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kit-cel/wt | SC468/LDPC_Optimization_AWGN.ipynb | 2 | 159277 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Optimization of Degree Distributions on the AWGN\n",
"\n",
"This code is provided as supplementary material of the lecture Channel Coding 2 - Advanced Methods.\n",
"\n",
"This code illustrates\n",
"* Using linear programming to optimize degree distributions on the AWGN channel using EXIT charts"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plot\n",
"from ipywidgets import interactive\n",
"import ipywidgets as widgets\n",
"import math\n",
"from pulp import *\n",
"%matplotlib inline "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Approximation of the J-function taken from [1] with\n",
"$$\n",
"J(\\mu) \\approx \\left(1 - 2^{-H_1\\cdot (2\\mu)^{H_2}}\\right)^{H_3}\n",
"$$\n",
"and its inverse function can be easily found as\n",
"$$\n",
"\\mu = J^{-1}(I) \\approx \\frac{1}{2}\\left(-\\frac{1}{H_1}\\log_2\\left(1-I^{\\frac{1}{H_3}}\\right)\\right)^{\\frac{1}{H_2}}\n",
"$$\n",
"with $H_1 = 0.3073$, $H_2=0.8935$, and $H_3 = 1.1064$.\n",
"\n",
"[1] F. Schreckenbach, _Iterative Decoding of Bit-Interleaved Coded Modulation_ , PhD thesis, TU Munich, 2007"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"H1 = 0.3073\n",
"H2 = 0.8935\n",
"H3 = 1.1064\n",
"\n",
"def J_fun(mu): \n",
" I = (1 - 2**(-H1*(2*mu)**H2))**H3\n",
" return I\n",
"\n",
"def invJ_fun(I):\n",
" if I > (1-1e-10):\n",
" return 100\n",
" mu = 0.5*(-(1/H1) * np.log2(1 - I**(1/H3)))**(1/H2)\n",
" return mu"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following function solves the optimization problem that returns the best $\\lambda(Z)$ for a given BI-AWGN channel quality $E_s/N_0$, corresponding to a $\\mu_c = 4\\frac{E_s}{N_0}$, for a regular check node degree $d_{\\mathtt{c}}$, and for a maximum variable node degree $d_{\\mathtt{v},\\max}$. This optimization problem is derived in the lecture as\n",
"$$\n",
"\\begin{aligned}\n",
"& \\underset{\\lambda_1,\\ldots,\\lambda_{d_{\\mathtt{v},\\max}}}{\\text{maximize}} & & \\sum_{i=1}^{d_{\\mathtt{v},\\max}}\\frac{\\lambda_i}{i} \\\\\n",
"& \\text{subject to} & & \\lambda_1 = 0 \\\\\n",
"& & & \\lambda_i \\geq 0, \\quad \\forall i \\in\\{2,3,\\ldots,d_{\\mathtt{v},\\max}\\} \\\\\n",
"& & & \\sum_{i=2}^{d_{\\mathtt{v},\\max}}\\lambda_i = 1 \\\\\n",
"& & & \\sum_{i=2}^{d_{\\mathtt{v},\\max}}\\lambda_i\\cdot J\\left(\\mu_c + (i-1)J^{-1}\\left(\\frac{j}{D}\\right)\\right) > 1 - J\\left(\\frac{1}{d_{\\mathtt{c}}-1}J^{-1}\\left(1-\\frac{j}{D}\\right)\\right),\\quad \\forall j \\in \\{1,\\ldots, D\\} \\\\\n",
"& & & \\lambda_2 \\leq \\frac{e^{\\frac{\\mu_c}{4}}}{d_{\\mathtt{c}}-1}\n",
"\\end{aligned}\n",
"$$\n",
"\n",
"If this optimization problem is feasible, then the function returns the polynomial $\\lambda(Z)$ as a coefficient array where the first entry corresponds to the largest exponent ($\\lambda_{d_{\\mathtt{v},\\max}}$) and the last entry to the lowest exponent ($\\lambda_1$). If the optimization problem has no solution (e.g., it is unfeasible), then the empty vector is returned."
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"def find_best_lambda(mu_c, v_max, dc): \n",
" # quantization of EXIT chart\n",
" D = 500\n",
" I_range = np.arange(0, D, 1)/D \n",
" \n",
" # Linear Programming model, maximize target expression\n",
" model = pulp.LpProblem(\"Finding best lambda problem\", pulp.LpMaximize)\n",
"\n",
" # definition of variables, v_max entries \\lambda_i that are between 0 and 1 (implicit declaration of constraint 2)\n",
" v_lambda = pulp.LpVariable.dicts(\"lambda\", range(v_max),0,1)\n",
" \n",
" # objective function\n",
" cv = 1/np.arange(v_max,0,-1) \n",
" model += pulp.lpSum(v_lambda[i]*cv[i] for i in range(v_max)) \n",
" \n",
" # constraints\n",
" # constraint 1, no variable nodes of degree 1\n",
" model += v_lambda[v_max-1] == 0\n",
" \n",
" # constraint 3, sum of lambda_i must be 1\n",
" model += pulp.lpSum(v_lambda[i] for i in range(v_max))==1\n",
" \n",
" # constraints 4, fixed point condition for all the descrete xi values (a total number of D, for each \\xi) \n",
" for myI in I_range: \n",
" model += pulp.lpSum(v_lambda[j] * J_fun(mu_c + (v_max-1-j)*invJ_fun(myI)) for j in range(v_max)) - 1 + J_fun(1/(dc-1)*invJ_fun(1-myI)) >= 0\n",
" \n",
" # constraint 5, stability condition\n",
" model += v_lambda[v_max-2] <= np.exp(mu_c/4)/(dc-1)\n",
"\n",
" model.solve()\n",
" if model.status != 1:\n",
" r_lambda = []\n",
" else:\n",
" r_lambda = [v_lambda[i].varValue for i in range(v_max)]\n",
" return r_lambda "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As an example, we consider the case of optimization carried out in the lecture after 10 iterations, where we have $\\mu_c = 3.8086$ and $d_{\\mathtt{c}} = 14$ with $d_{\\mathtt{v},\\max}=16$"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 15 6 5 2\n",
"0.335 Z + 0.04282 Z + 0.1768 Z + 0.246 Z + 0.1993 Z\n"
]
}
],
"source": [
"best_lambda = find_best_lambda(3.8086, 16, 14)\n",
"print(np.poly1d(best_lambda, variable='Z'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the following, we provide an interactive widget that allows you to choose the parameters of the optimization yourself and get the best possible $\\lambda(Z)$. Additionally, the EXIT chart is plotted to visualize the good fit of the obtained degree distribution."
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8e703c55b3f747a59677adc2fcf997f6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(FloatSlider(value=3.0, continuous_update=False, description='\\\\(\\\\mu_c\\\\)', layout=Layou…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def best_lambda_interactive(mu_c, v_max, dc):\n",
" # get lambda and rho polynomial from optimization and from c_avg, respectively\n",
" p_lambda = find_best_lambda(mu_c, v_max, dc)\n",
" \n",
" # if optimization successful, compute rate and show plot\n",
" if not p_lambda:\n",
" print('Optimization infeasible, no solution found')\n",
" else:\n",
" design_rate = 1 - 1/(dc * np.polyval(np.polyint(p_lambda),1))\n",
" if design_rate <= 0:\n",
" print('Optimization feasible, but no code with positive rate found')\n",
" else:\n",
" print(\"Lambda polynomial:\")\n",
" print(np.poly1d(p_lambda, variable='Z'))\n",
" print(\"Design rate r_d = %1.3f\" % design_rate)\n",
" \n",
" # Plot EXIT-Chart\n",
" print(\"EXIT Chart:\")\n",
" plot.figure(3) \n",
" x = np.linspace(0, 1, num=100)\n",
" y_v = [np.sum([p_lambda[j] * J_fun(mu_c + (v_max-1-j)*invJ_fun(xv)) for j in range(v_max)]) for xv in x] \n",
" y_c = [1-J_fun((dc-1)*invJ_fun(1-xv)) for xv in x] \n",
" plot.plot(x, y_v, '#7030A0')\n",
" plot.plot(y_c, x, '#008000') \n",
" plot.axis('equal')\n",
" plot.gca().set_aspect('equal', adjustable='box')\n",
" plot.xlim(0,1)\n",
" plot.ylim(0,1) \n",
" plot.grid()\n",
" plot.show()\n",
"\n",
"interactive_plot = interactive(best_lambda_interactive, \\\n",
" mu_c=widgets.FloatSlider(min=0.5,max=8,step=0.01,value=3, continuous_update=False, description=r'\\(\\mu_c\\)',layout=widgets.Layout(width='50%')), \\\n",
" v_max = widgets.IntSlider(min=3, max=20, step=1, value=16, continuous_update=False, description=r'\\(d_{\\mathtt{v},\\max}\\)'), \\\n",
" dc = widgets.IntSlider(min=3,max=20,step=1,value=4, continuous_update=False, description=r'\\(d_{\\mathtt{c}}\\)')) \n",
"output = interactive_plot.children[-1]\n",
"output.layout.height = '400px'\n",
"interactive_plot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we carry out the optimization over a wide range of $d_{\\mathtt{c},\\text{avg}}$ values for a given $\\epsilon$ and find the largest possible rate."
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b5c5065617254f2b98af5c331e8c1401",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(FloatSlider(value=2.0, continuous_update=False, description='\\\\(\\\\mu_c\\\\)', layout=Layou…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def find_best_rate(mu_c, dv_max, dc_max):\n",
" c_range = np.arange(3, dc_max+1)\n",
" rates = np.zeros_like(c_range,dtype=float)\n",
" \n",
" \n",
" # loop over all c_avg, add progress bar\n",
" f = widgets.FloatProgress(min=0, max=np.size(c_range))\n",
" display(f)\n",
" for index,dc in enumerate(c_range):\n",
" f.value += 1 \n",
" p_lambda = find_best_lambda(mu_c, dv_max, dc) \n",
" if p_lambda: \n",
" design_rate = 1 - 1/(dc * np.polyval(np.polyint(p_lambda),1))\n",
" if design_rate >= 0:\n",
" rates[index] = design_rate\n",
" \n",
" # find largest rate\n",
" largest_rate_index = np.argmax(rates)\n",
" best_lambda = find_best_lambda(mu_c, dv_max, c_range[largest_rate_index])\n",
" print(\"Found best code of rate %1.3f for average check node degree of %1.2f\" % (rates[largest_rate_index], c_range[largest_rate_index]))\n",
" print(\"Corresponding lambda polynomial\")\n",
" print(np.poly1d(best_lambda, variable='Z'))\n",
" \n",
" # Plot curve with all obtained results\n",
" plot.figure(4, figsize=(10,3)) \n",
" plot.plot(c_range, rates, 'b--s',color=(0, 0.59, 0.51))\n",
" plot.plot(c_range[largest_rate_index], rates[largest_rate_index], 'rs')\n",
" plot.xlim(3, dc_max)\n",
" plot.xticks(range(3,dc_max+1))\n",
" plot.ylim(0, 1)\n",
" plot.xlabel('$d_{\\mathtt{c}}$')\n",
" plot.ylabel('design rate $r_d$')\n",
" plot.grid()\n",
" plot.show()\n",
"\n",
" return rates[largest_rate_index]\n",
" \n",
"interactive_optim = interactive(find_best_rate, \\\n",
" mu_c=widgets.FloatSlider(min=0.1,max=10,step=0.01,value=2, continuous_update=False, description=r'\\(\\mu_c\\)',layout=widgets.Layout(width='50%')), \\\n",
" dv_max = widgets.IntSlider(min=3, max=20, step=1, value=16, continuous_update=False, description=r'\\(d_{\\mathtt{v},\\max}\\)'), \\\n",
" dc_max = widgets.IntSlider(min=3, max=40, step=1, value=22, continuous_update=False, description=r'\\(d_{\\mathtt{c},\\max}\\)'))\n",
"output = interactive_optim.children[-1]\n",
"output.layout.height = '400px'\n",
"interactive_optim"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Running binary search to find code with a given target rate for the AWGN channel"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running optimization for mu_c = 10.00000, corresponding to Es/N0 = 3.98 dB\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bc47af15f9164c6abe0b01b3d37b9444",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"FloatProgress(value=0.0, max=20.0)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found best code of rate 0.894 for average check node degree of 22.00\n",
"Corresponding lambda polynomial\n",
" 2\n",
"0.4199 Z + 0.5801 Z\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running optimization for mu_c = 5.00000, corresponding to Es/N0 = 0.97 dB\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "70001359c7b048ba8804fc1931396c2b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"FloatProgress(value=0.0, max=20.0)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found best code of rate 0.789 for average check node degree of 21.00\n",
"Corresponding lambda polynomial\n",
" 15 5 2\n",
"0.3837 Z + 0.1962 Z + 0.2455 Z + 0.1745 Z\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running optimization for mu_c = 2.50000, corresponding to Es/N0 = -2.04 dB\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2d055353a7f74326a78cc1cbc9f4d5df",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"FloatProgress(value=0.0, max=20.0)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found best code of rate 0.555 for average check node degree of 9.00\n",
"Corresponding lambda polynomial\n",
" 15 5 4 2\n",
"0.3299 Z + 0.17 Z + 0.03501 Z + 0.2316 Z + 0.2335 Z\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running optimization for mu_c = 1.25000, corresponding to Es/N0 = -5.05 dB\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cd4d6973601a4ce181f0b5c8434b44cb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"FloatProgress(value=0.0, max=20.0)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found best code of rate 0.339 for average check node degree of 5.00\n",
"Corresponding lambda polynomial\n",
" 13 12 6 5 2\n",
"0.1339 Z + 0.06836 Z + 0.007432 Z + 0.2035 Z + 0.2451 Z + 0.3417 Z\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running optimization for mu_c = 1.87500, corresponding to Es/N0 = -3.29 dB\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1485576035ad40eeadd0330e6fdddf35",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"FloatProgress(value=0.0, max=20.0)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found best code of rate 0.460 for average check node degree of 7.00\n",
"Corresponding lambda polynomial\n",
" 15 5 4 2\n",
"0.2868 Z + 0.2058 Z + 0.007592 Z + 0.2335 Z + 0.2663 Z\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running optimization for mu_c = 2.18750, corresponding to Es/N0 = -2.62 dB\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "136a7a9e589341e8ae11a9d00dd34fdc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"FloatProgress(value=0.0, max=20.0)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found best code of rate 0.511 for average check node degree of 8.00\n",
"Corresponding lambda polynomial\n",
" 15 5 4 2\n",
"0.3184 Z + 0.1648 Z + 0.04061 Z + 0.2294 Z + 0.2468 Z\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running optimization for mu_c = 2.03125, corresponding to Es/N0 = -2.94 dB\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d57da313c6ce4504aef904b73100217f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"FloatProgress(value=0.0, max=20.0)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found best code of rate 0.485 for average check node degree of 8.00\n",
"Corresponding lambda polynomial\n",
" 15 3 2\n",
"0.4249 Z + 0.1833 Z + 0.1544 Z + 0.2374 Z\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running optimization for mu_c = 2.10938, corresponding to Es/N0 = -2.78 dB\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4659d82f018d4443b2e60de15ae2a305",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"FloatProgress(value=0.0, max=20.0)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found best code of rate 0.499 for average check node degree of 8.00\n",
"Corresponding lambda polynomial\n",
" 15 4 2\n",
"0.3708 Z + 0.1796 Z + 0.2075 Z + 0.2421 Z\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running optimization for mu_c = 2.14844, corresponding to Es/N0 = -2.70 dB\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "08a9852ae0d24e5eb5042fc6ecedb6dc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"FloatProgress(value=0.0, max=20.0)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found best code of rate 0.505 for average check node degree of 8.00\n",
"Corresponding lambda polynomial\n",
" 15 5 4 2\n",
"0.3439 Z + 0.08126 Z + 0.1125 Z + 0.2179 Z + 0.2444 Z\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running optimization for mu_c = 2.12891, corresponding to Es/N0 = -2.74 dB\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e1d7d491df7c43069e3a908135ab05f0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"FloatProgress(value=0.0, max=20.0)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found best code of rate 0.502 for average check node degree of 8.00\n",
"Corresponding lambda polynomial\n",
" 15 5 4 2\n",
"0.3575 Z + 0.03598 Z + 0.1514 Z + 0.2118 Z + 0.2432 Z\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"target_rate = 0.7\n",
"dv_max = 16\n",
"dc_max = 22\n",
"\n",
"T_Delta = 0.01\n",
"mu_c = 10\n",
"Delta_mu = 10\n",
"\n",
"while Delta_mu >= T_Delta: \n",
" print('Running optimization for mu_c = %1.5f, corresponding to Es/N0 = %1.2f dB' % (mu_c, 10*np.log10(mu_c/4)))\n",
" \n",
" rate = find_best_rate(mu_c, dv_max, dc_max)\n",
" if rate > target_rate:\n",
" mu_c = mu_c - Delta_mu / 2\n",
" else:\n",
" mu_c = mu_c + Delta_mu / 2\n",
" \n",
" Delta_mu = Delta_mu / 2"
]
}
],
"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": "3.6.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-2.0 |
UWSEDS/LectureNotes | PreFall2018/02-Python-and-Data/Lecture-Python-and-Data.ipynb | 1 | 20527 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>\n",
" .rendered_html {font-size: 140%;}\n",
" .rendered_html h1, h2 {text-align:center;}\n",
"</style>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Some styling stuff... ignore this for now!\n",
"from IPython.display import HTML\n",
"HTML(\"\"\"<style>\n",
" .rendered_html {font-size: 140%;}\n",
" .rendered_html h1, h2 {text-align:center;}\n",
"</style>\"\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Software Engineering for Data Scientists\n",
"\n",
"## *Manipulating Data with Python*\n",
"## CSE 599 B1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Today's Objectives\n",
"\n",
"#### 1. Opening & Navigating the IPython Notebook\n",
"\n",
"#### 2. Simple Math in the IPython Notebook\n",
"\n",
"#### 3. Loading data with ``pandas``\n",
"\n",
"#### 4. Cleaning and Manipulating data with ``pandas``\n",
"\n",
"#### 5. Visualizing data with ``pandas``"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Opening and Navigating the IPython Notebook\n",
"\n",
"We will start today with the interactive environment that we will be using often through the course: the [IPython/Jupyter Notebook](http://ipython.org).\n",
"\n",
"We will walk through the following steps together:\n",
"\n",
"1. Download [miniconda]() (be sure to get Version 3.5) and install it on your system (hopefully you have done this before coming to class)\n",
" ```\n",
" ```\n",
"\n",
"2. Use the ``conda`` command-line tool to update your package listing and install the IPython notebook:\n",
"\n",
" Update ``conda``'s listing of packages for your system:\n",
" ```\n",
" $ conda update conda\n",
" ```\n",
" \n",
" Install IPython notebook and all its requirements\n",
" ```\n",
" $ conda install ipython-notebook\n",
" ```\n",
" \n",
"3. Navigate to the directory containing the course material. For example:\n",
"\n",
" ```\n",
" $ cd ~/courses/CSE599/\n",
" ```\n",
" \n",
" You should see a number of files in the directory, including these:\n",
" \n",
" ```\n",
" $ ls\n",
" ...\n",
" Breakout-Simple-Math.ipynb\n",
" CSE599_Lecture_2.ipynb\n",
" ...\n",
" ```\n",
"\n",
"4. Type ``ipython notebook`` in the terminal to start the notebook\n",
"\n",
" ```\n",
" $ ipython notebook\n",
" ```\n",
" \n",
" If everything has worked correctly, it should automatically launch your default browser\n",
" ```\n",
" ```\n",
" \n",
"5. Click on ``CSE599_Lecture_2.ipynb`` to open the notebook containing the content for this lecture.\n",
"\n",
"With that, you're set up to use the IPython notebook!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Simple Math in the IPython Notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have the IPython notebook up and running, we're going to do a short breakout exploring some of the mathematical functionality that Python offers.\n",
"\n",
"Please open [Breakout-Simple-Math.ipynb](Breakout-Simple-Math.ipynb), find a partner, and make your way through that notebook, typing and executing code along the way."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Loading data with ``pandas``\n",
"\n",
"With this simple Python computation experience under our belt, we can now move to doing some more interesting analysis."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Python's Data Science Ecosystem\n",
"\n",
"In addition to Python's built-in modules like the ``math`` module we explored above, there are also many often-used third-party modules that are core tools for doing data science with Python.\n",
"Some of the most important ones are:\n",
"\n",
"#### [``numpy``](http://numpy.org/): Numerical Python\n",
"\n",
"Numpy is short for \"Numerical Python\", and contains tools for efficient manipulation of arrays of data.\n",
"If you have used other computational tools like IDL or MatLab, Numpy should feel very familiar.\n",
"\n",
"#### [``scipy``](http://scipy.org/): Scientific Python\n",
"\n",
"Scipy is short for \"Scientific Python\", and contains a wide range of functionality for accomplishing common scientific tasks, such as optimization/minimization, numerical integration, interpolation, and much more.\n",
"We will not look closely at Scipy today, but we will use its functionality later in the course.\n",
"\n",
"#### [``pandas``](http://pandas.pydata.org/): Labeled Data Manipulation in Python\n",
"\n",
"Pandas is short for \"Panel Data\", and contains tools for doing more advanced manipulation of labeled data in Python, in particular with a columnar data structure called a *Data Frame*.\n",
"If you've used the [R](http://rstats.org) statistical language (and in particular the so-called \"Hadley Stack\"), much of the functionality in Pandas should feel very familiar.\n",
"\n",
"#### [``matplotlib``](http://matplotlib.org): Visualization in Python\n",
"\n",
"Matplotlib started out as a Matlab plotting clone in Python, and has grown from there in the 15 years since its creation. It is the most popular data visualization tool currently in the Python data world (though other recent packages are starting to encroach on its monopoly)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Installing Pandas & friends\n",
"\n",
"Because the above packages are not included in Python itself, you need to install them separately. While it is possible to install these from source (compiling the C and/or Fortran code that does the heavy lifting under the hood) it is much easier to use a package manager like ``conda``. All it takes is to run\n",
"\n",
"```\n",
"$ conda install numpy scipy pandas matplotlib\n",
"```\n",
"\n",
"and (so long as your conda setup is working) the packages will be downloaded and installed on your system."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Loading Data with Pandas"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because we'll use it so much, we often import under a shortened name using the ``import ... as ...`` pattern:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can use the ``read_csv`` command to read the comma-separated-value data:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Viewing Pandas Dataframes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ``head()`` and ``tail()`` methods show us the first and last rows of the data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ``shape`` attribute shows us the number of elements:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ``columns`` attribute gives us the column names"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ``index`` attribute gives us the index names"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ``dtypes`` attribute gives the data types of each column:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Manipulating data with ``pandas``\n",
"\n",
"Here we'll cover some key features of manipulating data with pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Access columns by name using square-bracket indexing:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mathematical operations on columns happen *element-wise*:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Columns can be created (or overwritten) with the assignment operator.\n",
"Let's create a *tripminutes* column with the number of minutes for each trip"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Working with Times"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One trick to know when working with columns of times is that Pandas ``DateTimeIndex`` provides a nice interface for working with columns of times:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With it, we can extract, the hour of the day, the day of the week, the month, and a wide range of other views of the time:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simple Grouping of Data\n",
"\n",
"The real power of Pandas comes in its tools for grouping and aggregating data. Here we'll look at *value counts* and the basics of *group-by* operations."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Value Counts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pandas includes an array of useful functionality for manipulating and analyzing tabular data.\n",
"We'll take a look at two of these here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ``pandas.value_counts`` returns statistics on the unique values within each column.\n",
"\n",
"We can use it, for example, to break down rides by gender:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or to break down rides by age:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What else might we break down rides by?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Group-by Operation\n",
"\n",
"One of the killer features of the Pandas dataframe is the ability to do group-by operations.\n",
"You can visualize the group-by like this (image borrowed from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import Image\n",
"Image('split_apply_combine.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, for example, we can use this to find the average length of a ride as a function of time of day:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The simplest version of a groupby looks like this, and you can use almost any aggregation function you wish (mean, median, sum, minimum, maximum, standard deviation, count, etc.)\n",
"\n",
"```\n",
"<data object>.groupby(<grouping values>).<aggregate>()\n",
"```\n",
"\n",
"You can even group by multiple values: for example we can look at the trip duration by time of day and by gender:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ``unstack()`` operation can help make sense of this type of multiply-grouped data:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Visualizing data with ``pandas``\n",
"\n",
"Of course, looking at tables of data is not very intuitive.\n",
"Fortunately Pandas has many useful plotting functions built-in, all of which make use of the ``matplotlib`` library to generate plots.\n",
"\n",
"Whenever you do plotting in the IPython notebook, you will want to first run this *magic command* which configures the notebook to work well with plots:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can simply call the ``plot()`` method of any series or dataframe to get a reasonable view of the data:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Adjusting the Plot Style\n",
"\n",
"The default formatting is not very nice; I often make use of the [Seaborn](http://stanford.edu/~mwaskom/software/seaborn/) library for better plotting defaults.\n",
"\n",
"First you'll have to\n",
"```\n",
"$ conda install seaborn\n",
"```\n",
"and then you can do this:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import seaborn\n",
"seaborn.set()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And now re-run the plot from above:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Other plot types\n",
"\n",
"Pandas supports a range of other plotting types; you can find these by using the <TAB> autocomplete on the ``plot`` method:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For example, we can create a histogram of trip durations:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you'd like to adjust the x and y limits of the plot, you can use the ``set_xlim()`` and ``set_ylim()`` method of the resulting object:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Breakout: Exploring the Data\n",
"\n",
"1. Make a plot of the total number of rides as a function of month of the year (You'll need to extract the month, use a ``groupby``, and find the appropriate aggregation to count the number in each group)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. Split this plot by gender. Do you see any seasonal ridership patterns by gender?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3. Split this plot by user type. Do you see any seasonal ridership patterns by usertype?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4. Repeat the above three steps, counting the number of rides by time of day rather thatn by month."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"5. Are there any other interesting insights you can discover in the data using these tools?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Looking Forward to Homework"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the homework this week, you will have a chance to apply some of these patterns to a brand new (but closely related) dataset."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"codemirror_mode": {
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"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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| bsd-2-clause |
Upward-Spiral-Science/grelliam | code/classification_simulation.ipynb | 1 | 76488 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulated Classifcation\n",
"1. State assumptions\n",
"2. Formally define classification/regression problem\n",
"3. provide algorithm for solving problem (including choosing hyperparameters as appropriate)\n",
"4. sample data from a simulation setting inspired by your data (from both null and alternative as defined before)\n",
"5. compute accuracy\n",
"6. plot accuracy vs. sample size in simulation\n",
"7. apply method directly on real data\n",
"8. explain the degree to which you believe the result and why\n",
" \n",
"### Step 1: State assumptions\n",
"$F_{X|0} = ER(p_0) = Bern(p_0)^{V \\times V}$ <br/>\n",
"$F_{X|1} = ER(p_1) = Bern(p_1)^{V \\times V}$\n",
"\n",
"$p_1 \\neq p_2$\n",
"\n",
"### Step 2: Formally define classification/regression problem\n",
"$G_i, Y_i \\sim \\mathscr{F}_{G,Y} = \\{ F_{G,Y}(\\cdot; \\theta) : \\theta \\in \\Theta \\}$.\n",
"\n",
"Since, all samples observed are graph matched (i.e. nodes are equal across graphs), we can look at just the distribution of adjacency matrices:\n",
"\n",
"$F_{G,Y} = F_{X,Y}$.\n",
"\n",
"Thus,\n",
"\n",
"$X_i = \\prod_{u,v}^{\\mathcal{E}} A_{uv}$, where $\\mathcal{E} \\subset V \\times V$ <br/>\n",
"$Y_i = \\{0,1\\}$\n",
"\n",
"As we are doing classification, we are trying to minimize expected error. Here, expected error can be defined as:\n",
"\n",
"$E[l] = \\sum \\Theta(\\hat{Y}_i \\neq Y_i)$\n",
"\n",
"Where $\\Theta$ is the indicator function.\n",
"\n",
"### Step 3: Provide algorithm for solving problem (including choosing hyperparameters as appropriate)\n",
"classification:\n",
"- lda (linear discriminant analysis): no parameters\n",
"- qda (quadratic discriminant analysis): no parameters\n",
"- svm (support vector machine): penalty parameters set to 0.5 because it was a default suggested \n",
"- knn (k-nearest neighbours): number of neighbors set to 3 because it was a default suggested\n",
"- rf (random forest): like the above, I didn't have better insight so went with defaults. Seemed like a simple starting point, as we always aim for.\n",
"\n",
"regression: linear regression, support vector regression, k-nearest neighbour regression, random forest regression, polynomial regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Setup Step"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"import csv\n",
"import igraph as ig\n",
"\n",
"from sklearn import cross_validation\n",
"from sklearn.cross_validation import LeaveOneOut\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.svm import SVC\n",
"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
"from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
"\n",
"%matplotlib inline\n",
"\n",
"np.random.seed(12345678) # for reproducibility, set random seed\n",
"r = 20 # define number of rois\n",
"N = 100 # number of samples at each iteration\n",
"p0 = 0.10\n",
"p1 = 0.15\n",
"# define number of subjects per class\n",
"S = np.array((8, 16, 20, 32, 40, 64, 80, 100, 120, 200, 320,\n",
" 400, 800, 1000))\n",
"\n",
"names = [\"Nearest Neighbors\", \"Linear SVM\", \"Random Forest\",\n",
" \"Linear Discriminant Analysis\", \"Quadratic Discriminant Analysis\"]\n",
"\n",
"classifiers = [\n",
" KNeighborsClassifier(3),\n",
" SVC(kernel=\"linear\", C=0.5),\n",
" RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n",
" LinearDiscriminantAnalysis(),\n",
" QuadraticDiscriminantAnalysis()]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Steps 4 & 5: Sample data from setting similar to data and record classification accuracy"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of Nearest Neighbors: 1.00 (+/- 0.00)\n",
"Accuracy of Linear SVM: 1.00 (+/- 0.00)\n",
"Accuracy of Random Forest: 1.00 (+/- 0.00)\n",
"Accuracy of Linear Discriminant Analysis: 1.00 (+/- 0.00)\n",
"Accuracy of Quadratic Discriminant Analysis: 0.50 (+/- 1.00)\n",
"Accuracy of Nearest Neighbors: 0.88 (+/- 0.66)\n",
"Accuracy of Linear SVM: 0.94 (+/- 0.48)\n",
"Accuracy of Random Forest: 0.94 (+/- 0.48)\n",
"Accuracy of Linear Discriminant Analysis: 0.94 (+/- 0.48)\n",
"Accuracy of Quadratic Discriminant Analysis: 0.88 (+/- 0.66)\n",
"Accuracy of Nearest Neighbors: 1.00 (+/- 0.00)\n",
"Accuracy of Linear SVM: 1.00 (+/- 0.00)\n",
"Accuracy of Random Forest: 0.95 (+/- 0.44)\n",
"Accuracy of Linear Discriminant Analysis: 1.00 (+/- 0.00)\n",
"Accuracy of Quadratic Discriminant Analysis: 0.95 (+/- 0.44)\n",
"Accuracy of Nearest Neighbors: 0.97 (+/- 0.35)\n",
"Accuracy of Linear SVM: 0.97 (+/- 0.35)\n",
"Accuracy of Random Forest: 0.97 (+/- 0.35)\n",
"Accuracy of Linear Discriminant Analysis: 0.97 (+/- 0.35)\n",
"Accuracy of Quadratic Discriminant Analysis: 0.97 (+/- 0.35)\n",
"Accuracy of Nearest Neighbors: 0.78 (+/- 0.84)\n",
"Accuracy of Linear SVM: 0.90 (+/- 0.60)\n",
"Accuracy of Random Forest: 0.90 (+/- 0.60)\n",
"Accuracy of Linear Discriminant Analysis: 0.90 (+/- 0.60)\n",
"Accuracy of Quadratic Discriminant Analysis: 0.88 (+/- 0.66)\n",
"Accuracy of Nearest Neighbors: 0.88 (+/- 0.66)\n",
"Accuracy of Linear SVM: 0.88 (+/- 0.66)\n",
"Accuracy of Random Forest: 0.83 (+/- 0.75)\n",
"Accuracy of Linear Discriminant Analysis: 0.91 (+/- 0.58)\n",
"Accuracy of Quadratic Discriminant Analysis: 0.84 (+/- 0.73)\n",
"Accuracy of Nearest Neighbors: 0.95 (+/- 0.44)\n",
"Accuracy of Linear SVM: 0.96 (+/- 0.38)\n",
"Accuracy of Random Forest: 0.95 (+/- 0.44)\n",
"Accuracy of Linear Discriminant Analysis: 0.96 (+/- 0.38)\n",
"Accuracy of Quadratic Discriminant Analysis: 0.97 (+/- 0.31)\n",
"Accuracy of Nearest Neighbors: 0.90 (+/- 0.60)\n",
"Accuracy of Linear SVM: 0.95 (+/- 0.44)\n",
"Accuracy of Random Forest: 0.90 (+/- 0.60)\n",
"Accuracy of Linear Discriminant Analysis: 0.95 (+/- 0.44)\n",
"Accuracy of Quadratic Discriminant Analysis: 0.94 (+/- 0.47)\n",
"Accuracy of Nearest Neighbors: 0.93 (+/- 0.53)\n",
"Accuracy of Linear SVM: 0.96 (+/- 0.40)\n",
"Accuracy of Random Forest: 0.93 (+/- 0.50)\n",
"Accuracy of Linear Discriminant Analysis: 0.95 (+/- 0.44)\n",
"Accuracy of Quadratic Discriminant Analysis: 0.97 (+/- 0.36)\n",
"Accuracy of Nearest Neighbors: 0.96 (+/- 0.37)\n",
"Accuracy of Linear SVM: 0.95 (+/- 0.41)\n",
"Accuracy of Random Forest: 0.96 (+/- 0.39)\n",
"Accuracy of Linear Discriminant Analysis: 0.94 (+/- 0.46)\n",
"Accuracy of Quadratic Discriminant Analysis: 0.95 (+/- 0.44)\n",
"Accuracy of Nearest Neighbors: 0.92 (+/- 0.54)\n",
"Accuracy of Linear SVM: 0.93 (+/- 0.51)\n",
"Accuracy of Random Forest: 0.91 (+/- 0.57)\n",
"Accuracy of Linear Discriminant Analysis: 0.93 (+/- 0.51)\n",
"Accuracy of Quadratic Discriminant Analysis: 0.93 (+/- 0.52)\n",
"Accuracy of Nearest Neighbors: 0.92 (+/- 0.54)\n",
"Accuracy of Linear SVM: 0.94 (+/- 0.49)\n",
"Accuracy of Random Forest: 0.92 (+/- 0.54)\n",
"Accuracy of Linear Discriminant Analysis: 0.94 (+/- 0.48)\n",
"Accuracy of Quadratic Discriminant Analysis: 0.92 (+/- 0.55)\n",
"Accuracy of Nearest Neighbors: 0.92 (+/- 0.53)\n",
"Accuracy of Linear SVM: 0.93 (+/- 0.50)\n",
"Accuracy of Random Forest: 0.94 (+/- 0.47)\n",
"Accuracy of Linear Discriminant Analysis: 0.95 (+/- 0.45)\n",
"Accuracy of Quadratic Discriminant Analysis: 0.94 (+/- 0.48)\n",
"Accuracy of Nearest Neighbors: 0.90 (+/- 0.59)\n",
"Accuracy of Linear SVM: 0.94 (+/- 0.49)\n",
"Accuracy of Random Forest: 0.94 (+/- 0.49)\n",
"Accuracy of Linear Discriminant Analysis: 0.94 (+/- 0.48)\n",
"Accuracy of Quadratic Discriminant Analysis: 0.93 (+/- 0.51)\n",
"[[[ 1. 0. ]\n",
" [ 1. 0. ]\n",
" [ 1. 0. ]\n",
" [ 1. 0. ]\n",
" [ 0.5 0.5 ]]\n",
"\n",
" [[ 0.875 0.33071891]\n",
" [ 0.9375 0.24206146]\n",
" [ 0.9375 0.24206146]\n",
" [ 0.9375 0.24206146]\n",
" [ 0.875 0.33071891]]\n",
"\n",
" [[ 1. 0. ]\n",
" [ 1. 0. ]\n",
" [ 0.95 0.21794495]\n",
" [ 1. 0. ]\n",
" [ 0.95 0.21794495]]\n",
"\n",
" [[ 0.96875 0.17399264]\n",
" [ 0.96875 0.17399264]\n",
" [ 0.96875 0.17399264]\n",
" [ 0.96875 0.17399264]\n",
" [ 0.96875 0.17399264]]\n",
"\n",
" [[ 0.775 0.41758233]\n",
" [ 0.9 0.3 ]\n",
" [ 0.9 0.3 ]\n",
" [ 0.9 0.3 ]\n",
" [ 0.875 0.33071891]]\n",
"\n",
" [[ 0.875 0.33071891]\n",
" [ 0.875 0.33071891]\n",
" [ 0.828125 0.37727176]\n",
" [ 0.90625 0.2914806 ]\n",
" [ 0.84375 0.36309219]]\n",
"\n",
" [[ 0.95 0.21794495]\n",
" [ 0.9625 0.18998355]\n",
" [ 0.95 0.21794495]\n",
" [ 0.9625 0.18998355]\n",
" [ 0.975 0.15612495]]\n",
"\n",
" [[ 0.9 0.3 ]\n",
" [ 0.95 0.21794495]\n",
" [ 0.9 0.3 ]\n",
" [ 0.95 0.21794495]\n",
" [ 0.94 0.23748684]]\n",
"\n",
" [[ 0.925 0.26339134]\n",
" [ 0.95833333 0.19982631]\n",
" [ 0.93333333 0.24944383]\n",
" [ 0.95 0.21794495]\n",
" [ 0.96666667 0.17950549]]\n",
"\n",
" [[ 0.965 0.18377976]\n",
" [ 0.955 0.20730412]\n",
" [ 0.96 0.19595918]\n",
" [ 0.945 0.22798026]\n",
" [ 0.95 0.21794495]]\n",
"\n",
" [[ 0.921875 0.26836819]\n",
" [ 0.93125 0.25302853]\n",
" [ 0.9125 0.28256636]\n",
" [ 0.93125 0.25302853]\n",
" [ 0.928125 0.25828082]]\n",
"\n",
" [[ 0.92 0.2712932 ]\n",
" [ 0.935 0.24652586]\n",
" [ 0.92 0.2712932 ]\n",
" [ 0.9375 0.24206146]\n",
" [ 0.9175 0.27512497]]\n",
"\n",
" [[ 0.92375 0.2653977 ]\n",
" [ 0.93375 0.24871859]\n",
" [ 0.94 0.23748684]\n",
" [ 0.94625 0.2255237 ]\n",
" [ 0.9375 0.24206146]]\n",
"\n",
" [[ 0.904 0.29459124]\n",
" [ 0.935 0.24652586]\n",
" [ 0.935 0.24652586]\n",
" [ 0.938 0.24115555]\n",
" [ 0.931 0.25345414]]]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python2.7/site-packages/sklearn/discriminant_analysis.py:387: UserWarning: Variables are collinear.\n",
" warnings.warn(\"Variables are collinear.\")\n",
"/usr/local/lib/python2.7/site-packages/sklearn/discriminant_analysis.py:688: UserWarning: Variables are collinear\n",
" warnings.warn(\"Variables are collinear\")\n",
"/usr/local/lib/python2.7/site-packages/sklearn/discriminant_analysis.py:712: RuntimeWarning: divide by zero encountered in power\n",
" X2 = np.dot(Xm, R * (S ** (-0.5)))\n",
"/usr/local/lib/python2.7/site-packages/sklearn/discriminant_analysis.py:715: RuntimeWarning: divide by zero encountered in log\n",
" u = np.asarray([np.sum(np.log(s)) for s in self.scalings_])\n"
]
}
],
"source": [
"accuracy = np.zeros((len(S), len(classifiers), 2), dtype=np.dtype('float64'))\n",
"for idx1, s in enumerate(S):\n",
" s0=s/2\n",
" s1=s/2\n",
"\n",
" g0 = 1 * (np.random.rand( r, r, s0) > 1-p0)\n",
" g1 = 1 * (np.random.rand( r, r, s1) > 1-p1)\n",
" mbar0 = 1.0*np.sum(g0, axis=(0,1))\n",
" mbar1 = 1.0*np.sum(g1, axis=(0,1))\n",
"\n",
" X = np.array((np.append(mbar0, mbar1), np.append(mbar0/( r**2), mbar1/( r**2 )))).T\n",
" y = np.append(np.zeros(s0), np.ones(s1))\n",
" \n",
" for idx2, cla in enumerate(classifiers):\n",
" X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.4, random_state=0)\n",
" clf = cla.fit(X_train, y_train)\n",
" loo = LeaveOneOut(len(X))\n",
" scores = cross_validation.cross_val_score(clf, X, y, cv=loo)\n",
" accuracy[idx1, idx2,] = [scores.mean(), scores.std()]\n",
" print(\"Accuracy of %s: %0.2f (+/- %0.2f)\" % (names[idx2], scores.mean(), scores.std() * 2))\n",
" \n",
"print accuracy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 6: Plot Accuracy versus N"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
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OXsrsObPpf0H/49toDX36wIgRph9fUZM7dOCa774j+6+/TgRniYnHg7McwFdr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\nJJUAkzdJkiRJKgH/B7PCsBMAL90tAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1139c1550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"font = {'weight' : 'bold',\n",
" 'size' : 14}\n",
"\n",
"import matplotlib\n",
"matplotlib.rc('font', **font)\n",
"\n",
"plt.figure(figsize=(8,5))\n",
"plt.errorbar(S, accuracy[:,0,0], yerr = accuracy[:,0,1]/np.sqrt(S), hold=True, label=names[0])\n",
"plt.errorbar(S, accuracy[:,1,0], yerr = accuracy[:,1,1]/np.sqrt(S), color='green', hold=True, label=names[1])\n",
"plt.errorbar(S, accuracy[:,2,0], yerr = accuracy[:,2,1]/np.sqrt(S), color='red', hold=True, label=names[2])\n",
"plt.errorbar(S, accuracy[:,3,0], yerr = accuracy[:,3,1]/np.sqrt(S), color='black', hold=True, label=names[3])\n",
"plt.errorbar(S, accuracy[:,4,0], yerr = accuracy[:,4,1]/np.sqrt(S), color='brown', hold=True, label=names[4])\n",
"plt.xscale('log')\n",
"plt.xlabel('Number of Samples')\n",
"plt.xlim((0,2100))\n",
"plt.ylim((-0.05, 1.05))\n",
"plt.ylabel('Accuracy')\n",
"plt.title('Gender Classification of Simulated Data')\n",
"plt.axhline(1, color='red', linestyle='--')\n",
"plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
"plt.savefig('../figs/general_classification.png')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 7: Apply technique to data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset: ../data/KKI2009\n",
"(70, 70, 42)\n"
]
}
],
"source": [
"# Initializing dataset names\n",
"dnames = list(['../data/KKI2009'])\n",
"print \"Dataset: \" + \", \".join(dnames)\n",
"\n",
"# Getting graph names\n",
"fs = list()\n",
"for dd in dnames:\n",
" fs.extend([root+'/'+file for root, dir, files in os.walk(dd) for file in files])\n",
"fs = fs[1:]\n",
"def loadGraphs(filenames, rois, printer=False):\n",
" A = np.zeros((rois, rois, len(filenames)))\n",
" for idx, files in enumerate(filenames):\n",
" if printer:\n",
" print \"Loading: \" + files\n",
" g = ig.Graph.Read_GraphML(files)\n",
" tempg = g.get_adjacency(attribute='weight')\n",
" A[:,:,idx] = np.asarray(tempg.data)\n",
" \n",
" return A\n",
"\n",
"# Load X\n",
"X = loadGraphs(fs, 70)\n",
"print X.shape\n",
"\n",
"# Load Y\n",
"ys = csv.reader(open('../data/kki42_subjectinformation.csv'))\n",
"y = [y[5] for y in ys]\n",
"y = [1 if x=='F' else 0 for x in y[1:]]\n",
"\n",
"xf = 1.0*np.sum(1.0*(X>0), axis=(0,1))\n",
"features = np.array((xf, xf/( 70**2 * 22))).T"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of Nearest Neighbors: 0.48 (+/- 1.00)\n",
"Accuracy of Linear SVM: 0.55 (+/- 1.00)\n",
"Accuracy of Random Forest: 0.57 (+/- 0.99)\n",
"Accuracy of Linear Discriminant Analysis: 0.45 (+/- 1.00)\n",
"Accuracy of Quadratic Discriminant Analysis: 0.71 (+/- 0.90)\n"
]
}
],
"source": [
"accuracy=np.zeros((len(classifiers),2))\n",
"for idx, cla in enumerate(classifiers):\n",
" X_train, X_test, y_train, y_test = cross_validation.train_test_split(features, y, test_size=0.4, random_state=0)\n",
" clf = cla.fit(X_train, y_train)\n",
" loo = LeaveOneOut(len(features))\n",
" scores = cross_validation.cross_val_score(clf, features, y, cv=loo)\n",
" accuracy[idx,] = [scores.mean(), scores.std()]\n",
" print(\"Accuracy of %s: %0.2f (+/- %0.2f)\" % (names[idx], scores.mean(), scores.std() * 2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 8: Reflect on result\n",
"\n",
"The classification accuracy on real data based on the five tested classifiers is, at best, 71%, and worst, chance. Next, I need to test my assumptions to see if they are accurate and adjust my processing/features to better represent my true scenario than the assumed conditions, if possible."
]
}
],
"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 |
hrayatnia/SciPy | ipython gallery/CS1001.py-master/recitation14.ipynb | 2 | 6936 | {
"metadata": {
"name": "recitation14"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CS1001.py\n",
"\n",
"## Extended Introduction to Computer Science with Python, Tel-Aviv University, Spring 2013\n",
"\n",
"# Recitation 14 - 17-20.6.2013\n",
"\n",
"## Last update: 16.6.2013"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Error detection and correction\n",
"\n",
"When we send data, we send data + error correction information (EC). \n",
"Although longer, this transmission will allow detecting and correcting errors in transmission.\n",
"\n",
"#### Definitions\n",
"\n",
"- `k` \u2013 len of original message \n",
"- `n` \u2013 len of transmitted message (data + EC) \n",
"\n",
"- The *Hamming distance* between two `n`-bit long messages is the numbers of differences between them.\n",
"Example: \n",
" \n",
"$$HD(01000,11010) = 2$$\n",
"\n",
"- `d` is the minimal HD between any 2 possible `n`-messages.\n",
"\n",
"- An `(n,k,d)` code is able to detect `d-1` errors, and fix \u0001$\\frac{d-1}{2}$ errors. \n",
"\n",
"## Repetition code\n",
"\n",
"Transmitting each bit `p` times.\n",
"\n",
"data = 10 \n",
"`p` = 4\n",
"data + EC = 11110000\n",
"`d` = 4\n",
"\n",
"## Other codes from lecture\n",
"\n",
"- Parity bit (xor) \n",
"- 2D parity bit (card magic) \n",
"- Israeli ID control digit \n",
"- Hamming (7,4,3) code \n",
"\n",
"## Index code\n",
"We will now introduce another example for a code.\n",
"\n",
"- $k = 2^m -1$\n",
"- EC: XOR over all active indices (containing `1`s) in data. (first index is 1)\n",
"\n",
"Example:\n",
"\n",
"- data = 0110110\n",
"- $k = 2^3-1=7$\n",
"- EC = 010 + 011 + 101 + 110 = 010 (active indices are 2,3,5,6)\n",
"- message = 0110110010\n",
"\n",
"#### Q1: What is the length of the EC? What is `n`?\n",
"\n",
"$|EC| = \\lfloor\\log_2{2^m-1}\\rfloor + 1$\n",
"\n",
"$n = 2^m + m - 1$\n",
"\n",
"#### Q2: What is `d`? How many errors can be detected and fixed?\n",
"\n",
"- `d` = 2\n",
"- detect: 1\n",
"- fix: 0\n",
"\n",
"Example:\n",
"0000000 000 \n",
"0001000 100\n",
"\n",
"#### Q3 Improvement A - transmit EC twice. What are `n`,`k`,`d` now?\n",
"\n",
"- $n = 2^m -1 + 2m$\n",
"- $k=2^m-1$\n",
"- `d`=3, can fix 1 error\n",
"\n",
"#### Q4: What's the decoding algorithm, given that we expect *no more than 1 error*? \n",
"Remember to treat the cases of 0 or 1 errors.\n",
"\n",
"**decode(transmitted message)**\n",
"\n",
"- Compute EC from first `k` bits\n",
"- If EC = EC1 or EC = EC2: return first `k` bits\n",
"- Else: XOR(EC,ECX) gives the index of the single error (where ECX is either EC1 or EC2, depending on which was not equal to EC)\n",
"\n",
"Example:\n",
"\n",
"1. Encoding: 0110110 -> 0110110 010 010\n",
"2. Transmission error: 0110110 010 010 -> 0110**0**10 010 010\n",
"3. Decoding: 0110010 010 010\n",
" - EC = 2 + 3 + 6 = 010 + 011 + 110 = 111 != 010\n",
" - Error bit at 111 + 010 = 101 = 5\n",
"4. Correction: 0110010 -> 0110110\n",
"\n",
"#### Q5: What happens if we have 2 errors?\n",
"\n",
"In this case we will think there is one error, try to correct, and will insert a third error.\n",
"\n",
"Example:\n",
"\n",
"3. Decoding: 0**0**10**0**10 010 010\n",
" - EC = 3 + 6 = 011 + 110 = 101 != 010\n",
" - Error bit at 101 + 010 = 111 = 7\n",
" - Correction: 0010010 -> 0010011 != 0110110\n",
"\n",
"#### Q6: Improvment B - add *parity* bit at the end\n",
"\n",
"What is the value of `d` now?\n",
"\n",
"Before, we had `d`=3. So the closet words were 3 bits apart.\n",
"Therefore, they differ in parity, and are now 4 bits apart - `d`=4.\n",
"We can now detect 3 and fix 1.\n",
"\n",
"#### Q7: Can be sitringuish between 0, 1 and 2 errors? How about 3 errors?\n",
"\n",
"<table>\n",
" <tr>\n",
" <th># errors</th><th>EC vs EC1 vs EC2</th><th>parity</th>\n",
" </tr>\n",
" <tr>\n",
" <td>0</td><td>V</td><td>V</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td><td>X or V (if error in parity)</td><td>X</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td><td>X</td><td>V</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td><td>X or V (example below)</td><td>X</td>\n",
" </tr>\n",
"</table>\n",
"\n",
"Examle for not detectin 3 bits by EC:\n",
"\n",
"0**0**10110 0**0**0 0**0**0 1\n",
"\n",
"#### Q8: What's the decoding algorithm given we expect no more than 2 errors?\n",
"\n",
"We use the above table.\n",
"\n",
"- EC and parity are V - no errors, return data\n",
"- Parity is X - single error, fix.\n",
"- EC is X, parity is V - two errors, don't fix.\n",
"\n",
"What can we do with the knowledge that we have an error if we cannot fix it?\n",
"\n",
"\n",
"#### Q9: Improvement C: iterated/turbo decoding\n",
"\n",
"Pack several messages into a matrix and add EC for matrix columns. Now, even if we have two errors in a row, we might be able to fix each of them, if they are the sole errors in their columns.\n",
"We might even be able to iterate and fix several errors.\n",
"\n",
"What is a scenario we cannot fix with at most 2 errors in evey row/column?\n",
"\n",
"A rectangle of 4 errors."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fin\n",
"This notebook is part of the [Extended introduction to computer science](http://tau-cs1001-py.wikidot.com/) course at Tel-Aviv University.\n",
"\n",
"The notebook was written using Python 3.2 and IPython 0.13.1.\n",
"\n",
"The code is available at <https://raw.github.com/yoavram/CS1001.py/master/recitation14.ipynb>.\n",
"\n",
"The notebook can be viewed online at <http://nbviewer.ipython.org/urls/raw.github.com/yoavram/CS1001.py/master/recitation14.ipynb>.\n",
"\n",
"This work is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](http://creativecommons.org/licenses/by-sa/3.0/)."
]
}
],
"metadata": {}
}
]
} | bsd-3-clause |
machinelearningnanodegree/stanford-cs231 | solutions/levin/assignment2/FullyConnectedNets.ipynb | 1 | 449922 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fully-Connected Neural Nets\n",
"In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures.\n",
"\n",
"In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
"\n",
"```python\n",
"def layer_forward(x, w):\n",
" \"\"\" Receive inputs x and weights w \"\"\"\n",
" # Do some computations ...\n",
" z = # ... some intermediate value\n",
" # Do some more computations ...\n",
" out = # the output\n",
" \n",
" cache = (x, w, z, out) # Values we need to compute gradients\n",
" \n",
" return out, cache\n",
"```\n",
"\n",
"The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
"\n",
"```python\n",
"def layer_backward(dout, cache):\n",
" \"\"\"\n",
" Receive derivative of loss with respect to outputs and cache,\n",
" and compute derivative with respect to inputs.\n",
" \"\"\"\n",
" # Unpack cache values\n",
" x, w, z, out = cache\n",
" \n",
" # Use values in cache to compute derivatives\n",
" dx = # Derivative of loss with respect to x\n",
" dw = # Derivative of loss with respect to w\n",
" \n",
" return dx, dw\n",
"```\n",
"\n",
"After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
"\n",
"In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch Normalization as a tool to more efficiently optimize deep networks.\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"run the following from the cs231n directory and try again:\n",
"python setup.py build_ext --inplace\n",
"You may also need to restart your iPython kernel\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/levin/anaconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
]
}
],
"source": [
"# As usual, a bit of setup\n",
"import sys\n",
"import os\n",
"sys.path.insert(0, os.path.abspath('..'))\n",
"import time\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from cs231n.classifiers.fc_net import *\n",
"from cs231n.data_utils import get_CIFAR10_data\n",
"from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
"from cs231n.solver import Solver\n",
"\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",
"# for auto-reloading external modules\n",
"# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"def rel_error(x, y):\n",
" \"\"\" returns relative error \"\"\"\n",
" return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X_val: (1000, 3, 32, 32)\n",
"X_train: (49000, 3, 32, 32)\n",
"X_test: (1000, 3, 32, 32)\n",
"y_val: (1000,)\n",
"y_train: (49000,)\n",
"y_test: (1000,)\n"
]
}
],
"source": [
"# Load the (preprocessed) CIFAR10 data.\n",
"\n",
"data = get_CIFAR10_data()\n",
"for k, v in data.iteritems():\n",
" print '%s: ' % k, v.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Affine layer: foward\n",
"Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
"\n",
"Once you are done you can test your implementaion by running the following:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing affine_forward function:\n",
"difference: 9.76984946819e-10\n"
]
}
],
"source": [
"# Test the affine_forward function\n",
"\n",
"num_inputs = 2\n",
"input_shape = (4, 5, 6)\n",
"output_dim = 3\n",
"\n",
"input_size = num_inputs * np.prod(input_shape)\n",
"weight_size = output_dim * np.prod(input_shape)\n",
"\n",
"x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
"w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
"b = np.linspace(-0.3, 0.1, num=output_dim)\n",
"\n",
"out, _ = affine_forward(x, w, b)\n",
"correct_out = np.array([[ 1.49834967, 1.70660132, 1.91485297],\n",
" [ 3.25553199, 3.5141327, 3.77273342]])\n",
"\n",
"# Compare your output with ours. The error should be around 1e-9.\n",
"print 'Testing affine_forward function:'\n",
"print 'difference: ', rel_error(out, correct_out)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Affine layer: backward\n",
"Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing affine_backward function:\n",
"dx error: 2.92895223181e-10\n",
"dw error: 2.29474519463e-10\n",
"db error: 4.1737760614e-11\n"
]
}
],
"source": [
"# Test the affine_backward function\n",
"\n",
"x = np.random.randn(10, 2, 3)\n",
"w = np.random.randn(6, 5)\n",
"b = np.random.randn(5)\n",
"dout = np.random.randn(10, 5)\n",
"\n",
"dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
"dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
"db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
"\n",
"_, cache = affine_forward(x, w, b)\n",
"dx, dw, db = affine_backward(dout, cache)\n",
"\n",
"# The error should be around 1e-10\n",
"print 'Testing affine_backward function:'\n",
"print 'dx error: ', rel_error(dx_num, dx)\n",
"print 'dw error: ', rel_error(dw_num, dw)\n",
"print 'db error: ', rel_error(db_num, db)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ReLU layer: forward\n",
"Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing relu_forward function:\n",
"difference: 4.99999979802e-08\n"
]
}
],
"source": [
"# Test the relu_forward function\n",
"\n",
"x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
"\n",
"out, _ = relu_forward(x)\n",
"correct_out = np.array([[ 0., 0., 0., 0., ],\n",
" [ 0., 0., 0.04545455, 0.13636364,],\n",
" [ 0.22727273, 0.31818182, 0.40909091, 0.5, ]])\n",
"\n",
"# Compare your output with ours. The error should be around 1e-8\n",
"print 'Testing relu_forward function:'\n",
"print 'difference: ', rel_error(out, correct_out)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ReLU layer: backward\n",
"Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing relu_backward function:\n",
"dx error: 3.27561383708e-12\n"
]
}
],
"source": [
"x = np.random.randn(10, 10)\n",
"dout = np.random.randn(*x.shape)\n",
"\n",
"dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
"\n",
"_, cache = relu_forward(x)\n",
"dx = relu_backward(dout, cache)\n",
"\n",
"# The error should be around 1e-12\n",
"print 'Testing relu_backward function:'\n",
"print 'dx error: ', rel_error(dx_num, dx)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# \"Sandwich\" layers\n",
"There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
"\n",
"For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing affine_relu_forward:\n",
"dx error: 4.61025112012e-10\n",
"dw error: 1.21121604714e-10\n",
"db error: 4.22957555078e-11\n"
]
}
],
"source": [
"from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
"\n",
"x = np.random.randn(2, 3, 4)\n",
"w = np.random.randn(12, 10)\n",
"b = np.random.randn(10)\n",
"dout = np.random.randn(2, 10)\n",
"\n",
"out, cache = affine_relu_forward(x, w, b)\n",
"dx, dw, db = affine_relu_backward(dout, cache)\n",
"\n",
"dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
"dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
"db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
"\n",
"print 'Testing affine_relu_forward:'\n",
"print 'dx error: ', rel_error(dx_num, dx)\n",
"print 'dw error: ', rel_error(dw_num, dw)\n",
"print 'db error: ', rel_error(db_num, db)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Loss layers: Softmax and SVM\n",
"You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
"\n",
"You can make sure that the implementations are correct by running the following:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing svm_loss:\n",
"loss: 8.9988134813\n",
"dx error: 3.62260263876e-09\n",
"\n",
"Testing softmax_loss:\n",
"loss: 2.30246686253\n",
"dx error: 8.69677164163e-09\n"
]
}
],
"source": [
"num_classes, num_inputs = 10, 50\n",
"x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
"y = np.random.randint(num_classes, size=num_inputs)\n",
"\n",
"dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
"loss, dx = svm_loss(x, y)\n",
"\n",
"# Test svm_loss function. Loss should be around 9 and dx error should be 1e-9\n",
"print 'Testing svm_loss:'\n",
"print 'loss: ', loss\n",
"print 'dx error: ', rel_error(dx_num, dx)\n",
"\n",
"dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
"loss, dx = softmax_loss(x, y)\n",
"\n",
"# Test softmax_loss function. Loss should be 2.3 and dx error should be 1e-8\n",
"print '\\nTesting softmax_loss:'\n",
"print 'loss: ', loss\n",
"print 'dx error: ', rel_error(dx_num, dx)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Two-layer network\n",
"In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
"\n",
"Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing initialization ... \n",
"Testing test-time forward pass ... \n",
"Testing training loss (no regularization)\n",
"Running numeric gradient check with reg = 0.0\n",
"W1 relative error: 1.83e-08\n",
"W2 relative error: 3.20e-10\n",
"b1 relative error: 9.83e-09\n",
"b2 relative error: 4.33e-10\n",
"Running numeric gradient check with reg = 0.7\n",
"W1 relative error: 2.53e-07\n",
"W2 relative error: 2.85e-08\n",
"b1 relative error: 1.56e-08\n",
"b2 relative error: 9.09e-10\n"
]
}
],
"source": [
"N, D, H, C = 3, 5, 50, 7\n",
"X = np.random.randn(N, D)\n",
"y = np.random.randint(C, size=N)\n",
"\n",
"std = 1e-2\n",
"model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
"\n",
"print 'Testing initialization ... '\n",
"W1_std = abs(model.params['W1'].std() - std)\n",
"b1 = model.params['b1']\n",
"W2_std = abs(model.params['W2'].std() - std)\n",
"b2 = model.params['b2']\n",
"assert W1_std < std / 10, 'First layer weights do not seem right'\n",
"assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
"assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
"assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
"\n",
"print 'Testing test-time forward pass ... '\n",
"model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
"model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
"model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
"model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
"X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
"scores = model.loss(X)\n",
"correct_scores = np.asarray(\n",
" [[11.53165108, 12.2917344, 13.05181771, 13.81190102, 14.57198434, 15.33206765, 16.09215096],\n",
" [12.05769098, 12.74614105, 13.43459113, 14.1230412, 14.81149128, 15.49994135, 16.18839143],\n",
" [12.58373087, 13.20054771, 13.81736455, 14.43418138, 15.05099822, 15.66781506, 16.2846319 ]])\n",
"scores_diff = np.abs(scores - correct_scores).sum()\n",
"assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
"\n",
"print 'Testing training loss (no regularization)'\n",
"y = np.asarray([0, 5, 1])\n",
"loss, grads = model.loss(X, y)\n",
"correct_loss = 3.4702243556\n",
"assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
"\n",
"model.reg = 1.0\n",
"loss, grads = model.loss(X, y)\n",
"correct_loss = 26.5948426952\n",
"assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
"\n",
"for reg in [0.0, 0.7]:\n",
" print 'Running numeric gradient check with reg = ', reg\n",
" model.reg = reg\n",
" loss, grads = model.loss(X, y)\n",
"\n",
" for name in sorted(grads):\n",
" f = lambda _: model.loss(X, y)[0]\n",
" grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
" print '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Solver\n",
"In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
"\n",
"Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(Iteration 1 / 4900) loss: 2.299175\n",
"(Epoch 0 / 10) train acc: 0.132000; val_acc: 0.114000\n",
"(Iteration 101 / 4900) loss: 1.745887\n",
"(Iteration 201 / 4900) loss: 1.841234\n",
"(Iteration 301 / 4900) loss: 1.469272\n",
"(Iteration 401 / 4900) loss: 1.459521\n",
"(Epoch 1 / 10) train acc: 0.442000; val_acc: 0.431000\n",
"(Iteration 501 / 4900) loss: 1.686477\n",
"(Iteration 601 / 4900) loss: 1.548239\n",
"(Iteration 701 / 4900) loss: 1.341739\n",
"(Iteration 801 / 4900) loss: 1.622524\n",
"(Iteration 901 / 4900) loss: 1.412256\n",
"(Epoch 2 / 10) train acc: 0.481000; val_acc: 0.471000\n",
"(Iteration 1001 / 4900) loss: 1.528274\n",
"(Iteration 1101 / 4900) loss: 1.430866\n",
"(Iteration 1201 / 4900) loss: 1.701945\n",
"(Iteration 1301 / 4900) loss: 1.355894\n",
"(Iteration 1401 / 4900) loss: 1.429757\n",
"(Epoch 3 / 10) train acc: 0.494000; val_acc: 0.473000\n",
"(Iteration 1501 / 4900) loss: 1.578617\n",
"(Iteration 1601 / 4900) loss: 1.404213\n",
"(Iteration 1701 / 4900) loss: 1.174549\n",
"(Iteration 1801 / 4900) loss: 1.319764\n",
"(Iteration 1901 / 4900) loss: 1.337004\n",
"(Epoch 4 / 10) train acc: 0.552000; val_acc: 0.485000\n",
"(Iteration 2001 / 4900) loss: 1.446253\n",
"(Iteration 2101 / 4900) loss: 1.512710\n",
"(Iteration 2201 / 4900) loss: 1.179128\n",
"(Iteration 2301 / 4900) loss: 1.237280\n",
"(Iteration 2401 / 4900) loss: 1.335179\n",
"(Epoch 5 / 10) train acc: 0.557000; val_acc: 0.490000\n",
"(Iteration 2501 / 4900) loss: 1.334219\n",
"(Iteration 2601 / 4900) loss: 1.292154\n",
"(Iteration 2701 / 4900) loss: 1.307421\n",
"(Iteration 2801 / 4900) loss: 1.261683\n",
"(Iteration 2901 / 4900) loss: 1.259764\n",
"(Epoch 6 / 10) train acc: 0.555000; val_acc: 0.505000\n",
"(Iteration 3001 / 4900) loss: 1.315673\n",
"(Iteration 3101 / 4900) loss: 1.237905\n",
"(Iteration 3201 / 4900) loss: 1.123620\n",
"(Iteration 3301 / 4900) loss: 1.053664\n",
"(Iteration 3401 / 4900) loss: 1.135636\n",
"(Epoch 7 / 10) train acc: 0.568000; val_acc: 0.495000\n",
"(Iteration 3501 / 4900) loss: 1.258151\n",
"(Iteration 3601 / 4900) loss: 0.986172\n",
"(Iteration 3701 / 4900) loss: 1.146398\n",
"(Iteration 3801 / 4900) loss: 1.087954\n",
"(Iteration 3901 / 4900) loss: 1.049108\n",
"(Epoch 8 / 10) train acc: 0.597000; val_acc: 0.522000\n",
"(Iteration 4001 / 4900) loss: 1.198095\n",
"(Iteration 4101 / 4900) loss: 1.152446\n",
"(Iteration 4201 / 4900) loss: 1.097323\n",
"(Iteration 4301 / 4900) loss: 1.230197\n",
"(Iteration 4401 / 4900) loss: 1.170906\n",
"(Epoch 9 / 10) train acc: 0.599000; val_acc: 0.493000\n",
"(Iteration 4501 / 4900) loss: 0.976991\n",
"(Iteration 4601 / 4900) loss: 1.412240\n",
"(Iteration 4701 / 4900) loss: 1.181796\n",
"(Iteration 4801 / 4900) loss: 1.224346\n",
"(Epoch 10 / 10) train acc: 0.595000; val_acc: 0.511000\n"
]
}
],
"source": [
"# model = TwoLayerNet()\n",
"# solver = None\n",
"\n",
"##############################################################################\n",
"# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least #\n",
"# 50% accuracy on the validation set. #\n",
"##############################################################################\n",
"input_dim=3*32*32\n",
"hidden_dim=100\n",
"num_classes=10\n",
"weight_scale=1e-3\n",
"reg=0.0\n",
"model = TwoLayerNet(input_dim=input_dim, hidden_dim=hidden_dim, num_classes=num_classes,\n",
" weight_scale=weight_scale, reg=reg)\n",
"\n",
"solver = Solver(model, data,\n",
" update_rule='sgd',\n",
" optim_config={\n",
" 'learning_rate': 1e-3,\n",
" },\n",
" lr_decay=0.95,\n",
" num_epochs=10, batch_size=100,\n",
" print_every=100)\n",
"solver.train()\n",
"##############################################################################\n",
"# END OF YOUR CODE #\n",
"##############################################################################"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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ZB3ReueXjwE1epq5H6uvrATjttCJeeGEVH3ywHreWrgi30bgrvfzzn2v4/OeXsXfv/z38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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7feb8b5c9b10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Run this cell to visualize training loss and train / val accuracy\n",
"\n",
"plt.subplot(2, 1, 1)\n",
"plt.title('Training loss')\n",
"plt.plot(solver.loss_history, 'o')\n",
"plt.xlabel('Iteration')\n",
"\n",
"plt.subplot(2, 1, 2)\n",
"plt.title('Accuracy')\n",
"plt.plot(solver.train_acc_history, '-o', label='train')\n",
"plt.plot(solver.val_acc_history, '-o', label='val')\n",
"plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
"plt.xlabel('Epoch')\n",
"plt.legend(loc='lower right')\n",
"plt.gcf().set_size_inches(15, 12)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multilayer network\n",
"Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
"\n",
"Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
"\n",
"Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch normalization; we will add those features soon."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initial loss and gradient check"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
"\n",
"For gradient checking, you should expect to see errors around 1e-6 or less."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running check with reg = 0\n",
"Initial loss: 2.30215742661\n",
"W1 relative error: 1.61e-06\n",
"W2 relative error: 6.56e-07\n",
"W3 relative error: 2.36e-07\n",
"b1 relative error: 7.68e-08\n",
"b2 relative error: 1.39e-08\n",
"b3 relative error: 1.11e-10\n",
"Running check with reg = 3.14\n",
"Initial loss: 6.82186799419\n",
"W1 relative error: 1.33e-07\n",
"W2 relative error: 2.15e-08\n",
"W3 relative error: 1.76e-08\n",
"b1 relative error: 1.31e-07\n",
"b2 relative error: 1.92e-08\n",
"b3 relative error: 2.75e-10\n"
]
}
],
"source": [
"N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
"X = np.random.randn(N, D)\n",
"y = np.random.randint(C, size=(N,))\n",
"\n",
"for reg in [0, 3.14]:\n",
" print 'Running check with reg = ', reg\n",
" model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
" reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
"\n",
" loss, grads = model.loss(X, y)\n",
" print 'Initial loss: ', loss\n",
"\n",
" for name in sorted(grads):\n",
" f = lambda _: model.loss(X, y)[0]\n",
" grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
" print '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. You will need to tweak the learning rate and initialization scale, but you should be able to overfit and achieve 100% training accuracy within 20 epochs."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(Iteration 1 / 40) loss: 2.290972\n",
"(Epoch 0 / 20) train acc: 0.260000; val_acc: 0.118000\n",
"(Epoch 1 / 20) train acc: 0.360000; val_acc: 0.140000\n",
"(Epoch 2 / 20) train acc: 0.340000; val_acc: 0.136000\n",
"(Epoch 3 / 20) train acc: 0.540000; val_acc: 0.140000\n",
"(Epoch 4 / 20) train acc: 0.340000; val_acc: 0.134000\n",
"(Epoch 5 / 20) train acc: 0.740000; val_acc: 0.190000\n",
"(Iteration 11 / 40) loss: 1.386780\n",
"(Epoch 6 / 20) train acc: 0.780000; val_acc: 0.188000\n",
"(Epoch 7 / 20) train acc: 0.720000; val_acc: 0.163000\n",
"(Epoch 8 / 20) train acc: 0.560000; val_acc: 0.132000\n",
"(Epoch 9 / 20) train acc: 0.840000; val_acc: 0.173000\n",
"(Epoch 10 / 20) train acc: 0.940000; val_acc: 0.189000\n",
"(Iteration 21 / 40) loss: 0.334002\n",
"(Epoch 11 / 20) train acc: 0.960000; val_acc: 0.207000\n",
"(Epoch 12 / 20) train acc: 0.940000; val_acc: 0.208000\n",
"(Epoch 13 / 20) train acc: 0.980000; val_acc: 0.212000\n",
"(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.203000\n",
"(Epoch 15 / 20) train acc: 0.980000; val_acc: 0.214000\n",
"(Iteration 31 / 40) loss: 0.046344\n",
"(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.199000\n",
"(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.208000\n",
"(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.206000\n",
"(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.206000\n",
"(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.204000\n"
]
},
{
"data": {
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U0u75m0SStqnZnGR8fIrVYNbqU9ZsTtZWk6TBYUd/SdqBldGXi4vLjI46+lLS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\nkiQVwFAmSZJUgP8PvXRKzi7u2toAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7feb8b34ba90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# TODO: Use a three-layer Net to overfit 50 training examples.\n",
"\n",
"num_train = 50\n",
"small_data = {\n",
" 'X_train': data['X_train'][:num_train],\n",
" 'y_train': data['y_train'][:num_train],\n",
" 'X_val': data['X_val'],\n",
" 'y_val': data['y_val'],\n",
"}\n",
"\n",
"learning_rate = 1e-2\n",
"weight_scale = 1e-2\n",
"model = FullyConnectedNet([100, 100],\n",
" weight_scale=weight_scale, dtype=np.float64)\n",
"solver = Solver(model, small_data,\n",
" print_every=10, num_epochs=20, batch_size=25,\n",
" update_rule='sgd',\n",
" optim_config={\n",
" 'learning_rate': learning_rate,\n",
" }\n",
" )\n",
"solver.train()\n",
"\n",
"plt.plot(solver.loss_history, 'o')\n",
"plt.title('Training loss history')\n",
"plt.xlabel('Iteration')\n",
"plt.ylabel('Training loss')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again you will have to adjust the learning rate and weight initialization, but you should be able to achieve 100% training accuracy within 20 epochs."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(Iteration 1 / 40) loss: 8.685991\n",
"(Epoch 0 / 20) train acc: 0.300000; val_acc: 0.092000\n",
"(Epoch 1 / 20) train acc: 0.140000; val_acc: 0.107000\n",
"(Epoch 2 / 20) train acc: 0.200000; val_acc: 0.127000\n",
"(Epoch 3 / 20) train acc: 0.540000; val_acc: 0.108000\n",
"(Epoch 4 / 20) train acc: 0.740000; val_acc: 0.147000\n",
"(Epoch 5 / 20) train acc: 0.820000; val_acc: 0.139000\n",
"(Iteration 11 / 40) loss: 0.407608\n",
"(Epoch 6 / 20) train acc: 0.880000; val_acc: 0.140000\n",
"(Epoch 7 / 20) train acc: 0.960000; val_acc: 0.144000\n",
"(Epoch 8 / 20) train acc: 0.980000; val_acc: 0.132000\n",
"(Epoch 9 / 20) train acc: 0.980000; val_acc: 0.133000\n",
"(Epoch 10 / 20) train acc: 0.980000; val_acc: 0.141000\n",
"(Iteration 21 / 40) loss: 0.271167\n",
"(Epoch 11 / 20) train acc: 0.980000; val_acc: 0.130000\n",
"(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.134000\n",
"(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.133000\n",
"(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.132000\n",
"(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.137000\n",
"(Iteration 31 / 40) loss: 0.099823\n",
"(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.133000\n",
"(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.134000\n",
"(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.129000\n",
"(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.139000\n",
"(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.139000\n"
]
},
{
"data": {
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iIzpwYE+S86u2ns/+/U4pALA5UsOIFheP5NChhVwIY4MxYouLR7rVBABMF2PELsHyrMlz\n55ayf79ZkwAwywzWBwDoxGB9AIApIogBAHQiiAEAdCKIAQB0sqUgVgOrVzEFAGAEFw1iVfXKqrpX\nVd09yU1J/qyqfmz8pQEA7G6baRF7RGvttiRPT3IiyWVJjoyzKACAWbCZIHbXqtqb5GlJ3tha+9sk\nbqgIAHCJNhPEXpbkI0nuk+QPquork9w+1qoAAGbAllfWr6pKctdhy9hYWVkfAJgWY1lZv6p+uKru\nNXz8X5K8Lcm3jFYiAADLNtM1+ezW2m1V9fgkD0jyA0lePN6yAAB2v80EseW+wScneVVr7V2bfB0A\nABvYTKB6V1X9VpLvSPLbVXWPXAhnAACM6KKD9avqLkkeneTPWmufqqr7JfmK1toNYy/OYH0AYEqM\nMlh/78V2aK19fhi+/slgwmT+oLX22yPWCADA0GZmTf50kp9IcvPw63lV9W/GXRgAwG63ma7Jdyd5\nVGvtc8Pv9yZ5Z2vtEWMvTtckADAlxrKO2NA913kMAMCILjpGLIM1w95ZVb+fpJLMJZkfZ1EAALNg\nU7c4qqoDSR4z/PZtrbWzY63qwnF1TQIAU2GUrsl1g1hVbTgGrLX27q0caBSCGAAwLbY7iP3RBq9r\nrbVv3cqBRiGIAQDTYluD2E4giAEA02KcsyYBANhmghgAQCeCGABAJxddR2yd2ZO3Jvloa21p+0sC\nAJgNm7nF0fVJLk/y3gwWdP3aJO/LYIX9Z7fWfn9sxRmsDwBMiXEN1v9wkke31i5vrT0yyaOTfCjJ\nE5L8hy1XCQBAks0Fsa9duXhra+2mJA9vrf3Z+MoCANj9NnOvyQ9U1TVJXj/8/pnDbXdL8rmxVQYA\nsMttZozY3ZNcneRxw01vTXJNkv8vyT1aa7eOrThjxACAKWFlfQCATkYJYptZvuKbkywkuWzl/q21\nh265QgAA7rCZrsn3J/mJJO9I8vnl7a21W8ZbmhYxAGB6jKVFLMltrbXfGLEmAADWsZkWsZ8dPvy1\nJJ9d3r5ySYtx0SIGAEyLsQzWr6o/WmNza61961YONApBDACYFmZNAgB0sq1jxKrqu1trr6uq56z1\nfGvtP261QAAALthosP59hv+9/yQKAQCYNbomAQC2wbgWdL1fkn+W5MG584Kuz95qgQAAXLCZdcTe\nmOS6JG/JigVdAQC4NJtZvuLG1trlE6pn9bF1TQIAU2GUrsk9m9jnt6vq8SPWBADAOjbTIvbpJPdO\n8tdJ/jZJZbCg633HXpwWMQBgSozrXpP3G7EeAAA2sNGCrl/dWvvTJF+3zi5jv9ckAMButm7XZFW9\nvLX2z91rEgDg4txrEgCgk3GNEUtVPSzJw5N88fK21tprt1YeAAArbWZl/X+d5PFJHpbkd5I8IYPF\nXQUxAIBLsJl1xJ6Z5IokH2+tfW+SRybZN9aqAABmwGaC2N+01j6f5HNVdc8kn0hy2XjLAgDY/TYz\nRuyGqvrSJL+c5E+S3Jbk7WOtCgBgBmw4a7KqKskDW2sfH37/VUnu1Vp750SKM2sSAJgSY1m+oqre\n01r7e5dU2YgEMQBgWozrpt83VtU3jFgTAADr2Ghl/b2ttc9V1XuTfE2SU0nO58JNvx819uK0iAEA\nU2K7F3R9e5JHJXnqJVUFAMCaNgpilSSttVMTqgUAYKZsFMTuX1U/tt6TrbWfG0M9AAAzY6Mgdpck\n98iwZQwAgO210WD9d05iQP5GDNYHAKbFdi9foSUMAGCMNmoRu29r7VMTrmd1DVrEAICpMJaV9Xva\nrUHs9OkzmZ8/nrNnl3LgwJ4sLh7JwYPuow4A00wQmwKnT5/JlVdek1OnjiXZl+R8Dh1ayIkTVwtj\nADDFxnWLI7bR/PzxFSEsSfbl1KljmZ8/3rEqAKAHQWzCzp5dyoUQtmxfzp1b6lEOANCRIDZhBw7s\nyeCWnSudz/79fhQAMGv89p+wxcUjOXRoIRfC2GCM2OLikW41AQB9GKzfwfKsyXPnlrJ/v1mTALAb\nTN2syarak+RPknystfbUNZ7flUEMANh9pnHW5I8keV/nGgAAuugWxKrqQUmenORlvWoAAOipZ4vY\nzyd5XhJ9jwDATNrb46BV9ZQkt7TWbqyquWxwg/GjR4/e8Xhubi5zc3PjLg8A4KJOnjyZkydPXtJ7\ndBmsX1U/k+SqJJ9L8iVJ7pnk11pr37dqP4P1AYCpMHWzJpOkqr4tyXPNmgQAptk0zpoEAJhZ3VvE\nNqJFDACYFlrEAACmiCAGANCJIAYA0IkgBgDQiSAGANCJIAYA0IkgBgDQiSAGANCJIAYA0IkgBgDQ\niSAGANCJIAYA0IkgBgDQiSAGANCJIAYA0IkgBgDQiSAGANCJIAYA0IkgBgDQiSAGANCJIAYA0Ikg\nBgDQiSAGANCJIAYA0IkgBgDQiSAGANCJIAYA0IkgBgDQiSAGANCJIAYA0IkgBgDQiSAGANCJIAYA\n0IkgBgDQiSAGANCJIAYA0IkgBgDQiSAGANCJIAYA0IkgBgDQiSAGANCJIAYA0IkgBgDQiSAGANCJ\nIAYA0IkgBgDQiSAGANCJIAYA0IkgBgDQiSAGANCJIAYA0IkgBgDQiSAGANCJIAYA0IkgBgDQiSAG\nANCJIAYA0IkgBgDQiSAGANCJIAYA0IkgBgDQiSAGANCJIAYA0IkgBgDQiSAGANCJIAYA0IkgBgDQ\niSAGANCJIAYA0IkgBgDQiSAGANCJIAYA0IkgBgDQiSAGANCJIAYA0IkgBgDQiSAGANCJIAYA0Ikg\nBgDQiSAGANBJlyBWVQ+qqjdX1Xur6qaqek6POgAAeqrW2uQPWvXAJA9srd1YVfdI8o4kT2utfWDV\nfq1HfQAAW1VVaa3VVl7TpUWstfaJ1tqNw8e3J3l/kgM9agEA6KX7GLGqenCSy5O8rW8lAACT1TWI\nDbsl35DkR4YtYwAAM2NvrwNX1d4MQtirWmtvXG+/o0eP3vF4bm4uc3NzY68NAOBiTp48mZMnT17S\ne3QZrJ8kVfXKJH/RWvuxDfYxWB8AmAqjDNbvNWvysUn+MMlNSdrw64Wttf+1aj9BDACYClMTxDZL\nEAMApsXULF8BAIAgBgDQjSAGANCJIAYA0IkgBgDQiSAGANCJIAYA0IkgBgDQiSAGANCJIAYA0Mne\n3gUwfqdPn8n8/PGcPbuUAwf2ZHHxSA4evKx3WQAw89xrcpc7ffpMrrzympw6dSzJviTnc+jQQk6c\nuFoYA4Bt5F6Tu9zp02dy1VXHcsUVC7nqqmM5ffrMRV8zP398RQhLkn05depY5uePj7NUAGATdE1O\nibVatq677uItW2fPLuVCCFu2L+fOLY2xWgBgM7SITYlRW7YOHNiT5Pyqreezf78fPQD05rfxlBi1\nZWtx8UgOHVrIhTA2GCO2uHhku0sEALZI1+SUuNCytTKMXbxl6+DBy3LixNWZn39Jzp1byv79e7K4\naKA+AOwEZk1OCbMfAWBnG2XWpCA2RZbXA7vQsmU9MADYKQQxAIBOrCMGADBFBDEAgE4EMQCATgQx\nAIBOBDEAgE4EMQCATgQxAIBOBDEAgE4EMQCATgQxAIBOBDEAgE4EMQCATgQxAIBOBDEAgE4EMQCA\nTgQxAIBOBDEAgE4EMQCATvb2LoCd6/TpM5mfP56zZ5dy4MCeLC4eycGDl/UuCwB2jWqt9a5hXVXV\ndnJ9u9np02dy5ZXX5NSpY0n2JTmfQ4cWcuLE1cIYAKyhqtJaq628Rtcka5qfP74ihCXJvpw6dSzz\n88c7VgUAu4sgxprOnl3KhRC2bF/OnVvqUQ4A7EqCGGs6cGBPkvOrtp7P/v0uGQDYLn6rsqbFxSM5\ndGghF8LYYIzY4uKRbjUBwG5jsD7rWp41ee7cUvbvN2sSADYyymB9QQwAYBuYNQkAMEUEMQCATgQx\nAIBOBDEAgE7ca5KZ5D6aAOwEZk0yc9xHE4BxMGsSNsF9NAHYKXRNsmNMqrvQfTQB2CkEMXaEtboL\nr7tuPN2FF+6juTKMuY8mAJPnNw87wiS7C91HE4CdQosYO8IkuwsPHrwsJ05cnfn5l6y4j6aB+gBM\nniDGjjDp7sKDBy/Lq1+9MJb3BoDN0jXJjqC7EIBZZB0xdozlWZMXugstsgrA9BhlHTFBDABgG4wS\nxIwRY9u5fRAAbI4WMbaV2wcBMKvc4oju3D4IADZP1yTbatK3D5qWbtBpqROAyRLE2FaTXA9skrdF\nuhTTUicAk6drkm01yfXApqUbdFrqBGDytIixrSZ5+6BJd4Mmo3Ux9qgTgOkgiLHtJnX7oEnfFmnU\nLsZJ19mDMXAAo7F8BVNr0ktlXHXVsbzmNT+e1YHq8OGXbBg8L6XOaQg4liwBGLCgKzNlkt2gyehd\njKPWeSmD/CcZ4NYfA7dxQO1hGoItMFsEMabapLpBk0vrYhylzlEDzqRnaU7LGDizV4GdaPcMUoEx\nm+SM0GT0gDPpWZoXAupKO28MnNmrwE6kRQw2adJdoaO2wE26hWpx8Uiuu27hC8aILS5ePZbjjWpa\nWu6A2SKIwRZMsit01IAz6Vmakw6oo5qF2avA9DFrEnaw5cHlFwLOxQeXT9MszUker8fsTpMDYLaM\nMmtSEINdaNIBbtQaewWjrZyXSzmWZT3YqfyRMB6CGDCyUddJm5bjTdpu/3y73W4OKtP0R8K0/Rys\nIwaMbNKD2adp8Pykb221m7uIp0GPpU5G/RmM8rppWftvZpacaa3t2K9BecAkHD58tCW3t6St+Lq9\nHT58dMcd7+abP9wOHz7a5uZe1A4fPtpuvvnDY6lx+ViHDj13Ra23t0OHnnvRY476+UY93vJrt3pe\nJn28aTDp/xdG/RmM+rq5uRet+myDryuueNF2fqwvqHWr18qkfw7bYZhbtpZ1tvqCSX4JYjA5l/IL\neZLHm3Sdkw5Uu/14y68dJcBN6nWXElQmGTgm/bpR7fbAuNJUBbEkT0zygSQfSvL8dfbZ0gmYFdde\ne23vEnYc52RtWz0vy/8IXXHFZFo4RjnedvwS2cp52Y5fylv5fKMeb9TzcufjXTv24006gI/yujt/\ntmvH/tlG/ZmP+rrtCNGPfOT37digubLOSbUOL5uaIJbBiv5/luSyJHdNcmOSh62x36Y//CxZWFjo\nXcKO45ysbTeel+34K3kr52Vaumy3J8AtTPh44/+lPMrr7vwLeWHTv5Cn4bOt/Ixb/SNh1PMy6cA4\n6dbhlUYJYr1WMvymJH/aWjvTWvu7JK9P8rROtQBTZNK3VJr0ra1GPd6o52XSxxt1EsMkX7e8SPHh\nwy/Jgx98bQ4ffsmmBoiPWuOoP4NLuTaXF6d+85uP5dWvXtjU4PdRbxM26rWy8udwxRULm/45jFpn\nrwlEvWZNHkjy0RXffyyDcAawoUnfUmnSdw4Y9XijnpeVx3vrW6/NYx9bYz3eqHc4mPTrloPK0aMt\nR48ubLjvdhxrlJ/5pK/NSwmao/4/O8rdTEats9fdN7qsI1ZVz0jyhNbas4ffX5Xkm1prz1m13+SL\nAwAYUZuSdcTOJvnKFd8/aLjtTrb6YQAApkmvMWLXJ/mqqrqsqr4oyXcleVOnWgAAuujSItZa+3xV\n/XCS380gDL68tfb+HrUAAPSyo+81CQCwm/XqmtxQVT2xqj5QVR+qquf3rmenqKoPV9W7quqGqnp7\n73p6qaqXV9UtVfXuFdvuU1W/W1UfrKrfqap796yxh3XOy0JVfayq3jn8emLPGietqh5UVW+uqvdW\n1U1V9Zzh9pm+XtY4L1cPt8/69XK3qnrb8N/Ym6pqYbh91q+X9c7LTF8vSVJVe4af/U3D77d8rey4\nFrGq2pPBavvfnuRcBuPJvqu19oGuhe0AVXVzkke31j7du5aequpxSW5P8srW2iOG2/5dkr9srb14\nGN7v01p7Qc86J22d87KQ5DOttZ/rWlwnVfXAJA9srd1YVfdI8o4M1ix8Vmb4etngvDwzM3y9JElV\n3b219tdVdZckb03ynCTPyAxfL8m65+VJcb38qySPTnKv1tpTR/ldtBNbxCz2ur7KzvyZTVRr7S1J\nVofRpyV5xfDxK5I8faJF7QDrnJdkcN3MpNbaJ1prNw4f357k/RnM0p7p62Wd83Jg+PTMXi9J0lr7\n6+HDu2UAFrnKAAAEz0lEQVQwjrplxq+XZN3zkszw9VJVD0ry5CQvW7F5y9fKTvylvtZirwfW2XfW\ntCQnqur6qvqB3sXsMF/eWrslGfySSfLlnevZSX64qm6sqpfNWpfKSlX14CSXJ7kuyQNcLwMrzsvb\nhptm+noZdjXdkOQTSU601q6P62W985LM9vXy80melwuhNBnhWtmJQYz1Pba19qgMEvgPDbuiWNvO\n6nPv5z8leUhr7fIM/gGdyS6EYffbG5L8yLAFaPX1MZPXyxrnZeavl9baUmvtGzJoOf2mqvq6uF7W\nOi8PzwxfL1X1lCS3DFuWN2oVvOi1shOD2KYWe51FrbWPD//750l+PW4LtdItVfWA5I7xL5/sXM+O\n0Fr783ZhIOgvJfnGnvX0UFV7Mwgbr2qtvXG4eeavl7XOi+vlgtbabUlOJnliXC93WHleZvx6eWyS\npw7Hbr8uyT+oqlcl+cRWr5WdGMQs9rqGqrr78K/XVNW+JI9P8p6+VXVVufNfIW9KcmT4+PuTvHH1\nC2bEnc7L8B+CZf8ks3nN/HKS97XWXrpim+tljfMy69dLVd1vuXutqr4kyZUZjJ+b6etlnfPygVm+\nXlprL2ytfWVr7SEZ5JQ3t9a+N8lvZIvXyo6bNZkMlq9I8tJcWOz133YuqbuqOphBK1jLYKDka2b1\nvFTVa5PMJfmyJLckWUjyP5L8apKvSHImyXe21v6qV409rHNershg/M9Skg8n+cHl8QuzoKoem+QP\nk9yUwf87LckLk7w9yX/LjF4vG5yX78lsXy9fn8EA6z3Dr//aWvvpqrpvZvt6We+8vDIzfL0sq6pv\nS/Lc4azJLV8rOzKIAQDMgp3YNQkAMBMEMQCATgQxAIBOBDEAgE4EMQCATgQxAIBOBDFgx6uqzwz/\ne1lVffc2v/dPrvr+Ldv5/gAbEcSAabC84OHBDBYd3bSqustFdnnhnQ7Umnu4AhMjiAHT5GeTPK6q\n3llVP1JVe6rqxVX1tqq6sap+IBmsdF1Vf1hVb0zy3uG2X6+q66vqpqr6F8NtP5vkS4bv96rhts8s\nH6yq/v1w/3dV1XeueO9rq+pXq+r9y68DGMXe3gUAbMELMryVSJIMg9dftdYeM7w37Vur6neH+35D\nkq9rrX1k+P2zWmt/VVVfnOT6qvrvrbWfrKofaq09asUx2vC9n5HkEa21r6+qLx++5g+G+1ye5OFJ\nPjE85t9vrf3vcX5wYHfSIgZMs8cn+b6quiHJ25LcN8lXD597+4oQliQ/WlU3JrkuyYNW7LeexyZ5\nXZK01j6Z5GSSb1zx3h9vg3vE3ZjkwZf+UYBZpEUMmGaV5OrW2ok7bRzchPf8qu//QZLHtNY+W1XX\nJvniFe+x2WMt++yKx5+Pf0uBEWkRA6bBcgj6TJJ7rtj+O0n+r6ramyRV9dVVdfc1Xn/vJJ8ehrCH\nJfnmFc/97fLrVx3rj5I8czgO7f5JviXJ27fhswDcwV9xwDRYnjX57iRLw67I4621l1bVg5O8s6oq\nySeTPH2N1/+vJP+yqt6b5INJ/njFc7+Y5N1V9Y7W2vcuH6u19utV9c1J3pVkKcnzWmufrKqvXac2\ngC2rwRAHAAAmTdckAEAnghgAQCeCGABAJ4IYAEAnghgAQCeCGABAJ4IYAEAn/z9sQ5hAWr3KTQAA\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7feb89965d50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# TODO: Use a five-layer Net to overfit 50 training examples.\n",
"\n",
"num_train = 50\n",
"small_data = {\n",
" 'X_train': data['X_train'][:num_train],\n",
" 'y_train': data['y_train'][:num_train],\n",
" 'X_val': data['X_val'],\n",
" 'y_val': data['y_val'],\n",
"}\n",
"\n",
"learning_rate = 1e-2\n",
"weight_scale = 6e-2\n",
"model = FullyConnectedNet([100, 100, 100, 100],\n",
" weight_scale=weight_scale, dtype=np.float64)\n",
"solver = Solver(model, small_data,\n",
" print_every=10, num_epochs=20, batch_size=25,\n",
" update_rule='sgd',\n",
" optim_config={\n",
" 'learning_rate': learning_rate,\n",
" }\n",
" )\n",
"solver.train()\n",
"\n",
"plt.plot(solver.loss_history, 'o')\n",
"plt.title('Training loss history')\n",
"plt.xlabel('Iteration')\n",
"plt.ylabel('Training loss')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Inline question: \n",
"Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net?\n",
"\n",
"# Answer:\n",
"It's much harder to find the right weight initialization and learning rate for five layer net. As the network grows deeper, we tend to have more dead activations, and thus kill the backward gradient.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Update rules\n",
"So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SGD+Momentum\n",
"Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochstic gradient descent.\n",
"\n",
"Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than 1e-8."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"next_w error: 8.88234703351e-09\n",
"velocity error: 4.26928774328e-09\n"
]
}
],
"source": [
"from cs231n.optim import sgd_momentum\n",
"\n",
"N, D = 4, 5\n",
"w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
"dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
"v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
"\n",
"config = {'learning_rate': 1e-3, 'velocity': v}\n",
"next_w, _ = sgd_momentum(w, dw, config=config)\n",
"\n",
"expected_next_w = np.asarray([\n",
" [ 0.1406, 0.20738947, 0.27417895, 0.34096842, 0.40775789],\n",
" [ 0.47454737, 0.54133684, 0.60812632, 0.67491579, 0.74170526],\n",
" [ 0.80849474, 0.87528421, 0.94207368, 1.00886316, 1.07565263],\n",
" [ 1.14244211, 1.20923158, 1.27602105, 1.34281053, 1.4096 ]])\n",
"expected_velocity = np.asarray([\n",
" [ 0.5406, 0.55475789, 0.56891579, 0.58307368, 0.59723158],\n",
" [ 0.61138947, 0.62554737, 0.63970526, 0.65386316, 0.66802105],\n",
" [ 0.68217895, 0.69633684, 0.71049474, 0.72465263, 0.73881053],\n",
" [ 0.75296842, 0.76712632, 0.78128421, 0.79544211, 0.8096 ]])\n",
"\n",
"print 'next_w error: ', rel_error(next_w, expected_next_w)\n",
"print 'velocity error: ', rel_error(expected_velocity, config['velocity'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"running with sgd\n",
"(Iteration 1 / 200) loss: 3.163584\n",
"(Epoch 0 / 5) train acc: 0.120000; val_acc: 0.133000\n",
"(Iteration 11 / 200) loss: 2.147519\n",
"(Iteration 21 / 200) loss: 2.134634\n",
"(Iteration 31 / 200) loss: 2.061858\n",
"(Epoch 1 / 5) train acc: 0.274000; val_acc: 0.258000\n",
"(Iteration 41 / 200) loss: 2.079632\n",
"(Iteration 51 / 200) loss: 1.965506\n",
"(Iteration 61 / 200) loss: 1.838539\n",
"(Iteration 71 / 200) loss: 1.967392\n",
"(Epoch 2 / 5) train acc: 0.352000; val_acc: 0.306000\n",
"(Iteration 81 / 200) loss: 1.837021\n",
"(Iteration 91 / 200) loss: 1.964120\n",
"(Iteration 101 / 200) loss: 1.848202\n",
"(Iteration 111 / 200) loss: 1.715296\n",
"(Epoch 3 / 5) train acc: 0.356000; val_acc: 0.323000\n",
"(Iteration 121 / 200) loss: 1.679853\n",
"(Iteration 131 / 200) loss: 1.830533\n",
"(Iteration 141 / 200) loss: 1.956518\n",
"(Iteration 151 / 200) loss: 1.767289\n",
"(Epoch 4 / 5) train acc: 0.417000; val_acc: 0.319000\n",
"(Iteration 161 / 200) loss: 1.718786\n",
"(Iteration 171 / 200) loss: 1.633583\n",
"(Iteration 181 / 200) loss: 1.552179\n",
"(Iteration 191 / 200) loss: 1.589140\n",
"(Epoch 5 / 5) train acc: 0.420000; val_acc: 0.337000\n",
"\n",
"running with sgd_momentum\n",
"(Iteration 1 / 200) loss: 2.734394\n",
"(Epoch 0 / 5) train acc: 0.150000; val_acc: 0.145000\n",
"(Iteration 11 / 200) loss: 2.118655\n",
"(Iteration 21 / 200) loss: 1.921783\n",
"(Iteration 31 / 200) loss: 1.844981\n",
"(Epoch 1 / 5) train acc: 0.256000; val_acc: 0.278000\n",
"(Iteration 41 / 200) loss: 1.872145\n",
"(Iteration 51 / 200) loss: 1.878514\n",
"(Iteration 61 / 200) loss: 1.812046\n",
"(Iteration 71 / 200) loss: 1.832228\n",
"(Epoch 2 / 5) train acc: 0.382000; val_acc: 0.327000\n",
"(Iteration 81 / 200) loss: 1.541768\n",
"(Iteration 91 / 200) loss: 1.757576\n",
"(Iteration 101 / 200) loss: 1.698475\n",
"(Iteration 111 / 200) loss: 1.757442\n",
"(Epoch 3 / 5) train acc: 0.459000; val_acc: 0.356000\n",
"(Iteration 121 / 200) loss: 1.538579\n",
"(Iteration 131 / 200) loss: 1.507526\n",
"(Iteration 141 / 200) loss: 1.456616\n",
"(Iteration 151 / 200) loss: 1.301375\n",
"(Epoch 4 / 5) train acc: 0.513000; val_acc: 0.360000\n",
"(Iteration 161 / 200) loss: 1.413942\n",
"(Iteration 171 / 200) loss: 1.516297\n",
"(Iteration 181 / 200) loss: 1.273159\n",
"(Iteration 191 / 200) loss: 1.249190\n",
"(Epoch 5 / 5) train acc: 0.496000; val_acc: 0.359000\n",
"\n"
]
},
{
"data": {
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A+D5CAV790DHy8l5Hb++xqG28wN71KH/2OnjPPSmU1XwgtFpmR3cH8kd40Pzh\nydiP8eHtQ0NB6+tvstRGLfX187B9e3SW803k5Nyd1EqjVqgWl4lXAoOIiIiIyKUY3CmUlIwAEB0A\nHQHOvBSYE4jKZr0Ij+cqw/tUB4yrEAm6ACuraeofYwKAn6Co6DuYOPHc8PDTn2P+/FrFvL1fY+OT\nfxoKnAKBVqDrdMh2H/JLRmD1E5Ehq1pDQQefE73aZWiY63OGVr9UDjdVz1dM/twYYaRmIhERERGR\nmzG4U1BlkgpbIoEdEC57EIA80Gp4n+qA0f55ZupjAMBJmD59ckxQtHHjaTHz9qqrZw8FZgUF6uGj\nelkz9XDQN7Fmzd3o73/E8D6is5x+f204Yxct9XPwjNRMJCIiIiJyMwZ3CspM0u7e3TiUq9goF+gZ\n6Da8T/XQwwGoA7Fj8Hi6UVVVZ6nem/oY2kMoowMp/Xl6+lkz9XDQtVGBnbF9RNMOUuOXfLCLkZqJ\nRERERERuxuBOQ8x8uYUtaOwLJtXpVwaMHk8vmpuXqhZYaW4eHTU3z9xcMyOraSqpA7MRMJtR1J6j\naD3zZjRItZtWzcSyHWXoHT3O8gqnREREREROYnCnw0ihdEPEAFD4DmROO/I8JXjipzeg4bFIINbT\nU4ANG5Yjmblm0UEpEFokpGphVdzVH9WBmfmsmTrTprWPNxEI7BoKkhLNybMSpNpBWTIjf4QHzW+d\njPUmhqgSuYlyLiy/nCAiIsp+LIVggF6h9OhttAIprULqpS+VYop3CrqPd6PEU4J//r0QL29frFqV\n0+9fhU2b6hK2y+gxfTt9Mas/qssQtAJ4ANErbCYqlA5oz7nLyYmec6f3WP8Y6eDm4urstJMe9X3p\nzvuMiIiIYiVbCoHBnQ30AqmqhVVozG+MDO08AmA7gEswtP3oP3nwYV8BMGd/1KqcpSg5NhGnf/oC\nVSfe9DER2qayp3Jo9UetDuBg/b2enjHhrJl+4BBd0y9SFP65OKtfatcN1AuanA5o/P5aNDWpg2q/\nvzZusG0Hvdfp1k47A053cfOXE0RERBQf69y5gN4y+qqVGF9DJLALb//h2G7gwm7Fqpz70b7ii2hv\nqoNyWKDpY4a3iV79UXsI5K2mO+XK4aAAcNFFXwKgtfql+Tl5WgHN1q2xReDNlF8wwsjCLnYHNFqv\nUzkUVD1PUj181+lAy0i7yVnqIdeAE6vOEhERUXoxuLOBZiD1AfDcS8/BP8+P4NtBoASRQEwiNqMG\nhKaqaazQfjF2AAAgAElEQVTKibxDQBeg7MTrBW9GV3/UCswSMRs4GJuTl3henzqgURaBfxO/+/0y\nDBSMAvI6gdeLsHXb7djStCxh2xK9Fr2FXeIFNCtXzrYcZBoJ3PQ67ekItIy02whm/+yTrlVniYiI\nKL0sB3dCiNMAPA6gCKF0zH9LKR/U2O5BAFcg1NOYJ6V8zeox3UoVSIWHXXZe1onO3E4gD8h5Ngf9\nl/eHthmAOvDS+lkfgN7oYCzSidcL3mxbCCaKlcBBHSRdjZycH6nm3CVaDVO/CPwKDHy6OabQ/P51\nXixePAZPPfWApdeit7CLdkDzPVx55a/Q2/uQ4fOT+HWG9hudbdHrtKcj0LIjS2Q1KGVAqC1dq84S\nERFReiWTuesHsERK+ZoQIg/Aq0KIv0op3xrcQAhxBQCflPJ0IcQFAFYAmJpck91HFUi9ithhlycD\n/dP6Ub65HN4zvPB8yoPmHc1oO78tssBKTinEDhHzM6zzAl3RwVikE1/9rRuw5san0T/z6ND2ORsK\nUP3oDQDUqz8We4pR/7B6IRgzrAQOWkFSdfUP0NBgfDVM3SLwhZs0C81v/+OmpF5LoqymdkCzNiqw\n095nIkayLXqd9nQEWqF27wYK74xaDOgWU1kiK9cWh4PGl65VZ4mIiCi9LAd3UsqDAA6G/90rhHgT\nocGHb0VtNguh7B6klC8LIQqEEEVSys4k2uw6ykBq9/HdOJR7KHajkwHvGV5sWhUKOFQrcK4MBXEx\nS/F/eDL2Y3x4B7Gd+IbHnkf/rheAjjuBvA6gtxj9Xbeg4bEncdGFXx5q1+DiKXawGjgkmpNnhG4R\n+LzeOENae+PuM9kgSDsQ+zipfRrJtuh12u0Yjmc20KpecCnW7PyS4ouGp1G94GnDx7TyftiVpcxW\nZodcExERUeazZc6dEKIcwHkAXlb8qgTA/qjH7eGfZVVwB8QGUlULq9DYp16pMnq+W7zAK/pnoSFn\n2p34UGd4EtC1OjwnL6SjY62trytauubx6BaB750EaBSav+CcyXH3aeW1RJee8OQWoLRsEfa33Y/B\nQCwv73X09ir3uRuBo3+Ef95WzVqDymGFoTl7ibMtiTrtRgJEvaGMZgOthv+7IhLYAUAu0D/zKBr+\n74qhLxr0WHk/uGgIZTsOOyYiIrOSDu7CQzKfBPATKWX8VMkwYtd8t0Sd+HQEWvECh+oFX48pll59\ndTUa1jbELZ5uhapAe1Tg6/H48PftJeiY2j50vou3l0DkTxgqnP61qybill/W4/3j3ThxpAd3/keN\nqTlJMaUnxoWOUXZWGWZNWYru7rHh4aY/x/z50fvcjZGTvojg5d0Ihtu1bcE2ND3WBG+5NyXDCvUy\ne0aOafbaMrIyqx4rc8S4aAhlMw47JiIiK5KqcyeEyAHwNIBnpJSqlSuEECsAbJZSrgk/fgvAxVrD\nMoUQsrY20nmvqKhARUWF5balm5HC50ntP031zlQ17RZcivl3z4sEsoeBnJeiFo/RKJ6eknZFne/8\nER40P39yVFZtI3DWHOAbPTHzE59Y9t94+k97ooKg+N+KG6kbqDw/77y3Bv/62l7Vc0qePhunF34L\nwaCyBiBgpRaZmW/3jdQ/M3ttGT03Rl+HkffDSjsTHTP63AGwvcTFcM2+DOfXnizWKiQiGh6amprQ\n1NQ09Liuri59RcyFEI8DeFdKuSTO778K4EdSyiuFEFMB3C+l1FxQJZOLmKeL2c5wKqg69k0Avoik\nO/pJtUnZKSr8DHDDG6o2lT97LgKvvm5on/55fjR5m9Q/D/iH5lEqFX3Bi0NXBtW/WHkR0LYFwDIA\nt6v3aaJQutkAx2hxdjPXVkxW00RAb0fHP5l7QOvclZYuhhCjI0N+k/zSxK1F550wnF+7HYzeq5Td\n+AUJ0fCTtiLmQogvAagE8IYQohmh6m1LAUwAIKWUDVLKPwshviqE+CdC46eut3o8UnPDggmqIXla\nNfxMDtHTEj3fTW+op2ouVl63ZpuOHO8xfHyjdQNj9ObFKW9RGn4wCk4vfhJvKKPHcyRmaG39knrD\n15aVlVntGnKWzD2gde727y8CcDOMnk8rxxgui74M59duBw47Jg7NJSIrklkt8wUAIw1s92OrxyD3\nUwU9AuqA5jAQeDsA/zy/oTl4ykCu+upqzL9rfsx8t+0/3h43M6TqFPV6NIOssSPzDb9OK/Mop51x\nCdavOxZTfw/rfFHlLeYBqAVgvRaZkUVFor/5LSjoRlnZ0pjMVGnZIjR/+Fe05bcZOr9azK7M6oaO\nv/a5G6HxM+uLtAznRV+G82u3Su9eZa3C4cUNn5NElHlsWS2T3MFMdssuqqBnkqJge3gOXvDyIIK5\nQd3AQWvhkvU3rkfvVb0xqzG2TG5Bzb01mgGFanGOrv8C/qCec/ebRx+NPXaC4S/eci9W/nwV5i66\nEUf6ezA2Jx8r73804fm9774l+EfFB9i/ogfIOwT0Hga6fg9g8DkTAHwP5eXfhdd7jqVaZHrf7scb\nejhz5nL09IxBcfEI9OQexoZT2xKeX7uHBqWq42+mndrnbkDjZ9azJUazL04MvXJ6eJdTmadsGbZm\n5F51a63CbHkP3IZfkBCRJVJKV/wXagpZtS+wT/qu9EkshcRySCyF9F3pk/sC+2w/TuVNlbJiboWs\nvKlS7gvsG/qZf65fVt5UKbds2zL0uHxaeaRNyyNtq7ypUnP/lTdVqre/UPF4OSQWQRadXxTTjph2\n7gvKysrl0u+/TVZWLpe/+/1aWf7Zc+XYyeWy/LPnyi1bt6m29/l+KoFeCUgJ9Eqf76dy376god/H\nPV9R7Zg1a5EsK1toeh96+0/UrsrK5VG/k0PbVFYuH9pHxdwK9fldDumf60/qtSdipF12nwsj25eW\n/sDW98hIm1Jxfq20Q+s5lZXLZUVF6B4y2x63vi63SsU94YRUvQfJXn/ZIFOvCSJKTjgmsh5TJfNk\nO/9jcJcczaAoQRBlVHQwN2vuLFl2WZmpAFIvcDC0fQViX9siSEyFrYGs3h9Ru/7IKoNOOzosifZZ\nUXGbos2h//z+2yKvXeva+RFk+bRyWTG3QpZ/9lwJPCtRWClRVhH6P3Y5GogZYeU90jp3dr9Hevtz\nogNn9hh2vT+puN6jZVPn18i96kZu+KImW/E8EA1PyQZ3HJaZJeyoNaakGiL5PIALYXh4JGBsIZLo\n4aTBt4OhMvfR208C8p7PQ++l4aGZrwK4xFw79OgNfzE6PEZvaGwqFsFJth6iamitcihtHoCPvwZc\n1R81d3Abnv3LWUN1BJXDsPSGaenV47PCyhCmeOfOzvdI6xjR52fPngBSPfTK7Lmxa65Pqhd9yqZh\na5m6gEoq3gPONQtJxeckEWU/BndZwtJqjjpq7q2JdPiB0FoTJlfC1FuIRBVA5inm7PUBvqAPK+9Z\niYa1Dejo7sDu47txKPdQwnaYnX+o17Ey0vHSmi9odmESJc06bGJAteBMvKLxRoqDK1e7DLwdQPDy\nYOS93o1IYAeE/j+nDe+tmBZeqj12BTejK7zZ3fFPVefY7rms6vNTk5J2RzNdmD5DgqZMDYi0GLlX\n3SgV70GmXH9OcMOq2ESUWZKqc2cn1rlLjtFaY2Y6qqrabk2wVMMuUUF3zQLYh4Hyf5TDe4ZXswC8\nXtFsK3XXNBczKFuEKZceRvfAUXhGFCgKo8f+vsRTgp5jPdhwygbbavwFAq24uOJ27O/tAfI6gd4i\nnPJJidxztqPt/DbDReON1IKLySQdezy2Pt9mAH5F444AWFcEiLOB3hKg6xZUVj6J1atrbSu+bHaR\nhlTUVbNawy8R9flpBfAAgHrb2q1kujB9hhTQjve6Vq6cjYaG5zJugQ8napdaua8SbZ+K+y5Trj8i\nolRIts4dg7sskiiIGvy9mY6qKog6AmA7IkMibejoWikOrvc69IK/eKI7Vh7PETR/+FQkiOoDynaU\nYcror6O7e6zm7z/5zCfx0ayPTL2WRL7+9Z9g/Vt/ii2l8NsTgG8fs7VovKpzVngtcMOa+MfQuA6w\nzoephV/FSy8+aEvxZasdRrs7x1avpUS0z08riooWY+LEc1PeqTdUmD6DCpArX1d19XTMn//HjGi7\n08y+r0a3t/u+S8kXNVm0omc2vRYiUktbEXNyH71aY6phlnolBZRDKscApaNLMeXgFPQc7zFUrFqP\nleGkekWzrc4/jB7+UrWwKlT3LepctZ3fhgt73sNTDz6g+fuP8j6ydWjsS29vigR24WOg9Fjs/qXi\neOHtzMy1VM1v6boTWPdK5NjK8hYacx4xpwUHn20CYM8wLatzbuwewmTlWtLreGmfn5MwffrklGYl\nzJybTJrro3xdVVV1pq8dOzrLmdDhNntfGd3e7vvO7usvm4qBZ9NrIaLUYHA3jJjtqGoGUSuTr50X\nPTS0AAUo21sWkwHTKw4+2LZkFnHRo3euNH//OWD0X0fjw8s+NPVa4srrVQduI6FfND7BgjVaQ3HV\n81u8wN7nUfTHmZj4hZNR7ClG9QPVQ3MeX+97He/lvhfbrlzglNPzAMSfO1RdPRtVVXWGOr/ac27e\nxXPP7Yy7iIsWZYe7unq6qeF6Zq8lIx2vTJlbpbcQTLoCGL02mJ2vZUdnOVM63GbPjV1z36xcN2YD\nxkTHcNMCLcneQ256LUTkTgzuhhGrWTKrw8+0aC06UtpWipkHZ9qWDdRbxMUIvXOl+fsxwGWTLkNe\nT55mRtGsqedMxoa+oGrl0DEbx+CDGR9oZ9X0FqzRWORFO5M0HtM/+02sXhXpLFz05YsAhIcq9qmH\nKvrGfwqA9rfu1dWzVUPlEnV+1W0KzUvr7HwCnZ3GOs/qDvebWLPmbvT3P5KwDcl8+WCk42VHViId\nQZZWALN162JMmVKA7u4xjrTDSBBlNnNsR2fZLR1ua1nj+OfGjiy8E4Gv3jHiBaktLe8b/sLJiXYa\nMZwXm3HDl0vDHd+DDJFMHQU7/wPr3KWcU4XOE0lVPT4lZWF1s69R71xZPZdaReATbausK1h2WVlM\nkXhl0XjlPo2cb9PFvy28dit11krLvi9ReE24tt45EthlqpaW+pj6bdB6baX+Ujlz/szI+d66Laa4\n8pYtfxt6PH78dxT7t79WWbpqX6nPZ1ACi021w8z1b6wNGu9hnOL0M2f+TLMgth315dxQo87IdWH6\nXrfhWjP6niVTsNxandI9Mi/vekfvIztqAmZTbUczWPMv/fgeOAdJ1rlLe1A31BAGd45INuhJltmi\n5umkd67MnksrQVGy75fm+V4EWXR+UUwH22yxabPtCnV+9ykKoe+L2/nVCmxxpi+0D4OdZ3WH22JB\n96hgWP3HbY/MyZkb9XhZyjteqerc6XWw1efTZMBuw5dLRoOo6Ot51qxFsqxsYdwOSeo63Htkefk3\n4p7PZAMaY21Qvw7T93qShej13jM7OoxWjpGXd5XjQZKR61fvuhiuHezhGtS6Cd8D5yQb3HFY5jBj\n9zBLs1JRjy9V9M6V2XNpdkEbK8cAdIrCh1e67LysE525nTHDNE0VpzbZroKCI8CZl8au/LnuRXg8\nV2luX3NvTWQoJDC0aAtW1ABdg8dNPDxMPaRMf4iZ3lxL9fC7tVHDPAHg+wjVrYsta2B2Pl2ioS92\nDTGLPkZBQTeamwfQ1nYHjA93NFkY3cL1Dyiu5+73AFwNYFLUFurrIGZxpKo6tLVFL6sfO2TSjjmQ\n6n28iZycuxEMPo5gUH0+UzFU0ehwPbNz2ZJdLEVvaKcdQ1r1jqE1FLql5Wxs325ueGOyQ9L02mnk\nusikxY7slKrhqBxmaNxwHhKcaRjckaPsmA+Xqayu4mmGblF4jZUujXSwkyULW9Urf84JQB5ojWn7\nYCd+zzt7gMsUO8kFkLcf6AKMdMDVHe6rMWLkdzFQMGqobmBpXj7q65cNPUfvywf1Hzfl4wkAvoFP\nFp+FT548EmNHerDy/kdNz6dL1MHT7iC+iZ2vt2P72wdDr+31Imzddju2NC0zuMR9DYCbkaiDrT6f\nAxrtiB9wtxxq0bz+Ww7ti38ulNdzCZDT9yX073oBoQBP/zowsjhPqC6e9c6yssMdCOxCMPg44p3P\nVMzRc2tBd73g2Y4Oo5EAXWtV1e3bjZ8vOwJyvXama3XSTJCK6zudCyFlYlDp1s8YUmNwR47SK2OQ\nzYxkLc0Umdeiyo6cDPRP60f55lBR+N3Hd+NQ7qHYJ9kcYGrpHjiqXvnzA+DlN7bDP8+PAhSg+UBz\nKFs3DsA+aJ6r8pPeh9dXa6gDruxwezxH8Peel9ExtX3oiwWxowwQS4eeo/flQ+iP226g8E4grx3o\nfQ/ois4kBYAzv4uP5vwLH+UCR/qA+XfPw8ZS47Ug9Tp4Wh3E0WN+jA9LAzGZ0f3rvFi8eAyeeuoB\nA8cYAb0Otvp89qK5eWlMtq+0dDF6ego0VzQ9+M4xwAfVe3rwnd7450Ij29c/8yjKc6+Dt2B23OtA\nne37IlD4RPg9KwC6xplanMeI6A63318bzthFi5zPVHwD7tZVWPUyTXZ0GOMt4pSo82z2fNkRkOud\ni0zKjDgdnKTi+k7XQkiZsrouoB7hUVYW+5nvhs8YUmNwR45L99DQdNELHIysbKlHMzt4MuA9w4tN\nqzbFXeky1cNiVYGtcnjo8wAujPr95wBsQkyhdN9OHzauW28q2FXWLuzIb1fVLozOWmp9+VD98xtQ\ns+xxtLcPIGfUfuRM+iL6Z3VHhpf+YRrw1ksAJgGFt6gylGYzo3odPK0O4rN/P4APNTKj2/+4yeAx\njHWwlRmD0B/+wWCvG83No7Fhw3JodViKcisQXPdBaHjt0NBcH04prIh/LuJku73nnoRNq5SF4MNt\n0sheo/8K4Gsy6rheYO8hAF6kokOnF7Ck4htwNw/XS5RpsqvTHn2MVAxvtCvwSnQuMiUzYnXl3GQC\nwlRc3+kKpt2yuq4erfe5tHQxZs5cjp6eMa76jKFYDO6IHKKXtbQ6JymaXnYwXcNiVcdVDg8doWjz\nWABTgaK/FmHipIm2ZHiNDouN/vJB9cet8Frghu7YIOobPSh/NpRJ2t37Mg4lWVTeSAdP2UEs+sIq\nzWL2yNPOiqmPMQ9W5grqz227Hpd88yqUnzsOnX3vAXsfAVY8AeR1AL3FQNct8FU+GXf/nhEFmtdz\n/ghP3Oeo7qPdiAR2wFDgGzt/094OnV7AYjWgUWb2q791Axoeez6ms+ymzqERqei0p2J4oxOBl1uz\nr0rq8/su9u/Pw/79yxEvmLYjW2X3cFSj76ndWcpMydBq3Uf799+Hiy66B+vXZ9bnzHDD4I7IQYmy\nlnbMydML3tI1LFZ5XNXwUK1i7GOA6dOm25bltbKYj+qPW16nZhA1mEmqWtiCRmVtQo1jJBp+a6WD\np1kTsQ+44JzJmturj3ESSkt7MWWK9W9k1R2WAHDmLAQvb0EwF+H5cteE5st1GZsvJ7omAC96FQvx\neCHOit8u1X0kESfw7QjP3wTs7qTrBSxWAhqtzP6aG5+OmX/o1qFdeuzutLtp2KuZwMDN2ddo6vO7\nCpEvhgCtYFo74P4eLrlkCcrLz9E8N6ke+mnkPU3FEMpMydBmShBKagzuiFzCjpVEjQRv6RoWG31c\n1fDQ86A5DNPOjKKVrKXqj1tv8plRveG3Vjp49992H15b0BxTbL1sRxnuf+w+ze21j3Gr6c5KwpUs\nC2siQzAB4/Plojp0e/a8Dxx6PpRlG8r21aO7eFXcNqnuI60vDvoA9I4PP7B/RVNAP2AxG9DEm3+I\njjvDGUh7hnZt3fY3zF10I94/3o0TR3rwm/sfxUUXftny/tLBLcNerQQGmbBYipWVc9WBQiuA/3F0\nRVklI+9pKoZQ2pGhdWLOY6YEoaQhmToKdv4H1rmjYc4NReadYqRYeCpet9n6fOq6PvskzvQmfI/0\njmGksLwTry1ZWu9hzjkFcqjYfNmFhmpaRtf1UtekM183UNWuH0HmfDYnpp1ll5XJWbMWWq7dlo5a\nY/FqhKLMH3N+kimcvmXrttB7qHhPt2zdZuMrscZMXUC31ILL5Lpgic63+vzq36fqc2Gl8Lzz586O\n2oRakqkf6dT17Zb7aDgC69wRZYfhtJKo5mtdmfi1JruS6OBxzWQt1d+wjkfph5diyoHD6BnotpQZ\nTVVJDKczsnorWQaOHkFQb3VY3ZIM5usGai6K80A1GtY2RK61x5K7r9KxIEK8zD56ozP7yX2rPnfR\njaFsoOI9nbvoRgRefd3yfpNlNouTzuGNsZnnADJxWJve+Taycq7yPlV/ln6MROfGLXXt7KhNqCWZ\nDK1Tnz+ZMkzYDsrrorp6Ohoanov72PWlK5KJDO38D8zcEVEc8bKaW7ZtkZU3VcqKuRWy8qZK1WO9\n7NVgtivR9sl8w6rFSubOyjfDqRYvkzSYmTOSiVZ/O39bKDtaWClRVhH6P7bJoqLZtp1/K5Tnf+rU\n/9D9Nt/2NgT2ybLLymKzapM8kUypDd+qF0yeoPmejp1cbuMrMc8tWRw9VjJaTrXLzOeHlfNt5HMy\nepvy8m84nrmzkonSe046rk0j2UQyTv0e75E5OXMTPE59BhPM3BFRttNcSbS8BVf+7Er0XtobyoQd\nBtb8ZE2oYLuBUhJGS0/YPQfG7Nw/t9ZE0psjaiQTrf52/ghw5qWKBVRexNSzrsJTT2mXPrCDMiv8\ntS/PxC133473j3cjX34Sx9+digMdj2Dw/OflXQezc1GSzjzLEZBvXQbs6AHyDgG943HSJ4AvzFxl\n27LkJ4704KjGezp2ZL7lfdrBqYUdkp3HpM6omM88291uK58fVs63kc9JdbkK+1eUTcRKxsuNtQnT\ntcqnHdzYJvV1sRb9/Y8keOzO0hXRGNwRketpDmXcjVBgF7Xkff/l/YZLSdhResIKzWGDN1fH7fiH\n/vBcDxQuCBfhLkFLyy2oqVmV1j8sRoJUvaGiqk5KYauqTiDmBCAPtKboVWgE+YeBxv/87VD5hKN9\nANb1AYjUxevtvRt5eTeht/chGOl4BoIBVCyoCC14E/4iYduCbWh6rMlUcfv9bfeHjhde5fMgjiE/\n375lyX9z/6O49MavRYZm9gE5Gwrwm0cftWX/g+weGmdXm8zWblO+jpaWDxRtnADgJygq+g4mTjw3\nJcPa9II3KwGNE+c7FSvK6rEaiLmtNqFdq3w6EWgpi6A3Nw/EDN91wxeV6utC7zGgdd24KXBlcEdE\ntrNjflw0zSzRccVjqXgMJJzLlqq5b0bE1NLTySD+s+U94MxZiuLf29Gy76spb2cidswRVXVS8ro0\n38OegW47mx67yufbQQT9QZN18c7GOed44PMZ63gu+sXiyEqm4X22nd+GRb9YjPUrnzLUZqfmID2x\n7L9xyy/rceR4D8aOzMdvHjW/WqbdmSQn6r+Zrd2m9Tq0M7onYfr0yZGMVTCAqoVVtn026gVvVq4b\nrfNdWroYPT0F8Ptrbeu42r2irB6rgZjdpWuSZccqn06MCNGfU20sA2Z3f0JJfV3oPQaU141bgulB\nDO6I0ijVH1rpYHS4oxlaWaK8I3no7YvK3MVZ8j5eKQk7Sk/YQS+D2NnXpCopgDktOPhsk6Pt1JLs\nIi7KToqRRViSpbo+98HQlwTKung+34mGO57bd+0MJXEU+3x5107D7bbSMdXrTMTrkGza+Ke4nQ6r\n+0wmk+TEwg5ma7dpvQ6tjG5p2SL05B6Gf54fBShA84HmmAxusp+NesGb9nXzJgKBXXEDNfWCKd1o\nbh6NDRuWI1WBgBOsBGKpKF1jB73AV++6SNWiLNGfD8HgLgSDjyM2SDKXASsoOILmD58yfc+YCaTU\n18XVyMn5UdRQTOVj9XXjhmA6GoM7GrbSHVilIghyg1QMd9QcyvhoNebfNT9yrElAzrM5kaGZOnPZ\nrNS9SwW9DOIpp58QKgKu+P0pp+c50r5Ui5mHE5wXuSdS9J6ors+RSH1dvN68OPtM/B5Gf0Z5cgtQ\nWrYoMjRTbyiogc6E2Q6eHftMxdA4O5it3ab9OmIzuh7PETR/+FdsODWctX0ewIWw9bNRL+hXd1zf\nRE7O3XHryw2KPt9VVXVoa/sZzAYCbhqmBlj7ksDI3zM31ibUuy5SMRJA/fmwTHEMCxmwwmuBG9pM\n3TN2rK5bXf0DNDTEf6y8btIVTMfD4I6GJTcEVuma85VqTi71r7fkfaK5bG4pPaGXQfSN92F733bV\n733jP+VkMx3hxHuiuj7PA7AZgB9DXxLgaREZmtkHjFzvQcVnPRg4Xmvpm/lpZ1yC9euOKRaK8WLq\nWZfEfY7WZ1TZWWWYNWUpurvH6rbDSGfCbAfPjn26tTCyOggaQKJ2xnsd0RndqoVVaMuP6piOgKmh\n49baHRv0q7LjAWU2Rb+TaSUQcMtCUFoBppnOdDqH75ulnN9WVha/PEUq7kP158MoxTHmQW+BIdU+\n8jpN3zNWRwcof3fRRV9K+DhaOoLpRBjc0bDkhsAqk/5omOHkcEetgO+iL18EwFgAb0dtOL0MsN7v\n9TKIbskwGpGOWoRmqa7PsQA+C5RvLof3DC+KPcX42q2h1TKH5p39b/PzzqLdd98S/KPiA+xfEVnp\nsjQvH/fdtyTuc7Q+o9rOb8OFPe/hqQcf0D2mkc6E2Q6eHftMxxwlI8zWbjPyOlSf8SaHjltpt1bQ\nH91x9ftrwxm7aIk7mVYCAacyFXbP71Sy8vcsHRlLrddaWroYM2cu11xNNxX3ofrzYR6AWgCDxzgJ\npaW9mDJFu02a++g1f/7TsYKp3vl0+kstBnc0LLkhsHLLnC+7uSUYcSKA1wsgjQaYeqtnrrx5ZWwR\nbgvZLDsCr0T7cEM23AjN6zPow8bfxrbz2quvtu2YXu8EbGlahpqaVejoODPcqVF3+KLP75539gCX\nKXZk4jPKSGfCbAfPjn26uTCy8tv7UCfd+mqOqs/48wBsAnAJhq69sh1l6B09LqmFSswMC7TSybQS\nCEknWWgAACAASURBVDjRwU7F/E6ldJWuMRsgar3W/fvvw0UXaa+mm4r7UH1tTQDwPZSXfxde7znh\nY9xqbmXcrnpg3Ysxox70+hPpGB2gdz4d/1IrmSJ5dv4HFjEnB1kpJG03I0WeM9VgcXD/XL+hYuKp\noFdk2w5615GlguU2XRfRBdpnzZ2lKoBtdp967XLDPWWUG65PrTbFnN8LkdT5NFqw2Ujx6VTuM5tp\n3TOl/lI5c/5M6Z/rlzOvnyVLy77vaHFkK4W8B59n5j11ori33jHsKvat/LzYsnVb3KLwdrxuK++R\nGwqbW7229PZRWvZ9OfP6WYY/r+1oRyqYuYfAIuZE5rkhu+SWOV+pkOqhdUY4kRnVywBbyRDbkXFU\nZdEMLOSgl9nTa5cbsuFGueH6VFKd389BleUx8xll9Jt5M1mfVOwzm2l+xq+M3FdVVXXYb2GhEj2J\nsj7x3sPB9sTLFJl9Ty2tTGkyWxXKDh4CCmuGaoCiq972+Z0xpWt0MnN2ZCzdWptQjx3ZQO3FTeah\noeE5dLcPACUjsL+tAzXLHk94rbpxdICTn4sM7mhYcktg5cZOZrZwIoDXCyCtBJh2BEmqQEFnIQcj\nQyr12pWtw4xTRRlMtxxqiT2/YwFMBYr+WoSJkyZa+oxKRWdCa5/pXnnYzRJ9xjuzYqF6WKDW8FO7\nFz8x28G20oaCgiPAmZcqFip6ER7PVQBSMxROL/CyI8iyqzZhOuay2vGZE7OCsuq6eBNr1twdU5ZA\nb7XX4Si9y1MRpdHgH91NqzZh9YOr2RnJMoMBfGVPJfwBPyp7Km2f/1W/pB6+nb5QpwKIBJBL6g39\nXstQkBTNZJDU3t0eG2QNLuQQZ5+JsnJG22XltdphsCi0f54fVQurEAgGUno8oxK1azCYbsxvRJO3\nCY35jdi1Z5f6/I4Bpk+b7urPKK3XMuPHM2x/H9z6PicjEghEs3vFwsHgY5WtzzFisIO9aVMdVq+u\nNT1nTK8NsrA1EtgB4RqggdDPEQkwKyvvgd9fi8rKe5JerVMv8KqvnwefrxaR93UwyJpn+Bja10Wk\nNmFVVR0CgdaY36bitWpx+j5UXxdrowI7wOi1Ggi0oqqqLu75yzbM3BFR1kp1ZlQvA2wlQ2xHxtHI\nQg7R+zSSLdRrVzqy4W5dxEWvXVrBdO9Fvch7Pg+9l/amdKi43Vk2NyxclKmcWbEQ0Mv66A1vdIKV\ndncPHNUckdAz0D300O4Mjl5mzo4hgXbUJtSS7Cqe6bgP1ddF5pblcBKDOyKiJOgFkGYDTDuCJFUg\nNgYoHV2KKQenoOd4j2qfRoZUGmmX08OM3VDSxEq7NIPpk4FzTjsHvh5fyoLjVHTOnJhr6db3OVnO\nrFgI6GUD9YY3OsFKu9MxFNxIQK417DXRfMbBbaIDr5UrZw8VzbZSm1DJjgAnHfeh+rrQuk4iWU2t\n8+t0AXE3YHBHROQyyQZJegs5KGll5UpfKkWPtwf+ef6YDE+6O9N2lgtIFavzE33FvpSe31R0ztK5\ncNFz219IqoSAG9idWbKSDYw7vPGAc0PXrLTbzOeWXYwE5NGfUZ4RBWh+/mTsb7sf8YIqvcDLSm1C\nJSMBjl5mLx2LZqmvi6uRk/OjqKGZ+lnNdNS9SzcGd0REWchMIKYMBj3woHlUMzacsmGo0+SGYXCq\nzNM+OPLNvdmhjHoBT7pW601F5yydCxd1tlyAzq46DIdhVkZZyQYaGd6Y6kVzrLQ7XZ9biQJyrew4\nRnsBHALghVZQ5YZFWoxk9uLdh4E33k3Zlyzaq2f+wFRWM1UriaajWL1RIlROIf2EENItbSEicrNU\nd7SqFlahMb9R9Ue8sqcyrZk7VbuOANgO1VxCOztzMZ01g8cw8pzB93Aos+rACpOpel+Vr6X66mo0\nrG2w7frUOp9Y5wX2Po9QhxkAjqGyMnuHWaWS3nVh5R5IBzd8bsVrA1ZUAl2RNvj9oYVmBv/d1FSn\n2tfgNlqBV2nZIky59DC6B44auseqqurQ2BhddgOIvmf0fg9o34c5GwrQv+sFAJMwmG118ksWvXMH\naAeupaWLMWVKAbq7x1ibf6ixTztfuxACUkph9fnM3BERZRAnJrW7tWadql02lAvQY2UooxvnJwKp\ny7LF1AFLwfWpPJ+7XzmMQ3s3IBLYAakYZjVcSjzoXReZMudR83PrA+C5l55L2TBNQ23IBZDXAXQN\n/iA2a2R2kRaP5wiaP/wrNpzaZjhDqTfs1cjQReV9GHjjXQR3/Q6hwC60vWqoZ4rvISNZOfX560Zz\n82hs2LAc8bKUeuwY5ppKSQV3Qoj/AfA1AJ1Sys9o/P5iAOsRGjwDAH+QUt6ezDGJiIYzJzpabq1Z\np9mucLmAVHUyrQa6bpufONixSvWKpqm6PqPPZ1VVHRrfHK/Ywt6Czdm6QqcWrS8jqm+udv3cViXV\n50M4s995WSc6czsNv4fJdMrjfXaid/B6Vc8l1Aq8SssWoSf3cExQOhg0VC2sQlt+m7kvnLwTsPLX\nX8fcRdNw5Hg3xo70YOX9j0aGXBocuhh9H/r9tQgOBXaDooZ6OnAPGZ2rGT2UtqqqDm1t0VlK8wus\n2DHMNZXBX7KZu18DeAjA4wm22SqlnJnkcYiICM5k1dI1J8yN7TIa6Loty5OoY5XKoNOR67PenhIC\nid6zTMlWWRHvdcfNvjo0t9VoO+Op/tYNWHPj0+ifGZ5D+CoiQ7YBQ+9hsqtKan1GnbrtVIw87TX0\nlpargirAfGbOyj0WCAYw/+55CF4eateRPmD+3fOwsTS0Tyv3lF5AqHkPlbfgkm9fgvIzym35nLQy\nV9OO0h+6r10ns5fq8gxJBXdSyr8JIfRaYXnMKBERxXIiq+ZkzTozHbh01NKLF1BW31yNqoVVaO9u\nRwEK0HygGW3nt7kmy5Ou4MTI9ZlsIGxHCQG9rIJbhyYny0g2RXXtfA4J62Smq51KDY89H5r/1XFn\naBjkqN1A7qHYjXTew2SXzddc5GV0M9ou3KsZVA09LzqzpJOZs/I3QO/zwMo9pTvUU3kPHQHwDyDo\nDyKYG7Ttc9LsirN2lP5IdphrqsszODHnbpoQ4jUA7QD+l5RyjwPHJCLKSk5lr5wYVmilA+f0cMd4\nQ9bm3zU/0u7nAVwIV2V50hWc6F2fdg3VSraEgF5WIfh2ECiB61ZiTXafRoL+dMxtVbLy5USoQz0p\ntHBJF4DCKqBPvbhJovfQjmXzY4YQL6xCW4m5IZR6966VvwFGPg/M3lN6AaEqCH0NgB+qc7HoF4uR\n3zfFsblpdpT+0H3tOpm9VJdnSHVw9yqAMinlB0KIKwA8BeCMFB+TiChr2ZW9csMwwkwZ+qYMKKsW\nVsW2ewQ0l5J3OssT/Z46FZwo6V2fbnnPdbMKeUDOsznov7w/ZV+ipGJOkh0ZyXTMbVWy8uWEqkPd\nVQ+sezEmQ6P3Htq9bL7R12Hm3rXyNyBVIz4SBYSqIPQ4ND8n//riP/DR3kakYniiFiOlP4xI+Nrr\nE2f2UlWeYVBKgzspZW/Uv58RQjwqhCiUUnZpbb98+fKhf1dUVKCioiKVzSMiykjJZq/cslhEpg59\nU7VbIO0L0KjeUweCk3gSXZ9uec91swonA/3T+lG+uRzeM7wZs/iM3j6NdPKNZIZSvgqihWBE3aEe\nj9IPL8WUA4fRM9Bt6D20az6nmddh5d41+zcgHfOVVatr9gcQ7AuqzsVHh7+IVA1P1GLl2jJ7vetl\n9tTX2TM48cR6jBv3hZhYyKqk69wJIcoB/ElKea7G74qklJ3hf38BwFopZXmc/bDOHRGRA9xQD8pN\n7TArHfX2TLcJAA4D5f+ICk40OiR6nRa7O/GprrVntJ2qml3PA7hUvZ0/4MemVZsstysR/zw/mrxN\nCY9p9nVp7vMIUPS3Ipw96ezY+aEJrtVEdRidqHtn9RiDKxBGOtTmh/jZsQ8zr8PqvWulLU7X1lQe\nX3kuPvnnE/HRa68itqxJbJ06J9qR6NpK1fWe6DpLa507IcRvAVQAGCeEaANQi9BLl1LKBgDfFEL8\nEMDHAD4EcE0yxyMiouS5JXvi1lU59ajaPQYoHV2KKQenoOd4jyNzkpQ039OTAe8Z3rjBiV4GNxUZ\n3lS851bnbhrJKqQy+6qXQbDyuoyUAyhtK8XMgzMTXquJMkNODK21Ovw82bmYAAAxABS+A5nTDnhK\nQo8tMvI6rNy7VtuSzi/NtM5F74RxWP9aasuaGGlHomsrZeVd7LhW40g6c2cXZu6IiJzhpoxZur9N\ntspt7bbynuo9J9VZNrvOnR3tdCIbZfaYVl6XZkYyerEfA/vQYyTjmKnScR246fM4WaYz6IFWXFxx\nO/b39gB5nUBvEUrz8rGlaZljBb/1pON6T2vmjoiIMo+bMmbp/jbZKre1OxUr6KUqw2v3ubOjnXYt\nVGSG3jGtvC7lPncf341DJssB6LG6OEeyQ3ydWAQqHQv+uOnzOBmWMv1iAOKsvwJRw4TFjjJALHWy\n6Qk5UX7IbgzuiIiGmXR0ZMl+ys7uyptXomFtg20r6GVKp8au2nrpCNgTHdPq+Vcuxd9oshyAHivB\nSLJDfO0aIqx3HTg1ZD3Ze9eNrATGNffWROZ/hp/Tdn6bq1ZMzsTgm8MyiYiIMowdw8f09pGOIWpW\naLWz9KVSTPFOQffxbsOLiLiNE+9xMm0zM7Q22aGHqRp6G32dlHhK0HOsBxtO2ZDSIZKZcl+ZZWX4\nYqYM8XV6GH6ywzIZ3BEREWUYu+bp6HValL+vvroaDWsb0lofUUt0Oz3wxAZzKZh35hQ7OpXpmh8a\nnZ3a884eHLrskGobo514O4IAI6vclr5UCjFKpPSLgGyaYxctFfN+hyvOuSMiIhpm7Bo+pjcUMfr3\nqaqPaGUuldZzoocitpVEDfVySZF5K+wYKpqO4aaqa2Ufkho6a8cQYdU98xoigR1C/98/bT9mtc/C\nhT0XpmyIpFtWK7ableGLVof4pnrupR3S2U4Gd0RERBkmHfPhUrHYhJWAUe856Swynykdz1RTXSuf\nA7AJqlqQg514vffUjnlPqntGQjPo70Y3nnrwKcuv3XQ7AFuKaKf72rMyl9vsc4x+XqTjXEQfM2Yo\nuI1fhBnFYZlEREQZJh3zdlIxP8aREg4OFZnP1rlUViQqpj5x0kTVEN/g20EE/cGE10Gyw0udKBNh\nqR02FNEeLtdeooLv5WeUo8RTguqrqzH/rvnpLWuS5LWV7LDM1FUJJCIiopQY/Ma7sqcS/oAflT2V\nKe/IDWUcoiWZAWvvbjc9ZFLvOfVL6uHb6Yu0NVxkfubBmSk9V4kym0YFggFULayCf54fVQurEAgG\nbG2jUzSvlTHA9GnTsWnVJtQvqcf8u+ajMb8RTd4mBHOCutfB4PDSTas2YfWDq02/f8p7ZtanZqFs\nR1mknYPZwCWpXQXR7L1r5Lqycu1l4rWmuvePAPgHEPQH0eRtQmN+I6688UrHz4Xq/Kd5KDiHZRIR\nEWUgp+dSpWJJcCtD1PSeoznUa2Xqh2UlO5cqVXMa00HvWlF1hkfCkaGzyntGlQ10qASBmXvXyHVl\n9trL1GtNde+/BsCPmECud2yvqcDKjnORzqHgWpi5IyIiIl2pyBaqsmwGsidGnpNslseKZDObdmT+\n3ELvWlFlYM4DsBlpyaIle52kOgNm5Loye+1l6rWmuvePQx3IDX5REC0F5yL6fQ++HYw95nkIzTF1\n+HoexDl3RERElDZac6kAJFwQIV3L+yeS7LynTKn5ZYdEc6e8Z3hd857qcWKuWyrm3GXytRZ97wfe\nDqjnah4G8nbkoffS3pSdC9X5PgzkvJSD/sv7VTUUe473mL6eWQqBiIiIMpbWUDm9YVLpWN5fj5XV\nAqNZXQE13askWqE5bDPow8bfuntYoFIqVpBVMnJdmb320rHarl3ilmeJuo5W3rMSDWsbUnYuVO/7\nyUD/tH6Ub476csKBoeDxMHNH9P/Zu/P4qKr7/+PvE/Yd2UmAJAREVmVTQYREi3Xfl0zBpa1fLf1J\nF9t+axcEv7FYvy7F1oparValCWhV/LpUsSSAKEuQLeyGJEACAQQS9kDm/P64k8kekkwms+T1fDzy\nSGbuveeeGbhJ3jnnng8AIGiESmHjhg5VtR19qXbJ9QCukuhLrcJgGn2tq1AdAQun1TUbfBXVWrwX\nDfHvXtM1w8gdAAAIG8Fa5NnfdaxqM/pSaVSz4pLrfhg5Opf6LkgRjKOvdRWqI2C+jjL7U13/UODr\n/6P6vBe+/rv7e0EbRu4AAEDQCMaRu2CpkVbpvUmVs1pgBY05chSM/17+FIwjp+fqZ21CUjBM7w2V\nEUVf+3mua4aROwAAEDb8UXLBV8FSxyrYllyvsk9SUIy0+kNVIy59dzk1FL0LZwTBCFhdR4bqO5LU\n0IGwMe5hbAi+jnz6+5oh3AEAgKARjFPGgiVUVZoOVrLk+hUKWBAO1amJ9VFV+Ng9brcmHp2ohX9e\nGNC+lVXXkFSfUOWPqYWh9IcCX6aD+vuaoc4dAADwm/rUAQtEnbqaVKojFqA6VpXqfLWV+rZxRo4a\nqvagz31q5JpejalSfT4pKMNHXftZn9flj1p5vtaKrI6/axHWlb+vGUbuAACAX/h74YDGUmmqqCdU\njdw3slGn41U5qhnAJder7VMQTE30h1AZpaxrP+vzuvwxyuaPKdnB8j2o4hTWvz9S+3INdcWCKgAA\nwC/CabGNcFi6H74J1wU/6vO6/HVtN/R1Fgzfg+r6/vq6oArhDgAA+EWo1gEDqhMqIb+u/azP/qEQ\ndIPhe1BdAyarZQIAgKAUKtPYgNoKlfp8de1nffYPhem4wfA9qLEXimHkDgAA+EWo/HUfQGXBUPvO\nV/X5HtTQr7uxR+4IdwAAwG9CZRobgFLh9IeZunwP8sfr5p47AAAAAAETDAuRBEIwLBTDPXcAAAAA\nGkwoFRRvSP563Y15ryZFzAEAAAB4+augeLALh9fNtEwAAAAAXuF0z11dBMPr5p47AAAAAA2qqS6G\nFOjXTbgDAAAAgDDga7jjnjsAAAAACAOEOwAAAAAIA4Q7AAAAAAgDhDsAAAAACAOEOwAAAAAIA4Q7\nAAAAAAgDhDsAAAAACAOEOwAAAAAIA4Q7AAAAAAgDhDsAAAAACAM+hTtjzKvGmHxjzIYa9vmzMWaH\nMWadMeYiX84HAAAAAKiaryN3r0n6bnUbjTHXSIqz1g6U9KCkF308H4BqpKWlBboLQEjjGgJ8wzUE\nBJ5P4c5a+4WkwzXscpOkNzz7rpTUyRjT05dzAqgaP1QB33ANAb7hGgICz9/33EVJ2l3mca7nOQAA\nAABAA2JBFQAAAAAIA8Za61sDxkRL+j9r7Ygqtr0oKdVaO9/zeKukSdba/Cr29a0jAAAAABDirLWm\nvsc2b4DzG89HVT6Q9P8kzTfGXCrpSFXBTvLtRQAAAABAU+dTuDPG/FNSvKSuxphdkmZKainJWmtf\nttZ+bIy51hjzjaTjkr7va4cBAAAAAJX5PC0TAAAAABB4AV9QxRhztTFmqzFmuzHm14HuDxAKjDHZ\nxpj1xpi1xphVnufOM8Z8ZozZZoz51BjTKdD9BIKJMeZVY0y+MWZDmeeqvW6MMb8xxuwwxmwxxlwV\nmF4DwaOaa2imMWaPMeZrz8fVZbZxDQFlGGP6GGMWG2M2GWM2GmN+4nm+wX4WBTTcGWMiJD0vpxD6\nUEkuY8wFgewTECLckuKttSOttRd7nntE0ufW2kGSFkv6TcB6BwSn1+T8vCmryuvGGDNE0p2SBku6\nRtILxhjuDUdTV9U1JEnPWmtHeT7+LUnGmMHiGgIqOivpYWvtUEnjJP0/T/ZpsJ9FgR65u1jSDmtt\njrX2jKQUOYXPAdTMqPL1e5Okf3i+/oekmxu1R0CQs9Z+Ielwhaeru25ulJRirT1rrc2WtEPOzyyg\nyarmGpKqXljvJnENAeVYa/dZa9d5vj4maYukPmrAn0WBDncVi5zvEUXOgdqwkhYZY1YbY+73PNez\nZDVaa+0+ST0C1jsgdPSo5rqp+PMpV/x8AqrzkDFmnTHmlTLTybiGgBoYY2IkXSRphar/Ha7O11Gg\nwx2A+rnMWjtK0rVyhvQvlxP4ymK1JKDuuG6AunlBUn9r7UWS9kl6JsD9AYKeMaa9pHck/dQzgtdg\nv8MFOtzlSupX5nEfz3MAamCt3ev5fEDS+3KG6PONMT0lyRjTS9L+wPUQCBnVXTe5kvqW2Y+fT0AV\nrLUHbOnS639T6ZQxriGgCsaY5nKC3ZvW2oWepxvsZ1Ggw91qSQOMMdHGmJaSEuUUPgdQDWNMW89f\nfGSMaSfpKkkb5Vw793l2u1fSwiobAJo2o/L3B1V33XwgKdEY09IYEytpgKRVjdVJIIiVu4Y8v4iW\nuFVShudrriGgan+XtNla+1yZ5xrsZ5FPRcx9Za0tNsY8JOkzOUHzVWvtlkD2CQgBPSW9Z4yxcq7h\nedbaz4wx6ZIWGGN+IClHzupKADyMMf+UFC+pqzFml6SZkv4o6e2K1421drMxZoGkzZLOSPpxmdEJ\noEmq5hpKMMZcJGcV52xJD0pcQ0BVjDGXSZoiaaMxZq2c6Ze/lfSkqvgdrj7XEUXMAQAAACAMBHpa\nJgAAAACgARDuAAAAACAMEO4AAAAAIAwQ7gAAAAAgDBDuAAAAACAMEO4AAAAAIAwQ7gAAIckYc9Tz\nOdoY42rgtn9T4fEXDdk+AAD+QLgDAISqkkKtsZK+V5cDjTHNzrHLb8udyNoJdWkfAIBAINwBAELd\nE5ImGGO+Nsb81BgTYYz5X2PMSmPMOmPMf0mSMWaSMWapMWahpE2e594zxqw2xmw0xtzvee4JSW08\n7b3pee5oycmMMU959l9vjLmzTNupxpi3jTFbSo4DAKAxNQ90BwAA8NEjkn5hrb1Rkjxh7oi19hJj\nTEtJy40xn3n2HSlpqLV2l+fx9621R4wxrSWtNsb8y1r7G2PM/7PWjipzDutp+zZJI6y1w40xPTzH\nLPHsc5GkIZL2ec453lr7pT9fOAAAZTFyBwAIN1dJuscYs1bSSkldJA30bFtVJthJ0s+MMeskrZDU\np8x+1blMUrIkWWv3S0qTNLZM23uttVbSOkkxvr8UAABqj5E7AEC4MZKmW2sXlXvSmEmSjld4fIWk\nS6y1p40xqZJal2mjtucqcbrM18XiZywAoJExcgcACFUlweqopA5lnv9U0o+NMc0lyRgz0BjTtorj\nO0k67Al2F0i6tMy2opLjK5xrmaS7PPf1dZd0uaRVDfBaAADwGX9VBACEqpLVMjdIcnumYb5urX3O\nGBMj6WtjjJG0X9LNVRz/b0k/MsZskrRN0ldltr0saYMxZo219u6Sc1lr3zPGXCppvSS3pF9Za/cb\nYwZX0zcAABqNcW4NAAAAAACEMqZlAgAAAEAYINwBAAAAQBgg3AEAAABAGCDcAQAAAEAYINwBAAAA\nQBgg3AEAAABAGCDcAQAAAEAYINwBAALKGBNhjDlqjOnTkPsCANDUUMQcAFAnxpijkkp+eLSTdFpS\nsee5B621yYHqGwAATRnhDgBQb8aYnZJ+aK1NrWGfZtba4kbsVkjifQIA+IppmQAAXxjPR+kTxiQZ\nY1KMMf80xhRImmKMudQY85Ux5rAxJtcY85wxppln/2bGGLcxpp/n8Zue7R8bYwqNMcuNMdF13dez\n/RpjzDbPef9sjPnCGHNPlS+khj56tg83xiwyxnxrjMkzxvyyTJ9mGGO+McYUGGNWGWN6GWPijDHu\nCudYVnJ+Y8wPjTFLPOf5VtLvjDEDjDGLPefYb4x5wxjToczx/Ywx73m27TfG/MkY08rT50Fl9utl\njDlujDmvXv+qAICQRLgDAPjDzZLestZ2kjRf0hlJP5HURdJlkr4r6cEy+1ecRuKS9DtJ50naLSmp\nrvsaY3p4zv0LSd0kZUkaW0Ofq+2jMaajpEWSFkrqJel8SWme4/5b0q2SrvK83vslnaqmrxWNl7TJ\n078n5QTlJEk9JA2RFCtphqcPzSR9JGm7pGhJfSUtsNae9rzOqWXa/Z6kf1trD5/j/ACAMEK4AwD4\nwxfW2o8lyVp72lq7xlq72jqyJf1N0qQy+5sKx79jrV3rmaY4T9JF9dj3OklrrbUfWmuLrbV/kvRt\ndR0+Rx9vlJRjrX3eWnvGWnvMWpvu2fZDSb+x1u70tLPBWnvkHO9PiRxr7cuec5621u6w1qZ6+ntQ\n0pwyfRgvqaukR6y1Jz37f+XZ9oakKWXavVvSm7XsAwAgTDQPdAcAAGFpd9kHnimDz0gaLamtpGaS\nVtZw/L4yX5+Q1L4e+0ZW7IekPdU1co4+9pWUWc2hfSXtrKF/Nan4PvWU9Gc5I4ftPX3Y79ncR1K2\nreJmeWvtl8aYM8aYyyQd8fTpo3r2CQAQohi5AwD4Q8UA8pKkjZL6e6YuzlTlEbiGtldOyCkrqob9\na+rjbkkDqjlul6S4Kp4/LknGmNZlnutVYZ+K79OTcqZ0DrXWdpZ0X4U+RBtjqnvf3pAzYne3nOma\nZ6rZDwAQpgh3AIDG0EFSgbX2pDFmsMrfb+cvH0oaaYy5zrPoyc/k3NtWnz5+IKmvMebHxpiWxpgO\nxpiS+/delfS4Maa/JBljLjTGdLbW7pMzqjjVU5/vATn3ytWkg5xQeNQY01fSL8ts+0rOtNLZxpg2\nxpjWxpjxZba/Jel2OfcgvnGO8wAAwhDhDgDgi9rW0/mFpPuMMYWS5kpKqaGdc7VZq32ttfsl3SXp\nT5IOylmcZK2cunx16qO1tlDSZDnhKV/SNkkTPZufkvS+pP94Vgd9SVLJaN1/yVns5YCk/pJWnOO1\nzZR0iZyple9LeqdMH4olXS9noZXdknIk3VZme46ckcfT1tpznQcAEIaocwcAaBKMMRGS8iTdZq1d\nHuj++IMx5h+SMq21/xPovgAAGh8LqgAAwpYx5rtyRstOSfqNpCJJqwLaKT/xTAu9UdLwQPcFq6EX\n2AAAIABJREFUABAYTMsEAISzCXJWssyXM63y5nBcaMQYM1vOlNM/WGurXREUABDemJYJAAAAAGEg\naKZlGmNImQAAAACaNGttvUsFBU24kyRGERGMZs2apVmzZgW6G0Al/N9EMOP/J4IV/zcRzKovZVo7\n3HMHAAAAAGGAcAcAAAAAYYBwB5xDfHx8oLsAVIn/mwhm/P9EsOL/JsJZ0KyWaYyxwdIXAAAAAGhs\nxpjwWVAFAMJBTEyMcnJyAt0NoEmIjo5WdnZ2oLsBAEGBkTsAaGCev7oFuhtAk8D1BiCc+Dpyxz13\nAAAAABAGCHcAAAAAEAYIdwAAAAAQBgh3AIA6+/73v69HH3000N0ISbx3AAB/IdwBAIAG8dhjj+me\ne+4JdDcAoMki3AEAAABAGCDcAUAjysrK0dSpjykhYaamTn1MWVl1q4fn6/GS9OSTT6pPnz7q2LGj\nBg8erNTUVJ06dUr33nuvunTpoqFDh+qpp55S3759vcesXbtWo0ePVqdOnZSYmKhTp07V+by+ysrO\n0tSfTFXCfQma+pOpysrOavQ2Guu9W7Jkifr27aunnnpKPXv2VFRUlBYuXKhPPvlEgwYNUrdu3fTE\nE0949y8qKtLPfvYzRUVFqU+fPvr5z3+uM2fO1Ksta63++Mc/asCAAerevbsSExN15MgRSVJOTo4i\nIiL0xhtvKDo6Wj169NDs2bMlSZ9++qlmz56t+fPnq0OHDho5cqQkKTY2VosXL/a2/9hjj+nuu+8u\n197rr7+ufv36qWvXrnrppZeUnp6uCy+8UF26dNH06dPr9G8EAE2atTYoPpyuAEDoq+772c6d2TYu\n7hdWOmYla6VjNi7uF3bnzuxatevr8dZau23bNtu3b1+7b98+a621OTk5dufOnfaRRx6x8fHxtqCg\nwObm5toRI0bYvn37WmutLSoqstHR0fa5556zZ8+ete+8845t0aKFnTFjRq3P66udWTtt3HVxVr+V\n1SxZ/VY27ro4uzNrZ6O10ZjvXVpamm3evLl9/PHH7dmzZ+3f/vY32717dztlyhR7/Phxu2nTJtum\nTRubne3828+YMcOOGzfOHjx40B48eNCOHz/ePvroo/Vqa86cOXbcuHE2Ly/PFhUV2R/96EfW5XJZ\na63Nzs62xhj7wAMP2NOnT9v169fbVq1a2a1bt1prrZ01a5a9++67y72WmJgY+5///Mf7uOw+Je1N\nmzbNnj592i5atMi2bt3a3nLLLfbgwYM2NzfX9ujRwy5durTa94rfHwCEE8/3tPpnKl8ObsgPvjkD\nCBfVfT+bMmVWmWBmvQFtypRZtWrX1+Ottfabb76xPXv2tJ9//rk9c+aM9/n+/fvbRYsWeR+/8sor\n3oCyZMkSGxUVVa6d8ePHN2q4mzJ9Smkom1UazqZMn9JobTTme5eWlmbbtm1r3W63tdbao0ePWmOM\nXb16tXef0aNH24ULF1prrY2Li7P//ve/vds+/fRTGxsbW6+2Bg8ebBcvXuzdlpeXZ1u0aGGLi4tt\ndna2jYiIsHl5ed7tF198sZ0/f761tn7hLiIiwu7du9e7vWvXrnbBggXex7fddpt97rnnqn2v+P0B\nQDjxNdw1D9SIIQA0Nbm5bkntKjzbTvPmuTVvXm1aqPr4vDx3rfsQFxenOXPmaNasWdq0aZOuvvpq\nPfPMM8rLy1OfPn28+5WdVrh3715FRUWVayc6OrrW52wIuYW5UtcKT7aU5m2Yp3mP1erNkzZISqjc\nRl5hXq0Ob+z3rmvXrjLGSJLatGkjSerRo4d3e5s2bXTs2DFJUl5envr161fuHHl5efVqKycnR7fc\ncosiIpw7N6y1atGihfLz87379+zZ0/t127ZtvcfWV8W+lG2/bN8AX2RlZ2nGszOUW5irqI5RSno4\nSbExsYHuFtCguOcOABpJVFSEpOMVnj2uKVMiyo3FVfcxZUrVx0dG1u1beWJiopYtW6Zdu3ZJkn79\n618rMjJSe/bs8e5Tsk2Sevfurdzc3HJtlN3eGKI6RklFFZ4skqaMmCI709bqY8qIKVW2Edkxstb9\nCNb3LjIyUjk5pfdf5uTkKDKy9q+rrH79+umTTz7RoUOHdOjQIR0+fFjHjx9X7969z3lsSYAsq127\ndjpx4oT38b59++rVL8AXWdlZmvzQZM3rME9psWma12GeJj80uV737gLBjHAHAI0kKek+xcXNVGlA\nO664uJlKSrqvUY6XpO3btys1NVVFRUVq2bKl2rRpo2bNmunOO+/U7NmzdeTIEeXm5uqvf/2r95hx\n48apefPm+stf/qKzZ8/q3Xff1apVq2p9zoaQ9HCS4tbHlYazIilufZySHk5qtDaC+b1zuVx6/PHH\ndfDgQR08eFBJSUneRUvq6sEHH9Rvf/tbbwg9cOCAPvjgA+92Z9ZQ1Xr27Kns7Oxy+1x00UVKSUnR\n2bNnlZ6ernfeeafcMTW1BzSUGc/OUOaFmVJLzxMtpcwLMzXj2RkB7RfQ0JiWCQCNJDY2WosWTdeM\nGU8rL8+tyMgIJSVNV2xs7abp+Xq8JJ0+fVqPPPKItm7dqhYtWmj8+PF6+eWX1bFjR/3oRz9SbGys\nIiMjNWXKFL322muSpBYtWujdd9/V/fffr9///ve69tprddttt9XrPaiv2JhYLXp+kWY8O0N5hXmK\n7BippOfrNqXK1zYC/d5VHBUr+/j3v/+9jh49qhEjRsgYozvvvFO/+93v6tXWT3/6U0nSVVddpb17\n96pHjx666667dOONN57z2DvuuENvvfWWunbtqv79+ys9PV1JSUlyuVzq0qWLJk2apClTpujQoUO1\n6ktVj4HaKjhVoI37N2pj/kalZqVKYyrs0FL61+Z/adNLm9SxVcfSj5Yd1al1p3LPdWpV/nHHVs4+\nrZq14v8ogooJlr+YGWNssPQFAHxhjAn50YgXX3xR8+fPV2pqaqC7EnJ47xpXOFxv8E1RcZG2Hdzm\nDXIb9zsf3574VkN7DNXwHsO1LmWd1sSuKR25k6Qi6YZvb9CsmbNUeLrQ+1FwqqD849PlH5c8V3Cq\nQJLKhb1zBsIKz5UcQ0hECc/3tHr/Z2DkDgCgffv2aefOnRo3bpy2b9+uZ555Rj/5yU8C3a2QwHsH\nNA5rrXYX7i4X4Dbmb9SOQzsU3Slaw3sO1/Aew/XDkT/U8J7D1f+8/oowzh1IWSOce+68UzM907Kf\ne/45xfau/6Iqp8+erjYElg2JuYW5KiyqOjgSEtGQfBq5M8ZcLWmOnHv3XrXWPllh+yRJCyXt9Dz1\nrrX28WraYuQOQFgIxZGEXbt26brrrlN2drY6d+4sl8ul2bNnq3lz/gZ4LvV975544gnNnj270i9i\nl19+uT766CN/djmshOL1hnMrO6WybJBr06KNhvdwQlxJmBvSfYjatGhzzjZLVsv0TssOotUyaxsS\nC08X1iokVgyIVQbCaqaaEhIDy9eRu3qHO2NMhKTtkq6UlCdptaREa+3WMvtMkvQLa+2NtWiPcAcg\nLPDLJtB4uN5CW22mVJYNct3bdQ90l4NaTSGxUlCsIiSWHGetJSQGSCCnZV4saYe1NsfTkRRJN0na\nWmE//kUBAACaMF+mVKL2WjVvpe7Nu/scgmsbEqubbnqukFiXqaaExLrxJdxFSdpd5vEeOYGvonHG\nmHWSciX9ylq72YdzAgAAIIjVZkrl5P6T9fClD9d6SiUalz9CYk1TTktCYnUL2hASa8/fN1OskdTP\nWnvCGHONpPclne/ncwIAAMDPajul8vYhtzOlsokKxpDo61RTf4XEkntCfeVLuMuV1K/M4z6e57ys\ntcfKfP2JMeYFY0wXa+0hVWHWrFner+Pj4xUfH+9D9wAAAOArplQi0MI5JC5ZskTvvf+e3vz4TR3u\nedjn98qXBVWaSdomZ0GVvZJWSXJZa7eU2aentTbf8/XFkhZYa2OqaY8FVQCEhZiYGOXk5AS6G0CT\nEB0drezs7EB3I2z4Y5VKINycKySWC4XVhMTC04VyW7c6te6kU5+f0rExx5wyHbMUmAVVrLXFxpiH\nJH2m0lIIW4wxDzqb7cuSbjfGTJN0RtJJSXfV93wAECr4RRNAsGNKJVB/DT2SeMP6G7Sy5coG6ZtP\nde4aEiN3AAAADau2UypLRuSYUgk0vqk/map5HeY1yMgd4Q4AACAMMKUSCE1Z2Vma/NBkZV6YKc0m\n3AEAADQZFP4Gwk/Japnz/jKPcAcAABBumFIJND3GGMIdAABAKGNKJQCJcAcAABAyqppSuSF/gw6d\nPMSUSgCEOwAAgGDDlEoA9UG4AwAACCCmVAJoKIQ7AACARsCUSgD+RrgDAABoQFVNqdyQv0HfHPqG\nKZUA/IpwBwAAUE9MqQQQTAh3AAAA58CUSgChgHAHAADgwZRKAKGMcAcAAJqkilMqN+RvUMb+DKZU\nAghZhDsAABDWmFIJoKkg3AEAgLDAlEoATR3hDgAABI2s7CzNeHaGcgtzFdUxSkkPJyk2JrbSfkyp\nBIDKCHcAACAoZGVnafJDk5V5YabUUlKR1H99fz0/83kdbn2YKZUAcA6EOwAAEBS+N/17Su6Y7AS7\nEkVSh/QO+u7932VKJQCcg6/hrnlDdgYAADQdxe5irdu3TmnZaUrLSdMnGZ9I8RV2aimN6T1Gb9/x\ndiC6CABNCuEOAADUSrG7WBvyNygtO02p2alatmuZerfvrfiYeN0z4h61HNxS7xa9W2nkLrJjZMD6\nDABNCdMyAQBAldzWrY35G5Wanaq07DQtzVmqHu16KCEmQfEx8ZoUM0m92vfy7l/VPXdx6+O06PlF\nVS6qAgAoj3vuAABAg3BbtzL2ZzjTLLPTtCRnibq17ab46HjFxzgfvTv0rrGNktUy8wrzFNkxstrV\nMgEAlRHuAABAvbitW5sPbPZOs1ySvUTntTlP8dHxSohN0KToSYrqGBXobgJAk0G4AwAAtWKt1ZaD\nW5Salaq0nDQtyV6iDq06eKdZxsfEq0/HPoHuJgA0WYQ7AABQJWutth7c6l3NMi07Te1atPMGufiY\nePXr1C/Q3QQAeBDuAACAJCfMbf92u3eaZVp2mlo1b6WEmAQlxCRoUswkxXSOCXQ3AQDVINwBANBE\nWWv1zaFvvEEuLTtNzSOaKyE2wXvfHGEOAEIH4Q4AgCbCWqudh3eWC3OSyoW52M6xMqbevxcAAAKI\ncAcAQJiy1irrSFa5aZbF7mIlxCZ4F0GJOy+OMAcAYYJwBwBAGMk+ku1dzTItO01FxUXeIJcQk6AB\nXQYQ5gAgTBHuAAAIYbsKdnnDXGpWqk6dPeVdyTIhJkHndz2fMAcATQThDgCAELK7YHe5aZbHio55\ng1x8TLwu6HYBYQ4AmijCHQAAQSy3MLfcAigFpwuckbloZ3RuSPchhDkAgCTCHQAAQSXvaJ43yKVm\np+rwycOaFDPJu5rlkO5DFGEiAt1NAEAQItwBABBAe4/u1ZKcJd775g4cP6BJMZO80yyH9RhGmAMA\n1ArhDgCARpR/LN87MpeWk6b8Y/maGD3RuwjKiJ4jCHMAgHrxNdw1b8jOAAAQbg4cP1BumuXeY3t1\neb/LFR8TrwdGP6ARPUeoWUSzQHcTABDCsrJyNGPG6z63w8gdAABlHDxxUEuyl3gXQdlTuEcT+k3w\nTrO8qNdFhDkAQIPJysrR5Ml/UWbmY5LaM3IHAEB9fXviWy3JWeIdncspyNGEfhMUHx2v129+XRf1\nukjNI/hxCYS6kpGR3Fy3oqIilJR0n2JjowPdLYQJt9v5KC6u++eHH37dE+za+dwPfloBAJqUQycP\naWnOUu80y6zDWbqs32WKj47XKze+olG9RxHmgDBTfmSknaTjWrFiphYtmh5yAa9siKhPkAi1z8HQ\nh9p8lqRmzaSIiLp/zstzqyGCncS0TABAmDt88rCW7VrmXc0y81CmxvUd551mObr3aLVo1iLQ3QTg\nRy7XY0pJ+aXK/wJ9XIMGPa3vfGdmUIQDf4eI+gaPYP4cDH0o+exLudKpUx/TvHkl/z9ZUAUAAK+C\nUwXlwtz2b7drXJ9xio+J11+v/avGRo4lzAFh7NQpacMGac0a5yM9XdqwoaqRkXYqLnZr0KDgDg0N\nGSIQnJKS7tOKFTM9I8u+IdwBAEJa4elCLctZ5p1mue3bbbok6hLFx8Trz1f/WWOjxqpls5aB7iYA\nPzh5snyQW7NG2r5dGjRIGj3a+XjgAelPf4pQSspxVRy5u+SSCE2fHqjeA47Y2GgtWjRdM2Y8rXnz\nfGuLaZkAgJBy9PRRfbHrC2+Y23xgsy6Outg7zfLiqIvVqnmrQHcTQAM7eVJav758kNuxQ7rggtIg\nN3q0NHy41Lp1+WOruucuLi4077lDeKOIOQAgrB0rOqblu5Z7SxNk7M/Q2Kixio92ioZf0ucStW7e\n+twNAQgZJ05UDnLffOMEuTFjyge5VrX8W07Japl5eW5FRrJaJoIT4Q4AEFaOFx3X8t3LvSNzG/M3\nanTkaMVHxyshNkGX9rmUMAeEkRMnpHXryge5zExp8ODKI3K1DXJAqApouDPGXC1pjqQISa9aa5+s\nZr+xkr6UdJe19t1q9iHcAUATdOLMCX25+0tvmFu/b71G9h7pnWY5rs84tWnRJtDdBNAAKga59HRp\n505pyJDyQW7YMIIcmqaAhTtjTISk7ZKulJQnabWkRGvt1ir2WyTppKS/E+4AoGk7eeakvtrzlXc1\ny7V71+qiXhcpPiZeCTEJGtd3nNq2aBvobgLw0fHjlUfksrKqDnItWfMIkBTYcHeppJnW2ms8jx+R\nZCuO3hljfiqpSNJYSR8S7gCgaTl19pS+2v2Vd2Tu671fa0TPEd4wN77veLVr2TDFWwEExrFjlYNc\ndrY0dGj5IDd0KEEOqImv4c6XUghRknaXebxH0sVldzDGREq62VqbYIwptw0AEJ5OnT2llXtWesNc\nel66hvUYpoSYBP3u8t/psn6XqX3L9oHuJoB6OnZMWru2fJDLyXFG4EaPliZNkh5+2AlyLSgpCTQq\nf9e5myPp12Ue15hCZ82a5f06Pj5e8fHxfukUAKDhnD57WqtyV3lXs1yVu0pDewxVfHS8HpnwiC7r\ne5k6tOoQ6G4CqIejRysHuV27SoNcQoL0y186Uy0JckDdpaWlKS0trcHa83Va5ixr7dWex5WmZRpj\ndpZ8KambpOOSHrDWflBFe0zLBIAQUFRcpFW5q7wjc6tyV+mCbhd4V7Oc0G+COrbqGOhuAqijwsLK\nQW73bmeVyrJTKwlygP8E8p67ZpK2yVlQZa+kVZJc1tot1ez/mqT/4547AAgtRcVFSs9L94a5FXtW\n6Pyu53tXs5zQb4I6t+4c6G4CqIOKQS49XdqzRxoxonKQa+7veV4AvIKhFMJzKi2F8EdjzINyRvBe\nrrDv38WCKgAQ9M4Un9GavWu8q1l+tfsrDegyQPExTtHwidETCXNACCkoqDwil5tbOcgNHkyQAwKN\nIuYAAJ+cdZ/Vmrw1SstOU1pOmpbvWq7+5/X3rmZ5efTl6tKmS6C7CaAWCgqkr78uH+Ty8qQLLywf\n5C64gCAHBCPCHQCgTs66z2rt3rXeaZbLdy9XdKdo7zTLidET1bVt10B3E8A5HDlSOcjt21d1kGvW\nLNC9BVAbhDsAQI2K3cVat2+ddzXLL3Z9ob6d+io+2plmOSlmkrq17RbobgKoweHDlYNcfn5pkBsz\nxvk8aBBBDghlhDsAaGKysrM049kZyi3MVVTHKCU9nKTYmFjv9mJ3sdbnr3emWWanadmuZYrsEOld\nzXJi9ET1aNcjgK8AQE0OHy4f4taskfbvly66qPyIHEEOCD+EOwBoQrKyszT5ocnKvDBTaimpSOq/\nvr/+PPPP2lG8Q6nZqVqWs0w92/f0TrOcFD1JPdv3DHTXAVTh0KHKI3IHDlQOcuefT5ADmgLCHQA0\nIVN/MlXzOsxzgl2JIqlDegd9b/r3vCta9mrfK2B9BFC1Q4cqj8gdPCiNHFk5yEVEBLq3AAKBcAcA\nTYS1VqNdo7V28NpK2xKyErT49cUB6BWAqnz7beUgd+hQ5SA3cCBBDkApX8Mdi+ACQJArPF2oN9e/\nqbnpc7X7yG6pSJVG7iI7Rgaqe0CTd/Bg5SB3+HBpkLv1Vmn2bGnAAIIcAP9i5A4AgtT6fes1N32u\n5m+ar8n9J+vHY3+sfrafrpp+Vbl77uLWx2nR84vKLaoCwD8OHKgc5AoKKo/IEeQA1AfTMgEgjJw6\ne0rvbH5HL6x+QbsLd+uBUQ/o/lH3q3eH3t59SlbLzCvMU2THyEqrZQJoGPv3Vw5yhYXSqFHlg1xc\nHEEOQMMg3AFAGNh5eKdeSn9Jr617TSN7j9S0MdN0/fnXq3kEs+eBxlBVkDt6tHKQ69+fIAfAfwh3\nABCiit3F+njHx3oh/QWl56Xr3gvv1YOjH9TArgMD3TUgrOXnVw5yx46VD3ElQc7U+1csAKg7wh0A\nhJj8Y/l6de2remnNS+rdvremjZmmO4feqTYt2gS6a0DY2bevcpA7caJykIuNJcgBCDzCHQCEAGut\nlu1aphdWv6BPMz/V7YNv17Sx0zSq96hAdw0IG3v3Vg5yJ0+WD3FjxkgxMQQ5AMGJcAcAQaxsGYNi\nW6xpY6bpngvvUefWnQPdNSCk5eVVDnKnT1cekSPIAQglhDsACELr9q3T3NVztWDzAm8Zg0nRk2T4\nLROos7w8KT29fJA7c6ZykIuOJsgBCG2EOwAIErUpYwCgetZWPSJ39mzlINevH0EOQPgh3AFAgFHG\nACiVlZWjGTNeV26uW1FREUpKuk+xsdGV9rNWys2tHOTc7spBrm9fghyApoFwBwABQBkDoLKsrBxN\nnvwXZWY+JqmdpOOKi5upzz6brhYtoisFOalykOvThyAHoOki3AFAI6KMAZoia50RtbNnpeJi56Oq\nr6dPf0wffPBLOcGuxHG1avW0OnWaWWnVyqgoghwAlOVruGPOEACcQ1VlDN67672AlTGo7bQ3VM/t\nrjmk1Obr+h4Xiudxu6WICKl5c6lZM+ej5Ouyz+3f71b5YCdJ7TRqlFvLlxPkAMDfCHcAUI2qyhi8\neP2LAS1jUNW0txUrZurf/56uvn2jQzY8NPZ5rK06nNT0nD++ruq5Fi0a5zx1+ToionbBbOrUCM2b\nd1wVR+76948g2AFAI2BaJgBUEKxlDE6elK6//jEtXlx52pv0tFq2nOnXX/AbK0g0RtsREQH6Rwxz\n1d1zt2jRdEaXAaAWmJYJAA2gqjIGm3+8OaBlDE6fllaulFJTnY/0dCkiouppbwkJbi1eHIheAqVi\nY6O1aNF0zZjxtPLy3IqMjFBSEsEOABoL4Q5Ak1axjMF/X/bfAStjcOaMs4Lg4sVOmFuxQho0SLri\nCunXv5YmTJCmTat62ltkJENRCA6xsdF6662Zge4GADRJTMsE0OQESxmD4mJp3brSMLd8uRQT44S5\nhARp4kSpc4Xb+5j2BgBA+KIUAgDUUv6xfL3y9St6+euXA1LGwO2WMjJKw9zSpVLv3k6Qu+IKadIk\nqVu3c7dTslpm6bQ3VssEACAcEO4AoAZVlTGYNnZao5QxsFbaurU0zKWlSeedVxrm4uOlXr383g0A\nABAiCHcAUIWqyhjcc+E9fi1jYK2UmVk+zLVqVT7M9e3rt9MDAIAQR7gDgDIau4xBTo4T5EoCndtd\nGuYSEqTYWL+cFgAAhCHCHYAm79TZU3p709uamz7XW8bg/lH3+6WMQW5uaWmC1FTp+HFnRK4kzA0c\nWLtizwAAABUR7gA0WTsP79SL6S/q9XWva2TvkZo2ZlqDlzHYv798mDt40Fn4pCTMDRlCmAMAAA2D\ncAegSfF3GYNDh6QlS0qnWe7ZI11+eelUyxEjpAhKygEAAD8g3AFoEvxVxqCgwClJUDIyl5kpjR9f\nOjI3cqTUvPHrmQMAgCaIcAcgbPmjjMGxY06x8JKRuc2bpUsuKR2ZGztWatGiAV8EAABALRHuAISd\nhixjcPKk9NVXpWFu/Xpp1KjSMHfJJVLr1n54EQAAAHVEuAMQNiqWMZg2ZpriY+LrVMagqEhaubI0\nzKWnS8OHl4a58eOltm39+CIAAADqiXAHIKT5WsbgzBlpzZrSMLdihTRoUGmYmzBB6tDBzy8CAACg\nARDuAISk+pYxKC6W1q0rDXPLl0sxMaVhbuJEqXPdZ28CAAAEHOEOQMioTxkDt1vKyCgNc0uXSr17\nl4a5SZOkbt0a8UUAAAD4CeEOQNCrSxkDa6WtW50gt3ixU3Ouc2cnzCUkSPHxTrgDAAAIN4Q7AEHJ\nWqulOUs1N31ujWUMrHVqy5WMzKWlSa1alY7MxcdLffsG5CUAAAA0KsIdgKBScKpAb25wyhi4rbvK\nMgY5OaUjc6mpztTLkpG5K66QYmMD+AIAAAAChHAHICjUVMYgL698mDt2rHyYGzhQqkO1AwAAgLBE\nuAMQMNWVMWh2srfS0krD3MGDzsInJWFuyBDCHAAAQEWEOwCNLvNQpl5a85K3jMHUQdPUZvf1WpLa\nXKmp0p490uWXl4a5ESOkiIhA9xoAACC4BTTcGWOuljRHUoSkV621T1bYfqOkJEluSWck/dxau7ya\ntgh3QBArW8ZgdW66Lu94rzp/86DW/WegMjOl8eNLw9zIkVLzmsvVAQAAoIKAhTtjTISk7ZKulJQn\nabWkRGvt1jL7tLXWnvB8PVzSAmvt4GraI9wBQSj/WL5eWPmKXlz1spqd7K3WG6cpP/VOXTq6jfe+\nubFjpZYtA91TAACA0OZruPPlb+sXS9phrc3xdCRF0k2SvOGuJNh5tJczggcgyJ04YfXSv5fqlfVz\nta34U5nNt2v46fd0w5hRSvipdGmy1Lp1oHsJAACAsnwJd1GSdpd5vEdO4CvHGHOzpCckdZd0nQ/n\nA+AnRUXSypXSx4sL9M72N5XZZa5atXZrQutp+sOEF3XVbzurbdtA9xIAAAA18ftdMdZ0lLmYAAAg\nAElEQVTa9yW9b4yZIOlxSZP9fU4ANTt7VkpPLy1PsDxzndrFz9XRfgs09pLJmnPl87p2iFPGAAAA\nAKHBl3CXK6lfmcd9PM9VyVr7hTGmvzGmi7X2UFX7zJo1y/t1fHy84uPjfegegBLFxdK6dWXC3HKp\nX/9Tipz8tnZfNVfnRezWj8Y8oPtHbVbvDr0D3V0AAIAmIS0tTWlpaQ3Wni8LqjSTtE3Ogip7Ja2S\n5LLWbimzT5y1NtPz9ShJC621fatpjwVVgAbidksZGaVhbulSqXdvZ/GTwZdlamu7l7Rgu1PGYNqY\nabr+/OvVPILlLQEAAAIpYAuqWGuLjTEPSfpMpaUQthhjHnQ225cl3WaMuUdSkaSTku6s7/kAVM9a\naevW0jC3ZInUubMT5hITpb++UKy1x5wyBvP3rNZ9F92n5T9YroFdBwa66wAAAGggFDEHQpC1UmZm\naZhLS3NKEVxxhbzlCfr2dcoYvPL1K3r565fVu31vTRszTXcOvVNtWrQJ9EsAAABABQEtYt6QCHdA\nzXJynDBXEujc7tIgl5AgxcZKxkjWWi3NWaq56XP1aeanun3w7Zo2dppG9R4V6JcAAACAGhDugDCV\nl1ca5FJTpWPHyoe58893wlyJglMFenPDm5qbPldu69a0MdN0z4X3qHPrzoF7EQAAAKg1wh0QJvbv\nd6ZXloS5gwelSZOcIHfFFdKQIeXDXIl1+9Zp7uq5WrB5gSb3n6xpY6YpPoYyBgAAAKEmYAuqAPDN\noUPOwiclo3N79kiXX+6EuQcflC68UIqIqPrYU2dP6e1Nb2tu+lztLtytB0Y9oM0/powBAABAU8bI\nHdBICgqkZctKR+YyM6Xx40unWY4aJTU/x59bMg9l6qU1L+n1dZQxAAAACDdMywSC1PHj0hdflIa5\nzZulSy4pDXNjxzorXJ5LsbtYH+34SHPT52p1rlPG4MHRD1LGAAAAIMwQ7oAgcfKk9NVXpdMs1693\nRuNKwtyll0qtW9e+PcoYAAAANC2EO8BPsrJyNGPG68rNdSsqKkJJSfcpNjbau72oSFq5sjTMpadL\nw4eXhrnLLpPatq3bOSljAAAA0HQR7gA/yMrK0eTJf1Fm5mOS2kk6rv79Z+qpp6Zr27ZoLV4srVgh\nDRpUGuYuv1zq0KF+56OMAQAAAAh3gB9MnfqY5s37pZxgV+K4Ond+WvfeO1MJCdLEidJ55/l2HsoY\nAAAAoASlEAA/+OYbt8oHO0lqp5Ej3Zozx7e2KWMAAAAAfyDcAWXs3Cn94Q/S2rURko6r4shdZGQ1\nhedqoWIZg/++7L8pYwAAAIAGU//fVIEwkp0t3X+/U54gKkpaufI+xcXNlBPwJOm44uJmKinpvjq1\nW+wu1gfbPtA1867RJa9cIrd1a/kPluvTqZ/q5gtuJtgBAACgwXDPHZq0nBxnpO5f/5KmTZMefljq\n0sXZVrJaZl6eW5GRlVfLrAllDAAAAFBXLKgC1MOuXdLs2dLbb0sPPij94hdS166+tUkZAwAAAPiC\nBVWAOti92wl1CxZIDzwgbdsmdevmW5tVlTF48foXKWMAAACARkW4Q5OwZ4/0xBNScrL0X/8lbd0q\nde/uW5sVyxg8f83zlDEAAABAwBDuENZyc6U//lGaN0/64Q+dUNejR+2OzcrO0oxnZyi3MFdRHaOU\n9HCSevfpTRkDAAAABCXuuUNYystzQt1bb0k/+IH0q19JPXvW/vis7CxNfmiyMi/MlFpKKpI6f9VZ\n5hKjsUPHatqYaZQxAAAAQIPinjugjL17pSeflN54Q7rvPmnzZqlXr7q3M+PZGaXBTpJaSkfGHdEN\nB2/QB1M/aMguAwAAAA2COncIC/v2ST//uTR0qPN40ybp2WfrHuzOFJ/RJzs+0ec7Py8NdiVaSsdO\nH2uQ/gIAAAANjZE7hLT8fOl//1d67TXp7ruljAwpMrJubbitW8tyliklI0XvbHlHcefFqU/HPsov\nyi8f8IqkyI51bBwAAABoJIQ7hKT9+6WnnpJefVWaMkXauFGKiqr98dZardm7RskbkzV/03x1bdtV\niUMTter+VYo9L1ZZ36l8z13c+jglPZ/kt9cEAAAA+IIFVRBSDhxwQt0rr0gul/Sb30h9+tT++M0H\nNit5Y7JSNqXIWivXMJdcw10a0n1IpX1LVsvMK8xTZMdIJT2cpNiY2AZ8NQAAAEApXxdUIdwhJBw8\nKD39tPS3v0l33eWEur59a3ds1uEspWSkKDkjWYdOHtJdQ++Sa7hLo3uPpiYdAAAAggarZSKsffut\n9Mwz0ksvSXfcIa1dK/Xrd+7j9h7dqwWbFig5I1mZhzN1++Db9fy1z2tCvwmKMKwjBAAAgPBDuENQ\nOnTICXUvvijddpv09ddSdPQ5jjl5SP/a/C8lZyRr7b61unHQjZoVP0tXxl6pFs1aNE7HAQAAgAAh\n3CGoHD7slDB44QXp1lul9HQptobb3I4VHdPCrQuVnJGsZbuW6aq4q/TQxQ/pmgHXqE2LNo3XcQAA\nACDACHcICkeOSH/6k/TXv0o33SStXi3171/1vqfOntInOz5RckayPs38VBP6TZBrmEv/vO2f6tiq\nY+N2HAAAAAgShDsEVEGBNGeO9Je/SDfcIK1cKcXFVd7vrPus/rPzP0rZlKKFWxfqwl4XyjXMpReu\ne0Hd2nZr/I4DAAAAQYZwh4AoKJCee84JddddJ61YIQ0YUH4ft3Vr+a7l3uLiMZ1j5Brm0uMJjyuq\nYx2K2gEAAABNAOEOjaqwUPrzn51gd8010pdfSgMHlm631urrvV8rJSNF8zfNV6fWneQa5tKXP/hS\ncV2qGNIDAAAAIIlwh0Zy9KgzSjdnjnTVVdIXX0iDBpVu33Jgi7cWXbEtVuLQRH085WMN6zEscJ0G\nAAAAQgjhDn519Kj0/PPOYimTJ0tLl0oXXOBsyz6SrfkZ85Wckaz9x/frrqF36a1b39LYyLEUFwcA\nAADqiHAHvzh2zFn58tlnpSuukJYskQYPlvYd26e/rHxbyRnJ2v7tdt02+DbNuXqOLu93uZpFNAt0\ntwEAAICQZay1ge6DJMkYY4OlL6i/48edUPfMM1J8vPToo1Jk/8N6d8u7Ss5IVnpeum4YdINcw1ya\n3H8yxcUBAAAAD2OMrLX1nsJGuEODOHHCKTz+9NPSxInSL397XJnNPlByRrKW5CzRd/p/R65hLl03\n8DqKiwMAAABVINwhoE6ckF58UXrqKenSCaeVcP+/9eXRZH3yzSca33e8XMNcuvmCmykuDgAAAJwD\n4Q4BcfKkE+qefOqsBkxOVbf4ZC3d/76G9xwu1zCXbh9yO8XFAQAAgDog3KFRnTwpvfiSW3/4x1fq\neFmyCvq8rf5d+8k1zKU7h96pPh37BLqLAAAAQEgi3KFRnDxpNfPFdXphWbLcF8xX727t9f0xLt01\n9C4N7Drw3A0AAAAAqBHhDn61IXebHpmXrEV7U9SizWklDnXpZ99J1PAew6lFBwAAADQgX8Mdde5Q\nya6CXXprXYpe/CJFuYV71a/wLr186+u67zuXEOgAAACAIEW4gyQp/1i+3t78tpI3pmh97lZpy60a\nap/W/J9P0rhLKC4OAAAABDumZTZhR04d0Xtb3lNyRrJW5a7SIHO9sv4vUSM7XqXHHm2pSy8NdA8B\nAACApoNpmaiT40XH9eH2D5WckazU7FQlRF+p2MP3a+vf3lfnAW218Clp3LhA9xIAAABAXfkU7owx\nV0uaIylC0qvW2icrbP+epF97Hh6VNM1au9GXc6LuioqL9Ok3nyo5I1kf7/hYl/a5VHcMTtSVR/+h\nZ3/bSQMGSMlvSJddFuieAgAAAKivek/LNMZESNou6UpJeZJWS0q01m4ts8+lkrZYaws8QXCWtbbK\nyX5My2xYxe5ipWWnKTkjWe9tfU9Dug+Ra5hLNw28XZ+910OPPy7FxkqzZkkTJgS6twAAAAACOS3z\nYkk7rLU5no6kSLpJkjfcWWtXlNl/haQoH86Hc7DWasWeFUrOSNbbm99WZIdIuYa5tO7Bderdrq/e\nekua5JL69ZNee02aODHQPQYAAADQUHwJd1GSdpd5vEdO4KvO/ZI+8eF8qIK1VhvyNyg5I1kpGSlq\n06KNXMNcWnLfEp3f9XydPSv9859SUpIUFSW98ooUHx/oXgMAAABoaI2yoIoxJkHS9yUxAbCB7Ph2\nh5IzkpWckayTZ04qcViiFiYu1IieI2SMUXGx9NZb0v/8j9Srl/Tyy1JCQqB7DQAAAMBffAl3uZL6\nlXncx/NcOcaYEZJelnS1tfZwTQ3OmjXL+3V8fLziGWIqZ3fBbs3fNF/JGcnKLczVnUPv1N9v/Lsu\n7XOpt7h4cbGUkuKEuu7dpblzpSuukKg9DgAAAASXtLQ0paWlNVh7viyo0kzSNjkLquyVtEqSy1q7\npcw+/ST9R9LdFe6/q6o9FlSpwoHjB/T25reVkpGiTQc26ZYLbpFrmEuTYiapeURpNi8ulhYscEJd\nly7SY49JV15JqAMAAABCRcAWVLHWFhtjHpL0mUpLIWwxxjzobLYvS5ohqYukF4wztHTGWlvTfXmQ\nVHCqQO9tfU8pGSlasWeFrh14rX41/le6Ku4qtWreqty+brf09ttOmOvUSXruOWnyZEIdAAAA0NTU\ne+SuoTX1kbsTZ07ow+0fKiUjRf/J+o8SYhLkGubS9edfr3Yt21Xa3+2W3nnHCXXt2zufv/tdQh0A\nAAAQqnwduSPcBVBRcZE+y/xMKRkp+nD7h7o46mIlDkvUrYNvVefWnas8xu2W3n3XCXNt2jifr76a\nUAcAAACEOsJdiCl2F2tJzhKlZKTo3S3v6oJuFyhxWKLuGHKHerbvWe1xbrf0/vtOmGvZ0ik+fu21\nhDoAAAAgXASyiDlqyVqrlbkrlZKRogWbFqhX+15KHJaoNQ+sUXTn6HMcWxrqmjWT/vAH6brrCHUA\nAAAAyiPc+Ym1Vhv3b1RKRopSMlLUolkLuYa5tPjexbqg2wW1OF764ANnhM4YZxXMG24g1AEAAACo\nGtMyG9g3h75RSkaKkjOSdfT0USUOS5RrmEsX9brIW4uuJtZKH37ohLriYufzTTcR6gAAAIBwxz13\nQSC3MNdbXHxXwS7dMeQOuYa5NK7vOEWYiFq1Ya300UdOmDtzpjTURdTucAAAAAAhjnAXIAdPHNQ7\nm99RckayNuZv1M0X3CzXMJcSYhPKFRc/F2ulTz5xwtzJk87nW24h1AEAAABNDeGuERWeLtT7W99X\nckayvtz9pa4ZcI1cw1y6esDVlYqLn4u10qefOmHu2DFp5kzpttsIdQAAAEBTRbjzs5NnTuqjHR8p\nOSNZn+/8XJOiJ8k1zKUbBt2g9i3b17k9a6XPPnNCXWGhE+puv51QBwAAADR1hDs/OFN8Rot2LlJy\nRrL+b9v/aUzkGLmGuXTL4FvUpU2XerVprfT5506YO3zY+XzHHU55AwAAAAAg3DWQYnexlu1apuSN\nyXp367sa2GWgXMNcumPoHerVvle927VWWrzYCXMHD0qPPirddRehDgAAAEB5FDH3gbVWq/NWK3lj\nshZsXqDubbvLNcyl1f+1WjGdY3xsW0pNdaZf5uc7oS4xkVAHAAAAwD+aZLjL2J/hLS4eYSLkGubS\n53d/rsHdBzdI+2lpzkhdXp4T6lwuqXmTfKcBAAAANJYmMy1z5+Gd3uLiR04dUeLQRCUOS9So3qNq\nVVy8NpYudULd7t3SjBnSlCmEOgAAAAC1wz13Ncg7mqcFmxYoOSNZWYezdMeQO5Q4LFGX9bus1sXF\na2PZMmf6ZXa2E+qmTiXUAQAAAKgbwl0F3574Vv/a8i8lZyRr3b51umnQTXINc+nK/lfWqbh4bSxf\n7ozU7dwp/f730t13Sy1aNOgpAAAAADQRhDtJR08f1cJtC5Wckawvdn2h78Z9V65hLl0z8Bq1bt66\ngXsqffWVE+q2b3dC3b33EuoAAAAA+KbJhrtTZ0/p4x0fKzkjWZ9lfqaJ0ROVODRR/5+9Ow+Pqjzf\nOP59EgiyRQh7AgkhoBUREJSqLOLPUhUr1uICghuKIgWLaK1VETCudal1aYWqtUpErVpFW6q4oIAi\nguwiQggBEvawb9ne3x8zGSeQkEkyyUwm9+e6cmVmzjnveWYMyJ13G3TyIBrXa1wlNc6f7xl++cMP\ncO+9nlAXE1MltxIRERERkVqmVoW7vII8Ps34lOkrpjNj9Qx6tOnBkFOHMLjz4ApvLh6IBQs8PXUr\nV3pC3Q03KNSJiIiIiEhwRXy4K3SFzN0wl+nLp/POqndIiUthyKlDuPLUK2nTuE2V1vTtt56eumXL\n4J57YMQIqFevSm8pIiIiIiK1VERtYj78tuGkjk+lfVJ7Fm1exPTl03lz5Zs0a9CMIacO4ZubviG5\naXKV17FokSfULV4Mf/wjvPuuQp2IiIiIiIS3sOq54x5oOr8pjfs2pm5cXYZ2GcrQ04bSuUXnaqnh\nu+88oe677+Duu+Gmm+CE4K/HIiIiIiIicoyI6rkjBnadtYufZ/2c/078b9A2Fy/LkiWeULdggSfU\nvfWWQp2IiIiIiNQswdvJO1hi4EjekWoJdkuXwm9+AwMHQv/+kJ4Ot92mYCciIiIiIjVP+IW7XIiP\nja/SWyxbBoMHw4UXQt++sHYtjBsH9etX6W1FRERERESqTHiFu1xIWZpC6vjUKml+xQq44gr45S/h\nnHM8PXW33w4NGlTJ7URERERERKpNWIW7YfuGMeu5WSS3D+6KmCtXwlVXwfnnQ69enlB3xx0KdSIi\nIiIiEjnCarXMYNfy/ffwwAPw+eeeMDd6NDRqFNRbiIiIiIiIBEVlV8sMq567YFm1Cq6+2rNISvfu\nnp66u+5SsBMRERERkcgVUeFu9WoYNgz69YMuXTyh7u67FepERERERCTyRUS4+/FHuOYa6NMHOnf2\nhLp77oHGjUNdmYiIiIiISPWo0eFuzRq47jro3RtOOsmzpcG990JsbKgrExERERERqV51Ql1ARaSn\nQ2oqfPghjB3rCXlNmoS6KhERERERkdCpUeFu3Tp48EGYMQPGjPH01CnUiYiIiIiI1JBhmRkZcNNN\ncOaZ0Latp6du0iQFOxERERERkSJhHe7Wr4eRI+GMM6B1a0+oe+ABaNo01JWJiIiIiIiEl7AKd8OH\nTyYjI5MNG+CWW6BnT2jZ0rMa5oMPQlxcqCsUEREREREJT+acC3UNAJiZg/3Exk4ExjJ6dBJ33AHN\nm4e6MhERERERkapnZjjnrMLXh1e4c8ABBg9+grffnhjqkkRERERERKpNZcNdWA3L9GhITk5hqIsQ\nERERERGpUcIw3B0gPj4MyxIREREREQljYZaiDpCSMpHU1OtDXYiIiIiIiEiNElbhbtiwJ5g1ayzJ\nyUmhLkVERERERKRGCasFVcKlFhERERERkeoWgQuqiIiIiIiISHkp3ImIiIiIiESASoU7M7vQzH4w\nsx/N7A8lHD/ZzL4ys8NmNr4y9xIJldmzZ4e6BJES6WdTwpl+PiVc6WdTIlmFw52ZRQHPARcApwJD\nzexnR522ExgLPF7hCkVCTP8TkHCln00JZ/r5lHCln02JZJXpuesFrHHOZTrn8oA3gEv9T3DO7XDO\nLQLyK3EfERERERERKUNlwl0CsNHv+SbvayIiIiIiIlLNKrwVgpkNBi5wzt3sfT4c6OWcu62EcycC\n+5xzTx2nPe2DICIiIiIitVpltkKoU4n7ZgGJfs/bel+rkMq8CRERERERkdquMsMyvwU6mlmSmcUA\nQ4AZxzlf4U1ERERERKSKVHhYJni2QgD+gickvuSce9TMbgGcc26qmbUCFgKNgUJgP9DZObe/8qWL\niIiIiIhIkUqFOxEREREREQkPldrEPBjK2ghdJFTM7CUz22pmy0Jdi4g/M2trZp+Z2UozW25mxyxk\nJRIKZlbPzL4xs8Xen82Joa5JxJ+ZRZnZd2Z2vKlEItXOzNab2VLv358LKtxOKHvuvBuh/wicD2Tj\nmcc3xDn3Q8iKEvEysz54hhK/6pzrGup6RIqYWWugtXNuiZk1AhYBl+rvTgkHZtbAOXfQzKKBecBt\nzrkK/0NFJJjM7HagJxDrnBsU6npEipjZOqCnc25XZdoJdc9dmRuhi4SKc24uUKk/YCJVwTm3xTm3\nxPt4P7AK7TMqYcI5d9D7sB6eVbk1/0PCgpm1BQYCL4a6FpESGEHIZqEOd9oIXUSkEsysPdAd+Ca0\nlYh4eIe9LQa2ALOcc9+GuiYRrz8Dv0e/cJDw5IBZZvatmY2saCOhDnciIlJB3iGZbwO/0yrEEi6c\nc4XOudPx7H/7czPrHOqaRMzsYmCrd9SDoS26JPz0ds71wNO7/Fvv9KByC3W4C+pG6CIitYWZ1cET\n7F5zzr0f6npEjuac2wt8DlwY6lpEgN7AIO+8punAeWb2aohrEvFxzm32ft8O/BvP9LVyC3W4K+9G\n6CLVTb/dk3D1MvC9c+4voS5EpIiZNTezE72P6wMDAC30IyHnnLvHOZfonOuA59+bnznnrg11XSLg\nWYjKOxoHM2sI/BJYUZG2QhrunHMFwBjgY2Al8IZzblUoaxIpYmavA18BJ5nZBjO7IdQ1iQCYWW9g\nGPB/3iWTvzMz9Y5IOGgDfG5mS/DMA/3IOfffENckIhLuWgFzvfOV5wMfOOc+rkhD2sRcREREREQk\nAoR6WKaIiIiIiIgEgcKdiIiIiIhIBFC4ExERERERiQAKdyIiIiIiIhFA4U5ERERERCQCKNyJiIiI\niIhEAIU7ERGJKGZW4N37r2gPwLuC2HaSmS0PVnsiIiLBVCfUBYiIiATZAedcjypsXxvEiohIWFLP\nnYiIRBor8UWzDDN7zMyWmdl8M+vgfT3JzD41syVmNsvM2npfb2lm73pfX2xmZ3mbqmNmU81shZn9\nz8zqVdP7EhEROS6FOxERiTT1jxqWeYXfsV3Oua7A88BfvK89C/zDOdcdeN37HOAZYLb39R7ASu/r\nnYBnnXNdgD3A4Cp+PyIiIgEx5zS6REREIoeZ7XXOxZbwegZwnnNuvZnVATY751qY2XagtXOuwPt6\ntnOupZltAxKcc3l+bSQBHzvnTvY+vwuo45x7uFrenIiIyHGo505ERGoTV8rj8jji97gAzV8XEZEw\noXAnIiKRpsQ5d15Xeb8PAb72Pp4HDPU+Hg7M8T7+BBgNYGZRZlbUG3i89kVEREJGv20UEZFIc4KZ\nfYcnhDngf865e7zHmprZUuAwPwW624B/mNmdwHbgBu/r44CpZnYjkA/cCmxBq2WKiEiY0pw7ERGp\nFbxz7no653JCXYuIiEhV0LBMERGpLfTbTBERiWjquRMREREREYkA6rkTERERERGJAAp3IiIiIiIi\nEUDhTkREREREJAIo3ImIiIiIiEQAhTsREakyZpZkZoVmFuV9/l8zuyaQcytwrz+a2dTK1CsiIlKT\nKdyJiEipzGymmU0q4fVLzWxzgEHMtyyzc26gc+61QM4to65zzWxjsQude8Q5d3Mg14uIiEQihTsR\nETmefwLDS3h9OPCac66wmuspYtSSfevMLDrUNYiISM2gcCciIsfzHtDMzPoUvWBmTYBfAa96nw80\ns+/MbI+ZZZrZxNIaM7PPzWyE93GUmT1hZtvNbC1w8VHnXm9m35vZXjNba2Y3e19vAPwXiDezfd7j\nrc1sopm95nf9IDNbYWY5ZvaZmf3M71iGmd1hZkvNbJeZTTezmFJq7mBmn5rZDjPbZmbTzCzW73hb\nM3vHe2y7mT3jd2yk33tYYWbdva8XmlkHv/P+YWYPeB+fa2YbzewuM9sMvGxmTczsA+89dnofx/td\n39TMXjazLO/xd72vLzezi/3Oq+OtsVtp/41ERKTmUrgTEZFSOecOA/8CrvV7+SpglXNuhff5fuAa\n59yJeALaKDMbFEDzNwMDgW7AGcDlRx3fCgx0zsUCNwB/NrPuzrmDwEVAtnOusXMu1jm3pahkADM7\nCXgduA1oAcwEPjCzOn7tXwH8Ekj21nB9KXUa8DDQGjgFaAtM8t4nCvgQyAASgQTgDe+xK4D7geHe\n9zAI2Olf53G0Bpp427wZz/+vXwbaeV87CDzvd/40oL63vpbAn72vvwr4z3G8GM/ntrSM+4uISA2k\ncCciImX5J3CFX8/WNd7XAHDOfemcW+l9vAJPuDk3gHavAJ52zmU753YDj/gfdM7NdM6t9z6eA3wM\n9A2w5iuBD51znznnCoAn8ISfc/zO+Ytzbqv33h8A3UtqyDmX7pz71DmX75zbiSc4Fb2/nwNtgLuc\nc4edc7nOua+8x24E/uSc+87bzjrnXNE8QSuj/gJgonMuzzl3xDmX45z7t/fxATyfVT8AM2sDXADc\n4pzb65wr8H5e4Al9F5lZI+/z4cDx5jyKiEgNpnAnIiLH5ZybB2wHfu0dSngmnl4xAMysl3fY4zYz\n2w3cAjQPoOl4wH9RlEz/g2Z2kZl97R1muAtPb10g7Ra17WvPOee890rwO2er3+ODQCNKYGYtvcM2\nN3nf3zS/OtoCmaXMPWwHpAdY79G2O+fy/Gqob2ZTzGy9t4YvgCZmZt4acpxze49uxDm3GZgHDDaz\nE/F8hmkVrElERMKcwp2IiATiNeA6PD0/Hznntvsdex3P3LwE51wTYApl90wBbMYTgIokFT3w9hK+\nDfwJaOGca4pnaGVRu2UNa8z2b8+rHbApgLqO9jBQCJzqfX/D/erYCCSWsmroRiCllDYPAg38nrc+\n6vjR7+8OoBNwpreGft7XzXufOP95gEcpGpp5BfCVN/CJiEgEUrgTEZFAvAr8ArgJvyGZXo2AXc65\nPDPrBVx91PHSgt5bwG1mlmBmTYE/+B2L8X7tcM4VmtlFeObHFdmKZ6GX0gLNW8DFZnaedxGRO4HD\nwNfHf5slaoxnXuE+M0sAfu93bAGekPqomTUws3pmVjT080XgTjPrAWBmKWZWFFcp45gAACAASURB\nVGYXA1d7F5W5kLKHsTYGDgF7zSwO75w/AO98w5nAX70Lr9QxM//hq+8BPfDMP3y1vG9eRERqDoU7\nEREpk3MuE/gKT2/TjKMOjwZSzWwPcB/w5tGXl/L478BHwFJgIfCO3/324wkj/zKzHGAI8L7f8dXA\ndGCddzXMYj1fzrkf8fSwPYdnSOnFwCXOufwS6ijLZKAnUDQ3z7/OQuASPL1qG/D0ol3pPfY28BDw\nupntBf4NxHkvHYdngZVdwFDvseN5Gs9nvwPPf4f/HnX8GiAf+AFP8P2dX42HvTUnA+8G/K5FRKTG\nMc80hDJO8vxW8Wk8YfAl59xjpZx3Jp7/6VzlnHu3PNeKiIhI1TCzCUAn59y1ZZ4sIiI1VpnhzjuP\n4EfgfDxzGL4FhjjnfijhvFl4ho287Jx7N9BrRUREpGp4h3F+BwzzLo4jIiIRKpBhmb2ANc65TO/K\nXW8Al5Zw3lg8k9+3VeBaERERCTIzuwnPcNH/KNiJiES+QMJdAsWXqt5E8aWkMbN44NfOub9RfOJ8\nmdeKiIhI1XDOveica+Sc+22oaxERkaoXrAVVnqb4KmciIiIiIiJSjeoEcE4WkOj3vK33NX9nAG94\nN1NtDlxkZvkBXguAmZVn5TIREREREZGI45wLZK/YEgUS7r4FOppZEp69fIbgWbbZv4AORY/N7B/A\nB865GWYWXda1R7VT/ncgUsUmTZrEpEmTQl2GyDH0synhTD+fEq70synhzNNXVnFlhjvnXIGZjQE+\n5qftDFaZ2S2ew27q0ZeUdW2lKhYREREREZFjBNJzh3Puf8DJR702pZRzR5R1rYiIiIiIiARXsBZU\nEYlY/fv3D3UJIiXSz6aEM/18SrjSz6ZEsjI3Ma8uZubCpRYREREREZHqZmZVvqCKiIiUQ/v27cnM\nzAx1GSK1QlJSEuvXrw91GSIiYUE9dyIiQeb9rVuoyxCpFfTnTUQiSWV77jTnTkREREREJAIo3ImI\niIiIiEQAhTsREREREZEIoHAnIiLldsMNN3D//feHuowaSZ+diIhUFYU7ERERCYrJkydz7bXXhroM\nEZFaS1shiIhUo4yMTCZMeIWsrEISEqJITb2e5OSkaru+JstYn8GEpyaQtTeLhNgEUsenktw+udrb\nEBERCVfquRMRqSYZGZkMGPAsaWl3Mnv2ZNLS7mTAgGfJyAhsT7zKXl/kscceo23btsTGxnLKKafw\n+eefc/jwYa677jri4uI49dRTefzxx2nXrp3vmsWLF9OzZ09OPPFEhgwZwuHDh8t1z8rKWJ/BgDED\nSGucxuzk2aQ1TmPAmAFkrM+o1jaq67P74osvaNeuHY8//jitWrUiISGB999/n5kzZ3LyySfTvHlz\nHnnkEd/5ubm5jBs3joSEBNq2bcvtt99OXl5ehdpyzvHoo4/SsWNHWrRowZAhQ9i9ezcAmZmZREVF\n8eqrr5KUlETLli15+OGHAfjoo494+OGHefPNN2ncuDGnn346AMnJyXz22We+9idPnsw111xTrL1X\nXnmFxMREmjVrxpQpU1i4cCHdunUjLi6OsWPHBvzfR0SktlO4ExGpJhMmvEJ6+mSgofeVhqSnT2bC\nhFeq5XqAH3/8keeff55Fixaxd+9ePvroI9q3b8/kyZPZsGED69evZ9asWUybNg0zzzY7eXl5XHbZ\nZVx33XXk5ORwxRVX8M477wR8z2CY8NQE0rulQ4z3hRhI75bOhKcmVFsb1f3ZbdmyhdzcXLKzs5k8\neTIjR44kLS2NxYsX8+WXX5KamkpmpifYP/jggyxYsIBly5axdOlSFixYwIMPPlihtp555hlmzJjB\nnDlzyM7OpmnTpowePbpYbfPmzWPNmjV88sknPPDAA6xevZoLLriAe+65h6uuuop9+/axePHiUt9b\n0edTZMGCBaxdu5Y333yTcePG8fDDD/PZZ5+xYsUK3nrrLebMmRPQZyYiUtsp3ImIVJOsrEJ+CmZF\nGpKWVogZZX6lpZV8fXZ2YcA1REdHk5uby4oVK8jPzycxMZHk5GTeeust7r33XmJjY4mPj+e2227z\nXfP111+Tn5/PbbfdRnR0NIMHD+bMM8+s6MdQIVl7s34KZUViIG1ZGjbZAvpKW5ZWYhvZe7MDqqG6\nP7uYmBjuueceoqOjGTJkCDt27GDcuHE0aNCAzp0707lzZ5YuXQrA66+/zsSJE2nWrBnNmjVj4sSJ\nvPbaaxVqa8qUKTz00EO0adOGunXrcv/99/P2229TWOj5OTMzJk2aRExMDF27dqVbt26+ayvCzLj/\n/vuJiYnhF7/4BQ0bNmTo0KE0a9aM+Ph4+vbte9ygKCIiP9GcOxGRapKQEAUcoHhAO8CwYVFMm1b2\n9cOHR5GWduz18fGB/54uJSWFp59+mkmTJrFy5UouvPBCnnzySbKzs2nbtq3vPP9hhZs3byYhIaFY\nO0lJ1TvPLyE2AXIpHs5yYVjXYUybGMCHBwzfOZy03LRj2oiPjQ/o+ur+7Jo1a+br4apfvz4ALVu2\n9B2vX78++/fvByA7O5vExMRi98jOzq5QW5mZmVx22WVERXl+rpxz1K1bl61bt/rOb9Wqle9xgwYN\nfNdW1NG1+LfvX5uIiByfeu5ERKpJaur1pKRMxBPwAA6QkjKR1NTrq+X6IkOGDGHOnDls2LABgD/8\n4Q/Ex8ezadMm3zlFxwDatGlDVlZWsTb8j1eH1PGppCxN8QQ8gFxIWZpC6vjUam0jXD+7+Ph437BK\n8AS0+PjAQuvREhMTmTlzJjk5OeTk5LBr1y4OHDhAmzZtyrz26OGWAA0bNuTgwYO+51u2bKlQXSIi\nUjaFOxGRapKcnMSsWWMZNuwJzjtvIsOGPcGsWWMDXu2ysteDZ97Y559/Tm5uLjExMdSvX5/o6Giu\nvPJKHn74YXbv3k1WVhbPP/+875qzzz6bOnXq8Oyzz5Kfn8+7777LggULyv3+KyO5fTKznpvFsH3D\nOC/jPIbtG8as52aVa6XLyrYRzp/d0KFDefDBB9mxYwc7duwgNTXVt2hJed1yyy3cc889vhC6fft2\nZsyY4TvunCv12latWrF+/fpi53Tv3p033niD/Px8Fi5cyNtvv13smuO1JyIi5aNhmSIi1Sg5OYlp\n0yaG7PojR45w991388MPP1C3bl3OOeccpk6dSmxsLKNGjSI5OZn4+HiGDRvGP/7xDwDq1q3Lu+++\ny0033cR9993HwIEDGTx4cIVrqKjk9slMeyawIZhV0UaoP7uje8X8n993333s27ePrl27YmZceeWV\n3HvvvRVq63e/+x0Av/zlL9m8eTMtW7bkqquuYtCgQWVee8UVVzBt2jSaNWtGhw4dWLhwIampqQwd\nOpS4uDjOPfdchg0bRk5OTkC1lPRcRERKZ+HyGzMzc+FSi4hIZZhZje+NeOGFF3jzzTf5/PPPQ11K\njaPPrnpFwp83EZEi3r/TKvxbLQ3LFBERtmzZwldffYVzjtWrV/Pkk0/ym9/8JtRl1Qj67EREJFwo\n3ImICLm5udxyyy3Exsbyi1/8gssuu4xbb7011GXVCBX97B555BEaN25MbGxssa+LL764GqoWEZFI\npGGZIiJBpmFiItVHf95EJJJoWKaIiIiIiIgo3ImIiIiIiEQChTsREREREZEIoH3uRESCLCkpSXtz\niVSTpKSkUJcgIhI2tKCKiIiIiES8jPUZTHhqAll7s0iITSB1fCrJ7ZNDXZZIMZVdUEU9dyIiIiIS\n0TLWZzBgzADSu6VDMyAX5o+Zz6znZingSVgo+uVDZWnOnYiIiIhEtPueus8T7GK8L8RAerd0/vjE\nHzmUd4jD+Yc5kn+E3IJc8gvzKSgsoNAVapsNqRZFv3xIa5xW6bY0LFNEREREjuGcI68wj7yCvGLf\ncwtyj3mtyr5X4lr/Ogs/K4Tzjn2PNtuo94t6viDncDjnPM/56d+lhmFmGEaURfkem3mfB3i8POdW\n9fEqvxeBnx/KzyEcanko9SE+af2J55cPk9CwTBEREQkPtX1eU2mBKJDvuQW5IQ9E/t8LXAF1oupQ\nN6oudaPrlvg9Jjqm1GPHfC/lWL3oejSKaRRYG+X8XlTfdTuu4/Xc13/quQPIhatPu5pp904r879p\nUdgrCoClhcFAjgezrdpcS4ErwBVWUS1U7+ewZNMSSAzO30HquRMREZGgKDavKQbIhZSlKWXOa6pM\nICpvD044BKLyfI+JKkd4CvL3OlF1Imbl34r+bIpUh+G3DfcMyQxCz53CnYiIiFTYobxDpO9KZ23O\nWiZPnsySDkuO6R1pvLAxzQc2r7ZAdLxeomPCU3SMAlEtUdSrnL03m/jY+FrXqyzhq9gvHx6uhnBn\nZhcCT+NZgOUl59xjRx0fBKQChUAecLtzbp732HpgT9Ex51yvUu6hcCciIhKGDuYdJD3HE+DW5Kwp\n9n37ge20b9KeTs06sWT6Ejb13HTM9b1+7MXrz76uQCQiUoqiXz6kPZtWteHOzKKAH4HzgWzgW2CI\nc+4Hv3MaOOcOeh+fBrzlnDvF+3wd0NM5t6uM+yjciYiIhMiB3AOk70pnzU5PaPMPcDsP7SS5STId\n4zrSMa4jneI6+R4nnphIdFQ0cNTQoiK5MGzfMKY9c/x5TSIiUj373PUC1jjnMr03fAO4FPCFu6Jg\n59UITy+dr0a05YKIiEjI7c/d7wtua3PWeoLcLs/3XYd30aFpB1946xnfk6u6XEWnuE60jW3rC3DH\nkzo+lflj5h8zryn1udSqf3MiIhJQuEsANvo934Qn8BVjZr8GHgFaABf7HXLALDMrAKY65/5e8XJF\nRETkePYd2XdMz1vR492Hd5PSNMUX4Hol9GJY12F0jOtI29i2RFnlfheb3D6ZWc/NKj6v6TnNaxIR\nqS6BDMscDFzgnLvZ+3w40Ms5d1sp5/cBJjrnBnift3HObTazFsAsYIxzbm4J12lYpoiISAD2Htlb\nYu/b2py17D2yl5S4FN/QSf8hlAmxCZUOcCIiUnWqY1hmFsV3Xmjrfa1Ezrm5ZtbBzOKccznOuc3e\n17eb2b/x9PodE+4AJk2a5Hvcv39/+vfvH0B5IiIikWfP4T0l9r6tzVnL/tz9vsDWsWlHerfrzXXd\nrqNTXCfaNG6jACciUkPMnj2b2bNnB629QHruooHVeBZU2QwsAIY651b5nZPinEv3Pu4BvO+ca2dm\nDYAo59x+M2sIfAxMds59XMJ91HMnIiK1yu7Du0tcwGRNzhoO5R0qcQGTTs060aZRG60uKSISgaq8\n5845V2BmY/AEs6KtEFaZ2S2ew24qMNjMrgVygUPAld7LWwH/NjPnvVdaScFOREQkUu06tOun0OYd\nQln0+EjBkWIBrn/7/ozsMZKOcR1p3ai1ApyIiJSLNjEXERGppJ0Hd5Y6hDKvIM/X49axqfe7N9C1\nathKAU5ERHwq23OncCciIlIG5xw7D+38qfftqCBX4AqKD50sWsykWSdaNGihACciIgFRuBMREQkC\n5xw7Du4osfdtzc41AL5et6ODXPMGzRXgRESk0hTuREREAuScY/vB7SX2vq3JWUOURdEprpNvCKVv\nOGVcR5rVb6YAJyIiVao6tkIQERGpMZxzbD2wtdR94OpG1y3W+3bJSZf4euGaNWgW6vJFRKQWysjI\nZMKEVyrdjnruRESkxnHOsWX/lmO2DygKdPWi65U4hLJjXEfi6seFunwRERGfjIxMBgx4lvT0yUAj\nDcsUEZHI45xj8/7NJe4DtzZnLQ3qNihxAZOUpik0rd801OWLiIgE5OqrJzN9+p1AQ0DDMkVEpIYq\ndIVs3re5xH3g1uaspVFMo2Lh7fLOl9MprhMpcSk0OaFJqMsXEREp0eHDsHUrbNtW8nf/x9u3F+IJ\ndpWncCciIlWq0BWStTfr2BUoc9aQnpNObL3YYvvAXXXqVXSM60hK0xROPOHEUJcvIiKCc7B377HB\nrKSwtm2bJ9y1bOn5atXK89WyJbRrBz17Fn9t/Pgopk8/QDACnoZliohIpRW6Qjbt3VTiPnDrdq3j\nxBNOLHEfuI5xHWlcr3GoyxcRkVqooAB27AgsrG3bBjExx4Y1/+/+j088EQJdYFlz7kREpNoVFBaw\nae+mEveBW7drHXH14zyBrWnHYouZpMSl0CimUajLFxGRWqA8wyF37YImTUoPa0eHtvr1q67uotUy\n09ImKdyJiEhwFBQWsGHPhhIXMMnYnUGz+s2O3cTbu4hJw5jgzBcQEREp4hzs2RNYWDvecMiSwlrz\n5lAnzCapaRNzEREpl/zCfF+A861E6d0Hbv3u9bRo2KLEIZQdmnZQgBMRkUoLl+GQ4UjhTkSklslY\nn8GEpyaQtTeLhNgEUsenktw+udg5+YX5ZO7OLHEfuMzdmbRs2NK3gEnREMqiRUzq163CcSciIhKR\nShsOWdJrOTnQtGnpAa06h0OGG4U7EZFaJGN9BgPGDCC9WzrEALnQZmEbRo4aye4TdvsC3IY9G2jd\nqHWJ+8AlN0lWgBMRkeMqbThkafPZavpwyHChcCciUkvkHMrhN7f+hi8SvvAEuyK50OmHToy6c5Qv\nyCU3TeaEOieErFYREQk/Gg4Z/iob7pSZRUTCVPa+bOZkzuHLzC+Zs2EOGbszqLOlDiQfdWIMtG3c\nlvFnjw9JnSL+ilZ8y8oqJCEhitTU60lOTgp1WSIRK1jDITt1qt3DISOFwp2ISBhwzpG+K90T5jZ8\nyZzMOeQcyqFvUl/6Jvbluu7XcXrr07lh8w2k5aYd03MXHxsfstpFihTfq6khcID58ycya9ZYBTyR\nAJVnOOTWrXDkSMnDIRMT4YwzNByyttGwTBGRECh0hazYtqJYmDMz+iX1o29iX/ol9aNzi85EWVSx\n60qac5eyNIVZz806ZlEVkaqSnw87d8L27Z6vbds836dMmcyKFXfiCXZFDtChwxOcf/5E6tYlJF/R\n0RouJqFV0nDI0uauHT0csqxhkRoOGVk0LFNEpAbIK8hj0eZFvjA3b8M8mjVoRr/EflzU8SIeOf8R\nkpskY2X8Hzq5fTKznpvFhKcmkL03m/jYeFKfO3a1TJHyKC2slfZ4zx7P0K4WLTxfLVt6vu/fX0jx\nYAfQkJiYQnr2hLy8Y78OHYK9e0s+FqyvwsLyB8KYmNAEUQXVqhPsIcNVNRyyRQto0CB471tqF/Xc\niYhUgYN5B5m/ab4vzC3IWkBK0xRfr1yfxD60adwm1GVKhApWWCvtcVycJ2QcbfjwyaSlHdtzN2zY\nE0ybNrG63v4xCgsrHgxzc6s2eFZHUA3XwFqdQbWkIcMpKcWHDAdrOKRWh5TK0GqZIiJhYNehXczb\nOM8X5pZtXUbXVl3pl9iPfkn9OKfdOTSt3zTUZUoNFaqwVl6B/ANaKqcyQTXcQmtlgmp5Q+s770xm\nyZJjf/EQH/8EbdpM9AW3evU0HFJCS8MyRURCYPO+zczZMMcX5tbtWkevhF70S+zHQ//3EGe1PYsG\ndTWuRkoWzLB22mlVF9bKKzk5iVmzxjJhwhNkZxcSHx9FaqqCXTBFRXkCSL16oa6k8oIVVMsKrYcO\nwc6dJQ8ZbtaskL/+VcMhJXIo3ImIlME5R8buDM+WBN4wt/PgTvok9qFvYl+m/moqPdr0oG503VCX\nKiESqWGtIpKTk0I6BFNqjuoMqpmZUaSlHeDonruuXaPo1avq7y9SXTQsU0TkKIWukO+3f+/bX+7L\nzC9xzhVbyfLUlqces5KlRI6aMgxSRAKjIcNSU2jOnYhIJeUV5PHd5u98QW7exnk0PaFpsTDXoWmH\nMleylPClsCYiRatl/jRkuHKrZYpUBYU7EZFyOph3kG82feMLc99kfUNyk2RfmOub1Jf4xtoUPJwp\nrImISCRSuBMRKcPuw7uZt2GeL8wt3bqU01qe5gtzvRN7E1c/LtRl1moKayIiIgp3IiLH2LJ/C3My\n5/jCXPqudM6MP9MX5s5qexYNY45eNU2CSWFNRESk/BTuRKRWc86xfvf6YoufbD+4nd7tevvCXM/4\nnsREx4S61BpNYU1ERKTqKdyJSK1S6ApZtX1VsTBX4Ap8C5/0TexLl5ZdiI6K3KRQtChAVlYhCQkV\nWxRAYU1ERCT8KNyJSETLL8xn8ebFvjA3Z8McmpzQpFiY6xjXsdasZFnact4zZ44lNjZJYU1ERKQG\nU7gTkYhyKO8QC7IW+MLc/E3zSWqSVCzMJcQmhLrMkLn88sm8886dHL0Rr9kTNG8+UWFNRESkBqts\nuKsT4E0uBJ4GooCXnHOPHXV8EJAKFAJ5wO3OuXmBXCsitduew3v4auNXvjC3eMtiurTsQt/Evozp\nNYbpg6fTrEGzUJcZEs7BunUwZw7Mnev5Wru2kOLBDqAh555byOefh6JKERERCRdlhjsziwKeA84H\nsoFvzex959wPfqd94pyb4T3/NOAt4JQArxWRWmTbgW3MyZzjC3M/7vyRMxPOpF9iPyb1n8RZbc+i\nUUyjUJcZEvn5sGxZ8TAXHQ19+0KfPjB2LDz2WBTTpx/g6J67hISoUJUtIiIiYSKQnrtewBrnXCaA\nmb0BXAr4Appz7qDf+Y3w9OAFdK2IRC7nHJl7MouFuS37t9A7sTf9Evvx3MDn6NmmJ/Xq1At1qSFx\n4AB8881PQW7+fEhM9AS5Sy+Fxx+HpCTwn0740EPXs2DBxGPm3KWmjg3V2xAREZEwEUi4SwA2+j3f\nhCe0FWNmvwYeAVoAF5fnWhGJDM45Vu1Y5QlzG75kTuYccgty6ZvUl76Jffltr99yWsvTInoly+PZ\nvv2nIDd3LqxcCd26/dQrN306NCtjBGpychKzZo1lwoQnyM4uJD4+itTUseVeLVNEREQiT0Bz7gLh\nnHsPeM/M+gAPAgOC1baIhKf8wnyWbFniC3NzN8ylcUxj+ib15f/a/x8Tz51Ip7hOtWYlS38lzZfb\nsgXOOccT5h5/HM48E+rXL3/byclJTJs2MfhFi4iISI0WSLjLAhL9nrf1vlYi59xcM+tgZnHlvXbS\npEm+x/3796d///4BlCci1eVw/mEWZC3whbn5m+bTLrYdfRP7cmXnK3n2omdpG9s21GWGRCDz5bp0\n0QqVIiIi8pPZs2cze/bsoLVX5lYIZhYNrMazKMpmYAEw1Dm3yu+cFOdcuvdxD+B951y7QK71a0Nb\nIYiEmb1H9vLVxq98YW7x5sV0btHZty1B78TeNG/QPNRlhsTx5ssVfR09X05ERETkeKplnzvvdgZ/\n4aftDB41s1sA55ybamZ3AdcCucAh4E7n3NelXVvKPRTuREJs+4Htno3CvWFu9Y7VnBF/hi/MndX2\nLBrXaxzqMkPiePPl+vTxDLcsa76ciIiIyPFoE3MRqbDM3ZnFwlz2vmx6t+vtC3NnxJ9RK1eydA7S\n04uHOf/5cn36VHy+nIiIiEhpFO5EJCDOOVbvXO3bkuDLzC85lHeIfkn9fGGua6uutXIly/x8WLq0\neJjzny/Xp4/my4mIiEjVU7gTkRIVFBawdOtSX5ibkzmHBnUbFAtzJzU7qVauZKn5ciIiIhKOFO5E\nBPCsZPlt1re+XrmvN31NQuMEX5jrm9SXxBMTy24oAm3bBvPmab6ciIiIhDeFO5Faat+RfXy96Wu+\nzPySLzO/5LvN3/Gz5j/zhbk+iX1o0bBFqMusdpovJyIiIjWVwp1ILbHj4A7mbpjrG2a5avsqerTp\n4QtzZ7c7m9h6saEus9ppvpyIiIhECoU7kQi1cc/GYoufZO3L4px253iGWCb25cyEMzmhzgmhLrPa\nlTRfrl274mFO8+VERESkJlK4E4kAzjl+3PljsTB3IO/AMStZ1omqE+pSq93R8+VWrPDMlysKc5ov\nJyIiIpFC4U6kBiooLGDZ1mU/rWS5YQ4n1DmhWJg7udnJtW4lS82XExERkdpM4U6kBjiSf4SF2Qt9\nYe6rjV/RpnEb+iX2o2+SZ5hlUpOkUJdZ7TRfTkREROQnCnciYWh/7n6+3vi1L8wtzF7Iyc1P9oW5\nPol9aNmwZajLrHaaLyciIiJSOoU7kTCw8+DOYitZfr/9e05vc7ovzJ3T7pxauZKl5suJiIiIBE7h\nTqSKZKzPYMJTE8jam0VCbAKp41NJbp8MwKa9m5iTOccX5jbs2cDZ7c72hbleCb1q3UqWmi8nIiIi\nUjkKdyJVIGN9BgPGDCC9WzrEALnQYkEL+lzehyWHl7D3yF76JvX1hbnurbvXupUsNV9OREREJLgU\n7kSqwPDbhpPWOM0T7IrkwpmZZ/LKn1/hZ81/RpRFhay+UNB8OREREZGqVdlwV7u6GkQCtHLbSjh6\nLlgMNKrTiM4tOoekpup2vPlyY8fC9OmaLyciIiISThTuRPzsObyH38/6Pat3roZcjum5i4+ND1Vp\nVaqs+XKPP675ciIiIiLhTsMyRbw+/PFDbv3PrQzsOJDfnvRbfnPHb4rNuUtZmsKs52b5FlWpyTRf\nTkRERCT8aM6dSCXtOLiD3/3vd8zfNJ8XL3mR85LPA35aLTN7bzbxsfHFVsusaTRfTkRERCT8KdyJ\nVJBzjrdWvsW4j8YxtMtQUs9LpWFMw1CXFRT+8+XmzIGVK7W/nIiIiEi4U7gTqYDsfdmM/s9o1uSs\n4aVBL3FW27NCXVKFHT1fbs4c2LpV+8uJiIiI1DQKdyLl4Jzj5cUvc/endzOq5yju63cf9erUC3VZ\n5aL5ciIiIiKRSeFOJEAZuzK4+cObyTmUw8uDXqZb626hLikgmi8nIiIiUjso3ImUodAV8tyC53jg\niwf4/Tm/545z7qBOVPjuAlLafLk+fTyBTvPlRERERCKTwp3Icfyw4wdunHEjhvHSoJc4ufnJoS6p\nmNLmy5199k89c5ovJyIiIlI7KNyJlCCvII8nvnqCJ79+kkn9JzH6zNFEWVSoyypxvlxUlCfIFYU5\nzZcTERERqZ0U7kSOsmTLEka8P4LmDZoz9ZKptG/SPmS1lDZfrmiIpebLgRHkVwAAHdZJREFUiYiI\niEgRhTsRr8P5h3nwyweZumgqfxrwJ67rdh1WzalJ8+VEREREpKIU7kSArzd+zYgZIzil+Sk8P/B5\n2jRuU+k2MzIymTDhFbKyCklIiCI19XqSk5N8xzVfTkRERESCSeFOarUDuQe497N7eXPlmzxz4TNc\n3vnyoPTWZWRkMmDAs6SnTwYaAgfo0GEif/7zWDIykjRfTkRERESCTuFOaq1P133KyA9G0juxN09f\n8DTNGgRvvOPw4ZNJS7sTT7ArcoDY2CcYMmSi5suJiIiISNBVNtyF72ZfIqXYc3gPd358Jx+lf8QL\nv3qBgZ0GBv0eGzYUUjzYATSkZ89CpkwJ+u1ERERERCot9GvDi5TDB6s/oMvfuhAdFc2K0SuCHuyc\ng/feg0WLooADRx09QHy8/siIiIiISHgK6F+qZnahmf1gZj+a2R9KOH61mS31fs01s65+x9Z7X19s\nZguCWbzUHtsPbOfqd67m9o9u57XLXuOFX71AbL3YoN5j/XoYNAj++Ef4+9+vJyVlIj8FvAOkpEwk\nNfX6oN5TRERERCRYyhyWaWZRwHPA+UA28K2Zve+c+8HvtHVAP+fcHjO7EJgKnOU9Vgj0d87tCm7p\nUhs453hz5ZuM+984hncdzrJbl9GgboOg3iM3F554Ap56CsaPh3fegZiYJM4+eywTJjxBdnYh8fFR\npKaOLbZapoiIiIhIOClzQRUzOwuY6Jy7yPv8bsA55x4r5fwmwHLnXDvv8wzgDOfczjLuowVVpJis\nvVmM/u9o0nPSeWnQS/y87c+Dfo/Zs+HWWyElBZ59FpKTg34LEREREZGAVHZBlUCGZSYAG/2eb/K+\nVpqbgJl+zx0wy8y+NbOR5S9RahvnHC9+9yLdp3Sne6vuLLp5UdCD3datcM01cO218Mgj8MEHCnYi\nIiIiUrMFdbVMMzsPuAHo4/dyb+fcZjNrgSfkrXLOzQ3mfSVyZOzKYOQHI9l9eDefXvspXVt1Lfui\ncigogKlTYeJEuP56+P57aNQoqLcQEREREQmJQMJdFpDo97yt97VivIuoTAUu9J9f55zb7P2+3cz+\nDfQCSgx3kyZN8j3u378//fv3D6A8iQQFhQU8t+A5Ur9M5a7edzH+7PHUiQruTh3ffQejRkG9evDZ\nZ54Nx0VEREREQmX27NnMnj07aO0FMucuGliNZ0GVzcACYKhzbpXfOYnAp8A1zrn5fq83AKKcc/vN\nrCHwMTDZOfdxCffRnLtaatX2Vdw440bqRNXhxUEvclKzk4La/p49cN998K9/eYZgXncdRGlHAxER\nEREJM1U+5845VwCMwRPMVgJvOOdWmdktZnaz97QJQBzw16O2PGgFzDWzxcB84IOSgp3UTnkFeTw8\n52H6/qMvw7sOZ/b1s4Ma7JyD6dOhc2c4cgRWroQbblCwExEREZHIVGbPXXVRz13tsnjzYkbMGEGr\nhq2Y8qspJDUJ7hYDP/4Iv/0tbNsGL7wAZ58d1OZFRERERIKuOlbLFAmaw/mHuffTe7lg2gWM+/k4\nZg6bGdRgd+gQ3H8/nHMODBwIixYp2ImIiIhI7RDcFStEjuOrjV9x44wb6dyiM8tuXUbrRq2D2v7M\nmTBmDPToAUuWQNu2QW1eRERERCSsKdxJldufu597P72Xf33/L5656Bku73x5UNvftAnGjYPFi+H5\n5+HCC4PavIiIiIhIjaBhmVKlPln3CV3/1pXdR3az/NblQQ12+fnw1FPQvTuceiqsWKFgJyIiIiK1\nl3rupErsPrybOz++k4/TP2bKr6ZwUaeLgtr+V1/BrbdCy5aexycFd/cEEREREZEaRz13EnQzVs+g\ny1+7EBMdw4rRK4Ia7HbuhJEj4Yor4I9/hI8/VrATEREREQH13EkQbT+wndv+dxsLsxeS9ps0zm1/\nbtDaLiyEV17xBLqrroLvv4cTTwxa8yIiIiIiNZ7CnVSac443VrzB7R/dzjVdr+GlUS/RoG6DoLW/\nfLlnCGZenmdFzB49gta0iIiIiEjEULiTSsnam8Wo/4xi/e71zBg6g14JvYLW9v79MHky/POf8MAD\nnuGY0dFBa15EREREJKJozp1UiHOOvy/6O92ndKdnm54sunlR0IKdc/Dvf0PnzrB1q6fnbtQoBTsR\nERERkeNRz52U27pd6xj5wUj2HtnLZ9d+xmmtTgta2xkZMHYsrFsHr74K/fsHrWkRERERkYimnjsJ\nWEFhAU/Pf5pef+/FRR0v4usbvw5asDtyBB56CM48E3r3hiVLFOxERERERMpDPXcSkFXbVzFixghi\nomP4+sav6dSsU9Da/uwzGD3as6XBwoXQvn3QmhYRERERqTUU7uS48gry+NO8P/H0N0/zQP8HuOWM\nW4iy4HT4bt0Kd9wBc+bAM8/ApZcGpVkRERERkVpJ4U5K9d3m7xjx/gjaNG7DopsXkXhiYlDaLSiA\nKVNg4kQYMcKzZ13DhkFpWkRERESk1lK4k2Mczj/M5NmTeXnJyzw+4HGu6XoNZhaUthcu9OxZV78+\nfP45dOkSlGZFRERERGo9hTspZt6Gedw440a6tOzC0lFLad2odVDa3b0b7rsP3n4bHnsMrr0WgpQX\nRUREREQEhTvx2p+7n3s+vYe3v3+bZy96lsGdBwelXedg+nS480645BLPEMy4uKA0LSIiIiIifhTu\nhFnps7j5w5s5N+lcVoxeQVz94KSv1as9q2Du3AnvvgtnnRWUZkVEREREpAQKd7XY7sO7ueOjO/gk\n4xOm/GoKF3a8MCjtHjoEDz8Mf/ubZyjmmDFQRz9pIiIiIiJVSpuY11Lv/fAep/71VE6ocwIrbl0R\ntGD33//Cqad6eu2WLoVx4xTsRERERESqg/7ZXctsO7CNsTPHsnjzYqYPnk6/pH5BaXfjRk+QW7bM\n02N3wQVBaVZERERERAKknrtawjlH2rI0uv6tK+1PbM/SUUuDEuzy8uDJJ+H00+G002D5cgU7ERER\nEZFQUM9dLbBp7yZGfTiKDXs28OHVH3JG/BlBaXfePM+eda1bw9dfQ6dOQWlWREREREQqQD13Ecw5\nx9RFUzl9yun0SujFwpsXBiXY7dgBN90EV17pWTDlo48U7EREREREQk09dxEqPSedkR+M5EDeAT6/\n7nO6tOxS6TYLC+Ef/4B77oGhQ2HVKoiNDUKxIiIiIiJSaQp3EaagsIC/fPMXHp7zMH/s80fGnTWO\n6KjoSre7bJlnCGZBAfzvf545diIiIiIiEj4U7iLIym0ruXHGjZxQ5wTm3zSfjnEdK93m/v0waRK8\n+iqkpsLIkRClwbwiIiIiImFH/0yPAHkFeaR+kUr/f/bnhu438Nl1n1U62DkH774LnTt75titWAG3\n3KJgJyIiIiISrtRzV8Mtyl7EiBkjaBvblu9u/o52J7ardJvr1sHYsbB+PUybBv2CsxWeiIiIiIhU\nIfXD1FCH8g5x9yd3M/D1gfz+nN/z4dAPKx3sjhyBBx+EXr08gW7xYgU7EREREZGaQj13NdDcDXO5\nccaNdGvVjWWjltGqUatKt/npp/Db38LJJ8OiRZCUFIRCRURERESk2gTUc2dmF5rZD2b2o5n9oYTj\nV5vZUu/XXDPrGui1Erh9R/Yx9r9juertq3j0/Ed564q3Kh3stmyBYcPgxhvhT3+C999XsBMRERER\nqYnKDHdmFgU8B1wAnAoMNbOfHXXaOqCfc64b8CAwtRzXSgA+Tv+Y0/52Gvvz9rPi1hVcdspllWqv\noACefx5OOw0SE2HlShg0KEjFioiIiIhItQtkWGYvYI1zLhPAzN4ALgV+KDrBOTff7/z5QEKg18rx\n7Tq0i/Efj+fzjM+Z8qspXNDxgkq3uXAhjBoFDRvCF194VsQUEREREZGaLZBhmQnARr/nm/gpvJXk\nJmBmBa8VP/9e9W+6/K0LDes2ZPmtyysd7Hbv9syru+QSuO02mD1bwU5EREREJFIEdUEVMzsPuAHo\nE8x2a5ut+7cyduZYlmxZwhuD36BvUt9KteccpKXB738Pl17qGYIZFxekYkVEREREJCwEEu6ygES/\n5229rxXjXURlKnChc25Xea4tMmnSJN/j/v37079//wDKixzOOdKWp3HHx3dwQ/cb+Oev/0n9uvUr\n1eYPP8Do0bBrF7z3Hvz850EqVkREREREKmX27NnMnj07aO2Zc+74J5hFA6uB84HNwAJgqHNuld85\nicCnwDX+8+8CudbvXFdWLZFs456NjPrPKDbt3cTLg16mZ3zPSrV38CA89BBMnQoTJngCXh1tfCEi\nIiIiErbMDOecVfT6MufcOecKgDHAx8BK4A3n3Cozu8XMbvaeNgGIA/5qZovNbMHxrq1osZGo0BUy\nZeEUekztwVkJZ/HtyG8rHez+8x/o0gXS02HpUs/8OgU7EREREZHIVmbPXXWpjT13a3PWMvKDkRzK\nO8RLg17i1JanVqq9jRvhd7+D5cs92xz88pdBKlRERERERKpclffcSfAVFBbw5FdPctaLZ3HJSZcw\nb8S8SgW7vDx4/HE4/XTo1s0T7hTsRERERERqFw3Wq2Yrt61kxIwRNKjbgPk3zadjXMdKtTd3Ltx6\nKyQkwPz50LFyzYmIiIiISA2lcFdNcgtyeXTuozy74Fke+r+HuKnHTURZxTtOd+yAu+6Cjz+GP/8Z\nLr8crMIduCIiIiIiUtMp3FWDhdkLGfH+CBJPTGTxLYtpG9u2wm0VFsLLL8O998LVV8P330NsbBCL\nFRERERGRGknhrgodyjvEpNmTeGXpKzz1y6e4+rSrsUp0ry1d6hmC6Rx89BF07x7EYkVEREREpEbT\ngipVZE7mHLq90I31e9az/NblDOs6rMLBbt8+GD8eBgyA66+HefMU7EREREREpDj13AXZviP7uPuT\nu3lv9Xs8P/B5fv2zX1e4LefgnXfg9tvhF7+AlSuhRYsgFisiIiIiIhFD4S6IPlr7ETd/eDPnJ5/P\niltX0LR+0wq3lZ4OY8Z49q57/XXo2zeIhYqIiIiISMRRuAuCnEM5jP9oPF9kfsGLl7zIgJQBFW7r\nyBH405/gL3/xrIZ5++1Qt24QixURERERkYikOXeV9O6qd+ny1y7E1otl+a3LKxXsPvkETjsNFi3y\nfN11l4KdiIiIiIgERj13FbR1/1bGzBzD8q3LeeuKt+iT2KfCbW3e7FkwZf58eOYZuOSSIBYqIiIi\nIiK1gnruysk5x2tLX6PrC13p2LQjS0YtqXCwKyiAZ5+Frl0hOdmzYIqCnYiIiIiIVIR67sphw54N\njPpwFNn7spk5bCY92vSocFvffgujRnk2IP/ySzjllCAWKiIiIiIitY567gJQ6Ar527d/o+fUnpzT\n7hy+HflthYPdrl0wejQMGgTjxsFnnynYiYiIiIhI5annrgxrdq5h5AcjOVJwhC+u/4LOLTpXqB3n\nYNo0zyIpv/41fP89NK34TgkiIiIiIiLFKNyVIr8wn6fnP82jcx/lvn73MbbXWKKjoivU1qpVnt66\nvXvh/fehV68gFysiIiIiIrWewl0JVmxbwYj3R9C4XmMWjFxAh6YdKtTOwYPw4IPw97/D/fd7Al50\nxfKhiIiIiIjIcWnOnZ//b+/eg6wu7zuOv79IgomNolPUKIpCWyMgRkjSGMykXqqMiVrFVhyajoIM\nQcFbVEz+CDjOZCaxqddioqVpvSSmQ+q4cTKERUUQGyDK/RJxGjThonhJABUXdr/94/ywG2cDe/bC\nOfz2/Zph9nd99ntmDrv7Oc/veZ6m5iZum3cbZ/7nmUwYPoG5X53b4WD35JMwZAhs2AArVsCUKQY7\nSZIkSd3HnrvCko1LGNcwjhP6nsDSiUvpf2j/DrXz6qtw3XWVZQ0efBDOOaeLC5UkSZKkNvT4nrv3\ndr3HzXNu5oIfX8A3zvgGDWMaOhTsdu2C734Xhg+v/FuxwmAnSZIkaf/p0T1381+Zz/iG8XzmmM+w\nYtIKjjzkyA61s2ABTJoExx0HixbBoEFdXKgkSZIk7UOPDHfb3t/GrXNvpeHXDcz48gwuPOnCDrWz\ndWtlaYO5c+HOO2H0aIjo4mIlSZIkqR163GOZs1+ezSn3n0JTcxOrrl7VoWDX0lIZTzd0KBxxRGXN\nuksvNdhJkiRJqp0e03P31ntvccMvbmD+K/OZeeFMzhnYsQFxy5ZVHsGMgDlz4NRTu7hQSZIkSeqA\nHtFzN2vNLIbOGMrhBx/OykkrOxTstm+HG26A886D8ePhuecMdpIkSZLqR6l77rbs2MI1P7+G1a+v\nZtY/zOILx32h6jYyYdasSrA791xYtQr69euGYiVJkiSpE0oZ7jKTh5Y/xC1zb+Gq067i0Use5eDe\nB1fdzssvw+TJsHEjPPYYnHFGNxQrSZIkSV2gdOHu1T+8ysQnJ7JlxxZmj53NaZ88reo2du6srFl3\nzz1w662VRck/8pFuKFaSJEmSukhpxty1ZAszlsxgxAMj+OLxX2TxVYs7FOwaG2HYsMrEKUuXwk03\nGewkSZIk1b9S9Nytf3M9V/3sKnY172L+FfM5ud/JVbexaRPceGNlEfJ774WvfKUbCpUkSZKkbnJA\n99ztbtnNHQvv4PSZp3PJpy5hwZULqg52u3fD3XdXeusGDYLVqw12kiRJkg48B2zP3crXVjKuYRyH\n9TmMxRMWM/DwgVW3sWhRZc26vn1hwQI4ufoOP0mSJEmqC+3quYuIURGxLiJeioipbZw/KSKej4id\nEXHjh85tiIjlEbE0IhZ3tuCm5iamPTONsx46i4kjJtL41caqg93bb8PXvgYXXwxf/zo89ZTBTpIk\nSdKBbZ89dxHRC7gPOBvYBCyJiCcyc12ry94EpgB/10YTLcDfZObbnS128cbFjHtiHIOOGMSyics4\n9tBjq7o/Ex5+GKZOhUsugTVrKr12kiRJknSga89jmZ8D1mfmKwAR8RhwEfBBuMvMN4A3IqKt0WpB\nJ8f2vbvrXb71zLd4ZMUj3DXqLi4bchkRUVUba9bA1VfDjh3Q0ACf/WxnKpIkSZKk+tKe0HUs8NtW\n+78rjrVXAo0RsSQiJlRTHMCzG57l1O+fyqbtm1g5aSVjho6pKti9805lrbovfQkuvbQyzs5gJ0mS\nJKls9seEKiMzc3NE9KMS8tZm5nP7umnb+9uY2jiVJ9c/yYzzZ3DBSRdU/Y0bGuDaa2HkSFi5Eo4+\nuiPlS5IkSVL9a0+42wgc32q/f3GsXTJzc/F1a0Q8TuUxzzbD3fTp04HKunWNLY1cdN5FrJy0kr4H\nVzcw7pVXKqFu3TqYORPOPruq2yVJkiSp282bN4958+Z1WXuRmXu/IOIg4NdUJlTZDCwGLs/MtW1c\nOw3YkZnfK/Y/DvTKzB0RcQgwB7gtM+e0cW+Ovno0zac0s/z95Tx4wYOcPbC6VNbUBHfeCXfcAddf\nDzffDH36VNWEJEmSJNVERJCZ1U0u0so+e+4yszkiJlMJZr2AmZm5NiImVk7nAxFxFPAr4BNAS0Rc\nBwwG+gGPR0QW3+vRtoLdHj/t+1MO+6/DWPj9hQwZOKSqF/Lss5UJUwYMgMWLYWD1y95JkiRJ0gFr\nnz13+0tEJNOBJhi7fSyP3PNIu+57/fVKD93TT8Ndd1WWOKhyIk1JkiRJqrnO9tx1aomCbvFR2LRt\n0z4va2mBH/wAhg6Ffv0qSx2MHm2wkyRJktQz7Y/ZMqvTBMccesxeL1m6FCZNgoMOgrlzYdiw/VSb\nJEmSJNWp+uq5a4JBywdx+423t3l627bKRCmjRsGECbBggcFOkiRJkqDOwt3Y7WNpvK+RE0848Y+O\nZ8JPfgKDB8P27bB6NYwfD73qqnpJkiRJqp26mlClrVrWr4fJk2HzZrj//sqC5JIkSZJUNuWbUKWw\ncydMnw6nnw7nngsvvGCwkyRJkqQ/pf4mVAHmzIFrrqmMp1u6FI47rtYVSZIkSVJ9q6twd/HFt9HU\ndAVr1w7gvvvg/PNrXZEkSZIkHRjqaswd7KBv32ksXDiFwYMH1LokSZIkSdpvSjbm7hB+//vb+Pa3\n/6PWhUiSJEnSAaXOwh3AIWza1FLrIiRJkiTpgFKH4e4djjmmDsuSJEmSpDpWZynqHQYNmsbtt19R\n60IkSZIk6YBSV+Fu7Nh/prFxCiee6GQqkiRJklSNupots15qkSRJkqT9rWSzZUqSJEmSOsJwJ0mS\nJEklYLiTJEmSpBIw3EmSJElSCRjuJEmSJKkEDHeSJEmSVAKGO0mSJEkqAcOdJEmSJJWA4U6SJEmS\nSsBwJ0mSJEklYLiTJEmSpBIw3EmSJElSCRjuJEmSJKkEDHeSJEmSVAKGO0mSJEkqAcOdJEmSJJWA\n4U6SJEmSSqBd4S4iRkXEuoh4KSKmtnH+pIh4PiJ2RsSN1dwrSZIkSeq8fYa7iOgF3AecBwwBLo+I\nT33osjeBKcAdHbhXqmvz5s2rdQlSm3xvqp75/lS98r2pMmtPz93ngPWZ+Upm7gIeAy5qfUFmvpGZ\nLwC7q71Xqnf+ElC98r2peub7U/XK96bKrD3h7ljgt632f1cca4/O3CtJkiRJaicnVJEkSZKkEojM\n3PsFEZ8HpmfmqGL/ViAz8zttXDsN2J6Z/9KBe/deiCRJkiSVXGZGR+/t3Y5rlgB/EREDgM3AGODy\nvVzfuph239uZFyFJkiRJPd0+w11mNkfEZGAOlcc4Z2bm2oiYWDmdD0TEUcCvgE8ALRFxHTA4M3e0\ndW+3vRpJkiRJ6qH2+VimJEmSJKn+1XxCFRc5V72KiJkR8VpErKh1LVJrEdE/Ip6OiNURsTIirq11\nTRJARPSJiEURsbR4b06rdU1SaxHRKyJejIiGWtcitRYRGyJiefHzc3GH26llz12xyPlLwNnAJipj\n9MZk5rqaFSUVIuIMYAfwUGYOq3U90h4RcTRwdGYui4g/A14ALvJnp+pBRHw8M9+NiIOAhcC1mdnh\nP1SkrhQRNwAjgEMz88Ja1yPtERH/C4zIzLc7006te+5c5Fx1KzOfAzr1H0zqDpm5JTOXFds7gLW4\nhqjqRGa+W2z2oTK23/EfqgsR0R84H/i3WtcitSHogmxW63DnIueS1AkRcQLwaWBRbSuRKorH3pYC\nW4DGzFxS65qkwp3AzfiBg+pTAo0RsSQiJnS0kVqHO0lSBxWPZM4Crit68KSay8yWzDwN6A/8dUQM\nrnVNUkR8GXiteOoh+OOlu6R6MDIzh1PpXb6mGB5UtVqHu43A8a32+xfHJEl7ERG9qQS7hzPziVrX\nI31YZm4DngFG1boWCRgJXFiMa/oxcGZEPFTjmqQPZObm4utW4HEqw9eqVutw98Ei5xHxUSqLnDt7\nkeqJn+6pXv07sCYz7651IdIeEfHnEXFYsf0x4G8BJ/pRzWXmNzPz+MwcSOXvzacz859qXZcElYmo\niqdxiIhDgHOBVR1pq6bhLjObgT2LnK8GHnORc9WLiPgR8DzwVxHxakRcWeuaJICIGAmMBc4qpkx+\nMSLsHVE9+CTwTEQsozIO9BeZ+fMa1yRJ9e4o4LlivPIvgZ9l5pyONOQi5pIkSZJUArV+LFOSJEmS\n1AUMd5IkSZJUAoY7SZIkSSoBw50kSZIklYDhTpIkSZJKwHAnSZIkSSVguJMklUpENBdr/+1ZA/CW\nLmx7QESs7Kr2JEnqSr1rXYAkSV3sncwc3o3tu0CsJKku2XMnSSqbaPNgxG8i4jsRsSIifhkRA4vj\nAyLiqYhYFhGNEdG/OH5kRPx3cXxpRHy+aKp3RDwQEasiYnZE9NlPr0uSpL0y3EmSyuZjH3os8+9b\nnXs7M4cB/wrcXRy7F/hhZn4a+FGxD3APMK84PhxYXRz/S+DezBwK/AEY3c2vR5KkdolMny6RJJVH\nRGzLzEPbOP4b4MzM3BARvYHNmdkvIrYCR2dmc3F8U2YeGRGvA8dm5q5WbQwA5mTmScX+LUDvzPz2\nfnlxkiTthT13kqSeJP/EdjXeb7XdjOPXJUl1wnAnSSqbNsfcFS4rvo4B/qfYXghcXmz/I7Cg2J4L\nXA0QEb0iYk9v4N7alySpZvy0UZJUNgdHxItUQlgCszPzm8W5wyNiObCT/w901wI/jIibgK3AlcXx\n64EHImI8sBuYBGzB2TIlSXXKMXeSpB6hGHM3IjPfqnUtkiR1Bx/LlCT1FH6aKUkqNXvuJEmSJKkE\n7LmTJEmSpBIw3EmSJElSCRjuJEmSJKkEDHeSJEmSVAKGO0mSJEkqAcOdJEmSJJXA/wGDbcppXcdw\nfAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7feb8971a450>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"num_train = 4000\n",
"small_data = {\n",
" 'X_train': data['X_train'][:num_train],\n",
" 'y_train': data['y_train'][:num_train],\n",
" 'X_val': data['X_val'],\n",
" 'y_val': data['y_val'],\n",
"}\n",
"\n",
"solvers = {}\n",
"\n",
"for update_rule in ['sgd', 'sgd_momentum']:\n",
" print 'running with ', update_rule\n",
" model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
"\n",
" solver = Solver(model, small_data,\n",
" num_epochs=5, batch_size=100,\n",
" update_rule=update_rule,\n",
" optim_config={\n",
" 'learning_rate': 1e-2,\n",
" },\n",
" verbose=True)\n",
" solvers[update_rule] = solver\n",
" solver.train()\n",
" print\n",
"\n",
"plt.subplot(3, 1, 1)\n",
"plt.title('Training loss')\n",
"plt.xlabel('Iteration')\n",
"\n",
"plt.subplot(3, 1, 2)\n",
"plt.title('Training accuracy')\n",
"plt.xlabel('Epoch')\n",
"\n",
"plt.subplot(3, 1, 3)\n",
"plt.title('Validation accuracy')\n",
"plt.xlabel('Epoch')\n",
"\n",
"for update_rule, solver in solvers.iteritems():\n",
" plt.subplot(3, 1, 1)\n",
" plt.plot(solver.loss_history, 'o', label=update_rule)\n",
" \n",
" plt.subplot(3, 1, 2)\n",
" plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
"\n",
" plt.subplot(3, 1, 3)\n",
" plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
" \n",
"for i in [1, 2, 3]:\n",
" plt.subplot(3, 1, i)\n",
" plt.legend(loc='upper center', ncol=4)\n",
"plt.gcf().set_size_inches(15, 15)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# RMSProp and Adam\n",
"RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
"\n",
"In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
"\n",
"[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
"\n",
"[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"next_w error: 9.52468751104e-08\n",
"cache error: 2.64779558072e-09\n"
]
}
],
"source": [
"# Test RMSProp implementation; you should see errors less than 1e-7\n",
"from cs231n.optim import rmsprop\n",
"\n",
"N, D = 4, 5\n",
"w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
"dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
"cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
"\n",
"config = {'learning_rate': 1e-2, 'cache': cache}\n",
"next_w, _ = rmsprop(w, dw, config=config)\n",
"\n",
"expected_next_w = np.asarray([\n",
" [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
" [-0.132737, -0.08078555, -0.02881884, 0.02316247, 0.07515774],\n",
" [ 0.12716641, 0.17918792, 0.23122175, 0.28326742, 0.33532447],\n",
" [ 0.38739248, 0.43947102, 0.49155973, 0.54365823, 0.59576619]])\n",
"expected_cache = np.asarray([\n",
" [ 0.5976, 0.6126277, 0.6277108, 0.64284931, 0.65804321],\n",
" [ 0.67329252, 0.68859723, 0.70395734, 0.71937285, 0.73484377],\n",
" [ 0.75037008, 0.7659518, 0.78158892, 0.79728144, 0.81302936],\n",
" [ 0.82883269, 0.84469141, 0.86060554, 0.87657507, 0.8926 ]])\n",
"\n",
"print 'next_w error: ', rel_error(expected_next_w, next_w)\n",
"print 'cache error: ', rel_error(expected_cache, config['cache'])"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"next_w error: 1.13956917985e-07\n",
"v error: 4.20831403811e-09\n",
"m error: 4.21496319311e-09\n"
]
}
],
"source": [
"# Test Adam implementation; you should see errors around 1e-7 or less\n",
"from cs231n.optim import adam\n",
"\n",
"N, D = 4, 5\n",
"w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
"dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
"m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
"v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
"\n",
"config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
"next_w, _ = adam(w, dw, config=config)\n",
"\n",
"expected_next_w = np.asarray([\n",
" [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
" [-0.1380274, -0.08544591, -0.03286534, 0.01971428, 0.0722929],\n",
" [ 0.1248705, 0.17744702, 0.23002243, 0.28259667, 0.33516969],\n",
" [ 0.38774145, 0.44031188, 0.49288093, 0.54544852, 0.59801459]])\n",
"expected_v = np.asarray([\n",
" [ 0.69966, 0.68908382, 0.67851319, 0.66794809, 0.65738853,],\n",
" [ 0.64683452, 0.63628604, 0.6257431, 0.61520571, 0.60467385,],\n",
" [ 0.59414753, 0.58362676, 0.57311152, 0.56260183, 0.55209767,],\n",
" [ 0.54159906, 0.53110598, 0.52061845, 0.51013645, 0.49966, ]])\n",
"expected_m = np.asarray([\n",
" [ 0.48, 0.49947368, 0.51894737, 0.53842105, 0.55789474],\n",
" [ 0.57736842, 0.59684211, 0.61631579, 0.63578947, 0.65526316],\n",
" [ 0.67473684, 0.69421053, 0.71368421, 0.73315789, 0.75263158],\n",
" [ 0.77210526, 0.79157895, 0.81105263, 0.83052632, 0.85 ]])\n",
"\n",
"print 'next_w error: ', rel_error(expected_next_w, next_w)\n",
"print 'v error: ', rel_error(expected_v, config['v'])\n",
"print 'm error: ', rel_error(expected_m, config['m'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"running with adam\n",
"(Iteration 1 / 200) loss: 2.705596\n",
"(Epoch 0 / 5) train acc: 0.138000; val_acc: 0.123000\n",
"(Iteration 11 / 200) loss: 2.133164\n",
"(Iteration 21 / 200) loss: 2.090119\n",
"(Iteration 31 / 200) loss: 1.924945\n",
"(Epoch 1 / 5) train acc: 0.359000; val_acc: 0.287000\n",
"(Iteration 41 / 200) loss: 1.667574\n",
"(Iteration 51 / 200) loss: 1.777399\n",
"(Iteration 61 / 200) loss: 1.653608\n",
"(Iteration 71 / 200) loss: 1.579225\n",
"(Epoch 2 / 5) train acc: 0.435000; val_acc: 0.336000\n",
"(Iteration 81 / 200) loss: 1.481105\n",
"(Iteration 91 / 200) loss: 1.526769\n",
"(Iteration 101 / 200) loss: 1.461250\n",
"(Iteration 111 / 200) loss: 1.430679\n",
"(Epoch 3 / 5) train acc: 0.474000; val_acc: 0.362000\n",
"(Iteration 121 / 200) loss: 1.448526\n",
"(Iteration 131 / 200) loss: 1.546457\n",
"(Iteration 141 / 200) loss: 1.396395\n",
"(Iteration 151 / 200) loss: 1.579234\n",
"(Epoch 4 / 5) train acc: 0.546000; val_acc: 0.376000\n",
"(Iteration 161 / 200) loss: 1.205349\n",
"(Iteration 171 / 200) loss: 1.374671\n",
"(Iteration 181 / 200) loss: 1.161410\n",
"(Iteration 191 / 200) loss: 1.267196\n",
"(Epoch 5 / 5) train acc: 0.575000; val_acc: 0.375000\n",
"\n",
"running with rmsprop\n",
"(Iteration 1 / 200) loss: 2.815276\n",
"(Epoch 0 / 5) train acc: 0.123000; val_acc: 0.127000\n",
"(Iteration 11 / 200) loss: 2.066750\n",
"(Iteration 21 / 200) loss: 2.089666\n",
"(Iteration 31 / 200) loss: 1.916847\n",
"(Epoch 1 / 5) train acc: 0.389000; val_acc: 0.285000\n",
"(Iteration 41 / 200) loss: 1.797897\n",
"(Iteration 51 / 200) loss: 1.828609\n",
"(Iteration 61 / 200) loss: 1.780695\n",
"(Iteration 71 / 200) loss: 1.702990\n",
"(Epoch 2 / 5) train acc: 0.427000; val_acc: 0.310000\n",
"(Iteration 81 / 200) loss: 1.829711\n",
"(Iteration 91 / 200) loss: 1.611500\n",
"(Iteration 101 / 200) loss: 1.522065\n",
"(Iteration 111 / 200) loss: 1.548703\n",
"(Epoch 3 / 5) train acc: 0.456000; val_acc: 0.319000\n",
"(Iteration 121 / 200) loss: 1.518567\n",
"(Iteration 131 / 200) loss: 1.506497\n",
"(Iteration 141 / 200) loss: 1.620115\n",
"(Iteration 151 / 200) loss: 1.792177\n",
"(Epoch 4 / 5) train acc: 0.484000; val_acc: 0.343000\n",
"(Iteration 161 / 200) loss: 1.539174\n",
"(Iteration 171 / 200) loss: 1.704846\n",
"(Iteration 181 / 200) loss: 1.364234\n",
"(Iteration 191 / 200) loss: 1.207458\n",
"(Epoch 5 / 5) train acc: 0.534000; val_acc: 0.345000\n",
"\n"
]
},
{
"data": {
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ZGeRlZVHmdndTa0VERERERKJHVCVUyb8+n43XbvSvuaMOXO+4yF+VD8DexCFU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22Bex+/RxNc+/teifpiqo8RcApZzQp44zO4s9KH0CxbvVRxXpVKViNugXGmqhMlhwpw118NHtuZ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\nXwHXP0QAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7feb8981be50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
"for update_rule in ['adam', 'rmsprop']:\n",
" print 'running with ', update_rule\n",
" model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
"\n",
" solver = Solver(model, small_data,\n",
" num_epochs=5, batch_size=100,\n",
" update_rule=update_rule,\n",
" optim_config={\n",
" 'learning_rate': learning_rates[update_rule]\n",
" },\n",
" verbose=True)\n",
" solvers[update_rule] = solver\n",
" solver.train()\n",
" print\n",
"\n",
"plt.subplot(3, 1, 1)\n",
"plt.title('Training loss')\n",
"plt.xlabel('Iteration')\n",
"\n",
"plt.subplot(3, 1, 2)\n",
"plt.title('Training accuracy')\n",
"plt.xlabel('Epoch')\n",
"\n",
"plt.subplot(3, 1, 3)\n",
"plt.title('Validation accuracy')\n",
"plt.xlabel('Epoch')\n",
"\n",
"for update_rule, solver in solvers.iteritems():\n",
" plt.subplot(3, 1, 1)\n",
" plt.plot(solver.loss_history, 'o', label=update_rule)\n",
" \n",
" plt.subplot(3, 1, 2)\n",
" plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
"\n",
" plt.subplot(3, 1, 3)\n",
" plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
" \n",
"for i in [1, 2, 3]:\n",
" plt.subplot(3, 1, i)\n",
" plt.legend(loc='upper center', ncol=4)\n",
"plt.gcf().set_size_inches(15, 15)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train a good model!\n",
"Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
"\n",
"If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
"\n",
"You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(Iteration 1 / 2450) loss: 2.393245\n",
"(Epoch 0 / 5) train acc: 0.124000; val_acc: 0.113000\n",
"(Iteration 11 / 2450) loss: 2.172244\n",
"(Iteration 21 / 2450) loss: 1.935820\n",
"(Iteration 31 / 2450) loss: 1.948361\n",
"(Iteration 41 / 2450) loss: 1.891365\n",
"(Iteration 51 / 2450) loss: 1.795540\n",
"(Iteration 61 / 2450) loss: 1.972566\n",
"(Iteration 71 / 2450) loss: 1.733027\n",
"(Iteration 81 / 2450) loss: 1.818204\n",
"(Iteration 91 / 2450) loss: 1.811869\n",
"(Iteration 101 / 2450) loss: 1.633671\n",
"(Iteration 111 / 2450) loss: 1.881841\n",
"(Iteration 121 / 2450) loss: 1.718194\n",
"(Iteration 131 / 2450) loss: 1.762939\n",
"(Iteration 141 / 2450) loss: 1.719082\n",
"(Iteration 151 / 2450) loss: 1.769248\n",
"(Iteration 161 / 2450) loss: 1.686924\n",
"(Iteration 171 / 2450) loss: 1.636663\n",
"(Iteration 181 / 2450) loss: 1.800643\n",
"(Iteration 191 / 2450) loss: 1.769995\n",
"(Iteration 201 / 2450) loss: 1.619124\n",
"(Iteration 211 / 2450) loss: 1.449260\n",
"(Iteration 221 / 2450) loss: 1.672231\n",
"(Iteration 231 / 2450) loss: 1.759620\n",
"(Iteration 241 / 2450) loss: 1.744652\n",
"(Iteration 251 / 2450) loss: 1.699259\n",
"(Iteration 261 / 2450) loss: 1.457534\n",
"(Iteration 271 / 2450) loss: 1.582331\n",
"(Iteration 281 / 2450) loss: 1.698536\n",
"(Iteration 291 / 2450) loss: 1.712866\n",
"(Iteration 301 / 2450) loss: 1.552364\n",
"(Iteration 311 / 2450) loss: 1.489530\n",
"(Iteration 321 / 2450) loss: 1.477179\n",
"(Iteration 331 / 2450) loss: 1.622095\n",
"(Iteration 341 / 2450) loss: 1.737046\n",
"(Iteration 351 / 2450) loss: 1.657767\n",
"(Iteration 361 / 2450) loss: 1.564889\n",
"(Iteration 371 / 2450) loss: 1.502038\n",
"(Iteration 381 / 2450) loss: 1.421695\n",
"(Iteration 391 / 2450) loss: 1.614303\n",
"(Iteration 401 / 2450) loss: 1.522058\n",
"(Iteration 411 / 2450) loss: 1.448205\n",
"(Iteration 421 / 2450) loss: 1.469830\n",
"(Iteration 431 / 2450) loss: 1.725102\n",
"(Iteration 441 / 2450) loss: 1.607295\n",
"(Iteration 451 / 2450) loss: 1.515946\n",
"(Iteration 461 / 2450) loss: 1.583637\n",
"(Iteration 471 / 2450) loss: 1.595459\n",
"(Iteration 481 / 2450) loss: 1.461294\n",
"(Epoch 1 / 5) train acc: 0.451000; val_acc: 0.480000\n",
"(Iteration 491 / 2450) loss: 1.494689\n",
"(Iteration 501 / 2450) loss: 1.661207\n",
"(Iteration 511 / 2450) loss: 1.609235\n",
"(Iteration 521 / 2450) loss: 1.596649\n",
"(Iteration 531 / 2450) loss: 1.572454\n",
"(Iteration 541 / 2450) loss: 1.472974\n",
"(Iteration 551 / 2450) loss: 1.653810\n",
"(Iteration 561 / 2450) loss: 1.523912\n",
"(Iteration 571 / 2450) loss: 1.429298\n",
"(Iteration 581 / 2450) loss: 1.320026\n",
"(Iteration 591 / 2450) loss: 1.448211\n",
"(Iteration 601 / 2450) loss: 1.462247\n",
"(Iteration 611 / 2450) loss: 1.459668\n",
"(Iteration 621 / 2450) loss: 1.574792\n",
"(Iteration 631 / 2450) loss: 1.462531\n",
"(Iteration 641 / 2450) loss: 1.529277\n",
"(Iteration 651 / 2450) loss: 1.434763\n",
"(Iteration 661 / 2450) loss: 1.508439\n",
"(Iteration 671 / 2450) loss: 1.620399\n",
"(Iteration 681 / 2450) loss: 1.633626\n",
"(Iteration 691 / 2450) loss: 1.270819\n",
"(Iteration 701 / 2450) loss: 1.400894\n",
"(Iteration 711 / 2450) loss: 1.454795\n",
"(Iteration 721 / 2450) loss: 1.401864\n",
"(Iteration 731 / 2450) loss: 1.617353\n",
"(Iteration 741 / 2450) loss: 1.530766\n",
"(Iteration 751 / 2450) loss: 1.437644\n",
"(Iteration 761 / 2450) loss: 1.629797\n",
"(Iteration 771 / 2450) loss: 1.502987\n",
"(Iteration 781 / 2450) loss: 1.415692\n",
"(Iteration 791 / 2450) loss: 1.512322\n",
"(Iteration 801 / 2450) loss: 1.564117\n",
"(Iteration 811 / 2450) loss: 1.334242\n",
"(Iteration 821 / 2450) loss: 1.646106\n",
"(Iteration 831 / 2450) loss: 1.466075\n",
"(Iteration 841 / 2450) loss: 1.351835\n",
"(Iteration 851 / 2450) loss: 1.473896\n",
"(Iteration 861 / 2450) loss: 1.505670\n",
"(Iteration 871 / 2450) loss: 1.332002\n",
"(Iteration 881 / 2450) loss: 1.483209\n",
"(Iteration 891 / 2450) loss: 1.441480\n",
"(Iteration 901 / 2450) loss: 1.358909\n",
"(Iteration 911 / 2450) loss: 1.432335\n",
"(Iteration 921 / 2450) loss: 1.512830\n",
"(Iteration 931 / 2450) loss: 1.395373\n",
"(Iteration 941 / 2450) loss: 1.568875\n",
"(Iteration 951 / 2450) loss: 1.484491\n",
"(Iteration 961 / 2450) loss: 1.554645\n",
"(Iteration 971 / 2450) loss: 1.598657\n",
"(Epoch 2 / 5) train acc: 0.518000; val_acc: 0.505000\n",
"(Iteration 981 / 2450) loss: 1.618098\n",
"(Iteration 991 / 2450) loss: 1.367231\n",
"(Iteration 1001 / 2450) loss: 1.484485\n",
"(Iteration 1011 / 2450) loss: 1.425154\n",
"(Iteration 1021 / 2450) loss: 1.437529\n",
"(Iteration 1031 / 2450) loss: 1.509744\n",
"(Iteration 1041 / 2450) loss: 1.437439\n",
"(Iteration 1051 / 2450) loss: 1.637276\n",
"(Iteration 1061 / 2450) loss: 1.369180\n",
"(Iteration 1071 / 2450) loss: 1.543416\n",
"(Iteration 1081 / 2450) loss: 1.386472\n",
"(Iteration 1091 / 2450) loss: 1.434196\n",
"(Iteration 1101 / 2450) loss: 1.515242\n",
"(Iteration 1111 / 2450) loss: 1.644874\n",
"(Iteration 1121 / 2450) loss: 1.275796\n",
"(Iteration 1131 / 2450) loss: 1.406213\n",
"(Iteration 1141 / 2450) loss: 1.327130\n",
"(Iteration 1151 / 2450) loss: 1.596233\n",
"(Iteration 1161 / 2450) loss: 1.493404\n",
"(Iteration 1171 / 2450) loss: 1.629423\n",
"(Iteration 1181 / 2450) loss: 1.433704\n",
"(Iteration 1191 / 2450) loss: 1.469942\n",
"(Iteration 1201 / 2450) loss: 1.556239\n",
"(Iteration 1211 / 2450) loss: 1.355049\n",
"(Iteration 1221 / 2450) loss: 1.504717\n",
"(Iteration 1231 / 2450) loss: 1.534259\n",
"(Iteration 1241 / 2450) loss: 1.406516\n",
"(Iteration 1251 / 2450) loss: 1.590167\n",
"(Iteration 1261 / 2450) loss: 1.557688\n",
"(Iteration 1271 / 2450) loss: 1.275411\n",
"(Iteration 1281 / 2450) loss: 1.465114\n",
"(Iteration 1291 / 2450) loss: 1.271140\n",
"(Iteration 1301 / 2450) loss: 1.415476\n",
"(Iteration 1311 / 2450) loss: 1.452175\n",
"(Iteration 1321 / 2450) loss: 1.233572\n",
"(Iteration 1331 / 2450) loss: 1.453561\n",
"(Iteration 1341 / 2450) loss: 1.406712\n",
"(Iteration 1351 / 2450) loss: 1.340919\n",
"(Iteration 1361 / 2450) loss: 1.436322\n",
"(Iteration 1371 / 2450) loss: 1.406518\n",
"(Iteration 1381 / 2450) loss: 1.183880\n",
"(Iteration 1391 / 2450) loss: 1.343576\n",
"(Iteration 1401 / 2450) loss: 1.397365\n",
"(Iteration 1411 / 2450) loss: 1.412249\n",
"(Iteration 1421 / 2450) loss: 1.286231\n",
"(Iteration 1431 / 2450) loss: 1.420857\n",
"(Iteration 1441 / 2450) loss: 1.247353\n",
"(Iteration 1451 / 2450) loss: 1.404727\n",
"(Iteration 1461 / 2450) loss: 1.316732\n",
"(Epoch 3 / 5) train acc: 0.542000; val_acc: 0.509000\n",
"(Iteration 1471 / 2450) loss: 1.404328\n",
"(Iteration 1481 / 2450) loss: 1.393383\n",
"(Iteration 1491 / 2450) loss: 1.314270\n",
"(Iteration 1501 / 2450) loss: 1.416495\n",
"(Iteration 1511 / 2450) loss: 1.315559\n",
"(Iteration 1521 / 2450) loss: 1.296240\n",
"(Iteration 1531 / 2450) loss: 1.378123\n",
"(Iteration 1541 / 2450) loss: 1.372484\n",
"(Iteration 1551 / 2450) loss: 1.463777\n",
"(Iteration 1561 / 2450) loss: 1.384762\n",
"(Iteration 1571 / 2450) loss: 1.310289\n",
"(Iteration 1581 / 2450) loss: 1.400576\n",
"(Iteration 1591 / 2450) loss: 1.387287\n",
"(Iteration 1601 / 2450) loss: 1.550186\n",
"(Iteration 1611 / 2450) loss: 1.442151\n",
"(Iteration 1621 / 2450) loss: 1.401800\n",
"(Iteration 1631 / 2450) loss: 1.220325\n",
"(Iteration 1641 / 2450) loss: 1.199102\n",
"(Iteration 1651 / 2450) loss: 1.453058\n",
"(Iteration 1661 / 2450) loss: 1.351824\n",
"(Iteration 1671 / 2450) loss: 1.319342\n",
"(Iteration 1681 / 2450) loss: 1.361695\n",
"(Iteration 1691 / 2450) loss: 1.482007\n",
"(Iteration 1701 / 2450) loss: 1.246076\n",
"(Iteration 1711 / 2450) loss: 1.638169\n",
"(Iteration 1721 / 2450) loss: 1.246328\n",
"(Iteration 1731 / 2450) loss: 1.569023\n",
"(Iteration 1741 / 2450) loss: 1.289674\n",
"(Iteration 1751 / 2450) loss: 1.232405\n",
"(Iteration 1761 / 2450) loss: 1.195796\n",
"(Iteration 1771 / 2450) loss: 1.433443\n",
"(Iteration 1781 / 2450) loss: 1.139838\n",
"(Iteration 1791 / 2450) loss: 1.206636\n",
"(Iteration 1801 / 2450) loss: 1.269741\n",
"(Iteration 1811 / 2450) loss: 1.270724\n",
"(Iteration 1821 / 2450) loss: 1.252650\n",
"(Iteration 1831 / 2450) loss: 1.383356\n",
"(Iteration 1841 / 2450) loss: 1.485217\n",
"(Iteration 1851 / 2450) loss: 1.221843\n",
"(Iteration 1861 / 2450) loss: 1.297372\n",
"(Iteration 1871 / 2450) loss: 1.351993\n",
"(Iteration 1881 / 2450) loss: 1.304564\n",
"(Iteration 1891 / 2450) loss: 1.328241\n",
"(Iteration 1901 / 2450) loss: 1.285023\n",
"(Iteration 1911 / 2450) loss: 1.432926\n",
"(Iteration 1921 / 2450) loss: 1.460258\n",
"(Iteration 1931 / 2450) loss: 1.394505\n",
"(Iteration 1941 / 2450) loss: 1.315463\n",
"(Iteration 1951 / 2450) loss: 1.382753\n",
"(Epoch 4 / 5) train acc: 0.546000; val_acc: 0.525000\n",
"(Iteration 1961 / 2450) loss: 1.409585\n",
"(Iteration 1971 / 2450) loss: 1.268464\n",
"(Iteration 1981 / 2450) loss: 1.278014\n",
"(Iteration 1991 / 2450) loss: 1.214101\n",
"(Iteration 2001 / 2450) loss: 1.354110\n",
"(Iteration 2011 / 2450) loss: 1.212669\n",
"(Iteration 2021 / 2450) loss: 1.443707\n",
"(Iteration 2031 / 2450) loss: 1.322949\n",
"(Iteration 2041 / 2450) loss: 1.220443\n",
"(Iteration 2051 / 2450) loss: 1.245847\n",
"(Iteration 2061 / 2450) loss: 1.406653\n",
"(Iteration 2071 / 2450) loss: 1.168552\n",
"(Iteration 2081 / 2450) loss: 1.144171\n",
"(Iteration 2091 / 2450) loss: 1.335821\n",
"(Iteration 2101 / 2450) loss: 1.296504\n",
"(Iteration 2111 / 2450) loss: 1.482875\n",
"(Iteration 2121 / 2450) loss: 1.325576\n",
"(Iteration 2131 / 2450) loss: 1.415418\n",
"(Iteration 2141 / 2450) loss: 1.262403\n",
"(Iteration 2151 / 2450) loss: 1.341821\n",
"(Iteration 2161 / 2450) loss: 1.433409\n",
"(Iteration 2171 / 2450) loss: 1.238393\n",
"(Iteration 2181 / 2450) loss: 1.296973\n",
"(Iteration 2191 / 2450) loss: 1.304204\n",
"(Iteration 2201 / 2450) loss: 1.329396\n",
"(Iteration 2211 / 2450) loss: 1.297929\n",
"(Iteration 2221 / 2450) loss: 1.455924\n",
"(Iteration 2231 / 2450) loss: 1.364676\n",
"(Iteration 2241 / 2450) loss: 1.134725\n",
"(Iteration 2251 / 2450) loss: 1.353628\n",
"(Iteration 2261 / 2450) loss: 1.331022\n",
"(Iteration 2271 / 2450) loss: 1.369687\n",
"(Iteration 2281 / 2450) loss: 1.365139\n",
"(Iteration 2291 / 2450) loss: 1.341802\n",
"(Iteration 2301 / 2450) loss: 1.446997\n",
"(Iteration 2311 / 2450) loss: 1.288457\n",
"(Iteration 2321 / 2450) loss: 1.197436\n",
"(Iteration 2331 / 2450) loss: 1.280263\n",
"(Iteration 2341 / 2450) loss: 1.272699\n",
"(Iteration 2351 / 2450) loss: 1.356410\n",
"(Iteration 2361 / 2450) loss: 1.301361\n",
"(Iteration 2371 / 2450) loss: 1.258524\n",
"(Iteration 2381 / 2450) loss: 1.369351\n",
"(Iteration 2391 / 2450) loss: 1.372183\n",
"(Iteration 2401 / 2450) loss: 1.471954\n",
"(Iteration 2411 / 2450) loss: 1.384708\n",
"(Iteration 2421 / 2450) loss: 1.322077\n",
"(Iteration 2431 / 2450) loss: 1.366167\n",
"(Iteration 2441 / 2450) loss: 1.150555\n",
"(Epoch 5 / 5) train acc: 0.577000; val_acc: 0.521000\n"
]
}
],
"source": [
"best_model = None\n",
"################################################################################\n",
"# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might #\n",
"# batch normalization and dropout useful. Store your best model in the #\n",
"# best_model variable. #\n",
"################################################################################\n",
"num_train = data['X_train'].shape[0]\n",
"small_data = {\n",
" 'X_train': data['X_train'][:num_train],\n",
" 'y_train': data['y_train'][:num_train],\n",
" 'X_val': data['X_val'],\n",
" 'y_val': data['y_val'],\n",
"}\n",
"dropout=0.1\n",
"model = FullyConnectedNet([100, 100, 100], weight_scale=5e-2, use_batchnorm=True, dropout=dropout)\n",
"\n",
"update_rule = 'adam'\n",
"learning_rate = 1e-3\n",
"solver = Solver(model, small_data,\n",
" num_epochs=5, batch_size=100,\n",
" update_rule=update_rule,\n",
" optim_config={\n",
" 'learning_rate': learning_rate\n",
" },\n",
" verbose=True)\n",
"solver.train()\n",
"best_model = model\n",
"################################################################################\n",
"# END OF YOUR CODE #\n",
"################################################################################"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test you model\n",
"Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation set accuracy: 0.524\n",
"Test set accuracy: 0.52\n"
]
}
],
"source": [
"X_test = data['X_test']\n",
"y_test = data['y_test']\n",
"X_val = data['X_val']\n",
"y_val = data['y_val']\n",
"y_test_pred = np.argmax(best_model.loss(X_test), axis=1)\n",
"y_val_pred = np.argmax(best_model.loss(X_val), axis=1)\n",
"print 'Validation set accuracy: ', (y_val_pred == y_val).mean()\n",
"print 'Test set accuracy: ', (y_test_pred == y_test).mean()"
]
},
{
"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 |
renekm/CD-atualizado- | MiniProjeto1/Projeto 1 feito.ipynb | 1 | 525255 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"# PROJETO 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <font color='red'>Rene Martinez</font>\n",
"___"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <font color='blue'>Aposentados X Aposentados</font>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introdução\n",
"\n",
"A partir da ideia de que há uma grande desigualdade social envolvida com a descriminação social, decidi ver qual é a relação que existe entre essa desigualdade com a descriminação racial em relação aos aposentados.\n",
"\n",
"No site http://www.cartaforense.com.br/conteudo/colunas/previdencia-e-a-desigualdade-social/5757 é visto que a distribuição de renda se torna mais injusta com o sistema previdenciário brasileiro.\n",
"\n",
"Para essa minha pesquisa, eu usei várias estatísticas qualitativas e quantitativas em uma ordem lógica que no final há uma conclusão acerca dessa relação estudada.\n",
"\n",
"Porém, essa pequisa só tem relação com a aposentadoria governamental, sendo que aprivada é algo totalmente diferente e pode ser analisado do mesmo modo\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"## Análise e Resultados"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Esperamos trabalhar no diretório\n",
"C:\\Users\\Rene Martinez\\Documents\\CDEng-master\\Projeto1\n"
]
}
],
"source": [
"print('Esperamos trabalhar no diretório')\n",
"print(os.getcwd())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\"\"\"Vamos ler a estrutura da PNAD como um dataframe.\n",
" São muitas colunas e precisamos usar a informação de tamanho para ler a PNAD de fato\n",
" como uma base de tamanho fixo\"\"\"\n",
"estrutura = pd.read_table(\"pes_py.txt\", sep=\";\")\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Coluna</th>\n",
" <th>Tamanho</th>\n",
" <th>Título</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>V0101</td>\n",
" <td>4</td>\n",
" <td>Ano de referência</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>UF</td>\n",
" <td>2</td>\n",
" <td>Unidade da Federação</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>V0102</td>\n",
" <td>6</td>\n",
" <td>Número de controle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>V0103</td>\n",
" <td>3</td>\n",
" <td>Número de série</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>V0301</td>\n",
" <td>2</td>\n",
" <td>Número de ordem</td>\n",
" </tr>\n",
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"</table>\n",
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"text/plain": [
" Coluna Tamanho Título\n",
"0 V0101 4 Ano de referência\n",
"1 UF 2 Unidade da Federação\n",
"2 V0102 6 Número de controle\n",
"3 V0103 3 Número de série\n",
"4 V0301 2 Número de ordem"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"estrutura.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Se quiser uma leitura mais rápida, use o arquivo descompactado. Não esqueça de adicionar ao seu .gitignore\n",
"pnad2014 = pd.read_fwf(\"PES2014.txt\", widths=estrutura.Tamanho, header=None)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
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" <th>332</th>\n",
" <th>333</th>\n",
" <th>334</th>\n",
" <th>335</th>\n",
" <th>336</th>\n",
" <th>337</th>\n",
" <th>338</th>\n",
" <th>339</th>\n",
" <th>340</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>19</td>\n",
" <td>8</td>\n",
" <td>1987</td>\n",
" <td>27</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>500</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
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" <td>500</td>\n",
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" <th>1</th>\n",
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" <td>4</td>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" <td>1986</td>\n",
" <td>28</td>\n",
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" <td>3</td>\n",
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" <th>2</th>\n",
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" <td>3</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" <td>5</td>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
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" <td>500</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>500</td>\n",
" <td>20160623</td>\n",
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" <tr>\n",
" <th>3</th>\n",
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" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>4</td>\n",
" <td>1963</td>\n",
" <td>51</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1150</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1150</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>11</td>\n",
" <td>1970</td>\n",
" <td>43</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1150</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1150</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1937</td>\n",
" <td>77</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>724</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>724</td>\n",
" <td>20160623</td>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>1975</td>\n",
" <td>39</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1700</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1700</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>1954</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1700</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1700</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>1998</td>\n",
" <td>16</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1700</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1700</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>21</td>\n",
" <td>5</td>\n",
" <td>1999</td>\n",
" <td>15</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1700</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1700</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>2001</td>\n",
" <td>12</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1700</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1700</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>13</td>\n",
" <td>7</td>\n",
" <td>1946</td>\n",
" <td>68</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>855</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>855</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>10</td>\n",
" <td>1978</td>\n",
" <td>35</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>855</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>855</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>21</td>\n",
" <td>7</td>\n",
" <td>1996</td>\n",
" <td>18</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>855</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>855</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>24</td>\n",
" <td>8</td>\n",
" <td>2000</td>\n",
" <td>14</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>855</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>855</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>20</td>\n",
" <td>11</td>\n",
" <td>1953</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>850</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>850</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>1956</td>\n",
" <td>58</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>850</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>850</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>1958</td>\n",
" <td>55</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>3220</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3220</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>1954</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>3220</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3220</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>6</td>\n",
" <td>1981</td>\n",
" <td>33</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>3220</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3220</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>6</td>\n",
" <td>1981</td>\n",
" <td>33</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>3220</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3220</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>1985</td>\n",
" <td>29</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>3220</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3220</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>1975</td>\n",
" <td>39</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>933</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>933</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>12</td>\n",
" <td>2</td>\n",
" <td>1972</td>\n",
" <td>42</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>933</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>933</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>2009</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>933</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>933</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>10</td>\n",
" <td>1948</td>\n",
" <td>65</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2362</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2362</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>24</td>\n",
" <td>6</td>\n",
" <td>1952</td>\n",
" <td>62</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2362</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>2362</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>20</td>\n",
" <td>12</td>\n",
" <td>1965</td>\n",
" <td>48</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>804</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>804</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>11</td>\n",
" <td>1965</td>\n",
" <td>48</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>804</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>804</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>11</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>1994</td>\n",
" <td>20</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>804</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>804</td>\n",
" <td>20160623</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",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\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>362597</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>9</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>1995</td>\n",
" <td>19</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>500</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>500</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362598</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>22</td>\n",
" <td>1</td>\n",
" <td>2000</td>\n",
" <td>14</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>500</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>500</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362599</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>21</td>\n",
" <td>7</td>\n",
" <td>1968</td>\n",
" <td>46</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1140</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1140</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362600</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>30</td>\n",
" <td>8</td>\n",
" <td>1965</td>\n",
" <td>49</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1140</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1140</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362601</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>12</td>\n",
" <td>1990</td>\n",
" <td>23</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1140</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1140</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362602</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>10</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" <td>1994</td>\n",
" <td>19</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1140</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1140</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362603</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>30</td>\n",
" <td>5</td>\n",
" <td>2001</td>\n",
" <td>13</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1140</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1140</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362604</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>29</td>\n",
" <td>9</td>\n",
" <td>1962</td>\n",
" <td>51</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1199</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1318</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362605</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1199</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1318</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362606</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>11</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>18</td>\n",
" <td>3</td>\n",
" <td>1992</td>\n",
" <td>22</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1199</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1318</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362607</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>29</td>\n",
" <td>8</td>\n",
" <td>1990</td>\n",
" <td>24</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1199</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1019</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362608</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>11</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>1995</td>\n",
" <td>19</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1199</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1019</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362609</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>24</td>\n",
" <td>12</td>\n",
" <td>1953</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>442</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>442</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362610</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>12</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1961</td>\n",
" <td>53</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>442</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>442</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362611</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>12</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>23</td>\n",
" <td>7</td>\n",
" <td>1987</td>\n",
" <td>27</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>442</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>442</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362612</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>12</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>22</td>\n",
" <td>5</td>\n",
" <td>1989</td>\n",
" <td>25</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>442</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>442</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362613</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>12</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>10</td>\n",
" <td>1994</td>\n",
" <td>19</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>442</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>442</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362614</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>12</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>13</td>\n",
" <td>9</td>\n",
" <td>1997</td>\n",
" <td>17</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>442</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>442</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362615</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>12</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>29</td>\n",
" <td>5</td>\n",
" <td>2001</td>\n",
" <td>13</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>442</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>442</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362616</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>1959</td>\n",
" <td>55</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>1266</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1266</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362617</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>13</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>31</td>\n",
" <td>10</td>\n",
" <td>1964</td>\n",
" <td>49</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>1266</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1266</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362618</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>13</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>17</td>\n",
" <td>6</td>\n",
" <td>2007</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>1266</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1266</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362619</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>23</td>\n",
" <td>6</td>\n",
" <td>1991</td>\n",
" <td>23</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>500</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>500</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362620</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>14</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>25</td>\n",
" <td>11</td>\n",
" <td>1989</td>\n",
" <td>24</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>500</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>500</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362621</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1987</td>\n",
" <td>27</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2000</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2000</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362622</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>16</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>1983</td>\n",
" <td>31</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2000</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2000</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362623</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>17</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1968</td>\n",
" <td>46</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>910</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>910</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362624</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>17</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>6</td>\n",
" <td>1978</td>\n",
" <td>36</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>910</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>910</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362625</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>17</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1996</td>\n",
" <td>18</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>910</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>910</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362626</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>17</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>1997</td>\n",
" <td>17</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>910</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>910</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>362627 rows × 341 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 7 8 9 ... 331 \\\n",
"0 2014 11 15 1 1 2 19 8 1987 27 ... 3 \n",
"1 2014 11 15 1 2 4 19 1 1986 28 ... 3 \n",
"2 2014 11 15 1 3 2 14 5 2013 1 ... 3 \n",
"3 2014 11 15 2 1 2 8 4 1963 51 ... 2 \n",
"4 2014 11 15 2 2 4 6 11 1970 43 ... 2 \n",
"5 2014 11 15 3 1 4 5 1 1937 77 ... 1 \n",
"6 2014 11 15 4 1 4 15 3 1975 39 ... 5 \n",
"7 2014 11 15 4 2 2 7 3 1954 60 ... 5 \n",
"8 2014 11 15 4 3 4 2 4 1998 16 ... 5 \n",
"9 2014 11 15 4 4 2 21 5 1999 15 ... 5 \n",
"10 2014 11 15 4 5 4 1 11 2001 12 ... 5 \n",
"11 2014 11 15 5 1 4 13 7 1946 68 ... 4 \n",
"12 2014 11 15 5 2 4 7 10 1978 35 ... 4 \n",
"13 2014 11 15 5 3 4 21 7 1996 18 ... 4 \n",
"14 2014 11 15 5 4 2 24 8 2000 14 ... 4 \n",
"15 2014 11 15 6 1 2 20 11 1953 60 ... 2 \n",
"16 2014 11 15 6 2 4 9 6 1956 58 ... 2 \n",
"17 2014 11 15 7 1 4 12 12 1958 55 ... 5 \n",
"18 2014 11 15 7 2 2 22 3 1954 60 ... 5 \n",
"19 2014 11 15 7 3 2 10 6 1981 33 ... 5 \n",
"20 2014 11 15 7 4 2 10 6 1981 33 ... 5 \n",
"21 2014 11 15 7 5 2 3 6 1985 29 ... 5 \n",
"22 2014 11 15 8 1 2 15 3 1975 39 ... 3 \n",
"23 2014 11 15 8 2 4 12 2 1972 42 ... 3 \n",
"24 2014 11 15 8 3 2 5 5 2009 5 ... 3 \n",
"25 2014 11 15 10 1 2 15 10 1948 65 ... 2 \n",
"26 2014 11 15 10 2 4 24 6 1952 62 ... 2 \n",
"27 2014 11 15 11 1 2 20 12 1965 48 ... 5 \n",
"28 2014 11 15 11 2 4 9 11 1965 48 ... 5 \n",
"29 2014 11 15 11 3 2 3 7 1994 20 ... 5 \n",
"... ... ... ... ... ... ... ... ... ... ... ... ... \n",
"362597 2014 53 2148 9 3 2 5 9 1995 19 ... 4 \n",
"362598 2014 53 2148 9 4 2 22 1 2000 14 ... 4 \n",
"362599 2014 53 2148 10 1 4 21 7 1968 46 ... 5 \n",
"362600 2014 53 2148 10 2 2 30 8 1965 49 ... 5 \n",
"362601 2014 53 2148 10 3 2 10 12 1990 23 ... 5 \n",
"362602 2014 53 2148 10 4 2 5 10 1994 19 ... 5 \n",
"362603 2014 53 2148 10 5 4 30 5 2001 13 ... 5 \n",
"362604 2014 53 2148 11 1 2 29 9 1962 51 ... 5 \n",
"362605 2014 53 2148 11 2 4 0 20 39 39 ... 5 \n",
"362606 2014 53 2148 11 3 2 18 3 1992 22 ... 5 \n",
"362607 2014 53 2148 11 4 2 29 8 1990 24 ... 5 \n",
"362608 2014 53 2148 11 5 4 21 1 1995 19 ... 5 \n",
"362609 2014 53 2148 12 1 2 24 12 1953 60 ... 7 \n",
"362610 2014 53 2148 12 2 4 3 1 1961 53 ... 7 \n",
"362611 2014 53 2148 12 3 4 23 7 1987 27 ... 7 \n",
"362612 2014 53 2148 12 4 2 22 5 1989 25 ... 7 \n",
"362613 2014 53 2148 12 5 2 7 10 1994 19 ... 7 \n",
"362614 2014 53 2148 12 6 2 13 9 1997 17 ... 7 \n",
"362615 2014 53 2148 12 7 4 29 5 2001 13 ... 7 \n",
"362616 2014 53 2148 13 1 2 10 2 1959 55 ... 3 \n",
"362617 2014 53 2148 13 2 4 31 10 1964 49 ... 3 \n",
"362618 2014 53 2148 13 3 2 17 6 2007 7 ... 3 \n",
"362619 2014 53 2148 14 1 2 23 6 1991 23 ... 2 \n",
"362620 2014 53 2148 14 2 4 25 11 1989 24 ... 2 \n",
"362621 2014 53 2148 16 1 2 2 1 1987 27 ... 2 \n",
"362622 2014 53 2148 16 2 4 5 5 1983 31 ... 2 \n",
"362623 2014 53 2148 17 1 2 2 2 1968 46 ... 4 \n",
"362624 2014 53 2148 17 2 4 8 6 1978 36 ... 4 \n",
"362625 2014 53 2148 17 3 2 1 6 1996 18 ... 4 \n",
"362626 2014 53 2148 17 4 4 11 4 1997 17 ... 4 \n",
"\n",
" 332 333 334 335 336 337 338 339 340 \n",
"0 500 3 3 1 2 2 1 500 20160623 \n",
"1 500 3 4 2 NaN 2 1 500 20160623 \n",
"2 500 3 1 NaN NaN NaN NaN 500 20160623 \n",
"3 1150 4 2 1 2 2 1 1150 20160623 \n",
"4 1150 4 3 1 2 2 1 1150 20160623 \n",
"5 724 3 2 2 NaN NaN 2 724 20160623 \n",
"6 1700 5 4 2 NaN 2 1 1700 20160623 \n",
"7 1700 5 1 1 2 2 1 1700 20160623 \n",
"8 1700 5 4 1 2 2 1 1700 20160623 \n",
"9 1700 5 3 2 NaN NaN 2 1700 20160623 \n",
"10 1700 5 2 2 NaN NaN 2 1700 20160623 \n",
"11 855 4 2 2 NaN NaN 2 855 20160623 \n",
"12 855 4 6 1 2 2 1 855 20160623 \n",
"13 855 4 4 1 2 2 1 855 20160623 \n",
"14 855 4 2 2 NaN NaN 2 855 20160623 \n",
"15 850 4 2 1 2 2 1 850 20160623 \n",
"16 850 4 2 1 2 2 1 850 20160623 \n",
"17 3220 6 2 1 2 2 1 3220 20160623 \n",
"18 3220 6 7 1 2 2 1 3220 20160623 \n",
"19 3220 6 7 1 2 2 1 3220 20160623 \n",
"20 3220 6 5 1 2 2 1 3220 20160623 \n",
"21 3220 6 7 1 2 2 1 3220 20160623 \n",
"22 933 4 5 1 2 2 1 933 20160623 \n",
"23 933 4 5 1 2 2 1 933 20160623 \n",
"24 933 4 1 2 NaN NaN 2 933 20160623 \n",
"25 2362 6 2 1 1 1 1 2362 20160623 \n",
"26 2362 6 5 2 NaN NaN 2 2362 20160623 \n",
"27 804 4 3 1 2 2 1 804 20160623 \n",
"28 804 4 5 1 2 2 1 804 20160623 \n",
"29 804 4 6 1 2 2 1 804 20160623 \n",
"... ... ... ... ... ... ... ... ... ... \n",
"362597 500 3 2 1 2 2 1 500 20160623 \n",
"362598 500 3 2 2 NaN NaN 2 500 20160623 \n",
"362599 1140 4 7 1 2 2 1 1140 20160623 \n",
"362600 1140 4 1 1 2 2 1 1140 20160623 \n",
"362601 1140 4 2 1 2 2 1 1140 20160623 \n",
"362602 1140 4 2 1 2 2 1 1140 20160623 \n",
"362603 1140 4 2 2 NaN NaN 2 1140 20160623 \n",
"362604 1199 4 2 1 2 2 1 1318 20160623 \n",
"362605 1199 4 3 1 2 2 1 1318 20160623 \n",
"362606 1199 4 5 1 2 2 1 1318 20160623 \n",
"362607 1199 4 5 1 2 2 1 1019 20160623 \n",
"362608 1199 4 5 1 2 2 1 1019 20160623 \n",
"362609 442 3 1 1 2 2 1 442 20160623 \n",
"362610 442 3 1 2 NaN NaN 2 442 20160623 \n",
"362611 442 3 5 1 2 2 1 442 20160623 \n",
"362612 442 3 2 1 2 2 1 442 20160623 \n",
"362613 442 3 5 1 2 2 1 442 20160623 \n",
"362614 442 3 2 2 NaN NaN 2 442 20160623 \n",
"362615 442 3 2 2 NaN NaN 2 442 20160623 \n",
"362616 1266 4 3 1 2 2 1 1266 20160623 \n",
"362617 1266 4 2 2 NaN NaN 2 1266 20160623 \n",
"362618 1266 4 1 2 NaN NaN 2 1266 20160623 \n",
"362619 500 3 2 2 NaN 2 1 500 20160623 \n",
"362620 500 3 4 1 2 2 1 500 20160623 \n",
"362621 2000 5 5 1 2 2 1 2000 20160623 \n",
"362622 2000 5 5 1 2 2 1 2000 20160623 \n",
"362623 910 4 1 1 2 2 1 910 20160623 \n",
"362624 910 4 1 1 2 2 1 910 20160623 \n",
"362625 910 4 3 2 NaN NaN 2 910 20160623 \n",
"362626 910 4 4 2 NaN NaN 2 910 20160623 \n",
"\n",
"[362627 rows x 341 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad2014"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Coluna', 'Tamanho', 'Título'], dtype='object')"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"estrutura.columns"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pnad2014.columns = estrutura.Coluna"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Coluna</th>\n",
" <th>V0101</th>\n",
" <th>UF</th>\n",
" <th>V0102</th>\n",
" <th>V0103</th>\n",
" <th>V0301</th>\n",
" <th>V0302</th>\n",
" <th>V3031</th>\n",
" <th>V3032</th>\n",
" <th>V3033</th>\n",
" <th>V8005</th>\n",
" <th>...</th>\n",
" <th>V4741</th>\n",
" <th>V4742</th>\n",
" <th>V4743</th>\n",
" <th>V4745</th>\n",
" <th>V4746</th>\n",
" <th>V4747</th>\n",
" <th>V4748</th>\n",
" <th>V4749</th>\n",
" <th>V4750</th>\n",
" <th>V9993</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>19</td>\n",
" <td>8</td>\n",
" <td>1987</td>\n",
" <td>27</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>500</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>500</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" <td>1986</td>\n",
" <td>28</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>500</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>500</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" <td>5</td>\n",
" <td>2013</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>500</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>500</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>4</td>\n",
" <td>1963</td>\n",
" <td>51</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1150</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1150</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>11</td>\n",
" <td>1970</td>\n",
" <td>43</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1150</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1150</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 341 columns</p>\n",
"</div>"
],
"text/plain": [
"Coluna V0101 UF V0102 V0103 V0301 V0302 V3031 V3032 V3033 V8005 \\\n",
"0 2014 11 15 1 1 2 19 8 1987 27 \n",
"1 2014 11 15 1 2 4 19 1 1986 28 \n",
"2 2014 11 15 1 3 2 14 5 2013 1 \n",
"3 2014 11 15 2 1 2 8 4 1963 51 \n",
"4 2014 11 15 2 2 4 6 11 1970 43 \n",
"\n",
"Coluna ... V4741 V4742 V4743 V4745 V4746 V4747 V4748 V4749 \\\n",
"0 ... 3 500 3 3 1 2 2 1 \n",
"1 ... 3 500 3 4 2 NaN 2 1 \n",
"2 ... 3 500 3 1 NaN NaN NaN NaN \n",
"3 ... 2 1150 4 2 1 2 2 1 \n",
"4 ... 2 1150 4 3 1 2 2 1 \n",
"\n",
"Coluna V4750 V9993 \n",
"0 500 20160623 \n",
"1 500 20160623 \n",
"2 500 20160623 \n",
"3 1150 20160623 \n",
"4 1150 20160623 \n",
"\n",
"[5 rows x 341 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad2014.head()"
]
},
{
"cell_type": "code",
"execution_count": 219,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"#Porcentagem de homens e mulheres aposentados no Brasil"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pnad2014.Aposentados = pnad2014[pnad2014.V9122 == 2].V0302.astype('category')"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pnad2014.Aposentados.cat.categories = ('Homem', 'Mulher')"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Mulher 50.793158\n",
"Homem 49.206842\n",
"dtype: float64"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad2014.Aposentados.value_counts(True)*100"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x25016496ba8>"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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IopUSQzDg1BKYUAv1fzYrOz50onyhiwCuJDMbA4kn4PJa+K5+APapNeLbIcAhRPPQQ1k0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\nQKiwRUQKhApbRKRAqLBFRArE/wdDFeuglteu0wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x25074802ac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pizza1 = pnad2014.Aposentados.value_counts(True)*100\n",
"pizza1.plot(kind='pie', colors=('blue', 'red'), autopct=\"%0.2f\",legend=False)\n",
"plt.title(\"Aposentados\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Quantidade de aposentados homens por cidade brasileira"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pnad2014.Regioes = pnad2014[(pnad2014.V9122 == 2)&(pnad2014.V0302 == 2)].UF.astype('category')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"São Paulo 2524\n",
"Minas Gerais 2101\n",
"Rio Grande do Sul 2079\n",
"Rio de Janeiro 1662\n",
"Bahia 1227\n",
"Paraná 1121\n",
"Pernambuco 1047\n",
"Ceará 882\n",
"Pará 762\n",
"Santa Catarina 729\n",
"Goiás 688\n",
"Maranhão 397\n",
"Espírito Santo 364\n",
"Distrito Federal 359\n",
"Amazonas 321\n",
"Mato Grosso 319\n",
"Paraíba 312\n",
"Sergipe 297\n",
"Piauí 294\n",
"Tocantins 281\n",
"Mato Grosso do Sul 266\n",
"Rondônia 264\n",
"Alagoas 242\n",
"Rio Grande do Norte 216\n",
"Acre 119\n",
"Amapá 75\n",
"Roraima 67\n",
"dtype: int64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad2014.Regioes.cat.categories = ('Rondônia', 'Acre', 'Amazonas', 'Roraima', 'Pará', 'Amapá', 'Tocantins', 'Maranhão', \n",
" 'Piauí', 'Ceará', 'Rio Grande do Norte', 'Paraíba', 'Pernambuco', 'Alagoas', 'Sergipe',\n",
" 'Bahia', 'Minas Gerais', 'Espírito Santo', 'Rio de Janeiro', 'São Paulo', 'Paraná', \n",
" 'Santa Catarina', 'Rio Grande do Sul', 'Mato Grosso do Sul', 'Mato Grosso', 'Goiás', \n",
" 'Distrito Federal')\n",
"pnad2014.Regioes.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x2500906ac18>"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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WwIlm9pyZ3QXcBqwnaQawqJldntIdk8kTBEEQjBO1BmMlzQTWAS4FljWz2eAfA2CZlGx5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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x25008fcd630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"barraH = pnad2014.Regioes.value_counts()\n",
"barraH.plot(kind='bar', color=('blue', 'red'), legend=False)\n",
"plt.title('Aposentados Homens/Região')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Qual é a raça desses aposentados homens "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pnad2014.Raca = pnad2014[(pnad2014.V9122 == 2)&(pnad2014.V0302 == 2)].V0404.astype('category')"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pnad2014.Raca.cat.categories = ('Indígena','Branca', 'Preta', 'Amarela', 'Parda')"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x25019e97630>"
]
},
"execution_count": 151,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x250183ea9b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"barraRaca = pnad2014.Raca.value_counts()\n",
"barraRaca.plot(kind='bar', color=('white', 'brown', 'black', 'yellow','brown'), legend=False)\n",
"plt.title(\"Raça dos homens aposentados\")"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Coluna</th>\n",
" <th>V0101</th>\n",
" <th>UF</th>\n",
" <th>V0102</th>\n",
" <th>V0103</th>\n",
" <th>V0301</th>\n",
" <th>V0302</th>\n",
" <th>V3031</th>\n",
" <th>V3032</th>\n",
" <th>V3033</th>\n",
" <th>V8005</th>\n",
" <th>...</th>\n",
" <th>V4741</th>\n",
" <th>V4742</th>\n",
" <th>V4743</th>\n",
" <th>V4745</th>\n",
" <th>V4746</th>\n",
" <th>V4747</th>\n",
" <th>V4748</th>\n",
" <th>V4749</th>\n",
" <th>V4750</th>\n",
" <th>V9993</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>23</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>25</td>\n",
" <td>1</td>\n",
" <td>1924</td>\n",
" <td>90</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>4.440000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>4.440000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>40</td>\n",
" <td>18</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>1947</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>8.060000e+02</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>8.060000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>232</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>58</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>1947</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>3.580000e+02</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3.580000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>347</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>74</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>12</td>\n",
" <td>1936</td>\n",
" <td>77</td>\n",
" <td>...</td>\n",
" <td>6</td>\n",
" <td>4.080000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>624</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>147</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>16</td>\n",
" <td>10</td>\n",
" <td>1946</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1218</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>317</td>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>28</td>\n",
" <td>7</td>\n",
" <td>1944</td>\n",
" <td>70</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1234</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>325</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>27</td>\n",
" <td>5</td>\n",
" <td>1950</td>\n",
" <td>64</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>9.740000e+02</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>9.740000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1254</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>333</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>12</td>\n",
" <td>5</td>\n",
" <td>1948</td>\n",
" <td>66</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.320000e+02</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.320000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1286</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>333</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>10</td>\n",
" <td>1949</td>\n",
" <td>64</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>5.600000e+01</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>5.600000e+01</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1402</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>350</td>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>22</td>\n",
" <td>11</td>\n",
" <td>1940</td>\n",
" <td>73</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>3.350000e+03</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.350000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1594</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>406</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>24</td>\n",
" <td>8</td>\n",
" <td>1944</td>\n",
" <td>70</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1595</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>406</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>12</td>\n",
" <td>5</td>\n",
" <td>1953</td>\n",
" <td>61</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1657</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>414</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>11</td>\n",
" <td>1948</td>\n",
" <td>65</td>\n",
" <td>...</td>\n",
" <td>9</td>\n",
" <td>2.050000e+02</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>2.050000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1675</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>414</td>\n",
" <td>14</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>1954</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.224000e+03</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.224000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1761</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>449</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>30</td>\n",
" <td>9</td>\n",
" <td>1949</td>\n",
" <td>64</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>9.120000e+02</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>9.120000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1973</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>503</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>23</td>\n",
" <td>10</td>\n",
" <td>1946</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>1.793000e+03</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.793000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2004</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>503</td>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>27</td>\n",
" <td>6</td>\n",
" <td>1947</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2114</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>538</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>1943</td>\n",
" <td>71</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>4.820000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4.820000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2242</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>562</td>\n",
" <td>14</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>1944</td>\n",
" <td>70</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2251</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>570</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>16</td>\n",
" <td>5</td>\n",
" <td>1945</td>\n",
" <td>69</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>4.820000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>4.820000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2276</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>570</td>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>17</td>\n",
" <td>6</td>\n",
" <td>1953</td>\n",
" <td>61</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>3.000000e+03</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.000000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2348</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>597</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" <td>1951</td>\n",
" <td>62</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>3.000000e+02</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.000000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2766</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>708</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1942</td>\n",
" <td>72</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2806</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>716</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>17</td>\n",
" <td>3</td>\n",
" <td>1943</td>\n",
" <td>71</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.250000e+02</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.250000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2966</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>740</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>1952</td>\n",
" <td>62</td>\n",
" <td>...</td>\n",
" <td>6</td>\n",
" <td>1.000000e+12</td>\n",
" <td>99</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.816000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2984</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>740</td>\n",
" <td>21</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>29</td>\n",
" <td>12</td>\n",
" <td>1945</td>\n",
" <td>68</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.086000e+03</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.086000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3156</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>791</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>1940</td>\n",
" <td>74</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1.000000e+12</td>\n",
" <td>99</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>2.312000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3234</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>813</td>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>1929</td>\n",
" <td>85</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>3.620000e+02</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.620000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3397</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>864</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>31</td>\n",
" <td>5</td>\n",
" <td>1951</td>\n",
" <td>63</td>\n",
" <td>...</td>\n",
" <td>9</td>\n",
" <td>5.000000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>5.000000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3398</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>864</td>\n",
" <td>15</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>23</td>\n",
" <td>4</td>\n",
" <td>1943</td>\n",
" <td>71</td>\n",
" <td>...</td>\n",
" <td>9</td>\n",
" <td>5.000000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>5.000000e+02</td>\n",
" <td>20160623</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",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\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>359993</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1567</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>1954</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.047000e+03</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.047000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360390</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1672</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>12</td>\n",
" <td>10</td>\n",
" <td>1942</td>\n",
" <td>71</td>\n",
" <td>...</td>\n",
" <td>6</td>\n",
" <td>3.000000e+02</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.000000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360421</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1672</td>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>23</td>\n",
" <td>2</td>\n",
" <td>1947</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>2.430000e+02</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>2.430000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360560</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1702</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>19</td>\n",
" <td>5</td>\n",
" <td>1952</td>\n",
" <td>62</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>9.120000e+02</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.333000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360571</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1702</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1952</td>\n",
" <td>62</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>1.600000e+03</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.600000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360579</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1702</td>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>17</td>\n",
" <td>4</td>\n",
" <td>1943</td>\n",
" <td>71</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>8.000000e+02</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>8.000000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360589</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1710</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>19</td>\n",
" <td>11</td>\n",
" <td>1949</td>\n",
" <td>64</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.000000e+02</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.000000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360590</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1710</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>1951</td>\n",
" <td>63</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.000000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.000000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360703</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1737</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>62</td>\n",
" <td>62</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>9.620000e+02</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>9.620000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360715</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1737</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>71</td>\n",
" <td>71</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360750</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1745</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.262000e+03</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.262000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360796</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1753</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>18</td>\n",
" <td>6</td>\n",
" <td>1954</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>1.813000e+03</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1.813000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360802</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1753</td>\n",
" <td>13</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>22</td>\n",
" <td>6</td>\n",
" <td>1954</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>1.062000e+03</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.062000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360953</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1788</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>15</td>\n",
" <td>10</td>\n",
" <td>1951</td>\n",
" <td>62</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.362000e+03</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.362000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361069</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1800</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>26</td>\n",
" <td>10</td>\n",
" <td>1948</td>\n",
" <td>65</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>4.060000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>4.060000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361158</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1826</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>1951</td>\n",
" <td>63</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>2.800000e+03</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>2.800000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361169</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1826</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>1942</td>\n",
" <td>72</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>4.084000e+03</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>4.084000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361173</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1826</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>17</td>\n",
" <td>6</td>\n",
" <td>1948</td>\n",
" <td>66</td>\n",
" <td>...</td>\n",
" <td>6</td>\n",
" <td>1.000000e+12</td>\n",
" <td>99</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.000000e+12</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361191</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1826</td>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>22</td>\n",
" <td>12</td>\n",
" <td>1953</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1.318000e+03</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2.750000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361204</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1834</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>21</td>\n",
" <td>12</td>\n",
" <td>1946</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>6</td>\n",
" <td>1.487000e+03</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.487000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361272</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1842</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" <td>9</td>\n",
" <td>1926</td>\n",
" <td>88</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>1.881000e+03</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1.881000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361564</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1907</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>20</td>\n",
" <td>2</td>\n",
" <td>1948</td>\n",
" <td>66</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>6.120000e+02</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>6.120000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361584</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1907</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>17</td>\n",
" <td>9</td>\n",
" <td>1947</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>1.774000e+03</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1.774000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362016</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2016</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>27</td>\n",
" <td>4</td>\n",
" <td>1938</td>\n",
" <td>76</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>8.120000e+02</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>8.120000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362077</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2024</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>1942</td>\n",
" <td>72</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>4.820000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>4.820000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362078</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2024</td>\n",
" <td>12</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1950</td>\n",
" <td>64</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>4.820000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>4.820000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362094</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2032</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>12</td>\n",
" <td>1951</td>\n",
" <td>62</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>9.870000e+02</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>9.870000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362105</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2032</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>23</td>\n",
" <td>4</td>\n",
" <td>1948</td>\n",
" <td>66</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.267000e+03</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.267000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362202</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2059</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" <td>1937</td>\n",
" <td>76</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1.448000e+03</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.448000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362239</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2059</td>\n",
" <td>14</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>1949</td>\n",
" <td>65</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1.800000e+02</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.800000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4340 rows × 341 columns</p>\n",
"</div>"
],
"text/plain": [
"Coluna V0101 UF V0102 V0103 V0301 V0302 V3031 V3032 V3033 V8005 \\\n",
"51 2014 11 23 1 5 2 25 1 1924 90 \n",
"203 2014 11 40 18 1 4 4 9 1947 67 \n",
"232 2014 11 58 10 2 2 8 8 1947 67 \n",
"347 2014 11 74 10 5 2 5 12 1936 77 \n",
"624 2014 11 147 7 2 4 16 10 1946 67 \n",
"1218 2014 11 317 14 1 4 28 7 1944 70 \n",
"1234 2014 11 325 7 1 2 27 5 1950 64 \n",
"1254 2014 11 333 1 2 4 12 5 1948 66 \n",
"1286 2014 11 333 12 1 2 15 10 1949 64 \n",
"1402 2014 11 350 14 1 2 22 11 1940 73 \n",
"1594 2014 11 406 1 1 4 24 8 1944 70 \n",
"1595 2014 11 406 1 2 2 12 5 1953 61 \n",
"1657 2014 11 414 10 1 2 7 11 1948 65 \n",
"1675 2014 11 414 14 2 4 5 6 1954 60 \n",
"1761 2014 11 449 6 1 2 30 9 1949 64 \n",
"1973 2014 11 503 2 4 4 23 10 1946 67 \n",
"2004 2014 11 503 14 1 4 27 6 1947 67 \n",
"2114 2014 11 538 10 1 2 6 7 1943 71 \n",
"2242 2014 11 562 14 2 4 6 3 1944 70 \n",
"2251 2014 11 570 2 1 2 16 5 1945 69 \n",
"2276 2014 11 570 14 1 2 17 6 1953 61 \n",
"2348 2014 11 597 7 1 2 10 11 1951 62 \n",
"2766 2014 11 708 9 1 2 3 1 1942 72 \n",
"2806 2014 11 716 8 2 4 17 3 1943 71 \n",
"2966 2014 11 740 15 1 4 8 8 1952 62 \n",
"2984 2014 11 740 21 2 2 29 12 1945 68 \n",
"3156 2014 11 791 11 1 4 1 9 1940 74 \n",
"3234 2014 11 813 14 1 4 8 9 1929 85 \n",
"3397 2014 11 864 15 1 2 31 5 1951 63 \n",
"3398 2014 11 864 15 2 4 23 4 1943 71 \n",
"... ... .. ... ... ... ... ... ... ... ... \n",
"359993 2014 53 1567 11 1 2 11 1 1954 60 \n",
"360390 2014 53 1672 6 2 2 12 10 1942 71 \n",
"360421 2014 53 1672 14 1 4 23 2 1947 67 \n",
"360560 2014 53 1702 9 1 4 19 5 1952 62 \n",
"360571 2014 53 1702 11 1 4 2 2 1952 62 \n",
"360579 2014 53 1702 14 1 2 17 4 1943 71 \n",
"360589 2014 53 1710 1 1 2 19 11 1949 64 \n",
"360590 2014 53 1710 1 2 4 2 6 1951 63 \n",
"360703 2014 53 1737 9 1 4 0 20 62 62 \n",
"360715 2014 53 1737 12 1 4 0 20 71 71 \n",
"360750 2014 53 1745 8 2 4 0 20 60 60 \n",
"360796 2014 53 1753 11 2 4 18 6 1954 60 \n",
"360802 2014 53 1753 13 2 2 22 6 1954 60 \n",
"360953 2014 53 1788 5 2 4 15 10 1951 62 \n",
"361069 2014 53 1800 6 1 4 26 10 1948 65 \n",
"361158 2014 53 1826 3 1 4 9 1 1951 63 \n",
"361169 2014 53 1826 7 2 2 15 3 1942 72 \n",
"361173 2014 53 1826 8 2 2 17 6 1948 66 \n",
"361191 2014 53 1826 14 1 4 22 12 1953 60 \n",
"361204 2014 53 1834 2 6 4 21 12 1946 67 \n",
"361272 2014 53 1842 8 1 2 14 9 1926 88 \n",
"361564 2014 53 1907 4 1 4 20 2 1948 66 \n",
"361584 2014 53 1907 10 2 4 17 9 1947 67 \n",
"362016 2014 53 2016 15 1 4 27 4 1938 76 \n",
"362077 2014 53 2024 12 1 2 5 2 1942 72 \n",
"362078 2014 53 2024 12 2 4 3 3 1950 64 \n",
"362094 2014 53 2032 2 1 2 10 12 1951 62 \n",
"362105 2014 53 2032 7 1 4 23 4 1948 66 \n",
"362202 2014 53 2059 2 1 4 10 11 1937 76 \n",
"362239 2014 53 2059 14 2 2 2 7 1949 65 \n",
"\n",
"Coluna ... V4741 V4742 V4743 V4745 V4746 V4747 V4748 \\\n",
"51 ... 5 4.440000e+02 3 1 2 NaN NaN \n",
"203 ... 1 8.060000e+02 4 1 1 2 2 \n",
"232 ... 3 3.580000e+02 2 1 2 NaN 1 \n",
"347 ... 6 4.080000e+02 3 1 2 NaN NaN \n",
"624 ... 2 7.240000e+02 3 2 2 NaN NaN \n",
"1218 ... 2 7.240000e+02 3 2 2 NaN NaN \n",
"1234 ... 2 9.740000e+02 4 1 1 1 1 \n",
"1254 ... 2 7.320000e+02 4 2 2 NaN NaN \n",
"1286 ... 2 5.600000e+01 1 1 2 NaN NaN \n",
"1402 ... 2 3.350000e+03 6 1 2 NaN NaN \n",
"1594 ... 2 7.240000e+02 3 1 2 NaN NaN \n",
"1595 ... 2 7.240000e+02 3 1 2 NaN NaN \n",
"1657 ... 9 2.050000e+02 2 1 2 NaN NaN \n",
"1675 ... 2 1.224000e+03 4 2 1 1 1 \n",
"1761 ... 2 9.120000e+02 4 5 1 2 2 \n",
"1973 ... 4 1.793000e+03 5 2 2 NaN NaN \n",
"2004 ... 3 7.240000e+02 3 1 2 NaN NaN \n",
"2114 ... 3 4.820000e+02 3 1 1 1 1 \n",
"2242 ... 2 7.240000e+02 3 2 2 NaN NaN \n",
"2251 ... 3 4.820000e+02 3 1 2 NaN NaN \n",
"2276 ... 1 3.000000e+03 6 5 2 NaN NaN \n",
"2348 ... 1 3.000000e+02 2 1 2 NaN NaN \n",
"2766 ... 2 7.240000e+02 3 2 1 1 1 \n",
"2806 ... 2 7.250000e+02 4 2 2 NaN NaN \n",
"2966 ... 6 1.000000e+12 99 1 1 1 1 \n",
"2984 ... 2 1.086000e+03 4 2 1 1 1 \n",
"3156 ... 5 1.000000e+12 99 1 2 NaN NaN \n",
"3234 ... 2 3.620000e+02 2 1 2 NaN NaN \n",
"3397 ... 9 5.000000e+02 3 2 1 2 2 \n",
"3398 ... 9 5.000000e+02 3 1 2 NaN NaN \n",
"... ... ... ... ... ... ... ... ... \n",
"359993 ... 2 1.047000e+03 4 2 2 NaN NaN \n",
"360390 ... 6 3.000000e+02 2 2 2 NaN NaN \n",
"360421 ... 3 2.430000e+02 2 2 2 NaN NaN \n",
"360560 ... 8 9.120000e+02 4 1 2 NaN NaN \n",
"360571 ... 3 1.600000e+03 5 3 2 NaN NaN \n",
"360579 ... 2 8.000000e+02 4 3 2 NaN NaN \n",
"360589 ... 2 7.000000e+02 3 5 2 NaN NaN \n",
"360590 ... 2 7.000000e+02 3 1 2 NaN NaN \n",
"360703 ... 2 9.620000e+02 4 3 1 2 2 \n",
"360715 ... 2 7.240000e+02 3 2 2 NaN NaN \n",
"360750 ... 2 1.262000e+03 4 5 2 NaN NaN \n",
"360796 ... 3 1.813000e+03 5 3 1 2 2 \n",
"360802 ... 4 1.062000e+03 4 2 2 NaN NaN \n",
"360953 ... 2 1.362000e+03 4 5 2 NaN NaN \n",
"361069 ... 4 4.060000e+02 3 2 2 NaN NaN \n",
"361158 ... 1 2.800000e+03 6 1 2 NaN NaN \n",
"361169 ... 4 4.084000e+03 7 1 2 NaN NaN \n",
"361173 ... 6 1.000000e+12 99 2 2 NaN NaN \n",
"361191 ... 5 1.318000e+03 4 2 1 2 2 \n",
"361204 ... 6 1.487000e+03 5 1 2 NaN NaN \n",
"361272 ... 3 1.881000e+03 5 1 1 2 2 \n",
"361564 ... 4 6.120000e+02 3 3 2 NaN NaN \n",
"361584 ... 3 1.774000e+03 5 2 1 2 2 \n",
"362016 ... 4 8.120000e+02 4 2 2 NaN NaN \n",
"362077 ... 3 4.820000e+02 3 2 2 NaN NaN \n",
"362078 ... 3 4.820000e+02 3 2 2 NaN NaN \n",
"362094 ... 3 9.870000e+02 4 2 1 2 2 \n",
"362105 ... 2 1.267000e+03 4 5 2 NaN NaN \n",
"362202 ... 1 1.448000e+03 4 5 2 NaN NaN \n",
"362239 ... 5 1.800000e+02 1 3 2 NaN NaN \n",
"\n",
"Coluna V4749 V4750 V9993 \n",
"51 2 4.440000e+02 20160623 \n",
"203 1 8.060000e+02 20160623 \n",
"232 1 3.580000e+02 20160623 \n",
"347 2 7.240000e+02 20160623 \n",
"624 2 7.240000e+02 20160623 \n",
"1218 2 7.240000e+02 20160623 \n",
"1234 1 9.740000e+02 20160623 \n",
"1254 2 7.320000e+02 20160623 \n",
"1286 2 5.600000e+01 20160623 \n",
"1402 2 3.350000e+03 20160623 \n",
"1594 2 7.240000e+02 20160623 \n",
"1595 2 7.240000e+02 20160623 \n",
"1657 2 2.050000e+02 20160623 \n",
"1675 1 1.224000e+03 20160623 \n",
"1761 1 9.120000e+02 20160623 \n",
"1973 2 1.793000e+03 20160623 \n",
"2004 2 7.240000e+02 20160623 \n",
"2114 1 4.820000e+02 20160623 \n",
"2242 2 7.240000e+02 20160623 \n",
"2251 2 4.820000e+02 20160623 \n",
"2276 2 3.000000e+03 20160623 \n",
"2348 2 3.000000e+02 20160623 \n",
"2766 1 7.240000e+02 20160623 \n",
"2806 2 7.250000e+02 20160623 \n",
"2966 1 1.816000e+03 20160623 \n",
"2984 1 1.086000e+03 20160623 \n",
"3156 2 2.312000e+03 20160623 \n",
"3234 2 3.620000e+02 20160623 \n",
"3397 1 5.000000e+02 20160623 \n",
"3398 2 5.000000e+02 20160623 \n",
"... ... ... ... \n",
"359993 2 1.047000e+03 20160623 \n",
"360390 2 3.000000e+02 20160623 \n",
"360421 2 2.430000e+02 20160623 \n",
"360560 2 1.333000e+03 20160623 \n",
"360571 2 1.600000e+03 20160623 \n",
"360579 2 8.000000e+02 20160623 \n",
"360589 2 7.000000e+02 20160623 \n",
"360590 2 7.000000e+02 20160623 \n",
"360703 1 9.620000e+02 20160623 \n",
"360715 2 7.240000e+02 20160623 \n",
"360750 2 1.262000e+03 20160623 \n",
"360796 1 1.813000e+03 20160623 \n",
"360802 2 1.062000e+03 20160623 \n",
"360953 2 1.362000e+03 20160623 \n",
"361069 2 4.060000e+02 20160623 \n",
"361158 2 2.800000e+03 20160623 \n",
"361169 2 4.084000e+03 20160623 \n",
"361173 2 1.000000e+12 20160623 \n",
"361191 1 2.750000e+03 20160623 \n",
"361204 2 1.487000e+03 20160623 \n",
"361272 1 1.881000e+03 20160623 \n",
"361564 2 6.120000e+02 20160623 \n",
"361584 1 1.774000e+03 20160623 \n",
"362016 2 8.120000e+02 20160623 \n",
"362077 2 4.820000e+02 20160623 \n",
"362078 2 4.820000e+02 20160623 \n",
"362094 1 9.870000e+02 20160623 \n",
"362105 2 1.267000e+03 20160623 \n",
"362202 2 1.448000e+03 20160623 \n",
"362239 2 1.800000e+02 20160623 \n",
"\n",
"[4340 rows x 341 columns]"
]
},
"execution_count": 146,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad2014[(pnad2014.V0404 == 4)&(pnad2014.V8005>=60)] #Quantidade Negros"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Só metade dos negros conseguem pagar o inss e receber a aposentadoria "
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Coluna</th>\n",
" <th>V0101</th>\n",
" <th>UF</th>\n",
" <th>V0102</th>\n",
" <th>V0103</th>\n",
" <th>V0301</th>\n",
" <th>V0302</th>\n",
" <th>V3031</th>\n",
" <th>V3032</th>\n",
" <th>V3033</th>\n",
" <th>V8005</th>\n",
" <th>...</th>\n",
" <th>V4741</th>\n",
" <th>V4742</th>\n",
" <th>V4743</th>\n",
" <th>V4745</th>\n",
" <th>V4746</th>\n",
" <th>V4747</th>\n",
" <th>V4748</th>\n",
" <th>V4749</th>\n",
" <th>V4750</th>\n",
" <th>V9993</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1937</td>\n",
" <td>77</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>1954</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1.700000e+03</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1.700000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>22</td>\n",
" <td>3</td>\n",
" <td>1954</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>3.220000e+03</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3.220000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>10</td>\n",
" <td>1948</td>\n",
" <td>65</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2.362000e+03</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2.362000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>23</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>22</td>\n",
" <td>7</td>\n",
" <td>1941</td>\n",
" <td>73</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>3.620000e+02</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.620000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>23</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>1949</td>\n",
" <td>65</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2.862000e+03</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2.862000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>23</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>1951</td>\n",
" <td>63</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.321000e+03</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1.321000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>150</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>40</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>27</td>\n",
" <td>3</td>\n",
" <td>1952</td>\n",
" <td>62</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>2.560000e+02</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2.560000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>202</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>40</td>\n",
" <td>17</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1949</td>\n",
" <td>65</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>3.620000e+02</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.620000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>213</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>58</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>8</td>\n",
" <td>1925</td>\n",
" <td>89</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>7.930000e+02</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.930000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>257</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>66</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>19</td>\n",
" <td>5</td>\n",
" <td>1937</td>\n",
" <td>77</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.162000e+03</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1.162000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>267</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>66</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>15</td>\n",
" <td>7</td>\n",
" <td>1937</td>\n",
" <td>77</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>3.840000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.840000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>280</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>66</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" <td>1951</td>\n",
" <td>63</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.174000e+03</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1.174000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>312</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>66</td>\n",
" <td>18</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>1951</td>\n",
" <td>63</td>\n",
" <td>...</td>\n",
" <td>6</td>\n",
" <td>4.330000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>5.660000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>373</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>82</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>11</td>\n",
" <td>1950</td>\n",
" <td>63</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>374</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>82</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1945</td>\n",
" <td>69</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>9.100000e+02</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>9.100000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>405</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>82</td>\n",
" <td>15</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>24</td>\n",
" <td>8</td>\n",
" <td>1947</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>4.620000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4.620000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>409</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>90</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>29</td>\n",
" <td>6</td>\n",
" <td>1952</td>\n",
" <td>62</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>8.000000e+02</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>8.000000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>468</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>90</td>\n",
" <td>27</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>1946</td>\n",
" <td>68</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.174000e+03</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.174000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>470</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>90</td>\n",
" <td>28</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>14</td>\n",
" <td>7</td>\n",
" <td>1946</td>\n",
" <td>68</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>504</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>104</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>23</td>\n",
" <td>12</td>\n",
" <td>1930</td>\n",
" <td>83</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1.448000e+03</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.448000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>527</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>112</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" <td>1941</td>\n",
" <td>72</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>1.041000e+03</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1.041000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>560</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>120</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" <td>11</td>\n",
" <td>1941</td>\n",
" <td>72</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>583</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>139</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>20</td>\n",
" <td>8</td>\n",
" <td>1947</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.000000e+12</td>\n",
" <td>99</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.000000e+12</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>591</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>139</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>17</td>\n",
" <td>12</td>\n",
" <td>1947</td>\n",
" <td>66</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2.362000e+03</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>2.362000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>596</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>139</td>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>25</td>\n",
" <td>12</td>\n",
" <td>1946</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.000000e+03</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1.000000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>614</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>147</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>20</td>\n",
" <td>8</td>\n",
" <td>1937</td>\n",
" <td>77</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>9.620000e+02</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>9.620000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>615</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>147</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>23</td>\n",
" <td>10</td>\n",
" <td>1951</td>\n",
" <td>62</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>5.090000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>9.570000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>623</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>147</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>11</td>\n",
" <td>1944</td>\n",
" <td>69</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>646</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>155</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>17</td>\n",
" <td>3</td>\n",
" <td>1945</td>\n",
" <td>69</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.862000e+03</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.862000e+03</td>\n",
" <td>20160623</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",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\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>361804</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1974</td>\n",
" <td>12</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1935</td>\n",
" <td>79</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.000000e+12</td>\n",
" <td>99</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.000000e+12</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361806</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1974</td>\n",
" <td>15</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>22</td>\n",
" <td>9</td>\n",
" <td>1941</td>\n",
" <td>73</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>4.333000e+03</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>4.333000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361808</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1974</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>12</td>\n",
" <td>1928</td>\n",
" <td>85</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>6.000000e+03</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>6.000000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361822</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1982</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>1950</td>\n",
" <td>64</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>5.130000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>5.130000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361838</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1982</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>17</td>\n",
" <td>9</td>\n",
" <td>1952</td>\n",
" <td>62</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>7.940000e+02</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>7.940000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361888</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1990</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>18</td>\n",
" <td>1</td>\n",
" <td>1950</td>\n",
" <td>64</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>4.820000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4.820000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361904</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1990</td>\n",
" <td>11</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1930</td>\n",
" <td>84</td>\n",
" <td>...</td>\n",
" <td>6</td>\n",
" <td>4.820000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>4.820000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361909</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1990</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1950</td>\n",
" <td>64</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>1.950000e+03</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.950000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362006</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2016</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>24</td>\n",
" <td>9</td>\n",
" <td>1951</td>\n",
" <td>63</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>9.800000e+02</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>9.800000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362032</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2024</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>22</td>\n",
" <td>5</td>\n",
" <td>1922</td>\n",
" <td>92</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>6.880000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>6.880000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362063</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2024</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>1920</td>\n",
" <td>94</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>1.931000e+03</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.931000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362072</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2024</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>20</td>\n",
" <td>6</td>\n",
" <td>1948</td>\n",
" <td>66</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1.220000e+03</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.220000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362073</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2024</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>20</td>\n",
" <td>6</td>\n",
" <td>1950</td>\n",
" <td>64</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>1.220000e+03</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.220000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362120</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2032</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>1943</td>\n",
" <td>71</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>1.666000e+03</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.666000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362133</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2032</td>\n",
" <td>15</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1938</td>\n",
" <td>76</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>1.465000e+03</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.465000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362164</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2040</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>24</td>\n",
" <td>11</td>\n",
" <td>1951</td>\n",
" <td>62</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>7.490000e+02</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.490000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362165</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2040</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>27</td>\n",
" <td>3</td>\n",
" <td>1927</td>\n",
" <td>87</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>7.490000e+02</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.490000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362172</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2040</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>12</td>\n",
" <td>1953</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>1.071000e+03</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1.071000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362194</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2040</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>13</td>\n",
" <td>5</td>\n",
" <td>1946</td>\n",
" <td>68</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>3.000000e+03</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.000000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362218</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2059</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>26</td>\n",
" <td>5</td>\n",
" <td>1950</td>\n",
" <td>64</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>8.000000e+02</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>8.000000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362369</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2083</td>\n",
" <td>10</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>20</td>\n",
" <td>9</td>\n",
" <td>1931</td>\n",
" <td>83</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>1.431000e+03</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.086000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362441</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2105</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>1931</td>\n",
" <td>83</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>3.620000e+02</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.620000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362442</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2105</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>1941</td>\n",
" <td>73</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>3.620000e+02</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.620000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362448</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2105</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>20</td>\n",
" <td>7</td>\n",
" <td>1947</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>3.862000e+03</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3.862000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362450</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2105</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>20</td>\n",
" <td>9</td>\n",
" <td>1947</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>3.620000e+02</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.620000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362470</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2113</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>23</td>\n",
" <td>1</td>\n",
" <td>1952</td>\n",
" <td>62</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>3.306000e+03</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.306000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362484</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2113</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>1938</td>\n",
" <td>76</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>3.640000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.640000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362547</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2130</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>16</td>\n",
" <td>6</td>\n",
" <td>1947</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>8.600000e+03</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>8.600000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362549</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2130</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1944</td>\n",
" <td>70</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>6.600000e+03</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>6.600000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362555</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2130</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>20</td>\n",
" <td>11</td>\n",
" <td>1939</td>\n",
" <td>74</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.000000e+12</td>\n",
" <td>99</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1.000000e+12</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>23586 rows × 341 columns</p>\n",
"</div>"
],
"text/plain": [
"Coluna V0101 UF V0102 V0103 V0301 V0302 V3031 V3032 V3033 V8005 \\\n",
"5 2014 11 15 3 1 4 5 1 1937 77 \n",
"7 2014 11 15 4 2 2 7 3 1954 60 \n",
"18 2014 11 15 7 2 2 22 3 1954 60 \n",
"25 2014 11 15 10 1 2 15 10 1948 65 \n",
"62 2014 11 23 6 1 4 22 7 1941 73 \n",
"66 2014 11 23 8 1 2 7 8 1949 65 \n",
"87 2014 11 23 16 1 4 3 5 1951 63 \n",
"150 2014 11 40 2 1 2 27 3 1952 62 \n",
"202 2014 11 40 17 2 4 1 6 1949 65 \n",
"213 2014 11 58 5 3 2 15 8 1925 89 \n",
"257 2014 11 66 1 1 2 19 5 1937 77 \n",
"267 2014 11 66 5 1 4 15 7 1937 77 \n",
"280 2014 11 66 9 1 2 7 9 1951 63 \n",
"312 2014 11 66 18 1 2 2 7 1951 63 \n",
"373 2014 11 82 3 2 4 7 11 1950 63 \n",
"374 2014 11 82 4 1 2 3 3 1945 69 \n",
"405 2014 11 82 15 2 4 24 8 1947 67 \n",
"409 2014 11 90 2 1 2 29 6 1952 62 \n",
"468 2014 11 90 27 1 4 6 2 1946 68 \n",
"470 2014 11 90 28 1 4 14 7 1946 68 \n",
"504 2014 11 104 6 1 4 23 12 1930 83 \n",
"527 2014 11 112 4 1 2 9 10 1941 72 \n",
"560 2014 11 120 10 1 2 14 11 1941 72 \n",
"583 2014 11 139 7 2 4 20 8 1947 67 \n",
"591 2014 11 139 11 2 4 17 12 1947 66 \n",
"596 2014 11 139 13 1 2 25 12 1946 67 \n",
"614 2014 11 147 5 4 4 20 8 1937 77 \n",
"615 2014 11 147 6 1 2 23 10 1951 62 \n",
"623 2014 11 147 7 1 2 5 11 1944 69 \n",
"646 2014 11 155 7 1 2 17 3 1945 69 \n",
"... ... .. ... ... ... ... ... ... ... ... \n",
"361804 2014 53 1974 12 2 2 4 2 1935 79 \n",
"361806 2014 53 1974 15 2 4 22 9 1941 73 \n",
"361808 2014 53 1974 16 1 4 3 12 1928 85 \n",
"361822 2014 53 1982 4 1 2 3 7 1950 64 \n",
"361838 2014 53 1982 9 2 4 17 9 1952 62 \n",
"361888 2014 53 1990 8 1 4 18 1 1950 64 \n",
"361904 2014 53 1990 11 6 4 2 2 1930 84 \n",
"361909 2014 53 1990 15 1 2 3 2 1950 64 \n",
"362006 2014 53 2016 12 1 2 24 9 1951 63 \n",
"362032 2014 53 2024 2 5 4 22 5 1922 92 \n",
"362063 2014 53 2024 9 4 2 5 6 1920 94 \n",
"362072 2014 53 2024 11 1 2 20 6 1948 66 \n",
"362073 2014 53 2024 11 2 4 20 6 1950 64 \n",
"362120 2014 53 2032 11 1 2 3 4 1943 71 \n",
"362133 2014 53 2032 15 2 4 5 3 1938 76 \n",
"362164 2014 53 2040 9 1 4 24 11 1951 62 \n",
"362165 2014 53 2040 9 2 4 27 3 1927 87 \n",
"362172 2014 53 2040 11 1 2 1 12 1953 60 \n",
"362194 2014 53 2040 16 1 2 13 5 1946 68 \n",
"362218 2014 53 2059 9 1 4 26 5 1950 64 \n",
"362369 2014 53 2083 10 6 4 20 9 1931 83 \n",
"362441 2014 53 2105 3 1 2 5 3 1931 83 \n",
"362442 2014 53 2105 3 2 4 4 7 1941 73 \n",
"362448 2014 53 2105 7 1 4 20 7 1947 67 \n",
"362450 2014 53 2105 8 1 4 20 9 1947 67 \n",
"362470 2014 53 2113 2 1 2 23 1 1952 62 \n",
"362484 2014 53 2113 7 5 4 2 4 1938 76 \n",
"362547 2014 53 2130 5 1 4 16 6 1947 67 \n",
"362549 2014 53 2130 7 2 4 4 3 1944 70 \n",
"362555 2014 53 2130 9 1 4 20 11 1939 74 \n",
"\n",
"Coluna ... V4741 V4742 V4743 V4745 V4746 V4747 V4748 \\\n",
"5 ... 1 7.240000e+02 3 2 2 NaN NaN \n",
"7 ... 5 1.700000e+03 5 1 1 2 2 \n",
"18 ... 5 3.220000e+03 6 7 1 2 2 \n",
"25 ... 2 2.362000e+03 6 2 1 1 1 \n",
"62 ... 2 3.620000e+02 2 5 2 NaN NaN \n",
"66 ... 2 2.862000e+03 6 5 1 1 1 \n",
"87 ... 2 1.321000e+03 4 1 1 2 2 \n",
"150 ... 4 2.560000e+02 2 2 1 2 2 \n",
"202 ... 2 3.620000e+02 2 2 2 NaN NaN \n",
"213 ... 4 7.930000e+02 4 1 2 NaN NaN \n",
"257 ... 2 1.162000e+03 4 1 1 2 2 \n",
"267 ... 5 3.840000e+02 3 2 2 NaN NaN \n",
"280 ... 2 1.174000e+03 4 2 1 2 2 \n",
"312 ... 6 4.330000e+02 3 1 1 2 2 \n",
"373 ... 2 7.240000e+02 3 1 1 1 1 \n",
"374 ... 2 9.100000e+02 4 1 1 1 1 \n",
"405 ... 2 4.620000e+02 3 1 1 1 1 \n",
"409 ... 1 8.000000e+02 4 5 1 2 2 \n",
"468 ... 2 1.174000e+03 4 1 2 NaN NaN \n",
"470 ... 2 7.240000e+02 3 1 2 NaN NaN \n",
"504 ... 1 1.448000e+03 4 1 2 NaN NaN \n",
"527 ... 3 1.041000e+03 4 2 1 2 2 \n",
"560 ... 2 7.240000e+02 3 1 1 1 1 \n",
"583 ... 2 1.000000e+12 99 2 1 1 1 \n",
"591 ... 2 2.362000e+03 6 2 2 NaN NaN \n",
"596 ... 2 1.000000e+03 4 2 1 2 2 \n",
"614 ... 4 9.620000e+02 4 1 1 1 1 \n",
"615 ... 8 5.090000e+02 3 2 1 1 1 \n",
"623 ... 2 7.240000e+02 3 2 2 NaN 1 \n",
"646 ... 2 1.862000e+03 5 1 2 NaN NaN \n",
"... ... ... ... ... ... ... ... ... \n",
"361804 ... 2 1.000000e+12 99 7 2 NaN NaN \n",
"361806 ... 3 4.333000e+03 7 5 2 NaN NaN \n",
"361808 ... 3 6.000000e+03 7 7 2 NaN NaN \n",
"361822 ... 5 5.130000e+02 3 2 1 2 2 \n",
"361838 ... 5 7.940000e+02 4 2 1 2 2 \n",
"361888 ... 3 4.820000e+02 3 2 1 2 2 \n",
"361904 ... 6 4.820000e+02 3 2 2 NaN NaN \n",
"361909 ... 4 1.950000e+03 5 3 2 NaN NaN \n",
"362006 ... 5 9.800000e+02 4 2 1 2 2 \n",
"362032 ... 5 6.880000e+02 3 1 2 NaN NaN \n",
"362063 ... 4 1.931000e+03 5 2 2 NaN NaN \n",
"362072 ... 5 1.220000e+03 4 2 2 NaN NaN \n",
"362073 ... 5 1.220000e+03 4 2 2 NaN NaN \n",
"362120 ... 3 1.666000e+03 5 2 2 NaN NaN \n",
"362133 ... 3 1.465000e+03 5 2 2 NaN NaN \n",
"362164 ... 3 7.490000e+02 4 3 2 NaN NaN \n",
"362165 ... 3 7.490000e+02 4 2 2 NaN NaN \n",
"362172 ... 7 1.071000e+03 4 1 1 2 2 \n",
"362194 ... 1 3.000000e+03 6 5 2 NaN NaN \n",
"362218 ... 5 8.000000e+02 4 7 2 NaN NaN \n",
"362369 ... 7 1.431000e+03 4 2 2 NaN NaN \n",
"362441 ... 2 3.620000e+02 2 2 2 NaN NaN \n",
"362442 ... 2 3.620000e+02 2 2 2 NaN NaN \n",
"362448 ... 2 3.862000e+03 7 2 2 NaN 2 \n",
"362450 ... 2 3.620000e+02 2 2 2 NaN NaN \n",
"362470 ... 4 3.306000e+03 6 5 2 NaN NaN \n",
"362484 ... 5 3.640000e+02 3 2 2 NaN NaN \n",
"362547 ... 1 8.600000e+03 7 7 1 2 2 \n",
"362549 ... 2 6.600000e+03 7 5 2 NaN NaN \n",
"362555 ... 2 1.000000e+12 99 5 1 2 2 \n",
"\n",
"Coluna V4749 V4750 V9993 \n",
"5 2 7.240000e+02 20160623 \n",
"7 1 1.700000e+03 20160623 \n",
"18 1 3.220000e+03 20160623 \n",
"25 1 2.362000e+03 20160623 \n",
"62 2 3.620000e+02 20160623 \n",
"66 1 2.862000e+03 20160623 \n",
"87 1 1.321000e+03 20160623 \n",
"150 1 2.560000e+02 20160623 \n",
"202 2 3.620000e+02 20160623 \n",
"213 2 7.930000e+02 20160623 \n",
"257 1 1.162000e+03 20160623 \n",
"267 2 3.840000e+02 20160623 \n",
"280 1 1.174000e+03 20160623 \n",
"312 1 5.660000e+02 20160623 \n",
"373 1 7.240000e+02 20160623 \n",
"374 1 9.100000e+02 20160623 \n",
"405 1 4.620000e+02 20160623 \n",
"409 1 8.000000e+02 20160623 \n",
"468 2 1.174000e+03 20160623 \n",
"470 2 7.240000e+02 20160623 \n",
"504 2 1.448000e+03 20160623 \n",
"527 1 1.041000e+03 20160623 \n",
"560 1 7.240000e+02 20160623 \n",
"583 1 1.000000e+12 20160623 \n",
"591 2 2.362000e+03 20160623 \n",
"596 1 1.000000e+03 20160623 \n",
"614 1 9.620000e+02 20160623 \n",
"615 1 9.570000e+02 20160623 \n",
"623 1 7.240000e+02 20160623 \n",
"646 2 1.862000e+03 20160623 \n",
"... ... ... ... \n",
"361804 2 1.000000e+12 20160623 \n",
"361806 2 4.333000e+03 20160623 \n",
"361808 2 6.000000e+03 20160623 \n",
"361822 1 5.130000e+02 20160623 \n",
"361838 1 7.940000e+02 20160623 \n",
"361888 1 4.820000e+02 20160623 \n",
"361904 2 4.820000e+02 20160623 \n",
"361909 2 1.950000e+03 20160623 \n",
"362006 1 9.800000e+02 20160623 \n",
"362032 2 6.880000e+02 20160623 \n",
"362063 2 1.931000e+03 20160623 \n",
"362072 2 1.220000e+03 20160623 \n",
"362073 2 1.220000e+03 20160623 \n",
"362120 2 1.666000e+03 20160623 \n",
"362133 2 1.465000e+03 20160623 \n",
"362164 2 7.490000e+02 20160623 \n",
"362165 2 7.490000e+02 20160623 \n",
"362172 1 1.071000e+03 20160623 \n",
"362194 2 3.000000e+03 20160623 \n",
"362218 2 8.000000e+02 20160623 \n",
"362369 2 1.086000e+03 20160623 \n",
"362441 2 3.620000e+02 20160623 \n",
"362442 2 3.620000e+02 20160623 \n",
"362448 1 3.862000e+03 20160623 \n",
"362450 2 3.620000e+02 20160623 \n",
"362470 2 3.306000e+03 20160623 \n",
"362484 2 3.640000e+02 20160623 \n",
"362547 1 8.600000e+03 20160623 \n",
"362549 2 6.600000e+03 20160623 \n",
"362555 1 1.000000e+12 20160623 \n",
"\n",
"[23586 rows x 341 columns]"
]
},
"execution_count": 147,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad2014[(pnad2014.V0404 == 2)&(pnad2014.V8005>=60)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Menos da metade dos brancos conseguem pagar o inss e receber a aposentadoria"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Coluna</th>\n",
" <th>V0101</th>\n",
" <th>UF</th>\n",
" <th>V0102</th>\n",
" <th>V0103</th>\n",
" <th>V0301</th>\n",
" <th>V0302</th>\n",
" <th>V3031</th>\n",
" <th>V3032</th>\n",
" <th>V3033</th>\n",
" <th>V8005</th>\n",
" <th>...</th>\n",
" <th>V4741</th>\n",
" <th>V4742</th>\n",
" <th>V4743</th>\n",
" <th>V4745</th>\n",
" <th>V4746</th>\n",
" <th>V4747</th>\n",
" <th>V4748</th>\n",
" <th>V4749</th>\n",
" <th>V4750</th>\n",
" <th>V9993</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>13</td>\n",
" <td>7</td>\n",
" <td>1946</td>\n",
" <td>68</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>8.550000e+02</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>8.550000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>20</td>\n",
" <td>11</td>\n",
" <td>1953</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>8.500000e+02</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>8.500000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>15</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>24</td>\n",
" <td>6</td>\n",
" <td>1952</td>\n",
" <td>62</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2.362000e+03</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>2.362000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>23</td>\n",
" <td>16</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>17</td>\n",
" <td>5</td>\n",
" <td>1952</td>\n",
" <td>62</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.321000e+03</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.321000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>31</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>71</td>\n",
" <td>71</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.620000e+02</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>7.620000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>31</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>11</td>\n",
" <td>8</td>\n",
" <td>1947</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>40</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>30</td>\n",
" <td>12</td>\n",
" <td>1950</td>\n",
" <td>63</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>8.740000e+02</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>8.740000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>184</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>40</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>1946</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>8.740000e+02</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>8.740000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>197</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>40</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1945</td>\n",
" <td>69</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>3.620000e+02</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.620000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>281</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>66</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>15</td>\n",
" <td>2</td>\n",
" <td>1954</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.174000e+03</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.174000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>348</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>74</td>\n",
" <td>10</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>27</td>\n",
" <td>3</td>\n",
" <td>1951</td>\n",
" <td>63</td>\n",
" <td>...</td>\n",
" <td>6</td>\n",
" <td>4.080000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>349</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>74</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>30</td>\n",
" <td>8</td>\n",
" <td>1947</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>74</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1953</td>\n",
" <td>61</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>7.440000e+02</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.440000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>372</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>82</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" <td>3</td>\n",
" <td>1948</td>\n",
" <td>66</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>375</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>82</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>16</td>\n",
" <td>5</td>\n",
" <td>1953</td>\n",
" <td>61</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>9.100000e+02</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>9.100000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>382</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>82</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>1946</td>\n",
" <td>68</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.274000e+03</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.274000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>383</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>82</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>28</td>\n",
" <td>5</td>\n",
" <td>1951</td>\n",
" <td>63</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.274000e+03</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.274000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>498</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>104</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>1940</td>\n",
" <td>73</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1.448000e+03</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.448000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>510</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>104</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>8</td>\n",
" <td>1950</td>\n",
" <td>64</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>9.650000e+02</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>9.650000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>528</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>112</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1951</td>\n",
" <td>63</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>1.041000e+03</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.041000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>537</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>112</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>1947</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1.084000e+03</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1.084000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>550</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>120</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>25</td>\n",
" <td>5</td>\n",
" <td>1936</td>\n",
" <td>78</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.000000e+12</td>\n",
" <td>99</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.000000e+12</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>554</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>120</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>19</td>\n",
" <td>11</td>\n",
" <td>1945</td>\n",
" <td>68</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>561</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>120</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>15</td>\n",
" <td>9</td>\n",
" <td>1945</td>\n",
" <td>69</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>582</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>139</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>12</td>\n",
" <td>4</td>\n",
" <td>1952</td>\n",
" <td>62</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.000000e+12</td>\n",
" <td>99</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.000000e+12</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>601</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>147</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>16</td>\n",
" <td>10</td>\n",
" <td>1941</td>\n",
" <td>72</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>6.740000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6.740000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>617</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>147</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" <td>1935</td>\n",
" <td>79</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>5.090000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>9.570000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>625</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>147</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>25</td>\n",
" <td>10</td>\n",
" <td>1949</td>\n",
" <td>64</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>4.462000e+03</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4.462000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>638</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>155</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>1945</td>\n",
" <td>68</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>1.316000e+03</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.316000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>639</th>\n",
" <td>2014</td>\n",
" <td>11</td>\n",
" <td>155</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>28</td>\n",
" <td>7</td>\n",
" <td>1946</td>\n",
" <td>68</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>1.316000e+03</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.316000e+03</td>\n",
" <td>20160623</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",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\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>361554</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1893</td>\n",
" <td>15</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>10</td>\n",
" <td>1938</td>\n",
" <td>75</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.000000e+12</td>\n",
" <td>99</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.000000e+12</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361565</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1907</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>1948</td>\n",
" <td>66</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>6.120000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>6.120000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361583</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1907</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>21</td>\n",
" <td>4</td>\n",
" <td>1942</td>\n",
" <td>72</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>1.774000e+03</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.774000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361611</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1915</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>18</td>\n",
" <td>6</td>\n",
" <td>1951</td>\n",
" <td>63</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.000000e+12</td>\n",
" <td>99</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.000000e+12</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361660</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1923</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>24</td>\n",
" <td>12</td>\n",
" <td>1948</td>\n",
" <td>65</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>2.600000e+04</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>2.600000e+04</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361667</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1923</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1943</td>\n",
" <td>71</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.100000e+04</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.100000e+04</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361715</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1931</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1951</td>\n",
" <td>63</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>5.120000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>5.120000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361762</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1966</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>14</td>\n",
" <td>4</td>\n",
" <td>1943</td>\n",
" <td>71</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>1.050000e+04</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1.050000e+04</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361805</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1974</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>8</td>\n",
" <td>1938</td>\n",
" <td>76</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>4.333000e+03</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>4.333000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361834</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1982</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>27</td>\n",
" <td>11</td>\n",
" <td>1952</td>\n",
" <td>61</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>9.500000e+02</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>9.500000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361837</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1982</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>17</td>\n",
" <td>10</td>\n",
" <td>1946</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>7.940000e+02</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.940000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361867</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>1990</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>1954</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>3.890000e+02</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.890000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361974</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2016</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>25</td>\n",
" <td>7</td>\n",
" <td>1948</td>\n",
" <td>66</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>9.050000e+02</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>9.050000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361978</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2016</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>22</td>\n",
" <td>9</td>\n",
" <td>1922</td>\n",
" <td>92</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1.448000e+03</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.448000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361982</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2016</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>1954</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361994</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2016</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>13</td>\n",
" <td>12</td>\n",
" <td>1951</td>\n",
" <td>62</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1.357000e+03</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1.357000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362025</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2024</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>26</td>\n",
" <td>5</td>\n",
" <td>1945</td>\n",
" <td>69</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>7.240000e+02</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>7.240000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362035</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2024</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>1954</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>6</td>\n",
" <td>1.659000e+03</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1.659000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362036</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2024</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1953</td>\n",
" <td>61</td>\n",
" <td>...</td>\n",
" <td>6</td>\n",
" <td>1.659000e+03</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.659000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362046</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2024</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>17</td>\n",
" <td>12</td>\n",
" <td>1931</td>\n",
" <td>82</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>2.710000e+02</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.620000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362108</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2032</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>30</td>\n",
" <td>1</td>\n",
" <td>1954</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>1.629000e+03</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1.629000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362205</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2059</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>15</td>\n",
" <td>11</td>\n",
" <td>1935</td>\n",
" <td>78</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>9.450000e+02</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>9.450000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362215</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2059</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>26</td>\n",
" <td>1</td>\n",
" <td>1954</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>5.400000e+02</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>5.400000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362344</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2083</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>20</td>\n",
" <td>9</td>\n",
" <td>1944</td>\n",
" <td>70</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>3.291000e+03</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.291000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362357</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2083</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>31</td>\n",
" <td>8</td>\n",
" <td>1947</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>3.500000e+02</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.500000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362384</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2083</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>1944</td>\n",
" <td>70</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>3.410000e+02</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.410000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362517</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2121</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>7</td>\n",
" <td>1953</td>\n",
" <td>61</td>\n",
" <td>...</td>\n",
" <td>6</td>\n",
" <td>3.000000e+02</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.000000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362518</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2121</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>20</td>\n",
" <td>4</td>\n",
" <td>1953</td>\n",
" <td>61</td>\n",
" <td>...</td>\n",
" <td>6</td>\n",
" <td>3.000000e+02</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3.000000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362548</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2130</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>1941</td>\n",
" <td>73</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>6.600000e+03</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6.600000e+03</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362609</th>\n",
" <td>2014</td>\n",
" <td>53</td>\n",
" <td>2148</td>\n",
" <td>12</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>24</td>\n",
" <td>12</td>\n",
" <td>1953</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>4.420000e+02</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4.420000e+02</td>\n",
" <td>20160623</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>19130 rows × 341 columns</p>\n",
"</div>"
],
"text/plain": [
"Coluna V0101 UF V0102 V0103 V0301 V0302 V3031 V3032 V3033 V8005 \\\n",
"11 2014 11 15 5 1 4 13 7 1946 68 \n",
"15 2014 11 15 6 1 2 20 11 1953 60 \n",
"26 2014 11 15 10 2 4 24 6 1952 62 \n",
"88 2014 11 23 16 2 2 17 5 1952 62 \n",
"98 2014 11 31 2 1 2 0 20 71 71 \n",
"103 2014 11 31 4 1 2 11 8 1947 67 \n",
"183 2014 11 40 11 1 2 30 12 1950 63 \n",
"184 2014 11 40 11 2 4 10 10 1946 67 \n",
"197 2014 11 40 16 1 4 5 1 1945 69 \n",
"281 2014 11 66 9 2 4 15 2 1954 60 \n",
"348 2014 11 74 10 6 4 27 3 1951 63 \n",
"349 2014 11 74 11 1 4 30 8 1947 67 \n",
"362 2014 11 74 16 1 4 1 5 1953 61 \n",
"372 2014 11 82 3 1 2 14 3 1948 66 \n",
"375 2014 11 82 4 2 4 16 5 1953 61 \n",
"382 2014 11 82 7 1 2 14 1 1946 68 \n",
"383 2014 11 82 7 2 4 28 5 1951 63 \n",
"498 2014 11 104 3 1 4 6 12 1940 73 \n",
"510 2014 11 104 8 1 2 15 8 1950 64 \n",
"528 2014 11 112 4 2 4 1 5 1951 63 \n",
"537 2014 11 112 6 1 2 9 1 1947 67 \n",
"550 2014 11 120 4 2 4 25 5 1936 78 \n",
"554 2014 11 120 8 1 2 19 11 1945 68 \n",
"561 2014 11 120 10 2 4 15 9 1945 69 \n",
"582 2014 11 139 7 1 2 12 4 1952 62 \n",
"601 2014 11 147 2 1 2 16 10 1941 72 \n",
"617 2014 11 147 6 3 4 7 9 1935 79 \n",
"625 2014 11 147 8 1 2 25 10 1949 64 \n",
"638 2014 11 155 3 1 2 10 10 1945 68 \n",
"639 2014 11 155 3 2 4 28 7 1946 68 \n",
"... ... .. ... ... ... ... ... ... ... ... \n",
"361554 2014 53 1893 15 2 2 8 10 1938 75 \n",
"361565 2014 53 1907 4 2 2 6 3 1948 66 \n",
"361583 2014 53 1907 10 1 2 21 4 1942 72 \n",
"361611 2014 53 1915 4 1 2 18 6 1951 63 \n",
"361660 2014 53 1923 7 1 4 24 12 1948 65 \n",
"361667 2014 53 1923 9 1 2 1 5 1943 71 \n",
"361715 2014 53 1931 7 1 2 3 2 1951 63 \n",
"361762 2014 53 1966 4 1 4 14 4 1943 71 \n",
"361805 2014 53 1974 15 1 2 10 8 1938 76 \n",
"361834 2014 53 1982 8 2 4 27 11 1952 61 \n",
"361837 2014 53 1982 9 1 2 17 10 1946 67 \n",
"361867 2014 53 1990 1 1 2 5 7 1954 60 \n",
"361974 2014 53 2016 2 1 2 25 7 1948 66 \n",
"361978 2014 53 2016 3 1 4 22 9 1922 92 \n",
"361982 2014 53 2016 5 1 4 2 8 1954 60 \n",
"361994 2014 53 2016 8 2 4 13 12 1951 62 \n",
"362025 2014 53 2024 1 2 4 26 5 1945 69 \n",
"362035 2014 53 2024 4 1 2 5 4 1954 60 \n",
"362036 2014 53 2024 4 2 4 3 1 1953 61 \n",
"362046 2014 53 2024 5 6 2 17 12 1931 82 \n",
"362108 2014 53 2032 8 2 4 30 1 1954 60 \n",
"362205 2014 53 2059 4 2 4 15 11 1935 78 \n",
"362215 2014 53 2059 7 2 4 26 1 1954 60 \n",
"362344 2014 53 2083 3 2 4 20 9 1944 70 \n",
"362357 2014 53 2083 8 1 2 31 8 1947 67 \n",
"362384 2014 53 2083 15 1 4 3 5 1944 70 \n",
"362517 2014 53 2121 2 5 2 9 7 1953 61 \n",
"362518 2014 53 2121 2 6 4 20 4 1953 61 \n",
"362548 2014 53 2130 7 1 2 3 7 1941 73 \n",
"362609 2014 53 2148 12 1 2 24 12 1953 60 \n",
"\n",
"Coluna ... V4741 V4742 V4743 V4745 V4746 V4747 V4748 \\\n",
"11 ... 4 8.550000e+02 4 2 2 NaN NaN \n",
"15 ... 2 8.500000e+02 4 2 1 2 2 \n",
"26 ... 2 2.362000e+03 6 5 2 NaN NaN \n",
"88 ... 2 1.321000e+03 4 2 2 NaN NaN \n",
"98 ... 2 7.620000e+02 4 2 1 2 2 \n",
"103 ... 1 7.240000e+02 3 1 2 NaN NaN \n",
"183 ... 3 8.740000e+02 4 2 1 1 1 \n",
"184 ... 3 8.740000e+02 4 1 1 2 2 \n",
"197 ... 4 3.620000e+02 2 1 2 NaN NaN \n",
"281 ... 2 1.174000e+03 4 2 2 NaN NaN \n",
"348 ... 6 4.080000e+02 3 1 2 NaN NaN \n",
"349 ... 1 7.240000e+02 3 2 2 NaN NaN \n",
"362 ... 5 7.440000e+02 4 2 2 NaN NaN \n",
"372 ... 2 7.240000e+02 3 1 1 1 1 \n",
"375 ... 2 9.100000e+02 4 2 2 NaN NaN \n",
"382 ... 2 1.274000e+03 4 1 1 1 1 \n",
"383 ... 2 1.274000e+03 4 2 1 1 1 \n",
"498 ... 1 1.448000e+03 4 1 2 NaN NaN \n",
"510 ... 3 9.650000e+02 4 1 1 2 2 \n",
"528 ... 3 1.041000e+03 4 2 2 NaN NaN \n",
"537 ... 1 1.084000e+03 4 3 1 2 2 \n",
"550 ... 2 1.000000e+12 99 1 1 1 1 \n",
"554 ... 1 7.240000e+02 3 1 1 1 1 \n",
"561 ... 2 7.240000e+02 3 2 2 NaN NaN \n",
"582 ... 2 1.000000e+12 99 3 1 1 1 \n",
"601 ... 3 6.740000e+02 3 2 1 1 1 \n",
"617 ... 8 5.090000e+02 3 2 2 NaN NaN \n",
"625 ... 2 4.462000e+03 7 2 1 1 1 \n",
"638 ... 3 1.316000e+03 4 5 2 NaN NaN \n",
"639 ... 3 1.316000e+03 4 7 2 NaN NaN \n",
"... ... ... ... ... ... ... ... ... \n",
"361554 ... 2 1.000000e+12 99 1 2 NaN NaN \n",
"361565 ... 4 6.120000e+02 3 1 2 NaN NaN \n",
"361583 ... 3 1.774000e+03 5 2 2 NaN NaN \n",
"361611 ... 2 1.000000e+12 99 5 2 NaN NaN \n",
"361660 ... 3 2.600000e+04 7 7 2 NaN NaN \n",
"361667 ... 2 1.100000e+04 7 7 2 NaN NaN \n",
"361715 ... 5 5.120000e+02 3 2 2 NaN NaN \n",
"361762 ... 3 1.050000e+04 7 7 1 2 2 \n",
"361805 ... 3 4.333000e+03 7 7 2 NaN NaN \n",
"361834 ... 4 9.500000e+02 4 2 2 NaN NaN \n",
"361837 ... 5 7.940000e+02 4 2 2 NaN NaN \n",
"361867 ... 3 3.890000e+02 3 3 2 NaN NaN \n",
"361974 ... 4 9.050000e+02 4 2 2 NaN NaN \n",
"361978 ... 1 1.448000e+03 4 1 2 NaN NaN \n",
"361982 ... 1 7.240000e+02 3 2 2 NaN NaN \n",
"361994 ... 2 1.357000e+03 4 2 1 2 2 \n",
"362025 ... 4 7.240000e+02 3 2 2 NaN NaN \n",
"362035 ... 6 1.659000e+03 5 2 1 2 2 \n",
"362036 ... 6 1.659000e+03 5 2 2 NaN NaN \n",
"362046 ... 8 2.710000e+02 2 2 2 NaN NaN \n",
"362108 ... 4 1.629000e+03 5 5 2 NaN NaN \n",
"362205 ... 2 9.450000e+02 4 2 2 NaN NaN \n",
"362215 ... 4 5.400000e+02 3 3 1 2 2 \n",
"362344 ... 3 3.291000e+03 6 1 2 NaN NaN \n",
"362357 ... 4 3.500000e+02 2 2 2 NaN NaN \n",
"362384 ... 3 3.410000e+02 2 1 2 NaN NaN \n",
"362517 ... 6 3.000000e+02 2 1 2 NaN NaN \n",
"362518 ... 6 3.000000e+02 2 2 2 NaN NaN \n",
"362548 ... 2 6.600000e+03 7 7 1 2 2 \n",
"362609 ... 7 4.420000e+02 3 1 1 2 2 \n",
"\n",
"Coluna V4749 V4750 V9993 \n",
"11 2 8.550000e+02 20160623 \n",
"15 1 8.500000e+02 20160623 \n",
"26 2 2.362000e+03 20160623 \n",
"88 2 1.321000e+03 20160623 \n",
"98 1 7.620000e+02 20160623 \n",
"103 2 7.240000e+02 20160623 \n",
"183 1 8.740000e+02 20160623 \n",
"184 1 8.740000e+02 20160623 \n",
"197 2 3.620000e+02 20160623 \n",
"281 2 1.174000e+03 20160623 \n",
"348 2 7.240000e+02 20160623 \n",
"349 2 7.240000e+02 20160623 \n",
"362 2 7.440000e+02 20160623 \n",
"372 1 7.240000e+02 20160623 \n",
"375 2 9.100000e+02 20160623 \n",
"382 1 1.274000e+03 20160623 \n",
"383 1 1.274000e+03 20160623 \n",
"498 2 1.448000e+03 20160623 \n",
"510 1 9.650000e+02 20160623 \n",
"528 2 1.041000e+03 20160623 \n",
"537 1 1.084000e+03 20160623 \n",
"550 1 1.000000e+12 20160623 \n",
"554 1 7.240000e+02 20160623 \n",
"561 2 7.240000e+02 20160623 \n",
"582 1 1.000000e+12 20160623 \n",
"601 1 6.740000e+02 20160623 \n",
"617 2 9.570000e+02 20160623 \n",
"625 1 4.462000e+03 20160623 \n",
"638 2 1.316000e+03 20160623 \n",
"639 2 1.316000e+03 20160623 \n",
"... ... ... ... \n",
"361554 2 1.000000e+12 20160623 \n",
"361565 2 6.120000e+02 20160623 \n",
"361583 2 1.774000e+03 20160623 \n",
"361611 2 1.000000e+12 20160623 \n",
"361660 2 2.600000e+04 20160623 \n",
"361667 2 1.100000e+04 20160623 \n",
"361715 2 5.120000e+02 20160623 \n",
"361762 1 1.050000e+04 20160623 \n",
"361805 2 4.333000e+03 20160623 \n",
"361834 2 9.500000e+02 20160623 \n",
"361837 2 7.940000e+02 20160623 \n",
"361867 2 3.890000e+02 20160623 \n",
"361974 2 9.050000e+02 20160623 \n",
"361978 2 1.448000e+03 20160623 \n",
"361982 2 7.240000e+02 20160623 \n",
"361994 1 1.357000e+03 20160623 \n",
"362025 2 7.240000e+02 20160623 \n",
"362035 1 1.659000e+03 20160623 \n",
"362036 2 1.659000e+03 20160623 \n",
"362046 2 3.620000e+02 20160623 \n",
"362108 2 1.629000e+03 20160623 \n",
"362205 2 9.450000e+02 20160623 \n",
"362215 1 5.400000e+02 20160623 \n",
"362344 2 3.291000e+03 20160623 \n",
"362357 2 3.500000e+02 20160623 \n",
"362384 2 3.410000e+02 20160623 \n",
"362517 2 3.000000e+02 20160623 \n",
"362518 2 3.000000e+02 20160623 \n",
"362548 1 6.600000e+03 20160623 \n",
"362609 1 4.420000e+02 20160623 \n",
"\n",
"[19130 rows x 341 columns]"
]
},
"execution_count": 148,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad2014[(pnad2014.V0404 == 8)&(pnad2014.V8005>=60)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Quase metade dos pardos conseguem pagar o inss e receber a aposentadoria "
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Qual a escolaridade desses aposentados homens"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pnad2014.Educação = pnad2014[(pnad2014.V9122 == 2)&(pnad2014.V0302 == 2)&(pnad2014.V6007 >= 1)&(pnad2014.V6007 < 10)].V6007.astype('category')"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pnad2014.Educação.cat.categories = ('Elementar','Médio 1° ciclo', 'Médio 2° ciclo', 'Ensino Fundamental', 'Ensino Médio',\n",
" 'Supletivo 1° grau', 'Supletivo 2° grau', 'Superior', 'Mestrado ou Doutorado')"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x25016722438>"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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Ce9j+8DDn9BjiGrd3FlUBj9OrRUTUQxK2NbR91KEb25+yvantLYF3ApfZfg9wPvDestvB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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2501667a400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"barraEdu = pnad2014.Educação.value_counts()\n",
"barraEdu.plot(kind='bar', color=('red', 'black'), legend=False)\n",
"plt.title(\"Escolaridade\")"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Qual a relaçao de escolaridade com raça entre esses aposentados homens no Brasil"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Branca 50.328688\n",
"Parda 39.547725\n",
"Preta 9.261110\n",
"Amarela 0.525901\n",
"Indígena 0.336576\n",
"dtype: float64"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pnad2014.Educação.value_counts(True)*100\n",
"pnad2014.Raca.value_counts(True)*100"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>V6007</th>\n",
" <th>Elementar</th>\n",
" <th>Médio 1° ciclo</th>\n",
" <th>Médio 2° ciclo</th>\n",
" <th>Ensino Fundamental</th>\n",
" <th>Ensino Médio</th>\n",
" <th>Supletivo 1° grau</th>\n",
" <th>Supletivo 2° grau</th>\n",
" <th>Superior</th>\n",
" <th>Mestrado ou Doutorado</th>\n",
" </tr>\n",
" <tr>\n",
" <th>V0404</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>Indígena</th>\n",
" <td>16</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Branca</th>\n",
" <td>3835</td>\n",
" <td>809</td>\n",
" <td>638</td>\n",
" <td>704</td>\n",
" <td>863</td>\n",
" <td>81</td>\n",
" <td>106</td>\n",
" <td>1373</td>\n",
" <td>92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Preta</th>\n",
" <td>627</td>\n",
" <td>152</td>\n",
" <td>66</td>\n",
" <td>198</td>\n",
" <td>148</td>\n",
" <td>23</td>\n",
" <td>24</td>\n",
" <td>91</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Amarela</th>\n",
" <td>35</td>\n",
" <td>12</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>24</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Parda</th>\n",
" <td>3008</td>\n",
" <td>478</td>\n",
" <td>326</td>\n",
" <td>683</td>\n",
" <td>495</td>\n",
" <td>93</td>\n",
" <td>74</td>\n",
" <td>349</td>\n",
" <td>21</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"V6007 Elementar Médio 1° ciclo Médio 2° ciclo Ensino Fundamental \\\n",
"V0404 \n",
"Indígena 16 7 3 5 \n",
"Branca 3835 809 638 704 \n",
"Preta 627 152 66 198 \n",
"Amarela 35 12 5 3 \n",
"Parda 3008 478 326 683 \n",
"\n",
"V6007 Ensino Médio Supletivo 1° grau Supletivo 2° grau Superior \\\n",
"V0404 \n",
"Indígena 1 0 1 6 \n",
"Branca 863 81 106 1373 \n",
"Preta 148 23 24 91 \n",
"Amarela 6 0 1 24 \n",
"Parda 495 93 74 349 \n",
"\n",
"V6007 Mestrado ou Doutorado \n",
"V0404 \n",
"Indígena 1 \n",
"Branca 92 \n",
"Preta 5 \n",
"Amarela 4 \n",
"Parda 21 "
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ct = pd.crosstab(pnad2014.Raca, pnad2014.Educação)\n",
"ct"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Diferenças nas educações entre diferentes raças em sp"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x2500278b518>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Y2lplJQSDNZtfsVGKO3l/g5s+LfZ9VLXKawMYJyJluHNoQ72XrgWeFpE/4BLA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\nMcZgycAYYwyWDIwxxmDJwBhjDPD/ATsDpvdDV0yTAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2500274e9b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"raca_sp = pnad2014.Raca[pnad2014.Regioes == 'São Paulo'].value_counts()\n",
"raca_sp.plot(kind='pie',autopct=\"%.2f\",legend=False)\n",
"plt.title(\"Raça de aposentados em SP\")"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x250027f9048>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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U5/clHtVMQUkp87cp6nQuQgFC++vdVViLUITQ7v3Sq7KCAJ0F7YhRCCwmwqZ3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\nDAaDIUb+H6ofATHM1CTkAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2500277d860>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"edu_sp1 = pnad2014.Educação[pnad2014.Raca == 'Branca'].value_counts(True)*100\n",
"edu_sp1.plot(kind='pie', autopct=\"%.2f\",legend = False)\n",
"plt.title(\"Educação aos Brancos em SP\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x2500287d668>"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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BWODsLFdi2TCay2JgchVMrgbeh1UXqOpHYVtlGGFjYsFoN4jIFtDlaqg+BM6OwPnpsEHY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\nGGNMuSxZMMYYY0y5/h9FbfQDfvWfIAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x250027eab38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"edu_sp2 = pnad2014.Educação[pnad2014.Raca == 'Preta'].value_counts(True)*100\n",
"edu_sp2.plot(kind='pie', autopct=\"%.2f\",legend = False)\n",
"plt.title(\"Educação aos Pretos em SP\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x25002905c50>"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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IIb6J7lfYJtIEKZqNqMUQQfyzHx4S0cNAZklmvU+S6RhR1jokEUk0b3vjTpGO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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x25002875a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"edu_sp3 = pnad2014.Educação[pnad2014.Raca == 'Parda'].value_counts(True)*100\n",
"edu_sp3.plot(kind='pie', autopct=\"%.2f\",legend = False)\n",
"plt.title(\"Educação aos Pardos em SP\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pnad2014.Renda = pnad2014[(pnad2014.V9122 == 2)&(pnad2014.V0302 == 2)&(pnad2014.V0404 == 2)&(pnad2014.UF==35)&(pnad2014.V4743>=0)&(pnad2014.V4743<=7)].V4743.astype('category')"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pnad2014.Renda.cat.categories = ('Até ¼ salário mínimo', 'Mais de ¼ até ½ salário mínimo', \n",
" 'Mais de ½ até 1 salário mínimo', 'Mais de 1 até 2 salários mínimos',\n",
" 'Mais de 2 até 3 salários mínimos','Mais de 3 até 5 salários mínimos',\n",
" 'Mais de 5 salários mínimos')"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x25025879fd0>"
]
},
"execution_count": 200,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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INyKOIc3a9u8mAhqlEyGNmouIR/K+XzcYT6dOUZ63PdLkRABI2gD4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\nVgkndDOzSvx/ifTNpQobIyMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x250257b36a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"barraRendaP = pnad2014.Renda.value_counts()\n",
"barraRendaP.plot(kind='bar', color=('white'), legend=False)\n",
"plt.title(\"Renda aposentados brancos\")"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pnad2014.Renda2 = pnad2014[(pnad2014.V9122 == 2)&(pnad2014.V0302 == 2)&(pnad2014.V0404==4)&(pnad2014.UF==35)&(pnad2014.V4743>=0)&(pnad2014.V4743<=7)].V4743.astype('category')\n"
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pnad2014.Renda2.cat.categories = ('Até ¼ salário mínimo', 'Mais de ¼ até ½ salário mínimo', \n",
" 'Mais de ½ até 1 salário mínimo', 'Mais de 1 até 2 salários mínimos',\n",
" 'Mais de 2 até 3 salários mínimos','Mais de 3 até 5 salários mínimos',\n",
" 'Mais de 5 salários mínimos')"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x250256a27f0>"
]
},
"execution_count": 191,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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R3eTpVvb8ZtYfJBERixWUjrpHIuLhend5YBkggJ2Bo+vxo4FdpiCnmZmNoaOiLWkpSZcD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\ns0T+P0uLLurXIoxHAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2502566e748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"barraRendaB = pnad2014.Renda2.value_counts()\n",
"barraRendaB.plot(kind='bar', color=('black'), legend=False)\n",
"plt.title(\"Renda aposentados pretos\")"
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pnad2014.Renda3 = pnad2014[(pnad2014.V9122 == 2)&(pnad2014.V0302 == 2)&(pnad2014.V0404==8)&(pnad2014.UF==35)&(pnad2014.V4743>=1)&(pnad2014.V4743<=7)].V4743.astype('category')\n"
]
},
{
"cell_type": "code",
"execution_count": 214,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pnad2014.Renda3.cat.categories = ( 'Mais de ¼ até ½ salário mínimo', \n",
" 'Mais de ½ até 1 salário mínimo', 'Mais de 1 até 2 salários mínimos',\n",
" 'Mais de 2 até 3 salários mínimos','Mais de 3 até 5 salários mínimos',\n",
" 'Mais de 5 salários mínimos')"
]
},
{
"cell_type": "code",
"execution_count": 215,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x250118327b8>"
]
},
"execution_count": 215,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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dqfZO7RqZW+i/kPQH4NXAuWXq1N8bjqkWEfEcc/sns0pbf0XK8pUZV39oOo4O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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x250117fa7f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"barraRendaPa = pnad2014.Renda3.value_counts()\n",
"barraRendaPa.plot(kind='bar', color=('brown'), legend=False)\n",
"plt.title(\"Renda aposentados pardos\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"___\n",
"## Conclusão "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
" Após ver que a população aposentada é bem parecida entre homens e mulheres, conclui que os dados vão acabar sendo parecidos e decidi pegar os homens para análise. \n",
" A maioria da população masculina que tem melhores condições de pagar INSS e receber a aposentadodia, fica nas áreas ricas do Brasil, nos famosos centros comerciais, como São Paulo, Rio de Janeiro, Rio Grande do Sul e etc. \n",
" Além disso, a divisão étnica desses aposentados se diferem bastante, tendo uma maioria brancos, pardos e negros, mas desconsiderando indigenas e amarelos por realmente não estarem em grandes quantidades no Brasil, há uma grande diferença entre a quantidade de negros e brancos, mostrando uma ideia de que os negros em geral nao conseguem pagar esse INSS e portanto há uma grande diferença na distribuição da renda brasileira. \n",
" Porém, ao analisarmos a quantidade total de pessoas acima dos 60 anos no Brasil (idade que se pode receber a aposentadoria em geral), vemos que tanto com brancos, pretos e pardos, apenas metade da população de cada raça consegue receber esse benefício, ou seja, não é apenas a população negra que não cosnsegue pagar ou não veem beneficio em pagar e sim todas.\n",
" Querendo ir mais fundo nesse assunto, decidi ver a escolaridade e a renda dessas pessoas e percebi que aqueles que usufruem desse beneficio possuem quase as mesmas caracteristicas de escolarização, sendo muito precária ,já que aproximadamente, apenas 15% de cada raça fizeram curso superior (vendo apenas em São Paulo, cidade mais populosa no quesito de aposentados).\n",
" A seguir, analisei quantas pessoas existem em cada grupo,na qual cada grupo representa a quantidade de renda das pessoas em salários mínimos. Todas as raças são aproximadamente iguais, tendo uma maioria no grupo de 1 a 2 salarios mínimos. \n",
" Ou seja, a previdência pública, mesmo que dando mais dinheiro a aqueles que cuntribuem mais para o governo, não aparenta ser o fator determinante e contribuidor da desigualdade social em relação com a desigualdade étnica, já que ela é defasada, precária e prejudica a todos, não ajudando em nenhum setor social. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
jurgnjn/biokludge | annot/notebooks/Fig2S3_import_Chen2013.ipynb | 2 | 204459 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2018-05-18T15:46:36.324807Z",
"start_time": "2018-05-18T15:46:30.244709Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/mnt/home3/jj374/anaconda36/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"os.getcwd(): /mnt/beegfs/scratch_copy/ahringer/jj374/lab/relmapping\n"
]
}
],
"source": [
"%run ~/relmapping/annot/notebooks/__init__.ipynb\n",
"def vp(fp): return os.path.join('annot/Fig2S3_tss/', fp) # \"verbose path\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2018-05-18T15:46:46.446823Z",
"start_time": "2018-05-18T15:46:36.327065Z"
},
"code_folding": [
0
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"73500 TICs in Supp_TableS2.csv\n",
"12738 number of wormbase_tss or raft_to_wormbase_tss TICs\n",
"12737 number of wormbase_tss or raft_to_wormbase_tss TICs with mode_position set\n",
"12665 marked as \"Live\" in WS260\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/mnt/home1/ahringer/jj374/relmapping/scripts/yarp/yarp.py:404: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
" df_name = df_name.convert_objects(convert_numeric=True)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"9995 with biotype protein_coding\n",
"9995 in final, cleaned-up set\n",
"9995 annot/Fig2S3_tss/Chen2013_tss.bed\r\n"
]
}
],
"source": [
"# (Chen et al., 2013) TSSs -- load\n",
"def WS260_geneIDs():\n",
" fp_geneIDs = lp('.wget/ftp.wormbase.org/pub/wormbase/releases/WS260/species/c_elegans/PRJNA13758/annotation/c_elegans.PRJNA13758.WS260.geneIDs.txt.gz')\n",
" df_geneIDs = pd.read_csv(fp_geneIDs, sep=',', names=('na', 'gene_id', 'locus', 'sequence_id', 'status'))\\\n",
" [['gene_id', 'locus', 'sequence_id', 'status']]\n",
" return df_geneIDs\n",
"\n",
"def WS260_genes():\n",
" df_genes = yp.read_wbgtf('WS260_ce10/WS260_ce10.genes.gtf.gz', parse_attr=False)\n",
" df_genes = yp.df_gfftags_unpack(df_genes, 'attribute')\n",
" df_genes = df_genes[list(yp.NAMES_GTF[:8]) + ['gene_id', 'gene_source', 'gene_biotype']]\n",
" #print(len(df_genes), 'gene records')\n",
" return df_genes\n",
"\n",
"#WS260_geneIDs()\n",
"#WS260_genes()\n",
"\n",
"fp_ = 'wget/genome.cshlp.org/content/suppl/2013/04/16/gr.153668.112.DC1/Supp_TableS2.csv'\n",
"df_ = pd.read_csv(fp_)\n",
"print('%d TICs in Supp_TableS2.csv' % (len(df_),))\n",
"df_ = df_.loc[(df_['assignment type'] == 'wormbase_tss') | (df_['assignment type'] == 'raft_to_wormbase_tss')]\n",
"print('%d number of wormbase_tss or raft_to_wormbase_tss TICs' % (len(df_),))\n",
"df_ = df_.query('mode_position == mode_position')\n",
"print('%d number of wormbase_tss or raft_to_wormbase_tss TICs with mode_position set' % (len(df_),))\n",
"df_ = df_.merge(WS260_geneIDs(), left_on='assigned gene name', right_on='sequence_id').query('status == \"Live\"')\n",
"print('%d marked as \"Live\" in WS260' % (len(df_),))\n",
"df_ = df_.merge(WS260_genes(), left_on='gene_id', right_on='gene_id').query('gene_biotype == \"protein_coding\"')\n",
"print('%d with biotype protein_coding' % (len(df_),))\n",
"\n",
"df_chen = pd.DataFrame()\n",
"df_chen['chrom'] = 'chr' + df_['chr']\n",
"df_chen['start'] = list(map(lambda mode_position: int(mode_position) - 1, df_['mode_position']))\n",
"df_chen['end'] = df_chen['start'] + 1\n",
"df_chen['name'] = df_['assigned gene name']\n",
"df_chen['score'] = df_['tag count']\n",
"assert all(df_['strand_x'] == df_['strand_y'])\n",
"df_chen['strand'] = df_['strand_x']\n",
"df_chen = df_chen.sort_values(['chrom', 'start', 'end', 'start']).reset_index(drop=True)\n",
"print('%d in final, cleaned-up set' % (len(df_chen),))\n",
"\n",
"fp_ = vp('Chen2013_tss.bed')\n",
"df_chen.to_csv(fp_, header=False, index=False, sep='\\t')\n",
"!wc -l {fp_}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2018-05-18T15:47:24.971729Z",
"start_time": "2018-05-18T15:46:46.450447Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/mnt/home3/jj374/anaconda36/lib/python3.6/site-packages/ipykernel_launcher.py:64: RuntimeWarning: Mean of empty slice\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"13054 of 42245 sites with CV values via promoter annotation\n",
"26764 of 42245 sites with CV values via \"associated gene\"\n"
]
},
{
"data": {
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dsD7PwkpgCSmMXeLqpZIkSd0nNNSPIY0SeDMwq9hqeo8Xglc7XrvLaZXtM3kWvgl8xS5Y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\nr5IkSZIkSZI0YAavkiRJkiRJkjRg/w9XDO0xIBMZ1gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f774676c358>"
]
},
"metadata": {
"image/png": {
"height": 322,
"width": 687
}
},
"output_type": "display_data"
}
],
"source": [
"df_regl_ = regl_Apr27(flank_len=150)[['chrom', 'start', 'end', 'annot']]\n",
"#df_regl_['annot'] = regl_()['annot']\n",
"\n",
"# (Chen et al., 2013)\n",
"gv = yp.GenomicVenn2(\n",
" BedTool.from_dataframe(df_regl_),\n",
" BedTool.from_dataframe(df_chen[yp.NAMES_BED3]),\n",
" label_a='Accessible sites',\n",
" label_b='(Chen et al., 2013)\\nTSSs',\n",
")\n",
"\n",
"plt.figure(figsize=(12,6)).subplots_adjust(wspace=0.2)\n",
"plt.subplot(1,2,1)\n",
"v = gv.plot(style='compact')\n",
"v.get_patch_by_id('10').set_color(yp.RED)\n",
"v.get_patch_by_id('01').set_color(yp.GREEN)\n",
"v.get_patch_by_id('11').set_color(yp.YELLOW)\n",
"\n",
"plt.subplot(1,2,2)\n",
"annot_count_ = gv.df_a_with_b['name'].value_counts()[config['annot']]\n",
"annot_count_.index = [\n",
" 'coding_promoter',\n",
" 'pseudogene_promoter',\n",
" 'unknown_promoter',\n",
" 'putative_enhancer',\n",
" 'non-coding_RNA',\n",
" 'other_element'\n",
"]\n",
"#plt.title('Annotation of %d accessible sites that overlap a TSS from (Chen et al., 2013)' % (len(gv.df_a_with_b),))\n",
"(patches, texts, autotexts) = plt.pie(\n",
" annot_count_.values,\n",
" #labels = ['%s (%d)' % (l, c) for l, c in annot_count_.iteritems()],\n",
" labels = ['%d' % (c,) for l, c in annot_count_.iteritems()],\n",
" colors=[yp.RED, yp.ORANGE, yp.YELLOW, yp.GREEN, '0.4', yp.BLUE],\n",
" counterclock=False,\n",
" startangle=90,\n",
" autopct='%.1f%%',\n",
" #explode=[0.05, 0.05, 0.05, 0.05, 0.05, 0.05],\n",
");\n",
"plt.gca().set_aspect('equal')\n",
"plt.savefig(vp('Chen2013_annot.pdf'), bbox_inches='tight', transparent=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2018-05-18T15:47:37.927033Z",
"start_time": "2018-05-18T15:47:24.975420Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/mnt/home3/jj374/anaconda36/lib/python3.6/site-packages/ipykernel_launcher.py:64: RuntimeWarning: Mean of empty slice\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"13054 of 42245 sites with CV values via promoter annotation\n",
"26764 of 42245 sites with CV values via \"associated gene\"\n"
]
},
{
"data": {
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PaD2OsuUGco3OuTH3d1FntIJ1zXiQZ9asB7sS+JG971sz4y5JJkZhtAwUtm55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\no2ZmZmZmZjbmHIyamZmZmZnZmPv/Rw4WZlkCc9sAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7721c34da0>"
]
},
"metadata": {
"image/png": {
"height": 226,
"width": 465
}
},
"output_type": "display_data"
}
],
"source": [
"df_regl_ = regl_Apr27(flank_len=150)[['chrom', 'start', 'end', 'annot']]\n",
"#df_regl_['annot'] = regl_()['annot']\n",
"\n",
"# (Chen et al., 2013)\n",
"gv = yp.GenomicVenn2(\n",
" BedTool.from_dataframe(df_regl_),\n",
" BedTool.from_dataframe(df_chen[yp.NAMES_BED3]),\n",
" label_a='Accessible sites',\n",
" label_b='(Chen et al.,\\n2013) TSSs',\n",
")\n",
"\n",
"plt.figure(figsize=(8,4)).subplots_adjust(wspace=0.2)\n",
"plt.subplot(1,2,1)\n",
"v = gv.plot(style='compact')\n",
"v.get_patch_by_id('10').set_color(yp.RED)\n",
"v.get_patch_by_id('01').set_color(yp.GREEN)\n",
"v.get_patch_by_id('11').set_color(yp.YELLOW)\n",
"\n",
"plt.subplot(1,2,2)\n",
"d_reduced_ = collections.OrderedDict([\n",
" ('coding_promoter', 'coding_promoter, pseudogene_promoter'),\n",
" ('pseudogene_promoter', 'coding_promoter, pseudogene_promoter'),\n",
" ('unknown_promoter', 'unknown_promoter'),\n",
" ('putative_enhancer', 'putative_enhancer'),\n",
" ('non-coding_RNA', 'other_element, non-coding_RNA'),\n",
" ('other_element', 'other_element, non-coding_RNA'),\n",
"])\n",
"\n",
"d_colour_ = collections.OrderedDict([\n",
" ('coding_promoter, pseudogene_promoter', yp.RED),\n",
" ('unknown_promoter', yp.YELLOW),\n",
" ('putative_enhancer', yp.GREEN),\n",
" ('other_element, non-coding_RNA', yp.BLUE),\n",
"])\n",
"\n",
"gv.df_a_with_b['name_reduced'] = [*map(lambda a: d_reduced_[a], gv.df_a_with_b['name'])]\n",
"annot_count_ = gv.df_a_with_b['name_reduced'].value_counts()[d_colour_.keys()]\n",
"\n",
"#plt.title('Annotation of %d accessible sites that overlap a TSS from (Chen et al., 2013)' % (len(gv.df_a_with_b),))\n",
"(patches, texts) = plt.pie(\n",
" annot_count_.values,\n",
" labels = yp.pct_(annot_count_.values),\n",
" colors=d_colour_.values(),\n",
" counterclock=False,\n",
" startangle=45,\n",
");\n",
"plt.gca().set_aspect('equal')\n",
"#plt.savefig(vp('Chen2013_annot.pdf'), bbox_inches='tight', transparent=True)\n",
"plt.savefig('annot_Apr27/Fig2S3A_Chen2013_annot.pdf', bbox_inches='tight', transparent=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2018-05-18T15:47:38.526195Z",
"start_time": "2018-05-18T15:47:37.928989Z"
}
},
"outputs": [
{
"data": {
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7Ozu31REdHc1js9lygUAgVR+G/+yzzyrS0tIMfv31V4GdnV3foUOH1lhZWbVW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\naNz9D2vxMS9Op8pdAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7746c672b0>"
]
},
"metadata": {
"image/png": {
"height": 86,
"width": 274
}
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(0.2,0.2))\n",
"plt.gca().axis('off')\n",
"plt.gca().legend(patches, annot_count_.index, loc='upper left')\n",
"#plt.savefig(vp('Chen2013_annot_legend.pdf'), bbox_inches='tight', transparent=True)\n",
"plt.savefig('annot_Apr27/Fig2S3A_Chen2013_legend.pdf', bbox_inches='tight', transparent=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2018-05-18T15:47:38.751033Z",
"start_time": "2018-05-18T15:47:38.528306Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"# (Chen et al., 2013) TSSs now annotated as putative_enhancer\n",
"#fp_ = 'annot/FigC_TSS/overlaps_Chen2013_TSS_to_putative_enhancer.bed'\n",
"#gv.df_a_with_b.query('name == \"putative_enhancer\"').to_csv(fp_, header=False, sep='\\t', index=False)\n",
"#!wc -l {fp_}"
]
}
],
"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": 2
}
| gpl-2.0 |
Ledoux/ShareYourSystem | Pythonlogy/draft/Object/Readme.ipynb | 1 | 2315 | {
"nbformat": 3,
"worksheets": [
{
"cells": [
{
"source": "\n<!--\nFrozenIsBool False\n-->\n\nView the Object sources on [Github](https://github.com/Ledoux/ShareYourSystem/tree/master/ShareYourSystem/Objects/Notebooker)\n\n",
"cell_type": "markdown",
"prompt_number": 0,
"metadata": {}
},
{
"source": "\n<!---\nFrozenIsBool True\n-->\n\n##Example\n\nLet's create an empty class, which will automatically receive\nspecial attributes from the decorating ClassorClass,\nspecially the NameStr, that should be the ClassStr\nwithout the TypeStr in the end.",
"cell_type": "markdown",
"prompt_number": 1,
"metadata": {}
},
{
"cell_type": "code",
"prompt_number": 2,
"language": "python",
"input": [
"\n",
"\n",
"\n",
"#ImportModules\n",
"import ShareYourSystem as SYS\n",
"from ShareYourSystem.Standards.Objects import Object\n",
"\n",
"#Definition a Getter\n",
"MyObject=Object.ObjectClass()\n",
"\t\t\n",
"#Definition the AttestedStr\n",
"SYS._attest(\n",
"\t[\n",
"\t\t'MyObject is '+SYS._str(\n",
"\t\t\tMyObject,\n",
"\t\t**{'RepresentingAlineaIsBool':False}\n",
"\t\t)\n",
"\t]\n",
") \n",
"\n",
"#Print\n",
"\n",
"\n"
],
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"\n",
"*****Start of the Attest *****\n",
"\n",
"MyObject is <ShareYourSystem.Standards.Objects.Object.ObjectClass object at 0x1075e6050>\n",
"\n",
"*****End of the Attest *****\n",
"\n",
"\n"
]
}
],
"collapsed": false,
"metadata": {}
}
]
}
],
"metadata": {
"name": "",
"signature": ""
},
"nbformat_minor": 0
} | mit |
callaghanmt/phd-flight-tracker | User_Input.ipynb | 1 | 77306 | {
"metadata": {
"name": "",
"signature": "sha256:fb7a0fb7b64f4c53b5d49e0d0728c76fc21b7867bddf09995c66d1b65abeabcb"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import math as ma\n",
"from haversine import flight_distance\n",
"from airport_data import airports\n",
"from airport_coords import coordinates\n",
"import draw_flight\n",
"%matplotlib inline\n",
"\n",
"no_flights_string = raw_input('Type the number of flights flown in a year:')\n",
"no_flights = int(no_flights_string)\n",
"assert no_flights > 0, \"You have not flown any flights!\"\n",
"assert no_flights < 1000, \"Number of flights must be an integer!\"\n",
"flight = 1\n",
"total=0\n",
"x1,x2,x3,x4, origin, destination = coordinates()\n",
"distance=flight_distance(x1,x2,x3,x4)\n",
"while no_flights != 0:\n",
" #origin_destination_airports = raw_input(\"Please enter the origin and destination airports (separated by a comma) for flight number \"+str(flight)+\".\").split(',')\n",
" #assert len(origin_destination_airports) == 2, 'This entry should be made of 2 airport names (strings), separated by comma!'\n",
" #assert type(origin_destination_airports) == <type 'list'>, \"This entry should be made of 2 airport names (strings), separated by comma!: %r\" % origin_destination_airports\n",
" #stopover_airports = raw_input(\"Please enter all stopover airports (separated by a comma) for flight number \"+str(flight)+\". If it was a direct flight (with no stopovers), please press number 0:\").split(',')\n",
" #assert type(stopover_airports) == <type 'list'>, \"This entry should be made of all airport stopovers (as strings), separated by comma!: %r\" % stopover_airports\n",
" #if str(stopover_airports[0]) == '0':\n",
" # print 'DIRECT FLIGHT!' #The code for calculating distance between aiports should go here\n",
" \n",
" \n",
" \n",
" #if str(stopover_airports[0]) != '0':\n",
" # no_flight_legs=len(stopover_airports)+1 #The number of flights between stopover airports is the no_airports plus one!\n",
" # flight_leg = 1\n",
" # while no_flight_legs != 0:\n",
" # \n",
" # #The code for calculating distance between aiports should go here\n",
" # if flight_leg == 1: print 'Distance for flight number '+str(flight)+', flight leg '+str(flight_leg)+', between '+origin_destination_airports[0]+' and '+stopover_airports[flight_leg-1]+' is '# + str(distance_between_aiports) This should show distance between individual airports \n",
" # if flight_leg > 1 and flight_leg == len(stopover_airports)+1: print 'Distance for flight number '+str(flight)+', flight leg '+str(flight_leg)+', between '+stopover_airports[flight_leg-2]+' and '+origin_destination_airports[1]+' is '# + str(distance_between_aiports) This should show distance between individual airports \n",
" # if flight_leg > 1 and flight_leg < len(stopover_airports)+1: print 'Distance for flight number '+str(flight)+', flight leg '+str(flight_leg)+', between '+stopover_airports[flight_leg-2]+' and '+stopover_airports[flight_leg-1]+' is '# + str(distance_between_aiports) This should show distance between individual airports \n",
" # flight_leg = flight_leg + 1\n",
" # no_flight_legs = no_flight_legs - 1\n",
" print 'Total distance for flight number ',str(flight),' is ' ,distance #str(Total) This should show distance flown for the whole flight trip (sum of all flights between all stopovers)\n",
" flight = flight + 1\n",
" no_flights = no_flights - 1\n",
" total = total + distance\n",
"print 'The total distance flown in a year is ' ,total #str(total_distance) This should be the sum of all the flights\n",
"\n",
"draw_flight.draw(x1,x2,x3,x4)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"Type the number of flights flown in a year:1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"Where are you flying from? LAX\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"Where are you flying to? SYD\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Coordinates for Los Angeles International [ LAX ] are (Lat / Lon): 33.9425 / -118.407222\n",
"Coordinates for Kingsford Smith [ SYD ] are (Lat / Lon): -33.946111 / 151.177222\n",
"Total distance for flight number 1 is 12053.6007051\n",
"The total distance flown in a year is 12053.6007051\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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0aOhrtbmDFKY1Wf0YTYKZv53hjQEtcNGWFon+7sx1Jv1ceXIbgPb1PNn5QPdK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tSJptUT9182fpyVhsYtF7Th4IwxxD9QJda3asWQsorHgCTCOR9r1jTqWoOIiwNpntaJOjPSKnYw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\nbNtmPD+wpklFeOWVx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uwm/tpXUoZGt6zk3PBrE1vJ7/HFF/L6zJ6t34KZyyUYOrTcuj0O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\n",
"text": [
"<matplotlib.figure.Figure at 0x108ce5cd0>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print str(flight_leg)\n",
"print len(stopover_airports)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2\n",
"1\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
teuben/astr288p | notebooks/orbits-02.ipynb | 1 | 3815 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Two Dimensional Galactic Orbits - Part 2\n",
"\n",
"We will be using an open source package, called GalPy, that has been used in research.\n",
"\n",
"The code is available via github: https://github.com/jobovy/galpy\n",
"\n",
"A paper that describes the code via ASCL http://ascl.net/galpy\n",
"\n",
"To install:\n",
"```\n",
" cd ASTR288\n",
" git clone https://github.com/jobovy/galpy\n",
" cd galpy\n",
" # first try out a build (compilation!) - \n",
" # make sure you use the right (miniconda3) python\n",
" which python\n",
" python setup.py build\n",
" \n",
" # if this all works, it can be \"installed\"\n",
" python setup.py install\n",
" \n",
" # this will install it within your `which python` tree\n",
" \n",
" # an alternative is to use pip:\n",
" pip install galpy\n",
" # which for this package happens to work.\n",
" \n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import galpy\n",
"import galpy.potential\n",
"# \n",
"print(galpy.__file__)\n",
"print(galpy.__version__)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print([p for p in dir(galpy.potential) if 'Potential' in p])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from galpy.orbit import Orbit"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from galpy.potential import PlummerPotential\n",
"pp = PlummerPotential(amp=1.0,b=1.0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pp.vcirc(1.0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"o1= Orbit(vxvv=[1.,0.1,1.1,0.,0.1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"ts = np.linspace(0,100,10000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"o1.integrate(ts,pp,method='leapfrog')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"o2= Orbit(vxvv=[1.,0.1,1.1,0.,0.1])\n",
"o2.integrate(ts,pp,method='odeint')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"o1.plot()\n",
"o2.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"o1.plotE(normed=True)\n",
"o2.plotE(normed=True)"
]
}
],
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
google/or-tools | examples/notebook/linear_solver/integer_programming_example.ipynb | 1 | 5587 | {
"cells": [
{
"cell_type": "markdown",
"id": "google",
"metadata": {},
"source": [
"##### Copyright 2021 Google LLC."
]
},
{
"cell_type": "markdown",
"id": "apache",
"metadata": {},
"source": [
"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",
" http://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.\n"
]
},
{
"cell_type": "markdown",
"id": "basename",
"metadata": {},
"source": [
"# integer_programming_example"
]
},
{
"cell_type": "markdown",
"id": "link",
"metadata": {},
"source": [
"<table align=\"left\">\n",
"<td>\n",
"<a href=\"https://colab.research.google.com/github/google/or-tools/blob/master/examples/notebook/linear_solver/integer_programming_example.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/master/tools/colab_32px.png\"/>Run in Google Colab</a>\n",
"</td>\n",
"<td>\n",
"<a href=\"https://github.com/google/or-tools/blob/master/ortools/linear_solver/samples/integer_programming_example.py\"><img src=\"https://raw.githubusercontent.com/google/or-tools/master/tools/github_32px.png\"/>View source on GitHub</a>\n",
"</td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "doc",
"metadata": {},
"source": [
"First, you must install [ortools](https://pypi.org/project/ortools/) package in this colab."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "install",
"metadata": {},
"outputs": [],
"source": [
"!pip install ortools"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "code",
"metadata": {},
"outputs": [],
"source": [
"#!/usr/bin/env python3\n",
"# Copyright 2010-2021 Google LLC\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",
"# http://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.\n",
"\"\"\"Small example to illustrate solving a MIP problem.\"\"\"\n",
"# [START program]\n",
"# [START import]\n",
"from ortools.linear_solver import pywraplp\n",
"# [END import]\n",
"\n",
"\n",
"def IntegerProgrammingExample():\n",
" \"\"\"Integer programming sample.\"\"\"\n",
" # [START solver]\n",
" # Create the mip solver with the SCIP backend.\n",
" solver = pywraplp.Solver.CreateSolver('SCIP')\n",
"\n",
" # [END solver]\n",
"\n",
" # [START variables]\n",
" # x, y, and z are non-negative integer variables.\n",
" x = solver.IntVar(0.0, solver.infinity(), 'x')\n",
" y = solver.IntVar(0.0, solver.infinity(), 'y')\n",
" z = solver.IntVar(0.0, solver.infinity(), 'z')\n",
" # [END variables]\n",
"\n",
" # [START constraints]\n",
" # 2*x + 7*y + 3*z <= 50\n",
" constraint0 = solver.Constraint(-solver.infinity(), 50)\n",
" constraint0.SetCoefficient(x, 2)\n",
" constraint0.SetCoefficient(y, 7)\n",
" constraint0.SetCoefficient(z, 3)\n",
"\n",
" # 3*x - 5*y + 7*z <= 45\n",
" constraint1 = solver.Constraint(-solver.infinity(), 45)\n",
" constraint1.SetCoefficient(x, 3)\n",
" constraint1.SetCoefficient(y, -5)\n",
" constraint1.SetCoefficient(z, 7)\n",
"\n",
" # 5*x + 2*y - 6*z <= 37\n",
" constraint2 = solver.Constraint(-solver.infinity(), 37)\n",
" constraint2.SetCoefficient(x, 5)\n",
" constraint2.SetCoefficient(y, 2)\n",
" constraint2.SetCoefficient(z, -6)\n",
" # [END constraints]\n",
"\n",
" # [START objective]\n",
" # Maximize 2*x + 2*y + 3*z\n",
" objective = solver.Objective()\n",
" objective.SetCoefficient(x, 2)\n",
" objective.SetCoefficient(y, 2)\n",
" objective.SetCoefficient(z, 3)\n",
" objective.SetMaximization()\n",
" # [END objective]\n",
"\n",
" # Solve the problem and print the solution.\n",
" # [START print_solution]\n",
" solver.Solve()\n",
" # Print the objective value of the solution.\n",
" print('Maximum objective function value = %d' % solver.Objective().Value())\n",
" print()\n",
" # Print the value of each variable in the solution.\n",
" for variable in [x, y, z]:\n",
" print('%s = %d' % (variable.name(), variable.solution_value()))\n",
" # [END print_solution]\n",
"\n",
"\n",
"IntegerProgrammingExample()\n",
"# [END program]\n",
"\n"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
| apache-2.0 |
ktmud/deep-learning | gan_mnist/Intro_to_GANs_Exercises.ipynb | 6 | 23066 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generative Adversarial Network\n",
"\n",
"In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!\n",
"\n",
"GANs were [first reported on](https://arxiv.org/abs/1406.2661) in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. Here are a few examples to check out:\n",
"\n",
"* [Pix2Pix](https://affinelayer.com/pixsrv/) \n",
"* [CycleGAN](https://github.com/junyanz/CycleGAN)\n",
"* [A whole list](https://github.com/wiseodd/generative-models)\n",
"\n",
"The idea behind GANs is that you have two networks, a generator $G$ and a discriminator $D$, competing against each other. The generator makes fake data to pass to the discriminator. The discriminator also sees real data and predicts if the data it's received is real or fake. The generator is trained to fool the discriminator, it wants to output data that looks _as close as possible_ to real data. And the discriminator is trained to figure out which data is real and which is fake. What ends up happening is that the generator learns to make data that is indistiguishable from real data to the discriminator.\n",
"\n",
"\n",
"\n",
"The general structure of a GAN is shown in the diagram above, using MNIST images as data. The latent sample is a random vector the generator uses to contruct it's fake images. As the generator learns through training, it figures out how to map these random vectors to recognizable images that can fool the discriminator.\n",
"\n",
"The output of the discriminator is a sigmoid function, where 0 indicates a fake image and 1 indicates an real image. If you're interested only in generating new images, you can throw out the discriminator after training. Now, let's see how we build this thing in TensorFlow."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import pickle as pkl\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from tensorflow.examples.tutorials.mnist import input_data\n",
"mnist = input_data.read_data_sets('MNIST_data')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model Inputs\n",
"\n",
"First we need to create the inputs for our graph. We need two inputs, one for the discriminator and one for the generator. Here we'll call the discriminator input `inputs_real` and the generator input `inputs_z`. We'll assign them the appropriate sizes for each of the networks.\n",
"\n",
">**Exercise:** Finish the `model_inputs` function below. Create the placeholders for `inputs_real` and `inputs_z` using the input sizes `real_dim` and `z_dim` respectively."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def model_inputs(real_dim, z_dim):\n",
" inputs_real = \n",
" inputs_z = \n",
" \n",
" return inputs_real, inputs_z"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generator network\n",
"\n",
"\n",
"\n",
"Here we'll build the generator network. To make this network a universal function approximator, we'll need at least one hidden layer. We should use a leaky ReLU to allow gradients to flow backwards through the layer unimpeded. A leaky ReLU is like a normal ReLU, except that there is a small non-zero output for negative input values.\n",
"\n",
"#### Variable Scope\n",
"Here we need to use `tf.variable_scope` for two reasons. Firstly, we're going to make sure all the variable names start with `generator`. Similarly, we'll prepend `discriminator` to the discriminator variables. This will help out later when we're training the separate networks.\n",
"\n",
"We could just use `tf.name_scope` to set the names, but we also want to reuse these networks with different inputs. For the generator, we're going to train it, but also _sample from it_ as we're training and after training. The discriminator will need to share variables between the fake and real input images. So, we can use the `reuse` keyword for `tf.variable_scope` to tell TensorFlow to reuse the variables instead of creating new ones if we build the graph again.\n",
"\n",
"To use `tf.variable_scope`, you use a `with` statement:\n",
"```python\n",
"with tf.variable_scope('scope_name', reuse=False):\n",
" # code here\n",
"```\n",
"\n",
"Here's more from [the TensorFlow documentation](https://www.tensorflow.org/programmers_guide/variable_scope#the_problem) to get another look at using `tf.variable_scope`.\n",
"\n",
"#### Leaky ReLU\n",
"TensorFlow doesn't provide an operation for leaky ReLUs, so we'll need to make one . For this you can just take the outputs from a linear fully connected layer and pass them to `tf.maximum`. Typically, a parameter `alpha` sets the magnitude of the output for negative values. So, the output for negative input (`x`) values is `alpha*x`, and the output for positive `x` is `x`:\n",
"$$\n",
"f(x) = max(\\alpha * x, x)\n",
"$$\n",
"\n",
"#### Tanh Output\n",
"The generator has been found to perform the best with $tanh$ for the generator output. This means that we'll have to rescale the MNIST images to be between -1 and 1, instead of 0 and 1.\n",
"\n",
">**Exercise:** Implement the generator network in the function below. You'll need to return the tanh output. Make sure to wrap your code in a variable scope, with 'generator' as the scope name, and pass the `reuse` keyword argument from the function to `tf.variable_scope`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def generator(z, out_dim, n_units=128, reuse=False, alpha=0.01):\n",
" ''' Build the generator network.\n",
" \n",
" Arguments\n",
" ---------\n",
" z : Input tensor for the generator\n",
" out_dim : Shape of the generator output\n",
" n_units : Number of units in hidden layer\n",
" reuse : Reuse the variables with tf.variable_scope\n",
" alpha : leak parameter for leaky ReLU\n",
" \n",
" Returns\n",
" -------\n",
" out: \n",
" '''\n",
" with tf.variable_scope # finish this\n",
" # Hidden layer\n",
" h1 = \n",
" # Leaky ReLU\n",
" h1 = \n",
" \n",
" # Logits and tanh output\n",
" logits = \n",
" out = \n",
" \n",
" return out"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Discriminator\n",
"\n",
"The discriminator network is almost exactly the same as the generator network, except that we're using a sigmoid output layer.\n",
"\n",
">**Exercise:** Implement the discriminator network in the function below. Same as above, you'll need to return both the logits and the sigmoid output. Make sure to wrap your code in a variable scope, with 'discriminator' as the scope name, and pass the `reuse` keyword argument from the function arguments to `tf.variable_scope`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def discriminator(x, n_units=128, reuse=False, alpha=0.01):\n",
" ''' Build the discriminator network.\n",
" \n",
" Arguments\n",
" ---------\n",
" x : Input tensor for the discriminator\n",
" n_units: Number of units in hidden layer\n",
" reuse : Reuse the variables with tf.variable_scope\n",
" alpha : leak parameter for leaky ReLU\n",
" \n",
" Returns\n",
" -------\n",
" out, logits: \n",
" '''\n",
" with tf.variable_scope # finish this\n",
" # Hidden layer\n",
" h1 =\n",
" # Leaky ReLU\n",
" h1 =\n",
" \n",
" logits =\n",
" out =\n",
" \n",
" return out, logits"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hyperparameters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Size of input image to discriminator\n",
"input_size = 784 # 28x28 MNIST images flattened\n",
"# Size of latent vector to generator\n",
"z_size = 100\n",
"# Sizes of hidden layers in generator and discriminator\n",
"g_hidden_size = 128\n",
"d_hidden_size = 128\n",
"# Leak factor for leaky ReLU\n",
"alpha = 0.01\n",
"# Label smoothing \n",
"smooth = 0.1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build network\n",
"\n",
"Now we're building the network from the functions defined above.\n",
"\n",
"First is to get our inputs, `input_real, input_z` from `model_inputs` using the sizes of the input and z.\n",
"\n",
"Then, we'll create the generator, `generator(input_z, input_size)`. This builds the generator with the appropriate input and output sizes.\n",
"\n",
"Then the discriminators. We'll build two of them, one for real data and one for fake data. Since we want the weights to be the same for both real and fake data, we need to reuse the variables. For the fake data, we're getting it from the generator as `g_model`. So the real data discriminator is `discriminator(input_real)` while the fake discriminator is `discriminator(g_model, reuse=True)`.\n",
"\n",
">**Exercise:** Build the network from the functions you defined earlier."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"# Create our input placeholders\n",
"input_real, input_z = \n",
"\n",
"# Generator network here\n",
"g_model = \n",
"# g_model is the generator output\n",
"\n",
"# Disriminator network here\n",
"d_model_real, d_logits_real = \n",
"d_model_fake, d_logits_fake = "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Discriminator and Generator Losses\n",
"\n",
"Now we need to calculate the losses, which is a little tricky. For the discriminator, the total loss is the sum of the losses for real and fake images, `d_loss = d_loss_real + d_loss_fake`. The losses will be sigmoid cross-entropies, which we can get with `tf.nn.sigmoid_cross_entropy_with_logits`. We'll also wrap that in `tf.reduce_mean` to get the mean for all the images in the batch. So the losses will look something like \n",
"\n",
"```python\n",
"tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels))\n",
"```\n",
"\n",
"For the real image logits, we'll use `d_logits_real` which we got from the discriminator in the cell above. For the labels, we want them to be all ones, since these are all real images. To help the discriminator generalize better, the labels are reduced a bit from 1.0 to 0.9, for example, using the parameter `smooth`. This is known as label smoothing, typically used with classifiers to improve performance. In TensorFlow, it looks something like `labels = tf.ones_like(tensor) * (1 - smooth)`\n",
"\n",
"The discriminator loss for the fake data is similar. The logits are `d_logits_fake`, which we got from passing the generator output to the discriminator. These fake logits are used with labels of all zeros. Remember that we want the discriminator to output 1 for real images and 0 for fake images, so we need to set up the losses to reflect that.\n",
"\n",
"Finally, the generator losses are using `d_logits_fake`, the fake image logits. But, now the labels are all ones. The generator is trying to fool the discriminator, so it wants to discriminator to output ones for fake images.\n",
"\n",
">**Exercise:** Calculate the losses for the discriminator and the generator. There are two discriminator losses, one for real images and one for fake images. For the real image loss, use the real logits and (smoothed) labels of ones. For the fake image loss, use the fake logits with labels of all zeros. The total discriminator loss is the sum of those two losses. Finally, the generator loss again uses the fake logits from the discriminator, but this time the labels are all ones because the generator wants to fool the discriminator."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Calculate losses\n",
"d_loss_real = \n",
"\n",
"d_loss_fake = \n",
"\n",
"d_loss = \n",
"\n",
"g_loss = "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Optimizers\n",
"\n",
"We want to update the generator and discriminator variables separately. So we need to get the variables for each part and build optimizers for the two parts. To get all the trainable variables, we use `tf.trainable_variables()`. This creates a list of all the variables we've defined in our graph.\n",
"\n",
"For the generator optimizer, we only want to generator variables. Our past selves were nice and used a variable scope to start all of our generator variable names with `generator`. So, we just need to iterate through the list from `tf.trainable_variables()` and keep variables that start with `generator`. Each variable object has an attribute `name` which holds the name of the variable as a string (`var.name == 'weights_0'` for instance). \n",
"\n",
"We can do something similar with the discriminator. All the variables in the discriminator start with `discriminator`.\n",
"\n",
"Then, in the optimizer we pass the variable lists to the `var_list` keyword argument of the `minimize` method. This tells the optimizer to only update the listed variables. Something like `tf.train.AdamOptimizer().minimize(loss, var_list=var_list)` will only train the variables in `var_list`.\n",
"\n",
">**Exercise: ** Below, implement the optimizers for the generator and discriminator. First you'll need to get a list of trainable variables, then split that list into two lists, one for the generator variables and another for the discriminator variables. Finally, using `AdamOptimizer`, create an optimizer for each network that update the network variables separately."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"# Optimizers\n",
"learning_rate = 0.002\n",
"\n",
"# Get the trainable_variables, split into G and D parts\n",
"t_vars = \n",
"g_vars = \n",
"d_vars = \n",
"\n",
"d_train_opt = \n",
"g_train_opt = "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"batch_size = 100\n",
"epochs = 100\n",
"samples = []\n",
"losses = []\n",
"saver = tf.train.Saver(var_list = g_vars)\n",
"with tf.Session() as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
" for e in range(epochs):\n",
" for ii in range(mnist.train.num_examples//batch_size):\n",
" batch = mnist.train.next_batch(batch_size)\n",
" \n",
" # Get images, reshape and rescale to pass to D\n",
" batch_images = batch[0].reshape((batch_size, 784))\n",
" batch_images = batch_images*2 - 1\n",
" \n",
" # Sample random noise for G\n",
" batch_z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
" \n",
" # Run optimizers\n",
" _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})\n",
" _ = sess.run(g_train_opt, feed_dict={input_z: batch_z})\n",
" \n",
" # At the end of each epoch, get the losses and print them out\n",
" train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})\n",
" train_loss_g = g_loss.eval({input_z: batch_z})\n",
" \n",
" print(\"Epoch {}/{}...\".format(e+1, epochs),\n",
" \"Discriminator Loss: {:.4f}...\".format(train_loss_d),\n",
" \"Generator Loss: {:.4f}\".format(train_loss_g)) \n",
" # Save losses to view after training\n",
" losses.append((train_loss_d, train_loss_g))\n",
" \n",
" # Sample from generator as we're training for viewing afterwards\n",
" sample_z = np.random.uniform(-1, 1, size=(16, z_size))\n",
" gen_samples = sess.run(\n",
" generator(input_z, input_size, n_units=g_hidden_size, reuse=True, alpha=alpha),\n",
" feed_dict={input_z: sample_z})\n",
" samples.append(gen_samples)\n",
" saver.save(sess, './checkpoints/generator.ckpt')\n",
"\n",
"# Save training generator samples\n",
"with open('train_samples.pkl', 'wb') as f:\n",
" pkl.dump(samples, f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training loss\n",
"\n",
"Here we'll check out the training losses for the generator and discriminator."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fig, ax = plt.subplots()\n",
"losses = np.array(losses)\n",
"plt.plot(losses.T[0], label='Discriminator')\n",
"plt.plot(losses.T[1], label='Generator')\n",
"plt.title(\"Training Losses\")\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generator samples from training\n",
"\n",
"Here we can view samples of images from the generator. First we'll look at images taken while training."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def view_samples(epoch, samples):\n",
" fig, axes = plt.subplots(figsize=(7,7), nrows=4, ncols=4, sharey=True, sharex=True)\n",
" for ax, img in zip(axes.flatten(), samples[epoch]):\n",
" ax.xaxis.set_visible(False)\n",
" ax.yaxis.set_visible(False)\n",
" im = ax.imshow(img.reshape((28,28)), cmap='Greys_r')\n",
" \n",
" return fig, axes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Load samples from generator taken while training\n",
"with open('train_samples.pkl', 'rb') as f:\n",
" samples = pkl.load(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These are samples from the final training epoch. You can see the generator is able to reproduce numbers like 5, 7, 3, 0, 9. Since this is just a sample, it isn't representative of the full range of images this generator can make."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"_ = view_samples(-1, samples)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below I'm showing the generated images as the network was training, every 10 epochs. With bonus optical illusion!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rows, cols = 10, 6\n",
"fig, axes = plt.subplots(figsize=(7,12), nrows=rows, ncols=cols, sharex=True, sharey=True)\n",
"\n",
"for sample, ax_row in zip(samples[::int(len(samples)/rows)], axes):\n",
" for img, ax in zip(sample[::int(len(sample)/cols)], ax_row):\n",
" ax.imshow(img.reshape((28,28)), cmap='Greys_r')\n",
" ax.xaxis.set_visible(False)\n",
" ax.yaxis.set_visible(False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It starts out as all noise. Then it learns to make only the center white and the rest black. You can start to see some number like structures appear out of the noise. Looks like 1, 9, and 8 show up first. Then, it learns 5 and 3."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sampling from the generator\n",
"\n",
"We can also get completely new images from the generator by using the checkpoint we saved after training. We just need to pass in a new latent vector $z$ and we'll get new samples!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"saver = tf.train.Saver(var_list=g_vars)\n",
"with tf.Session() as sess:\n",
" saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n",
" sample_z = np.random.uniform(-1, 1, size=(16, z_size))\n",
" gen_samples = sess.run(\n",
" generator(input_z, input_size, n_units=g_hidden_size, reuse=True, alpha=alpha),\n",
" feed_dict={input_z: sample_z})\n",
"view_samples(0, [gen_samples])"
]
}
],
"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
}
| mit |
Hugovdberg/timml | notebooks/timml_notebook1_sol.ipynb | 1 | 365402 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TimML Notebook 1\n",
"## A well in uniform flow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Consider a well in the middle aquifer of a three aquifer system. Aquifer properties are given in Table 1. The well is located at $(x,y)=(0,0)$, the discharge is $Q=10,000$ m$^3$/d and the radius is 0.2 m. There is a uniform flow from West to East with a gradient of 0.002. The head is fixed to 20 m at a distance of 10,000 m downstream of the well. Here is the cookbook recipe to build this model:\n",
" \n",
"* Import pylab to use numpy and plotting: `from pylab import *`\n",
"* Set figures to be in the notebook with `%matplotlib notebook`\n",
"* Import everything from TimML: `from timml import *`\n",
"* Create the model and give it a name, for example `ml` with the command `ml = ModelMaq(kaq, z, c)` (substitute the correct lists for `kaq`, `z`, and `c`).\n",
"* Enter the well with the command `w = Well(ml, xw, yw, Qw, rw, layers)`, where the well is called `w`.\n",
"* Enter uniform flow with the command `Uflow(ml, slope, angle)`.\n",
"* Enter the reference head with `Constant(ml, xr, yr, head, layer)`.\n",
"* Solve the model `ml.solve()`\n",
"\n",
"#### Table 1: Aquifer data for exercise 1\n",
"|Layer |$k$ (m/d)|$z_b$ (m)|$z_t$|$c$ (days)|\n",
"|-------------|--------:|--------:|----:|---------:|\n",
"|Aquifer 0 | 10 | -20 | 0 | - |\n",
"|Leaky Layer 1| - | -40 | -20 | 4000 | \n",
"|Aquifer 1 | 20 | -80 | -40 | - |\n",
"|Leaky Layer 2| - | -90 | -80 | 10000 | \n",
"|Aquifer 2 | 5 | -140 | -90 | - ||\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from pylab import *\n",
"from timml import *\n",
"figsize=(8, 8)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of elements, Number of equations: 3 , 1\n",
"...\n",
"solution complete\n"
]
}
],
"source": [
"ml = ModelMaq(kaq=[10, 20, 5],\n",
" z=[0, -20, -40, -80, -90, -140], \n",
" c=[4000, 10000])\n",
"w = Well(ml, xw=0, yw=0, Qw=10000, rw=0.2, layers=1)\n",
"Constant(ml, xr=10000, yr=0, hr=20, layer=0)\n",
"Uflow(ml, slope=0.002, angle=0)\n",
"ml.solve()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Questions:\n",
"#### Exercise 1a\n",
"What are the leakage factors of the aquifer system?"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The leakage factors of the aquifers are:\n",
"[ 0. 1430.58042146 790.84743012]\n"
]
}
],
"source": [
"print('The leakage factors of the aquifers are:')\n",
"print(ml.aq.lab)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Exercise 1b\n",
"What is the head at the well?"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The head at the well is:\n",
"[20.06196743]\n"
]
}
],
"source": [
"print('The head at the well is:')\n",
"print(w.headinside())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Exercise 1c\n",
"Create a contour plot of the head in the three aquifers. Use a window with lower left hand corner $(x,y)=(−3000,−3000)$ and upper right hand corner $(x,y)=(3000,3000)$. Notice that the heads in the three aquifers are almost equal at three times the largest leakage factor."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ml.contour(win=[-3000, 3000, -3000, 3000], ngr=50, layers=[0, 1, 2], levels=10, \n",
" legend=True, figsize=figsize)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Exercise 1d\n",
"Create a contour plot of the head in aquifer 1 with labels along the contours. Labels are added when the `labels` keyword argument is set to `True`. The number of decimal places can be set with the `decimals` keyword argument, which is zero by default."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ml.contour(win=[-3000, 3000, -3000, 3000], ngr=50, layers=[1], levels=np.arange(30, 45, 1), \n",
" labels=True, legend=['layer 1'], figsize=figsize)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Exercise 1e\n",
"Create a contour plot with a vertical cross-section below it. Start three pathlines from $(x,y)=(-2000,-1000)$ at levels $z=-120$, $z=-60$, and $z=-10$. Try a few other starting locations."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"...\n",
"...\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"win=[-3000, 3000, -3000, 3000]\n",
"ml.plot(win=win, orientation='both', figsize=figsize)\n",
"ml.tracelines(-2000 * ones(3), -1000 * ones(3), [-120, -60, -10], hstepmax=50, \n",
" win=win, orientation='both')\n",
"ml.tracelines(0 * ones(3), 1000 * ones(3), [-120, -50, -10], hstepmax=50, \n",
" win=win, orientation='both')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Exercise 1f\n",
"Add an abandoned well that is screened in both aquifer 0 and aquifer 1, located at $(x, y) = (100, 100)$ and create contour plot of all aquifers near the well (from (-200,-200) till (200,200)). What are the discharge and the head at the abandoned well? Note that you have to solve the model again!"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of elements, Number of equations: 4 , 3\n",
"....\n",
"solution complete\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ml = ModelMaq(kaq=[10, 20, 5],\n",
" z=[0, -20, -40, -80, -90, -140], \n",
" c=[4000, 10000])\n",
"w = Well(ml, xw=0, yw=0, Qw=10000, rw=0.2, layers=1)\n",
"Constant(ml, xr=10000, yr=0, hr=20, layer=0)\n",
"Uflow(ml, slope=0.002, angle=0)\n",
"wabandoned = Well(ml, xw=100, yw=100, Qw=0, rw=0.2, layers=[0, 1])\n",
"ml.solve()\n",
"ml.contour(win=[-200, 200, -200, 200], ngr=50, layers=[0, 2], \n",
" levels=20, color=['C0', 'C1', 'C2'], legend=True, figsize=figsize)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The head at the abandoned well is:\n",
"[33.62101294 33.62101294]\n",
"The discharge at the abandoned well is:\n",
"[ 431.40914098 -431.40914098 0. ]\n"
]
}
],
"source": [
"print('The head at the abandoned well is:')\n",
"print(wabandoned.headinside())\n",
"print('The discharge at the abandoned well is:')\n",
"print(wabandoned.discharge())"
]
}
],
"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"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autoclose": false,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 1,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
StevenCHowell/code_sas_modeling | notebooks/Standard Deviation versus Standard Error.ipynb | 1 | 29032 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "81c0606f-dc29-4fae-895c-f5f4c6d8d58d"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Standard Deviation versus Standard Error\n",
"## Steven C. Howell\n",
"### 5 January 2017"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "c3b4e8ae-7bdd-490d-bd9e-a3ed0719d7e9"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## References:\n",
"1. [Brown, G. W. Standard deviation, standard error: Which ‘standard’ should we use? American Journal of Diseases of Children 136, 937–941 (1982).](http://archpedi.jamanetwork.com/article.aspx?articleid=510667)\n",
"2. [Cumming, G., Fidler, F. & Vaux, D. L. Error bars in experimental biology. The Journal of Cell Biology 177, 7–11 (2007).](http://jcb.rupress.org/content/177/1/7)\n",
"3. [Biau, D. J. In Brief: Standard Deviation and Standard Error. Clin Orthop Relat Res 469, 2661–2664 (2011).](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3148365/)"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "3f08db14-4e69-408b-bd93-f325d77b2270"
},
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"source": [
"## Representing a Distribution\n",
"What is the best way to represent or summarize a group of measurements or counts?"
]
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"source": [
"### central value\n",
"- mode: the most frequent value\n",
"- median: the value midway between the lowest and highest value\n",
"- mean ($\\mu$): the average of all values "
]
},
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"cell_type": "markdown",
"metadata": {
"collapsed": true
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"source": [
"### dispersion\n",
"- range: lowest and highest value\n",
"- quartile range: range for percentiles (ideal for asymmetric distributions)\n",
"- variance: average of the squared distances from the mean \n",
"- standard deviation ($\\sigma$): square root of the variance (also referred to as the \"root-mean-square\"). Typical or (roughly speaking) average difference between the data points and their mean."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Standard Deviation (descriptive statistic)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mean of a population: $\\quad \\mu = \\frac{\\sum_i^N x_i}{N}$\n",
"\n",
"Variance of a population: $\\quad \\sigma^2 = \\frac{\\sum_i^N{(x_i - \\mu)^2}}{N}$\n",
"\n",
"Standard deviation of a population: $\\quad \\sigma = \\sqrt{\\frac{\\sum_i^N{(x_i - \\mu)^2}}{N}}$\n",
"\n",
"Number in Population: $\\quad N$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For gaussion distributions, the mean and standard deviation completely describe the distribution. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### What about non-gaussian distributions?\n",
"\n",
"A frequent pitfall occurs when reporting a mean and standard deviation for a non-gaussian distribution. When a distribution is not gaussian, the standard deviation can still be calculated and reported, but the meaning is easily misinterpreted.\n",
"\n",
"Consider the example from reference [1] in which certain 1976 medical articles had an average of 4.9 authors $\\pm7.3$ (SD). As face value, this indicates that 95% of articles had $4.9\\pm(1.96\\times7.3)$ authors, or from $-9.4$ to $+19.2$; or that 25% of articles had zero or fewer authors. \n",
"\n",
"In this situation, a better description would be provided using the mean and range, or quartile ranges."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Absolute versus Estimate Statistics for a Population\n",
"The mean and standard deviation are used to describe the distribution of a population of measurements and to estimate the distribution a population based on a sample of measurements. \n",
"\n",
"This difference means there are two slight changes in the calculation of the standard deviation for the second case. First instead of the mean being the exact population mean, $\\mu$, the mean is an estimate of the larger population mean, $\\bar{x}$, though it is calculated the same way. The second difference, which actually changes the result, is that the sum of the squared differences from the mean is divided by $n-1$ rather than $N$. This change produces a wider distribution, corresponding to the sample underestimating the full spread of values present in the entire population."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Estimate of the population mean: $\\quad \\bar{x} = \\frac{\\sum_i^n x_i}{n}$\n",
"\n",
"Estimate of the population standard deviation from a sample: Typical or average difference between the data points and their mean. $$\\quad SD = \\sqrt{\\frac{\\sum_i^n{(x_i - \\bar{x})^2}}{n-1}}$$\n",
"\n",
"Number in Sample: $\\quad n$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While there is only a slight difference in calculating these two standard deviations, there is a real difference in their meaning. Unfortunately it is rare to see a distinction made when reporting a standard deviation value."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Standard Error (inferential statistic)\n",
"The standard error provides a prediction of the confidence interval for which the true value should be; it does not describe the distribution of values (it also has nothing to do with standards or errors). Whenever used, it should be described what statistic it corresponds to, e.g., standard error of the mean (SEM).\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Standard Error of the Mean, SEM"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Standard error of the mean: A measure of hov variable the mean will be, if you repeat the whole study many times. $$\\quad SEM = SD_{\\bar{x}} = \\frac{SD}{\\sqrt{n}} = \\sqrt{\\frac{\\sum_i^n{(x_i - \\bar{x})^2}}{n\\, (n-1)}}$$ "
]
},
{
"cell_type": "markdown",
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"source": [
"The SEM can be use to declare something like the following: \"The mean of the sample was 73 mg/dL, with an SE of the mean of 73 mg/dL. This implies that the mean of the population from which the sample was randomly taken will fall, with 95% probability, in the interval of $73\\pm(1.96*3) mg/dL, which is from 67.12 to 78.88 mg/dL.\" (from reference [1])"
]
},
{
"cell_type": "markdown",
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"The above formula for the $SEM$ can be rearranged for more efficient computation as follows.\n",
"$$ SEM^2 = \\frac{\\sum_i^n{(x_i-\\bar{x})^2}}{n (n-1)}$$\n",
"\n",
"$$ SEM^2 = \\frac{\\sum_i^n{(x_i^2 - 2x_i\\bar{x} + (\\bar{x})^2)}}{n (n-1)}$$\n",
"\n",
"$$ SEM^2 = \\frac{1}{n-1} \\left[\\frac{\\sum_i^n x_i^2}{n} - 2\\,\\bar{x}\\,\\frac{\\sum_i^n x_i}{n} + (\\bar{x})^2\\,\\frac{\\sum_i^n 1}{n}\\right]$$\n",
"Note that $$\\frac{\\sum_i^n x_i}{n} = \\bar{n}\\,,$$ and $$\\frac{\\sum_i^n 1}{n} = 1\\,.$$ Making these replacements, we are left with,\n",
"$$ SEM^2 = \\frac{1}{n-1} \\left[\\frac{\\sum_i^n x_i^2}{n} - 2(\\bar{x})^2 + (\\bar{x})^2\\right]\\,,$$\n",
"or simply,\n",
"$$ SEM^2 = \\frac{1}{n-1} \\left[\\frac{\\sum_i^n x_i^2}{n} - (\\bar{x})^2\\right]\\,.$$\n",
"\n",
"Written with angled braces to represent averages this is simply,\n",
"$$ SEM^2 = \\frac{\\langle x^2 \\rangle - \\langle x \\rangle^2}{n-1}\\,.$$\n",
"\n",
"This form allows for calculating the variance (part in square braces) in two passes of the numerical series, one summing $x^2$ (first term in square braces), and another summaing $x$ to get the mean. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Standard Error of Proportion, SEp"
]
},
{
"cell_type": "markdown",
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"source": [
"The standard error of proportion is used when describing the proportion of group that have a certain classification. An example from reference [1] is when six of ten patients with zymurgy exhibit so-and-so. The natural interpretation is that we should expect to see so-and-so for 60% of patients with zymurgy. \n",
"\n",
"The standard error of proportion provide an estimate for confidence intervals in this situation. \n",
"\n",
"Standard error of proportion: $\\quad SE_p = \\sqrt{\\frac{p (1-p)}{n}} $\n",
"\n",
"Proportion estimated from sample: $\\quad p$"
]
},
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"execution_count": 7,
"metadata": {
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"source": [
"def calc_sep(p, n):\n",
" return np.sqrt(p * (1-p)/n)\n",
"\n",
"p = 0.6"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Example with 10 patients"
]
},
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"cell_type": "code",
"execution_count": 16,
"metadata": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"sep : 0.155\n",
"95% ci for n=10: 0.296 to 0.904\n"
]
}
],
"source": [
"n=10\n",
"sep = calc_sep(p, n)\n",
"ci95 = 1.96 * sep\n",
"interval = p - ci95, p + ci95\n",
"print('sep : {:0.3}\\n95% ci for n={}: {:0.3} to {:0.3}'.format(sep, n, interval[0], interval[1]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So when 6 of 10 patients have a certain classification, the 95% confidence interval for predicting the classification of the entire population is $60\\% \\pm (1.96\\times 0.155)$ or from $29.6\\%$ to $90.4\\%$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Example with large patients populations"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sep : 0.049\n",
"95% ci for n=100: 0.504 to 0.696\n"
]
}
],
"source": [
"n=100\n",
"sep = calc_sep(p, n)\n",
"ci95 = 1.96 * sep\n",
"interval = p - ci95, p + ci95\n",
"print('sep : {:0.3}\\n95% ci for n={}: {:0.3} to {:0.3}'.format(sep, n, interval[0], interval[1]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Increasing the population size to 100 reduces the 95% confidence interval that the population exhibit so-and-so to be between $50.4\\%$ to $69.6\\%$."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sep : 0.0155\n",
"95% ci for n=1000: 0.57 to 0.63\n"
]
}
],
"source": [
"n=1000\n",
"sep = calc_sep(p, n)\n",
"ci95 = 1.96 * sep\n",
"interval = p - ci95, p + ci95\n",
"print('sep : {:0.3}\\n95% ci for n={}: {:0.3} to {:0.3}'.format(sep, n, interval[0], interval[1]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Increasing the population size to 1000 reduces the 95% confidence interval that the population exhibit so-and-so to be between $57\\%$ to $63\\%$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Notes\n",
"- SEM is most appropriate for comparing two different curves to conclude similarity or difference (how likely are the differences due to random noise?)\n",
"- In small-angle scattering (SAS), the error reported relates to the fidelity with which repeated measurement were performed, either from repeated scans or from measurements of different pixels at the same $q$-vector.\n",
"- \"Whenever you see a figure with very small error bars (such as Fig. 3), you should ask yourself whether the very small variation implied by the error bars is due to analysis of replicates rather than independent samples. If so, the bars are useless for making the inference you are considering.\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rules for effective use and interpretation of error bars (reference [2])\n",
"1. when showing error bars, always describe in the figure legends what they are\n",
"2. the value of $n$ (i.e., the sample size, or the number of independently performed experiments) must be stated in the figure legend.\n",
"3. error bars and statistics should only be shown for independently repeated experiments, and never for replicates. If a “representative” experiment is shown, it should not have error bars or P values, because in such an experiment, n = 1 (Fig. 3 shows what not to do).\n",
"4. because experimental biologists are usually trying to compare experimental results with controls, it is usually appropriate to show inferential error bars, such as SE or CI, rather than SD. However, if n is very small (for example n = 3), rather than showing error bars and statistics, it is better to simply plot the individual data points.\n",
"5. 95% CIs capture $\\mu$ on 95% of occasions, so you can be 95% confident your interval includes $\\mu$. SE bars can be doubled in width to get the approximate 95% CI, provided n is 10 or more. If n = 3, SE bars must be multiplied by 4 to get the approximate 95% CI.\n",
"6. when n = 3, and double the SE bars don’t overlap, P < 0.05, and if double the SE bars just touch, P is close to 0.05. If n is 10 or more, a gap of SE indicates P ≈ 0.05 and a gap of 2 SE indicates P ≈ 0.01.\n",
"7. with 95% CIs and n=3, overlap of one full arm indicates $P\\approx 0.05$, and overlap of half an arm indicates $P\\approx 0.01$.\n",
"8. in the case of repeated measurements on the same group (e.g., of animals, individuals, cultures, or reactions), CIs or SE bars are irrelevant to comparisons within the same group."
]
},
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Standard Error or Standard Deviation\n",
"If one wishes to provide a\n",
"description of the sample, then the standard deviations of\n",
"the relevant parameters are of interest. For instance we\n",
"would provide the mean age of the patients and standard\n",
"deviation, the mean size of tumors and standard deviation,\n",
"etc. \n",
"\n",
"If, on the other hand, one wishes to have the precision\n",
"of the sample value as it relates to that of the true value in\n",
"the population, then it is the standard error that should be\n",
"reported. For instance, when reporting the survival probability\n",
"of a sample we should provide the standard error\n",
"together with this estimated probability. However, because\n",
"the confidence interval is more useful and readable than the\n",
"standard error, it can be provided instead as it avoids\n",
"having the readers do the math."
]
},
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"cell_type": "markdown",
"metadata": {
"collapsed": true
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"source": [
"## How does this apply to Small-Angle Scattering (SAS)? What error is reported, and where does it originate?\n",
"\n",
"1. Pauw, B. R. Everything SAXS: small-angle scattering pattern collection and correction. Journal of Physics: Condensed Matter 25, 383201 (2013).\n",
"2. Hura G, Sorenson J M, Glaeser R M and Head-Gordon T 2000 A J. Chem. Phys. 113 9140–9148\n",
"3. Ilavsky, J. Nika : software for two-dimensional data reduction. Journal of Applied Crystallography 45, 324–328 (2012).\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Uncertainty in the scattering intensity should be calculated from two sources, the photon counting statistics, and the standard error of the mean of the pixel values. \"The photon counting (Poisson) statistics defines the absolute minimum possible uncertainty in any counting procedure. It does not consider other contributors to noise such as the variance between pixel sensitivities or electronic noise\" [1]. Additionally, we can set a lower limit on the error in scattering intensity to never have a relative uncertainty estimate lower than 1%, as beamlines report it is challenging to be more accurate than this [2]."
]
},
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"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\sigma_{\\!_{Q_\\text{bin}}} \\!= \\max\\left\\{{\n",
" \\begin{array}{l l l}\n",
" \\dfrac{1}{N_{Q_\\text{bin}}}\\sqrt{\\displaystyle\\sum_{Q_j\\in\\left[Q_k,\\,Q_{k+1}\\right]}\\!\\!\\!\\!\\!\\!\\!\\!\\sigma_j^2} & {\\text{photon counting error}}\\\\\n",
" \\dfrac{1}{\\sqrt{N_{Q_\\text{bin}}}}\\sqrt{\\dfrac{\\displaystyle\\sum_{Q_j\\in\\left[Q_k,\\,Q_{k+1}\\right]}\\!\\!\\!\\!\\!\\!\\!\\!\\left(I_j-I_{Q_\\text{bin}}\\right)^2}{N_{Q_\\text{bin}}-1}} & \\mbox{standard error of the mean}\\\\\n",
" \\dfrac{I_{Q_\\text{bin}}}{100} & \\mbox{1% of } I_{Q_\\text{bin}}\n",
" \\end{array}} \\right\\}\\,,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This leaves the choice as to what the grid spacing should be. Typically users opt for either uniform, or logaritmically spaced $Q$-grids, the later providing more points at lower $Q$ values [3]."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So in SAS, the error is the standard error of the mean. Least-squares fits should be weighted by the variance, but typically the number of $Q$-bins, $N_{Q_\\text{bin}}$, is not reported. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"y = m x + b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Standard deviation in $Q$: $\\sigma_Q$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Often, the standard deviation of the $Q$-values are also reported. Similar to the procedure for the scattering intensity, this can be converted to a standard error of the mean by dividing by $\\sqrt{N_{Q_\\text{bin}}}$,\n",
"\n",
"$$\n",
"\\sigma_{Q} = \\dfrac{1}{\\sqrt{N_{Q_\\text{bin}}}} \\sqrt{\\dfrac{\\displaystyle\\sum_{Q_j\\in\\left[Q_k,\\,Q_{k+1}\\right]}\\!\\!\\!\\!\\!\\!\\!\\!\\left(Q_j-\\bar{Q}\\right)^2}{N_{Q_\\text{bin}}-1}} \\,,\n",
"$$\n",
"\n",
"where \n",
"\n",
"$$\n",
"\\bar{Q} = \\langle Q_j\\in\\left[Q_k,\\,Q_{k+1}\\right]\\rangle\\,.\n",
"$$\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I have never seen the $Q$ standard deviation or standard error of the mean factored into the Guinier fitting, or even plotted for that matter. What difference would this make? Should it be included?"
]
},
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| gpl-3.0 |
dsevilla/bdge | mongo/sesion4.ipynb | 1 | 292595 | {
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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"Esta hoja muestra cómo acceder a bases de datos MongoDB y también a conectar la salida con Jupyter. Se puede utilizar el *shell* propio de MongoDB en la máquina virtual usando el programa `mongo`. La diferencia es que ese programa espera código Javascript y aquí trabajaremos con Python."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install --upgrade pymongo"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pprint import pprint as pp\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"\n",
"%matplotlib inline\n",
"matplotlib.style.use('ggplot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Usaremos la librería `pymongo` para python. La cargamos a continuación."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pymongo\n",
"from pymongo import MongoClient"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La conexión se inicia con `MongoClient` en el `host` descrito en el fichero `docker-compose.yml` (`mongo`)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"client = MongoClient(\"mongo\",27017)\n",
"client"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"client.list_database_names()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" - Format: 7zipped\n",
" - Files:\n",
" - **badges**.xml\n",
" - UserId, e.g.: \"420\"\n",
" - Name, e.g.: \"Teacher\"\n",
" - Date, e.g.: \"2008-09-15T08:55:03.923\"\n",
" - **comments**.xml\n",
" - Id\n",
" - PostId\n",
" - Score\n",
" - Text, e.g.: \"@Stu Thompson: Seems possible to me - why not try it?\"\n",
" - CreationDate, e.g.:\"2008-09-06T08:07:10.730\"\n",
" - UserId\n",
" - **posts**.xml\n",
" - Id\n",
" - PostTypeId\n",
" - 1: Question\n",
" - 2: Answer\n",
" - ParentID (only present if PostTypeId is 2)\n",
" - AcceptedAnswerId (only present if PostTypeId is 1)\n",
" - CreationDate\n",
" - Score\n",
" - ViewCount\n",
" - Body\n",
" - OwnerUserId\n",
" - LastEditorUserId\n",
" - LastEditorDisplayName=\"Jeff Atwood\"\n",
" - LastEditDate=\"2009-03-05T22:28:34.823\"\n",
" - LastActivityDate=\"2009-03-11T12:51:01.480\"\n",
" - CommunityOwnedDate=\"2009-03-11T12:51:01.480\"\n",
" - ClosedDate=\"2009-03-11T12:51:01.480\"\n",
" - Title=\n",
" - Tags=\n",
" - AnswerCount\n",
" - CommentCount\n",
" - FavoriteCount\n",
" - **posthistory**.xml\n",
"\t - Id\n",
"\t - PostHistoryTypeId\n",
"\t\t\t- 1: Initial Title - The first title a question is asked with.\n",
"\t\t\t- 2: Initial Body - The first raw body text a post is submitted with.\n",
"\t\t\t- 3: Initial Tags - The first tags a question is asked with.\n",
"\t\t\t- 4: Edit Title - A question's title has been changed.\n",
"\t\t\t- 5: Edit Body - A post's body has been changed, the raw text is stored here as markdown.\n",
"\t\t\t- 6: Edit Tags - A question's tags have been changed.\n",
"\t\t\t- 7: Rollback Title - A question's title has reverted to a previous version.\n",
"\t\t\t- 8: Rollback Body - A post's body has reverted to a previous version - the raw text is stored here.\n",
"\t\t\t- 9: Rollback Tags - A question's tags have reverted to a previous version.\n",
"\t\t\t- 10: Post Closed - A post was voted to be closed.\n",
"\t\t\t- 11: Post Reopened - A post was voted to be reopened.\n",
"\t\t\t- 12: Post Deleted - A post was voted to be removed.\n",
"\t\t\t- 13: Post Undeleted - A post was voted to be restored.\n",
"\t\t\t- 14: Post Locked - A post was locked by a moderator.\n",
"\t\t\t- 15: Post Unlocked - A post was unlocked by a moderator.\n",
"\t\t\t- 16: Community Owned - A post has become community owned.\n",
"\t\t\t- 17: Post Migrated - A post was migrated.\n",
"\t\t\t- 18: Question Merged - A question has had another, deleted question merged into itself.\n",
"\t\t\t- 19: Question Protected - A question was protected by a moderator\n",
"\t\t\t- 20: Question Unprotected - A question was unprotected by a moderator\n",
"\t\t\t- 21: Post Disassociated - An admin removes the OwnerUserId from a post.\n",
"\t\t\t- 22: Question Unmerged - A previously merged question has had its answers and votes restored.\n",
"\t\t- PostId\n",
"\t\t- RevisionGUID: At times more than one type of history record can be recorded by a single action. All of these will be grouped using the same RevisionGUID\n",
"\t\t- CreationDate: \"2009-03-05T22:28:34.823\"\n",
"\t\t- UserId\n",
"\t\t- UserDisplayName: populated if a user has been removed and no longer referenced by user Id\n",
"\t\t- Comment: This field will contain the comment made by the user who edited a post\n",
"\t\t- Text: A raw version of the new value for a given revision\n",
"\t\t\t- If PostHistoryTypeId = 10, 11, 12, 13, 14, or 15 this column will contain a JSON encoded string with all users who have voted for the PostHistoryTypeId\n",
"\t\t\t- If PostHistoryTypeId = 17 this column will contain migration details of either \"from <url>\" or \"to <url>\"\n",
"\t\t- CloseReasonId\n",
"\t\t\t- 1: Exact Duplicate - This question covers exactly the same ground as earlier questions on this topic; its answers may be merged with another identical question.\n",
"\t\t\t- 2: off-topic\n",
"\t\t\t- 3: subjective\n",
"\t\t\t- 4: not a real question\n",
"\t\t\t- 7: too localized\n",
" - **postlinks**.xml\n",
" - Id\n",
" - CreationDate\n",
" - PostId\n",
" - RelatedPostId\n",
" - PostLinkTypeId\n",
" - 1: Linked\n",
" - 3: Duplicate\n",
" - **users**.xml\n",
" - Id\n",
" - Reputation\n",
" - CreationDate\n",
" - DisplayName\n",
" - EmailHash\n",
" - LastAccessDate\n",
" - WebsiteUrl\n",
" - Location\n",
" - Age\n",
" - AboutMe\n",
" - Views\n",
" - UpVotes\n",
" - DownVotes\n",
" - **votes**.xml\n",
" - Id\n",
" - PostId\n",
" - VoteTypeId\n",
" - ` 1`: AcceptedByOriginator\n",
" - ` 2`: UpMod\n",
" - ` 3`: DownMod\n",
" - ` 4`: Offensive\n",
" - ` 5`: Favorite - if VoteTypeId = 5 UserId will be populated\n",
" - ` 6`: Close\n",
" - ` 7`: Reopen\n",
" - ` 8`: BountyStart\n",
" - ` 9`: BountyClose\n",
" - `10`: Deletion\n",
" - `11`: Undeletion\n",
" - `12`: Spam\n",
" - `13`: InformModerator\n",
" - CreationDate\n",
" - UserId (only for VoteTypeId 5)\n",
" - BountyAmount (only for VoteTypeId 9)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Las bases de datos se crean conforme se nombran. Se puede utilizar la notación punto o la de diccionario. Las colecciones también."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"db = client.stackoverflow\n",
"db = client['stackoverflow']\n",
"db"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Las bases de datos están compuestas por un conjunto de **colecciones**. Cada colección aglutina a un conjunto de objetos (documentos) del mismo tipo, aunque como vimos en teoría, cada documento puede tener un conjunto de atributos diferente."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"posts = db.posts\n",
"posts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Importación de los ficheros CSV. Por ahora creamos una colección diferente para cada uno. Después estudiaremos cómo poder optimizar el acceso usando agregación."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import os.path as path\n",
"from urllib.request import urlretrieve\n",
"\n",
"def download_csv_upper_dir(baseurl, filename):\n",
" file = path.abspath(path.join(os.getcwd(),os.pardir,filename))\n",
" if not os.path.isfile(file):\n",
" urlretrieve(baseurl + '/' + filename, file)\n",
"\n",
"baseurl = 'http://neuromancer.inf.um.es:8080/es.stackoverflow/'\n",
"download_csv_upper_dir(baseurl, 'Posts.csv')\n",
"download_csv_upper_dir(baseurl, 'Users.csv')\n",
"download_csv_upper_dir(baseurl, 'Tags.csv')\n",
"download_csv_upper_dir(baseurl, 'Comments.csv')\n",
"download_csv_upper_dir(baseurl, 'Votes.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import csv\n",
"from datetime import datetime\n",
"\n",
"def csv_to_mongo(file, coll):\n",
" \"\"\"\n",
" Carga un fichero CSV en Mongo. file especifica el fichero, coll la colección\n",
" dentro de la base de datos, y date_cols las columnas que serán interpretadas\n",
" como fechas.\n",
" \"\"\"\n",
" # Convertir todos los elementos que se puedan a números\n",
" def to_numeric(d):\n",
" try:\n",
" return int(d)\n",
" except ValueError:\n",
" try:\n",
" return float(d)\n",
" except ValueError:\n",
" return d\n",
" \n",
" def to_date(d):\n",
" \"\"\"To ISO Date. If this cannot be converted, return NULL (None)\"\"\"\n",
" try:\n",
" return datetime.strptime(d, \"%Y-%m-%dT%H:%M:%S.%f\")\n",
" except ValueError:\n",
" return None\n",
" \n",
" coll.drop()\n",
"\n",
" with open(file, encoding='utf-8') as f:\n",
" # La llamada csv.reader() crea un iterador sobre un fichero CSV\n",
" reader = csv.reader(f, dialect='excel')\n",
" \n",
" # Se leen las columnas. Sus nombres se usarán para crear las diferentes columnas en la familia\n",
" columns = next(reader)\n",
" \n",
" # Las columnas que contienen 'Date' se interpretan como fechas\n",
" func_to_cols = list(map(lambda c: to_date if 'date' in c.lower() else to_numeric, columns))\n",
" \n",
" docs=[]\n",
" for row in reader:\n",
" row = [func(e) for (func,e) in zip(func_to_cols, row)]\n",
" docs.append(dict(zip(columns, row)))\n",
" coll.insert_many(docs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"csv_to_mongo('../Posts.csv',db.posts)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"csv_to_mongo('../Users.csv',db.users)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"csv_to_mongo('../Votes.csv',db.votes)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"csv_to_mongo('../Comments.csv',db.comments)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"csv_to_mongo('../Tags.csv',db.tags)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"posts.count_documents()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"El API de colección en Python se puede encontrar aquí: https://api.mongodb.com/python/current/api/pymongo/collection.html. La mayoría de libros y referencias muestran el uso de mongo desde Javascript, ya que el *shell* de MongoDB acepta ese lenguaje. La sintaxis con respecto a Python cambia un poco, y se puede seguir en el enlace anterior."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creación de índices\n",
"\n",
"Para que el proceso map-reduce y de agregación funcione mejor, voy a crear índices sobre los atributos que se usarán como índice... Ojo, si no se crea las consultas pueden tardar mucho."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(\n",
" db.posts.create_index([('Id', pymongo.HASHED)]),\n",
" db.comments.create_index([('Id', pymongo.HASHED)]),\n",
" db.users.create_index([('Id', pymongo.HASHED)])\n",
")"
]
},
{
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]
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Map-Reduce\n",
"\n",
"Mongodb incluye dos APIs para procesar y buscar documentos: el API de Map-Reduce y el API de agregación. Veremos primero el de Map-Reduce. Manual: https://docs.mongodb.com/manual/aggregation/#map-reduce\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from bson.code import Code"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"map = Code(\n",
"'''\n",
"function () {\n",
" emit(this.OwnerUserId, 1);\n",
"}\n",
"''')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"reduce = Code(\n",
"'''\n",
"function (key, values)\n",
"{\n",
" return Array.sum(values);\n",
"}\n",
"''')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"results = posts.map_reduce(map, reduce, \"posts_by_userid\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"posts_by_userid = db.posts_by_userid\n",
"list(posts_by_userid.find())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Se le puede añadir una etiqueta para especificar sobre qué elementos queremos trabajar (`query`):"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La función `map_reduce` puede llevar añadida una serie de *keywords*, los mismos especificados en la documentación:\n",
"\n",
"- `query`: Restringe los datos que se tratan\n",
"- `sort`: Ordena los documentos de entrada por alguna clave\n",
"- `limit`: Limita el número de resultados\n",
"- `out`: Especifica la colección de salida y otras opciones. Lo veremos después.\n",
"- etc."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En el parámetro `out` se puede especificar en qué colección se quedarán los datos resultado del map-reduce. Por defecto, en la colección origen. (Todos los parámetros aquí: https://docs.mongodb.com/manual/reference/command/mapReduce/#mapreduce-out-cmd). En la operación `map_reduce()` podemos especificar la colección de salida, pero también podemos añadir un parámetro final `out={...}`.\n",
"\n",
"Hay varias posibilidades para `out`:\n",
"\n",
"- `replace`: Sustituye la colección, si la hubiera, con la especificada (p. ej.: `out={ \"replace\" : \"coll\" }`.\n",
"- `merge`: Mezcla la colección existente, sustituyendo los documentos que existan por los generados.\n",
"- `reduce`: Si existe un documento con el mismo \\_id en la colección, se aplica la función `reduce` para *fusionar* ambos documentos y producir un nuevo documento.\n",
"\n",
"Veremos a continuación, al resolver el ejercicio de crear `post_comments` con map-reduce cómo se utilizan estas posibilidades."
]
},
{
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]
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"También hay operaciones específicas de la coleción, como `count()`, `groupby()` y `distinct()`:\n",
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"db.posts.distinct('Score')"
]
},
{
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"metadata": {},
"source": [
"## EJERCICIO (resuelto): Construir, con el API de Map-Reduce, una colección 'post_comments', donde se añade el campo 'Comments' a cada Post con la lista de todos los comentarios referidos a un Post."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Veremos la resolución de este ejercicio para que haga de ejemplo para los siguientes a implementar. En primer lugar, una operación map/reduce sólo se puede ejecutar sobre una colección, así que sólo puede contener resultados de la misma. Por lo tanto, con sólo una operación map/reduce no va a ser posible realizar todo el ejercicio.\n",
"\n",
"Así, en primer lugar, parece interesante agrupar todos los comentarios que se han producido de un Post en particular. En cada comentario, el atributo `PostId` marca una referencia al Post al que se refiere.\n",
"\n",
"Es importante cómo se construyen las operaciones `map()` y `reduce()`. Primero, la función `map()` se ejecutará para todos los documentos (o para todos los que cumplan la condición si se utiliza el modificador `query=`). Sin embargo, la función `reduce()` no se ejecutará a no ser que haya más de un elemento asociado a la misma clave.\n",
"\n",
"Por lo tanto, la salida de la función `map()` debe ser la misma que la de la función `reduce()`. En nuestro caso, es un objeto JSON de la forma:\n",
"\n",
" { type: 'comment', comments: [ {comentario1, comentario2} ] }\n",
"\n",
"En el caso de que sólo se ejecute la función `map()`, nótese cómo el objeto tiene la misma composición, pero con un array de sólo un elemento (comentario): sí mismo."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from bson.code import Code\n",
"\n",
"comments_map = Code('''\n",
"function () {\n",
" emit(this.PostId, { type: 'comment', comments: [this]});\n",
"}\n",
"''')\n",
"\n",
"comments_reduce = Code('''\n",
"function (key, values) {\n",
" comments = [];\n",
" values.forEach(function(v) {\n",
" if ('comments' in v)\n",
" comments = comments.concat(v.comments)\n",
" })\n",
" return { type: 'comment', comments: comments };\n",
"}\n",
"''')\n",
"\n",
"db.comments.map_reduce(comments_map, comments_reduce, \"post_comments\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"list(db.post_comments.find()[:10])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Esto demuestra que en general el esquema de datos en MongoDB no estaría así desde el principio."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Después del primer paso de map/reduce, tenemos que construir la colección final que asocia cada Post con sus comentarios. Como hemos construido antes la colección `post_comments` indizada por el `Id` del Post, podemos utilizar ahora una ejecución de map/reduce que *mezcle* los datos en `post_comments` con los datos en `posts`.\n",
"\n",
"La segunda ejecución de map/reduce la haremos sobre `posts`, para que el resultado sea completo, incluso para los Posts que no aparecen en comentarios, y por lo tanto tendrán el atributo `comments` vacío.\n",
"\n",
"En este caso, debemos hacer que la función `map()` produzca una salida de documentos que también están indizados con el atributo `Id`, y, como sólo hay uno para cada `Id`, la función `reduce()` no se ejecutará. **Tan sólo se ejecutará para mezclar ambas colecciones**, así que la función `reduce()` tendrá que estar preparada para mezclar objetos de tipo \"comment\" y Posts. En cualquier caso, como se puede ver, es válida también aunque sólo se llame con un objeto de tipo Post. Finalmente, la función `map()` prepara a cada objeto Post, inicialmente, con una lista de comentarios vacíos"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"posts_map = Code(\"\"\"\n",
"function () {\n",
" this.comments = [];\n",
" emit(this.Id, this);\n",
"}\n",
"\"\"\")\n",
"\n",
"posts_reduce = Code(\"\"\"\n",
"function (key, values) {\n",
" comments = []; // The set of comments\n",
" obj = {}; // The object to return\n",
" \n",
" values.forEach(function(v) {\n",
" if (v['type'] === 'comment')\n",
" comments = comments.concat(v.comments);\n",
" else // Object\n",
" {\n",
" obj = v;\n",
" // obj.comments will always be there because of the map() operation\n",
" comments = comments.concat(obj.comments);\n",
" }\n",
" })\n",
" \n",
" // Finalize: Add the comments to the object to return\n",
" obj.comments = comments;\n",
"\n",
" return obj;\n",
"}\n",
"\"\"\")\n",
"\n",
"db.posts.map_reduce(posts_map, posts_reduce, out={'reduce' : 'post_comments'})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"list(db.post_comments.find()[:10])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Framework de Agregación\n",
"\n",
"Framework de agregación: https://docs.mongodb.com/manual/reference/operator/aggregation/. Y aquí una presentación interesante sobre el tema: https://www.mongodb.com/presentations/aggregation-framework-0?jmp=docs&_ga=1.223708571.1466850754.1477658152\n",
"\n",
"<video style=\"width:100%;\" src=\"https://docs.mongodb.com/manual/_images/agg-pipeline.mp4\" controls> </video>\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Proyección:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"respuestas = db['posts'].aggregate( [ {'$project' : { 'Id' : True }}, {'$limit': 20} ])\n",
"list(respuestas)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_Lookup_!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"respuestas = posts.aggregate( [\n",
" {'$match': { 'Score' : {'$gte': 40}}},\n",
" {'$lookup': {\n",
" 'from': \"users\", \n",
" 'localField': \"OwnerUserId\",\n",
" 'foreignField': \"Id\",\n",
" 'as': \"owner\"}\n",
" }\n",
" ])\n",
"list(respuestas)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"El `$lookup` genera un _array_ con todos los resultados. El operador `$arrayElementAt` accede al primer elemento."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"respuestas = db.posts.aggregate( [\n",
" {'$match': { 'Score' : {'$gte': 40}}},\n",
" {'$lookup': {\n",
" 'from': \"users\", \n",
" 'localField': \"OwnerUserId\",\n",
" 'foreignField': \"Id\",\n",
" 'as': \"owner\"}\n",
" },\n",
" { '$project' :\n",
" {\n",
" 'Id' : True,\n",
" 'Score' : True,\n",
" 'username' : {'$arrayElemAt' : ['$owner.DisplayName', 0]},\n",
" 'owner.DisplayName' : True\n",
" }}\n",
" ])\n",
"list(respuestas)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`$unwind` también puede usarse. \"Desdobla\" cada fila por cada elemento del array. En este caso, como sabemos que el array sólo contiene un elemento, sólo habrá una fila por fila original, pero sin el _array_. Finalmente se puede proyectar el campo que se quiera."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"respuestas = db.posts.aggregate( [\n",
" {'$match': { 'Score' : {'$gte': 40}}},\n",
" {'$lookup': {\n",
" 'from': \"users\", \n",
" 'localField': \"OwnerUserId\",\n",
" 'foreignField': \"Id\",\n",
" 'as': \"owner\"}\n",
" },\n",
" { '$unwind': '$owner'},\n",
" { '$project' : \n",
" {\n",
" 'username': '$owner.DisplayName'\n",
" }\n",
" }\n",
" ])\n",
"list(respuestas)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ejemplo de realización de la consulta RQ4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Como ejemplo de consulta compleja con el Framework de Agregación, adjunto una posible solución a la consulta RQ4:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"RQ4 = db.posts.aggregate( [\n",
" { \"$match\" : {\"PostTypeId\": 2}},\n",
" {'$lookup': {\n",
" 'from': \"posts\", \n",
" 'localField': \"ParentId\",\n",
" 'foreignField': \"Id\",\n",
" 'as': \"question\"\n",
" }\n",
" },\n",
" {\n",
" '$unwind' : '$question'\n",
" },\n",
" {\n",
" '$project' : { 'OwnerUserId': True, \n",
" 'OP' : '$question.OwnerUserId'\n",
" }\n",
" },\n",
" {\n",
" '$group' : {'_id' : {'min' : { '$min' : ['$OwnerUserId' , '$OP'] },\n",
" 'max' : { '$max' : ['$OwnerUserId' , '$OP'] }},\n",
" 'pairs' : {'$addToSet' : { '0q': '$OP', '1a': '$OwnerUserId'}}\n",
" }\n",
" },\n",
" {\n",
" '$project': {\n",
" 'pairs' : True,\n",
" 'npairs' : { '$size' : '$pairs'}\n",
" }\n",
" },\n",
" {\n",
" '$match' : { 'npairs' : { '$eq' : 2}}\n",
" }\n",
" ])\n",
"RQ4 = list(RQ4)\n",
"RQ4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La explicación es como sigue:\n",
"\n",
"1. Se eligen sólo las respuestas\n",
"2. Se accede a la tabla `posts` para recuperar los datos de la pregunta\n",
"3. A continuación se proyectan sólo el usuario que pregunta y el que hace la respuesta\n",
"4. El paso más imaginativo es el de agrupación. Lo que se intenta es que ambos pares de usuarios que están relacionados como preguntante -> respondiente y viceversa, caigan en la misma clave. Por ello, se coge el máximo y el mínimo de ambos identificadores de usuarios y se construye una clave con ambos números en las mismas posiciones. Así, ambas combinaciones de usuario que pregunta y que responde caerán en la misma clave. También se usa un conjunto (en `pairs`), y sólo se añadirá una vez las posibles combinaciones iguales de preguntador/respondiente.\n",
"5. Sólo nos interesan aquellas tuplas cuyo tamaño del conjunto de pares de pregunta/respuesta sea igual a dos (en un elemento uno de los dos usuarios habrá preguntado y el otro habrá respondido y en el otro viceversa).\n",
"\n",
"La implementación en Map-Reduce se puede realizar con la misma idea."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En el caso de que queramos tener como referencia las preguntas y respuestas a las que se refiere la conversación, se puede añadir un campo más que guarde todas las preguntas junto con sus respuestas consideradas "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"RQ4 = db.posts.aggregate( [\n",
" {'$match': { 'PostTypeId' : 2}},\n",
" {'$lookup': {\n",
" 'from': \"posts\", \n",
" 'localField': \"ParentId\",\n",
" 'foreignField': \"Id\",\n",
" 'as': \"question\"}\n",
" },\n",
" {\n",
" '$unwind' : '$question'\n",
" },\n",
" {\n",
" '$project' : {'OwnerUserId': True,\n",
" 'QId' : '$question.Id',\n",
" 'AId' : '$Id',\n",
" 'OP' : '$question.OwnerUserId'\n",
" }\n",
" },\n",
" {\n",
" '$group' : {'_id' : {'min' : { '$min' : ['$OwnerUserId' , '$OP'] },\n",
" 'max' : { '$max' : ['$OwnerUserId' , '$OP'] }},\n",
" 'pairs' : {'$addToSet' : { '0q':'$OP', '1a': '$OwnerUserId'}},\n",
" 'considered_pairs' : { '$push' : {'QId' : '$QId', 'AId' : '$AId'}}\n",
" }\n",
" },\n",
" {\n",
" '$project': {\n",
" 'pairs' : True,\n",
" 'npairs' : { '$size' : '$pairs'},\n",
" 'considered_pairs' : True\n",
" }\n",
" },\n",
" {\n",
" '$match' : { 'npairs' : { '$eq' : 2}}\n",
" }\n",
" ])\n",
"RQ4 = list(RQ4)\n",
"RQ4"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(db.posts.find_one({'Id': 238}), db.posts.find_one({'Id': 243}),\n",
"db.posts.find_one({'Id': 222}), db.posts.find_one({'Id': 223}))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ejemplo de consulta: Tiempo medio desde que se hace una pregunta hasta que se le da la primera respuesta\n",
"\n",
"Veamos cómo calcular el tiempo medio desde que se hace una pregunta hasta que se le da la primera respuesta. En este caso se puede utilizar las respuestas para apuntar a qué pregunta correspondieron. No se considerarán pues las preguntas que no tienen respuesta, lo cual es razonable. Sin embargo, la función map debe guardar también las preguntas para poder calcular el tiempo menor (la primera repuesta)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from bson.code import Code\n",
"\n",
"# La función map agrupará todas las respuestas, pero también necesita las \n",
"mapcode = Code(\"\"\"\n",
"function () {\n",
" if (this.PostTypeId == 2)\n",
" emit(this.ParentId, {q: null, a: {Id: this.Id, CreationDate: this.CreationDate}, diff: null})\n",
" else if (this.PostTypeId == 1)\n",
" emit(this.Id, {q: {Id: this.Id, CreationDate: this.CreationDate}, a: null, diff: null})\n",
"}\n",
"\"\"\")\n",
"\n",
"reducecode = Code(\"\"\"\n",
"function (key, values) {\n",
" q = null // Pregunta\n",
" a = null // Respuesta con la fecha más cercana a la pregunta\n",
" \n",
" values.forEach(function(v) {\n",
" if (v.q != null) // Pregunta\n",
" q = v.q\n",
" if (v.a != null) // Respuesta\n",
" {\n",
" if (a == null || v.a.CreationDate < a.CreationDate)\n",
" a = v.a\n",
" }\n",
" })\n",
"\n",
" mindiff = null\n",
" if (q != null && a != null)\n",
" mindiff = a.CreationDate - q.CreationDate;\n",
"\n",
" return {q: q, a: a, diff: mindiff}\n",
"}\n",
"\"\"\")\n",
"\n",
"db.posts.map_reduce(mapcode, reducecode, \"min_response_time\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"mrt = list(db.min_response_time.find())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pandas.io.json import json_normalize\n",
"\n",
"df = json_normalize(mrt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.index=df[\"_id\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df['value.diff'].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Esto sólo calcula el tiempo mínimo de cada pregunta a su respuesta. Después habría que aplicar lo visto en otros ejemplos para calcular la media. Con agregación, a continuación, sí que se puede calcular la media de forma relativament sencilla:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"min_answer_time = db.posts.aggregate([\n",
" {\"$match\" : {\"PostTypeId\" : 2}},\n",
" {\n",
" '$group' : {'_id' : '$ParentId',\n",
" # 'answers' : { '$push' : {'Id' : \"$Id\", 'CreationDate' : \"$CreationDate\"}},\n",
" 'min' : {'$min' : \"$CreationDate\"}\n",
" }\n",
" },\n",
" { \"$lookup\" : {\n",
" 'from': \"posts\", \n",
" 'localField': \"_id\",\n",
" 'foreignField': \"Id\",\n",
" 'as': \"post\"}\n",
" },\n",
" { \"$unwind\" : \"$post\"},\n",
" {\"$project\" :\n",
" {\"_id\" : True,\n",
" \"min\" : True,\n",
" #\"post\" : True,\n",
" \"diff\" : {\"$subtract\" : [\"$min\", \"$post.CreationDate\"]}}\n",
" },\n",
" # { \"$sort\" : {'_id' : 1} }\n",
" {\n",
" \"$group\" : {\n",
" \"_id\" : None,\n",
" \"avg\" : { \"$avg\" : \"$diff\"}\n",
" }\n",
" }\n",
"])\n",
"min_answer_time = list(min_answer_time)\n",
"min_answer_time"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## EJERCICIO: Con Map-Reduce, construir las colecciones que asocian un usuario con sus tags y los tags con los usuarios que las utilizan (E1)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## EJERCICIO: Con el Framework de Agregación, generar la colección `StackOverflowFacts` vista en la sesión 2 (E2)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## EJERCICIO: Con Map-Reduce, implementar la consulta RQ3 de la sesión 2."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## EJERCICIO (difícil, opcional): Con Agregación, calcular, enla tabla `StackOverflowFacts` la media de tiempo que pasa desde que los usuarios se registran hasta que publican su primera pregunta."
]
},
{
"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": 4
}
| mit |
nkmk/python-snippets | notebook/opencv_drawing.ipynb | 1 | 5147 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import cv2\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.3.0\n"
]
}
],
"source": [
"print(cv2.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img = np.full((210, 425, 3), 128, dtype=np.uint8)\n",
"\n",
"cv2.rectangle(img, (50, 10), (125, 60), (255, 0, 0))\n",
"cv2.rectangle(img, (50, 80), (125, 130), (0, 255, 0), thickness=-1)\n",
"cv2.rectangle(img, (50, 150), (125, 200), (0, 0, 255), thickness=-1)\n",
"cv2.rectangle(img, (50, 150), (125, 200), (255, 255, 0))\n",
"\n",
"cv2.rectangle(img, (175, 10), (250, 60), (255, 255, 255), thickness=8, lineType=cv2.LINE_4)\n",
"cv2.line(img, (175, 10), (250, 60), (0, 0, 0), thickness=1, lineType=cv2.LINE_4)\n",
"cv2.rectangle(img, (175, 80), (250, 130), (255, 255, 255), thickness=8, lineType=cv2.LINE_8)\n",
"cv2.line(img, (175, 80), (250, 130), (0, 0, 0), thickness=1, lineType=cv2.LINE_8)\n",
"cv2.rectangle(img, (175, 150), (250, 200), (255, 255, 255), thickness=8, lineType=cv2.LINE_AA)\n",
"cv2.line(img, (175, 150), (250, 200), (0, 0, 0), thickness=1, lineType=cv2.LINE_AA)\n",
"\n",
"cv2.rectangle(img, (600, 20), (750, 120), (0, 0, 0), lineType=cv2.LINE_AA, shift=1)\n",
"cv2.rectangle(img, (601, 160), (751, 260), (0, 0, 0), lineType=cv2.LINE_AA, shift=1)\n",
"cv2.rectangle(img, (602, 300), (752, 400), (0, 0, 0), lineType=cv2.LINE_AA, shift=1)\n",
"\n",
"cv2.imwrite('data/dst/opencv_draw_argument.png', img)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"img_rect = cv2.rectangle(img, (10, 10), (110, 60), (255, 0, 0))\n",
"print(img is img_rect)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img = np.full((210, 425, 3), 128, dtype=np.uint8)\n",
"\n",
"cv2.line(img, (50, 10), (125, 60), (255, 0, 0))\n",
"cv2.line(img, (50, 60), (125, 10), (0, 255, 255), thickness=4, lineType=cv2.LINE_AA)\n",
"\n",
"cv2.arrowedLine(img, (50, 80), (125, 130), (0, 255, 0), thickness=4)\n",
"cv2.arrowedLine(img, (50, 130), (125, 80), (255, 0, 255), tipLength=0.3)\n",
"\n",
"cv2.rectangle(img, (50, 150), (125, 200), (255, 255, 0))\n",
"\n",
"cv2.circle(img, (190, 35), 15, (255, 255, 255), thickness=-1)\n",
"cv2.circle(img, (240, 35), 20, (0, 0, 0), thickness=3, lineType=cv2.LINE_AA)\n",
"\n",
"cv2.ellipse(img, ((190, 105), (20, 50), 0), (255, 255, 255))\n",
"cv2.ellipse(img, ((240, 105), (20, 50), 30), (0, 0, 0), thickness=-1)\n",
"\n",
"cv2.ellipse(img, (190, 175), (10, 25), 0, 0, 270, (255, 255, 255))\n",
"cv2.ellipse(img, (240, 175), (10, 25), 30, 0, 270, (0, 0, 0), thickness=-1)\n",
"\n",
"cv2.drawMarker(img, (300, 20), (255, 0, 0))\n",
"cv2.drawMarker(img, (337, 20), (0, 255, 0), markerType=cv2.MARKER_TILTED_CROSS, markerSize=15)\n",
"cv2.drawMarker(img, (375, 20), (0, 0, 255), markerType=cv2.MARKER_STAR, markerSize=10)\n",
"\n",
"cv2.drawMarker(img, (300, 50), (0, 255, 255), markerType=cv2.MARKER_DIAMOND)\n",
"cv2.drawMarker(img, (337, 50), (255, 0, 255), markerType=cv2.MARKER_SQUARE, markerSize=15)\n",
"cv2.drawMarker(img, (375, 50), (255, 255, 0), markerType=cv2.MARKER_TRIANGLE_UP, markerSize=10)\n",
"\n",
"pts = np.array(((300, 80), (300, 130), (335, 130)))\n",
"cv2.polylines(img, [pts], True, (255, 255, 255), thickness=2)\n",
"\n",
"pts = np.array(((335, 80), (375, 80), (375, 130)))\n",
"cv2.fillPoly(img, [pts], (0, 0, 0))\n",
"\n",
"cv2.putText(img, 'nkmk', (300, 170), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 255, 255), thickness=2)\n",
"cv2.putText(img, 'nkmk', (300, 195), cv2.FONT_HERSHEY_COMPLEX, 0.8, (0, 0, 0), lineType=cv2.LINE_AA)\n",
"\n",
"cv2.imwrite('data/dst/opencv_draw_etc.png', img)"
]
}
],
"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": 2
}
| mit |
PubuduSaneth/genome4d | looplist2bed.ipynb | 1 | 12518 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Convert looplist file to different file formats"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Looplist file format](http://dx.doi.org/10.1016/j.cell.2014.11.021) <br>\n",
"\n",
"1. chromosome = the chromosome that the loop is located on\n",
"\n",
"2. x1,x2 = the coordinates of the upstream locus corresponding to the peak pixel (see the Experimental Procedures and VI.a.5.iv of the Extended Experimental Procedures of Rao, Huntley, et al., Cell 2014 for a definition of the peak pixel)\n",
"\n",
"3. chromosome\n",
"\n",
"4. y1,y2 = the coordinates of the downstream locus corresponding to the peak pixel (see the Experimental Procedures and VI.a.5.iv of the Extended Experimental Procedures of Rao, Huntley, et al., Cell 2014 for a definition of the peak pixel)\n",
"\n",
"5. color = the color that the feature will be rendered as if loaded in [Juicebox](www.aidenlab.org/juicebox)\n",
"\n",
"6. observed = the raw observed counts at the peak pixel (see the Experimental Procedures and VI.a.5.iv of the Extended Experimental Procedures of Rao, Huntley, et al., Cell 2014 for a definition of the peak pixel)\n",
"\n",
"7. expected_[bottom_left, donut, horizontal, vertical] = the expected counts calculated using the [bottom_left, donut, horizontal, vertical] filter (see Figure 3 and section VI.a.5.i of the Extended Experimental Procedures of Rao, Huntley, et al., Cell 2014)\n",
"\n",
"8. fdr_[bottom_left, donut, horizontal, vertical] = the q-value of the loop calculated using the [bottom_left, donut, horizontal, vertical] filter (see VI.a.5.ii of the Extended Experimental Procedures of Rao, Huntley, et al., Cell 2014)\n",
"\n",
"9. number_collapsed = the number of pixels that were clustered together as part of the loop call (see section VI.a.5.iv of the Extended Experimental Procedures of Rao, Huntley, et al., Cell 2014)\n",
"\n",
"10. centroid1 = the upstream coordinate of the centroid of the cluster of pixels corresponding to the loop (see section VI.a.5.iv of the Extended Experimental Procedures of Rao, Huntley, et al., Cell 2014)\n",
"\n",
"11. centroid2 = the downstream coordinate of the centroid of the cluster of pixels corresponding to the loop (see section VI.a.5.iv of the Extended Experimental Procedures of Rao, Huntley, et al., Cell 2014)\n",
"\n",
"12. radius = the Euclidean distance from the centroid of the cluster of pixels to the farthest pixel in the cluster of pixels (see section VI.a.5.iv of the Extended Experimental Procedures of Rao, Huntley, et al., Cell 2014)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [BED12 output file format](http://bedtools.readthedocs.io/en/latest/content/general-usage.html)</br>\n",
"\n",
"1. chromosome_1 (upstream locus)\n",
"2. start_1 (upstream locus)\n",
"3. end_2 (downstream locus)\n",
"4. interaction (eg. chr:start_1..end_1-chr:start_2..end_2)\n",
"5. Score\n",
"6. strand\n",
"7. start_1\n",
"8. end_2\n",
"9. itemRgb\n",
"10. blockCount\n",
"11. blockSizes\n",
"12. blockStarts(distance)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [BEDPE output file format](http://bedtools.readthedocs.io/en/latest/content/general-usage.html)</br>\n",
"\n",
"1. chromosome_1 (upstream locus)\n",
"2. start_1 (upstream locus)\n",
"3. end_1 (upstream locus)\n",
"4. chromosome_2 (downstream locus)\n",
"5. Start_2 (downstream locus)\n",
"6. end_2 (downstream locus)\n",
"7. interaction (eg. chr:start_1..end_1-chr:start_2..end_2)\n",
"8. score (fdr_donut)\n",
"9. strand_1\n",
"10. strand_2\n",
"11. fdr_bl\n",
"12. fdr_h\n",
"13. fdr_v\n",
"14. distance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [HOMER](http://homer.salk.edu/homer/interactions/HiCinteractions.html)</br>\n",
"\n",
"1. Interaction ID (must be unique)\n",
"2. Peak ID for region 1\n",
"3. chr for region 1\n",
"4. start position for region 1\n",
"5. end position for region 1\n",
"6. strand for region 1\n",
"7. total reads for region 1\n",
"8. Peak ID for region 2\n",
"9. chr for region 2\n",
"10. start position for region 2\n",
"11. end position for region 2\n",
"12. strand for region 2\n",
"13. total reads for region 2\n",
"14. Distance between regions (or \"interchromosomal\")\n",
"15. Interaction Reads (total Hi-C reads connecting the regions)\n",
"16. Expected Interaction Reads (total expected Hi-C reads based on background model)\n",
"17. Modified Z-score\n",
"18. Natural log of the p-value for the interaction (binomial)\n",
"19. False Discovery Rate (based on Benjamini correction)\n",
"20. Circos Thickness (used for visualization by Circos)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#header of a looplist file\n",
"loop_head = ['chr1', 'x1', 'x2', 'chr2', 'y1', 'y2', 'color', 'o', 'e_bl', 'e_donut', 'e_h', 'e_v', 'fdr_bl', 'fdr_donut', 'fdr_h', 'fdr_v', 'number collapsed', 'centroid1', 'centroid2', 'radius']"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def to_bed12(looplist, out_file):\n",
" # conver a looplist file to bed12\n",
" #i=0\n",
" bed = open(out_file, \"w\")\n",
" with open (looplist, \"r\") as f:\n",
" f.readline()\n",
" for l in f:\n",
" l = l.replace(\"\\n\",\"\").split(\"\\t\")\n",
" d = dict(zip(loop_head,l))\n",
" out = (\"{}\\t{}\\t{}\\t\".format(d['chr1'],d['x1'],d['y2']) + \n",
" \"{}:{}..{}-{}:{}-{}\\t\".format(d['chr1'],d['x1'],d['x2'],d['chr2'],d['y1'],d['y2']) +\n",
" \"{}\\t\".format(d['fdr_donut']) +\n",
" \".\\t\" +\n",
" \"{}\\t{}\\t\".format(d['x1'],d['y2']) +\n",
" \"0,0,0\\t\" +\n",
" \"2\\t\" +\n",
" \"100000,100000\\t\" +\n",
" \"0,{}\\n\".format(int(d['y2'])-int(d['x1'])) )\n",
" bed.write(out)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def to_bedpe(looplist, out_file):\n",
" #i=0\n",
" bedpe = open(out_file, \"w\")\n",
" with open (looplist, \"r\") as f:\n",
" f.readline()\n",
" for l in f:\n",
" l = l.replace(\"\\n\",\"\").split(\"\\t\")\n",
" d = dict(zip(loop_head,l))\n",
" #print l\n",
" out = (\"{}\\t{}\\t{}\\t\".format(d['chr1'],d['x1'],d['x2']) + \n",
" \"{}\\t{}\\t{}\\t\".format(d['chr2'],d['y1'],d['y2']) + \n",
" \"{}:{}..{}-{}:{}-{}\\t\".format(d['chr1'],d['x1'],d['x2'],d['chr2'],d['y1'],d['y2']) +\n",
" \"{}\\t\".format(d['fdr_donut']) +\n",
" \"*\\t*\\t\" +\n",
" \"{}\\t{}\\t{}\\t\".format(d['fdr_bl'], d['fdr_h'], d['fdr_v']) + \n",
" \"0,{}\\n\".format(int(d['y2'])-int(d['x1'])) )\n",
" bedpe.write(out)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def to_hiBrowsein(looplist, out_file):\n",
" #i=0\n",
" hibrowsein = open(out_file, \"w\")\n",
" hibrowsein_head = (\"chr1\\tx1\\tx2\\tchr2\\ty1\\ty2\\tfdr_bl\\n\")\n",
" hibrowsein.write(hibrowsein_head)\n",
" #chr1\tx1\tx2\tchr2\ty1\ty2\tfdr_bl\n",
" with open (looplist, \"r\") as f:\n",
" f.readline()\n",
" for l in f:\n",
" l = l.replace(\"\\n\",\"\").split(\"\\t\")\n",
" d = dict(zip(loop_head,l))\n",
" #print l\n",
" out = (\"{}\\t{}\\t{}\\t{}\\t{}\\t{}\\t{}\\n\".format(d['chr1'],d['x1'],d['x2'],d['chr2'],d['y1'],d['y2'],d['fdr_bl']))\n",
" hibrowsein.write(out)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def to_homer(looplist, out_file):\n",
" #i=0\n",
" homer = open(out_file, \"w\")\n",
" homer_head = (\"InteractionID\\t\" +\n",
" \"PeakID(1)\\t\" + \n",
" \"chr(1)\\t\" + \n",
" \"start(1)\\t\" + \n",
" \"end(1)\\t\" + \n",
" \"strand(1)\\t\" + \n",
" \"Total Reads(1)\\t\" + \n",
" \"PeakID(2)\\t\" + \n",
" \"chr(2)\\t\" + \n",
" \"start(2)\\t\" + \n",
" \"end(2)\\t\" + \n",
" \"strand(2)\\t\" + \n",
" \"Total Reads(2)\\t\" + \n",
" \"Distance\\t\" + \n",
" \"Interaction Reads\\t\" + \n",
" \"Expected Reads\\t\" + \n",
" \"Z-score\\t\" + \"LogP\\t\" + \"FDR\\t\" + \"Circos Thickness\\n\") #(Benjamini, based on 4.90e+08 total tests)\n",
" homer.write(homer_head)\n",
" with open (looplist, \"r\") as f:\n",
" f.readline()\n",
" for l in f:\n",
" l = l.replace(\"\\n\",\"\").split(\"\\t\")\n",
" d = dict(zip(loop_head,l))\n",
" #print l\n",
" out = (\"{}:{}..{}-{}:{}-{}\\t\".format(d['chr1'],d['x1'],d['x2'],d['chr2'],d['y1'],d['y2']) +\n",
" \"{}-{}\\t\".format(d['chr1'],d['x1']) +\n",
" \"{}\\t{}\\t{}\\t\".format(d['chr1'],d['x1'],d['x2']) + \n",
" \".\\t0\\t\" +\n",
" \"{}-{}\\t\".format(d['chr2'],d['y1']) +\n",
" \"{}\\t{}\\t{}\\t\".format(d['chr2'],d['y1'],d['y2']) + \n",
" \".\\t0\\t\" +\n",
" \"{}\\t\".format(int(d['y2'])-int(d['x1'])) + \n",
" \"0\\t0\\t0\\t\" +\n",
" \"{}\\t{}\\t\".format(d['fdr_bl'], d['fdr_donut']) +\n",
" \"2\\n\" )\n",
" homer.write(out)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"to_bedpe(\"/Users/pubudu/Documents/HiC-runs/hic_formatConversion/chr1-looplist.txt\",'/Users/pubudu/Documents/HiC-runs/hic_formatConversion/chr1-looplist.bedpe2.bed') # to_bedpe(<looplist file>, <output file name>)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"to_homer(\"/Users/pubudu/Documents/HiC-runs/hic_formatConversion/chr1-looplist.txt\",'/Users/pubudu/Documents/HiC-runs/hic_formatConversion/chr1-looplist.homer2.bed') # to_homer(<looplist file>, <output file name>)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"to_hiBrowsein(\"/Users/pubudu/Documents/HiC-runs/hic_formatConversion/chr1-looplist.txt\",'/Users/pubudu/Documents/HiC-runs/hic_formatConversion/chr1-looplist.hibrowseIn.txt') # to_homer(<looplist file>, <output file name>)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python (saneth)",
"language": "python",
"name": "saneth"
},
"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
}
| gpl-3.0 |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/sdk/sdk_automl_image_object_detection_online_export_edge.ipynb | 1 | 37331 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "copyright"
},
"outputs": [],
"source": [
"# Copyright 2021 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": "title"
},
"source": [
"# Vertex SDK: AutoML training image object detection model for export to edge\n",
"\n",
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/tree/master/notebooks/official/automl/sdk_automl_image_object_detection_online_export_edge.ipynb\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/master/notebooks/official/automl/sdk_automl_image_object_detection_online_export_edge.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",
" <td>\n",
" <a href=\"https://console.cloud.google.com/ai/platform/notebooks/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/master/notebooks/official/automl/sdk_automl_image_object_detection_online_export_edge.ipynb\">\n",
" Open in Google Cloud Notebooks\n",
" </a>\n",
" </td>\n",
"</table>\n",
"<br/><br/><br/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "overview:automl,export_edge"
},
"source": [
"## Overview\n",
"\n",
"\n",
"This tutorial demonstrates how to use the Vertex SDK to create image object detection models to export as an Edge model using a Google Cloud AutoML model."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dataset:salads,iod"
},
"source": [
"### Dataset\n",
"\n",
"The dataset used for this tutorial is the Salads category of the [OpenImages dataset](https://www.tensorflow.org/datasets/catalog/open_images_v4) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). This dataset does not require any feature engineering. The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the bounding box locations and corresponding type of salad items in an image from a class of five items: salad, seafood, tomato, baked goods, or cheese."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "objective:automl,training,export_edge"
},
"source": [
"### Objective\n",
"\n",
"In this tutorial, you create a AutoML image object detection model from a Python script using the Vertex SDK, and then export the model as an Edge model in TFLite format. You can alternatively create models with AutoML using the `gcloud` command-line tool or online using the Cloud Console.\n",
"\n",
"The steps performed include:\n",
"\n",
"- Create a Vertex `Dataset` resource.\n",
"- Train the model.\n",
"- Export the `Edge` model from the `Model` resource to Cloud Storage.\n",
"- Download the model locally.\n",
"- Make a local prediction."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "costs"
},
"source": [
"### 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": "setup_local"
},
"source": [
"### Set up your local development environment\n",
"\n",
"If you are using Colab or Google Cloud Notebooks, your environment already meets all the requirements to run this notebook. You can skip this step.\n",
"\n",
"Otherwise, make sure your environment meets this notebook's requirements. You need the following:\n",
"\n",
"- The Cloud Storage SDK\n",
"- Git\n",
"- Python 3\n",
"- virtualenv\n",
"- Jupyter notebook running in a virtual environment with Python 3\n",
"\n",
"The Cloud Storage guide to [Setting up a Python development environment](https://cloud.google.com/python/setup) and the [Jupyter installation guide](https://jupyter.org/install) provide detailed instructions for meeting these requirements. The following steps provide a condensed set of instructions:\n",
"\n",
"1. [Install and initialize the SDK](https://cloud.google.com/sdk/docs/).\n",
"\n",
"2. [Install Python 3](https://cloud.google.com/python/setup#installing_python).\n",
"\n",
"3. [Install virtualenv](https://cloud.google.com/python/setup#installing_and_using_virtualenv) and create a virtual environment that uses Python 3. Activate the virtual environment.\n",
"\n",
"4. To install Jupyter, run `pip3 install jupyter` on the command-line in a terminal shell.\n",
"\n",
"5. To launch Jupyter, run `jupyter notebook` on the command-line in a terminal shell.\n",
"\n",
"6. Open this notebook in the Jupyter Notebook Dashboard.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "install_aip:mbsdk"
},
"source": [
"## Installation\n",
"\n",
"Install the latest version of Vertex SDK for Python."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "install_aip:mbsdk"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"# Google Cloud Notebook\n",
"if os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n",
" USER_FLAG = \"--user\"\n",
"else:\n",
" USER_FLAG = \"\"\n",
"\n",
"! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "install_storage"
},
"source": [
"Install the latest GA version of *google-cloud-storage* library as well."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "install_storage"
},
"outputs": [],
"source": [
"! pip3 install -U google-cloud-storage $USER_FLAG"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "install_tensorflow"
},
"outputs": [],
"source": [
"if os.environ[\"IS_TESTING\"]:\n",
" ! pip3 install --upgrade tensorflow $USER_FLAG"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "restart"
},
"source": [
"### Restart the kernel\n",
"\n",
"Once you've installed the additional packages, you need to restart the notebook kernel so it can find the packages."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "restart"
},
"outputs": [],
"source": [
"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": "before_you_begin:nogpu"
},
"source": [
"## Before you begin\n",
"\n",
"### GPU runtime\n",
"\n",
"This tutorial does not require a GPU runtime.\n",
"\n",
"### 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). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
"\n",
"2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n",
"\n",
"3. [Enable the following APIs: Vertex AI APIs, Compute Engine APIs, and Cloud Storage.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component,storage-component.googleapis.com)\n",
"\n",
"4. If you are running this notebook locally, you will need to install the [Cloud SDK]((https://cloud.google.com/sdk)).\n",
"\n",
"5. 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 `$`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "set_project_id"
},
"outputs": [],
"source": [
"PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "autoset_project_id"
},
"outputs": [],
"source": [
"if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n",
" # Get your GCP project id from gcloud\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": "code",
"execution_count": null,
"metadata": {
"id": "set_gcloud_project_id"
},
"outputs": [],
"source": [
"! gcloud config set project $PROJECT_ID"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "region"
},
"source": [
"#### Region\n",
"\n",
"You can also change the `REGION` variable, which is used for operations\n",
"throughout the rest of this notebook. Below are regions supported for Vertex AI. We recommend that you choose the region closest to you.\n",
"\n",
"- Americas: `us-central1`\n",
"- Europe: `europe-west4`\n",
"- Asia Pacific: `asia-east1`\n",
"\n",
"You may not use a multi-regional bucket for training with Vertex AI. Not all regions provide support for all Vertex AI services.\n",
"\n",
"Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "region"
},
"outputs": [],
"source": [
"REGION = \"us-central1\" # @param {type: \"string\"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "timestamp"
},
"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 the timestamp onto the name of resources you create in this tutorial."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "timestamp"
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"\n",
"TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gcp_authenticate"
},
"source": [
"### Authenticate your Google Cloud account\n",
"\n",
"**If you are using Google Cloud Notebooks**, your environment is already authenticated. Skip this step.\n",
"\n",
"**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
"\n",
"**Otherwise**, follow these steps:\n",
"\n",
"In the Cloud Console, go to the [Create service account key](https://console.cloud.google.com/apis/credentials/serviceaccountkey) page.\n",
"\n",
"**Click Create service account**.\n",
"\n",
"In the **Service account name** field, enter a name, and click **Create**.\n",
"\n",
"In the **Grant this service account access to project** section, click the Role drop-down list. Type \"Vertex\" into the filter box, and select **Vertex Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
"\n",
"Click Create. A JSON file that contains your key downloads to your local environment.\n",
"\n",
"Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gcp_authenticate"
},
"outputs": [],
"source": [
"# 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",
"import os\n",
"import sys\n",
"\n",
"# If on Google Cloud Notebook, then don't execute this code\n",
"if not os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\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, replace the string below with the\n",
" # path to your service account key and run this cell to authenticate your GCP\n",
" # account.\n",
" elif not os.getenv(\"IS_TESTING\"):\n",
" %env GOOGLE_APPLICATION_CREDENTIALS ''"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bucket:mbsdk"
},
"source": [
"### Create a Cloud Storage bucket\n",
"\n",
"**The following steps are required, regardless of your notebook environment.**\n",
"\n",
"When you initialize the Vertex SDK for Python, you specify a Cloud Storage staging bucket. The staging bucket is where all the data associated with your dataset and model resources are retained across sessions.\n",
"\n",
"Set the name of your Cloud Storage bucket below. Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bucket"
},
"outputs": [],
"source": [
"BUCKET_NAME = \"gs://[your-bucket-name]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "autoset_bucket"
},
"outputs": [],
"source": [
"if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"gs://[your-bucket-name]\":\n",
" BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_bucket"
},
"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": "create_bucket"
},
"outputs": [],
"source": [
"! gsutil mb -l $REGION $BUCKET_NAME"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "validate_bucket"
},
"source": [
"Finally, validate access to your Cloud Storage bucket by examining its contents:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "validate_bucket"
},
"outputs": [],
"source": [
"! gsutil ls -al $BUCKET_NAME"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "setup_vars"
},
"source": [
"### Set up variables\n",
"\n",
"Next, set up some variables used throughout the tutorial.\n",
"### Import libraries and define constants"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "import_aip:mbsdk"
},
"outputs": [],
"source": [
"import google.cloud.aiplatform as aip"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "init_aip:mbsdk"
},
"source": [
"## Initialize Vertex SDK for Python\n",
"\n",
"Initialize the Vertex SDK for Python for your project and corresponding bucket."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "init_aip:mbsdk"
},
"outputs": [],
"source": [
"aip.init(project=PROJECT_ID, staging_bucket=BUCKET_NAME)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tutorial_start:automl"
},
"source": [
"# Tutorial\n",
"\n",
"Now you are ready to start creating your own AutoML image object detection model."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "import_file:u_dataset,csv"
},
"source": [
"#### Location of Cloud Storage training data.\n",
"\n",
"Now set the variable `IMPORT_FILE` to the location of the CSV index file in Cloud Storage."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "import_file:salads,csv,iod"
},
"outputs": [],
"source": [
"IMPORT_FILE = \"gs://cloud-samples-data/vision/salads.csv\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "quick_peek:csv"
},
"source": [
"#### Quick peek at your data\n",
"\n",
"This tutorial uses a version of the Salads dataset that is stored in a public Cloud Storage bucket, using a CSV index file.\n",
"\n",
"Start by doing a quick peek at the data. You count the number of examples by counting the number of rows in the CSV index file (`wc -l`) and then peek at the first few rows."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "quick_peek:csv"
},
"outputs": [],
"source": [
"if \"IMPORT_FILES\" in globals():\n",
" FILE = IMPORT_FILES[0]\n",
"else:\n",
" FILE = IMPORT_FILE\n",
"\n",
"count = ! gsutil cat $FILE | wc -l\n",
"print(\"Number of Examples\", int(count[0]))\n",
"\n",
"print(\"First 10 rows\")\n",
"! gsutil cat $FILE | head"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_dataset:image,iod"
},
"source": [
"### Create the Dataset\n",
"\n",
"Next, create the `Dataset` resource using the `create` method for the `ImageDataset` class, which takes the following parameters:\n",
"\n",
"- `display_name`: The human readable name for the `Dataset` resource.\n",
"- `gcs_source`: A list of one or more dataset index files to import the data items into the `Dataset` resource.\n",
"- `import_schema_uri`: The data labeling schema for the data items.\n",
"\n",
"This operation may take several minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_dataset:image,iod"
},
"outputs": [],
"source": [
"dataset = aip.ImageDataset.create(\n",
" display_name=\"Salads\" + \"_\" + TIMESTAMP,\n",
" gcs_source=[IMPORT_FILE],\n",
" import_schema_uri=aip.schema.dataset.ioformat.image.bounding_box,\n",
")\n",
"\n",
"print(dataset.resource_name)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_automl_pipeline:image,edge,iod"
},
"source": [
"### Create and run training pipeline\n",
"\n",
"To train an AutoML model, you perform two steps: 1) create a training pipeline, and 2) run the pipeline.\n",
"\n",
"#### Create training pipeline\n",
"\n",
"An AutoML training pipeline is created with the `AutoMLImageTrainingJob` class, with the following parameters:\n",
"\n",
"- `display_name`: The human readable name for the `TrainingJob` resource.\n",
"- `prediction_type`: The type task to train the model for.\n",
" - `classification`: An image classification model.\n",
" - `object_detection`: An image object detection model.\n",
"- `multi_label`: If a classification task, whether single (`False`) or multi-labeled (`True`).\n",
"- `model_type`: The type of model for deployment.\n",
" - `CLOUD`: Deployment on Google Cloud\n",
" - `CLOUD_HIGH_ACCURACY_1`: Optimized for accuracy over latency for deployment on Google Cloud.\n",
" - `CLOUD_LOW_LATENCY_`: Optimized for latency over accuracy for deployment on Google Cloud.\n",
" - `MOBILE_TF_VERSATILE_1`: Deployment on an edge device.\n",
" - `MOBILE_TF_HIGH_ACCURACY_1`:Optimized for accuracy over latency for deployment on an edge device.\n",
" - `MOBILE_TF_LOW_LATENCY_1`: Optimized for latency over accuracy for deployment on an edge device.\n",
"- `base_model`: (optional) Transfer learning from existing `Model` resource -- supported for image classification only.\n",
"\n",
"The instantiated object is the DAG (directed acyclic graph) for the training job."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_automl_pipeline:image,edge,iod"
},
"outputs": [],
"source": [
"dag = aip.AutoMLImageTrainingJob(\n",
" display_name=\"salads_\" + TIMESTAMP,\n",
" prediction_type=\"object_detection\",\n",
" multi_label=False,\n",
" model_type=\"MOBILE_TF_LOW_LATENCY_1\",\n",
" base_model=None,\n",
")\n",
"\n",
"print(dag)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "run_automl_pipeline:image"
},
"source": [
"#### Run the training pipeline\n",
"\n",
"Next, you run the DAG to start the training job by invoking the method `run`, with the following parameters:\n",
"\n",
"- `dataset`: The `Dataset` resource to train the model.\n",
"- `model_display_name`: The human readable name for the trained model.\n",
"- `training_fraction_split`: The percentage of the dataset to use for training.\n",
"- `test_fraction_split`: The percentage of the dataset to use for test (holdout data).\n",
"- `validation_fraction_split`: The percentage of the dataset to use for validation.\n",
"- `budget_milli_node_hours`: (optional) Maximum training time specified in unit of millihours (1000 = hour).\n",
"- `disable_early_stopping`: If `True`, training maybe completed before using the entire budget if the service believes it cannot further improve on the model objective measurements.\n",
"\n",
"The `run` method when completed returns the `Model` resource.\n",
"\n",
"The execution of the training pipeline will take upto 20 minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "run_automl_pipeline:image"
},
"outputs": [],
"source": [
"model = dag.run(\n",
" dataset=dataset,\n",
" model_display_name=\"salads_\" + TIMESTAMP,\n",
" training_fraction_split=0.8,\n",
" validation_fraction_split=0.1,\n",
" test_fraction_split=0.1,\n",
" budget_milli_node_hours=20000,\n",
" disable_early_stopping=False,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "evaluate_the_model:mbsdk"
},
"source": [
"## Review model evaluation scores\n",
"After your model has finished training, you can review the evaluation scores for it.\n",
"\n",
"First, you need to get a reference to the new model. As with datasets, you can either use the reference to the model variable you created when you deployed the model or you can list all of the models in your project."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "evaluate_the_model:mbsdk"
},
"outputs": [],
"source": [
"# Get model resource ID\n",
"models = aip.Model.list(filter=\"display_name=salads_\" + TIMESTAMP)\n",
"\n",
"# Get a reference to the Model Service client\n",
"client_options = {\"api_endpoint\": f\"{REGION}-aiplatform.googleapis.com\"}\n",
"model_service_client = aip.gapic.ModelServiceClient(client_options=client_options)\n",
"\n",
"model_evaluations = model_service_client.list_model_evaluations(\n",
" parent=models[0].resource_name\n",
")\n",
"model_evaluation = list(model_evaluations)[0]\n",
"print(model_evaluation)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "export_model:mbsdk,image"
},
"source": [
"## Export as Edge model\n",
"\n",
"You can export an AutoML image object detection model as a `Edge` model which you can then custom deploy to an edge device or download locally. Use the method `export_model()` to export the model to Cloud Storage, which takes the following parameters:\n",
"\n",
"- `artifact_destination`: The Cloud Storage location to store the SavedFormat model artifacts to.\n",
"- `export_format_id`: The format to save the model format as. For AutoML image object detection there is just one option:\n",
" - `tf-saved-model`: TensorFlow SavedFormat for deployment to a container.\n",
" - `tflite`: TensorFlow Lite for deployment to an edge or mobile device.\n",
" - `edgetpu-tflite`: TensorFlow Lite for TPU\n",
" - `tf-js`: TensorFlow for web client\n",
" - `coral-ml`: for Coral devices\n",
"\n",
"- `sync`: Whether to perform operational sychronously or asynchronously."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "export_model:mbsdk,image"
},
"outputs": [],
"source": [
"response = model.export_model(\n",
" artifact_destination=BUCKET_NAME, export_format_id=\"tflite\", sync=True\n",
")\n",
"\n",
"model_package = response[\"artifactOutputUri\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "download_model_artifacts:tflite"
},
"source": [
"#### Download the TFLite model artifacts\n",
"\n",
"Now that you have an exported TFLite version of your model, you can test the exported model locally, but first downloading it from Cloud Storage."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "download_model_artifacts:tflite"
},
"outputs": [],
"source": [
"! gsutil ls $model_package\n",
"# Download the model artifacts\n",
"! gsutil cp -r $model_package tflite\n",
"\n",
"tflite_path = \"tflite/model.tflite\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "instantiate_tflite_interpreter"
},
"source": [
"#### Instantiate a TFLite interpreter\n",
"\n",
"The TFLite version of the model is not a TensorFlow SavedModel format. You cannot directly use methods like predict(). Instead, one uses the TFLite interpreter. You must first setup the interpreter for the TFLite model as follows:\n",
"\n",
"- Instantiate an TFLite interpreter for the TFLite model.\n",
"- Instruct the interpreter to allocate input and output tensors for the model.\n",
"- Get detail information about the models input and output tensors that will need to be known for prediction."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "instantiate_tflite_interpreter"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"\n",
"interpreter = tf.lite.Interpreter(model_path=tflite_path)\n",
"interpreter.allocate_tensors()\n",
"\n",
"input_details = interpreter.get_input_details()\n",
"output_details = interpreter.get_output_details()\n",
"input_shape = input_details[0][\"shape\"]\n",
"\n",
"print(\"input tensor shape\", input_shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "get_test_item"
},
"source": [
"### Get test item\n",
"\n",
"You will use an arbitrary example out of the dataset as a test item. Don't be concerned that the example was likely used in training the model -- we just want to demonstrate how to make a prediction."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "get_test_item:image,224x224"
},
"outputs": [],
"source": [
"test_items = ! gsutil cat $IMPORT_FILE | head -n1\n",
"test_item = test_items[0].split(\",\")[0]\n",
"\n",
"with tf.io.gfile.GFile(test_item, \"rb\") as f:\n",
" content = f.read()\n",
"test_image = tf.io.decode_jpeg(content)\n",
"print(\"test image shape\", test_image.shape)\n",
"\n",
"test_image = tf.image.resize(test_image, (224, 224))\n",
"print(\"test image shape\", test_image.shape, test_image.dtype)\n",
"\n",
"test_image = tf.cast(test_image, dtype=tf.uint8).numpy()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "invoke_tflite_interpreter"
},
"source": [
"#### Make a prediction with TFLite model\n",
"\n",
"Finally, you do a prediction using your TFLite model, as follows:\n",
"\n",
"- Convert the test image into a batch of a single image (`np.expand_dims`)\n",
"- Set the input tensor for the interpreter to your batch of a single image (`data`).\n",
"- Invoke the interpreter.\n",
"- Retrieve the softmax probabilities for the prediction (`get_tensor`).\n",
"- Determine which label had the highest probability (`np.argmax`)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "invoke_tflite_interpreter"
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"data = np.expand_dims(test_image, axis=0)\n",
"\n",
"interpreter.set_tensor(input_details[0][\"index\"], data)\n",
"\n",
"interpreter.invoke()\n",
"\n",
"softmax = interpreter.get_tensor(output_details[0][\"index\"])\n",
"\n",
"label = np.argmax(softmax)\n",
"\n",
"print(label)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cleanup:mbsdk"
},
"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",
"\n",
"Otherwise, you can delete the individual resources you created in this tutorial:\n",
"\n",
"- Dataset\n",
"- Pipeline\n",
"- Model\n",
"- Endpoint\n",
"- AutoML Training Job\n",
"- Batch Job\n",
"- Custom Job\n",
"- Hyperparameter Tuning Job\n",
"- Cloud Storage Bucket"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cleanup:mbsdk"
},
"outputs": [],
"source": [
"delete_all = True\n",
"\n",
"if delete_all:\n",
" # Delete the dataset using the Vertex dataset object\n",
" try:\n",
" if \"dataset\" in globals():\n",
" dataset.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the model using the Vertex model object\n",
" try:\n",
" if \"model\" in globals():\n",
" model.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the endpoint using the Vertex endpoint object\n",
" try:\n",
" if \"endpoint\" in globals():\n",
" endpoint.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the AutoML or Pipeline trainig job\n",
" try:\n",
" if \"dag\" in globals():\n",
" dag.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the custom trainig job\n",
" try:\n",
" if \"job\" in globals():\n",
" job.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the batch prediction job using the Vertex batch prediction object\n",
" try:\n",
" if \"batch_predict_job\" in globals():\n",
" batch_predict_job.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the hyperparameter tuning job using the Vertex hyperparameter tuning object\n",
" try:\n",
" if \"hpt_job\" in globals():\n",
" hpt_job.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" if \"BUCKET_NAME\" in globals():\n",
" ! gsutil rm -r $BUCKET_NAME"
]
}
],
"metadata": {
"colab": {
"name": "sdk_automl_image_object_detection_online_export_edge.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
QuantumTechDevStudio/RUDNEVGAUSS | archive/Rodion/Gauss/NumPy/old/gaussianCompositionGradientDescent_Minibatch.ipynb | 1 | 59040 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x5ab3e90>]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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Znpi15bVEJGIZv1lUrk2JzrpWv4LLi5u7t6rqjcBO4Asi8iuLLaSqu1V1m6pua2lp8aBY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\n8/8B3U9k/0lWggkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x374cf10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import math\n",
"import random\n",
"import numpy as np\n",
"from matplotlib import mlab\n",
"from matplotlib import pylab as plt\n",
"%matplotlib inline\n",
"\n",
"# Построение графика композиции гауссианов с заданными параметрами и определение функции вычисления её значения: \n",
"\n",
"def gaussian(omg,sgm,omg0):\n",
" return (1/(sgm*math.sqrt(2*math.pi)))*math.exp(-(omg-omg0)**2/(2*sgm**2))\n",
"\n",
"def gaussComp(omg, sgm, omg0):\n",
" r = 0\n",
" for i in range(len(sgm)):\n",
" r = r + gaussian(omg, sgm[i], omg0[i])\n",
" return r\n",
"\n",
"sigma = [1.5, 1.5]\n",
"omega_0 = [0, 30]\n",
"\n",
"dOmega = 0.01\n",
"omegaMin = -10\n",
"omegaMax = 50\n",
"\n",
"omegaList = mlab.frange (omegaMin, omegaMax, dOmega)\n",
"gaussList = [gaussComp(omega, sigma, omega_0) for omega in omegaList]\n",
"\n",
"plt.plot (omegaList, gaussList,'green')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x374cbd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# выборка из \"непрерывной\" композиции гауссианов с заданными параметрами тренировочного набора данных:\n",
"dOmegaTrain = 0.3\n",
"\n",
"omegaTrain = mlab.frange(omegaMin, omegaMax, dOmegaTrain)\n",
"gaussTrain = [gaussComp(omega, sigma, omega_0) for omega in omegaTrain]\n",
"\n",
"plt.plot (omegaTrain, gaussTrain,'x')\n",
"\n",
"#Учебный набор в виде списка списков:\n",
"trainData = [[omega] for omega in omegaTrain]\n",
"m = len(omegaTrain)\n",
"for i in range(m):\n",
" trainData[i].append(gaussTrain[i])\n",
"\n",
"#Пусть так же известно и колическо гауссианов в комбинации, так уже легко можно будет построить гипотезу:\n",
"gaussN = len(sigma)\n",
"\n",
"#Запишем в отдельную переменную длину учебного набора:\n",
"m = len(omegaTrain)\n",
"#Параметр определяющий количество последовательных узлов для формирования mini-batch набора\n",
"L = 4"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def J(sgm, omg0): #Весовая функция, в виде суммы квадратов разности значений гипотезы и заданной функции в узлах. Пакетная.\n",
" cost = 0\n",
" for i in range(m):\n",
" omg = trainData[i][0]\n",
" y = trainData[i][1]\n",
" cost = cost + (1/(2*m))*(gaussComp(omg, sgm, omg0)-y)**2\n",
" return cost\n",
"\n",
"#Функции вычисления частных производных. К счастью, переменные каждого отдельного гауссиана встречаются только в них, поэтому \n",
"#производные не отличаются от производных для одного единственного гауссиана.\n",
"\n",
"def JSmg_Der(k, sgm, omg0): # Частная производная функции J по sigma\n",
" der = 0\n",
" for i in range(L):\n",
" omg = trainData[k+i][0]\n",
" y = trainData[k+i][1]\n",
" der = der + (1/L)*(gaussian(omg, sgm, omg0)-y)*((-1/(math.sqrt(2*math.pi)*sgm**2))*math.exp(-(omg-omg0)**2/(2*sgm**2)) + gaussian(omg, sgm, omg0)*((omg-omg0)**2/sgm**3))\n",
" return der\n",
" \n",
" \n",
"\n",
"def Jomg0_Der(k, sgm, omg0): # Частная производная функции J по omega0\n",
" der = 0\n",
" for i in range(L):\n",
" omg = trainData[k+i][0]\n",
" y = trainData[k+i][1]\n",
" der = der + (1/L)*(gaussian(omg, sgm, omg0)-y)*gaussian(omg, sgm, omg0)*(omg-omg0)*(1/(sgm**2))\n",
" return der\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"alpha = 0.5 #скорость обучения\n",
"\n",
"# Стартовый набор коэффицентов проинициализируем единицами(нулями нельзя, т.к. сигма встречается в знаменателе, и получим ошибку)\n",
"# А так же костыль для временного набора параметров, нулями\n",
"#prm = []\n",
"#prmTemp0 = []\n",
"#for i in range(gaussN):\n",
"# prm.append([1, 1])\n",
"# prmTemp0.append([0, 0])\n",
"\n",
"prm = [[1, 2], [5, 35]]\n",
"\n",
"prmTemp0 = [[0,0],[0,0]] \n",
" \n",
"\n",
"\n",
"prmTemp = prmTemp0\n",
"for i in range(100000): # основной цикл реализации градиентного спуска\n",
" k = random.randint(0,m-L-1)\n",
" for j in range(gaussN): \n",
" prmTemp[j][0] = prm[j][0] - alpha * JSmg_Der(k, prm[j][0], prm[j][1])\n",
" prmTemp[j][1] = prm[j][1] - alpha * Jomg0_Der(k, prm[j][0], prm[j][1])\n",
" prm = prmTemp\n",
" prmTemp = prmTemp0\n",
"\n",
"\n",
" \n",
"#Разделим лист параметров на отдельные листы для сигма и омега_0. \n",
"#Удобно из-за копирования кода рассчитанного на единственный гауссиан(чтобы не переписывать производные заново)\n",
"apprSigma = []\n",
"apprOmega_0 = []\n",
"for i in range(gaussN):\n",
" apprSigma.append(prm[i][0])\n",
" apprOmega_0.append(prm[i][1])\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x6bd46f0>]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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T8NfACxfmODzYRU97WXPgVV17a4Iju7t5oXjmIdHyQjH4r90bTvADvHFv72oD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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x5b80d50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"apprGauss = [gaussComp(omega, apprSigma, apprOmega_0) for omega in omegaList]\n",
"plt.plot(omegaList, apprGauss)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"J(apprSigma, apprOmega_0)\n",
"prm\n",
"2+3"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x6c457b0>]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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8mWOfF4BeM9tqZi2kFxLsW6D+PuAuM0uY2VbSCwueD1N0w2Z2S1jl9tk835nt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\nAY+47GrwCuJGAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x5bb68d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"error = []\n",
"for i in range(len(omegaList)):\n",
" error.append(gaussList[i] - apprGauss[i])\n",
"plt.plot(omegaList, error)"
]
}
],
"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
}
| gpl-3.0 |
AtmaMani/pyChakras | python_crash_course/python_cheat_sheet_2.ipynb | 1 | 15703 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "6eb6ae54-534b-4581-9172-4e35fd5b7e94"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Python cheat sheet - iterations\n",
"\n",
"**Table of contents**\n",
" - [Functions](#Functions)\n",
" - [Classes](#Classes)\n",
" - [Exception handling](#Exception-handling)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"nbpresent": {
"id": "85e9447a-7fa5-44a7-b804-bc73024c1a62"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Functions\n",
"Specify optional parameters in the end. Specify the default values for optional parameters with = value notation\n",
"\n",
" def func_name(arg1, arg2=None):\n",
" operations\n",
" return value"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"nbpresent": {
"id": "c2aebfaf-f880-4e8d-9853-c9ddbcfce385"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"def func_add_numbers(num1, num2=10):\n",
" return (num1 + num2)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"nbpresent": {
"id": "0e4b1912-beb1-405c-9d18-50f02bc8c680"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"12"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"func_add_numbers(2)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"nbpresent": {
"id": "8877b2bb-cdcb-48fc-b9e9-ddc2a226490c"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"36"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"func_add_numbers(2,34)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"nbpresent": {
"id": "3b04b7e7-2a2a-4e73-b321-3933b4a216ed"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"ename": "TypeError",
"evalue": "func_add_numbers() missing 1 required positional argument: 'num1'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-4-0d259ef4c90e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfunc_add_numbers\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m: func_add_numbers() missing 1 required positional argument: 'num1'"
]
}
],
"source": [
"func_add_numbers()"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "f9ee4b69-c3e6-4b15-90dc-06ffaadbd451"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Classes\n",
"Everything is an object in Python including native types. You define class names with camel casing.\n",
"You define the constructor with special name `__init__()`. The fields (private) are denoted with `_variable_name` specification and properties are decorated with `@property` decorator.\n",
"\n",
"Fields and properties are accessed within the class using `self.name` notation. This helps differentiate a class field / property from a local variable or method argument of the same name.\n",
"\n",
"### A simple class\n",
" class MyClass:\n",
" _local_variables = \"value\"\n",
" \n",
" def __init__(self, args): #constructor\n",
" statements\n",
" self._local_variables = args # assign values to fields\n",
" \n",
" def func_1(self, args):\n",
" statements\n",
"\n",
"You use this method by instantiating an object.\n",
"\n",
" obj1 = myClass(args_defined_in_constructor)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"nbpresent": {
"id": "f6b2524f-d31a-4e7b-ae7d-b8333c456224"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"# Define a class to hold a satellite or aerial imagery file. Its properties give information\n",
"# such as location of the ground, area, dimensions, spatial and spectral resolution etc.\n",
"\n",
"class ImageryObject:\n",
" _default_gsd = 5.0\n",
" \n",
" def __init__(self, file_path):\n",
" self._file_path = file_path\n",
" self._gps_location = (3,4)\n",
" \n",
" @property\n",
" def bands(self):\n",
" #count number of bands\n",
" count = 3\n",
" return count\n",
" \n",
" @property\n",
" def gsd(self):\n",
" # logic to calculate the ground sample distance\n",
" gsd = 10.0\n",
" return gsd\n",
" \n",
" @property\n",
" def address(self):\n",
" # logic to reverse geocode the self._gps_location to get address\n",
" # reverse geocode self._gps_location\n",
" address = \"123 XYZ Street\"\n",
" return address\n",
" \n",
" #class methods\n",
" def display(self):\n",
" #logic to display picture\n",
" print(\"image is displayed\")\n",
" \n",
" def shuffle_bands(self):\n",
" #logic to shift RGB combination\n",
" print(\"shifting pands\")\n",
" self.display()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"nbpresent": {
"id": "b37c8960-7a37-4793-92a6-5b481f2ff20c"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"# class instantiation\n",
"img1 = ImageryObject(\"user\\img\\file.img\") #pass value to constructor"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"nbpresent": {
"id": "cca870e6-a1c6-4c76-a149-b9611bd11080"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'123 XYZ Street'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img1.address"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5.0"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img1._default_gsd"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"nbpresent": {
"id": "0b8fc2fa-0647-43d5-83e7-a161c3921239"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(3, 4)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img1._gps_location"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"nbpresent": {
"id": "66330b26-7740-4322-866c-7f9d7baee10d"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"shifting pands\n",
"image is displayed\n"
]
}
],
"source": [
"img1.shuffle_bands()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"nbpresent": {
"id": "f14ee926-7346-48b8-b75e-ceb1103bd89e"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on ImageryObject in module __main__ object:\n",
"\n",
"class ImageryObject(builtins.object)\n",
" | Methods defined here:\n",
" | \n",
" | __init__(self, file_path)\n",
" | Initialize self. See help(type(self)) for accurate signature.\n",
" | \n",
" | display(self)\n",
" | #class methods\n",
" | \n",
" | shuffle_bands(self)\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data descriptors defined here:\n",
" | \n",
" | __dict__\n",
" | dictionary for instance variables (if defined)\n",
" | \n",
" | __weakref__\n",
" | list of weak references to the object (if defined)\n",
" | \n",
" | address\n",
" | \n",
" | bands\n",
" | \n",
" | gsd\n",
"\n"
]
}
],
"source": [
"# Get help on any object. Only public methods, properties are displayed.\n",
"# fields are private, properties are public. Class variables beginning with _ are private fields.\n",
"help(img1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "e4830d3a-9992-40a0-8f5b-ff17d9fb5265"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Exception handling\n",
"Exceptions are classes. You can define your own by inheriting from `Exception` class.\n",
"\n",
" try:\n",
" statements\n",
" \n",
" except Exception_type1 as e1:\n",
" handling statements\n",
" \n",
" except Exception_type2 as e2:\n",
" specific handling statements\n",
" \n",
" except Exception as generic_ex:\n",
" generic handling statements\n",
" \n",
" else:\n",
" some more statements\n",
" \n",
" finally:\n",
" default statements which will always be executed"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"nbpresent": {
"id": "1b6b0d20-99dd-4464-9aa7-63e4e3884ced"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"image is displayed\n"
]
}
],
"source": [
"try:\n",
" img2 = ImageryObject(\"user\\img\\file2.img\")\n",
" img2.display()\n",
"except:\n",
" print(\"something bad happened\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"image is displayed\n",
"else block\n",
"finally block\n"
]
}
],
"source": [
"try:\n",
" img2 = ImageryObject(\"user\\img\\file2.img\")\n",
" img2.display()\n",
"except:\n",
" print(\"something bad happened\")\n",
"else:\n",
" print(\"else block\")\n",
"finally:\n",
" print(\"finally block\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"nbpresent": {
"id": "7d94a383-045b-4dc6-ba2e-552137b64348"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"something bad happened\n",
"finally block\n"
]
}
],
"source": [
"try:\n",
" img2 = ImageryObject()\n",
" img2.display()\n",
"except:\n",
" print(\"something bad happened\")\n",
"else:\n",
" print(\"else block\")\n",
"finally:\n",
" print(\"finally block\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"nbpresent": {
"id": "08b7f669-3eca-4930-8c50-44aa905cf79c"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"something bad happened\n",
"exactly what whent bad? : __init__() missing 1 required positional argument: 'file_path'\n"
]
}
],
"source": [
"try:\n",
" img2 = ImageryObject()\n",
" img2.display()\n",
"\n",
"except Exception as ex:\n",
" print(\"something bad happened\")\n",
" print(\"exactly what whent bad? : \" + str(ex))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"nbpresent": {
"id": "c5a5c54a-8559-4cb7-b76f-840ff3b402fc"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"nope, it went worng here: 'ImageryObject' object has no attribute 'dddisplay'\n"
]
}
],
"source": [
"try:\n",
" img2 = ImageryObject('path')\n",
" img2.dddisplay()\n",
"\n",
"except TypeError as terr:\n",
" print(\"looks like you forgot a parameter\")\n",
"except Exception as ex:\n",
" print(\"nope, it went worng here: \" + str(ex))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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"
},
"nbpresent": {
"slides": {
"5004b7d3-2f6e-4b0b-ae28-dc334d829c7e": {
"id": "5004b7d3-2f6e-4b0b-ae28-dc334d829c7e",
"prev": "df1608ed-5dce-41a0-b82b-a5e6fed11664",
"regions": {
"9b95bea7-f729-4641-9c88-b56d2c0767a2": {
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"d855b7eb-dc83-44c0-9e6c-a9faf5d0e8ed": {
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| mit |
tbarrongh/cosc-learning-labs | src/notebook/Menu.ipynb | 2 | 1280 | {
"metadata": {
"name": "",
"signature": "sha256:aaa9a3e75d6a07b02f40f4b5f3732fe8b64422386d162dc87bf7030e5f0fc551"
},
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"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"COSC Learning Lab: Menu"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Sample Scripts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* [Inventory](Inventory.ipynb)\n",
"* [Interface](Interface.ipynb)\n",
"* [Capabilities](Capabilities.ipynb)\n",
"* [Properties](Properties.ipynb)\n",
"* [Configure](Configure.ipynb)\n",
"* [Access Control](AccessControl.ipynb)"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Guides"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* [Objectives](Objectives.ipynb)\n",
"* [How to set up your computer](HowToSetUpYourComputer.ipynb)\n",
"* [Settings](Settings.ipynb)\n",
"* [Http](Http.ipynb)\n",
"* [Copyright](Copyright.ipynb)"
]
}
],
"metadata": {}
}
]
} | apache-2.0 |
datahac/jup | UX_analytics-D.ipynb | 1 | 62595 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "raw",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# iPython notebook - Daryna analysis - Conversions by website pages\n",
"\n",
"\n",
"## 1. Import libraries"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline \n",
"\n",
"import numpy as np\n",
"import scipy as sp\n",
"import matplotlib as mpl\n",
"import matplotlib.cm as cm\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"\n",
"pd.set_option('display.width', 500)\n",
"pd.set_option('display.max_columns', 100)\n",
"pd.set_option('display.notebook_repr_html', True)\n",
"import seaborn as sns #sets up styles and gives us more plotting options\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" 2. Settings"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Time period 24th Jan - 24th April (arbitrary )\n",
"\n",
"# API credentials\n",
"# Email address [email protected]\n",
"# Key IDs 948ee8e2a420ef14a5d5a29bd35104fe2f1e6ed4\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# open file. It is requested via API explorer using request parameters:\n",
"\n",
"#Account: TMRW Tech Hub\n",
"#Property: TMRW\n",
"#View: All Web Site Data\n",
"#ids: ga:123303369\n",
"#start-date: 2017-01-24\n",
"#end-date: 2017-04-24\n",
"\n",
"#metrics\n",
"#ga:sessions\n",
"#ga:sessionsWithEvent\n",
"\n",
"#dimensions\n",
"#ga:pagePath\n",
"\n",
"#sort\n",
"#-ga:sessionsWithEvent\n",
"\n",
"#filter\n",
"#ga:sessions>10\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ga:pagePath</th>\n",
" <th>ga:sessions</th>\n",
" <th>ga:sessionsWithEvent</th>\n",
" <th>cr</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>'/</td>\n",
" <td>4436</td>\n",
" <td>82</td>\n",
" <td>0.018485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>'/TMRW_FAQs.php</td>\n",
" <td>100</td>\n",
" <td>26</td>\n",
" <td>0.260000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>'/TMRW_Byte_Cafe.php</td>\n",
" <td>218</td>\n",
" <td>23</td>\n",
" <td>0.105505</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>'/TMRW_the_team.php</td>\n",
" <td>99</td>\n",
" <td>10</td>\n",
" <td>0.101010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>'/trainstrikes.php</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>'/voteforbyte.php</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ga:pagePath ga:sessions ga:sessionsWithEvent cr\n",
"0 '/ 4436 82 0.018485\n",
"1 '/TMRW_FAQs.php 100 26 0.260000\n",
"2 '/TMRW_Byte_Cafe.php 218 23 0.105505\n",
"3 '/TMRW_the_team.php 99 10 0.101010\n",
"4 '/trainstrikes.php 13 0 0.000000\n",
"5 '/voteforbyte.php 31 0 0.000000"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Open file\n",
"# original file exported from GA includes ga:pagePath,ga:sessions,ga:sessionsWithEvent\n",
"# Calculate \"rate\" as \"Sessions with event\"/\"Sessions\" for each page.\n",
"\n",
"TMRW_events= pd.read_csv(\"files/TMRW_events.csv\")\n",
"TMRW_events\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"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>page</th>\n",
" <th>sessions</th>\n",
" <th>events</th>\n",
" <th>rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>'/</td>\n",
" <td>4436</td>\n",
" <td>82</td>\n",
" <td>0.018485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>'/TMRW_FAQs.php</td>\n",
" <td>100</td>\n",
" <td>26</td>\n",
" <td>0.260000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>'/TMRW_Byte_Cafe.php</td>\n",
" <td>218</td>\n",
" <td>23</td>\n",
" <td>0.105505</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>'/TMRW_the_team.php</td>\n",
" <td>99</td>\n",
" <td>10</td>\n",
" <td>0.101010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>'/trainstrikes.php</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>'/voteforbyte.php</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" page sessions events rate\n",
"0 '/ 4436 82 0.018485\n",
"1 '/TMRW_FAQs.php 100 26 0.260000\n",
"2 '/TMRW_Byte_Cafe.php 218 23 0.105505\n",
"3 '/TMRW_the_team.php 99 10 0.101010\n",
"4 '/trainstrikes.php 13 0 0.000000\n",
"5 '/voteforbyte.php 31 0 0.000000"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"TMRW_events.columns=[\"page\",\"sessions\",\"events\",\"rate\"]\n",
"TMRW_events"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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>page</th>\n",
" <th>sessions</th>\n",
" <th>events</th>\n",
" <th>rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>'/</td>\n",
" <td>4436</td>\n",
" <td>82</td>\n",
" <td>0.018485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>'/TMRW_FAQs.php</td>\n",
" <td>100</td>\n",
" <td>26</td>\n",
" <td>0.260000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>'/TMRW_Byte_Cafe.php</td>\n",
" <td>218</td>\n",
" <td>23</td>\n",
" <td>0.105505</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>'/TMRW_the_team.php</td>\n",
" <td>99</td>\n",
" <td>10</td>\n",
" <td>0.101010</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" page sessions events rate\n",
"0 '/ 4436 82 0.018485\n",
"1 '/TMRW_FAQs.php 100 26 0.260000\n",
"2 '/TMRW_Byte_Cafe.php 218 23 0.105505\n",
"3 '/TMRW_the_team.php 99 10 0.101010"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"TMRW_events_filter = TMRW_events[TMRW_events.rate > 0]\n",
"TMRW_events_filter"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
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"<div>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sessions</th>\n",
" <th>events</th>\n",
" <th>rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>4.00000</td>\n",
" <td>4.000000</td>\n",
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" <tr>\n",
" <th>mean</th>\n",
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" <td>0.121250</td>\n",
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" <tr>\n",
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" <th>min</th>\n",
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" <td>10.000000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
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" <tr>\n",
" <th>50%</th>\n",
" <td>159.00000</td>\n",
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" <tr>\n",
" <th>75%</th>\n",
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"text/plain": [
" sessions events rate\n",
"count 4.00000 4.000000 4.000000\n",
"mean 1213.25000 35.250000 0.121250\n",
"std 2149.22612 31.930915 0.100780\n",
"min 99.00000 10.000000 0.018485\n",
"25% 99.75000 19.750000 0.080379\n",
"50% 159.00000 24.500000 0.103257\n",
"75% 1272.50000 40.000000 0.144128\n",
"max 4436.00000 82.000000 0.260000"
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" var render_items = [{\"docid\":\"e58a95fa-664f-40ec-bc3f-9a20ef72a0b0\",\"elementid\":\"61b76fa9-8ce1-4e61-807d-8ee2c8350e26\",\"modelid\":\"704e4640-6ce5-4078-9d59-94a123d08a59\"}];\n",
" \n",
" Bokeh.embed.embed_items(docs_json, render_items);\n",
" });\n",
" },\n",
" function(Bokeh) {\n",
" }\n",
" ];\n",
" \n",
" function run_inline_js() {\n",
" \n",
" if ((window.Bokeh !== undefined) || (force === \"1\")) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }if (force === \"1\") {\n",
" display_loaded();\n",
" }} else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!window._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" window._bokeh_failed_load = true;\n",
" } else if (!force) {\n",
" var cell = $(\"#61b76fa9-8ce1-4e61-807d-8ee2c8350e26\").parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
" \n",
" }\n",
" \n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
" }(this));\n",
"</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#import numpy as np\n",
"from bokeh.io import output_notebook\n",
"from bokeh.charts import Bar, show\n",
"\n",
"output_notebook()\n",
"p = Bar(TMRW_events_filter, 'page', values='rate', title=\"Events per page\")\n",
"show(p)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"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>rate</th>\n",
" </tr>\n",
" <tr>\n",
" <th>page</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>'/</th>\n",
" <td>0.0185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'/TMRW_Byte_Cafe.php</th>\n",
" <td>0.1055</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'/TMRW_FAQs.php</th>\n",
" <td>0.2600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'/TMRW_the_team.php</th>\n",
" <td>0.1010</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rate\n",
"page \n",
"'/ 0.0185\n",
"'/TMRW_Byte_Cafe.php 0.1055\n",
"'/TMRW_FAQs.php 0.2600\n",
"'/TMRW_the_team.php 0.1010"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"TMRW_events_data = TMRW_events_filter.groupby(['page']).mean()\n",
"TMRW_events_data"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"page\n",
"'/ 0.0185\n",
"'/TMRW_Byte_Cafe.php 0.1055\n",
"'/TMRW_FAQs.php 0.2600\n",
"'/TMRW_the_team.php 0.1010\n",
"Name: rate, dtype: float64"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"selected=TMRW_events_data.loc[:,\"rate\"]\n",
"selected"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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FlEkpCyjjU2SH+T7Jz+fMXu7msrNiMU4rKeH0Xr24rmtXKqtpf1Lv3pzZqxdn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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xe588510>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"labels = selected.index\n",
"sizes = TMRW_events_filter['rate']\n",
"colors = ['green','yellow', 'red', 'lightskyblue']\n",
"explode = (0, 0, 0,0)\n",
"plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
" autopct='%1.1f%%', shadow=False, startangle=90)\n",
"plt.legend(patches, labels, loc=\"best\")\n",
"plt.axis('equal')\n",
"plt.title('Conversions by pages ')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
the-deep-learners/TensorFlow-LiveLessons | notebooks/live_training/natural_language_preprocessing_best_practices_LT.ipynb | 1 | 14243 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Best Practices for Preprocessing Natural Language Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook, we improve the quality of our Project Gutenberg word vectors by adopting best-practices for preprocessing natural language data.\n",
"\n",
"**N.B.:** Some, all or none of these preprocessing steps may be helpful to a given downstream application. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Load dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# the initial block is copied from creating_word_vectors_with_word2vec.ipynb\n",
"import nltk\n",
"from nltk import word_tokenize, sent_tokenize\n",
"from nltk.corpus import stopwords\n",
"from nltk.stem.porter import *\n",
"import gensim\n",
"from gensim.models.word2vec import Word2Vec\n",
"from gensim.models.phrases import Phraser, Phrases\n",
"from sklearn.manifold import TSNE\n",
"import pandas as pd\n",
"from bokeh.io import output_notebook, output_file\n",
"from bokeh.plotting import show, figure\n",
"import string\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nltk.download('punkt')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nltk.download('stopwords')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Load data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nltk.download('gutenberg')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from nltk.corpus import gutenberg"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(gutenberg.fileids())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gutenberg.fileids()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gberg_sent_tokens = sent_tokenize(gutenberg.raw())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gberg_sent_tokens[0:5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gberg_sent_tokens[1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"word_tokenize(gberg_sent_tokens[1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"word_tokenize(gberg_sent_tokens[1])[14]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gberg_sents = gutenberg.sents()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gberg_sents[0:5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(gutenberg.words())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Iteratively preprocess a sentence"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### a tokenized sentence: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gberg_sents[4]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### to lowercase: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### remove stopwords and punctuation: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"stpwrds = stopwords.words('english') + list(string.punctuation)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"stpwrds"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### stem words: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"stemmer = PorterStemmer()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### handle bigram collocations:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"phrases = Phrases(gberg_sents) # train detector"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bigram = Phraser(phrases) # create a more efficient Phraser object for transforming sentences"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bigram.phrasegrams # output count and score of each bigram"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"Jon lives in New York City\".split()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Preprocess the corpus"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lower_sents = []\n",
"for s in gberg_sents:\n",
" lower_sents.append( # CODE HERE )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lower_sents[0:5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lower_bigram = Phraser(Phrases(lower_sents))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lower_bigram.phrasegrams # miss taylor, mr woodhouse, mr weston"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lower_bigram[\"jon lives in new york city\".split()]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lower_bigram = Phraser(Phrases(lower_sents, min_count=32, threshold=64))\n",
"lower_bigram.phrasegrams"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# as in Maas et al. (2001):\n",
"# - leave in stop words (\"indicative of sentiment\")\n",
"# - no stemming (\"model learns similar representations of words of the same stem when data suggests it\")\n",
"clean_sents = []\n",
"for s in lower_sents:\n",
" clean_sents.append( # CODE HERE )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"clean_sents[0:9]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"clean_sents[6] # could consider removing stop words or common words"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Run word2vec"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# max_vocab_size can be used instead of min_count (which has increased here)\n",
"# model = Word2Vec(sentences=clean_sents, size=64, sg=1, window=10, min_count=10, seed=42, workers=8)\n",
"# model.save('../clean_gutenberg_model.w2v')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Explore model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# skip re-training the model with the next line: \n",
"model = gensim.models.Word2Vec.load('../clean_gutenberg_model.w2v')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(model.wv.vocab) # 17k with raw data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(model['dog'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model['dog']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.most_similar('dog')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.most_similar('think')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.most_similar('day')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.doesnt_match(\"morning afternoon evening dog\".split())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.similarity('morning', 'dog')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.most_similar('ma_am') "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.most_similar(positive=['father', 'woman'], negative=['man']) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Reduce word vector dimensionality with t-SNE"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# tsne = TSNE(n_components=2, n_iter=1000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# X_2d = tsne.fit_transform(model[model.wv.vocab])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# coords_df = pd.DataFrame(X_2d, columns=['x','y'])\n",
"# coords_df['token'] = model.wv.vocab.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# coords_df.to_csv('../clean_gutenberg_tsne.csv', index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Visualise "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"coords_df = pd.read_csv('../clean_gutenberg_tsne.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"coords_df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"_ = coords_df.plot.scatter('x', 'y', figsize=(12,12), marker='.', s=10, alpha=0.2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"output_notebook()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"subset_df = coords_df.sample(n=5000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"p = figure(plot_width=800, plot_height=800)\n",
"_ = p.text(x=subset_df.x, y=subset_df.y, text=subset_df.token)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"show(p)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# output_file() here"
]
}
],
"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
}
| mit |
paoloRais/lightfm | examples/quickstart/quickstart.ipynb | 1 | 8044 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quickstart\n",
"In this example, we'll build an implicit feedback recommender using the Movielens 100k dataset (http://grouplens.org/datasets/movielens/100k/).\n",
"\n",
"The code behind this example is available as a [Jupyter notebook](https://github.com/lyst/lightfm/tree/master/examples/quickstart/quickstart.ipynb)\n",
"\n",
"LightFM includes functions for getting and processing this dataset, so obtaining it is quite easy."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"from lightfm.datasets import fetch_movielens\n",
"\n",
"data = fetch_movielens(min_rating=5.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This downloads the dataset and automatically pre-processes it into sparse matrices suitable for further calculation. In particular, it prepares the sparse user-item matrices, containing positive entries where a user interacted with a product, and zeros otherwise.\n",
"\n",
"We have two such matrices, a training and a testing set. Both have around 1000 users and 1700 items. We'll train the model on the train matrix but test it on the test matrix."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<943x1682 sparse matrix of type '<type 'numpy.int32'>'\n",
"\twith 19048 stored elements in COOrdinate format>\n",
"<943x1682 sparse matrix of type '<type 'numpy.int32'>'\n",
"\twith 2153 stored elements in COOrdinate format>\n"
]
}
],
"source": [
"print(repr(data['train']))\n",
"print(repr(data['test']))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We need to import the model class to fit the model:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from lightfm import LightFM"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We're going to use the WARP (Weighted Approximate-Rank Pairwise) model. WARP is an implicit feedback model: all interactions in the training matrix are treated as positive signals, and products that users did not interact with they implicitly do not like. The goal of the model is to score these implicit positives highly while assigining low scores to implicit negatives.\n",
"\n",
"Model training is accomplished via SGD (stochastic gradient descent). This means that for every pass through the data --- an epoch --- the model learns to fit the data more and more closely. We'll run it for 10 epochs in this example. We can also run it on multiple cores, so we'll set that to 2. (The dataset in this example is too small for that to make a difference, but it will matter on bigger datasets.)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.55 s, sys: 4 ms, total: 1.56 s\n",
"Wall time: 838 ms\n"
]
},
{
"data": {
"text/plain": [
"<lightfm.lightfm.LightFM at 0x7f978c58ea50>"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = LightFM(loss='warp')\n",
"%time model.fit(data['train'], epochs=30, num_threads=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Done! We should now evaluate the model to see how well it's doing. We're most interested in how good the ranking produced by the model is. Precision@k is one suitable metric, expressing the percentage of top k items in the ranking the user has actually interacted with. `lightfm` implements a number of metrics in the `evaluation` module. "
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from lightfm.evaluation import precision_at_k"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll measure precision in both the train and the test set."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train precision: 0.43\n",
"Test precision: 0.04\n"
]
}
],
"source": [
"print(\"Train precision: %.2f\" % precision_at_k(model, data['train'], k=5).mean())\n",
"print(\"Test precision: %.2f\" % precision_at_k(model, data['test'], k=5).mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unsurprisingly, the model fits the train set better than the test set.\n",
"\n",
"For an alternative way of judging the model, we can sample a couple of users and get their recommendations. To make predictions for given user, we pass the id of that user and the ids of all products we want predictions for into the `predict` method."
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"User 3\n",
" Known positives:\n",
" Contact (1997)\n",
" Air Force One (1997)\n",
" In & Out (1997)\n",
" Recommended:\n",
" Air Force One (1997)\n",
" Assignment, The (1997)\n",
" Kiss the Girls (1997)\n",
"User 25\n",
" Known positives:\n",
" Fargo (1996)\n",
" Godfather, The (1972)\n",
" L.A. Confidential (1997)\n",
" Recommended:\n",
" L.A. Confidential (1997)\n",
" Titanic (1997)\n",
" Fargo (1996)\n",
"User 450\n",
" Known positives:\n",
" Event Horizon (1997)\n",
" Scream (1996)\n",
" Conspiracy Theory (1997)\n",
" Recommended:\n",
" Independence Day (ID4) (1996)\n",
" Scream (1996)\n",
" Ransom (1996)\n"
]
}
],
"source": [
"def sample_recommendation(model, data, user_ids):\n",
" \n",
"\n",
" n_users, n_items = data['train'].shape\n",
"\n",
" for user_id in user_ids:\n",
" known_positives = data['item_labels'][data['train'].tocsr()[user_id].indices]\n",
" \n",
" scores = model.predict(user_id, np.arange(n_items))\n",
" top_items = data['item_labels'][np.argsort(-scores)]\n",
" \n",
" print(\"User %s\" % user_id)\n",
" print(\" Known positives:\")\n",
" \n",
" for x in known_positives[:3]:\n",
" print(\" %s\" % x)\n",
"\n",
" print(\" Recommended:\")\n",
" \n",
" for x in top_items[:3]:\n",
" print(\" %s\" % x)\n",
" \n",
"sample_recommendation(model, data, [3, 25, 450]) "
]
},
{
"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
}
| apache-2.0 |
jo-tez/aima-python | csp.ipynb | 1 | 231531 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CONSTRAINT SATISFACTION PROBLEMS\n",
"\n",
"This IPy notebook acts as supporting material for topics covered in **Chapter 6 Constraint Satisfaction Problems** of the book* Artificial Intelligence: A Modern Approach*. We make use of the implementations in **csp.py** module. Even though this notebook includes a brief summary of the main topics, familiarity with the material present in the book is expected. We will look at some visualizations and solve some of the CSP problems described in the book. Let us import everything from the csp module to get started."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from csp import *\n",
"from notebook import psource, pseudocode, plot_NQueens\n",
"%matplotlib inline\n",
"\n",
"# Hide warnings in the matplotlib sections\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CONTENTS\n",
"\n",
"* Overview\n",
"* Graph Coloring\n",
"* N-Queens\n",
"* AC-3\n",
"* Backtracking Search\n",
"* Tree CSP Solver\n",
"* Graph Coloring Visualization\n",
"* N-Queens Visualization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## OVERVIEW\n",
"\n",
"CSPs are a special kind of search problems. Here we don't treat the space as a black box but the state has a particular form and we use that to our advantage to tweak our algorithms to be more suited to the problems. A CSP State is defined by a set of variables which can take values from corresponding domains. These variables can take only certain values in their domains to satisfy the constraints. A set of assignments which satisfies all constraints passes the goal test. Let us start by exploring the CSP class which we will use to model our CSPs. You can keep the popup open and read the main page to get a better idea of the code."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
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"\n",
"<div class=\"highlight\"><pre><span></span><span class=\"k\">class</span> <span class=\"nc\">CSP</span><span class=\"p\">(</span><span class=\"n\">search</span><span class=\"o\">.</span><span class=\"n\">Problem</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""This class describes finite-domain Constraint Satisfaction Problems.</span>\n",
"<span class=\"sd\"> A CSP is specified by the following inputs:</span>\n",
"<span class=\"sd\"> variables A list of variables; each is atomic (e.g. int or string).</span>\n",
"<span class=\"sd\"> domains A dict of {var:[possible_value, ...]} entries.</span>\n",
"<span class=\"sd\"> neighbors A dict of {var:[var,...]} that for each variable lists</span>\n",
"<span class=\"sd\"> the other variables that participate in constraints.</span>\n",
"<span class=\"sd\"> constraints A function f(A, a, B, b) that returns true if neighbors</span>\n",
"<span class=\"sd\"> A, B satisfy the constraint when they have values A=a, B=b</span>\n",
"\n",
"<span class=\"sd\"> In the textbook and in most mathematical definitions, the</span>\n",
"<span class=\"sd\"> constraints are specified as explicit pairs of allowable values,</span>\n",
"<span class=\"sd\"> but the formulation here is easier to express and more compact for</span>\n",
"<span class=\"sd\"> most cases. (For example, the n-Queens problem can be represented</span>\n",
"<span class=\"sd\"> in O(n) space using this notation, instead of O(N^4) for the</span>\n",
"<span class=\"sd\"> explicit representation.) In terms of describing the CSP as a</span>\n",
"<span class=\"sd\"> problem, that's all there is.</span>\n",
"\n",
"<span class=\"sd\"> However, the class also supports data structures and methods that help you</span>\n",
"<span class=\"sd\"> solve CSPs by calling a search function on the CSP. Methods and slots are</span>\n",
"<span class=\"sd\"> as follows, where the argument 'a' represents an assignment, which is a</span>\n",
"<span class=\"sd\"> dict of {var:val} entries:</span>\n",
"<span class=\"sd\"> assign(var, val, a) Assign a[var] = val; do other bookkeeping</span>\n",
"<span class=\"sd\"> unassign(var, a) Do del a[var], plus other bookkeeping</span>\n",
"<span class=\"sd\"> nconflicts(var, val, a) Return the number of other variables that</span>\n",
"<span class=\"sd\"> conflict with var=val</span>\n",
"<span class=\"sd\"> curr_domains[var] Slot: remaining consistent values for var</span>\n",
"<span class=\"sd\"> Used by constraint propagation routines.</span>\n",
"<span class=\"sd\"> The following methods are used only by graph_search and tree_search:</span>\n",
"<span class=\"sd\"> actions(state) Return a list of actions</span>\n",
"<span class=\"sd\"> result(state, action) Return a successor of state</span>\n",
"<span class=\"sd\"> goal_test(state) Return true if all constraints satisfied</span>\n",
"<span class=\"sd\"> The following are just for debugging purposes:</span>\n",
"<span class=\"sd\"> nassigns Slot: tracks the number of assignments made</span>\n",
"<span class=\"sd\"> display(a) Print a human-readable representation</span>\n",
"<span class=\"sd\"> """</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">variables</span><span class=\"p\">,</span> <span class=\"n\">domains</span><span class=\"p\">,</span> <span class=\"n\">neighbors</span><span class=\"p\">,</span> <span class=\"n\">constraints</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Construct a CSP problem. If variables is empty, it becomes domains.keys()."""</span>\n",
" <span class=\"n\">variables</span> <span class=\"o\">=</span> <span class=\"n\">variables</span> <span class=\"ow\">or</span> <span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"n\">domains</span><span class=\"o\">.</span><span class=\"n\">keys</span><span class=\"p\">())</span>\n",
"\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">variables</span> <span class=\"o\">=</span> <span class=\"n\">variables</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">domains</span> <span class=\"o\">=</span> <span class=\"n\">domains</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">neighbors</span> <span class=\"o\">=</span> <span class=\"n\">neighbors</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">constraints</span> <span class=\"o\">=</span> <span class=\"n\">constraints</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">initial</span> <span class=\"o\">=</span> <span class=\"p\">()</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">curr_domains</span> <span class=\"o\">=</span> <span class=\"bp\">None</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">nassigns</span> <span class=\"o\">=</span> <span class=\"mi\">0</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">assign</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Add {var: val} to assignment; Discard the old value if any."""</span>\n",
" <span class=\"n\">assignment</span><span class=\"p\">[</span><span class=\"n\">var</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">val</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">nassigns</span> <span class=\"o\">+=</span> <span class=\"mi\">1</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">unassign</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Remove {var: val} from assignment.</span>\n",
"<span class=\"sd\"> DO NOT call this if you are changing a variable to a new value;</span>\n",
"<span class=\"sd\"> just call assign for that."""</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">var</span> <span class=\"ow\">in</span> <span class=\"n\">assignment</span><span class=\"p\">:</span>\n",
" <span class=\"k\">del</span> <span class=\"n\">assignment</span><span class=\"p\">[</span><span class=\"n\">var</span><span class=\"p\">]</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">nconflicts</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Return the number of conflicts var=val has with other variables."""</span>\n",
" <span class=\"c1\"># Subclasses may implement this more efficiently</span>\n",
" <span class=\"k\">def</span> <span class=\"nf\">conflict</span><span class=\"p\">(</span><span class=\"n\">var2</span><span class=\"p\">):</span>\n",
" <span class=\"k\">return</span> <span class=\"p\">(</span><span class=\"n\">var2</span> <span class=\"ow\">in</span> <span class=\"n\">assignment</span> <span class=\"ow\">and</span>\n",
" <span class=\"ow\">not</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">constraints</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">var2</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">[</span><span class=\"n\">var2</span><span class=\"p\">]))</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">count</span><span class=\"p\">(</span><span class=\"n\">conflict</span><span class=\"p\">(</span><span class=\"n\">v</span><span class=\"p\">)</span> <span class=\"k\">for</span> <span class=\"n\">v</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">neighbors</span><span class=\"p\">[</span><span class=\"n\">var</span><span class=\"p\">])</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">display</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Show a human-readable representation of the CSP."""</span>\n",
" <span class=\"c1\"># Subclasses can print in a prettier way, or display with a GUI</span>\n",
" <span class=\"k\">print</span><span class=\"p\">(</span><span class=\"s1\">'CSP:'</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"s1\">'with assignment:'</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">)</span>\n",
"\n",
" <span class=\"c1\"># These methods are for the tree and graph-search interface:</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">actions</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">state</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Return a list of applicable actions: nonconflicting</span>\n",
"<span class=\"sd\"> assignments to an unassigned variable."""</span>\n",
" <span class=\"k\">if</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">state</span><span class=\"p\">)</span> <span class=\"o\">==</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">variables</span><span class=\"p\">):</span>\n",
" <span class=\"k\">return</span> <span class=\"p\">[]</span>\n",
" <span class=\"k\">else</span><span class=\"p\">:</span>\n",
" <span class=\"n\">assignment</span> <span class=\"o\">=</span> <span class=\"nb\">dict</span><span class=\"p\">(</span><span class=\"n\">state</span><span class=\"p\">)</span>\n",
" <span class=\"n\">var</span> <span class=\"o\">=</span> <span class=\"n\">first</span><span class=\"p\">([</span><span class=\"n\">v</span> <span class=\"k\">for</span> <span class=\"n\">v</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">variables</span> <span class=\"k\">if</span> <span class=\"n\">v</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"n\">assignment</span><span class=\"p\">])</span>\n",
" <span class=\"k\">return</span> <span class=\"p\">[(</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">)</span> <span class=\"k\">for</span> <span class=\"n\">val</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">domains</span><span class=\"p\">[</span><span class=\"n\">var</span><span class=\"p\">]</span>\n",
" <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">nconflicts</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">)</span> <span class=\"o\">==</span> <span class=\"mi\">0</span><span class=\"p\">]</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">result</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">state</span><span class=\"p\">,</span> <span class=\"n\">action</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Perform an action and return the new state."""</span>\n",
" <span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">)</span> <span class=\"o\">=</span> <span class=\"n\">action</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">state</span> <span class=\"o\">+</span> <span class=\"p\">((</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">),)</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">goal_test</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">state</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""The goal is to assign all variables, with all constraints satisfied."""</span>\n",
" <span class=\"n\">assignment</span> <span class=\"o\">=</span> <span class=\"nb\">dict</span><span class=\"p\">(</span><span class=\"n\">state</span><span class=\"p\">)</span>\n",
" <span class=\"k\">return</span> <span class=\"p\">(</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">assignment</span><span class=\"p\">)</span> <span class=\"o\">==</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">variables</span><span class=\"p\">)</span>\n",
" <span class=\"ow\">and</span> <span class=\"nb\">all</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">nconflicts</span><span class=\"p\">(</span><span class=\"n\">variables</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">[</span><span class=\"n\">variables</span><span class=\"p\">],</span> <span class=\"n\">assignment</span><span class=\"p\">)</span> <span class=\"o\">==</span> <span class=\"mi\">0</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">variables</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">variables</span><span class=\"p\">))</span>\n",
"\n",
" <span class=\"c1\"># These are for constraint propagation</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">support_pruning</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Make sure we can prune values from domains. (We want to pay</span>\n",
"<span class=\"sd\"> for this only if we use it.)"""</span>\n",
" <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">curr_domains</span> <span class=\"ow\">is</span> <span class=\"bp\">None</span><span class=\"p\">:</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">curr_domains</span> <span class=\"o\">=</span> <span class=\"p\">{</span><span class=\"n\">v</span><span class=\"p\">:</span> <span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">domains</span><span class=\"p\">[</span><span class=\"n\">v</span><span class=\"p\">])</span> <span class=\"k\">for</span> <span class=\"n\">v</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">variables</span><span class=\"p\">}</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">suppose</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">value</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Start accumulating inferences from assuming var=value."""</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">support_pruning</span><span class=\"p\">()</span>\n",
" <span class=\"n\">removals</span> <span class=\"o\">=</span> <span class=\"p\">[(</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">a</span><span class=\"p\">)</span> <span class=\"k\">for</span> <span class=\"n\">a</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">curr_domains</span><span class=\"p\">[</span><span class=\"n\">var</span><span class=\"p\">]</span> <span class=\"k\">if</span> <span class=\"n\">a</span> <span class=\"o\">!=</span> <span class=\"n\">value</span><span class=\"p\">]</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">curr_domains</span><span class=\"p\">[</span><span class=\"n\">var</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">value</span><span class=\"p\">]</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">removals</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">prune</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">value</span><span class=\"p\">,</span> <span class=\"n\">removals</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Rule out var=value."""</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">curr_domains</span><span class=\"p\">[</span><span class=\"n\">var</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">remove</span><span class=\"p\">(</span><span class=\"n\">value</span><span class=\"p\">)</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">removals</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"bp\">None</span><span class=\"p\">:</span>\n",
" <span class=\"n\">removals</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">((</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">value</span><span class=\"p\">))</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">choices</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Return all values for var that aren't currently ruled out."""</span>\n",
" <span class=\"k\">return</span> <span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">curr_domains</span> <span class=\"ow\">or</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">domains</span><span class=\"p\">)[</span><span class=\"n\">var</span><span class=\"p\">]</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">infer_assignment</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Return the partial assignment implied by the current inferences."""</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">support_pruning</span><span class=\"p\">()</span>\n",
" <span class=\"k\">return</span> <span class=\"p\">{</span><span class=\"n\">v</span><span class=\"p\">:</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">curr_domains</span><span class=\"p\">[</span><span class=\"n\">v</span><span class=\"p\">][</span><span class=\"mi\">0</span><span class=\"p\">]</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">v</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">variables</span> <span class=\"k\">if</span> <span class=\"mi\">1</span> <span class=\"o\">==</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">curr_domains</span><span class=\"p\">[</span><span class=\"n\">v</span><span class=\"p\">])}</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">restore</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">removals</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Undo a supposition and all inferences from it."""</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">B</span><span class=\"p\">,</span> <span class=\"n\">b</span> <span class=\"ow\">in</span> <span class=\"n\">removals</span><span class=\"p\">:</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">curr_domains</span><span class=\"p\">[</span><span class=\"n\">B</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">b</span><span class=\"p\">)</span>\n",
"\n",
" <span class=\"c1\"># This is for min_conflicts search</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">conflicted_vars</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">current</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Return a list of variables in current assignment that are in conflict"""</span>\n",
" <span class=\"k\">return</span> <span class=\"p\">[</span><span class=\"n\">var</span> <span class=\"k\">for</span> <span class=\"n\">var</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">variables</span>\n",
" <span class=\"k\">if</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">nconflicts</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">current</span><span class=\"p\">[</span><span class=\"n\">var</span><span class=\"p\">],</span> <span class=\"n\">current</span><span class=\"p\">)</span> <span class=\"o\">></span> <span class=\"mi\">0</span><span class=\"p\">]</span>\n",
"</pre></div>\n",
"</body>\n",
"</html>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"psource(CSP)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The __ _ _init_ _ __ method parameters specify the CSP. Variables can be passed as a list of strings or integers. Domains are passed as dict (dictionary datatpye) where \"key\" specifies the variables and \"value\" specifies the domains. The variables are passed as an empty list. Variables are extracted from the keys of the domain dictionary. Neighbor is a dict of variables that essentially describes the constraint graph. Here each variable key has a list of its values which are the variables that are constraint along with it. The constraint parameter should be a function **f(A, a, B, b**) that **returns true** if neighbors A, B **satisfy the constraint** when they have values **A=a, B=b**. We have additional parameters like nassings which is incremented each time an assignment is made when calling the assign method. You can read more about the methods and parameters in the class doc string. We will talk more about them as we encounter their use. Let us jump to an example."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## GRAPH COLORING\n",
"\n",
"We use the graph coloring problem as our running example for demonstrating the different algorithms in the **csp module**. The idea of map coloring problem is that the adjacent nodes (those connected by edges) should not have the same color throughout the graph. The graph can be colored using a fixed number of colors. Here each node is a variable and the values are the colors that can be assigned to them. Given that the domain will be the same for all our nodes we use a custom dict defined by the **UniversalDict** class. The **UniversalDict** Class takes in a parameter and returns it as a value for all the keys of the dict. It is very similar to **defaultdict** in Python except that it does not support item assignment."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['R', 'G', 'B']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = UniversalDict(['R','G','B'])\n",
"s[5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For our CSP we also need to define a constraint function **f(A, a, B, b)**. In this, we need to ensure that the neighbors don't have the same color. This is defined in the function **different_values_constraint** of the module."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
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" </style>\n",
"</head>\n",
"<body>\n",
"<h2></h2>\n",
"\n",
"<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">different_values_constraint</span><span class=\"p\">(</span><span class=\"n\">A</span><span class=\"p\">,</span> <span class=\"n\">a</span><span class=\"p\">,</span> <span class=\"n\">B</span><span class=\"p\">,</span> <span class=\"n\">b</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""A constraint saying two neighboring variables must differ in value."""</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">a</span> <span class=\"o\">!=</span> <span class=\"n\">b</span>\n",
"</pre></div>\n",
"</body>\n",
"</html>\n"
],
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"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"psource(different_values_constraint)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The CSP class takes neighbors in the form of a Dict. The module specifies a simple helper function named **parse_neighbors** which allows us to take input in the form of strings and return a Dict of a form that is compatible with the **CSP Class**."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"%pdoc parse_neighbors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The **MapColoringCSP** function creates and returns a CSP with the above constraint function and states. The variables are the keys of the neighbors dict and the constraint is the one specified by the **different_values_constratint** function. **Australia**, **USA** and **France** are three CSPs that have been created using **MapColoringCSP**. **Australia** corresponds to ** Figure 6.1 ** in the book."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
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"\n",
"<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">MapColoringCSP</span><span class=\"p\">(</span><span class=\"n\">colors</span><span class=\"p\">,</span> <span class=\"n\">neighbors</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Make a CSP for the problem of coloring a map with different colors</span>\n",
"<span class=\"sd\"> for any two adjacent regions. Arguments are a list of colors, and a</span>\n",
"<span class=\"sd\"> dict of {region: [neighbor,...]} entries. This dict may also be</span>\n",
"<span class=\"sd\"> specified as a string of the form defined by parse_neighbors."""</span>\n",
" <span class=\"k\">if</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">neighbors</span><span class=\"p\">,</span> <span class=\"nb\">str</span><span class=\"p\">):</span>\n",
" <span class=\"n\">neighbors</span> <span class=\"o\">=</span> <span class=\"n\">parse_neighbors</span><span class=\"p\">(</span><span class=\"n\">neighbors</span><span class=\"p\">)</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">CSP</span><span class=\"p\">(</span><span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"n\">neighbors</span><span class=\"o\">.</span><span class=\"n\">keys</span><span class=\"p\">()),</span> <span class=\"n\">UniversalDict</span><span class=\"p\">(</span><span class=\"n\">colors</span><span class=\"p\">),</span> <span class=\"n\">neighbors</span><span class=\"p\">,</span>\n",
" <span class=\"n\">different_values_constraint</span><span class=\"p\">)</span>\n",
"</pre></div>\n",
"</body>\n",
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"## N-QUEENS\n",
"\n",
"The N-queens puzzle is the problem of placing N chess queens on an N×N chessboard in a way such that no two queens threaten each other. Here N is a natural number. Like the graph coloring problem, NQueens is also implemented in the csp module. The **NQueensCSP** class inherits from the **CSP** class. It makes some modifications in the methods to suit this particular problem. The queens are assumed to be placed one per column, from left to right. That means position (x, y) represents (var, val) in the CSP. The constraint that needs to be passed to the CSP is defined in the **queen_constraint** function. The constraint is satisfied (true) if A, B are really the same variable, or if they are not in the same row, down diagonal, or up diagonal. "
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"<body>\n",
"<h2></h2>\n",
"\n",
"<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">queen_constraint</span><span class=\"p\">(</span><span class=\"n\">A</span><span class=\"p\">,</span> <span class=\"n\">a</span><span class=\"p\">,</span> <span class=\"n\">B</span><span class=\"p\">,</span> <span class=\"n\">b</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Constraint is satisfied (true) if A, B are really the same variable,</span>\n",
"<span class=\"sd\"> or if they are not in the same row, down diagonal, or up diagonal."""</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">A</span> <span class=\"o\">==</span> <span class=\"n\">B</span> <span class=\"ow\">or</span> <span class=\"p\">(</span><span class=\"n\">a</span> <span class=\"o\">!=</span> <span class=\"n\">b</span> <span class=\"ow\">and</span> <span class=\"n\">A</span> <span class=\"o\">+</span> <span class=\"n\">a</span> <span class=\"o\">!=</span> <span class=\"n\">B</span> <span class=\"o\">+</span> <span class=\"n\">b</span> <span class=\"ow\">and</span> <span class=\"n\">A</span> <span class=\"o\">-</span> <span class=\"n\">a</span> <span class=\"o\">!=</span> <span class=\"n\">B</span> <span class=\"o\">-</span> <span class=\"n\">b</span><span class=\"p\">)</span>\n",
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"The **NQueensCSP** method implements methods that support solving the problem via **min_conflicts** which is one of the many popular techniques for solving CSPs. Because **min_conflicts** hill climbs the number of conflicts to solve, the CSP **assign** and **unassign** are modified to record conflicts. More details about the structures: **rows**, **downs**, **ups** which help in recording conflicts are explained in the docstring."
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"<div class=\"highlight\"><pre><span></span><span class=\"k\">class</span> <span class=\"nc\">NQueensCSP</span><span class=\"p\">(</span><span class=\"n\">CSP</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Make a CSP for the nQueens problem for search with min_conflicts.</span>\n",
"<span class=\"sd\"> Suitable for large n, it uses only data structures of size O(n).</span>\n",
"<span class=\"sd\"> Think of placing queens one per column, from left to right.</span>\n",
"<span class=\"sd\"> That means position (x, y) represents (var, val) in the CSP.</span>\n",
"<span class=\"sd\"> The main structures are three arrays to count queens that could conflict:</span>\n",
"<span class=\"sd\"> rows[i] Number of queens in the ith row (i.e val == i)</span>\n",
"<span class=\"sd\"> downs[i] Number of queens in the \\ diagonal</span>\n",
"<span class=\"sd\"> such that their (x, y) coordinates sum to i</span>\n",
"<span class=\"sd\"> ups[i] Number of queens in the / diagonal</span>\n",
"<span class=\"sd\"> such that their (x, y) coordinates have x-y+n-1 = i</span>\n",
"<span class=\"sd\"> We increment/decrement these counts each time a queen is placed/moved from</span>\n",
"<span class=\"sd\"> a row/diagonal. So moving is O(1), as is nconflicts. But choosing</span>\n",
"<span class=\"sd\"> a variable, and a best value for the variable, are each O(n).</span>\n",
"<span class=\"sd\"> If you want, you can keep track of conflicted variables, then variable</span>\n",
"<span class=\"sd\"> selection will also be O(1).</span>\n",
"<span class=\"sd\"> >>> len(backtracking_search(NQueensCSP(8)))</span>\n",
"<span class=\"sd\"> 8</span>\n",
"<span class=\"sd\"> """</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">n</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Initialize data structures for n Queens."""</span>\n",
" <span class=\"n\">CSP</span><span class=\"o\">.</span><span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">n</span><span class=\"p\">)),</span> <span class=\"n\">UniversalDict</span><span class=\"p\">(</span><span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">n</span><span class=\"p\">))),</span>\n",
" <span class=\"n\">UniversalDict</span><span class=\"p\">(</span><span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">n</span><span class=\"p\">))),</span> <span class=\"n\">queen_constraint</span><span class=\"p\">)</span>\n",
"\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">rows</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">n</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">ups</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"mi\">2</span><span class=\"o\">*</span><span class=\"n\">n</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">)</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">downs</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"mi\">2</span><span class=\"o\">*</span><span class=\"n\">n</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">)</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">nconflicts</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""The number of conflicts, as recorded with each assignment.</span>\n",
"<span class=\"sd\"> Count conflicts in row and in up, down diagonals. If there</span>\n",
"<span class=\"sd\"> is a queen there, it can't conflict with itself, so subtract 3."""</span>\n",
" <span class=\"n\">n</span> <span class=\"o\">=</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">variables</span><span class=\"p\">)</span>\n",
" <span class=\"n\">c</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">rows</span><span class=\"p\">[</span><span class=\"n\">val</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">downs</span><span class=\"p\">[</span><span class=\"n\">var</span><span class=\"o\">+</span><span class=\"n\">val</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">ups</span><span class=\"p\">[</span><span class=\"n\">var</span><span class=\"o\">-</span><span class=\"n\">val</span><span class=\"o\">+</span><span class=\"n\">n</span><span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">]</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">assignment</span><span class=\"o\">.</span><span class=\"n\">get</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"bp\">None</span><span class=\"p\">)</span> <span class=\"o\">==</span> <span class=\"n\">val</span><span class=\"p\">:</span>\n",
" <span class=\"n\">c</span> <span class=\"o\">-=</span> <span class=\"mi\">3</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">c</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">assign</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Assign var, and keep track of conflicts."""</span>\n",
" <span class=\"n\">oldval</span> <span class=\"o\">=</span> <span class=\"n\">assignment</span><span class=\"o\">.</span><span class=\"n\">get</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"bp\">None</span><span class=\"p\">)</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">val</span> <span class=\"o\">!=</span> <span class=\"n\">oldval</span><span class=\"p\">:</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">oldval</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"bp\">None</span><span class=\"p\">:</span> <span class=\"c1\"># Remove old val if there was one</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">record_conflict</span><span class=\"p\">(</span><span class=\"n\">assignment</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">oldval</span><span class=\"p\">,</span> <span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">record_conflict</span><span class=\"p\">(</span><span class=\"n\">assignment</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"o\">+</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
" <span class=\"n\">CSP</span><span class=\"o\">.</span><span class=\"n\">assign</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">)</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">unassign</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Remove var from assignment (if it is there) and track conflicts."""</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">var</span> <span class=\"ow\">in</span> <span class=\"n\">assignment</span><span class=\"p\">:</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">record_conflict</span><span class=\"p\">(</span><span class=\"n\">assignment</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">[</span><span class=\"n\">var</span><span class=\"p\">],</span> <span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n",
" <span class=\"n\">CSP</span><span class=\"o\">.</span><span class=\"n\">unassign</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">)</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">record_conflict</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">delta</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Record conflicts caused by addition or deletion of a Queen."""</span>\n",
" <span class=\"n\">n</span> <span class=\"o\">=</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">variables</span><span class=\"p\">)</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">rows</span><span class=\"p\">[</span><span class=\"n\">val</span><span class=\"p\">]</span> <span class=\"o\">+=</span> <span class=\"n\">delta</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">downs</span><span class=\"p\">[</span><span class=\"n\">var</span> <span class=\"o\">+</span> <span class=\"n\">val</span><span class=\"p\">]</span> <span class=\"o\">+=</span> <span class=\"n\">delta</span>\n",
" <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">ups</span><span class=\"p\">[</span><span class=\"n\">var</span> <span class=\"o\">-</span> <span class=\"n\">val</span> <span class=\"o\">+</span> <span class=\"n\">n</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">]</span> <span class=\"o\">+=</span> <span class=\"n\">delta</span>\n",
"\n",
" <span class=\"k\">def</span> <span class=\"nf\">display</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Print the queens and the nconflicts values (for debugging)."""</span>\n",
" <span class=\"n\">n</span> <span class=\"o\">=</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">variables</span><span class=\"p\">)</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">val</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">n</span><span class=\"p\">):</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">var</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">n</span><span class=\"p\">):</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">assignment</span><span class=\"o\">.</span><span class=\"n\">get</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"s1\">''</span><span class=\"p\">)</span> <span class=\"o\">==</span> <span class=\"n\">val</span><span class=\"p\">:</span>\n",
" <span class=\"n\">ch</span> <span class=\"o\">=</span> <span class=\"s1\">'Q'</span>\n",
" <span class=\"k\">elif</span> <span class=\"p\">(</span><span class=\"n\">var</span> <span class=\"o\">+</span> <span class=\"n\">val</span><span class=\"p\">)</span> <span class=\"o\">%</span> <span class=\"mi\">2</span> <span class=\"o\">==</span> <span class=\"mi\">0</span><span class=\"p\">:</span>\n",
" <span class=\"n\">ch</span> <span class=\"o\">=</span> <span class=\"s1\">'.'</span>\n",
" <span class=\"k\">else</span><span class=\"p\">:</span>\n",
" <span class=\"n\">ch</span> <span class=\"o\">=</span> <span class=\"s1\">'-'</span>\n",
" <span class=\"k\">print</span><span class=\"p\">(</span><span class=\"n\">ch</span><span class=\"p\">,</span> <span class=\"n\">end</span><span class=\"o\">=</span><span class=\"s1\">' '</span><span class=\"p\">)</span>\n",
" <span class=\"k\">print</span><span class=\"p\">(</span><span class=\"s1\">' '</span><span class=\"p\">,</span> <span class=\"n\">end</span><span class=\"o\">=</span><span class=\"s1\">' '</span><span class=\"p\">)</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">var</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">n</span><span class=\"p\">):</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">assignment</span><span class=\"o\">.</span><span class=\"n\">get</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"s1\">''</span><span class=\"p\">)</span> <span class=\"o\">==</span> <span class=\"n\">val</span><span class=\"p\">:</span>\n",
" <span class=\"n\">ch</span> <span class=\"o\">=</span> <span class=\"s1\">'*'</span>\n",
" <span class=\"k\">else</span><span class=\"p\">:</span>\n",
" <span class=\"n\">ch</span> <span class=\"o\">=</span> <span class=\"s1\">' '</span>\n",
" <span class=\"k\">print</span><span class=\"p\">(</span><span class=\"nb\">str</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">nconflicts</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">))</span> <span class=\"o\">+</span> <span class=\"n\">ch</span><span class=\"p\">,</span> <span class=\"n\">end</span><span class=\"o\">=</span><span class=\"s1\">' '</span><span class=\"p\">)</span>\n",
" <span class=\"k\">print</span><span class=\"p\">()</span>\n",
"</pre></div>\n",
"</body>\n",
"</html>\n"
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"cell_type": "markdown",
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"source": [
"The _ ___init___ _ method takes only one parameter **n** i.e. the size of the problem. To create an instance, we just pass the required value of n into the constructor."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"eight_queens = NQueensCSP(8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have defined our CSP. \n",
"Now, we need to solve this.\n",
"\n",
"### Min-conflicts\n",
"As stated above, the `min_conflicts` algorithm is an efficient method to solve such a problem.\n",
"<br>\n",
"In the start, all the variables of the CSP are _randomly_ initialized. \n",
"<br>\n",
"The algorithm then randomly selects a variable that has conflicts and violates some constraints of the CSP.\n",
"<br>\n",
"The selected variable is then assigned a value that _minimizes_ the number of conflicts.\n",
"<br>\n",
"This is a simple **stochastic algorithm** which works on a principle similar to **Hill-climbing**.\n",
"The conflicting state is repeatedly changed into a state with fewer conflicts in an attempt to reach an approximate solution.\n",
"<br>\n",
"This algorithm sometimes benefits from having a good initial assignment.\n",
"Using greedy techniques to get a good initial assignment and then using `min_conflicts` to solve the CSP can speed up the procedure dramatically, especially for CSPs with a large state space."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
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"\n",
"<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">min_conflicts</span><span class=\"p\">(</span><span class=\"n\">csp</span><span class=\"p\">,</span> <span class=\"n\">max_steps</span><span class=\"o\">=</span><span class=\"mi\">100000</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Solve a CSP by stochastic hillclimbing on the number of conflicts."""</span>\n",
" <span class=\"c1\"># Generate a complete assignment for all variables (probably with conflicts)</span>\n",
" <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">current</span> <span class=\"o\">=</span> <span class=\"n\">current</span> <span class=\"o\">=</span> <span class=\"p\">{}</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">var</span> <span class=\"ow\">in</span> <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">variables</span><span class=\"p\">:</span>\n",
" <span class=\"n\">val</span> <span class=\"o\">=</span> <span class=\"n\">min_conflicts_value</span><span class=\"p\">(</span><span class=\"n\">csp</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">current</span><span class=\"p\">)</span>\n",
" <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">assign</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">current</span><span class=\"p\">)</span>\n",
" <span class=\"c1\"># Now repeatedly choose a random conflicted variable and change it</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">max_steps</span><span class=\"p\">):</span>\n",
" <span class=\"n\">conflicted</span> <span class=\"o\">=</span> <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">conflicted_vars</span><span class=\"p\">(</span><span class=\"n\">current</span><span class=\"p\">)</span>\n",
" <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"n\">conflicted</span><span class=\"p\">:</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">current</span>\n",
" <span class=\"n\">var</span> <span class=\"o\">=</span> <span class=\"n\">random</span><span class=\"o\">.</span><span class=\"n\">choice</span><span class=\"p\">(</span><span class=\"n\">conflicted</span><span class=\"p\">)</span>\n",
" <span class=\"n\">val</span> <span class=\"o\">=</span> <span class=\"n\">min_conflicts_value</span><span class=\"p\">(</span><span class=\"n\">csp</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">current</span><span class=\"p\">)</span>\n",
" <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">assign</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">current</span><span class=\"p\">)</span>\n",
" <span class=\"k\">return</span> <span class=\"bp\">None</span>\n",
"</pre></div>\n",
"</body>\n",
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's use this algorithm to solve the `eight_queens` CSP."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"solution = min_conflicts(eight_queens)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is indeed a valid solution. \n",
"<br>\n",
"`notebook.py` has a helper function to visualize the solution space."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
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njtZXAQBAQ1iaNGHvfr8h539Pn5HaZ/en9wna1TPKq9OUHy9JR5+QJo4bWh7laXq3S2PfE1jdiuVKWRYx+/LehrRf9hWgDVmaNCvahjV2/PCLK993zm8sv9BgDQBIBQG7xURZLGXJ6sr3tX58fvar8ZQLAEhP7AHbzP7OzI6Y2c/izhue+7YNLf3GrcnUAwDQPEn0sDdJ+lQC+WbayrXR0za7tzuU8obyOQAA8Yk9YDvnnpZ0LO58s25tzCt7fv62aOnivutX3J8DABAN57Bb1KIV4fu//YD3vGOP//6tT3vPQffVLrlyVeX7ay+vXTcAQPOlErDNbJmZdZtZnDeBzLTp76t8/+jOaMfNW+a//dMRe8LV12ff85VoxwEAmiuVgO2c+45zrivKdWdF8ZO7B29bsDz8mI6QpUYlafzHw/evWBO+HwDQOhgSb5aZ4SuETZk0eNtjNZYFPV7jZh69J8P3r98cvt/X+T11HAQAaFQSl3VtlvRTSR8yswNm9n/EXUYmtU2s67CkZoxfdVOdB7ZPiLUeAIBo2uLO0Dm3NO48Eb/vb0+7BgCAoWBIvIVM7ki3/NnnpVs+ACAYN/9I2KDvN+QmIFL9Q+Af+YAX8PcflH55oL48at4tbNbgpuLGA9mX9zak/bKvAG0Y6eYfsQ+JozGuOzhoL5zT2P2yL7tB2vZscLkAgNZFwG62qXdKB8JnfPVul8bN814f3iZNqhoqv+4W6Z5Hohc5Z6a0c4P0+F0D2/YflGZc4b0+FGVt8mnfjF4gACB2DIknzPf7rTEsLnm97FKvd8s2aenq8PRD8d2vSUsvG1xOKJ/hcInhuDzIexvSftlXgDaMNCROwE6Y7/d76qi01+fC6ypRz2cvnitdv1iaN0s6flL66V7p1o3Sz/dFqF+UYH1+T+DlXPxnkX15b0PaL/sK0Iacw25Z7Z11H7p1rRegg4wfI82YIl29oHL7zhekSz5XZ6Fcew0AqaOHnbDQ7zfi0Hh7m/TOs4O3R65DVS+6fbZ0+kxjQ+Hv1oNf95mX9zak/bKvAG1ID7vlzXKRgnYpWNd7yVf5cWeel049FzGvGsEaANA8LJyStum1F/S2ruAAe8sy6fhTXm+59Ojb5W33M+yiiMF6+vciJAIANAtD4gmL9P0G9LKrA+uV86QH76y/LktXezPOywUOi0fsXTMcl315b0PaL/sK0IbMEm8Fkb/fPaMk93bFJuuSep6UJoytTDp6rvRmX/Q6dIyR3vhx5bavb5JuvssnYE/fLHUsiZw3/1lkX97bkPbLvgK0IeewM+XC/ghc1dtuGyZNv0J65WD9WR87Udlb/9Ujg3vakjhnDQAtjHPYraYsaLpu6aEdjQVrP+cu8q7bruhdE6wBoKUxJJ6wur/fU8ekvU24/vn8Iw1dF85wXPblvQ1pv+wrQBtGGhKnh92q2ju8Xu+0dcnkP229l38DwRoA0Dz0sBMW6/cb4ZrtmmIe+ubXffblvQ1pv+wrQBvSw86dWW7gMfP4oN2r/Drj579eeRwAIJPoYScs7e83afy6z768tyHtl30FaEN62AAA5AUBGwCADCBgAwCQAamvdDZr1ix1d0e5z2M25f38Ut7PLUm0YdbRftmX9zaMih42AAAZkHoPGwCAZgm8Q+EQRLpFcQLoYQMAcu2ma7xAHUewlgbyWnl1PPlFRcAGAORSxxgvsN7xxWTyX3Ojl/+kjmTyr8aQOAAgd+LqTUdxuP92xUkPldPDBgDkSjODdTPLJWADAHLht8+kF6xLXLf0559MJm8CNgAg81y3NGJ44/nccHvjeWy5LZkfDpzDBgBk2tu7Gs+j/Pzz39zvPTcadH/7jDTyjxvLoxw9bABApo0cUTtN53zp3h/67wuaLNboJLI4evzlCNgAgMyq1Qu2Lu/R0yt95q8bD8Kl/EqP8/6ssfoNBQEbAJBJtYLht+7z315v0PY77qV9tY+LK2gTsAEAmdMZYbGS5XckXw8p2g+ACWMbL4eADQDInCPb4ssrqAcc53B2z5ON58EscQBApvzFNQOv/Xq3pUDruqMPf7tu6WSfNGaudOJpafSo6PXZ+OVo9VmxVPrG5uj5VqOHDQDIlNv71wYPCsYHjgy8njNz8P6gnnMpSAcF66DjrlvsPf/6kP/+Uj3XrfLfHxUBGwCQK9MWDrzeuaEy0IYNc3/wKu95wqXBaarzKn9/7qKh1XOoCNgAgMxo9Lzya0eC9738qvd87ERwmrB9UTRSfwI2ACBXFs4J3jd1YfC+KMJ634suaSzvWgjYAIBM6gtYkvTR9c2tR8nD6/y3v/1MPPkTsAEAmTB5QuX7s0Z4Q8xnlS1NGmXIedPD9ZX/0I7aacrLHzXSez+yaonSiePqK5+ADQDIhEOP+2/v2yWdes57HeUyruu/Mnjb6TOV73t6B6e5MsIs71L5vdult3b6pzn6RO18/BCwAQCZ1zasseOHX1z5vnN+Y/mNfU9jx/shYAMAciVKL3vJ6sr3zoWn/+xX4ym3EQRsAEDh3DfEpU03bk2mHkMRe8A2s2lm9pSZ/cLMXjKzL8ZdBgCgeFaujZ426d5uI+UN5XOUS6KHfVrSKufc/yzpYkn/ycz+IIFyAAAFsnZlvPl9/rZo6eK+61e9nyP2gO2ce905t6f/9UlJv5A0Je5yAAAIs2hF+P5vP+A979jjv3/r095z0H21S6pnj197ee261SPRc9hm9n5JH5X0XNX2ZWbWbWbdR48eTbIKAICCmP6+yvePBlxWVW3eMv/tn47YE66+Pvsen8vG4pBYwDaz90h6QNIK51zF6qvOue8457qcc12dnZ1JVQEAUCA/uXvwtgXLw4/pCFlqVJLGfzx8/4o14fvjlEjANrN2ecH6XufcPyZRBgCgWCZ+Inz/lEmDtz1WY1nQ4zVu5tF7Mnz/+jrubx22HnmYJGaJm6QNkn7hnKtzLhwAAJXe+E19xyU1Y/yqm+o7rt47fiXRw54j6RpJl5rZC/2PBu+PAgBAa/n+9uaW1xZ3hs65nZIs7nwBAKhlcod0+Fh65c8+L7m8WekMAJAZtYa3Dw1xBbNyH/mANP8i6fen1p/Hs5vC9zcyPB97DxsAgDS57uDAuHBOY/fLvuwGaduzweUmiYANAMiUVeukNTeGp+ndLo2b570+vE2a1FG5/7pbpHseiV7mnJnSzg3S43cNbNt/UJpxhfc6Ss/+Cw2umGau1i1KEtbV1eW6uxP+WZIib9J8fqX976cZaMNso/2yz68No/RmrWsg3ZZt0tLV4emH4rtfk5ZeNricWvUJsNs5V3OwnICdMP6zyD7aMNtov+zza8OJ46SjT0Q4NuI548VzpesXS/NmScdPSj/dK926Ufr5vtrHRgnWEy4NvZwrUsBmSBwAkDk9vfUfu3WtF6CDjB8jzZgiXb2gcvvOF6RLPldfmfVee12OgA0AyKQoQ9GlCWjtbdI7VZPFhjJj23VLH7tgoLz22dLpMw0PhQ8JARsAkFlRzx+XgnW9wbP8uDPPS6eei5ZXnKuscR02ACDTltxcO411BQfPW5ZJx5/yAn/p0bfL2+5n2EXRAvGffql2mqFg0lnCmPCSfbRhttF+2RelDYN62dWB9cp50oN31l+Xpau9Gef1lB2CSWcAgGKwLumtndKokYP39TwpTRhbuW30XOnNvuj5d4yR3vixtPlW7yFJX98k3XzX4LRLbpbu+1H0vKMiYAMAcuHsj3nP1T3etmHS9CukVw7Wn/exE5U95l89MrinLSV3ZzCJc9gAgJwpD5quW3poR2PB2s+5i7zrtst/HCQZrCV62ACAHLIuafxo6dhT0rWXe4+kdM5v7LrwqOhhAwBy6fhJL3CvWJNM/svv8PJvRrCW6GEDAHJu/WbvIcVzR62kh76D0MMGABRG6Xps6xq4m1e5VesGbzvnssrj0kIPGwBQSL950z8Ar723+XWJgh42AAAZQMAGACADCNgAAGQAARsAgAxI/eYfZpbrlevT/n6TlvcbK0i0YdbRftlXgDaMdPMPetgAEjFudOXtCl23tPLqwdvOmZB2TYFsoIedsLS/36Tx6z774mzDVlyUgvbLvgK0IT1sAMm76ZqB3nIcynvjAAbQw05Y2t9v0vh1n331tmHp/sBJm/wn0pFj9R9P+2VfAdowUg+blc4ADFlcvekoDvffczjNJSGBVsCQOIAhaWawboVygVZBwAYQyW+fST9oum7pzz+Zbh2AtBCwAdTkuqURwxvP54bbG89jy23p/3AA0sCks4Sl/f0mjQkv2VerDd/eJY0c0WAZPuefGw26v3tHGvnHtdMVvf3yoABtyGVdABoXJVh3zpfu/aH/vqDJYo1OIoujxw9kCT3shKX9/SaNX/fZF9aGtXrBUXrOYYG5VtoPz5B+dv/Q61BRRoHbLy8K0Ib0sAHUr1aw/tZ9/tvr7Tn7HffSvtrHcT4bRUHABjBIZ0ftNMvvSL4eUrQfABPGJl8PIG0EbACDHNkWX15BPeA4e8Y9T8aXF9CqWOkMQIW/uGbgddg5atcdffjbdUsn+6Qxc6UTT0ujR0Wvz8YvR6vPiqXSNzZHzxfIGnrYACrc/kXvOSgYHzgy8HrOzMH7g3rOpSAdFKyDjrtusff860P++0v1XLfKfz+QFwRsAEMybeHA650bKgNt2DD3B6/ynidcGpymOq/y9+cuGlo9gbwhYAN4V6PnlV87Erzv5Ve952MngtOE7YuCGePIMwI2gCFZOCd439SFwfuiCOt9L7qksbyBrCNgA/DVt8t/+6Prm1uPkofX+W9/+5nm1gNICwEbgCRp8oTK92eN8IaYzypbmjTKkPOmh+sr/6EdtdOUlz9qpPd+ZNUSpRPH1Vc+0OpYmjRhaX+/SWNZxOwrtWFYMD59RmqfrcB01TPKq9OUHy9JR58YHFhr5VGepne7NPY9wfUtz6so7ZdnBWhDliYFEI+2YY0dP/ziyved8xvLLyxYA3lFwAYwJFEWS1myuvJ9rQ7SZ78aT7lAnsUesM1spJk9b2YvmtlLZvaVuMsA0NruG+LSphu3JlMPIE+S6GH/TtKlzrmZki6Q9Ckzu7jGMQBStnJt9LTN7u0OpbyhfA4gS2IP2M7zZv/b9v5HvmcMADmwdmW8+X3+tmjp4r7rV9yfA2gViZzDNrNhZvaCpCOSfuSce65q/zIz6zYz1iUCMmrRivD9337Ae96xx3//1qe956D7apdcWbVG+LWX164bkEeJXtZlZuMkPSjpC865nwWkyXXvuwCXI6RdhcQVpQ1rXWM94wpp/8HKbaVjgoasa93RK2x/UN5RrgXnsq58KUAbpn9Zl3OuV9J2SZ9KshwAyfvJ3YO3LVgefkxHyFKjkjT+4+H7V6wJ3w8USRKzxDv7e9Yys7MkzZf0r3GXAyBeEz8Rvn/KpMHbHquxLOjxGjfz6D0Zvn99Hfe3DluPHMiytgTyfK+ke8xsmLwfBPc75x5JoBwAMXrjN/Udl9SM8atuqu+4Ru/4BbSq2AO2c26vpI/GnS+AYvn+9rRrALQWVjoDENnkjnTLn31euuUDaeLmHwlL+/tNGjNUs6+6DWvNwq53CPwjH/AC/v6D0i8P1JdHPXUrWvvlUQHaMNIs8STOYQPIsbBLsRbOaex+2ZfdIG17NrhcoMgI2AAqrFonrbkxPE3vdmncPO/14W3SpKqh8utuke4ZwlTTOTOlnRukx+8a2Lb/oHfttyQdirA2+RdiXjENaDUMiScs7e83aQzHZZ9fG0ZdnKSUbss2aenq8PRD8d2vSUsvG1xOrfr4KWL75U0B2jDSkDgBO2Fpf79J4z+L7PNrw4njpKNPRDg24vnsxXOl6xdL82ZJx09KP90r3bpR+vm+2sdGCdYTLg2+nKuI7Zc3BWhDzmEDqE9Pb/3Hbl3rBegg48dIM6ZIVy+o3L7zBemSz9VXJtdeowjoYScs7e83afy6z76wNow6FN3eJr3z7ODtUVWX0z5bOn2msaHwd/MucPvlRQHakB42gMZEPX9cCtb1XvJVftyZ56VTz0XLq9n35QbSxMIpAEItubl2GusKDp63LJOOP+UF/tKjb5e33c+wi6IF4j/9Uu00QJ4wJJ6wtL/fpDEcl31R2jCol10dWK+cJz14Z/3uE0VqAAAgAElEQVR1Wbram3FeT9lBaL/sK0AbMku8FaT9/SaN/yyyL2obvrVTGjWy6tguqedJacLYyu2j50pv9kWvQ8cY6Y0fV277+ibp5rsGB+wlN0v3/Sh63rRf9hWgDTmHDSA+Z3/Me64OoG3DpOlXSK8crD/vYycqe8y/emRwT1vinDWKjXPYAIakPGi6bumhHY0Faz/nLvKu2y7/cUCwRtExJJ6wtL/fpDEcl331tuH40dKxp2KujI/O+Y1dF077ZV8B2jDSkDg9bAB1OX7S6/WuWJNM/svv6D9H3kCwBvKEHnbC0v5+k8av++yLsw3juKNW3EPftF/2FaAN6WEDaK7S9djWNXA3r3Kr1g3eds5llccB8EcPO2Fpf79J49d99uW9DWm/7CtAG9LDBgAgLwjYAABkAAEbAIAMSH2ls1mzZqm7O4appS0q7+eX8n5uSaINs472y768t2FU9LABAMiA1HvYAIAWsjuG3uys/Pf600APGwCK7vAdXqCOI1hLA3kdTmgZvIIiYANAUZ16wwusB76UTP4HbvLyP3U4mfwLhiFxACiiuHrTUew9x3tmqLwh9LABoGiaGaxbodycIGADQFHsGZF+0Nxt0rEt6dYhowjYAFAEu01y7zSczQ23x1CX/UvT/+GQQZzDBoC82zOy4SzK76T2N/d7zw3fTnXPCOnC3zWYSXHQwwaAvHO1g2LnfOneH/rvC7rtacO3Q42hx18kBGwAyLMaQ8+l+5D39Eqf+evGg3D5vc2tSzrvzxqrHwYQsAEgr2oEw2/d57+93qDtd9xL+yIcSNCOhIANAHl0+kjNJMvvaEI9FPEHwOmexOuRdQRsAMijFyfHllXQ5LKGJ52Ve7EzxszyiVniAJA3rw9ce+XXuy0FWtcdffjbdUsn+6Qxc6UTT0ujR0WvzsYvD7wOq48OrZPOuTF6xgVDDxsA8ubgX0oKDsYHykbL58wcvD+o51wK0kHBOui46xZ7z78+5L//3Xq+ttI/ASQRsAGgcKYtHHi9c0NloA0b5v7gVd7zhEuD01TnVf7+3EVDqycqEbABIE8anHH9WshctZdf9Z6PnQhOE7YvEmaMByJgA0DBLJwTvG/qwuB9UYT1vhdd0ljeRUfABoCc6tvlv/3R9c2tR8nD6/y3v/1Mc+uRVQRsAMiLU5Wzus4a4Z1DPmvEwLYol2Jteri+4h/aUTtNefmjRnrvRw6vSnTqaH0VyDkCNgDkxd73+m7u2yWdes57HeUyruu/Mnjb6TOV73t6B6e5clXtvEvl926X3toZkGjvpNoZFRABGwAKoG1YY8cPv7jyfef8xvIb+57Gji8iAjYAFEyUXvaS1ZXvnQtP/9mvxlMugiUSsM1smJn9s5k9kkT+AIBk3bdtaOk3bk2mHhiQVA/7i5J+kVDeAAAfK9dGT9vs3u5QyhvK5yiS2AO2mU2VdLmku+POGwAQbG3MK3t+/rZo6eK+61fcnyMvkuhhf0PSlyT9j6AEZrbMzLrNrPvoUabvA0AaFq0I3//tB7znHXv892992nsOuq92SfXs8Wsvr103DBZrwDazRZKOOOd2h6Vzzn3HOdflnOvq7OSWagDQDNPfV/n+0aDLqqrMW+a//dMRe8LV12ff43PZGGqLu4c9R9IVZvaKpC2SLjWzv4+5DABAHX7ic6JywfLwYzpClhqVpPEfD9+/Yk34fkQXa8B2zt3snJvqnHu/pCWSfuyc+0ycZQAAAswMP8U4xWc9ksdqLAt6vMbNPHpPhu9fvzl8v6/ze+o4KP+4DhsA8qJtYl2HJTVj/Kqb6jywfUKs9ciLtqQyds5tl7Q9qfwBAK3t+9vTrkG+0MMGgAKZ3JFu+bPPS7f8LCNgA0CezApfQ/TQEFcwK/eRD0jzL5J+f2r9eTy7qUaCGvUvssSGxAEArcl1B5+3XjinsftlX3aDtO3Z4HJRPwI2AOTN1DulA+Ezvnq3S+Pmea8Pb5MmVQ2VX3eLdM8Q7gYxZ6a0c4P0+F0D2/YflGZc4b2O1LOf9s3oBRYQQ+IAkDeTa9+YunR7S9ftBest27xed+kxlGAtSbterDx+8+PeQi2lXnWkc+eTvjC0QgvGXK17piWsq6vLdXfnd5zEzNKuQqLS/vfTDLRhthW2/U4dlfb6XHhdJeolXYvnStcvlubNko6flH66V7p1o/TzfRHqGOW/+PN7Ai/nynsbStrtnKvZEgyJA0Aetde/7PPWtV6ADjJ+jDRjinT1gsrtO1+QLvlcnYVy7XVNBGwAyKtZTtod3jstTUBrb5PeqZosNpQFVVy39LELBnrT7bOl02ci9q6ZGR4JARsA8ixC0JYGgnW9q56VH3fmeenUcxHzIlhHxqQzAMi76bUX9C5NFvNzyzLp+FNeb7n06Nvlbfcz7KKIwXr69yIkQgmTzhKW98kSaf/7aQbaMNtov34BvezqwHrlPOnBO+uvz9LV3ozzcoHD4hF713lvQzHpDADwrllO2jNKcm8P2tXzpDRhbOW20XOlN/uiZ98xRnrjx9LmW72HJH19k3TzXT6Jp2+WOpZEzxySCNgAUBwX9kfgqt522zBp+hXSKwfrz/rYicre+q8eGdzTlsQ56wZwDhsAiqYsaLpu6aEdjQVrP+cu8q7brhgOJ1g3hB42ABTRLCedOibtnaBrL5euvTzBss4/0tB14fDQwwaAomrv8AL3tHXJ5D9tvZc/wToW9LABoOgmrfAeUqRrtmti6DsR9LABAANmuYHHzOODdq/y64yf/3rlcUgEPWwAgL+2cYMC8Jq/T6kuoIcNAEAWELABAMgAAjYAABmQ+lriZpbrGQppf79JK8Aav7RhxtF+2VeANoy0ljg9bAAAMiA3s8Qj3SS9hnrvAwsAQNIy3cO+6ZqBe7PGoZTXyqvjyQ8AgLhk8hx26TZuSZv8J9KRY43lkfb3mzTOn2Vf3tuQ9su+ArRhPu+HHVdvOorD/beGY6gcAJC2TA2JNzNYt0K5AACUZCJg//aZ9IOm65b+/JPp1gEAUFwtH7BdtzRieOP53HB743lsuS39Hw4AgGJq6Ulnb++SRo5oMH+f88+NBt3fvSON/ONoadP+fpPGhJfsy3sb0n7ZV4A2zP7CKVGCded86d4f+u8LmizW6CSyOHr8AAAMRcv2sGv1gqP0nMMCc620H54h/ez+oddhUDn5/2WYdhUSRxtmG+2XfQVow+z2sGsF62/d57+93p6z33Ev7at9HOezAQDN0nIBu7OjdprldyRfDynaD4AJY5OvBwAALRewj2yLL6+gHnCcPeOeJ+PLCwCAIC210tlfXDPwOuwcteuOPvztuqWTfdKYudKJp6XRo6LXZ+OXo9VnxVLpG5uj5wsAwFC1VA/79i96z0HB+MCRgddzZg7eH9RzLgXpoGAddNx1i73nXx/y31+q57pV/vsBAIhLSwXsWqYtHHi9c0NloA0b5v7gVd7zhEuD01TnVf7+3EVDqycAAHFrmYDd6Hnl144E73v5Ve/52IngNGH7omDGOAAgSS0TsKNYOCd439SFwfuiCOt9L7qksbwBAGhUSwbsvl3+2x9d39x6lDy8zn/72880tx4AgOJqiYA9eULl+7NGeEPMZ5UtTRplyHnTw/WV/9CO2mnKyx810ns/smqJ0onj6isfAIBaWmJp0rBgfPqM1D7be+2XrnpGeXWa8uMl6egTgwNrrTzK0/Rul8a+J7i+g/LK/5J6aVchcbRhttF+2VeANszu0qTl2oY1dvzwiyvfd85vLL+wYA0AQFJaPmCXi7JYypLVle9r/TD77FfjKRcAgCQlErDN7BUz+xcze8HMmnrB031DXNp049Zk6gEAQJyS7GF/3Dl3QZRx+ZVro2fa7N7uUMobyucAAGAoWmJIfO3KePP7/G3R0sV916+4PwcAACVJBWwnaZuZ7TazZdU7zWyZmXXXO1y+aEX4/m8/4D3v2OO/f+vT3nPQfbVLrqxaI/zay2vXDQCAJCRyWZeZvc85d9DMJkn6kaQvOOeeDkgbelmXJM24Qtp/sHJb6ZigIetad/QK2x+Ud5RrwbmsK39ow2yj/bKvAG2Y3mVdzrmD/c9HJD0o6aJG8vvJ3YO3LVgefkxHyFKjkjT+4+H7V6wJ3w8AQDPFHrDN7GwzG116LelPJP0s7JiJnwjPc8qkwdseq7Es6PEaN/PoPRm+f30d97cOW48cAIBGtCWQ52RJD/YP07RJ+q5z7rGwA974TX0FJTVj/Kqb6juu0Tt+AQAQJPaA7ZzbJ2lm3Pk20/e3p10DAAAqtcRlXVFM7ki3/NnnpVs+AKDYWuLmH6XXtWZh1zsE/pEPeAF//0Hplwfqy6PeuqX9/SaNGarZl/c2pP2yrwBtGGmWeBLnsBMTdinWwjmN3S/7shukbc8GlwsAQJpaKmCvWietuTE8Te92adw87/XhbdKkqqHy626R7nkkeplzZko7N0iP3zWwbf9B79pvSToUYW3yL8S8YhoAANVaakhcir44SSndlm3S0tXh6Yfiu1+Tll42uJxa9QmS9vebNIbjsi/vbUj7ZV8B2jDSkHjLBeyJ46SjT0Q4LuL57MVzpesXS/NmScdPSj/dK926Ufr5vtrHRgnWEy4Nv5wr7e83afxnkX15b0PaL/sK0IbZPIfd01v/sVvXegE6yPgx0owp0tULKrfvfEG65HP1lcm11wCAZmi5HnZJ1KHo9jbpnWcHb4+qupz22dLpM40Phb+bf/5/GaZdhcTRhtlG+2VfAdowmz3skqjnj0vBut5LvsqPO/O8dOq5aHk1+77cAIBia+mFU5bcXDuNdQUHz1uWScef8gJ/6dG3y9vuZ9hF0QLxn36pdhoAAOLUskPiJUG97OrAeuU86cE766/H0tXejPN6yg6T9vebNIbjsi/vbUj7ZV8B2jCbs8T9vLVTGjWy6rguqedJacLYyu2j50pv9kUvv2OM9MaPK7d9fZN0812DA/aSm6X7fhQ9b6kQ/9DSrkLiaMNso/2yrwBtmO1z2OXO/pj3XB1A24ZJ06+QXjlYf97HTlT2mH/1yOCetsQ5awBAulr6HHa18qDpuqWHdjQWrP2cu8i7brv8xwHBGgCQtkwMiVcbP1o69lQStanUOb+x68KlQgzlpF2FxNGG2Ub7ZV8B2jDSkHimetglx096vd4Va5LJf/kd/efIGwzWAADEJZM9bD9x3FEriaHvtL/fpPHrPvvy3oa0X/YVoA3z28P2U7oe27oG7uZVbtW6wdvOuazyOAAAWlVuetitKu3vN2n8us++vLch7Zd9BWjDYvWwAQDIMwI2AAAZQMAGACADUl/pbNasWerujmGKd4vK+/mlvJ9bkmjDrKP9si/vbRgVPWwAADKAgA0AQAakPiQOvGt3DMNes/I/PAigmOhhI12H7/ACdRzBWhrI63BC69YCQEoI2EjHqTe8wHrgS8nkf+AmL/9Th5PJHwCajCFxNF9cveko9p7jPTNUDiDj6GGjuZoZrFuhXACICQEbzbFnRPpBc7dJx7akWwcAqBMBG8nbbZJ7p+Fsbrg9hrrsX5r+DwcAqAPnsJGsPSMbzqL81qd/c7/33PD9z/eMkC78XYOZAEDz0MNGslztoNg5X7r3h/77gu5T3vD9y2Po8QNAMxGwkZwaQ8/W5T16eqXP/HXjQbiUX+lx3p81Vj8AaCUEbCSjRjD81n3+2+sN2n7HvbQvwoEEbQAZQcBG/E4fqZlk+R1NqIci/gA43ZN4PQCgUQRsxO/FybFlFTS5rOFJZ+Ve7IwxMwBIBrPEEa/XB6698uvdlgKt644+/O26pZN90pi50omnpdGjoldn45cHXofVR4fWSefcGD1jAGgyetiI18G/lBQcjA+UjZbPmTl4f1DPuRSkg4J10HHXLfaef33If/+79XxtpX8CAGgRBGw01bSFA693bqgMtGHD3B+8ynuecGlwmuq8yt+fu2ho9QSAVkPARnwanHH9WshctZdf9Z6PnQhOE7YvEmaMA2hhBGw01cI5wfumLgzeF0VY73vRJY3lDQBpI2AjEX27/Lc/ur659Sh5eJ3/9refaW49AKBeBGzE41TlrK6zRnjnkM8aMbAtyqVYmx6ur/iHdtROU17+qJHe+5HDqxKdOlpfBQAgYQRsxGPve3039+2STj3nvY5yGdf1Xxm87fSZyvc9vYPTXLmqdt6l8nu3S2/tDEi0d1LtjAAgBQRsJK5tWGPHD7+48n3n/MbyG/uexo4HgDQkErDNbJyZ/YOZ/auZ/cLM/iiJcpA9UXrZS1ZXvncuPP1nvxpPuQDQypLqYa+X9Jhz7t9JminpFwmVgxy6b9vQ0m/cmkw9AKCVxB6wzWyMpLmSNkiSc+4d55zPWUfkycq10dM2u7c7lPKG8jkAoJmS6GHPkHRU0kYz+2czu9vMzk6gHLSQtTGv7Pn526Kli/uuX3F/DgCISxIBu03ShZL+1jn3UUlvSfqr8gRmtszMus2s++hRLqMpokUrwvd/+wHvecce//1bn/aeg+6rXVI9e/zay2vXDQBaURIB+4CkA865/ot59A/yAvi7nHPfcc51Oee6Oju5tWERTH9f5ftHgy6rqjJvmf/2T0fsCVdfn32Pz2VjAJAFsQds59whSa+a2Yf6N31C0s/jLgfZ8pO7B29bsDz8mI6QpUYlafzHw/evWBO+HwCyJKn7YX9B0r1mNlzSPknXJ1QOWsXMo9KLwaMlU3zWI3msxrKgx2vczKP3ZPj+9ZvD9/s6v6eOgwAgeYkEbOfcC5K48rVI2ibWdVhSM8avuqnOA9snxFoPAIgLK50hl76/Pe0aAEC8CNhomskd6ZY/+7x0yweARhCwEZ9Z4WuIHhriCmblPvIBaf5F0u9PrT+PZzfVSFCj/gCQpqQmnQG+XHfweeuFcxq7X/ZlN0jbng0uFwCyjICNeE29UzoQPuOrd7s0bp73+vA2aVLVUPl1t0j3PBK9yDkzpZ0bpMfvGti2/6A04wrvdaSe/bRvRi8QAFLAkDjiNbn2jalLt7d03V6w3rLN63WXHkMJ1pK068XK4zc/7i3UUupVRzp3PukLQysUAJrMXK17Fyasq6vLdXfnd7zSzNKuQqJ8//2cOirt9bnwukrUS7oWz5WuXyzNmyUdPyn9dK9060bp5/si1C/KP63ze0Iv5ypkG+YI7Zd9eW9DSbudczX/R2RIHPFrr3+52a1rvQAdZPwYacYU6eoFldt3viBd8rk6C+XaawAZQMBGMmY5aXf4r+LSBLT2NumdqsliQ1lQxXVLH7tgoDfdPls6fSZi75qZ4QAygoCN5EQI2tJAsK531bPy4848L516LmJeBGsAGcKkMyRreu0FvUuTxfzcskw6/pTXWy49+nZ52/0MuyhisJ7+vQiJAKB1MOksYXmfLBHp309AL7s6sF45T3rwzvrrsnS1N+O8XOCw+BB617RhttF+2Zf3NhSTztAyZjlpzyjJvT1oV8+T0oSxldtGz5Xe7IuefccY6Y0fS5tv9R6S9PVN0s13+SSevlnqWBI9cwBoEQRsNMeF/RG4qrfdNkyafoX0ysH6sz52orK3/qtHBve0JXHOGkCmcQ4bzVUWNF239NCOxoK1n3MXeddtVwyHE6wBZBw9bDTfLCedOibtnaBrL5euvTzBss4/0tB14QDQKuhhIx3tHV7gnrYumfynrffyJ1gDyAl62EjXpBXeQ4p0zXZNDH0DyCl62Ggds9zAY+bxQbtX+XXGz3+98jgAyCl62GhNbeMGBeA1f59SXQCgBdDDBgAgAwjYAABkAAEbAIAMSH0tcTPL9UyhtL/fpBVgjV/aMONov+wrQBtGWkucHjYAABnALHEAiIq1ApAietgAEObwHV6gjiNYSwN5HV4TT34oDM5hJyzt7zdpnD/Lvry3Yd3td+oNae/EeCvj5/xDUvvkug/Pe/tJhfgb5H7YAFCXuHrTUew9x3tmqBw1MCQOAOWaGaxboVxkBgEbACRpz4j0g+Zuk45tSbcOaFkEbADYbZJ7p+Fsbrg9hrrsX5r+Dwe0JCadJSzt7zdpTHjJvry3Yc322zNScr9rqAzzmS7kuhvKUrLh0oW165X39pMK8TfIwikAUFOEYN05X7r3h/77/IJ12PbIYujxI1/oYScs7e83afy6z768t2Fo+9UYeo7Scw4LzLXSfniG9LP7Q6tQc/Z43ttPKsTfID1sAAhUI1h/6z7/7fX2nP2Oe2lfhAM5n41+BGwAxXP6SM0ky+9oQj0U8QfA6Z7E64HWR8AGUDwv1r+yWLWgyWUNTzor92JnjJkhq1jpDECxvD5w7VXYOWrXHX3423VLJ/ukMXOlE09Lo0dFr87GLw+8Dj1nfmiddM6N0TNG7tDDBlAsB/9SUnAwPlA2Wj5n5uD9QT3nUpAOCtZBx1232Hv+9SH//e/W87WV/glQGARsACgzbeHA650bKgNt2DD3B6/ynidcGpymOq/y9+cuGlo9UTwEbADF0eCM69dC5qq9/Kr3fOxEcJqwfZEwY7zQCNgAUGbhnOB9UxcG74sirPe96JLG8kb+EbABFFLfLv/tj65vbj1KHl7nv/3tZ5pbD7QuAjaAYjhVOavrrBHeOeSzRgxsi3Ip1qaH6yv+oR2105SXP2qk937k8KpEp47WVwFkHkuTJizt7zdpLIuYfXlvw3fbL+T87+kzUvvs/vQ+Qbt6Rnl1mvLjJenoE9LEcUPLozxN73Zp7HsCq1uxXGne208qxN8gS5MCQBRtwxo7fvjFle875zeWX2iwRmERsAGgTJTFUpasrnxfqwP42a/GUy6KLfaAbWYfMrMXyh4nzGxF3OUAQFru2za09Bu3JlMPFEvsAds592/OuQuccxdImiWpT9KDcZcDAEOxcm30tM3u7Q6lvKF8DuRL0kPin5D0S+fcrxIuBwBCrY15Zc/P3xYtXdx3/Yr7cyA7kg7YSyRtrt5oZsvMrNvM4ryfDQDEZlGNE3nffsB73rHHf//Wp73noPtql1y5qvL9tZfXrhuKKbHLusxsuKSDkj7snDscki7X8/ULcDlC2lVIHG2YbVEu65KkGVdI+w9WHdvfpQgasq51R6+w/UF5R7otJ5d15UorXNa1QNKesGANAK3iJ3cP3rZgefgxHSFLjUrS+I+H71+xJnw/UC7JgL1UPsPhAJCKmeErhE2ZNHjbYzWWBT1e42YevSfD96+v53/I83vqOAh5kEjANrNRkj4p6R+TyB8AhqxtYl2HJTVj/Kqb6jywfUKs9UB2tCWRqXOuTxL/qgAgwPe3p10DZA0rnQFAv8kd6ZY/+7x0y0dr4+YfCUv7+00aM1SzL+9tOKj9aswWr3cI/CMf8AL+/oPSLw/Ul0fNGeKzBv9bzHv7SYX4G4w0SzyRIXEAyKqwS7EWzmnsftmX3SBteza4XCAMARtAsUy9UzoQPuOrd7s0bp73+vA2aVLVUPl1t0j3PBK9yDkzpZ0bpMfvGti2/6B37bckHYqyNvm0b0YvELnEkHjC0v5+k8ZwXPblvQ1926/GsLjk9bJLvd4t26Slq8PTD8V3vyYtvWxwOaF8hsOl/LefVIi/wUhD4gTshKX9/SaN/yyyL+9t6Nt+p45Ke30uvK4S9Xz24rnS9YulebOk4yeln+6Vbt0o/XxfhPpFCdbn9wRezpX39pMK8TfIOWwA8NXeWfehW9d6ATrI+DHSjCnS1Qsqt+98Qbrkc3UWyrXXED3sxKX9/SaNX/fZl/c2DG2/iEPj7W3SO88O3h65DlW96PbZ0ukzjQ2Fv1uPnLefVIi/QXrYABBqlosUtEvBut5LvsqPO/O8dOq5iHnVCNYoFhZOAVBs02sv6G1dwQH2lmXS8ae83nLp0bfL2+5n2EURg/X070VIhCJhSDxhaX+/SWM4Lvvy3oaR2i+gl10dWK+cJz14Z/11Wbram3FeLnBYPGLvOu/tJxXib5BZ4q0g7e83afxnkX15b8PI7bdnlOTerthkXVLPk9KEsZVJR8+V3uyLXoeOMdIbP67c9vVN0s13+QTs6ZuljiWR8857+0mF+BvkHDYARHZhfwSu6m23DZOmXyG9crD+rI+dqOyt/+qRwT1tSZyzRijOYQNAubKg6bqlh3Y0Fqz9nLvIu267ondNsEYNDIknLO3vN2kMx2Vf3tuw7vY7dUza24Trn88/0tB14XlvP6kQf4ORhsTpYQOAn/YOr9c7bV0y+U9b7+XfQLBGsdDDTlja32/S+HWffXlvw1jbL8I12zXFPPSd9/aTCvE3SA8bAGI1yw08Zh4ftHuVX2f8/NcrjwPqRA87YWl/v0nj13325b0Nab/sK0Ab0sMGACAvCNgAAGQAARsAgAxohZXOeiT9qonlTewvsylSOr/U1M+Ygry3Ie0XI9ovdk3/fAVow3OjJEp90lmzmVl3lJP7WZb3z8jnyzY+X7bl/fNJrfsZGRIHACADCNgAAGRAEQP2d9KuQBPk/TPy+bKNz5dtef98Uot+xsKdwwYAIIuK2MMGACBzCNgAAGRAoQK2mX3KzP7NzF42s79Kuz5xMrO/M7MjZvaztOuSBDObZmZPmdkvzOwlM/ti2nWKm5mNNLPnzezF/s/4lbTrFDczG2Zm/2xmj6RdlySY2Stm9i9m9oKZdaddn7iZ2Tgz+wcz+9f+v8U/SrtOcTGzD/W3W+lxwsxWpF2vcoU5h21mwyT9f5I+KemApH+StNQ59/NUKxYTM5sr6U1J/805d17a9Ymbmb1X0nudc3vMbLSk3ZKuzEv7SZJ5q0Oc7Zx708zaJe2U9EXn3LMpVy02ZrZSUpekMc65RWnXJ25m9oqkLudcLhdOMbN7JP3EOXe3mQ2XNMo515t2veLWHy9ekzTbOdfMhb1CFamHfZGkl51z+5xz70jaIunTKdcpNs65pyUdS7seSXHOve6c29P/+qSkX0iakm6t4uU8b/a/be9/5OYXtZlNlXS5pLvTrguGzszGSD53FtgAAAJLSURBVJoraYMkOefeyWOw7vcJSb9spWAtFStgT5H0atn7A8rZf/hFYWbvl/RRSc+lW5P49Q8ZvyDpiKQfOefy9Bm/IelLkv5H2hVJkJO0zcx2m9mytCsTsxmSjkra2H9a424zOzvtSiVkiaTNaVeiWpECtt9itLnpvRSFmb1H0gOSVjjnTqRdn7g558445y6QNFXSRWaWi9MbZrZI0hHn3O6065KwOc65CyUtkPSf+k9V5UWbpAsl/a1z7qOS3pKUq7lAktQ/1H+FpO+lXZdqRQrYByRNK3s/VdLBlOqCOvSf131A0r3OuX9Muz5J6h9q3C7pUylXJS5zJF3Rf453i6RLzezv061S/JxzB/ufj0h6UN6puLw4IOlA2ajPP8gL4HmzQNIe59zhtCtSrUgB+58kfdDMpvf/gloiaWvKdUJE/ROyNkj6hXNubdr1SYKZdZrZuP7XZ0maL+lf061VPJxzNzvnpjrn3i/vb+/HzrnPpFytWJnZ2f0TItU/VPwnknJz1YZz7pCkV83sQ/2bPiEpN5M+yyxVCw6HS61xe82mcM6dNrMbJD0uaZikv3POvZRytWJjZpslzZM00cwOSPqyc25DurWK1RxJ10j6l/5zvJK02jn3gxTrFLf3Srqnf4bq70m63zmXy8ufcmqypAf7bwXZJum7zrnH0q1S7L4g6d7+Ts8+SdenXJ9YmdkoeVcS/ce06+KnMJd1AQCQZUUaEgcAILMI2AAAZAABGwCADCBgAwCQAQRsAAAygIANAEAGELABAMiA/x8yMOc/us4UiAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 504x504 with 9 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_NQueens(solution)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets' see if we can find a different solution."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x504 with 9 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"eight_queens = NQueensCSP(8)\n",
"solution = min_conflicts(eight_queens)\n",
"plot_NQueens(solution)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The solution is a bit different this time. \n",
"Running the above cell several times should give you different valid solutions.\n",
"<br>\n",
"In the `search.ipynb` notebook, we will see how NQueensProblem can be solved using a **heuristic search method** such as `uniform_cost_search` and `astar_search`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Helper Functions\n",
"\n",
"We will now implement a few helper functions that will allow us to visualize the Coloring Problem; we'll also make a few modifications to the existing classes and functions for additional record keeping. To begin, we modify the **assign** and **unassign** methods in the **CSP** in order to add a copy of the assignment to the **assignment_history**. We name this new class as **InstruCSP**; it will allow us to see how the assignment evolves over time. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"import copy\n",
"class InstruCSP(CSP):\n",
" \n",
" def __init__(self, variables, domains, neighbors, constraints):\n",
" super().__init__(variables, domains, neighbors, constraints)\n",
" self.assignment_history = []\n",
" \n",
" def assign(self, var, val, assignment):\n",
" super().assign(var,val, assignment)\n",
" self.assignment_history.append(copy.deepcopy(assignment))\n",
" \n",
" def unassign(self, var, assignment):\n",
" super().unassign(var,assignment)\n",
" self.assignment_history.append(copy.deepcopy(assignment))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we define **make_instru** which takes an instance of **CSP** and returns an instance of **InstruCSP**."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def make_instru(csp):\n",
" return InstruCSP(csp.variables, csp.domains, csp.neighbors, csp.constraints)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will now use a graph defined as a dictionary for plotting purposes in our Graph Coloring Problem. The keys are the nodes and their values are the corresponding nodes they are connected to."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"neighbors = {\n",
" 0: [6, 11, 15, 18, 4, 11, 6, 15, 18, 4], \n",
" 1: [12, 12, 14, 14], \n",
" 2: [17, 6, 11, 6, 11, 10, 17, 14, 10, 14], \n",
" 3: [20, 8, 19, 12, 20, 19, 8, 12], \n",
" 4: [11, 0, 18, 5, 18, 5, 11, 0], \n",
" 5: [4, 4], \n",
" 6: [8, 15, 0, 11, 2, 14, 8, 11, 15, 2, 0, 14], \n",
" 7: [13, 16, 13, 16], \n",
" 8: [19, 15, 6, 14, 12, 3, 6, 15, 19, 12, 3, 14], \n",
" 9: [20, 15, 19, 16, 15, 19, 20, 16], \n",
" 10: [17, 11, 2, 11, 17, 2], \n",
" 11: [6, 0, 4, 10, 2, 6, 2, 0, 10, 4], \n",
" 12: [8, 3, 8, 14, 1, 3, 1, 14], \n",
" 13: [7, 15, 18, 15, 16, 7, 18, 16], \n",
" 14: [8, 6, 2, 12, 1, 8, 6, 2, 1, 12], \n",
" 15: [8, 6, 16, 13, 18, 0, 6, 8, 19, 9, 0, 19, 13, 18, 9, 16], \n",
" 16: [7, 15, 13, 9, 7, 13, 15, 9], \n",
" 17: [10, 2, 2, 10], \n",
" 18: [15, 0, 13, 4, 0, 15, 13, 4], \n",
" 19: [20, 8, 15, 9, 15, 8, 3, 20, 3, 9], \n",
" 20: [3, 19, 9, 19, 3, 9]\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we are ready to create an InstruCSP instance for our problem. We are doing this for an instance of **MapColoringProblem** class which inherits from the **CSP** Class. This means that our **make_instru** function will work perfectly for it."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"coloring_problem = MapColoringCSP('RGBY', neighbors)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"coloring_problem1 = make_instru(coloring_problem)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### CONSTRAINT PROPAGATION\n",
"Algorithms that solve CSPs have a choice between searching and or doing a _constraint propagation_, a specific type of inference.\n",
"The constraints can be used to reduce the number of legal values for another variable, which in turn can reduce the legal values for some other variable, and so on. \n",
"<br>\n",
"Constraint propagation tries to enforce _local consistency_.\n",
"Consider each variable as a node in a graph and each binary constraint as an arc.\n",
"Enforcing local consistency causes inconsistent values to be eliminated throughout the graph, \n",
"a lot like the `GraphPlan` algorithm in planning, where mutex links are removed from a planning graph.\n",
"There are different types of local consistencies:\n",
"1. Node consistency\n",
"2. Arc consistency\n",
"3. Path consistency\n",
"4. K-consistency\n",
"5. Global constraints\n",
"\n",
"Refer __section 6.2__ in the book for details.\n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## AC-3\n",
"Before we dive into AC-3, we need to know what _arc-consistency_ is.\n",
"<br>\n",
"A variable $X_i$ is __arc-consistent__ with respect to another variable $X_j$ if for every value in the current domain $D_i$ there is some value in the domain $D_j$ that satisfies the binary constraint on the arc $(X_i, X_j)$.\n",
"<br>\n",
"A network is arc-consistent if every variable is arc-consistent with every other variable.\n",
"<br>\n",
"\n",
"AC-3 is an algorithm that enforces arc consistency.\n",
"After applying AC-3, either every arc is arc-consistent, or some variable has an empty domain, indicating that the CSP cannot be solved.\n",
"Let's see how `AC3` is implemented in the module."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
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"<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">AC3</span><span class=\"p\">(</span><span class=\"n\">csp</span><span class=\"p\">,</span> <span class=\"n\">queue</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"n\">removals</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""[Figure 6.3]"""</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">queue</span> <span class=\"ow\">is</span> <span class=\"bp\">None</span><span class=\"p\">:</span>\n",
" <span class=\"n\">queue</span> <span class=\"o\">=</span> <span class=\"p\">[(</span><span class=\"n\">Xi</span><span class=\"p\">,</span> <span class=\"n\">Xk</span><span class=\"p\">)</span> <span class=\"k\">for</span> <span class=\"n\">Xi</span> <span class=\"ow\">in</span> <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">variables</span> <span class=\"k\">for</span> <span class=\"n\">Xk</span> <span class=\"ow\">in</span> <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">neighbors</span><span class=\"p\">[</span><span class=\"n\">Xi</span><span class=\"p\">]]</span>\n",
" <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">support_pruning</span><span class=\"p\">()</span>\n",
" <span class=\"k\">while</span> <span class=\"n\">queue</span><span class=\"p\">:</span>\n",
" <span class=\"p\">(</span><span class=\"n\">Xi</span><span class=\"p\">,</span> <span class=\"n\">Xj</span><span class=\"p\">)</span> <span class=\"o\">=</span> <span class=\"n\">queue</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">()</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">revise</span><span class=\"p\">(</span><span class=\"n\">csp</span><span class=\"p\">,</span> <span class=\"n\">Xi</span><span class=\"p\">,</span> <span class=\"n\">Xj</span><span class=\"p\">,</span> <span class=\"n\">removals</span><span class=\"p\">):</span>\n",
" <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">curr_domains</span><span class=\"p\">[</span><span class=\"n\">Xi</span><span class=\"p\">]:</span>\n",
" <span class=\"k\">return</span> <span class=\"bp\">False</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">Xk</span> <span class=\"ow\">in</span> <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">neighbors</span><span class=\"p\">[</span><span class=\"n\">Xi</span><span class=\"p\">]:</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">Xk</span> <span class=\"o\">!=</span> <span class=\"n\">Xj</span><span class=\"p\">:</span>\n",
" <span class=\"n\">queue</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">((</span><span class=\"n\">Xk</span><span class=\"p\">,</span> <span class=\"n\">Xi</span><span class=\"p\">))</span>\n",
" <span class=\"k\">return</span> <span class=\"bp\">True</span>\n",
"</pre></div>\n",
"</body>\n",
"</html>\n"
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{
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"`AC3` also employs a helper function `revise`."
]
},
{
"cell_type": "code",
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"\n",
"<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">revise</span><span class=\"p\">(</span><span class=\"n\">csp</span><span class=\"p\">,</span> <span class=\"n\">Xi</span><span class=\"p\">,</span> <span class=\"n\">Xj</span><span class=\"p\">,</span> <span class=\"n\">removals</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Return true if we remove a value."""</span>\n",
" <span class=\"n\">revised</span> <span class=\"o\">=</span> <span class=\"bp\">False</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">x</span> <span class=\"ow\">in</span> <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">curr_domains</span><span class=\"p\">[</span><span class=\"n\">Xi</span><span class=\"p\">][:]:</span>\n",
" <span class=\"c1\"># If Xi=x conflicts with Xj=y for every possible y, eliminate Xi=x</span>\n",
" <span class=\"k\">if</span> <span class=\"nb\">all</span><span class=\"p\">(</span><span class=\"ow\">not</span> <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">constraints</span><span class=\"p\">(</span><span class=\"n\">Xi</span><span class=\"p\">,</span> <span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">Xj</span><span class=\"p\">,</span> <span class=\"n\">y</span><span class=\"p\">)</span> <span class=\"k\">for</span> <span class=\"n\">y</span> <span class=\"ow\">in</span> <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">curr_domains</span><span class=\"p\">[</span><span class=\"n\">Xj</span><span class=\"p\">]):</span>\n",
" <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">prune</span><span class=\"p\">(</span><span class=\"n\">Xi</span><span class=\"p\">,</span> <span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">removals</span><span class=\"p\">)</span>\n",
" <span class=\"n\">revised</span> <span class=\"o\">=</span> <span class=\"bp\">True</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">revised</span>\n",
"</pre></div>\n",
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"`AC3` maintains a queue of arcs to consider which initially contains all the arcs in the CSP.\n",
"An arbitrary arc $(X_i, X_j)$ is popped from the queue and $X_i$ is made _arc-consistent_ with respect to $X_j$.\n",
"<br>\n",
"If in doing so, $D_i$ is left unchanged, the algorithm just moves to the next arc, \n",
"but if the domain $D_i$ is revised, then we add all the neighboring arcs $(X_k, X_i)$ to the queue.\n",
"<br>\n",
"We repeat this process and if at any point, the domain $D_i$ is reduced to nothing, then we know the whole CSP has no consistent solution and `AC3` can immediately return failure.\n",
"<br>\n",
"Otherwise, we keep removing values from the domains of variables until the queue is empty.\n",
"We finally get the arc-consistent CSP which is faster to search because the variables have smaller domains."
]
},
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"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see how `AC3` can be used.\n",
"<br>\n",
"We'll first define the required variables."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"neighbors = parse_neighbors('A: B; B: ')\n",
"domains = {'A': [0, 1, 2, 3, 4], 'B': [0, 1, 2, 3, 4]}\n",
"constraints = lambda X, x, Y, y: x % 2 == 0 and (x + y) == 4 and y % 2 != 0\n",
"removals = []"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll now define a `CSP` object."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"csp = CSP(variables=None, domains=domains, neighbors=neighbors, constraints=constraints)"
]
},
{
"cell_type": "code",
"execution_count": 24,
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"False"
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"source": [
"AC3(csp, removals=removals)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This configuration is inconsistent."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"constraints = lambda X, x, Y, y: (x % 2) == 0 and (x + y) == 4\n",
"removals = []\n",
"csp = CSP(variables=None, domains=domains, neighbors=neighbors, constraints=constraints)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
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{
"data": {
"text/plain": [
"True"
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"execution_count": 26,
"metadata": {},
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],
"source": [
"AC3(csp,removals=removals)"
]
},
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"cell_type": "markdown",
"metadata": {},
"source": [
"This configuration is consistent."
]
},
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"cell_type": "markdown",
"metadata": {},
"source": [
"## BACKTRACKING SEARCH\n",
"\n",
"The main issue with using Naive Search Algorithms to solve a CSP is that they can continue to expand obviously wrong paths; whereas, in **backtracking search**, we check the constraints as we go and we deal with only one variable at a time. Backtracking Search is implemented in the repository as the function **backtracking_search**. This is the same as **Figure 6.5** in the book. The function takes as input a CSP and a few other optional parameters which can be used to speed it up further. The function returns the correct assignment if it satisfies the goal. However, we will discuss these later. For now, let us solve our **coloring_problem1** with **backtracking_search**."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"result = backtracking_search(coloring_problem1)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{0: 'R',\n",
" 1: 'R',\n",
" 2: 'R',\n",
" 3: 'R',\n",
" 4: 'G',\n",
" 5: 'R',\n",
" 6: 'G',\n",
" 7: 'R',\n",
" 8: 'B',\n",
" 9: 'R',\n",
" 10: 'G',\n",
" 11: 'B',\n",
" 12: 'G',\n",
" 13: 'G',\n",
" 14: 'Y',\n",
" 15: 'Y',\n",
" 16: 'B',\n",
" 17: 'B',\n",
" 18: 'B',\n",
" 19: 'G',\n",
" 20: 'B'}"
]
},
"execution_count": 28,
"metadata": {},
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"source": [
"result # A dictonary of assignments."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us also check the number of assignments made."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
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"21"
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"coloring_problem1.nassigns"
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let us check the total number of assignments and unassignments, which would be the length of our assignment history. We can see it by using the command below. "
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
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"Now let us explore the optional keyword arguments that the **backtracking_search** function takes. These optional arguments help speed up the assignment further. Along with these, we will also point out the methods in the CSP class that help to make this work. \n",
"\n",
"The first one is **select_unassigned_variable**. It takes in, as a parameter, a function that helps in deciding the order in which the variables will be selected for assignment. We use a heuristic called Most Restricted Variable which is implemented by the function **mrv**. The idea behind **mrv** is to choose the variable with the least legal values left in its domain. The intuition behind selecting the **mrv** or the most constrained variable is that it allows us to encounter failure quickly before going too deep into a tree if we have selected a wrong step before. The **mrv** implementation makes use of another function **num_legal_values** to sort out the variables by the number of legal values left in its domain. This function, in turn, calls the **nconflicts** method of the **CSP** to return such values."
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"<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">mrv</span><span class=\"p\">(</span><span class=\"n\">assignment</span><span class=\"p\">,</span> <span class=\"n\">csp</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Minimum-remaining-values heuristic."""</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">argmin_random_tie</span><span class=\"p\">(</span>\n",
" <span class=\"p\">[</span><span class=\"n\">v</span> <span class=\"k\">for</span> <span class=\"n\">v</span> <span class=\"ow\">in</span> <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">variables</span> <span class=\"k\">if</span> <span class=\"n\">v</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"n\">assignment</span><span class=\"p\">],</span>\n",
" <span class=\"n\">key</span><span class=\"o\">=</span><span class=\"k\">lambda</span> <span class=\"n\">var</span><span class=\"p\">:</span> <span class=\"n\">num_legal_values</span><span class=\"p\">(</span><span class=\"n\">csp</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">))</span>\n",
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"<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">num_legal_values</span><span class=\"p\">(</span><span class=\"n\">csp</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">):</span>\n",
" <span class=\"k\">if</span> <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">curr_domains</span><span class=\"p\">:</span>\n",
" <span class=\"k\">return</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">curr_domains</span><span class=\"p\">[</span><span class=\"n\">var</span><span class=\"p\">])</span>\n",
" <span class=\"k\">else</span><span class=\"p\">:</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">count</span><span class=\"p\">(</span><span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">nconflicts</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">)</span> <span class=\"o\">==</span> <span class=\"mi\">0</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">val</span> <span class=\"ow\">in</span> <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">domains</span><span class=\"p\">[</span><span class=\"n\">var</span><span class=\"p\">])</span>\n",
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"<div class=\"highlight\"><pre><span></span> <span class=\"k\">def</span> <span class=\"nf\">nconflicts</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Return the number of conflicts var=val has with other variables."""</span>\n",
" <span class=\"c1\"># Subclasses may implement this more efficiently</span>\n",
" <span class=\"k\">def</span> <span class=\"nf\">conflict</span><span class=\"p\">(</span><span class=\"n\">var2</span><span class=\"p\">):</span>\n",
" <span class=\"k\">return</span> <span class=\"p\">(</span><span class=\"n\">var2</span> <span class=\"ow\">in</span> <span class=\"n\">assignment</span> <span class=\"ow\">and</span>\n",
" <span class=\"ow\">not</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">constraints</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">var2</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">[</span><span class=\"n\">var2</span><span class=\"p\">]))</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">count</span><span class=\"p\">(</span><span class=\"n\">conflict</span><span class=\"p\">(</span><span class=\"n\">v</span><span class=\"p\">)</span> <span class=\"k\">for</span> <span class=\"n\">v</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">neighbors</span><span class=\"p\">[</span><span class=\"n\">var</span><span class=\"p\">])</span>\n",
"</pre></div>\n",
"</body>\n",
"</html>\n"
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"cell_type": "markdown",
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"source": [
"Another ordering related parameter **order_domain_values** governs the value ordering. Here we select the Least Constraining Value which is implemented by the function **lcv**. The idea is to select the value which rules out least number of values in the remaining variables. The intuition behind selecting the **lcv** is that it allows a lot of freedom to assign values later. The idea behind selecting the mrc and lcv makes sense because we need to do all variables but for values, and it's better to try the ones that are likely. So for vars, we face the hard ones first."
]
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{
"cell_type": "code",
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"\n",
"<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">lcv</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">,</span> <span class=\"n\">csp</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""Least-constraining-values heuristic."""</span>\n",
" <span class=\"k\">return</span> <span class=\"nb\">sorted</span><span class=\"p\">(</span><span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">choices</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">),</span>\n",
" <span class=\"n\">key</span><span class=\"o\">=</span><span class=\"k\">lambda</span> <span class=\"n\">val</span><span class=\"p\">:</span> <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">nconflicts</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">))</span>\n",
"</pre></div>\n",
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"source": [
"Finally, the third parameter **inference** can make use of one of the two techniques called Arc Consistency or Forward Checking. The details of these methods can be found in the **Section 6.3.2** of the book. In short the idea of inference is to detect the possible failure before it occurs and to look ahead to not make mistakes. **mac** and **forward_checking** implement these two techniques. The **CSP** methods **support_pruning**, **suppose**, **prune**, **choices**, **infer_assignment** and **restore** help in using these techniques. You can find out more about these by looking up the source code."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let us compare the performance with these parameters enabled vs the default parameters. We will use the Graph Coloring problem instance 'usa' for comparison. We will call the instances **solve_simple** and **solve_parameters** and solve them using backtracking and compare the number of assignments."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
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"source": [
"solve_simple = copy.deepcopy(usa)\n",
"solve_parameters = copy.deepcopy(usa)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
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"data": {
"text/plain": [
"{'NJ': 'R',\n",
" 'DE': 'G',\n",
" 'PA': 'B',\n",
" 'MD': 'R',\n",
" 'NY': 'G',\n",
" 'WV': 'G',\n",
" 'VA': 'B',\n",
" 'OH': 'R',\n",
" 'KY': 'Y',\n",
" 'IN': 'G',\n",
" 'IL': 'R',\n",
" 'MO': 'G',\n",
" 'TN': 'R',\n",
" 'AR': 'B',\n",
" 'OK': 'R',\n",
" 'IA': 'B',\n",
" 'NE': 'R',\n",
" 'MI': 'B',\n",
" 'TX': 'G',\n",
" 'NM': 'B',\n",
" 'LA': 'R',\n",
" 'KA': 'B',\n",
" 'NC': 'G',\n",
" 'GA': 'B',\n",
" 'MS': 'G',\n",
" 'AL': 'Y',\n",
" 'CO': 'G',\n",
" 'WY': 'B',\n",
" 'SC': 'R',\n",
" 'FL': 'R',\n",
" 'UT': 'R',\n",
" 'ID': 'G',\n",
" 'SD': 'G',\n",
" 'MT': 'R',\n",
" 'ND': 'B',\n",
" 'DC': 'G',\n",
" 'NV': 'B',\n",
" 'OR': 'R',\n",
" 'MN': 'R',\n",
" 'CA': 'G',\n",
" 'AZ': 'Y',\n",
" 'WA': 'B',\n",
" 'WI': 'G',\n",
" 'CT': 'R',\n",
" 'MA': 'B',\n",
" 'VT': 'R',\n",
" 'NH': 'G',\n",
" 'RI': 'G',\n",
" 'ME': 'R'}"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
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"source": [
"backtracking_search(solve_simple)\n",
"backtracking_search(solve_parameters, order_domain_values=lcv, select_unassigned_variable=mrv, inference=mac)"
]
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{
"cell_type": "code",
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"cell_type": "markdown",
"metadata": {},
"source": [
"## TREE CSP SOLVER\n",
"\n",
"The `tree_csp_solver` function (**Figure 6.11** in the book) can be used to solve problems whose constraint graph is a tree. Given a CSP, with `neighbors` forming a tree, it returns an assignment that satisfies the given constraints. The algorithm works as follows:\n",
"\n",
"First it finds the *topological sort* of the tree. This is an ordering of the tree where each variable/node comes after its parent in the tree. The function that accomplishes this is `topological_sort`; it builds the topological sort using the recursive function `build_topological`. That function is an augmented DFS (Depth First Search), where each newly visited node of the tree is pushed on a stack. The stack in the end holds the variables topologically sorted.\n",
"\n",
"Then the algorithm makes arcs between each parent and child consistent. *Arc-consistency* between two variables, *a* and *b*, occurs when for every possible value of *a* there is an assignment in *b* that satisfies the problem's constraints. If such an assignment cannot be found, the problematic value is removed from *a*'s possible values. This is done with the use of the function `make_arc_consistent`, which takes as arguments a variable `Xj` and its parent, and makes the arc between them consistent by removing any values from the parent which do not allow for a consistent assignment in `Xj`.\n",
"\n",
"If an arc cannot be made consistent, the solver fails. If every arc is made consistent, we move to assigning values.\n",
"\n",
"First we assign a random value to the root from its domain and then we assign values to the rest of the variables. Since the graph is now arc-consistent, we can simply move from variable to variable picking any remaining consistent values. At the end we are left with a valid assignment. If at any point though we find a variable where no consistent value is left in its domain, the solver fails.\n",
"\n",
"Run the cell below to see the implementation of the algorithm:"
]
},
{
"cell_type": "code",
"execution_count": 39,
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"<h2></h2>\n",
"\n",
"<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">tree_csp_solver</span><span class=\"p\">(</span><span class=\"n\">csp</span><span class=\"p\">):</span>\n",
" <span class=\"sd\">"""[Figure 6.11]"""</span>\n",
" <span class=\"n\">assignment</span> <span class=\"o\">=</span> <span class=\"p\">{}</span>\n",
" <span class=\"n\">root</span> <span class=\"o\">=</span> <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">variables</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span>\n",
" <span class=\"n\">X</span><span class=\"p\">,</span> <span class=\"n\">parent</span> <span class=\"o\">=</span> <span class=\"n\">topological_sort</span><span class=\"p\">(</span><span class=\"n\">csp</span><span class=\"p\">,</span> <span class=\"n\">root</span><span class=\"p\">)</span>\n",
"\n",
" <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">support_pruning</span><span class=\"p\">()</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">Xj</span> <span class=\"ow\">in</span> <span class=\"nb\">reversed</span><span class=\"p\">(</span><span class=\"n\">X</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">:]):</span>\n",
" <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"n\">make_arc_consistent</span><span class=\"p\">(</span><span class=\"n\">parent</span><span class=\"p\">[</span><span class=\"n\">Xj</span><span class=\"p\">],</span> <span class=\"n\">Xj</span><span class=\"p\">,</span> <span class=\"n\">csp</span><span class=\"p\">):</span>\n",
" <span class=\"k\">return</span> <span class=\"bp\">None</span>\n",
"\n",
" <span class=\"n\">assignment</span><span class=\"p\">[</span><span class=\"n\">root</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">csp</span><span class=\"o\">.</span><span class=\"n\">curr_domains</span><span class=\"p\">[</span><span class=\"n\">root</span><span class=\"p\">][</span><span class=\"mi\">0</span><span class=\"p\">]</span>\n",
" <span class=\"k\">for</span> <span class=\"n\">Xi</span> <span class=\"ow\">in</span> <span class=\"n\">X</span><span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">:]:</span>\n",
" <span class=\"n\">assignment</span><span class=\"p\">[</span><span class=\"n\">Xi</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">assign_value</span><span class=\"p\">(</span><span class=\"n\">parent</span><span class=\"p\">[</span><span class=\"n\">Xi</span><span class=\"p\">],</span> <span class=\"n\">Xi</span><span class=\"p\">,</span> <span class=\"n\">csp</span><span class=\"p\">,</span> <span class=\"n\">assignment</span><span class=\"p\">)</span>\n",
" <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"n\">assignment</span><span class=\"p\">[</span><span class=\"n\">Xi</span><span class=\"p\">]:</span>\n",
" <span class=\"k\">return</span> <span class=\"bp\">None</span>\n",
" <span class=\"k\">return</span> <span class=\"n\">assignment</span>\n",
"</pre></div>\n",
"</body>\n",
"</html>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"psource(tree_csp_solver)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will now use the above function to solve a problem. More specifically, we will solve the problem of coloring Australia's map. We have two colors at our disposal: Red and Blue. As a reminder, this is the graph of Australia:\n",
"\n",
"`\"SA: WA NT Q NSW V; NT: WA Q; NSW: Q V; T: \"`\n",
"\n",
"Unfortunately, as you can see, the above is not a tree. However, if we remove `SA`, which has arcs to `WA`, `NT`, `Q`, `NSW` and `V`, we are left with a tree (we also remove `T`, since it has no in-or-out arcs). We can now solve this using our algorithm. Let's define the map coloring problem at hand:"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"australia_small = MapColoringCSP(list('RB'),\n",
" 'NT: WA Q; NSW: Q V')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will input `australia_small` to the `tree_csp_solver` and print the given assignment."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'NT': 'R', 'Q': 'B', 'NSW': 'R', 'V': 'B', 'WA': 'B'}\n"
]
}
],
"source": [
"assignment = tree_csp_solver(australia_small)\n",
"print(assignment)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`WA`, `Q` and `V` got painted with the same color and `NT` and `NSW` got painted with the other."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## GRAPH COLORING VISUALIZATION\n",
"\n",
"Next, we define some functions to create the visualisation from the assignment_history of **coloring_problem1**. The readers need not concern themselves with the code that immediately follows as it is the usage of Matplotib with IPython Widgets. If you are interested in reading more about these, visit [ipywidgets.readthedocs.io](http://ipywidgets.readthedocs.io). We will be using the **networkx** library to generate graphs. These graphs can be treated as graphs that need to be colored or as constraint graphs for this problem. If interested you can check out a fairly simple tutorial [here](https://www.udacity.com/wiki/creating-network-graphs-with-python). We start by importing the necessary libraries and initializing matplotlib inline.\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import networkx as nx\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"import time"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ipython widgets we will be using require the plots in the form of a step function such that there is a graph corresponding to each value. We define the **make_update_step_function** which returns such a function. It takes in as inputs the neighbors/graph along with an instance of the **InstruCSP**. The example below will elaborate it further. If this sounds confusing, don't worry. This is not part of the core material and our only goal is to help you visualize how the process works."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"def make_update_step_function(graph, instru_csp):\n",
" \n",
" #define a function to draw the graphs\n",
" def draw_graph(graph):\n",
" \n",
" G=nx.Graph(graph)\n",
" pos = nx.spring_layout(G,k=0.15)\n",
" return (G, pos)\n",
" \n",
" G, pos = draw_graph(graph)\n",
" \n",
" def update_step(iteration):\n",
" # here iteration is the index of the assignment_history we want to visualize.\n",
" current = instru_csp.assignment_history[iteration]\n",
" # We convert the particular assignment to a default dict so that the color for nodes which \n",
" # have not been assigned defaults to black.\n",
" current = defaultdict(lambda: 'Black', current)\n",
"\n",
" # Now we use colors in the list and default to black otherwise.\n",
" colors = [current[node] for node in G.node.keys()]\n",
" # Finally drawing the nodes.\n",
" nx.draw(G, pos, node_color=colors, node_size=500)\n",
"\n",
" labels = {label:label for label in G.node}\n",
" # Labels shifted by offset so that nodes don't overlap\n",
" label_pos = {key:[value[0], value[1]+0.03] for key, value in pos.items()}\n",
" nx.draw_networkx_labels(G, label_pos, labels, font_size=20)\n",
"\n",
" # display the graph\n",
" plt.show()\n",
"\n",
" return update_step # <-- this is a function\n",
"\n",
"def make_visualize(slider):\n",
" ''' Takes an input a slider and returns \n",
" callback function for timer and animation\n",
" '''\n",
" \n",
" def visualize_callback(Visualize, time_step):\n",
" if Visualize is True:\n",
" for i in range(slider.min, slider.max + 1):\n",
" slider.value = i\n",
" time.sleep(float(time_step))\n",
" \n",
" return visualize_callback\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally let us plot our problem. We first use the function below to obtain a step function."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"step_func = make_update_step_function(neighbors, coloring_problem1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we set the canvas size."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"matplotlib.rcParams['figure.figsize'] = (18.0, 18.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, our plot using ipywidget slider and matplotib. You can move the slider to experiment and see the colors change. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click. The **Visualize Button** will automatically animate the slider for you. The **Extra Delay Box** allows you to set time delay in seconds (upto one second) for each time step."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "26b425b8fade4789a075632715b1afcd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(IntSlider(value=0, description='iteration', max=20), Output()), _dom_classes=('widget-in…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "179048eb3f8e41a1afc1ec22343dece4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(ToggleButton(value=False, description='Visualize'), ToggleButtons(description='Extra Del…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import ipywidgets as widgets\n",
"from IPython.display import display\n",
"\n",
"iteration_slider = widgets.IntSlider(min=0, max=len(coloring_problem1.assignment_history)-1, step=1, value=0)\n",
"w=widgets.interactive(step_func,iteration=iteration_slider)\n",
"display(w)\n",
"\n",
"visualize_callback = make_visualize(iteration_slider)\n",
"\n",
"visualize_button = widgets.ToggleButton(description = \"Visualize\", value = False)\n",
"time_select = widgets.ToggleButtons(description='Extra Delay:',options=['0', '0.1', '0.2', '0.5', '0.7', '1.0'])\n",
"\n",
"a = widgets.interactive(visualize_callback, Visualize = visualize_button, time_step=time_select)\n",
"display(a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## N-QUEENS VISUALIZATION\n",
"\n",
"Just like the Graph Coloring Problem, we will start with defining a few helper functions to help us visualize the assignments as they evolve over time. The **make_plot_board_step_function** behaves similar to the **make_update_step_function** introduced earlier. It initializes a chess board in the form of a 2D grid with alternating 0s and 1s. This is used by **plot_board_step** function which draws the board using matplotlib and adds queens to it. This function also calls the **label_queen_conflicts** which modifies the grid placing a 3 in any position where there is a conflict."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"def label_queen_conflicts(assignment,grid):\n",
" ''' Mark grid with queens that are under conflict. '''\n",
" for col, row in assignment.items(): # check each queen for conflict\n",
" conflicts = {temp_col:temp_row for temp_col,temp_row in assignment.items() \n",
" if (temp_row == row and temp_col != col\n",
" or (temp_row+temp_col == row+col and temp_col != col)\n",
" or (temp_row-temp_col == row-col and temp_col != col)}\n",
" \n",
" # Place a 3 in positions where this is a conflict\n",
" for col, row in conflicts.items():\n",
" grid[col][row] = 3\n",
"\n",
" return grid\n",
"\n",
"def make_plot_board_step_function(instru_csp):\n",
" '''ipywidgets interactive function supports\n",
" single parameter as input. This function\n",
" creates and return such a function by taking\n",
" in input other parameters.\n",
" '''\n",
" n = len(instru_csp.variables)\n",
" \n",
" \n",
" def plot_board_step(iteration):\n",
" ''' Add Queens to the Board.'''\n",
" data = instru_csp.assignment_history[iteration]\n",
" \n",
" grid = [[(col+row+1)%2 for col in range(n)] for row in range(n)]\n",
" grid = label_queen_conflicts(data, grid) # Update grid with conflict labels.\n",
" \n",
" # color map of fixed colors\n",
" cmap = matplotlib.colors.ListedColormap(['white','lightsteelblue','red'])\n",
" bounds=[0,1,2,3] # 0 for white 1 for black 2 onwards for conflict labels (red).\n",
" norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)\n",
" \n",
" fig = plt.imshow(grid, interpolation='nearest', cmap = cmap,norm=norm)\n",
"\n",
" plt.axis('off')\n",
" fig.axes.get_xaxis().set_visible(False)\n",
" fig.axes.get_yaxis().set_visible(False)\n",
"\n",
" # Place the Queens Unicode Symbol\n",
" for col, row in data.items():\n",
" fig.axes.text(row, col, u\"\\u265B\", va='center', ha='center', family='Dejavu Sans', fontsize=32)\n",
" plt.show()\n",
" \n",
" return plot_board_step"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let us visualize a solution obtained via backtracking. We make use of the previosuly defined **make_instru** function for keeping a history of steps."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"twelve_queens_csp = NQueensCSP(12)\n",
"backtracking_instru_queen = make_instru(twelve_queens_csp)\n",
"result = backtracking_search(backtracking_instru_queen)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"backtrack_queen_step = make_plot_board_step_function(backtracking_instru_queen) # Step Function for Widgets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now finally we set some matplotlib parameters to adjust how our plot will look like. The font is necessary because the Black Queen Unicode character is not a part of all fonts. You can move the slider to experiment and observe how the queens are assigned. It is also possible to move the slider using arrow keys or to jump to the value by directly editing the number with a double click. The **Visualize Button** will automatically animate the slider for you. The **Extra Delay Box** allows you to set time delay in seconds of upto one second for each time step."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fa243795d27f47c0af2cd12cbefa5e52",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(IntSlider(value=0, description='iteration', max=473, step=0), Output()), _dom_classes=('…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bdea801600cb441697ea3a810cb747a9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(ToggleButton(value=False, description='Visualize'), ToggleButtons(description='Extra Del…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"matplotlib.rcParams['figure.figsize'] = (8.0, 8.0)\n",
"matplotlib.rcParams['font.family'].append(u'Dejavu Sans')\n",
"\n",
"iteration_slider = widgets.IntSlider(min=0, max=len(backtracking_instru_queen.assignment_history)-1, step=0, value=0)\n",
"w=widgets.interactive(backtrack_queen_step,iteration=iteration_slider)\n",
"display(w)\n",
"\n",
"visualize_callback = make_visualize(iteration_slider)\n",
"\n",
"visualize_button = widgets.ToggleButton(description = \"Visualize\", value = False)\n",
"time_select = widgets.ToggleButtons(description='Extra Delay:',options=['0', '0.1', '0.2', '0.5', '0.7', '1.0'])\n",
"\n",
"a = widgets.interactive(visualize_callback, Visualize = visualize_button, time_step=time_select)\n",
"display(a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let us finally repeat the above steps for **min_conflicts** solution."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"conflicts_instru_queen = make_instru(twelve_queens_csp)\n",
"result = min_conflicts(conflicts_instru_queen)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"conflicts_step = make_plot_board_step_function(conflicts_instru_queen)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This visualization has same features as the one above; however, this one also highlights the conflicts by labeling the conflicted queens with a red background."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3bf64b599e5e4f128da23ecce08f3f53",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(IntSlider(value=0, description='iteration', max=52, step=0), Output()), _dom_classes=('w…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e4ccaba569f34a78857f2de8af4f01f2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(ToggleButton(value=False, description='Visualize'), ToggleButtons(description='Extra Del…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"iteration_slider = widgets.IntSlider(min=0, max=len(conflicts_instru_queen.assignment_history)-1, step=0, value=0)\n",
"w=widgets.interactive(conflicts_step,iteration=iteration_slider)\n",
"display(w)\n",
"\n",
"visualize_callback = make_visualize(iteration_slider)\n",
"\n",
"visualize_button = widgets.ToggleButton(description = \"Visualize\", value = False)\n",
"time_select = widgets.ToggleButtons(description='Extra Delay:',options=['0', '0.1', '0.2', '0.5', '0.7', '1.0'])\n",
"\n",
"a = widgets.interactive(visualize_callback, Visualize = visualize_button, time_step=time_select)\n",
"display(a)"
]
}
],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
ChristosChristofidis/h2o-3 | h2o-py/demos/imputation.ipynb | 2 | 48105 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import h2o"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime: </td>\n",
"<td>7 minutes 34 seconds 146 milliseconds </td></tr>\n",
"<tr><td>H2O cluster version: </td>\n",
"<td>3.1.0.99999</td></tr>\n",
"<tr><td>H2O cluster name: </td>\n",
"<td>spencer</td></tr>\n",
"<tr><td>H2O cluster total nodes: </td>\n",
"<td>1</td></tr>\n",
"<tr><td>H2O cluster total memory: </td>\n",
"<td>14.22 GB</td></tr>\n",
"<tr><td>H2O cluster total cores: </td>\n",
"<td>8</td></tr>\n",
"<tr><td>H2O cluster allowed cores: </td>\n",
"<td>8</td></tr>\n",
"<tr><td>H2O cluster healthy: </td>\n",
"<td>True</td></tr>\n",
"<tr><td>H2O Connection ip: </td>\n",
"<td>127.0.0.1</td></tr>\n",
"<tr><td>H2O Connection port: </td>\n",
"<td>54321</td></tr></table></div>"
],
"text/plain": [
"-------------------------- -------------------------------------\n",
"H2O cluster uptime: 7 minutes 34 seconds 146 milliseconds\n",
"H2O cluster version: 3.1.0.99999\n",
"H2O cluster name: spencer\n",
"H2O cluster total nodes: 1\n",
"H2O cluster total memory: 14.22 GB\n",
"H2O cluster total cores: 8\n",
"H2O cluster allowed cores: 8\n",
"H2O cluster healthy: True\n",
"H2O Connection ip: 127.0.0.1\n",
"H2O Connection port: 54321\n",
"-------------------------- -------------------------------------"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"h2o.init()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Parse Progress: [##################################################] 100%\n",
"Uploaded py2eedb6c5-0858-4b7f-833f-b24ab23140f8 into cluster with 43,978 rows and 31 cols\n"
]
}
],
"source": [
"air = h2o.upload_file(h2o.locate(\"smalldata/airlines/allyears2k_headers.zip\"))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[43978, 31]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"air.dim()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1086.0\n"
]
}
],
"source": [
"numNAs = air[\"DepTime\"].isna().sum()\n",
"print numNAs"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1345.84666138\n"
]
}
],
"source": [
"DepTime_mean = air[\"DepTime\"].mean(na_rm=True)\n",
"print DepTime_mean"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0\n"
]
}
],
"source": [
"air.impute(\"DepTime\", method = \"median\", combine_method=\"low\") \n",
"numNAs = air[\"DepTime\"].isna().sum()\n",
"print numNAs"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Parse Progress: [##################################################] 100%\n",
"Uploaded py74d01579-0aba-47ba-91a9-c9df3c8b4649 into cluster with 43,978 rows and 31 cols\n"
]
}
],
"source": [
"air = h2o.upload_file(h2o.locate(\"smalldata/airlines/allyears2k_headers.zip\"))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First 10 rows and first 31 columns: \n"
]
},
{
"data": {
"text/html": [
"<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>Year</td>\n",
"<td>Month</td>\n",
"<td>DayofMonth</td>\n",
"<td>DayOfWeek</td>\n",
"<td>DepTime</td>\n",
"<td>CRSDepTime</td>\n",
"<td>ArrTime</td>\n",
"<td>CRSArrTime</td>\n",
"<td>UniqueCarrier</td>\n",
"<td>FlightNum</td>\n",
"<td>TailNum</td>\n",
"<td>ActualElapsedTime</td>\n",
"<td>CRSElapsedTime</td>\n",
"<td>AirTime</td>\n",
"<td>ArrDelay</td>\n",
"<td>DepDelay</td>\n",
"<td>Origin</td>\n",
"<td>Dest</td>\n",
"<td>Distance</td>\n",
"<td>TaxiIn</td>\n",
"<td>TaxiOut</td>\n",
"<td>Cancelled</td>\n",
"<td>CancellationCode</td>\n",
"<td>Diverted</td>\n",
"<td>CarrierDelay</td>\n",
"<td>WeatherDelay</td>\n",
"<td>NASDelay</td>\n",
"<td>SecurityDelay</td>\n",
"<td>LateAircraftDelay</td>\n",
"<td>IsArrDelayed</td>\n",
"<td>IsDepDelayed</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>14</td>\n",
"<td>3</td>\n",
"<td>741</td>\n",
"<td>730</td>\n",
"<td>912</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>91</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>23</td>\n",
"<td>11</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>YES</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>15</td>\n",
"<td>4</td>\n",
"<td>729</td>\n",
"<td>730</td>\n",
"<td>903</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>94</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>14</td>\n",
"<td>-1</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>NO</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>17</td>\n",
"<td>6</td>\n",
"<td>741</td>\n",
"<td>730</td>\n",
"<td>918</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>97</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>29</td>\n",
"<td>11</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>YES</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>18</td>\n",
"<td>7</td>\n",
"<td>729</td>\n",
"<td>730</td>\n",
"<td>847</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>78</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>-2</td>\n",
"<td>-1</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>NO</td>\n",
"<td>NO</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>19</td>\n",
"<td>1</td>\n",
"<td>749</td>\n",
"<td>730</td>\n",
"<td>922</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>93</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>33</td>\n",
"<td>19</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>YES</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>21</td>\n",
"<td>3</td>\n",
"<td>728</td>\n",
"<td>730</td>\n",
"<td>848</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>80</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>-1</td>\n",
"<td>-2</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>NO</td>\n",
"<td>NO</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>22</td>\n",
"<td>4</td>\n",
"<td>728</td>\n",
"<td>730</td>\n",
"<td>852</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>84</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>3</td>\n",
"<td>-2</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>NO</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>23</td>\n",
"<td>5</td>\n",
"<td>731</td>\n",
"<td>730</td>\n",
"<td>902</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>91</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>13</td>\n",
"<td>1</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>YES</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>24</td>\n",
"<td>6</td>\n",
"<td>744</td>\n",
"<td>730</td>\n",
"<td>908</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>84</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>19</td>\n",
"<td>14</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>YES</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>25</td>\n",
"<td>7</td>\n",
"<td>729</td>\n",
"<td>730</td>\n",
"<td>851</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>82</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>2</td>\n",
"<td>-1</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>NO</td></tr></table></div>"
],
"text/plain": [
" Year Month DayofMonth DayOfWeek DepTime CRSDepTime ArrTime CRSArrTime UniqueCarrier FlightNum TailNum ActualElapsedTime CRSElapsedTime AirTime ArrDelay DepDelay Origin Dest Distance TaxiIn TaxiOut Cancelled CancellationCode Diverted CarrierDelay WeatherDelay NASDelay SecurityDelay LateAircraftDelay IsArrDelayed IsDepDelayed\n",
"------ ------- ------------ ----------- --------- ------------ --------- ------------ --------------- ----------- --------- ------------------- ---------------- --------- ---------- ---------- -------- ------ ---------- -------- --------- ----------- ------------------ ---------- -------------- -------------- ---------- --------------- ------------------- -------------- --------------\n",
" 1987 10 14 3 741 730 912 849 PS 1451 NA 91 79 23 11 SAN SFO 447 0 NA 0 YES YES\n",
" 1987 10 15 4 729 730 903 849 PS 1451 NA 94 79 14 -1 SAN SFO 447 0 NA 0 YES NO\n",
" 1987 10 17 6 741 730 918 849 PS 1451 NA 97 79 29 11 SAN SFO 447 0 NA 0 YES YES\n",
" 1987 10 18 7 729 730 847 849 PS 1451 NA 78 79 -2 -1 SAN SFO 447 0 NA 0 NO NO\n",
" 1987 10 19 1 749 730 922 849 PS 1451 NA 93 79 33 19 SAN SFO 447 0 NA 0 YES YES\n",
" 1987 10 21 3 728 730 848 849 PS 1451 NA 80 79 -1 -2 SAN SFO 447 0 NA 0 NO NO\n",
" 1987 10 22 4 728 730 852 849 PS 1451 NA 84 79 3 -2 SAN SFO 447 0 NA 0 YES NO\n",
" 1987 10 23 5 731 730 902 849 PS 1451 NA 91 79 13 1 SAN SFO 447 0 NA 0 YES YES\n",
" 1987 10 24 6 744 730 908 849 PS 1451 NA 84 79 19 14 SAN SFO 447 0 NA 0 YES YES\n",
" 1987 10 25 7 729 730 851 849 PS 1451 NA 82 79 2 -1 SAN SFO 447 0 NA 0 YES NO"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"air.impute(\"DepTime\", method = \"mean\", by = [\"Origin\", \"Distance\"]).show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Parse Progress: [##################################################] 100%\n",
"Uploaded pyf6d4c8d3-78a3-4c26-9adb-a282583ae563 into cluster with 43,978 rows and 31 cols\n"
]
}
],
"source": [
"air = h2o.upload_file(h2o.locate(\"smalldata/airlines/allyears2k_headers.zip\"))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First 10 rows and first 31 columns: \n"
]
},
{
"data": {
"text/html": [
"<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>Year</td>\n",
"<td>Month</td>\n",
"<td>DayofMonth</td>\n",
"<td>DayOfWeek</td>\n",
"<td>DepTime</td>\n",
"<td>CRSDepTime</td>\n",
"<td>ArrTime</td>\n",
"<td>CRSArrTime</td>\n",
"<td>UniqueCarrier</td>\n",
"<td>FlightNum</td>\n",
"<td>TailNum</td>\n",
"<td>ActualElapsedTime</td>\n",
"<td>CRSElapsedTime</td>\n",
"<td>AirTime</td>\n",
"<td>ArrDelay</td>\n",
"<td>DepDelay</td>\n",
"<td>Origin</td>\n",
"<td>Dest</td>\n",
"<td>Distance</td>\n",
"<td>TaxiIn</td>\n",
"<td>TaxiOut</td>\n",
"<td>Cancelled</td>\n",
"<td>CancellationCode</td>\n",
"<td>Diverted</td>\n",
"<td>CarrierDelay</td>\n",
"<td>WeatherDelay</td>\n",
"<td>NASDelay</td>\n",
"<td>SecurityDelay</td>\n",
"<td>LateAircraftDelay</td>\n",
"<td>IsArrDelayed</td>\n",
"<td>IsDepDelayed</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>14</td>\n",
"<td>3</td>\n",
"<td>741</td>\n",
"<td>730</td>\n",
"<td>912</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>91</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>23</td>\n",
"<td>11</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>YES</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>15</td>\n",
"<td>4</td>\n",
"<td>729</td>\n",
"<td>730</td>\n",
"<td>903</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>94</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>14</td>\n",
"<td>-1</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>NO</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>17</td>\n",
"<td>6</td>\n",
"<td>741</td>\n",
"<td>730</td>\n",
"<td>918</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>97</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>29</td>\n",
"<td>11</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>YES</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>18</td>\n",
"<td>7</td>\n",
"<td>729</td>\n",
"<td>730</td>\n",
"<td>847</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>78</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>-2</td>\n",
"<td>-1</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>NO</td>\n",
"<td>NO</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>19</td>\n",
"<td>1</td>\n",
"<td>749</td>\n",
"<td>730</td>\n",
"<td>922</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>93</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>33</td>\n",
"<td>19</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>YES</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>21</td>\n",
"<td>3</td>\n",
"<td>728</td>\n",
"<td>730</td>\n",
"<td>848</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>80</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>-1</td>\n",
"<td>-2</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>NO</td>\n",
"<td>NO</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>22</td>\n",
"<td>4</td>\n",
"<td>728</td>\n",
"<td>730</td>\n",
"<td>852</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>84</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>3</td>\n",
"<td>-2</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>NO</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>23</td>\n",
"<td>5</td>\n",
"<td>731</td>\n",
"<td>730</td>\n",
"<td>902</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>91</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>13</td>\n",
"<td>1</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>YES</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>24</td>\n",
"<td>6</td>\n",
"<td>744</td>\n",
"<td>730</td>\n",
"<td>908</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>84</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>19</td>\n",
"<td>14</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>YES</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>25</td>\n",
"<td>7</td>\n",
"<td>729</td>\n",
"<td>730</td>\n",
"<td>851</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>82</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>2</td>\n",
"<td>-1</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>NO</td></tr></table></div>"
],
"text/plain": [
" Year Month DayofMonth DayOfWeek DepTime CRSDepTime ArrTime CRSArrTime UniqueCarrier FlightNum TailNum ActualElapsedTime CRSElapsedTime AirTime ArrDelay DepDelay Origin Dest Distance TaxiIn TaxiOut Cancelled CancellationCode Diverted CarrierDelay WeatherDelay NASDelay SecurityDelay LateAircraftDelay IsArrDelayed IsDepDelayed\n",
"------ ------- ------------ ----------- --------- ------------ --------- ------------ --------------- ----------- --------- ------------------- ---------------- --------- ---------- ---------- -------- ------ ---------- -------- --------- ----------- ------------------ ---------- -------------- -------------- ---------- --------------- ------------------- -------------- --------------\n",
" 1987 10 14 3 741 730 912 849 PS 1451 NA 91 79 23 11 SAN SFO 447 0 NA 0 YES YES\n",
" 1987 10 15 4 729 730 903 849 PS 1451 NA 94 79 14 -1 SAN SFO 447 0 NA 0 YES NO\n",
" 1987 10 17 6 741 730 918 849 PS 1451 NA 97 79 29 11 SAN SFO 447 0 NA 0 YES YES\n",
" 1987 10 18 7 729 730 847 849 PS 1451 NA 78 79 -2 -1 SAN SFO 447 0 NA 0 NO NO\n",
" 1987 10 19 1 749 730 922 849 PS 1451 NA 93 79 33 19 SAN SFO 447 0 NA 0 YES YES\n",
" 1987 10 21 3 728 730 848 849 PS 1451 NA 80 79 -1 -2 SAN SFO 447 0 NA 0 NO NO\n",
" 1987 10 22 4 728 730 852 849 PS 1451 NA 84 79 3 -2 SAN SFO 447 0 NA 0 YES NO\n",
" 1987 10 23 5 731 730 902 849 PS 1451 NA 91 79 13 1 SAN SFO 447 0 NA 0 YES YES\n",
" 1987 10 24 6 744 730 908 849 PS 1451 NA 84 79 19 14 SAN SFO 447 0 NA 0 YES YES\n",
" 1987 10 25 7 729 730 851 849 PS 1451 NA 82 79 2 -1 SAN SFO 447 0 NA 0 YES NO"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"air.impute(\"TailNum\", method = \"mode\").show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Parse Progress: [##################################################] 100%\n",
"Uploaded py77ce3a79-0fa0-4116-91d4-c928a7084e68 into cluster with 43,978 rows and 31 cols\n"
]
}
],
"source": [
"air = h2o.upload_file(h2o.locate(\"smalldata/airlines/allyears2k_headers.zip\"))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First 10 rows and first 31 columns: \n"
]
},
{
"data": {
"text/html": [
"<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>Year</td>\n",
"<td>Month</td>\n",
"<td>DayofMonth</td>\n",
"<td>DayOfWeek</td>\n",
"<td>DepTime</td>\n",
"<td>CRSDepTime</td>\n",
"<td>ArrTime</td>\n",
"<td>CRSArrTime</td>\n",
"<td>UniqueCarrier</td>\n",
"<td>FlightNum</td>\n",
"<td>TailNum</td>\n",
"<td>ActualElapsedTime</td>\n",
"<td>CRSElapsedTime</td>\n",
"<td>AirTime</td>\n",
"<td>ArrDelay</td>\n",
"<td>DepDelay</td>\n",
"<td>Origin</td>\n",
"<td>Dest</td>\n",
"<td>Distance</td>\n",
"<td>TaxiIn</td>\n",
"<td>TaxiOut</td>\n",
"<td>Cancelled</td>\n",
"<td>CancellationCode</td>\n",
"<td>Diverted</td>\n",
"<td>CarrierDelay</td>\n",
"<td>WeatherDelay</td>\n",
"<td>NASDelay</td>\n",
"<td>SecurityDelay</td>\n",
"<td>LateAircraftDelay</td>\n",
"<td>IsArrDelayed</td>\n",
"<td>IsDepDelayed</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>14</td>\n",
"<td>3</td>\n",
"<td>741</td>\n",
"<td>730</td>\n",
"<td>912</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>91</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>23</td>\n",
"<td>11</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>YES</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>15</td>\n",
"<td>4</td>\n",
"<td>729</td>\n",
"<td>730</td>\n",
"<td>903</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>94</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>14</td>\n",
"<td>-1</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>NO</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>17</td>\n",
"<td>6</td>\n",
"<td>741</td>\n",
"<td>730</td>\n",
"<td>918</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>97</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>29</td>\n",
"<td>11</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>YES</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>18</td>\n",
"<td>7</td>\n",
"<td>729</td>\n",
"<td>730</td>\n",
"<td>847</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>78</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>-2</td>\n",
"<td>-1</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>NO</td>\n",
"<td>NO</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>19</td>\n",
"<td>1</td>\n",
"<td>749</td>\n",
"<td>730</td>\n",
"<td>922</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>93</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>33</td>\n",
"<td>19</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>YES</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>21</td>\n",
"<td>3</td>\n",
"<td>728</td>\n",
"<td>730</td>\n",
"<td>848</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>80</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>-1</td>\n",
"<td>-2</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>NO</td>\n",
"<td>NO</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>22</td>\n",
"<td>4</td>\n",
"<td>728</td>\n",
"<td>730</td>\n",
"<td>852</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>84</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>3</td>\n",
"<td>-2</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>NO</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>23</td>\n",
"<td>5</td>\n",
"<td>731</td>\n",
"<td>730</td>\n",
"<td>902</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>91</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>13</td>\n",
"<td>1</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>YES</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>24</td>\n",
"<td>6</td>\n",
"<td>744</td>\n",
"<td>730</td>\n",
"<td>908</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>84</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>19</td>\n",
"<td>14</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>YES</td></tr>\n",
"<tr><td>1987</td>\n",
"<td>10</td>\n",
"<td>25</td>\n",
"<td>7</td>\n",
"<td>729</td>\n",
"<td>730</td>\n",
"<td>851</td>\n",
"<td>849</td>\n",
"<td>PS</td>\n",
"<td>1451</td>\n",
"<td>NA</td>\n",
"<td>82</td>\n",
"<td>79</td>\n",
"<td></td>\n",
"<td>2</td>\n",
"<td>-1</td>\n",
"<td>SAN</td>\n",
"<td>SFO</td>\n",
"<td>447</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>0</td>\n",
"<td>NA</td>\n",
"<td>0</td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td></td>\n",
"<td>YES</td>\n",
"<td>NO</td></tr></table></div>"
],
"text/plain": [
" Year Month DayofMonth DayOfWeek DepTime CRSDepTime ArrTime CRSArrTime UniqueCarrier FlightNum TailNum ActualElapsedTime CRSElapsedTime AirTime ArrDelay DepDelay Origin Dest Distance TaxiIn TaxiOut Cancelled CancellationCode Diverted CarrierDelay WeatherDelay NASDelay SecurityDelay LateAircraftDelay IsArrDelayed IsDepDelayed\n",
"------ ------- ------------ ----------- --------- ------------ --------- ------------ --------------- ----------- --------- ------------------- ---------------- --------- ---------- ---------- -------- ------ ---------- -------- --------- ----------- ------------------ ---------- -------------- -------------- ---------- --------------- ------------------- -------------- --------------\n",
" 1987 10 14 3 741 730 912 849 PS 1451 NA 91 79 23 11 SAN SFO 447 0 NA 0 YES YES\n",
" 1987 10 15 4 729 730 903 849 PS 1451 NA 94 79 14 -1 SAN SFO 447 0 NA 0 YES NO\n",
" 1987 10 17 6 741 730 918 849 PS 1451 NA 97 79 29 11 SAN SFO 447 0 NA 0 YES YES\n",
" 1987 10 18 7 729 730 847 849 PS 1451 NA 78 79 -2 -1 SAN SFO 447 0 NA 0 NO NO\n",
" 1987 10 19 1 749 730 922 849 PS 1451 NA 93 79 33 19 SAN SFO 447 0 NA 0 YES YES\n",
" 1987 10 21 3 728 730 848 849 PS 1451 NA 80 79 -1 -2 SAN SFO 447 0 NA 0 NO NO\n",
" 1987 10 22 4 728 730 852 849 PS 1451 NA 84 79 3 -2 SAN SFO 447 0 NA 0 YES NO\n",
" 1987 10 23 5 731 730 902 849 PS 1451 NA 91 79 13 1 SAN SFO 447 0 NA 0 YES YES\n",
" 1987 10 24 6 744 730 908 849 PS 1451 NA 84 79 19 14 SAN SFO 447 0 NA 0 YES YES\n",
" 1987 10 25 7 729 730 851 849 PS 1451 NA 82 79 2 -1 SAN SFO 447 0 NA 0 YES NO"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"air.impute(\"TailNum\", method = \"mode\", by=[\"Month\", \"Year\"]).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": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
aqibsaeed/Human-Activity-Recognition-using-CNN | Activity Detection.ipynb | 1 | 8534 | {
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy import stats\n",
"import tensorflow as tf\n",
"\n",
"%matplotlib inline\n",
"plt.style.use('ggplot')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def read_data(file_path):\n",
" column_names = ['user-id','activity','timestamp', 'x-axis', 'y-axis', 'z-axis']\n",
" data = pd.read_csv(file_path,header = None, names = column_names)\n",
" return data\n",
"\n",
"def feature_normalize(dataset):\n",
" mu = np.mean(dataset,axis = 0)\n",
" sigma = np.std(dataset,axis = 0)\n",
" return (dataset - mu)/sigma\n",
" \n",
"def plot_axis(ax, x, y, title):\n",
" ax.plot(x, y)\n",
" ax.set_title(title)\n",
" ax.xaxis.set_visible(False)\n",
" ax.set_ylim([min(y) - np.std(y), max(y) + np.std(y)])\n",
" ax.set_xlim([min(x), max(x)])\n",
" ax.grid(True)\n",
" \n",
"def plot_activity(activity,data):\n",
" fig, (ax0, ax1, ax2) = plt.subplots(nrows = 3, figsize = (15, 10), sharex = True)\n",
" plot_axis(ax0, data['timestamp'], data['x-axis'], 'x-axis')\n",
" plot_axis(ax1, data['timestamp'], data['y-axis'], 'y-axis')\n",
" plot_axis(ax2, data['timestamp'], data['z-axis'], 'z-axis')\n",
" plt.subplots_adjust(hspace=0.2)\n",
" fig.suptitle(activity)\n",
" plt.subplots_adjust(top=0.90)\n",
" plt.show()\n",
" \n",
"def windows(data, size):\n",
" start = 0\n",
" while start < data.count():\n",
" yield int(start), int(start + size)\n",
" start += (size / 2)\n",
"\n",
"def segment_signal(data,window_size = 90):\n",
" segments = np.empty((0,window_size,3))\n",
" labels = np.empty((0))\n",
" for (start, end) in windows(data['timestamp'], window_size):\n",
" x = data[\"x-axis\"][start:end]\n",
" y = data[\"y-axis\"][start:end]\n",
" z = data[\"z-axis\"][start:end]\n",
" if(len(dataset['timestamp'][start:end]) == window_size):\n",
" segments = np.vstack([segments,np.dstack([x,y,z])])\n",
" labels = np.append(labels,stats.mode(data[\"activity\"][start:end])[0][0])\n",
" return segments, labels\n",
"\n",
"def weight_variable(shape):\n",
" initial = tf.truncated_normal(shape, stddev = 0.1)\n",
" return tf.Variable(initial)\n",
"\n",
"def bias_variable(shape):\n",
" initial = tf.constant(0.0, shape = shape)\n",
" return tf.Variable(initial)\n",
"\n",
"def depthwise_conv2d(x, W):\n",
" return tf.nn.depthwise_conv2d(x,W, [1, 1, 1, 1], padding='VALID')\n",
"\n",
"def apply_depthwise_conv(x,kernel_size,num_channels,depth):\n",
" weights = weight_variable([1, kernel_size, num_channels, depth])\n",
" biases = bias_variable([depth * num_channels])\n",
" return tf.nn.relu(tf.add(depthwise_conv2d(x, weights),biases))\n",
" \n",
"def apply_max_pool(x,kernel_size,stride_size):\n",
" return tf.nn.max_pool(x, ksize=[1, 1, kernel_size, 1], \n",
" strides=[1, 1, stride_size, 1], padding='VALID')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dataset = read_data('actitracker_raw.txt')\n",
"dataset['x-axis'] = feature_normalize(dataset['x-axis'])\n",
"dataset['y-axis'] = feature_normalize(dataset['y-axis'])\n",
"dataset['z-axis'] = feature_normalize(dataset['z-axis'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for activity in np.unique(dataset[\"activity\"]):\n",
" subset = dataset[dataset[\"activity\"] == activity][:180]\n",
" plot_activity(activity,subset)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"segments, labels = segment_signal(dataset)\n",
"labels = np.asarray(pd.get_dummies(labels), dtype = np.int8)\n",
"reshaped_segments = segments.reshape(len(segments), 1,90, 3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_test_split = np.random.rand(len(reshaped_segments)) < 0.70\n",
"train_x = reshaped_segments[train_test_split]\n",
"train_y = labels[train_test_split]\n",
"test_x = reshaped_segments[~train_test_split]\n",
"test_y = labels[~train_test_split]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"input_height = 1\n",
"input_width = 90\n",
"num_labels = 6\n",
"num_channels = 3\n",
"\n",
"batch_size = 10\n",
"kernel_size = 60\n",
"depth = 60\n",
"num_hidden = 1000\n",
"\n",
"learning_rate = 0.0001\n",
"training_epochs = 8\n",
"\n",
"total_batches = train_x.shape[0] // batch_size"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X = tf.placeholder(tf.float32, shape=[None,input_height,input_width,num_channels])\n",
"Y = tf.placeholder(tf.float32, shape=[None,num_labels])\n",
"\n",
"c = apply_depthwise_conv(X,kernel_size,num_channels,depth)\n",
"p = apply_max_pool(c,20,2)\n",
"c = apply_depthwise_conv(p,6,depth*num_channels,depth//10)\n",
"\n",
"shape = c.get_shape().as_list()\n",
"c_flat = tf.reshape(c, [-1, shape[1] * shape[2] * shape[3]])\n",
"\n",
"f_weights_l1 = weight_variable([shape[1] * shape[2] * depth * num_channels * (depth//10), num_hidden])\n",
"f_biases_l1 = bias_variable([num_hidden])\n",
"f = tf.nn.tanh(tf.add(tf.matmul(c_flat, f_weights_l1),f_biases_l1))\n",
"\n",
"out_weights = weight_variable([num_hidden, num_labels])\n",
"out_biases = bias_variable([num_labels])\n",
"y_ = tf.nn.softmax(tf.matmul(f, out_weights) + out_biases)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"loss = -tf.reduce_sum(Y * tf.log(y_))\n",
"optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(loss)\n",
"\n",
"correct_prediction = tf.equal(tf.argmax(y_,1), tf.argmax(Y,1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"cost_history = np.empty(shape=[1],dtype=float)\n",
"\n",
"with tf.Session() as session:\n",
" tf.global_variables_initializer().run()\n",
" for epoch in range(training_epochs):\n",
" for b in range(total_batches): \n",
" offset = (b * batch_size) % (train_y.shape[0] - batch_size)\n",
" batch_x = train_x[offset:(offset + batch_size), :, :, :]\n",
" batch_y = train_y[offset:(offset + batch_size), :]\n",
" _, c = session.run([optimizer, loss],feed_dict={X: batch_x, Y : batch_y})\n",
" cost_history = np.append(cost_history,c)\n",
" print \"Epoch: \",epoch,\" Training Loss: \",c,\" Training Accuracy: \",\n",
" session.run(accuracy, feed_dict={X: train_x, Y: train_y})\n",
" \n",
" print \"Testing Accuracy:\", session.run(accuracy, feed_dict={X: test_x, Y: test_y})"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
alsrgv/tensorflow | tensorflow/contrib/eager/python/examples/workshop/1_basic.ipynb | 2 | 7695 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "TFE Workshop: control flow",
"version": "0.3.2",
"provenance": [],
"include_colab_link": true
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"[View in Colaboratory](https://colab.research.google.com/gist/alextp/664b2f8700485ff6801f4d26293bd567/tfe-workshop-control-flow.ipynb)"
]
},
{
"metadata": {
"id": "9BpQzh9BvJlj",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 37
},
"outputId": "0b336886-8204-4815-89fa-5291a49d5784"
},
"cell_type": "code",
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
"tf.enable_eager_execution()"
],
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"id": "0roIB19GvOjI",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Eager execution basics\n",
"\n",
"When eager execution is enabled TensorFlow immediately executes operations, and Tensors are always available. "
]
},
{
"metadata": {
"id": "jeO8F-V-vN24",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
},
"outputId": "aeb3bdec-50b7-440d-93d8-5a171f091081"
},
"cell_type": "code",
"source": [
"t = tf.constant([[1, 2], [3, 4]])\n",
"t"
],
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tf.Tensor: id=0, shape=(2, 2), dtype=int32, numpy=\n",
"array([[1, 2],\n",
" [3, 4]], dtype=int32)>"
]
},
"metadata": {
"tags": []
},
"execution_count": 2
}
]
},
{
"metadata": {
"id": "Y17RwSFxvlDL",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
},
"outputId": "cfcc10c7-707b-4997-99b3-a5f382c5166b"
},
"cell_type": "code",
"source": [
"tf.matmul(t, t)"
],
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tf.Tensor: id=2, shape=(2, 2), dtype=int32, numpy=\n",
"array([[ 7, 10],\n",
" [15, 22]], dtype=int32)>"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"metadata": {
"id": "Dab1bS3TvmRE",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "8a624f3d-a658-4359-c586-1c5f6bf4c8b7"
},
"cell_type": "code",
"source": [
"# It's also possible to have Python control flow which depends on the value of tensors.\n",
"if t[0, 0] > 0.5:\n",
" print(\"T is bigger\")\n",
"else:\n",
" print(\"T is smaller\")"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"T is bigger\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "dPgptJcGwIon",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "c4f27f2b-0848-4475-dde5-2534dac65a5c"
},
"cell_type": "code",
"source": [
"# Tensors are also usable as numpy arrays\n",
"np.prod(t)"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"24"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"metadata": {
"id": "p3DTfQXnwXzj",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Exercise\n",
"\n",
"The algorithm for bisecting line search is a pretty simple way to find a zero of a continuous scalar function in an interval [a,b] where f(a) and f(b) have different signs. Simply evaluate f((a+b)/2), and narrow the interval by replacing either a or b with (a+b)/2 such that the function when applied on the boundary of the interval still has different signs.\n",
"\n",
"Implement a python function `bisecting_line_search(f, a, b, epsilon)` which returns a value such that `tf.abs(f(value)) < epsilon`.\n",
"\n",
"One thing to keep in mind: python's `==` operator is not overloaded on Tensors, so you need to use `tf.equal` to compare for equality."
]
},
{
"metadata": {
"id": "6eq0YuI6ykm5",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# Example test harness to get you going\n",
"\n",
"def test_f(x):\n",
" return x - 0.1234\n",
"def bisecting_line_search(f, a, b, epsilon):\n",
" # Return x such that f(x) <= epsilon.\n",
" pass\n",
"a = tf.constant(0.0)\n",
"b = tf.constant(1.0)\n",
"epsilon = tf.constant(0.001)\n",
"x = bisecting_line_search(test_f, a, b, epsilon)\n"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "LcMmEfd_xvej",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 170
},
"outputId": "f402aa50-8ce3-4416-f755-8bbcd1af7809"
},
"cell_type": "code",
"source": [
"#@title Double-click to see the solution\n",
"\n",
"def bisecting_line_search(f, a, b, epsilon):\n",
" f_a = f(a)\n",
" f_b = f(b)\n",
" probe = (a + b) / 2\n",
" f_probe = f(probe)\n",
" while tf.abs(f_probe) > epsilon:\n",
" if tf.equal(tf.sign(f_probe), tf.sign(f_a)):\n",
" a = probe\n",
" f_a = f_probe\n",
" else:\n",
" b = probe\n",
" f_b = f_probe\n",
" probe = (a + b) / 2\n",
" f_probe = f(probe)\n",
" print(\"new probe\", probe)\n",
" return probe\n",
"\n",
"bisecting_line_search(test_f, 0., 1., 0.001)"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"('new probe', 0.25)\n",
"('new probe', 0.125)\n",
"('new probe', 0.0625)\n",
"('new probe', 0.09375)\n",
"('new probe', 0.109375)\n",
"('new probe', 0.1171875)\n",
"('new probe', 0.12109375)\n",
"('new probe', 0.123046875)\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.123046875"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
}
]
}
| apache-2.0 |
KristianJensen/cameo | examples/visbio_new_interact_feature.ipynb | 1 | 2920248 | null | apache-2.0 |
AstroHackWeek/AstroHackWeek2016 | notebook-tutorial/notebooks/01-Tips-and-tricks.ipynb | 1 | 98605 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Best practices\n",
"\n",
"Let's start with pep8 (https://www.python.org/dev/peps/pep-0008/)\n",
"\n",
"> Imports should be grouped in the following order:\n",
"\n",
"> - standard library imports\n",
"> - related third party imports\n",
"> - local application/library specific imports\n",
"\n",
"> You should put a blank line between each group of imports.\n",
"Put any relevant __all__ specification after the imports.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format='retina' \n",
"\n",
"# Add this to python2 code to make life easier\n",
"from __future__ import absolute_import, division, print_function\n",
"\n",
"from itertools import combinations\n",
"import string\n",
"\n",
"from IPython.display import IFrame, HTML, YouTubeVideo\n",
"import matplotlib as mpl\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib.pyplot import GridSpec\n",
"import seaborn as sns\n",
"import mpld3\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os, sys\n",
"import warnings\n",
"\n",
"sns.set();\n",
"plt.rcParams['figure.figsize'] = (12, 8)\n",
"sns.set_style(\"darkgrid\")\n",
"sns.set_context(\"poster\", font_scale=1.3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pivot Tables w/ pandas\n",
"\n",
"http://nicolas.kruchten.com/content/2015/09/jupyter_pivottablejs/"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/jpeg": 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"text/html": [
"\n",
" <iframe\n",
" width=\"400\"\n",
" height=\"300\"\n",
" src=\"https://www.youtube.com/embed/ZbrRrXiWBKc\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x106c15eb8>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"YouTubeVideo(\"ZbrRrXiWBKc\", width=400, height=300)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fetching package metadata .........\n",
"Solving package specifications: ..........\n",
"\n",
"# All requested packages already installed.\n",
"# packages in environment at /Users/jonathan/miniconda3/envs/py3:\n",
"#\n",
"pivottablejs 0.1.0 py35_0 \n"
]
}
],
"source": [
"!conda install pivottablejs -y"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.read_csv(\"../data/mps.csv\", encoding=\"ISO-8859-1\")"
]
},
{
"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>Name</th>\n",
" <th>Party</th>\n",
" <th>Province</th>\n",
" <th>Age</th>\n",
" <th>Gender</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Liu, Laurin</td>\n",
" <td>NDP</td>\n",
" <td>Quebec</td>\n",
" <td>22.0</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Mourani, Maria</td>\n",
" <td>Bloc Quebecois</td>\n",
" <td>Quebec</td>\n",
" <td>43.0</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Sellah, Djaouida</td>\n",
" <td>NDP</td>\n",
" <td>Quebec</td>\n",
" <td>NaN</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>St-Denis, Lise</td>\n",
" <td>NDP</td>\n",
" <td>Quebec</td>\n",
" <td>72.0</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Fry, Hedy</td>\n",
" <td>Liberal</td>\n",
" <td>British Columbia</td>\n",
" <td>71.0</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Turmel, Nycole</td>\n",
" <td>NDP</td>\n",
" <td>Quebec</td>\n",
" <td>70.0</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Sgro, Judy</td>\n",
" <td>Liberal</td>\n",
" <td>Ontario</td>\n",
" <td>68.0</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Raynault, Francine</td>\n",
" <td>NDP</td>\n",
" <td>Quebec</td>\n",
" <td>67.0</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Davidson, Patricia</td>\n",
" <td>Conservative</td>\n",
" <td>Ontario</td>\n",
" <td>66.0</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Smith, Joy</td>\n",
" <td>Conservative</td>\n",
" <td>Manitoba</td>\n",
" <td>65.0</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Party Province Age Gender\n",
"0 Liu, Laurin NDP Quebec 22.0 Female\n",
"1 Mourani, Maria Bloc Quebecois Quebec 43.0 Female\n",
"2 Sellah, Djaouida NDP Quebec NaN Female\n",
"3 St-Denis, Lise NDP Quebec 72.0 Female\n",
"4 Fry, Hedy Liberal British Columbia 71.0 Female\n",
"5 Turmel, Nycole NDP Quebec 70.0 Female\n",
"6 Sgro, Judy Liberal Ontario 68.0 Female\n",
"7 Raynault, Francine NDP Quebec 67.0 Female\n",
"8 Davidson, Patricia Conservative Ontario 66.0 Female\n",
"9 Smith, Joy Conservative Manitoba 65.0 Female"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pivottablejs import pivot_ui"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Enhanced Pandas Dataframe Display"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"100%\"\n",
" height=\"500\"\n",
" src=\"pivottablejs.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x111d0bfd0>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pivot_ui(df)\n",
"# Province, Party, Average, Age, Heatmap"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Keyboard shortcuts\n",
"\n",
"For help, `ESC` + `h`"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# in select mode, shift j/k (to select multiple cells at once)\n",
"# split cell with ctrl shift -"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"first = 1"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"second = 2"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"third = 3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also get syntax highlighting if you tell it the language that you're including: \n",
"\n",
"```bash\n",
"mkdir toc\n",
"cd toc\n",
"\n",
"wget https://raw.githubusercontent.com/minrk/ipython_extensions/master/nbextensions/toc.js\n",
"\n",
"wget https://raw.githubusercontent.com/minrk/ipython_extensions/master/nbextensions/toc.css\n",
"cd ..\n",
"\n",
"jupyter-nbextension install --user toc\n",
"jupyter-nbextension enable toc/toc\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```SQL\n",
"SELECT *\n",
"FROM tablename\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/jonathan/github/AstroHackWeek2016/notebook-tutorial/notebooks\n",
" 108 343 2905 00-Overview.ipynb\n",
" 767 1589 97891 01-Tips-and-tricks.ipynb\n",
" 133 250 2410 02-Visualization-and-code-organization.ipynb\n",
" 921 2234 499442 03-Pandas-and-Plotting.ipynb\n",
" 492 1144 13128 04-SQL-Example.ipynb\n",
" 169 381 23836 05-interactive-splines.ipynb\n",
" 393 841 204811 06-R-stuff.ipynb\n",
" 1069 2174 20704 07-Some_basics.ipynb\n",
" 1028 2025 17918 08-More_basics.ipynb\n",
" 422 943 8408 09-Extras.ipynb\n",
" 569 1672 21017 Data_Cleaning.ipynb\n",
"\n",
"break\n",
"\n",
"4.0K\t00-Overview.ipynb\n",
" 96K\t01-Tips-and-tricks.ipynb\n",
"4.0K\t02-Visualization-and-code-organization.ipynb\n",
"488K\t03-Pandas-and-Plotting.ipynb\n",
" 16K\t04-SQL-Example.ipynb\n",
" 24K\t05-interactive-splines.ipynb\n",
"204K\t06-R-stuff.ipynb\n",
" 24K\t07-Some_basics.ipynb\n",
" 20K\t08-More_basics.ipynb\n",
" 12K\t09-Extras.ipynb\n",
" 24K\tData_Cleaning.ipynb\n"
]
}
],
"source": [
"%%bash\n",
"pwd \n",
"for i in *.ipynb\n",
"do\n",
" wc $i\n",
"done\n",
"echo \n",
"echo \"break\"\n",
"echo\n",
"du -h *ipynb"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def silly_absolute_value_function(xval):\n",
" \"\"\"Takes a value and returns the value.\"\"\"\n",
" xval_sq = xval ** 2.0\n",
" xval_abs = np.sqrt(xval_sq)\n",
" return xval_abs"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"silly_absolute_value_function?"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"silly_absolute_value_function??"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# shift-tab\n",
"silly_absolute_value_function()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# shift-tab-tab\n",
"silly_absolute_value_function()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# shift-tab-tab-tab\n",
"silly_absolute_value_function()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.sin??"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Stop here for now"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# R\n",
"\n",
" - [pyRserve](https://pypi.python.org/pypi/pyRserve)\n",
" - [rpy2](http://rpy.sourceforge.net/)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# !conda install -c r rpy2 -y"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import rpy2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%load_ext rpy2.ipython"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X = np.array([0,1,2,3,4])\n",
"Y = np.array([3,5,4,6,7])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Object `%%R` not found.\n"
]
}
],
"source": [
"%%R?"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\n",
"Call:\n",
"lm(formula = Y ~ X)\n",
"\n",
"Residuals:\n",
" 1 2 3 4 5 \n",
"-0.2 0.9 -1.0 0.1 0.2 \n",
"\n",
"Coefficients:\n",
" Estimate Std. Error t value Pr(>|t|) \n",
"(Intercept) 3.2000 0.6164 5.191 0.0139 *\n",
"X 0.9000 0.2517 3.576 0.0374 *\n",
"---\n",
"Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
"\n",
"Residual standard error: 0.7958 on 3 degrees of freedom\n",
"Multiple R-squared: 0.81,\tAdjusted R-squared: 0.7467 \n",
"F-statistic: 12.79 on 1 and 3 DF, p-value: 0.03739\n",
"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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gWHPD2AydYwhn8BkpBqMuUEzYEQEcIpDRKGAsAaMiEOZsYBkJAys0DszPwigKxkRQSeFF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OK4udD1GoULkTlnJhn8O04XgqUzomAhQxii2CEUJQgMuG7dugRRcSSUm+MRI7sSPCXga/zVV18R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\ngg==\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R -i X,Y -o XYcoef\n",
"XYlm = lm(Y~X)\n",
"XYcoef = coef(XYlm)\n",
"print(summary(XYlm))\n",
"par(mfrow=c(2,2))\n",
"plot(XYlm)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 3.2, 0.9])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"XYcoef"
]
},
{
"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
}
| mit |
phobson/bokeh | examples/howto/notebook_comms/Basic Usage.ipynb | 1 | 4415 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from bokeh.io import push_notebook, show, output_notebook\n",
"from bokeh.layouts import row\n",
"from bokeh.plotting import figure\n",
"output_notebook()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"opts = dict(plot_width=250, plot_height=250, min_border=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"p1 = figure(**opts)\n",
"r1 = p1.circle([1,2,3], [4,5,6], size=20)\n",
"\n",
"p2 = figure(**opts)\n",
"r2 = p2.circle([1,2,3], [4,5,6], size=20)\n",
"\n",
"# get a handle to update the shown cell with\n",
"t = show(row(p1, p2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# the comms handle repr show what cell it can be used to update\n",
"t"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# this will update the left plot circle color with an explicit handle\n",
"r1.glyph.fill_color = \"white\"\n",
"push_notebook(handle=t)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# and this will update the right plot circle color because it was in the last shown cell\n",
"r2.glyph.fill_color = \"pink\"\n",
"push_notebook()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"p3 = figure(**opts)\n",
"r3 = p3.circle([1,2,3], [4,5,6], size=20)\n",
"\n",
"# get a handle to update the shown cell with\n",
"t2 = show(p3) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# show which cell t2 handles\n",
"t2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# this updates the immediately previous cell with an explicit handle\n",
"r3.glyph.fill_color = \"orange\"\n",
"push_notebook(handle=t2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# this updates the left plot at the top with an explicit handle\n",
"r1.glyph.fill_color = \"orange\"\n",
"push_notebook(handle=t)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# get a handle to update the shown cell with\n",
"t3 = show(p2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# this will update the immediately previous plot circle color because it was in the last shown cell\n",
"r2.glyph.fill_color = \"red\"\n",
"push_notebook()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# this will update the immediately previous plot circle color with an explicit handle\n",
"r2.glyph.fill_color = \"blue\"\n",
"push_notebook(handle=t3)"
]
},
{
"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": "3.4.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
crs4/omero.biobank | notebooks/biobank-basics-10.ipynb | 1 | 14122 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Micro arrays datasets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Omerobiobank has specialized objects to deal with the description of micro arrays dataset. The basic objects that should be captured are:\n",
"\n",
" * the physical chip that contain the microarray hibridization beads/grids\n",
" * (possibly) the platform (usually a slide) where the chip is mounted\n",
" * the raw datasets obtained by reading the hibridizaton results\n",
"\n",
"The raw datasets will then be converted to biologically informative data, but this is outside the scope of this notebook."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sys, os, uuid\n",
"from bl.vl.kb import KnowledgeBase\n",
"\n",
"OME_HOST = os.getenv('OME_HOST', 'localhost')\n",
"OME_USER = os.getenv('OME_USER', 'test')\n",
"OME_PASSWD = os.getenv('OME_PASSWD', 'test')\n",
"CHECK_OME_VERSION = os.getenv('CHECK_OME_VERSION', \"True\") == \"True\"\n",
"\n",
"BaseProxy = KnowledgeBase(driver='omero')\n",
"\n",
"class Proxy(BaseProxy):\n",
" def get_objects_dict(self, klass):\n",
" return dict((o.label, o) for o in super(Proxy, self).get_objects(klass))\n",
"\n",
"kb = Proxy(OME_HOST, OME_USER, OME_PASSWD, check_ome_version=CHECK_OME_VERSION)\n",
"kb.connect()\n",
"kb.start_keep_alive()\n",
"\n",
"def cleanup():\n",
" print \"# disconnecting the kb\"\n",
" kb.disconnect()\n",
"\n",
"def make_barcode():\n",
" return uuid.uuid4().hex\n",
" \n",
" \n",
"sys.exitfunc = cleanup\n",
"\n",
"print\n",
"print \"### KB ENV PRELOADED ###\"\n",
"print \"# connected to %s\" % OME_HOST\n",
"print \"# knowledge base: kb\"\n",
"print \"# extra method: kb.get_objects_dict\"\n",
"print \"########################\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"### KB ENV PRELOADED ###\n",
"# connected to 192.168.56.101\n",
"# knowledge base: kb\n",
"# extra method: kb.get_objects_dict\n",
"########################\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the following we will need a container to aliquot DNA from. For the time being, we define a function to build a TiterPlate with 24 wells filled with DNA."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def create_titer_plate(label, action):\n",
" conf = {'rows': 6, 'columns': 2, 'label': label,\n",
" 'barcode': make_barcode(),\n",
" 'status': kb.ContainerStatus.READY,\n",
" 'action': action,\n",
" }\n",
" titer_plate = kb.factory.create(kb.TiterPlate, conf).save()\n",
" for row in range(titer_plate.rows):\n",
" for column in range(titer_plate.columns):\n",
" conf = {'row': row + 1, 'column': column + 1,\n",
" 'container': titer_plate,\n",
" 'initialVolume': 1.0,\n",
" 'currentVolume': 1.0,\n",
" 'content': kb.VesselContent.DNA,\n",
" 'status': kb.VesselStatus.CONTENTUSABLE,\n",
" 'action': action}\n",
" kb.factory.create(kb.PlateWell, conf).save()\n",
" return titer_plate"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with kb.context.sandbox():\n",
" action = kb.create_an_action()\n",
" titer_plate = create_titer_plate('a-foo-plate', action)\n",
" for well in kb.get_wells_by_plate(titer_plate):\n",
" print well.label, well.slot"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"A1 1\n",
"A2 2\n",
"B1 3\n",
"B2 4\n",
"C1 5\n",
"C2 6\n",
"D1 7\n",
"D2 8\n",
"E1 9\n",
"E2 10\n",
"F1 11\n",
"F2 12\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Actions that create a chip"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Basic actions that bring a chip into Omero.biobank:\n",
"\n",
" * import with an ActionCategory.IMPORT, no target, to create a chip with VesselStatus.UNUSED, basically something like an import in the stockroom;\n",
" * import with an ActionCategory.ALIQUOTING from a target of type derived from Vessel, typically Tube or PlateWell, to create a chip with, if everything is ok, a VesselStatus.CONTENTUSABLE.\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def create_action_on_well(titer_plate, row, column):\n",
" return kb.create_an_action(target=kb.get_well_on_plate(titer_plate, row, column),\n",
" acat=kb.ActionCategory.ALIQUOTING)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Physical chips"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Affymetrix chips"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are currently handling two major micro array technologies, Affymetrix and Illumina.\n",
"\n",
"The affymetrix chips we have are individually packaged in a stand-alone object with a barcode. We model it using an object `AffymetrixArray`, that is understood as derived from `Tube` but with assigned volume. The instantiation of a `AffymetrixArray` requires the assignement of a value from the enum `AffymetrixAssayType`, besides all other field that are coming from being a `Tube`, that is a `label`, a `barcode`, and a `content`. FIXME: `content` should be DNA, `status`."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def create_affy_chip(label, action):\n",
" conf={'label': label,\n",
" 'assayType': kb.AffymetrixAssayType.GENOMEWIDESNP_6,\n",
" 'barcode': make_barcode(),\n",
" 'content': kb.VesselContent.DNA,\n",
" 'status': kb.VesselStatus.UNUSED, # FIXME, this is confusing\n",
" 'action': action}\n",
" return kb.factory.create(kb.AffymetrixArray, conf).save()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with kb.context.sandbox():\n",
" action = kb.create_an_action()\n",
" affy_chip = create_affy_chip('an-affy-chip', action)\n",
" print affy_chip.label, affy_chip.barcode"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"an-affy-chip ae1a03912a5c4a30a5c5beab4994f2f0\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Illumina micro arrays"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Illumina chips are more complex, since they are packages, typically, 12 on a glass support (slide). The slide is modelled with a class `IlluminaArrayOfArrays`. A `IlluminaArrayOfArray` is a complex object with a `type`, `arrayClass`, and `assayType` choosen, respectively, between the ones listed by the enum `IlluminaArrayOfArraysType`, `IlluminaArrayOfArraysClass` and `IlluminaArrayOfArraysAssayType`.\n",
"The model `IlluminaArrayOfArrays` is derived from `TiterPlate`."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def create_illumina_array_of_array(label, action):\n",
" conf={'status': kb.ContainerStatus.READY,\n",
" 'assayType': kb.IlluminaArrayOfArraysAssayType.Infinium_HD,\n",
" 'rows': 6, 'columns': 2,\n",
" 'arrayClass': kb.IlluminaArrayOfArraysClass.Slide,\n",
" 'barcode': make_barcode(), 'label': label,\n",
" 'type': kb.IlluminaArrayOfArraysType.UNKNOWN,\n",
" 'action': action}\n",
" return kb.factory.create(kb.IlluminaArrayOfArrays, conf).save()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with kb.context.sandbox():\n",
" action = kb.create_an_action()\n",
" array = create_illumina_array_of_array('a label', action)\n",
" print array.label, array.barcode"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"a label 3d5b8efbc9e643aab881330db7bf586c\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The actual Illumina chips are describe by the model `IlluminaBeadChipArray`, derived from `PlateWell`.\n",
"Apart from all the standard `PlateWell` properties, an `IlluminaBeadChipArray` instance has the property `assayType` chosen from the enum `IlluminaBeadChipAssayType`."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def create_bead_chip_array(array, assay_type, row, column, action):\n",
" conf = {\n",
" 'content': kb.VesselContent.DNA, 'status': kb.VesselStatus.CONTENTUSABLE,\n",
" 'container': array, 'assayType': assay_type,\n",
" 'row': row, 'column': column,'action': action,\n",
" }\n",
" return kb.factory.create(kb.IlluminaBeadChipArray, conf).save()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with kb.context.sandbox():\n",
" action = kb.create_an_action()\n",
" titer_plate = create_titer_plate('a-fake-plate-well', action)\n",
" print 'we have a titer_plate', titer_plate\n",
" array = create_illumina_array_of_array('a-fake-illumina-array', action)\n",
" print 'we have an array'\n",
" assay_type = kb.IlluminaBeadChipAssayType.HumanOmni5_4v1_B\n",
" for row in range(1, array.rows + 1):\n",
" for column in range(1, array.columns + 1):\n",
" action_on_well = create_action_on_well(titer_plate, row, column)\n",
" create_bead_chip_array(array, assay_type, row, column, action_on_well)\n",
" print 'created well ({}, {})'.format(row, column)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"we have a titer_plate <bl.vl.kb.drivers.omero.objects_collections.TiterPlate object at 0x4857990>\n",
"we have an array"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"created well (1, 1)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"created well (1, 2)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"created well (2, 1)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"created well (2, 2)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"created well (3, 1)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"created well (3, 2)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"created well (4, 1)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"created well (4, 2)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"created well (5, 1)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"created well (5, 2)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"created well (6, 1)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"created well (6, 2)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 10
}
],
"metadata": {}
}
]
} | gpl-2.0 |
bird-house/twitcher | notebooks/twitcher-client.ipynb | 2 | 5667 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using Twitcher Client"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# disable ssl warnings\n",
"import urllib3\n",
"urllib3.disable_warnings()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from twitcher.client import TwitcherService\n",
"base_url = 'http://localhost:8000'\n",
"twitcher = TwitcherService(base_url, username='demo', password='demo', verify=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Register Client APP"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"client = twitcher.add_client_app(name='test1', redirect_uri='http://demo/test1')\n",
"client"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Register WPS service in Twitcher OWS Proxy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"service = twitcher.register_service(name=\"emu_demo\", url=\"http://localhost:5000/wps\")\n",
"service"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## WPS GetCapabilities request"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"\n",
"url = \"{}/ows/proxy/emu_demo?service=WPS&request=GetCapabilities\".format(base_url)\n",
"url"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"resp = requests.get(url, verify=False)\n",
"resp.ok"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## WPS DescribeProcess request"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"url = \"{}/ows/proxy/emu_demo?service=WPS&version=1.0.0&request=DescribeProcess&identifier=hello\".format(base_url)\n",
"url"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"resp = requests.get(url, verify=False)\n",
"resp.ok"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## WPS Execute request ... restricted access"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"url = \"{}/ows/proxy/emu_demo?service=WPS&version=1.0.0&request=Execute&identifier=hello&DataInputs=name=Tux\".format(base_url)\n",
"url"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"resp = requests.get(url, verify=False)\n",
"resp.ok"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Access forbidden without access token."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'AccessForbidden' in resp.text"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get Access token for compute"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"compute_token = twitcher.fetch_token(client_id=client['client_id'], client_secret=client['client_secret'], scope='compute')\n",
"compute_token"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Add access token to parameters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"url = \"{}/ows/proxy/emu_demo?service=WPS&version=1.0.0&request=Execute&identifier=hello&DataInputs=name=Tux&access_token={}\".format(\n",
" base_url, compute_token['access_token'])\n",
"url"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"resp = requests.get(url, verify=False)\n",
"resp.ok"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'Hello Tux' in resp.text"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"... or add access token to headers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"headers = {'Authorization': 'Bearer {}'.format(compute_token['access_token'])}\n",
"headers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"resp = requests.get(\n",
" \"{}/ows/proxy/emu_demo?service=WPS&version=1.0.0&request=Execute&identifier=hello&DataInputs=name=Tux\".format(base_url),\n",
" headers=headers,\n",
" verify=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'Hello Tux' in resp.text"
]
}
],
"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": 4
}
| apache-2.0 |
BrownDwarf/ApJdataFrames | notebooks/Hernandez2014.ipynb | 1 | 11741 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`ApJdataFrames` Hernandez2014\n",
"---\n",
"`Title`: A SPECTROSCOPIC CENSUS IN YOUNG STELLAR REGIONS: THE σ ORIONIS CLUSTER \n",
"`Authors`: Jesus Hernandez, Nuria Calvet, Alice Perez, Cesar Briceno, Lorenzo Olguin, Maria E Contreras, Lee Hartmann, Lori E Allen, Catherine Espaillat, and Ramírez Hernan \n",
"\n",
"Data is from this paper: \n",
"http://iopscience.iop.org/0004-637X/794/1/36/article"
]
},
{
"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\n",
"\n",
"import seaborn as sns\n",
"sns.set_context(\"notebook\", font_scale=1.5)\n",
"\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from astropy.io import ascii"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Table 4 - Low Resolution Analysis"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading http://iopscience.iop.org/0004-637X/794/1/36/suppdata/apj500669t4_mrt.txt [Done]\n"
]
}
],
"source": [
"tbl4 = ascii.read(\"http://iopscience.iop.org/0004-637X/794/1/36/suppdata/apj500669t4_mrt.txt\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<Table masked=True length=4>\n",
"<table id=\"table4614934288\">\n",
"<thead><tr><th>Name</th><th>f_Name</th><th>2MASS</th><th>Inst</th><th>f_Inst</th><th>SpType</th><th>e_SpType</th><th>EWLiI</th><th>f_EWLiI</th><th>EWHa</th><th>f_EWHa</th><th>EWNaI</th><th>e_EWNaI</th><th>f_EWNaI</th><th>AVKH95</th><th>AVPM13</th></tr></thead>\n",
"<thead><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>0.1 nm</th><th></th><th>0.1 nm</th><th></th><th>0.1 nm</th><th>0.1 nm</th><th></th><th>mag</th><th>mag</th></tr></thead>\n",
"<thead><tr><th>string48</th><th>string8</th><th>string136</th><th>string48</th><th>string8</th><th>string32</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>string48</th><th>float64</th><th>float64</th><th>string8</th><th>float64</th><th>float64</th></tr></thead>\n",
"<tr><td>--</td><td>--</td><td>05384476-0236001</td><td>F</td><td>--</td><td>B0.0</td><td>1.5</td><td>0.0</td><td>0</td><td>3.1</td><td>E_abs_</td><td>--</td><td>--</td><td>--</td><td>0.0</td><td>0.0</td></tr>\n",
"<tr><td>SO724</td><td>--</td><td>05384719-0235405</td><td>S</td><td>--</td><td>B2.0</td><td>1.5</td><td>0.0</td><td>0</td><td>2.0</td><td>E_abs_</td><td>--</td><td>--</td><td>--</td><td>0.0</td><td>0.0</td></tr>\n",
"<tr><td>--</td><td>--</td><td>05384561-0235588</td><td>F</td><td>--</td><td>B2.0</td><td>1.5</td><td>0.0</td><td>0</td><td>4.3</td><td>E_abs_</td><td>--</td><td>--</td><td>--</td><td>0.1</td><td>0.0</td></tr>\n",
"<tr><td>--</td><td>--</td><td>05402018-0226082</td><td>F</td><td>--</td><td>B4.0</td><td>1.5</td><td>0.0</td><td>0</td><td>5.0</td><td>E_abs_</td><td>--</td><td>--</td><td>--</td><td>0.17</td><td>0.0</td></tr>\n",
"</table>"
],
"text/plain": [
"<Table masked=True length=4>\n",
" Name f_Name 2MASS Inst ... e_EWNaI f_EWNaI AVKH95 AVPM13\n",
" ... 0.1 nm mag mag \n",
"string48 string8 string136 string48 ... float64 string8 float64 float64\n",
"-------- ------- ---------------- -------- ... ------- ------- ------- -------\n",
" -- -- 05384476-0236001 F ... -- -- 0.0 0.0\n",
" SO724 -- 05384719-0235405 S ... -- -- 0.0 0.0\n",
" -- -- 05384561-0235588 F ... -- -- 0.1 0.0\n",
" -- -- 05402018-0226082 F ... -- -- 0.17 0.0"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tbl4[0:4]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 57 sources with Na I line detections out of 340 sources in the catalog\n"
]
}
],
"source": [
"Na_mask = ((tbl4[\"f_EWNaI\"] == \"Y\") | (tbl4[\"f_EWNaI\"] == \"N\"))\n",
"print \"There are {} sources with Na I line detections out of {} sources in the catalog\".format(Na_mask.sum(), len(tbl4))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tbl4_late = tbl4[['Name', '2MASS', 'SpType', 'e_SpType','EWHa', 'f_EWHa', 'EWNaI', 'e_EWNaI', 'f_EWNaI']][Na_mask]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Name 2MASS SpType e_SpType EWHa f_EWHa EWNaI e_EWNaI f_EWNaI\n",
" 0.1 nm 0.1 nm 0.1 nm \n",
"------ ----------------- ------ -------- ------ ------ ------ ------- -------\n",
" SO576 05383302-0239279 M2.0 1.5 0.0 -- 2.37 0.22 N\n",
" SO444 05381824-0248143 M3.0 0.5 -3.8 nAcr 0.83 0.08 Y\n",
" SO220 05375220-0233379 M3.0 0.5 -3.9 nAcr 0.92 0.08 Y\n",
"SO1219 05394799-0240320 M3.0 0.5 -2.8 nAcr 0.9 0.07 Y\n",
" SO804 05385543-0241297 M3.0 0.5 0.5 nAcr 1.96 0.09 N\n",
" SO374 05380994-0251377 M3.0 0.5 -22.3 Acr 1.04 0.09 Y\n",
" SO117 05373666-0234003 M3.0 0.5 -4.3 nAcr 1.06 0.1 Y\n",
" SO525 05382774-0243009 M3.0 1.0 -5.8 nAcr 1.35 0.09 Y\n",
" SO60 05372831-0224182 M3.0 1.0 -3.5 nAcr 1.37 0.1 Y\n",
" SO27 05372306-0232465 M3.0 1.0 -4.4 nAcr 1.47 0.11 Y\n",
" SO795 05385434-0240029 M3.0 1.0 0.0 -- 2.48 0.19 N\n",
" SO908 05390878-0231115 M3.0 1.0 -19.8 Acr 0.74 0.1 Y\n",
" SO520 05382750-0235041 M3.5 0.5 -28.4 Acr 0.52 0.07 Y\n",
" SO366 05380897-0220109 M3.5 0.5 -5.8 nAcr 1.18 0.11 Y\n",
" SO426 05381610-0238049 M3.5 0.5 -5.3 nAcr 0.69 0.11 Y\n",
" SO655 05383972-0240197 M3.5 0.5 2.7 nAcr 0.57 0.13 Y\n",
" SO865 05390357-0246269 M3.5 1.0 -50.2 Acr 0.94 0.09 Y\n",
" SO955 05391505-0218444 M3.5 1.0 -2.7 nAcr 1.21 0.11 Y\n",
"SO1071 05392883-0217513 M3.5 1.0 -14.4 Acr? 1.41 0.09 Y\n",
"SO1083 05393132-0248528 M3.5 1.0 -8.0 nAcr 1.33 0.13 Y\n",
" SO514 05382684-0238460 M3.5 1.0 0.0 -- 1.23 0.19 Y\n",
" SO562 05383141-0236338 M3.5 1.5 -77.7 Acr 0.52 0.1 Y\n",
"SO1053 05392650-0252152 M4.0 0.5 -3.6 nAcr 1.02 0.09 Y\n",
" SO967 05391582-0236507 M4.0 0.5 -5.5 nAcr 1.08 0.08 Y\n",
" SO484 05382332-0244142 M4.0 0.5 -4.8 nAcr 1.15 0.08 Y\n",
" SO940 05391346-0237391 M4.0 0.5 -0.5 nAcr 0.99 0.12 Y\n",
" SO397 05381319-0226088 M4.0 1.0 -38.5 Acr 1.21 0.1 Y\n",
" SO901 05390808-0228447 M4.0 1.0 2.7 nAcr 0.47 0.1 Y\n",
" SO999 05392023-0238258 M4.0 1.0 -1.0 nAcr 0.81 0.1 Y\n",
" SO490 05382358-0220475 M4.0 1.0 -138.7 Acr 0.99 0.1 Y\n",
" SO462 05382050-0234089 M4.0 1.0 -22.0 Acr 0.43 0.06 Y\n",
" SO723 05384718-0234368 M4.0 1.5 -77.8 Acr 0.61 0.13 Y\n",
" SO465 05382088-0246132 M4.0 4.0 7.6 nAcr 0.48 0.13 Y\n",
" SO740 05384828-0236409 M4.5 0.5 -5.2 nAcr 0.46 0.11 Y\n",
" SO300 05380107-0245379 M4.5 0.5 -92.6 Acr 0.65 0.1 Y\n",
" SO489 05382354-0241317 M4.5 0.5 -4.6 nAcr 1.13 0.1 Y\n",
" SO624 05383687-0236432 M4.5 0.5 -2.9 nAcr 0.36 0.09 Y\n",
" SO866 05390387-0220081 M4.5 1.0 -13.8 Acr? 0.86 0.09 Y\n",
" SO165 05374527-0228521 M4.5 1.0 -8.0 nAcr 1.04 0.11 Y\n",
" SO658 05384008-0250370 M4.5 1.0 -1.1 nAcr 0.57 0.11 Y\n",
" SO628 05383745-0250236 M4.5 1.5 -5.1 nAcr 1.16 0.09 Y\n",
" SO500 05382543-0242412 M4.5 2.0 -181.6 Acr 0.97 0.15 Y\n",
" SO247 05375486-0241092 M5.0 0.5 -24.1 Acr 0.6 0.1 Y\n",
" SO460 05382021-0238016 M5.0 0.5 -10.6 nAcr 1.44 0.14 Y\n",
" SO435 05381778-0240500 M5.0 0.5 -13.8 nAcr 1.41 0.1 Y\n",
" SO297 05380055-0245097 M5.0 0.5 -9.5 nAcr 0.74 0.07 Y\n",
"SO1207 05394661-0226313 M5.0 0.5 -9.9 nAcr 1.23 0.11 Y\n",
" SO714 05384597-0245231 M5.0 0.5 -0.3 nAcr 1.02 0.12 Y\n",
" SO933 05391232-0230064 M5.0 0.5 -5.5 nAcr 0.83 0.09 Y\n",
" SO432 05381718-0222256 M5.0 0.5 -13.1 nAcr 0.97 0.14 Y\n",
" SO566 J053832.13-023243 M5.0 1.0 -6.9 nAcr 0.4 0.08 Y\n",
" SO271 05375745-0238444 M5.0 1.0 -15.5 Acr? 1.34 0.18 Y\n",
" SO689 05384333-0232008 M5.0 2.0 -3.7 nAcr 0.96 0.12 Y\n",
" SO107 05373514-0226577 M5.5 0.5 -14.3 nAcr 0.78 0.12 Y\n",
" SO467 05382119-0254110 M5.5 0.5 -6.6 nAcr 0.84 0.12 Y\n",
" SO283 05375840-0241262 M5.5 0.5 -14.3 Acr? 0.99 0.13 Y\n",
" SO398 05381321-0224075 M5.5 1.0 -11.6 nAcr 1.03 0.14 Y\n"
]
}
],
"source": [
"tbl4_late.pprint(max_lines=100, )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Meh... not a lot of late type sources... M5.5 is the latest. Oh well."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Script finished.*"
]
}
],
"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
}
| mit |
alephcero/adsProject | olds/dataMunging.ipynb | 1 | 36424 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"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>CODUSU</th>\n",
" <th>NRO_HOGAR</th>\n",
" <th>COMPONENTE</th>\n",
" <th>AGLOMERADO</th>\n",
" <th>PONDERA</th>\n",
" <th>familyRelation</th>\n",
" <th>female</th>\n",
" <th>age</th>\n",
" <th>schooled</th>\n",
" <th>schoolYear</th>\n",
" <th>finishedYear</th>\n",
" <th>lastYear</th>\n",
" <th>activity</th>\n",
" <th>educLevel</th>\n",
" <th>empCond</th>\n",
" <th>unempCond</th>\n",
" <th>ITF</th>\n",
" <th>IPCF</th>\n",
" <th>P47T</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>302468</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>32</td>\n",
" <td>1287</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>20</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4000</td>\n",
" <td>2000.0</td>\n",
" <td>2000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>302468</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>32</td>\n",
" <td>1287</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>20</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4000</td>\n",
" <td>2000.0</td>\n",
" <td>2000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>307861</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>32</td>\n",
" <td>1674</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>42</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>5800</td>\n",
" <td>1450.0</td>\n",
" <td>3000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>307861</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>32</td>\n",
" <td>1674</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>44</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>5800</td>\n",
" <td>1450.0</td>\n",
" <td>2800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>307861</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>32</td>\n",
" <td>1674</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>5800</td>\n",
" <td>1450.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CODUSU NRO_HOGAR COMPONENTE AGLOMERADO PONDERA familyRelation female \\\n",
"0 302468 1 1 32 1287 1 2 \n",
"1 302468 1 2 32 1287 10 2 \n",
"2 307861 1 1 32 1674 1 1 \n",
"3 307861 1 2 32 1674 2 2 \n",
"4 307861 1 3 32 1674 3 1 \n",
"\n",
" age schooled schoolYear finishedYear lastYear activity educLevel \\\n",
"0 20 1 7 2 1.0 3 5 \n",
"1 20 1 6 2 1.0 3 5 \n",
"2 42 2 2 1 NaN 1 2 \n",
"3 44 2 7 1 NaN 1 6 \n",
"4 13 1 4 2 0.0 3 3 \n",
"\n",
" empCond unempCond ITF IPCF P47T \n",
"0 0 3 4000 2000.0 2000 \n",
"1 0 3 4000 2000.0 2000 \n",
"2 3 0 5800 1450.0 3000 \n",
"3 3 0 5800 1450.0 2800 \n",
"4 0 3 5800 1450.0 0 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ind = pd.read_csv('data/cleanData.csv')\n",
"ind.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(8360, 19)\n"
]
}
],
"source": [
"print ind.shape\n",
"#CHEQUEAR\n",
"ind.drop_duplicates(subset = ['CODUSU',\n",
" 'NRO_HOGAR',\n",
" 'AGLOMERADO',\n",
" 'PONDERA',\n",
" 'familyRelation',\n",
" 'female',\n",
" 'age',\n",
" 'schooled',\n",
" 'schoolYear',\n",
" 'finishedYear',\n",
" 'lastYear',\n",
" 'activity',\n",
" 'educLevel',\n",
" 'empCond',\n",
" 'unempCond',\n",
" 'ITF',\n",
" 'IPCF',\n",
" 'P47T'], inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(8347, 19)\n"
]
}
],
"source": [
"print ind.shape"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2 4447\n",
"1 3913\n",
"Name: female, dtype: int64\n"
]
}
],
"source": [
"print ind.female.value_counts()\n"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# create a boolean variable for females\n",
"#1 = male\n",
"#2 = female\n",
"ind.female = ind.female == 2\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pd.crosstab(ind.schooled)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2 5506\n",
"1 2376\n",
"0 248\n",
"3 210\n",
"9 7\n",
"Name: schooled, dtype: int64\n",
"2.0 5506\n",
"1.0 2376\n",
"3.0 210\n",
"Name: schooled, dtype: int64\n"
]
}
],
"source": [
"#CH10 - ¿Asiste o asistió a algún establecimiento\n",
"#educativo (colegio, escuela, universidad)? \n",
"#1 = Si, asiste\n",
"#2 = No asiste, pero asistió\n",
"#3 = Nunca asistió \n",
"print ind.schooled.value_counts()\n",
"ind.schooled.replace(to_replace=[0,9], value=[np.nan,np.nan] , inplace=True, axis=None) \n",
"print ind.schooled.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2 4043\n",
"1 3817\n",
"0 465\n",
"9 22\n",
"Name: finishedYear, dtype: int64\n",
"2.0 4043\n",
"1.0 3817\n",
"Name: finishedYear, dtype: int64\n"
]
}
],
"source": [
"#CH13 = finishedYear\n",
"#¿Finalizó ese nivel?\n",
"#1 = Sí\n",
"#2 = No\n",
"#9 = Ns./Nr. \n",
"print ind.finishedYear.value_counts()\n",
"ind.finishedYear.replace(to_replace=[0,9], value=[np.nan,np.nan] , inplace=True, axis=None) \n",
"print ind.finishedYear.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 3759\n",
"3 3072\n",
"4 1175\n",
"2 337\n",
"0 4\n",
"Name: activity, dtype: int64\n",
"1.0 3759\n",
"3.0 3072\n",
"4.0 1175\n",
"2.0 337\n",
"Name: activity, dtype: int64\n"
]
}
],
"source": [
"#ESTADO N(1) CONDICIÓN DE ACTIVIDAD\n",
"#0 = Entrevista individual no realizada\n",
"#1 = Ocupado\n",
"#2 = Desocupado\n",
"#3 = Inactivo\n",
"#4 = Menor de 10 años\n",
"print ind.activity.value_counts()\n",
"ind.activity.replace(to_replace=0, value=np.nan , inplace=True, axis=None) \n",
"print ind.activity.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.0 1667\n",
"1.0 1044\n",
"5.0 706\n",
"4.0 682\n",
"7.0 106\n",
"6.0 30\n",
"2.0 21\n",
"Name: unempCond, dtype: int64\n",
"3.0 1667\n",
"1.0 1044\n",
"5.0 706\n",
"4.0 682\n",
"7.0 106\n",
"6.0 30\n",
"2.0 21\n",
"Name: unempCond, dtype: int64\n"
]
}
],
"source": [
"#N(1)CAT_INAC = unempCond\n",
"#CATEGORÍA DE INACTIVIDAD\n",
"#1 = Jubilado / Pensionado\n",
"#2 = Rentista\n",
"#3 = Estudiante\n",
"#4 = Ama de casa\n",
"#5 = Menor de 6 años\n",
"#6 = Discapacitado\n",
"#7 = Otros\n",
"\n",
"print ind.unempCond.value_counts()\n",
"ind.unempCond.replace(to_replace=0, value=np.nan , inplace=True, axis=None) \n",
"print ind.unempCond.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 4333\n",
"3 3116\n",
"2 700\n",
"1 176\n",
"4 35\n",
"Name: empCond, dtype: int64\n",
"3.0 3116\n",
"2.0 700\n",
"1.0 176\n",
"4.0 35\n",
"Name: empCond, dtype: int64\n"
]
}
],
"source": [
"#CAT OCUP\n",
"#CATEGORÍA OCUPACIONAL (Para ocupados y\n",
"#desocupados con ocupación anterior)\n",
"#1 = Patrón\n",
"#2 = Cuenta propia\n",
"#3 = Obrero o empleado\n",
"#4 = Trabajador familiar sin remuneración\n",
"#9 = Ns./Nr.\n",
"\n",
"print ind.empCond.value_counts()\n",
"ind.empCond.replace(to_replace=0, value=np.nan , inplace=True, axis=None) \n",
"print ind.empCond.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3 1633\n",
"4 1415\n",
"2 1381\n",
"1 1321\n",
"6 1000\n",
"5 830\n",
"7 767\n",
"Name: educLevel, dtype: int64\n",
"3 1633\n",
"4 1415\n",
"2 1381\n",
"1 1321\n",
"6 1000\n",
"5 830\n",
"0 767\n",
"Name: educLevel, dtype: int64\n"
]
}
],
"source": [
"#NIVEL_ED N(1) NIVEL EDUCATIVO\n",
"#1 = Primaria Incompleta (incluye educación especial)\n",
"#2 = Primaria Completa\n",
"#3 = Secundaria Incompleta\n",
"#4 = Secundaria Completa\n",
"#5 = Superior Universitaria Incompleta\n",
"#6 = Superior Universitaria Completa\n",
"#7 = Sin instrucción\n",
"#9 = Ns./ Nr.\n",
"\n",
"#we replace 7 (no instruction, with 0 so the variable has an increasing order)\n",
"print ind.educLevel.value_counts()\n",
"ind.educLevel.replace(to_replace = 7, value = 0, inplace = True, axis = None)\n",
"print ind.educLevel.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3.110223642172524"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ind.lastYear[(ind.educLevel == 1) & (ind.lastYear < 98)].mean()"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/pipe/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" app.launch_new_instance()\n",
"/home/pipe/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"/home/pipe/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
}
],
"source": [
"ind['primary'] = 0\n",
"#ind.Esc1[ind.educLevel == 0] = 0\n",
"ind.primary[ind.educLevel > 1] = 7\n",
"ind.primary[(ind.educLevel == 1) & (ind.lastYear > 7)] = int(ind.lastYear[(ind.educLevel == 1) & (ind.lastYear < 98)].mean())\n",
"ind.primary[(ind.educLevel == 1) & (ind.lastYear <= 7)] = ind.lastYear[(ind.educLevel == 1) & (ind.lastYear <= 7)]\n",
"\n",
"\n",
"# if educLevel > 1, esc1 = 7\n",
"# if educLevel = 0, esc1 = 0\n",
"# if educLevel ==1 and\n",
"#Esc1 = lastYear, si dicen 98 o 99, mean \n",
"#there are 8 cases with spetial educacion, and two that never finished primary but don't remember their last year. Those are 0\n"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <tr>\n",
" <th>primary</th>\n",
" <th></th>\n",
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" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0</td>\n",
" <td>137</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1381</td>\n",
" <td>1633</td>\n",
" <td>1415</td>\n",
" <td>830</td>\n",
" <td>1000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"educLevel 0 1 2 3 4 5 6\n",
"primary \n",
"0 767 150 0 0 0 0 0\n",
"1 0 117 0 0 0 0 0\n",
"2 0 232 0 0 0 0 0\n",
"3 0 276 0 0 0 0 0\n",
"4 0 213 0 0 0 0 0\n",
"5 0 196 0 0 0 0 0\n",
"6 0 137 0 0 0 0 0\n",
"7 0 0 1381 1633 1415 830 1000"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(ind.primary,ind.educLevel)"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <th>3</th>\n",
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" <th>6</th>\n",
" </tr>\n",
" <tr>\n",
" <th>lastYear</th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
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" <tr>\n",
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" <td>0</td>\n",
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" <tr>\n",
" <th>5.0</th>\n",
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" <td>37</td>\n",
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" <tr>\n",
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" <td>65</td>\n",
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" <tr>\n",
" <th>8.0</th>\n",
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" <td>116</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98.0</th>\n",
" <td>0</td>\n",
" <td>33</td>\n",
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" <td>0</td>\n",
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" <tr>\n",
" <th>99.0</th>\n",
" <td>3</td>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>31</td>\n",
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"</div>"
],
"text/plain": [
"educLevel 0 1 2 3 5 6\n",
"lastYear \n",
"0.0 286 140 0 195 180 8\n",
"1.0 5 117 0 300 148 4\n",
"2.0 2 232 0 410 213 1\n",
"3.0 2 214 0 304 135 2\n",
"4.0 1 213 0 143 95 0\n",
"5.0 0 196 0 13 37 0\n",
"6.0 0 137 0 0 4 0\n",
"7.0 0 0 10 65 0 0\n",
"8.0 0 0 0 116 1 0\n",
"98.0 0 33 0 0 0 0\n",
"99.0 3 29 1 31 17 0"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(ind.lastYear,ind.educLevel)"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/pipe/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"/home/pipe/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:7: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"/home/pipe/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:10: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"/home/pipe/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:13: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"/home/pipe/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:17: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"/home/pipe/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:18: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"/home/pipe/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:19: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"/home/pipe/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:22: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"/home/pipe/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:25: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
}
],
"source": [
"ind['secondary'] = 999\n",
"\n",
"#dont know their last level\n",
"ind.secondary[ind.schoolYear == 99] = 0\n",
"\n",
"#dont know if they went to school\n",
"ind.secondary[ind.schooled == 0] = 0\n",
"\n",
"#bellow uncomplete secondary\n",
"ind.secondary[ind.educLevel < 3] = 0\n",
"\n",
"#above uncomplete secondary\n",
"ind.secondary[ind.educLevel > 3] = 5\n",
"\n",
"#special cases (another school system with 7,8,9 grades)\n",
"\n",
"ind.secondary[(ind.educLevel == 3) & (ind.lastYear == 7)] = 1\n",
"ind.secondary[(ind.educLevel == 3) & (ind.lastYear == 8)] = 2\n",
"ind.secondary[(ind.educLevel == 3) & (ind.lastYear == 9)] = 3\n",
"\n",
"#error, finished 9 grade EGB system\n",
"ind.secondary[(ind.schoolYear == 3) & (ind.finishedYear == 1)] = 3\n",
"\n",
"#they get their last aproved year\n",
"ind.secondary[(ind.educLevel == 3) & (ind.lastYear <= 5)] = ind.lastYear[(ind.educLevel == 3) & (ind.lastYear <= 5)]\n",
"\n",
"#dont know their last aproved year, so they get the mean\n",
"ind.secondary[(ind.educLevel == 3) & (ind.lastYear > 9)] = int(ind.lastYear[(ind.educLevel == 3) & (ind.lastYear < 98)].mean())\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>educLevel</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",
" </tr>\n",
" <tr>\n",
" <th>secondary</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>0</th>\n",
" <td>767</td>\n",
" <td>1321</td>\n",
" <td>1381</td>\n",
" <td>198</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>365</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>557</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>354</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>143</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>1415</td>\n",
" <td>830</td>\n",
" <td>1000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"educLevel 0 1 2 3 4 5 6\n",
"secondary \n",
"0 767 1321 1381 198 0 0 0\n",
"1 0 0 0 365 0 0 0\n",
"2 0 0 0 557 0 0 0\n",
"3 0 0 0 354 0 0 0\n",
"4 0 0 0 143 0 0 0\n",
"5 0 0 0 13 1415 830 1000\n",
"999 0 0 0 3 0 0 0"
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(ind.secondary,ind.educLevel)"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4 2\n",
"2 1\n",
"Name: schoolYear, dtype: int64"
]
},
"execution_count": 147,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#SEGUIR DESPUES ACA\n",
"ind.schoolYear[ind.secondary == 999].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['CODUSU',\n",
" 'NRO_HOGAR',\n",
" 'COMPONENTE',\n",
" 'AGLOMERADO',\n",
" 'PONDERA',\n",
" 'familyRelation',\n",
" 'female',\n",
" 'age',\n",
" 'schooled',\n",
" 'schoolYear',\n",
" 'finishedYear',\n",
" 'lastYear',\n",
" 'activity',\n",
" 'educLevel',\n",
" 'empCond',\n",
" 'unempCond',\n",
" 'ITF',\n",
" 'IPCF',\n",
" 'P47T',\n",
" 'primary',\n",
" 'secondary']"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(ind.columns)"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(8360, 21)\n",
"(8347, 21)\n"
]
}
],
"source": [
"print ind.shape\n",
"print ind.drop_duplicates(subset = ['CODUSU',\n",
" 'NRO_HOGAR',\n",
" 'AGLOMERADO',\n",
" 'PONDERA',\n",
" 'familyRelation',\n",
" 'female',\n",
" 'age',\n",
" 'schooled',\n",
" 'schoolYear',\n",
" 'finishedYear',\n",
" 'lastYear',\n",
" 'activity',\n",
" 'educLevel',\n",
" 'empCond',\n",
" 'unempCond',\n",
" 'ITF',\n",
" 'IPCF',\n",
" 'P47T',\n",
" 'primary',\n",
" 'secondary']\n",
" ).shape\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# anos de escolaridad por nivel\n",
"# funcion en R https://github.com/alephcero/incomeMapBuenosAires/blob/master/src/schoolYears.R\n",
"\n",
"\n",
"# ver la curva de ingresos segun edad y la curva de ingresos segun anos de escolaridad, partirla en 3 o usar x y x2\n",
"#construir igual anos de escolaridad\n",
"\n",
"\n",
"# variables dummy para cada grupo de edad \n",
"\n",
"#r1. Esc_1 + r2. Esc_2 + r3. Esc_3 + v_14a24 GBA + v_25a34GBA + m_14a24 GBA + m_25a34GBA + m_35ymás GBA + \n",
"\n",
"\n",
"\n",
"#http://stackoverflow.com/questions/26777832/replicating-rows-in-a-pandas-data-frame-by-a-column-value/26778637#26778637\n",
"ind = indNoW.loc[np.repeat(indNoW.index.values,indNoW.PONDERA)]\n",
"ind.shape"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"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
}
| gpl-3.0 |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/sdk/sdk_automl_image_object_detection_online.ipynb | 1 | 35843 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "copyright"
},
"outputs": [],
"source": [
"# Copyright 2021 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": "title"
},
"source": [
"# Vertex SDK: AutoML training image object detection model for online prediction\n",
"\n",
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/tree/master/notebooks/official/automl/sdk_automl_image_object_detection_online.ipynb\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
" </a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/master/notebooks/official/automl/sdk_automl_image_object_detection_online.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",
" <td>\n",
" <a href=\"https://console.cloud.google.com/ai/platform/notebooks/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/master/notebooks/official/automl/sdk_automl_image_object_detection_online.ipynb\">\n",
" Open in Google Cloud Notebooks\n",
" </a>\n",
" </td>\n",
"</table>\n",
"<br/><br/><br/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "overview:automl"
},
"source": [
"## Overview\n",
"\n",
"\n",
"This tutorial demonstrates how to use the Vertex SDK to create image object detection models and do online prediction using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dataset:salads,iod"
},
"source": [
"### Dataset\n",
"\n",
"The dataset used for this tutorial is the Salads category of the [OpenImages dataset](https://www.tensorflow.org/datasets/catalog/open_images_v4) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). This dataset does not require any feature engineering. The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the bounding box locations and corresponding type of salad items in an image from a class of five items: salad, seafood, tomato, baked goods, or cheese."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "objective:automl,training,online_prediction"
},
"source": [
"### Objective\n",
"\n",
"In this tutorial, you create an AutoML image object detection model and deploy for online prediction from a Python script using the Vertex SDK. You can alternatively create and deploy models using the `gcloud` command-line tool or online using the Cloud Console.\n",
"\n",
"The steps performed include:\n",
"\n",
"- Create a Vertex `Dataset` resource.\n",
"- Train the model.\n",
"- View the model evaluation.\n",
"- Deploy the `Model` resource to a serving `Endpoint` resource.\n",
"- Make a prediction.\n",
"- Undeploy the `Model`."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "costs"
},
"source": [
"### 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": "setup_local"
},
"source": [
"### Set up your local development environment\n",
"\n",
"If you are using Colab or Google Cloud Notebooks, your environment already meets all the requirements to run this notebook. You can skip this step.\n",
"\n",
"Otherwise, make sure your environment meets this notebook's requirements. You need the following:\n",
"\n",
"- The Cloud Storage SDK\n",
"- Git\n",
"- Python 3\n",
"- virtualenv\n",
"- Jupyter notebook running in a virtual environment with Python 3\n",
"\n",
"The Cloud Storage guide to [Setting up a Python development environment](https://cloud.google.com/python/setup) and the [Jupyter installation guide](https://jupyter.org/install) provide detailed instructions for meeting these requirements. The following steps provide a condensed set of instructions:\n",
"\n",
"1. [Install and initialize the SDK](https://cloud.google.com/sdk/docs/).\n",
"\n",
"2. [Install Python 3](https://cloud.google.com/python/setup#installing_python).\n",
"\n",
"3. [Install virtualenv](https://cloud.google.com/python/setup#installing_and_using_virtualenv) and create a virtual environment that uses Python 3. Activate the virtual environment.\n",
"\n",
"4. To install Jupyter, run `pip3 install jupyter` on the command-line in a terminal shell.\n",
"\n",
"5. To launch Jupyter, run `jupyter notebook` on the command-line in a terminal shell.\n",
"\n",
"6. Open this notebook in the Jupyter Notebook Dashboard.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "install_aip:mbsdk"
},
"source": [
"## Installation\n",
"\n",
"Install the latest version of Vertex SDK for Python."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "install_aip:mbsdk"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"# Google Cloud Notebook\n",
"if os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n",
" USER_FLAG = \"--user\"\n",
"else:\n",
" USER_FLAG = \"\"\n",
"\n",
"! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "install_storage"
},
"source": [
"Install the latest GA version of *google-cloud-storage* library as well."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "install_storage"
},
"outputs": [],
"source": [
"! pip3 install -U google-cloud-storage $USER_FLAG"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "install_tensorflow"
},
"outputs": [],
"source": [
"if os.environ[\"IS_TESTING\"]:\n",
" ! pip3 install --upgrade tensorflow $USER_FLAG"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "restart"
},
"source": [
"### Restart the kernel\n",
"\n",
"Once you've installed the additional packages, you need to restart the notebook kernel so it can find the packages."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "restart"
},
"outputs": [],
"source": [
"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": "before_you_begin:nogpu"
},
"source": [
"## Before you begin\n",
"\n",
"### GPU runtime\n",
"\n",
"This tutorial does not require a GPU runtime.\n",
"\n",
"### 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). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
"\n",
"2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n",
"\n",
"3. [Enable the following APIs: Vertex AI APIs, Compute Engine APIs, and Cloud Storage.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component,storage-component.googleapis.com)\n",
"\n",
"4. If you are running this notebook locally, you will need to install the [Cloud SDK]((https://cloud.google.com/sdk)).\n",
"\n",
"5. 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 `$`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "set_project_id"
},
"outputs": [],
"source": [
"PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "autoset_project_id"
},
"outputs": [],
"source": [
"if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n",
" # Get your GCP project id from gcloud\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": "code",
"execution_count": null,
"metadata": {
"id": "set_gcloud_project_id"
},
"outputs": [],
"source": [
"! gcloud config set project $PROJECT_ID"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "region"
},
"source": [
"#### Region\n",
"\n",
"You can also change the `REGION` variable, which is used for operations\n",
"throughout the rest of this notebook. Below are regions supported for Vertex AI. We recommend that you choose the region closest to you.\n",
"\n",
"- Americas: `us-central1`\n",
"- Europe: `europe-west4`\n",
"- Asia Pacific: `asia-east1`\n",
"\n",
"You may not use a multi-regional bucket for training with Vertex AI. Not all regions provide support for all Vertex AI services.\n",
"\n",
"Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "region"
},
"outputs": [],
"source": [
"REGION = \"us-central1\" # @param {type: \"string\"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "timestamp"
},
"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 the timestamp onto the name of resources you create in this tutorial."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "timestamp"
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"\n",
"TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gcp_authenticate"
},
"source": [
"### Authenticate your Google Cloud account\n",
"\n",
"**If you are using Google Cloud Notebooks**, your environment is already authenticated. Skip this step.\n",
"\n",
"**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
"\n",
"**Otherwise**, follow these steps:\n",
"\n",
"In the Cloud Console, go to the [Create service account key](https://console.cloud.google.com/apis/credentials/serviceaccountkey) page.\n",
"\n",
"**Click Create service account**.\n",
"\n",
"In the **Service account name** field, enter a name, and click **Create**.\n",
"\n",
"In the **Grant this service account access to project** section, click the Role drop-down list. Type \"Vertex\" into the filter box, and select **Vertex Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
"\n",
"Click Create. A JSON file that contains your key downloads to your local environment.\n",
"\n",
"Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gcp_authenticate"
},
"outputs": [],
"source": [
"# 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",
"import os\n",
"import sys\n",
"\n",
"# If on Google Cloud Notebook, then don't execute this code\n",
"if not os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\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, replace the string below with the\n",
" # path to your service account key and run this cell to authenticate your GCP\n",
" # account.\n",
" elif not os.getenv(\"IS_TESTING\"):\n",
" %env GOOGLE_APPLICATION_CREDENTIALS ''"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bucket:mbsdk"
},
"source": [
"### Create a Cloud Storage bucket\n",
"\n",
"**The following steps are required, regardless of your notebook environment.**\n",
"\n",
"When you initialize the Vertex SDK for Python, you specify a Cloud Storage staging bucket. The staging bucket is where all the data associated with your dataset and model resources are retained across sessions.\n",
"\n",
"Set the name of your Cloud Storage bucket below. Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bucket"
},
"outputs": [],
"source": [
"BUCKET_NAME = \"gs://[your-bucket-name]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "autoset_bucket"
},
"outputs": [],
"source": [
"if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"gs://[your-bucket-name]\":\n",
" BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_bucket"
},
"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": "create_bucket"
},
"outputs": [],
"source": [
"! gsutil mb -l $REGION $BUCKET_NAME"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "validate_bucket"
},
"source": [
"Finally, validate access to your Cloud Storage bucket by examining its contents:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "validate_bucket"
},
"outputs": [],
"source": [
"! gsutil ls -al $BUCKET_NAME"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "setup_vars"
},
"source": [
"### Set up variables\n",
"\n",
"Next, set up some variables used throughout the tutorial.\n",
"### Import libraries and define constants"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "import_aip:mbsdk"
},
"outputs": [],
"source": [
"import google.cloud.aiplatform as aip"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "init_aip:mbsdk"
},
"source": [
"## Initialize Vertex SDK for Python\n",
"\n",
"Initialize the Vertex SDK for Python for your project and corresponding bucket."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "init_aip:mbsdk"
},
"outputs": [],
"source": [
"aip.init(project=PROJECT_ID, staging_bucket=BUCKET_NAME)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tutorial_start:automl"
},
"source": [
"# Tutorial\n",
"\n",
"Now you are ready to start creating your own AutoML image object detection model."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "import_file:u_dataset,csv"
},
"source": [
"#### Location of Cloud Storage training data.\n",
"\n",
"Now set the variable `IMPORT_FILE` to the location of the CSV index file in Cloud Storage."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "import_file:salads,csv,iod"
},
"outputs": [],
"source": [
"IMPORT_FILE = \"gs://cloud-samples-data/vision/salads.csv\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "quick_peek:csv"
},
"source": [
"#### Quick peek at your data\n",
"\n",
"This tutorial uses a version of the Salads dataset that is stored in a public Cloud Storage bucket, using a CSV index file.\n",
"\n",
"Start by doing a quick peek at the data. You count the number of examples by counting the number of rows in the CSV index file (`wc -l`) and then peek at the first few rows."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "quick_peek:csv"
},
"outputs": [],
"source": [
"if \"IMPORT_FILES\" in globals():\n",
" FILE = IMPORT_FILES[0]\n",
"else:\n",
" FILE = IMPORT_FILE\n",
"\n",
"count = ! gsutil cat $FILE | wc -l\n",
"print(\"Number of Examples\", int(count[0]))\n",
"\n",
"print(\"First 10 rows\")\n",
"! gsutil cat $FILE | head"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_dataset:image,iod"
},
"source": [
"### Create the Dataset\n",
"\n",
"Next, create the `Dataset` resource using the `create` method for the `ImageDataset` class, which takes the following parameters:\n",
"\n",
"- `display_name`: The human readable name for the `Dataset` resource.\n",
"- `gcs_source`: A list of one or more dataset index files to import the data items into the `Dataset` resource.\n",
"- `import_schema_uri`: The data labeling schema for the data items.\n",
"\n",
"This operation may take several minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_dataset:image,iod"
},
"outputs": [],
"source": [
"dataset = aip.ImageDataset.create(\n",
" display_name=\"Salads\" + \"_\" + TIMESTAMP,\n",
" gcs_source=[IMPORT_FILE],\n",
" import_schema_uri=aip.schema.dataset.ioformat.image.bounding_box,\n",
")\n",
"\n",
"print(dataset.resource_name)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_automl_pipeline:image,iod"
},
"source": [
"### Create and run training pipeline\n",
"\n",
"To train an AutoML model, you perform two steps: 1) create a training pipeline, and 2) run the pipeline.\n",
"\n",
"#### Create training pipeline\n",
"\n",
"An AutoML training pipeline is created with the `AutoMLImageTrainingJob` class, with the following parameters:\n",
"\n",
"- `display_name`: The human readable name for the `TrainingJob` resource.\n",
"- `prediction_type`: The type task to train the model for.\n",
" - `classification`: An image classification model.\n",
" - `object_detection`: An image object detection model.\n",
"- `multi_label`: If a classification task, whether single (`False`) or multi-labeled (`True`).\n",
"- `model_type`: The type of model for deployment.\n",
" - `CLOUD`: Deployment on Google Cloud\n",
" - `CLOUD_HIGH_ACCURACY_1`: Optimized for accuracy over latency for deployment on Google Cloud.\n",
" - `CLOUD_LOW_LATENCY_`: Optimized for latency over accuracy for deployment on Google Cloud.\n",
" - `MOBILE_TF_VERSATILE_1`: Deployment on an edge device.\n",
" - `MOBILE_TF_HIGH_ACCURACY_1`:Optimized for accuracy over latency for deployment on an edge device.\n",
" - `MOBILE_TF_LOW_LATENCY_1`: Optimized for latency over accuracy for deployment on an edge device.\n",
"- `base_model`: (optional) Transfer learning from existing `Model` resource -- supported for image classification only.\n",
"\n",
"The instantiated object is the DAG (directed acyclic graph) for the training job."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "create_automl_pipeline:image,iod"
},
"outputs": [],
"source": [
"dag = aip.AutoMLImageTrainingJob(\n",
" display_name=\"salads_\" + TIMESTAMP,\n",
" prediction_type=\"object_detection\",\n",
" multi_label=False,\n",
" model_type=\"CLOUD\",\n",
" base_model=None,\n",
")\n",
"\n",
"print(dag)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "run_automl_pipeline:image"
},
"source": [
"#### Run the training pipeline\n",
"\n",
"Next, you run the DAG to start the training job by invoking the method `run`, with the following parameters:\n",
"\n",
"- `dataset`: The `Dataset` resource to train the model.\n",
"- `model_display_name`: The human readable name for the trained model.\n",
"- `training_fraction_split`: The percentage of the dataset to use for training.\n",
"- `test_fraction_split`: The percentage of the dataset to use for test (holdout data).\n",
"- `validation_fraction_split`: The percentage of the dataset to use for validation.\n",
"- `budget_milli_node_hours`: (optional) Maximum training time specified in unit of millihours (1000 = hour).\n",
"- `disable_early_stopping`: If `True`, training maybe completed before using the entire budget if the service believes it cannot further improve on the model objective measurements.\n",
"\n",
"The `run` method when completed returns the `Model` resource.\n",
"\n",
"The execution of the training pipeline will take upto 60 minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "run_automl_pipeline:image"
},
"outputs": [],
"source": [
"model = dag.run(\n",
" dataset=dataset,\n",
" model_display_name=\"salads_\" + TIMESTAMP,\n",
" training_fraction_split=0.8,\n",
" validation_fraction_split=0.1,\n",
" test_fraction_split=0.1,\n",
" budget_milli_node_hours=20000,\n",
" disable_early_stopping=False,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "evaluate_the_model:mbsdk"
},
"source": [
"## Review model evaluation scores\n",
"After your model has finished training, you can review the evaluation scores for it.\n",
"\n",
"First, you need to get a reference to the new model. As with datasets, you can either use the reference to the model variable you created when you deployed the model or you can list all of the models in your project."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "evaluate_the_model:mbsdk"
},
"outputs": [],
"source": [
"# Get model resource ID\n",
"models = aip.Model.list(filter=\"display_name=salads_\" + TIMESTAMP)\n",
"\n",
"# Get a reference to the Model Service client\n",
"client_options = {\"api_endpoint\": f\"{REGION}-aiplatform.googleapis.com\"}\n",
"model_service_client = aip.gapic.ModelServiceClient(client_options=client_options)\n",
"\n",
"model_evaluations = model_service_client.list_model_evaluations(\n",
" parent=models[0].resource_name\n",
")\n",
"model_evaluation = list(model_evaluations)[0]\n",
"print(model_evaluation)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "deploy_model:mbsdk,automatic"
},
"source": [
"## Deploy the model\n",
"\n",
"Next, deploy your model for online prediction. To deploy the model, you invoke the `deploy` method."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "deploy_model:mbsdk,automatic"
},
"outputs": [],
"source": [
"endpoint = model.deploy()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "make_prediction"
},
"source": [
"## Send a online prediction request\n",
"\n",
"Send a online prediction to your deployed model."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "get_test_item"
},
"source": [
"### Get test item\n",
"\n",
"You will use an arbitrary example out of the dataset as a test item. Don't be concerned that the example was likely used in training the model -- we just want to demonstrate how to make a prediction."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "get_test_item:automl,iod,csv"
},
"outputs": [],
"source": [
"test_items = !gsutil cat $IMPORT_FILE | head -n1\n",
"cols = str(test_items[0]).split(\",\")\n",
"if len(cols) == 11:\n",
" test_item = str(cols[1])\n",
" test_label = str(cols[2])\n",
"else:\n",
" test_item = str(cols[0])\n",
" test_label = str(cols[1])\n",
"\n",
"print(test_item, test_label)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "predict_request:mbsdk,iod"
},
"source": [
"### Make the prediction\n",
"\n",
"Now that your `Model` resource is deployed to an `Endpoint` resource, you can do online predictions by sending prediction requests to the Endpoint resource.\n",
"\n",
"#### Request\n",
"\n",
"Since in this example your test item is in a Cloud Storage bucket, you open and read the contents of the image using `tf.io.gfile.Gfile()`. To pass the test data to the prediction service, you encode the bytes into base64 -- which makes the content safe from modification while transmitting binary data over the network.\n",
"\n",
"The format of each instance is:\n",
"\n",
" { 'content': { 'b64': base64_encoded_bytes } }\n",
"\n",
"Since the `predict()` method can take multiple items (instances), send your single test item as a list of one test item.\n",
"\n",
"#### Response\n",
"\n",
"The response from the `predict()` call is a Python dictionary with the following entries:\n",
"\n",
"- `ids`: The internal assigned unique identifiers for each prediction request.\n",
"- `displayNames`: The class names for each class label.\n",
"- `confidences`: The predicted confidence, between 0 and 1, per class label.\n",
"- `bboxes`: The bounding box of each detected object.\n",
"- `deployed_model_id`: The Vertex AI identifier for the deployed Model resource which did the predictions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "predict_request:mbsdk,iod"
},
"outputs": [],
"source": [
"import base64\n",
"\n",
"import tensorflow as tf\n",
"\n",
"with tf.io.gfile.GFile(test_item, \"rb\") as f:\n",
" content = f.read()\n",
"\n",
"# The format of each instance should conform to the deployed model's prediction input schema.\n",
"instances = [{\"content\": base64.b64encode(content).decode(\"utf-8\")}]\n",
"\n",
"prediction = endpoint.predict(instances=instances)\n",
"\n",
"print(prediction)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "undeploy_model:mbsdk"
},
"source": [
"## Undeploy the model\n",
"\n",
"When you are done doing predictions, you undeploy the model from the `Endpoint` resouce. This deprovisions all compute resources and ends billing for the deployed model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "undeploy_model:mbsdk"
},
"outputs": [],
"source": [
"endpoint.undeploy_all()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cleanup:mbsdk"
},
"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",
"\n",
"Otherwise, you can delete the individual resources you created in this tutorial:\n",
"\n",
"- Dataset\n",
"- Pipeline\n",
"- Model\n",
"- Endpoint\n",
"- AutoML Training Job\n",
"- Batch Job\n",
"- Custom Job\n",
"- Hyperparameter Tuning Job\n",
"- Cloud Storage Bucket"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cleanup:mbsdk"
},
"outputs": [],
"source": [
"delete_all = True\n",
"\n",
"if delete_all:\n",
" # Delete the dataset using the Vertex dataset object\n",
" try:\n",
" if \"dataset\" in globals():\n",
" dataset.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the model using the Vertex model object\n",
" try:\n",
" if \"model\" in globals():\n",
" model.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the endpoint using the Vertex endpoint object\n",
" try:\n",
" if \"endpoint\" in globals():\n",
" endpoint.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the AutoML or Pipeline trainig job\n",
" try:\n",
" if \"dag\" in globals():\n",
" dag.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the custom trainig job\n",
" try:\n",
" if \"job\" in globals():\n",
" job.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the batch prediction job using the Vertex batch prediction object\n",
" try:\n",
" if \"batch_predict_job\" in globals():\n",
" batch_predict_job.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" # Delete the hyperparameter tuning job using the Vertex hyperparameter tuning object\n",
" try:\n",
" if \"hpt_job\" in globals():\n",
" hpt_job.delete()\n",
" except Exception as e:\n",
" print(e)\n",
"\n",
" if \"BUCKET_NAME\" in globals():\n",
" ! gsutil rm -r $BUCKET_NAME"
]
}
],
"metadata": {
"colab": {
"name": "sdk_automl_image_object_detection_online.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
Cyb3rWard0g/HELK | docker/helk-jupyter/notebooks/sigma/proxy_download_susp_dyndns.ipynb | 1 | 6762 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Download from Suspicious Dyndns Hosts\n",
"Detects download of certain file types from hosts with dynamic DNS names (selected list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Content\n",
"```\n",
"- title: Download from Suspicious Dyndns Hosts\n",
" id: 195c1119-ef07-4909-bb12-e66f5e07bf3c\n",
" status: experimental\n",
" description: Detects download of certain file types from hosts with dynamic DNS\n",
" names (selected list)\n",
" references:\n",
" - https://www.alienvault.com/blogs/security-essentials/dynamic-dns-security-and-potential-threats\n",
" author: Florian Roth\n",
" date: 2017/11/08\n",
" logsource:\n",
" category: proxy\n",
" product: null\n",
" service: null\n",
" detection:\n",
" selection:\n",
" c-uri-extension:\n",
" - exe\n",
" - vbs\n",
" - bat\n",
" - rar\n",
" - ps1\n",
" - doc\n",
" - docm\n",
" - xls\n",
" - xlsm\n",
" - pptm\n",
" - rtf\n",
" - hta\n",
" - dll\n",
" - ws\n",
" - wsf\n",
" - sct\n",
" - zip\n",
" r-dns:\n",
" - '*.hopto.org'\n",
" - '*.no-ip.org'\n",
" - '*.no-ip.info'\n",
" - '*.no-ip.biz'\n",
" - '*.no-ip.com'\n",
" - '*.noip.com'\n",
" - '*.ddns.name'\n",
" - '*.myftp.org'\n",
" - '*.myftp.biz'\n",
" - '*.serveblog.net'\n",
" - '*.servebeer.com'\n",
" - '*.servemp3.com'\n",
" - '*.serveftp.com'\n",
" - '*.servequake.com'\n",
" - '*.servehalflife.com'\n",
" - '*.servehttp.com'\n",
" - '*.servegame.com'\n",
" - '*.servepics.com'\n",
" - '*.myvnc.com'\n",
" - '*.ignorelist.com'\n",
" - '*.jkub.com'\n",
" - '*.dlinkddns.com'\n",
" - '*.jumpingcrab.com'\n",
" - '*.ddns.info'\n",
" - '*.mooo.com'\n",
" - '*.dns-dns.com'\n",
" - '*.strangled.net'\n",
" - '*.adultdns.net'\n",
" - '*.craftx.biz'\n",
" - '*.ddns01.com'\n",
" - '*.dns53.biz'\n",
" - '*.dnsapi.info'\n",
" - '*.dnsd.info'\n",
" - '*.dnsdynamic.com'\n",
" - '*.dnsdynamic.net'\n",
" - '*.dnsget.org'\n",
" - '*.fe100.net'\n",
" - '*.flashserv.net'\n",
" - '*.ftp21.net'\n",
" - '*.http01.com'\n",
" - '*.http80.info'\n",
" - '*.https443.com'\n",
" - '*.imap01.com'\n",
" - '*.kadm5.com'\n",
" - '*.mysq1.net'\n",
" - '*.ns360.info'\n",
" - '*.ntdll.net'\n",
" - '*.ole32.com'\n",
" - '*.proxy8080.com'\n",
" - '*.sql01.com'\n",
" - '*.ssh01.com'\n",
" - '*.ssh22.net'\n",
" - '*.tempors.com'\n",
" - '*.tftpd.net'\n",
" - '*.ttl60.com'\n",
" - '*.ttl60.org'\n",
" - '*.user32.com'\n",
" - '*.voip01.com'\n",
" - '*.wow64.net'\n",
" - '*.x64.me'\n",
" - '*.xns01.com'\n",
" - '*.dyndns.org'\n",
" - '*.dyndns.info'\n",
" - '*.dyndns.tv'\n",
" - '*.dyndns-at-home.com'\n",
" - '*.dnsomatic.com'\n",
" - '*.zapto.org'\n",
" - '*.webhop.net'\n",
" - '*.25u.com'\n",
" - '*.slyip.net'\n",
" condition: selection\n",
" fields:\n",
" - cs-ip\n",
" - c-uri\n",
" falsepositives:\n",
" - Software downloads\n",
" level: medium\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Querying Elasticsearch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from elasticsearch import Elasticsearch\n",
"from elasticsearch_dsl import Search\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Initialize Elasticsearch client"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"es = Elasticsearch(['http://helk-elasticsearch:9200'])\n",
"searchContext = Search(using=es, index='logs-*', doc_type='doc')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run Elasticsearch Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s = searchContext.query('query_string', query='(c-uri-extension:(\"exe\" OR \"vbs\" OR \"bat\" OR \"rar\" OR \"ps1\" OR \"doc\" OR \"docm\" OR \"xls\" OR \"xlsm\" OR \"pptm\" OR \"rtf\" OR \"hta\" OR \"dll\" OR \"ws\" OR \"wsf\" OR \"sct\" OR \"zip\") AND r-dns.keyword:(*.hopto.org OR *.no\\-ip.org OR *.no\\-ip.info OR *.no\\-ip.biz OR *.no\\-ip.com OR *.noip.com OR *.ddns.name OR *.myftp.org OR *.myftp.biz OR *.serveblog.net OR *.servebeer.com OR *.servemp3.com OR *.serveftp.com OR *.servequake.com OR *.servehalflife.com OR *.servehttp.com OR *.servegame.com OR *.servepics.com OR *.myvnc.com OR *.ignorelist.com OR *.jkub.com OR *.dlinkddns.com OR *.jumpingcrab.com OR *.ddns.info OR *.mooo.com OR *.dns\\-dns.com OR *.strangled.net OR *.adultdns.net OR *.craftx.biz OR *.ddns01.com OR *.dns53.biz OR *.dnsapi.info OR *.dnsd.info OR *.dnsdynamic.com OR *.dnsdynamic.net OR *.dnsget.org OR *.fe100.net OR *.flashserv.net OR *.ftp21.net OR *.http01.com OR *.http80.info OR *.https443.com OR *.imap01.com OR *.kadm5.com OR *.mysq1.net OR *.ns360.info OR *.ntdll.net OR *.ole32.com OR *.proxy8080.com OR *.sql01.com OR *.ssh01.com OR *.ssh22.net OR *.tempors.com OR *.tftpd.net OR *.ttl60.com OR *.ttl60.org OR *.user32.com OR *.voip01.com OR *.wow64.net OR *.x64.me OR *.xns01.com OR *.dyndns.org OR *.dyndns.info OR *.dyndns.tv OR *.dyndns\\-at\\-home.com OR *.dnsomatic.com OR *.zapto.org OR *.webhop.net OR *.25u.com OR *.slyip.net))')\n",
"response = s.execute()\n",
"if response.success():\n",
" df = pd.DataFrame((d.to_dict() for d in s.scan()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show Results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.head()"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-3.0 |
musketeer191/job_analytics | .ipynb_checkpoints/jobtitle_skill-checkpoint.ipynb | 1 | 7600 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Building JobTitle-Skill matrix\n",
"\n",
"Running LDA on document-skill matrix, where each document is a job post, still does not give good results!!! What is the problem here?\n",
"\n",
"It seems that the job post level has too many noises:\n",
"+ other info not relating to skills i.e. salary, location, working time, required experience.\n",
"\n",
"Thus, we now try putting all posts of the same job title together so that the aggregated skill info can win over the noises."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import cluster_skill_helpers as cluster_skill_helpers\n",
"\n",
"from cluster_skill_helpers import *"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"HOME_DIR = 'd:/larc_projects/job_analytics/'; DATA_DIR = HOME_DIR + 'data/clean/'\n",
"SKILL_DIR = DATA_DIR + 'skill_cluster/'; RES_DIR = HOME_DIR + 'results/reports/skill_cluster/'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"jobs = pd.read_csv(DATA_DIR + 'jobs.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"skill_df = pd.read_csv(SKILL_DIR + 'skill_df.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Collapse all posts of the same job title into a single document"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"by_job_title = jobs.groupby('title')\n",
"job_title_df = by_job_title.agg({'job_id': lambda x: ','.join(x), 'doc': lambda x: 'next_doc'.join(x)})\n",
"\n",
"job_title_df = job_title_df.add_prefix('agg_').job_title_dfet_index()\n",
"job_title_df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n_job_title = by_job_title.ngroups\n",
"print('# job titles: %d' %n_job_title)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reload(cluster_skill_helpers)\n",
"from cluster_skill_helpers import *"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"jd_docs = job_title_df['agg_doc']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# This version of skills still contain stopwords\n",
"doc_skill = buildDocSkillMat(jd_docs, skill_df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ Concat matrices doc_unigram, doc_bigram and doc_trigram to get occurrences of all skills:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scipy.sparse import hstack\n",
"jobtitle_skill = hstack([doc_unigram, doc_bigram, doc_trigram])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with(open(SKILL_DIR + 'jobtitle_skill.mtx', 'w')) as f:\n",
" mmwrite(f, jobtitle_skill)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"jobtitle_skill.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"jobtitle_skill = jobtitle_skill.toarray()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Most popular skills by job title"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"job_title_df.head(1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"idx_of_top_skill = np.apply_along_axis(np.argmax, 1, jobtitle_skill)\n",
"\n",
"# skill_df = skills\n",
"skills = skill_df['skill']\n",
"top_skill_by_job_title = pd.DataFrame({'job_title': job_titles, 'idx_of_top_skill': idx_of_top_skill})\n",
"top_skill_by_job_title['top_skill'] = top_skill_by_job_title['idx_of_top_skill'].apply(lambda i: skills[i])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"top_skill_by_job_title.head(30)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with(open(SKILL_DIR + 'jobtitle_skill.mtx', 'r')) as f:\n",
" jobtitle_skill = mmread(f)\n",
"\n",
"jobtitle_skill = jobtitle_skill.tocsr()\n",
"jobtitle_skill.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"job_titles = job_title_df['title']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# for each row (corresponding to a jobtitle) in matrix jobtitle_skill, get non-zero freqs\n",
"global k\n",
"k = 3\n",
"\n",
"def getTopK_Skills(idx):\n",
" title = job_titles[idx]\n",
" print('Finding top-{} skills of job title {}...'.format(k, title))\n",
" \n",
" skill_occur = jobtitle_skill.getrow(idx)\n",
" tmp = find(skill_occur)\n",
" nz_indices = tmp[1]\n",
" values = tmp[2]\n",
" res = pd.DataFrame({'job_title': title, 'skill_found_in_jd': skills[nz_indices], 'occur_freq': values})\n",
"\n",
" res.sort_values('occur_freq', ascending=False, inplace=True)\n",
" return res.head(k)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# getTopK_Skills(0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"frames = map(getTopK_Skills, range(n_job_title))\n",
"res = pd.concat(frames) # concat() is great as it can concat as many df as possible\n",
"res.head(30)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"res.to_csv(RES_DIR + 'top3_skill_by_jobtitle.csv', index=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
afeiguin/comp-phys | 01_03_eqs_of_motion.ipynb | 1 | 2039828 | null | mit |
halexand/NB_Distribution | .ipynb_checkpoints/KL rambling notes on Python-checkpoint.ipynb | 2 | 32618 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use this to keep track of useful code bits as I learn Python\n",
"Krista, August 19, 2015"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"Shortcut \tAction\n",
"Shift-Enter \trun cell\n",
"Ctrl-Enter \trun cell in-place\n",
"Alt-Enter \trun cell, insert below\n",
"\n",
"***\n",
"Ctrl / (Ctrl and then the slash)...will comment out any selected text within a block of code"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#First up...list the files in a directory\n",
"import os,sys\n",
"os.listdir(os.getcwd())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#read the CSV file into a data frame and use the pandas head tool to show me the first five rows. \n",
"#note that this doesn't seem to work: pd.head(CO_RawData)\n",
"CO_RawData=pd.read_csv(mtabFile, index_col='RInumber')\n",
"CO_RawData.head(n=5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#insert an image...the gif file here would be in the folder\n",
"from IPython.display import Image\n",
"Image(url=\"R02485.gif\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for x in range(0, 3):\n",
" print(\"hello\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fig.suptitle(CO + ' working') #use the plus sign to concatenate strings for the title"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from IPython.core.debugger import Tracer #used this to step into the function and debug it, also need line with Tracer()() \n",
"for i, CO in enumerate(CO_withKO):\n",
" #if i==2:\n",
" #break\n",
" kos=CO_withKO[CO]['Related KO']\n",
" cos=CO_withKO[CO]['Related CO']\n",
" for k in kos: \n",
" if k in KO_RawData.index: \n",
" kData=KO_RawData.loc[kos].dropna()\n",
" kData=(kData.T/kData.sum(axis=1)).T\n",
" cData=CO_RawData.loc[cos].dropna()\n",
" cData=(cData.T/cData.sum(axis=1)).T\n",
" \n",
" fig, ax=plt.subplots(1)\n",
" kData.T.plot(color='r', ax=ax)\n",
" cData.T.plot(color='k', ax=ax)\n",
" \n",
" Tracer()()\n",
" \n",
" getKmeans = CcoClust.loc['C01909']['kmeans']\n",
" makeStringLabel = CO + '_kmeansCluster_' + str(getKmeans)\n",
" #fig.suptitle(CO)\n",
" fig.suptitle(makeStringLabel)\n",
" \n",
" #fig.savefig(CO+'.png') #stop saving all the images for now...\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Ou2no/q/njDcDu5X3Hw78adnGuFXSrcCTgX0BJB0r6XuSNpaPPRPYa+i5NkbElqHxXUPP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\nC7OZWce4MJuZdYwLs5lZx7gwm5l1zP8DPhytDueAfrgAAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#here, tData is a pandas data frame that I want to plot into a bar graph\n",
"#tData.plot(kind = \"bar\") ##this would be the code to run if tData existed...\n",
"#instead I am reading in the file saved and present in my working directory using this:\n",
"from IPython.display import Image\n",
"Image(filename=\"SampleBarGraph.png\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#indexing in Python is a bit bizarre, or at least takes some getting used to.\n",
"# df.ix[0,'cNumber'] #this will allow me to mix index from integers with index by label\n",
"#other way apparently uses iloc and loc, to use integers and labels respectively\n",
"# this would be df.iloc[0].loc['cNumber] {can't get that to work in the if statement}"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'CcoClust' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-8-73c35b442f8f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mCcoClust\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'C05356'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'kmeans'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'CcoClust' is not defined"
]
}
],
"source": [
"#ways to subset data...\n",
"CcoClust.loc['C05356']['kmeans']\n",
"tData = CcoClust.loc['C05356']\n",
"type(tData)\n",
"\n",
"#want to select only the first group in the kmeans clusters \n",
"#(baby steps, eventually do this for each cluster)\n",
"CcoClust[CcoClust.kmeans==1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"/...this is where I learned to not use pip install with scikit-learn...\n",
"To upgrade scikit-learn:\n",
"conda update scikit-learn\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sklearn.cluster\n",
"#from sklearn.cluster import KMeans"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"silAverage = [0.4227, 0.33299, 0.354, 0.3768, 0.3362, 0.3014, 0.3041, 0.307, 0.313, 0.325,\n",
"0.3109, 0.2999, 0.293, 0.289, 0.2938, 0.29, 0.288, 0.3, 0.287]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"OK...can I get a simple scatter plot?"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(-1, 20)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nASd7M7Mh8P8BqK8x/QUUFkgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xbea20b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(range(0,len(silAverage)), silAverage)\n",
"plt.grid() #put on a grid\n",
"\n",
"plt.xlim(-1,20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#get list of column names in pandas data frame\n",
"list(my_dataframe.columns.values)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for i in range(0,len(ut)):\n",
" if i == 10:\n",
" break\n",
" p = ut.iloc[i,:]\n",
" n = p.name\n",
" if n[0] == 'R':\n",
" #do the plotting, \n",
" #print 'yes'\n",
" CO = p.KEGG\n",
" kos = CO_withKO[CO]['Related KO']\n",
" cos = CO_withKO[CO]['Related CO']\n",
" #Tracer()()\n",
" for k in kos: \n",
" if k in KO_RawData.index: \n",
" kData=KO_RawData.loc[kos].dropna()\n",
" kData=(kData.T/kData.sum(axis=1)).T\n",
" #? why RawData, the output from the K-means will have the normalized data, use that for CO \n",
" #bc easier since that is the file I am working with right now.\n",
" #cData=CO_RawData.loc[cos].dropna()\n",
" #cData=(cData.T/cData.sum(axis=1)).T\n",
" cData = pd.DataFrame(p[dayList]).T\n",
" \n",
" #go back and check, but I think this next step is already done\n",
" #cData=(cData.T/cData.sum(axis=1)).T\n",
"\n",
" fig, ax=plt.subplots(1)\n",
" kData.T.plot(color='r', ax=ax)\n",
" cData.T.plot(color='k', ax=ax)\n",
" \n",
" else:\n",
" #skip over the KO plotting, so effectively doing nothing\n",
" #print 'no'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Write a function to match RI number and cNumbers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def findRInumber(dataIn,KEGGin):\n",
" #find possible RI numbers for a given KEGG number. \n",
" for i,KEGG in enumerate(dataIn['KEGG']):\n",
" if KEGG == KEGGin:\n",
" t = dataIn.index[i]\n",
" print t\n",
"\n",
"#For example: this will give back one row, C18028 will be multiple\n",
"m = findRInumber(forRelatedness,'C00031') \n",
"m"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#to copy a matrix I would think this works: NOPE\n",
"#forRelatedness = CcoClust# this is NOT making a new copy...\n",
"#instead it makes a new pointing to an existing data frame. So you now have two ways to \n",
"#reference the same data frame. Make a change with one term and you can see the same change\n",
"#using the other name. Odd. No idea why you would want that.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"##this is the test that finally let me understand enumerate\n",
"\n",
"# for index, KEGG in enumerate(useSmall['KEGG']):\n",
"# print index,KEGG"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Windows\n",
"chrome_path = 'C:/Program Files (x86)/Google/Chrome/Application/chrome.exe %s'\n",
"\n",
"url = \"http://www.genome.jp/dbget-bin/www_bget?cpd:C00019\"\n",
"webbrowser.get(chrome_path).open_new(url)\n",
"#while a nice idea, this stays open until you close the web browser window."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<iframe src = http://www.genome.jp/dbget-bin/www_bget?cpd:C02265 width=700 height=350></iframe>\n",
"<iframe src = http://www.genome.jp/dbget-bin/www_bget?cpd:C00001 width=700 height=350></iframe>\n"
]
}
],
"source": [
"from IPython.display import HTML\n",
"tList = ['C02265','C00001']\n",
"for i in tList:\n",
" ml = '<iframe src = http://www.genome.jp/dbget-bin/www_bget?cpd:' + i + ' width=700 height=350></iframe>'\n",
" print ml"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe src = http://www.genome.jp/dbget-bin/www_bget?cpd:C02265 width=700 height=350></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"CO='C02265'\n",
"HTML('<iframe src = http://www.genome.jp/dbget-bin/www_bget?cpd:' + CO + ' width=700 height=350></iframe>')\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": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
endgameinc/youarespecial | BSidesLV -- your model isn't that special -- (1) MLP.ipynb | 1 | 17732 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preliminaries\n",
"\n",
"We're going to build and compare a few malware machine learning models in this series of Jupyter notebooks. Some of them require a GPU. I've used a Titan X GPU for this exercise. If yours isn't as beefy, you may get tensorflow memory errors that may require modifying some of the code, namely `file_chunks` and `file_chunk_size`. (I'll point to it later.) But, to get started, the first few exercises will work on even that GPU you're embarrassed to tell people about, or if you're willing to wait, no GPU at all.\n",
"\n",
"For the fancy folks who have multiple GPUs, we're going to restrict usage to the first one."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"env: CUDA_VISIBLE_DEVICES=0 # limit GPU usage, if any to this GPU\n"
]
}
],
"source": [
"%env CUDA_VISIBLE_DEVICES=0 # limit GPU usage, if any to this GPU"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Also note that this exercise assumes you've already populated a `malicious/` and a `benign/` directory with samples that you consider malicious and benign, respectively. How many samples? In this notebook, I'm using 50K of each for demonstration purposes. Sadly, you must bring your own. If you don't populate these subdirectories for binaries (each renamed to the sha256 hash of its contents!), the code will bicker and complain incessently."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Feature extraction for feature-based models\n",
"\n",
"There is a lot of domain knowledge on what malware authors *can* do, and what malware authors *actually* do when crafting malicious files. Furthermore, there are some things malware authors *seldom* do that would indicate that a file is benign. For each file we want to analyze, we're going to encapsulate that domain knowledge about malicious and benign files in a single feature vector. See the source code at [classifier/pefeatures.py](classifier/pefeatures.py).\n",
"\n",
"Note that the feature extraction we use here contains many elements from published malware classification papers. Some of those are slightly modified. And there are additional features in this particular feature extraction that are included because, well, they were just sitting there in the [LIEF](https://lief.quarkslab.com/) parser patiently waiting for a chair at the feature vector table. Read: there's really no secret sauce in there, and to turn this into something commercially viable would take a bit of work. But, be my guest.\n",
"\n",
"A note about LIEF. What a cool tool with a great mission! It aims to *parse* _and_ *manipulate* binary files for Windows (PE), Linux (ELF) and MacOS (macho). Of course, we're using only the PE subset here. At the time of this writing, LIEF is still very much a new tool, and I've worked with the authors to help resolve some kinks. It's a growing project with more warts to find and fix. Nevertheless, we're using it as the backbone for features that requires one to parse a PE file."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from classifier import common"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"took 3.0163469314575195 seconds\n"
]
}
],
"source": [
"# this will take a LONG time the first time you run it (and cache features to disk for next time)\n",
"# it's also chatty. Parts of feature extraction require LIEF, and LIEF is quite chatty.\n",
"# the output you see below is *after* I've already run feature extraction, so that\n",
"# X and sample_index are being read from cache on disk\n",
"X, y, sha256list = common.extract_features_and_persist() \n",
"\n",
"# split our features, labels and hashes into training and test sets\n",
"from sklearn.model_selection import train_test_split\n",
"import numpy as np\n",
"np.random.seed(123)\n",
"X_train, X_test, y_train, y_test, sha256_train, sha256_test = train_test_split( X, y, sha256list, test_size=1000) \n",
"# a random train_test split, but for a malware classifier, we should really be holding out *future* malicious and benign \n",
"# samples, to better capture how we'll generalize to malware yet to be seen in the wild. ...an exercise left to the reader.."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multilayer perceptron\n",
"We'll use the features we extracted to train a multilayer perceptron (MLP). An MLP is an artificial neural network with at least one hidden layer. Is a multilayer perceptron \"deep learning\"? Well, it's a matter of semantics, but \"deep learning\" may imply that the features and model are optimized together, end-to-end. So, it that sense, no: since we're using domain knowledge to extract features, then pass it to an artificial neural network, we'll remain conservative and call this an MLP. (As we'll see, don't get fooled just because we're not calling this \"deep learning\": this MLP is no slouch.) The network architecture is defined in [classifier/simple_multilayer.py](classifier/simple_multilayer.py)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 98997 samples, validate on 1000 samples\n",
"Epoch 1/200\n",
"98997/98997 [==============================] - 17s - loss: 0.2164 - acc: 0.9148 - val_loss: 0.1322 - val_acc: 0.9470\n",
"Epoch 2/200\n",
"98997/98997 [==============================] - 11s - loss: 0.1541 - acc: 0.9408 - val_loss: 0.1200 - val_acc: 0.9500\n",
"Epoch 3/200\n",
"98997/98997 [==============================] - 12s - loss: 0.1344 - acc: 0.9485 - val_loss: 0.1143 - val_acc: 0.9500\n",
"Epoch 4/200\n",
"98997/98997 [==============================] - 11s - loss: 0.1227 - acc: 0.9531 - val_loss: 0.1147 - val_acc: 0.9510\n",
"Epoch 5/200\n",
"98997/98997 [==============================] - 11s - loss: 0.1150 - acc: 0.9559 - val_loss: 0.1097 - val_acc: 0.9580\n",
"Epoch 6/200\n",
"98997/98997 [==============================] - 11s - loss: 0.1061 - acc: 0.9601 - val_loss: 0.1078 - val_acc: 0.9590\n",
"Epoch 7/200\n",
"98997/98997 [==============================] - 11s - loss: 0.1015 - acc: 0.9611 - val_loss: 0.1158 - val_acc: 0.9580\n",
"Epoch 8/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0949 - acc: 0.9637 - val_loss: 0.1053 - val_acc: 0.9610\n",
"Epoch 9/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0929 - acc: 0.9640 - val_loss: 0.1111 - val_acc: 0.9620\n",
"Epoch 10/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0886 - acc: 0.9663 - val_loss: 0.1055 - val_acc: 0.9610\n",
"Epoch 11/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0862 - acc: 0.9669 - val_loss: 0.1048 - val_acc: 0.9600\n",
"Epoch 12/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0819 - acc: 0.9684 - val_loss: 0.1049 - val_acc: 0.9620\n",
"Epoch 13/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0810 - acc: 0.9691 - val_loss: 0.1077 - val_acc: 0.9640\n",
"Epoch 14/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0784 - acc: 0.9703 - val_loss: 0.0999 - val_acc: 0.9620\n",
"Epoch 15/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0757 - acc: 0.9713 - val_loss: 0.1065 - val_acc: 0.9650\n",
"Epoch 16/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0723 - acc: 0.9727 - val_loss: 0.1069 - val_acc: 0.9650\n",
"Epoch 17/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0709 - acc: 0.9732 - val_loss: 0.1099 - val_acc: 0.9670\n",
"Epoch 18/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0699 - acc: 0.9732 - val_loss: 0.1046 - val_acc: 0.9680\n",
"Epoch 19/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0688 - acc: 0.9738 - val_loss: 0.1051 - val_acc: 0.9650\n",
"Epoch 20/200\n",
"98944/98997 [============================>.] - ETA: 0s - loss: 0.0673 - acc: 0.9746\n",
"Epoch 00019: reducing learning rate to 0.0009999999776482583.\n",
"98997/98997 [==============================] - 11s - loss: 0.0672 - acc: 0.9747 - val_loss: 0.1020 - val_acc: 0.9640\n",
"Epoch 21/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0616 - acc: 0.9761 - val_loss: 0.1001 - val_acc: 0.9660\n",
"Epoch 22/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0606 - acc: 0.9767 - val_loss: 0.1006 - val_acc: 0.9670\n",
"Epoch 23/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0611 - acc: 0.9763 - val_loss: 0.1030 - val_acc: 0.9670\n",
"Epoch 24/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0619 - acc: 0.9764 - val_loss: 0.1014 - val_acc: 0.9680\n",
"Epoch 25/200\n",
"98944/98997 [============================>.] - ETA: 0s - loss: 0.0599 - acc: 0.9772\n",
"Epoch 00024: reducing learning rate to 9.999999310821295e-05.\n",
"98997/98997 [==============================] - 11s - loss: 0.0599 - acc: 0.9772 - val_loss: 0.1024 - val_acc: 0.9670\n",
"Epoch 26/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0601 - acc: 0.9767 - val_loss: 0.1016 - val_acc: 0.9670\n",
"Epoch 27/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0594 - acc: 0.9773 - val_loss: 0.1017 - val_acc: 0.9670\n",
"Epoch 28/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0588 - acc: 0.9777 - val_loss: 0.1019 - val_acc: 0.9670\n",
"Epoch 29/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0602 - acc: 0.9772 - val_loss: 0.1017 - val_acc: 0.9670\n",
"Epoch 30/200\n",
"98944/98997 [============================>.] - ETA: 0s - loss: 0.0595 - acc: 0.9774\n",
"Epoch 00029: reducing learning rate to 9.999999019782991e-06.\n",
"98997/98997 [==============================] - 11s - loss: 0.0595 - acc: 0.9774 - val_loss: 0.1022 - val_acc: 0.9670\n",
"Epoch 31/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0587 - acc: 0.9776 - val_loss: 0.1028 - val_acc: 0.9670\n",
"Epoch 32/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0594 - acc: 0.9774 - val_loss: 0.1028 - val_acc: 0.9670\n",
"Epoch 33/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0604 - acc: 0.9773 - val_loss: 0.1020 - val_acc: 0.9670\n",
"Epoch 34/200\n",
"98997/98997 [==============================] - 11s - loss: 0.0596 - acc: 0.9769 - val_loss: 0.1014 - val_acc: 0.9670\n",
"Epoch 35/200\n",
"98816/98997 [============================>.] - ETA: 0s - loss: 0.0607 - acc: 0.9768\n",
"Epoch 00034: reducing learning rate to 9.99999883788405e-07.\n",
"98997/98997 [==============================] - 11s - loss: 0.0607 - acc: 0.9769 - val_loss: 0.1023 - val_acc: 0.9670\n",
"** Multilayer perceptron **\n",
"ROC AUC = 0.993257095265552\n",
"threshold=0.8988195061683655: 0.9070247933884298 TP rate @ 0.009689922480620155 FP rate\n",
"confusion matrix @ threshold:\n",
"[[511 5]\n",
" [ 46 438]]\n",
"accuracy @ threshold = 0.949\n"
]
},
{
"data": {
"text/plain": [
"(0.99325709526555195,\n",
" 0.89881951,\n",
" 0.0096899224806201549,\n",
" 0.90702479338842978,\n",
" array([[511, 5],\n",
" [ 46, 438]]),\n",
" 0.94899999999999995)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# StandardScaling the data can be important to multilayer perceptron\n",
"from sklearn.preprocessing import StandardScaler\n",
"scaler = StandardScaler().fit(X_train)\n",
"\n",
"# Note that we're using scaling info form X_train to transform both\n",
"X_train = scaler.transform(X_train) # scale for multilayer perceptron\n",
"X_test = scaler.transform(X_test)\n",
"\n",
"from classifier import simple_multilayer\n",
"from keras.callbacks import LearningRateScheduler, EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\n",
"model = simple_multilayer.create_model(\n",
" input_shape=(X_train.shape[1], ), # input dimensions\n",
" input_dropout=0.05, # this prevents the model becoming a fanboy of (overfitting to) any particular input feature\n",
" hidden_dropout=0.1, # same, but for hidden units. Dropping out hidden layers can create a sort of ensemble learner\n",
" hidden_layers=[4096, 2048, 1024, 512] # this is \"art\". making up # of hidden layers and width of each. don't be afraid to change this\n",
")\n",
"model.fit(X_train, y_train,\n",
" batch_size=128,\n",
" epochs=200,\n",
" verbose=1,\n",
" callbacks=[\n",
" EarlyStopping( patience=20 ),\n",
" ModelCheckpoint( 'multilayer.h5', save_best_only=True),\n",
" ReduceLROnPlateau( patience=5, verbose=1)],\n",
" validation_data=(X_test, y_test))\n",
"\n",
"from keras.models import load_model\n",
"# we'll load the \"best\" model (in this case, the penultimate model) that was saved \n",
"# by our ModelCheckPoint callback\n",
"model = load_model('multilayer.h5')\n",
"\n",
"y_pred = model.predict(X_test)\n",
"common.summarize_performance(y_pred, y_test, \"Multilayer perceptron\") \n",
"# The astute reader will note we should be doing this on a separate holdout, since we've explicitly\n",
"# saved the model that works best on X_test, y_test...an exercise for left for the reader...\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sanity check: random forest classifier\n",
"Alright. Is that good? Let's compare to another model. We'll reach for the simple and reliable random forest classifier?\n",
"\n",
"One nice thing about tree-based classifiers like a random forest classifier is that they are invariant to linear scaling and shifting of the dataset (the model will automatically learn those transformations). Nevertheless, for a sanity check, we're going to use the scaled/transformed features in a random forest classifier."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"** RF Classifier **\n",
"ROC AUC = 0.9944763437760266\n",
"threshold=0.7: 0.9276859504132231 TP rate @ 0.009689922480620155 FP rate\n",
"confusion matrix @ threshold:\n",
"[[512 4]\n",
" [ 36 448]]\n",
"accuracy @ threshold = 0.96\n"
]
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
"# you can increase performance by increasing n_estimators, and removing the restriction on max_depth\n",
"# I've kept those in there because I want a quick-and-dirty look at how the MLP above\n",
"rf = RandomForestClassifier( \n",
" n_estimators=40, \n",
" n_jobs=-1, \n",
" max_depth=30\n",
").fit(X_train, y_train)\n",
"\n",
"y_pred = rf.predict_proba(X_test)[:,-1] # get probabiltiy of malicious (last class == last column )\n",
"_ = common.summarize_performance(y_pred, y_test, \"RF Classifier\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How can we improve?\n",
"\n",
"Really, it's not a terrible model, *but it's nothing special*. But, we'd really like to get to the realm of > 99% true positive rate at < 1% false positive rate.\n",
"\n",
"Seems like we can do one of two things here:\n",
"1. Spend some time working on our dataset, our labels, and our feature extraction, but use the same model.\n",
"2. Make our model special. *Really special.*\n",
"\n",
"Hey, end-to-end deep learning disrupted object detection, image recognition, speech recognition and machine translation. And that sounds way more interesting than item 1, so let's pull out some end-to-end deep learning for static malware detection!"
]
}
],
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"display_name": "Python 2",
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"name": "python2"
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"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
cmgerber/PLOS_Cloud_Explorer | ipython_notebooks/Batch_data_collection_full.ipynb | 1 | 215256 | {
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"metadata": {},
"source": [
"Batch Data Collection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook will download all 120k+ articles with abstracts from the PLOS API."
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Imports"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import numpy as np \n",
"import settings\n",
"import requests\n",
"import urllib\n",
"import time\n",
"from retrying import retry\n",
"import os"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"API Call Function"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#adapted from Raymond's notebook\n",
"\n",
"def plos_search(q,start=0,rows=100,fl=None, extras=None):\n",
"\n",
" BASE_URL = 'http://api.plos.org/search'\n",
" DEFAULT_FL = ('abstract','author',\n",
" 'id','journal','publication_date',\n",
" 'score','title_display', 'subject','subject_level')\n",
" #removed elements: eissn, article_type\n",
" \n",
" # fl indicates fields to return\n",
" # http://wiki.apache.org/solr/CommonQueryParameters#fl\n",
" \n",
" if fl is None:\n",
" fl_ = \",\".join(DEFAULT_FL)\n",
" else:\n",
" fl_ = \",\".join(fl)\n",
" \n",
" query = {'q':q,\n",
" 'start':start,\n",
" 'rows':rows,\n",
" 'api_key':settings.PLOS_KEY,\n",
" 'wt':'json',\n",
" 'fl':fl_,\n",
" 'fq': 'doc_type:full AND !article_type_facet:\"Issue Image\"'}\n",
" \n",
" if extras is not None:\n",
" query.update(extras)\n",
" \n",
" query_url = BASE_URL + \"?\" +urllib.urlencode(query)\n",
" \n",
" r = requests.get(query_url)\n",
" return r"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Finding Parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Need to make sure the calls do not exceed the following: \n",
"\n",
"* 7200 requests a day, 300 per hour, 10 per minute and allow 5 seconds for your search to return results.\n",
"\n",
"To be safe there will be a 15 second wait between each call:\n",
"\n",
"* 15 sec per call\n",
"* 4 calls per minute\n",
"* 240 calls per hour"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Call for all articles"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"r = plos_search(q='*')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Check the total number of articles with abstracts."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tot_articles = r.json()['response']['numFound']\n",
"tot_articles"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"126718"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With <s>118545</s> <s>119637</s> 126718 acticles total that means that we will have to perform ~1.2k API requests at 100 articles per request.\n",
"\n",
"At 240 requests per hour it should take about 5.5 hours to get all the data needed."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Looping Function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This function will call the plos_search function every 15 seconds while incrementing the start number so that all of the articles can be pulled."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"@retry(wait='exponential_sleep', wait_exponential_multiplier=1000, wait_exponential_max=10000, stop='stop_after_attempt', stop_max_attempt_number=200)\n",
"def data_request(end, start=0):\n",
" current_end = end - start\n",
" rows_per_query = 100\n",
" loops = (current_end/rows_per_query) + 1\n",
" for n in range(loops):\n",
" current_pickle = '../data/pickles/plos_pickle_{0}.pkl'.format(n)\n",
" \n",
" #skips rows that have already been downloaded\n",
" if os.path.exists(current_pickle):\n",
" start += rows_per_query\n",
" continue\n",
" \n",
" r = plos_search(q='subject:*', start=start, rows=rows_per_query)\n",
" \n",
" #increment the start for the next request\n",
" start += rows_per_query\n",
" \n",
" #store data before next call\n",
" data = r.json()['response']['docs']\n",
" new_df = pd.DataFrame(data)\n",
" \n",
" #every request pickle the dataframe\n",
" new_df.to_pickle(current_pickle)\n",
" \n",
" #prints status of job\n",
" print start, \" articles pickled\"\n",
" \n",
" #wait a few seconds before the next loop\n",
" time.sleep(7)\n",
" print \"DONE!\"\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can run the function inputing the tot_articles as the end parameter.\n",
"\n",
"### Before proceeding with this notebook to obtain/update your dataset:\n",
"\n",
"- Make sure you have a `data/pickles/` directory and it is empty.\n",
"- Make sure that `data/all_plos_df.pkl` is suitably backed up (or does not exist).\n",
"\n",
"#### Data collection log\n",
"\n",
"First collection: crashed at 12100, 43800, 49500, 53500\n",
"\n",
"Update 07-2014: *What! No crashes!*"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_plos_df = data_request(end=tot_articles)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"100 articles pickled\n",
"200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"1000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"1100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"1200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"1300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"1400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"1500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"1600"
]
},
{
"output_type": "stream",
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"text": [
" articles pickled\n",
"1700"
]
},
{
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" articles pickled\n",
"1800"
]
},
{
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"stream": "stdout",
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" articles pickled\n",
"1900"
]
},
{
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" articles pickled\n",
"2000"
]
},
{
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"stream": "stdout",
"text": [
" articles pickled\n",
"2100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"2200"
]
},
{
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"stream": "stdout",
"text": [
" articles pickled\n",
"2300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"2400"
]
},
{
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"stream": "stdout",
"text": [
" articles pickled\n",
"2500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"2600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"2700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"2800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"2900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"3000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"3100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"3200"
]
},
{
"output_type": "stream",
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"text": [
" articles pickled\n",
"3300"
]
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{
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" articles pickled\n",
"3400"
]
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{
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" articles pickled\n",
"3500"
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{
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" articles pickled\n",
"3600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"3700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"3800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"3900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"4000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"4100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"4200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"4300"
]
},
{
"output_type": "stream",
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" articles pickled\n",
"4400"
]
},
{
"output_type": "stream",
"stream": "stdout",
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" articles pickled\n",
"4500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"4600"
]
},
{
"output_type": "stream",
"stream": "stdout",
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" articles pickled\n",
"4700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"4800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"4900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"5000"
]
},
{
"output_type": "stream",
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" articles pickled\n",
"5100"
]
},
{
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" articles pickled\n",
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]
},
{
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" articles pickled\n",
"5300"
]
},
{
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" articles pickled\n",
"5400"
]
},
{
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" articles pickled\n",
"5500"
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"5700"
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{
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" articles pickled\n",
"5800"
]
},
{
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" articles pickled\n",
"5900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"6000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"6100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"6200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"6300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"6400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"6500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"6600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"6700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"6800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"6900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"7000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"7100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"7200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"7300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"7400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"7500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"7600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"7700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"7800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"7900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"8000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"8100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"8200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"8300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"8400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"8500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"8600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"8700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"8800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"8900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"9000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"9100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"9200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"9300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"9400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"9500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"9600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"9700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"9800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"9900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"10000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"10100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"10200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"10300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"10400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"10500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"10600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"10700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"10800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"10900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"11000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"11100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"11200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"11300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"11400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"11500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"11600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"11700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"11800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"11900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"12000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"12100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"12200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"12300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"12400"
]
},
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{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"114400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"114500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"114600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"114700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"114800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"114900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"115000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"115100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"115200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"115300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"115400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"115500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"115600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"115700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"115800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"115900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"116000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"116100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"116200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"116300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"116400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"116500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"116600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"116700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"116800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"116900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"117000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"117100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"117200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"117300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"117400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"117500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"117600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"117700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"117800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"117900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"118000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"118100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"118200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"118300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"118400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"118500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"118600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"118700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"118800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"118900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"119000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"119100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"119200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"119300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"119400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"119500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"119600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"119700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"119800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"119900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"120000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"120100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"120200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"120300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"120400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"120500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"120600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"120700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"120800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"120900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"121000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"121100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"121200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"121300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"121400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"121500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"121600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"121700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"121800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"121900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"122000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"122100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"122200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"122300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"122400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"122500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"122600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"122700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"122800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"122900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"123000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"123100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"123200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"123300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"123400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"123500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"123600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"123700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"123800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"123900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"124000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"124100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"124200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"124300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"124400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"124500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"124600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"124700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"124800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"124900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"125000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"125100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"125200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"125300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"125400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"125500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"125600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"125700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"125800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"125900"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"126000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"126100"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"126200"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"126300"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"126400"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"126500"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"126600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"126700"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"126800"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" articles pickled\n",
"DONE!"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Exploring Output"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#concatenates pickled files\n",
"\n",
"pieces = []\n",
"\n",
"for i in range(0,1267): # replace end of range(0,x) with the number of the last file!\n",
" path = '../data/pickles/plos_pickle_{0}.pkl'.format(i)\n",
" frame = pd.read_pickle(path)\n",
" \n",
" pieces.append(frame)\n",
"\n",
"all_plos_df_for_export = pd.concat(pieces)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_plos_df_for_export.to_pickle('../data/all_plos_df.pkl')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_plos_df = pd.read_pickle('../data/all_plos_df.pkl')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# total number of articles (originally 115489)\n",
"len(all_plos_df)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"121450"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_plos_df.sort(columns = 'id').head()"
],
"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>abstract</th>\n",
" <th>author</th>\n",
" <th>id</th>\n",
" <th>journal</th>\n",
" <th>publication_date</th>\n",
" <th>score</th>\n",
" <th>subject</th>\n",
" <th>title_display</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>27</th>\n",
" <td> [\\n During lymphocyte development, V(D)...</td>\n",
" <td> [Alfred Ian Lee, Sebastian D Fugmann, Lindsay ...</td>\n",
" <td> 10.1371/journal.pbio.0000001</td>\n",
" <td> PLoS Biology</td>\n",
" <td> 2003-10-13T00:00:00Z</td>\n",
" <td> 1</td>\n",
" <td> [/Biology and life sciences/Molecular biology/...</td>\n",
" <td> A Functional Analysis of the Spacer of V(D)J R...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td> [\\n Because of the constant threat pose...</td>\n",
" <td> [David Wang, Anatoly Urisman, Yu-Tsueng Liu, M...</td>\n",
" <td> 10.1371/journal.pbio.0000002</td>\n",
" <td> PLoS Biology</td>\n",
" <td> 2003-11-17T00:00:00Z</td>\n",
" <td> 1</td>\n",
" <td> [/Biology and life sciences/Molecular biology/...</td>\n",
" <td> Viral Discovery and Sequence Recovery Using DN...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td> []</td>\n",
" <td> NaN</td>\n",
" <td> 10.1371/journal.pbio.0000003</td>\n",
" <td> PLoS Biology</td>\n",
" <td> 2003-11-17T00:00:00Z</td>\n",
" <td> 1</td>\n",
" <td> [/Biology and life sciences/Organisms/Viruses/...</td>\n",
" <td> Gene Chip for Viral Discovery</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td> []</td>\n",
" <td> NaN</td>\n",
" <td> 10.1371/journal.pbio.0000004</td>\n",
" <td> PLoS Biology</td>\n",
" <td> 2003-10-13T00:00:00Z</td>\n",
" <td> 1</td>\n",
" <td> [/Medicine and health sciences/Immunology/Immu...</td>\n",
" <td> Functional Analysis of RSS Spacers</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td> [\\n Plasmodium falciparum is the causat...</td>\n",
" <td> [Zbynek Bozdech, Manuel Llin\u00e1s, Brian Lee Pull...</td>\n",
" <td> 10.1371/journal.pbio.0000005</td>\n",
" <td> PLoS Biology</td>\n",
" <td> 2003-08-18T00:00:00Z</td>\n",
" <td> 1</td>\n",
" <td> [/Biology and life sciences/Biochemistry/Prote...</td>\n",
" <td> The Transcriptome of the Intraerythrocytic Dev...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 8 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
" abstract \\\n",
"27 [\\n During lymphocyte development, V(D)... \n",
"59 [\\n Because of the constant threat pose... \n",
"65 [] \n",
"51 [] \n",
"69 [\\n Plasmodium falciparum is the causat... \n",
"\n",
" author \\\n",
"27 [Alfred Ian Lee, Sebastian D Fugmann, Lindsay ... \n",
"59 [David Wang, Anatoly Urisman, Yu-Tsueng Liu, M... \n",
"65 NaN \n",
"51 NaN \n",
"69 [Zbynek Bozdech, Manuel Llin\u00e1s, Brian Lee Pull... \n",
"\n",
" id journal publication_date score \\\n",
"27 10.1371/journal.pbio.0000001 PLoS Biology 2003-10-13T00:00:00Z 1 \n",
"59 10.1371/journal.pbio.0000002 PLoS Biology 2003-11-17T00:00:00Z 1 \n",
"65 10.1371/journal.pbio.0000003 PLoS Biology 2003-11-17T00:00:00Z 1 \n",
"51 10.1371/journal.pbio.0000004 PLoS Biology 2003-10-13T00:00:00Z 1 \n",
"69 10.1371/journal.pbio.0000005 PLoS Biology 2003-08-18T00:00:00Z 1 \n",
"\n",
" subject \\\n",
"27 [/Biology and life sciences/Molecular biology/... \n",
"59 [/Biology and life sciences/Molecular biology/... \n",
"65 [/Biology and life sciences/Organisms/Viruses/... \n",
"51 [/Medicine and health sciences/Immunology/Immu... \n",
"69 [/Biology and life sciences/Biochemistry/Prote... \n",
"\n",
" title_display \n",
"27 A Functional Analysis of the Spacer of V(D)J R... \n",
"59 Viral Discovery and Sequence Recovery Using DN... \n",
"65 Gene Chip for Viral Discovery \n",
"51 Functional Analysis of RSS Spacers \n",
"69 The Transcriptome of the Intraerythrocytic Dev... \n",
"\n",
"[5 rows x 8 columns]"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(list(all_plos_df.id))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"121450"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print list(all_plos_df.subject)[0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[u'/Medicine and health sciences/Pathology and laboratory medicine/Signs and symptoms/Fatigue', u'/Biology and life sciences/Anatomy/Musculoskeletal system/Muscles/Muscle fibers', u'/Medicine and health sciences/Physiology/Muscle physiology/Muscle contraction', u'/Biology and life sciences/Cell biology/Cellular types/Animal cells/Muscle fibers', u'/Biology and life sciences/Physiology/Muscle physiology/Muscle functions', u'/Medicine and health sciences/Anatomy/Musculoskeletal system/Muscles/Skeletal muscles', u'/Biology and life sciences/Anatomy/Musculoskeletal system/Muscles/Skeletal muscles', u'/Medicine and health sciences/Physiology/Muscle physiology/Muscle functions', u'/Biology and life sciences/Cell biology/Cellular types/Animal cells/Muscle fibers/Skeletal muscle fibers', u'/Biology and life sciences/Anatomy/Musculoskeletal system/Muscles/Muscle fibers/Skeletal muscle fibers', u'/Biology and life sciences/Physiology/Physiological processes/Homeostasis', u'/Biology and life sciences/Physiology/Muscle physiology/Muscle contraction', u'/Medicine and health sciences/Physiology/Physiological processes/Homeostasis', u'/Research and analysis methods/Imaging techniques/Fluorescence imaging', u'/Medicine and health sciences/Anatomy/Musculoskeletal system/Muscles/Muscle fibers', u'/Medicine and health sciences/Anatomy/Musculoskeletal system/Muscles/Muscle fibers/Skeletal muscle fibers']\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"all_plos_df.tail()"
],
"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>abstract</th>\n",
" <th>author</th>\n",
" <th>id</th>\n",
" <th>journal</th>\n",
" <th>publication_date</th>\n",
" <th>score</th>\n",
" <th>subject</th>\n",
" <th>title_display</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>45</th>\n",
" <td> [Background: Besides having an impact on human...</td>\n",
" <td> [Anna Puig-Oliveras, Yuliaxis Ramayo-Caldas, J...</td>\n",
" <td> 10.1371/journal.pone.0099720</td>\n",
" <td> PLoS ONE</td>\n",
" <td> 2014-06-13T00:00:00Z</td>\n",
" <td> 1</td>\n",
" <td> [/Biology and life sciences/Biochemistry/Lipid...</td>\n",
" <td> Differences in Muscle Transcriptome among Pigs...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td> [\\nThe nuclear receptors and xenosensors const...</td>\n",
" <td> [Marianne Math\u00e4s, Christian Nu\u00dfhag , Oliver Bu...</td>\n",
" <td> 10.1371/journal.pone.0096263</td>\n",
" <td> PLoS ONE</td>\n",
" <td> 2014-05-05T00:00:00Z</td>\n",
" <td> 1</td>\n",
" <td> [/Biology and life sciences/Biochemistry/Prote...</td>\n",
" <td> Structural and Functional Similarity of Amphib...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td> [Background: Body dissatisfaction is the most ...</td>\n",
" <td> [Blanca Ortega-Rold\u00e1n, Sonia Rodr\u00edguez-Ruiz, P...</td>\n",
" <td> 10.1371/journal.pone.0102595</td>\n",
" <td> PLoS ONE</td>\n",
" <td> 2014-07-18T00:00:00Z</td>\n",
" <td> 1</td>\n",
" <td> [/Medicine and health sciences/Anatomy/Integum...</td>\n",
" <td> The Emotional and Attentional Impact of Exposu...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td> [Objective: Maternal mortality (MM) is a core ...</td>\n",
" <td> [Juliana C Giordano, Mary A Parpinelli, Jose G...</td>\n",
" <td> 10.1371/journal.pone.0097401</td>\n",
" <td> PLoS ONE</td>\n",
" <td> 2014-05-13T00:00:00Z</td>\n",
" <td> 1</td>\n",
" <td> [/Medicine and health sciences/Women's health/...</td>\n",
" <td> The Burden of Eclampsia: Results from a Multic...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td> [\\nOur previous studies showed positive correl...</td>\n",
" <td> [Mohammed M H Al-Gayyar, Barbara A Mysona, Sur...</td>\n",
" <td> 10.1371/journal.pone.0054692</td>\n",
" <td> PLoS ONE</td>\n",
" <td> 2013-01-24T00:00:00Z</td>\n",
" <td> 1</td>\n",
" <td> [/Biology and life sciences/Biochemistry/Enzym...</td>\n",
" <td> Diabetes and Overexpression of proNGF Cause Re...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 8 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
" abstract \\\n",
"45 [Background: Besides having an impact on human... \n",
"46 [\\nThe nuclear receptors and xenosensors const... \n",
"47 [Background: Body dissatisfaction is the most ... \n",
"48 [Objective: Maternal mortality (MM) is a core ... \n",
"49 [\\nOur previous studies showed positive correl... \n",
"\n",
" author \\\n",
"45 [Anna Puig-Oliveras, Yuliaxis Ramayo-Caldas, J... \n",
"46 [Marianne Math\u00e4s, Christian Nu\u00dfhag , Oliver Bu... \n",
"47 [Blanca Ortega-Rold\u00e1n, Sonia Rodr\u00edguez-Ruiz, P... \n",
"48 [Juliana C Giordano, Mary A Parpinelli, Jose G... \n",
"49 [Mohammed M H Al-Gayyar, Barbara A Mysona, Sur... \n",
"\n",
" id journal publication_date score \\\n",
"45 10.1371/journal.pone.0099720 PLoS ONE 2014-06-13T00:00:00Z 1 \n",
"46 10.1371/journal.pone.0096263 PLoS ONE 2014-05-05T00:00:00Z 1 \n",
"47 10.1371/journal.pone.0102595 PLoS ONE 2014-07-18T00:00:00Z 1 \n",
"48 10.1371/journal.pone.0097401 PLoS ONE 2014-05-13T00:00:00Z 1 \n",
"49 10.1371/journal.pone.0054692 PLoS ONE 2013-01-24T00:00:00Z 1 \n",
"\n",
" subject \\\n",
"45 [/Biology and life sciences/Biochemistry/Lipid... \n",
"46 [/Biology and life sciences/Biochemistry/Prote... \n",
"47 [/Medicine and health sciences/Anatomy/Integum... \n",
"48 [/Medicine and health sciences/Women's health/... \n",
"49 [/Biology and life sciences/Biochemistry/Enzym... \n",
"\n",
" title_display \n",
"45 Differences in Muscle Transcriptome among Pigs... \n",
"46 Structural and Functional Similarity of Amphib... \n",
"47 The Emotional and Attentional Impact of Exposu... \n",
"48 The Burden of Eclampsia: Results from a Multic... \n",
"49 Diabetes and Overexpression of proNGF Cause Re... \n",
"\n",
"[5 rows x 8 columns]"
]
}
],
"prompt_number": 14
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Cleaning Output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Testing retry decorator\n",
"\n",
"This is making sure that the retry decorator works"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import random\n",
"\n",
"@retry\n",
"def do_something_unreliable():\n",
" if random.randint(0, 2) > 1:\n",
" raise IOError(\"Broken sauce, everything is hosed!!!111one\")\n",
" else:\n",
" return \"Awesome sauce!\"\n",
"\n",
"print do_something_unreliable()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Awesome sauce!\n"
]
}
],
"prompt_number": 15
}
],
"metadata": {}
}
]
} | agpl-3.0 |
gena/paper-osm-2015 | notebooks/TileCatchmentsToGrid.ipynb | 1 | 565841 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generates tiles using a given grid for a set of rasters generated per-catchment"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"from pykml import parser\n",
"import shapely.geometry, shapely.wkt\n",
"from shapely.geometry.polygon import LinearRing\n",
"from shapely.geometry.polygon import Polygon\n",
"import shapely as sl\n",
"import fiona\n",
"import numpy as np\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import pylab\n",
"\n",
"from utils.shapely_plot import draw\n",
"\n",
"pylab.rcParams['figure.figsize'] = (17.0, 15.0)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# read grid cells\n",
"\n",
"doc = file(\"../data/grid.kml\").read()\n",
"\n",
"root = parser.fromstring(doc)\n",
"\n",
"placemarks = [c for c in root.Document.Placemark]\n",
"\n",
"def get_feature(placemark):\n",
" data = placemark.ExtendedData.Data\n",
" id = int([d.value for d in placemark.ExtendedData.Data if d.get('name') == 'id'][0])\n",
"\n",
" scoords = str([d for d in placemark.Polygon.outerBoundaryIs.LinearRing.coordinates][0])[:-1]\n",
" coords = [(float(c[0]), float(c[1])) for c in [p.split(',') for p in scoords.split(' ')]]\n",
" geom = Polygon(LinearRing(coords))\n",
" \n",
" return (id, geom) "
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cells = [get_feature(placemark) for placemark in placemarks]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1964\n"
]
},
{
"data": {
"image/svg+xml": [
"<svg\n",
" preserveAspectRatio=\"xMinYMin meet\"\n",
" viewBox=\"143.511933 -28.7792029968 2.2 2.200035792\"\n",
" width=\"100.0\"\n",
" height=\"100.0\"\n",
" transform=\"translate(0, 100.0),scale(1, -1)\">\n",
" \n",
" <g fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" \n",
" stroke-width=\"0.04400071584\" opacity=\"0.6\">\n",
" <path d=\"M 144.511933,-27.779167 L 144.536933,-27.779183 L 144.561933,-27.779194 L 144.586933,-27.779201 L 144.611933,-27.779203 L 144.636933,-27.779201 L 144.661933,-27.779194 L 144.686933,-27.779183 L 144.711933,-27.779167 L 144.711933,-27.579167 L 144.686933,-27.579183 L 144.661933,-27.579194 L 144.636933,-27.579201 L 144.611933,-27.579203 L 144.586933,-27.579201 L 144.561933,-27.579194 L 144.536933,-27.579183 L 144.511933,-27.579167 L 144.511933,-27.77916 L 144.511933,-27.779167 z\" />\n",
" </g>\n",
" </svg>"
],
"text/plain": [
"<shapely.geometry.polygon.Polygon at 0x7fdf732a22d0>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cell50 = cells[50]\n",
"\n",
"# de\n",
"print(cell50[0])\n",
"cell50[1]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"g = cell50[1]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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X/sto9PSkIqca/P66yvb2Ty4QhIhOr3eGxoy56Tm1M6VaPN5vb2pacbpWa5u2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\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4ETyEJOMih2PfSorKZHg80Y+4JJvDyZ/PzzsLh1MAW7fugHK5zL0qVbWn0LFMpt7ed+6G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evnFe31hKIYJ5Wae3O5sKFYMUySz74DSKXmoNd7colAII7fqF0Y7+4ZjU2ntNq5Z5LJAefw8MEt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\nNACTAQDgMAAAbN4MPmlpeetzgqAPCQITQVFCIUk8e3qf8caNr9Mkq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"text/plain": [
"<matplotlib.figure.Figure at 0x7fdf67cc3d50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# read catchments\n",
"\n",
"catchments_path = r'../data/HydroBASINS/without_lakes/hybas_au_lev06_v1c.shp'\n",
"\n",
"fig = plt.figure()\n",
"axes = plt.axes()\n",
"axes.set_aspect('equal', 'datalim')\n",
"\n",
"bounds = (138.8119327121311, -37.679166666666646, 152.4875, -24.591666666666637)\n",
"axes.set_xlim(bounds[0], bounds[2])\n",
"axes.set_ylim(bounds[1], bounds[3])\n",
"\n",
"with fiona.collection(catchments_path, \"r\") as input:\n",
" for f in input:\n",
" geom = sl.geometry.shape(f['geometry'])\n",
" pfaf_id = int(f['properties']['PFAF_ID'])\n",
" \n",
" if pfaf_id >= 564000 and pfaf_id <= 564999:\n",
" draw(geom, fill='#aaaaff', alpha=0.5)\n",
" \n",
"draw(cell50[1], outline='#ffaaaa', lw=2.0) \n",
"\n",
"plt.show() \n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5060073410\n"
]
},
{
"data": {
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" transform=\"translate(0, 100.0),scale(1, -1)\">\n",
" \n",
" <g fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" \n",
" stroke-width=\"0.0800696435602\" opacity=\"0.6\">\n",
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" </g>\n",
" </svg>"
],
"text/plain": [
"<shapely.geometry.polygon.Polygon at 0x7fdf6584bd10>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"catchments = []\n",
"with fiona.collection(catchments_path, \"r\") as input:\n",
" for f in input:\n",
" geom = sl.geometry.shape(f['geometry'])\n",
" pfaf_id = int(f['properties']['PFAF_ID'])\n",
" id = int(f['properties']['HYBAS_ID'])\n",
" \n",
" if pfaf_id >= 564000 and pfaf_id <= 564999:\n",
" catchments.append((id, geom))\n",
"\n",
"print(catchments[0][0])\n",
"catchments[0][1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* convert all catchments to shp files id_catchment.shp\n",
"* convert all grid cells to shp files id_cell.shp\n",
"\n",
"* foreach grid cell:\n",
" * find overlapping catchments\n",
"\t* foreach overlapping raster\n",
"\t\t* clip raster by its catchment:\n",
"\t* merge all overlapping and clipped rasters"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from shapely.geometry import mapping\n",
"import fiona\n",
"import subprocess\n",
"import os\n",
"\n",
"def remove_file(path):\n",
" if os.path.exists(path):\n",
" os.remove(path)\n",
"\n",
"def clip_catchment_by_cell(cell_geom, catchment_geom):\n",
" return cell_geom.intersection(catchment_geom)\n",
"\n",
"def write_shp(geom, shp_path, id):\n",
" schema = { 'geometry': 'Polygon', 'properties': {'id': 'int'}, }\n",
"\n",
" with fiona.open(shp_path, 'w', 'ESRI Shapefile', schema) as c:\n",
" c.write({'geometry': mapping(geom), 'properties': {'id': id}})\n",
"\n",
"def clip_raster(in_path, out_path, shp_path):\n",
" cmd = u'gdalwarp -dstnodata -99999 -q -cutline {0} -crop_to_cutline -of GTiff {1} {2}'.format(shp_path, in_path, out_path)\n",
" subprocess.check_call(cmd, shell=True)\n",
" pass\n",
"\n",
"def merge_rasters(in_paths, out_path):\n",
" # merge_cmd = r'python C:\\OSGeo4W32\\bin\\gdal_merge.py'\n",
" merge_cmd = r'gdal_merge.py'\n",
" cmd = merge_cmd + u' -co COMPRESS=DEFLATE -co PREDICTOR=2 -co ZLEVEL=6 -n -99999 -a_nodata -99999 -of GTiff -o {0} {1}'.format(out_path, in_paths)\n",
" subprocess.check_call(cmd, shell=True)\n",
" pass\n",
"\n",
"def generate_tile_raster(cell, catchments, catchment_raster_format, cell_raster_format, temp_dir):\n",
" # print('Merging sub-catchments raster for cell {0}: '.format(cell[0]))\n",
" \n",
" cell_id = cell[0]\n",
" cell_geom = cell[1]\n",
" \n",
" out_catchment_rasters = []\n",
" for (idx, catchment) in enumerate(cell_catchments):\n",
" catchment_id = catchment[0]\n",
" catchment_geom = catchment[1]\n",
"\n",
" # write (catchment x cell) to Shapefile\n",
" g = clip_catchment_by_cell(cell_geom, catchment_geom)\n",
" \n",
" if g.area < 1e-5:\n",
" continue\n",
" \n",
" clip_shp = temp_dir + str(idx) + '.shp'\n",
" write_shp(g, clip_shp, idx)\n",
" \n",
" # clip catchment raster by the polygon from previous step\n",
" in_catchment_raster = catchment_raster_format.format(catchment_id)\n",
" out_catchment_raster = temp_dir + str(idx) + '.tif'\n",
" \n",
" # print('Clipping ' + in_catchment_raster + ' ...')\n",
" clip_raster(in_catchment_raster, out_catchment_raster, clip_shp)\n",
"\n",
" out_catchment_rasters.append(out_catchment_raster)\n",
"\n",
" # clean-up\n",
" remove_file(temp_dir + str(idx) + '.cpg')\n",
" remove_file(temp_dir + str(idx) + '.dbf')\n",
" remove_file(temp_dir + str(idx) + '.shp')\n",
" remove_file(temp_dir + str(idx) + '.shx')\n",
"\n",
" if len(out_catchment_rasters) == 0:\n",
" return\n",
" \n",
" # merge sub-catchment rasters clipped by intersection of (cell x catchment)\n",
" tile_path = cell_raster_format.format('{0:05}'.format(cell_id))\n",
" # print('Genereting tile raster ' + tile_path + ' ...')\n",
" merge_rasters(' '.join(out_catchment_rasters), tile_path)\n",
" \n",
" # clean-up\n",
" for path in out_catchment_rasters:\n",
" remove_file(path)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cell: 0, id: 2240, catchment_count: 1\n",
"cell: 1, id: 2239, catchment_count: 1\n",
"cell: 2, id: 2173, catchment_count: 1\n",
"cell: 3, id: 2238, catchment_count: 1\n",
"cell: 4, id: 2172, catchment_count: 1\n",
"cell: 5, id: 2237, catchment_count: 1\n",
"cell: 6, id: 2236, catchment_count: 1\n",
"cell: 7, id: 2170, catchment_count: 2\n",
"cell: 8, id: 2171, catchment_count: 1\n",
"cell: 9, id: 2105, catchment_count: 1\n",
"cell: 10, id: 2103, catchment_count: 1\n",
"cell: 11, id: 2104, catchment_count: 2\n",
"cell: 12, id: 2169, catchment_count: 2\n",
"cell: 13, id: 2235, catchment_count: 2\n",
"cell: 14, id: 2234, catchment_count: 1\n",
"cell: 15, id: 2233, catchment_count: 2\n",
"cell: 16, id: 2167, catchment_count: 1\n",
"cell: 17, id: 2168, catchment_count: 1\n",
"cell: 18, id: 2102, catchment_count: 1\n",
"cell: 19, id: 2101, catchment_count: 1\n",
"cell: 20, id: 2035, catchment_count: 1\n",
"cell: 21, id: 2036, catchment_count: 1\n",
"cell: 22, id: 2037, catchment_count: 1\n",
"cell: 23, id: 1966, catchment_count: 1\n",
"cell: 24, id: 1967, catchment_count: 1\n",
"cell: 25, id: 1968, catchment_count: 1\n",
"cell: 26, id: 2034, catchment_count: 1\n",
"cell: 27, id: 2100, catchment_count: 1\n",
"cell: 28, id: 2166, catchment_count: 1\n",
"cell: 29, id: 2232, catchment_count: 2\n",
"cell: 30, id: 2231, catchment_count: 1\n",
"cell: 31, id: 2165, catchment_count: 1\n",
"cell: 32, id: 2230, catchment_count: 2\n",
"cell: 33, id: 2229, catchment_count: 2\n",
"cell: 34, id: 2164, catchment_count: 1\n",
"cell: 35, id: 2033, catchment_count: 1\n",
"cell: 36, id: 2099, catchment_count: 1\n",
"cell: 37, id: 2032, catchment_count: 1\n",
"cell: 38, id: 2098, catchment_count: 1\n",
"cell: 39, id: 2096, catchment_count: 1\n",
"cell: 40, id: 2097, catchment_count: 1\n",
"cell: 41, id: 2163, catchment_count: 1\n",
"cell: 42, id: 2228, catchment_count: 1\n",
"cell: 43, id: 2162, catchment_count: 1\n",
"cell: 44, id: 2227, catchment_count: 1\n",
"cell: 45, id: 2226, catchment_count: 1\n",
"cell: 46, id: 2160, catchment_count: 1\n",
"cell: 47, id: 2161, catchment_count: 1\n",
"cell: 48, id: 2095, catchment_count: 1\n",
"cell: 49, id: 1963, catchment_count: 2\n",
"cell: 50, id: 1964, catchment_count: 2\n",
"cell: 51, id: 2029, catchment_count: 1\n",
"cell: 52, id: 2030, catchment_count: 1\n",
"cell: 53, id: 2031, catchment_count: 1\n",
"cell: 54, id: 1965, catchment_count: 1\n",
"cell: 55, id: 1899, catchment_count: 2\n",
"cell: 56, id: 1898, catchment_count: 2\n",
"cell: 57, id: 1832, catchment_count: 1\n",
"cell: 58, id: 1831, catchment_count: 1\n",
"cell: 59, id: 1830, catchment_count: 1\n",
"cell: 60, id: 1829, catchment_count: 1\n",
"cell: 61, id: 1895, catchment_count: 1\n",
"cell: 62, id: 1894, catchment_count: 1\n",
"cell: 63, id: 1960, catchment_count: 1\n",
"cell: 64, id: 1961, catchment_count: 2\n",
"cell: 65, id: 1896, catchment_count: 1\n",
"cell: 66, id: 1897, catchment_count: 2\n",
"cell: 67, id: 1962, catchment_count: 2\n",
"cell: 68, id: 2028, catchment_count: 1\n",
"cell: 69, id: 2027, catchment_count: 2\n",
"cell: 70, id: 2093, catchment_count: 1\n",
"cell: 71, id: 2094, catchment_count: 1\n",
"cell: 72, id: 2225, catchment_count: 1\n",
"cell: 73, id: 2224, catchment_count: 1\n",
"cell: 74, id: 2159, catchment_count: 1\n",
"cell: 75, id: 2223, catchment_count: 1\n",
"cell: 76, id: 2157, catchment_count: 1\n",
"cell: 77, id: 2158, catchment_count: 1\n",
"cell: 78, id: 2026, catchment_count: 2\n",
"cell: 79, id: 2092, catchment_count: 1\n",
"cell: 80, id: 2091, catchment_count: 1\n",
"cell: 81, id: 2090, catchment_count: 1\n",
"cell: 82, id: 2156, catchment_count: 1\n",
"cell: 83, id: 2222, catchment_count: 1\n",
"cell: 84, id: 2221, catchment_count: 1\n",
"cell: 85, id: 2155, catchment_count: 1\n",
"cell: 86, id: 2220, catchment_count: 2\n",
"cell: 87, id: 2154, catchment_count: 1\n",
"cell: 88, id: 2089, catchment_count: 1\n",
"cell: 89, id: 2088, catchment_count: 1\n",
"cell: 90, id: 2022, catchment_count: 1\n",
"cell: 91, id: 1957, catchment_count: 1\n",
"cell: 92, id: 2023, catchment_count: 1\n",
"cell: 93, id: 2024, catchment_count: 1\n",
"cell: 94, id: 2025, catchment_count: 2\n",
"cell: 95, id: 1959, catchment_count: 2\n",
"cell: 96, id: 1958, catchment_count: 2\n",
"cell: 97, id: 1893, catchment_count: 1\n",
"cell: 98, id: 1828, catchment_count: 1\n",
"cell: 99, id: 1827, catchment_count: 1\n",
"cell: 100, id: 1826, catchment_count: 2\n",
"cell: 101, id: 1892, catchment_count: 2\n",
"cell: 102, id: 1891, catchment_count: 1\n",
"cell: 103, id: 1825, catchment_count: 1\n",
"cell: 104, id: 1760, catchment_count: 2\n",
"cell: 105, id: 1761, catchment_count: 1\n",
"cell: 106, id: 1762, catchment_count: 1\n",
"cell: 107, id: 1763, catchment_count: 1\n",
"cell: 108, id: 1764, catchment_count: 1\n",
"cell: 109, id: 1765, catchment_count: 1\n",
"cell: 110, id: 357, catchment_count: 1\n",
"cell: 111, id: 356, catchment_count: 1\n",
"cell: 112, id: 290, catchment_count: 1\n",
"cell: 113, id: 291, catchment_count: 1\n",
"cell: 114, id: 225, catchment_count: 1\n",
"cell: 115, id: 144, catchment_count: 1\n",
"cell: 116, id: 79, catchment_count: 1\n",
"cell: 117, id: 80, catchment_count: 1\n",
"cell: 118, id: 145, catchment_count: 1\n",
"cell: 119, id: 146, catchment_count: 1\n",
"cell: 120, id: 277, catchment_count: 1\n",
"cell: 121, id: 211, catchment_count: 1\n",
"cell: 122, id: 210, catchment_count: 1\n",
"cell: 123, id: 276, catchment_count: 1\n",
"cell: 124, id: 342, catchment_count: 1\n",
"cell: 125, id: 343, catchment_count: 1\n",
"cell: 126, id: 344, catchment_count: 1\n",
"cell: 127, id: 278, catchment_count: 1\n",
"cell: 128, id: 279, catchment_count: 1\n",
"cell: 129, id: 280, catchment_count: 1\n",
"cell: 130, id: 345, catchment_count: 1\n",
"cell: 131, id: 346, catchment_count: 2\n",
"cell: 132, id: 347, catchment_count: 2\n",
"cell: 133, id: 349, catchment_count: 3\n",
"cell: 134, id: 348, catchment_count: 5\n",
"cell: 135, id: 283, catchment_count: 2\n",
"cell: 136, id: 282, catchment_count: 4\n",
"cell: 137, id: 281, catchment_count: 2\n",
"cell: 138, id: 150, catchment_count: 1\n",
"cell: 139, id: 216, catchment_count: 2\n",
"cell: 140, id: 217, catchment_count: 3\n",
"cell: 141, id: 152, catchment_count: 1\n",
"cell: 142, id: 151, catchment_count: 3\n",
"cell: 143, id: 86, catchment_count: 2\n",
"cell: 144, id: 21, catchment_count: 2\n",
"cell: 145, id: 20, catchment_count: 1\n",
"cell: 146, id: 19, catchment_count: 1\n",
"cell: 147, id: 85, catchment_count: 3\n",
"cell: 148, id: 84, catchment_count: 1\n",
"cell: 149, id: 149, catchment_count: 1\n",
"cell: 150, id: 215, catchment_count: 1\n",
"cell: 151, id: 214, catchment_count: 1\n",
"cell: 152, id: 83, catchment_count: 1\n",
"cell: 153, id: 148, catchment_count: 1\n",
"cell: 154, id: 213, catchment_count: 1\n",
"cell: 155, id: 212, catchment_count: 1\n",
"cell: 156, id: 147, catchment_count: 1\n",
"cell: 157, id: 81, catchment_count: 1\n",
"cell: 158, id: 82, catchment_count: 1\n",
"cell: 159, id: 158, catchment_count: 1\n",
"cell: 160, id: 157, catchment_count: 1\n",
"cell: 161, id: 156, catchment_count: 2\n",
"cell: 162, id: 90, catchment_count: 2\n",
"cell: 163, id: 23, catchment_count: 1\n",
"cell: 164, id: 22, catchment_count: 1\n",
"cell: 165, id: 88, catchment_count: 1\n",
"cell: 166, id: 87, catchment_count: 2\n",
"cell: 167, id: 153, catchment_count: 1\n",
"cell: 168, id: 154, catchment_count: 1\n",
"cell: 169, id: 89, catchment_count: 1\n",
"cell: 170, id: 155, catchment_count: 2\n",
"cell: 171, id: 286, catchment_count: 2\n",
"cell: 172, id: 220, catchment_count: 1\n",
"cell: 173, id: 219, catchment_count: 1\n",
"cell: 174, id: 218, catchment_count: 1\n",
"cell: 175, id: 284, catchment_count: 1\n",
"cell: 176, id: 285, catchment_count: 2\n",
"cell: 177, id: 350, catchment_count: 2\n",
"cell: 178, id: 351, catchment_count: 2\n",
"cell: 179, id: 352, catchment_count: 1\n",
"cell: 180, id: 353, catchment_count: 1\n",
"cell: 181, id: 287, catchment_count: 2\n",
"cell: 182, id: 221, catchment_count: 2\n",
"cell: 183, id: 222, catchment_count: 1\n",
"cell: 184, id: 288, catchment_count: 1\n",
"cell: 185, id: 223, catchment_count: 1\n",
"cell: 186, id: 224, catchment_count: 1\n",
"cell: 187, id: 289, catchment_count: 1\n",
"cell: 188, id: 354, catchment_count: 1\n",
"cell: 189, id: 355, catchment_count: 1\n",
"cell: 190, id: 420, catchment_count: 1\n",
"cell: 191, id: 485, catchment_count: 1\n",
"cell: 192, id: 486, catchment_count: 1\n",
"cell: 193, id: 617, catchment_count: 1\n",
"cell: 194, id: 551, catchment_count: 1\n",
"cell: 195, id: 616, catchment_count: 1\n",
"cell: 196, id: 615, catchment_count: 1\n",
"cell: 197, id: 550, catchment_count: 1\n",
"cell: 198, id: 419, catchment_count: 1\n",
"cell: 199, id: 484, catchment_count: 1\n",
"cell: 200, id: 418, catchment_count: 1\n",
"cell: 201, id: 483, catchment_count: 1\n",
"cell: 202, id: 482, catchment_count: 1\n",
"cell: 203, id: 417, catchment_count: 1\n",
"cell: 204, id: 416, catchment_count: 1\n",
"cell: 205, id: 415, catchment_count: 3\n",
"cell: 206, id: 481, catchment_count: 1\n",
"cell: 207, id: 547, catchment_count: 2\n",
"cell: 208, id: 612, catchment_count: 2\n",
"cell: 209, id: 613, catchment_count: 2\n",
"cell: 210, id: 548, catchment_count: 1\n",
"cell: 211, id: 549, catchment_count: 1\n",
"cell: 212, id: 614, catchment_count: 1\n",
"cell: 213, id: 680, catchment_count: 2\n",
"cell: 214, id: 679, catchment_count: 2\n",
"cell: 215, id: 678, catchment_count: 2\n",
"cell: 216, id: 744, catchment_count: 1\n",
"cell: 217, id: 743, catchment_count: 1\n",
"cell: 218, id: 809, catchment_count: 2\n",
"cell: 219, id: 745, catchment_count: 2\n",
"cell: 220, id: 810, catchment_count: 2\n",
"cell: 221, id: 811, catchment_count: 2\n",
"cell: 222, id: 812, catchment_count: 1\n",
"cell: 223, id: 746, catchment_count: 2\n",
"cell: 224, id: 681, catchment_count: 1\n",
"cell: 225, id: 747, catchment_count: 1\n",
"cell: 226, id: 682, catchment_count: 1\n",
"cell: 227, id: 683, catchment_count: 1\n",
"cell: 228, id: 748, catchment_count: 1\n",
"cell: 229, id: 813, catchment_count: 1\n",
"cell: 230, id: 814, catchment_count: 1\n",
"cell: 231, id: 880, catchment_count: 1\n",
"cell: 232, id: 945, catchment_count: 2\n",
"cell: 233, id: 879, catchment_count: 1\n",
"cell: 234, id: 878, catchment_count: 1\n",
"cell: 235, id: 943, catchment_count: 3\n",
"cell: 236, id: 944, catchment_count: 2\n",
"cell: 237, id: 1009, catchment_count: 2\n",
"cell: 238, id: 1010, catchment_count: 3\n",
"cell: 239, id: 1011, catchment_count: 2\n",
"cell: 240, id: 1077, catchment_count: 2\n",
"cell: 241, id: 1076, catchment_count: 2\n",
"cell: 242, id: 1142, catchment_count: 1\n",
"cell: 243, id: 1208, catchment_count: 2\n",
"cell: 244, id: 1273, catchment_count: 3\n",
"cell: 245, id: 1207, catchment_count: 3\n",
"cell: 246, id: 1272, catchment_count: 2\n",
"cell: 247, id: 1206, catchment_count: 2\n",
"cell: 248, id: 1075, catchment_count: 1\n",
"cell: 249, id: 1141, catchment_count: 2\n",
"cell: 250, id: 1140, catchment_count: 3\n",
"cell: 251, id: 1139, catchment_count: 2\n",
"cell: 252, id: 1205, catchment_count: 1\n",
"cell: 253, id: 1271, catchment_count: 2\n",
"cell: 254, id: 1270, catchment_count: 1\n",
"cell: 255, id: 1204, catchment_count: 2\n",
"cell: 256, id: 1269, catchment_count: 1\n",
"cell: 257, id: 1268, catchment_count: 3\n",
"cell: 258, id: 1203, catchment_count: 3\n",
"cell: 259, id: 1138, catchment_count: 2\n",
"cell: 260, id: 1137, catchment_count: 3\n",
"cell: 261, id: 1006, catchment_count: 3\n",
"cell: 262, id: 1072, catchment_count: 1\n",
"cell: 263, id: 1073, catchment_count: 3\n",
"cell: 264, id: 1074, catchment_count: 5\n",
"cell: 265, id: 1008, catchment_count: 5\n",
"cell: 266, id: 1007, catchment_count: 3\n",
"cell: 267, id: 942, catchment_count: 4\n",
"cell: 268, id: 877, catchment_count: 2\n",
"cell: 269, id: 876, catchment_count: 4\n",
"cell: 270, id: 875, catchment_count: 3\n",
"cell: 271, id: 941, catchment_count: 2\n",
"cell: 272, id: 940, catchment_count: 3\n",
"cell: 273, id: 939, catchment_count: 4\n",
"cell: 274, id: 937, catchment_count: 2\n",
"cell: 275, id: 938, catchment_count: 3\n",
"cell: 276, id: 1003, catchment_count: 2\n",
"cell: 277, id: 1004, catchment_count: 2\n",
"cell: 278, id: 1005, catchment_count: 1\n",
"cell: 279, id: 1071, catchment_count: 1\n",
"cell: 280, id: 1070, catchment_count: 2\n",
"cell: 281, id: 1136, catchment_count: 3\n",
"cell: 282, id: 1202, catchment_count: 2\n",
"cell: 283, id: 1267, catchment_count: 3\n",
"cell: 284, id: 1201, catchment_count: 1\n",
"cell: 285, id: 1266, catchment_count: 3\n",
"cell: 286, id: 1265, catchment_count: 2\n",
"cell: 287, id: 1200, catchment_count: 3\n",
"cell: 288, id: 1069, catchment_count: 3\n",
"cell: 289, id: 1135, catchment_count: 1\n",
"cell: 290, id: 1134, catchment_count: 3\n",
"cell: 291, id: 1133, catchment_count: 1\n",
"cell: 292, id: 1199, catchment_count: 2\n",
"cell: 293, id: 1264, catchment_count: 2\n",
"cell: 294, id: 1198, catchment_count: 2\n",
"cell: 295, id: 1263, catchment_count: 2\n",
"cell: 296, id: 1262, catchment_count: 2\n",
"cell: 297, id: 1197, catchment_count: 1\n",
"cell: 298, id: 1132, catchment_count: 1\n",
"cell: 299, id: 1131, catchment_count: 2\n",
"cell: 300, id: 1000, catchment_count: 4\n",
"cell: 301, id: 1066, catchment_count: 2\n",
"cell: 302, id: 1067, catchment_count: 1\n",
"cell: 303, id: 1068, catchment_count: 3\n",
"cell: 304, id: 1002, catchment_count: 2\n",
"cell: 305, id: 1001, catchment_count: 4\n",
"cell: 306, id: 936, catchment_count: 1\n",
"cell: 307, id: 935, catchment_count: 2\n",
"cell: 308, id: 934, catchment_count: 2\n",
"cell: 309, id: 869, catchment_count: 1\n",
"cell: 310, id: 803, catchment_count: 1\n",
"cell: 311, id: 737, catchment_count: 2\n",
"cell: 312, id: 672, catchment_count: 2\n",
"cell: 313, id: 738, catchment_count: 3\n",
"cell: 314, id: 673, catchment_count: 1\n",
"cell: 315, id: 674, catchment_count: 1\n",
"cell: 316, id: 739, catchment_count: 2\n",
"cell: 317, id: 804, catchment_count: 2\n",
"cell: 318, id: 870, catchment_count: 1\n",
"cell: 319, id: 805, catchment_count: 2\n",
"cell: 320, id: 871, catchment_count: 2\n",
"cell: 321, id: 806, catchment_count: 2\n",
"cell: 322, id: 872, catchment_count: 2\n",
"cell: 323, id: 873, catchment_count: 1\n",
"cell: 324, id: 874, catchment_count: 2\n",
"cell: 325, id: 808, catchment_count: 1\n",
"cell: 326, id: 807, catchment_count: 1\n",
"cell: 327, id: 742, catchment_count: 1\n",
"cell: 328, id: 677, catchment_count: 1\n",
"cell: 329, id: 676, catchment_count: 1\n",
"cell: 330, id: 741, catchment_count: 1\n",
"cell: 331, id: 740, catchment_count: 1\n",
"cell: 332, id: 675, catchment_count: 1\n",
"cell: 333, id: 609, catchment_count: 1\n",
"cell: 334, id: 610, catchment_count: 1\n",
"cell: 335, id: 611, catchment_count: 2\n",
"cell: 336, id: 545, catchment_count: 2\n",
"cell: 337, id: 546, catchment_count: 2\n",
"cell: 338, id: 480, catchment_count: 2\n",
"cell: 339, id: 479, catchment_count: 1\n",
"cell: 340, id: 414, catchment_count: 3\n",
"cell: 341, id: 413, catchment_count: 1\n",
"cell: 342, id: 544, catchment_count: 1\n",
"cell: 343, id: 478, catchment_count: 2\n",
"cell: 344, id: 412, catchment_count: 2\n",
"cell: 345, id: 411, catchment_count: 1\n",
"cell: 346, id: 476, catchment_count: 2\n",
"cell: 347, id: 477, catchment_count: 2\n",
"cell: 348, id: 543, catchment_count: 1\n",
"cell: 349, id: 608, catchment_count: 2\n",
"cell: 350, id: 542, catchment_count: 2\n",
"cell: 351, id: 607, catchment_count: 2\n",
"cell: 352, id: 606, catchment_count: 2\n",
"cell: 353, id: 541, catchment_count: 2\n",
"cell: 354, id: 410, catchment_count: 1\n",
"cell: 355, id: 475, catchment_count: 1\n",
"cell: 356, id: 409, catchment_count: 1\n",
"cell: 357, id: 474, catchment_count: 1\n",
"cell: 358, id: 473, catchment_count: 1\n",
"cell: 359, id: 408, catchment_count: 1\n",
"cell: 360, id: 407, catchment_count: 1\n",
"cell: 361, id: 538, catchment_count: 1\n",
"cell: 362, id: 604, catchment_count: 1\n",
"cell: 363, id: 539, catchment_count: 1\n",
"cell: 364, id: 540, catchment_count: 1\n",
"cell: 365, id: 605, catchment_count: 1\n",
"cell: 366, id: 670, catchment_count: 3\n",
"cell: 367, id: 671, catchment_count: 2\n",
"cell: 368, id: 802, catchment_count: 2\n",
"cell: 369, id: 736, catchment_count: 2\n",
"cell: 370, id: 801, catchment_count: 1\n",
"cell: 371, id: 800, catchment_count: 1\n",
"cell: 372, id: 734, catchment_count: 1\n",
"cell: 373, id: 735, catchment_count: 1\n",
"cell: 374, id: 669, catchment_count: 1\n",
"cell: 375, id: 799, catchment_count: 1\n",
"cell: 376, id: 927, catchment_count: 1\n",
"cell: 377, id: 993, catchment_count: 1\n",
"cell: 378, id: 1190, catchment_count: 1\n",
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"cell: 2623, id: 4082, catchment_count: 1\n",
"cell: 2624, id: 4017, catchment_count: 2\n",
"cell: 2625, id: 4016, catchment_count: 1\n",
"cell: 2626, id: 4081, catchment_count: 1\n",
"cell: 2627, id: 4080, catchment_count: 1\n",
"cell: 2628, id: 4014, catchment_count: 1\n",
"cell: 2629, id: 3948, catchment_count: 1\n",
"cell: 2630, id: 3949, catchment_count: 2\n",
"cell: 2631, id: 3817, catchment_count: 2\n",
"cell: 2632, id: 3685, catchment_count: 1\n",
"cell: 2633, id: 3751, catchment_count: 1\n",
"cell: 2634, id: 3686, catchment_count: 1\n",
"cell: 2635, id: 3752, catchment_count: 1\n",
"cell: 2636, id: 3818, catchment_count: 1\n",
"cell: 2637, id: 3753, catchment_count: 1\n",
"cell: 2638, id: 3819, catchment_count: 1\n",
"cell: 2639, id: 3884, catchment_count: 1\n",
"cell: 2640, id: 3950, catchment_count: 2\n",
"cell: 2641, id: 3885, catchment_count: 1\n",
"cell: 2642, id: 3951, catchment_count: 2\n",
"cell: 2643, id: 3820, catchment_count: 1\n",
"cell: 2644, id: 4083, catchment_count: 1\n",
"cell: 2645, id: 4084, catchment_count: 1\n",
"cell: 2646, id: 4149, catchment_count: 1\n",
"cell: 2647, id: 3100, catchment_count: 2\n",
"cell: 2648, id: 3035, catchment_count: 1\n",
"cell: 2649, id: 2969, catchment_count: 1\n",
"cell: 2650, id: 2771, catchment_count: 1\n",
"cell: 2651, id: 4124, catchment_count: 1\n",
"cell: 2652, id: 4343, catchment_count: 1\n",
"cell: 2653, id: 4211, catchment_count: 1\n",
"cell: 2654, id: 4208, catchment_count: 1\n",
"cell: 2655, id: 4340, catchment_count: 1\n",
"cell: 2656, id: 4469, catchment_count: 1\n",
"cell: 2657, id: 4337, catchment_count: 1\n",
"cell: 2658, id: 4205, catchment_count: 2\n",
"cell: 2659, id: 4334, catchment_count: 1\n",
"cell: 2660, id: 4331, catchment_count: 2\n",
"cell: 2661, id: 3995, catchment_count: 1\n",
"cell: 2662, id: 4132, catchment_count: 1\n",
"cell: 2663, id: 3936, catchment_count: 3\n",
"cell: 2664, id: 4135, catchment_count: 2\n",
"cell: 2665, id: 4006, catchment_count: 3\n",
"cell: 2666, id: 3544, catchment_count: 3\n",
"cell: 2667, id: 3348, catchment_count: 2\n",
"cell: 2668, id: 3026, catchment_count: 1\n",
"cell: 2669, id: 3163, catchment_count: 1\n",
"cell: 2670, id: 3034, catchment_count: 2\n",
"cell: 2671, id: 3687, catchment_count: 1\n",
"cell: 2672, id: 3555, catchment_count: 1\n",
"cell: 2673, id: 3289, catchment_count: 2\n",
"cell: 2674, id: 3681, catchment_count: 2\n",
"cell: 2675, id: 3485, catchment_count: 2\n",
"cell: 2676, id: 3684, catchment_count: 2\n",
"cell: 2677, id: 3880, catchment_count: 2\n",
"cell: 2678, id: 4009, catchment_count: 1\n",
"cell: 2679, id: 4012, catchment_count: 2\n",
"cell: 2680, id: 4147, catchment_count: 1\n",
"cell: 2681, id: 4015, catchment_count: 1\n",
"cell: 2682, id: 3883, catchment_count: 2\n",
"cell: 2683, id: 3886, catchment_count: 1\n"
]
}
],
"source": [
"def get_overlapping_catchments(g):\n",
" for catchment in catchments:\n",
" id = catchment[0]\n",
" geom = catchment[1]\n",
" if geom.intersects(g):\n",
" yield (id, geom)\n",
" \n",
"\n",
"# find overlapping catchments for every cell and clip+merge corresponding rasters\n",
"idx = 0\n",
"for cell in cells[idx:]:\n",
" cell_id = cell[0]\n",
" cell_geom = cell[1]\n",
" cell_catchments = list(get_overlapping_catchments(cell_geom))\n",
" \n",
" print('cell: {0}, id: {1}, catchment_count: {2}'.format(idx, cell_id, len(cell_catchments)))\n",
" \n",
" generate_tile_raster(cell, cell_catchments, \n",
" '../output_lddout/SRTM_30_Murray_Darling_{0}_dist.tif', \n",
" '../output_lddout_tiled/SRTM_30_Murray_Darling_cell_{0}_dist.tif', \n",
" r'../temp/')\n",
" \n",
" idx = idx + 1\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": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
rsignell-usgs/notebook | SOS/NDBC_SOS.ipynb | 2 | 70964 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Wind Speed and Gust from NDBC SOS service\n",
"Get CSV data from NDBC SOS service using OWSlib, then read CSV data and plot using Pandas"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"import cStringIO\n",
"import pandas as pd\n",
"from owslib.sos import SensorObservationService"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# pick a buoy, any buoy\n",
"#sta_id='44066' # texas tower\n",
"sta_id='44013' # boston buoy"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# pick a start & stop time\n",
"start = '2013-06-12T00:00:00Z'\n",
"stop = '2013-06-14T00:00:00Z'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe src=http://www.ndbc.noaa.gov/station_page.php?station=44013 width=950 height=400></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.core.display import HTML\n",
"HTML('<iframe src=http://www.ndbc.noaa.gov/station_page.php?station=%s width=950 height=400></iframe>' % sta_id)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ndbc=SensorObservationService('http://sdf.ndbc.noaa.gov/sos/server.php?request=GetCapabilities&service=SOS')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'National Data Buoy Center SOS'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"id=ndbc.identification\n",
"id.title"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'All stations on the NDBC SOS server'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"contents = ndbc.contents\n",
"network = contents['network-all']\n",
"network.description"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'National Data Buoy Center SOS'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"id.title"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rfs = network.response_formats"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"text/xml;subtype=\"om/1.0.0\"\n",
"text/csv\n",
"text/tab-separated-values\n",
"application/vnd.google-earth.kml+xml\n",
"text/xml;schema=\"ioos/0.6.1\"\n",
"application/ioos+xml;version=0.6.1\n"
]
}
],
"source": [
"print '\\n'.join(rfs)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"station = contents['station-%s' % sta_id] "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'urn:ioos:station:wmo:44013'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"station.name"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'BOSTON 16 NM East of Boston, MA'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"station.description"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"getob = ndbc.get_operation_by_name('getobservation')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'observedProperty': {'values': ['air_temperature',\n",
" 'air_pressure_at_sea_level',\n",
" 'sea_water_electrical_conductivity',\n",
" 'currents',\n",
" 'sea_water_salinity',\n",
" 'sea_floor_depth_below_sea_surface',\n",
" 'sea_water_temperature',\n",
" 'waves',\n",
" 'winds']},\n",
" 'responseFormat': {'values': ['text/xml;subtype=\"om/1.0.0\"',\n",
" 'text/csv',\n",
" 'text/tab-separated-values',\n",
" 'application/vnd.google-earth.kml+xml',\n",
" 'text/xml;schema=\"ioos/0.6.1\"',\n",
" 'application/ioos+xml;version=0.6.1']}}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"getob.parameters"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# issue the SOS get_obs request\n",
"response = ndbc.get_observation(offerings=['urn:ioos:station:wmo:%s' % sta_id],\n",
" responseFormat='text/csv',\n",
" observedProperties=['winds'],\n",
" eventTime='%s/%s' % (start,stop))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'station_id,sensor_id,\"latitude (degree)\",\"longitude (degree)\",date_time,\"depth (m)\",\"wind_from_direction (degree)\",\"wind_speed (m/s)\",\"wind_speed_of_gust (m/s)\",\"upward_air_velocity (m/s)\"\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T00:50:00Z,-5.00,20.0,5.00,6.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T01:50:00Z,-5.00,30.0,3.00,3.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T02:50:00Z,-5.00,10.0,3.00,3.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T03:50:00Z,-5.00,250.0,2.00,2.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T04:50:00Z,-5.00,230.0,3.00,3.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T05:50:00Z,-5.00,240.0,2.00,3.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T06:50:00Z,-5.00,250.0,2.00,3.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T07:50:00Z,-5.00,270.0,3.00,3.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T08:50:00Z,-5.00,300.0,3.00,3.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T09:50:00Z,-5.00,300.0,5.00,5.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T10:50:00Z,-5.00,280.0,5.00,5.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T11:50:00Z,-5.00,270.0,5.00,5.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T12:50:00Z,-5.00,270.0,5.00,6.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T13:50:00Z,-5.00,300.0,8.00,9.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T14:50:00Z,-5.00,300.0,7.00,8.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T15:50:00Z,-5.00,300.0,6.00,7.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T16:50:00Z,-5.00,300.0,7.00,8.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T17:50:00Z,-5.00,310.0,8.00,9.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T18:50:00Z,-5.00,360.0,6.00,8.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T19:50:00Z,-5.00,340.0,3.00,4.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T20:50:00Z,-5.00,340.0,7.00,8.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T21:50:00Z,-5.00,330.0,6.00,7.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T22:50:00Z,-5.00,350.0,5.00,5.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-12T23:50:00Z,-5.00,330.0,5.00,6.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T00:50:00Z,-5.00,350.0,5.00,6.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T01:50:00Z,-5.00,320.0,4.00,5.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T02:50:00Z,-5.00,330.0,5.00,5.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T03:50:00Z,-5.00,300.0,4.00,6.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T04:50:00Z,-5.00,300.0,5.00,6.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T05:50:00Z,-5.00,280.0,5.00,5.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T06:50:00Z,-5.00,290.0,5.00,6.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T07:50:00Z,-5.00,300.0,4.00,5.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T08:50:00Z,-5.00,300.0,4.00,5.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T09:50:00Z,-5.00,270.0,3.00,3.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T10:50:00Z,-5.00,260.0,3.00,3.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T11:50:00Z,-5.00,260.0,3.00,3.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T12:50:00Z,-5.00,230.0,2.00,2.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T13:50:00Z,-5.00,80.0,2.00,3.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T14:50:00Z,-5.00,110.0,2.00,3.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T15:50:00Z,-5.00,160.0,3.00,3.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T16:50:00Z,-5.00,160.0,4.00,4.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T17:50:00Z,-5.00,160.0,2.00,3.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T18:50:00Z,-5.00,140.0,5.00,6.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T19:50:00Z,-5.00,180.0,5.00,7.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T20:50:00Z,-5.00,160.0,3.00,4.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T21:50:00Z,-5.00,130.0,2.00,3.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T22:50:00Z,-5.00,110.0,3.00,4.00,\\nurn:ioos:station:wmo:44013,urn:ioos:sensor:wmo:44013::anemometer1,42.35,-70.69,2013-06-13T23:50:00Z,-5.00,50.0,4.00,5.00,\\n'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"response"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df2 = pd.read_csv(cStringIO.StringIO(response.strip()),index_col='date_time',parse_dates=True) # skip the units row "
]
},
{
"cell_type": "code",
"execution_count": 20,
"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>station_id</th>\n",
" <th>sensor_id</th>\n",
" <th>latitude (degree)</th>\n",
" <th>longitude (degree)</th>\n",
" <th>depth (m)</th>\n",
" <th>wind_from_direction (degree)</th>\n",
" <th>wind_speed (m/s)</th>\n",
" <th>wind_speed_of_gust (m/s)</th>\n",
" <th>upward_air_velocity (m/s)</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date_time</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>2013-06-12 00:50:00</th>\n",
" <td>urn:ioos:station:wmo:44013</td>\n",
" <td>urn:ioos:sensor:wmo:44013::anemometer1</td>\n",
" <td>42.35</td>\n",
" <td>-70.69</td>\n",
" <td>-5.0</td>\n",
" <td>20.0</td>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-06-12 01:50:00</th>\n",
" <td>urn:ioos:station:wmo:44013</td>\n",
" <td>urn:ioos:sensor:wmo:44013::anemometer1</td>\n",
" <td>42.35</td>\n",
" <td>-70.69</td>\n",
" <td>-5.0</td>\n",
" <td>30.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-06-12 02:50:00</th>\n",
" <td>urn:ioos:station:wmo:44013</td>\n",
" <td>urn:ioos:sensor:wmo:44013::anemometer1</td>\n",
" <td>42.35</td>\n",
" <td>-70.69</td>\n",
" <td>-5.0</td>\n",
" <td>10.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-06-12 03:50:00</th>\n",
" <td>urn:ioos:station:wmo:44013</td>\n",
" <td>urn:ioos:sensor:wmo:44013::anemometer1</td>\n",
" <td>42.35</td>\n",
" <td>-70.69</td>\n",
" <td>-5.0</td>\n",
" <td>250.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-06-12 04:50:00</th>\n",
" <td>urn:ioos:station:wmo:44013</td>\n",
" <td>urn:ioos:sensor:wmo:44013::anemometer1</td>\n",
" <td>42.35</td>\n",
" <td>-70.69</td>\n",
" <td>-5.0</td>\n",
" <td>230.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" station_id \\\n",
"date_time \n",
"2013-06-12 00:50:00 urn:ioos:station:wmo:44013 \n",
"2013-06-12 01:50:00 urn:ioos:station:wmo:44013 \n",
"2013-06-12 02:50:00 urn:ioos:station:wmo:44013 \n",
"2013-06-12 03:50:00 urn:ioos:station:wmo:44013 \n",
"2013-06-12 04:50:00 urn:ioos:station:wmo:44013 \n",
"\n",
" sensor_id \\\n",
"date_time \n",
"2013-06-12 00:50:00 urn:ioos:sensor:wmo:44013::anemometer1 \n",
"2013-06-12 01:50:00 urn:ioos:sensor:wmo:44013::anemometer1 \n",
"2013-06-12 02:50:00 urn:ioos:sensor:wmo:44013::anemometer1 \n",
"2013-06-12 03:50:00 urn:ioos:sensor:wmo:44013::anemometer1 \n",
"2013-06-12 04:50:00 urn:ioos:sensor:wmo:44013::anemometer1 \n",
"\n",
" latitude (degree) longitude (degree) depth (m) \\\n",
"date_time \n",
"2013-06-12 00:50:00 42.35 -70.69 -5.0 \n",
"2013-06-12 01:50:00 42.35 -70.69 -5.0 \n",
"2013-06-12 02:50:00 42.35 -70.69 -5.0 \n",
"2013-06-12 03:50:00 42.35 -70.69 -5.0 \n",
"2013-06-12 04:50:00 42.35 -70.69 -5.0 \n",
"\n",
" wind_from_direction (degree) wind_speed (m/s) \\\n",
"date_time \n",
"2013-06-12 00:50:00 20.0 5.0 \n",
"2013-06-12 01:50:00 30.0 3.0 \n",
"2013-06-12 02:50:00 10.0 3.0 \n",
"2013-06-12 03:50:00 250.0 2.0 \n",
"2013-06-12 04:50:00 230.0 3.0 \n",
"\n",
" wind_speed_of_gust (m/s) upward_air_velocity (m/s) \n",
"date_time \n",
"2013-06-12 00:50:00 6.0 NaN \n",
"2013-06-12 01:50:00 3.0 NaN \n",
"2013-06-12 02:50:00 3.0 NaN \n",
"2013-06-12 03:50:00 2.0 NaN \n",
"2013-06-12 04:50:00 3.0 NaN "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.head()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7effdbdf3590>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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i2tqypeSCxK0ZW2nfsD3PxD3DnFVzXM5SP/QQLF9ubgHuKc8+a9rY1a/v2nkf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\nH2hArZRSSimllA/+H/MXAeb61/zCAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7effe34eb090>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df2[['wind_speed_of_gust (m/s)','wind_speed (m/s)']].plot(figsize=(12,4),title=station.description,legend=True)"
]
},
{
"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": 0
}
| mit |
justincely/lightcurve_pipeline | dev/poisson_hist.ipynb | 1 | 2889 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from lightcurve_pipeline.utils.utils import SETTINGS\n",
"from lightcurve_pipeline.database.database_interface import engine\n",
"from lightcurve_pipeline.database.database_interface import session\n",
"from lightcurve_pipeline.database.database_interface import Stats"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Make plot of 'poisson factor' for all lightcurves\n",
"stats = session.query(Stats).all()\n",
"poisson_factor_all = [item.poisson_factor for item in stats if item.poisson_factor is not None]\n",
"poisson_factor = [pf for pf in poisson_factor_all if pf < 10]\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"ax.set_xlim((0, 10))\n",
"ax.hist(poisson_factor, bins=100)\n",
"ax.set_xlabel(r'stdev / $\\mu$')\n",
"ax.set_title('Poisson Factor (all)')\n",
"ax.text(4, 4000, '{} lightcuves > 10 (max={})'.format(len(poisson_factor_all) - len(poisson_factor), max(poisson_factor_all)), size=10)\n",
"plt.savefig('poisson_histogram.png')\n",
"plt.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Make plot of 'poisson factor' for only composite lightcurves\n",
"stats = session.query(Stats).all()\n",
"stats = session.query(Stats).all()\n",
"poisson_factor_all = [item.poisson_factor for item in stats if item.poisson_factor is not None and 'composite' in item.lightcurve_path]\n",
"poisson_factor = [pf for pf in poisson_factor_all if pf < 10]\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"ax.set_xlim((0, 10))\n",
"ax.hist(poisson_factor, bins=100)\n",
"ax.set_xlabel(r'stdev / $\\mu$')\n",
"ax.set_title('Poisson Factor (composite only)')\n",
"ax.text(4, 1000, '{} lightcuves > 10 (max={})'.format(len(poisson_factor_all) - len(poisson_factor), max(poisson_factor_all)), size=10)\n",
"plt.savefig('poisson_histogram_composite.png')\n",
"plt.close()"
]
},
{
"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
}
| bsd-3-clause |
mmaelicke/felis_python1 | felis_python1/lectures/06_Classes.ipynb | 1 | 20135 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Classes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One of the main features in the Python programming language is its **_object oriented_** structure. Thus, beside procedual programming (*scripting*) it's also possible to use Python for object oriented Programming (OOP). \n",
"In a nutshell, everything in Python is an object and can be understood as an **_instance_** of a specific *class*. Therefore, a class is like a blueprint of how an object is structured and how it should behave. With that in mind, learning to write your own custom classes means implementing more or less any functionality into Python that you can think of. Nowadays, there is hardly anything in the field of information science, that is not implemented in Python."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's have a look at some of the *objects*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"f = open('afile.txt', 'w')\n",
"print(f)\n",
"print(f.__class__)\n",
"print(type(f))\n",
"print(f.readline)\n",
"f.close()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"The *file object* is already implemented in Python, just like thousands of other classes, therefore we do not have to bother with reading and writing files in Pthon. Therefore, let's have a look at defining our own classes. \n",
"A class can be defined using the <span style=\"color: green\">class</span> statement followed by a *class name*. This is very similar to <span style=\"color: green\">def</span>. Everything inside the *class namespace* is now part of that class. The shortest possible class does now define nothing inside the namespace (and will therefore have no attributes and no functionality). Nevertheless, it can be *instantiated* and a *reference* to the *class instance* can be assigned to a variable."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# define class\n",
"class Car:\n",
" pass\n",
"\n",
"# create two instances\n",
"vw = Car()\n",
"audi= Car()\n",
"\n",
"print('vw: ', type(vw), 'audi: ', type(audi))\n",
"print('vw: ', vw.__class__, 'audi: ', audi.__class__)\n",
"print('vw: ', str(vw), 'audi: ', str(audi))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Methods"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The shown class <span style='color: blue'>Car</span> is not really useful. But we can define functions *inside* the class namespace. These functions are called **_methods_**. To be correct here: they are called *instance methods* and should not be confused with *class methods*, which will not be covered here. \n",
"Although, we did not define methods so far, there are already some methods assigned to <span style='color: blue'>Car</span>, which Python created for us. These very generic methods handle the return of the <span style=\"color: green\">type</span> or <span style=\"color: green\">str</span> function if invoked on a <span style='color: blue'>Car</span> instance. \n",
"We will first focus on a special method, the **\\_\\_init\\_\\_**. This method is already defined, but doesn't do anything. But we can do that and fill the method. It will be called on object instantiation. This way we can set default values and define what a <span style='color: blue'>Car</span> instance should look like after creation. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's define an actual speed and maximum speed for our car, because this is what a car needs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# redefine class\n",
"class Car:\n",
" def __init__(self):\n",
" self.speed = 0\n",
" self.max_speed = 100\n",
"\n",
"# create two instances\n",
"vw = Car()\n",
"audi = Car()\n",
"print('vw: speed: %d max speed: %d' % (vw.speed, vw.max_speed))\n",
"print('audi: speed: %d max speed: %d' % (audi.speed, audi.max_speed))\n",
"\n",
"audi.max_speed = 250\n",
"audi.speed = 260\n",
"vw.speed = - 50.4\n",
"\n",
"print('vw: speed: %d max speed: %d' % (vw.speed, vw.max_speed))\n",
"print('audi: speed: %d max speed: %d' % (audi.speed, audi.max_speed))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is better, but still somehow wrong. A car should not be allowed to drive faster than the maximum possible speed. A Volkswagen might not be the best car in the world, but it can do definitely better than negative speeds. A better approach would be to define some methods for accelerating and decelerating the car.<br>\n",
"Define two methods *accelerate* and *decelerate* that accept a value and set the new speed for the car. Prevent the car from negative speeds and stick to the maximum speed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# redefine class\n",
"class Car:\n",
" pass \n",
" \n",
"vw = Car()\n",
"print(vw.speed)\n",
"vw.accelerate(60)\n",
"print(vw.speed)\n",
"vw.accelerate(45)\n",
"print(vw.speed)\n",
"vw.decelerate(10)\n",
"print(vw.speed)\n",
"vw.decelerate(2000)\n",
"print(vw.speed)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Magic Methods"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Maybe you recognized the two underscores in the **\\_\\_init\\_\\_** method. A defined set of function names following this name pattern are called *magic methods* in Python, because they are influcencing the object behaviour using magic. Beside **\\_\\_init\\_\\_** two other very important magic methods are **\\_\\_repr\\_\\_** and **\\_\\_str\\_\\_**. <br>\n",
"The return value of **\\_\\_str\\_\\_** defines the string representation of the object instance. This way you can define the return value whenever <span style=\"color: green\">str</span> is called on an object instance. The **\\_\\_repr\\_\\_** method is very similar, but returns the *object* representation. Whenever possible, the object shall be recoverable from this returned string. However, with most custom classes this is not easily possible and **\\_\\_repr\\_\\_** shall return a one line string that clearly identifies the object **instance**. This is really useful for debugging your code."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print('str(vw) old:' , str(vw))\n",
"\n",
"class Car:\n",
" pass\n",
" \n",
"\n",
"vw = Car()\n",
"vw.accelerate(45)\n",
"print('str(vw) new:', str(vw))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using these functions, almost any behaviour of the <span style='color: blue'>Car</span> instance can be influenced. \n",
"Imagine you are using it in a conditional statement and test two instances for equality or if one instance is *bigger* than the other one.<br>\n",
" \n",
" * Are these two variables equal if they reference *exactly* the same instance?\n",
" * Are they equal in case they are of the same *model*\n",
" * Is one instance *bigger* in case it's actually faster?\n",
" * or has the higher maximum speed?\n",
"\n",
"Let's define a new attribute *model*, which is requested by **\\_\\_init\\_\\_** as an argument. Then the magic method **\\_\\_eq\\_\\_** can be used to check the models of the two instances.<br>\n",
"The **\\_\\_eq\\_\\_** method can be defined like: **\\_\\_eq\\_\\_(self, other)** and return either <span style='color: green'>True</span> or <span style='color: green'>False</span>."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class Car:\n",
" pass\n",
"\n",
"vw = Car('vw')\n",
"vw2 = Car('vw')\n",
"audi = Car('audi')\n",
"\n",
"print('vw equals vw2? ',vw == vw2)\n",
"print('vw equals vw? ',vw == vw)\n",
"print('vw equals audi? ', vw == audi)\n",
"print('is vw exactly 9? ', vw == 9)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### private methods and attributes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The <span style='color: blue'>Car</span> class has two methods which are meant to be used for mainpulating the actual speed. Nevertheless, one could directly assign new values, even of other types than integers, to the speed and max\\_speed attribute. Thus, one would call these attributes *public* attributes, just like *accelerate* and *decelerate* are *public methods*. This implies to other developers, *'It's ok to directly use these attributes and methods, that's why I putted them there.'*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"vw = Car('audi')\n",
"print('Speed: ', vw.speed)\n",
"vw.speed = 900\n",
"print('Speed: ', vw.speed)\n",
"vw.speed = -11023048282\n",
"print('Speed: ', vw.speed)\n",
"vw.speed = Car('vw')\n",
"print('Speed: ', vw.speed)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Consequently, we want to protect this attribute from access from outside the class itself. Other languages use the keyword <span style=\"color: blue\">private</span> to achieve this. Here, Python is not very explicit, as it does not define a keyword or statement for this. You'll have to prefix your attribute or method name with double underscores. Renaming *Car.speed* to *Car.\\_\\_speed* will therefore not work like shown above."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As the user or other developers cannot access the speed anymore, we have to offer a new interface for accessing this attribute. We could either define a method *getSpeed* returning the actual speed or implement a so called *property*. This will be introduced in a later shown example.<br>\n",
"**Note:** Some jupyter notebooks allow accessing a protected attribute, but your Python console won't allow this."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class Car:\n",
" pass\n",
"\n",
"\n",
"vw = Car('vw')\n",
"vw.accelerate(45)\n",
"print(vw)\n",
"vw.decelerate(20)\n",
"print(vw)\n",
"print(vw.getSpeed())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### class attributes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All attributes and methods defined so far have one thing in common. They are bound to the instance. That means you can only access or invoke them using a reference to this instance. In most cases this is exactly what you want and would expect, as altering one instance won't influence other class instances. But in some cases this is exactly the desired behaviour. A typical example is counting object instances. For our <span style='color: blue'>Car</span> class this would mean an attribute storing the current amount of instanciated cars. It is not possible to implement this using instance attibutes and methods. <br>\n",
"One (bad) solution would be shifting the declaration of <span style='color: blue'>Car</span> from the global namespace to a function returning a new car instance. Then the function could increment a global variable. The downside is, that destroyed car instances won't decrement this global variable. A function like this would, by the way, be called a *ClassFactory* in the Python world.<br>\n",
"The second (way better) solution are using a *class attribute*. These attributes are bound to the class, not an instance of that class. That means all instances will operate on the *same* variable. In the field of data analysis one would implement a counter like this for example for counting the instances of a class handling large data amounts like a raster image. Then the amount of instances could be limited."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class Car:\n",
" pass\n",
"\n",
"\n",
"vw = Car('vw')\n",
"print(vw.count)\n",
"audi = Car('audi')\n",
"print(audi.count)\n",
"\n",
"\n",
"bmw = Car('bmw')\n",
"print('BMW:', bmw.max_speed)\n",
"print('VW:', vw.max_speed)\n",
"print('Audi:', audi.max_speed)\n",
"print(vw.count)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Inheritance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a proper OOP language Python does also implement inheritance. This means, that one can define a class which inherits the attibutes and classes from another class. You can put other classes into the parenthesis of your class signature and the new class will inherit from these classes. One would call this new class a *child* class and the class it inherits from a *parent* class. Every of that *child* classes can of course inherit to as many *children* as needed. Then these children will inherit from its parent and all their parents.<br>\n",
"In case a method or attribute gets re-defined, the child method or attribute will overwrtie the parent methods and attributes.<br>\n",
"A real world example of this concept is the definition of a class that can read different file formats and transform the content into a inner-application special format. You could then first write a class that can do the transformation. Next, another class is defined inheriting from this base class. This class can now read all text files on a very generic level. From here different class can be defined, each one capable of exactly one specific text-based format, like a CSV or JSON reader. Now, each of these specific classes know all the methods from all prent classes and the transformation does not have to be redefined on each level. The second advantage is, that at a later point of time one could decide to implement a generic database reader as well. Then different database engine specific reader could be defined and again inherit all the transformation stuff."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Here, we will use this concept to write two inheriting class es **VW** and **Audi**, which both just set the model into a protected attribute.<br> How could this concept be extended?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class VW(Car):\n",
" def __init__(self):\n",
" super(VW, self).__init__('vw')\n",
"\n",
"class Audi(Car):\n",
" def __init__(self):\n",
" super(Audi, self).__init__('audi')\n",
" \n",
"vw = VW()\n",
"audi = Audi()\n",
"\n",
"vw.accelerate(40)\n",
"audi.accelerate(400)\n",
"print(vw)\n",
"print(audi)\n",
"print(vw == audi)\n",
"print(isinstance(vw, VW))\n",
"print(isinstance(vw, Car))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Property"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes it would be really handy if an attribute could be altered or calculated before returning it to the user. Or even better: if one could make a function to behave like an attribute. That's exactly what a *property* does. These are methods with no other argument than *self* and therefore be executed without parentheses. Using a property like this enables us to reimplement the *speed* attribute. We're just using a property.<br>\n",
"The property function is a built-in function that needs a function as only argument and returns exactly the same function again with the *added* property behaviour. In information science a function expecting another function, altering it and returing it back for usage are called *decorators* (a concept borrowed from Java). Decorating functions is in Python even easier as you can just use the *decorator operator*: **@**."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class MyInt(int):\n",
" def as_string(self):\n",
" return 'The value is %s' % self\n",
"\n",
"i = MyInt(5)\n",
"print(i.as_string())\n",
"\n",
"class MyInt(int):\n",
" @property\n",
" def as_string(self):\n",
" return 'The value is %s' % self\n",
" \n",
"x = MyInt(7)\n",
"print(x.as_string)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class Car: \n",
" pass\n",
"\n",
"class VW(Car):\n",
" def __init__(self):\n",
" super(VW, self).__init__('vw')\n",
" \n",
"vw = VW()\n",
"vw.accelerate(60)\n",
"print(vw.speed)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Property.setter "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Obviously, the protectec *\\_\\_speed* attribute cannot be changed and the *speed* property is a function and thus, cannot be set. In the example of the Car, this absolutely makes sense, but nevertheless, setting a property is also possible. This time the property function is defined again accepting an additional positional argument. This will be filled by the assigned value. The Decorator for the redefinition is the *@property.setter* function."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class Model(object):\n",
" def __init__(self, name):\n",
" self.__model = self.check_model(name)\n",
" \n",
" def check_model(self, name):\n",
" if name.lower() not in ('vw', 'audi'):\n",
" return 'VW'\n",
" else:\n",
" return name.upper()\n",
" \n",
" @property\n",
" def model(self):\n",
" return self.__model\n",
" \n",
" @model.setter\n",
" def model(self, value):\n",
" self.__model = self.check_model(value)\n",
" \n",
"car = Model('audi')\n",
"print(car.model)\n",
"car.model = 'vw'\n",
"print(car.model)\n",
"car.model = 'mercedes'\n",
"print(car.model)\n",
"setattr(car, '__model', 'mercedes')\n",
"print(car.model)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:py3-dev]",
"language": "python",
"name": "conda-env-py3-dev-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
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"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
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| mit |
omoju/Fundamentals | CS/Part_4_Graphs_ShortestPath.ipynb | 1 | 3414 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from IPython.display import display\n",
"from IPython.display import HTML\n",
"import IPython.core.display as di # Example: di.display_html('<h3>%s:</h3>' % str, raw=True)\n",
"\n",
"# This line will hide code by default when the notebook is exported as HTML\n",
"di.display_html('<script>jQuery(function() {if (jQuery(\"body.notebook_app\").length == 0) { jQuery(\".input_area\").toggle(); jQuery(\".prompt\").toggle();}});</script>', raw=True)\n",
"\n",
"# This line will add a button to toggle visibility of code blocks, for use with the HTML export version\n",
"di.display_html('''<button onclick=\"jQuery('.input_area').toggle(); jQuery('.prompt').toggle();\">Toggle code</button>''', raw=True)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Unique Shortest Path\n",
"\n",
"Shortest paths are not always unique: sometimes there are two or more different paths with the minimum possible length. Show how to solve the following problem in $O((|V| + |E|)\\log|V|)$ time.\n",
"\n",
"*Input*: An undirected graph $G = (V, E)$; edge lengths $l_e > 0$; starting vertex $s \\in V$.\n",
"\n",
"*Output*: A Boolean array `usp[.]`: for each node $u$, the entry `usp[u]` should be true if and only if there is a *unique* shortest path from $s$ to $u$. (Note: `usp[s]` = true.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solution Explanation\n",
"This can be done by slightly modifying Dijkstra's algorithm. The array `usp[.]` is initialized\n",
"to true in the initialization loop. The main loop is modified as follows:\n",
"<img src=\"hw4.png\" alt=\"img text\" width = 400/>\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This will run in the required time when the heap is implemented as a binary heap.\n",
"\n",
"*Correctness:* By Djikstra's proof of correctness, this algorithm will identify the shortest paths from the source $u$ to the other vertices. For uniqueness, we consider some vertex $v$. If there are multiple shortest paths, then they either all share the same final edge $(w, v)$ (for some vertex $w$), or they have different final edges. In the former case, there must be multiple shortest paths from $u$ to $w$. This will be detected in the first conditional statement when the algorithm explores from $w$ and updates the distance to $v$ by taking edge $(w, v)$. In the latter case, the algorithm will detect the existence of multiple shortest paths with the second conditional statement by testing for equality of two distances."
]
},
{
"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": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
philippgrafendorfe/stackedautoencoders | MNIST_SAE.ipynb | 1 | 2602711 | null | mit |
amirziai/learning | spark/Median for RDDs, Datasets, and Dataframes.ipynb | 1 | 14150 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Median for RDDs, Datasets, and Dataframes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Getting `spark` up and running"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"143 new artifact(s)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"143 new artifacts in macro\n",
"143 new artifacts in runtime\n",
"143 new artifacts in compile\n"
]
},
{
"data": {
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"classpath.add(\n",
" \"org.apache.spark\" %% \"spark-core\" % \"2.0.2\",\n",
" \"org.apache.spark\" %% \"spark-sql\" % \"2.0.2\",\n",
" \"org.apache.spark\" %% \"spark-mllib\" % \"2.0.2\"\n",
");"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[32mimport \u001b[36morg.apache.spark.sql.{SparkSession, DataFrame, Dataset}\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import org.apache.spark.sql.{SparkSession, DataFrame, Dataset}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties\n",
"17/08/03 23:24:04 INFO SparkContext: Running Spark version 2.0.2\n",
"17/08/03 23:24:04 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n",
"17/08/03 23:24:04 INFO SecurityManager: Changing view acls to: amir.ziai\n",
"17/08/03 23:24:04 INFO SecurityManager: Changing modify acls to: amir.ziai\n",
"17/08/03 23:24:04 INFO SecurityManager: Changing view acls groups to: \n",
"17/08/03 23:24:04 INFO SecurityManager: Changing modify acls groups to: \n",
"17/08/03 23:24:04 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(amir.ziai); groups with view permissions: Set(); users with modify permissions: Set(amir.ziai); groups with modify permissions: Set()\n",
"17/08/03 23:24:04 INFO Utils: Successfully started service 'sparkDriver' on port 53119.\n",
"17/08/03 23:24:04 INFO SparkEnv: Registering MapOutputTracker\n",
"17/08/03 23:24:04 INFO SparkEnv: Registering BlockManagerMaster\n",
"17/08/03 23:24:04 INFO DiskBlockManager: Created local directory at /private/var/folders/8n/xtn3n2c50tbgtcr2pnh21dl4002rn2/T/blockmgr-ba771fc8-e73c-4b14-a159-787d0bb2a583\n",
"17/08/03 23:24:04 INFO MemoryStore: MemoryStore started with capacity 2004.6 MB\n",
"17/08/03 23:24:04 INFO SparkEnv: Registering OutputCommitCoordinator\n",
"17/08/03 23:24:05 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.\n",
"17/08/03 23:24:05 INFO Utils: Successfully started service 'SparkUI' on port 4041.\n",
"17/08/03 23:24:05 INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://192.168.0.4:4041\n",
"17/08/03 23:24:05 INFO Executor: Starting executor ID driver on host localhost\n",
"17/08/03 23:24:05 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 53120.\n",
"17/08/03 23:24:05 INFO NettyBlockTransferService: Server created on 192.168.0.4:53120\n",
"17/08/03 23:24:05 INFO BlockManagerMaster: Registering BlockManager BlockManagerId(driver, 192.168.0.4, 53120)\n",
"17/08/03 23:24:05 INFO BlockManagerMasterEndpoint: Registering block manager 192.168.0.4:53120 with 2004.6 MB RAM, BlockManagerId(driver, 192.168.0.4, 53120)\n",
"17/08/03 23:24:05 INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, 192.168.0.4, 53120)\n",
"17/08/03 23:24:05 WARN SparkContext: Use an existing SparkContext, some configuration may not take effect.\n",
"17/08/03 23:24:05 INFO SharedState: Warehouse path is 'file:/Users/amir.ziai/Dropbox/jupyter/spark-warehouse'.\n"
]
},
{
"data": {
"text/plain": [
"\u001b[36mspark\u001b[0m: \u001b[32mSparkSession\u001b[0m = org.apache.spark.sql.SparkSession@6bdd6f2a"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val spark = SparkSession.builder().master(\"local[*]\").getOrCreate()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[32mimport \u001b[36mspark.implicits._\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import spark.implicits._"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating a `Dataset[Double]`"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36mds1\u001b[0m: \u001b[32mDataset\u001b[0m[\u001b[32mDouble\u001b[0m] = [value: double]\n",
"\u001b[36mds2\u001b[0m: \u001b[32mDataset\u001b[0m[\u001b[32mDouble\u001b[0m] = [value: double]\n",
"\u001b[36mds3\u001b[0m: \u001b[32mDataset\u001b[0m[\u001b[32mDouble\u001b[0m] = [value: double]\n",
"\u001b[36mds4\u001b[0m: \u001b[32mDataset\u001b[0m[\u001b[32mDouble\u001b[0m] = [value: double]\n",
"\u001b[36mds5\u001b[0m: \u001b[32mDataset\u001b[0m[\u001b[32mDouble\u001b[0m] = [value: double]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val ds1 = spark.createDataset(Seq(1)).map(_.toDouble)\n",
"val ds2 = spark.createDataset(Seq(1, 2)).map(_.toDouble)\n",
"val ds3 = spark.createDataset(Seq(1, 2, 3)).map(_.toDouble)\n",
"val ds4 = spark.createDataset(Seq(1, 2, 3, 4)).map(_.toDouble)\n",
"val ds5 = spark.createDataset(Seq(1, 2, 3, 4, 5)).map(_.toDouble)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Dataset with odd number of elements"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36mmedian\u001b[0m: \u001b[32mDouble\u001b[0m = \u001b[32m3.0\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val Array(median) = ds5.stat.approxQuantile(\"value\",\n",
" Array(0.5),\n",
" relativeError = 0.1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is strange to me. My understanding is that `relativeError=0` is supposed to result in an exact median calculation. I will have to look into this further."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36mmedian\u001b[0m: \u001b[32mDouble\u001b[0m = \u001b[32m4.0\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val Array(median) = ds5.stat.approxQuantile(\"value\",\n",
" Array(0.5),\n",
" relativeError = 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Dataset with even number of elements"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36mmedian\u001b[0m: \u001b[32mDouble\u001b[0m = \u001b[32m2.0\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val Array(median) = ds4.stat.approxQuantile(\"value\",\n",
" Array(0.5),\n",
" relativeError = 0.1)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36mmedian\u001b[0m: \u001b[32mDouble\u001b[0m = \u001b[32m3.0\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val Array(median) = ds4.stat.approxQuantile(\"value\",\n",
" Array(0.5),\n",
" relativeError = 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Dataset of 1 element"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36mmedian\u001b[0m: \u001b[32mDouble\u001b[0m = \u001b[32m1.0\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val Array(median) = ds1.stat.approxQuantile(\"value\",\n",
" Array(0.5),\n",
" relativeError = 0.1)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36mmedian\u001b[0m: \u001b[32mDouble\u001b[0m = \u001b[32m1.0\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val Array(median) = ds1.stat.approxQuantile(\"value\",\n",
" Array(0.5),\n",
" relativeError = 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exact median calculation with RDDs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is not an efficient implementation but it works."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[32mimport \u001b[36morg.apache.spark.sql.Dataset\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import org.apache.spark.sql.Dataset"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"defined \u001b[32mfunction \u001b[36mmedian\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def median(ds: Dataset[Double], column: String = \"value\"): Double = {\n",
" // Order the dataset\n",
" val dsOrdered = ds.orderBy(column)\n",
" val count = ds.count()\n",
" val dsDouble = dsOrdered.select(column).as[Double]\n",
" \n",
" // Zip the Dataset with index so we can lookup \n",
" // values by index\n",
" val dsWithIndex = dsDouble.rdd.zipWithIndex()\n",
" if (count % 2 == 0) {\n",
" val left = dsWithIndex\n",
" .filter(_._2 == count / 2 - 1)\n",
" .collect()(0)._1\n",
" val right = dsWithIndex\n",
" .filter(_._2 == count / 2)\n",
" .collect()(0)._1\n",
" (left + right) / 2\n",
" } else {\n",
" dsWithIndex.\n",
" filter(_._2 == count / 2)\n",
" .collect()(0)._1\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36mres37\u001b[0m: \u001b[32mDouble\u001b[0m = \u001b[32m3.0\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"median(ds5)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36mres38\u001b[0m: \u001b[32mDouble\u001b[0m = \u001b[32m2.5\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"median(ds4)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36mres39\u001b[0m: \u001b[32mDouble\u001b[0m = \u001b[32m1.0\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"median(ds1)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36mres40\u001b[0m: \u001b[32mDouble\u001b[0m = \u001b[32m1.5\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"median(ds2)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Scala 2.11",
"language": "scala211",
"name": "scala211"
},
"language_info": {
"codemirror_mode": "text/x-scala",
"file_extension": ".scala",
"mimetype": "text/x-scala",
"name": "scala211",
"pygments_lexer": "scala",
"version": "2.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
arcyfelix/Courses | 17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/10-Quantopian-Platform/02-Basic-Algorithm-Methods.ipynb | 2 | 14693 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Basic Algorithm Methods\n",
"\n",
"Let's algorithmically test our earlier optimized tech portfolio strategy with Quantopian!\n",
"\n",
"#### THIS CODE ONLY WORKS ON QUANTOPIAN. EACH CELL CORRESPONDS WITH A PART OF THE VIDEO LECTURE. MAKE SURE TO WATCH THE VIDEOS FOR CLARITY ON THIS!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**initialize()**\n",
"\n",
"initialize() is called exactly once when our algorithm starts and requires context as input.\n",
"\n",
"context is an augmented Python dictionary used for maintaining state during our backtest or live trading, and can be referenced in different parts of our algorithm. context should be used instead of global variables in the algorithm. Properties can be accessed using dot notation (context.some_property)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** handle_data() **\n",
"\n",
"handle_data() is called once at the end of each minute and requires context and data as input. context is a reference to the same dictionary in initialize() and data is an object that stores several API functions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Our Tech Stock Optimized Portfolio\n",
"\n",
"Let's use the tech stock portfolio we calculated earlier. Keep in mind that handle_data() is readjusting our portfolio every minute! That may be unreasonable for certain algorithms, but for this example, we will just continue with these basics functions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def initialize(context):\n",
" # Reference to Tech Stocks\n",
" context.aapl = sid(24)\n",
" context.csco = sid(1900)\n",
" context.amzn = sid(16841)\n",
"\n",
"def handle_data(context, data):\n",
" # Position our portfolio optimization!\n",
" order_target_percent(context.aapl, .27)\n",
" order_target_percent(context.csco, .20)\n",
" order_target_percent(context.amzn, .53)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Grabbing Current Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### data.current()\n",
"data.current() can be used to retrieve the most recent value of a given field(s) for a given asset(s). data.current() requires two arguments: the asset or list of assets, and the field or list of fields being queried. Possible fields include 'price', 'open', 'high', 'low', 'close', and 'volume'. The output type will depend on the input types"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def initialize(context):\n",
" # Reference to Tech Stocks\n",
" context.techies = [sid(16841),\n",
" sid(24),\n",
" sid(1900)]\n",
"\n",
"def handle_data(context, data):\n",
" # Position our portfolio optimization!\n",
" tech_close = data.current(context.techies, 'close')\n",
" print(type(tech_close)) # Pandas Series\n",
" print(tech_close) # Closing Prices "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Note! You can use data.is_stale(sid(#)) to check if the results of data.current() where generated at the current bar (the timeframe) or were forward filled from a previous time."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Checking for trading"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### data.can_trade()\n",
"\n",
"data.can_trade() is used to determine if an asset(s) is currently listed on a supported exchange and can be ordered. If data.can_trade() returns True for a particular asset in a given minute bar, we are able to place an order for that asset in that minute. This is an important guard to have in our algorithm if we hand-pick the securities that we want to trade. It requires a single argument: an asset or a list of assets."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def initialize(context):\n",
" # Reference to amazn\n",
" context.amzn = sid(16841)\n",
" \n",
"def handle_data(context, data):\n",
" # This insures we don't hit an exception!\n",
" if data.can_trade(sid(16841)):\n",
" order_target_percent(context.amzn, 1.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Checking Historical Data\n",
"\n",
"When your algorithm calls data.history on equities, the returned data is adjusted for splits, mergers, and dividends as of the current simulation date. In other words, when your algorithm asks for a historical window of prices, and there is a split in the middle of that window, the first part of that window will be adjusted for the split. This adustment is done so that your algorithm can do meaningful calculations using the values in the window.\n",
"\n",
"This code queries the last 20 days of price history for a static set of securities. Specifically, this returns the closing daily price for the last 20 days, including the current price for the current day. Equity prices are split- and dividend-adjusted as of the current date in the simulation:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"def initialize(context):\n",
" # AAPL, MSFT, and SPY\n",
" context.assets = [sid(24), sid(1900), sid(16841)]\n",
"\n",
"def before_trading_start(context,data):\n",
" price_history = data.history(context.assets,\n",
" fields = \"price\", \n",
" bar_count = 5, \n",
" frequency = \"1d\")\n",
" \n",
" print(price_history)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The bar_count field specifies the number of days or minutes to include in the pandas DataFrame returned by the history function. This parameter accepts only integer values.\n",
"\n",
"The frequency field specifies how often the data is sampled: daily or minutely. Acceptable inputs are ‘1d’ or ‘1m’. For other frequencies, use the pandas resample function."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Examples\n",
"Below are examples of code along with explanations of the data returned.\n",
"\n",
"### Daily History\n",
"\n",
"Use \"1d\" for the frequency. The dataframe returned is always in daily bars. The bars never span more than one trading day. For US equities, a daily bar captures the trade activity during market hours (usually 9:30am-4:00pm ET). For US futures, a daily bar captures the trade activity from 6pm-6pm ET (24 hours). For example, the Monday daily bar captures trade activity from 6pm the day before (Sunday) to 6pm on the Monday. Tuesday's daily bar will run from 6pm Monday to 6pm Tuesday, etc. For either asset class, the last bar, if partial, is built using the minutes of the current day.\n",
"\n",
"### Examples (assuming context.assets exists):\n",
"\n",
"* data.history(context.assets, \"price\", 1, \"1d\") returns the current price.\n",
"* data.history(context.assets, \"volume\", 1, \"1d\") returns the volume since the current day's open, even if it is partial.\n",
"* data.history(context.assets, \"price\", 2, \"1d\") returns yesterday's close price and the current price.\n",
"* data.history(context.assets, \"price\", 6, \"1d\") returns the prices for the previous 5 days and the current price.\n",
"\n",
"\n",
"### Minute History\n",
"\n",
"Use \"1m\" for the frequency.\n",
"\n",
"Examples (assuming context.assets exists):\n",
"\n",
"* data.history(context.assets, \"price\", 1, \"1m\") returns the current price.\n",
"* data.history(context.assets, \"price\", 2, \"1m\") returns the previous minute's close price and the current price.\n",
"* data.history(context.assets, \"volume\", 60, \"1m\") returns the volume for the previous 60 minutes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Scheduling\n",
"\n",
"Use schedule_function to indicate when you want other functions to occur. The functions passed in must take context and data as parameters."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def initialize(context):\n",
" context.appl = sid(49051)\n",
"\n",
" # At ebginning of trading week\n",
" # At Market Open, set 10% of portfolio to be apple\n",
" schedule_function(open_positions, \n",
" date_rules.week_start(), \n",
" time_rules.market_open())\n",
" \n",
" # At end of trading week\n",
" # 30 min before market close, dump all apple stock.\n",
" schedule_function(close_positions, \n",
" date_rules.week_end(), \n",
" time_rules.market_close(minutes = 30))\n",
"\n",
"def open_positions(context, data):\n",
" order_target_percent(context.appl, 0.10)\n",
"\n",
"def close_positions(context, data):\n",
" order_target_percent(context.appl, 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Portfolio Information\n",
"\n",
"You can get portfolio information and record it!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def initialize(context):\n",
" context.amzn = sid(16841)\n",
" context.ibm = sid(3766)\n",
"\n",
" schedule_function(rebalance, \n",
" date_rules.every_day(), \n",
" time_rules.market_open())\n",
" schedule_function(record_vars, \n",
" date_rules.every_day(), \n",
" time_rules.market_close())\n",
"\n",
"def rebalance(context, data):\n",
" # Half of our portfolio long on amazn\n",
" order_target_percent(context.amzn, 0.50)\n",
" # Half is shorting IBM\n",
" order_target_percent(context.ibm, -0.50)\n",
"\n",
"def record_vars(context, data):\n",
"\n",
" # Plot the counts\n",
" record(amzn_close=data.current(context.amzn, 'close'))\n",
" record(ibm_close=data.current(context.ibm, 'close'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Slippage and Commision \n",
"\n",
"### Slippage\n",
"Slippage is where a simulation estimates the impact of orders on the fill rate and execution price they receive. When an order is placed for a trade, the market is affected. Buy orders drive prices up, and sell orders drive prices down; this is generally referred to as the price_impact of a trade. Additionally, trade orders do not necessarily fill instantaneously. Fill rates are dependent on the order size and current trading volume of the ordered security. The volume_limit determines the fraction of a security's trading volume that can be used by your algorithm.\n",
"\n",
"In backtesting and non-brokerage paper trading (Quantopian paper trading), a slippage model can be specified in initialize() using set_slippage(). There are different builtin slippage models that can be used, as well as the option to set a custom model. By default (if a slippage model is not specified), the following volume share slippage model is used:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"set_slippage(slippage.VolumeShareSlippage(volume_limit = 0.025, \n",
" price_impact = 0.1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the default model, if an order of 60 shares is placed for a given stock, then 1000 shares of that stock trade in each of the next several minutes and the volume_limit is 0.025, then our trade order will be split into three orders (25 shares, 25 shares, and 10 shares) that execute over the next 3 minutes.\n",
"\n",
"At the end of each day, all open orders are canceled, so trading liquid stocks is generally a good idea. Additionally, orders placed exactly at market close will not have time to fill, and will be canceled."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Commision\n",
"\n",
"To set the cost of trades, we can specify a commission model in initialize() using set_commission(). By default (if a commission model is not specified), the following commission model is used:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"set_commission(commission.PerShare(cost = 0.0075, \n",
" min_trade_cost = 1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The default commission model charges 0.0075 dollar per share, with a minimum trade cost of $1.\n",
"\n",
"Slippage and commission models can have an impact on the performance of a backtest. The default models used by Quantopian are fairly realistic, and it is highly recommended that you use them."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Great Job!\n",
"\n",
"Those are all the basics of Quantopians Tutorial! With these key functions you actually know enough to begin trading! "
]
}
],
"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
}
| apache-2.0 |
jasonding1354/ScalaFAQ | type_system/phantom_type.ipynb | 1 | 13852 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1 虚类型\n",
"我们将定义好的没有任何实例的类型称为虚类型。\n",
"\n",
"虚类型一般作为一个标志而存在，表明我们不会使用该类型的任何实例，它是用来解决设计问题而存在的。\n",
"\n",
"**对于定义必须按照某一特定顺序执行的工作流而言，虚类型作用很大。**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"下面，我们举一个计算工资的例子。工资计算器必须首先执行“扣税前”的减项操作，然后进行扣税，最后计算器会扣除扣税后的其他减项并算出净收入。"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"defined \u001b[32mtrait \u001b[36mPreTaxDeductions\u001b[0m\n",
"defined \u001b[32mtrait \u001b[36mPostTaxDeductions\u001b[0m\n",
"defined \u001b[32mtrait \u001b[36mFinal\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"// 定义了密封的特质，起到标志的作用\n",
"sealed trait PreTaxDeductions\n",
"sealed trait PostTaxDeductions\n",
"sealed trait Final"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"defined \u001b[32mclass \u001b[36mEmployee\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"// 为了简单起见，此处用Float类型表示金额\n",
"case class Employee(\n",
" name: String,\n",
" annualSalary: Float,\n",
" taxRate: Float, // 所有税种税率相同\n",
" insurancePremiumsPerPayPeriod: Float,\n",
" _401kDeductionRate: Float, // 税前扣除项，美国退休储蓄计划扣款\n",
" postTaxDeductions: Float)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pay[Step]对象中包含Step参数，它表示了当前执行的步骤"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"defined \u001b[32mclass \u001b[36mPay\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"case class Pay[Step](employee: Employee, netPay: Float)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"defined \u001b[32mobject \u001b[36mPayroll\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"object Payroll {\n",
" // 每两周发一次薪水，为了简单，认定每年正好52周\n",
" \n",
" // 调用Payroll类的每一个方法均需要传入Pay[Step]对象\n",
" def start(employee: Employee): Pay[PreTaxDeductions] =\n",
" Pay[PreTaxDeductions](employee, employee.annualSalary / 26.0F)\n",
" \n",
" def minusInsurance(pay: Pay[PreTaxDeductions]): Pay[PreTaxDeductions] = {\n",
" val newNet = pay.netPay - pay.employee.insurancePremiumsPerPayPeriod\n",
" pay copy (netPay = newNet)\n",
" }\n",
" \n",
" def minus401k(pay: Pay[PreTaxDeductions]): Pay[PreTaxDeductions] = {\n",
" val newNet = pay.netPay - (pay.employee._401kDeductionRate * pay.netPay)\n",
" pay copy (netPay = newNet)\n",
" }\n",
" \n",
" def minusTax(pay: Pay[PreTaxDeductions]): Pay[PostTaxDeductions] = {\n",
" val newNet = pay.netPay - (pay.employee.taxRate * pay.netPay)\n",
" pay copy (netPay = newNet)\n",
" }\n",
" \n",
" def minusFinalDeductions(pay: Pay[PostTaxDeductions]): Pay[Final] = {\n",
" val newNet = pay.netPay - pay.employee.postTaxDeductions\n",
" pay copy (netPay = newNet)\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"defined \u001b[32mobject \u001b[36mCalculatePayroll\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"object CalculatePayroll {\n",
" def main(args: Array[String]) = {\n",
" val e = Employee(\"Buck Trends\", 100000.0F, 0.25F, 200F, 0.10F, 0.05F)\n",
" val pay1 = Payroll start e\n",
" // 401K和保险扣除的顺序可以交换\n",
" val pay2 = Payroll minus401k pay1\n",
" val pay3 = Payroll minusInsurance pay2\n",
" val pay4 = Payroll minusTax pay3\n",
" val pay = Payroll minusFinalDeductions pay4\n",
" val twoWeekGross = e.annualSalary / 26.0F\n",
" val twoWeekNet = pay.netPay\n",
" val percent = (twoWeekNet / twoWeekGross) * 100\n",
" println(s\"For ${e.name}, the gross vs. net pay every 2 weeks is:\")\n",
" println(\n",
" f\" $$${twoWeekGross}%.2f vs. $$${twoWeekNet}%.2f or ${percent}%.1f%%\")\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For Buck Trends, the gross vs. net pay every 2 weeks is:\n",
" $3846.15 vs. $2446.10 or 63.6%\n"
]
},
{
"data": {
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"CalculatePayroll.main(Array.empty)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2 引入管道操作符\n",
"为了使得多个流程环节之间表达式更加美观简洁，这里引入“管道”操作符。‘"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"defined \u001b[32mobject \u001b[36mPipeline\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"object Pipeline {\n",
" implicit class toPiped[V](value:V) {\n",
" def |>[R] (f : V => R) = f(value)\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"defined \u001b[32mobject \u001b[36mCalculatePayroll2\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"object CalculatePayroll2 {\n",
" def main(args: Array[String]) = {\n",
" import Pipeline._\n",
" import Payroll._\n",
" val e = Employee(\"Buck Trends\", 100000.0F, 0.25F, 200F, 0.10F, 0.05F)\n",
" val pay = start(e) |>\n",
" minus401k |>\n",
" minusInsurance |>\n",
" minusTax |>\n",
" minusFinalDeductions\n",
" val twoWeekGross = e.annualSalary / 26.0F\n",
" val twoWeekNet = pay.netPay\n",
" val percent = (twoWeekNet / twoWeekGross) * 100\n",
" println(s\"For ${e.name}, the gross vs. net pay every 2 weeks is:\")\n",
" println(\n",
" f\" $$${twoWeekGross}%.2f vs. $$${twoWeekNet}%.2f or ${percent}%.1f%%\")\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For Buck Trends, the gross vs. net pay every 2 weeks is:\n",
" $3846.15 vs. $2446.10 or 63.6%\n"
]
},
{
"data": {
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"CalculatePayroll2.main(Array.empty)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"管道操作符实际上只是重排了表达式中各个标记的次序。\n",
"\n",
"例如：|>操作符对`pay1 |> Payroll.minus401k`进行转化，转化后的表达式是`Payroll.minus401k(pay1)`。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3 发射火箭的例子"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"defined \u001b[32mtrait \u001b[36mNoFuel\u001b[0m\n",
"defined \u001b[32mtrait \u001b[36mFueled\u001b[0m\n",
"defined \u001b[32mtrait \u001b[36mNoO2\u001b[0m\n",
"defined \u001b[32mtrait \u001b[36mHasO2\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"// 虚类型\n",
"sealed trait NoFuel\n",
"sealed trait Fueled\n",
"sealed trait NoO2\n",
"sealed trait HasO2"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"defined \u001b[32mclass \u001b[36mRocket\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"final case class Rocket[Fuel, O2] ()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"defined \u001b[32mfunction \u001b[36mcreateRocket\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def createRocket = Rocket[NoFuel, NoO2]()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"defined \u001b[32mfunction \u001b[36maddFuel\u001b[0m\n",
"defined \u001b[32mfunction \u001b[36maddO2\u001b[0m\n",
"defined \u001b[32mfunction \u001b[36mlaunch\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def addFuel[O2](x: Rocket[NoFuel, O2]) = Rocket[Fueled, O2]()\n",
"\n",
"def addO2[Fuel](x : Rocket[Fuel, NoO2]) = Rocket[Fuel, HasO2]()\n",
"\n",
"def launch(x : Rocket[Fueled, HasO2]) = \"blastoff\""
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36mfueledRocket\u001b[0m: \u001b[32mRocket\u001b[0m[\u001b[32mFueled\u001b[0m, \u001b[32mNoO2\u001b[0m] = \u001b[33mRocket\u001b[0m()"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val fueledRocket = addFuel(createRocket)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36mhasFuelO2Rocket\u001b[0m: \u001b[32mRocket\u001b[0m[\u001b[32mFueled\u001b[0m, \u001b[32mHasO2\u001b[0m] = \u001b[33mRocket\u001b[0m()"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val hasFuelO2Rocket = addO2(fueledRocket)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36mlaunchRocket\u001b[0m: \u001b[32mString\u001b[0m = \u001b[32m\"blastoff\"\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val launchRocket = launch(hasFuelO2Rocket)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**使用管道操作符**"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[32mimport \u001b[36mPipeline._\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import Pipeline._"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"defined \u001b[32mfunction \u001b[36mlaunchRocketProcess\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def launchRocketProcess = createRocket |> addFuel |> addO2 |> launch"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36mres21\u001b[0m: \u001b[32mString\u001b[0m = \u001b[32m\"blastoff\"\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"launchRocketProcess // 小火箭发射成功"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Scala 2.11",
"language": "scala211",
"name": "scala211"
},
"language_info": {
"codemirror_mode": "text/x-scala",
"file_extension": ".scala",
"mimetype": "text/x-scala",
"name": "scala211",
"pygments_lexer": "scala",
"version": "2.11.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
cathalmccabe/PYNQ | boards/Pynq-Z1/base/notebooks/pmod/pmod_adc.ipynb | 4 | 15012 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Pmod ADC Reading Waveforms\n",
"\n",
"This demonstration shows how to use the Pmod ADC (AD2). \n",
"\n",
"The Pmod ADC, and an analog signal generator are required for this demonstration. \n",
"\n",
"In this demonstration, an analog waveform is generated using the Digilent Analog Discovery 2, and the Waveforms software:\n",
"\n",
"[Digilent Analog Discovery 2](http://store.digilentinc.com/analog-discovery-2-100msps-usb-oscilloscope-logic-analyzer-and-variable-power-supply/)\n",
"\n",
"<td> <img src=\"http://cdn6.bigcommerce.com/s-7gavg/products/468/images/2617/Analog_Discovery_2_obl_Academic_600__01249.1447804398.1280.1280.png\" alt=\"Drawing\" style=\"width: 250px;\"/> </td>\n",
"\n",
"[WaveForms 2015](https://reference.digilentinc.com/waveforms3#newest):\n",
"\n",
"<td> <img src=\"https://reference.digilentinc.com/_media/reference/software/waveforms/waveforms-3/waveforms3-0.png\" alt=\"Drawing\" style=\"width: 250px;\"/> </td>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"For the waveform to be displayed, we collect multiple points in each period. However, according to the Nyquist theorem, the sample rate only has to be $2\\times$ the frequency of the signal.\n",
"\n",
"The following block of code is just an example. For the Pmod ADC, the minimum delay between two samples is around $0.3\\,$ms (corresponding to a sampling period of $3\\,$kHz). So the maximum frequency of the input signal can be $1.5\\,$kHz.\n",
"\n",
"For the interface ID used in the following example, if the Pmod ADC is connected to interface PMODA, type in `PMODA`; if the Pmod ADC is connected to interface PMODB, type in `PMODB`.\n",
"\n",
"For the WaveForms configuration, this example uses the following parameters:\n",
"\n",
"| Wavegen Parameters | Configuration |\n",
"| ---------------------- |\n",
"| Type | Sine |\n",
"| Amplitude | 1V |\n",
"| Offset | 1V |\n",
"| Symmetry | 50% |\n",
"| Phase | 0 |\n",
"\n",
"Channel 0 (V1) on Pmod ADC is connected to port W1 on Digilent Analog Discovery 2."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Type in the interface ID used (PMODA or PMODB): PMODB\n",
"Type in the frequency/Hz of the waveform: 200\n"
]
},
{
"data": {
"image/png": 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X02sMDSUHAO7Zk9w76FsXmTKP36xpSmmSpYyZUpokOLCatZG3+K19nCZplgkO/NY+Pr2G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\nFcz/B+Q7cYG3FoRZAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2fa3f3d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from time import sleep\n",
"from pynq.overlays.base import BaseOverlay\n",
"from pynq.lib import Pmod_ADC\n",
"\n",
"base = BaseOverlay(\"base.bit\")\n",
"\n",
"\n",
"if_id = input(\"Type in the interface ID used (PMODA or PMODB): \")\n",
"if if_id.upper()=='PMODA':\n",
" adc = Pmod_ADC(base.PMODA)\n",
"else:\n",
" adc = Pmod_ADC(base.PMODB)\n",
"\n",
"freq = int(input(\"Type in the frequency/Hz of the waveform: \"))\n",
"period = 1/freq\n",
"log_interval_us = 0\n",
"\n",
"# Assume Channel 0 is connected to the waveform generator\n",
"adc.start_log(1,0,0,log_interval_us)\n",
"sleep(3*period)\n",
"log = adc.get_log()\n",
"\n",
"# Draw the figure\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"plt.plot(range(len(log)), log, 'ro')\n",
"plt.title('PMOD ADC Waveform')\n",
"plt.axis([0, len(log), min(log), max(log)])\n",
"plt.show()\n",
"\n",
"adc.reset()\n",
"del adc,base"
]
}
],
"metadata": {
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"display_name": "Python 3",
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"name": "python3"
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| bsd-3-clause |
ivannz/study_notes | year_15_16/fall_2015/game theoretic foundations of ml/labs/SVM-lab.ipynb | 1 | 11833 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Применение машины опорных векторов к выявлению фальшивых купюр"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Подключим необходимые библиотеки."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np, pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import *\n",
"%matplotlib inline\n",
"\n",
"random_state = np.random.RandomState( None )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def collect_result( grid_, names = [ ] ) :\n",
" df = pd.DataFrame( { \"2-Отклонение\" : [ np.std(v_[ 2 ] ) for v_ in grid_.grid_scores_ ],\n",
" \"1-Точность\" : [ v_[ 1 ] for v_ in grid_.grid_scores_ ], },\n",
" index = pd.MultiIndex.from_tuples(\n",
" [ v_[ 0 ].values() for v_ in grid_.grid_scores_ ],\n",
" names = names ) )\n",
" df.sort_index( )\n",
" return df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Данные были взяты из репозитория UCI Machine Learning Repository по адресу http://archive.ics.uci.edu/ml/datasets/banknote+authentication.\n",
"\n",
"Выборка сконструирована при помощи вейвлет преобразования избражений фальшивых и аутентичных банкнот в градациях серого."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.read_csv( 'data_banknote_authentication.txt', sep = \",\", decimal = \".\", header = None,\n",
" names = [ \"variance\", \"skewness\", \"curtosis\", \"entropy\", \"class\" ] )\n",
"\n",
"y = df.xs( \"class\", axis = 1 )\n",
"X = df.drop( \"class\", axis = 1 )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"В исследуемых данных мы имеем следующее число точек:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print len( X )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Загруженные данные разбиваем на две выборки: обучающую ($\\text{*_train}$) и тестовую. которая будет **не** будет использоваться при обучении ($\\text{*_test}$).\n",
"\n",
"Разобьём выборку на обучающую и тестовую в соотношении 2:3."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = cross_validation.train_test_split( X, y, test_size = 0.60,\n",
" random_state = random_state )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"В обучающей выборке имеем столько наблюдений:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print len( X_train )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Рассмотрим SVM в линейно неразделимом случае с $L^1$ нормой на зазоры $(\\xi_i)_{i=1}^n$:\n",
"$$ \\frac{1}{2} \\|\\beta\\|^2 + C \\sum_{i=1}^n \\xi_i \\to \\min_{\\beta, \\beta_0, (\\xi_i)_{i=1}^n} \\,, $$\n",
"при условиях: для любого $i=1,\\ldots,n$ требуется $\\xi_i \\geq 0$ и \n",
"$$ \\bigl( \\beta' \\phi(x_i) + \\beta_0 \\bigr) y_i \\geq 1 - \\xi_i \\,.$$\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"svm_clf_ = svm.SVC( probability = True, max_iter = 100000 )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Параметры вида ядра (и соответственно отображений признаков $\\phi:\\mathcal{X}\\to\\mathcal{H}$) и параметр регуляризации $C$ будем искать с помощью переборного поиска на сетке с $5$-fold кроссвалидацией на тренировочной выборке $\\text{X_train}$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Рассмотрим три ядра: гауссовское\n",
"$$ K( x, y ) = \\text{exp}\\bigl\\{ -\\frac{1}{2\\gamma^2} \\|x-y\\|^2 \\bigr\\} \\,,$$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Вид ядра : Гауссовское ядро\n",
"grid_rbf_ = grid_search.GridSearchCV( svm_clf_, param_grid = {\n",
"## Параметр регуляризции: C = 0.0001, 0.001, 0.01, 0.1, 1, 10.\n",
" \"C\" : np.logspace( -4, 1, num = 6 ),\n",
" \"kernel\" : [ \"rbf\" ],\n",
"## Параметр \"концентрации\" Гауссовского ядра\n",
" \"gamma\" : np.logspace( -2, 2, num = 10 ),\n",
" }, cv = 5, n_jobs = -1, verbose = 0 ).fit( X_train, y_train )\n",
"df_rbf_ = collect_result( grid_rbf_, names = [ \"Ядро\", \"C\", \"Параметр\" ] )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"полимониальное\n",
"$$ K( x, y ) = \\bigl( 1 + \\langle x, y\\rangle\\bigr)^p \\,, $$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Вид ядра : Полиномиальное ядро\n",
"grid_poly_ = grid_search.GridSearchCV( svm.SVC( probability = True, max_iter = 20000, kernel = \"poly\" ), param_grid = {\n",
"## Параметр регуляризции: C = 0.0001, 0.001, 0.01, 0.1, 1, 10.\n",
" \"C\" : np.logspace( -4, 1, num = 6 ),\n",
" \"kernel\" : [ \"poly\" ], \n",
"## Степень полиномиального ядра\n",
" \"degree\" : [ 2, 3, 5, 7 ],\n",
" }, cv = 5, n_jobs = -1, verbose = 0 ).fit( X_train, y_train )\n",
"df_poly_ = collect_result( grid_poly_, names = [ \"Ядро\", \"C\", \"Параметр\" ] )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"и линейное (в $\\mathbb{R}^d$)\n",
"$$ K( x, y ) = \\langle x, y\\rangle \\,,$$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"## Вид ядра : линейное ядро\n",
"grid_linear_ = grid_search.GridSearchCV( svm_clf_, param_grid = {\n",
"## Параметр регуляризции: C = 0.0001, 0.001, 0.01, 0.1, 1, 10.\n",
" \"C\" : np.logspace( -4, 1, num = 6 ),\n",
" \"kernel\" : [ \"linear\" ],\n",
" \"degree\" : [ 0 ]\n",
" }, cv = 5, n_jobs = -1, verbose = 0 ).fit( X_train, y_train )\n",
"df_linear_ = collect_result( grid_linear_, names = [ \"Ядро\", \"C\", \"Параметр\" ] )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Результаты поиска приведены ниже:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pd.concat( [ df_linear_, df_poly_, df_rbf_ ], axis = 0 ).sort_index( )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Посмотрим точность лучших моделей в каждом классе ядер на тестовтй выборке.\n",
"\n",
"Линейное ядро"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print grid_linear_.best_estimator_\n",
"print \"Accuracy: %0.3f%%\" % ( grid_linear_.best_estimator_.score( X_test, y_test ) * 100, )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Гауссовское ядро"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print grid_rbf_.best_estimator_\n",
"print \"Accuracy: %0.3f%%\" % ( grid_rbf_.best_estimator_.score( X_test, y_test ) * 100, )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Полимониальное ядро"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print grid_poly_.best_estimator_\n",
"print \"Accuracy: %0.3f%%\" % ( grid_poly_.best_estimator_.score( X_test, y_test ) * 100, )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Построим ROC-AUC кривую для лучшей моделей."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"result_ = { name_: metrics.roc_curve( y_test, estimator_.predict_proba( X_test )[:,1] )\n",
" for name_, estimator_ in {\n",
" \"Linear\": grid_linear_.best_estimator_,\n",
" \"Polynomial\": grid_poly_.best_estimator_,\n",
" \"RBF\": grid_rbf_.best_estimator_ }.iteritems( ) }"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig = plt.figure( figsize = ( 16, 9 ) )\n",
"ax = fig.add_subplot( 111 )\n",
"ax.set_ylim( -0.1, 1.1 ) ; ax.set_xlim( -0.1, 1.1 )\n",
"\n",
"ax.set_xlabel( \"FPR\" ) ; ax.set_ylabel( u\"TPR\" )\n",
"ax.set_title( u\"ROC-AUC\" )\n",
"\n",
"for name_, value_ in result_.iteritems( ) :\n",
" fpr, tpr, _ = value_\n",
" ax.plot( fpr, tpr, lw=2, label = name_ )\n",
"\n",
"ax.legend( loc = \"lower right\" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Невероятный результат: на тестовой выборке достигается точность $\\geq 99\\%$. И SVM порождает почти идеальный классификатор! Так уж леги данные."
]
}
],
"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 |
myuuuuun/various | STP/SDE.ipynb | 2 | 2339541 | null | mit |
deeplycloudy/dressanalysis | Dress color analysis.ipynb | 1 | 481646 | {
"metadata": {
"name": "",
"signature": "sha256:e934d7b83a14b0b65f03314b6d2880b9ddc9747752981842cbd0508323e97189"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import display, Image\n",
"dress_full = Image('dress.jpeg')\n",
"dress_full"
],
"language": "python",
"metadata": {},
"outputs": [
{
"jpeg": 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PI8UBY4UGuCtjVDSJtTpOo9T034hfGi91mZ7aylyOmc157bmSe4Mky+Y7cljW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\nlv8A9d0/nTuB/9k=\n",
"metadata": {},
"output_type": "pyout",
"prompt_number": 1,
"text": [
"<IPython.core.display.Image at 0x103555050>"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"from io import BytesIO\n",
"import PIL.Image as PILImg\n",
"\n",
"# Thanks to @minrk for these functions\n",
"# http://nbviewer.ipython.org/gist/minrk/7076095\n",
"\n",
"def img2array(im):\n",
" if im.mode != 'RGB':\n",
" im = im.convert(mode='RGB')\n",
" return np.fromstring(im.tostring(), dtype='uint8').reshape((im.size[1], im.size[0], 3))\n",
"\n",
"def display_img_array(ima):\n",
" im = PILImg.fromarray(ima)\n",
" bio = BytesIO()\n",
" im.save(bio, format='png')\n",
" display(Image(bio.getvalue(), format='png'))\n",
"\n",
"dress_img = img2array(PILImg.open('dress.jpeg'))\n",
"# print dress_img.shape\n",
"xi = slice(100,300)\n",
"yi = slice(0,640)\n",
"sliced_dress = dress_img[yi, xi, :]\n",
"display_img_array(sliced_dress)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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V6N0XDV+cWwAwXRPPLY+t1UpO93OSO2Ltp3mfc5kqUDFs2FC2biCn0Tz/vEYr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\nJ1UAaNUJkSB9UnoDfXbTpTo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vTif1er3++uvX19dXa829mbWjfSWxMTNpjDh7f4/R1SMi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\njBvNA262qkhZMx1Wj2WnzNGckAZyu9sGc445x5ePi+Qdy91CzXb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72165JMn9zzOAj9B/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vgmRdHfqV3e+8gEc4SHF0FX9aGUdepKaB7vwqqaivfcVES29kiRwXvYEd8HO0EckTlg/AYJ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\n",
"text": [
"<IPython.core.display.Image at 0x107106f10>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"r = sliced_dress[:,:,0]\n",
"g = sliced_dress[:,:,1]\n",
"b = sliced_dress[:,:,2]\n",
"b.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"(640, 200)"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"white_or_blue = (b > g) & (b > r)\n",
"gold_or_black = ~white_or_blue\n",
"\n",
"white_blue_only = np.zeros_like(sliced_dress)\n",
"white_blue_only[white_or_blue,0] = r[white_or_blue]\n",
"white_blue_only[white_or_blue,1] = g[white_or_blue]\n",
"white_blue_only[white_or_blue,2] = b[white_or_blue]\n",
"\n",
"gold_black_only = np.zeros_like(sliced_dress)\n",
"gold_black_only[gold_or_black,0] = r[gold_or_black]\n",
"gold_black_only[gold_or_black,1] = g[gold_or_black]\n",
"gold_black_only[gold_or_black,2] = b[gold_or_black]\n",
"\n",
"display_img_array(white_blue_only)\n",
"display_img_array(gold_black_only)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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IxCHde2a3Ap4oV3gScbt/uO/7vu9f+cpXLPSL3/BN3/CN3/yN3/QfXN99oYNtFp0hmXQn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"text": [
"<IPython.core.display.Image at 0x10720b950>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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6LASXmeMc7IuZ+by0ORRJrqloHObpQNS10OmkkcdbTi9fOW3IM47DXYLwvAfp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\n0+yUXUgeNVOVwa9bWyPXzKzNbBGZ+X8AAos70waPG6kAAAAASUVORK5CYII=\n",
"text": [
"<IPython.core.display.Image at 0x10354b4d0>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lace_avg_r = gold_black_only[gold_or_black,0].mean()\n",
"lace_avg_g = gold_black_only[gold_or_black,1].mean()\n",
"lace_avg_b = gold_black_only[gold_or_black,2].mean()\n",
"print lace_avg_r, lace_avg_g, lace_avg_b\n",
"\n",
"lines_avg_r = white_blue_only[white_or_blue,0].mean()\n",
"lines_avg_g = white_blue_only[white_or_blue,1].mean()\n",
"lines_avg_b = white_blue_only[white_or_blue,2].mean()\n",
"print lines_avg_r, lines_avg_g, lines_avg_b"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"99.3273143822 87.1049950261 69.3338709191\n",
"114.093943621 123.171732572 154.975800435\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"averaged_dress = np.zeros_like(sliced_dress)\n",
"averaged_dress[white_or_blue,:] = (lines_avg_r, lines_avg_g, lines_avg_b)\n",
"averaged_dress[gold_or_black,:] = (lace_avg_r, lace_avg_g, lace_avg_b)\n",
"display_img_array(averaged_dress)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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"text": [
"<IPython.core.display.Image at 0x1035550d0>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
xpharry/Udacity-DLFoudation | your-first-network/.ipynb_checkpoints/dlnd-your-first-neural-network-checkpoint.ipynb | 1 | 238056 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Your first neural network\n",
"\n",
"In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load and prepare the data\n",
"\n",
"A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_path = 'Bike-Sharing-Dataset/hour.csv'\n",
"\n",
"rides = pd.read_csv(data_path)"
]
},
{
"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>instant</th>\n",
" <th>dteday</th>\n",
" <th>season</th>\n",
" <th>yr</th>\n",
" <th>mnth</th>\n",
" <th>hr</th>\n",
" <th>holiday</th>\n",
" <th>weekday</th>\n",
" <th>workingday</th>\n",
" <th>weathersit</th>\n",
" <th>temp</th>\n",
" <th>atemp</th>\n",
" <th>hum</th>\n",
" <th>windspeed</th>\n",
" <th>casual</th>\n",
" <th>registered</th>\n",
" <th>cnt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2011-01-01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.24</td>\n",
" <td>0.2879</td>\n",
" <td>0.81</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2011-01-01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.22</td>\n",
" <td>0.2727</td>\n",
" <td>0.80</td>\n",
" <td>0.0</td>\n",
" <td>8</td>\n",
" <td>32</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>2011-01-01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.22</td>\n",
" <td>0.2727</td>\n",
" <td>0.80</td>\n",
" <td>0.0</td>\n",
" <td>5</td>\n",
" <td>27</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2011-01-01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.24</td>\n",
" <td>0.2879</td>\n",
" <td>0.75</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>10</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>2011-01-01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.24</td>\n",
" <td>0.2879</td>\n",
" <td>0.75</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" instant dteday season yr mnth hr holiday weekday workingday \\\n",
"0 1 2011-01-01 1 0 1 0 0 6 0 \n",
"1 2 2011-01-01 1 0 1 1 0 6 0 \n",
"2 3 2011-01-01 1 0 1 2 0 6 0 \n",
"3 4 2011-01-01 1 0 1 3 0 6 0 \n",
"4 5 2011-01-01 1 0 1 4 0 6 0 \n",
"\n",
" weathersit temp atemp hum windspeed casual registered cnt \n",
"0 1 0.24 0.2879 0.81 0.0 3 13 16 \n",
"1 1 0.22 0.2727 0.80 0.0 8 32 40 \n",
"2 1 0.22 0.2727 0.80 0.0 5 27 32 \n",
"3 1 0.24 0.2879 0.75 0.0 3 10 13 \n",
"4 1 0.24 0.2879 0.75 0.0 0 1 1 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rides.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Checking out the data\n",
"\n",
"This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the `cnt` column. You can see the first few rows of the data above.\n",
"\n",
"Below is a plot showing the number of bike riders over the first 10 days in the data set. You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc7fa9fdc50>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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ffapq86tvD37PeA3MPoSUuDz4PvcDevBJ3Ykx6Co7Dz6bbAkhdcUsPOLk4I8q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IBSn8HaWr+QMbbb6j8Amz28DldMcAq+twKfFsqiFVrRyFTw62PRqZ1ZTBV86r/eurRLaEFQiXZu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\ndm5i60+ePo1b7rwPT9d4rZRMvmgMfErEI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/nu3u/efPfc55xjJeIC38zMzMysRFzgm5mZ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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc7fac51e80>"
]
},
"metadata": {
"image/png": {
"height": 263,
"width": 380
}
},
"output_type": "display_data"
}
],
"source": [
"rides[:24*10].plot(x='dteday', y='cnt')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dummy variables\n",
"Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to `get_dummies()`."
]
},
{
"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>yr</th>\n",
" <th>holiday</th>\n",
" <th>temp</th>\n",
" <th>hum</th>\n",
" <th>windspeed</th>\n",
" <th>casual</th>\n",
" <th>registered</th>\n",
" <th>cnt</th>\n",
" <th>season_1</th>\n",
" <th>season_2</th>\n",
" <th>...</th>\n",
" <th>hr_21</th>\n",
" <th>hr_22</th>\n",
" <th>hr_23</th>\n",
" <th>weekday_0</th>\n",
" <th>weekday_1</th>\n",
" <th>weekday_2</th>\n",
" <th>weekday_3</th>\n",
" <th>weekday_4</th>\n",
" <th>weekday_5</th>\n",
" <th>weekday_6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.24</td>\n",
" <td>0.81</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</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>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.22</td>\n",
" <td>0.80</td>\n",
" <td>0.0</td>\n",
" <td>8</td>\n",
" <td>32</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</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>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.22</td>\n",
" <td>0.80</td>\n",
" <td>0.0</td>\n",
" <td>5</td>\n",
" <td>27</td>\n",
" <td>32</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</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>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.24</td>\n",
" <td>0.75</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>10</td>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</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>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.24</td>\n",
" <td>0.75</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</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>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 59 columns</p>\n",
"</div>"
],
"text/plain": [
" yr holiday temp hum windspeed casual registered cnt season_1 \\\n",
"0 0 0 0.24 0.81 0.0 3 13 16 1 \n",
"1 0 0 0.22 0.80 0.0 8 32 40 1 \n",
"2 0 0 0.22 0.80 0.0 5 27 32 1 \n",
"3 0 0 0.24 0.75 0.0 3 10 13 1 \n",
"4 0 0 0.24 0.75 0.0 0 1 1 1 \n",
"\n",
" season_2 ... hr_21 hr_22 hr_23 weekday_0 weekday_1 weekday_2 \\\n",
"0 0 ... 0 0 0 0 0 0 \n",
"1 0 ... 0 0 0 0 0 0 \n",
"2 0 ... 0 0 0 0 0 0 \n",
"3 0 ... 0 0 0 0 0 0 \n",
"4 0 ... 0 0 0 0 0 0 \n",
"\n",
" weekday_3 weekday_4 weekday_5 weekday_6 \n",
"0 0 0 0 1 \n",
"1 0 0 0 1 \n",
"2 0 0 0 1 \n",
"3 0 0 0 1 \n",
"4 0 0 0 1 \n",
"\n",
"[5 rows x 59 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n",
"for each in dummy_fields:\n",
" dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n",
" rides = pd.concat([rides, dummies], axis=1)\n",
"\n",
"fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \n",
" 'weekday', 'atemp', 'mnth', 'workingday', 'hr']\n",
"data = rides.drop(fields_to_drop, axis=1)\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scaling target variables\n",
"To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.\n",
"\n",
"The scaling factors are saved so we can go backwards when we use the network for predictions."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n",
"# Store scalings in a dictionary so we can convert back later\n",
"scaled_features = {}\n",
"for each in quant_features:\n",
" mean, std = data[each].mean(), data[each].std()\n",
" scaled_features[each] = [mean, std]\n",
" data.loc[:, each] = (data[each] - mean)/std"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Splitting the data into training, testing, and validation sets\n",
"\n",
"We'll save the last 21 days of the data to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Save the last 21 days \n",
"test_data = data[-21*24:]\n",
"data = data[:-21*24]\n",
"\n",
"# Separate the data into features and targets\n",
"target_fields = ['cnt', 'casual', 'registered']\n",
"features, targets = data.drop(target_fields, axis=1), data[target_fields]\n",
"test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Hold out the last 60 days of the remaining data as a validation set\n",
"train_features, train_targets = features[:-60*24], targets[:-60*24]\n",
"val_features, val_targets = features[-60*24:], targets[-60*24:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Time to build the network\n",
"\n",
"Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.\n",
"\n",
"The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called *forward propagation*.\n",
"\n",
"We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called *backpropagation*.\n",
"\n",
"> **Hint:** You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.\n",
"\n",
"Below, you have these tasks:\n",
"1. Implement the sigmoid function to use as the activation function. Set `self.activation_function` in `__init__` to your sigmoid function.\n",
"2. Implement the forward pass in the `train` method.\n",
"3. Implement the backpropagation algorithm in the `train` method, including calculating the output error.\n",
"4. Implement the forward pass in the `run` method.\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class NeuralNetwork(object):\n",
" def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n",
" # Set number of nodes in input, hidden and output layers.\n",
" self.input_nodes = input_nodes\n",
" self.hidden_nodes = hidden_nodes\n",
" self.output_nodes = output_nodes\n",
"\n",
" # Initialize weights\n",
" self.weights_input_to_hidden = np.random.normal(0.0, self.hidden_nodes**-0.5, \n",
" (self.hidden_nodes, self.input_nodes))\n",
"\n",
" self.weights_hidden_to_output = np.random.normal(0.0, self.output_nodes**-0.5, \n",
" (self.output_nodes, self.hidden_nodes))\n",
" self.lr = learning_rate\n",
" \n",
" #### Set this to your implemented sigmoid function ####\n",
" # Activation function is the sigmoid function\n",
" self.activation_function = self.sigmoid\n",
" \n",
" def sigmoid(self, x):\n",
" return 1 / (1 + np.exp(-x))\n",
" \n",
" def train(self, inputs_list, targets_list):\n",
" # Convert inputs list to 2d array\n",
" inputs = np.array(inputs_list, ndmin=2).T\n",
" targets = np.array(targets_list, ndmin=2).T\n",
" \n",
" #### Implement the forward pass here ####\n",
" ### Forward pass ###\n",
" # TODO: Hidden layer\n",
" hidden_inputs = np.dot(self.weights_input_to_hidden, inputs) # signals into hidden layer\n",
" hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n",
" \n",
" # TODO: Output layer\n",
" final_inputs = np.dot(self.weights_hidden_to_output, hidden_outputs) # signals into final output layer\n",
" final_outputs = self.activation_function(final_inputs) # signals from final output layer\n",
" \n",
" #### Implement the backward pass here ####\n",
" ### Backward pass ###\n",
" \n",
" # TODO: Output error\n",
" output_errors = targets - final_outputs # Output layer error is the difference between desired target and actual output.\n",
" \n",
" # TODO: Backpropagated error\n",
" hidden_errors = np.dot(self.weights_hidden_to_output, output_error) # errors propagated to the hidden layer\n",
" hidden_grad = hidden_outputs * (1 - hidden_outputs) # hidden layer gradients\n",
" \n",
" # TODO: Update the weights\n",
" self.weights_hidden_to_output += self.lr * np.dot(hidden_outputs, output_errors).T # update hidden-to-output weights with gradient descent step\n",
" self.weights_input_to_hidden += self.lr * np.dot(inputs, hidden_errors * hidden_grad).T # update input-to-hidden weights with gradient descent step\n",
" \n",
" \n",
" def run(self, inputs_list):\n",
" # Run a forward pass through the network\n",
" inputs = np.array(inputs_list, ndmin=2).T\n",
" \n",
" #### Implement the forward pass here ####\n",
" # TODO: Hidden layer\n",
" hidden_inputs = np.dot(self.weights_input_to_hidden, inputs) # signals into hidden layer\n",
" hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n",
" \n",
" # TODO: Output layer\n",
" final_inputs = np.dot(self.weights_hidden_to_output, hidden_outputs) # signals into final output layer\n",
" final_outputs = self.activation_function(final_inputs) # signals from final output layer \n",
" \n",
" return final_outputs"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def MSE(y, Y):\n",
" return np.mean((y-Y)**2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training the network\n",
"\n",
"Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.\n",
"\n",
"You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.\n",
"\n",
"### Choose the number of epochs\n",
"This is the number of times the dataset will pass through the network, each time updating the weights. As the number of epochs increases, the network becomes better and better at predicting the targets in the training set. You'll need to choose enough epochs to train the network well but not too many or you'll be overfitting.\n",
"\n",
"### Choose the learning rate\n",
"This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.\n",
"\n",
"### Choose the number of hidden nodes\n",
"The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose."
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1, 1)\n",
"(3, 3)\n"
]
},
{
"ename": "ValueError",
"evalue": "shapes (56,1) and (3,3) not aligned: 1 (dim 1) != 3 (dim 0)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-89-5346589e9cfa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 16\u001b[0m for record, target in zip(train_features.ix[batch].values, \n\u001b[1;32m 17\u001b[0m train_targets.ix[batch]['cnt']):\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mnetwork\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecord\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;31m# Printing out the training progress\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-87-cd50e945b945>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, inputs_list, targets_list)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_grad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_hidden_to_output\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_errors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;31m# update hidden-to-output weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_input_to_hidden\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhidden_errors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mhidden_grad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;31m# update input-to-hidden weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: shapes (56,1) and (3,3) not aligned: 1 (dim 1) != 3 (dim 0)"
]
}
],
"source": [
"import sys\n",
"\n",
"### Set the hyperparameters here ###\n",
"epochs = 1000\n",
"learning_rate = 0.05\n",
"hidden_nodes = 3\n",
"output_nodes = 1\n",
"\n",
"N_i = train_features.shape[1]\n",
"network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)\n",
"\n",
"losses = {'train':[], 'validation':[]}\n",
"for e in range(epochs):\n",
" # Go through a random batch of 128 records from the training data set\n",
" batch = np.random.choice(train_features.index, size=128)\n",
" for record, target in zip(train_features.ix[batch].values, \n",
" train_targets.ix[batch]['cnt']):\n",
" network.train(record, target)\n",
" \n",
" # Printing out the training progress\n",
" train_loss = MSE(network.run(train_features), train_targets['cnt'].values)\n",
" val_loss = MSE(network.run(val_features), val_targets['cnt'].values)\n",
" sys.stdout.write(\"\\rProgress: \" + str(100 * e/float(epochs))[:4] \\\n",
" + \"% ... Training loss: \" + str(train_loss)[:5] \\\n",
" + \" ... Validation loss: \" + str(val_loss)[:5])\n",
" \n",
" losses['train'].append(train_loss)\n",
" losses['validation'].append(val_loss)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1.0802171755827887, 0.5)"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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QAYD58+dTVlbGypUrG+XP9lJzUv7XqrKyMubPnw/833utIZRiGcwOxePrtdTP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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc7f5d10cf8>"
]
},
"metadata": {
"image/png": {
"height": 252,
"width": 373
}
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(losses['train'], label='Training loss')\n",
"plt.plot(losses['validation'], label='Validation loss')\n",
"plt.legend()\n",
"plt.ylim(ymax=0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check out your predictions\n",
"\n",
"Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly."
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/peng/lib/anaconda3/envs/dlnd/lib/python3.6/site-packages/ipykernel/__main__.py:21: RuntimeWarning: overflow encountered in exp\n"
]
},
{
"data": {
"image/png": 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XVgP+C6v//KFynx9Vv54thNg8cJ/pAL4EYADAT00XXxi+lMevehW462vA49e2eiVERERE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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc7f5bac9b0>"
]
},
"metadata": {
"image/png": {
"height": 272,
"width": 502
}
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8,4))\n",
"\n",
"mean, std = scaled_features['cnt']\n",
"predictions = network.run(test_features)*std + mean\n",
"ax.plot(predictions[0], label='Prediction')\n",
"ax.plot((test_targets['cnt']*std + mean).values, label='Data')\n",
"ax.set_xlim(right=len(predictions))\n",
"ax.legend()\n",
"\n",
"dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])\n",
"dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
"ax.set_xticks(np.arange(len(dates))[12::24])\n",
"_ = ax.set_xticklabels(dates[12::24], rotation=45)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Thinking about your results\n",
" \n",
"Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?\n",
"\n",
"> **Note:** You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter\n",
"\n",
"#### Your answer below"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Unit tests\n",
"\n",
"Run these unit tests to check the correctness of your network implementation. These tests must all be successful to pass the project."
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"...FF\n",
"======================================================================\n",
"FAIL: test_run (__main__.TestMethods)\n",
"----------------------------------------------------------------------\n",
"Traceback (most recent call last):\n",
" File \"<ipython-input-77-f992b33152d9>\", line 51, in test_run\n",
" self.assertTrue(np.allclose(network.run(inputs), 0.09998924))\n",
"AssertionError: False is not true\n",
"\n",
"======================================================================\n",
"FAIL: test_train (__main__.TestMethods)\n",
"----------------------------------------------------------------------\n",
"Traceback (most recent call last):\n",
" File \"<ipython-input-77-f992b33152d9>\", line 40, in test_train\n",
" np.array([[ 0.37275328, -0.03172939]])))\n",
"AssertionError: False is not true\n",
"\n",
"----------------------------------------------------------------------\n",
"Ran 5 tests in 0.003s\n",
"\n",
"FAILED (failures=2)\n"
]
},
{
"data": {
"text/plain": [
"<unittest.runner.TextTestResult run=5 errors=0 failures=2>"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import unittest\n",
"\n",
"inputs = [0.5, -0.2, 0.1]\n",
"targets = [0.4]\n",
"test_w_i_h = np.array([[0.1, 0.4, -0.3], \n",
" [-0.2, 0.5, 0.2]])\n",
"test_w_h_o = np.array([[0.3, -0.1]])\n",
"\n",
"class TestMethods(unittest.TestCase):\n",
" \n",
" ##########\n",
" # Unit tests for data loading\n",
" ##########\n",
" \n",
" def test_data_path(self):\n",
" # Test that file path to dataset has been unaltered\n",
" self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')\n",
" \n",
" def test_data_loaded(self):\n",
" # Test that data frame loaded\n",
" self.assertTrue(isinstance(rides, pd.DataFrame))\n",
" \n",
" ##########\n",
" # Unit tests for network functionality\n",
" ##########\n",
"\n",
" def test_activation(self):\n",
" network = NeuralNetwork(3, 2, 1, 0.5)\n",
" # Test that the activation function is a sigmoid\n",
" self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))\n",
"\n",
" def test_train(self):\n",
" # Test that weights are updated correctly on training\n",
" network = NeuralNetwork(3, 2, 1, 0.5)\n",
" network.weights_input_to_hidden = test_w_i_h.copy()\n",
" network.weights_hidden_to_output = test_w_h_o.copy()\n",
" \n",
" network.train(inputs, targets)\n",
" self.assertTrue(np.allclose(network.weights_hidden_to_output, \n",
" np.array([[ 0.37275328, -0.03172939]])))\n",
" self.assertTrue(np.allclose(network.weights_input_to_hidden,\n",
" np.array([[ 0.10562014, 0.39775194, -0.29887597],\n",
" [-0.20185996, 0.50074398, 0.19962801]])))\n",
"\n",
" def test_run(self):\n",
" # Test correctness of run method\n",
" network = NeuralNetwork(3, 2, 1, 0.5)\n",
" network.weights_input_to_hidden = test_w_i_h.copy()\n",
" network.weights_hidden_to_output = test_w_h_o.copy()\n",
"\n",
" self.assertTrue(np.allclose(network.run(inputs), 0.09998924))\n",
"\n",
"suite = unittest.TestLoader().loadTestsFromModule(TestMethods())\n",
"unittest.TextTestRunner().run(suite)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"celltoolbar": "Raw Cell Format",
"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 |
escientists/cervical-cancer | notebooks/keras attempt.ipynb | 1 | 239115 | {
"cells": [
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions\n",
"from keras.preprocessing import image\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = VGG16?"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5\n",
"553467904/553467096 [==============================] - 49s \n"
]
}
],
"source": [
"model = VGG16(include_top=True, weights='imagenet')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1, 224, 224, 3)\n",
"-14.9529 -123.68 151.061\n"
]
}
],
"source": [
"img_path = '0.jpg'\n",
"img = image.load_img(img_path, target_size=(224, 224))\n",
"x = image.img_to_array(img)\n",
"x = np.expand_dims(x, axis=0)\n",
"x = preprocess_input(x)\n",
"print(x.shape)\n",
"print(np.mean(x), np.min(x), np.max(x))\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f9be9858e10>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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zz0N1PIx0i4F7pqmB62goXa6TaKa48rCs/t/Tk2y8Hf36xsBzDPjHgHsq3ZCOWji9v9aa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KPhtVzvRv69F5JTRIw4uRk3HJaFUiohNO5f70Fy7yTzMGqtzOVZ+HYT8H0AvEhNn68q5gVQ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\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I9mGH8GgDMOZPSn5tDKUuFQ2B3tVoxsOWwdoHiNkAL+uRHM22WZvtB2KKDGwxKsOgp9oq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\nXUQkQSruIiIJUnEXEUmQiruISIJ+AlnhVC1hCnzcAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9beb15e2b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(x[0])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ValueError",
"evalue": "Error when checking : expected input_1 to have shape (None, 224, 224, 3) but got array with shape (1, 500, 500, 3)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-25-ae07b2745649>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\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[0m",
"\u001b[0;32m/home/christiaan/anaconda3/lib/python3.5/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose)\u001b[0m\n\u001b[1;32m 1199\u001b[0m x = standardize_input_data(x, self.input_names,\n\u001b[1;32m 1200\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minternal_input_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1201\u001b[0;31m check_batch_dim=False)\n\u001b[0m\u001b[1;32m 1202\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstateful\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1203\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\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[0mbatch_size\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\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[0mbatch_size\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[0;32m/home/christiaan/anaconda3/lib/python3.5/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mstandardize_input_data\u001b[0;34m(data, names, shapes, check_batch_dim, exception_prefix)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[0;34m' to have shape '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshapes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\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 112\u001b[0m \u001b[0;34m' but got array with shape '\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m str(array.shape))\n\u001b[0m\u001b[1;32m 114\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Error when checking : expected input_1 to have shape (None, 224, 224, 3) but got array with shape (1, 500, 500, 3)"
]
}
],
"source": [
"result = model.predict(x)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1, 1000)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result.shape"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: [('n01968897', 'chambered_nautilus', 0.18836045), ('n01910747', 'jellyfish', 0.17890556), ('n03724870', 'mask', 0.1336593)]\n"
]
}
],
"source": [
"print('Predicted:', decode_predictions(result, top=3)[0])\n"
]
}
],
"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
}
| apache-2.0 |
ktmud/deep-learning | student-admissions/StudentAdmissions.ipynb | 9 | 11422 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Predicting Student Admissions with Neural Networks\n",
"In this notebook, we predict student admissions to graduate school at UCLA based on three pieces of data:\n",
"- GRE Scores (Test)\n",
"- GPA Scores (Grades)\n",
"- Class rank (1-4)\n",
"\n",
"The dataset originally came from here: http://www.ats.ucla.edu/\n",
"\n",
"## Loading the data\n",
"To load the data and format it nicely, we will use two very useful packages called Pandas and Numpy. You can read on the documentation here:\n",
"- https://pandas.pydata.org/pandas-docs/stable/\n",
"- https://docs.scipy.org/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Importing pandas and numpy\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"# Reading the csv file into a pandas DataFrame\n",
"data = pd.read_csv('student_data.csv')\n",
"\n",
"# Printing out the first 10 rows of our data\n",
"data[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting the data\n",
"\n",
"First let's make a plot of our data to see how it looks. In order to have a 2D plot, let's ingore the rank."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Importing matplotlib\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Function to help us plot\n",
"def plot_points(data):\n",
" X = np.array(data[[\"gre\",\"gpa\"]])\n",
" y = np.array(data[\"admit\"])\n",
" admitted = X[np.argwhere(y==1)]\n",
" rejected = X[np.argwhere(y==0)]\n",
" plt.scatter([s[0][0] for s in rejected], [s[0][1] for s in rejected], s = 25, color = 'red', edgecolor = 'k')\n",
" plt.scatter([s[0][0] for s in admitted], [s[0][1] for s in admitted], s = 25, color = 'cyan', edgecolor = 'k')\n",
" plt.xlabel('Test (GRE)')\n",
" plt.ylabel('Grades (GPA)')\n",
" \n",
"# Plotting the points\n",
"plot_points(data)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Roughly, it looks like the students with high scores in the grades and test passed, while the ones with low scores didn't, but the data is not as nicely separable as we hoped it would. Maybe it would help to take the rank into account? Let's make 4 plots, each one for each rank."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Separating the ranks\n",
"data_rank1 = data[data[\"rank\"]==1]\n",
"data_rank2 = data[data[\"rank\"]==2]\n",
"data_rank3 = data[data[\"rank\"]==3]\n",
"data_rank4 = data[data[\"rank\"]==4]\n",
"\n",
"# Plotting the graphs\n",
"plot_points(data_rank1)\n",
"plt.title(\"Rank 1\")\n",
"plt.show()\n",
"plot_points(data_rank2)\n",
"plt.title(\"Rank 2\")\n",
"plt.show()\n",
"plot_points(data_rank3)\n",
"plt.title(\"Rank 3\")\n",
"plt.show()\n",
"plot_points(data_rank4)\n",
"plt.title(\"Rank 4\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This looks more promising, as it seems that the lower the rank, the higher the acceptance rate. Let's use the rank as one of our inputs. In order to do this, we should one-hot encode it.\n",
"\n",
"## TODO: One-hot encoding the rank\n",
"Use the `get_dummies` function in numpy in order to one-hot encode the data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO: Make dummy variables for rank\n",
"one_hot_data = pass\n",
"\n",
"# TODO: Drop the previous rank column\n",
"one_hot_data = pass\n",
"\n",
"# Print the first 10 rows of our data\n",
"one_hot_data[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TODO: Scaling the data\n",
"The next step is to scale the data. We notice that the range for grades is 1.0-4.0, whereas the range for test scores is roughly 200-800, which is much larger. This means our data is skewed, and that makes it hard for a neural network to handle. Let's fit our two features into a range of 0-1, by dividing the grades by 4.0, and the test score by 800."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Making a copy of our data\n",
"processed_data = one_hot_data[:]\n",
"\n",
"# TODO: Scale the columns\n",
"\n",
"# Printing the first 10 rows of our procesed data\n",
"processed_data[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Splitting the data into Training and Testing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to test our algorithm, we'll split the data into a Training and a Testing set. The size of the testing set will be 10% of the total data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sample = np.random.choice(processed_data.index, size=int(len(processed_data)*0.9), replace=False)\n",
"train_data, test_data = processed_data.iloc[sample], processed_data.drop(sample)\n",
"\n",
"print(\"Number of training samples is\", len(train_data))\n",
"print(\"Number of testing samples is\", len(test_data))\n",
"print(train_data[:10])\n",
"print(test_data[:10])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Splitting the data into features and targets (labels)\n",
"Now, as a final step before the training, we'll split the data into features (X) and targets (y)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"features = train_data.drop('admit', axis=1)\n",
"targets = train_data['admit']\n",
"features_test = test_data.drop('admit', axis=1)\n",
"targets_test = test_data['admit']\n",
"\n",
"print(features[:10])\n",
"print(targets[:10])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training the 2-layer Neural Network\n",
"The following function trains the 2-layer neural network. First, we'll write some helper functions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Activation (sigmoid) function\n",
"def sigmoid(x):\n",
" return 1 / (1 + np.exp(-x))\n",
"def sigmoid_prime(x):\n",
" return sigmoid(x) * (1-sigmoid(x))\n",
"def error_formula(y, output):\n",
" return - y*np.log(output) - (1 - y) * np.log(1-output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TODO: Backpropagate the error\n",
"Now it's your turn to shine. Write the error term. Remember that this is given by the equation $$ -(y-\\hat{y}) \\sigma'(x) $$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# TODO: Write the error term formula\n",
"def error_term_formula(y, output):\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Neural Network hyperparameters\n",
"epochs = 1000\n",
"learnrate = 0.5\n",
"\n",
"# Training function\n",
"def train_nn(features, targets, epochs, learnrate):\n",
" \n",
" # Use to same seed to make debugging easier\n",
" np.random.seed(42)\n",
"\n",
" n_records, n_features = features.shape\n",
" last_loss = None\n",
"\n",
" # Initialize weights\n",
" weights = np.random.normal(scale=1 / n_features**.5, size=n_features)\n",
"\n",
" for e in range(epochs):\n",
" del_w = np.zeros(weights.shape)\n",
" for x, y in zip(features.values, targets):\n",
" # Loop through all records, x is the input, y is the target\n",
"\n",
" # Activation of the output unit\n",
" # Notice we multiply the inputs and the weights here \n",
" # rather than storing h as a separate variable \n",
" output = sigmoid(np.dot(x, weights))\n",
"\n",
" # The error, the target minus the network output\n",
" error = error_formula(y, output)\n",
"\n",
" # The error term\n",
" # Notice we calulate f'(h) here instead of defining a separate\n",
" # sigmoid_prime function. This just makes it faster because we\n",
" # can re-use the result of the sigmoid function stored in\n",
" # the output variable\n",
" error_term = error_term_formula(y, output)\n",
"\n",
" # The gradient descent step, the error times the gradient times the inputs\n",
" del_w += error_term * x\n",
"\n",
" # Update the weights here. The learning rate times the \n",
" # change in weights, divided by the number of records to average\n",
" weights += learnrate * del_w / n_records\n",
"\n",
" # Printing out the mean square error on the training set\n",
" if e % (epochs / 10) == 0:\n",
" out = sigmoid(np.dot(features, weights))\n",
" loss = np.mean((out - targets) ** 2)\n",
" print(\"Epoch:\", e)\n",
" if last_loss and last_loss < loss:\n",
" print(\"Train loss: \", loss, \" WARNING - Loss Increasing\")\n",
" else:\n",
" print(\"Train loss: \", loss)\n",
" last_loss = loss\n",
" print(\"=========\")\n",
" print(\"Finished training!\")\n",
" return weights\n",
" \n",
"weights = train_nn(features, targets, epochs, learnrate)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculating the Accuracy on the Test Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Calculate accuracy on test data\n",
"tes_out = sigmoid(np.dot(features_test, weights))\n",
"predictions = tes_out > 0.5\n",
"accuracy = np.mean(predictions == targets_test)\n",
"print(\"Prediction accuracy: {:.3f}\".format(accuracy))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
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"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
yubhrraj/PredictAddict | Random_Forest_Model/RF.ipynb | 1 | 99606 | {
"cells": [
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv(\"student-alcohol-consumption/student-mat.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
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" <td>10</td>\n",
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"<p>5 rows × 31 columns</p>\n",
"</div>"
],
"text/plain": [
" school sex age address famsize Pstatus Medu Fedu Mjob Fjob ... \\\n",
"0 GP F 18 U GT3 A 4 4 at_home teacher ... \n",
"1 GP F 17 U GT3 T 1 1 at_home other ... \n",
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"[5 rows x 31 columns]"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"y = np.array(data[[\"Dalc\",\"Walc\"]])"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"labels = 2*y[:,0] + y[:,1]"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"data.drop([\"Dalc\",\"Walc\"], inplace=True, axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"data.drop([\"school\", \"age\", \"reason\",\"guardian\", \"schoolsup\", \"famsup\", \"nursery\", \"higher\",\"internet\", \"romantic\", \"freetime\",\"health\", \"absences\"], inplace = True, axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
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" <td>other</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>F</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>A</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>other</td>\n",
" <td>teacher</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>LE3</td>\n",
" <td>A</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>services</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>16</td>\n",
" <td>18</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>14</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>F</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>teacher</td>\n",
" <td>health</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>10</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>F</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>services</td>\n",
" <td>other</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>health</td>\n",
" <td>services</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>teacher</td>\n",
" <td>other</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>A</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>F</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>health</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>F</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>services</td>\n",
" <td>services</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>F</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>services</td>\n",
" <td>services</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>health</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>teacher</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>13</td>\n",
" <td>14</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>health</td>\n",
" <td>health</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>12</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>teacher</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>F</td>\n",
" <td>R</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>services</td>\n",
" <td>health</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>F</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>services</td>\n",
" <td>services</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>health</td>\n",
" <td>services</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>15</td>\n",
" <td>16</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>LE3</td>\n",
" <td>A</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>services</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>11</td>\n",
" <td>11</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>teacher</td>\n",
" <td>teacher</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" <td>12</td>\n",
" <td>11</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",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\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>365</th>\n",
" <td>M</td>\n",
" <td>R</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>at_home</td>\n",
" <td>other</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>366</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>teacher</td>\n",
" <td>services</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367</th>\n",
" <td>F</td>\n",
" <td>R</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>other</td>\n",
" <td>services</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>368</th>\n",
" <td>F</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>at_home</td>\n",
" <td>services</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>11</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>369</th>\n",
" <td>F</td>\n",
" <td>R</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>other</td>\n",
" <td>teacher</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" <td>12</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>370</th>\n",
" <td>F</td>\n",
" <td>U</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>services</td>\n",
" <td>services</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>371</th>\n",
" <td>M</td>\n",
" <td>R</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>at_home</td>\n",
" <td>services</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>14</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>372</th>\n",
" <td>F</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>other</td>\n",
" <td>at_home</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>11</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>373</th>\n",
" <td>F</td>\n",
" <td>R</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>374</th>\n",
" <td>F</td>\n",
" <td>R</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>19</td>\n",
" <td>18</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>375</th>\n",
" <td>F</td>\n",
" <td>R</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>376</th>\n",
" <td>F</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>health</td>\n",
" <td>other</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>15</td>\n",
" <td>14</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>377</th>\n",
" <td>F</td>\n",
" <td>R</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>teacher</td>\n",
" <td>services</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>378</th>\n",
" <td>F</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>379</th>\n",
" <td>F</td>\n",
" <td>R</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>at_home</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>380</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>teacher</td>\n",
" <td>teacher</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>15</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>381</th>\n",
" <td>M</td>\n",
" <td>R</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>382</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>other</td>\n",
" <td>services</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>11</td>\n",
" <td>11</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>383</th>\n",
" <td>M</td>\n",
" <td>R</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>other</td>\n",
" <td>services</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>384</th>\n",
" <td>M</td>\n",
" <td>R</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>385</th>\n",
" <td>F</td>\n",
" <td>R</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>at_home</td>\n",
" <td>other</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>386</th>\n",
" <td>F</td>\n",
" <td>R</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>teacher</td>\n",
" <td>at_home</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>387</th>\n",
" <td>F</td>\n",
" <td>R</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>services</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>388</th>\n",
" <td>F</td>\n",
" <td>U</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>teacher</td>\n",
" <td>services</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>389</th>\n",
" <td>F</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>390</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>LE3</td>\n",
" <td>A</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>services</td>\n",
" <td>services</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>391</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>services</td>\n",
" <td>services</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>14</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>392</th>\n",
" <td>M</td>\n",
" <td>R</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>10</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>393</th>\n",
" <td>M</td>\n",
" <td>R</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>services</td>\n",
" <td>other</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>12</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>394</th>\n",
" <td>M</td>\n",
" <td>U</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>other</td>\n",
" <td>at_home</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>395 rows × 18 columns</p>\n",
"</div>"
],
"text/plain": [
" sex address famsize Pstatus Medu Fedu Mjob Fjob traveltime \\\n",
"0 F U GT3 A 4 4 at_home teacher 2 \n",
"1 F U GT3 T 1 1 at_home other 1 \n",
"2 F U LE3 T 1 1 at_home other 1 \n",
"3 F U GT3 T 4 2 health services 1 \n",
"4 F U GT3 T 3 3 other other 1 \n",
"5 M U LE3 T 4 3 services other 1 \n",
"6 M U LE3 T 2 2 other other 1 \n",
"7 F U GT3 A 4 4 other teacher 2 \n",
"8 M U LE3 A 3 2 services other 1 \n",
"9 M U GT3 T 3 4 other other 1 \n",
"10 F U GT3 T 4 4 teacher health 1 \n",
"11 F U GT3 T 2 1 services other 3 \n",
"12 M U LE3 T 4 4 health services 1 \n",
"13 M U GT3 T 4 3 teacher other 2 \n",
"14 M U GT3 A 2 2 other other 1 \n",
"15 F U GT3 T 4 4 health other 1 \n",
"16 F U GT3 T 4 4 services services 1 \n",
"17 F U GT3 T 3 3 other other 3 \n",
"18 M U GT3 T 3 2 services services 1 \n",
"19 M U LE3 T 4 3 health other 1 \n",
"20 M U GT3 T 4 3 teacher other 1 \n",
"21 M U GT3 T 4 4 health health 1 \n",
"22 M U LE3 T 4 2 teacher other 1 \n",
"23 M U LE3 T 2 2 other other 2 \n",
"24 F R GT3 T 2 4 services health 1 \n",
"25 F U GT3 T 2 2 services services 1 \n",
"26 M U GT3 T 2 2 other other 1 \n",
"27 M U GT3 T 4 2 health services 1 \n",
"28 M U LE3 A 3 4 services other 1 \n",
"29 M U GT3 T 4 4 teacher teacher 1 \n",
".. .. ... ... ... ... ... ... ... ... \n",
"365 M R GT3 T 1 3 at_home other 2 \n",
"366 M U LE3 T 4 4 teacher services 2 \n",
"367 F R GT3 T 1 1 other services 3 \n",
"368 F U GT3 T 2 3 at_home services 2 \n",
"369 F R GT3 T 4 4 other teacher 3 \n",
"370 F U LE3 T 3 2 services services 2 \n",
"371 M R LE3 T 1 2 at_home services 3 \n",
"372 F U GT3 T 2 2 other at_home 1 \n",
"373 F R GT3 T 1 2 other other 1 \n",
"374 F R LE3 T 4 4 other other 2 \n",
"375 F R GT3 T 1 1 other other 4 \n",
"376 F U GT3 T 4 2 health other 2 \n",
"377 F R LE3 T 4 4 teacher services 1 \n",
"378 F U GT3 T 3 3 other other 1 \n",
"379 F R GT3 T 3 1 at_home other 1 \n",
"380 M U GT3 T 4 4 teacher teacher 1 \n",
"381 M R GT3 T 2 1 other other 2 \n",
"382 M U GT3 T 2 3 other services 2 \n",
"383 M R GT3 T 1 1 other services 2 \n",
"384 M R GT3 T 4 2 other other 2 \n",
"385 F R GT3 T 2 2 at_home other 2 \n",
"386 F R GT3 T 4 4 teacher at_home 3 \n",
"387 F R GT3 T 2 3 services other 1 \n",
"388 F U LE3 T 3 1 teacher services 1 \n",
"389 F U GT3 T 1 1 other other 2 \n",
"390 M U LE3 A 2 2 services services 1 \n",
"391 M U LE3 T 3 1 services services 2 \n",
"392 M R GT3 T 1 1 other other 1 \n",
"393 M R LE3 T 3 2 services other 3 \n",
"394 M U LE3 T 1 1 other at_home 1 \n",
"\n",
" studytime failures paid activities famrel goout G1 G2 G3 \n",
"0 2 0 no no 4 4 5 6 6 \n",
"1 2 0 no no 5 3 5 5 6 \n",
"2 2 3 yes no 4 2 7 8 10 \n",
"3 3 0 yes yes 3 2 15 14 15 \n",
"4 2 0 yes no 4 2 6 10 10 \n",
"5 2 0 yes yes 5 2 15 15 15 \n",
"6 2 0 no no 4 4 12 12 11 \n",
"7 2 0 no no 4 4 6 5 6 \n",
"8 2 0 yes no 4 2 16 18 19 \n",
"9 2 0 yes yes 5 1 14 15 15 \n",
"10 2 0 yes no 3 3 10 8 9 \n",
"11 3 0 no yes 5 2 10 12 12 \n",
"12 1 0 yes yes 4 3 14 14 14 \n",
"13 2 0 yes no 5 3 10 10 11 \n",
"14 3 0 no no 4 2 14 16 16 \n",
"15 1 0 no no 4 4 14 14 14 \n",
"16 3 0 yes yes 3 3 13 14 14 \n",
"17 2 0 no yes 5 2 8 10 10 \n",
"18 1 3 no yes 5 5 6 5 5 \n",
"19 1 0 yes yes 3 3 8 10 10 \n",
"20 2 0 no no 4 1 13 14 15 \n",
"21 1 0 yes no 5 2 12 15 15 \n",
"22 2 0 no yes 4 1 15 15 16 \n",
"23 2 0 no yes 5 4 13 13 12 \n",
"24 3 0 yes yes 4 2 10 9 8 \n",
"25 1 2 yes no 1 2 6 9 8 \n",
"26 1 0 yes no 4 2 12 12 11 \n",
"27 1 0 yes no 2 4 15 16 15 \n",
"28 2 0 no yes 5 3 11 11 11 \n",
"29 2 0 yes yes 4 5 10 12 11 \n",
".. ... ... ... ... ... ... .. .. .. \n",
"365 2 0 yes no 3 4 10 10 10 \n",
"366 3 0 yes no 4 2 13 13 13 \n",
"367 1 1 yes no 5 1 7 6 0 \n",
"368 1 0 yes no 5 3 11 10 10 \n",
"369 2 0 yes no 3 2 14 12 11 \n",
"370 2 2 no yes 3 2 7 7 9 \n",
"371 1 0 yes yes 4 3 14 12 12 \n",
"372 3 0 no yes 3 3 13 11 11 \n",
"373 1 0 no yes 3 5 6 5 5 \n",
"374 3 0 no no 5 4 19 18 19 \n",
"375 3 0 no no 4 2 8 8 10 \n",
"376 3 2 yes no 5 3 15 14 15 \n",
"377 2 0 yes yes 5 3 8 9 10 \n",
"378 2 0 yes no 4 3 15 15 15 \n",
"379 2 0 yes yes 4 4 10 10 10 \n",
"380 2 0 yes yes 3 4 15 14 14 \n",
"381 1 0 no yes 4 3 7 6 7 \n",
"382 2 0 no yes 4 3 11 11 10 \n",
"383 1 1 no no 4 2 6 5 0 \n",
"384 1 1 yes no 5 3 6 5 5 \n",
"385 3 0 yes no 5 3 10 9 10 \n",
"386 1 0 yes yes 4 3 6 5 6 \n",
"387 3 1 no yes 5 2 7 5 0 \n",
"388 2 0 yes no 4 4 7 9 8 \n",
"389 2 1 no yes 1 1 6 5 0 \n",
"390 2 2 yes no 5 4 9 9 9 \n",
"391 1 0 no no 2 5 14 16 16 \n",
"392 1 3 no no 5 3 10 8 7 \n",
"393 1 0 no no 4 1 11 12 10 \n",
"394 1 0 no no 3 3 8 9 9 \n",
"\n",
"[395 rows x 18 columns]"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data\n"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"grades = np.array(data[[\"G1\",\"G2\",\"G3\"]])"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"per = grades[:,0] + grades[:,1] + grades[:, 2]"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"per = per*5/3"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(395,)"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"per.shape"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"data.drop([\"G1\", \"G2\", \"G3\"], inplace = True, axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
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" .dataframe tbody tr th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sex</th>\n",
" <th>address</th>\n",
" <th>famsize</th>\n",
" <th>Pstatus</th>\n",
" <th>Medu</th>\n",
" <th>Fedu</th>\n",
" <th>Mjob</th>\n",
" <th>Fjob</th>\n",
" <th>traveltime</th>\n",
" <th>studytime</th>\n",
" <th>failures</th>\n",
" <th>paid</th>\n",
" <th>activities</th>\n",
" <th>famrel</th>\n",
" <th>goout</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>GT3</td>\n",
" <td>A</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>at_home</td>\n",
" <td>teacher</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>at_home</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>at_home</td>\n",
" <td>other</td>\n",
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" <td>3</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
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" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>health</td>\n",
" <td>services</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>GT3</td>\n",
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" </tr>\n",
" <tr>\n",
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" <td>yes</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>GT3</td>\n",
" <td>A</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>other</td>\n",
" <td>teacher</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>LE3</td>\n",
" <td>A</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>services</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sex address famsize Pstatus Medu Fedu Mjob Fjob traveltime \\\n",
"0 0 0 GT3 A 4 4 at_home teacher 2 \n",
"1 0 0 GT3 T 1 1 at_home other 1 \n",
"2 0 0 LE3 T 1 1 at_home other 1 \n",
"3 0 0 GT3 T 4 2 health services 1 \n",
"4 0 0 GT3 T 3 3 other other 1 \n",
"5 1 0 LE3 T 4 3 services other 1 \n",
"6 1 0 LE3 T 2 2 other other 1 \n",
"7 0 0 GT3 A 4 4 other teacher 2 \n",
"8 1 0 LE3 A 3 2 services other 1 \n",
"9 1 0 GT3 T 3 4 other other 1 \n",
"\n",
" studytime failures paid activities famrel goout \n",
"0 2 0 no no 4 4 \n",
"1 2 0 no no 5 3 \n",
"2 2 3 yes no 4 2 \n",
"3 3 0 yes yes 3 2 \n",
"4 2 0 yes no 4 2 \n",
"5 2 0 yes yes 5 2 \n",
"6 2 0 no no 4 4 \n",
"7 2 0 no no 4 4 \n",
"8 2 0 yes no 4 2 \n",
"9 2 0 yes yes 5 1 "
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"U 307\n",
"R 88\n",
"Name: address, dtype: int64"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['address'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [],
"source": [
"di = { 'U' : 0, 'R' : 1}\n",
"data.replace({'address':di},inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GT3 281\n",
"LE3 114\n",
"Name: famsize, dtype: int64"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['famsize'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
"di = {'LE3' : 0,'GT3' : 1}\n",
"data.replace({'famsize':di},inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <td>5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sex address famsize Pstatus Medu Fedu Mjob Fjob traveltime \\\n",
"0 0 0 1 A 4 4 at_home teacher 2 \n",
"1 0 0 1 T 1 1 at_home other 1 \n",
"2 0 0 0 T 1 1 at_home other 1 \n",
"3 0 0 1 T 4 2 health services 1 \n",
"4 0 0 1 T 3 3 other other 1 \n",
"5 1 0 0 T 4 3 services other 1 \n",
"\n",
" studytime failures paid activities famrel goout \n",
"0 2 0 no no 4 4 \n",
"1 2 0 no no 5 3 \n",
"2 2 3 yes no 4 2 \n",
"3 3 0 yes yes 3 2 \n",
"4 2 0 yes no 4 2 \n",
"5 2 0 yes yes 5 2 "
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head(6)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
"di = { 'A' : 0, 'T' : 1}\n",
"data.replace({'Pstatus':di},inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
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" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
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" <td>5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sex address famsize Pstatus Medu Fedu Mjob Fjob traveltime \\\n",
"0 0 0 1 0 4 4 at_home teacher 2 \n",
"1 0 0 1 1 1 1 at_home other 1 \n",
"2 0 0 0 1 1 1 at_home other 1 \n",
"3 0 0 1 1 4 2 health services 1 \n",
"4 0 0 1 1 3 3 other other 1 \n",
"5 1 0 0 1 4 3 services other 1 \n",
"\n",
" studytime failures paid activities famrel goout \n",
"0 2 0 no no 4 4 \n",
"1 2 0 no no 5 3 \n",
"2 2 3 yes no 4 2 \n",
"3 3 0 yes yes 3 2 \n",
"4 2 0 yes no 4 2 \n",
"5 2 0 yes yes 5 2 "
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head(6)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"other 141\n",
"services 103\n",
"at_home 59\n",
"teacher 58\n",
"health 34\n",
"Name: Mjob, dtype: int64"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Mjob'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [],
"source": [
"di = { 'teacher' : 0, 'health' : 1, 'services' : 2, 'at_home' : 3, 'other' : 4}\n",
"data.replace({'Mjob':di},inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
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" <th>traveltime</th>\n",
" <th>studytime</th>\n",
" <th>failures</th>\n",
" <th>paid</th>\n",
" <th>activities</th>\n",
" <th>famrel</th>\n",
" <th>goout</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>services</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sex address famsize Pstatus Medu Fedu Mjob Fjob traveltime \\\n",
"0 0 0 1 0 4 4 3 0 2 \n",
"1 0 0 1 1 1 1 3 4 1 \n",
"2 0 0 0 1 1 1 3 4 1 \n",
"3 0 0 1 1 4 2 1 services 1 \n",
"4 0 0 1 1 3 3 4 4 1 \n",
"5 1 0 0 1 4 3 2 4 1 \n",
"\n",
" studytime failures paid activities famrel goout \n",
"0 2 0 no no 4 4 \n",
"1 2 0 no no 5 3 \n",
"2 2 3 yes no 4 2 \n",
"3 3 0 yes yes 3 2 \n",
"4 2 0 yes no 4 2 \n",
"5 2 0 yes yes 5 2 "
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head(6)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"other 217\n",
"services 111\n",
"teacher 29\n",
"at_home 20\n",
"health 18\n",
"Name: Fjob, dtype: int64"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Fjob'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
"di = { 'teacher' : 0, 'health' : 1, 'services' : 2, 'at_home' : 3, 'other' : 4}\n",
"data.replace({'Fjob':di},inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 96,
"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>sex</th>\n",
" <th>address</th>\n",
" <th>famsize</th>\n",
" <th>Pstatus</th>\n",
" <th>Medu</th>\n",
" <th>Fedu</th>\n",
" <th>Mjob</th>\n",
" <th>Fjob</th>\n",
" <th>traveltime</th>\n",
" <th>studytime</th>\n",
" <th>failures</th>\n",
" <th>paid</th>\n",
" <th>activities</th>\n",
" <th>famrel</th>\n",
" <th>goout</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
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" <td>4</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sex address famsize Pstatus Medu Fedu Mjob Fjob traveltime \\\n",
"0 0 0 1 0 4 4 3 0 2 \n",
"1 0 0 1 1 1 1 3 4 1 \n",
"2 0 0 0 1 1 1 3 4 1 \n",
"3 0 0 1 1 4 2 1 2 1 \n",
"4 0 0 1 1 3 3 4 4 1 \n",
"5 1 0 0 1 4 3 2 4 1 \n",
"\n",
" studytime failures paid activities famrel goout \n",
"0 2 0 no no 4 4 \n",
"1 2 0 no no 5 3 \n",
"2 2 3 yes no 4 2 \n",
"3 3 0 yes yes 3 2 \n",
"4 2 0 yes no 4 2 \n",
"5 2 0 yes yes 5 2 "
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head(6)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"no 214\n",
"yes 181\n",
"Name: paid, dtype: int64"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['paid'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [],
"source": [
"di = { 'no' : 0, 'yes' : 1}\n",
"data.replace({'paid':di},inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
"di = { 'no' : 0, 'yes' : 1}\n",
"data.replace({'activities':di},inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(395, 15)"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"test = np.array(data)"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(395, 15)"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.shape"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [],
"source": [
"train = np.zeros((395,16))"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [],
"source": [
"train[:,:15] = test[:,:]"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [],
"source": [
"train[:,15] = per"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(395, 16)"
]
},
"execution_count": 130,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.shape"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [],
"source": [
"labels = labels // 10"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(395,)"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"labels.shape"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {},
"outputs": [],
"source": [
"clf = RandomForestClassifier(n_estimators=60)"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(train, labels, test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(79, 16)"
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test.shape"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(79,)"
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_test.shape"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
" max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=60, n_jobs=1,\n",
" oob_score=False, random_state=None, verbose=0,\n",
" warm_start=False)"
]
},
"execution_count": 161,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [],
"source": [
"y_A = clf.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
]
},
"execution_count": 171,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_A"
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
]
},
"execution_count": 172,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_test"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"92.40506329113924"
]
},
"execution_count": 177,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"100*float((y_A==y_test).sum())/y_test.shape[0]"
]
}
],
"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
}
| mit |
kit-cel/wt | nt1/vorlesung/extra/dsss.ipynb | 2 | 96432 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Content and Objectives"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Show spreading in time and frequency domain\n",
"- BPSk symbols are being pulse-shaped by rectangular w. and wo. spreading"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# importing\n",
"import numpy as np\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"\n",
"# showing figures inline\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# plotting options \n",
"font = {'size' : 20}\n",
"plt.rc('font', **font)\n",
"plt.rc('text', usetex=True)\n",
"\n",
"matplotlib.rc('figure', figsize=(18, 6) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Parameters"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# number of realizations along which to average the psd estimate\n",
"n_real = 100\n",
"\n",
"# modulation scheme and constellation points\n",
"constellation = [ -1, 1 ]\n",
"\n",
"# number of symbols \n",
"n_symb = 100\n",
"t_symb = 1.0 \n",
"\n",
"chips_per_symbol = 8\n",
"samples_per_chip = 8\n",
"samples_per_symbol = samples_per_chip * chips_per_symbol\n",
"\n",
"\n",
"# parameters for frequency regime\n",
"N_fft = 512\n",
"omega = np.linspace( -np.pi, np.pi, N_fft )\n",
"f_vec = omega / ( 2 * np.pi * t_symb / samples_per_symbol )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Real data-modulated Tx-signal"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# define rectangular function responses \n",
"rect = np.ones( samples_per_symbol )\n",
"rect /= np.linalg.norm( rect )\n",
"\n",
"\n",
"# number of realizations along which to average the psd estimate\n",
"n_real = 10\n",
"\n",
"\n",
"# initialize two-dimensional field for collecting several realizations along which to average \n",
"RECT_PSD = np.zeros( (n_real, N_fft ) ) \n",
"DSSS_PSD = np.zeros( (n_real, N_fft ) )\n",
"\n",
"\n",
"# get chips and signature\n",
"\n",
"# NOTE: looping until number of +-1 chips in | sum ones - 0.5 N_chips | < 0.2 N_chips,\n",
"# i.e., number of +1,-1 is approximately 1/2 (up to 20 percent)\n",
"while True:\n",
" dsss_chips = (-1) ** np.random.randint( 0, 2, size = chips_per_symbol )\n",
"\n",
" if np.abs( np.sum( dsss_chips > 0) - chips_per_symbol/2 ) / chips_per_symbol < .2:\n",
" break\n",
"\n",
"# generate signature out of chips by putting samples_per_symbol samples with chip amplitude \n",
"# normalize signature to energy 1\n",
"dsss_signature = np.ones( samples_per_symbol )\n",
"for n in range( chips_per_symbol ):\n",
" dsss_signature[ n * samples_per_chip : (n+1) * samples_per_chip ] *= dsss_chips[ n ] \n",
"dsss_signature /= np.linalg.norm( dsss_signature ) \n",
" \n",
" \n",
"# activate switch if chips should be resampled for every simulation\n",
"# this would average (e.g., for PSD) instead of showing \"one reality\"\n",
"new_chips_per_sim = 1\n",
" \n",
" \n",
"# loop for realizations\n",
"for k in np.arange( n_real ):\n",
"\n",
" if new_chips_per_sim:\n",
" \n",
" # resample signature using identical method as above\n",
" while True:\n",
" dsss_chips = (-1) ** np.random.randint( 0, 2, size = chips_per_symbol )\n",
" if np.abs( np.sum( dsss_chips > 0) - chips_per_symbol/2 ) / chips_per_symbol < .2:\n",
" break\n",
" \n",
" # get signature \n",
" dsss_signature = np.ones( samples_per_symbol )\n",
" for n in range( chips_per_symbol ):\n",
" dsss_signature[ n * samples_per_chip : (n+1) * samples_per_chip ] *= dsss_chips[ n ]\n",
" dsss_signature /= np.linalg.norm( dsss_signature ) \n",
" \n",
" # generate random binary vector and modulate\n",
" data = np.random.randint( 2, size = n_symb )\n",
" mod = [ constellation[ d ] for d in data ]\n",
"\n",
" # get signals by putting symbols and filtering\n",
" s_up = np.zeros( n_symb * samples_per_symbol ) \n",
" s_up[ :: samples_per_symbol ] = mod\n",
"\n",
" \n",
" # apply RECTANGULAR and CDMA shaping in time domain\n",
" s_rect = np.convolve( rect, s_up ) \n",
" s_dsss = np.convolve( dsss_signature, s_up )\n",
"\n",
" \n",
" # get spectrum \n",
" RECT_PSD[ k, :] = np.abs( np.fft.fftshift( np.fft.fft( s_rect, N_fft ) ) )**2\n",
" DSSS_PSD[ k, :] = np.abs( np.fft.fftshift( np.fft.fft( s_dsss, N_fft ) ) )**2\n",
"\n",
"# average along realizations\n",
"RECT_av = np.average( RECT_PSD, axis=0 )\n",
"RECT_av /= np.max( RECT_av )\n",
"\n",
"DSSS_av = np.average( DSSS_PSD, axis=0 )\n",
"DSSS_av /= np.max( DSSS_av )"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,1,'$|S(f)|^2$')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1296x432 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# show limited amount of symbols in time domain\n",
"N_syms_plot = 5\n",
"t_plot = np.arange( 0, N_syms_plot * t_symb, t_symb / samples_per_symbol )\n",
"\n",
"\n",
"# plot\n",
"plt.figure()\n",
"\n",
"plt.subplot(121)\n",
"plt.plot( t_plot, s_rect[ : N_syms_plot * samples_per_symbol], linewidth=2.0, label='Rect') \n",
"plt.plot( t_plot, s_dsss[ : N_syms_plot * samples_per_symbol ], linewidth=2.0, label='DS-SS') \n",
"\n",
"plt.ylim( (-1.1, 1.1 ) ) \n",
"plt.grid( True )\n",
"plt.legend(loc='upper right') \n",
"plt.xlabel('$t/T$')\n",
"plt.title('$s(t)$')\n",
"\n",
"plt.subplot(122)\n",
"\n",
"np.seterr(divide='ignore') # ignore warning for logarithm of 0\n",
"plt.plot( f_vec, 10*np.log10( RECT_av ), linewidth=2.0, label='Rect., sim.' ) \n",
"plt.plot( f_vec, 10*np.log10( DSSS_av ), linewidth=2.0, label='DS-SS, sim.' ) \n",
"np.seterr(divide='warn') # enable warning for logarithm of 0\n",
"\n",
"plt.grid(True) \n",
"plt.legend(loc='lower right') \n",
"plt.ylim( (-60, 10 ) )\n",
"\n",
"plt.xlabel('$fT$')\n",
"plt.title('$|S(f)|^2$')"
]
},
{
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-2.0 |
spacedrabbit/PythonBootcamp | GUI/Widget Basics & Events.ipynb | 1 | 4819 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from ipywidgets import *"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"IntSlider()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from IPython.display import display\n",
"w = IntSlider()\n",
"display(w)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"display(w)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"w.close() # close all of the widgets"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"display(w)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"32"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w.value # gets the current values"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"w.value = 50 # sets the current value"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['_view_name',\n",
" 'orientation',\n",
" 'color',\n",
" '_view_module',\n",
" 'disabled',\n",
" 'visible',\n",
" 'readout_format',\n",
" '_model_module',\n",
" 'font_style',\n",
" 'layout',\n",
" 'min',\n",
" '_range',\n",
" 'background_color',\n",
" 'slider_color',\n",
" 'continuous_update',\n",
" 'font_family',\n",
" '_dom_classes',\n",
" 'description',\n",
" '_model_name',\n",
" 'max',\n",
" 'readout',\n",
" 'font_weight',\n",
" 'step',\n",
" 'font_size',\n",
" 'msg_throttle',\n",
" 'value']"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w.keys # possible values that can be changed for the widgets"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Text(value='Hello World', disabled=True) # cannot be edited since disabled"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# traitlet link function\n",
"from traitlets import link\n",
"a = FloatText()\n",
"b = FloatSlider()\n",
"display(a, b)\n",
"mylink = link((a, 'value'), (b, 'value'))\n",
"# the tuple here represents: (thing to link, trait of the thing to link)\n",
"# they can be unlinked using\n",
"# mylink.unlink()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"100\n"
]
}
],
"source": [
"print w.max"
]
},
{
"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"
},
"widgets": {
"state": {
"16d197e5664b49e6a9581d0ea8c4aa53": {
"views": [
{
"cell_index": 10
}
]
},
"20e9d4374a5c4714a34be9be227e23da": {
"views": [
{
"cell_index": 10
}
]
},
"41c63eacbb514683b725baecbca7769b": {
"views": [
{
"cell_index": 5
}
]
},
"754f9c49b9e34078a67e624693b58bf6": {
"views": [
{
"cell_index": 10
}
]
},
"8c1f8ffe83b5442da57ebd4391068b76": {
"views": [
{
"cell_index": 9
}
]
}
},
"version": "1.2.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
ioam/geoviews | examples/gallery/matplotlib/filled_contours.ipynb | 1 | 1887 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import geoviews as gv\n",
"import geoviews.feature as gf\n",
"import cartopy.crs as ccrs\n",
"\n",
"gv.extension('matplotlib')\n",
"\n",
"gv.output(fig='svg', size=200)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def sample_data(shape=(73, 145)):\n",
" \"\"\"Returns ``lons``, ``lats`` and ``data`` of some fake data.\"\"\"\n",
" nlats, nlons = shape\n",
" ys = np.linspace(-np.pi / 2, np.pi / 2, nlats)\n",
" xs = np.linspace(0, 2 * np.pi, nlons)\n",
" lons, lats = np.meshgrid(xs, ys)\n",
" wave = 0.75 * (np.sin(2 * lats) ** 8) * np.cos(4 * lons)\n",
" mean = 0.5 * np.cos(2 * lats) * ((np.sin(2 * lats)) ** 2 + 2)\n",
"\n",
" lats = np.rad2deg(ys)\n",
" lons = np.rad2deg(xs)\n",
" data = wave + mean\n",
"\n",
" return lons, lats, data\n",
"\n",
"lons, lats, data = sample_data()\n",
"\n",
"# Make sure we declare the central longitude\n",
"contours = gv.FilledContours((lons, lats, data), crs=ccrs.PlateCarree(central_longitude=180))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"contours.opts(colorbar=True, levels=8, color_levels=10, cmap='nipy_spectral', projection=ccrs.Mollweide()) * gf.coastline"
]
}
],
"metadata": {
"language_info": {
"name": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-3-clause |
Jay-Jay-D/LeanSTP | Jupyter/KitchenSinkQuantBookTemplate.ipynb | 4 | 12193 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Welcome to The QuantConnect Research Page\n",
"#### Refer to this page for documentation https://www.quantconnect.com/docs#Introduction-to-Jupyter\n",
"#### Contribute to this template file https://github.com/QuantConnect/Lean/blob/master/Jupyter/BasicQuantBookTemplate.ipynb"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## QuantBook Basics\n",
"\n",
"### Start QuantBook\n",
"- Add the references and imports\n",
"- Create a QuantBook instance"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"# Imports\n",
"from clr import AddReference\n",
"AddReference(\"System\")\n",
"AddReference(\"QuantConnect.Common\")\n",
"AddReference(\"QuantConnect.Jupyter\")\n",
"AddReference(\"QuantConnect.Indicators\")\n",
"from System import *\n",
"from QuantConnect import *\n",
"from QuantConnect.Data.Custom import *\n",
"from QuantConnect.Data.Market import TradeBar, QuoteBar\n",
"from QuantConnect.Jupyter import *\n",
"from QuantConnect.Indicators import *\n",
"from datetime import datetime, timedelta\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"\n",
"# Create an instance\n",
"qb = QuantBook()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Selecting Asset Data\n",
"Checkout the QuantConnect [docs](https://www.quantconnect.com/docs#Initializing-Algorithms-Selecting-Asset-Data) to learn how to select asset data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"spy = qb.AddEquity(\"SPY\")\n",
"eur = qb.AddForex(\"EURUSD\")\n",
"btc = qb.AddCrypto(\"BTCUSD\")\n",
"fxv = qb.AddData[FxcmVolume](\"EURUSD_Vol\", Resolution.Hour)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Historical Data Requests\n",
"\n",
"We can use the QuantConnect API to make Historical Data Requests. The data will be presented as multi-index pandas.DataFrame where the first index is the Symbol.\n",
"\n",
"For more information, please follow the [link](https://www.quantconnect.com/docs#Historical-Data-Historical-Data-Requests)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# Gets historical data from the subscribed assets, the last 360 datapoints with daily resolution\n",
"h1 = qb.History(qb.Securities.Keys, 360, Resolution.Daily)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Plot closing prices from \"SPY\" \n",
"h1.loc[\"SPY\"][\"close\"].plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Gets historical data from the subscribed assets, from the last 30 days with daily resolution\n",
"h2 = qb.History(qb.Securities.Keys, timedelta(360), Resolution.Daily)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Plot high prices from \"EURUSD\" \n",
"h2.loc[\"EURUSD\"][\"high\"].plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# Gets historical data from the subscribed assets, between two dates with daily resolution\n",
"h3 = qb.History([btc.Symbol], datetime(2014,1,1), datetime.now(), Resolution.Daily)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Plot closing prices from \"BTCUSD\" \n",
"h3.loc[\"BTCUSD\"][\"close\"].plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Only fetchs historical data from a desired symbol\n",
"h4 = qb.History([spy.Symbol], 360, Resolution.Daily)\n",
"# or qb.History([\"SPY\"], 360, Resolution.Daily)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Only fetchs historical data from a desired symbol\n",
"h5 = qb.History([eur.Symbol], timedelta(360), Resolution.Daily)\n",
"# or qb.History([\"EURUSD\"], timedelta(30), Resolution.Daily)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Fetchs custom data\n",
"h6 = qb.History([fxv.Symbol], timedelta(360))\n",
"h6.loc[fxv.Symbol.Value][\"volume\"].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Historical Options Data Requests\n",
"- Select the option data\n",
"- Sets the filter, otherwise the default will be used SetFilter(-1, 1, timedelta(0), timedelta(35))\n",
"- Get the OptionHistory, an object that has information about the historical options data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"goog = qb.AddOption(\"GOOG\")\n",
"goog.SetFilter(-2, 2, timedelta(0), timedelta(180))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"option_history = qb.GetOptionHistory(goog.Symbol, datetime(2017, 1, 4))\n",
"print (option_history.GetStrikes())\n",
"print (option_history.GetExpiryDates())\n",
"h7 = option_history.GetAllData()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Historical Future Data Requests\n",
"- Select the future data\n",
"- Sets the filter, otherwise the default will be used SetFilter(timedelta(0), timedelta(35))\n",
"- Get the FutureHistory, an object that has information about the historical future data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"es = qb.AddFuture(\"ES\")\n",
"es.SetFilter(timedelta(0), timedelta(180))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"future_history = qb.GetFutureHistory(es.Symbol, datetime(2017, 1, 4))\n",
"print (future_history.GetExpiryDates())\n",
"h7 = future_history.GetAllData()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get Fundamental Data\n",
"\n",
"- *GetFundamental([symbol], selector, start_date = datetime(1998,1,1), end_date = datetime.now())*\n",
"\n",
"We will get a pandas.DataFrame with fundamental data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = qb.GetFundamental([\"AAPL\",\"AIG\",\"BAC\",\"GOOG\",\"IBM\"], \"ValuationRatios.PERatio\")\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Indicators\n",
"\n",
"We can easily get the indicator of a given symbol with QuantBook. \n",
"\n",
"For all indicators, please checkout QuantConnect Indicators [Reference Table](https://www.quantconnect.com/docs#Indicators-Reference-Table)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Example with BB, it is a datapoint indicator\n",
"# Define the indicator\n",
"bb = BollingerBands(30, 2)\n",
"\n",
"# Gets historical data of indicator\n",
"bbdf = qb.Indicator(bb, \"SPY\", 360, Resolution.Daily)\n",
"\n",
"# drop undesired fields\n",
"bbdf = bbdf.drop('standarddeviation', 1)\n",
"\n",
"# Plot\n",
"bbdf.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# For EURUSD\n",
"bbdf = qb.Indicator(bb, \"EURUSD\", 360, Resolution.Daily)\n",
"bbdf = bbdf.drop('standarddeviation', 1)\n",
"bbdf.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Example with ADX, it is a bar indicator\n",
"adx = AverageDirectionalIndex(\"adx\", 14)\n",
"adxdf = qb.Indicator(adx, \"SPY\", 360, Resolution.Daily)\n",
"adxdf.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# For EURUSD\n",
"adxdf = qb.Indicator(adx, \"EURUSD\", 360, Resolution.Daily)\n",
"adxdf.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Example with ADO, it is a tradebar indicator (requires volume in its calculation)\n",
"ado = AccumulationDistributionOscillator(\"ado\", 5, 30)\n",
"adodf = qb.Indicator(ado, \"SPY\", 360, Resolution.Daily)\n",
"adodf.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# For EURUSD. \n",
"# Uncomment to check that this SHOULD fail, since Forex is data type is not TradeBar.\n",
"# adodf = qb.Indicator(ado, \"EURUSD\", 360, Resolution.Daily)\n",
"# adodf.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# SMA cross:\n",
"symbol = \"EURUSD\"\n",
"# Get History \n",
"hist = qb.History([symbol], 500, Resolution.Daily)\n",
"# Get the fast moving average\n",
"fast = qb.Indicator(SimpleMovingAverage(50), symbol, 500, Resolution.Daily)\n",
"# Get the fast moving average\n",
"slow = qb.Indicator(SimpleMovingAverage(200), symbol, 500, Resolution.Daily)\n",
"\n",
"# Remove undesired columns and rename others \n",
"fast = fast.drop('rollingsum', 1).rename(columns={'simplemovingaverage': 'fast'})\n",
"slow = slow.drop('rollingsum', 1).rename(columns={'simplemovingaverage': 'slow'})\n",
"\n",
"# Concatenate the information and plot \n",
"df = pd.concat([hist.loc[symbol][\"close\"], fast, slow], axis=1).dropna(axis=0)\n",
"df.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Get indicator defining a lookback period in terms of timedelta\n",
"ema1 = qb.Indicator(ExponentialMovingAverage(50), \"SPY\", timedelta(100), Resolution.Daily)\n",
"# Get indicator defining a start and end date\n",
"ema2 = qb.Indicator(ExponentialMovingAverage(50), \"SPY\", datetime(2016,1,1), datetime(2016,10,1), Resolution.Daily)\n",
"\n",
"ema = pd.concat([ema1, ema2], axis=1)\n",
"ema.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"rsi = RelativeStrengthIndex(14)\n",
"\n",
"# Selects which field we want to use in our indicator (default is Field.Close)\n",
"rsihi = qb.Indicator(rsi, \"SPY\", 360, Resolution.Daily, Field.High)\n",
"rsilo = qb.Indicator(rsi, \"SPY\", 360, Resolution.Daily, Field.Low)\n",
"rsihi = rsihi.rename(columns={'relativestrengthindex': 'high'})\n",
"rsilo = rsilo.rename(columns={'relativestrengthindex': 'low'})\n",
"rsi = pd.concat([rsihi['high'], rsilo['low']], axis=1)\n",
"rsi.plot()"
]
}
],
"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": 2
}
| apache-2.0 |
ParuninPavel/lenta4_hack | dl_module/Dataset_preprocess.ipynb | 1 | 1424312 | null | mit |
zacwentzell/BIA-660-C-Spring2017 | In-class Lectures/February 02/inclass_lecture_3.ipynb | 1 | 8982 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import datetime"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"datetime.datetime(2017, 2, 2, 18, 45, 32, 547720)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datetime.datetime.now()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"datetime.datetime(2017, 2, 2, 23, 45, 59, 519592)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datetime.datetime.utcnow()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dt = datetime.datetime.now()\n",
"v = str(dt)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'2017-02-02 18:46:35.523684'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datetime.datetime."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'2017-02-02 18:46:35'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v[:v.rfind('.')]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"datetime.datetime(2017, 2, 2, 18, 46, 35)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dt = datetime.datetime.strptime(v[:v.rfind('.')], '%Y-%m-%d %H:%M:%S')\n",
"dt"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"datetime.datetime(2017, 2, 3, 18, 46, 35)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dt2 = dt + datetime.timedelta(days=1)\n",
"dt2"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"datetime.timedelta(0, 658, 675389)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datetime.datetime.now() - dt"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2017-02-02 19:00:31.354712\n"
]
},
{
"data": {
"text/plain": [
"'2017***************02-02 19:00:31'"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print str(datetime.datetime.now())\n",
"datetime.datetime.now().strftime('%Y***************%m-%d %H:%M:%S')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datetime.datetime.now() == dt"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def fun(name):\n",
" print 'Hello {}'.format(name)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello Zac\n"
]
}
],
"source": [
"fun('Zac')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<function __main__.fun>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"var = fun\n",
"var"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello Bob\n"
]
}
],
"source": [
"var('Bob')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def run_a_func(func):\n",
" func(3)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello 3\n"
]
}
],
"source": [
"run_a_func(var)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<function __main__.fun>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"var"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list([1,2,3])"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"l = [('z', 22), ('b', 15), ('p', 27)]"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"l.sort()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 2, 6, 8]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(l, )"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[('b', 15), ('g', 22), ('p', 27)]"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(l)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def fun(arg1, arg2):\n",
" do_stuff"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<function __main__.<lambda>>"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lambda x: x[1]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[('b', 15), ('z', 22), ('p', 27)]"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(l, key=lambda x: x[1])"
]
},
{
"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": 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 |
johnbachman/emcee | docs/_static/notebooks/quickstart.ipynb | 2 | 28454 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"%config InlineBackend.figure_format = \"retina\"\n",
"\n",
"from matplotlib import rcParams\n",
"rcParams[\"savefig.dpi\"] = 100\n",
"rcParams[\"figure.dpi\"] = 100\n",
"rcParams[\"font.size\"] = 20"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quickstart "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The easiest way to get started with using emcee is to use it for a project. To get you started, here’s an annotated, fully-functional example that demonstrates a standard usage pattern.\n",
"\n",
"## How to sample a multi-dimensional Gaussian\n",
"\n",
"We’re going to demonstrate how you might draw samples from the multivariate Gaussian density given by:\n",
"\n",
"$$\n",
"p(\\vec{x}) \\propto \\exp \\left [ - \\frac{1}{2} (\\vec{x} -\n",
" \\vec{\\mu})^\\mathrm{T} \\, \\Sigma ^{-1} \\, (\\vec{x} - \\vec{\\mu})\n",
" \\right ]\n",
"$$\n",
"\n",
"where $\\vec{\\mu}$ is an $N$-dimensional vector position of the mean of the density and $\\Sigma$ is the square N-by-N covariance matrix.\n",
"\n",
"The first thing that we need to do is import the necessary modules:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, we’ll code up a Python function that returns the density $p(\\vec{x})$ for specific values of $\\vec{x}$, $\\vec{\\mu}$ and $\\Sigma^{-1}$. In fact, emcee actually requires the logarithm of $p$. We’ll call it `log_prob`:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def log_prob(x, mu, cov):\n",
" diff = x - mu\n",
" return -0.5 * np.dot(diff, np.linalg.solve(cov, diff))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is important that the first argument of the probability function is\n",
"the position of a single \"walker\" (a *N* dimensional\n",
"`numpy` array). The following arguments are going to be constant every\n",
"time the function is called and the values come from the `args` parameter\n",
"of our :class:`EnsembleSampler` that we'll see soon.\n",
"\n",
"Now, we'll set up the specific values of those \"hyperparameters\" in 5\n",
"dimensions:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"ndim = 5\n",
"\n",
"np.random.seed(42)\n",
"means = np.random.rand(ndim)\n",
"\n",
"cov = 0.5 - np.random.rand(ndim ** 2).reshape((ndim, ndim))\n",
"cov = np.triu(cov)\n",
"cov += cov.T - np.diag(cov.diagonal())\n",
"cov = np.dot(cov, cov)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and where `cov` is $\\Sigma$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How about we use 32 walkers? Before we go on, we need to guess a starting point for each\n",
"of the 32 walkers. This position will be a 5-dimensional vector so the\n",
"initial guess should be a 32-by-5 array.\n",
"It's not a very good guess but we'll just guess a\n",
"random number between 0 and 1 for each component:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"nwalkers = 32\n",
"p0 = np.random.rand(nwalkers, ndim)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we've gotten past all the bookkeeping stuff, we can move on to\n",
"the fun stuff. The main interface provided by `emcee` is the\n",
":class:`EnsembleSampler` object so let's get ourselves one of those:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import emcee\n",
"\n",
"sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob, args=[means, cov])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remember how our function `log_prob` required two extra arguments when it\n",
"was called? By setting up our sampler with the `args` argument, we're\n",
"saying that the probability function should be called as:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-2.5960945890854434"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"log_prob(p0[0], means, cov)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we didn't provide any\n",
"`args` parameter, the calling sequence would be `log_prob(p0[0])` instead.\n",
"\n",
"It's generally a good idea to run a few \"burn-in\" steps in your MCMC\n",
"chain to let the walkers explore the parameter space a bit and get\n",
"settled into the maximum of the density. We'll run a burn-in of 100\n",
"steps (yep, I just made that number up... it's hard to really know\n",
"how many steps of burn-in you'll need before you start) starting from\n",
"our initial guess ``p0``:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"state = sampler.run_mcmc(p0, 100)\n",
"sampler.reset()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You'll notice that I saved the final position of the walkers (after the\n",
"100 steps) to a variable called `state`. You can check out what will be\n",
"contained in the other output variables by looking at the documentation for\n",
"the :func:`EnsembleSampler.run_mcmc` function. The call to the\n",
":func:`EnsembleSampler.reset` method clears all of the important bookkeeping\n",
"parameters in the sampler so that we get a fresh start. It also clears the\n",
"current positions of the walkers so it's a good thing that we saved them\n",
"first.\n",
"\n",
"Now, we can do our production run of 10000 steps:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"sampler.run_mcmc(state, 10000);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The samples can be accessed using the :func:`EnsembleSampler.get_chain` method.\n",
"This will return an array\n",
"with the shape `(10000, 32, 5)` giving the parameter values for each walker\n",
"at each step in the chain.\n",
"Take note of that shape and make sure that you know where each of those numbers come from.\n",
"You can make histograms of these samples to get an estimate of the density that you were sampling:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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5lsHBrjIoWa0+uB8RH8QyHrw38kgAAABwsG4aDl6I5YqD6b6fkrBvOefXYxkP7o89CwAAAByqm4aDWUS8knP+fB/DDG0tHgAAAABXuHeTF+ecv7evQcaSc35j86sAAADgPN3qcYwAAADAebjRigMAOATT6TS6riteb9t2wGkAAE6bcADA0em6LhaLxdhjAACcBeEAgKM1mUyiruvi9aqqBpwGAOA0CQcAHK26rmM+n489Bhy9tm2jaZprX1NVVcxms4EmAuCQCAcAAGeu73vbfwAoEg4AAM7UNtt52raNvu8HmAaAQ3VW4SCl9GlEPJNzPqt/bgCAq2yz9aBpGqsRAM7cZOwBBvZsRKSxhwAAAIBjcW7hAAAAALgB4QAAAAAoEg4AAACAooM6JDClNIuIr489BwAAALB0UOEgIv4uIn409hAAAADA0qGFg7djGQ5yRHywh/u/tod7AgAAwMk6qHCQc/48pfRBRHwrIr6fc/7FLu+fUup3eT8AAAA4dYd4OOLfRkSKiO+OPQgAAACcu0MMB09Wv7466hQAAADAQYeDZ0edAgAAADisMw4ivjjn4POIeH4Pt/8gIr6yh/sCAADASTq4cBARkXN+bk/3fX0f9wUAAIBTdYhbFQAAAIADIRwAAAAARcIBAAAAULS3Mw5SSn8QES/E8pDD+xHxq9Wlz2L55IRZzvnX+/r5AAAAwN3tLByklJ6JiO9GxLcj4sUtvydiGREeR8SHEfEo5/yLXc0EAAAA3M1OwkFK6W9iGQ2++FLhpfmK6/djuTLhtdW9PouIH0fEOznnf9rFfAAAAMDt3CkcpJS+EhGzWL7xvyoWPInl1oQLL0TEs1fdau3z5yLiQUQ8WEWEtyPiR7Y1AAAAwPDuejjix7FcMXDhUUS8HhH3c86TnPM3cs7TtY/nYxkGXlq97mEsw0KOp6sRIpYhIa1e+zAi/iWl9Ner7RAAAADAQG4dDlJK34nlCoILr+ac/zTn/EHO+ZPS9+WcP885/2z1uv8QyxULFysOXo+Id2O5UuGLH7X6eBDLgPBXt50ZAAAAuJm7rDh4ffVrjogHOeeP7jrMKiY8yDl/I5arDb4Xy4MTI54GhIcppf+ZUvrju/48AAAA4Hp3CQfPr33+d3cd5LLVyoR3cs7TWG5teHd1KUXENyLiUUrpr3f9cwEAAICn7hIOnmx+yW6stjY8iOUqhP+w+nKK5QGKP3H2AQAAAOzHXcLBj9c+/+ZdB9nGahXCw1iuOPhZLOPBS7FcfSAeAAAAwI7dJRw8Wvv8zbsOchM55yc555diufrgi3gw5AwAAABwDm4dDnLOn8fy8MIUEfdTSn+7s6m2n+FhRPxgNcNLKaW/GHoGAAAAOGV3WXEQOed3IuKjWL5xfz2l9F92MtXNZngrIj5YzfBWSulrQ88AAAAAp+reXW+Qc341pfRxRPxhRLyaUvpJRHwn5/zf7zzd9v7viPjz1ecPIuIvB/zZAOzQdDqNruuufU3btgNNAwDAncNBRETO+aW1ePBSRDxOKb0dET/IOf96Fz9jw8//PKX0OCJejIhXQjgAOFpd18VisRh7DAAAVnYSDiK+iAdvR8R3IiLH8r/8P1h97a2c8y929bM2eHGgnwPAHk0mk6jr+trXVFU10DQAAOdrZ+EgIiLn/CCl9GFEvBMRz66+fBEQHsfyEY7v7ykiCAYAJ6Su65jP52OPAQBw9u50OOJVcs7v55yfj+WjEiOWhxamWL6xfzMifp5S+ueU0l+llP5kFz8zpfTjix8fHssIAAAAO7PzcHBh9ajEb8TyiQcRy3hw8ev9iHgYER9GxDcvviel9BfbPhUhpfRMSunfpZT+OSJeW7v08G6TAwAAABd2ulXhspzzk1g+pvErEfFWLM8/WJdiuUogrz5/K5aPVIyIeBIRn0XEp2u/RkS8sPaxfp+IiO/lnP9p9/8kAAAAcJ72Gg4u5Jw/j6dnHfx5RHw7nq4SuHjTny/9fj0MXCWtff4kIh7knD/awbgAAFzStm00TVO8XlVVzGazAScCYCiDhIN1OecPYrV9IaX0zYh4NZ4+RnFdis3ej4i3BQMAgP3q+96jUgHO1ODhYN3qDf8Xb/pTSl+P5UqDZ1e/3l9d+mz1669iubrgcc75kwFHBQA4S5see9q2bfR9P9A0AIxh1HBw2SoGCAIAAAdi0/aDpmmsRAA4cXt7qgIAAABw/IQDAAAAoEg4AAAAAIqEAwAAAKDoRuEgpfStfQ0yllP8ZwIAAIBduemKg/83pfRXe5lkBCmlP4+I98aeAwAAAA7VTcPBv0TEw1OIB6uVBqIBAAAAXOOm4eBhRKRYxoO/3cM8g0gp/btYRoMcy38mAAAA4Ao3Cgc558cR8b1YxoPXU0r/M6X0+3uZbA9SSs+klP4xIt6M5T/D+znn/zjyWAAAAHCwbvxUhZzzO/E0HtyPiMfHsHVhtTXhk4h4JZazv51z/va4UwEAAMBhu9XjGFfx4I1YvgGPWG5d+P9SSv9mZ5PtSErpD1JKP43l1oTnYjnz93PO/3bcyQAAAODw3SocRETknN+PiGlE/DqWb8afj4j3U0r/nFL6v3Y0362llP5kFQw+jogX42nkeN32BAAAANjOrcNBxBdnHnwtIj5YfSlFxAsR8U5K6Tcppb9KKf3B3UbcXkrpaymlH6aUfhURH8bTYJAi4nFE3M85f3DdPQAAAICn7hQOIiJyzp/nnF+PiD+N5RkCae3jYUR8nFL6VUrpxymlb6WUvnbXn3lhddjht1JKf5NS+ueI+HlEfD+WWxJiNcNnEfEg5zzNOX+yq58NAAAA5+Derm6Uc34UEfdTSt+N5Zv3F+Lp9oDnIuK11UeklCKWKwCerD5+HhGfxvJN/qdX3P6FWG6FeDaWBzJOY7maYF264vdvRcQPc86f3+EfDQAAAM7WzsLBhdXBie+klF6LiB/E0zf4KSJyPH2D/2L89pv/m7i4z/o9I5ZB4u2c87t3uDcAAAAQewgHF1aHJ76fUvpKRDyI5VMY1kPB+hv/y1+78paF11ycX/AoIn6cc/7ZrYcGAAAAvmRv4eDCapvAW6uPSCl9M5YB4eXVry9seauLYHCxveFxRHyYc/5opwMDAAAAX9h7OLhs9Ub/S2/2V6sSno9yRPg0Ip44qwDg+E2n0+i6rni9bdsBpwEAYJPBw8FVVkHg81g+lQGAE9Z1XSwWi7HHAABgSwcRDgA4P5PJJOq6Ll6vqmrAaYC7ats2mqYpXq+qKmaz2YATAbArwgEAo6jrOubz+dhjADvS973VRAAnSjgAAODWNq0Oats2+r4faBoA9uFgwkFK6ZlYHpD4ac7512PPAwDAZpu2HzRNYyUCwJEbLRysQsF3I+LbsXyawrNr1yIiPovlYxcfRcSPc87/NMKYAAAAcNZGCQcppb+JZTSIiEiFlz0XES+uPr6fUnoSEf85It61IgEAAACGMRnyh6WUnkkp/TSW0SCtPvLaR/FbI+J+RLwVEZ+klP5i37MCAAAAw684eBwRX4+nwSDi6YqDJ6vrn8Zym8ILsVxt8MKlezwXEW+llL4dEa/nnP/XvocGAACAczVYOEgpfSeWEWB9dUGKiPcj4oc5559d872vRMTDiPjm2penEfEkpfRKzvm/7WdqAAAAOG9DblV4sPb5xSqDV3POb1wXDSIics6Pcs6vRsQ3IuKjeLpiIUXEo5TSH+9jYAAAADh3Q4aDi9UGF2/638g5f3STG+Scn6wCwhvx2/Hg93c8LwAAAJy9QQ9HXPM45/zBbb855/x+LLcqfB5P48F7q0c8AgAAADsyZDj4NJ6uEnj7rjfLOT+OiFfW7nk/In501/sCAAAATw0ZDh6tff7pLm64igffi6ePdnxg1QEAAADszpDh4L21zz/b1U1zzu9ExPrhij/Y1b0BAADg3A0WDlYHIX6++u2LO779w7XPX93xvQEAAOBsDX044sNYbil4Y5c3zTk/iuUqhhS7jxIAAABwtu4N+cNyzu+klB5GxEsppd/POf/3Hd5+FsvDEiOl9LWc8y92eG8AtjCdTqPrumtf07btQNMAALALg4aDlddj+Sb/vYj4P3Z434unLETs6PBFAG6m67pYLBZjjwEAwA4NFg5SSn8QEc/HMhq8HhHvpZT+Ouf8/+zoRzx78UnO+dc7uicAtzCZTKKu62tfU1XVQNMAAHAXQ644+D8j4t+vPr94qsKDlNLzEfGjnPM/3fH+04jI8eXHPgIwgrquYz6fjz0GAAA7MPThiGn18Vws3+SnWK4++Dil9JuU0j+mlH6YUvqTlNIzW980pT+Mp4ciPrzutQAAAMD2xjjjYF1e/ZpWv76y+vh+RERK6bNYriD4aUQ8umpVwmoLxEer335vBysXAAAAgJXBwkHO+QcppR/HckvBS7EMBC8UXn4REp6LiNdWH5FSioh4EsuY8POIeHl17bOIeCXn/F/3NT8AAACco6Efx/iziPhZRLx78bWU0jdjuc3g5VjGhGev/u4vYsL9+HJwSLE8cPHFVViYORwRAAAAdmPsrQqRc/4onm41iJTSV2K5KuHVWAaFaZRjQsRyu8PFFoeLezyJ5eMZi1scAAAAgM1GDweX5Zw/j2VIWI8JX49lRHg1liHhxau+NX57VcJVWxw+jmVM+MVe/gEAAPgtbdtG0zTXvqaqqpjNZgNNBMC2Di4cXCXn/ElEfBIRH1x8bfUkhZucl7C+xSHHkfyzAwCcgr7vY7FYjD0GALdwtG+eC+cl/GEsI8J15yWkK74GAMAeVFW18TVt20bf9wNMA8BtHG04uMpaTIiIW52XAADADm2z9aBpGqsRAA7YSYWDyzaclwAAAABscNLh4Cpr5yUAAAAAG0zGHgAAAAA4XMIBAAAAUCQcAAAAAEXCAQAAAFAkHAAAAABFwgEAAABQdHaPYwQA4DC1bRtN0xSvV1UVs9lswIkAiBAOAAA4EH3fx2KxGHsMAC4RDgAAGFVVVddeb9s2+r4faBoALhMOAAAY1abtB03TWIkAMCKHIwIAAABFwgEAAABQJBwAAAAARcIBAAAAUCQcAAAAAEXCAQAAAFAkHAAAAABFwgEAAABQJBwAAAAARcIBAAAAUHRv7AEAOB7T6TS6riteb9t2wGkAABiCcADA1rqui8ViMfYYAAAMSDgA4MYmk0nUdV28XlXVgNMAALBPwgEAN1bXdczn87HHAABgAA5HBAAAAIqEAwAAAKBIOAAAAACKhAMAAACgSDgAAAAAioQDAAAAoEg4AAAAAIqEAwAAAKBIOAAAAACKhAMAAACgSDgAAAAAioQDAAAAoEg4AAAAAIqEAwAAAKBIOAAAAACKhAMAAACgSDgAAAAAioQDAAAAoEg4AAAAAIqEAwAAAKBIOAAAAACK7o09AACHYTqdRtd1176mbduBpgEA4FAIBwBERETXdbFYLMYeAwCAAyMcAPAlk8kk6rq+9jVVVQ00DQAAYxMOAPiSuq5jPp+PPQYAAAdCOAAA4Ci0bRtN01z7mqqqYjabDTQRwHkQDgAAOAp93zuLBWAEwgEAAAdtm3NV2raNvu8HmAbg/AgHAAActG22HjRNYzUCwJ5Mxh4AAAAAOFzCAQAAAFAkHAAAAABFwgEAAABQJBwAAAAARcIBAAAAUCQcAAAAAEXCAQAAAFAkHAAAAABFwgEAAABQdG/sAQAYxnQ6ja7ritfbth1wGgAAjoVwAHAmuq6LxWIx9hgAABwZ4QDgzEwmk6jruni9qqoBpwEA4NAJBwBnpq7rmM/nY48BAMCRcDgiAAAAUCQcAAAAAEW2KgAAcDLato2maYrXq6qK2Ww24EQAx084AADgZPR97wkyADsmHAAAcPQ2PRGmbdvo+36gaQBOi3AAAMDR27T9oGkaKxEAbsnhiAAAAECRcAAAAAAUCQcAAABAkXAAAAAAFAkHAAAAQJFwAAAAABQJBwAAAECRcAAAAAAUCQcAAABAkXAAAAAAFAkHAAAAQJFwAAAAABQJBwAAAECRcAAAAAAUCQcAAABAkXAAAAAAFAkHAAAAQJFwAAAAABTdG3sAAHZjOp1G13XF623bDjgNAACnQjgAOBFd18VisRh7DICD1rZtNE1z7WuqqorZbDbQRACtlPCOAAAWBklEQVSHTzgAODGTySTqui5er6pqwGkADkvf9yIrwA0JBwAnpq7rmM/nY48BcFC2iaZt20bf9wNMA3BchAMAAE7eNlsPmqaxGgHgCp6qAAAAABQJBwAAAECRcAAAAAAUCQcAAABAkXAAAAAAFAkHAAAAQJFwAAAAABTdG3sAAAA4JG3bRtM0xetVVcVsNhtwIoBxCQcAALCm7/tYLBZjjwFwMIQDAACI5UqC67RtG33fDzQNwOEQDgAAIGLj9oOmaaxEAM6SwxEBAACAIuEAAAAAKLJVAeAITKfT6Lru2te0bTvQNAAAnBPhAOAIdF1nXy0AAKMQDgCOyGQyibqur33NplPBAQDgJoQDgCNS13XM5/OxxwAA4Iw4HBEAAAAoEg4AAACAIuEAAAAAKBIOAAAAgCLhAAAAACgSDgAAAIAi4QAAAAAoEg4AAACAIuEAAAAAKBIOAAAAgCLhAAAAACgSDgAAAICie2MPAAAAx6Rt22ia5trXVFUVs9lsoIkA9ks4AACAG+j7PhaLxdhjAAxGOAAAgC1UVbXxNW3bRt/3A0wDMBzhAAAAtrDN1oOmaaxGAE6OwxEBAACAIisOAA7AdDqNruuK19u2HXAaAAB4SjgAOABd11naCgDAQRIOAA7IZDKJuq6L17c5mAsAAHZJOAA4IHVdx3w+H3sMAAD4gsMRAQAAgCLhAAAAACgSDgAAAIAi4QAAAAAoEg4AAACAIuEAAAAAKBIOAAAAgCLhAAAAACi6N/YAAABwatq2jaZpiterqorZbDbgRAC3JxwAAMCO9X0fi8Vi7DEAdkI4AACAHamq6trrbdtG3/cDTQOwG8IBAADsyKbtB03TWIkAHB2HIwIAAABFwgEAAABQJBwAAAAARc44ANiz6XQaXddd+5q2bQeaBgAAbkY4ANizruschAUAwNESDgAGMplMoq7ra1+z6TFeAAAwNOEAYCB1Xcd8Ph97DAAAuBGHIwIAAABFwgEAAABQJBwAAAAARcIBAAAAUORwRIA7mk6n0XVd8XrbtgNOAwAAuyUcANxR13WxWCzGHgMAAPZCOADYkclkEnVdF69XVTXgNAAAsBvCAcCO1HUd8/l87DEAAGCnHI4IAAAAFAkHAAAAQJFwAAAAABQJBwAAAECRwxEBAGBgbdtG0zR3ukdVVTGbzXY0EUCZcAAAAAPr+z4Wi8XYYwBsRTgAAICBVFV153u0bRt93+9gGoDtCAcAADCQXWwtaJrGagVgUA5HBAAAAIqEAwAAAKBIOAAAAACKhAMAAACgyOGIANeYTqfRdd21r2nbdqBpAABgeMIBwDW6rnNyNQAAZ004ANjCZDKJuq6vfc0uns0NAACHRjgA2EJd1zGfz8ceAwAABudwRAAAAKBIOAAAAACKbFUAztqmpyZ4YgIAAOdOOADOmqcmAADA9YQDgNj81ARPTAAA4FwJBwDhqQkAAFDicEQAAACgyIoDAAA4Qm3bRtM0xetVVcVsNhtwIuBUCQcAAHCE+r53wC8wCOEAAACOyKYDe9u2jb7vB5oGOAfCAQAAHJFN2w+aprESAdgphyMCAAAARcIBAAAAUCQcAAAAAEXOOABO1nQ6ja7rrn1N27YDTQMAAMdJOABOVtd1DocCAIA7Eg6AkzeZTKKu62tfs+nRVgBwbNq2jaZprn1NVVUbn9IAIBwAJ6+u65jP52OPAQCD6vveyjtgJ4QDAAA4IdusomvbNvq+H2Aa4BQIBwAAcEK22XrQNI3VCMDWPI4RAAAAKBIOAAAAgCJbFQAA4ExtevKCpy4AEcIBcMSm02l0XVe83rbtgNMAwPHx5AVgG8IBcLS6rvOXHQC4hU1PXvDUBWCdcAAcvclkEnVdF69v81gqADgnm7YfeOoCsE44AI5eXdcxn8/HHgMAAE6SpyoAAAAARVYcAAdp08GHEQ4/BACAIQgHwEFy8CEAjG/T4xojPLIRzoFwABy0TQcfRjj8EAD2xeMagQjhADhwDj4EgOFtE+U9shHOh3AAAAB8yTZbDzyyEc6HcACMYtPhhw4+BIDjsOkcBGcgwPETDoBROPwQAE6DcxDg9AkHwKg2HX7o4EMAOEyb/h3tDAQ4HcIBMCqHHwLAcdq0/cAZCHA6JmMPAAAAABwu4QAAAAAoslUBuLFNT0TYhqcmAADAcRAOgBvzRAQAADgfwgFwa5ueiLANT00AAIDDJhwAt+aJCAAAcPocjggAAAAUWXEAAADsTdu20TTNta+pqipms9lAEwE3JRwAAAB70/e9Q5XhyAkHAADAzm1zAHLbttH3ffH6TR4BbdUC7I9wAAAA7Nw2b+Kbprl2NYJHQMNhEA4AAIBRlc5BaNs2Iq5/BPSmVQvA3QkHAADAqDadg3DdI6A3rVoA7k44gDNzk72CJRf1HwDgLrY5B+EmrwP2QziAM2OvIABwKBxmCMdBOIAzdd1ewW2p/wAAcPqEAzhT1+0VBAA4NqUDFi94XCPcnnAAAAAcvU0HLAK3JxzAEXGwIQDAl23aOnnxuMZNKxIu7mVVAvw24QCOiIMNAQC+bNMb/YvHNW6zImGxWNjuAFcQDuCAbFpRcLFawMGGAADb2ebvPOtBwX+kgd8mHMAB2XZFgYMNAQC2s80KgW3+48022x2sSOBUCQewI9ueP7DNv1A2rSiwWgAAYHd2ud0BTpFwAFvYJgps+y+R6/bOXWxFsKIAAOBw7PIAxiFY+cCuCQe79TsXnzi5fjh/9md/Fr/85S/3+jNu+iSDq/7lsn6PTZHhN7/5jXAAAHAg/v7v//7a6y+//HJ0XXcwKxIWi8XG87C++tWvxj/8wz/c6v7b/v37Lj+D7V167/k7pdfdRco57+O+ZymlNI2In449BwAAAGfp5ZzzzpebTHZ9QwAAAOB0WHGwQymlfxURv7f67S8j4jcjjsN+VPF0VcnLEXGzPQwAN+PPHGBI/syB4/Q7EfHV1ef/I+f8v3f9A5xxsEOr/4GcQnLCUkrrv+1yzg4iAPbGnznAkPyZA0ftf+3z5rYqAAAAAEXCAQAAAFAkHAAAAABFwgEAAABQJBwAAAAARcIBAAAAUCQcAAAAAEUp5zz2DAAAAMCBsuIAAAAAKBIOAAAAgCLhAAAAACgSDgAAAIAi4QAAAAAoEg4AAACAIuEAAAAAKBIOAAAAgCLhAAAAACgSDgAAAIAi4QAAAAAoEg4AAACAIuEAAA5ESum1lNJ7KaWfp5T+ZfXxcUrp7ZTSK2PPBwCcp5RzHnsGADhrKaXXIuLdiHh2w0s/i4jv5Jzf3/9UAABLwgEAjCil9GZEfP+KS09Wv75wxbX3c86v728q4ByklL4bEa9GxIsR8Xw8jZdPVh8fxvLPmydX3wE4F8IBAIwkpfR2RHx37UuPIuLNnPOjS697MSLejIj17QqPcs6v7n9K4NRcEyxLHkXEw5zz4z2NBBw44QAARrDanvDe2pce5Jzf2fX3AFxIKT0bER/H1SuZtvEw5/zWDkcCjoRwAAADW/3l/ZN4uix46wBwRTx4Luf82Y5HBE5QSunn8eVo8FYstyPMLv4cWa1wmkbE6/HlVU4X3sk5P9j3rMBhEQ5gAPYQAusuLRN+knO+f8Pv/zCe/oX+rZzzw13OB5yeS39uPI6I1zf9vSOl9EIsQ+WLly5ZeQBnRjiAPbKHELhKSulf4mlAfP2mT0lY/RfBj1e//Szn/Nwu5wNOy+o/YLy9+u1tYuXl81giIu77Dx5wPoQD2AN7CIGSlNIrsVxlFHGHN/13jQ/A+bj058Wt3vCnlN6LiNfWvmTLApyRydgDwIm6HA3eiuVWhedyzinnnCLipYh4EMtVBpe9uar7wOlZf4ziVf//39b693q6AnCl1WqDi2jw1h1WCXwnItbPU7m8AgE4YcIB7NhqD+FFNHgcy7L/MOf8aP0As5zz45zzO6vHqd1fvXbdd1NKN9nmAByH9cPGfnqH+3y49vkbd7gPcNrWVwX88LY3Wf0d5kuHuK5WUAFnQDiAHVpV/Yt/iT7JOb+0TdnPOT/JOb8Ul/6FHMuVB7fd7gAcpvX/T9/lPJP1P1ueXW2RArjs4s+K93fwBJYPL/3+8qGJwIkSDmC33lz7/MZLh1d7BS/vU3ZaOpyI1aGG6+5ysNjl7xUZgat8J5Z/Xvx4B/eaXfr9v97BPYEjIBzAjthDCGzh8pv7T+9wr8vfO73DvYATtVpl8Grc7UwV4MwJB7A79hACmzy//pu7LBu+4nttVQCutNoSeddtChGX/gyLiF/t4J7AERAOYHfsIQQ28eYeOGaX/z5yl3NagCMiHMDu2EMIbLLP/y/7cwLYt2+v/ybnbPsDnIl7Yw8ApyLn/FlK6dW4255lgNuymgHYm9WTW15b+9LlJ0Hx/7d3v0dxo0kcgLuvHAC+ywAywHsR2GSAzxlABqY2givIADLYszMwzmBNBstFsLtk0PcBcSO0vIBmNMwfnqfKVZIsDf1pauY33e8LW0xwABNaYEHEITOEsJ2m/nJ/s4TXBHjIz4Nzuz7BK2JUAdaTGUIAYC10W8l+7l06nmixRWBDCA5gPZkhhO3kgzawib70ji+rypgCvDKCA1gzZghhq009dtQfUxBKAJPLzG8RsdudXlXVwSrrAVZDcADrxwwhbK9lfrm3Fgowqcz8EhEfutPriHi/wnKAFRIcwBoxQwivS9dhNNWz3iuAyXSdBncdkJdVteczCbxeggNYL2YIYbsNd17ZffCu5xnuvmIrWGASXWhw12lwYTwBsB0jrAkzhPAq/Do4H375H2MYOky1HSzwSnWdTD9i9v5y7EcMIELHAawFM4TwOnRtvv1W3+HWq2Pce7aqbNsKzK0bl/xvzEKDA6EBcEfHARuhl4Av8uvcov6IiHdTz/cN2gEvdRrA1ruO2Zf+fy7wOv1nhQbA3DLzMGbjkjdx+3lHFxPwf4IDNsVPsdgs8BR2uhom+4D+wAzh8VSvDayty5gFB1N1HAxHIACeJTOPIuK8O72OJfxIAmy+rKpV1wDP0rXQrarjYCcirqdqBTZDCK9XZu5GxG+9S3tjf9l74DXeGVUAxsrMzxFx2p1eRcR7oQHwEMEBvLAuAPket2FExO0M4eUKSwJeWGb+iFnHwNeq+jjy+fOIOOpOr6tqb8r6gO2Xmacx2wJ69PsQ8LpYHBFeUDdD+CNuQ4ObuP2lUWgAr8957/iw6yB4lu7eo96l89a9AA/pwse70OBMaAA8RccBvBAzhEBfZv4Zs86jZ7UId2NO32PWraDbABil28npsDs9qaqzVdYDbAYdB/ACuhnCu9DgKoQGQET/F779iPj+WOdB93/90CAiwoKqwLN1izLfhQYfhQbAc+k4gCUzQwi0DBYmu/M1In6J286km7gNCg7i/nhChEVVgWd6oFvJ+krAKIIDWKLBAmZnVXWyynqA9TMYY3quj1X1dRn1ANvlgZ2cDmKiLVx1T8LrITiAJTFDCDxXN4ZwEn/tKhi6iNv3Ex/WgWcZdD5O7aKqjEzBK/Bm1QXANupmCD90p34ZBB5VVddxu17BcWZ+iNt24n/E7eKJv0XEZVVdrbBEYHPtPH0LwOMEBzAhM4TAorr3DO8bAMDaMKoAEzFDCAAAbCMdBzCdn2MWGkREfJvqhTPTDCEAALASf1t1AbBFzBACAABbR3AAAAAANFnjAAAAAGjScQAAAAA0CQ4AAACAJsEBAAAA0CQ4AAAAAJoEBwAAAECT4AAAAABoEhwAAAAATYIDAAAAoElwAAAAADQJDgAAAIAmwQEAAADQJDgAAAAAmt6sugAAYLtl5k5EfIiITxGxGxF/RMSXqrpYaWEAwLNkVa26BgBgS2XmUUScN/77OiLeVdXNC5YEAIwkOAAAliIzv8Vtp8Fjrqtq7yXqAQDmY40DAGBymfkjZqHBdUR8jIi33b/j3q27mdnqSAAA1oCOAwBgUl1osN+dfq2qjw/c8yEivvUuvTWyAADrSccBADCZrnvgLjS4fCg0iIioqsuIuOxdOlp2bQDAfAQHAMAkMvMwZgHAVVUdPPFIf0Th03KqAgAWZVQBAJhEZv4ZETvd6buqunri/t2I+O3uvKpyieUBAHPScQAALCwzT2MWGlw8FRpERFTV9eA19lv3AgCrIzgAABbSdQ587l06GfF4PzzYnaYiAGBKggMAYFGnveOLkbsj9O/9+0T1AAATEhwAAHPLzJ2IOOxdOm3d+wx7C5YDACyB4AAAWEQ/KLgarlsw0u+LFgMATE9wAAAs4qh3/Mscz1vXAADWnOAAAJhLZh4OLn0d+fxOzHZiiLi/3gEAsCYEBwDAvD71jq/nGFMYdhssMuYAACyJ4AAAmFe/42BUt0FnGBz8ukAtAMCSCA4AgNEy88Pg0rc5XuZecDByG0cA4IW8WXUBAMBGOhicn2TmycjX+Kl3bEwBANaU4AAAmMf+4HzYgTDW1YLPAwBLYlQBAJjHokHB0DyjDgDACxAcAACjZOZwUcOTqsox/yLi4+A1Ll+ofABgJMEBADDWMDiYZ0eF/hoJN2O2cszMncz8nJk/5vi7AMBI1jgAAMba6Z+M+dLf0x91+M9jN2bmTtyGFbsR8Slm20DahQEAXoDgAAAYq99xMDo06AUBd84fuffPGAQVAMDLMqoAAIy11zue51f/f/Wfr6qndlS4jttdF866vz3PaAQAMCcdBwDAIv6Y45nj3vHFYzdW1dvhtcyc408CAPPScQAAjHXTOH5SZu5HxH7v0r8nqQgAWBrBAQCwiLEdB/e6DarKAocAsOYEBwDAWL/P81C3KOJR79LJNOUAAMskOAAAxurvpLDbvOuvfu4dn+k2AIDNIDgAAMbq74LwrOAgM3cj4nN3el1Vug0AYEMIDgCAUarqXsdBN4LwlC+94+PmXQDA2hEcAADz+No7PmreFRGZeRqznRQuqupyaVUBAJMTHAAA8zjvHTc7CDLzc8xGFK6qSrcBAGwYwQEAMFrXNXA3srCbmf1RhMjMne7aaXfpKiLev2CJAMBE3qy6AABgYx1HxLfu+DAzK2YLJ+737ruKiPd2UQCAzaTjAACYS9d1MBw92I/7ocFJVb0TGgDA5hIcAABzq6qLiHgXEf0FD68i4iwi9qrqbCWFAQCTMaoAACykqq4i4mDVdQAAy6HjAAAAAGgSHAAAAABNggMAAACgSXAAAAAANAkOAAAAgCbBAQAAANCUVbXqGgAAHpSZXyLicMQjN1X1dln1AMBrpOMAAFhnO0u+HwB4go4DAAAAoEnHAQAAANAkOAAAAACaBAcAAABAk+AAAAAAaBIcAAAAAE2CAwAAAKBJcAAAAAA0CQ4AAACAJsEBAAAA0CQ4AAAAAJoEBwAAAECT4AAAAABoEhwAAAAATYIDAAAAoElwAAAAADQJDgAAAIAmwQEAAADQJDgAAAAAmgQHAAAAQJPgAAAAAGgSHAAAAABNggMAAACgSXAAAAAANAkOAAAAgCbBAQAAANAkOAAAAACaBAcAAABAk+AAAAAAaBIcAAAAAE2CAwAAAKBJcAAAAAA0/Q+lMhlBvGyovgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 600x400 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 393,
"width": 519
},
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"samples = sampler.get_chain(flat=True)\n",
"plt.hist(samples[:, 0], 100, color=\"k\", histtype=\"step\")\n",
"plt.xlabel(r\"$\\theta_1$\")\n",
"plt.ylabel(r\"$p(\\theta_1)$\")\n",
"plt.gca().set_yticks([]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another good test of whether or not the sampling went well is to check\n",
"the mean acceptance fraction of the ensemble using the\n",
":func:`EnsembleSampler.acceptance_fraction` property:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean acceptance fraction: 0.552\n"
]
}
],
"source": [
"print(\"Mean acceptance fraction: {0:.3f}\".format(np.mean(sampler.acceptance_fraction)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and the integrated autocorrelation time (see the :ref:`autocorr` tutorial for more details)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean autocorrelation time: 57.112 steps\n"
]
}
],
"source": [
"print(\n",
" \"Mean autocorrelation time: {0:.3f} steps\".format(\n",
" np.mean(sampler.get_autocorr_time())\n",
" )\n",
")"
]
},
{
"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": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
airbnb/knowledge-repo | tests/test_posts/one_plus_one.ipynb | 1 | 8516 | {
"cells": [
{
"cell_type": "raw",
"metadata": {},
"source": [
"---\n",
"title: \"My bright idea!\"\n",
"authors:\n",
" - resident_innovator\n",
"created_at: 2017-01-01 00:00:00\n",
"updated_at: 2017-01-25 00:00:00\n",
"tags:\n",
" - proofs\n",
" - novel\n",
"tldr: |\n",
" In this post we prove that one plus one equals two. Various forms of evidence are provided.\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Programmatic Evidence"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1 + 1 == 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Graphical Evidence"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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JliQPJtmX5JoZ+p+a5BNd/5eTbBrp++Ou/cEkrxhf6ZKkhZgz+JOsAa4HXgmcB1yR5Lxp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\nBr8kNcbgl6TGGPyS1BiDX5Ia8/+OpuyCLbVvgwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111db6668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"_ = plt.bar([1,2,3], [1,1,2], color=['r','r','b'])"
]
}
],
"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": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| apache-2.0 |
atitus/PHY3110 | notes/03-conservation-laws/.ipynb_checkpoints/barbell-checkpoint.ipynb | 1 | 6070 | {
"metadata": {
"name": "",
"signature": "sha256:883b5d98b5eb193382fb6e953e2578a443e5f1524493c0f1bad15021d29d4104"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Barbell Angular Momentum"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This program is based on the program *10_barbell_ang_mom.py* by Bruce Sherwood. At the beginning of the program, select the rotation option and direction option."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division, print_function\n",
"from ivisual import *\n",
"from math import *"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div id=\"scene\"><div id=\"glowscript\" class=\"glowscript\"></div></div>"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x103497e50>"
]
},
{
"javascript": [
"require.undef(\"nbextensions/glow.1.0.min\");"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x1048d9250>"
]
},
{
"javascript": [
"require.undef(\"nbextensions/glowcomm\");"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x1048d9290>"
]
},
{
"javascript": [
"require([\"nbextensions/glowcomm\"], function(){console.log(\"glowcomm loaded\");})"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x1048d92d0>"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#rotation options\n",
"rotation = 0 #barbell orientation doesn't change (mounted on frictionless axle, no torque acts on it)\n",
"#rotation = 1 #barbell rotates at same rate as rod\n",
"#rotation = 2 #barbell rotates a lot\n",
"\n",
"#direction of rotation of barbell about its CM\n",
"direction = 1 #counterclockwise\n",
"#direction = -1 #clockwise\n",
"\n",
"#create scene\n",
"scene=canvas(title=\"Barbell Angular Momentum\")\n",
"\n",
"#set background color\n",
"#scene.background = color.white\n",
"\n",
"#mass of particles on barbell\n",
"rmass = 0.2\n",
"\n",
"#length of the barbell\n",
"L = 2\n",
"\n",
"#rod from origin to CM of barbell\n",
"rod=cylinder(pos=(0,0,0), axis=(4,0,0), radius=0.03, color=(1,.9,0))\n",
"\n",
"#create a frame\n",
"barbell = frame()\n",
"\n",
"#particle 1\n",
"s1=sphere(frame = barbell, pos=(0,L/2.,0), radius=rmass, color=(1,0,0))\n",
"s1.mass=0.01\n",
"\n",
"#particle 2\n",
"s2=sphere(frame = barbell, pos=(0,-L/2.,0), radius=rmass, color=(0,0,1))\n",
"s2.mass=0.01\n",
"\n",
"#barbell rod\n",
"rd=cylinder(frame = barbell, pos=s1.pos, axis=(s2.pos-s1.pos),\n",
" color=(1,1,1), radius=0.04)\n",
"\n",
"#barbell moment of inertia about CM\n",
"barbell.Icm = 2*s1.mass*(L/2)**2\n",
"barbell.pos = rod.pos+rod.axis\n",
"\n",
"#moment of inertia of CM of barbell about origin\n",
"barbell.Iorig = 2*s1.mass*(mag(rod.axis)**2)\n",
"\n",
"#angular velocity of the barbell about its CM\n",
"omegaCM = vector(0,0,pi)\n",
"\n",
"#angular velocity of the CM of the barbell about the origin\n",
"omega = vector(0,0,pi/5)\n",
"\n",
"t = 0.0\n",
"dt = 0.01\n",
"scene.range=5\n",
"Lscale = 2.0/0.1\n",
"\n",
"LT=arrow(pos=rod.pos, axis=(0,0,0), color=color.cyan,\n",
" shaftwidth = 0.2)\n",
"LR=arrow(pos=barbell.pos, axis=(0,0,0), color=color.green,\n",
" shaftwidth = 0.2)\n",
"\n",
"theta=0\n",
"phi=0\n",
"dphi=mag(omega)*dt\n",
"dtheta=mag(omegaCM)*dt\n",
"\n",
"T=2*pi/mag(omega)\n",
"\n",
"while t<2*T:\n",
" rate(150)\n",
" theta = theta + mag(omegaCM)*dt\n",
" phi = phi + mag(omega)*dt\n",
" rod.axis=4*vector(cos(phi), sin(phi),0)\n",
" barbell.pos = rod.pos+rod.axis\n",
" Ltrans = barbell.Iorig*omega\n",
" Lrot = vector(0,0,0)\n",
" if rotation == 1:\n",
" barbell.rotate(angle=dphi, axis=omegaCM, origin=(barbell.pos))\n",
" Lrot = barbell.Icm*omega \n",
" if rotation == 2:\n",
" barbell.rotate(angle=direction*dtheta, axis=omegaCM, origin=(barbell.pos))\n",
" Lrot = direction*barbell.Icm*omegaCM\n",
" LT.axis = Ltrans*Lscale\n",
" LR.pos = barbell.pos\n",
" LR.axis = Lrot*Lscale\n",
" t = t+dt\n",
"\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div id=\"scene10\"><div id=\"glowscript\" class=\"glowscript\"></div></div>"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x104a7c4d0>"
]
},
{
"javascript": [
"window.__context = { glowscript_container: $(\"#glowscript\").removeAttr(\"id\")}"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x104a7c510>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
decisionstats/pythonfordatascience | matplotlib+cars.ipynb | 1 | 28731 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cars=pd.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/datasets/mtcars.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cars=cars.drop(\"Unnamed: 0\",1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"var=cars.groupby('cyl').mpg.mean().reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x941db00>]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.plot(var.cyl,var.mpg)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x904b1d0>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.xlabel(\"Cylinders\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x93df898>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.ylabel(\"Mean Mileage Per Gallon\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x93fe668>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.title(\"Mileage versus Cylinders\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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97shERETKl9gTDjMbZGZvmdkqM/vezKab2f6FtDvAzJ4ys5/NbLWZzTezOiW/P5x6aujt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\nW1NSz00AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x9011a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
rssalessio/PythonVRFT | examples/notebook_example_2.ipynb | 1 | 171001 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## VRFT with measurement noise (no instrumental variables)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Copyright (c) [2021] Alessio Russo [[email protected]]. All rights reserved.\n",
"# This file is part of PythonVRFT.\n",
"# PythonVRFT is free software: you can redistribute it and/or modify\n",
"# it under the terms of the MIT License. You should have received a copy of\n",
"# the MIT License along with PythonVRFT.\n",
"# If not, see <https://opensource.org/licenses/MIT>.\n",
"#\n",
"# Code author: [Alessio Russo - [email protected]]\n",
"# Last update: 10th January 2020, by [email protected]\n",
"#\n",
"\n",
"# Example 2\n",
"# ------------\n",
"# In this example we see how to apply VRFT to a simple SISO model\n",
"# with colored measurement noise (no instrumental variables)\n",
"# Input data is generated using random normal noise\n",
"#"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load libraries"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import scipy.signal as scipysig\n",
"from vrft import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### System, Reference Model and Control law"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# System\n",
"dt = 1e-2\n",
"num = [0.5]\n",
"den = [1, -0.9]\n",
"sys = ExtendedTF(num, den, dt=dt)\n",
"\n",
"# Reference Model\n",
"refModel = ExtendedTF([0.6], [1, -0.4], dt=dt)\n",
"\n",
"# Control law\n",
"control = [ExtendedTF([1], [1, -1], dt=dt),\n",
" ExtendedTF([1, 0], [1, -1], dt=dt)]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generate signals"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def generateNoise(t):\n",
" # Generate colored noise\n",
" omega = 2*np.pi*100\n",
" xi = 0.9\n",
" dt = t[1] - t[0]\n",
" noise = np.random.normal(0,0.1,t.size)\n",
" tf = scipysig.TransferFunction([10*omega**2], [1, 2*xi*omega, omega**2])\n",
" # Second order system\n",
" _, yn, _ = scipysig.lsim(tf, noise, t)\n",
" return yn\n",
"\n",
"# Generate input siganl\n",
"t_start = 0\n",
"t_end = 10\n",
"t = np.arange(t_start, t_end, dt)\n",
"u = np.random.normal(size = t.size)\n",
"\n",
"# Open loop experiment\n",
"t, y = scipysig.dlsim(sys, u, t)\n",
"y = y.flatten() + generateNoise(t)\n",
"\n",
"# Save data into an IDDATA Object with 0 initial condition\n",
"# Length of the initial condition depends on the reference model\n",
"data = iddata(y, u, dt, [0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### VRFT"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loss: 0.3207682187262347\n",
"Theta: [-0.2482335 0.28201084]\n",
"Controller: ExtendedTF(\n",
"array([ 0.28201084, -0.2482335 ]),\n",
"array([ 1., -1.]),\n",
"dt: 0.01\n",
")\n"
]
}
],
"source": [
"# VRFT Pre-filter\n",
"prefilter = refModel * (1 - refModel)\n",
"\n",
"# VRFT method\n",
"theta, r, loss, C = compute_vrft(data, refModel, control, prefilter)\n",
"\n",
"#Obtained controller\n",
"print(\"Loss: {}\\nTheta: {}\\nController: {}\".format(loss, theta, C))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Verify performance"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1200x800 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Closed loop system\n",
"closed_loop = (C * sys).feedback()\n",
"\n",
"t = t[:len(r)]\n",
"u = np.ones(len(t))\n",
"\n",
"_, yr = scipysig.dlsim(refModel, u, t)\n",
"_, yc = scipysig.dlsim(closed_loop, u, t)\n",
"_, ys = scipysig.dlsim(sys, u, t)\n",
"\n",
"yr = np.array(yr).flatten()\n",
"ys = np.array(ys).flatten()\n",
"yc = np.array(yc).flatten()\n",
"fig, ax = plt.subplots(4, sharex=True, figsize=(12,8), dpi= 100, facecolor='w', edgecolor='k')\n",
"ax[0].plot(t, yr,label='Reference System')\n",
"ax[0].plot(t, yc, label='CL System')\n",
"ax[0].set_title('Systems response')\n",
"ax[0].grid(True)\n",
"ax[1].plot(t, ys, label='OL System')\n",
"ax[1].set_title('OL Systems response')\n",
"ax[1].grid(True)\n",
"ax[2].plot(t, y[:len(r)])\n",
"ax[2].grid(True)\n",
"ax[2].set_title('Experiment data')\n",
"ax[3].plot(t, r)\n",
"ax[3].grid(True)\n",
"ax[3].set_title('Virtual Reference')\n",
"\n",
"# Now add the legend with some customizations.\n",
"legend = ax[0].legend(loc='lower right', shadow=True)\n",
"\n",
"# The frame is matplotlib.patches.Rectangle instance surrounding the legend.\n",
"frame = legend.get_frame()\n",
"frame.set_facecolor('0.90')\n",
"\n",
"# Set the fontsize\n",
"for label in legend.get_texts():\n",
" label.set_fontsize('large')\n",
"\n",
"for label in legend.get_lines():\n",
" label.set_linewidth(1.5) # the legend line width\n",
"plt.show()\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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-3.0 |
scottquiring/Udacity_Deeplearning | reinforcement/Q-learning-cart.ipynb | 1 | 9788836 | null | mit |
sdpython/pyquickhelper | _unittests/ut_helpgen/data/completion_profiling.ipynb | 1 | 1402712 | null | mit |
ekostat/ekostat_calculator | notebooks/.ipynb_checkpoints/get_data_from_sharkweb-checkpoint.ipynb | 1 | 60353 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get data from SHARKweb."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"import pathlib\n",
"import urllib"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## SHARKweb class"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"class SharkWebReader():\n",
" \"\"\" \"\"\"\n",
" def __init__(self, \n",
" sharkweb_url='https://sharkweb.smhi.se',\n",
" debug=False):\n",
" \"\"\" \"\"\"\n",
" self.sharkweb_url = sharkweb_url\n",
" self.debug = debug\n",
" self.clear()\n",
" \n",
" def clear(self):\n",
" \"\"\" \"\"\"\n",
" self.options = {}\n",
" self.location_options = {}\n",
" self.view_options = {}\n",
" self.data_params = {\n",
" # Time.\n",
" 'year_from': '', \n",
" 'year_to': '', \n",
" 'month_list': [], \n",
" \n",
" # Position.\n",
" 'bounds': '', \n",
" 'county_list': [], # Example: ['Blekinge län', 'Kalmar län']\n",
" 'municipality_list': [], \n",
" 'water_district_list': [], \n",
" 'svar_sea_area_list': [], \n",
" 'water_category': [], \n",
" 'type_area_list': [], \n",
" 'sea_basin': [], \n",
" 'helcom_ospar': [], \n",
" 'economic_zone': [], \n",
" \n",
" # Standard search. \n",
" 'datatype': '', \n",
" 'parameter': '', \n",
" 'station_name': '', \n",
" 'station_name_option': '', \n",
" 'taxon_name': '', \n",
" 'taxon_name_option': '', \n",
" \n",
" # Advanced search.\n",
" 'adv_datatype_list': '', \n",
" 'adv_parameter_list': '', \n",
" 'adv_deliverer_list': '', \n",
" 'adv_orderer_list': '', \n",
" 'adv_project_list': '', \n",
" 'adv_dataset_name': '', \n",
" 'adv_dataset_name_option': '', \n",
" 'adv_check_status': '', \n",
" 'adv_checked_by_list': '', \n",
" 'adv_quality_flag_list': '', \n",
" 'adv_min_depth': '', \n",
" 'adv_max_depth': '', \n",
" 'adv_red_list_category': '', \n",
" \n",
" # Selected columns.\n",
" 'sample_table_view': 'sample_col_std', \n",
" \n",
" # Not used for data download.\n",
" # 'limit\"': '', \n",
" # 'db_read_offset': '', \n",
" \n",
" # File format.\n",
" 'delimiters': 'point-tab', \n",
" 'lineend': 'unix', \n",
" 'encoding': 'utf-8', \n",
" 'headerlang': 'internal', \n",
" }\n",
" self.data = None\n",
" \n",
" def set_data_params(self, data_params):\n",
" \"\"\" \"\"\"\n",
" self.data_params = data_params\n",
" \n",
" def get_data_params(self):\n",
" \"\"\" \"\"\"\n",
" return self.data_params\n",
" \n",
" def get_options(self):\n",
" \"\"\" \"\"\"\n",
" return self.options\n",
" \n",
" def get_view_options(self):\n",
" \"\"\" \"\"\"\n",
" return self.view_options\n",
" \n",
" def get_location_options(self):\n",
" \"\"\" \"\"\"\n",
" return self.location_options\n",
" \n",
" def read_options(self):\n",
" \"\"\" \"\"\"\n",
" url = self.sharkweb_url + '/shark_php.php?action=get_options'\n",
" r = requests.get(url)\n",
" self.options = r.json()\n",
" \n",
" if self.debug:\n",
" print('DEBUG: Status: ', r.status_code)\n",
" print('DEBUG: Header: ', r.headers['content-type'])\n",
" print('DEBUG: Encoding: ', r.encoding)\n",
"\n",
" def read_view_options(self):\n",
" \"\"\" \"\"\"\n",
" url = self.sharkweb_url + '/shark_php.php?action=get_shark_settings&settings_key=sample_view_list_json'\n",
" r = requests.get(url)\n",
" self.view_options = r.text\n",
" \n",
" if self.debug:\n",
" print('DEBUG: Status: ', r.status_code)\n",
" print('DEBUG: Header: ', r.headers['content-type'])\n",
" print('DEBUG: Encoding: ', r.encoding)\n",
"\n",
" def read_location_options(self):\n",
" \"\"\" \"\"\"\n",
" url = self.sharkweb_url + '/shark_php.php?action=get_location_options'\n",
" r = requests.get(url)\n",
" self.location_options = r.json()\n",
" \n",
" if self.debug:\n",
" print('DEBUG: Status: ', r.status_code)\n",
" print('DEBUG: Header: ', r.headers['content-type'])\n",
" print('DEBUG: Encoding: ', r.encoding)\n",
" \n",
" def read_data(self, data_params=None):\n",
" \"\"\" \"\"\"\n",
" url = self.sharkweb_url + '/shark_save.php?action=download_sample'\n",
" #\n",
" params = data_params\n",
" if params is None:\n",
" params = self.data_params\n",
" \n",
" # \n",
" self.encodeURIComponent(params, 'month_list')\n",
" self.encodeURIComponent(params, 'adv_datatype_list')\n",
" self.encodeURIComponent(params, 'adv_parameter_list')\n",
" self.encodeURIComponent(params, 'adv_deliverer_list')\n",
" self.encodeURIComponent(params, 'adv_orderer_list')\n",
" self.encodeURIComponent(params, 'adv_project_list')\n",
" self.encodeURIComponent(params, 'county_list')\n",
" self.encodeURIComponent(params, 'municipality_list')\n",
" self.encodeURIComponent(params, 'water_district_list')\n",
" self.encodeURIComponent(params, 'svar_sea_area_list')\n",
" self.encodeURIComponent(params, 'water_category')\n",
" self.encodeURIComponent(params, 'type_area_list')\n",
" self.encodeURIComponent(params, 'sea_basin')\n",
" self.encodeURIComponent(params, 'helcom_ospar')\n",
" self.encodeURIComponent(params, 'economic_zone')\n",
" # print(params)\n",
" \n",
" #\n",
" self.params = params\n",
" r = requests.get(url, params)\n",
" self.data = r.text\n",
" \n",
" if self.debug:\n",
" print('DEBUG: Status: ', r.status_code)\n",
" print('DEBUG: Header: ', r.headers['content-type'])\n",
" print('DEBUG: Encoding: ', r.encoding)\n",
" \n",
" def save_data(self, file_name='sharkweb_data.txt'):\n",
" \"\"\" \"\"\"\n",
" if self.data is None:\n",
" if self.debug:\n",
" print('DEBUG: No data available.')\n",
" return\n",
" #\n",
" file_path = pathlib.Path(file_name)\n",
" with file_path.open('w') as f:\n",
" f.write(self.data)\n",
" \n",
" def encodeURIComponent(self, params, key):\n",
" \"\"\" \"\"\"\n",
" string = '[or]'.join(params[key])\n",
" string = urllib.parse.quote(string, safe='~()*!.\\'\\\\')\n",
" params[key] = string\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#reader = SharkWebReader(sharkweb_url='http://localhost/w_sharkweb/p_sharkweb/sharkweb/public_html/sharkweb', \n",
"# debug=True)\n",
"reader = SharkWebReader(debug=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"data_params = reader.get_data_params()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'adv_check_status': '',\n",
" 'adv_checked_by_list': '',\n",
" 'adv_dataset_name': '',\n",
" 'adv_dataset_name_option': '',\n",
" 'adv_datatype_list': '',\n",
" 'adv_deliverer_list': '',\n",
" 'adv_max_depth': '',\n",
" 'adv_min_depth': '',\n",
" 'adv_orderer_list': '',\n",
" 'adv_parameter_list': '',\n",
" 'adv_project_list': '',\n",
" 'adv_quality_flag_list': '',\n",
" 'adv_red_list_category': '',\n",
" 'bounds': '',\n",
" 'county_list': [],\n",
" 'datatype': '',\n",
" 'delimiters': 'point-tab',\n",
" 'economic_zone': [],\n",
" 'encoding': 'utf-8',\n",
" 'headerlang': 'internal',\n",
" 'helcom_ospar': [],\n",
" 'lineend': 'unix',\n",
" 'month_list': [],\n",
" 'municipality_list': [],\n",
" 'parameter': '',\n",
" 'sample_table_view': 'sample_col_std',\n",
" 'sea_basin': [],\n",
" 'station_name': '',\n",
" 'station_name_option': '',\n",
" 'svar_sea_area_list': [],\n",
" 'taxon_name': '',\n",
" 'taxon_name_option': '',\n",
" 'type_area_list': [],\n",
" 'water_category': [],\n",
" 'water_district_list': [],\n",
" 'year_from': '',\n",
" 'year_to': ''}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_params"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"data_params = reader.get_data_params()\n",
"data_params['datatype'] = 'Harbour Porpoise'\n",
"data_params['sample_table_view'] = 'sample_col_harbourporpoise'\n",
"\n",
"data_params['county_list'] = ['Blekinge län', 'Kalmar län']"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DEBUG: Status: 200\n",
"DEBUG: Header: application/octet-stream; charset=utf-8\n",
"DEBUG: Encoding: utf-8\n"
]
}
],
"source": [
"reader.read_data(data_params=data_params)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"reader.save_data()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DEBUG: Status: 200\n",
"DEBUG: Header: application/json; charset=utf-8\n",
"DEBUG: Encoding: utf-8\n",
"{'year_option': {'header': ['visit_year'], 'rows': [['2018'], ['2017'], ['2016'], ['2015'], ['2014'], ['2013'], ['2012'], ['2011'], ['2010'], ['2009'], ['2008'], ['2007'], ['2006'], ['2005'], ['2004'], ['2003'], ['2002'], ['2001'], ['2000'], ['1999'], ['1998'], ['1997'], ['1996'], ['1995'], ['1994'], ['1993'], ['1992'], ['1991'], ['1990'], ['1989'], ['1988'], ['1987'], ['1986'], ['1985'], ['1984'], ['1983'], ['1982'], ['1981'], ['1980'], ['1979'], ['1978'], ['1977'], ['1976'], ['1975'], ['1974'], ['1973'], ['1972'], ['1971'], ['1970'], ['1969'], ['1968'], ['1967'], ['1966'], ['1965'], ['1964'], ['1963'], ['1962'], ['1961'], ['1960'], ['1959'], ['1958'], ['1957'], ['1956'], ['1955'], ['1954'], ['1953'], ['1952'], ['1951'], ['1950'], ['1949'], ['1948'], ['1947'], ['1946'], ['1945'], ['1944'], ['1943'], ['1942'], ['1941'], ['1940'], ['1939'], ['1938'], ['1937'], ['1936'], ['1935'], ['1934'], ['1933'], ['1932'], ['1931'], ['1930'], ['1929'], ['1928'], ['1927'], ['1926'], ['1925'], ['1924'], ['1923'], ['1922'], ['1921'], ['1920'], ['1916'], ['1915'], ['1914'], ['1913'], ['1912'], ['1911'], ['1910'], ['1909'], ['1908'], ['1907'], ['1906'], ['1905'], ['1904'], ['1903'], ['1902'], ['1901'], ['1900'], ['1899'], ['1898'], ['1897'], ['1896'], ['1894'], ['1893'], [' ']]}, 'datatype_option': {'header': ['sample_datatype'], 'rows': [['Bacterioplankton'], ['Chlorophyll'], ['Epibenthos'], ['Grey seal'], ['Harbour Porpoise'], ['Harbour seal'], ['Physical and Chemical'], ['Phytoplankton'], ['Picoplankton'], ['Primary production'], ['Ringed seal'], ['Seal pathology'], ['Sedimentation'], ['Zoobenthos'], ['Zooplankton']]}, 'parameter_option': {'header': ['parameter'], 'rows': [['Abundance'], ['Abundance class'], ['Adreno cort-hyperplasia class'], ['Age'], ['Alkalinity'], ['Alkalinity_2'], ['Aluminium'], ['Ammonium NH4-N'], ['Arteriosclerosis class'], ['Autolysis class'], ['Average shoot length'], ['Bacterial cell carbon content'], ['Bacterial cell volume'], ['Bacterial concentration'], ['Bacterial production'], ['Bare substrate cover'], ['Biovolume concentration'], ['Blubber thickness'], ['Body length'], ['Body weigth'], ['Bottom water dissolved oxygen'], ['Bottom water oxygen saturation'], ['Bottom water salinity'], ['Bottom water temperature'], ['Boulder cover'], ['BQIm'], ['Calculated # counted'], ['Carbon concentration'], ['Carbon content'], ['Carbon prod in light'], ['Carbon production'], ['Carbon production/day'], ['Carbon production/hour'], ['Carbon production in darkness'], ['Chlorophyll-a'], ['Chlorophyll-a bottle'], ['Claw lesions class'], ['Colored dissolved organic matter CDOM'], ['Conductivity_25 bottle'], ['Conductivity CTD'], ['# counted'], ['Cover'], ['Cover (%)'], ['Cover class'], ['Cover class filamentous algae'], ['Cronic cholangiohepatitis'], ['Current direction'], ['Current velocity'], ['Debris cover'], ['Density in covered area'], ['Depth distribution (max depth)'], ['Dissolved oxygen'], ['Dissolved oxygen O2 bottle'], ['Dissolved oxygen O2 CTD'], ['Dissovled organic carbon DOC'], ['Dry weight'], ['Gravel cover'], ['H2S smell'], ['Hard clay cover'], ['Humus'], ['Hydrogen sulphide H2S'], ['Intestinal ulcer class'], ['Length (mean)'], ['Length (median)'], ['Lignin'], ['Loss of ignition'], ['Max shoot length'], ['Min shoot length'], ['Net carbon prod_prod-resp'], ['Nitrate NO3-N'], ['Nitrite+Nitrate NO2+NO3-N'], ['Nitrite NO2-N'], ['Non-quantified observation'], ['No species found.'], ['No species in sample'], ['Observed species'], ['PAR at sampling depth'], ['Particulate organic carbon POC'], ['Particulate org nitrogen PON'], ['pH'], ['pH Laboratory'], ['Phosphate PO4-P'], ['Pneumonia'], ['Porpoise positive minute'], ['Porpoise positive minutes'], ['Pressure CTD'], ['# pups counted on land'], ['Redox potential'], ['Regional skin changes class'], ['Rhizome biomass'], ['Rhizome juice sugar content'], ['Rock cover'], ['Salinity'], ['Salinity bottle'], ['Salinity CTD'], ['Sand cover'], ['Secchi depth'], ['Sedimentation Al-fraction'], ['Sedimentation C-fraction'], ['Sedimentation Fe-fraction'], ['Sedimentation Mg-fraction'], ['Sedimentation Mn-fraction'], ['Sedimentation N-fraction'], ['Sedimentation P-fraction'], ['Sedimentation rate (dw)'], ['Sedimentation Si-fraction'], ['Sediment colour'], ['Sediment colour code'], ['Sediment depos cover (class)'], ['Sediment deposition cover (%)'], ['Sediment dry weight content'], ['Sediment nitrogen content'], ['Sediment phosphorus content'], ['Sediment redox potential'], ['Sediment water content'], ['Shoot biomass'], ['Shoot density'], ['Silicate SiO3-Si'], ['Silt soft clay cover'], ['Species distribution max depth'], ['Species distribution min depth'], ['Stone cover'], ['Substrate specific cover'], ['Temperature'], ['Temperature bottle'], ['Temperature CTD'], ['Temperature pH Laboratory'], ['Total CO2'], ['Total # counted in water'], ['Total # counted on land'], ['Total cover of all species (%)'], ['Total Nitrogen Tot-N'], ['Total organic carbon TOC'], ['Total phosphorus Tot-P'], ['Turbidity'], ['Unidentified algae cover'], ['Urea'], ['Uterus pregnant'], ['Uterus stenosis occlusion'], ['Uterus tumour'], ['Wet weight'], ['Wet weight/area'], ['Wet weight/volume'], ['Yellow substance']]}, 'deliverer_option': {'header': ['reporting_institute_name'], 'rows': [[''], ['Alcontrol AB'], ['AquaBiota'], ['Blekingekustens vattenvårdsförbund'], ['Bohuskustens vattenvårdsförbund'], ['Calluna AB'], ['Danish Centre for Environment and Energy'], ['Fiskeriverket'], ['Fiskeriverket Havsfiskelaboratoriet'], ['Fiskeriverket Kustfiskelaboratoriet'], ['Göteborgs kommun'], ['Göteborgs universitet'], ['Hafok AB'], ['Helsingsborgs miljöförvaltning'], ['Linnéuniversitetet'], ['Litoralis Natur AB'], ['Lunds Universitet'], ['Länsstyrelsen Blekinge'], ['Länsstyrelsen Gotland'], ['Länsstyrelsen Gävleborg'], ['Länsstyrelsen Halland'], ['Länsstyrelsen Norrbotten'], ['Länsstyrelsen ospecificerad'], ['Länsstyrelsen Skåne'], ['Länsstyrelsen Stockholm'], ['Länsstyrelsen Södermanland'], ['Länsstyrelsen Uppsala'], ['Länsstyrelsen Västerbotten'], ['Länsstyrelsen Västernorrland'], ['Länsstyrelsen Västra Götaland'], ['Länsstyrelsen Östergötland'], ['Marine Monitoring AB'], ['Marin Miljöanalys AB'], ['Medins Havs och Vattenkonsulter'], ['Naturhistoriska riksmuseet'], ['Naturvatten i Roslagen AB'], ['Ospecificerat'], ['PAG Miljöundersökningar'], ['Pelagia Miljökonsult AB'], ['Stockholms universitet'], ['Stockholms universitets marina forskningscentrum'], ['Stockholm Vatten AB'], ['SWECO'], ['Sven Lovén centrum för marina vetenskaper-Kristineberg'], ['Sven Lovén centrum för marina vetenskaper-Tjärnö'], ['Svensk Ekologiskonsult AB'], ['Sveriges lantbruksuniversitet'], ['Sveriges lantbruksuniversitet - Havsfiskelaboratoriet'], ['Sveriges lantbruksuniversitet Institutionen för akvatiska resurser'], ['Sveriges meteorologiska och hydrologiska institut\\xa0'], ['Sveriges Vattenekologer AB'], ['Sydkustens vattenvårdsförbund'], ['Toxicon AB'], ['Umeå Universitet'], ['WEAQ AB'], ['Öresunds vattenvårdsförbund']]}, 'project_option': {'header': ['lookup_project'], 'rows': [['-'], ['HIST Bornö hängbro'], ['HIST Fyrskepp'], ['HIST Havsfiskelaboratoriet Lysekil'], ['NAT Algövervakning'], ['NAT Atlanto Scandian Herring'], ['NAT Baltic International Acoustic Surveys BIAS'], ['NAT Baltic International Trawl Survey BITS'], ['NAT Gavik KKP'], ['NAT GOV kalibrering Argos Ancylus'], ['NAT International Bottom Trawl Survey IBTS'], ['NAT International Young Fish Survey IYFS'], ['NAT Nationell BasVerksamhet'], ['NAT Nationell miljöövervakning'], ['NAT Nationell miljöövervakning extra provtagning'], ['NAT SYKEs BasVerksamhet'], ['NAT Östersjöns övervakningsprogram'], ['PROJ Alsbäck forskningsdata'], ['PROJ Automatisk Kasun Boj'], ['PROJ Baltic Open Sea Experiment'], ['PROJ Bank kartering vindkraft'], ['PROJ Basinventering'], ['PROJ Bottenhavet pelagisk verifikation'], ['PROJ Bottenviken pelagial mätkampanj'], ['PROJ Bottenviken pelagisk verifikation'], ['PROJ Bottniska viken året'], ['PROJ Brofjorden'], ['PROJ Byfjorden BOX'], ['PROJ DIAMIX'], ['PROJ Eg östersjön pelagial mätkampanj'], ['PROJ Enningsdalsälven INTERREG'], ['PROJ Ferrybox Transpaper'], ['PROJ Gotland pelagial mätkampanj'], ['PROJ Gotlands grunda vikar'], ['PROJ Grunda vikar'], ['PROJ Göteborg säkrare farled'], ['PROJ HABILE'], ['PROJ Inventering Gävleborg'], ['PROJ Inventering ROV Koster-Väderöfjorden'], ['PROJ JERICO'], ['PROJ Kartering Hoburgs bank'], ['PROJ Kartering rev sandbankar Skåne'], ['PROJ Laholm eutrophication project'], ['PROJ Levande kust Baltic2020'], ['PROJ Läsö sektion'], ['PROJ Marin Modellering Stockholm Södermanland'], ['PROJ Marin Naturvärdes kartering'], ['PROJ MARMONI'], ['PROJ Nya varvet'], ['PROJ Ospecificerat'], ['PROJ Patchiness experiment PEX'], ['PROJ PMK_Programmet_för_Miljökontroll'], ['PROJ Råneåfjärden KK BDlän'], ['PROJ Skagerrak närsaltstransport'], ['PROJ SKAGEX'], ['PROJ Slussen'], ['PROJ Svenska Offentliga Data'], ['PROJ Transp sec hansth Kristand'], ['PROJ Utsjöbanksinventeringen'], ['PROJ WATERS'], ['PROJ Östergötland Inre Kärrö'], ['REG Blekinge SRK'], ['REG Bohuskustens extra vårprovtagning'], ['REG Bohuskustens grunda vikar'], ['REG Bohuskustens KKP SRK'], ['REG Bohuskustens KKP SRK fjordmodell 96'], ['REG Bohuskustens KKP SRK götaälv extremflöde'], ['REG Boliden RK'], ['REG Bottniska viken'], ['REG Bureå RK'], ['REG Byske RK'], ['REG Dalälven SRK'], ['REG Domsjö RK'], ['REG Edsvikens miljökontrollprogram'], ['REG Elleholm RK'], ['REG Figeholm RK'], ['REG Fiskodling Kalmar RK'], ['REG Fiskodling Omne Ullånger Nätrafjärden RK'], ['REG Forsmark RK'], ['REG Gotland'], ['REG Gullmarns KP'], ['REG Gästriklands kustvatten SRK'], ['REG Gästriklands kustvatten SRK pelagisk verifikation'], ['REG Gävleborgs län'], ['REG Halland Lilla Middelgrund'], ['REG Hallands KVK'], ['REG Hallands län'], ['REG Halland Vendelsöarna'], ['REG Hallsta RK'], ['REG Helsingborg KKP'], ['REG Holmen'], ['REG Holmöreservatet'], ['REG Husum RK'], ['REG Hörnefors RK'], ['REG Iggesund RK'], ['REG Kalmar KVK'], ['REG Kalmar län'], ['REG Kungsbackafjorden'], ['REG Kåge RK'], ['REG Marum och Ösbyfjärden'], ['REG Motala ströms SRK'], ['REG Mätkampanj'], ['REG Nedre ångermanälven SRK'], ['REG Nordmalingsfjärden SRK'], ['REG Nordvästskånes kustvattenkommitté SRK'], ['REG Nordöstra Hälsinglands kustvatten SRK'], ['REG Norrbottenskusten Kalix SRK'], ['REG Norrbottenskusten Luleå SRK'], ['REG Norrbottenskusten Piteå SRK'], ['REG Norrbottens län'], ['REG Norrsundetsbruk RK'], ['REG Norrtäljes kommun RK'], ['REG Ospecificerat'], ['REG Skatan RK'], ['REG Skellefteälven SRK'], ['REG Stockholms län'], ['REG Stockholm vatten SRK'], ['REG Sundsvallsbuktens SRK'], ['REG Svealands KKP synoptiska karteringar'], ['REG Sydkustens SRK'], ['REG Södermanlands län'], ['REG Södra Hälsinglands kustvatten SRK'], ['REG Sölvesborgs hamn deponering RK'], ['REG Täby kommun RK'], ['REG Täfteå RK'], ['REG Ume och Vindelälvens SRK'], ['REG Vindkraft Lillgrund RK'], ['REG Väröbruk RK'], ['REG Västernorrlands län'], ['REG Västra Hanöbukten SRK'], ['REG Öresunds vattenvårdsförbund SRK'], ['REG Östergötlands län'], ['REG Östhammars kommun RK']]}, 'orderer_option': {'header': ['lookup_orderer'], 'rows': [['-'], ['ABB'], ['Airicole AB'], ['Artdatabanken'], ['BalticSea2020'], ['Billerud Karlsborg AB'], ['Blekingekustens vattenvårdsförbund'], ['Bohuskustens vattenvårdsförbund'], ['Boliden Group'], ['Borgholms kommun'], ['Dalälvens vattenvårdsförbund'], ['Domsjö fabriker AB'], ['Edsviken vattensamverkan'], ['Ekologigruppen AB'], ['Europeiska kommisionen LIFE programmet'], ['Finlands miljöcentral'], ['Fiskeriverket'], ['Fiskeriverket Havsfiskelaboratoriet'], ['Fiskeriverket Kustfiskelaboratoriet'], ['Forskningsdata'], ['Forsmarks kraftgrupp AB'], ['Föreningen Nenningesund'], ['Gästriklands vattenvårdsförbund'], ['Göteborgs kommun'], ['Göteborgs universitet'], ['Haninge kommun'], ['Havs- och vattenmyndigheten'], ['Helsingsborgs miljöförvaltning'], ['Holmen Paper'], ['Intresseföreningen för Nordöstra Hälsinglands SRK'], ['Kalix kommun'], ['Kalmar kommun'], ['Kalmar läns kustvattenkommitté'], ['Karlshamns hamn AB'], ['Karlskrona kommun'], ['Kristianstads kommun'], ['Kungliga Vetenskapsakademin'], ['Ljusnan-Voxnans vattenvårdsförbund'], ['Ljusterö vatten och fiskevårdsförening'], ['Länsstyrelsen Blekinge'], ['Länsstyrelsen Gotland'], ['Länsstyrelsen Gävleborg'], ['Länsstyrelsen Halland'], ['Länsstyrelsen Kalmar'], ['Länsstyrelsen Norrbotten'], ['Länsstyrelsen ospecificerad'], ['Länsstyrelsen Skåne'], ['Länsstyrelsen Stockholm'], ['Länsstyrelsen Södermanland'], ['Länsstyrelsen Uppsala'], ['Länsstyrelsen Västerbotten'], ['Länsstyrelsen Västernorrland'], ['Länsstyrelsen Västra Götaland'], ['Länsstyrelsen Östergötland'], ['Länstyrelsen Västra Götaland och Bohusläns vattenvårdsförbund'], ['Metsä Board Sverige AB'], ['Motala Ströms vattenvårdsförbund'], ['Mönsterås kommun'], ['Nacka kommun'], ['Naturvårdsverket'], ['Naturvårdsverket Kustlaboratorium\\xa0'], ['Nordmalings kommun'], ['Nordvästskånes kustvattenkommitté'], ['Norrköpings kommun'], ['Norrtälje Energi'], ['Norrtälje kommun'], ['Nynäshamns kommun'], ['Oskarshamns kommun'], ['Oskarshamns Kraftgrupp AB'], ['Ospecificerat'], ['Piteå Renhållning och Vatten AB'], ['Ringhals AB kärnkraftverk'], ['SCA Packaging'], ['Schweden Splitt AB'], ['Smurfit Kappa Kraftliner AB'], ['Sollentuna kommun'], ['SSAB'], ['Stockholms stad'], ['Stockholms universitet'], ['Stockholm Vatten AB'], ['Sundsvallsbuktens vattenvårdsförening'], ['Svealands kustvattenvårdsförbund'], ['Svensk Kärnbränslehantering AB'], ['Sveriges meteorologiska och hydrologiska institut\\xa0'], ['Sydkustens vattenvårdsförbund'], ['Södra Cell Värö massafabrik'], ['Sölvesborgs kommun'], ['TYRÉNS'], ['Täby kommun'], ['Ume- och Vindelälvens vattenvårdsförbund'], ['Umeå Universitet'], ['WATERS'], ['Vattenfall'], ['Vattenmyndigheten i Bottenviken'], ['Vattenvårdsförbundet för västra Hanöbukten'], ['Veolia'], ['WSP Environmental'], ['Västerviks kommun'], ['Ålands Fiskförädling AB'], ['Öresundsbrokonsortiet'], ['Öresunds vattenvårdsförbund']]}, 'check_status_option': {'header': ['check_status'], 'rows': [['Klar'], ['Pågående-SMHI']]}, 'quality_flag_option': {'header': ['quality_flag'], 'rows': [[''], ['<'], ['>'], ['?'], ['0'], ['1'], ['A'], ['B'], ['Blank'], ['D'], ['E'], ['I'], ['R'], ['S'], ['Z']]}, 'red_list_category_option': {'header': ['taxon_red_list_category'], 'rows': [[''], ['Akut hotad (CR)'], ['Ej rödlistad'], ['Kunskapsbrist (DD)'], ['Nära hotad (NT)'], ['Starkt hotad (EN)'], ['Sårbar (VU)']]}}\n"
]
}
],
"source": [
"reader.read_options()\n",
"options = reader.get_options()\n",
"print(options)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DEBUG: Status: 200\n",
"DEBUG: Header: application/json; charset=utf-8\n",
"DEBUG: Encoding: utf-8\n",
"[{\"value\":\"sample_col_std\",\"text\":\"Tabell: Översikt\"},{\"value\":\"sample_col_all\",\"text\":\"Tabell: Alla\"},{\"value\":\"sample_col_physicalchemical\",\"text\":\"Tabell: Fysik/kemi\"},{\"value\":\"sample_col_physicalchemical_columnparams\",\"text\":\"Tabell: Fysik/kemi, kolumner\"},{\"value\":\"sample_col_bacterioplankton\",\"text\":\"Tabell: Bacterioplankton\"},{\"value\":\"sample_col_chlorophyll\",\"text\":\"Tabell: Chlorophyll\"},{\"value\":\"sample_col_epibenthos\",\"text\":\"Tabell: Epibenthos\"},{\"value\":\"sample_col_epibenthos_dropvideo\",\"text\":\"Tabell: Epibenthos-dropvideo\"},{\"value\":\"sample_col_greyseal\",\"text\":\"Tabell: Greyseal\"},{\"value\":\"sample_col_harbourseal\",\"text\":\"Tabell: Harbourseal\"},{\"value\":\"sample_col_harbourporpoise\",\"text\":\"Tabell: Harbourporpoise\"},{\"value\":\"sample_col_phytoplankton\",\"text\":\"Tabell: Phytoplankton\"},{\"value\":\"sample_col_picoplankton\",\"text\":\"Tabell: Picoplankton\"},{\"value\":\"sample_col_primaryproduction\",\"text\":\"Tabell: Primaryproduction\"},{\"value\":\"sample_col_ringedseal\",\"text\":\"Tabell: Ringedseal\"},{\"value\":\"sample_col_sealpathology\",\"text\":\"Tabell: Sealpathology\"},{\"value\":\"sample_col_sedimentation\",\"text\":\"Tabell: Sedimentation\"},{\"value\":\"sample_col_zoobenthos\",\"text\":\"Tabell: Zoobenthos\"},{\"value\":\"sample_col_zooplankton\",\"text\":\"Tabell: Zooplankton\"},{\"value\":\"sample_sum_1\",\"text\":\"Rapport: Summa per år och parameter\"},{\"value\":\"sample_sum\",\"text\":\"Rapport: Summa per år, parameter och taxon\"},{\"value\":\"sample_rep_1\",\"text\":\"Rapport: Provtagning per station\"},{\"value\":\"sample_rep_2\",\"text\":\"Rapport: Observerade arter\"},{\"value\":\"sample_rep_4\",\"text\":\"Rapport: Stationer\"},{\"value\":\"sample_rep_5\",\"text\":\"Rapport: Arter\"},{\"value\":\"graph_std\",\"text\":\"Diagram: Standard\"},{\"value\":\"graph_depth\",\"text\":\"Diagram: Djupintervall\"},{\"value\":\"graph_sum\",\"text\":\"Diagram: Summa/dag\"},{\"value\":\"graph_mean\",\"text\":\"Diagram: Medelvärde/dag\"}]\n"
]
}
],
"source": [
"reader.read_view_options()\n",
"view_options = reader.get_view_options()\n",
"print(view_options)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DEBUG: Status: 200\n",
"DEBUG: Header: application/json; charset=utf-8\n",
"DEBUG: Encoding: utf-8\n",
"{'county_option': {'header': ['location_county'], 'rows': [['-'], ['Blekinge län'], ['Gotlands län'], ['Gävleborgs län'], ['Hallands län'], ['Jämtlands län'], ['Jönköpings län'], ['Kalmar län'], ['Norrbottens län'], ['Skåne län'], ['Stockholms län'], ['Södermanlands län'], ['Uppsala län'], ['Utanför gränser'], ['Västerbottens län'], ['Västernorrlands län'], ['Västra Götalands län'], ['Östergötlands län']]}, 'municipality_option': {'header': ['location_municipality'], 'rows': [['-'], ['Berg'], ['Borgholm'], ['Botkyrka'], ['Bromölla'], ['Bräcke'], ['Båstad'], ['Danderyd'], ['Ekerö'], ['Falkenberg'], ['Gislaved'], ['Gotland'], ['Gullspång'], ['Gävle'], ['Göteborg'], ['Halmstad'], ['Haninge'], ['Haparanda'], ['Helsingborg'], ['Hudiksvall'], ['Härnösand'], ['Höganäs'], ['Jönköping'], ['Kalix'], ['Kalmar'], ['Karlshamn'], ['Karlskrona'], ['Kramfors'], ['Kristianstad'], ['Kungsbacka'], ['Kungälv'], ['Kävlinge'], ['Laholm'], ['Landskrona'], ['Lidingö'], ['Linköping'], ['Lomma'], ['Luleå'], ['Lysekil'], ['Malmö'], ['Malå'], ['Mark'], ['Munkedal'], ['Mönsterås'], ['Mörbylånga'], ['Nacka'], ['Nordanstig'], ['Nordmaling'], ['Norrköping'], ['Norrtälje'], ['Nyköping'], ['Nynäshamn'], ['Orust'], ['Oskarshamn'], ['Oxelösund'], ['Piteå'], ['Ragunda'], ['Robertsfors'], ['Ronneby'], ['Simrishamn'], ['Skellefteå'], ['Skurup'], ['Sollefteå'], ['Sollentuna'], ['Solna'], ['Sotenäs'], ['Stenungsund'], ['Stockholm'], ['Strömstad'], ['Sundsvall'], ['Sävsjö'], ['Söderhamn'], ['Söderköping'], ['Södertälje'], ['Sölvesborg'], ['Tanum'], ['Tierp'], ['Timrå'], ['Tjörn'], ['Torsås'], ['Trelleborg'], ['Trosa'], ['Tyresö'], ['Täby'], ['Uddevalla'], ['Umeå'], ['Uppsala'], ['Utanför gränser'], ['Valdemarsvik'], ['Varberg'], ['Vaxholm'], ['Vellinge'], ['Vännäs'], ['Värmdö'], ['Värnamo'], ['Västervik'], ['Ystad'], ['Älvkarleby'], ['Ängelholm'], ['Öckerö'], ['Örnsköldsvik'], ['Österåker'], ['Östhammar']]}, 'water_district_option': {'header': ['location_water_district'], 'rows': [['-'], ['Bottenhavets vattendistrikt'], ['Bottenvikens vattendistrikt'], ['Norra Östersjöns vattendistrikt'], ['Södra Östersjöns vattendistrikt'], ['Västerhavets vattendistrikt']]}, 'svar_sea_area_option': {'header': ['location_svar_sea_area_code', 'location_svar_sea_area_name'], 'rows': [['-', '-'], ['613500-172500', 'Agöfjärden sek namn'], ['575920-191650', 'Ajkesvik'], ['622500-172430', 'Alnösundet'], ['582820-165920', 'Arköfjärden sek namn'], ['583000-165600', 'Arkösund'], ['560750-152500', 'Arpöfjärden sek namn'], ['574500-164500', 'Asken'], ['580500-114725', 'Askeröfjorden'], ['573500-115150', 'Askims fjord'], ['592290-181600', 'Askrikefjärden'], ['584870-174310', 'Asköfjärden'], ['584215-170800', 'Aspafjärden'], ['573860-115000', 'Asperöfjorden sek namn'], ['582600-165680', 'Aspöfjärden'], ['610100-171245', 'Axmarfjärden'], ['591760-181955', 'Baggensfjärden'], ['651800-214740', 'Baggholmsdraget'], ['570900-121060', 'Balgöarkipelagen'], ['652465-214080', 'Bastafjärden'], ['652855-224000', 'Bastaskärsfjärden'], ['575430-163640', 'Bergholmsfjärden'], ['656840-222800', 'Bergnäsfjärden'], ['593750-183962', 'Bergshamraviken'], ['654291-224000', 'Bergöfjärden'], ['584333-172895', 'Bergöområdet'], ['590665-184210', 'Biskopsfjärden'], ['642950-213400', 'Bjuröfjärden'], ['592000-190500', 'Björkskärsfjärden'], ['574370-114250', 'Björköfjorden'], ['594800-190220', 'Björköfjärden'], ['621265-173125', 'Björköfjärden'], ['729849-180191', 'Björköfjärden'], ['591400-183200', 'Björnöfjärden'], ['593820-185500', 'Blidösund'], ['654640-233190', 'Bodöfjärden'], ['640066-167754', 'Bogevik'], ['560895-145975', 'Boköfjärden'], ['625900-174360', 'Bollstafjärden'], ['651075-213700', 'Bondöfjärden'], ['581520-113750', 'Borgilefjorden'], ['572565-164000', 'Borholmsfjärden'], ['583370-165290', 'Bosöfjärden sek namn'], ['582850-111760', 'Bottnefjorden'], ['644730-210650', 'Boviksfjärden'], ['591500-185300', 'Brandfjärden'], ['622384-147046', 'Bredasund'], ['591755-183895', 'Breviken'], ['582150-112530', 'Brofjorden'], ['658507-162696', 'Brunnsviken'], ['583121-171401', 'Bråvikens kustvatten'], ['652385-214180', 'Brändöfjärden'], ['573797-114618', 'Brännö- Styrsöområdet'], ['591175-185000', 'Bulleröfjärden'], ['643700-211940', 'Burefjärden'], ['570270-181160', 'Burgsviken'], ['650460-213400', 'Bursfjärden'], ['582000-115270', 'Byfjorden'], ['645500-212000', 'Byskefjärden'], ['654110-224850', 'Båtöfjärden'], ['631346-184241', 'Bäckfjärden'], ['643160-212730', 'Bäckfjärden'], ['654490-220870', 'Börstskärsfjärden'], ['574000-114230', 'Dana fjord'], ['561000-153320', 'Danmarksfjärden'], ['634040-193330', 'Degerfjärden'], ['653140-224000', 'Degeröfjärden'], ['631460-185000', 'Dekarsöfjärden'], ['551617-133102', 'Del av Arkonahavets utsjövatten'], ['555851-160709', 'Del av Bornholmshavets utsjövatten'], ['603634-183531', 'Del av Bottenhavets utsjövatten'], ['620333-175418', 'Del av Bottenhavets utsjövatten'], ['650320-220650', 'Del av Bottenvikens utsjövatten'], ['555420-145140', 'Del av Hanöbuktens utsjövatten'], ['591454-192215', 'Del av N Gotlandshavets utsjövatten'], ['582950-192156', 'Del av N Gotlandshavets utsjövatten'], ['570714-115613', 'Del av n Kattegatts utsjövatten'], ['634223-210932', 'Del av N n Kvarkens utsjövatten'], ['595913-190752', 'Del av N Ålands havs utsjövatten'], ['582008-105731', 'Del av Skagerraks utsjövatten'], ['570714-115613', 'Del av s Kattegatts utsjövatten'], ['632213-201821', 'Del av S n Kvarkens utsjövatten'], ['594211-193824', 'Del av S Ålands havs utsjövatten'], ['615085-130626', 'Del av S Öresunds utsjövatten'], ['580109-171030', 'Del av V Gotlandshavets utsjövatten'], ['583649-180707', 'Del av V Gotlandshavets utsjövatten'], ['574755-181120', 'Del av V Gotlandshavets utsjövatten'], ['573224-190746', 'Del av Ö Gotlandshavets utsjövatten'], ['560290-154710', 'Djupfjärden sek namn'], ['630180-182080', 'Dockstafjärden'], ['622126-172430', 'Draget'], ['584960-175280', 'Dragfjärden sek namn'], ['584400-172270', 'Dragviksfjärden'], ['585990-111125', 'Dynekilen'], ['600740-183460', 'Edeboviken'], ['659024-162417', 'Edsviken'], ['662116-166449', 'Edsviken'], ['625416-182696', 'Edsätterfjärden'], ['591905-185275', 'Eknösundet'], ['581120-112680', 'Ellösefjorden'], ['570500-163750', 'Emområdet sek namn'], ['653870-235570', 'Enskärsfjärden'], ['613240-171000', 'Enångersfjärden'], ['575782-165143', 'Erskärområdet sek namn'], ['654860-219880', 'Ersnäsfjärden'], ['591400-182320', 'Erstaviken'], ['585200-174000', 'Fifångsdjupet'], ['572000-163835', 'Figeholmsområdets kustvatten'], ['581820-165500', 'Finnfjärden'], ['653900-223280', 'Fjuksöfjärden'], ['583710-111535', 'Fjällbacka inre skärgård'], ['583625-111300', 'Fjällbacka yttre skärgård'], ['636570-203590', 'Fjärdgrundsområdet sek namn'], ['580890-165500', 'Flisdjupet'], ['585200-173600', 'Fågelöfjärden'], ['572205-163500', 'Fågelöfjärden'], ['590500-182000', 'Fåglaröfjärden sek namn'], ['585345-174950', 'Fållnäsviken'], ['580150-191251', 'Fårö n kustvatten'], ['575150-190400', 'Fårösund'], ['575300-191801', 'Fårö sö kustvatten'], ['582630-113515', 'Färlevfjorden'], ['601000-183510', 'Galtfjärden'], ['570850-182920', 'Gansviken'], ['625180-181655', 'Gaviksfjärden'], ['652686-221500', 'Germandöfjärden'], ['591790-185500', 'Getholmsfjärden sek namn'], ['585040-173535', 'Gillsviken'], ['592500-191750', 'Gillögafjärden'], ['575880-164000', 'Gisslöfjärden sek namn'], ['575675-185101', 'Gotlands n kustvatten'], ['574520-182151', 'Gotlands nv kustvatten'], ['582350-191651', 'Gotskasandöns n kustvatten'], ['582200-191201', 'Gotskasandöns v kustvatten'], ['594250-191040', 'Granhamnsfjärden'], ['572650-164000', 'Granholmsfjärden'], ['572110-170620', 'Grankullaviken'], ['656620-222480', 'Granöfjärden'], ['584030-111400', 'Grebbestad inre skärgård'], ['591815-182670', 'Grisslingen'], ['582000-164145', 'Gropviken'], ['580950-170601', 'Gryts skärgårds kustvatten'], ['591050-182740', 'Gränöfjärden'], ['594000-190500', 'Gräsköfjärden'], ['624800-181030', 'Grönsviksfjärden'], ['573885-163740', 'Grönvållsfjärden'], ['575000-163620', 'Gudingen'], ['581700-113000', 'Gullmarn centralbassäng'], ['631710-188130', 'Gullviksfjärden sek namn'], ['641250-210560', 'Gumbodafjärden'], ['584820-172920', 'Gunnarbofjärden'], ['584600-173200', 'Gupafjärden'], ['654470-222700', 'Gussöfjärden'], ['613760-171000', 'Gårdsfjärden'], ['585145-175690', 'Gårdsfjärden'], ['560500-154435', 'Gåsefjärden'], ['573500-163500', 'Gåsfjärden'], ['602400-183190', 'Gällfjärden'], ['593080-184500', 'Gälnan'], ['585400-173870', 'Gälöfjärden'], ['604200-174400', 'Gävlebuktens utsjövatten'], ['574931-113131', 'Göteborgs n n skärgårds kustvatten'], ['574160-113351', 'Göteborgs n skärgårds kustvatten'], ['573300-113801', 'Göteborgs s skärgårds kustvatten'], ['582420-111370', 'Haby bukt'], ['575700-114240', 'Hake fjord'], ['591150-113700', 'Halden'], ['560795-154730', 'Hallarumsviken'], ['590740-174135', 'Hallsfjärden'], ['580688-114860', 'Halsefjorden'], ['580735-165296', 'Halsöfjärden'], ['583160-111551', 'Hamburgsundsområdet'], ['605390-171558', 'Hamnskär'], ['654150-240380', 'Hamnskärsfjärden'], ['590000-183000', 'Hanstensfjärden'], ['654560-246250', 'Haparandafjärden sek namn'], ['651940-213930', 'Haraholmsfjärden'], ['601070-182870', 'Hargsviken'], ['604675-172125', 'Harkskärsfjärden'], ['652500-213500', 'Harrbäcksfjärden'], ['583906-170998', 'Hasselöområdet'], ['631406-185500', 'Havsfjärden sek namn'], ['594845-191240', 'Havssvalget'], ['581740-114820', 'Havstensfjorden'], ['562290-124131', 'Helsingborgsområdet'], ['624335-180000', 'Hemsösundet sek namn'], ['581240-165220', 'Hesselöfjärden'], ['604900-171700', 'Hilleviksfjärden'], ['590000-174400', 'Himmerfjärden'], ['653303-222900', 'Hindersöfjärden'], ['575760-112671', 'Hjärteröfjorden'], ['652475-215750', 'Holfjärden'], ['634210-202020', 'Holmsund'], ['590385-180890', 'Horsfjärden'], ['563770-161670', 'Hossmoviken'], ['582210-111880', 'Hovenäset området'], ['614165-171500', 'Hudiksvallsfjärden'], ['582665-111706', 'Hunnebostrand skärgård'], ['658436-162998', 'Husarviken'], ['631840-191130', 'Husumbukten'], ['652250-213430', 'Håkansöfjärden'], ['585075-173130', 'Hållsviken'], ['613380-171450', 'Hålsängesfjärden'], ['581660-165710', 'Håsköfjärden sek namn'], ['560740-152650', 'Hästholmsfjärden'], ['593000-193000', 'Högfjärden'], ['552800-125430', 'Höllviken'], ['635660-199490', 'Hörnefors området sek namn'], ['631646-185280', 'Idbyfjärden'], ['590860-113810', 'Idefjorden'], ['574205-164500', 'Idöfjärden'], ['590990-174015', 'Igelstaviken'], ['605140-171674', 'Iggösundet'], ['591300-182800', 'Ingaröfjärden'], ['583926-161744', 'Inre Bråviken'], ['651818-212790', 'Inrefjärden'], ['604055-171248', 'Inre Fjärden'], ['575150-162700', 'Inre Gamlebyviken'], ['590020-114520', 'Inre Idefjorden'], ['572472-120302', 'Inre Kungsbackafjorden'], ['645000-212100', 'Inre Kågefjärden'], ['729159-179002', 'Inre Lulefjärden'], ['641840-211540', 'Inre Lövselefjärden'], ['571552-162848', 'Inre Oskarshamnsområdet'], ['560825-144215', 'Inre Pukaviksbukten'], ['590900-112300', 'Inre Singlefjorden'], ['582705-163350', 'Inre Slätbaken'], ['585200-111140', 'Inre Tjärnöarkipelagen'], ['624380-176450', 'Inre Tynderösundet'], ['581000-164020', 'Inre Valdemarsviken'], ['584045-170882', 'Inre Ålöfjärden'], ['634350-202000', 'Inre Österfjärden'], ['575170-183550', 'Irevik'], ['590835-183000', 'Jungfrufjärden'], ['622900-174790', 'Juniskär-Bergsfjärden'], ['561000-150390', 'Järnavikafjärden sek namn'], ['600920-183090', 'Järsjöviken'], ['650750-213500', 'Jävrefjärden'], ['580000-164060', 'Kaggebofjärden'], ['590550-174540', 'Kaggfjärden'], ['591330-184225', 'Kalkkobbsfjärden'], ['602120-181610', 'Kallriga Fjärden'], ['593000-190500', 'Kallskärsfjärden'], ['591280-182070', 'Kalvfjärden'], ['581540-114000', 'Kalvöfjord'], ['580610-113615', 'Kalvöfjorden'], ['592000-184700', 'Kanholmsfjärden'], ['594340-190448', 'Kapellskärs hamnområde'], ['594350-190530', 'Kapellskärsområdet'], ['575480-184830', 'Kappelshamnsviken'], ['603190-174000', 'Karlholmsfjärden'], ['560900-145280', 'Karlshamnsfjärden'], ['601440-184000', 'Kasfjärden sek namn'], ['654130-249500', 'Katajafjärden'], ['650280-213110', 'Kinnbäcksfjärden'], ['622860-173000', 'Klingerfjärden'], ['572350-180930', 'Klintehamnsviken sek namn'], ['571240-121000', 'Klosterfjorden'], ['575747-113237', 'Klädesholmenområdet'], ['653840-247900', 'Knivskärsfjärden'], ['593180-191280', 'Kobbfjärden'], ['581260-113220', 'Koljö fjord'], ['591745-182250', 'Kolström'], ['584840-175400', 'Konabbsfjärden'], ['581960-164890', 'Korsfjärden'], ['584340-174401', 'Krabbfjärden'], ['625500-175153', 'Kramforsfjärden sek namn'], ['580338-112901', 'Kråke fjord'], ['584390-172085', 'Kråkfjärden'], ['573322-115478', 'Kräklingeområdet'], ['583720-172571', 'Kränkfjärden sek namn'], ['581800-170000', 'Kullskärsdjupet'], ['582302-111451', 'Kungshamn n skärgård'], ['582147-111771', 'Kungshamn s skärgård'], ['610500-171586', 'Kusöfjärden sek namn'], ['580205-165162', 'Kvädöfjärden'], ['572227-115662', 'Kyrkefjälls sund'], ['592600-181135', 'Kyrkfjärden'], ['575480-191200', 'Kyrkviken'], ['645000-213500', 'Kågefjärden'], ['561000-152500', 'Kålfjärden'], ['560385-154500', 'Kållafjärden'], ['575335-165000', 'Kåröområdet'], ['574630-113940', 'Källö fjord'], ['580550-112460', 'Käringöfjorden'], ['582050-165820', 'Kärrfjärden'], ['582070-164820', 'Lagnöströmmen'], ['563330-124600', 'Laholmsbukten'], ['563000-123351', 'Laholmsbuktens kustvatten'], ['555685-142290', 'Landöbukten sek namn'], ['571800-184300', 'Lausvik'], ['580375-164500', 'Licknevarpefjärden'], ['658352-163189', 'Lilla Värtan'], ['634950-202940', 'Lillfjärden'], ['592547-182720', 'Lindalssundet'], ['581975-164500', 'Lindensfjärden'], ['580000-164500', 'Lindödjupet'], ['584725-111050', 'Lindöfjorden sek namn'], ['581260-115280', 'Ljungs kile'], ['583896-170790', 'Ljungskärsflagen'], ['611213-171063', 'Ljusnefjärden'], ['554040-125750', 'Lommabukten'], ['621720-175130', 'Lubban'], ['554810-125240', 'Lundåkrabukten'], ['574440-164160', 'Lusärnafjärden'], ['561080-153835', 'Lyckebyfjärden'], ['594260-185580', 'Långfjärden'], ['612791-171130', 'Långvindsfjärden'], ['593330-192540', 'Lökharfjärden'], ['573972-164250', 'Lökholmsdjupet'], ['582630-165210', 'Lönshuvudfjärden'], ['603650-174500', 'Lövstabukten'], ['565800-163000', 'Lövöområdet sek namn'], ['553757-130820', 'Malmö hamnområde'], ['652400-220070', 'Mannöfjärden'], ['575340-113000', 'Marstrandsfjorden'], ['583970-170280', 'Marsviken'], ['560950-145810', 'Matviksfjärden'], ['580500-111801', 'M Bohusläns skärgårds kustvatten'], ['636150-199220', 'Megrundsområdet'], ['584435-170450', 'Mellanfjärden'], ['622011-146303', 'Mellersta Blekinge skärgårds kustvatten'], ['583825-163500', 'Mellersta Bråviken'], ['560740-144375', 'Mellersta Pukaviksbukten'], ['582600-163810', 'Merumsfjärden'], ['611766-171305', 'Midsommarfjärden'], ['573500-164660', 'Misterhults skärgårds inre kustvatten'], ['573150-165001', 'Misterhults skärgårds kustvatten'], ['630383-183500', 'Mjältöfjärden sek namn'], ['601204-182670', 'Mjölkfjärden'], ['584200-105901', 'M n Bohusläns skärgårds kustvatten'], ['565400-163600', 'M n Kalmarsunds utsjövatten'], ['580240-112501', 'Mollöfjorden'], ['583721-161110', 'Motala Ström'], ['656300-222750', 'Mulöviken'], ['562050-160820', 'M v s Kalmarsunds kustvatten'], ['585797-181090', 'Mysingen'], ['655120-220380', 'Måttsundsfjärden'], ['592090-185125', 'Möja söderfjärd'], ['592500-185000', 'Möja västerfjärd'], ['570080-163430', 'Mönsteråsområdet sek namn'], ['655180-218660', 'Möröfjärden'], ['633400-195000', 'N Bottenhavets kustvatten'], ['657608-164193', 'Neglingeviken'], ['630210-187470', 'N Höga kustens kustvatten'], ['613591-171000', 'Njutångersfjärden'], ['585400-110400', 'N Kosterfjorden'], ['580765-112501', 'n Käringöfjorden inre skärgård'], ['594800-190655', 'N Lidöfjärden sek namn'], ['584400-116000', 'n Långebyområdet'], ['619690-175690', 'N M Bottenhavets kustvatten'], ['649640-214530', 'N m Bottenvikens kustvatten'], ['570000-120701', 'N m Hallands kustvatten'], ['555545-124332', 'N m Öresunds kustvatten'], ['585660-112590', 'N n Bohusläns skärgårds kustvatten'], ['591760-105425', 'N n Bohusläns skärgårds kustvatten'], ['570900-164501', 'N n Kalmarsunds utsjövatten'], ['635300-205251', 'N n Kvarkens kustvatten'], ['625000-180075', 'Norafjärden'], ['633043-193300', 'Nordmalingsfjärden'], ['574650-114360', 'Nordre Älvs fjord'], ['601190-182870', 'Norra Hargsviken'], ['623810-180350', 'Norra sundet'], ['592468-182000', 'Norra Vaxholmsfjärden'], ['652400-223501', 'Norrbottens skärgårds kustvatten'], ['630203-182615', 'Norrfjärden'], ['601300-184180', 'Norrfjärden'], ['590730-183763', 'Norrfjärden'], ['593300-183600', 'Norrfjärden sek namn'], ['605760-171000', 'Norrsundet'], ['594670-185500', 'Norrtäljeviken'], ['590148-183625', 'Norstensfjärden'], ['654330-222200', 'N. Sigfridsöfjärden'], ['612520-172080', 'N S M Bottenhavets kustvatten'], ['563100-161500', 'N v s Kalmarsunds kustvatten'], ['585170-175445', 'Nynäsviken'], ['593860-192000', 'Nåtfjärden'], ['591200-183600', 'Nämdöfjärden'], ['630760-183315', 'Näskefjärden'], ['590400-174090', 'Näslandsfjärden'], ['630685-184305', 'Nätrafjärden'], ['593500-191660', 'Nö Kobbfjärden sek namn'], ['570000-170351', 'N Ölands kustvatten'], ['652000-213210', 'Nördfjärden'], ['561030-122821', 'N Öresunds kustvatten'], ['631450-185200', 'Nötbolandsfjärden'], ['625710-183000', 'Omnefjärden'], ['572540-114801', 'Onsala kustvatten'], ['593000-192000', 'Ormskärsfjärden sek namn'], ['581520-165000', 'Orren'], ['600565-184600', 'Ortalaviken'], ['571450-163320', 'Oskarshamnsområdet'], ['635040-204196', 'Ostnäsfjärden'], ['583960-170700', 'Oxelösunds hamnområde'], ['583718-161687', 'Pampusfjärden'], ['565460-163000', 'Pataholmsviken'], ['658180-166649', 'Prästmaren'], ['633846-154163', 'Påskallavikområdet'], ['601310-183700', 'Raggaröfjärden'], ['637070-204260', 'Raggavaviken'], ['624870-175500', 'Ramöfjärden sek namn'], ['654500-232000', 'Repskärsfjärden'], ['582590-165000', 'Rimmöfjärden sek namn'], ['584420-172515', 'Ringsöfjärden sek namn'], ['631500-190270', 'Risöfjärden'], ['584227-171600', 'Risöområdet sek namn'], ['573044-115355', 'Risö-Säröarkipelagen'], ['574050-114780', 'Rivö fjord'], ['560940-151740', 'Ronnebyfjärden'], ['575095-164630', 'Rågödjupet'], ['654820-222660', 'Rånefjärden'], ['654570-225230', 'Rånöfjärden'], ['584890-110950', 'Råssö-Resöfjorden sek namn'], ['652150-213000', 'Rävahavet'], ['591910-185600', 'Rödkobbsfjärden'], ['622080-176120', 'Salen'], ['582500-113890', 'Saltkällefjorden'], ['581748-112411', 'Saltö fjord'], ['611600-171500', 'Sandarnesfjärden sek namn'], ['590635-182120', 'Sandemars fjärd sek namn'], ['581815-164320', 'Sandfjärden'], ['652450-222116', 'Sandgrönnfjärden'], ['552670-142281', 'Sandhammaren-Simrishamn'], ['560895-145500', 'Sandviksfjärden'], ['645670-214290', 'Sandvikssundet'], ['653176-222000', 'Sandöfjärden'], ['592280-183550', 'Sandöfjärden'], ['632760-191300', 'Sannafjärden'], ['584450-111445', 'Sannäsfjorden sek namn'], ['641250-211751', 'S Bottenvikens kustvatten'], ['654575-234250', 'Seskaröfjärden'], ['623890-178030', 'S Höga kustens kustvatten'], ['654990-224540', 'Siknäsfjärden'], ['604028-171724', 'Sikvik'], ['583875-170270', 'Sillöfjärden'], ['643920-211500', 'Simpan'], ['572500-164500', 'Simpevarpsområdet'], ['590670-111380', 'Singlefjorden'], ['601000-184030', 'Singöfjärden'], ['613500-171000', 'Siviksfjärden'], ['584430-170665', 'Sjösafjärden'], ['592400-184400', 'Skagsfjärden'], ['561400-161201', 'S Kalmarsunds utsjövatten'], ['593460-184890', 'Skatfjärden'], ['646360-213700', 'Skelleftebukten'], ['644070-211650', 'Skelleftehamnsfjärden'], ['574560-163950', 'Skeppsbrofjärden sek namn'], ['584905-172980', 'Skettnefjärden'], ['654360-235780', 'Skomakarfjärden'], ['584695-175315', 'S Konabbsfjärden sek namn'], ['585100-110600', 'S Kosterfjorden'], ['591800-181360', 'Skurusundet'], ['604250-173000', 'Skutskärsfjärden sek namn'], ['562000-123800', 'Skälderviken'], ['562450-122751', 'Skäldervikens kustvatten'], ['573500-163900', 'Skälöfjärden'], ['580025-113168', 'Skärhamnområdet'], ['612303-171075', 'Skärsåfjärden sek namn'], ['573100-115580', 'Skörvallaviken'], ['570200-182500', 'Slesviken'], ['584363-110971', 's Långebyområdet'], ['647050-213980', 'S m Bottenvikens kustvatten'], ['564500-122601', 'S m Hallands kustvatten'], ['575060-164170', 'Smågöfjärden'], ['554500-125001', 'S m Öresunds kustvatten'], ['564250-162500', 'S n Kalmarsund'], ['633550-200700', 'S n Kvarkens kustvatten'], ['581365-112910', 'Snäckedjupet'], ['592245-184400', 'Sollenkrokafjärden'], ['592315-182620', 'Solöfjärden'], ['582700-110451', 'Sotefjorden'], ['560900-151260', 'Spjälköområdet'], ['654100-234100', 'S. Seskaröfjärden sek namn'], ['654000-222430', 'S. Sigfridsöfjärden'], ['605660-172380', 'S S M Bottenhavets kustvatten'], ['584434-170260', 'Stadsfjärden'], ['572980-115576', 'Stallviken'], ['581900-171101', 'St Anna skärgårds kustvatten'], ['590200-173765', 'Stavbofjärden'], ['593500-193255', 'Stenfjärden'], ['580325-113500', 'Stigfjorden'], ['592100-192001', 'Stockholms skärgårds m kustvatten'], ['595000-191501', 'Stockholms skärgårds n n'], ['585350-182001', 'Stockholms skärgårds s kustvatten'], ['594000-193501', 'Stockholms skärgårds s n'], ['573860-164725', 'Stora Hökhallen'], ['574330-114000', 'Stora Kalvsund'], ['592400-180800', 'Stora Värtan'], ['652066-214400', 'Storfjärden'], ['624615-180500', 'Storfjärden'], ['654416-230000', 'Storöfjärden'], ['584750-111185', 'Stridsfjorden'], ['591920-180800', 'Strömmen'], ['585600-110880', 'Strömstadsfjorden'], ['573547-114617', 'Styrsö- Vrångöområdet'], ['561480-148220', 'Stärnö Sandvik'], ['623300-176210', 'Sundsvallsbukten'], ['622339-172190', 'Sundsvallsfjärden'], ['593500-190000', 'Svartlögafjärden'], ['622000-172300', 'Svartviksfjärden'], ['652020-211930', 'Svensbyfjärden'], ['583730-162500', 'Svensksundsviken'], ['560700-155801', 'S v s Kalmarsunds kustvatten'], ['585000-174600', 'Svärdsfjärden'], ['575670-163500', 'Syrsan'], ['592600-181600', 'Säbyvik'], ['574870-113795', 'Sälö fjord'], ['594200-192000', 'Söderarms skärgård'], ['611676-171000', 'Söderhamnsfjärden'], ['623340-175556', 'Södra Sundet'], ['592420-182210', 'Södra Vaxholmsfjärden'], ['562410-164001', 'S Ölands kustvatten'], ['560205-143545', 'Sölvesborgsviken'], ['652920-222650', 'Sörbrändöfjärden'], ['552500-124461', 'S Öresunds kustvatten'], ['644040-211260', 'Sörfjärden'], ['628750-183300', 'Sörleviken'], ['570730-163715', 'Taktöområdet sek namn'], ['592400-181860', 'Tallaröfjärden'], ['584670-111300', 'Tanumskilen'], ['637640-204160', 'Tavlefjärden'], ['632090-189470', 'Tennviken'], ['654200-222920', 'Tistersöfjärden'], ['594590-190600', 'Tjocköfjärden'], ['561005-150250', 'Tjäröfjärden'], ['560500-154880', 'Torhamnsfjärden'], ['654240-241500', 'Torniofjärden sek namn'], ['574750-164500', 'Torröfjärden sek namn'], ['592135-182700', 'Torsbyfjärden'], ['555950-142740', 'Tostebergabukten'], ['591655-183200', 'Tranaröfjärden'], ['585200-173430', 'Trosafjärden'], ['581853-112736', 'Trälebergskile'], ['592605-182310', 'Trälhavet'], ['582460-164500', 'Trännöfjärden'], ['592640-184500', 'Träsköfjärden'], ['581740-170260', 'Turmulefjärden'], ['584520-172495', 'Tvären'], ['634740-203020', 'Täftefjärden'], ['560790-145850', 'Tärnöfjärden sek namn'], ['655260-224280', 'Törefjärden'], ['593760-192625', 'Uddjupet'], ['630000-183500', 'Ullångersfjärden'], ['632670-190860', 'Ultråfjärden'], ['644150-211000', 'Ursviksfjärden'], ['574083-164115', 'Uvöfjärden'], ['580380-170001', 'Valdemarsviks kustvatten'], ['560200-143175', 'Valjeviken'], ['611000-171500', 'Vallviksfjärden sek namn'], ['651475-214300', 'Vargödraget'], ['572072-115880', 'Varren'], ['574160-163610', 'Verkebäcksviken'], ['572000-180001', 'V Gotlands m kustvatten'], ['570450-180651', 'V Gotlands s kustvatten'], ['554800-142001', 'V Hanöbuktens kustvatten'], ['593920-191440', 'Vidingefjärden'], ['560930-150810', 'Vierydfjorden'], ['591090-182300', 'Vissvassfjärden'], ['573865-164160', 'Vistingsdjupet'], ['575370-164220', 'Vivassen'], ['553730-128890', 'V sydkustens kustvatten'], ['591655-183530', 'Våmfjärden'], ['657412-164249', 'Vårgärdssjön'], ['595730-185850', 'Väddö kustvatten'], ['583450-110750', 'Väderöfjorden'], ['571720-120640', 'Vändelsöarkipelagen'], ['641000-210500', 'Vändskärsfjärden'], ['652830-222116', 'Västantillfjärden'], ['634230-201605', 'Västerfjärden'], ['574450-165451', 'Västerviks kustvatten'], ['621688-144133', 'Västra Blekinge skärgårds kustvatten'], ['560775-153055', 'Västra fjärden'], ['592650-182815', 'Västra Saxarfjärden'], ['563825-161810', 'Västra sjön'], ['573940-163560', 'Västrumsfjärden'], ['595000-185600', 'Vätösundet'], ['634640-203710', 'Ytterbodafjärden'], ['644030-218500', 'Ytterviksfjärden'], ['581280-170070', 'Ytteröområdet'], ['621920-175280', 'Yttre Björköfjärden'], ['582000-112350', 'Yttre Brofjorden'], ['583730-164501', 'Yttre Bråviken'], ['651500-213108', 'Yttrefjärden'], ['673283-158060', 'Yttre Fjärden'], ['574820-163550', 'Yttre Gamlebyviken'], ['628480-183070', 'Yttre Gaviksfjärden'], ['572135-120141', 'Yttre Kungsbackafjorden'], ['728806-179329', 'Yttre Lulefjärden'], ['641720-211520', 'Yttre Lövselefjärden'], ['632690-193500', 'Yttre Nordmalingfjärden'], ['561150-147620', 'Yttre Pukaviksbukten'], ['560780-153500', 'Yttre redden'], ['637310-204860', 'Yttre Täftefjärden'], ['580585-164720', 'Yttre Valdemarsviken'], ['634110-201920', 'Yttre Österfjärden'], ['593750-184900', 'Yxlaområdet'], ['582230-112255', 'Åbyfjorden'], ['645950-212650', 'Åbyfjärden'], ['594384-185542', 'Åkeröfjärden'], ['594100-185690', 'Ålandsfjärden sek namn'], ['583755-163200', 'Ållonöfjärden'], ['584067-171125', 'Ålöfjärden'], ['591050-182320', 'Åvaviken'], ['622795-174565', 'Åvikebukten'], ['632030-187600', 'Åvikfjärden'], ['623980-175600', 'Älandsfjärden'], ['575500-113750', 'Älgöfjorden'], ['592040-184000', 'Älgöfjärden'], ['591160-182400', 'Ällmorafjärden'], ['632090-190370', 'Ällöviken'], ['601660-183550', 'Ängsfjärden sek namn'], ['572810-164500', 'Ärnöområdet sek namn'], ['572308-115550', 'Öckerösund'], ['570340-163710', 'Ödänglaområdet sek namn'], ['573200-185701', 'Ö Gotlands m kustvatten'], ['574170-190001', 'Ö Gotlands n kustvatten'], ['571000-184001', 'Ö Gotlands s kustvatten'], ['633000-195000', 'Örefjärden'], ['603000-181500', 'Öregrundsgrepen'], ['603870-181301', 'Öregrunds kustvatten'], ['631610-184500', 'Örnsköldsviksfjärden'], ['584085-171600', 'Örsbaken'], ['562000-162271', 'Ö s Kalmarsunds kustvatten'], ['636910-204040', 'Österlångslädan'], ['601300-182880', 'Östhammarfjärden sek namn'], ['601020-185050', 'Östhammars kustvatten'], ['621157-148904', 'Östra Blekinge skärgårds kustvatten'], ['560810-153980', 'Östra fjärden'], ['592790-183000', 'Östra Saxarfjärden'], ['560850-150580', 'Östre fjorden'], ['552170-130626', 'Ö sydkustens kustvatten'], ['592575-181770', 'Överbyfjärden']]}, 'water_category_option': {'header': ['location_water_category'], 'rows': [['-'], ['1 - Estuarie (övergångsvatten)'], ['2 - Havsområde innanför 1 NM'], ['3 - Havsområde mellan 1 NM och 12 NM'], ['4 - Utsjövatten']]}, 'type_area_option': {'header': ['location_type_area'], 'rows': [['-'], ['01n - Västkustens inre kustvatten'], ['01s - Västkustens inre kustvatten'], ['02 - Västkustens fjordar'], ['03 - Västkustens yttre kustvatten. Skagerrak'], ['04 - Västkustens yttre kustvatten. Kattegatt'], ['05 - Södra Hallands och norra Öresunds kustvatten'], ['06 - Öresunds kustvatten'], ['07 - Skånes kustvatten'], ['08 - Blekinge skärgård och Kalmarsund. Inre kustvatten'], ['09 - Blekinge skärgård och Kalmarsund. Yttre kustvatten'], ['10 - Ölands och Gotlands kustvatten'], ['11 - Gotlands nordvästra kustvatten'], ['12n - Östergötlands och Stockholms skärgård. Mellankustvatten'], ['12s - Östergötlands och Stockholms skärgård. Mellankustvatten'], ['13 - Östergötlands inre kustvatten'], ['14 - Östergötlands yttre kustvatten'], ['15 - Stockholms skärgård. Yttre kustvatten'], ['16 - Södra Bottenhavet. Inre kustvatten'], ['17 - Södra Bottenhavet. Yttre kustvatten'], ['18 - Norra Bottenhavet. Höga kusten. Inre kustvatten'], ['19 - Norra Bottenhavet. Höga kusten. Yttre kustvatten'], ['20 - Norra Kvarkens inre kustvatten'], ['21 - Norra Kvarkens yttre kustvatten'], ['22 - Norra Bottenviken. Inre kustvatten'], ['23 - Norra Bottenviken. Yttre kustvatten'], ['24 - Stockholms inre skärgård och Hallsfjärden'], ['25 - Göta älvs- och Nordre älvs estuarie']]}, 'sea_basin_option': {'header': ['location_sea_basin'], 'rows': [['-'], ['Arkonahavet och S Öresund'], ['Bornholmshavet och Hanöbukten'], ['Bottenhavet'], ['Bottenviken'], ['N Gotlandshavet'], ['N Kattegatt'], ['N Kvarken'], ['Skagerrak'], ['S Kattegatt'], ['V Gotlandshavet'], ['Ålands hav'], ['Ö Gotlandshavet'], ['Öresund']]}, 'economic_zone_option': {'header': ['location_economic_zone'], 'rows': [['-'], ['Svensk ekonomisk zon']]}}\n"
]
}
],
"source": [
"reader.read_location_options()\n",
"location_options = reader.get_location_options()\n",
"print(location_options)"
]
},
{
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
scios/fricas_kernel | screenshots/test2.ipynb | 2 | 135994 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" \n",
"Value = \"Wednesday August 5, 2009 at 19:05:50 \"\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
")version"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" \n",
" 5 4 3 2\n",
" (1) x + 5x + 10x + 10x + 5x + 1\n",
" Type: Polynomial Integer\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(1+x)^5"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" \n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
")set output tex on"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"{x \\sp 5}+{5 \\ {x \\sp 4}}+{{10} \\ {x \\sp 3}}+{{10} \\ {x \\sp 2}}+{5 \\ x}+1 \n",
"\\leqno(2)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Polynomial Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" 5 4 3 2\n",
" (2) x + 5x + 10x + 10x + 5x + 1\n",
"$$\n",
"{x \\sp 5}+{5 \\ {x \\sp 4}}+{{10} \\ {x \\sp 3}}+{{10} \\ {x \\sp 2}}+{5 \\ x}+1 \n",
"\\leqno(2)\n",
"$$\n",
"\n",
" Type: Polynomial Integer\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(1+x)^5"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"{x \\sp 4}+{2 \\ {x \\sp 2}}+1 \n",
"\\leqno(3)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Polynomial Integer}} \\\\\n",
"\n",
"{\\color{black} \\normalsize \n",
"{x \\sp 6}+{3 \\ {x \\sp 4}}+{3 \\ {x \\sp 2}}+1 \n",
"\\leqno(4)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Polynomial Integer}} \\\\\n",
"\n",
"{\\color{black} \\normalsize \n",
"{x \\sp 8}+{4 \\ {x \\sp 6}}+{6 \\ {x \\sp 4}}+{4 \\ {x \\sp 2}}+1 \n",
"\\leqno(5)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Polynomial Integer}} \\\\\n",
"\n",
"{\\color{black} \\normalsize \n",
"{x \\sp {10}}+{5 \\ {x \\sp 8}}+{{10} \\ {x \\sp 6}}+{{10} \\ {x \\sp 4}}+{5 \\ \n",
"{x \\sp 2}}+1 \n",
"\\leqno(6)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Polynomial Integer}} \\\\\n",
"\n",
"{\\color{black} \\normalsize \n",
"35 \n",
"\\leqno(7)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{PositiveInteger}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" 4 2\n",
" (3) x + 2x + 1\n",
"$$\n",
"{x \\sp 4}+{2 \\ {x \\sp 2}}+1 \n",
"\\leqno(3)\n",
"$$\n",
"\n",
" Type: Polynomial Integer\n",
"\n",
" 6 4 2\n",
" (4) x + 3x + 3x + 1\n",
"$$\n",
"{x \\sp 6}+{3 \\ {x \\sp 4}}+{3 \\ {x \\sp 2}}+1 \n",
"\\leqno(4)\n",
"$$\n",
"\n",
" Type: Polynomial Integer\n",
"\n",
" 8 6 4 2\n",
" (5) x + 4x + 6x + 4x + 1\n",
"$$\n",
"{x \\sp 8}+{4 \\ {x \\sp 6}}+{6 \\ {x \\sp 4}}+{4 \\ {x \\sp 2}}+1 \n",
"\\leqno(5)\n",
"$$\n",
"\n",
" Type: Polynomial Integer\n",
"\n",
" 10 8 6 4 2\n",
" (6) x + 5x + 10x + 10x + 5x + 1\n",
"$$\n",
"{x \\sp {10}}+{5 \\ {x \\sp 8}}+{{10} \\ {x \\sp 6}}+{{10} \\ {x \\sp 4}}+{5 \\ \n",
"{x \\sp 2}}+1 \n",
"\\leqno(6)\n",
"$$\n",
"\n",
" Type: Polynomial Integer\n",
"\n",
" (7) 35\n",
"$$\n",
"35 \n",
"\\leqno(7)\n",
"$$\n",
"\n",
" Type: PositiveInteger\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(1+x^2)^2\n",
"(1+x^2)^3\n",
"(1+x^2)^4\n",
"(1+x^2)^5\n",
"binomial(7,3)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"35 \n",
"\\leqno(8)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{PositiveInteger}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" (8) 35\n",
"$$\n",
"35 \n",
"\\leqno(8)\n",
"$$\n",
"\n",
" Type: PositiveInteger\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"c+b+a \n",
"\\leqno(9)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Polynomial Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" (9) c + b + a\n",
"$$\n",
"c+b+a \n",
"\\leqno(9)\n",
"$$\n",
"\n",
" Type: Polynomial Integer\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a+b+c"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"{c \\sp 4}+{{\\left( {4 \\ b}+{4 \\ a} \n",
"\\right)}\n",
"\\ {c \\sp 3}}+{{\\left( {6 \\ {b \\sp 2}}+{{12} \\ a \\ b}+{6 \\ {a \\sp 2}} \n",
"\\right)}\n",
"\\ {c \\sp 2}}+{{\\left( {4 \\ {b \\sp 3}}+{{12} \\ a \\ {b \\sp 2}}+{{12} \\ {a \n",
"\\sp 2} \\ b}+{4 \\ {a \\sp 3}} \n",
"\\right)}\n",
"\\ c}+{b \\sp 4}+{4 \\ a \\ {b \\sp 3}}+{6 \\ {a \\sp 2} \\ {b \\sp 2}}+{4 \\ {a \n",
"\\sp 3} \\ b}+{a \\sp 4} \n",
"\\leqno(10)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Polynomial Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" (10)\n",
" 4 3 2 2 2 3 2 2 3\n",
" c + (4b + 4a)c + (6b + 12a b + 6a )c + (4b + 12a b + 12a b + 4a )c\n",
" + \n",
" 4 3 2 2 3 4\n",
" b + 4a b + 6a b + 4a b + a\n",
"$$\n",
"{c \\sp 4}+{{\\left( {4 \\ b}+{4 \\ a} \n",
"\\right)}\n",
"\\ {c \\sp 3}}+{{\\left( {6 \\ {b \\sp 2}}+{{12} \\ a \\ b}+{6 \\ {a \\sp 2}} \n",
"\\right)}\n",
"\\ {c \\sp 2}}+{{\\left( {4 \\ {b \\sp 3}}+{{12} \\ a \\ {b \\sp 2}}+{{12} \\ {a \n",
"\\sp 2} \\ b}+{4 \\ {a \\sp 3}} \n",
"\\right)}\n",
"\\ c}+{b \\sp 4}+{4 \\ a \\ {b \\sp 3}}+{6 \\ {a \\sp 2} \\ {b \\sp 2}}+{4 \\ {a \n",
"\\sp 3} \\ b}+{a \\sp 4} \n",
"\\leqno(10)\n",
"$$\n",
"\n",
" Type: Polynomial Integer\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%^4"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"1 \\over {{x \\sp 2}+1} \n",
"\\leqno(11)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Fraction Polynomial Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" 1\n",
" (11) ------\n",
" 2\n",
" x + 1\n",
"$$\n",
"1 \\over {{x \\sp 2}+1} \n",
"\\leqno(11)\n",
"$$\n",
"\n",
" Type: Fraction Polynomial Integer\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(1+x^2)^4/(1+x^2)^5"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"{{\\erf \n",
"\\left(\n",
"{x} \n",
"\\right)}\n",
"\\ {\\sqrt {\\pi}}} \\over 2 \n",
"\\leqno(12)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Union(Expression Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" +---+\n",
" erf(x)\\|%pi\n",
" (12) ------------\n",
" 2\n",
"$$\n",
"{{\\erf \n",
"\\left(\n",
"{x} \n",
"\\right)}\n",
"\\ {\\sqrt {\\pi}}} \\over 2 \n",
"\\leqno(12)\n",
"$$\n",
"\n",
" Type: Union(Expression Integer,...)\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"integrate(exp(-x^2),x)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"3+ \\zag{1}{7}+ \\zag{1}{{15}}+ \\zag{1}{1}+ \\zag{1}{{292}}+ \\zag{1}{1}+ \n",
"\\zag{1}{1}+ \\zag{1}{1}+ \\zag{1}{2}+ \\zag{1}{1}+ \\zag{1}{3}+\\ldots \n",
"\\leqno(13)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{ContinuedFraction Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" (13)\n",
" 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |\n",
" 3 + +---+ + +----+ + +---+ + +-----+ + +---+ + +---+ + +---+ + +---+\n",
" | 7 | 15 | 1 | 292 | 1 | 1 | 1 | 2\n",
" + \n",
" 1 | 1 |\n",
" +---+ + +---+ + ...\n",
" | 1 | 3\n",
"$$\n",
"3+ \\zag{1}{7}+ \\zag{1}{{15}}+ \\zag{1}{1}+ \\zag{1}{{292}}+ \\zag{1}{1}+ \n",
"\\zag{1}{1}+ \\zag{1}{1}+ \\zag{1}{2}+ \\zag{1}{1}+ \\zag{1}{3}+\\ldots \n",
"\\leqno(13)\n",
"$$\n",
"\n",
" Type: ContinuedFraction Integer\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"continuedFraction %pi"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"{x \\sp 5}+{5 \\ {x \\sp 4}}+{{10} \\ {x \\sp 3}}+{{10} \\ {x \\sp 2}}+{5 \\ x}+1 \n",
"\\leqno(14)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Polynomial Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" 5 4 3 2\n",
" (14) x + 5x + 10x + 10x + 5x + 1\n",
"$$\n",
"{x \\sp 5}+{5 \\ {x \\sp 4}}+{{10} \\ {x \\sp 3}}+{{10} \\ {x \\sp 2}}+{5 \\ x}+1 \n",
"\\leqno(14)\n",
"$$\n",
"\n",
" Type: Polynomial Integer\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(1+x)^5"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"3+ \\zag{1}{7}+ \\zag{1}{{15}}+ \\zag{1}{1}+ \\zag{1}{{292}}+ \\zag{1}{1}+ \n",
"\\zag{1}{1}+ \\zag{1}{1}+ \\zag{1}{2}+ \\zag{1}{1}+ \\zag{1}{3}+\\ldots \n",
"\\leqno(15)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{ContinuedFraction Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" (15)\n",
" 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |\n",
" 3 + +---+ + +----+ + +---+ + +-----+ + +---+ + +---+ + +---+ + +---+\n",
" | 7 | 15 | 1 | 292 | 1 | 1 | 1 | 2\n",
" + \n",
" 1 | 1 |\n",
" +---+ + +---+ + ...\n",
" | 1 | 3\n",
"$$\n",
"3+ \\zag{1}{7}+ \\zag{1}{{15}}+ \\zag{1}{1}+ \\zag{1}{{292}}+ \\zag{1}{1}+ \n",
"\\zag{1}{1}+ \\zag{1}{1}+ \\zag{1}{2}+ \\zag{1}{1}+ \\zag{1}{3}+\\ldots \n",
"\\leqno(15)\n",
"$$\n",
"\n",
" Type: ContinuedFraction Integer\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"continuedFraction %pi"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"{-{a \\ x} -b} \\over {z -{c \\ y}} \n",
"\\leqno(16)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Fraction Polynomial Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" - a x - b\n",
" (16) ---------\n",
" z - c y\n",
"$$\n",
"{-{a \\ x} -b} \\over {z -{c \\ y}} \n",
"\\leqno(16)\n",
"$$\n",
"\n",
" Type: Fraction Polynomial Integer\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(a*x+b)/(c*y-z)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" )credits : list the people who have contributed to OpenAxiom\r\n",
"\r\n",
" )help <command> gives more information\r\n",
" )quit : exit OpenAxiom \r\n",
"\r\n",
" )abbreviation : query, set and remove abbreviations for constructors\r\n",
" )cd : set working directory\r\n",
" )clear : remove declarations, definitions or values\r\n",
" )close : throw away an interpreter client and workspace\r\n",
" )compile : invoke constructor compiler\r\n",
" )display : display Library operations and objects in your workspace\r\n",
" )edit : edit a file\r\n",
" )frame : manage interpreter workspaces\r\n",
" )history : manage aspects of interactive session\r\n",
" )library : introduce new constructors \r\n",
" )lisp : evaluate a LISP expression\r\n",
" )read : execute AXIOM commands from a file\r\n",
" )savesystem : save LISP image to a file\r\n",
" )set : view and set system variables\r\n",
" )show : show constructor information\r\n",
" )spool : log input and output to a file\r\n",
" )synonym : define an abbreviation for system commands\r\n",
" )system : issue shell commands\r\n",
" )trace : trace execution of functions\r\n",
" )undo : restore workspace to earlier state\r\n",
" )what : search for various things by name\r\n",
"\r\n",
"\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
")summary"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"\\left[\n",
"\\begin{array}{cccc}\n",
"1 & 2 & 3 & 4 \\\\ \n",
"5 & 6 & 7 & 8 \\\\ \n",
"0 & 3 & 2 & 5 \\\\ \n",
"5 & 8 & 3 & 4 \n",
"\\end{array}\n",
"\\right]\n",
"\\leqno(17)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Matrix Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" +1 2 3 4+\n",
" | |\n",
" |5 6 7 8|\n",
" (17) | |\n",
" |0 3 2 5|\n",
" | |\n",
" +5 8 3 4+\n",
"$$\n",
"\\left[\n",
"\\begin{array}{cccc}\n",
"1 & 2 & 3 & 4 \\\\ \n",
"5 & 6 & 7 & 8 \\\\ \n",
"0 & 3 & 2 & 5 \\\\ \n",
"5 & 8 & 3 & 4 \n",
"\\end{array}\n",
"\\right]\n",
"\\leqno(17)\n",
"$$\n",
"\n",
" Type: Matrix Integer\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"M := matrix [[1,2,3,4],[5,6,7,8],[0,3,2,5],[5,8,3,4]] ; M"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"\\left[\n",
"1, \\: 3, \\: 5, \\: 7 \n",
"\\right]\n",
"\\leqno(18)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Vector PositiveInteger}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" (18) [1,3,5,7]\n",
"$$\n",
"\\left[\n",
"1, \\: 3, \\: 5, \\: 7 \n",
"\\right]\n",
"\\leqno(18)\n",
"$$\n",
"\n",
" Type: Vector PositiveInteger\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"v := vector [1,3,5,7] ; v"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"\\left[\n",
"{236970}, \\: {596498}, \\: {269400}, \\: {491702} \n",
"\\right]\n",
"\\leqno(19)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Vector Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" (19) [236970,596498,269400,491702]\n",
"$$\n",
"\\left[\n",
"{236970}, \\: {596498}, \\: {269400}, \\: {491702} \n",
"\\right]\n",
"\\leqno(19)\n",
"$$\n",
"\n",
" Type: Vector Integer\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"M^4*v"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"{12}+{{17} \\ i} \n",
"\\leqno(20)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Complex Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" (20) 12 + 17%i\n",
"$$\n",
"{12}+{{17} \\ i} \n",
"\\leqno(20)\n",
"$$\n",
"\n",
" Type: Complex Integer\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"z_0 := 12+17*%i"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"-{145439} -{{118320} \\ i} \n",
"\\leqno(21)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Complex Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" (21) - 145439 - 118320%i\n",
"$$\n",
"-{145439} -{{118320} \\ i} \n",
"\\leqno(21)\n",
"$$\n",
"\n",
" Type: Complex Integer\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"z_0^4"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"{1158} -{{741} \\ i} \n",
"\\leqno(22)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Complex Integer}} \\\\\n",
"\n",
"{\\color{black} \\normalsize \n",
"{26493}+{{10794} \\ i} \n",
"\\leqno(23)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Complex Integer}} \\\\\n",
"\n",
"{\\color{black} \\normalsize \n",
"\\left[\n",
"{{12}+{{17} \\ i}}, \\: {{1158} -{{741} \\ i}}, \\: {{26493}+{{10794} \\ i}} \n",
"\\right]\n",
"\\leqno(24)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{List Complex Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" (22) 1158 - 741%i\n",
"$$\n",
"{1158} -{{741} \\ i} \n",
"\\leqno(22)\n",
"$$\n",
"\n",
" Type: Complex Integer\n",
"\n",
" (23) 26493 + 10794%i\n",
"$$\n",
"{26493}+{{10794} \\ i} \n",
"\\leqno(23)\n",
"$$\n",
"\n",
" Type: Complex Integer\n",
"\n",
" (24) [12 + 17%i,1158 - 741%i,26493 + 10794%i]\n",
"$$\n",
"\\left[\n",
"{{12}+{{17} \\ i}}, \\: {{1158} -{{741} \\ i}}, \\: {{26493}+{{10794} \\ i}} \n",
"\\right]\n",
"\\leqno(24)\n",
"$$\n",
"\n",
" Type: List Complex Integer\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"z_1:=z_0 * (3-%i*66)\n",
"z_2:=z_0*z_1\n",
"[z_0,z_1,z_2]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" \"Hello\"\n",
" Type: Void\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print \"Hello\""
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"\\left[\n",
"\\mbox{\\tt \"{x \\sp 2}+2\"} \n",
"\\right]\n",
"\\leqno(26)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{List String}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" (26) [\"{x \\sp 2}+2\"]\n",
"$$\n",
"\\left[\n",
"\\mbox{\\tt \"{x \\sp 2}+2\"} \n",
"\\right]\n",
"\\leqno(26)\n",
"$$\n",
"\n",
" Type: List String\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tex(x^2+2)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" \n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"-- abc"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"hello \n",
"\\leqno(27)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Variable hello}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" (27) hello\n",
"$$\n",
"hello \n",
"\\leqno(27)\n",
"$$\n",
"\n",
" Type: Variable hello\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"hello"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
")logo"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"Welcome! \n",
"\\leqno(28)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Variable Welcome}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" (28) Welcome!\n",
"$$\n",
"Welcome! \n",
"\\leqno(28)\n",
"$$\n",
"\n",
" Type: Variable Welcome!\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Welcome!"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
")plot"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
")logo"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
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"\\begin{array}{cccc}\n",
"-{1 \\over {x -2}} & -{1 \\over {x -3}} & -{1 \\over {x -4}} & -{1 \\over {x -5}} \\\\ \n",
"-{1 \\over {x -3}} & -{1 \\over {x -4}} & -{1 \\over {x -5}} & -{1 \\over {x -6}} \\\\ \n",
"-{1 \\over {x -4}} & -{1 \\over {x -5}} & -{1 \\over {x -6}} & -{1 \\over {x -7}} \\\\ \n",
"-{1 \\over {x -5}} & -{1 \\over {x -6}} & -{1 \\over {x -7}} & -{1 \\over {x -8}} \n",
"\\end{array}\n",
"\\right]\n",
"\\leqno(48)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Matrix Fraction Polynomial Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" + 1 1 1 1 +\n",
" |- ----- - ----- - ----- - -----|\n",
" | x - 2 x - 3 x - 4 x - 5|\n",
" | |\n",
" | 1 1 1 1 |\n",
" |- ----- - ----- - ----- - -----|\n",
" | x - 3 x - 4 x - 5 x - 6|\n",
" (48) | |\n",
" | 1 1 1 1 |\n",
" |- ----- - ----- - ----- - -----|\n",
" | x - 4 x - 5 x - 6 x - 7|\n",
" | |\n",
" | 1 1 1 1 |\n",
" |- ----- - ----- - ----- - -----|\n",
" + x - 5 x - 6 x - 7 x - 8+\n",
"$$\n",
"\\left[\n",
"\\begin{array}{cccc}\n",
"-{1 \\over {x -2}} & -{1 \\over {x -3}} & -{1 \\over {x -4}} & -{1 \\over {x -5}} \\\\ \n",
"-{1 \\over {x -3}} & -{1 \\over {x -4}} & -{1 \\over {x -5}} & -{1 \\over {x -6}} \\\\ \n",
"-{1 \\over {x -4}} & -{1 \\over {x -5}} & -{1 \\over {x -6}} & -{1 \\over {x -7}} \\\\ \n",
"-{1 \\over {x -5}} & -{1 \\over {x -6}} & -{1 \\over {x -7}} & -{1 \\over {x -8}} \n",
"\\end{array}\n",
"\\right]\n",
"\\leqno(48)\n",
"$$\n",
"\n",
" Type: Matrix Fraction Polynomial Integer\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"matrix ([[1/( i + j - x) for i in 1..4] for j in 1..4])"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"\\left[\n",
"\\begin{array}{ccc}\n",
"1 & 1 & 1 \\\\ \n",
"x & y & z \\\\ \n",
"{x \\sp 2} & {y \\sp 2} & {z \\sp 2} \n",
"\\end{array}\n",
"\\right]\n",
"\\leqno(49)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Matrix Polynomial Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" +1 1 1 +\n",
" | |\n",
" (49) |x y z |\n",
" | |\n",
" | 2 2 2|\n",
" +x y z +\n",
"$$\n",
"\\left[\n",
"\\begin{array}{ccc}\n",
"1 & 1 & 1 \\\\ \n",
"x & y & z \\\\ \n",
"{x \\sp 2} & {y \\sp 2} & {z \\sp 2} \n",
"\\end{array}\n",
"\\right]\n",
"\\leqno(49)\n",
"$$\n",
"\n",
" Type: Matrix Polynomial Integer\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"vm := matrix [[1 ,1 ,1] , [x,y,z], [x*x,y*y,z*z]]"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"{\\csc \n",
"\\left(\n",
"{{a \\ x}} \n",
"\\right)}\n",
"\\over {\\csch \n",
"\\left(\n",
"{{b \\ x}} \n",
"\\right)}\n",
"\\leqno(50)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Expression Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" csc(a x)\n",
" (50) ---------\n",
" csch(b x)\n",
"$$\n",
"{\\csc \n",
"\\left(\n",
"{{a \\ x}} \n",
"\\right)}\n",
"\\over {\\csch \n",
"\\left(\n",
"{{b \\ x}} \n",
"\\right)}\n",
"\\leqno(50)\n",
"$$\n",
"\n",
" Type: Expression Integer\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g := csc (a*x) / csch(b*x)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"b \\over a \n",
"\\leqno(51)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Union(OrderedCompletion Expression Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" b\n",
" (51) -\n",
" a\n",
"$$\n",
"b \\over a \n",
"\\leqno(51)\n",
"$$\n",
"\n",
" Type: Union(OrderedCompletion Expression Integer,...)\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"limit (g,x=0)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"{{x+k} \\over x} \\sp x \n",
"\\leqno(52)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Expression Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" x + k x\n",
" (52) (-----)\n",
" x\n",
"$$\n",
"{{x+k} \\over x} \\sp x \n",
"\\leqno(52)\n",
"$$\n",
"\n",
" Type: Expression Integer\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"h := (1 + k/x)^x"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"\\(\\def\\sp{^}\\def\\sb{_}\\def\\leqno(#1){}\\)\\(\\def\\erf\\{\\mathrm{erf}}\\def\\sinh{\\mathrm{sinh}}\\)\\(\\def\\zag#1#2{{{ \\left.{#1}\\right|}\\over{\\left|{#2}\\right.}}}\\)\\(\\require{color}\\)$$\n",
"{\\color{black} \\normalsize \n",
"e \\sp k \n",
"\\leqno(53)\n",
"} \\\\[0.9ex] {\\color{blue} \\scriptsize \\text{Union(OrderedCompletion Expression Integer}} \\\\\n",
"$$"
],
"text/plain": [
" \n",
" k\n",
" (53) %e\n",
"$$\n",
"e \\sp k \n",
"\\leqno(53)\n",
"$$\n",
"\n",
" Type: Union(OrderedCompletion Expression Integer,...)\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"limit (h,x=%plusInfinity)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" Current Values of output Variables \n",
"\n",
"Variable Description Current Value\n",
"-----------------------------------------------------------------------------\n",
"abbreviate abbreviate type names off \n",
"algebra display output in algebraic form On:CONSOLE \n",
"characters choose special output character set plain \n",
"fortran create output in FORTRAN format Off:CONSOLE \n",
"fraction how fractions are formatted vertical \n",
"length line length of output displays 77 \n",
"openmath create output in OpenMath style Off:CONSOLE \n",
"script display output in SCRIPT formula format Off:CONSOLE \n",
"scripts show subscripts,... linearly off \n",
"showeditor view output of )show in editor off \n",
"tex create output in TeX style On:CONSOLE \n",
"mathml create output in MathML style Off:CONSOLE \n",
"\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
")set output"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" OpenAxiom is distributed under terms of the Modified BSD license.\r\n",
"OpenAxiom is an evolution of Axiom which was released under this license \r\n",
"as of September 3, 2002.\r\n",
"\r\n",
"Copyrights remain with the original copyright holders.\r\n",
"Use of this material is by permission and/or license.\r\n",
"Individual files contain reference to these applicable copyrights.\r\n",
"The copyright and license statements are collected here for reference.\r\n",
"\r\n",
"Portions Copyright (C) 2007- Gabriel Dos Reis\r\n",
"All modifications applied to the build-improvements branch of Axiom,\r\n",
"and to the OpenAxiom project are covered by this copyright and\r\n",
"the BSD-type License reproduced below.\r\n",
"\r\n",
"Portions Copyright (c) 2003-2007 The Axiom Team\r\n",
"\r\n",
"The Axiom Team is the collective name for the people who have\r\n",
"contributed to the Axiom project. Where no other copyright statement\r\n",
"is noted in a file this copyright will apply. \r\n",
"\r\n",
"Portions Copyright (c) 1991-2002, The Numerical ALgorithms Group Ltd.\r\n",
"All rights reserved.\r\n",
"\r\n",
"Redistribution and use in source and binary forms, with or without\r\n",
"modification, are permitted provided that the following conditions are\r\n",
"met:\r\n",
"\r\n",
" - Redistributions of source code must retain the above copyright\r\n",
" notice, this list of conditions and the following disclaimer.\r\n",
"\r\n",
" - Redistributions in binary form must reproduce the above copyright\r\n",
" notice, this list of conditions and the following disclaimer in\r\n",
" the documentation and/or other materials provided with the\r\n",
" distribution.\r\n",
"\r\n",
" - Neither the name of The Numerical ALgorithms Group Ltd. nor the\r\n",
" names of its contributors may be used to endorse or promote products\r\n",
" derived from this software without specific prior written permission.\r\n",
"\r\n",
"THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS\r\n",
"IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED\r\n",
"TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A\r\n",
"PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER\r\n",
"OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\r\n",
"EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\r\n",
"PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\r\n",
"PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF\r\n",
"LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\r\n",
"NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\r\n",
"SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\r\n",
"\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
")copyright"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "FriCAS",
"language": "fricas",
"name": "fricas"
},
"language_info": {
"mimetype": "text/plain",
"name": "spad"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-2-clause |
turi-code/tutorials | dss-2016/churn_prediction/churn-tutorial.ipynb | 2 | 52796 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Forecasting customer churn \n",
"\n",
"Churn prediction is the task of identifying users that are likely to stop using a service, product or website. In this notebook, you will learn how to:\n",
"\n",
"#### Train & consume a model to forecast user churn\n",
"* Define the boundary at which churn happens.\n",
"* Define a churn period.\n",
"* Train a model using data from the past.\n",
"* Make predictions for probability of churn for each user."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Let's get started!"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import graphlab as gl\n",
"import datetime\n",
"gl.canvas.set_target('ipynb') # make sure plots appear inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Load previously saved data\n",
"\n",
"In the previous notebook, we had saved the data in a binary format. Let us try and load the data back."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"interactions_ts = gl.TimeSeries(\"data/user_activity_data.ts/\")\n",
"users = gl.SFrame(\"data/users.sf/\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training a churn predictor\n",
"\n",
"We define churn to be **no activity** within a period of time (called the `churn_period`). Hence,\n",
"a user/customer is said to have churned if periods of activity is followed\n",
"by no activity for a `churn_period` (for example, 30 days). \n",
"\n",
"<img src=\"https://dato.com/learn/userguide/churn_prediction/images/churn-illustration.png\", align=\"left\">"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"churn_period_oct = datetime.datetime(year = 2011, month = 10, day = 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Making a train-validation split\n",
"\n",
"Next, we perform a **train-validation** split where we randomly split the data such that one split contains data for a `fraction` of the users while the second split contains all data for the rest of the users."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"(train, valid) = gl.churn_predictor.random_split(interactions_ts, user_id = 'CustomerID', fraction = 0.9, seed = 12)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Users in the training dataset : 3899\n",
"Users in the validation dataset : 441\n"
]
}
],
"source": [
"print \"Users in the training dataset : %s\" % len(train['CustomerID'].unique())\n",
"print \"Users in the validation dataset : %s\" % len(valid['CustomerID'].unique())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training a churn predictor model"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROGRESS: Grouping observation_data by user.\n",
"PROGRESS: Resampling grouped observation_data by time-period 1 day, 0:00:00.\n"
]
},
{
"data": {
"text/html": [
"<pre>InvoiceNo is a categorical variable with too many different values (16841) and will be ignored.</pre>"
],
"text/plain": [
"InvoiceNo is a categorical variable with too many different values (16841) and will be ignored."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>StockCode is a categorical variable with too many different values (3649) and will be ignored.</pre>"
],
"text/plain": [
"StockCode is a categorical variable with too many different values (3649) and will be ignored."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Description is a categorical variable with too many different values (3845) and will be ignored.</pre>"
],
"text/plain": [
"Description is a categorical variable with too many different values (3845) and will be ignored."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROGRESS: Generating features at time-boundaries.\n",
"PROGRESS: --------------------------------------------------\n",
"PROGRESS: Features for 2011-09-30 17:00:00\n",
"PROGRESS: Joining user_data with aggregated features.\n",
"PROGRESS: --------------------------------------------------\n",
"PROGRESS: Training a classifier model.\n"
]
},
{
"data": {
"text/html": [
"<pre>Boosted trees classifier:</pre>"
],
"text/plain": [
"Boosted trees classifier:"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>--------------------------------------------------------</pre>"
],
"text/plain": [
"--------------------------------------------------------"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of examples : 3242</pre>"
],
"text/plain": [
"Number of examples : 3242"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of classes : 2</pre>"
],
"text/plain": [
"Number of classes : 2"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of feature columns : 17</pre>"
],
"text/plain": [
"Number of feature columns : 17"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of unpacked features : 152</pre>"
],
"text/plain": [
"Number of unpacked features : 152"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>+-----------+--------------+-------------------+-------------------+</pre>"
],
"text/plain": [
"+-----------+--------------+-------------------+-------------------+"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| Iteration | Elapsed Time | Training-accuracy | Training-log_loss |</pre>"
],
"text/plain": [
"| Iteration | Elapsed Time | Training-accuracy | Training-log_loss |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>+-----------+--------------+-------------------+-------------------+</pre>"
],
"text/plain": [
"+-----------+--------------+-------------------+-------------------+"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| 1 | 0.016911 | 0.783159 | 0.588051 |</pre>"
],
"text/plain": [
"| 1 | 0.016911 | 0.783159 | 0.588051 |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| 2 | 0.031428 | 0.795188 | 0.528285 |</pre>"
],
"text/plain": [
"| 2 | 0.031428 | 0.795188 | 0.528285 |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| 3 | 0.046039 | 0.808452 | 0.487217 |</pre>"
],
"text/plain": [
"| 3 | 0.046039 | 0.808452 | 0.487217 |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| 4 | 0.063003 | 0.805367 | 0.461014 |</pre>"
],
"text/plain": [
"| 4 | 0.063003 | 0.805367 | 0.461014 |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| 5 | 0.076169 | 0.810611 | 0.439372 |</pre>"
],
"text/plain": [
"| 5 | 0.076169 | 0.810611 | 0.439372 |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| 6 | 0.095362 | 0.812461 | 0.422827 |</pre>"
],
"text/plain": [
"| 6 | 0.095362 | 0.812461 | 0.422827 |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>+-----------+--------------+-------------------+-------------------+</pre>"
],
"text/plain": [
"+-----------+--------------+-------------------+-------------------+"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Decision tree regression:</pre>"
],
"text/plain": [
"Decision tree regression:"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>--------------------------------------------------------</pre>"
],
"text/plain": [
"--------------------------------------------------------"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of examples : 3242</pre>"
],
"text/plain": [
"Number of examples : 3242"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of features : 17</pre>"
],
"text/plain": [
"Number of features : 17"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of unpacked features : 152</pre>"
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"text/plain": [
"Number of unpacked features : 152"
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"+-----------+--------------+--------------------+---------------+"
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"<pre>| Iteration | Elapsed Time | Training-max_error | Training-rmse |</pre>"
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"| Iteration | Elapsed Time | Training-max_error | Training-rmse |"
]
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"<pre>| 1 | 0.019569 | 0.381705 | 0.224819 |</pre>"
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"text/plain": [
"| 1 | 0.019569 | 0.381705 | 0.224819 |"
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"text/html": [
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"text/plain": [
"+-----------+--------------+--------------------+---------------+"
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"name": "stdout",
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"text": [
"PROGRESS: --------------------------------------------------\n",
"PROGRESS: Model training complete: Next steps\n",
"PROGRESS: --------------------------------------------------\n",
"PROGRESS: (1) Evaluate the model at various timestamps in the past:\n",
"PROGRESS: metrics = model.evaluate(data, time_in_past)\n",
"PROGRESS: (2) Make a churn forecast for a timestamp in the future:\n",
"PROGRESS: predictions = model.predict(data, time_in_future)\n"
]
}
],
"source": [
"model = gl.churn_predictor.create(train, user_id='CustomerID', \n",
" user_data = users, time_boundaries = [churn_period_oct])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
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{
"data": {
"text/plain": [
"Class : ChurnPredictor\n",
"\n",
"Schema\n",
"------\n",
"Number of observations : 362700\n",
"Number of users : 3899\n",
"Number of feature columns : 5\n",
"Features used : ['InvoiceNo', 'StockCode', 'Description', 'Quantity', 'UnitPrice']\n",
"\n",
"Parameters\n",
"----------\n",
"Lookback periods : [7, 14, 21, 60, 90]\n",
"Number of time boundaries : 1\n",
"Time period : 1 day, 0:00:00\n",
"Churn period : 30 days, 0:00:00"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
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"source": [
"model"
]
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{
"cell_type": "markdown",
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"source": [
"## Consuming predictions made by the model"
]
},
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"cell_type": "markdown",
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"source": [
"Here the question to ask is will they churn after a certain period of time. To validate we can see if they user has used us after that evaluation period. Voila! I was confusing it with expiration time (customer churn not usage churn)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
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"name": "stdout",
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"text": [
"PROGRESS: Making a churn forecast for the time window:\n",
"PROGRESS: --------------------------------------------------\n",
"PROGRESS: Start : 2011-12-09 12:08:00\n",
"PROGRESS: End : 2012-01-08 12:08:00\n",
"PROGRESS: --------------------------------------------------\n",
"PROGRESS: Grouping dataset by user.\n",
"PROGRESS: Resampling grouped observation_data by time-period 1 day, 0:00:00.\n"
]
},
{
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"<pre>InvoiceNo is a categorical variable with too many different values (16841) and will be ignored.</pre>"
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"text/plain": [
"InvoiceNo is a categorical variable with too many different values (16841) and will be ignored."
]
},
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"data": {
"text/html": [
"<pre>StockCode is a categorical variable with too many different values (3649) and will be ignored.</pre>"
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"text/plain": [
"StockCode is a categorical variable with too many different values (3649) and will be ignored."
]
},
"metadata": {},
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{
"data": {
"text/html": [
"<pre>Description is a categorical variable with too many different values (3845) and will be ignored.</pre>"
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"text/plain": [
"Description is a categorical variable with too many different values (3845) and will be ignored."
]
},
"metadata": {},
"output_type": "display_data"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"PROGRESS: Generating features for boundary 2011-12-09 12:08:00.\n",
"PROGRESS: Joining user_data with aggregated features.\n"
]
},
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.38116106391</td>\n",
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" <tr>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.783465206623</td>\n",
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" <tr>\n",
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" <tr>\n",
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"[441 rows x 2 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
"</div>"
],
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"Columns:\n",
"\tCustomerID\tstr\n",
"\tprobability\tfloat\n",
"\n",
"Rows: 441\n",
"\n",
"Data:\n",
"+------------+----------------+\n",
"| CustomerID | probability |\n",
"+------------+----------------+\n",
"| 16200 | 0.38116106391 |\n",
"| 17383 | 0.885241806507 |\n",
"| 15910 | 0.740143716335 |\n",
"| 16718 | 0.783465206623 |\n",
"| 16222 | 0.783465206623 |\n",
"| 16899 | 0.143798291683 |\n",
"| 12732 | 0.946555435658 |\n",
"| 13194 | 0.946781158447 |\n",
"| 14625 | 0.743798315525 |\n",
"| 13242 | 0.918005168438 |\n",
"+------------+----------------+\n",
"[441 rows x 2 columns]\n",
"Note: Only the head of the SFrame is printed.\n",
"You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
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"source": [
"predictions = model.predict(valid, user_data=users)\n",
"predictions"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
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" });\n",
" })();\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"predictions['probability'].show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluating the model"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROGRESS: Making a churn forecast for the time window:\n",
"PROGRESS: --------------------------------------------------\n",
"PROGRESS: Start : 2011-10-01 00:00:00\n",
"PROGRESS: End : 2011-10-31 00:00:00\n",
"PROGRESS: --------------------------------------------------\n",
"PROGRESS: Grouping dataset by user.\n",
"PROGRESS: Resampling grouped observation_data by time-period 1 day, 0:00:00.\n"
]
},
{
"data": {
"text/html": [
"<pre>InvoiceNo is a categorical variable with too many different values (16841) and will be ignored.</pre>"
],
"text/plain": [
"InvoiceNo is a categorical variable with too many different values (16841) and will be ignored."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>StockCode is a categorical variable with too many different values (3649) and will be ignored.</pre>"
],
"text/plain": [
"StockCode is a categorical variable with too many different values (3649) and will be ignored."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Description is a categorical variable with too many different values (3845) and will be ignored.</pre>"
],
"text/plain": [
"Description is a categorical variable with too many different values (3845) and will be ignored."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROGRESS: Generating features for boundary 2011-10-01 00:00:00.\n",
"PROGRESS: Joining user_data with aggregated features.\n",
"PROGRESS: Not enough data to make predictions for 66 user(s). \n"
]
},
{
"data": {
"text/plain": [
"{'auc': 0.7990228370663153, 'evaluation_data': Columns:\n",
" \tCustomerID\tstr\n",
" \tprobability\tfloat\n",
" \tlabel\tint\n",
" \n",
" Rows: 375\n",
" \n",
" Data:\n",
" +------------+----------------+-------+\n",
" | CustomerID | probability | label |\n",
" +------------+----------------+-------+\n",
" | 16200 | 0.632646918297 | 1 |\n",
" | 15910 | 0.430852562189 | 0 |\n",
" | 16718 | 0.703077316284 | 1 |\n",
" | 16222 | 0.768735051155 | 1 |\n",
" | 16899 | 0.583611965179 | 1 |\n",
" | 12732 | 0.894502520561 | 1 |\n",
" | 13194 | 0.817718148232 | 1 |\n",
" | 14625 | 0.618298172951 | 1 |\n",
" | 13242 | 0.940870046616 | 1 |\n",
" | 15894 | 0.828248143196 | 1 |\n",
" +------------+----------------+-------+\n",
" [375 rows x 3 columns]\n",
" Note: Only the head of the SFrame is printed.\n",
" You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns., 'precision': 0.7863501483679525, 'precision_recall_curve': Columns:\n",
" \tcutoffs\tfloat\n",
" \tprecision\tfloat\n",
" \trecall\tfloat\n",
" \n",
" Rows: 5\n",
" \n",
" Data:\n",
" +---------+----------------+----------------+\n",
" | cutoffs | precision | recall |\n",
" +---------+----------------+----------------+\n",
" | 0.1 | 0.743243243243 | 0.996376811594 |\n",
" | 0.25 | 0.763231197772 | 0.992753623188 |\n",
" | 0.5 | 0.786350148368 | 0.960144927536 |\n",
" | 0.75 | 0.912371134021 | 0.641304347826 |\n",
" | 0.9 | 0.953703703704 | 0.373188405797 |\n",
" +---------+----------------+----------------+\n",
" [5 rows x 3 columns], 'recall': 0.9601449275362319, 'roc_curve': Columns:\n",
" \tthreshold\tfloat\n",
" \tfpr\tfloat\n",
" \ttpr\tfloat\n",
" \tp\tint\n",
" \tn\tint\n",
" \n",
" Rows: 100001\n",
" \n",
" Data:\n",
" +-----------+-----+-----+-----+----+\n",
" | threshold | fpr | tpr | p | n |\n",
" +-----------+-----+-----+-----+----+\n",
" | 0.0 | 1.0 | 1.0 | 276 | 99 |\n",
" | 1e-05 | 1.0 | 1.0 | 276 | 99 |\n",
" | 2e-05 | 1.0 | 1.0 | 276 | 99 |\n",
" | 3e-05 | 1.0 | 1.0 | 276 | 99 |\n",
" | 4e-05 | 1.0 | 1.0 | 276 | 99 |\n",
" | 5e-05 | 1.0 | 1.0 | 276 | 99 |\n",
" | 6e-05 | 1.0 | 1.0 | 276 | 99 |\n",
" | 7e-05 | 1.0 | 1.0 | 276 | 99 |\n",
" | 8e-05 | 1.0 | 1.0 | 276 | 99 |\n",
" | 9e-05 | 1.0 | 1.0 | 276 | 99 |\n",
" +-----------+-----+-----+-----+----+\n",
" [100001 rows x 5 columns]\n",
" Note: Only the head of the SFrame is printed.\n",
" You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"metrics = model.evaluate(valid, user_data=users, time_boundary=churn_period_oct)\n",
"metrics"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model.save('data/churn_model.mdl')"
]
}
],
"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"
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| apache-2.0 |
FranciscoBraga/AprendendoPython | while/Trabalhando com While no Python.ipynb | 1 | 3738 | {
"cells": [
{
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"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n",
"4\n",
"5\n",
"6\n",
"7\n",
"8\n",
"9\n"
]
}
],
"source": [
"#usando while para imprimir valores\n",
"counter=0\n",
"while counter < 10:\n",
" print(counter)\n",
" counter = counter +1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pass, Break e Continue"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n"
]
}
],
"source": [
"contador = 0\n",
"while contador < 100:\n",
" if contador ==4:\n",
" break\n",
" else:\n",
" pass\n",
" print(contador)\n",
" contador = contador+1\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P\n",
"y\n",
"t\n",
"o\n",
"n\n"
]
}
],
"source": [
"#não executando nenhuma ação com continue\n",
"for verificador in \"Python\":\n",
" if verificador == \"h\":\n",
" continue\n",
" print(verificador)\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"while e for juntos"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2é um número primo\n",
"3é um número primo\n",
"4é um número primo\n",
"5é um número primo\n",
"6é um número primo\n",
"7é um número primo\n",
"8é um número primo\n",
"9é um número primo\n",
"10é um número primo\n",
"11é um número primo\n",
"12é um número primo\n",
"13é um número primo\n",
"14é um número primo\n",
"15é um número primo\n",
"16é um número primo\n",
"17é um número primo\n",
"18é um número primo\n",
"19é um número primo\n",
"20é um número primo\n",
"21é um número primo\n",
"22é um número primo\n",
"23é um número primo\n",
"24é um número primo\n",
"25é um número primo\n",
"26é um número primo\n",
"27é um número primo\n",
"28é um número primo\n",
"29é um número primo\n"
]
}
],
"source": [
"for i in range(2,30):\n",
" j=2\n",
" contador =0\n",
" while j < i:\n",
" if j<1: \n",
" contador =1\n",
" j = j + 1\n",
" else:\n",
" j = j +1\n",
" if contador == 0:\n",
" print(str(i)+\"é um número primo\")\n",
" contador = 0\n",
" else:\n",
" contador=0"
]
},
{
"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 |
canercandan/dim | notebooks/experiments_tsp_problem_with_uniform_operators.ipynb | 1 | 567491 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Test avec des op\u00e9rateurs de permutation uniforme sur le probl\u00e8me du TSP"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous d\u00e9finissons ici le nombre d'ile \u00e0 utiliser ainsi que le niveau de lissage des courbes."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"N=3\n",
"A=1000\n",
"OPS=['1swap (0)','1swap (1)','1swap (2)','1swap (3)']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Puis quelques routines\u2026"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"import matplotlib as mpl\n",
"mpl.rc('figure', figsize=(10, 8))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%script /Dev/dim/release/application/TSP/tsp -h"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
">> Loading [benchs/ali535.tsp]\n",
"/Dev/dim/release/application/TSP/tsp: \n",
"\n",
"Usage: /Dev/dim/release/application/TSP/tsp [Options]\n",
"Options of the form \"-f[=Value]\" or \"--Name[=value]\"\n",
"Where:\n",
"\n",
"### GENERAL ########################################################################\n",
"-h, --help :\tPrints this message (optional, default: 0)\n",
"--stopOnUnknownParam :\tStop if unkown param entered (optional, default: 1)\n",
"-S, --seed :\tRandom number seed (optional, default: 0)\n",
"\n",
"### EVOLUTION ENGINE ###############################################################\n",
"-P, --popSize :\tPopulation Size (optional, default: 100)\n",
"\n",
"### ISLANDS MODEL ##################################################################\n",
"--nislands :\tNumber of islands (see --smp) (optional, default: 4)\n",
"-a, --alpha :\tAlpha Probability (optional, default: 0.2)\n",
"-A, --alphaF :\tAlpha Fitness (optional, default: 0.01)\n",
"-b, --beta :\tBeta Probability (optional, default: 0.01)\n",
"-d, --probaSame :\tProbability for an individual to stay in the same island (optional, default: 25)\n",
"-I, --initG :\tinitG (optional, default: 1)\n",
"--nmigrations :\tNumber of migrations to do at each generation (0=all individuals are migrated) (optional, default: 1)\n",
"--stepTimer :\tstepTimer (optional, default: 0)\n",
"--deltaUpdate :\tdeltaUpdate (optional, default: 1)\n",
"--deltaFeedback :\tdeltaFeedback (optional, default: 1)\n",
"--sensitivity :\tsensitivity of delta{t} (1/sensitivity) (optional, default: 1)\n",
"--rewardStrategy :\tStrategy of rewarding: best or avg (optional, default: best)\n",
"--operator0 :\tSet an operator between 1swap, 2swap, shift and 2opt (optional, default: 1swap)\n",
"--operator1 :\tSet an operator between 1swap, 2swap, shift and 2opt (optional, default: 1swap)\n",
"--operator2 :\tSet an operator between 1swap, 2swap, shift and 2opt (optional, default: 1swap)\n",
"--operator3 :\tSet an operator between 1swap, 2swap, shift and 2opt (optional, default: 1swap)\n",
"\n",
"### LOGGER #########################################################################\n",
"-v, --verbose :\tSet the verbose level (optional, default: quiet)\n",
"-l, --print-verbose-levels :\tPrint verbose levels (optional, default: 0)\n",
"-o, --output :\tRedirect a standard output to a file (optional, default: )\n",
"\n",
"### OUTPUT #########################################################################\n",
"--monitorPrefix :\tMonitor prefix filenames (optional, default: result)\n",
"\n",
"### PARALLELIZATION ################################################################\n",
"--parallelize-dynamic :\tEnable dynamic memory shared parallelization (optional, default: 1)\n",
"--parallelize-do-measure :\tDo some measures during execution (optional, default: 0)\n",
"\n",
"### PARALLELIZATION WITH MPI #######################################################\n",
"--parallelize-packet-size :\tNumber of elements which should be sent in a single message during a parallel evaluation based on message passing. (optional, default: 1)\n",
"\n",
"### PERSISTENCE ####################################################################\n",
"-L, --Load :\tA save file to restart from (optional, default: )\n",
"-r, --recomputeFitness :\tRecompute the fitness after re-loading the pop.? (optional, default: 0)\n",
"--status :\tStatus file (optional, default: /Dev/dim/release/application/TSP/tsp.status)\n",
"\n",
"### PROBLEM ########################################################################\n",
"--tspInstance :\tfilename of the instance for TSP problem (optional, default: benchs/ali535.tsp)\n",
"\n",
"### STOPPING CRITERION #############################################################\n",
"-T, --targetFitness :\tStop when fitness reaches (optional, default: 0)\n",
"-G, --maxGen :\tMaximum number of generations () = none) (optional, default: 10000)\n",
"\n",
"@param_file \t defines a file where the parameters are stored\n",
"\n",
"You can use an edited copy of file /Dev/dim/release/application/TSP/tsp.status as parameter file\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Param\u00e8tres"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous lan\u00e7ons le test suivant: DIM auquel s'applique le probl\u00e8me du TSP avec les param\u00e9tres:\n",
"\n",
"* 4 iles avec l'op\u00e9rateur de permutation \u00ab 1swap \u00bb assign\u00e9s uniform\u00e9ment.\n",
"* appliqu\u00e9 \u00e0 l'instance \u00ab benchs/ali535.tsp \u00bb\n",
"* avec un crit\u00e8re d'arr\u00eat \u00ab genmax \u00bb d\u00e9finit \u00e0 10000 (pas de targetFitness)\n",
"* en mode SMP (synchrone)\n",
"* $\\alpha_p = 0.2$\n",
"* $\\beta_p = 0.01$\n",
"* $\\alpha_f = 0.2$\n",
"* stat\u00e9gie de r\u00e9compense:\u00a0MAX\n",
"* $P=100$\n",
"* $$M = \\begin{bmatrix}\n",
"25 & 25 & 25 & 25 \\\\\\\n",
"25 & 25 & 25 & 25 \\\\\\\n",
"25 & 25 & 25 & 25 \\\\\\\n",
"25 & 25 & 25 & 25 \\\\\\\n",
"\\end{bmatrix}$$"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Ex\u00e9cution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous lan\u00e7ons ici l'algorithme DIM utilisant le probl\u00e8me du TSP avec le jeu de param\u00e8tres d\u00e9finis ci-dessus."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%script /Dev/dim/release/application/TSP/tsp --monitorPrefix=/tmp/tsp_uniform_test_result --operator0=1swap --operator1=1swap --operator2=1swap --operator3=1swap -G=10000 --tspInstance=/Dev/dim/release/application/TSP/benchs/ali535.tsp --nislands=4"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
">> Loading [/Dev/dim/release/application/TSP/benchs/ali535.tsp]\n",
"Migration probability (in %) among islands\n",
"\t0\t1\t2\t3\n",
"0\t25\t25\t25\t25\n",
"1\t25\t25\t25\t25\n",
"2\t25\t25\t25\t25\n",
"3\t25\t25\t25\t25\n",
"\n",
"sum\t100\t100\t100\t100\n",
"size: 535\n",
"island 0\n",
"4 0 1swap\n",
"island 1\n",
"4 1 1swap\n",
"island 2\n",
"4 2 1swap\n",
"island 3\n",
"4 3 1swap\n",
"end\n",
"end\n",
"end\n",
"end\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!head /tmp/tsp_uniform_test_result_monitor_*"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"==> /tmp/tsp_uniform_test_result_monitor_0 <==\r\n",
"Island,Time,DateTime,migration,nb_individual_isl0,sending_queue_size_isl0,receiving_queue_size_isl0,avg_ones_isl0,delta_avg_ones_isl0,best_value_isl0,nb_input_ind_isl0,nb_output_ind_isl0,P0to0,P0to1,P0to2,P0to3,P0to*,P*to0,F0to0,F0to1,F0to2,F0to3,nb_migrants_isl0to0,nb_migrants_isl0to1,nb_migrants_isl0to2,nb_migrants_isl0to3\r\n",
"0,0.006,2013-Jul-11 16:50:59.848273,0,100,0,0,-39353.2,-39353.2,-37787,0,0,25,25,25,25,100,0,0,0,0,0,0,0,0,0\r\n",
"0,0.011,2013-Jul-11 16:50:59.852569,1,104,0,0,-39276.5,-39276.5,-37488,104,100,39.8,20,20,20.2,100,0,0.2997,0,0,0,37,20,19,24\r\n",
"0,0.014,2013-Jul-11 16:50:59.856350,2,99,0,0,-39096.4,-39096.4,-37298,99,104,51.5,16.1,16.1,16.3,100,0,0.469676,0.2365,0.137368,0.392917,55,24,11,14\r\n",
"0,0.017,2013-Jul-11 16:50:59.859319,3,87,0,0,-39146.5,-39146.5,-37282,87,99,60.8,12.7,13.3,13.2,100,0,0.750252,0.532468,0.283267,0.636845,59,11,14,15\r\n",
"0,0.02,2013-Jul-11 16:50:59.861421,4,89,0,0,-39293.3,-39293.3,-37164,89,87,48.5,10.3,10.7,30.5,100,0,0.927156,0.999871,0.444721,1.05381,41,9,8,29\r\n",
"0,0.023,2013-Jul-11 16:50:59.864991,5,111,0,0,-39112.5,-39112.5,-37319,111,89,38.5,8.4,8.8,44.3,100,0,1.32959,1.19765,0.866523,1.41293,37,12,7,33\r\n",
"0,0.027,2013-Jul-11 16:50:59.868816,6,144,0,0,-39164.9,-39164.9,-37164,144,111,50.5,6.9,7.1,35.5,100,0,1.78251,1.43901,1.245,1.47789,60,9,7,35\r\n",
"0,0.03,2013-Jul-11 16:50:59.871605,7,134,0,0,-39252.9,-39252.9,-36815,134,144,40.2,5.6,6,48.2,100,0,1.95002,1.97795,1.66969,2.01111,52,10,9,73\r\n",
"0,0.034,2013-Jul-11 16:50:59.875878,8,152,0,0,-39033.6,-39033.6,-36815,152,134,31.9,4.7,5.1,58.3,100,0,2.21302,2.20917,1.91189,2.32018,40,7,6,81\r\n",
"\r\n",
"==> /tmp/tsp_uniform_test_result_monitor_1 <==\r\n",
"Island,Time,DateTime,migration,nb_individual_isl1,sending_queue_size_isl1,receiving_queue_size_isl1,avg_ones_isl1,delta_avg_ones_isl1,best_value_isl1,nb_input_ind_isl1,nb_output_ind_isl1,P1to0,P1to1,P1to2,P1to3,P1to*,P*to1,F1to0,F1to1,F1to2,F1to3,nb_migrants_isl1to0,nb_migrants_isl1to1,nb_migrants_isl1to2,nb_migrants_isl1to3\r\n",
"1,0.004,2013-Jul-11 16:50:59.848273,0,100,0,0,-39243.3,-39243.3,-37166,0,0,25,25,25,25,100,0,0,0,0,0,0,0,0,0\r\n",
"1,0.009,2013-Jul-11 16:50:59.852583,1,97,0,0,-39315.5,-39315.5,-37164,97,100,20.3,39.8,19.8,20.1,100,0,0,0.3044,0,0,19,37,21,23\r\n",
"1,0.012,2013-Jul-11 16:50:59.856335,2,104,0,0,-39370.2,-39370.2,-37017,104,97,16.2,51.5,15.7,16.6,100,0,0.381053,0.441897,0.321429,0.173043,11,49,18,19\r\n",
"1,0.015,2013-Jul-11 16:50:59.859403,3,132,0,0,-39168.5,-39168.5,-37004,132,104,13.1,60.7,12.4,13.8,100,0,0.85906,0.928702,0.820992,0.58605,13,70,11,10\r\n",
"1,0.017,2013-Jul-11 16:50:59.861440,4,112,0,0,-39159,-39159,-37499,112,132,11,48.1,29.8,11.1,100,0,1.13124,1.0717,1.16369,0.757189,13,66,36,17\r\n",
"1,0.021,2013-Jul-11 16:50:59.864928,5,75,0,0,-39124.6,-39124.6,-37391,75,112,8.8,38.3,43.8,9.1,100,0,1.293,1.34947,1.47622,1.02491,8,47,50,7\r\n",
"1,0.025,2013-Jul-11 16:50:59.868733,6,65,0,0,-39002,-39002,-37271,65,75,7.1,30.6,54.7,7.6,100,0,1.56632,1.56576,1.74406,1.41895,3,30,37,5\r\n",
"1,0.027,2013-Jul-11 16:50:59.871441,7,40,0,0,-39004.1,-39004.1,-37164,40,65,6,24.5,63.1,6.4,100,0,1.89399,1.8541,1.97094,1.48676,3,16,42,4\r\n",
"1,0.03,2013-Jul-11 16:50:59.874080,8,33,0,0,-39172.2,-39172.2,-37591,33,40,4.8,19.6,69.9,5.7,100,0,2.34839,2.00431,2.39814,1.69939,0,9,31,0\r\n",
"\r\n",
"==> /tmp/tsp_uniform_test_result_monitor_2 <==\r\n",
"Island,Time,DateTime,migration,nb_individual_isl2,sending_queue_size_isl2,receiving_queue_size_isl2,avg_ones_isl2,delta_avg_ones_isl2,best_value_isl2,nb_input_ind_isl2,nb_output_ind_isl2,P2to0,P2to1,P2to2,P2to3,P2to*,P*to2,F2to0,F2to1,F2to2,F2to3,nb_migrants_isl2to0,nb_migrants_isl2to1,nb_migrants_isl2to2,nb_migrants_isl2to3\r\n",
"2,0.002,2013-Jul-11 16:50:59.848273,0,100,0,0,-39246.3,-39246.3,-37022,0,0,25,25,25,25,100,0,0,0,0,0,0,0,0,0\r\n",
"2,0.008,2013-Jul-11 16:50:59.854463,1,100,0,0,-39332.9,-39332.9,-37022,100,100,20.3,19.8,39.6,20.3,100,0,0,0,0.2952,0,26,19,38,17\r\n",
"2,0.01,2013-Jul-11 16:50:59.856363,2,104,0,0,-39456.5,-39456.5,-37781,104,100,16.4,15.9,51.4,16.3,100,0,0.348846,0.538947,0.558564,0.180588,13,18,57,12\r\n",
"2,0.013,2013-Jul-11 16:50:59.859359,3,100,0,0,-39391,-39391,-37781,100,104,13.3,12.8,60.5,13.4,100,0,0.816896,0.691891,0.91368,0.397949,10,19,64,11\r\n",
"2,0.015,2013-Jul-11 16:50:59.861453,4,118,0,0,-39068.8,-39068.8,-37004,118,100,30.6,10.4,48.1,10.9,100,0,1.58573,1.11287,1.16564,0.797606,28,12,49,11\r\n",
"2,0.019,2013-Jul-11 16:50:59.865103,5,146,0,0,-39146.4,-39146.4,-37004,146,118,44.1,8.4,38.3,9.2,100,0,1.89916,1.26841,1.41867,1.11327,62,5,45,6\r\n",
"2,0.023,2013-Jul-11 16:50:59.868743,6,111,0,0,-39169.5,-39169.5,-37004,111,146,54.7,6.7,30.8,7.8,100,0,2.17162,1.40372,1.58915,1.42213,75,12,48,11\r\n",
"2,0.026,2013-Jul-11 16:50:59.872418,7,100,0,0,-39019.9,-39019.9,-37344,100,111,63.7,5.3,24.4,6.6,100,0,2.3855,1.41552,1.88972,1.69609,73,5,24,9\r\n",
"2,0.03,2013-Jul-11 16:50:59.875751,8,77,0,0,-39047,-39047,-37128,77,100,70.5,4.4,19.4,5.7,100,0,2.77041,1.45736,2.22916,1.99247,77,4,14,5\r\n",
"\r\n",
"==> /tmp/tsp_uniform_test_result_monitor_3 <==\r\n",
"Island,Time,DateTime,migration,nb_individual_isl3,sending_queue_size_isl3,receiving_queue_size_isl3,avg_ones_isl3,delta_avg_ones_isl3,best_value_isl3,nb_input_ind_isl3,nb_output_ind_isl3,P3to0,P3to1,P3to2,P3to3,P3to*,P*to3,F3to0,F3to1,F3to2,F3to3,nb_migrants_isl3to0,nb_migrants_isl3to1,nb_migrants_isl3to2,nb_migrants_isl3to3\r\n",
"3,0,2013-Jul-11 16:50:59.848318,0,100,0,0,-39392.2,-39392.2,-37678,0,0,25,25,25,25,100,0,0,0,0,0,0,0,0,0\r\n",
"3,0.005,2013-Jul-11 16:50:59.853398,1,99,0,0,-39196.5,-39196.5,-37629,99,100,19.8,19.8,20,40.4,100,0,0,0,0,0.2412,22,21,22,35\r\n",
"3,0.008,2013-Jul-11 16:50:59.856346,2,93,0,0,-39061.8,-39061.8,-37629,93,99,15.9,15.7,16.1,52.3,100,0,0.213182,0.38619,0.260909,0.551074,20,13,18,48\r\n",
"3,0.011,2013-Jul-11 16:50:59.859333,3,81,0,0,-39159.9,-39159.9,-37656,81,93,12.7,32.2,13,42.1,100,0,0.42955,1.10156,0.751078,0.823063,5,32,11,45\r\n",
"3,0.013,2013-Jul-11 16:50:59.861402,4,81,0,0,-39298.4,-39298.4,-37451,81,81,10.4,25.7,30.3,33.6,100,0,0.727254,1.34211,1.3572,1.04461,7,25,25,24\r\n",
"3,0.016,2013-Jul-11 16:50:59.864919,5,68,0,0,-39299.2,-39299.2,-37276,68,81,8.2,20.6,43.9,27.3,100,0,1.06855,1.61829,1.63483,1.50875,4,11,44,22\r\n",
"3,0.022,2013-Jul-11 16:50:59.870051,6,80,0,0,-39125.7,-39125.7,-37489,80,68,6.8,16.6,35,41.6,100,0,1.06037,1.71938,1.87507,1.97821,6,14,19,29\r\n",
"3,0.023,2013-Jul-11 16:50:59.871681,7,126,0,0,-39039.3,-39039.3,-37211,126,80,5.4,13.2,28.3,53.1,100,0,1.83643,1.97647,2.14948,2.27256,6,9,25,40\r\n",
"3,0.026,2013-Jul-11 16:50:59.874343,8,138,0,0,-39090.1,-39090.1,-37646,138,126,24.2,10.5,22.7,42.6,100,0,2.6714,2.48559,2.38079,2.65984,35,13,26,52\r\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Data mining"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous r\u00e9cup\u00e9rons l'ensemble des r\u00e9sultats g\u00e9n\u00e9r\u00e9s par l'ex\u00e9cution de l'algorithme dont chaque ligne est index\u00e9 par une pair \u00ab DateTime \u00bb correspondant \u00e0 la date de l'enregistrement de la ligne dans le fichier de r\u00e9sultat et \u00ab migration \u00bb correspondant au num\u00e9ro de migration. Nous cr\u00e9eons \u00e9galement la variable \u00ab mig_set \u00bb qui correspond \u00e0 l'ensemble des r\u00e9sultats utilisant uniquement comme indice \u00ab migration \u00bb."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data_set = [pd.read_csv('/home/candan/Dev/dim/release/application/TSP/result_monitor_%d' % i, index_col=['DateTime','migration'], parse_dates='DateTime') for i in range(N)]\n",
"mig_set = [data_set[i].reset_index(0) for i in range(N)]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Attractivit\u00e9 des op\u00e9rateurs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous illustrons ci-dessous l'actractivit\u00e9 sans et avec le cumul des valeurs."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"attractiveness = pd.concat([pd.concat([mig_set[j]['P%dto%d' % (j,i)] for j in range(N)],axis=1).sum(axis=1) for i in range(N)],axis=1)\n",
"attractiveness.rename(columns=dict(zip(attractiveness.columns,OPS)), inplace=True)\n",
"fig, axes = subplots(nrows=2,ncols=2)\n",
"for affinity, ax in [(1,axes[0,0]), (10,axes[0,1]), (100,axes[1,0]), (1000,axes[1,1])]:\n",
" attractiveness[::affinity].plot(ax=ax, title=\"smoothing = %d\" % affinity).set_ylabel('nb individuals')\n",
"attractiveness.cumsum().plot(title='sum of cumul').set_ylabel('cumul')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"<matplotlib.text.Text at 0x7fba3f5d6090>"
]
},
{
"metadata": {},
"output_type": "display_data",
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8FrQNGjSo3u7u9u7x9fHiiy/y4osvujy2L4M2e+8pTzNtGRkZDB06VPdBm12n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c3utPXtm6wLp01PPXr0pLUhvPHGGyQlJeHv789dd93laTlOSU9PJz09nVGjRjnWffrpp7Rv356g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\n",
"text": [
"<matplotlib.figure.Figure at 0x7fba3f66e5d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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UqFCB7OxsRo4cyZkzZy77/JSUFL744gtycnJ4//33KVmyJE2aNMk31tatW5Oc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\n",
"text": [
"<matplotlib.figure.Figure at 0x7fba3f0c7550>"
]
}
],
"prompt_number": 19
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Evolution des populations"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nbindis = pd.concat([mig_set[i]['nb_individual_isl%d' % i] for i in range(N)], axis=1)\n",
"nbindis.rename(columns=dict(zip(nbindis.columns,OPS)), inplace=True)\n",
"nbindis_max = nbindis.max(axis=1); nbindis_max.name = 'nbindis max'\n",
"nbindis_avg = nbindis.mean(axis=1); nbindis_avg.name = 'nbindis avg'\n",
"#nbindis = nbindis.join(nbindis_max).join(nbindis_avg)\n",
"fig, axes = subplots(nrows=2,ncols=2)\n",
"for affinity, ax in [(1,axes[0,0]), (10,axes[0,1]), (100,axes[1,0]), (1000,axes[1,1])]:\n",
" nbindis[::affinity].plot(ax=ax, title=\"smoothing = %d\" % affinity).set_ylabel('nb individuals')\n",
"[ax.set_ylabel('nb individuals') for ax in nbindis[::A].plot(subplots=True, title=\"smoothing = %d\" % A)]\n",
"nbindis.cumsum().plot(title='sum of cumul').set_ylabel('cumul of nb individuals')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"<matplotlib.text.Text at 0x7fba3ebeed90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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+tbmWc43kzGSljSH9wvvx9K9Pk3srl8ZBjQFw17oT6h/KwaSDDGsxjL8S/2Lf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\n2s4IyG6P3BWuKRKT9Zfrcaf+AsTRYVY9be+9955+MEvMnGl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TgbIk7dxix5K15eor56DV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zrUtV0d9tBDREePsveHD7Nr+J9/iP74g+jAAYtN+vQh2raNaPBgortRragUSip6+XW6ujeZALb/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eLY7t27fTqFEj6tevz3vvvWducwgMDKR58+a0bNmSdu3aAZCUlETPnj1p0KABjzzy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\nQgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7fba3ecc6e50>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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3ZE1NDQ4ePIhvvvkGSUlJKC0tVVmQymISRkRE6lBXV4cjR45ALpdj+/btcHFx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\nIiIi4gMlYSIiIiI++AFFlPT5ltmBUAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7fba3f371a10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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pL/Fwm9va2P7tt9/qVTyGsK3t/5/vurILB8oP4NikYzh17JTi16vE9qOfU1NT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\nAlBEREQkwKgAFBEREQkw/w9cttSERGh/fQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7fba3f371490>"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = subplots()\n",
"ax.set_ylabel('nb individuals')\n",
"nbindis.boxplot(ax=ax)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
"{'boxes': [<matplotlib.lines.Line2D at 0x7fba3f204910>,\n",
" <matplotlib.lines.Line2D at 0x7fba3f359ed0>,\n",
" <matplotlib.lines.Line2D at 0x7fba3eb35b50>],\n",
" 'caps': [<matplotlib.lines.Line2D at 0x7fba3ee5f410>,\n",
" <matplotlib.lines.Line2D at 0x7fba3eb2c0d0>,\n",
" <matplotlib.lines.Line2D at 0x7fba3ebd4ed0>,\n",
" <matplotlib.lines.Line2D at 0x7fba3f359fd0>,\n",
" <matplotlib.lines.Line2D at 0x7fba3ec39190>,\n",
" <matplotlib.lines.Line2D at 0x7fba3ec39950>],\n",
" 'fliers': [<matplotlib.lines.Line2D at 0x7fba3eada1d0>,\n",
" <matplotlib.lines.Line2D at 0x7fba3ec1b0d0>,\n",
" <matplotlib.lines.Line2D at 0x7fba3efb36d0>,\n",
" <matplotlib.lines.Line2D at 0x7fba3eb65990>,\n",
" <matplotlib.lines.Line2D at 0x7fba3ec3f210>,\n",
" <matplotlib.lines.Line2D at 0x7fba3f479590>],\n",
" 'medians': [<matplotlib.lines.Line2D at 0x7fba3f204b50>,\n",
" <matplotlib.lines.Line2D at 0x7fba3ec4ac90>,\n",
" <matplotlib.lines.Line2D at 0x7fba3ec3fd90>],\n",
" 'whiskers': [<matplotlib.lines.Line2D at 0x7fba3f2a16d0>,\n",
" <matplotlib.lines.Line2D at 0x7fba3eae9c90>,\n",
" <matplotlib.lines.Line2D at 0x7fba3ec1b750>,\n",
" <matplotlib.lines.Line2D at 0x7fba3ec1bf10>,\n",
" <matplotlib.lines.Line2D at 0x7fba3eb2fb50>,\n",
" <matplotlib.lines.Line2D at 0x7fba3eb7f390>]}"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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reWFlnPAfuegWN0AgZ1ycTQn4gvyHNd1n5qRAtbUJSczM+YZiDgAA9OpHP5Le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\nIMIo5gAAACKMYg4AACDC/j8gj1ROkGWlYwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7fba3edc8b90>"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nbindis.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>1swap (0)</th>\n",
" <th>1swap (1)</th>\n",
" <th>1swap (2)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 2678.000000</td>\n",
" <td> 2678.000000</td>\n",
" <td> 2678.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 94.795743</td>\n",
" <td> 190.877894</td>\n",
" <td> 14.326363</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 104.685257</td>\n",
" <td> 108.430703</td>\n",
" <td> 26.053584</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 2.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 10.000000</td>\n",
" <td> 106.000000</td>\n",
" <td> 5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 15.000000</td>\n",
" <td> 277.000000</td>\n",
" <td> 7.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 153.000000</td>\n",
" <td> 284.000000</td>\n",
" <td> 9.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 298.000000</td>\n",
" <td> 295.000000</td>\n",
" <td> 122.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
" 1swap (0) 1swap (1) 1swap (2)\n",
"count 2678.000000 2678.000000 2678.000000\n",
"mean 94.795743 190.877894 14.326363\n",
"std 104.685257 108.430703 26.053584\n",
"min 2.000000 0.000000 0.000000\n",
"25% 10.000000 106.000000 5.000000\n",
"50% 15.000000 277.000000 7.000000\n",
"75% 153.000000 284.000000 9.000000\n",
"max 298.000000 295.000000 122.000000"
]
}
],
"prompt_number": 22
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"\u00c9volution des solutions"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bests = pd.concat([mig_set[i]['best_value_isl%d' % i] for i in range(N)], axis=1)\n",
"bests.rename(columns=dict(zip(bests.columns,OPS)), inplace=True)\n",
"bests_max = bests.max(axis=1); bests_max.name = 'bests max'\n",
"bests_avg = bests.mean(axis=1); bests_avg.name = 'bests avg'\n",
"#bests = bests.join(bests_max).join(bests_avg)\n",
"bests[::].plot().set_ylabel('fitness')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"<matplotlib.text.Text at 0x7fba3f494a50>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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fXywtLalSpQru7u4cOnSIiIgI4uPj8fLyAmDIkCH89ttvgDZv4KuvvgpAnz59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QBY5740OjoKk8mEyMhI+VYBf9Q5B45e4KTfvtXU1ASr1QqLxYK9e/eisbHRbbskSXf9HfD38+Du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\nz18pAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7fba3f47c590>"
]
}
],
"prompt_number": 23
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"\u00c9volution de la fitness"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nbindis_bests_max = nbindis.join(bests_max).set_index('bests max')\n",
"fig, axes = subplots(nrows=2,ncols=2)\n",
"for affinity, ax in [(1,axes[0,0]), (10,axes[0,1]), (100,axes[1,0]), (1000,axes[1,1])]:\n",
" ax = nbindis_bests_max[::affinity].plot(ax=ax, title='smoothing = %d' % affinity)\n",
" ax.set_ylabel('nb individuals')\n",
" ax.set_xlabel('fitness')\n",
"ax = nbindis_bests_max.cumsum().plot()\n",
"ax.set_ylabel('cumul of nb individuals')\n",
"ax.set_xlabel('fitness')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"<matplotlib.text.Text at 0x7fba3f1dac90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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4t3MVbSFCZYiJiZWsDK4c4yG1fDmfuyUevdu32bAExgw9U/K5oef4CB49Oddj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\nAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7fba3f114f90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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S+Okn2x7GPXugfn2zq3ENs/sWHZ6WMjRfY0y5GFMuxpSLI2ViTLkYu5ZcvvgC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\nX0WjGo0I+uUfsv6h2ksxKSR2S3S6GSL+xxirwDl3Do4fP1/wuAsgu8e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5nvXTp09n48aN\nbNy4kd27d3Pbbbdx88038/XXXxMREcGwYcOYNWsWNWvW5LvvviMxMZG33nqLESNGeLU/IiKXQnsa\nRUQuQvXq1Tlx4kSpr+vduzfJycl0796d0NBQduzYQWRkJD///DMRERGMGDGC7OxsNmzYQN++fQkL\nC2PAgAHExcXx29/+1gs9ERG5PCoaRUSKCAoKKjQBDB48mIceeog33niD//znP7bvARgxYgRpaWm0\nb98eYwx169Zl/vz5pKSkMGXKFMLCwqhevTrvvfce6enpPPDAA7hcLgAmT57svU6KiFwiXXJHRERE\nREqlMY0iIiIiUioVjSIiIiJSKhWNIiIiIlIqFY0iIiIiUioVjSIiIiJSKhWNIiIiIlIqFY0iIiIi\nUqr/Dy+Zavi+8Hk5AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7fba3f37b410>"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
}
],
"metadata": {}
}
]
} | lgpl-2.1 |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_linear_regression_raw.ipynb | 1 | 3411 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n========================================\nRegression on continuous data (rER[P/F])\n========================================\n\nThis demonstrates how rER[P/F]s - regressing the continuous data - is a\ngeneralisation of traditional averaging. If all preprocessing steps\nare the same, no overlap between epochs exists, and if all\npredictors are binary, regression is virtually identical to traditional\naveraging.\nIf overlap exists and/or predictors are continuous, traditional averaging\nis inapplicable, but regression can estimate effects, including those of\ncontinuous predictors.\n\nrERPs are described in:\nSmith, N. J., & Kutas, M. (2015). Regression-based estimation of ERP\nwaveforms: II. Non-linear effects, overlap correction, and practical\nconsiderations. Psychophysiology, 52(2), 169-189.\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Authors: Jona Sassenhagen <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.stats.regression import linear_regression_raw\n\n# Load and preprocess data\ndata_path = sample.data_path()\nraw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\nraw = mne.io.read_raw_fif(raw_fname, preload=True).pick_types(\n meg='grad', stim=True, eeg=False).filter(1, None) # high-pass\n\n# Set up events\nevents = mne.find_events(raw)\nevent_id = {'Aud/L': 1, 'Aud/R': 2}\ntmin, tmax = -.1, .5\n\n# regular epoching\npicks = mne.pick_types(raw.info, meg=True)\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, reject=None,\n baseline=None, preload=True, verbose=False)\n\n# rERF\nevokeds = linear_regression_raw(raw, events=events, event_id=event_id,\n reject=None, tmin=tmin, tmax=tmax)\n# linear_regression_raw returns a dict of evokeds\n# select conditions similarly to mne.Epochs objects\n\n# plot both results, and their difference\ncond = \"Aud/L\"\nfig, (ax1, ax2, ax3) = plt.subplots(3, 1)\nparams = dict(spatial_colors=True, show=False, ylim=dict(grad=(-200, 200)))\nepochs[cond].average().plot(axes=ax1, **params)\nevokeds[cond].plot(axes=ax2, **params)\ncontrast = mne.combine_evoked([evokeds[cond], -epochs[cond].average()],\n weights='equal')\ncontrast.plot(axes=ax3, **params)\nax1.set_title(\"Traditional averaging\")\nax2.set_title(\"rERF\")\nax3.set_title(\"Difference\")\nplt.show()"
],
"outputs": [],
"metadata": {
"collapsed": false
}
}
],
"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": "2.7.13",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} | bsd-3-clause |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_info.ipynb | 1 | 8511 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n\nThe :class:`Info <mne.Info>` data structure\n===========================================\n\nThe :class:`Info <mne.Info>` data object is typically created\nwhen data is imported into MNE-Python and contains details such as:\n\n- date, subject information, and other recording details\n- the sampling rate\n- information about the data channels (name, type, position, etc.)\n- digitized points\n- sensor\u2013head coordinate transformation matrices\n\nand so forth. See the :class:`the API reference <mne.Info>`\nfor a complete list of all data fields. Once created, this object is passed\naround throughout the data analysis pipeline.\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"import mne\nimport os.path as op"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
":class:`mne.Info` behaves as a nested Python dictionary:\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Read the info object from an example recording\ninfo = mne.io.read_info(\n op.join(mne.datasets.sample.data_path(), 'MEG', 'sample',\n 'sample_audvis_raw.fif'), verbose=False)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"List all the fields in the info object\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"print('Keys in info dictionary:\\n', info.keys())"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Obtain the sampling rate of the data\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"print(info['sfreq'], 'Hz')"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"List all information about the first data channel\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"print(info['chs'][0])"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\nObtaining subsets of channels\n-----------------------------\n\nThere are a number of convenience functions to obtain channel indices, given\nan :class:`mne.Info` object.\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"Get channel indices by name\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"channel_indices = mne.pick_channels(info['ch_names'], ['MEG 0312', 'EEG 005'])"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Get channel indices by regular expression\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"channel_indices = mne.pick_channels_regexp(info['ch_names'], 'MEG *')"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Channel types\n-------------\n\nMNE supports different channel types:\n\n- eeg : For EEG channels with data stored in Volts (V)\n- meg (mag) : For MEG magnetometers channels stored in Tesla (T)\n- meg (grad) : For MEG gradiometers channels stored in Tesla/Meter (T/m)\n- ecg : For ECG channels stored in Volts (V)\n- seeg : For Stereotactic EEG channels in Volts (V).\n- ecog : For Electrocorticography (ECoG) channels in Volts (V).\n- fnirs (HBO) : Functional near-infrared spectroscopy oxyhemoglobin data.\n- fnirs (HBR) : Functional near-infrared spectroscopy deoxyhemoglobin data.\n- emg : For EMG channels stored in Volts (V)\n- bio : For biological channels (AU).\n- stim : For the stimulus (a.k.a. trigger) channels (AU)\n- resp : For the response-trigger channel (AU)\n- chpi : For HPI coil channels (T).\n- exci : Flux excitation channel used to be a stimulus channel.\n- ias : For Internal Active Shielding data (maybe on Triux only).\n- syst : System status channel information (on Triux systems only).\n\nGet channel indices by type\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"channel_indices = mne.pick_types(info, meg=True) # MEG only\nchannel_indices = mne.pick_types(info, eeg=True) # EEG only"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"MEG gradiometers and EEG channels\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"channel_indices = mne.pick_types(info, meg='grad', eeg=True)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Get a dictionary of channel indices, grouped by channel type\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"channel_indices_by_type = mne.io.pick.channel_indices_by_type(info)\nprint('The first three magnetometers:', channel_indices_by_type['mag'][:3])"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Obtaining information about channels\n------------------------------------\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Channel type of a specific channel\nchannel_type = mne.io.pick.channel_type(info, 75)\nprint('Channel #75 is of type:', channel_type)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Channel types of a collection of channels\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"meg_channels = mne.pick_types(info, meg=True)[:10]\nchannel_types = [mne.io.pick.channel_type(info, ch) for ch in meg_channels]\nprint('First 10 MEG channels are of type:\\n', channel_types)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Dropping channels from an info structure\n----------------------------------------\n\nIt is possible to limit the info structure to only include a subset of\nchannels with the :func:`mne.pick_info` function:\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Only keep EEG channels\neeg_indices = mne.pick_types(info, meg=False, eeg=True)\nreduced_info = mne.pick_info(info, eeg_indices)\n\nprint(reduced_info)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
}
],
"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": "2.7.14",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} | bsd-3-clause |
mkudija/Map-Tools | (2)_plot_trips.ipynb | 1 | 206656 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
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" 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>Year</th>\n",
" <th>Name_Orig</th>\n",
" <th>Lat_Orig</th>\n",
" <th>Lng_Orig</th>\n",
" <th>Name_Des</th>\n",
" <th>Lat_Des</th>\n",
" <th>Lng_Des</th>\n",
" <th>Distance (nm)</th>\n",
" <th>Distance (mi)</th>\n",
" <th>Distance (km)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>2018</td>\n",
" <td>Los Angeles, CA</td>\n",
" <td>33.9416</td>\n",
" <td>-118.4085</td>\n",
" <td>Palm Springs, CA</td>\n",
" <td>33.8303</td>\n",
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" <tr>\n",
" <th>84</th>\n",
" <td>2018</td>\n",
" <td>Palm Springs, CA</td>\n",
" <td>33.8303</td>\n",
" <td>-116.5453</td>\n",
" <td>Paso Robles, CA</td>\n",
" <td>35.6370</td>\n",
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" <td>2018</td>\n",
" <td>Paso Robles, CA</td>\n",
" <td>35.6370</td>\n",
" <td>-120.6550</td>\n",
" <td>Paso Robles, CA</td>\n",
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" <td>2018</td>\n",
" <td>Paso Robles, CA</td>\n",
" <td>35.6370</td>\n",
" <td>-120.6550</td>\n",
" <td>Montana De Oro, CA</td>\n",
" <td>35.2723</td>\n",
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" <tr>\n",
" <th>87</th>\n",
" <td>2018</td>\n",
" <td>Paso Robles, CA</td>\n",
" <td>35.6370</td>\n",
" <td>-120.6550</td>\n",
" <td>Los Angeles, CA</td>\n",
" <td>33.9416</td>\n",
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"text/plain": [
" Year Name_Orig Lat_Orig Lng_Orig Name_Des Lat_Des \\\n",
"83 2018 Los Angeles, CA 33.9416 -118.4085 Palm Springs, CA 33.8303 \n",
"84 2018 Palm Springs, CA 33.8303 -116.5453 Paso Robles, CA 35.6370 \n",
"85 2018 Paso Robles, CA 35.6370 -120.6550 Paso Robles, CA 35.6370 \n",
"86 2018 Paso Robles, CA 35.6370 -120.6550 Montana De Oro, CA 35.2723 \n",
"87 2018 Paso Robles, CA 35.6370 -120.6550 Los Angeles, CA 33.9416 \n",
"\n",
" Lng_Des Distance (nm) Distance (mi) Distance (km) \n",
"83 -116.5453 0.0 0.0 0.0 \n",
"84 -120.6550 0.0 0.0 0.0 \n",
"85 -120.6550 0.0 0.0 0.0 \n",
"86 -120.8868 0.0 0.0 0.0 \n",
"87 -118.4085 0.0 0.0 0.0 "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#ENTER YEAR --------\n",
"year = 2015\n",
"#-------------------\n",
"\n",
"%matplotlib inline\n",
"from mpl_toolkits.basemap import Basemap\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import os\n",
"import pandas as pd\n",
"\n",
"# df = pd.read_csv('data/locations_{}.csv'.format(year))\n",
"df = pd.read_csv('data/locations.csv')\n",
"df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def plot_travel(df, title):\n",
" # Basemap parameter options here: http://matplotlib.org/basemap/api/basemap_api.html\n",
"\n",
" # US+Europe\n",
" lat_0 = 0\n",
" lon_0 = 0\n",
" llcrnrlat = 20\n",
" llcrnrlon = -140\n",
" urcrnrlat = 65\n",
" urcrnrlon = 20\n",
"\n",
" fig=plt.figure()\n",
" m = Basemap(projection='merc', \n",
" lat_0=lat_0, lon_0=lon_0, lat_ts=20, area_thresh=500,\n",
" llcrnrlon=llcrnrlon,llcrnrlat=llcrnrlat,urcrnrlon=urcrnrlon,urcrnrlat=urcrnrlat,\n",
" resolution='l')\n",
"\n",
" m.drawmapboundary(fill_color='#FFFFFF')\n",
" m.fillcontinents(color='#B1B2B4',lake_color='#FFFFFF',zorder=0)\n",
"\n",
" m.drawcoastlines(linewidth=0.25, zorder=8)\n",
" m.drawstates(linewidth=0.25, color='#A8A8A8', zorder=6)\n",
" m.drawcountries(linewidth=0.25, color='#707070', zorder=7)\n",
"\n",
" for row in range(0,df.shape[0]): \n",
" lat_orig = df.loc[df.index[row],'Lat_Orig']\n",
" lng_orig = df.loc[df.index[row],'Lng_Orig']\n",
" lat_des = df.loc[df.index[row],'Lat_Des']\n",
" lng_des = df.loc[df.index[row],'Lng_Des']\n",
" m.drawgreatcircle(lon1=lng_orig, lat1=lat_orig, lon2=lng_des, lat2=lat_des,\n",
" linewidth=.5,color='#2E5FAC', zorder=9)\n",
" x, y = m(lng_orig,lat_orig)\n",
" m.scatter(x,y,2,marker='.',edgecolors='#CF5300',c='#CF5300', zorder=10)\n",
" x, y = m(lng_des,lat_des)\n",
" m.scatter(x,y,2,marker='.',edgecolors='#CF5300',c='#CF5300', zorder=10)\n",
"\n",
" plt.show()\n",
" fig.savefig(title, dpi=350, bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mkudija/anaconda/lib/python3.5/site-packages/mpl_toolkits/basemap/__init__.py:1767: MatplotlibDeprecationWarning: The get_axis_bgcolor function was deprecated in version 2.0. Use get_facecolor instead.\n",
" axisbgc = ax.get_axis_bgcolor()\n",
"/Users/mkudija/anaconda/lib/python3.5/site-packages/mpl_toolkits/basemap/__init__.py:3260: MatplotlibDeprecationWarning: The ishold function was deprecated in version 2.0.\n",
" b = ax.ishold()\n",
"/Users/mkudija/anaconda/lib/python3.5/site-packages/mpl_toolkits/basemap/__init__.py:3269: MatplotlibDeprecationWarning: axes.hold is deprecated.\n",
" See the API Changes document (http://matplotlib.org/api/api_changes.html)\n",
" for more details.\n",
" ax.hold(b)\n",
"/Users/mkudija/anaconda/lib/python3.5/site-packages/mpl_toolkits/basemap/__init__.py:3222: MatplotlibDeprecationWarning: The ishold function was deprecated in version 2.0.\n",
" b = ax.ishold()\n",
"/Users/mkudija/anaconda/lib/python3.5/site-packages/mpl_toolkits/basemap/__init__.py:3231: MatplotlibDeprecationWarning: axes.hold is deprecated.\n",
" See the API Changes document (http://matplotlib.org/api/api_changes.html)\n",
" for more details.\n",
" ax.hold(b)\n"
]
},
{
"data": {
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O8ttJS0sjKSkJWzt7SktLG8yOqNFo2LZ1C8/Ofh6NRoNSqWyW+2JCQgKTJk0C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"text/plain": [
"<matplotlib.figure.Figure at 0x119fa7e10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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cO3qEESNG4urq2uJx3dUZsiAIdf0q9KSstJTS0tJW9TlmzDiKiorIuHGjyXLLly0lMTEB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"text/plain": [
"<matplotlib.figure.Figure at 0x11c961dd8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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L7963thjv/zfGjbMuB/n1ZGdnk56ejoOjExUVFc1mRxQEgQ3r1/H4E39EEASM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\nwS1XG5DJ6irdxsV1s9QJPH7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3VWBvK79lm5RymZToIGeig+pMH6Iokp6nZfzs5ziWfIHw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\numRxWjWG3HPPCs6cPzTTzqFQ24ZKhzsZPWQYnPiRGg4jyXLUL7702DG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i2yj3ORECUhMk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Tvbxei8MRz/+mtKfv0b5s+/NSHyRDC8WLNmLSNGjux+wCBfq222DFxuNz5fY49lbPuKpmk0Njai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\nAkGKIARZIBAIUgQhyAKBQJAi/D+TyJtMCUyF+QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c96d1d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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B4WTei1IulxMSGsZ7771HcHAQJSXFXLl61exztdlkMXLkKB588AFcm9HYBEFA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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c6d4908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"years = df.Year.unique()\n",
"for year in years:\n",
" dfPlot = df[df['Year'].isin([year])]\n",
" title = '{}_travel.png'\n",
" plot_travel(dfPlot, title)"
]
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| mit |
arsenovic/galgebra | doc/galgebra_guide.ipynb | 1 | 156568 | {
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"What is Geometric Algebra?\n",
"==========================\n",
"\n",
"<script type=\"text/x-mathjax-config\">\n",
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"\n",
"Basics of Geometric Algebra\n",
"---------------------------\n",
"\n",
"Geometric algebra is the Clifford algebra of a real finite dimensional vector space or the algebra that results when the vector space is extended with a product of vectors (geometric product) that is associative, left and right distributive, and yields a real number for the square (geometric product) of any vector , . The elements of the geometric algebra are called multivectors and consist of the linear combination of scalars, vectors, and the geometric product of two or more vectors. The additional axioms for the geometric algebra are that for any vectors $a$, $b$, and $c$ in the base vector space (,p85):\n",
"\n",
"$$\\begin{array}{c}\n",
" a\\lp bc \\rp = \\lp ab \\rp c \\\\\n",
" a\\lp b+c \\rp = ab+ac \\\\\n",
" \\lp a + b \\rp c = ac+bc \\\\\n",
" aa = a^{2} \\in \\Re.\n",
" \\end{array}$$\n",
"\n",
"If the dot (inner) product of two vectors is defined by (,p86)\n",
"\n",
"$$\\be a\\cdot b \\equiv (ab+ba)/2, \\ee$$\n",
"\n",
"then we have\n",
"\n",
"$$\\begin{aligned}\n",
" c &= a+b \\\\\n",
" c^{2} &= (a+b)^{2} \\\\\n",
" c^{2} &= a^{2}+ab+ba+b^{2} \\\\\n",
" a\\cdot b &= (c^{2}-a^{2}-b^{2})/2 \\in \\Re\n",
" \\end{aligned}$$\n",
"\n",
"Thus $a\\cdot b$ is real. The objects generated from linear combinations of the geometric products of vectors are called multivectors. If a basis for the underlying vector space are the vectors ${\\left \\{{{{\\eb}}_{1},\\dots,{{\\eb}}_{n}} \\rbrc}$ (we use boldface $\\eb$’s to denote basis vectors) a complete basis for the geometric algebra is given by the scalar $1$, the vectors ${{\\eb}}_{1},\\dots,{{\\eb}}_{n}$ and all geometric products of vectors\n",
"\n",
"$$\\be {{\\eb}}_{i_{1}}{{\\eb}}_{i_{2}}\\dots {{\\eb}}_{i_{r}} \\mbox{ where } 0\\le r \\le n\\mbox{, }0 \\le i_{j} \\le n \\mbox{ and } i_{1}<i_{2}<\\dots<i_{r} \\ee$$\n",
"\n",
"Each base of the complete basis is represented by a non-commutative symbol (except for the scalar 1) with name ${{\\eb}}_{i_{1}}\\dots {{\\eb}}_{i_{r}}$ so that the general multivector ${\\boldsymbol{A}}$ is represented by ($A$ is the scalar part of the multivector and the $A^{i_{1},\\dots,i_{r}}$ are scalars)\n",
"\n",
"$$\\be {\\boldsymbol{A}} = A + \\sum_{r=1}^{n}\\sum_{\\substack{i_{1},\\dots,i_{r}\\\\ 0\\le i_{j}<i_{j+1} \\le n}}\n",
" A^{i_{1},\\dots,i_{r}}{{\\eb}}_{i_{1}}{{\\eb}}_{i_{2}}\\dots {{\\eb}}_{r} \\ee$$\n",
"\n",
"The critical operation in setting up the geometric algebra is reducing the geometric product of any two bases to a linear combination of bases so that we can calculate a multiplication table for the bases. Since the geometric product is associative we can use the operation (by definition for two vectors $a\\cdot b \\equiv (ab+ba)/2$ which is a scalar)\n",
"\n",
"$$\\be \\label{reduce}\n",
" {{\\eb}}_{i_{j+1}}{{\\eb}}_{i_{j}} = 2{{\\eb}}_{i_{j+1}}\\cdot {{\\eb}}_{i_{j}} - {{\\eb}}_{i_{j}}{{\\eb}}_{i_{j+1}} \\ee$$\n",
"\n",
"These processes are repeated until every basis list in ${\\boldsymbol{A}}$ is in normal (ascending) order with no repeated elements. As an example consider the following\n",
"\n",
"$$\\begin{aligned}\n",
" {{\\eb}}_{3}{{\\eb}}_{2}{{\\eb}}_{1} &= (2({{\\eb}}_{2}\\cdot {{\\eb}}_{3}) - {{\\eb}}_{2}{{\\eb}}_{3}){{\\eb}}_{1} \\\\\n",
" &= 2{\\lp {{{\\eb}}_{2}\\cdot {{\\eb}}_{3}} \\rp }{{\\eb}}_{1} - {{\\eb}}_{2}{{\\eb}}_{3}{{\\eb}}_{1} \\\\\n",
" &= 2{\\lp {{{\\eb}}_{2}\\cdot {{\\eb}}_{3}} \\rp }{{\\eb}}_{1} - {{\\eb}}_{2}{\\lp {2{\\lp {{{\\eb}}_{1}\\cdot {{\\eb}}_{3}} \\rp }-{{\\eb}}_{1}{{\\eb}}_{3}} \\rp } \\\\\n",
" &= 2{\\lp {{\\lp {{{\\eb}}_{2}\\cdot {{\\eb}}_{3}} \\rp }{{\\eb}}_{1}-{\\lp {{{\\eb}}_{1}\\cdot {{\\eb}}_{3}} \\rp }{{\\eb}}_{2}} \\rp }+{{\\eb}}_{2}{{\\eb}}_{1}{{\\eb}}_{3} \\\\\n",
" &= 2{\\lp {{\\lp {{{\\eb}}_{2}\\cdot {{\\eb}}_{3}} \\rp }{{\\eb}}_{1}-{\\lp {{{\\eb}}_{1}\\cdot {{\\eb}}_{3}} \\rp }{{\\eb}}_{2}+\n",
" {\\lp {{{\\eb}}_{1}\\cdot {{\\eb}}_{2}} \\rp }{{\\eb}}_{3}} \\rp }-{{\\eb}}_{1}{{\\eb}}_{2}{{\\eb}}_{3}\n",
" \\end{aligned}$$\n",
"\n",
"which results from repeated application of eq. ($\\ref{reduce}$). If the product of basis vectors contains repeated factors eq. ($\\ref{reduce}$) can be used to bring the repeated factors next to one another so that if ${{\\eb}}_{i_{j}} = {{\\eb}}_{i_{j+1}}$ then ${{\\eb}}_{i_{j}}{{\\eb}}_{i_{j+1}} = {{\\eb}}_{i_{j}}\\cdot {{\\eb}}_{i_{j+1}}$ which is a scalar that commutes with all the terms in the product and can be brought to the front of the product. Since every repeated pair of vectors in a geometric product of $r$ factors reduces the number of non-commutative factors in the product by $r-2$. The number of bases in the multivector algebra is $2^{n}$ and the number containing $r$ factors is ${n\\choose r}$ which is the number of combinations or $n$ things taken $r$ at a time (binomial coefficient).\n",
"\n",
"The other construction required for formulating the geometric algebra is the outer or wedge product (symbol ${\\wedge}$) of $r$ vectors denoted by $a_{1}{\\wedge}\\dots{\\wedge}a_{r}$. The wedge product of $r$ vectors is called an $r$-blade and is defined by (,p86)\n",
"\n",
"$$\\be a_{1}{\\wedge}\\dots{\\wedge}a_{r} \\equiv \\sum_{i_{j_{1}}\\dots i_{j_{r}}} \\epsilon^{i_{j_{1}}\\dots i_{j_{r}}}a_{i_{j_{1}}}\\dots a_{i_{j_{1}}} \\ee$$\n",
"\n",
"where $\\epsilon^{i_{j_{1}}\\dots i_{j_{r}}}$ is the contravariant permutation symbol which is $+1$ for an even permutation of the superscripts, $0$ if any superscripts are repeated, and $-1$ for an odd permutation of the superscripts. From the definition $a_{1}{\\wedge}\\dots{\\wedge}a_{r}$ is antisymmetric in all its arguments and the following relation for the wedge product of a vector $a$ and an $r$-blade $B_{r}$ can be derived\n",
"\n",
"$$\\be \\label{wedge}\n",
" a{\\wedge}B_{r} = (aB_{r}+(-1)^{r}B_{r}a)/2 \\ee$$\n",
"\n",
"Using eq. ($\\ref{wedge}$) one can represent the wedge product of all the basis vectors in terms of the geometric product of all the basis vectors so that one can solve (the system of equations is lower diagonal) for the geometric product of all the basis vectors in terms of the wedge product of all the basis vectors. Thus a general multivector ${\\boldsymbol{B}}$ can be represented as a linear combination of a scalar and the basis blades.\n",
"\n",
"$$\\be {\\boldsymbol{B}} = B + \\sum_{r=1}^{n}\\sum_{i_{1},\\dots,i_{r},\\;\\forall\\; 0\\le i_{j} \\le n} B^{i_{1},\\dots,i_{r}}{{\\eb}}_{i_{1}}{\\wedge}{{\\eb}}_{i_{2}}{\\wedge}\\dots{\\wedge}{{\\eb}}_{r} \\ee$$\n",
"\n",
"Using the blades ${{\\eb}}_{i_{1}}{\\wedge}{{\\eb}}_{i_{2}}{\\wedge}\\dots{\\wedge}{{\\eb}}_{r}$ creates a graded algebra where $r$ is the grade of the basis blades. The grade-$r$ part of ${\\boldsymbol{B}}$ is the linear combination of all terms with grade $r$ basis blades.\n",
"\n",
"### Grade Projection\n",
"\n",
"The scalar part of ${\\boldsymbol{B}}$ is defined to be grade-$0$. Now that the blade expansion of ${\\boldsymbol{B}}$ is defined we can also define the grade projection operator ${\\left <{{\\boldsymbol{B}}} \\right >_{r}}$ by\n",
"\n",
"$$\\be {\\left <{{\\boldsymbol{B}}} \\right >_{r}} = \\sum_{i_{1},\\dots,i_{r},\\;\\forall\\; 0\\le i_{j} \\le n} B^{i_{1},\\dots,i_{r}}{{\\eb}}_{i_{1}}{\\wedge}{{\\eb}}_{i_{2}}{\\wedge}\\dots{\\wedge}{{\\eb}}_{r} \\ee$$\n",
"\n",
"and\n",
"\n",
"$$\\be {\\left <{{\\boldsymbol{B}}} \\right >_{}} \\equiv {\\left <{{\\boldsymbol{B}}} \\right >_{0}} = B \\ee$$\n",
"\n",
"### Multivector Products\n",
"\n",
"Then if ${\\boldsymbol{A}}_{r}$ is an $r$-grade multivector and ${\\boldsymbol{B}}_{s}$ is an $s$-grade multivector we have\n",
"\n",
"$$\\be {\\boldsymbol{A}}_{r}{\\boldsymbol{B}}_{s} = {\\left <{{\\boldsymbol{A}}_{r}{\\boldsymbol{B}}_{s}} \\right >_{{\\left |{r-s}\\right |}}}+{\\left <{{\\boldsymbol{A}}_{r}{\\boldsymbol{B}}_{s}} \\right >_{{\\left |{r-s}\\right |}+2}}+\\cdots\n",
" {\\left <{{\\boldsymbol{A}}_{r}{\\boldsymbol{B}}_{s}} \\right >_{r+s}} \\ee$$\n",
"\n",
"and define (,p6)\n",
"\n",
"$$\\begin{aligned}\n",
" {\\boldsymbol{A}}_{r}{\\wedge}{\\boldsymbol{B}}_{s} &\\equiv {\\left <{{\\boldsymbol{A}}_{r}{\\boldsymbol{B}}_{s}} \\right >_{r+s}} \\\\\n",
" {\\boldsymbol{A}}_{r}\\cdot{\\boldsymbol{B}}_{s} &\\equiv {\\left \\{ { \\begin{array}{cc}\n",
" r\\mbox{ and }s \\ne 0: & {\\left <{{\\boldsymbol{A}}_{r}{\\boldsymbol{B}}_{s}} \\right >_{{\\left |{r-s}\\right |}}} \\\\\n",
" r\\mbox{ or }s = 0: & 0 \\end{array}} \\right \\}}\n",
" \\end{aligned}$$\n",
"\n",
"where ${\\boldsymbol{A}}_{r}\\cdot{\\boldsymbol{B}}_{s}$ is called the dot or inner product of two pure grade multivectors. For the case of two non-pure grade multivectors\n",
"\n",
"$$\\begin{aligned}\n",
" {\\boldsymbol{A}}{\\wedge}{\\boldsymbol{B}} &= \\sum_{r,s}{\\left <{{\\boldsymbol{A}}} \\right >_{r}}{\\wedge}{\\left <{{\\boldsymbol{B}}} \\right >_{{s}}} \\\\\n",
" {\\boldsymbol{A}}\\cdot{\\boldsymbol{B}} &= \\sum_{r,s\\ne 0}{\\left <{{\\boldsymbol{A}}} \\right >_{r}}\\cdot{\\left <{{\\boldsymbol{B}}} \\right >_{{s}}}\n",
" \\end{aligned}$$\n",
"\n",
"Two other products, the left ($\\rfloor$) and right ($\\lfloor$) contractions, are defined by\n",
"\n",
"$$\\begin{aligned}\n",
" {\\boldsymbol{A}}\\lfloor{\\boldsymbol{B}} &\\equiv \\sum_{r,s}{\\left \\{ {\\begin{array}{cc} {\\left <{{\\boldsymbol{A}}_r{\\boldsymbol{B}}_{s}} \\right >_{r-s}} & r \\ge s \\\\\n",
" 0 & r < s \\end{array}} \\right \\}} \\\\\n",
" {\\boldsymbol{A}}\\rfloor{\\boldsymbol{B}} &\\equiv \\sum_{r,s}{\\left \\{ {\\begin{array}{cc} {\\left <{{\\boldsymbol{A}}_{r}{\\boldsymbol{B}}_{s}} \\right >_{s-r}} & s \\ge r \\\\\n",
" 0 & s < r\\end{array}} \\right \\}}\n",
" \\end{aligned}$$\n",
"\n",
"### Reverse of Multivector\n",
"\n",
"A final operation for multivectors is the reverse. If a multivector ${\\boldsymbol{A}}$ is the geometric product of $r$ vectors (versor) so that ${\\boldsymbol{A}} = a_{1}\\dots a_{r}$ the reverse is defined by\n",
"\n",
"$$\\begin{aligned}\n",
" {\\boldsymbol{A}}^{{\\dagger}} \\equiv a_{r}\\dots a_{1}\n",
" \\end{aligned}$$\n",
"\n",
"where for a general multivector we have (the the sum of the reverse of versors)\n",
"\n",
"$$\\be {\\boldsymbol{A}}^{{\\dagger}} = A + \\sum_{r=1}^{n}(-1)^{r(r-1)/2}\\sum_{i_{1},\\dots,i_{r},\\;\\forall\\; 0\\le i_{j} \\le n} A^{i_{1},\\dots,i_{r}}{{\\eb}}_{i_{1}}{\\wedge}{{\\eb}}_{i_{2}}{\\wedge}\\dots{\\wedge}{{\\eb}}_{r} \\ee$$\n",
"\n",
"note that if ${\\boldsymbol{A}}$ is a versor then ${\\boldsymbol{A}}{\\boldsymbol{A}}^{{\\dagger}}\\in\\Re$ and ($AA^{{\\dagger}} \\ne 0$)\n",
"\n",
"$$\\be {\\boldsymbol{A}}^{-1} = {\\displaystyle\\frac{{\\boldsymbol{A}}^{{\\dagger}}}{{\\boldsymbol{AA}}^{{\\dagger}}}} \\ee$$\n",
"\n",
"The reverse is important in the theory of rotations in $n$-dimensions. If $R$ is the product of an even number of vectors and $RR^{{\\dagger}} = 1$ then $RaR^{{\\dagger}}$ is a composition of rotations of the vector $a$. If $R$ is the product of two vectors then the plane that $R$ defines is the plane of the rotation. That is to say that $RaR^{{\\dagger}}$ rotates the component of $a$ that is projected into the plane defined by $a$ and $b$ where $R=ab$. $R$ may be written $R = e^{\\frac{\\theta}{2}U}$, where $\\theta$ is the angle of rotation and $U$ is a unit blade $\\lp U^{2} = \\pm 1\\rp$ that defines the plane of rotation.\n",
"\n",
"### Reciprocal Frames\n",
"\n",
"If we have $M$ linearly independent vectors (a frame), $a_{1},\\dots,a_{M}$, then the reciprocal frame is $a^{1},\\dots,a^{M}$ where $a_{i}\\cdot a^{j} = \\delta_{i}^{j}$, $\\delta_{i}^{j}$ is the Kronecker delta (zero if $i \\ne j$ and one if $i = j$). The reciprocal frame is constructed as follows:\n",
"\n",
"$$\\be E_{M} = a_{1}{\\wedge}\\dots{\\wedge}a_{M} \\ee$$\n",
"\n",
"$$\\be E_{M}^{-1} = {\\displaystyle\\frac{E_{M}}{E_{M}^{2}}} \\ee$$\n",
"\n",
"Then\n",
"\n",
"$$\\be a^{i} = \\lp -1\\rp ^{i-1}\\lp a_{1}{\\wedge}\\dots{\\wedge}\\breve{a}_{i} {\\wedge}\\dots{\\wedge}a_{M}\\rp E_{M}^{-1} \\ee$$\n",
"\n",
"where $\\breve{a}_{i}$ indicates that $a_{i}$ is to be deleted from the product. In the standard notation if a vector is denoted with a subscript the reciprocal vector is denoted with a superscript. The set of reciprocal vectors will be calculated if a coordinate set is given when a geometric algebra is instantiated since they are required for geometric differentiation when the `Ga` member function `Ga.mvr()` is called to return the reciprocal basis in terms of the basis vectors.\n",
"\n",
"Manifolds and Submanifolds\n",
"--------------------------\n",
"\n",
"A $m$-dimensional vector manifold[4], $\\mathcal{M}$, is defined by a coordinate tuple (tuples are indicated by the vector accent “$\\vec{\\;\\;\\;}$”)\n",
"\n",
"$$\\be \\vec{x} = \\paren{x^{1},\\dots,x^{m}}, \\ee$$\n",
"\n",
"and the differentiable mapping ($U^{m}$ is an $m$-dimensional subset of $\\Re^{m}$)\n",
"\n",
"$$\\be \\f{\\bm{e}^{\\mathcal{M}}}{\\vec{x}}\\colon U^{m}\\subseteq\\Re^{m}\\rightarrow \\mathcal{V}, \\ee$$\n",
"\n",
"where $\\mathcal{V}$ is a vector space with an inner product[5] ($\\cdot$) and is of ${{\\dim}\\lp {\\mathcal{V}} \\rp } \\ge m$.\n",
"\n",
"Then a set of basis vectors for the tangent space of $\\mathcal{M}$ at $\\vec{x}$, ${{{\\mathcal{T}_{\\vec{x}}}\\lp {\\mathcal{M}} \\rp }}$, are\n",
"\n",
"$$\\be \\bm{e}_{i}^{\\mathcal{M}} = \\pdiff{\\bm{e}^{\\mathcal{M}}}{x^{i}} \\ee$$\n",
"\n",
"and\n",
"\n",
"$$\\be \\f{g_{ij}^{\\mathcal{M}}}{\\vec{x}} = \\bm{e}_{i}^{\\mathcal{M}}\\cdot\\bm{e}_{j}^{\\mathcal{M}}. \\ee$$\n",
"\n",
"A $n$-dimensional ($n\\le m$) submanifold $\\mathcal{N}$ of $\\mathcal{M}$ is defined by a coordinate tuple\n",
"\n",
"$$\\be \\vec{u} = \\paren{u^{1},\\dots,u^{n}}, \\ee$$\n",
"\n",
"and a differentiable mapping\n",
"\n",
"$$\\be \\label{eq_79}\n",
" \\f{\\vec{x}}{\\vec{u}}\\colon U^{n}\\subseteq\\Re^{n}\\rightarrow U^{m}\\subseteq\\Re^{m},\n",
" \\ee$$\n",
"\n",
"Then the basis vectors for the tangent space ${{{\\mathcal{T}_{\\vec{u}}}\\lp {\\mathcal{N}} \\rp }}$ are (using ${{{{\\eb}}^{\\mathcal{N}}}\\lp {\\vec{u}} \\rp } = {{{{\\eb}}^{\\mathcal{M}}}\\lp {{{\\vec{x}}\\lp {\\vec{u}} \\rp }} \\rp }$ and the chain rule)[6]\n",
"\n",
"$$\\be \\f{\\bm{e}_{i}^{\\mathcal{N}}}{\\vec{u}} = \\pdiff{\\f{\\bm{e}^{\\mathcal{N}}}{\\vec{u}}}{u^{i}}\n",
" = \\pdiff{\\f{\\bm{e}^{\\mathcal{M}}}{\\vec{x}}}{x^{j}}\\pdiff{x^{j}}{u^{i}}\n",
" = \\f{\\bm{e}_{j}^{\\mathcal{M}}}{\\f{\\vec{x}}{\\vec{u}}}\\pdiff{x^{j}}{u^{i}}, \\ee$$\n",
"\n",
"and\n",
"\n",
"$$\\be \\label{eq_81}\n",
" \\f{g_{ij}^{\\mathcal{N}}}{\\vec{u}} = \\pdiff{x^{k}}{u^{i}}\\pdiff{x^{l}}{u^{j}}\n",
" \\f{g_{kl}^{\\mathcal{M}}}{\\f{\\vec{x}}{\\vec{u}}}.\n",
" \\ee$$\n",
"\n",
"Going back to the base manifold, $\\mathcal{M}$, note that the mapping ${{{\\eb}^{\\mathcal{M}}}\\lp {\\vec{x}} \\rp }\\colon U^{n}\\subseteq\\Re^{n}\\rightarrow \\mathcal{V}$ allows us to calculate an unnormalized pseudo-scalar for ${{{\\mathcal{T}_{\\vec{x}}}\\lp {\\mathcal{M}} \\rp }}$, \n",
"\n",
"$$\\be \\f{I^{\\mathcal{M}}}{\\vec{x}} = \\f{\\bm{e}_{1}^{\\mathcal{M}}}{\\vec{x}}\n",
" \\W\\dots\\W\\f{\\bm{e}_{m}^{\\mathcal{M}}}{\\vec{x}}. \\ee$$\n",
"\n",
"With the pseudo-scalar we can define a projection operator from $\\mathcal{V}$ to the tangent space of $\\mathcal{M}$ by \n",
"\n",
"$$\\be \\f{P_{\\vec{x}}}{\\bm{v}} = (\\bm{v}\\cdot \\f{I^{\\mathcal{M}}}{\\vec{x}})\n",
" \\paren{\\f{I^{\\mathcal{M}}}{\\vec{x}}}^{-1} \\;\\forall\\; \\bm{v}\\in\\mathcal{V}. \\ee$$\n",
"\n",
"In fact for each tangent space ${{{\\mathcal{T}_{\\vec{x}}}\\lp {\\mathcal{M}} \\rp }}$ we can define a geometric algebra ${{\\mathcal{G}}\\lp {{{{\\mathcal{T}_{\\vec{x}}}\\lp {\\mathcal{M}} \\rp }}} \\rp }$ with pseudo-scalar $I^{\\mathcal{M}}$ so that if $A \\in {{\\mathcal{G}}\\lp {\\mathcal{V}} \\rp }$ then \n",
"\n",
"$$\\be \\f{P_{\\vec{x}}}{A} = \\paren{A\\cdot \\f{I^{\\mathcal{M}}}{\\vec{x}}}\n",
" \\paren{\\f{I^{\\mathcal{M}}}{\\vec{x}}}^{-1}\n",
" \\in \\f{\\mathcal{G}}{\\Tn{\\mathcal{M}}{\\vec{x}}}\\;\\forall\\;\n",
" A \\in \\f{\\mathcal{G}}{\\mathcal{V}} \\ee$$\n",
"\n",
"and similarly for the submanifold $\\mathcal{N}$.\n",
"\n",
"If the embedding ${{{\\eb}^{\\mathcal{M}}}\\lp {\\vec{x}} \\rp }\\colon U^{n}\\subseteq\\Re^{n}\\rightarrow \\mathcal{V}$ is not given, but the metric tensor ${{g_{ij}^{\\mathcal{M}}}\\lp {\\vec{x}} \\rp }$ is given the geometric algebra of the tangent space can be constructed. Also the derivatives of the basis vectors of the tangent space can be calculated from the metric tensor using the Christoffel symbols, ${{\\Gamma_{ij}^{k}}\\lp {\\vec{u}} \\rp }$, where the derivatives of the basis vectors are given by\n",
"\n",
"$$\\be \\pdiff{\\bm{e}_{j}^{\\mathcal{M}}}{x^{i}} =\\f{\\Gamma_{ij}^{k}}{\\vec{u}}\\bm{e}_{k}^{\\mathcal{M}}. \\ee$$\n",
"\n",
"If we have a submanifold, $\\mathcal{N}$, defined by eq. ($\\ref{eq_79}$) we can calculate the metric of $\\mathcal{N}$ from eq. ($\\ref{eq_81}$) and hence construct the geometric algebra and calculus of the tangent space, ${{{\\mathcal{T}_{\\vec{u}}}\\lp {\\mathcal{N}} \\rp }}\\subseteq {{{\\mathcal{T}_{{{\\vec{x}}\\lp {\\vec{u}} \\rp }}}\\lp {\\mathcal{M}} \\rp }}$.\n",
"\n",
"**Note:**\n",
"\n",
"If the base manifold is normalized (use the hat symbol to denote normalized tangent vectors, $\\hat{{\\eb}}_{i}^{\\mathcal{M}}$, and the resulting metric tensor, $\\hat{g}_{ij}^{\\mathcal{M}}$) we have $\\hat{{\\eb}}_{i}^{\\mathcal{M}}\\cdot\\hat{{\\eb}}_{i}^{\\mathcal{M}} = \\pm 1$ and $\\hat{g}_{ij}^{\\mathcal{M}}$ does not posses enough information to calculate $g_{ij}^{\\mathcal{N}}$. In that case we need to know $g_{ij}^{\\mathcal{M}}$, the metric tensor of the base manifold before normalization. Likewise, for the case of a vector manifold unless the mapping, ${{{\\eb}^{\\mathcal{M}}}\\lp {\\vec{x}} \\rp }\\colon U^{m}\\subseteq\\Re^{m}\\rightarrow \\mathcal{V}$, is constant the tangent vectors and metric tensor can only be normalized after the fact (one cannot have a mapping that automatically normalizes all the tangent vectors).\n",
"\n",
"Geometric Derivative\n",
"--------------------\n",
"\n",
"The directional derivative of a multivector field ${{F}\\lp {x} \\rp }$ is defined by ($a$ is a vector and $h$ is a scalar) \n",
"\n",
"$$\\be \\paren{a\\cdot\\nabla_{x}}F \\equiv \\lim_{h\\rightarrow 0}\\bfrac{\\f{F}{x+ah}-\\f{F}{x}}{h}. \\label{eq_50} \\ee$$\n",
"\n",
"Note that $a\\cdot\\nabla_{x}$ is a scalar operator. It will give a result containing only those grades that are already in $F$. ${\\lp {a\\cdot\\nabla_{x}} \\rp }F$ is the best linear approximation of ${{F}\\lp {x} \\rp }$ in the direction $a$. Equation ($\\ref{eq_50}$) also defines the operator $\\nabla_{x}$ which for the basis vectors, ${\\left \\{{{\\eb}_{i}} \\rbrc}$, has the representation (note that the ${\\left \\{{{\\eb}^{j}} \\rbrc}$ are reciprocal basis vectors)\n",
"\n",
"$$\\be \\nabla_{x} F = {\\eb}^{j}{\\displaystyle\\frac{\\partial F}{\\partial x^{j}}} \\ee$$\n",
"\n",
"If $F_{r}$ is a $r$-grade multivector (if the independent vector, $x$, is obvious we suppress it in the notation and just write $\\nabla$) and $F_{r} = F_{r}^{i_{1}\\dots i_{r}}{\\eb}_{i_{1}}{\\wedge}\\dots{\\wedge}{\\eb}_{i_{r}}$ then\n",
"\n",
"$$\\be \\nabla F_{r} = {\\displaystyle\\frac{\\partial F_{r}^{i_{1}\\dots i_{r}}}{\\partial x^{j}}}{\\eb}^{j}\\lp {\\eb}_{i_{1}}{\\wedge}\\dots{\\wedge}{\\eb}_{i_{r}} \\rp \\ee$$\n",
"\n",
"Note that ${\\eb}^{j}\\lp {\\eb}_{i_{1}}{\\wedge}\\dots{\\wedge}{\\eb}_{i_{r}} \\rp$ can only contain grades $r-1$ and $r+1$ so that $\\nabla F_{r}$ also can only contain those grades. For a grade-$r$ multivector $F_{r}$ the inner (div) and outer (curl) derivatives are\n",
"\n",
"$$\\be \\nabla\\cdot F_{r} = \\left < \\nabla F_{r}\\right >_{r-1} = {\\eb}^{j}\\cdot {{\\displaystyle\\frac{\\partial {F_{r}}}{\\partial {x^{j}}}}} \\ee$$\n",
"\n",
"and\n",
"\n",
"$$\\be \\nabla{\\wedge}F_{r} = \\left < \\nabla F_{r}\\right >_{r+1} = {\\eb}^{j}{\\wedge}{{\\displaystyle\\frac{\\partial {F_{r}}}{\\partial {x^{j}}}}} \\ee$$\n",
"\n",
"For a general multivector function $F$ the inner and outer derivatives are just the sum of the inner and outer derivatives of each grade of the multivector function.\n",
"\n",
"### Geometric Derivative on a Manifold\n",
"\n",
"In the case of a manifold the derivatives of the ${\\eb}_{i}$’s are functions of the coordinates, ${\\left \\{{x^{i}} \\rbrc}$, so that the geometric derivative of a $r$-grade multivector field is\n",
"\n",
"$$\\begin{aligned}\n",
" \\nabla F_{r} &= {\\eb}^{i}{{\\displaystyle\\frac{\\partial {F_{r}}}{\\partial {x^{i}}}}} = {\\eb}^{i}{{\\displaystyle\\frac{\\partial {}}{\\partial {x^{i}}}}}\n",
" {\\lp {F_{r}^{i_{1}\\dots i_{r}}{\\eb}_{i_{1}}{\\wedge}\\dots{\\wedge}{\\eb}_{i_{r}}} \\rp } \\nonumber \\\\\n",
" &= {{\\displaystyle\\frac{\\partial {F_{r}^{i_{1}\\dots i_{r}}}}{\\partial {x^{i}}}}}{\\eb}^{i}{\\lp {{\\eb}_{i_{1}}{\\wedge}\\dots{\\wedge}{\\eb}_{i_{r}}} \\rp }\n",
" +F_{r}^{i_{1}\\dots i_{r}}{\\eb}^{i}{{\\displaystyle\\frac{\\partial {}}{\\partial {x^{i}}}}}{\\lp {{\\eb}_{i_{1}}{\\wedge}\\dots{\\wedge}{\\eb}_{i_{r}}} \\rp }\\end{aligned}$$\n",
"\n",
"where the multivector functions ${\\eb}^{i}{{\\displaystyle\\frac{\\partial {}}{\\partial {x^{i}}}}}{\\lp {{\\eb}_{i_{1}}{\\wedge}\\dots{\\wedge}{\\eb}_{i_{r}}} \\rp }$ are the connection for the manifold.[7]\n",
"\n",
"The directional (material/convective) derivative, ${\\lp {v\\cdot\\nabla} \\rp }F_{r}$ is given by\n",
"\n",
"$$\\begin{aligned}\n",
" {\\lp {v\\cdot\\nabla} \\rp } F_{r} &= v^{i}{{\\displaystyle\\frac{\\partial {F_{r}}}{\\partial {x^{i}}}}} = v^{i}{{\\displaystyle\\frac{\\partial {}}{\\partial {x^{i}}}}}\n",
" {\\lp {F_{r}^{i_{1}\\dots i_{r}}{\\eb}_{i_{1}}{\\wedge}\\dots{\\wedge}{\\eb}_{i_{r}}} \\rp } \\nonumber \\\\\n",
" &= v^{i}{{\\displaystyle\\frac{\\partial {F_{r}^{i_{1}\\dots i_{r}}}}{\\partial {x^{i}}}}}{\\lp {{\\eb}_{i_{1}}{\\wedge}\\dots{\\wedge}{\\eb}_{i_{r}}} \\rp }\n",
" +v^{i}F_{r}^{i_{1}\\dots i_{r}}{{\\displaystyle\\frac{\\partial {}}{\\partial {x^{i}}}}}{\\lp {{\\eb}_{i_{1}}{\\wedge}\\dots{\\wedge}{\\eb}_{i_{r}}} \\rp },\\end{aligned}$$\n",
"\n",
"so that the multivector connection functions for the directional derivative are ${{\\displaystyle\\frac{\\partial {}}{\\partial {x^{i}}}}}{\\lp {{\\eb}_{i_{1}}{\\wedge}\\dots{\\wedge}{\\eb}_{i_{r}}} \\rp }$. Be careful and note that ${\\lp {v\\cdot\\nabla} \\rp } F_{r} \\ne v\\cdot {\\lp {\\nabla F_{r}} \\rp }$ since the dot and geometric products are not associative with respect to one another ($v\\cdot\\nabla$ is a scalar operator).\n",
"\n",
"### Normalizing Basis for Derivatives\n",
"\n",
"The basis vector set, ${\\left \\{\n",
"{{\\eb}_{i}} \\rbrc}$, is not in general normalized. We define a normalized set of basis vectors, ${\\left \\{{{\\boldsymbol{\\hat{e}}}_{i}} \\rbrc}$, by\n",
"\n",
"$$\\be {\\boldsymbol{\\hat{e}}}_{i} = {\\displaystyle\\frac{{\\eb}_{i}}{\\sqrt{{\\left |{{\\eb}_{i}^{2}}\\right |}}}} = {\\displaystyle\\frac{{\\eb}_{i}}{{\\left |{{\\eb}_{i}}\\right |}}}. \\ee$$\n",
"\n",
"This works for all ${\\eb}_{i}^{2} \\neq 0$. Note that ${\\boldsymbol{\\hat{e}}}_{i}^{2} = \\pm 1$.\n",
"\n",
"Thus the geometric derivative for a set of normalized basis vectors is (where\n",
"$F_{r} = F_{r}^{i_{1}\\dots i_{r}} \\bm{\\hat{e}}_{i_{1}}\\W\\dots\\W\\bm{\\hat{e}}_{i_{r}}$ and [no summation]\n",
"$\\hat{F}_{r}^{i_{1}\\dots i_{r}} = F_{r}^{i_{1}\\dots i_{r}} \\abs{\\bm{\\hat{e}}_{i_{1}}}\\dots\\abs{\\bm{\\hat{e}}_{i_{r}}}$).\n",
"\n",
"$$\\be \\nabla F_{r} = \\eb^{i}\\pdiff{F_{r}}{x^{i}} =\n",
" \\pdiff{F_{r}^{i_{1}\\dots i_{r}}}{x^{i}}\\bm{e}^{i}\n",
" \\paren{\\bm{\\hat{e}}_{i_{1}}\\W\\dots\\W\\bm{\\hat{e}}_{i_{r}}}\n",
" +F_{r}^{i_{1}\\dots i_{r}}\\bm{e}^{i}\\pdiff{}{x^{i}}\n",
" \\paren{\\bm{\\hat{e}}_{i_{1}}\\W\\dots\\W\\bm{\\hat{e}}_{i_{r}}}. \\ee$$\n",
"\n",
"To calculate ${\\eb}^{i}$ in terms of the ${\\boldsymbol{\\hat{e}}}_{i}$’s we have\n",
"\n",
"$$\\begin{aligned}\n",
" {\\eb}^{i} &= g^{ij}{\\eb}_{j} \\nonumber \\\\\n",
" {\\eb}^{i} &= g^{ij}{\\left |{{\\eb}_{j}}\\right |}{\\boldsymbol{\\hat{e}}}_{j}.\\end{aligned}$$\n",
"\n",
"This is the general (non-orthogonal) formula. If the basis vectors are orthogonal then (no summation over repeated indexes)\n",
"\n",
"$$\\begin{aligned}\n",
" {\\eb}^{i} &= g^{ii}{\\left |{{\\eb}_{i}}\\right |}{\\boldsymbol{\\hat{e}}}_{i} \\nonumber \\\\\n",
" {\\eb}^{i} &= {\\displaystyle\\frac{{\\left |{{\\eb}_{i}}\\right |}}{g_{ii}}}{\\boldsymbol{\\hat{e}}}_{i} = {\\displaystyle\\frac{{\\left |{{\\boldsymbol{\\hat{e}}}_{i}}\\right |}}{{\\eb}_{i}^{2}}}{\\boldsymbol{\\hat{e}}}_{i}.\\end{aligned}$$\n",
"\n",
"Additionally, one can calculate the connection of the normalized basis as follows\n",
"\n",
"$$\\begin{aligned}\n",
" {{\\displaystyle\\frac{\\partial {{\\lp {{\\left |{{\\eb}_{i}}\\right |}{\\boldsymbol{\\hat{e}}}_{i}} \\rp }}}{\\partial {x^{j}}}}} =& {{\\displaystyle\\frac{\\partial {{\\eb}_{i}}}{\\partial {x^{j}}}}}, \\nonumber \\\\\n",
" {{\\displaystyle\\frac{\\partial {{\\left |{{\\eb}_{i}}\\right |}}}{\\partial {x^{j}}}}}{\\boldsymbol{\\hat{e}}}_{i}\n",
" +{\\left |{{\\eb}_{i}}\\right |}{{\\displaystyle\\frac{\\partial {{\\boldsymbol{\\hat{e}}}_{i}}}{\\partial {x^{j}}}}} =& {{\\displaystyle\\frac{\\partial {{\\eb}_{i}}}{\\partial {x^{j}}}}}, \\nonumber \\\\\n",
" {{\\displaystyle\\frac{\\partial {{\\boldsymbol{\\hat{e}}}_{i}}}{\\partial {x^{j}}}}} =& {\\displaystyle\\frac{1}{{\\left |{{\\eb}_{i}}\\right |}}}{\\lp {{{\\displaystyle\\frac{\\partial {{\\eb}_{i}}}{\\partial {x^{j}}}}}\n",
" -{{\\displaystyle\\frac{\\partial {{\\left |{{\\eb}_{i}}\\right |}}}{\\partial {x^{j}}}}}{\\boldsymbol{\\hat{e}}}_{i}} \\rp },\\nonumber \\\\\n",
" =& {\\displaystyle\\frac{1}{{\\left |{{\\eb}_{i}}\\right |}}}{{\\displaystyle\\frac{\\partial {{\\eb}_{i}}}{\\partial {x^{j}}}}}\n",
" -{\\displaystyle\\frac{1}{{\\left |{{\\eb}_{i}}\\right |}}}{{\\displaystyle\\frac{\\partial {{\\left |{{\\eb}_{i}}\\right |}}}{\\partial {x^{j}}}}}{\\boldsymbol{\\hat{e}}}_{i},\\nonumber \\\\\n",
" =& {\\displaystyle\\frac{1}{{\\left |{{\\eb}_{i}}\\right |}}}{{\\displaystyle\\frac{\\partial {{\\eb}_{i}}}{\\partial {x^{j}}}}}\n",
" -{\\displaystyle\\frac{1}{2g_{ii}}}{{\\displaystyle\\frac{\\partial {g_{ii}}}{\\partial {x^{j}}}}}{\\boldsymbol{\\hat{e}}}_{i},\\end{aligned}$$\n",
"\n",
"where ${{\\displaystyle\\frac{\\partial {{\\eb}_{i}}}{\\partial {x^{j}}}}}$ is expanded in terms of the ${\\boldsymbol{\\hat{e}}}_{i}$’s.\n",
"\n",
"### Linear Differential Operators\n",
"\n",
"First a note on partial derivative notation. We shall use the following notation for a partial derivative where the manifold coordinates are $x_{1},\\dots,x_{n}$:\n",
"\n",
"$$\\be\\label{eq_66a}\n",
" \\bfrac{\\partial^{j_{1}+\\cdots+j_{n}}}{\\partial x_{1}^{j_{1}}\\dots\\partial x_{n}^{j_{n}}} = \\partial_{j_{1}\\dots j_{n}}.\n",
"\\ee$$\n",
"\n",
"If $j_{k}=0$ the partial derivative with respect to the $k^{th}$ coordinate is not taken. If $j_{k} = 0$ for all $1 \\le k \\le n$ then the partial derivative operator is the scalar one. If we consider a partial derivative where the $x$’s are not in normal order such as\n",
"\n",
"$$\\be {\\displaystyle\\frac{\\partial^{j_{1}+\\cdots+j_{n}}}{\\partial x_{i_{1}}^{j_{1}}\\dots\\partial x_{i_{n}}^{j_{n}}}}, \\ee$$\n",
"\n",
"and the $i_{k}$’s are not in ascending order. The derivative can always be put in the form in eq ($\\ref{eq_66a}$) since the order of differentiation does not change the value of the partial derivative (for the smooth functions we are considering). Additionally, using our notation the product of two partial derivative operations is given by \n",
"\n",
"$$\\be \\partial_{i_{1}\\dots i_{n}}\\partial_{j_{1}\\dots j_{n}} = \\partial_{i_{1}+j_{1},\\dots, i_{n}+j_{n}}. \\ee$$\n",
"\n",
"A general general multivector linear differential operator is a linear combination of multivectors and partial derivative operators denoted by\n",
"\n",
"$$\\be\\label{eq_66b}\n",
" D \\equiv D^{i_{1}\\dots i_{n}}\\partial_{i_{1}\\dots i_{n}}.\n",
"\\ee$$\n",
"\n",
"Equation ($\\ref{eq_66b}$) is the normal form of the differential operator in that the partial derivative operators are written to the right of the multivector coefficients and do not operate upon the multivector coefficients. The operator of eq ($\\ref{eq_66b}$) can operate on mulitvector functions, returning a multivector function via the following definitions.\n",
"\n",
"$F$ as \n",
"\n",
"$$\\be D\\circ F = D^{j_{1}\\dots j_{n}}\\circ\\partial_{j_{1}\\dots j_{n}}F,\\label{eq_67a} \\ee$$\n",
"\n",
", or \n",
"\n",
"$$\\be F\\circ D = \\partial_{j_{1}\\dots j_{n}}F\\circ D^{j_{1}\\dots j_{n}},\\label{eq_68a} \\ee$$\n",
"\n",
" where the $D^{j_{1}\\dots j_{n}}$ are multivector functions and $\\circ$ is any of the multivector multiplicative operations.\n",
"\n",
"Equations ($\\ref{eq_67a}$) and ($\\ref{eq_68a}$) are not the most general multivector linear differential operators, the most general would be \n",
"\n",
"$$\\be D \\left( F \\right) = {D^{j_{1}\\dots j_{n}}}\\left({\\partial_{j_{1}\\dots j_{n}}F}\\right), \\ee$$\n",
"\n",
"where ${{D^{j_{1}\\dots j_{n}}}\\lp {} \\rp }$ are linear multivector functionals.\n",
"\n",
"The definition of the sum of two differential operators is obvious since any multivector operator, $\\circ$, is a bilinear operator ${\\lp {{\\lp {D_{A}+D_{B}} \\rp }\\circ F = D_{A}\\circ F+D_{B}\\circ F} \\rp }$, the product of two differential operators $D_{A}$ and $D_{B}$ operating on a multivector function $F$ is defined to be ($\\circ_{1}$ and $\\circ_{2}$ are any two multivector multiplicative operations)\n",
"\n",
"$$\\begin{aligned}\n",
" {\\lp {D_{A}\\circ_{1}D_{B}} \\rp }\\circ_{2}F &\\equiv {\\lp {D_{A}^{i_{1}\\dots i_{n}}\\circ_{1}\n",
" \\partial_{i_{1}\\dots i_{n}}{\\lp {D_{B}^{j_{1}\\dots j_{n}}\n",
" \\partial_{j_{1}\\dots j_{n}}} \\rp }} \\rp }\\circ_{2}F \\nonumber \\\\\n",
" &= {\\lp {D_{A}^{i_{1}\\dots i_{n}}\\circ_{1}\n",
" {\\lp {{\\lp {\\partial_{i_{1}\\dots i_{n}}D_{B}^{j_{1}\\dots j_{n}}} \\rp }\n",
" \\partial_{j_{1}\\dots j_{n}}+\n",
" D_{B}^{j_{1}\\dots j_{n}}} \\rp }\n",
" \\partial_{i_{1}+j_{1},\\dots, i_{n}+j_{n}}} \\rp }\\circ_{2}F \\nonumber \\\\\n",
" &= {\\lp {D_{A}^{i_{1}\\dots i_{n}}\\circ_{1}{\\lp {\\partial_{i_{1}\\dots i_{n}}D_{B}^{j_{1}\\dots j_{n}}} \\rp }} \\rp }\n",
" \\circ_{2}\\partial_{j_{1}\\dots j_{n}}F+\n",
" {\\lp {D_{A}^{i_{1}\\dots i_{n}}\\circ_{1}D_{B}^{j_{1}\\dots j_{n}}} \\rp }\n",
" \\circ_{2}\\partial_{i_{1}+j_{1},\\dots, i_{n}+j_{n}}F,\\end{aligned}$$\n",
"\n",
"where we have used the fact that the $\\partial$ operator is a scalar operator and commutes with $\\circ_{1}$ and $\\circ_{2}$.\n",
"\n",
"Thus for a pure operator product $D_{A}\\circ D_{B}$ we have \n",
"\n",
"$$\\be D_{A}\\circ D_{B} = \\paren{D_{A}^{i_{1}\\dots i_{n}}\\circ\\paren{\\partial_{i_{1}\\dots i_{n}}D_{B}^{j_{1}\\dots j_{n}}}}\n",
" \\partial_{j_{1}\\dots j_{n}}+\n",
" \\paren{D_{A}^{i_{1}\\dots i_{n}}\\circ_{1}D_{B}^{j_{1}\\dots j_{n}}}\n",
" \\partial_{i_{1}+j_{1},\\dots, i_{n}+j_{n}} \\label{eq_71a} \\ee$$\n",
"\n",
"and the form of eq ($\\ref{eq_71a}$) is the same as eq ($\\ref{eq_67a}$). The basis of eq ($\\ref{eq_71a}$) is that the $\\partial$ operator operates on all object to the right of it as products so that the product rule must be used in all differentiations. Since eq ($\\ref{eq_71a}$) puts the product of two differential operators in standard form we also evaluate $F\\circ_{2}{\\lp {D_{A}\\circ_{1}D_{B}} \\rp }$.\n",
"\n",
"We now must distinguish between the following cases. If $D$ is a differential operator and $F$ a multivector function should $D\\circ F$ and $F\\circ D$ return a differential operator or a multivector. In order to be consistent with the standard vector analysis we have $D\\circ F$ return a multivector and $F\\circ D$ return a differential operator. Then we define the complementary differential operator $\\bar{D}$ which is identical to $D$ except that $\\bar{D}\\circ F$ returns a differential operator according to eq ($\\ref{eq_71a}$)[8] and $F\\circ\\bar{D}$ returns a multivector according to eq ($\\ref{eq_68a}$).\n",
"\n",
"A general differential operator is built from repeated applications of the basic operator building blocks ${\\lp {\\bar{\\nabla}\\circ A} \\rp }$, ${\\lp {A\\circ\\bar{\\nabla}} \\rp }$, ${\\lp {\\bar{\\nabla}\\circ\\bar{\\nabla}} \\rp }$, and ${\\lp {A\\pm \\bar{\\nabla}} \\rp }$. Both $\\nabla$ and $\\bar{\\nabla}$ are represented by the operator\n",
"\n",
"$$\\be \n",
" \\nabla = \\bar{\\nabla} = e^{i}\\pdiff{}{x^{i}},\n",
" \\ee$$\n",
"\n",
"but are flagged to produce the appropriate result.\n",
"\n",
"In the our notation the directional derivative operator is $a\\cdot\\nabla$, the Laplacian $\\nabla\\cdot\\nabla$ and the expression for the Riemann tensor, $R^{i}_{jkl}$, is\n",
"\n",
"$$\\be \\paren{\\nabla\\W\\nabla}\\eb^{i} = \\half R^{i}_{jkl}\\paren{\\eb^{j}\\W\\eb^{k}}\\eb^{l}. \\ee$$\n",
"\n",
"We would use the complement if we wish a quantum mechanical type commutator defining\n",
"\n",
"$$\\be\n",
" \\com{x,\\nabla} \\equiv x\\nabla - \\bar{\\nabla}x,\n",
"\\ee$$\n",
"\n",
", or if we wish to simulate the dot notation (Doran and Lasenby)\n",
"\n",
"$$\\be\n",
" \\dot{F}\\dot{\\nabla} = F\\bar{\\nabla}.\n",
"\\ee$$\n",
"\n",
"### Split Differential Operator\n",
"\n",
"To implement the general “dot” notation for differential operators in python is not possible. Another type of symbolic notation is required. I propose what one could call the “split differential operator.” For $\\nabla$ denote the corresponding split operator by two operators ${{\\nabla}_{\\mathcal{G}}}$ and ${{\\nabla}_{\\mathcal{D}}}$ where in practice ${{\\nabla}_{\\mathcal{G}}}$ is a tuple of vectors and ${{\\nabla}_{\\mathcal{D}}}$ is a tuple of corresponding partial derivatives. Then the equivalent of the “dot” notation would be\n",
"\n",
"$$\\be \\dot{\\nabla}{\\lp {A\\dot{B}C} \\rp } = {{\\nabla}_{\\mathcal{G}}}{\\lp {A{\\lp {{{\\nabla}_{\\mathcal{D}}}B} \\rp }C} \\rp }.\\label{splitopV} \\ee$$\n",
"\n",
"We are using the $\\mathcal{G}$ subscript to indicate the geometric algebra parts of the multivector differential operator and the $\\mathcal{D}$ subscript to indicate the scalar differential operator parts of the multivector differential operator. An example of this notation in 3D Euclidean space is\n",
"\n",
"$$\\begin{aligned}\n",
" {{\\nabla}_{\\mathcal{G}}} &= {\\lp {{{\\eb}}_{x},{{\\eb}}_{y},{{\\eb}}_{z}} \\rp }, \\\\\n",
" {{\\nabla}_{\\mathcal{D}}} &= {\\lp {{{\\displaystyle\\frac{\\partial {}}{\\partial {x}}}},{{\\displaystyle\\frac{\\partial {}}{\\partial {y}}}},{{\\displaystyle\\frac{\\partial {}}{\\partial {x}}}}} \\rp },\\end{aligned}$$\n",
"\n",
"To implement ${{\\nabla}_{\\mathcal{G}}}$ and ${{\\nabla}_{\\mathcal{D}}}$ we have in the example\n",
"\n",
"$$\\begin{aligned}\n",
" {{\\nabla}_{\\mathcal{D}}}B &= {\\lp {{{\\displaystyle\\frac{\\partial {B}}{\\partial {x}}}},{{\\displaystyle\\frac{\\partial {B}}{\\partial {y}}}},{{\\displaystyle\\frac{\\partial {B}}{\\partial {z}}}}} \\rp } \\\\\n",
" {\\lp {{{\\nabla}_{\\mathcal{D}}}B} \\rp }C &= {\\lp {{{\\displaystyle\\frac{\\partial {B}}{\\partial {x}}}}C,{{\\displaystyle\\frac{\\partial {B}}{\\partial {y}}}}C,{{\\displaystyle\\frac{\\partial {B}}{\\partial {z}}}}C} \\rp } \\\\\n",
" A{\\lp {{{\\nabla}_{\\mathcal{D}}}B} \\rp }C &= {\\lp {A{{\\displaystyle\\frac{\\partial {B}}{\\partial {x}}}}C,A{{\\displaystyle\\frac{\\partial {B}}{\\partial {y}}}}C,A{{\\displaystyle\\frac{\\partial {B}}{\\partial {z}}}}C} \\rp }.\\end{aligned}$$\n",
"\n",
"Then the final evaluation is\n",
"\n",
"$$\\be {{\\nabla}_{\\mathcal{G}}}{\\lp {A{\\lp {{{\\nabla}_{\\mathcal{D}}}B} \\rp }C} \\rp } = {{\\eb}}_{x}A{{\\displaystyle\\frac{\\partial {B}}{\\partial {x}}}}C+{{\\eb}}_{y}A{{\\displaystyle\\frac{\\partial {B}}{\\partial {y}}}}C+{{\\eb}}_{z}A{{\\displaystyle\\frac{\\partial {B}}{\\partial {z}}}}C, \\ee$$\n",
"\n",
"which could be called the “dot” product of two tuples. Note that $\\nabla = {{\\nabla}_{\\mathcal{G}}}{{\\nabla}_{\\mathcal{D}}}$ and $\\dot{F}\\dot{\\nabla} = F\\bar{\\nabla} = {\\lp {{{\\nabla}_{\\mathcal{D}}}F} \\rp }{{\\nabla}_{\\mathcal{G}}}$.\n",
"\n",
"For the general multivector differential operator, $D$, the split operator parts are ${{D}_{\\mathcal{G}}}$, a tuple of basis blade multivectors and ${{D}_{\\mathcal{D}}}$, a tuple of scalar differential operators that correspond to the coefficients of the basis-blades in the total operator $D$ so that\n",
"\n",
"$$\\be \\dot{D}{\\lp {A\\dot{B}C} \\rp } = {{D}_{\\mathcal{G}}}{\\lp {A{\\lp {{{D}_{\\mathcal{D}}}B} \\rp }C} \\rp }. \\label{splitopM} \\ee$$\n",
"\n",
"If the index set for the basis blades of a geometric algebra is denoted by ${\\left \\{{n} \\rbrc}$ where ${\\left \\{{n} \\rbrc}$ contains $2^{n}$ indices for an $n$ dimensional geometric algebra then the most general multivector differential operator can be written[9]\n",
"\n",
"$$\\be D = {{\\displaystyle}\\sum_{l\\in{\\left \\{\n",
"{n} \\rbrc}}{{\\eb}}^{l}D_{{\\left \\{\n",
"{l} \\rbrc}}} \\ee$$\n",
"\n",
"$$\\be \\dot{D}{\\lp {A\\dot{B}C} \\rp } = {{D}_{\\mathcal{G}}}{\\lp {A{\\lp {{{D}_{\\mathcal{D}}}B} \\rp }C} \\rp } = {{\\displaystyle}\\sum_{l\\in{\\left \\{\n",
"{n} \\rbrc}}{{\\eb}}^{l}{\\lp {A{\\lp {D_{l}B} \\rp }C} \\rp }} \\ee$$\n",
"\n",
"or\n",
"\n",
"$$\\be {\\lp {A\\dot{B}C} \\rp }\\dot{D} = {\\lp {A{\\lp {{{D}_{\\mathcal{D}}}B} \\rp }C} \\rp }{{D}_{\\mathcal{G}}} = {{\\displaystyle}\\sum_{l\\in{\\left \\{\n",
"{n} \\rbrc}}{\\lp {A{\\lp {D_{l}B} \\rp }C} \\rp }{{\\eb}}^{l}}. \\ee$$\n",
"\n",
"The implementation of equations [splitopV] and [splitopM] is described in sections [makeMV] and [makeMVD].\n",
"\n",
"Linear Transformations/Outermorphisms\n",
"-------------------------------------\n",
"\n",
"In the tangent space of a manifold, $\\mathcal{M}$, (which is a vector space) a linear transformation is the mapping $\\underline{T}\\colon{{{\\mathcal{T}_{\\vec{x}}}\\lp {\\mathcal{M}} \\rp }}\\rightarrow{{{\\mathcal{T}_{\\vec{x}}}\\lp {\\mathcal{M}} \\rp }}$ (we use an underline to indicate a linear transformation) where for all $x,y\\in {{{\\mathcal{T}_{\\vec{x}}}\\lp {\\mathcal{M}} \\rp }}$ and $\\alpha\\in\\Re$ we have\n",
"\n",
"$$\\begin{aligned}\n",
" {{\\underline{T}}\\lp {x+y} \\rp } =& {{\\underline{T}}\\lp {x} \\rp } + {{\\underline{T}}\\lp {y} \\rp } \\\\\n",
" {{\\underline{T}}\\lp {\\alpha x} \\rp } =& \\alpha{{\\underline{T}}\\lp {x} \\rp }\\end{aligned}$$\n",
"\n",
"The outermorphism induced by $\\underline{T}$ is defined for $x_{1},\\dots,x_{r}\\in{{{\\mathcal{T}_{\\vec{x}}}\\lp {\\mathcal{M}} \\rp }}$ where $\\newcommand{\\f}[2]{{#1}\\lp {#2} \\rp } \\newcommand{\\Tn}[2]{\\f{\\mathcal{T}_{#2}}{#1}} r\\le\\f{\\dim}{\\Tn{\\mathcal{M}}{\\vec{x}}}$\n",
"\n",
"$$\\be \\newcommand{\\f}[2]{{#1}\\lp {#2} \\rp }\n",
"\\newcommand{\\W}{\\wedge}\n",
"\\f{\\underline{T}}{x_{1}\\W\\dots\\W x_{r}} \\equiv \\f{\\underline{T}}{x_{1}}\\W\\dots\\W\\f{\\underline{T}}{x_{r}} \\ee$$\n",
"\n",
"If $I$ is the pseudo scalar for ${{{\\mathcal{T}_{\\vec{x}}}\\lp {\\mathcal{M}} \\rp }}$ we also have the following definitions for determinate, trace, and adjoint ($\\overline{T}$) of $\\underline{T}$\n",
"\n",
"$$\\begin{align}\n",
" \\f{\\underline{T}}{I} \\equiv&\\; \\f{\\det}{\\underline{T}}I\\text{,} \\label{eq_82}\\\\\n",
" \\f{\\tr}{\\underline{T}} \\equiv&\\; \\nabla_{y}\\cdot\\f{\\underline{T}}{y}\\text{,} \\label{eq_83}\\\\ \n",
" x\\cdot \\f{\\overline{T}}{y} \\equiv&\\; y\\cdot \\f{\\underline{T}}{x}.\\ \\label{eq_84}\\\\\n",
"\\end{align}$$\n",
"\n",
"If ${\\left \\{{{{\\eb}}_{i}} \\rbrc}$ is a basis for ${{{\\mathcal{T}_{\\vec{x}}}\\lp {\\mathcal{M}} \\rp }}$ then we can represent $\\underline{T}$ with the matrix $\\underline{T}_{i}^{j}$ used as follows (Einstein summation convention as usual) - \n",
"\n",
"$$\\be \\f{\\underline{T}}{\\eb_{i}} = \\underline{T}_{i}^{j}\\eb_{j}, \\label{eq_85} \\ee$$\n",
"\n",
"The let ${\\lp {\\underline{T}^{-1}} \\rp }_{m}^{n}$ be the inverse matrix of $\\underline{T}_{i}^{j}$ so that ${\\lp {\\underline{T}^{-1}} \\rp }_{m}^{k}\\underline{T}_{k}^{j} = \\delta^{j}_{m}$ and\n",
"\n",
"$$\\be \\underline{T}^{-1}{\\lp {a^{i}{{\\eb}}_{i}} \\rp } = a^{i}{\\lp {\\underline{T}^{-1}} \\rp }_{i}^{j}{{\\eb}}_{j} \\label{eq_85a} \\ee$$\n",
"\n",
"and calculate\n",
"\n",
"$$\\begin{aligned}\n",
" \\underline{T}^{-1}{\\lp {\\underline{T}{\\lp {a} \\rp }} \\rp } &= \\underline{T}^{-1}{\\lp {\\underline{T}{\\lp {a^{i}{{\\eb}}_{i}} \\rp }} \\rp } \\nonumber \\\\\n",
" &= \\underline{T}^{-1}{\\lp {a^{i}\\underline{T}_{i}^{j}{{\\eb}}_{j}} \\rp } \\nonumber \\\\\n",
" &= a^{i}{\\lp {\\underline{T}^{-1}} \\rp }_{i}^{j} \\underline{T}_{j}^{k}{{\\eb}}_{k} \\nonumber \\\\\n",
" &= a^{i}\\delta_{i}^{j}{{\\eb}}_{j} = a^{i}{{\\eb}}_{i} = a.\\end{aligned}$$\n",
"\n",
"Thus if eq $\\ref{eq_85a}$ is used to define the $\\underline{T}_{i}^{j}$ then the linear transformation defined by the matrix ${\\lp {\\underline{T}^{-1}} \\rp }_{m}^{n}$ is the inverse of $\\underline{T}$.\n",
"\n",
"In eq. ($\\ref{eq_85}$) the matrix, $\\underline{T}_{i}^{j}$, only has it’s usual meaning if the ${\\left \\{{{{\\eb}}_{i}} \\rbrc}$ form an orthonormal Euclidean basis (Minkowski spaces not allowed). Equations ($\\ref{eq_82}$) through ($\\ref{eq_84}$) become\n",
"\n",
"$$\\begin{aligned}\n",
" {{\\det}\\lp {\\underline{T}} \\rp } =&\\; {{\\underline{T}}\\lp {{{\\eb}}_{1}{\\wedge}\\dots{\\wedge}{{\\eb}}_{n}} \\rp }{\\lp {{{\\eb}}_{1}{\\wedge}\\dots{\\wedge}{{\\eb}}_{n}} \\rp }^{-1},\\\\\n",
" {{{\\mbox{tr}}}\\lp {\\underline{T}} \\rp } =&\\; \\underline{T}_{i}^{i},\\\\\n",
" \\overline{T}_{j}^{i} =&\\; g^{il}g_{jp}\\underline{T}_{l}^{p}.\\end{aligned}$$\n",
"\n",
"A important form of linear transformation with a simple representation is the spinor transformation. If $S$ is an even multivector we have $SS^{{\\dagger}} = \\rho^{2}$, where $\\rho^{2}$ is a scalar. Then $S$ is a spinor transformation is given by ($v$ is a vector)\n",
"\n",
"$$\\be {{S}\\lp {v} \\rp } = SvS^{{\\dagger}} \\ee$$\n",
"\n",
"if ${{S}\\lp {v} \\rp }$ is a vector and\n",
"\n",
"$$\\be {{S^{-1}}\\lp {v} \\rp } = \\frac{S^{{\\dagger}}vS}{\\rho^{4}}. \\ee$$\n",
"\n",
"Thus\n",
"\n",
"$$\\begin{aligned}\n",
" {{S^{-1}}\\lp {{{S}\\lp {v} \\rp }} \\rp } &= \\frac{S^{{\\dagger}}SvS^{{\\dagger}}S}{\\rho^{4}} \\nonumber \\\\\n",
" &= \\frac{\\rho^{2}v\\rho^{2}}{\\rho^{4}} \\nonumber \\\\\n",
" &= v. \\end{aligned}$$\n",
"\n",
"One more topic to consider is whether or not $T^{i}_{j}$ should be called the matrix representation of $T$ ? The reason that this is a question is that for a general metric $g_{ij}$ is that because of the dependence of the dot product on the metric $T^{i}_{j}$ does not necessarily show the symmetries of the underlying transformation $T$. Consider the expression\n",
"\n",
"$$\\begin{aligned}\n",
" a\\cdot{{T}\\lp {b} \\rp } &= a^{i}{{\\eb}}_{i}\\cdot{{T}\\lp {b^{j}{{\\eb}}_{j}} \\rp } \\nonumber \\\\\n",
" &= a^{i}{{\\eb}}_{i}\\cdot {{T}\\lp {{{\\eb}}_{j}} \\rp }b^{j} \\nonumber \\\\\n",
" &= a^{i}{{\\eb}}_{i}\\cdot{{\\eb}}_{k} T_{j}^{k}b^{j} \\nonumber \\\\\n",
" &= a^{i}g_{ik}T_{j}^{k}b^{j}.\\end{aligned}$$\n",
"\n",
"It is\n",
"\n",
"$$\\be T_{ij} = g_{ik}T_{j}^{k} \\ee$$\n",
"\n",
"that has the proper symmetry for self adjoint transformations $(a\\cdot{{T}\\lp {b} \\rp } = b\\cdot{{T}\\lp {a} \\rp })$ in the sense that if $T = \\overline{T}$ then $T_{ij} = T_{ji}$. Of course if we are dealing with a manifold where the $g_{ij}$’s are functions of the coordinates then the matrix representation of a linear transformation will also be a function of the coordinates. Assuming we use $T_{ij}$ for the matrix representation of the linear transformation, $T$, then if we given the matrix representation, $T_{ij}$, we can construct the linear transformation given by $T^{i}_{j}$ as follows\n",
"\n",
"$$\\begin{aligned}\n",
" T_{ij} &= g_{ik}T_{j}^{k} \\nonumber \\\\\n",
" g^{li}T_{ij} &= g^{li}g_{ik}T_{j}^{k} \\nonumber \\\\\n",
" g^{li}T_{ij} &= \\delta_{k}^{l}T_{j}^{k} \\nonumber \\\\\n",
" g^{li}T_{ij} &= T_{j}^{l}.\\end{aligned}$$\n",
"\n",
"Any program/code that represents $T$ should allow one to define $T$ in terms of $T_{ij}$ or $T_{j}^{l}$ and likewise given a linear transformation $T$ obtain both $T_{ij}$ and $T_{j}^{l}$ from it. Please note that these considerations come into play for any non-Euclidean metric with respect to the trace and adjoint of a linear transformation since calculating either requires a dot product.\n",
"\n",
"Multilinear Functions\n",
"---------------------\n",
"\n",
"A multivector multilinear function[10] is a multivector function ${{T}\\lp {A_{1},\\dots,A_{r}} \\rp }$ that is linear in each of it arguments[11] (it could be implicitly non-linearly dependent on a set of additional arguments such as the position coordinates, but we only consider the linear arguments). $T$ is a *tensor* of degree $r$ if each variable $A_{j}$ is restricted to the vector space $\\mathcal{V}_{n}$. More generally if each $A_{j}\\in{{\\mathcal{G}}\\lp {\\mathcal{V}_{n}} \\rp }$ (the geometric algebra of $\\mathcal{V}_{n}$), we call $T$ an *extensor* of degree-$r$ on ${{\\mathcal{G}}\\lp {\\mathcal{V}_{n}} \\rp }$.\n",
"\n",
"If the values of ${{T} \\lp {a_{1},\\dots,a_{r}} \\rp }$ $\\lp a_{j}\\in\\mathcal{V}_{n}\\;\\forall\\; 1\\le j \\le r \\rp$ are $s$-vectors (pure grade $s$ multivectors in ${{\\mathcal{G}}\\lp {\\mathcal{V}_{n}} \\rp }$) we say that $T$ has grade $s$ and rank $r+s$. A tensor of grade zero is called a *multilinear form*.\n",
"\n",
"In the normal definition of tensors as multilinear functions the tensor is defined as a mapping $$T:{\\huge \\times}_{i=1}^{r}\\mathcal{V}_{i}\\rightarrow\\Re,$$so that the standard tensor definition is an example of a grade zero degree/rank$ r $ tensor in our definition.\n",
"\n",
"### Algebraic Operations\n",
"\n",
"The properties of tensors are ($\\alpha\\in\\Re$, $a_{j},b\\in\\mathcal{V}_{n}$, $T$ and $S$ are tensors of rank $r$, and $\\circ$ is any multivector multiplicative operation)\n",
"\n",
"$$\\begin{aligned}\n",
" {{T}\\lp {a_{1},\\dots,\\alpha a_{j},\\dots,a_{r}} \\rp } =& \\alpha{{T}\\lp {a_{1},\\dots,a_{j},\\dots,a_{r}} \\rp }, \\\\\n",
" {{T}\\lp {a_{1},\\dots,a_{j}+b,\\dots,a_{r}} \\rp } =& {{T}\\lp {a_{1},\\dots,a_{j},\\dots,a_{r}} \\rp }+ {{T}\\lp {a_{1},\\dots,a_{j-1},b,a_{j+1},\\dots,a_{r}} \\rp }, \\\\\n",
" {{\\lp T\\pm S\\rp }\\lp {a_{1},\\dots,a_{r}} \\rp } \\equiv& {{T}\\lp {a_{1},\\dots,a_{r}} \\rp }\\pm{{S}\\lp {a_{1},\\dots,a_{r}} \\rp }.\\end{aligned}$$\n",
"\n",
"Now let $T$ be of rank $r$ and $S$ of rank $s$ then the product of the two tensors is \n",
"\n",
"$$\\be \\f{\\lp T\\circ S\\rp}{a_{1},\\dots,a_{r+s}} \\equiv \\f{T}{a_{1},\\dots,a_{r}}\\circ\\f{S}{a_{r+1},\\dots,a_{r+s}}, \\ee$$\n",
"\n",
"where “$\\circ$” is any multivector multiplicative operation.\n",
"\n",
"### Covariant, Contravariant, and Mixed Representations\n",
"\n",
"The arguments (vectors) of the multilinear function can be represented in terms of the basis vectors or the reciprocal basis vectors\n",
"\n",
"$$\\begin{aligned}\n",
" a_{j} =& a^{i_{j}}{{\\eb}}_{i_{j}}, \\label{vrep}\\\\\n",
" =& a_{i_{j}}{{\\eb}}^{i_{j}}. \\label{rvrep}\\end{aligned}$$\n",
"\n",
"Equation ([vrep]) gives $a_{j}$ in terms of the basis vectors and eq ([rvrep]) in terms of the reciprocal basis vectors. The index $j$ refers to the argument slot and the indices $i_{j}$ the components of the vector in terms of the basis. The covariant representation of the tensor is defined by\n",
"\n",
"$\\newcommand{\\indices}[1]{#1}\\begin{aligned}\n",
" T\\indices{_{i_{1}\\dots i_{r}}} \\equiv& {{T}\\lp {{{\\eb}}_{i_{1}},\\dots,{{\\eb}}_{i_{r}}} \\rp } \\\\\n",
" {{T}\\lp {a_{1},\\dots,a_{r}} \\rp } =& {{T}\\lp {a^{i_{1}}{{\\eb}}_{i_{1}},\\dots,a^{i_{r}}{{\\eb}}_{i_{r}}} \\rp } \\nonumber \\\\\n",
" =& {{T}\\lp {{{\\eb}}_{i_{1}},\\dots,{{\\eb}}_{i_{r}}} \\rp }a^{i_{1}}\\dots a^{i_{r}} \\nonumber \\\\\n",
" =& T\\indices{_{i_{1}\\dots i_{r}}}a^{i_{1}}\\dots a^{i_{r}}.\\end{aligned}$$\n",
"\n",
"Likewise for the contravariant representation\n",
"\n",
"$$\\begin{aligned}\n",
"T\\indices{^{i_{1}\\dots i_{r}}} \\equiv& {{T}\\lp {{{\\eb}}^{i_{1}},\\dots,{{\\eb}}^{i_{r}}} \\rp } \\\\\n",
" {{T}\\lp {a_{1},\\dots,a_{r}} \\rp } =& {{T}\\lp {a_{i_{1}}{{\\eb}}^{i_{1}},\\dots,a_{i_{r}}{{\\eb}}^{i_{r}}} \\rp } \\nonumber \\\\\n",
" =& {{T}\\lp {{{\\eb}}^{i_{1}},\\dots,{{\\eb}}^{i_{r}}} \\rp }a_{i_{1}}\\dots a_{i_{r}} \\nonumber \\\\\n",
" =& T\\indices{^{i_{1}\\dots i_{r}}}a_{i_{1}}\\dots a_{i_{r}}.\\end{aligned}$$\n",
"\n",
"One could also have a mixed representation\n",
"\n",
"$$\\begin{aligned}\n",
"T\\indices{_{i_{1}\\dots i_{s}}^{i_{s+1}\\dots i_{r}}} \\equiv& {{T}\\lp {{{\\eb}}_{i_{1}},\\dots,{{\\eb}}_{i_{s}},{{\\eb}}^{i_{s+1}}\\dots{{\\eb}}^{i_{r}}} \\rp } \\\\\n",
" {{T}\\lp {a_{1},\\dots,a_{r}} \\rp } =& {{T}\\lp {a^{i_{1}}{{\\eb}}_{i_{1}},\\dots,a^{i_{s}}{{\\eb}}_{i_{s}},\n",
" a_{i_{s+1}}{{\\eb}}^{i_{s}}\\dots,a_{i_{r}}{{\\eb}}^{i_{r}}} \\rp } \\nonumber \\\\\n",
" =& {{T}\\lp {{{\\eb}}_{i_{1}},\\dots,{{\\eb}}_{i_{s}},{{\\eb}}^{i_{s+1}},\\dots,{{\\eb}}^{i_{r}}} \\rp }\n",
" a^{i_{1}}\\dots a^{i_{s}}a_{i_{s+1}},\\dots a^{i_{r}} \\nonumber \\\\\n",
" =& T\\indices{_{i_{1}\\dots i_{s}}^{i_{s+1}\\dots i_{r}}}a^{i_{1}}\\dots a^{i_{s}}a_{i_{s+1}}\\dots a^{i_{r}}.\\end{aligned}$$\n",
"\n",
"In the representation of $T$ one could have any combination of covariant (lower) and contravariant (upper) indexes.\n",
"\n",
"To convert a covariant index to a contravariant index simply consider\n",
"\n",
"$$\\begin{aligned}\n",
" \\f{T}{\\eb_{i_{1}},\\dots,\\eb^{i_{j}},\\dots,\\eb_{i_{r}}} =& \\f{T}{\\eb_{i_{1}},\\dots,g^{i_{j}k_{j}}\\eb_{k_{j}},\\dots,\\eb_{i_{r}}} \\nonumber \\\\\n",
" =& g^{i_{j}k_{j}}\\f{T}{\\eb_{i_{1}},\\dots,\\eb_{k_{j}},\\dots,\\eb_{i_{r}}} \\nonumber \\\\\n",
" T_{i_{1}\\dots}{}^{i_{j}}{}_{\\dots i_{r}} =& g^{i_{j}k_{j}}T\\indices{_{i_{1}\\dots i_{j}\\dots i_{r}}}.\n",
"\\end{aligned}$$\n",
"\n",
"Similarly one could lower an upper index with $g_{i_{j}k_{j}}$.\n",
"\n",
"### Contraction and Differentiation\n",
"\n",
"The contraction of a tensor between the $j^{th}$ and $k^{th}$ variables (slots) is \n",
"\n",
"$$\\be \\f{T}{a_{i},\\dots,a_{j-1},\\nabla_{a_{k}},a_{j+1},\\dots,a_{r}} = \\nabla_{a_{j}}\\cdot\\lp \\nabla_{a_{k}}\\f{T}{a_{1},\\dots,a_{r}}\\rp . \\ee$$\n",
"\n",
"This operation reduces the rank of the tensor by two. This definition gives the standard results for *metric contraction* which is proved as follows for a rank $r$ grade zero tensor (the circumflex “$\\breve{\\:\\:}$” indicates that a term is to be deleted from the product).\n",
"\n",
"$$\\begin{align}\n",
" \\f{T}{a_{1},\\dots,a_{r}} =& a^{i_{1}}\\dots a^{i_{r}}T_{i_{1}\\dots i_{r}} \\\\\n",
" \\nabla_{a_{j}}T =& \\eb^{l_{j}} a^{i_{1}}\\dots\\lp\\partial_{a^{l_j}}a^{i_{j}}\\rp\\dots a_{i_{r}}T_{i_{1}\\dots i_{r}} \\nonumber \\\\\n",
" =& \\eb^{l_{j}}\\delta_{l_{j}}^{i_{j}} a^{i_{1}}\\dots \\breve{a}^{i_{j}}\\dots a^{i_{r}}T_{i_{1}\\dots i_{r}} \\\\\n",
" \\nabla_{a_{m}}\\cdot\\lp\\nabla_{a_{j}}T\\rp =& \\eb^{k_{m}}\\cdot\\eb^{l_{j}}\\delta_{l_{j}}^{i_{j}}\n",
" a^{i_{1}}\\dots \\breve{a}^{i_{j}}\\dots\\lp\\partial_{a^{k_m}}a^{i_{m}}\\rp\n",
" \\dots a^{i_{r}}T_{i_{1}\\dots i_{r}} \\nonumber \\\\\n",
" =& g^{k_{m}l_{j}}\\delta_{l_{j}}^{i_{j}}\\delta_{k_{m}}^{i_{m}}\n",
" a^{i_{1}}\\dots \\breve{a}^{i_{j}}\\dots\\breve{a}^{i_{m}}\n",
" \\dots a^{i_{r}}T_{i_{1}\\dots i_{r}} \\nonumber \\\\\n",
" =& g^{i_{m}i_{j}}a^{i_{1}}\\dots \\breve{a}^{i_{j}}\\dots\\breve{a}^{i_{m}}\n",
" \\dots a^{i_{r}}T_{i_{1}\\dots i_{j}\\dots i_{m}\\dots i_{r}} \\nonumber \\\\\n",
" =& g^{i_{j}i_{m}}a^{i_{1}}\\dots \\breve{a}^{i_{j}}\\dots\\breve{a}^{i_{m}}\n",
" \\dots a^{i_{r}}T_{i_{1}\\dots i_{j}\\dots i_{m}\\dots i_{r}} \\nonumber \\\\\n",
" =& \\lp g^{i_{j}i_{m}}T_{i_{1}\\dots i_{j}\\dots i_{m}\\dots i_{r}}\\rp a^{i_{1}}\\dots\n",
" \\breve{a}^{i_{j}}\\dots\\breve{a}^{i_{m}}\\dots a^{i_{r}} \\label{eq108}\n",
"\\end{align}$$\n",
"\n",
"Equation ($\\ref{eq108}$) is the correct formula for the metric contraction of a tensor.\n",
"\n",
"If we have a mixed representation of a tensor, $T\\indices{_{i_{1}\\dots}{}^{i_{j}}{}_{\\dots i_{k}\\dots i_{r}}}$, and wish to contract between an upper and lower index ($i_{j}$ and $i_{k}$) first lower the upper index and then use eq ($\\ref{eq108}$) to contract the result. Remember lowering the index does *not* change the tensor, only the *representation* of the tensor, while contraction results in a *new* tensor. First lower index\n",
"\n",
"$$\\be T\\indices{_{i_{1}\\dots}{}^{i_{j}}{}_{\\dots i_{k}\\dots i_{r}}} \\xRightarrow{\\small Lower Index} g_{i_{j}k_{j}}T\\indices{_{i_{1}\\dots}{}^{k_{j}}{}_{\\dots i_{k}\\dots i_{r}}} \\ee$$\n",
"\n",
"Now contract between $i_{j}$ and $i_{k}$ and use the properties of the metric tensor.\n",
"\n",
"$$\\begin{aligned}\n",
" g_{i_{j}k_{j}}T\\indices{_{i_{1}\\dots}{}^{k_{j}}{}_{\\dots i_{k}\\dots i_{r}}} \\xRightarrow{\\small Contract}&\n",
" g^{i_{j}i_{k}}g_{i_{j}k_{j}}T\\indices{_{i_{1}\\dots}{}^{k_{j}}{}_{\\dots i_{k}\\dots i_{r}}} \\nonumber \\\\\n",
" =& \\delta_{k_{j}}^{i_{k}}T\\indices{_{i_{1}\\dots}{}^{k_{j}}{}_{\\dots i_{k}\\dots i_{r}}}. \\label{114a}\\end{aligned}$$\n",
"\n",
"Equation ([114a]) is the standard formula for contraction between upper and lower indexes of a mixed tensor.\n",
"\n",
"Finally if ${{T}\\lp {a_{1},\\dots,a_{r}} \\rp }$ is a tensor field (implicitly a function of position) the tensor derivative is defined as\n",
"\n",
"$$\\begin{aligned}\n",
" {{T}\\lp {a_{1},\\dots,a_{r};a_{r+1}} \\rp } \\equiv \\lp a_{r+1}\\cdot\\nabla\\rp {{T}\\lp {a_{1},\\dots,a_{r}} \\rp },\\end{aligned}$$\n",
"\n",
"assuming the $a^{i_{j}}$ coefficients are not a function of the coordinates.\n",
"\n",
"This gives for a grade zero rank $r$ tensor\n",
"\n",
"$$\\begin{aligned}\n",
" \\lp a_{r+1}\\cdot\\nabla\\rp {{T}\\lp {a_{1},\\dots,a_{r}} \\rp } =& a^{i_{r+1}}\\partial_{x^{i_{r+1}}}a^{i_{1}}\\dots a^{i_{r}}\n",
" T_{i_{1}\\dots i_{r}}, \\nonumber \\\\\n",
" =& a^{i_{1}}\\dots a^{i_{r}}a^{i_{r+1}}\n",
" \\partial_{x^{i_{r+1}}}T_{i_{1}\\dots i_{r}}.\\end{aligned}$$\n",
"\n",
"### From Vector to Tensor\n",
"\n",
"A rank one tensor is a vector since it satisfies all the axioms for a vector space, but a vector in not necessarily a tensor since not all vectors are multilinear (actually in the case of vectors a linear function) functions. However, there is a simple isomorphism between vectors and rank one tensors defined by the mapping ${{v}\\lp {a} \\rp }:\\mathcal{V}\\rightarrow\\Re$ such that if $v,a \\in\\mathcal{V}$ \n",
"\n",
"$$\\be \\f{v}{a} \\equiv v\\cdot a. \\ee$$\n",
"\n",
"So that if $v = v^{i}{{\\eb}}_{i} = v_{i}{{\\eb}}^{i}$ the covariant and contravariant representations of $v$ are (using ${{\\eb}}^{i}\\cdot{{\\eb}}_{j} = \\delta^{i}_{j}$)\n",
"\n",
"$$\\be \\f{v}{a} = v_{i}a^{i} = v^{i}a_{i}. \\ee$$\n",
"\n",
"### Parallel Transport and Covariant Derivatives\n",
"\n",
"The covariant derivative of a tensor field ${{T}\\lp {a_{1},\\dots,a_{r};x} \\rp }$ ($x$ is the coordinate vector of which $T$ can be a non-linear function) in the direction $a_{r+1}$ is (remember $a_{j} = a_{j}^{k}{{\\eb}}_{k}$ and the ${{\\eb}}_{k}$ can be functions of $x$) the directional derivative of ${{T}\\lp {a_{1},\\dots,a_{r};x} \\rp }$ where all the arguments of $T$ are parallel transported. The definition of parallel transport is if $a$ and $b$ are tangent vectors in the tangent spaced of the manifold then\n",
"\n",
"$$\\be \\paren{a\\cdot\\nabla_{x}}b = 0 \\label{eq108a} \\ee$$\n",
"\n",
"if $b$ is parallel transported. Since $b = b^{i}{{\\eb}}_{i}$ and the derivatives of ${{\\eb}}_{i}$ are functions of the $x^{i}$’s then the $b^{i}$’s are also functions of the $x^{i}$’s so that in order for eq ($\\ref{eq108a}$) to be satisfied we have\n",
"\n",
"$$\\begin{aligned}\n",
" {\\lp {a\\cdot\\nabla_{x}} \\rp }b =& a^{i}\\partial_{x^{i}}{\\lp {b^{j}{{\\eb}}_{j}} \\rp } \\nonumber \\\\\n",
" =& a^{i}{\\lp {{\\lp {\\partial_{x^{i}}b^{j}} \\rp }{{\\eb}}_{j} + b^{j}\\partial_{x^{i}}{{\\eb}}_{j}} \\rp } \\nonumber \\\\\n",
" =& a^{i}{\\lp {{\\lp {\\partial_{x^{i}}b^{j}} \\rp }{{\\eb}}_{j} + b^{j}\\Gamma_{ij}^{k}{{\\eb}}_{k}} \\rp } \\nonumber \\\\\n",
" =& a^{i}{\\lp {{\\lp {\\partial_{x^{i}}b^{j}} \\rp }{{\\eb}}_{j} + b^{k}\\Gamma_{ik}^{j}{{\\eb}}_{j}} \\rp }\\nonumber \\\\\n",
" =& a^{i}{\\lp {{\\lp {\\partial_{x^{i}}b^{j}} \\rp } + b^{k}\\Gamma_{ik}^{j}} \\rp }{{\\eb}}_{j} = 0.\\end{aligned}$$\n",
"\n",
"Thus for $b$ to be parallel transported we must have\n",
"\n",
"$$\\be \\partial_{x^{i}}b^{j} = -b^{k}\\Gamma_{ik}^{j}. \\label{eq121a} \\ee$$\n",
"\n",
"The geometric meaning of parallel transport is that for an infinitesimal rotation and dilation of the basis vectors (cause by infinitesimal changes in the $x^{i}$’s) the direction and magnitude of the vector $b$ does not change.\n",
"\n",
"If we apply eq ($\\ref{eq121a}$) along a parametric curve defined by ${{x^{j}}\\lp {s} \\rp }$ we have\n",
"\n",
"$$\\begin{align}\n",
" \\deriv{b^{j}}{s}{} =& \\deriv{x^{i}}{s}{}\\pdiff{b^{j}}{x^{i}} \\nonumber \\\\\n",
" =& -b^{k}\\deriv{x^{i}}{s}{}\\Gamma_{ik}^{j}, \\label{eq122a}\n",
"\\end{align}$$\n",
"\n",
"and if we define the initial conditions ${{b^{j}}\\lp {0} \\rp }{{\\eb}}_{j}$. Then eq ($\\ref{eq122a}$) is a system of first order linear differential equations with initial conditions and the solution, ${{b^{j}}\\lp {s} \\rp }{{\\eb}}_{j}$, is the parallel transport of the vector ${{b^{j}}\\lp {0} \\rp }{{\\eb}}_{j}$.\n",
"\n",
"An equivalent formulation for the parallel transport equation is to let ${{\\gamma}\\lp {s} \\rp }$ be a parametric curve in the manifold defined by the tuple ${{\\gamma}\\lp {s} \\rp } = {\\lp {{{x^{1}}\\lp {s} \\rp },\\dots,{{x^{n}}\\lp {s} \\rp }} \\rp }$. Then the tangent to ${{\\gamma}\\lp {s} \\rp }$ is given by\n",
"\n",
"$$\\be \\deriv{\\gamma}{s}{} \\equiv \\deriv{x^{i}}{s}{}\\eb_{i} \\ee$$\n",
"\n",
"and if ${{v}\\lp {x} \\rp }$ is a vector field on the manifold then\n",
"\n",
"$$\\begin{align}\n",
" \\paren{\\deriv{\\gamma}{s}{}\\cdot\\nabla_{x}}v =& \\deriv{x^{i}}{s}{}\\pdiff{}{x^{i}}\\paren{v^{j}\\eb_{j}} \\nonumber \\\\\n",
" =&\\deriv{x^{i}}{s}{}\\paren{\\pdiff{v^{j}}{x^{i}}\\eb_{j}+v^{j}\\pdiff{\\eb_{j}}{x^{i}}} \\nonumber \\\\\n",
" =&\\deriv{x^{i}}{s}{}\\paren{\\pdiff{v^{j}}{x^{i}}\\eb_{j}+v^{j}\\Gamma^{k}_{ij}\\eb_{k}} \\nonumber \\\\\n",
" =&\\deriv{x^{i}}{s}{}\\pdiff{v^{j}}{x^{i}}\\eb_{j}+\\deriv{x^{i}}{s}{}v^{k}\\Gamma^{j}_{ik}\\eb_{j} \\nonumber \\\\\n",
" =&\\paren{\\deriv{v^{j}}{s}{}+\\deriv{x^{i}}{s}{}v^{k}\\Gamma^{j}_{ik}}\\eb_{j} \\nonumber \\\\\n",
" =& 0. \\label{eq124a}\n",
"\\end{align}$$\n",
"\n",
"Thus eq ($\\ref{eq124a}$) is equivalent to eq ($\\ref{eq122a}$) and parallel transport of a vector field along a curve is equivalent to the directional derivative of the vector field in the direction of the tangent to the curve being zero.\n",
"\n",
"If the tensor component representation is contra-variant (superscripts instead of subscripts) we must use the covariant component representation of the vector arguments of the tensor, $a = a_{i}{{\\eb}}^{i}$. Then the definition of parallel transport gives\n",
"\n",
"$$\\begin{aligned}\n",
" {\\lp {a\\cdot\\nabla_{x}} \\rp }b =& a^{i}\\partial_{x^{i}}{\\lp {b_{j}{{\\eb}}^{j}} \\rp } \\nonumber \\\\\n",
" =& a^{i}{\\lp {{\\lp {\\partial_{x^{i}}b_{j}} \\rp }{{\\eb}}^{j} + b_{j}\\partial_{x^{i}}{{\\eb}}^{j}} \\rp },\\end{aligned}$$\n",
"\n",
"and we need\n",
"\n",
"$$\\be \\paren{\\partial_{x^{i}}b_{j}}\\eb^{j} + b_{j}\\partial_{x^{i}}\\eb^{j} = 0. \\label{eq111a} \\ee$$\n",
"\n",
"To satisfy equation ($\\ref{eq111a}$) consider the following\n",
"\n",
"$$\\begin{aligned}\n",
" \\partial_{x^{i}}{\\lp {{{\\eb}}^{j}\\cdot{{\\eb}}_{k}} \\rp } =& 0 \\nonumber \\\\\n",
" {\\lp {\\partial_{x^{i}}{{\\eb}}^{j}} \\rp }\\cdot{{\\eb}}_{k} + {{\\eb}}^{j}\\cdot{\\lp {\\partial_{x^{i}}{{\\eb}}_{k}} \\rp } =& 0 \\nonumber \\\\\n",
" {\\lp {\\partial_{x^{i}}{{\\eb}}^{j}} \\rp }\\cdot{{\\eb}}_{k} + {{\\eb}}^{j}\\cdot{{\\eb}}_{l}\\Gamma_{ik}^{l} =& 0 \\nonumber \\\\\n",
" {\\lp {\\partial_{x^{i}}{{\\eb}}^{j}} \\rp }\\cdot{{\\eb}}_{k} + \\delta_{l}^{j}\\Gamma_{ik}^{l} =& 0 \\nonumber \\\\\n",
" {\\lp {\\partial_{x^{i}}{{\\eb}}^{j}} \\rp }\\cdot{{\\eb}}_{k} + \\Gamma_{ik}^{j} =& 0 \\nonumber \\\\\n",
" {\\lp {\\partial_{x^{i}}{{\\eb}}^{j}} \\rp }\\cdot{{\\eb}}_{k} =& -\\Gamma_{ik}^{j}\\end{aligned}$$\n",
"\n",
"Now dot eq ($\\ref{eq111a}$) into ${{\\eb}}_{k}$ giving\n",
"\n",
"$$\\begin{aligned}\n",
" {\\lp {\\partial_{x^{i}}b_{j}} \\rp }{{\\eb}}^{j}\\cdot{{\\eb}}_{k} + b_{j}{\\lp {\\partial_{x^{i}}{{\\eb}}^{j}} \\rp }\\cdot{{\\eb}}_{k} =& 0 \\nonumber \\\\\n",
" {\\lp {\\partial_{x^{i}}b_{j}} \\rp }\\delta_{j}^{k} - b_{j}\\Gamma_{ik}^{j} =& 0 \\nonumber \\\\\n",
" {\\lp {\\partial_{x^{i}}b_{k}} \\rp } = b_{j}\\Gamma_{ik}^{j}.\\end{aligned}$$\n",
"\n",
"Thus if we have a mixed representation of a tensor\n",
"\n",
"$$\\be \\f{T}{a_{1},\\dots,a_{r};x} =\n",
" \\f{T\\indices{_{i_{1}\\dots i_{s}}^{i_{s+1}\\dots i_{r}}}}{x}a^{i_{1}}\\dots a^{i_{s}}a_{i_{s+1}}\\dots a_{i_{r}}, \\ee$$\n",
"\n",
"the covariant derivative of the tensor is\n",
"\n",
"$$\\begin{align}\n",
" {\\lp {a_{r+1}\\cdot D} \\rp } {{T}\\lp {a_{1},\\dots,a_{r};x} \\rp } =&\n",
" {{\\displaystyle\\frac{\\partial {T\\indices{_{i_{1}\\dots i_{s}}^{i_{s+1}\\dots i_{r}}}}}{\\partial {x^{r+1}}}}}a^{i_{1}}\\dots a^{i_{s}}a_{i_{s+1}}\\dots a^{r}_{i_{r}}\n",
" a^{i_{r+1}} \\nonumber \\\\\n",
" &\\hspace{-0.5in}+ \\sum_{p=1}^{s}{{\\displaystyle\\frac{\\partial {a^{i_{p}}}}{\\partial {x^{i_{r+1}}}}}}T\\indices{_{i_{1}\\dots i_{s}}^{i_{s+1}\\dots i_{r}}}a^{i_{1}}\\dots\n",
" \\breve{a}^{i_{p}}\\dots a^{i_{s}}a_{i_{s+1}}\\dots a_{i_{r}}a^{i_{r+1}} \\nonumber \\\\\n",
" &\\hspace{-0.5in}+ \\sum_{q=s+1}^{r}{{\\displaystyle\\frac{\\partial {a_{i_{p}}}}{\\partial {x^{i_{r+1}}}}}}T\\indices{_{i_{1}\\dots i_{s}}^{i_{s+1}\\dots i_{r}}}a^{i_{1}}\\dots\n",
" a^{i_{s}}a_{i_{s+1}}\\dots\\breve{a}_{i_{q}}\\dots a_{i_{r}}a^{i_{r+1}} \\nonumber \\\\\n",
" =& {{\\displaystyle\\frac{\\partial {T\\indices{_{i_{1}\\dots i_{s}}^{i_{s+1}\\dots i_{r}}}}}{\\partial {x^{r+1}}}}}a^{i_{1}}\\dots a^{i_{s}}a_{i_{s+1}}\\dots a^{r}_{i_{r}}\n",
" a^{i_{r+1}} \\nonumber \\\\\n",
" &\\hspace{-0.5in}- \\sum_{p=1}^{s}\\Gamma_{i_{r+1}l_{p}}^{i_{p}}T\\indices{_{i_{1}\\dots i_{p}\\dots i_{s}}^{i_{s+1}\n",
" \\dots i_{r}}}a^{i_{1}}\\dots\n",
" a^{l_{p}}\\dots a^{i_{s}}a_{i_{s+1}}\\dots a_{i_{r}}a^{i_{r+1}} \\nonumber \\\\\n",
" &\\hspace{-0.5in}+ \\sum_{q=s+1}^{r}\\Gamma_{i_{r+1}i_{q}}^{l_{q}}T\\indices{_{i_{1}\\dots i_{s}}^{i_{s+1}\\dots i_{q}\n",
" \\dots i_{r}}}a^{i_{1}}\\dots\n",
" a^{i_{s}}a_{i_{s+1}}\\dots a_{l_{q}}\\dots a_{i_{r}}a^{i_{r+1}} . \\label{eq126a} \\\\\n",
"\\end{align}$$\n",
"\n",
"From eq ($\\ref{eq126a}$) we obtain the components of the covariant derivative to be\n",
"\n",
"$$\\begin{aligned}\n",
" {{\\displaystyle\\frac{\\partial {T\\indices{_{i_{1}\\dots i_{s}}^{i_{s+1}\\dots i_{r}}}}}{\\partial {x^{r+1}}}}}\n",
" - \\sum_{p=1}^{s}\\Gamma_{i_{r+1}l_{p}}^{i_{p}}T\\indices{_{i_{1}\\dots i_{p}\\dots i_{s}}^{i_{s+1}\\dots i_{r}}}\n",
" + \\sum_{q=s+1}^{r}\\Gamma_{i_{r+1}i_{q}}^{l_{q}}T\\indices{_{i_{1}\\dots i_{s}}^{i_{s+1}\\dots i_{q}\\dots i_{r}}}.\\end{aligned}$$\n",
"\n",
"The component free form of the covariant derivative (the one used to calculate it in the code) is\n",
"\n",
"$$\\be \\mathcal{D}_{a_{r+1}} {{T}\\lp {a_{1},\\dots,a_{r};x} \\rp } \\equiv \\nabla T\n",
" - \\sum_{k=1}^{r}{{T}\\lp {a_{1},\\dots,{\\lp {a_{r+1}\\cdot\\nabla} \\rp } a_{k},\\dots,a_{r};x} \\rp }. \\ee$$\n",
"\n",
"Representation of Multivectors in *sympy*\n",
"-----------------------------------------\n",
"\n",
"The *sympy* python module offers a simple way of representing multivectors using linear combinations of commutative expressions (expressions consisting only of commuting *sympy* objects) and non-commutative symbols. We start by defining $n$ non-commutative *sympy* symbols as a basis for the vector space\n",
"\n",
"`(e_1,...,e_n) = symbols(’e_1,...,e_n’,commutative=False,real=True)`\n",
"\n",
"Several software packages for numerical geometric algebra calculations are available from Doran-Lasenby group and the Dorst group. Symbolic packages for Clifford algebra using orthogonal bases such as ${{\\eb}}_{i}{{\\eb}}_{j}+{{\\eb}}_{j}{{\\eb}}_{i} = 2\\eta_{ij}$, where $\\eta_{ij}$ is a numeric array are available in Maple and Mathematica. The symbolic algebra module, *ga*, developed for python does not depend on an orthogonal basis representation, but rather is generated from a set of $n$ arbitrary symbolic vectors ${{\\eb}}_{1},{{\\eb}}_{2},\\dots,{{\\eb}}_{n}$ and a symbolic metric tensor $g_{ij} = {{\\eb}}_{i}\\cdot {{\\eb}}_{j}$ (the symbolic metric can be symbolic constants or symbolic function in the case of a manifold).\n",
"\n",
"In order not to reinvent the wheel all scalar symbolic algebra is handled by the python module *sympy* and the abstract basis vectors are encoded as non-commuting *sympy* symbols.\n",
"\n",
"The basic geometric algebra operations will be implemented in python by defining a geometric algebra class, *Ga*, that performs all required geometric algebra an calculus operations on *sympy* expressions of the form (Einstein summation convention) \n",
"\n",
"$$\\be F +\\sum_{r=1}^{n}F^{i_{1}\\dots i_{r}}\\eb_{i_{1}}\\dots\\eb_{i_{r}} \\ee$$\n",
"\n",
"where the $F$’s are *sympy* symbolic constants or functions of the coordinates and a multivector class, *Mv*, that wraps *Ga* and overloads the python operators to provide all the needed multivector operations as shown in Table [ops] where $A$ and $B$ are any two multivectors (In the case of $+$, $-$, $*$, ${\\wedge}$, $|$, $<$, and $>$ the operation is also defined if $A$ or $B$ is a *sympy* symbol or a *sympy* real number).\n",
"\n",
"| | |\n",
"|:-:|:-:|\n",
"|$A+B$|sum of multivectors|\n",
"|$A-B$|difference of multivectors|\n",
"|$A*B$|geometric product of multivectors|\n",
"|$A{\\wedge}B$|outer product of multivectors|\n",
"|$A|B$|inner product of multivectors|\n",
"|$A<B$|left contraction of multivectors|\n",
"|$A>B$|right contraction of multivectors|\n",
"|$A/B$|division of multivectors|\n",
"\n",
"### Multivector operations for GA\n",
"\n",
"Since `<` and `>` have no r-forms (in python for the `<` and `>` operators there are no `__rlt__()` and `__rgt__()` member functions to overload) we can only have mixed modes (sympy scalars and multivectors) if the first operand is a multivector.\n",
"\n",
"Except for `<` and `>` all the multivector operators have r-forms so that as long as one of the operands, left or right, is a multivector the other can be a multivector or a scalar (*sympy* symbol or number).\n",
"\n",
"### Operator Precedence\n",
"\n",
"**Note that** the operator order precedence is determined by python and is not necessarily that used by geometric algebra. It is *absolutely essential* to use parenthesis in multivector expressions containing `^`, `|`, `<`, and/or `>`. As an example let `A` and `B` be any two multivectors. Then `A + A*B = A +(A*B)`, but `A+A^B = (2*A)^B` since in python the `^` operator has a lower precedence than the `+` operator. In geometric algebra the outer and inner products and the left and right contractions have a higher precedence than the geometric product and the geometric product has a higher precedence than addition and subtraction. In python the `^`, `|`, `>`, and `<` all have a lower precedence than `+` and `-` while `*` has a higher precedence than `+` and `-`.\n",
"\n",
"**Additional care has to be used** when using the operators `!=` and `==` with the operators `<` and `>`. All these operators have the same precedence and are evaluated chained from left to right. To be completely safe for expressions such as `A == B` or `A != B` always user `(A) == (B)` and `(A) != (B)` if `A` or `B` contains a left, `<`, or right, `>`, contraction.\n",
"\n",
"For those users who wish to define a default operator precedence the functions `def_prec()` and `GAeval()` are available in the module printer.\n",
"\n",
"`def_prec(gd,op_ord='<>|,^,*')`\n",
"\n",
"> Define the precedence of the multivector operations. The function `def_prec()` must be called from the main program and the first argument `gd` must be set to `globals()`. The second argument `op_ord` determines the operator precedence for expressions input to the function `GAeval()`. The default value of `op_ord` is `'<>|,^,*'`. For the default value the `<`, `>`, and `|` operations have equal precedence followed by `^`, and `^` is followed by `*`.\n",
"\n",
"`GAeval(s,pstr=False)`\n",
"\n",
"> The function `GAeval()` returns a multivector expression defined by the string `s` where the operations in the string are parsed according to the precedences defined by `def_prec()`. `pstr` is a flag to print the input and output of `GAeval()` for debugging purposes. `GAeval()` works by adding parenthesis to the input string `s` with the precedence defined by `op_ord=’<>|,,*’`. Then the parsed string is converted to a *sympy* expression using the python `eval()` function. For example consider where `X`, `Y`, `Z`, and `W` are multivectors\n",
">\n",
"> ```python\n",
"> def_prec(globals())\n",
"> V = GAeval('X|Y^Z*W')\n",
"> ```\n",
">\n",
"> The *sympy* variable `V` would evaluate to `((X|Y)^Z)*W`.\n",
"\n",
"Vector Basis and Metric\n",
"-----------------------\n",
"\n",
"The two structures that define the `metric` class (inherited by the geometric algebra class) are the symbolic basis vectors and the symbolic metric. The symbolic basis vectors are input as a string with the symbol name separated by spaces. For example if we are calculating the geometric algebra of a system with three vectors that we wish to denote as `a0`, `a1`, and `a2` we would define the string variable:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"basis = 'a0 a1 a2'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"that would be input into the geometric algebra class instantiation function, `Ga()`. The next step would be to define the symbolic metric for the geometric algebra of the basis we have defined. The default metric is the most general and is the matrix of the following symbols\n",
"\n",
"$$\\begin{equation}\\label{metric}\n",
" g = \\lbrk\n",
" \\begin{array}{ccc}\n",
" (a0.a0) & (a0.a1) & (a0.a2) \\\\\n",
" (a0.a1) & (a1.a1) & (a1.a2) \\\\\n",
" (a0.a2) & (a1.a2) & (a2.a2) \\\\\n",
" \\end{array}\n",
" \\rbrk\n",
" \\end{equation}$$\n",
"\n",
"where each of the $g_{ij}$ is a symbol representing all of the dot products of the basis vectors. Note that the symbols are named so that $g_{ij} = g_{ji}$ since for the symbol function $(a0.a1) \\ne (a1.a0)$.\n",
"\n",
"Note that the strings shown in the above equation are only used when the values of $g_{ij}$ are output (printed). In the ga module (library) the $g_{ij}$ symbols are stored in a member of the geometric algebra instance so that if `o3d` is a geometric algebra then `o3d.g` is the metric tensor ( $g_{ij} =$ `o3d.g[i,j]`) for that algebra.\n",
"\n",
"The default definition of $g$ can be overwritten by specifying a string that will define $g$. As an example consider a symbolic representation for conformal geometry. Define for a basis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"basis = 'a0 a1 a2 n nbar'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and for a metric"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = '# # # 0 0, # # # 0 0, # # # 0 0, 0 0 0 0 2, 0 0 0 2 0'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"then calling `cf3d = Ga(basis,g=g)` would initialize the metric tensor\n",
"\n",
"$$\\be g = \\lbrk\\begin{array}{ccccc}\n",
" (a0.a0) & (a0.a1) & (a0.a2) & 0 & 0\\\\\n",
" (a0.a1) & (a1.a1) & (a1.a2) & 0 & 0\\\\\n",
" (a0.a2) & (a1.a2) & (a2.a2) & 0 & 0 \\\\\n",
" 0 & 0 & 0 & 0 & 2 \\\\\n",
" 0 & 0 & 0 & 2 & 0\n",
" \\end{array}\n",
" \\rbrk \\ee$$\n",
"\n",
"for the `cf3d` (conformal 3-d) geometric algebra.\n",
"\n",
"Here we have specified that `n` and `nbar` are orthogonal to all the `a`’s, `(n.n) = (nbar.nbar) = 0`, and `(n.nbar) = 2`. Using `#` in the metric definition string just tells the program to use the default symbol for that value.\n",
"\n",
"When `Ga` is called multivector representations of the basis local to the program are instantiated. For the case of an orthogonal 3-d vector space that means the symbolic vectors named `a0`, `a1`, and `a2` are created. We can instantiate the geometric algebra and obtain the basis vectors with -"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"o3d = Ga('a_1 a_2 a_3',g=[1,1,1])\n",
"(a_1,a_2,a_3) = o3d.mv()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"or use the `Ga.build()` function -"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(o3d,a_1,a_2,a_3) = Ga.build('a_1 a_2 a_3',g=[1,1,1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the python variable name for a basis vector does not have to correspond to the name give in `Ga()` or `Ga.build()`, one may wish to use a shortened python variable name to reduce programming (typing) errors, for example one could use -"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(o3d,a1,a2,a3) = Ga.build('a_1 a_2 a_3',g=[1,1,1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"or"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(st4d,g0,g1,g2,g3) = Ga.build('gamma_0 gamma_1 gamma_2 gamma_3',\\\n",
" g=[1,-1,-1,-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"for Minkowski space time.\n",
"\n",
"If the latex printer is used `e1` would print as ${\\boldsymbol{e_{1}}}$ and `g1` as ${\\boldsymbol{\\gamma_{1}}}$.\n",
"\n",
"Representation and Reduction of Multivector Bases\n",
"-------------------------------------------------\n",
"\n",
"In our symbolic geometric algebra all multivectors can be obtained from the symbolic basis vectors we have input, via the different operations available to geometric algebra. The first problem we have is representing the general multivector in terms terms of the basis vectors. To do this we form the ordered geometric products of the basis vectors and develop an internal representation of these products in terms of python classes. The ordered geometric products are all multivectors of the form $a_{i_{1}}a_{i_{2}}\\dots a_{i_{r}}$ where $i_{1}<i_{2}<\\dots <i_{r}$ and $r \\le n$. We call these multivectors bases and represent them internally with non-commutative symbols so for example $a_{1}a_{2}a_{3}$ is represented by"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Symbol('a_1*a_2*a_3',commutative=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the simplest case of two basis vectors `a_1` and `a_2` we have a list of bases"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"self.bases = [[Symbol('a_1',commutative=False,real=True),\\\n",
" Symbol('a_2',commutative=False,real=True)],\\\n",
" [Symbol('a_1*a_2',commutative=False,real=True)]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the case of the basis blades we have"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"self.blades = [[Symbol('a_1',commutative=False,real=True),\\\n",
" Symbol('a_2',commutative=False,real=True)],\\\n",
" [Symbol('a_1^a_2',commutative=False,real=True)]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The index tuples for the bases of each pseudo grade and each grade for the case of dimension 3 is"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"self.indexes = (((0,),(1,),(2,)),((0,1),(0,2),(1,2)),((0,1,2)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then the non-commutative symbol representing each base is constructed from each index tuple. For example for `self.indexes[1][1]` the symbol is `Symbol('a_1*a_3',commutative=False)`.\n",
"\n",
"Base Representation of Multivectors\n",
"-----------------------------------\n",
"\n",
"In terms of the bases defined as non-commutative *sympy* symbols the general multivector is a linear combination (scalar *sympy* coefficients) of bases so that for the case of two bases the most general multivector is given by -"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"A = A_0+A__1*self.bases[1][0]+A__2*self.bases[1][1]+\\\n",
" A__12*self.bases[2][0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we have another multivector `B` to multiply with `A` we can calculate the product in terms of a linear combination of bases if we have a multiplication table for the bases.\n",
"\n",
"Blade Representation of Multivectors\n",
"------------------------------------\n",
"\n",
"Since we can now calculate the symbolic geometric product of any two multivectors we can also calculate the blades corresponding to the product of the symbolic basis vectors using the formula\n",
"\n",
"$$\\be A_{r}{\\wedge}b = {\\frac{1}{2}}\\lp A_{r}b+\\lp -1 \\rp ^{r}bA_{r} \\rp , \\ee$$\n",
"\n",
"where $A_{r}$ is a multivector of grade $r$ and $b$ is a vector. For our example basis the result is shown in Table [bladexpand].\n",
"\n",
"[h]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"1 = 1\n",
"a0 = a0\n",
"a1 = a1\n",
"a2 = a2\n",
"a0^a1 = {-(a0.a1)}1+a0a1\n",
"a0^a2 = {-(a0.a2)}1+a0a2\n",
"a1^a2 = {-(a1.a2)}1+a1a2\n",
"a0^a1^a2 = {-(a1.a2)}a0+{(a0.a2)}a1+{-(a0.a1)}a2+a0a1a2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[bladexpand]\n",
"\n",
"The important thing to notice about Table [bladexpand] is that it is a triagonal (lower triangular) system of equations so that using a simple back substitution algorithm we can solve for the pseudo bases in terms of the blades giving Table [baseexpand]."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"1 = 1\n",
"a0 = a0\n",
"a1 = a1\n",
"a2 = a2\n",
"a0a1 = {(a0.a1)}1+a0^a1\n",
"a0a2 = {(a0.a2)}1+a0^a2\n",
"a1a2 = {(a1.a2)}1+a1^a2\n",
"a0a1a2 = {(a1.a2)}a0+{-(a0.a2)}a1+{(a0.a1)}a2+a0^a1^a2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[baseexpand]\n",
"\n",
"Using Table [baseexpand] and simple substitution we can convert from a base multivector representation to a blade representation. Likewise, using Table [bladexpand] we can convert from blades to bases.\n",
"\n",
"Using the blade representation it becomes simple to program functions that will calculate the grade projection, reverse, even, and odd multivector functions.\n",
"\n",
"Note that in the multivector class `Mv` there is a class variable for each instantiation, `self.is_blade_rep`, that is set to `False` for a base representation and `True` for a blade representation. One needs to keep track of which representation is in use since various multivector operations require conversion from one representation to the other.\n",
"\n",
"Module Components\n",
"=================\n",
"\n",
"The geometric algebra module consists of the following files and classes\n",
"\n",
"| File | Classes | Usage |\n",
"|:----:|:-------:|:------|\n",
"| `metric.py` | `Metric` | Instantiates metric tensor and derivatives of basis vectors. Normalized basis if required. |\n",
"|`ga.py` | `Ga` | Instantiates geometric algebra (inherits $\\T{Metric}$), generates bases, blades, multiplication tables, reciprocal basis, and left and right geometric derivative operators. |\n",
"| | `Sm` | Instantiates geometric algebra for submainfold (inherits $\\T{Ga}$). |\n",
"|`mv.py` | `Mv` | Instantiates multivector. |\n",
"| | `Dop` | Instantiates linear multivector differential operator. |\n",
"| `lt.py` | `Lt` | Instantiates multivector linear transformation. |\n",
"|`printer.py` | `Eprint` | Starts enhanced text printing on ANSI terminal (requires $\\T{ConEmu}$ on Windows). |\n",
"| | `GaPrinter` | Text printer for all geometric algebra classes (inherits from $\\T{sympy}$ $\\T{StringPrinter}$). |\n",
"| | `GaLatexPrinter` | $\\LaTeX$printer for all geometric algebra classes (inherits from$\\T{sympy}$ $\\T{LatexPrinter}$). |\n",
"\n",
"Instantiating a Geometric Algebra\n",
"---------------------------------\n",
"\n",
"The geometric algebra class is instantiated with\n",
"\n",
"`Ga(basis,g=None,coords=None,X=None,norm=False,sig=’e’,Isq=’-’,wedge=True,debug=False)`\n",
"\n",
"> The `basis` and `g` parameters were described in section [BasisMetric]. If the metric is a function of position, if we have multivector fields, or we wish to calculate geometric derivatives a coordinate set, `coords`, is required. `coords` is a list of *sympy* symbols. For the case of instantiating a 3-d geometric algebra in spherical coordinates we have\n",
">\n",
"> ```python\n",
"> (r, th, phi) = coords = symbols('r,theta,phi', real=True)\n",
"> basis = 'e_r e_theta e_phi'\n",
"> g = [1, r**2, r**2*sin(th)**2]\n",
"> sp3d = Ga(basis,g=g,coords=coords,norm=True)\n",
"> ```\n",
">\n",
"> The input `X` allows the metric to be input as a vector manifold. `X` is a list of functions of `coords` of dimension, $m$, equal to or greater than the number of coordinates. If `g=None` it is assumed that `X` is a vector in an $m$-dimensional orthonormal Euclidean vector space. If it is wished the embedding vector space to be non-Euclidean that condition is specified with `g`. For example if we wish the embedding space to be a 5-dimensional Minkowski space then `g=[-1,1,1,1,1]`. Then the Ga class uses `X` to calculate the manifold basis vectors as a function of the coordinates and from them the metric tensor.[12]\n",
">\n",
"> If `norm=True` the basis vectors of the manifold are normalized so that the absolute values of the squares of the basis vectors are one. *Currently you should only use this option for diagonal metric tensors, and even there due so with caution, due to the possible problems with taking the square root of a general *sympy* expression (one that has an unknown sign).*\n",
">\n",
"> **When a geometric algebra is created the unnormalized metric tensor is always saved so that submanifolds created from the normalized manifold can be calculated correctly.**\n",
">\n",
"> `sig` indicates the signature of the vector space in the following ways.[13]\n",
">\n",
"> 1. If the metric tensor is purely numerical (the components are not symbolic or functions of the coordinates) and is diagonal (orthogonal basis vectors) the signature is computed from the metric tensor.\n",
">\n",
"> 2. If the metric tensor is not purely numerical and orthogonal the following hints are used (dimension of vector space is $n$)\n",
">\n",
"> 1. `sig=’e’` the default hint assumes the signature is for a Euclidean space with signature $(n,0)$.\n",
">\n",
"> 2. `sig=’m+’` assumes the signature if for the Minkowski space $(n-1,1)$.\n",
">\n",
"> 3. `sig=’m-’` assumes the signature if for the Minkowski space $(1,n-1)$.\n",
">\n",
"> 4. `sig=p` where `p` is an integer $p\\le n$ and the signature it $(p,n-p)$.\n",
">\n",
"> If the metric tensor contains no symbolic constants, but is a function of the coordinates, it is possible to determine the signature of the metric numerically by specifying a allowed numerical coordinate tuple due to the invariance of the signature. This will be implemented in the future.\n",
">\n",
"> Currently one need not be concerned about inputting `sig` unless one in using the *Ga* member function `Ga.I()` or the functions `Mv.dual()` or `cross()` which also use `Ga.I()`.\n",
">\n",
"> If $I^{2}$ is numeric it is calculated if it is not numeric then `Isq=’-’` is the sign of the square of the pseudo-scalar. This is needed for some operations. The default is chosen for the case of a general 3D Euclidean metric.\n",
">\n",
"> If `wedge=True` the basis blades of a multivector are printed using the `^` symbol between basis vectors. If `wedge=False` the subscripts of each individual basis vector (assuming that the basis vector symbols are of the form root symbol with a subscript[14]). For example in three dimensions if the basis vectors are ${{\\eb}}_{x}$, ${{\\eb}}_{y}$, and ${{\\eb}}_{z}$ the grade 3 basis blade would be printed as ${{\\eb}}_{xyz}$.\n",
">\n",
"> If `debug=True` the data structures required to initialize the Ga class are printed out.\n",
">\n",
"> To get the basis vectors for `sp3d` we would have to use the member function `Ga.mv()` in the form\n",
">\n",
"> ```python\n",
"> (er,eth,ephi) = sp3d.mv() \n",
"> ```\n",
"\n",
"To access the reciprocal basis vectors of the geometric algebra use the member function `mvr()`\n",
"\n",
"`Ga.mvr(norm=’True’)`\n",
"\n",
"> `Ga.mvr(norm)` returns the reciprocal basis vectors as a tuple. This allows the programmer to attach any python variable names to the reciprocal basis vectors that is convenient. For example (demonstrating the use of both `mv()` and `mvr()`)\n",
">\n",
"> ```python\n",
"> (e_x,e_y,e_z) = o3d.mv()\n",
"> (e__x,e__y,e__z) = o3d.mvr()\n",
"> ```\n",
"> \n",
"> If `norm=’True’` or the basis vectors are orthogonal the dot product of the basis vector and the corresponding reciprocal basis vector is one ${\\lp {e_{i}\\cdot e^{j}=\\delta_{i}^{j}} \\rp }$. If `norm=’False’` and the basis is non-orthogonal The dot product of the basis vector and the corresponding reciprocal basis vector is the square of the pseudo scalar, $I^{2}$, of the geometric algebra ${\\lp {e_{i}\\cdot e^{j}=E^{2}\\delta_{i}^{j}} \\rp }$.\n",
"\n",
"In addition to the basis vectors, if coordinates are defined for the geometric algebra, the left and right geometric derivative operators are calculated and accessed with the `Ga` member function `grads()`.\n",
"\n",
"`Ga.grads()`\n",
"\n",
"> `Ga.grads()` returns a tuple with the left and right geometric derivative operators. A typical usage would be\n",
">\n",
"> ```python\n",
"> (grad,rgrad) = sp3d.grads()\n",
"> ```\n",
">\n",
"> for the spherical 3-d geometric algebra. The left derivative ${\\lp {{\\texttt{grad}} ={\\boldsymbol{\\nabla}}} \\rp }$ and the right derivative ${\\lp {{\\texttt{rgrad}} = {\\boldsymbol{\\bar{\\nabla}}}} \\rp }$ have been explained in section [ldops]. Again the names `grad` and `rgrad` used in a program are whatever the user chooses them to be. In the previous example `grad` and `rgrad` are used.\n",
"\n",
"an alternative instantiation method is\n",
"\n",
"`Ga.build(basis, g=None, coords=None, X=None, norm=False, debug=False)`\n",
"\n",
"> The input parameters for `Ga.build()` are the same as for `Ga()`. The difference is that in addition to returning the geometric algebra `Ga.build()` returns the basis vectors at the same time. Using `Ga.build()` in the previous example gives\n",
"\n",
"> ```python\n",
"> (r, th, phi) = coords = symbols('r,theta,phi', real=True)\n",
"> basis = 'e_r e_theta e_phi'\n",
"> g = [1, r**2, r**2*sin(th)**2]\n",
"> (sp3d,er,eth,ephi) = Ga.build(basis,g=g,coord=coords,norm=True)\n",
"> ```\n",
"> \n",
"\n",
"To access the pseudo scalar of the geometric algebra use the member function `I()`.\n",
"\n",
"`Ga.I()`\n",
"\n",
"> `Ga.I()` returns the normalized pseudo scalar ${\\lp {{\\left |{I^{2}}\\right |}=1} \\rp }$ for the geometric algebra. For example $I = \\mbox{{\\texttt{o3d.I()}}}$ for the `o3d` geometric algebra. This function requires the signature of the vector space (see instantiating a geometric algebra).\n",
"\n",
"`Ga.E()`\n",
"\n",
"> `Ga.E()` returns the unnormalized pseudo scalar $E_{n} = {\\eb}_{1}{\\wedge}\\dots{\\wedge}{\\eb}_{n}$ for the geometric algebra.\n",
"\n",
"In general we have defined member functions of the `Ga` class that will instantiate objects of other classes since the objects of the other classes are all associated with a particular geometric algebra object. Thus we have\n",
"\n",
"|Object|Class|`Ga` method|\n",
"|:-:|:-:|:-:|\n",
"|multivector|`Mv`|`mv`|\n",
"|submanifold|`Sm`|`sm`|\n",
"|linear transformation|`Lt`|`lt`|\n",
"|differential operator|`Dop`|`dop`|\n",
"\n",
"for the instantiation of various objects from the `Ga` class. This means that in order to instantiate any of these objects we need only to import `Ga` into our program.\n",
"\n",
"Instantiating a Multivector\n",
"---------------------------\n",
"\n",
"Since we need to associate each multivector with the geometric algebra that contains it we use a member function of Ga to instantiate every multivector[15] The multivector is instantiated with:\n",
"\n",
"`Ga.mv(name, mode, f=False)`\n",
"\n",
"> As an example of both instantiating a geometric algebra and multivectors consider the following code fragment for a 3-d Euclidean geometric algebra.\n",
">\n",
"> ```python\n",
"> from sympy import symbols\n",
"> from ga import Ga\n",
"> (x, y, z) = coords = symbols('x,y,z',real=True)\n",
"> o3d = Ga('e_x e_y e_z', g=[1,1,1], coords=coords)\n",
"> (ex, ey, ez) = o3d.mv()\n",
"> V = o3d.mv('V','vector',f=True)\n",
"> f = o3d.mv(x*y*z)\n",
"> B = o3d.mv('B',2)\n",
"> ```\n",
">\n",
"> First consider the multivector instantiation in line 6,\n",
">\n",
"> `V = o3d.mv(’V’,’vector’,f=True)`\n",
">\n",
"> .Here a 3-dimensional multivector field that is a function of `x`, `y`, and `z` (`f=True`) is being instantiated. If latex output were used (to be discussed later) the multivector `V` would be displayed as\n",
"> \n",
"> $$\\be V^{x}\\eb_{x} + V^{y}\\eb_{y} + V^{z}\\eb_{z} \\ee$$\n",
"> \n",
"> Where the coefficients of the basis vectors are generalized *sympy* functions of the coordinates. If `f=(x,y)` then the coefficients would be functions of `x` and `y`. In general is `f` is a tuple of symbols then the coefficients of the basis would be functions of those symbols. The superscripts[16] are formed from the coordinate symbols or if there are no coordinates from the subscripts of the basis vectors. The types of name and modes available for multivector instantiation are\n",
">\n",
"> |`name`|`mode`|result|\n",
"> |:-:|:-:|:-|\n",
"> |string s|`scalar`|symbolic scalar of value Symbol(s)|\n",
"> |string s|`vector`|symbolic vector|\n",
"> |string s|`grade2` or `bivector`|symbolic bivector|\n",
"> |string s|`r` (integer)|symbolic r-grade multivector|\n",
"> |string s|`pseudo`|symbolic pseudoscalar|\n",
"> |string s|`spinor`|symbolic even multivector|\n",
"> |string s|`mv`|symbolic general multivector|\n",
"> |scalar c|None|zero grade multivector with coefficient value c|\n",
">\n",
"> Line 5 of the previous listing illustrates the case of using the `mv` member function with no arguments. The code does not return a multivector, but rather a tuple or the basis vectors of the geometric algebra `o3d`. The elements of the tuple then can be used to construct multivectors, or multivector fields through the operations of addition, subtraction, multiplication (geometric, inner, and outer products and left and right contraction). As an example we could construct the vector function\n",
">\n",
"> ```python\n",
"> F = x**2*ex + z*ey + x*y*ez\n",
"> ```\n",
">\n",
"> or the bivector function\n",
">\n",
"> ```python\n",
"> B = z*(ex^ey) + y*(ey^ez) + y*(ex^ez).\n",
"> ```\n",
">\n",
"> Line 7 is an example of instantiating a multivector scalar function (a multivector with only a scalar part). If we print `f` the result is `x*y*z`. Line 8 is an example of instantiating a grade $r$ (in the example a grade 2) multivector where\n",
">\n",
"> $$\\be B = B^{xy}{\\eb}_{x}{\\wedge}{\\eb}_{y}+B^{yz}{\\eb}_{y}{\\wedge}{\\eb}_{z}+B^{xz}{\\eb}_{x}{\\wedge}{\\eb}_{z}. \\ee$$\n",
"\n",
"If one wished to calculate the left and right geometric derivatives of `F` and `B` the required code would be"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(grad,rgrad) = o3d.grads()\n",
"dF = grad*F\n",
"dB = grad*B\n",
"dFr = F*rgrad\n",
"dBr = B*rgrad"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`dF`, `dB`, `dFr`, and `dBr` are all multivector functions. For the code where the order of the operations are reversed"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"(grad,rgrad) = o3d.grads()\n",
"dFop = F*grad\n",
"dBop = B*grad\n",
"dFrop = rgrad*F\n",
"dBrop = rgrad*B"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`dFop`, `dBop`, `dFrop`, and `dBrop` are all multivector differential operators (again see section [ldops]).\n",
"\n",
"Backward Compatibility Class MV\n",
"-------------------------------\n",
"\n",
"In order to be backward compatible with older versions of *galgebra* we introduce the class MV which is inherits it’s functions from then class Mv. To instantiate a geometric algebra using MV use the static function\n",
"\n",
"`MV.setup(basis, metric=None, coords=None, rframe=False, debug=False, curv=(None,None))`\n",
"\n",
"> This function allows a single geometric algebra to be created. If the function is called more than once the old geometric algebra is overwritten by the new geometric algebra. The named input `metric` is the same as the named input `g` in the current version of *galgebra*. Likewise, `basis`, `coords`, and `debug` are the same in the old and current versions of *galgebra*[17]. Due to improvements in *sympy* the inputs `rframe` and `curv[1]` are no longer required. `curv[0]` is the vector function (list or tuple of scalar functions) of the coordinates required to define a vector manifold. For compatibility with the old version of *galgebra* if `curv` is used `metric` should be a orthonormal Euclidean metric of the same dimension as `curv[0]`. It is strongly suggested that one use the new methods of defining a geometric algebra on a manifold.\n",
"\n",
"`MV(base, mvtype, fct=False, blade_rep=True)`\n",
"\n",
"> For the instantiation of multivector using `MV` the `base` and `mvtype` arguments are the same as for new methods of multivector instantiation. The `fct` input is the same and the `g` input in the new methods. `blade_rep` is not used in the new methods so setting `blade_rep=False` will do nothing. Effectively `blade_rep=False` was not used in the old examples.\n",
"\n",
"`Fmt(self, fmt=1, title=None)`\n",
"\n",
"> `Fmt` in `MV` has inputs identical to `Fmt` in `Mv` except that if `A` is a multivector then `A.Fmt(2,’A’)` executes a print statement from `MV` and returns `None`, while from `Mv`, `A.Fmt(2,’A’)` returns a string so that the function is compatible with use in *ipython notebook*.\n",
"\n",
"Basic Multivector Class Functions\n",
"---------------------------------\n",
"\n",
"If we can instantiate multivectors we can use all the multivector class functions as described as follows.\n",
"\n",
"`blade_coefs(self,basis_lst)`\n",
"\n",
"> Find coefficients (sympy expressions) of multivector basis blade expansion corresponding to basis blades in `basis_lst`. For example if $V = V^{x}{{\\eb}}_{x}+V^{y}{{\\eb}}_{x}+V^{z}{{\\eb}}_{x}$ Then $V\\text{.blade_coefs}([{{\\eb}}_{z},{{\\eb}}_{x}]) = [V^{z},V^{x}]$ or if $B = B^{xy}{{\\eb}}_{x}{\\wedge}{{\\eb}}_{y}+V^{yz}{{\\eb}}_{y}{\\wedge}{{\\eb}}_{z}$ then $B\\text{.blade_coefs}([{{\\eb}}_{x}{\\wedge}{{\\eb}}_{y}]) = [B^{xy}]$.\n",
"\n",
"`convert_to_blades(self)`\n",
"\n",
"> Convert multivector from the base representation to the blade representation. If multivector is already in blade representation nothing is done.\n",
"\n",
"`convert_from_blades(self)`\n",
"\n",
"> Convert multivector from the blade representation to the base representation. If multivector is already in base representation nothing is done.\n",
"\n",
"`diff(self,var)`\n",
"\n",
"> Calculate derivative of each multivector coefficient with respect to variable `var` and form new multivector from coefficients.\n",
"\n",
"`dual(self)`\n",
"\n",
"> The mode of the `dual()` function is set by the `Ga` class static member function, `GA.dual_mode(mode=’I+’)` of the `GA` geometric galgebra which sets the following return values ($I$ is the pseudo-scalar for the geometric algebra `GA`)\n",
">\n",
"> |`mode`|Return Value|\n",
"> |:-:|:-:|\n",
"> |`’+I’`|$IA$|\n",
"> |`’I+’`|$AI$|\n",
"> |`’-I’`|$-IA$|\n",
"> |`’I-’`|$-AI$|\n",
"> |`’+Iinv’`|$I^{-1}A$|\n",
"> |`’Iinv+’`|$AI^{-1}$|\n",
"> |`’-Iinv’`|$-I^{-1}A$|\n",
"> |`’Iinv-’`|$-AI^{-1}$|\n",
">\n",
"> For example if the geometric algebra is `o3d`, `A` is a multivector in `o3d`, and we wish to use `mode=’I-’`. We set the mode with the function `o3d.dual(’I-’)` and get the dual of `A` with the function `A.dual()` which returns $-AI$.\n",
">\n",
"> If `o3d.dual(mode)` is not called the default for the dual mode is `mode=’I+’` and `A*I` is returned.\n",
">\n",
"> Note that `Ga.dual(mode)` used the function `Ga.I()` to calculate the normalized pseudoscalar. Thus if the metric tensor is not numerical and orthogonal the correct hint for then`sig` input of the *Ga* constructor is required.\n",
"\n",
"`even(self)`\n",
"\n",
"> Return the even grade components of the multivector.\n",
"\n",
"`exp(self,hint=’-’)`\n",
"\n",
"> If $A$ is a multivector then $e^{A}$ is defined for any $A$ via the series expansion for $e$. However as a practical matter we only have a simple closed form formula for $e^{A}$ if $A^{2}$ is a scalar.[18] If $A^{2}$ is a scalar and we know the sign of $A^{2}$ we have the following formulas for $e^{A}$.\n",
">\n",
"> $$$\\begin{aligned}\n",
"A^{2} > 0 : & & &\\\\\n",
" A &= \\sqrt{A^{2}} {\\displaystyle\\frac{A}{\\sqrt{A^{2}}}} ,& e^{A} &= {{\\cosh}\\lp {\\sqrt{A^{2}}} \\rp }+{{\\sinh}\\lp {\\sqrt{A^{2}}} \\rp }{\\displaystyle\\frac{A}{\\sqrt{A^{2}}}} \\\\\n",
" A^{2} < 0 : & & &\\\\\n",
" A &= \\sqrt{-A^{2}} {\\displaystyle\\frac{A}{\\sqrt{-A^{2}}}} ,& e^{A} &= {{\\cos}\\lp {\\sqrt{-A^{2}}} \\rp }+{{\\sin}\\lp {\\sqrt{-A^{2}}} \\rp }{\\displaystyle\\frac{A}{\\sqrt{-A^{2}}}} \\\\\n",
" A^{2} = 0 : & & &\\\\\n",
" A &=0 ,& e^{A} &= 1 + A \n",
"\\end{aligned}$$\n",
">\n",
"> The hint is required for symbolic multivectors $A$ since in general *sympy* cannot determine if $A^{2}$ is positive or negative. If $A$ is purely numeric the hint is ignored since the sign can be calculated.\n",
"\n",
"`expand(self)`\n",
"\n",
"> Return multivector in which each coefficient has been expanded using *sympy* `expand()` function.\n",
"\n",
"`factor(self)`\n",
"\n",
"> Apply the `sympy` `factor` function to each coefficient of the multivector.\n",
"\n",
"`Fmt(self, fmt=1,title=None)`\n",
"\n",
"> Fuction to print multivectors in different formats where\n",
">\n",
"> |`fmt`| |\n",
"> |:-:|:--|\n",
"> |1|Print entire multivector on one line.|\n",
"> |2|Print each grade of multivector on one line.|\n",
"> |3|Print each base of multivector on one line.|\n",
">\n",
"> `title` appends a title string to the beginning of the output. An equal sign in the title string is not required, but is added as a default. Note that `Fmt` only overrides the the global multivector printing format for the particular instance being printed. To reset the global multivector printing format use the function `Fmt()` in the printer module.\n",
"\n",
"`func(self,fct)`\n",
"\n",
"> Apply the `sympy` scalar function `fct` to each coefficient of the multivector.\n",
"\n",
"`grade(self,igrade=0)`\n",
"\n",
"> Return a multivector that consists of the part of the multivector of grade equal to `igrade`. If the multivector has no `igrade` part return a zero multivector.\n",
"\n",
"`inv(self)`\n",
"\n",
"> Return the inverse of the multivector $M$ (`M.inv()`). If $M$ is a non-zero scalar return $1/M$. If $M^{2}$ is a non-zero scalar return $M/{\\lp {M^{2}} \\rp }$, If $MM^{{\\dagger}}$ is a non-zero scalar return $M^{{\\dagger}}/{\\lp {MM^{{\\dagger}}} \\rp }$. Otherwise exit the program with an error message.\n",
">\n",
"> All division operators (`/`, `/=`) use right multiplication by the inverse.\n",
"\n",
"`norm(self,hint=’+’)`\n",
"\n",
"> Return the norm of the multivector $M$ (`M.norm()`) defined by $\\sqrt{{\\left |{MM^{{\\dagger}}}\\right |}}$. If $MM^{{\\dagger}}$ is a scalar (a *sympy* scalar is returned). If $MM^{{\\dagger}}$ is not a scalar the program exits with an error message. If $MM^{{\\dagger}}$ is a number *sympy* can determine if it is positive or negative and calculate the absolute value. If $MM^{{\\dagger}}$ is a *sympy* expression (function) *sympy* cannot determine the sign of the expression so that `hint=’+’` or `hint=’-’` is needed to determine if the program should calculate $\\sqrt{MM^{{\\dagger}}}$ or $\\sqrt{-MM^{{\\dagger}}}$. For example if we are in a Euclidean space and `M` is a vector then `hint=’+’`, if `M` is a bivector then let `hint=’-’`. If `hint=’0’` and $MM^{{\\dagger}}$ is a symbolic scalar `sqrt(Abs(M*M.rev()))` is returned where `Abs()` is the *sympy* symbolic absolute value function.\n",
"\n",
"`norm2(self)`\n",
"\n",
"> Return the the scalar defined by $MM^{{\\dagger}}$ if $MM^{{\\dagger}}$ is a scalar. If $MM^{{\\dagger}}$ is not a scalar the program exits with an error message.\n",
"\n",
"`proj(self,bases_lst)`\n",
"\n",
"> Return the projection of the multivector $M$ (`M.proj(bases_lst)`) onto the subspace defined by the list of bases (`bases_lst`).\n",
"\n",
"`proj(self,lst)`\n",
"\n",
"> Return the projection of the mutivector $A$ onto the list, $lst$, of basis blades. For example if $A = A^{x}{{\\eb}}_{x}+A^{y}{{\\eb}}_{y}+A^{z}{{\\eb}}_{z}$ then $A.proj{\\lp {[{{\\eb}}_{x},{{\\eb}}_{y}]} \\rp } = A^{x}{{\\eb}}_{x}+A^{y}{{\\eb}}_{y}$. Similarly if $A = A^{xy}{{\\eb}}_{x}{\\wedge}{{\\eb}}_{y}+A^{yz}{{\\eb}}_{y}{\\wedge}{{\\eb}}_{z}$ then $A.proj{\\lp {[{{\\eb}}_{x}{\\wedge}{{\\eb}}_{y}]} \\rp } = A^{xy}{{\\eb}}_{x}{\\wedge}{{\\eb}}_{y}$.\n",
"\n",
"`project_in_blade(self,blade)`\n",
"\n",
"> Return the projection of the mutivector $A$ in subspace defined by the blade, $B$, using the formula ${\\lp {A\\rfloor B} \\rp }B^{-1}$ in , page 121.\n",
"\n",
"`pure_grade(self)`\n",
"\n",
"> If the multivector $A$ is pure (only contains one grade) return, $A.pure\\_grade()$, the index (’0’ for a scalar, ’1’ for vector, ’2’ for a bi-vector, etc.) of the non-zero grade. If $A$ is not pure return the negative of the highest non-zero grade index.\n",
"\n",
"`odd(self)`\n",
"\n",
"> Return odd part of multivector.\n",
"\n",
"`reflect_in_blade(self,blade)`\n",
"\n",
"> Return the reflection of the mutivector $A$ in the subspace defined by the $r$-grade blade, $B_{r}$, using the formula (extended to multivectors) $\\sum_{i} {\\lp {-1} \\rp }^{r{\\lp {i+1} \\rp }}{B}_{r}{\\left < {A} \\right >}_{i}B_{r}^{-1}$ in , page 129.\n",
"\n",
"`rev(self)`\n",
"\n",
"> Return the reverse of the multivector.\n",
"\n",
"`rotate_multivector(self,itheta,hint=’-’)`\n",
"\n",
"> Rotate the multivector $A$ via the operation $e^{-\\theta i/2}Ae^{\\theta i/2}$ where itheta = $\\theta i$, $\\theta$ is a scalar, and $i$ is a unit, $i^{2} = \\pm 1$, 2-blade. If ${\\lp {\\theta i} \\rp }^{2}$ is not a number `hint` is required to determine the sign of the square of `itheta`. The default chosen, `hint=’-’`, is correct for any Euclidean space.\n",
"\n",
"`scalar(self)`\n",
"\n",
"> Return the coefficient (*sympy* scalar) of the scalar part of a multivector.\n",
"\n",
"`simplify(self,mode=simplify)`\n",
"\n",
"> `mode` is a *sympy* simplification function of a list/tuple of *sympy* simplification functions that are applied in sequence (if more than one function) each coefficient of the multivector. For example if we wished to applied `trigsimp` and `ratsimp` *sympy* functions to the multivector `F` the code would be\n",
">\n",
"> ```python\n",
"> Fsimp = F.simplify(mode=[trigsimp,ratsimp]).\n",
"> ```\n",
">\n",
"> Actually `simplify` could be used to apply any scalar *sympy* function to the coefficients of the multivector.\n",
"\n",
"`set_coef(self,grade,base,value)`\n",
"\n",
"> Set the multivector coefficient of index `(grade,base)` to `value`.\n",
"\n",
"`subs(self,x)`\n",
"\n",
"> Return multivector where *sympy* subs function has been applied to each coefficient of multivector for argument dictionary/list `x`.\n",
"\n",
"`trigsimp(self,**kwargs)`\n",
"\n",
"> Apply the `sympy` trigonometric simplification function `trigsimp` to each coefficient of the multivector. `**kwargs` are the arguments of trigsimp. See `sympy` documentation on `trigsimp` for more information.\n",
"\n",
"Basic Multivector Functions\n",
"---------------------------\n",
"\n",
"`com(A,B)`\n",
"\n",
"> Calculate commutator of multivectors $A$ and $B$. Returns $(AB-BA)/2$.\n",
">\n",
"> Additionally, commutator and anti-commutator operators are defined by\n",
">\n",
"> $$$\\begin{aligned}\n",
"> \\texttt{A >> B} \\equiv & {\\displaystyle\\frac{AB - BA}{2}} \\\\\n",
"> \\texttt{A << B} \\equiv & {\\displaystyle\\frac{AB + BA}{2}}.\n",
"> \\end{aligned}$$\n",
"\n",
"`cross(v1,v2)`\n",
"\n",
"> If `v1` and `v2` are 3-dimensional Euclidean vectors the vector cross product is returned, $v_{1}\\times v_{2} = -I{\\lp {v_{1}{\\wedge}v_{2}} \\rp }$.\n",
"\n",
"`def_prec(gd,op_ord='<>|,^,*')`[19]\n",
"\n",
"> This is used with the `GAeval()` function to evaluate a string representing a multivector expression with a revised operator precedence. `def_prec()` redefines the operator precedence for multivectors. `def_prec()` must be called in the main program an the argument `gd` must be `globals()`. The argument `op_ord` defines the order of operator precedence from high to low with groups of equal precedence separated by commas. the default precedence `op_ord='<>|,^,\\*'` is that used by Hestenes (,p7,,p38).\n",
"\n",
"`dual(A,mode=’I+’)`\n",
"\n",
"> Return the dual of the multivector `A`. The default operation is $AI$. For other modes see member function`Mv.dual(mode)`\n",
"\n",
"`even(A)`\n",
"\n",
"> Return even part of $A$.\n",
"\n",
"`exp(A,hint=’-’)`\n",
"\n",
"> If $A$ is a multivector then `A.exp(hint)` is returned. If $A$ is a *sympy* expression the *sympy* expression $e^{A}$ is returned (see `sympy.exp(A)` member function).\n",
"\n",
"`GAeval(s,pstr=False)`[20]\n",
"\n",
"> Returns multivector expression for string `s` with operator precedence for string `s` defined by inputs to function `def_prec()`. if `pstr=True` `s` and `s` with parenthesis added to enforce operator precedence are printed.\n",
"\n",
"`grade(A,r=0)`\n",
"\n",
"> If $A$ is a multivector ${\\left < {A} \\right >}_{r}$ is returned.\n",
"\n",
"`inv(A)`\n",
"\n",
"> If $A$ is a multivector and $AA^{{\\dagger}}$ is a non-zero scalar then $A^{-1} = A^{{\\dagger}}/(AA^{{\\dagger}})$ is returned otherwise an exception is returned.\n",
"\n",
"`Nga(x,prec=5)`\n",
"\n",
"> If `x` is a multivector with coefficients that contain floating point numbers, `Nga()` rounds all these numbers to a precision of `prec` and returns the rounded multivector.\n",
"\n",
"`norm(A,hint=’-’)`\n",
"\n",
"> If $A$ is a multivector and $AA^{{\\dagger}}$ is a number (not a scalar function) then $\\sqrt{{\\left |{AA^{{\\dagger}}}\\right |}}$ is returned. If $AA^{{\\dagger}}$ is a scalar *sympy* expression, but not a number, and `hint=’-’` then return $\\sqrt{-AA^{{\\dagger}}}$ otherwise return $\\sqrt{AA^{{\\dagger}}}$.\n",
"\n",
"`norm2(A)`\n",
"\n",
"> If $A$ is a multivector and $AA^{{\\dagger}}$ is a scalar return ${\\left |{AA^{{\\dagger}}}\\right |}$.\n",
"\n",
"`odd(A)`\n",
"\n",
"> Return odd part of $A$.\n",
"\n",
"`proj(B,A)`\n",
"\n",
"> Project blade `A` on blade `B` returning ${\\lp {A\\rfloor B} \\rp }B^{-1}$.\n",
"\n",
"`ReciprocalFrame(basis,mode=’norm’)`\n",
"\n",
"> If `basis` is a list/tuple of vectors, `ReciprocalFrame()` returns a tuple of reciprocal vectors. If `mode=norm` the vectors are normalized. If `mode` is anything other than `norm` the vectors are unnormalized and the normalization coefficient is added to the end of the tuple. One must divide by this coefficient to normalize the vectors.\n",
"\n",
"`refl(B,A)`\n",
"\n",
"> Reflect multivector $A$ in blade $B$. If $s$ is grade of $B$ returns $\\sum_{r}(-1)^{s(r+1)}B{\\left < {A} \\right >}_{r}B^{-1}$.\n",
"\n",
"`rev(A)`\n",
"\n",
"> If $A$ is a multivector return $A^{{\\dagger}}$.\n",
"\n",
"`rot(itheta,A,hint=’-’)`\n",
"\n",
"> If `A` is a multivector return `A.rotate_multivector(itheta,hint)` where `itheta` is the bi-vector blade defining the rotation. For the use of `hint` see the member function `Mv.rotate_multivector(self,itheta,hint)`.\n",
"\n",
"Multivector Derivatives\n",
"-----------------------\n",
"\n",
"The various derivatives of a multivector function is accomplished by multiplying the gradient operator vector with the function. The gradient operation vector is returned by the `Ga.grads()` function if coordinates are defined. For example if we have for a 3-D vector space"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X = (x,y,z) = symbols('x y z')\n",
"o3d = Ga('e*x|y|z',metric='[1,1,1]',coords=X)\n",
"(ex,ey,ez) = o3d.mv()\n",
"(grad,rgrad) = o3d.grads()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then the gradient operator vector is `grad` (actually the user can give it any name he wants to). The derivatives of the multivector function `F = o3d.mv(’F’,’mv’,f=True)` are given by multiplying by the left geometric derivative operator and the right geometric derivative operator ($\\T{grad} = \\nabla$ and $\\T{rgrad} = \\bar{\\nabla}$). Another option is to use the radiant operator members of the geometric algebra directly where we have $\\nabla = {\\texttt{o3d.grad}}$ and $\\bar{\\nabla} = {\\texttt{o3d.rgrad}}$.\n",
"\n",
"$$\\begin{aligned}\n",
" \\nabla F &= \\texttt{grad*F} \\\\\n",
" F \\bar{\\nabla} &= \\texttt{F*rgrad} \\\\\n",
" \\nabla {\\wedge}F &= \\texttt{grad^F} \\\\\n",
" F {\\wedge}\\bar{\\nabla} &= \\texttt{F^rgrad} \\\\\n",
" \\nabla \\cdot F &= \\texttt{grad|F} \\\\\n",
" F \\cdot \\bar{\\nabla} &= \\texttt{F|rgrad} \\\\\n",
" \\nabla \\rfloor F &= \\texttt{grad<F} \\\\\n",
" F \\rfloor \\bar{\\nabla} &= \\texttt{F<rgrad} \\\\\n",
" \\nabla \\lfloor F &= \\texttt{grad>F} \\\\\n",
" F \\lfloor \\bar{\\nabla} &= \\texttt{F>rgrad}\n",
" \\end{aligned}$$\n",
"\n",
"The preceding list gives examples of all possible multivector derivatives of the multivector function `F` where the operation returns a multivector function. The complementary operations\n",
"\n",
"$$\\begin{aligned}\n",
" F \\nabla &= \\texttt{F*grad} \\\\\n",
" \\bar{\\nabla} F &= \\texttt{rgrad*F} \\\\\n",
" F {\\wedge}\\nabla &= \\texttt{F^grad} \\\\\n",
" \\bar{\\nabla} {\\wedge}F &= \\texttt{rgrad^F} \\\\\n",
" F \\cdot \\nabla &= \\texttt{F|grad} \\\\\n",
" \\bar{\\nabla}\\cdot F &= \\texttt{rgrad|F} \\\\\n",
" F \\rfloor \\nabla &= \\texttt{F<grad} \\\\\n",
" \\bar{\\nabla} \\rfloor F &= \\texttt{rgrad<F} \\\\\n",
" F \\lfloor \\nabla &= \\texttt{F>grad} \\\\\n",
" \\bar{\\nabla} \\lfloor F &= \\texttt{rgrad>F}\n",
" \\end{aligned}$$\n",
"\n",
"all return multivector linear differential operators.\n",
"\n",
"Submanifolds\n",
"------------\n",
"\n",
"In general the geometric algebra that the user defines exists on the tangent space of a manifold (see section [sect_manifold]). The submanifold class, `Sm`, is derived from the `Ga` class and allows one to define a submanifold of a manifold by defining a coordinate mapping between the submanifold coordinates and the manifold coordinates. What is returned as the submanifold is the geometric algebra of the tangent space of the submanifold. The submanifold for a geometric algebra is instantiated with\n",
"\n",
"`Ga.sm(map,coords,root=’e’,norm=False)`\n",
"\n",
"> To define the submanifold we must def a coordinate map from the coordinates of the submanifold to each of the coordinates of the base manifold. Thus the arguments `map` and `coords` are respectively lists of functions and symbols. The list of symbols, `coords`, are the coordinates of the submanifold and are of length equal to the dimension of the submanifold. The list of functions, `map`, define the mapping from the coordinate space of the submanifold to the coordinate space of the base manifold. The length of `map` is equal to the dimension of the base manifold and each function in `map` is a function of the coordinates of the submanifold. `root` is the root of the string that is used to name the basis vectors of the submanifold. The default value of `root` is `e`. The result of this is that if the *sympy* symbols for the coordinates are `u` and `v` (two dimensional manifold) the text symbols for the basis vectors are `e_u` and `e_v` or in LaTeX $e_{u}$ and $e_{v}$. As a concrete example consider the following code. The output of this program (using LaTeX) is\n",
">\n",
"> \n",
">\n",
"> The base manifold, `sp3d`, is a 3-d Euclidean space using standard spherical coordinates. The submanifold `sph2d` of `sp3d` is a spherical surface of radius $1$. To take the sumanifold operation one step further the submanifold `cir1d` of `sph2d` is a circle in `sph2d` where the latitude of the circle is $\\pi/8$.\n",
">\n",
"> In each case, for demonstration purposes, a scalar and vector function on each manifold is defined (`f` and `F` for the 2-d manifold and `h` and `H` for the 1-d manifold) and the geometric derivative of each function is taken. The manifold mapping and the metric tensor for `cir1d` of `sph2d` are also shown. Note that if the submanifold basis vectors are not normalized[21] the program output is\n",
">\n",
"> \n",
"\n",
"Linear Transformations\n",
"----------------------\n",
"\n",
"The mathematical background for linear transformations is in section [Ltrans]. Linear transformations on the tangent space of the manifold are instantiated with the `Ga` member function `lt` (the actual class being instantiated is `Lt`) as shown in lines 12, 20, 26, and 44 of the code listing `Ltrans.py`. In all of the examples in `Ltrans.py` the default instantiation is used which produces a general (all the coefficients of the linear transformation are symbolic constants) linear transformation. *Note that to instantiate linear transformations coordinates, ${\\left \\{\n",
"{{\\eb}_{i}} \\rbrc}$, must be defined when the geometric algebra associated with the linear transformation is instantiated. This is due to the naming conventions of the general linear transformation (coordinate names are used) and for the calculation of the trace of the linear transformation which requires taking a divergence.* To instantiate a specific linear transformation the usage of `lt()` is `Ga.lt(M,f=False,mode=’g’)`\n",
"\n",
"> `M` is an expression that can define the coefficients of the linear transformation in various ways defined as follows.\n",
">\n",
"> | `M` | Result |\n",
"> |:---:|:-------|\n",
"> | string `M` | Coefficients are symbolic constants with names $\\T{M}^{x_{i}x_{j}}$ where $x_{i}$ and $x_{j}$ are the names of the $i^{th}$ and $j^{th}$ coordinates (see output of $\\T{Ltrans.py}$). | \n",
"> | char `mode` | If $\\T{M}$ is a string then $\\T{mode}$determines whether the linear transformation is general,$\\T{mode='g'}$, symmetric, $\\T{mode='s'}$, or antisymmetric, $\\T{mode='a'}$. The default is $\\T{mode='g'}$. |\n",
"> | list `M` | If $\\T{M}$ is a list of vectors equal in length to the dimension of the vector space then the linear transformation is $\\f{L}{\\ebf_{i}} = \\T{M}\\mat{i}$. If $\\T{M}$is a list of lists of scalars where all lists are equal in length to the dimension of the vector space then the linear transformation is$\\f{L}{\\ebf_{i}} = \\T{M}\\mat{i}\\mat{j}\\ebf_{j}$. |\n",
"> | dict `M` | If $\\T{M}$ is a dictionary the linear transformation is defined by $\\f{L}{\\ebf_{i}} = \\T{M}\\mat{\\ebf_{i}}$. If $\\ebf_{i}$ is not in the dictionary then $\\f{L}{\\ebf_{i}} =0$. |\n",
"> | rotor `M` | If $\\T{M}$ is a rotor, $\\T{M}\\T{M}^{\\R}=1$, the linear transformation is defined by $\\f{L}{{\\ebf}_{i}} = \\T{M}{\\ebf}_{i}\\T{M}^{\\R}$ .|\n",
"> | multivector function `M` | If $\\T{M}$ is a general multivector function, the function is tested for linearity, and if linear the coefficients of the linear transformation are calculated from $\\f{L}{\\ebf_{i}} = \\f{\\T{M}}{\\ebf_{i}}$. |\n",
">\n",
"> `f` is `True` or `False`. If `True` the symbolic coefficients of the general linear transformation are instantiated as functions of the coordinates.\n",
"\n",
"The different methods of instantiation are demonstrated in the code `LtransInst.py` with output\n",
"\n",
"\n",
"\n",
"The member function of the `Lt` class are\n",
"\n",
"`Lt(A)`\n",
"\n",
"> Returns the image of the multivector $A$ under the linear transformation $L$ where ${{L}\\lp {A} \\rp }$ is defined by the linearity of $L$, the vector values ${{L}\\lp {{{\\eb}}_{i}} \\rp }$, and the definition ${{L}\\lp {{{\\eb}}_{i_{1}}{\\wedge}\\dots{\\wedge}{{\\eb}}_{i_{r}}} \\rp } = {{L}\\lp {{{\\eb}}_{i_{1}}} \\rp }{\\wedge}\\dots{\\wedge}{{L}\\lp {{{\\eb}}_{i_{r}}} \\rp }$.\n",
"\n",
"`Lt.det()`\n",
"\n",
"> Returns the determinant (a scalar) of the linear transformation, $L$, defined by ${{\\det}\\lp {L} \\rp }I = {{L}\\lp {I} \\rp }$.\n",
"\n",
"`Lt.adj()`\n",
"\n",
"> Returns the adjoint (a linear transformation) of the linear transformation, $L$, defined by $a\\cdot{{L}\\lp {b} \\rp } = b\\cdot{{\\bar{L}}\\lp {a} \\rp }$ where $a$ and $b$ are any two vectors in the tangent space and $\\bar{L}$ is the adjoint of $L$.\n",
"\n",
"`Lt.tr()`\n",
"\n",
"> Returns the trace (a scalar) of the linear transformation, $L$, defined by ${{\\operatorname{tr}}\\lp {L} \\rp }=\\nabla_{a}\\cdot{{L}\\lp {a} \\rp }$ where $a$ is a vector in the tangent space.\n",
"\n",
"`Lt.matrix()`\n",
"\n",
"> Returns the matrix representation (*sympy* `Matrix`) of the linear transformation, $L$, defined by ${{L}\\lp {{{\\eb}}_{i}} \\rp } = L_{ij}{{\\eb}}_{j}$ where $L_{ij}$ is the matrix representation.\n",
"\n",
"The `Ltrans.py` demonstrate the use of the various `Lt` member functions and operators. The operators that can be used with linear transformations are `+`, `-`, and `*`. If $A$ and $B$ are linear transformations, $V$ a multivector, and $\\alpha$ a scalar then ${{{\\lp {A\\pm B} \\rp }}\\lp {V} \\rp } = {{A}\\lp {V} \\rp }\\pm{{B}\\lp {V} \\rp }$, ${{{\\lp {AB} \\rp }}\\lp {V} \\rp } = {{A}\\lp {{{B}\\lp {V} \\rp }} \\rp }$, and ${{{\\lp {\\alpha A} \\rp }}\\lp {V} \\rp } = \\alpha{{A}\\lp {V} \\rp }$.\n",
"\n",
"The `matrix()` member function returns a *sympy* `Matrix` object which can be printed in IPython notebook. To directly print an linear transformation in *ipython notebook* one must implement (yet to be done) a printing method similar to `mv.Fmt()`.\n",
"\n",
"Note that in `Ltrans.py` lines 30 and 49 are commented out since the latex output of those statements would run off the page. The use can uncomment those statements and run the code in the “LaTeX docs” directory to see the output. The output of this code is.\n",
"\n",
"\n",
"\n",
"Differential Operators\n",
"----------------------\n",
"\n",
"For the mathematical treatment of linear multivector differential operators see section [ldops]. The is a differential operator class `Dop`. However, one never needs to use it directly. The operators are constructed from linear combinations of multivector products of the operators `Ga.grad` and `Ga.rgrad` as shown in the following code for both orthogonal rectangular and spherical 3-d coordinate systems. The output of this code is.\n",
"\n",
"\n",
"\n",
"Note that for print an operator in the IPython notebook one must implement (yet to be done) a printing method similar to `mv.Fmt()`.\n",
"\n",
"Instantiating a Multi-linear Functions (Tensors)\n",
"------------------------------------------------\n",
"\n",
"The mathematical background for multi-linear functions is in section [MLtrans]. To instantiate a multi-linear function use\n",
"\n",
"`Mlt(self, f, Ga, nargs=None, fct=False)`\n",
"\n",
"> Where the arguments are\n",
">\n",
"> | | |\n",
"> |:-:|:--|\n",
"> |`f`| Either a string for a general tensor (this option is included mainly for debugging of the $\\T{Mlt}$ class) or a multi-linear function of manifold tangent vectors (multi-vectors of grade one) to scalar. For example one could generate a custom python function such as shown in $\\T{TensorDef.py}$ .|\n",
"> |`Ga`| Geometric algebra that tensor is associated with. |\n",
"> |`nargs`| If $\\T{f}$ is a string then $\\T{nargs}$ is the number of vector arguments of the tensor. If $\\T{f}$ is anything other than a string $\\T{nargs}$ is not required since $\\T{Mlt}$ determines the number of vector arguments from $\\T{f}$. |\n",
"> |`fct`| If $\\T{f}$ is a string then $\\T{fct=True}$ forces the tensor to be a tensor field (function of the coordinates. If $\\T{f}$ anything other than a string $\\T{fct}$ is not required since $\\T{Mlt}$ determines whether the tensor is a tensor field from $\\T{f}$ . |\n",
"\n",
"\n",
"Basic Multilinear Function Class Functions\n",
"------------------------------------------\n",
"\n",
"If we can instantiate multilinear functions we can use all the multilinear function class functions as described as follows. See section [MLtrans] for the mathematical description of each operation.\n",
"\n",
"`self(kargs)`\n",
"\n",
"> Calling function to evaluates multilinear function for `kargs` list of vector arguments and returns a value. Note that a sympy scalar is returned, *not* a multilinear function.\n",
"\n",
"`self.contract(slot1,slot2)`\n",
"\n",
"> Returns contraction of tensor between `slot1` and `slot2` where `slot1` is the index of the first vector argument and `slot2` is the index of the second vector argument of the tensor. For example if we have a rank two tensor, `T(a1,a2)`, then `T.contract(1,2)` is the contraction of `T`. For this case since there are only two slots there can only be one contraction.\n",
"\n",
"`self.pdiff(slot)`\n",
"\n",
"> Returns gradient of tensor, `T`, with respect to slot vector. For example if the tensor is ${{T}\\lp {a_{1},a_{2}} \\rp }$ then `T.pdiff(2)` is $\\nabla_{a_{2}}T$. Since `T` is a scalar function, `T.pdiff(2)` is a vector function.\n",
"\n",
"`self.cderiv()`\n",
"\n",
"> Returns covariant derivative of tensor field. If `T` is a tensor of rank $k$ then `T.cderiv()` is a tensor of rank $k+1$. The operation performed is defined in section [MLtrans].\n",
"\n",
"Standard Printing\n",
"-----------------\n",
"\n",
"Printing of multivectors is handled by the module `printer` which contains a string printer class derived from the *sympy* string printer class and a latex printer class derived from the *sympy* latex printer class. Additionally, there is an `Eprint` class that enhances the console output of *sympy* to make the printed output multivectors, functions, and derivatives more readable. `Eprint` requires an ansi console such as is supplied in linux or the program *ConEmu* replaces `cmd.exe`.\n",
"\n",
"For a windows user the simplest way to implement *ConEmu* is to use the *geany* editor and in the Edit$\\rightarrow$Preferences$\\rightarrow$Tools menu replace `cmd.exe` with[22]\n",
"\n",
"`\"C:\\Program Files\\ConEmu\\ConEmu64.exe\" /WndW 180 /cmd %c`\n",
"\n",
"and then run an example *galgeba* program that used `Eprint`. The default background and foreground colors make the output unreadable. To change these parameters to reasonable values:[23]\n",
"\n",
"1. Right click on title bar of console.\n",
"\n",
"2. Open *setting* window.\n",
"\n",
"3. Open *colors* window.\n",
"\n",
"4. Set the following parameters to the indicated values:\n",
"\n",
"- Text: #0\n",
"- Back: #7\n",
"- Popup: #0\n",
"- Back: #7\n",
"- $\\rlap{ \\checkmark }\\square$ Extend foreground colors with background \\#13\n",
"\n",
"If `Eprint` is called in a program (linux) when multivectors are printed the basis blades or bases are printed in bold text, functions are printed in red, and derivative operators in green.\n",
"\n",
"For formatting the multivector output there is the member function `Fmt(self,fmt=1,title=None)` which is documented in the multivector member functions. This member function works in the same way for LaTeX printing.\n",
"\n",
"There are two functions for returning string representations of multivectors. If `A` is a multivector then `str(A)` returns a string in which the scalar coefficients of the multivector bases have been simplified (grouped, factored, etc.). The member function `A.raw_str()` returns a string in which the scalar coefficients of the multivector bases have not been simplified.\n",
"\n",
"Latex Printing\n",
"--------------\n",
"\n",
"For latex printing one uses one functions from the `ga` module and one function from the `printer` module. The functions are\n",
"\n",
"`Format(Fmode=True,Dmode=True)`\n",
"\n",
"> This function from the `ga` module turns on latex printing with the following options\n",
">\n",
"> |Argument|Value|Result|\n",
"> |:-:|:-:|:--|\n",
"> |`Fmode`|`True`|Print functions without argument list, $f$|\n",
"> ||`False`|Print functions with standard *sympy* latex formatting, ${{f}\\lp {x,y,z} \\rp }$ |\n",
"> |`Dmode`|`True`|Print partial derivatives with condensed notation, $\\partial_{x}f$ |\n",
"> ||`False`|Print partial derivatives with standard *sympy* latex formatting, $\\pdiff{f}{x}$ |\n",
">\n",
"> `Format()` is also required for printing from *ipython notebook* (note that `xpdf()` is not needed to print from *ipython notebook*).\n",
"\n",
"`Fmt(obj,fmt=1)`\n",
"\n",
"> `Fmt()` can be used to set the global multivector printing format or to print a tuple, list, of dictionary.[24] The modes and operation of `Fmt()` are as follows:\n",
">\n",
"> | `obj` | Effect |\n",
"> |:-:|:--|\n",
"> | `obj=1,2,3` | Global multivector format is set to 1, 2, or 3 depending on $\\T{obj}$. See multivector member function $\\T{Fmt()}$for effect of$\\T{obj}$ value. |\n",
"> | obj=tuple/list/dict | The printing format of an object that is a tuple, list, or dict is controlled by the $\\T{fmt}$ argument in $\\T{Fmt}$ : |\n",
"> | | $\\T{fmt=1}$: Print complete $\\T{obj}$ on one line. |\n",
"> | | $\\T{fmt=2}$: Print one element of $\\T{obj}$ on each line. |\n",
"\n",
"`xpdf(filename=None,debug=False,paper=(14,11),crop=False)`\n",
"\n",
"> This function from the `printer` module post-processes the output captured from print statements, writes the resulting latex strings to the file `filename`, processes the file with pdflatex, and displays the resulting pdf file. All latex files except the pdf file are deleted. If `debug = True` the file `filename` is printed to standard output for debugging purposes and `filename` (the tex file) is saved. If `filename` is not entered the default filename is the root name of the python program being executed with `.tex` appended. The `paper` option defines the size of the paper sheet for latex. The format for the `paper` is\n",
">\n",
"> | | |\n",
"> |:--|:--|\n",
"> |`paper=(w,h)`|`w` is paper width in inches and|\n",
"> ||`h` is paper height in inches|\n",
"> |`paper=’letter’`|paper is standard letter size 8.5 in $\\times$ 11 in |\n",
"> |`paper=’landscape’`|paper is standard letter size but 11 in $\\times$ 8.5 in|\n",
">\n",
"> The default of `paper=(14,11)` was chosen so that long multivector expressions would not be truncated on the display.\n",
">\n",
"> If the `crop` input is `True` the linux `pdfcrop` program is used to crop the pdf output (if output is one page). This only works for linux installations (where `pdfcrop` is installed).\n",
">\n",
"> The `xpdf` function requires that latex and a pdf viewer be installed on the computer.\n",
">\n",
"> `xpdf` *is not required when printing latex in IPython notebook.*\n",
"\n",
"As an example of using the latex printing options when the following code is executed"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from printer import Format, xpdf\n",
"from ga import Ga\n",
"Format()\n",
"g3d = Ga('e*x|y|z')\n",
"A = g3d.mv('A','mv')\n",
"print r'\\bm{A} =',A\n",
"print A.Fmt(2,r'\\bm{A}')\n",
"print A.Fmt(3,r'\\bm{A}')\n",
"xpdf()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following is displayed\n",
"\n",
"$$\\begin{aligned}\n",
" {\\boldsymbol{A}} = & A+A^{x}{\\boldsymbol{e_{x}}}+A^{y}{\\boldsymbol{e_{y}}}+A^{z}{\\boldsymbol{e_{z}}}+A^{xy}{\\boldsymbol{e_{x}{\\wedge}e_{y}}}+A^{xz}{\\boldsymbol{e_{x}{\\wedge}e_{z}}}+A^{yz}{\\boldsymbol{e_{y}{\\wedge}e_{z}}}+A^{xyz}{\\boldsymbol{e_{x}{\\wedge}e_{y}{\\wedge}e_{z}}} \\\\\n",
" {\\boldsymbol{A}} = & A \\\\ & +A^{x}{\\boldsymbol{e_{x}}}+A^{y}{\\boldsymbol{e_{y}}}+A^{z}{\\boldsymbol{e_{z}}} \\\\ & +A^{xy}{\\boldsymbol{e_{x}{\\wedge}e_{y}}}+A^{xz}{\\boldsymbol{e_{x}{\\wedge}e_{z}}}+A^{yz}{\\boldsymbol{e_{y}{\\wedge}e_{z}}} \\\\ & +A^{xyz}{\\boldsymbol{e_{x}{\\wedge}e_{y}{\\wedge}e_{z}}} \\\\\n",
" {\\boldsymbol{A}} = & A \\\\ & +A^{x}{\\boldsymbol{e_{x}}} \\\\ & +A^{y}{\\boldsymbol{e_{y}}} \\\\ & +A^{z}{\\boldsymbol{e_{z}}} \\\\ & +A^{xy}{\\boldsymbol{e_{x}{\\wedge}e_{y}}} \\\\ & +A^{xz}{\\boldsymbol{e_{x}{\\wedge}e_{z}}} \\\\ & +A^{yz}{\\boldsymbol{e_{y}{\\wedge}e_{z}}} \\\\ & +A^{xyz}{\\boldsymbol{e_{x}{\\wedge}e_{y}{\\wedge}e_{z}}}\\end{aligned}$$\n",
"\n",
"For the cases of derivatives the code is"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from printer import Format, xpdf\n",
"from ga import Ga\n",
"\n",
"Format()\n",
"X = (x,y,z) = symbols('x y z')\n",
"o3d = Ga('e_x e_y e_z',g=[1,1,1],coords=X)\n",
"\n",
"f = o3d.mv('f','scalar',f=True)\n",
"A = o3d.mv('A','vector',f=True)\n",
"B = o3d.mv('B','grade2',f=True)\n",
"\n",
"print r'\\bm{A} =',A\n",
"print r'\\bm{B} =',B\n",
"\n",
"print 'grad*f =',o3d.grad*f\n",
"print r'grad|\\bm{A} =',o3d.grad|A\n",
"(o3d.grad*A).Fmt(2,r'grad*\\bm{A}')\n",
"\n",
"print r'-I*(grad^\\bm{A}) =',-o3g.mv_I*(o3d.grad^A)\n",
"print (o3d.grad*B).Fmt(2,r'grad*\\bm{B}')\n",
"print r'grad^\\bm{B} =',o3d.grad^B\n",
"print r'grad|\\bm{B} =',o3d.grad|B\n",
"\n",
"xpdf()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and the latex displayed output is ($f$ is a scalar function)\n",
"\n",
"$$\\be {\\boldsymbol{A}} = A^{x}{\\boldsymbol{e_{x}}}+A^{y}{\\boldsymbol{e_{y}}}+A^{z}{\\boldsymbol{e_{z}}} \\ee$$\n",
"\n",
"$$\\be {\\boldsymbol{B}} = B^{xy}{\\boldsymbol{e_{x}{\\wedge}e_{y}}}+B^{xz}{\\boldsymbol{e_{x}{\\wedge}e_{z}}}+B^{yz}{\\boldsymbol{e_{y}{\\wedge}e_{z}}} \\ee$$\n",
"\n",
"$$\\be {\\boldsymbol{\\nabla}} f = \\partial_{x} f{\\boldsymbol{e_{x}}}+\\partial_{y} f{\\boldsymbol{e_{y}}}+\\partial_{z} f{\\boldsymbol{e_{z}}} \\ee$$\n",
"\n",
"$$\\be {\\boldsymbol{\\nabla}} \\cdot {\\boldsymbol{A}} = \\partial_{x} A^{x} + \\partial_{y} A^{y} + \\partial_{z} A^{z} \\ee$$\n",
"\n",
"$$\\begin{aligned}\n",
" {\\boldsymbol{\\nabla}} {\\boldsymbol{A}} = & \\partial_{x} A^{x} + \\partial_{y} A^{y} + \\partial_{z} A^{z} \\\\ & +\\lp - \\partial_{y} A^{x} + \\partial_{x} A^{y}\\rp {\\boldsymbol{e_{x}{\\wedge}e_{y}}}+\\lp - \\partial_{z} A^{x} + \\partial_{x} A^{z}\\rp {\\boldsymbol{e_{x}{\\wedge}e_{z}}}+\\lp - \\partial_{z} A^{y} + \\partial_{y} A^{z}\\rp {\\boldsymbol{e_{y}{\\wedge}e_{z}}} \\\\ \\end{aligned}$$\n",
"\n",
"$$\\be -I ({\\boldsymbol{\\nabla}} {\\wedge}{\\boldsymbol{A}}) = \\lp - \\partial_{z} A^{y} + \\partial_{y} A^{z}\\rp {\\boldsymbol{e_{x}}}+\\lp \\partial_{z} A^{x} - \\partial_{x} A^{z}\\rp {\\boldsymbol{e_{y}}}+\\lp - \\partial_{y} A^{x} + \\partial_{x} A^{y}\\rp {\\boldsymbol{e_{z}}} \\ee$$\n",
"\n",
"$$\\begin{aligned}\n",
" {\\boldsymbol{\\nabla}} {\\boldsymbol{B}} = & \\lp - \\partial_{y} B^{xy} - \\partial_{z} B^{xz}\\rp {\\boldsymbol{e_{x}}}+\\lp \\partial_{x} B^{xy} - \\partial_{z} B^{yz}\\rp {\\boldsymbol{e_{y}}}+\\lp \\partial_{x} B^{xz} + \\partial_{y} B^{yz}\\rp {\\boldsymbol{e_{z}}} \\\\ & +\\lp \\partial_{z} B^{xy} - \\partial_{y} B^{xz} + \\partial_{x} B^{yz}\\rp {\\boldsymbol{e_{x}{\\wedge}e_{y}{\\wedge}e_{z}}} \\\\ \\end{aligned}$$\n",
"\n",
"$$\\be {\\boldsymbol{\\nabla}} {\\wedge}{\\boldsymbol{B}} = \\lp \\partial_{z} B^{xy} - \\partial_{y} B^{xz} + \\partial_{x} B^{yz}\\rp {\\boldsymbol{e_{x}{\\wedge}e_{y}{\\wedge}e_{z}}} \\ee$$\n",
"\n",
"$$\\be {\\boldsymbol{\\nabla}} \\cdot {\\boldsymbol{B}} = \\lp - \\partial_{y} B^{xy} - \\partial_{z} B^{xz}\\rp {\\boldsymbol{e_{x}}}+\\lp \\partial_{x} B^{xy} - \\partial_{z} B^{yz}\\rp {\\boldsymbol{e_{y}}}+\\lp \\partial_{x} B^{xz} + \\partial_{y} B^{yz}\\rp {\\boldsymbol{e_{z}}} \\ee$$\n",
"\n",
"This example also demonstrates several other features of the latex printer. In the case that strings are input into the latex printer such as `r'grad*\\bm{A}'`, `r'grad^\\bm{A}'`, or `r'grad*\\bm{A}'`. The text symbols `grad`, `^`, `|`, and `*` are mapped by the `xpdf()` post-processor as follows if the string contains an `=`.\n",
"\n",
"| | | |\n",
"|:-:|:-:|:-:|\n",
"|original|replacement|displayed latex|\n",
"|`grad*A`|`\\bm{\\nabla}A`|${\\boldsymbol{\\nabla}}A$|\n",
"|`A^B`|`A\\wedge B`|$A\\wedge B$|\n",
"|`A|B`|`A\\cdot B`|$A\\cdot B$|\n",
"|`A*B`|`AB`|$AB$|\n",
"|`A<B`|`A\\rfloor B`|$A\\rfloor B$|\n",
"|`A>B`|`A\\lfloor B`|$A\\lfloor B$|\n",
"|`A>>B`|`A\\times B`|$A\\times B$|\n",
"|`A<<B`|`A\\bar{\\times} B`|$A\\bar{\\times} B$|\n",
"\n",
"If the first character in the string to be printed is a `%` none of the above substitutions are made before the latex processor is applied. In general for the latex printer strings are assumed to be in a math environment (equation or align) unless the first character in the string is a `#`.[25]\n",
"\n",
"There are two member functions for returning LaTeX string representations of multivectors. If `A` is a multivector then `A.Mv_latex_str()` returns a LaTeX string in which the scalar coefficients of the multivector bases have been simplified (grouped, factored, etc.). This function is used when using `print` in the LaTeX mode. The member function `A.raw_latex_str()` returns a LaTeX string in which the scalar coefficients of the multivector bases have not been simplified.\n",
"\n",
"### Printing Lists/Tuples of Multivectors/Differential Operators\n",
"\n",
"Since the expressions for multivectors or differential operators can be very long printing lists or tuples of such items can easily exceed the page with when printing in LaTeX or in “ipython notebook.” I order to alleviate this problem the function `Fmt` can be used.\n",
"\n",
"`Fmt(obj,fmt=0)`\n",
"\n",
"> This function from the `printer` module allows the formatted printing of lists/tuples or multivectors/differential operators.\n",
">\n",
"> | | |\n",
"> |:--|:--|\n",
"> |`obj`|`obj` is a list or tuple of multivectors and/or differential operators.|\n",
"> |`fmt=0`|`fmt=0` prints each element of the list/tuple on an individual lines[26].|\n",
"> ||`fmt=1` prints all elements of the list/tuple on a single line.|\n",
">\n",
"> If l is a list or tuple to print in the LaTeX environment use the command\n",
">\n",
"> ```python\n",
"> print Fmt(l) # One element of l per line\n",
"> ```\n",
">\n",
"> or\n",
">\n",
"> ```python\n",
"> print Fmt(l,1) # All elements of l on one line\n",
"> ```\n",
">\n",
"> If you are printing in “ipython notebook” then enter\n",
">\n",
"> ```python\n",
"> Fmt(l) # One element of l per line\n",
"> ```\n",
">\n",
"> or\n",
"\n",
"> ```python\n",
"> Fmt(l,1) # All elements of l on one line\n",
"> ```\n",
"\n",
"Bibliography\n",
"================\n",
"\n",
"1. Chris Doran and Anthony Lasenby, “Geometric Algebra for Physicists,” Cambridge University Press, 2003. <http://www.mrao.cam.ac.uk/~clifford>\n",
"2. David Hestenes and Garret Sobczyk, “Clifford Algebra to Geometric Calculus,” Kluwer Academic Publishers, 1984. <http://geocalc.clas.asu.edu/html/CA_to_GC.html>\n",
"3. Alan Macdonald, “Linear and Geometric Algebra,” 2010. <http://faculty.luther.edu/~macdonal/laga>\n",
"4. Alan Macdonald, “Vector and Geometric Calculus,” 2012. <http://faculty.luther.edu/~macdonal/vagc>\n",
"5. D. Hestenes, “*New Foundations for Classical Mechanics*,” Kluwer Academic Publishers, 1999. <http://geocalc.clas.asu.edu/html/NFCM.html>\n",
"6. L. Dorst, D. Fontijne, S. Mann, “*Geometric Algebra for Computer Science*: An Object-Oriented Approach to Geometry*,” Morgan Kaufmann, $2^{\\text{nd}}$ printing, 2009. <http://www.geometricalgebra.net/>\n",
"7. Christian Perwass, “*Geometric Algebra with Applications in Engineering*,” Springer, 2008\n",
"8. John W. Arthur, “*Understanding Geometric Algebra for Electromagnetic Theory*,” Wiley-IEEE Press, 2011.\n",
"\n",
"[4] By the manifold embedding theorem any $m$-dimensional manifold is isomorphic to a $m$-dimensional vector manifold\n",
"\n",
"[5] This product in not necessarily positive definite.\n",
"\n",
"[6] In this section and all following sections we are using the Einstein summation convention unless otherwise stated.\n",
"\n",
"[7] We use the Christoffel symbols of the first kind to calculate the derivatives of the basis vectors and the product rule to calculate the derivatives of the basis blades where (<http://en.wikipedia.org/wiki/Christoffel_symbols>)\n",
"\n",
"$$\\be \\Gamma_{ijk} = {\\frac{1}{2}}{\\lp {{{\\displaystyle\\frac{\\partial {g_{jk}}}{\\partial {x^{i}}}}}+{{\\displaystyle\\frac{\\partial {g_{ik}}}{\\partial {x^{j}}}}}-{{\\displaystyle\\frac{\\partial {g_{ij}}}{\\partial {x^{k}}}}}} \\rp }, \\ee$$\n",
"\n",
"and\n",
"\n",
"$$\\be {{\\displaystyle\\frac{\\partial {{{\\eb}}_{j}}}{\\partial {x^{i}}}}} = \\Gamma_{ijk}{{\\eb}}^{k}. \\ee$$\n",
"\n",
"The Christoffel symbols of the second kind,\n",
"\n",
"$$\\be \\Gamma_{ij}^{k} = {\\frac{1}{2}}g^{kl}{\\lp {{{\\displaystyle\\frac{\\partial {g_{li}}}{\\partial {x^{j}}}}}+{{\\displaystyle\\frac{\\partial {g_{lj}}}{\\partial {x^{i}}}}}-{{\\displaystyle\\frac{\\partial {g_{ij}}}{\\partial {x^{l}}}}}} \\rp }, \\ee$$\n",
"\n",
"could also be used to calculate the derivatives in term of the original basis vectors, but since we need to calculate the reciprocal basis vectors for the geometric derivative it is more efficient to use the symbols of the first kind.\n",
"\n",
"[8] In this case $D_{B}^{j_{1}\\dots j_{n}} = F$ and $\\partial_{j_{1}\\dots j_{n}} = 1$.\n",
"\n",
"[9] For example in three dimensions ${\\left \\{{3} \\rbrc} = (0,1,2,3,(1,2),(2,3),(1,3),(1,2,3))$ and as an example of how the superscript would work with each grade ${{\\eb}}^{0}=1$, ${{\\eb}}^{1}={{\\eb}}^{1}$, ${{\\eb}}^{{\\lp {1,2} \\rp }}={{\\eb}}^{1}{\\wedge}{{\\eb}}^{2}$, and ${{\\eb}}^{{\\lp {1,2,3} \\rp }}={{\\eb}}^{1}{\\wedge}{{\\eb}}^{2}{\\wedge}{{\\eb}}^{3}$.\n",
"\n",
"[10] We are following the treatment of Tensors in section 3–10 of .\n",
"\n",
"[11] We assume that the arguments are elements of a vector space or more generally a geometric algebra so that the concept of linearity is meaningful.\n",
"\n",
"[12] Since `X` or the metric tensor can be functions of coordinates the vector space that the geometric algebra is constructed from is not necessarily flat so that the geometric algebra is actually constructed on the tangent space of the manifold which is a vector space.\n",
"\n",
"[13] The signature of the vector space, $(p,q)$, is required to determine whether the square of the normalized pseudoscalar, $I$, is $+1$ or $-1$. In the future the metric tensor would be required to create a generalized spinor .\n",
"\n",
"[14] Using LaTeX output if a basis vector is denoted by ${{\\eb}}_{x}$ then ${{\\eb}}$ is the root symbol and $x$ is the subscript\n",
"\n",
"[15] There is a multivector class, `Mv`, but in order the insure that every multivector is associated with the correct geometric algebra we always use the member function `Ga.mv` to instantiate the multivector.\n",
"\n",
"[16] Denoted in text output by `A__x`, etc. so that for text output `A` would be printed as `A__x*e_x+A__y*e_y+A__z*e_z`.\n",
"\n",
"[17] If the metric is input as a list or list or lists the object is no longer quoted (input as a string). For example the old `metric=’[1,1,1]’` becomes `metric=[1,1,1]`.\n",
"\n",
"[18] In the future it should be possible to generate closed form expressions for $e^{A}$ if $A^{r}$ is a scalar for some interger $r$.\n",
"\n",
"[19] See footnote [fn_6].\n",
"\n",
"[20] `GAeval` is in the `printer` module.[fn_6]\n",
"\n",
"[21] Remember that normalization is currently supported only for orthogonal systems (diagonal metric tensors).\n",
"\n",
"[22] The 180 in the *ConEmu* command line is the width of the console you wish to display in characters. Change the number to suit you.\n",
"\n",
"[23] I am not exactly sure what the different parameter setting do. I achieved the result I wished for by trial and error. I encourage the users to experiment and share their results.\n",
"\n",
"[24] In *Ipython notebook* tuples, or lists, or dictionarys of multivectors do print correctly. One mode of `Fmt()` corrects this deficiency.\n",
"\n",
"[25] Preprocessing do not occur for the Ipython notebook and the string post processing commands `%` and `#` are not used in this case.\n",
"\n",
"[26] The formatting of each element is respected as applied by `A.Fmt(fmt=1,2, or 3)` where `A` is an element of `obj `so that if multivector/differential operation have been formatted to print on multiple lines it will printed on multiple lines.[Fmt_format]"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-3-clause |
computational-class/cjc2016 | code/04.PythonCrawler_selenium.ipynb | 1 | 53032 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"\n",
"***\n",
"***\n",
"# 数据抓取\n",
" > # 使用Selenium操纵浏览器\n",
"\n",
"***\n",
"***\n",
"\n",
"王成军 \n",
"\n",
"[email protected]\n",
"\n",
"计算传播网 http://computational-communication.com\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Selenium 是一套完整的web应用程序测试系统，包含了\n",
"- 测试的录制（selenium IDE）\n",
"- 编写及运行（Selenium Remote Control）\n",
"- 测试的并行处理（Selenium Grid）。\n",
"\n",
"Selenium的核心Selenium Core基于JsUnit，完全由JavaScript编写，因此可以用于任何支持JavaScript的浏览器上。selenium可以模拟真实浏览器，自动化测试工具，支持多种浏览器，爬虫中主要用来解决JavaScript渲染问题。https://www.cnblogs.com/zhaof/p/6953241.html"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"上面我们知道了selenium支持很多的浏览器，但是如果想要声明并调用浏览器则需要：\n",
"https://pypi.org/project/selenium/"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-17T00:57:02.726390Z",
"start_time": "2019-10-17T00:56:56.947418Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting selenium\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/80/d6/4294f0b4bce4de0abf13e17190289f9d0613b0a44e5dd6a7f5ca98459853/selenium-3.141.0-py2.py3-none-any.whl (904kB)\n",
"\u001b[K 100% |████████████████████████████████| 911kB 9.3MB/s ta 0:00:011\n",
"\u001b[?25hRequirement already satisfied: urllib3 in /Users/datalab/anaconda3/lib/python3.7/site-packages (from selenium) (1.24.1)\n",
"Installing collected packages: selenium\n",
"Successfully installed selenium-3.141.0\n"
]
}
],
"source": [
"!pip install selenium"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Webdriver\n",
"- 主要用的是selenium的Webdriver\n",
"- 我们可以通过下面的方式先看看Selenium.Webdriver支持哪些浏览器\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-17T00:57:07.111400Z",
"start_time": "2019-10-17T00:57:07.067485Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"from selenium import webdriver"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-17T00:57:10.624675Z",
"start_time": "2019-10-17T00:57:10.619107Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on package selenium.webdriver in selenium:\n",
"\n",
"NAME\n",
" selenium.webdriver\n",
"\n",
"DESCRIPTION\n",
" # Licensed to the Software Freedom Conservancy (SFC) under one\n",
" # or more contributor license agreements. See the NOTICE file\n",
" # distributed with this work for additional information\n",
" # regarding copyright ownership. The SFC licenses this file\n",
" # to you under the Apache License, Version 2.0 (the\n",
" # \"License\"); you may not use this file except in compliance\n",
" # with the License. You may obtain a copy of the License at\n",
" #\n",
" # http://www.apache.org/licenses/LICENSE-2.0\n",
" #\n",
" # Unless required by applicable law or agreed to in writing,\n",
" # software distributed under the License is distributed on an\n",
" # \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
" # KIND, either express or implied. See the License for the\n",
" # specific language governing permissions and limitations\n",
" # under the License.\n",
"\n",
"PACKAGE CONTENTS\n",
" android (package)\n",
" blackberry (package)\n",
" chrome (package)\n",
" common (package)\n",
" edge (package)\n",
" firefox (package)\n",
" ie (package)\n",
" opera (package)\n",
" phantomjs (package)\n",
" remote (package)\n",
" safari (package)\n",
" support (package)\n",
" webkitgtk (package)\n",
"\n",
"VERSION\n",
" 3.14.1\n",
"\n",
"FILE\n",
" /Users/datalab/anaconda3/lib/python3.7/site-packages/selenium/webdriver/__init__.py\n",
"\n",
"\n"
]
}
],
"source": [
"help(webdriver) "
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### 下载和设置Webdriver\n",
"\n",
"对于Chrome需要的webdriver下载地址\n",
"\n",
"http://chromedriver.storage.googleapis.com/index.html\n",
"\n",
"需要将webdriver放在系统路径下：\n",
"- 确保anaconda在系统路径名里\n",
"- 把下载的webdriver 放在`Anaconda的bin文件夹`下"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### PhantomJS\n",
"\n",
"PhantomJS是一个而基于WebKit的服务端JavaScript API,支持Web而不需要浏览器支持，其快速、原生支持各种Web标准：Dom处理，CSS选择器，JSON等等。PhantomJS可以用用于页面自动化、网络监测、网页截屏，以及无界面测试"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-17T00:57:17.147546Z",
"start_time": "2019-10-17T00:57:14.749313Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"#browser = webdriver.Firefox() # 打开Firefox浏览器\n",
"browser = webdriver.Chrome() # 打开Chrome浏览器"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# 访问页面"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-17T03:39:01.788430Z",
"start_time": "2019-10-17T03:38:58.474675Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<!DOCTYPE html><html xmlns=\"http://www.w3.org/1999/xhtml\"><head>\n",
"<meta charset=\"utf-8\" />\n",
"<meta name=\"baidu_ssp_verify\" content=\"39f14c78c537175eb4b5192c72d002c1\" />\n",
"<meta name=\"baidu-site-verification\" content=\"cNhJHKEzsD\" />\n",
"<meta name=\"360-site-verification\" content=\"e37aef53e3922913e2a6a4682e479b84\" />\n",
"<meta name=\"sogou_site_verification\" content=\"7zFjYjJaMq\" />\n",
"<meta name=\"msvalidate.01\" content=\"0CA3171633345524D8CBED5E95C75FFF\" />\n",
"<meta name=\"google-site-verification\" content=\"rh2irYN2Lu028orAseOD3aXd5u7Eu1mqTfhoVaw2Ihg\" />\n",
"<meta name=\"shenma-site-verification\" content=\"12da4afc02bfe908ed0667f287167d11_1555581349\" />\n",
"<meta property=\"qc:admins\" content=\"27354635321361636375\" />\n",
"<link rel=\"canonical\" href=\"https://music.163.com/\" />\n",
"<meta name=\"applicable-device\" content=\"pc,mobile\" />\n",
"<title>网易云音乐</title>\n",
"<meta name=\"keywords\" content=\"网易云音乐，音乐，播放器，网易，下载，播放，DJ，免费，明星，精选，歌单，识别音乐，收藏，分享音乐，音乐互动，高音质，320K，音乐社交，官网，music.163.com\" />\n",
"<meta name=\"description\" content=\"网易云音乐是一款专注于发现与分享的音乐产品，依托专业音乐人、DJ、好友推荐及社交功能，为用户打造全新的音乐生活。\" />\n",
"<meta property=\"og:title\" content=\"网易云音乐\" />\n",
"<meta property=\"og:type\" content=\"website\" />\n",
"<meta property=\"og:image\" content=\"http://p3.music.126.net/tBTNafgjNnTL1KlZMt7lVA==/18885211718935735.jpg\" />\n",
"<meta property=\"og:url\" content=\"https://music.163.com/\" />\n",
"<meta property=\"og:site_name\" content=\"网易云音乐\" />\n",
"<script type=\"text/javascript\">\n",
"var GDownloadLink=\"\";\n",
"var GDevice = \"phone\";\n",
"var GFrom=\"\";\n",
"var GClient=\"\";\n",
"var GPlatform=\"other\";\n",
"var GRef = '';\n",
"var GInApp = false;\n",
"var GMobile = false;\n",
"var GAbroad = false;\n",
"var GUser={};\n",
"var GAllowRejectComment = false;\n",
"var GEnc = true;\n",
"var GEnvType = \"online\";\n",
"var GWebpSupport = \"1\";\n",
"var vipWebCashierRedirect = \"1\"\n",
"window.NEJ_CONF = {p_csrf:{cookie:'__csrf',param:'csrf_token'}};\n",
"GUtil = {\n",
"getBase:function(){\n",
"return location.protocol+'//'+location.hostname;\n",
"},\n",
"getPathAndHash:function(_url){//获取URL path 之后的所有内容,并将/#/替换成/m/使之成为path的一部分\n",
"if(!_url) return '';\n",
"var _reg0 = /^https?:\\/\\/.*?\\//i,\n",
"_reg1 = /\\/?#\\/?/i;\n",
"return _url.replace(_reg0,'/').replace(_reg1,'/m/');\n",
"},\n",
"composeRefer:function(_url,_ref){//对所有的页面请求都加上ref参数表示被嵌套的来源\n",
"if(!_ref) return _url;\n",
"var _hi = _url.indexOf('#'),\n",
"_si = _url.indexOf('?');\n",
"if(_si>0&&(_si<_hi||_hi<0)){\n",
"return _url.substring(0,_si+1)+'ref='+_ref+'&'+_url.substring(_si+1);\n",
"}else if(_hi>0&&(_si<0||_si>_hi)){\n",
"return _url.substring(0,_hi)+'?ref='+_ref+_url.substring(_hi);\n",
"}else{\n",
"return _url+'?ref='+_ref;\n",
"}\n",
"}\n",
"};(function(){\n",
"var _ua = window.navigator.userAgent,\n",
"_isMobile = /(mobile|mobi|wap|iphone)/i.test(_ua),\n",
"_isAndroid = /android/i.test(_ua),\n",
"_isIpad = /(ipad)/i.test(_ua),\n",
"_igList = [/^\\/xiami$/,/^\\/live$/],//不需要以单页面打开的列表，比如某些活动页面\n",
"_pn = location.pathname,\n",
"_idx = _pn.lastIndexOf('/'),\n",
"_pReg = /\\s*(\\w+)\\s*=\\s*(\\d+)\\s*/,\n",
"_redirect2mobile = function() {\n",
"var _type,_murl,\n",
"_id = 0,\n",
"_hash = location.hash,\n",
"_mReg = /^#\\/?m?\\/(share|song|playlist|djradio|dj|program|album|mv|artist|topic|radio|zysf|drqp|qp|activity|store|user|event|video|discover\\/toplist)(\\/(\\d+))?/,\n",
"_base = location.protocol+'//'+location.hostname,\n",
"_sindex = _hash.lastIndexOf('?'),\n",
"_search = _sindex>-1?_hash.substring(_sindex+1):'',\n",
"_match = _mReg.exec(_hash);\n",
"// 用户等级页特殊处理\n",
"if (_hash === '#/user/level') {\n",
"location.href = _base + '/store/m/gain/mylevel';\n",
"return;\n",
"}\n",
"// 无hash || 不匹配 || 匹配但是商品之外不带参数 || 匹配且是排行榜\n",
"if (!_hash.length || !_match || (_match[1] != 'store' && !_search) || /share|discover\\/toplist/.test(_match[1])) {\n",
"// 有hash && (没有参数 || 排行榜)\n",
"if ((!_search || /share|discover\\/toplist/.test(_match[1])) && _hash.length) {\n",
"location.href = _base + '/' + _hash.replace('#', 'm');\n",
"} else {\n",
"location.href = _base + '/m/';\n",
"}\n",
"return;\n",
"}\n",
"_type = _match[1];\n",
"_id = _match[3];\n",
"if (_type == 'dj') _type = 'program';\n",
"if (_type == 'store') {\n",
"_murl = /^#\\/store\\/(product|concert)\\/detail/.test(_hash) ? _hash.replace('#/store', '/store/m') : '/store/m/product/index';\n",
"} else {\n",
"_murl = '/' + _type + '?' + (_id ? 'id=' + _id + '&' : '') + _search;\n",
"}\n",
"location.href = _base + _murl;\n",
"};\n",
"if(_isMobile || _isAndroid || _isIpad){\n",
"_redirect2mobile();\n",
"return;\n",
"}\n",
"if(!_pn||_pn=='/') return;\n",
"for(var i in _igList){\n",
"if(_igList[i].test(_pn)) return;\n",
"}\n",
"if(top==self){\n",
"location.href = '/#'+GUtil.getPathAndHash(location.href);\n",
"return;\n",
"}\n",
"//搜索引擎过来的内容页连接\n",
"if(top==self&&/^\\/static\\/(song|playlist|album|artist)/i.test(_pn)){\n",
"location.href = '/#'+_pn.substring(0,_idx).replace('/static/','/')+'?id='+_pn.substring(_idx+1);\n",
"}\n",
"})();\n",
"(function(){\n",
"var _addEvent = function(_node,_type,_cb){\n",
"if(_node.addEventListener){\n",
"_node.addEventListener(_type,_cb);\n",
"}else if(_node.attachEvent){\n",
"_node.attachEvent('on'+_type,_cb);\n",
"}\n",
"},\n",
"_pathPrefixArray = [\n",
"'/store/', // 商城\n",
"'/m/at/', // 活动\n",
"'/prime/m/', // 会员移动端页面\n",
"'/oauth2/', // 授权\n",
"'/m/oauth2/', // 授权\n",
"'/octave/', // 新数字专辑\n",
"'/v/', // 新数字专辑\n",
"'/st/', // 静态页面\n",
"'/nmusician/',// 音乐人\n",
"'/nact/', // 新活动\n",
"'/m/topic/', // 专栏移动端\n",
"'/show/m/', //演出移动端\n",
"],\n",
"_isNotMainsitePagePath = function(_pagePath){\n",
"// 对于非主站内的页面的跳转 需要排除\n",
"var _path = (_pagePath||'').replace(/^https?:\\/\\/.*?\\//i, '/').split(/[?#]/)[0];\n",
"for(var i=0;i<_pathPrefixArray.length;i++){\n",
"if(_path.indexOf(_pathPrefixArray[i])===0) return true;\n",
"}\n",
"return false;\n",
"},\n",
"_onAnchorClick = function(_event){//截获所有<a>标签的点击事件，自定义页面的跳转\n",
"_event = _event||window.event;\n",
"var _el = _event.target||_event.srcElement,\n",
"_base = location.protocol+'//'+location.host;\n",
"while(_el&&_el!=document){\n",
"if(_el.tagName&&_el.tagName.toLowerCase()=='a'){\n",
"//fix ie6下有时javascript:;不能阻止默认事件的bug.\n",
"if(_el.href.indexOf('javascript:')>=0){\n",
"!!_event.preventDefault\n",
"? _event.preventDefault()\n",
": _event.returnValue = !1;\n",
"return;\n",
"}\n",
"if(_event.button==2) return;//ff 右键会触发click事件\n",
"//商城有独立地顶栏了，排除掉。但会员、数字专辑、单曲的商品、订单页仍保持主站frame，\n",
"//这些url往往是通过/vip2, /payfee这样的地址跳转的，也没有问题，如果真的有，URL用#配置就好了\n",
"if(_isNotMainsitePagePath(_el.href)) return;\n",
"//新窗口打开的链接、云音乐单页面形式的链接、站外的链接不做拦截处理。\n",
"if(_el.target=='_blank'\n",
"||_el.target=='blank'\n",
"||_isNotSameHost(_el.href)\n",
"||_el.href==_base\n",
"||_el.href.indexOf(_base+'/#')>=0) return;\n",
"!!_event.preventDefault\n",
"? _event.preventDefault()\n",
": _event.returnValue = !1;\n",
"location.dispatch2(_el.href);\n",
"break;\n",
"}else{\n",
"_el = _el.parentNode;\n",
"}\n",
"}\n",
"},\n",
"_isNotSameHost = function(_href){\n",
"var _same = true;\n",
"if(_href.charAt(0)!='/'){\n",
"var _index = _href.indexOf('//'+location.hostname);\n",
"if(_index > 0){\n",
"var _index2 = _href.indexOf('?');\n",
"if(_index2 > 0 && _index2 < _index){\n",
"_same = false;\n",
"}\n",
"}else{\n",
"_same = false;\n",
"}\n",
"}\n",
"return !_same;\n",
"};\n",
"_addEvent(document,'click',_onAnchorClick);\n",
"//扩展一个js中直接使用的页面跳转的方法，以拦截js中的页面跳转行为\n",
"location.dispatch2 = function(_url,_replace){\n",
"var delegate = false;\n",
"try{\n",
"delegate = !!top.GDispatcher;\n",
"}catch(e){\n",
"delegate = false;\n",
"}\n",
"// 处理对于非主站内的页面的跳转\n",
"if(_isNotMainsitePagePath(_url)){\n",
"if(delegate && top.location && top.location.href){\n",
"top.location.href = _url;\n",
"}else{\n",
"location.href = _url;\n",
"}\n",
"return;\n",
"}\n",
"if(delegate){\n",
"top.GDispatcher.dispatch(_url,_replace);\n",
"}else{\n",
"_url = GUtil.composeRefer(_url,GRef);\n",
"//邮箱音乐盒中，每次链接的跳转都要将proxy.html的地址合并到hash中\n",
"if(GRef&&GRef=='mail'){\n",
"var _hindex,_sindex,\n",
"_reg = /(https?:\\/\\/.+\\/proxy.html)/,\n",
"_hreg = /#(.*?)$/,\n",
"_href = decodeURIComponent(location.href);\n",
"if(!_reg.test(decodeURIComponent(_url))&&_reg.test(_href)){\n",
"_hindex = _url.indexOf('#');\n",
"_sindex = _url.lastIndexOf('?');\n",
"if(_hindex>0){\n",
"_url = _url+(_sindex>_hindex?'&':'?')+'proxy='+encodeURIComponent(RegExp.$1);\n",
"}else{\n",
"_url = _url+'#proxy='+encodeURIComponent(RegExp.$1);\n",
"}\n",
"}\n",
"}\n",
"if(_replace){\n",
"location.replace(_url);\n",
"}else{\n",
"location.href = _url;\n",
"}\n",
"}\n",
"};\n",
"})();\n",
"(function start() {\n",
"var targetUrl = 'https://music.163.com';\n",
"// 首先检测hash规则, 在白名单内才进行跳转\n",
"var hashWhite = /^(\\/discover|\\/download|\\/login)/ig;\n",
"// 如果当前域不是163域名，那么强制跳转到163.com\n",
"var siteReg = /^(https?:\\/\\/)?([a-zA-Z0-9]+(-?[a-zA-Z0-9])*\\.){1,}?(163\\.com)$/ig;\n",
"if(hashWhite.test(window.location.pathname) && !siteReg.test(window.location.hostname)){\n",
"top.location.href = targetUrl;\n",
"}\n",
"})();</script>\n",
"<link rel=\"shortcut icon\" href=\"//s1.music.126.net/style/favicon.ico?v20180823\" />\n",
"<link href=\"//s2.music.126.net/web/s/core_50ad2145ad526d20456118b24db986d7.css?50ad2145ad526d20456118b24db986d7\" type=\"text/css\" rel=\"stylesheet\" /><link href=\"//s2.music.126.net/web/s/pt_frame_48c8b54589cccecb9b345836a6e82767.css?48c8b54589cccecb9b345836a6e82767\" type=\"text/css\" rel=\"stylesheet\" />\n",
"<style>html,body{overflow:hidden;}</style>\n",
"<script>if(top!=self)top.location=self.location;</script>\n",
"<script type=\"text/javascript\" src=\"//s5.music.126.net/static_public/5c51482cf8a93b7fc8cf42cb/0.6.0-beta1/vipcashier.umd.js\"></script><script type=\"text/javascript\" charset=\"UTF-8\" async=\"\" src=\"https://acstatic-dun.126.net/2.5.5_26766d7a/watchman.min.js\"></script><script type=\"text/javascript\" charset=\"UTF-8\" async=\"\" src=\"https://acstatic-dun.126.net/2.5.5_26766d7a/watchman.min.js\"></script></head>\n",
"<body>\n",
"<div id=\"g-topbar\" class=\"g-topbar\" style=\"width: 1200px; top: 0px;\">\n",
"<div class=\"m-top\">\n",
"<div class=\"wrap f-cb\">\n",
"<h1 class=\"logo\"><a hidefocus=\"true\" href=\"/#\">网易云音乐</a></h1>\n",
"<ul class=\"m-nav j-tflag\">\n",
"<li class=\"fst\">\n",
"<span><a hidefocus=\"true\" href=\"/#\" data-module=\"discover\" class=\"z-slt\"><em>发现音乐</em><sub class=\"cor\"> </sub></a></span>\n",
"</li>\n",
"<li>\n",
"<span><a data-action=\"bilog\" data-log-action=\"page\" data-log-json=\"{"type":"my"}\" hidefocus=\"true\" href=\"/my/\" data-module=\"my\"><em>我的音乐</em><sub class=\"cor\"> </sub></a></span>\n",
"</li>\n",
"<li>\n",
"<span><a hidefocus=\"true\" href=\"/friend\" data-module=\"friend\"><em>朋友</em><sub class=\"cor\"> </sub><i class=\"dot j-t\" style=\"display:none;\"></i></a></span>\n",
"</li>\n",
"<li>\n",
"<span><a hidefocus=\"true\" href=\"/store/product\" target=\"_blank\" data-module=\"store\"><em>商城</em></a></span>\n",
"</li>\n",
"<li>\n",
"<span><a hidefocus=\"true\" href=\"/nmusician/web/recruit\" target=\"_blank\" data-module=\"musician\"><em>音乐人</em></a></span>\n",
"</li>\n",
"<li class=\"lst\">\n",
"<span><a id=\"topbar-download-link\" data-action=\"bilog\" data-log-action=\"page\" data-log-json=\"{"type":"downloadapp","source":"tab"}\" hidefocus=\"true\" href=\"/download\" data-module=\"download\"><em>下载客户端</em><sub class=\"cor\"> </sub></a></span><sup class=\"hot\"> </sup>\n",
"</li>\n",
"</ul>\n",
"<div class=\"m-tophead f-pr j-tflag\" id=\"auto-id-TAJ80gQuBk105lNT\"><a hidefocus=\"true\" href=\"#\" class=\"link s-fc3\" data-action=\"login\">登录</a>\n",
"<div class=\"m-tlist j-uflag\" style=\"display:none;\">\n",
"<div class=\"inner\">\n",
"</div>\n",
"<i class=\"arr\"></i>\n",
"</div>\n",
"</div>\n",
"<a data-action=\"bilog\" data-log-action=\"click\" data-log-json=\"{"target":"uploadvideo","page":"homepage"}\" href=\"/login?targetUrl=%2Fst/creator\" target=\"_blank\" class=\"m-topvd f-pr m-creator-center\">创作者中心</a>\n",
"<div class=\"m-srch f-pr j-suggest\" id=\"g_search\">\n",
"<div class=\"srchbg\">\n",
"<span class=\"parent\">\n",
"<input type=\"text\" name=\"srch\" id=\"srch\" class=\"txt j-flag\" value=\"\" style=\"opacity: 1;\" />\n",
"<label class=\"ph j-flag\" id=\"auto-id-urmKeZT09eNeWEmB\">音乐/视频/电台/用户</label>\n",
"</span>\n",
"</div>\n",
"<div class=\"j-showoff u-showoff\"><p>现在支持搜索MV啦~</p></div>\n",
"<span class=\"j-flag\" style=\"display:none;\" id=\"auto-id-EuiwIrNb4WA90NoG\"> </span>\n",
"<div class=\"u-lstlay j-flag\" style=\"display:none;\" id=\"auto-id-MT1wJD6X0far5svk\"></div>\n",
"</div>\n",
"</div>\n",
"</div>\n",
"<div class=\"m-subnav m-subnav-up f-pr j-tflag f-hide\"></div>\n",
"<div id=\"g_nav2\" class=\"m-subnav j-tflag\">\n",
"<div class=\"wrap f-pr\">\n",
"<ul class=\"nav\">\n",
"<li><a hidefocus=\"true\" data-module=\"discover\" href=\"/discover\" class=\"z-slt\"><em>推荐</em></a></li>\n",
"<li><a hidefocus=\"true\" data-module=\"toplist\" href=\"/discover/toplist\"><em>排行榜</em></a></li>\n",
"<li><a hidefocus=\"true\" data-module=\"playlist\" href=\"/discover/playlist\"><em class=\"f-pr\" style=\"padding: 0 15px 0 11px;\">歌单<span class=\"f-pa f-r-white-icon\" style=\"display:inline-block;width:8px;height:8px;top:2px;background-size:cover;\"></span></em></a></li>\n",
"<li><a hidefocus=\"true\" data-module=\"djradio\" href=\"/discover/djradio\"><em>主播电台</em></a></li>\n",
"<li><a hidefocus=\"true\" data-module=\"artist\" href=\"/discover/artist\"><em>歌手</em></a></li>\n",
"<li><a hidefocus=\"true\" data-module=\"album\" href=\"/discover/album\"><em>新碟上架</em></a></li>\n",
"</ul>\n",
"</div>\n",
"</div>\n",
"</div>\n",
"<iframe name=\"contentFrame\" id=\"g_iframe\" class=\"g-iframe\" scrolling=\"auto\" frameborder=\"0\" src=\"about:blank\" allowfullscreen=\"true\"></iframe>\n",
"<script type=\"text/javascript\">\n",
"var GUserAcc={topic:1, reward:false};\n",
"(function(){\n",
"var topbar = document.getElementById('g-topbar'),\n",
"scrollBarWidth = document.body.clientWidth - topbar.clientWidth;\n",
"topbar.style.width = topbar.clientWidth+'px';\n",
"topbar.className = 'g-topbar';\n",
"if(window.addEventListener){\n",
"window.addEventListener('resize', onResize)\n",
"}else{\n",
"window.attachEvent('onresize', onResize)\n",
"}\n",
"function onResize(){\n",
"topbar.style.width = (document.body.clientWidth-scrollBarWidth)+'px';\n",
"};\n",
"})();/*!\n",
"* Copyright (c) 2009-2011 Andreas Blixt <[email protected]>\n",
"* Contributors: Aaron Ogle <[email protected]>,\n",
"* Matti Virkkunen <[email protected]>,\n",
"* Simon Chester <[email protected]>\n",
"* http://github.com/blixt/js-hash\n",
"* MIT License: http://www.opensource.org/licenses/mit-license.php\n",
"*\n",
"* Hash handler\n",
"* Keeps track of the history of changes to the hash part in the address bar.\n",
"*/\n",
"/* WARNING for Internet Explorer 7 and below:\n",
"* If an element on the page has the same ID as the hash used, the history will\n",
"* get messed up.\n",
"*\n",
"* Does not support history in Safari 2 and below.\n",
"*\n",
"* Example:\n",
"* function handler(newHash, initial) {\n",
"* if (initial)\n",
"* alert('Hash is \"' + newHash + '\"');\n",
"* else\n",
"* alert('Hash changed to \"' + newHash + '\"');\n",
"* }\n",
"* Hash.init(handler);\n",
"* Hash.go('abc123');\n",
"*\n",
"*\n",
"* Updated by Simon Chester ([email protected]) on 2011-05-16:\n",
"* - Removed the need for blank.html and the iframe argument by creating the\n",
"* iframe on initialization.\n",
"*\n",
"* Updated by Matti Virkkunen ([email protected]) on 2009-11-16:\n",
"* - Added second argument to callback that indicates whether the callback is\n",
"* due to initial state (true) or due to an actual change to the hash\n",
"* (false).\n",
"*\n",
"* Updated by Aaron Ogle ([email protected]) on 2009-08-11:\n",
"* - Fixed bug where Firefox automatically unescapes location.hash but no\n",
"* other browsers do. Always get the hash by parsing location.href and\n",
"* never use location.hash.\n",
"*/\n",
"var Hash = (function () {\n",
"var\n",
"// Import globals\n",
"window = this,\n",
"documentMode = document.documentMode,\n",
"history = window.history,\n",
"// Plugin variables\n",
"callback, hash,\n",
"// IE-specific\n",
"iframe,\n",
"getHash = function () {\n",
"// Internet Explorer 6 (and possibly other browsers) extracts the query\n",
"// string out of the location.hash property into the location.search\n",
"// property, so we can't rely on it. The location.search property can't be\n",
"// relied on either, since if the URL contains a real query string, that's\n",
"// what it will be set to. The only way to get the whole hash is to parse\n",
"// it from the location.href property.\n",
"//\n",
"// Another thing to note is that in Internet Explorer 6 and 7 (and possibly\n",
"// other browsers), subsequent hashes are removed from the location.href\n",
"// (and location.hash) property if the location.search property is set.\n",
"//\n",
"// Via Aaron: Firefox 3.5 (and below?) always unescape location.hash which\n",
"// causes poll to fire the hashchange event twice on escaped hashes. This\n",
"// is because the hash variable (escaped) will not match location.hash\n",
"// (unescaped.) The only consistent option is to rely completely on\n",
"// location.href.\n",
"var index = window.location.href.indexOf('#');\n",
"return (index == -1 ? '' : window.location.href.substr(index + 1));\n",
"},\n",
"// Used by all browsers except Internet Explorer 7 and below.\n",
"poll = function () {\n",
"var curHash = getHash();\n",
"if (curHash != hash) {\n",
"hash = curHash;\n",
"callback(curHash, false);\n",
"}\n",
"},\n",
"// From:\n",
"// http://perfectionkills.com/detecting-event-support-without-browser-sniffing/\n",
"isHashChangeSupported = function() {\n",
"var eventName = 'onhashchange';\n",
"var isSupported = (eventName in document.body);\n",
"if (!isSupported) {\n",
"document.body.setAttribute(eventName, 'return;');\n",
"isSupported = typeof document.body[eventName] == 'function';\n",
"}\n",
"// documentMode logic from YUI to filter out IE8 Compat Mode (which\n",
"// generates false positives).\n",
"return isSupported && (document.documentMode === undefined ||\n",
"document.documentMode > 7);\n",
"},\n",
"createIframe = function () {\n",
"var tempEl = document.createElement();\n",
"tempEl.innerHTML = '<iframe src=\"javascript:void(0)\" tabindex=\"-1\" ' +\n",
"'style=\"display: none;\"></iframe>';\n",
"var frame = tempEl.childNodes[0];\n",
"document.body.appendChild(frame);\n",
"return frame;\n",
"},\n",
"// Used to create a history entry with a value in the iframe.\n",
"setIframe = function (newHash) {\n",
"try {\n",
"var doc = iframe.contentWindow.document;\n",
"doc.open();\n",
"doc.write('<html><body>' + newHash + '</body></html>');\n",
"doc.close();\n",
"hash = newHash;\n",
"} catch (e) {\n",
"setTimeout(function () { setIframe(newHash); }, 10);\n",
"}\n",
"},\n",
"// Used by Internet Explorer 7 and below to set up an iframe that keeps track\n",
"// of history changes.\n",
"setUpIframe = function () {\n",
"// Don't run until access to the body is allowed.\n",
"try {\n",
"iframe = createIframe();\n",
"} catch (e) {\n",
"setTimeout(setUpIframe, 10);\n",
"return;\n",
"}\n",
"// Create a history entry for the initial state.\n",
"setIframe(hash);\n",
"var data = hash;\n",
"setInterval(function () {\n",
"var curData, curHash;\n",
"try {\n",
"curData = iframe.contentWindow.document.body.innerText;\n",
"if (curData != data) {\n",
"data = curData;\n",
"window.location.hash = hash = curData;\n",
"callback(curData, true);\n",
"} else {\n",
"curHash = getHash();\n",
"if (curHash != hash) setIframe(curHash);\n",
"}\n",
"} catch (e) {\n",
"}\n",
"}, 50);\n",
"};\n",
"return {\n",
"init: function (cb) {\n",
"// init can only be called once.\n",
"if (callback) return;\n",
"callback = cb;\n",
"// Keep track of the hash value.\n",
"hash = getHash();\n",
"cb(hash, true);\n",
"if (isHashChangeSupported()) {\n",
"if (window.addEventListener){\n",
"window.addEventListener('hashchange', poll, false);\n",
"} else if (window.attachEvent){\n",
"window.attachEvent('onhashchange', poll);\n",
"}\n",
"} else {\n",
"// Run specific code for Internet Explorer.\n",
"if (window.ActiveXObject) {\n",
"if (!documentMode || documentMode < 8) {\n",
"// Internet Explorer 5.5/6/7 need an iframe for history\n",
"// support.\n",
"setUpIframe();\n",
"}\n",
"} else {\n",
"// Change Opera navigation mode to improve history support.\n",
"if (history.navigationMode) {\n",
"history.navigationMode = 'compatible';\n",
"}\n",
"setInterval(poll, 50);\n",
"}\n",
"}\n",
"},\n",
"go: function (newHash) {\n",
"// Cancel if the new hash is the same as the current one, since there\n",
"// is no cross-browser way to keep track of navigation to the exact\n",
"// same hash multiple times in a row. A wrapper can handle this by\n",
"// adding an incrementing counter to the end of the hash.\n",
"if (newHash == hash) return;\n",
"if (iframe) {\n",
"setIframe(newHash);\n",
"} else {\n",
"window.location.hash = hash = newHash;\n",
"callback(newHash, false);\n",
"}\n",
"}\n",
"};\n",
"})();var GDispatcher = (function(){\n",
"var _lastPage = '',\n",
"_igReg = /f=(.*?)/,\n",
"_hReg = /\\/?#.*/,\n",
"_xssReg = /(java|vb)script/,//xss注入\n",
"_default = '/discover';\n",
"function _isIE10plus(){\n",
"var _ua = window.navigator.userAgent;\n",
"return (/msie\\s+(.*?);/i.test(_ua)||/trident\\/.+rv:([\\d\\.]+)/i.test(_ua))&&(parseInt(RegExp.$1)>=10);\n",
"};\n",
"function _onHashChange(_hash){\n",
"var _url,\n",
"_iframe = document.getElementById('g_iframe');\n",
"if(!_hash||_igReg.test(_hash)||_xssReg.test(_hash)){//忽略统计来源的hash\n",
"_url = _default;\n",
"}else{\n",
"if(_hash.indexOf('/') !== 0) {\n",
"_hash = '/' + _hash;\n",
"}\n",
"_hash = _hash.replace(/\\/+/g, '/');//#29664 http://music.163.com/#// 会死循环\n",
"var _midx = -1;\n",
"if((_midx=_hash.indexOf('store/m/'))>=0){\n",
"_url = _hash.substring(0, _midx+8)+(_hash.substring(_midx+8).replace('/m/','/#/'));\n",
"}else{\n",
"_url = _hash.replace('/m/','/#/');\n",
"// debugger;\n",
"if(/^\\/member\\/?(\\?[\\s\\S]*)?$/.test(_url) && typeof vipWebCashierRedirect !== 'undefined' && '' + vipWebCashierRedirect === '1') {\n",
"_url = _url.replace('/member','/prime/member');\n",
"}\n",
"}\n",
"}\n",
"if(_url.indexOf('http://')<0){\n",
"_url = location.protocol+'//'+location.hostname+_url;\n",
"}\n",
"//针对ie10+ location.replace的bug做特殊处理\n",
"if(_isIE10plus()){\n",
"if(_lastPage.replace(_hReg,'')==_url.replace(_hReg,'')){//只是hash的改变\n",
"_iframe.contentWindow.location.replace(_url);\n",
"} else{\n",
"_iframe.parentNode.removeChild(_iframe);\n",
"_iframe = document.createElement('iframe');\n",
"_iframe.id = 'g_iframe';\n",
"_iframe.src = 'about:blank';\n",
"_iframe.className = 'g-iframe';\n",
"_iframe.setAttribute('allowfullscreen', true);\n",
"document.body.insertAdjacentElement('afterBegin',_iframe);\n",
"_iframe.contentWindow.location.href = _url;\n",
"}\n",
"}else{\n",
"_iframe.contentWindow.location.replace(_url);\n",
"}\n",
"_lastPage = _url;\n",
"if(typeof window.onHashChange=='function'){\n",
"window.onHashChange(_hash);\n",
"}\n",
"};\n",
"Hash.init(_onHashChange);\n",
"return {\n",
"dispatch:function(_url,_replace){\n",
"var _ph = GUtil.getPathAndHash(_url);\n",
"if(!_ph) return;\n",
"if(_replace){\n",
"location.replace(GUtil.getBase()+'#'+_ph);\n",
"}else{\n",
"location.hash = _ph;\n",
"}\n",
"},\n",
"refreshIFrame:function(_url){\n",
"_onHashChange(_url);\n",
"}\n",
"};\n",
"})();</script>\n",
"<div class=\"g-btmbar\">\n",
"<div class=\"m-playbar m-playbar-unlock\" style=\"top: -53px; visibility: visible;\" id=\"auto-id-8JF3ENN3VAFToxxh\">\n",
"<div class=\"updn\">\n",
"<div class=\"left f-fl\"><a href=\"javascript:;\" class=\"btn\" hidefocus=\"true\" data-action=\"lock\"></a></div>\n",
"<div class=\"right f-fl\"></div>\n",
"</div>\n",
"<div class=\"bg\"></div>\n",
"<div class=\"hand\" title=\"展开播放条\"></div>\n",
"<div class=\"wrap\" id=\"g_player\" style=\"margin-left: -490px;\">\n",
"<div class=\"btns\">\n",
"<a href=\"javascript:;\" hidefocus=\"true\" data-action=\"prev\" class=\"prv\" title=\"上一首(ctrl+←)\">上一首</a>\n",
"<a href=\"javascript:;\" hidefocus=\"true\" data-action=\"play\" class=\"ply j-flag\" title=\"播放/暂停(p)\">播放/暂停</a>\n",
"<a href=\"javascript:;\" hidefocus=\"true\" data-action=\"next\" class=\"nxt\" title=\"下一首(ctrl+→)\">下一首</a>\n",
"</div>\n",
"<div class=\"head j-flag\"><img src=\"http://s4.music.126.net/style/web2/img/default/default_album.jpg\" /><a href=\"javascript:;\" hidefocus=\"true\" class=\"mask\"></a></div>\n",
"<div class=\"play\">\n",
"<div class=\"j-flag words\"></div>\n",
"<div class=\"m-pbar\" data-action=\"noop\">\n",
"<div class=\"barbg j-flag\" id=\"auto-id-XT3AmP2zq1lUqJcF\">\n",
"<div class=\"rdy\" style=\"width:0%;\"></div>\n",
"<div class=\"cur\" style=\"width:0%;\"><span class=\"btn f-tdn f-alpha\" id=\"auto-id-HsQavKpTMfk9nN00\"><i></i></span></div>\n",
"</div>\n",
"<span class=\"j-flag time\"><em>00:00</em> / 00:00</span>\n",
"</div>\n",
"</div>\n",
"<div class=\"oper f-fl\">\n",
"<a href=\"javascript:;\" hidefocus=\"true\" data-action=\"like\" class=\"icn icn-add j-flag\" title=\"收藏\">收藏</a>\n",
"<a href=\"javascript:;\" hidefocus=\"true\" data-action=\"share\" class=\"icn icn-share\" title=\"分享\">分享</a>\n",
"</div>\n",
"<div class=\"ctrl f-fl f-pr j-flag\">\n",
"<div class=\"m-vol\" style=\"visibility:hidden;\" id=\"auto-id-F3iGHU1MamHcdwZt\">\n",
"<div class=\"barbg\"></div>\n",
"<div class=\"vbg j-t\" id=\"auto-id-8QOlkbDIX70QxLCA\"><div class=\"curr j-t\" style=\"height: 74.4px;\"></div>\n",
"<span class=\"btn f-alpha j-t\" style=\"top: 16.2px;\"></span></div>\n",
"</div>\n",
"<a href=\"javascript:;\" hidefocus=\"true\" data-action=\"volume\" class=\"icn icn-vol\"></a>\n",
"<a href=\"javascript:;\" hidefocus=\"true\" data-action=\"mode\" class=\"icn icn-loop\" title=\"循环\"></a>\n",
"<span class=\"add f-pr\">\n",
"<span class=\"tip\" style=\"display:none;\">已添加到播放列表</span>\n",
"<a href=\"javascript:;\" title=\"播放列表\" hidefocus=\"true\" data-action=\"panel\" class=\"icn icn-list s-fc3\">0</a>\n",
"</span>\n",
"<div class=\"tip tip-1\" style=\"display:none;\">循环</div>\n",
"</div>\n",
"</div>\n",
"</div>\n",
"</div>\n",
"\n",
"<script src=\"//s3.music.126.net/web/s/core_b0a178a6096b06b7ede4786484719e64.js?b0a178a6096b06b7ede4786484719e64\" type=\"text/javascript\"></script><iframe frameborder=\"0\" id=\"auto-id-SCGcud22FsyTJrOJ\" style=\"display: none;\" src=\"about:blank\"></iframe><script type=\"text/javascript\" src=\"https://acstatic-dun.126.net/tool.min.js\"></script><script src=\"//s3.music.126.net/web/s/pt_frame_index_72027f888ebe95226812e45a754a3fad.js?72027f888ebe95226812e45a754a3fad\" type=\"text/javascript\"></script><script type=\"text/javascript\" src=\"https://acstatic-dun.126.net/tool.min.js\"></script>\n",
"\n",
"<script type=\"text/javascript\">\n",
"var _gaq=_gaq||[];\n",
"_gaq.push(['_setAccount','UA-38766552-1'],['_setLocalGifPath','/UA-38766552-1/__utm.gif'],['_setLocalRemoteServerMode']);\n",
"_gaq.push(['_trackPageview']);\n",
"//fix ipad下的一个bug\n",
"if (navigator.userAgent.indexOf('iPad') != -1) {\n",
"iframeHeight = Math.max(\n",
"Math.max(document.body.scrollHeight, document.documentElement.scrollHeight),\n",
"Math.max(document.body.offsetHeight, document.documentElement.offsetHeight),\n",
"Math.max(document.body.clientHeight, document.documentElement.clientHeight)\n",
");\n",
"top.document.body.style.height = iframeHeight + 20 + 'px';\n",
"}</script>\n",
"<div><div></div></div></body></html>\n"
]
}
],
"source": [
"from selenium import webdriver\n",
"\n",
"browser = webdriver.Chrome()\n",
" \n",
"browser.get(\"http://music.163.com\") \n",
"print(browser.page_source)\n",
"#browser.close() "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# 查找元素\n",
"单个元素查找"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-17T03:39:32.451564Z",
"start_time": "2019-10-17T03:39:28.999764Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<selenium.webdriver.remote.webelement.WebElement (session=\"c3cc1a9b94f4916ca4876894a7181c24\", element=\"0.058239897806812824-1\")>\n",
"<selenium.webdriver.remote.webelement.WebElement (session=\"c3cc1a9b94f4916ca4876894a7181c24\", element=\"0.058239897806812824-1\")>\n",
"<selenium.webdriver.remote.webelement.WebElement (session=\"c3cc1a9b94f4916ca4876894a7181c24\", element=\"0.058239897806812824-1\")>\n"
]
}
],
"source": [
"from selenium import webdriver\n",
"\n",
"browser = webdriver.Chrome()\n",
"\n",
"browser.get(\"http://music.163.com\")\n",
"input_first = browser.find_element_by_id(\"g_search\")\n",
"input_second = browser.find_element_by_css_selector(\"#g_search\")\n",
"input_third = browser.find_element_by_xpath('//*[@id=\"g_search\"]')\n",
"print(input_first)\n",
"print(input_second)\n",
"print(input_third)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"这里我们通过三种不同的方式去获取响应的元素，第一种是通过id的方式，第二个中是CSS选择器，第三种是xpath选择器，结果都是相同的。\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## 常用的查找元素方法：\n",
"\n",
"- find_element_by_name\n",
"- find_element_by_id\n",
"- find_element_by_xpath\n",
"- find_element_by_link_text\n",
"- find_element_by_partial_link_text\n",
"- find_element_by_tag_name\n",
"- find_element_by_class_name\n",
"- find_element_by_css_selector"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-17T03:40:12.762331Z",
"start_time": "2019-10-17T03:40:12.759968Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"# 下面这种方式是比较通用的一种方式：这里需要记住By模块所以需要导入\n",
"from selenium.webdriver.common.by import By"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-17T03:40:18.546410Z",
"start_time": "2019-10-17T03:40:14.277771Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<selenium.webdriver.remote.webelement.WebElement (session=\"804f73390e4b2b1f3d258d3c64cfcdf6\", element=\"0.8756856460866715-1\")>\n"
]
}
],
"source": [
"browser = webdriver.Chrome()\n",
"browser.get(\"http://music.163.com\")\n",
"input_first = browser.find_element(By.ID,\"g_search\")\n",
"print(input_first)\n",
"browser.close()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## 多个元素查找\n",
"\n",
"其实多个元素和单个元素的区别，举个例子：find_elements,单个元素是find_element,其他使用上没什么区别，通过其中的一个例子演示："
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-17T03:40:36.622392Z",
"start_time": "2019-10-17T03:40:32.247072Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[<selenium.webdriver.remote.webelement.WebElement (session=\"16c18e686f0c8bf5cc368774e0dfbf4e\", element=\"0.356934675169573-1\")>]\n"
]
}
],
"source": [
"browser = webdriver.Chrome()\n",
"browser.get(\"http://music.163.com\")\n",
"lis = browser.find_elements_by_css_selector('body')\n",
"print(lis)\n",
"browser.close() "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"当然上面的方式也是可以通过导入`from selenium.webdriver.common.by import By` 这种方式实现\n",
"\n",
"> lis = browser.find_elements(By.CSS_SELECTOR,'.service-bd li')\n",
"\n",
"同样的在单个元素中查找的方法在多个元素查找中同样存在：\n",
"- find_elements_by_name\n",
"- find_elements_by_id\n",
"- find_elements_by_xpath\n",
"- find_elements_by_link_text\n",
"- find_elements_by_partial_link_text\n",
"- find_elements_by_tag_name\n",
"- find_elements_by_class_name\n",
"- find_elements_by_css_selector"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 元素交互操作\n",
"对于获取的元素调用交互方法"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-17T03:40:57.466649Z",
"start_time": "2019-10-17T03:40:51.101641Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"from selenium import webdriver\n",
"import time\n",
"browser = webdriver.Chrome()\n",
"\n",
"browser.get(\"https://music.163.com/\")\n",
"input_str = browser.find_element_by_id('srch')\n",
"input_str.send_keys(\"周杰伦\")\n",
"time.sleep(3) #休眠，模仿人工搜索\n",
"input_str.clear()\n",
"input_str.send_keys(\"林俊杰\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"运行的结果可以看出程序会自动打开Chrome浏览器并打开淘宝输入ipad,然后删除，重新输入MacBook pro，并点击搜索\n",
"\n",
"Selenium所有的api文档：http://selenium-python.readthedocs.io/api.html#module-selenium.webdriver.common.action_chains"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 执行JavaScript\n",
"这是一个非常有用的方法，这里就可以直接调用js方法来实现一些操作，\n",
"下面的例子是通过登录知乎然后通过js翻到页面底部，并弹框提示"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2019-10-17T01:16:20.950284Z",
"start_time": "2019-10-17T01:16:17.156296Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"from selenium import webdriver\n",
"browser = webdriver.Chrome()\n",
"browser.get(\"https://www.zhihu.com/explore/\")\n",
"browser.execute_script('window.scrollTo(0, document.body.scrollHeight)')\n",
"browser.execute_script('alert(\"To Bottom\")')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# 一个例子"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T06:32:02.234295Z",
"start_time": "2019-06-08T06:30:56.716427Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"```pyton\n",
"from selenium import webdriver\n",
"\n",
"browser = webdriver.Chrome()\n",
"browser.get(\"https://www.privco.com/home/login\") #需要翻墙打开网址\n",
"username = 'fake_username'\n",
"password = 'fake_password'\n",
"browser.find_element_by_id(\"username\").clear()\n",
"browser.find_element_by_id(\"username\").send_keys(username) \n",
"browser.find_element_by_id(\"password\").clear()\n",
"browser.find_element_by_id(\"password\").send_keys(password)\n",
"browser.find_element_by_css_selector(\"#login-form > div:nth-child(5) > div > button\").click()\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T06:33:11.197128Z",
"start_time": "2019-06-08T06:33:11.169229Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"# url = \"https://www.privco.com/private-company/329463\"\n",
"def download_excel(url):\n",
" browser.get(url)\n",
" name = url.split('/')[-1]\n",
" title = browser.title\n",
" source = browser.page_source\n",
" with open(name+'.html', 'w') as f:\n",
" f.write(source)\n",
" try:\n",
" soup = BeautifulSoup(source, 'html.parser')\n",
" url_new = soup.find('span', {'class', 'profile-name'}).a['href']\n",
" url_excel = url_new + '/export'\n",
" browser.get(url_excel)\n",
" except Exception as e:\n",
" print(url, 'no excel')\n",
" pass\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T06:32:13.789332Z",
"start_time": "2019-06-08T06:32:13.785931Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"urls = [ 'https://www.privco.com/private-company/1135789',\n",
" 'https://www.privco.com/private-company/542756',\n",
" 'https://www.privco.com/private-company/137908',\n",
" 'https://www.privco.com/private-company/137138']"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T06:33:19.547094Z",
"start_time": "2019-06-08T06:33:15.569463Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"https://www.privco.com/private-company/1135789 no excel\n",
"1\n",
"https://www.privco.com/private-company/542756 no excel\n",
"2\n",
"https://www.privco.com/private-company/137908 no excel\n",
"3\n",
"https://www.privco.com/private-company/137138 no excel\n"
]
}
],
"source": [
"for k, url in enumerate(urls):\n",
" print(k)\n",
" try:\n",
" download_excel(url)\n",
" except Exception as e:\n",
" print(url, e)"
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"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"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autoclose": false,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 0,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": false,
"sideBar": false,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {
"height": "647px",
"left": "1361px",
"top": "123px",
"width": "340px"
},
"toc_section_display": false,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
evanbiederstedt/RRBSfun | genomic_regions_distance/Pairs_Genomic_Regions_12August2016_NormalBCD19pCD27mcell23_44.ipynb | 2 | 538131 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import glob\n",
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"pd.set_option('display.max_columns', 50) # print all rows\n",
"\n",
"\n",
"import os\n",
"os.chdir('/Users/evanbiederstedt/Downloads/RRBS_data_files')\n",
"\n",
"import statsmodels.api as sm"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'\\n\\nCD19cell_regions.csv\\ncw154_regions.csv\\nNormal_B_regions.csv\\ntrito_regions.csv\\npcell_regions.csv\\nmcell_regions.csv\\n\\n'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
"\n",
"CD19cell_regions.csv\n",
"cw154_regions.csv\n",
"Normal_B_regions.csv\n",
"trito_regions.csv\n",
"pcell_regions.csv\n",
"mcell_regions.csv\n",
"\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"trito = pd.read_csv(\"trito_regions.csv\")\n",
"normal = pd.read_csv(\"Normal_B_regions.csv\")\n",
"pcell = pd.read_csv(\"pcell_regions.csv\")\n",
"mcell = pd.read_csv(\"mcell_regions.csv\")\n",
"cw154 = pd.read_csv(\"cw154_regions.csv\")\n",
"cd19cell = pd.read_csv(\"CD19cell_regions.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(44, 39)\n",
"(136, 39)\n",
"(90, 39)\n",
"(88, 39)\n",
"(66, 39)\n",
"(89, 39)\n"
]
}
],
"source": [
"print(trito.shape)\n",
"print(normal.shape) # remove 2cell files\n",
"print(pcell.shape)\n",
"print(mcell.shape)\n",
"print(cw154.shape)\n",
"print(cd19cell.shape)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"trito[\"filename\"] = trito[\"filename\"].str[:33]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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>filename</th>\n",
" <th>methylation_tssDistance</th>\n",
" <th>methylation_genesDistance</th>\n",
" <th>methylation_exonsDistance</th>\n",
" <th>methylation_intronsDistance</th>\n",
" <th>methylation_promoterDistance</th>\n",
" <th>methylation_cgiDistance</th>\n",
" <th>methylation_ctcfDistance</th>\n",
" <th>methylation_ctcfUpDistance</th>\n",
" <th>methylation_ctcfDownDistance</th>\n",
" <th>methylation_geneDistalRegulatoryModulesDistance</th>\n",
" <th>methylation_vistaEnhancersDistance</th>\n",
" <th>methylation_3PrimeUTRDistance</th>\n",
" <th>methylation_5PrimeUTRDistance</th>\n",
" <th>methylation_firstExonDistance</th>\n",
" <th>methylation_geneDistalRegulatoryModulesK562Distance</th>\n",
" <th>methylation_hypoInHues64Distance</th>\n",
" <th>methylation_intergenic</th>\n",
" <th>methylation_shore</th>\n",
" <th>methylation_shelf</th>\n",
" <th>PDR_tssDistance</th>\n",
" <th>PDR_genesDistance</th>\n",
" <th>PDR_exonsDistance</th>\n",
" <th>PDR_intronsDistance</th>\n",
" <th>PDR_promoterDistance</th>\n",
" <th>PDR_cgiDistance</th>\n",
" <th>PDR_ctcfDistance</th>\n",
" <th>PDR_ctcfUpDistance</th>\n",
" <th>PDR_ctcfDownDistance</th>\n",
" <th>PDR_geneDistalRegulatoryModulesDistance</th>\n",
" <th>PDR_vistaEnhancersDistance</th>\n",
" <th>PDR_3PrimeUTRDistance</th>\n",
" <th>PDR_5PrimeUTRDistance</th>\n",
" <th>PDR_firstExonDistance</th>\n",
" <th>PDR_geneDistalRegulatoryModulesK562Distance</th>\n",
" <th>PDR_hypoInHues64Distance</th>\n",
" <th>PDR_intergenic</th>\n",
" <th>PDR_shore</th>\n",
" <th>PDR_shelf</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.ACAACC</td>\n",
" <td>0.0</td>\n",
" <td>0.570721</td>\n",
" <td>0.404589</td>\n",
" <td>0.594704</td>\n",
" <td>0.146371</td>\n",
" <td>0.169386</td>\n",
" <td>0.199815</td>\n",
" <td>0.0</td>\n",
" <td>0.199815</td>\n",
" <td>0.342986</td>\n",
" <td>0.444522</td>\n",
" <td>0.680480</td>\n",
" <td>0.305206</td>\n",
" <td>0.145730</td>\n",
" <td>0.254209</td>\n",
" <td>0.877142</td>\n",
" <td>0.820339</td>\n",
" <td>0.607671</td>\n",
" <td>0.803716</td>\n",
" <td>0.0</td>\n",
" <td>0.343014</td>\n",
" <td>0.371890</td>\n",
" <td>0.339596</td>\n",
" <td>0.347748</td>\n",
" <td>0.388709</td>\n",
" <td>0.385297</td>\n",
" <td>0.0</td>\n",
" <td>0.385297</td>\n",
" <td>0.401254</td>\n",
" <td>0.509555</td>\n",
" <td>0.386285</td>\n",
" <td>0.324236</td>\n",
" <td>0.367271</td>\n",
" <td>0.354046</td>\n",
" <td>0.240359</td>\n",
" <td>0.386809</td>\n",
" <td>0.377907</td>\n",
" <td>0.359143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.ACGTGG</td>\n",
" <td>0.0</td>\n",
" <td>0.545781</td>\n",
" <td>0.383371</td>\n",
" <td>0.568638</td>\n",
" <td>0.141545</td>\n",
" <td>0.161519</td>\n",
" <td>0.191404</td>\n",
" <td>0.0</td>\n",
" <td>0.191404</td>\n",
" <td>0.326140</td>\n",
" <td>0.589834</td>\n",
" <td>0.670559</td>\n",
" <td>0.290196</td>\n",
" <td>0.140779</td>\n",
" <td>0.240221</td>\n",
" <td>0.809942</td>\n",
" <td>0.816166</td>\n",
" <td>0.573089</td>\n",
" <td>0.795932</td>\n",
" <td>0.0</td>\n",
" <td>0.348110</td>\n",
" <td>0.381251</td>\n",
" <td>0.341950</td>\n",
" <td>0.349891</td>\n",
" <td>0.398898</td>\n",
" <td>0.415058</td>\n",
" <td>0.0</td>\n",
" <td>0.415058</td>\n",
" <td>0.408417</td>\n",
" <td>0.548192</td>\n",
" <td>0.382172</td>\n",
" <td>0.332749</td>\n",
" <td>0.373615</td>\n",
" <td>0.359217</td>\n",
" <td>0.364148</td>\n",
" <td>0.391925</td>\n",
" <td>0.386808</td>\n",
" <td>0.354120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.ACTCAC</td>\n",
" <td>0.0</td>\n",
" <td>0.564547</td>\n",
" <td>0.401760</td>\n",
" <td>0.588136</td>\n",
" <td>0.148529</td>\n",
" <td>0.174413</td>\n",
" <td>0.209041</td>\n",
" <td>0.0</td>\n",
" <td>0.209041</td>\n",
" <td>0.346473</td>\n",
" <td>0.553062</td>\n",
" <td>0.696068</td>\n",
" <td>0.296809</td>\n",
" <td>0.148360</td>\n",
" <td>0.255392</td>\n",
" <td>0.795883</td>\n",
" <td>0.832812</td>\n",
" <td>0.609544</td>\n",
" <td>0.812564</td>\n",
" <td>0.0</td>\n",
" <td>0.338412</td>\n",
" <td>0.371890</td>\n",
" <td>0.332321</td>\n",
" <td>0.351391</td>\n",
" <td>0.393829</td>\n",
" <td>0.392313</td>\n",
" <td>0.0</td>\n",
" <td>0.392313</td>\n",
" <td>0.412311</td>\n",
" <td>0.471703</td>\n",
" <td>0.378630</td>\n",
" <td>0.327488</td>\n",
" <td>0.370494</td>\n",
" <td>0.338321</td>\n",
" <td>0.334783</td>\n",
" <td>0.378580</td>\n",
" <td>0.378799</td>\n",
" <td>0.353949</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.AGGATG</td>\n",
" <td>0.0</td>\n",
" <td>0.567309</td>\n",
" <td>0.399934</td>\n",
" <td>0.592890</td>\n",
" <td>0.143897</td>\n",
" <td>0.168936</td>\n",
" <td>0.200661</td>\n",
" <td>0.0</td>\n",
" <td>0.200661</td>\n",
" <td>0.342257</td>\n",
" <td>0.665920</td>\n",
" <td>0.661426</td>\n",
" <td>0.308680</td>\n",
" <td>0.141673</td>\n",
" <td>0.242236</td>\n",
" <td>0.787966</td>\n",
" <td>0.824659</td>\n",
" <td>0.602995</td>\n",
" <td>0.799836</td>\n",
" <td>0.0</td>\n",
" <td>0.342724</td>\n",
" <td>0.374419</td>\n",
" <td>0.337654</td>\n",
" <td>0.346109</td>\n",
" <td>0.389718</td>\n",
" <td>0.399153</td>\n",
" <td>0.0</td>\n",
" <td>0.399153</td>\n",
" <td>0.405627</td>\n",
" <td>0.359189</td>\n",
" <td>0.391002</td>\n",
" <td>0.324431</td>\n",
" <td>0.360431</td>\n",
" <td>0.343730</td>\n",
" <td>0.304035</td>\n",
" <td>0.380413</td>\n",
" <td>0.373345</td>\n",
" <td>0.347372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.ATAGCG</td>\n",
" <td>0.0</td>\n",
" <td>0.529224</td>\n",
" <td>0.367743</td>\n",
" <td>0.555131</td>\n",
" <td>0.136090</td>\n",
" <td>0.156827</td>\n",
" <td>0.175426</td>\n",
" <td>0.0</td>\n",
" <td>0.175426</td>\n",
" <td>0.307402</td>\n",
" <td>0.479145</td>\n",
" <td>0.644411</td>\n",
" <td>0.273473</td>\n",
" <td>0.134137</td>\n",
" <td>0.220729</td>\n",
" <td>0.815944</td>\n",
" <td>0.808981</td>\n",
" <td>0.575050</td>\n",
" <td>0.788587</td>\n",
" <td>0.0</td>\n",
" <td>0.349254</td>\n",
" <td>0.376307</td>\n",
" <td>0.342617</td>\n",
" <td>0.343348</td>\n",
" <td>0.388623</td>\n",
" <td>0.403861</td>\n",
" <td>0.0</td>\n",
" <td>0.403861</td>\n",
" <td>0.390288</td>\n",
" <td>0.471324</td>\n",
" <td>0.392438</td>\n",
" <td>0.332882</td>\n",
" <td>0.358450</td>\n",
" <td>0.319824</td>\n",
" <td>0.401641</td>\n",
" <td>0.398275</td>\n",
" <td>0.373236</td>\n",
" <td>0.363320</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" filename methylation_tssDistance \\\n",
"0 RRBS_trito_pool_1_TAAGGCGA.ACAACC 0.0 \n",
"1 RRBS_trito_pool_1_TAAGGCGA.ACGTGG 0.0 \n",
"2 RRBS_trito_pool_1_TAAGGCGA.ACTCAC 0.0 \n",
"3 RRBS_trito_pool_1_TAAGGCGA.AGGATG 0.0 \n",
"4 RRBS_trito_pool_1_TAAGGCGA.ATAGCG 0.0 \n",
"\n",
" methylation_genesDistance methylation_exonsDistance \\\n",
"0 0.570721 0.404589 \n",
"1 0.545781 0.383371 \n",
"2 0.564547 0.401760 \n",
"3 0.567309 0.399934 \n",
"4 0.529224 0.367743 \n",
"\n",
" methylation_intronsDistance methylation_promoterDistance \\\n",
"0 0.594704 0.146371 \n",
"1 0.568638 0.141545 \n",
"2 0.588136 0.148529 \n",
"3 0.592890 0.143897 \n",
"4 0.555131 0.136090 \n",
"\n",
" methylation_cgiDistance methylation_ctcfDistance \\\n",
"0 0.169386 0.199815 \n",
"1 0.161519 0.191404 \n",
"2 0.174413 0.209041 \n",
"3 0.168936 0.200661 \n",
"4 0.156827 0.175426 \n",
"\n",
" methylation_ctcfUpDistance methylation_ctcfDownDistance \\\n",
"0 0.0 0.199815 \n",
"1 0.0 0.191404 \n",
"2 0.0 0.209041 \n",
"3 0.0 0.200661 \n",
"4 0.0 0.175426 \n",
"\n",
" methylation_geneDistalRegulatoryModulesDistance \\\n",
"0 0.342986 \n",
"1 0.326140 \n",
"2 0.346473 \n",
"3 0.342257 \n",
"4 0.307402 \n",
"\n",
" methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance \\\n",
"0 0.444522 0.680480 \n",
"1 0.589834 0.670559 \n",
"2 0.553062 0.696068 \n",
"3 0.665920 0.661426 \n",
"4 0.479145 0.644411 \n",
"\n",
" methylation_5PrimeUTRDistance methylation_firstExonDistance \\\n",
"0 0.305206 0.145730 \n",
"1 0.290196 0.140779 \n",
"2 0.296809 0.148360 \n",
"3 0.308680 0.141673 \n",
"4 0.273473 0.134137 \n",
"\n",
" methylation_geneDistalRegulatoryModulesK562Distance \\\n",
"0 0.254209 \n",
"1 0.240221 \n",
"2 0.255392 \n",
"3 0.242236 \n",
"4 0.220729 \n",
"\n",
" methylation_hypoInHues64Distance methylation_intergenic \\\n",
"0 0.877142 0.820339 \n",
"1 0.809942 0.816166 \n",
"2 0.795883 0.832812 \n",
"3 0.787966 0.824659 \n",
"4 0.815944 0.808981 \n",
"\n",
" methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance \\\n",
"0 0.607671 0.803716 0.0 0.343014 \n",
"1 0.573089 0.795932 0.0 0.348110 \n",
"2 0.609544 0.812564 0.0 0.338412 \n",
"3 0.602995 0.799836 0.0 0.342724 \n",
"4 0.575050 0.788587 0.0 0.349254 \n",
"\n",
" PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance \\\n",
"0 0.371890 0.339596 0.347748 \n",
"1 0.381251 0.341950 0.349891 \n",
"2 0.371890 0.332321 0.351391 \n",
"3 0.374419 0.337654 0.346109 \n",
"4 0.376307 0.342617 0.343348 \n",
"\n",
" PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance \\\n",
"0 0.388709 0.385297 0.0 \n",
"1 0.398898 0.415058 0.0 \n",
"2 0.393829 0.392313 0.0 \n",
"3 0.389718 0.399153 0.0 \n",
"4 0.388623 0.403861 0.0 \n",
"\n",
" PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance \\\n",
"0 0.385297 0.401254 \n",
"1 0.415058 0.408417 \n",
"2 0.392313 0.412311 \n",
"3 0.399153 0.405627 \n",
"4 0.403861 0.390288 \n",
"\n",
" PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance \\\n",
"0 0.509555 0.386285 0.324236 \n",
"1 0.548192 0.382172 0.332749 \n",
"2 0.471703 0.378630 0.327488 \n",
"3 0.359189 0.391002 0.324431 \n",
"4 0.471324 0.392438 0.332882 \n",
"\n",
" PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance \\\n",
"0 0.367271 0.354046 \n",
"1 0.373615 0.359217 \n",
"2 0.370494 0.338321 \n",
"3 0.360431 0.343730 \n",
"4 0.358450 0.319824 \n",
"\n",
" PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf \n",
"0 0.240359 0.386809 0.377907 0.359143 \n",
"1 0.364148 0.391925 0.386808 0.354120 \n",
"2 0.334783 0.378580 0.378799 0.353949 \n",
"3 0.304035 0.380413 0.373345 0.347372 \n",
"4 0.401641 0.398275 0.373236 0.363320 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trito.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"normal[\"filename\"] = normal[\"filename\"].str[:40]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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>filename</th>\n",
" <th>methylation_tssDistance</th>\n",
" <th>methylation_genesDistance</th>\n",
" <th>methylation_exonsDistance</th>\n",
" <th>methylation_intronsDistance</th>\n",
" <th>methylation_promoterDistance</th>\n",
" <th>methylation_cgiDistance</th>\n",
" <th>methylation_ctcfDistance</th>\n",
" <th>methylation_ctcfUpDistance</th>\n",
" <th>methylation_ctcfDownDistance</th>\n",
" <th>methylation_geneDistalRegulatoryModulesDistance</th>\n",
" <th>methylation_vistaEnhancersDistance</th>\n",
" <th>methylation_3PrimeUTRDistance</th>\n",
" <th>methylation_5PrimeUTRDistance</th>\n",
" <th>methylation_firstExonDistance</th>\n",
" <th>methylation_geneDistalRegulatoryModulesK562Distance</th>\n",
" <th>methylation_hypoInHues64Distance</th>\n",
" <th>methylation_intergenic</th>\n",
" <th>methylation_shore</th>\n",
" <th>methylation_shelf</th>\n",
" <th>PDR_tssDistance</th>\n",
" <th>PDR_genesDistance</th>\n",
" <th>PDR_exonsDistance</th>\n",
" <th>PDR_intronsDistance</th>\n",
" <th>PDR_promoterDistance</th>\n",
" <th>PDR_cgiDistance</th>\n",
" <th>PDR_ctcfDistance</th>\n",
" <th>PDR_ctcfUpDistance</th>\n",
" <th>PDR_ctcfDownDistance</th>\n",
" <th>PDR_geneDistalRegulatoryModulesDistance</th>\n",
" <th>PDR_vistaEnhancersDistance</th>\n",
" <th>PDR_3PrimeUTRDistance</th>\n",
" <th>PDR_5PrimeUTRDistance</th>\n",
" <th>PDR_firstExonDistance</th>\n",
" <th>PDR_geneDistalRegulatoryModulesK562Distance</th>\n",
" <th>PDR_hypoInHues64Distance</th>\n",
" <th>PDR_intergenic</th>\n",
" <th>PDR_shore</th>\n",
" <th>PDR_shelf</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>131</th>\n",
" <td>RRBS_normal_B_cell_H1_22_TAGGCATG.GTGAGG</td>\n",
" <td>0.0</td>\n",
" <td>0.476010</td>\n",
" <td>0.319477</td>\n",
" <td>0.501080</td>\n",
" <td>0.112156</td>\n",
" <td>0.125920</td>\n",
" <td>0.140172</td>\n",
" <td>0.0</td>\n",
" <td>0.140172</td>\n",
" <td>0.266191</td>\n",
" <td>0.520634</td>\n",
" <td>0.568097</td>\n",
" <td>0.250866</td>\n",
" <td>0.108529</td>\n",
" <td>0.199670</td>\n",
" <td>0.743346</td>\n",
" <td>0.709026</td>\n",
" <td>0.512304</td>\n",
" <td>0.723009</td>\n",
" <td>0.0</td>\n",
" <td>0.413186</td>\n",
" <td>0.417787</td>\n",
" <td>0.411922</td>\n",
" <td>0.371247</td>\n",
" <td>0.397676</td>\n",
" <td>0.361732</td>\n",
" <td>0.0</td>\n",
" <td>0.361732</td>\n",
" <td>0.449309</td>\n",
" <td>0.538398</td>\n",
" <td>0.431550</td>\n",
" <td>0.378186</td>\n",
" <td>0.390782</td>\n",
" <td>0.390614</td>\n",
" <td>0.433456</td>\n",
" <td>0.526395</td>\n",
" <td>0.438160</td>\n",
" <td>0.468283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td>RRBS_normal_B_cell_H1_22_TAGGCATG.GTTGAG</td>\n",
" <td>0.0</td>\n",
" <td>0.561826</td>\n",
" <td>0.421027</td>\n",
" <td>0.581238</td>\n",
" <td>0.194894</td>\n",
" <td>0.241158</td>\n",
" <td>0.227289</td>\n",
" <td>0.0</td>\n",
" <td>0.227289</td>\n",
" <td>0.355498</td>\n",
" <td>0.652030</td>\n",
" <td>0.675360</td>\n",
" <td>0.320042</td>\n",
" <td>0.195983</td>\n",
" <td>0.239473</td>\n",
" <td>0.848333</td>\n",
" <td>0.775009</td>\n",
" <td>0.603093</td>\n",
" <td>0.765059</td>\n",
" <td>0.0</td>\n",
" <td>0.378380</td>\n",
" <td>0.397910</td>\n",
" <td>0.375394</td>\n",
" <td>0.389046</td>\n",
" <td>0.412999</td>\n",
" <td>0.390730</td>\n",
" <td>0.0</td>\n",
" <td>0.390730</td>\n",
" <td>0.430454</td>\n",
" <td>0.628598</td>\n",
" <td>0.406830</td>\n",
" <td>0.371516</td>\n",
" <td>0.395340</td>\n",
" <td>0.389081</td>\n",
" <td>0.268990</td>\n",
" <td>0.455415</td>\n",
" <td>0.396795</td>\n",
" <td>0.385031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133</th>\n",
" <td>RRBS_normal_B_cell_H1_22_TAGGCATG.TAGCGG</td>\n",
" <td>0.0</td>\n",
" <td>0.403834</td>\n",
" <td>0.204255</td>\n",
" <td>0.435666</td>\n",
" <td>0.094118</td>\n",
" <td>0.093855</td>\n",
" <td>0.072948</td>\n",
" <td>0.0</td>\n",
" <td>0.072948</td>\n",
" <td>0.253112</td>\n",
" <td>0.333333</td>\n",
" <td>0.591837</td>\n",
" <td>0.223590</td>\n",
" <td>0.096000</td>\n",
" <td>0.223404</td>\n",
" <td>0.818182</td>\n",
" <td>0.820471</td>\n",
" <td>0.605528</td>\n",
" <td>0.801020</td>\n",
" <td>0.0</td>\n",
" <td>0.320128</td>\n",
" <td>0.375319</td>\n",
" <td>0.314522</td>\n",
" <td>0.375959</td>\n",
" <td>0.383838</td>\n",
" <td>0.258359</td>\n",
" <td>0.0</td>\n",
" <td>0.258359</td>\n",
" <td>0.396266</td>\n",
" <td>1.000000</td>\n",
" <td>0.346939</td>\n",
" <td>0.411282</td>\n",
" <td>0.409333</td>\n",
" <td>0.386525</td>\n",
" <td>0.818182</td>\n",
" <td>0.311942</td>\n",
" <td>0.381910</td>\n",
" <td>0.306122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td>RRBS_normal_B_cell_H1_22_TAGGCATG.TATCTC</td>\n",
" <td>0.0</td>\n",
" <td>0.601704</td>\n",
" <td>0.464233</td>\n",
" <td>0.621206</td>\n",
" <td>0.137723</td>\n",
" <td>0.148328</td>\n",
" <td>0.140719</td>\n",
" <td>0.0</td>\n",
" <td>0.140719</td>\n",
" <td>0.266187</td>\n",
" <td>0.400000</td>\n",
" <td>0.706186</td>\n",
" <td>0.334928</td>\n",
" <td>0.135086</td>\n",
" <td>0.218656</td>\n",
" <td>1.000000</td>\n",
" <td>0.878793</td>\n",
" <td>0.656104</td>\n",
" <td>0.840000</td>\n",
" <td>0.0</td>\n",
" <td>0.286919</td>\n",
" <td>0.323835</td>\n",
" <td>0.287354</td>\n",
" <td>0.384461</td>\n",
" <td>0.407314</td>\n",
" <td>0.347305</td>\n",
" <td>0.0</td>\n",
" <td>0.347305</td>\n",
" <td>0.350719</td>\n",
" <td>0.000000</td>\n",
" <td>0.368557</td>\n",
" <td>0.362440</td>\n",
" <td>0.391198</td>\n",
" <td>0.270812</td>\n",
" <td>0.000000</td>\n",
" <td>0.243974</td>\n",
" <td>0.306151</td>\n",
" <td>0.357500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>135</th>\n",
" <td>RRBS_normal_B_cell_H1_22_TAGGCATG.TCTCTG</td>\n",
" <td>0.0</td>\n",
" <td>0.560233</td>\n",
" <td>0.366567</td>\n",
" <td>0.591090</td>\n",
" <td>0.120401</td>\n",
" <td>0.128137</td>\n",
" <td>0.182218</td>\n",
" <td>0.0</td>\n",
" <td>0.182218</td>\n",
" <td>0.329330</td>\n",
" <td>0.558873</td>\n",
" <td>0.703213</td>\n",
" <td>0.278496</td>\n",
" <td>0.111726</td>\n",
" <td>0.241188</td>\n",
" <td>0.930269</td>\n",
" <td>0.887614</td>\n",
" <td>0.628778</td>\n",
" <td>0.865630</td>\n",
" <td>0.0</td>\n",
" <td>0.276382</td>\n",
" <td>0.338157</td>\n",
" <td>0.268336</td>\n",
" <td>0.360595</td>\n",
" <td>0.380319</td>\n",
" <td>0.401676</td>\n",
" <td>0.0</td>\n",
" <td>0.401676</td>\n",
" <td>0.369472</td>\n",
" <td>0.293523</td>\n",
" <td>0.278300</td>\n",
" <td>0.314054</td>\n",
" <td>0.366833</td>\n",
" <td>0.346789</td>\n",
" <td>0.255155</td>\n",
" <td>0.244022</td>\n",
" <td>0.313503</td>\n",
" <td>0.248764</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" filename methylation_tssDistance \\\n",
"131 RRBS_normal_B_cell_H1_22_TAGGCATG.GTGAGG 0.0 \n",
"132 RRBS_normal_B_cell_H1_22_TAGGCATG.GTTGAG 0.0 \n",
"133 RRBS_normal_B_cell_H1_22_TAGGCATG.TAGCGG 0.0 \n",
"134 RRBS_normal_B_cell_H1_22_TAGGCATG.TATCTC 0.0 \n",
"135 RRBS_normal_B_cell_H1_22_TAGGCATG.TCTCTG 0.0 \n",
"\n",
" methylation_genesDistance methylation_exonsDistance \\\n",
"131 0.476010 0.319477 \n",
"132 0.561826 0.421027 \n",
"133 0.403834 0.204255 \n",
"134 0.601704 0.464233 \n",
"135 0.560233 0.366567 \n",
"\n",
" methylation_intronsDistance methylation_promoterDistance \\\n",
"131 0.501080 0.112156 \n",
"132 0.581238 0.194894 \n",
"133 0.435666 0.094118 \n",
"134 0.621206 0.137723 \n",
"135 0.591090 0.120401 \n",
"\n",
" methylation_cgiDistance methylation_ctcfDistance \\\n",
"131 0.125920 0.140172 \n",
"132 0.241158 0.227289 \n",
"133 0.093855 0.072948 \n",
"134 0.148328 0.140719 \n",
"135 0.128137 0.182218 \n",
"\n",
" methylation_ctcfUpDistance methylation_ctcfDownDistance \\\n",
"131 0.0 0.140172 \n",
"132 0.0 0.227289 \n",
"133 0.0 0.072948 \n",
"134 0.0 0.140719 \n",
"135 0.0 0.182218 \n",
"\n",
" methylation_geneDistalRegulatoryModulesDistance \\\n",
"131 0.266191 \n",
"132 0.355498 \n",
"133 0.253112 \n",
"134 0.266187 \n",
"135 0.329330 \n",
"\n",
" methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance \\\n",
"131 0.520634 0.568097 \n",
"132 0.652030 0.675360 \n",
"133 0.333333 0.591837 \n",
"134 0.400000 0.706186 \n",
"135 0.558873 0.703213 \n",
"\n",
" methylation_5PrimeUTRDistance methylation_firstExonDistance \\\n",
"131 0.250866 0.108529 \n",
"132 0.320042 0.195983 \n",
"133 0.223590 0.096000 \n",
"134 0.334928 0.135086 \n",
"135 0.278496 0.111726 \n",
"\n",
" methylation_geneDistalRegulatoryModulesK562Distance \\\n",
"131 0.199670 \n",
"132 0.239473 \n",
"133 0.223404 \n",
"134 0.218656 \n",
"135 0.241188 \n",
"\n",
" methylation_hypoInHues64Distance methylation_intergenic \\\n",
"131 0.743346 0.709026 \n",
"132 0.848333 0.775009 \n",
"133 0.818182 0.820471 \n",
"134 1.000000 0.878793 \n",
"135 0.930269 0.887614 \n",
"\n",
" methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance \\\n",
"131 0.512304 0.723009 0.0 0.413186 \n",
"132 0.603093 0.765059 0.0 0.378380 \n",
"133 0.605528 0.801020 0.0 0.320128 \n",
"134 0.656104 0.840000 0.0 0.286919 \n",
"135 0.628778 0.865630 0.0 0.276382 \n",
"\n",
" PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance \\\n",
"131 0.417787 0.411922 0.371247 \n",
"132 0.397910 0.375394 0.389046 \n",
"133 0.375319 0.314522 0.375959 \n",
"134 0.323835 0.287354 0.384461 \n",
"135 0.338157 0.268336 0.360595 \n",
"\n",
" PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance \\\n",
"131 0.397676 0.361732 0.0 \n",
"132 0.412999 0.390730 0.0 \n",
"133 0.383838 0.258359 0.0 \n",
"134 0.407314 0.347305 0.0 \n",
"135 0.380319 0.401676 0.0 \n",
"\n",
" PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance \\\n",
"131 0.361732 0.449309 \n",
"132 0.390730 0.430454 \n",
"133 0.258359 0.396266 \n",
"134 0.347305 0.350719 \n",
"135 0.401676 0.369472 \n",
"\n",
" PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance \\\n",
"131 0.538398 0.431550 0.378186 \n",
"132 0.628598 0.406830 0.371516 \n",
"133 1.000000 0.346939 0.411282 \n",
"134 0.000000 0.368557 0.362440 \n",
"135 0.293523 0.278300 0.314054 \n",
"\n",
" PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance \\\n",
"131 0.390782 0.390614 \n",
"132 0.395340 0.389081 \n",
"133 0.409333 0.386525 \n",
"134 0.391198 0.270812 \n",
"135 0.366833 0.346789 \n",
"\n",
" PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf \n",
"131 0.433456 0.526395 0.438160 0.468283 \n",
"132 0.268990 0.455415 0.396795 0.385031 \n",
"133 0.818182 0.311942 0.381910 0.306122 \n",
"134 0.000000 0.243974 0.306151 0.357500 \n",
"135 0.255155 0.244022 0.313503 0.248764 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"normal.tail()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pcell[\"protocol\"] = pcell[\"filename\"].str[:31]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" if __name__ == '__main__':\n",
"/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" from ipykernel import kernelapp as app\n",
"/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" app.launch_new_instance()\n",
"/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
}
],
"source": [
"pcell[\"filename\"][pcell[\"protocol\"]=='RRBS_NormalBCD19pCD27pcell1_22_'] = pcell[\"filename\"].str[:46]\n",
"pcell[\"filename\"][pcell[\"protocol\"]=='RRBS_NormalBCD19pCD27pcell23_44'] = pcell[\"filename\"].str[:47]\n",
"pcell[\"filename\"][pcell[\"protocol\"]=='RRBS_NormalBCD19pCD27pcell45_66'] = pcell[\"filename\"].str[:47]\n",
"pcell[\"filename\"][pcell[\"protocol\"]=='RRBS_NormalBCD19pCD27pcell67_88'] = pcell[\"filename\"].str[:47]"
]
},
{
"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>filename</th>\n",
" <th>methylation_tssDistance</th>\n",
" <th>methylation_genesDistance</th>\n",
" <th>methylation_exonsDistance</th>\n",
" <th>methylation_intronsDistance</th>\n",
" <th>methylation_promoterDistance</th>\n",
" <th>methylation_cgiDistance</th>\n",
" <th>methylation_ctcfDistance</th>\n",
" <th>methylation_ctcfUpDistance</th>\n",
" <th>methylation_ctcfDownDistance</th>\n",
" <th>methylation_geneDistalRegulatoryModulesDistance</th>\n",
" <th>methylation_vistaEnhancersDistance</th>\n",
" <th>methylation_3PrimeUTRDistance</th>\n",
" <th>methylation_5PrimeUTRDistance</th>\n",
" <th>methylation_firstExonDistance</th>\n",
" <th>methylation_geneDistalRegulatoryModulesK562Distance</th>\n",
" <th>methylation_hypoInHues64Distance</th>\n",
" <th>methylation_intergenic</th>\n",
" <th>methylation_shore</th>\n",
" <th>methylation_shelf</th>\n",
" <th>PDR_tssDistance</th>\n",
" <th>PDR_genesDistance</th>\n",
" <th>PDR_exonsDistance</th>\n",
" <th>PDR_intronsDistance</th>\n",
" <th>PDR_promoterDistance</th>\n",
" <th>PDR_cgiDistance</th>\n",
" <th>PDR_ctcfDistance</th>\n",
" <th>PDR_ctcfUpDistance</th>\n",
" <th>PDR_ctcfDownDistance</th>\n",
" <th>PDR_geneDistalRegulatoryModulesDistance</th>\n",
" <th>PDR_vistaEnhancersDistance</th>\n",
" <th>PDR_3PrimeUTRDistance</th>\n",
" <th>PDR_5PrimeUTRDistance</th>\n",
" <th>PDR_firstExonDistance</th>\n",
" <th>PDR_geneDistalRegulatoryModulesK562Distance</th>\n",
" <th>PDR_hypoInHues64Distance</th>\n",
" <th>PDR_intergenic</th>\n",
" <th>PDR_shore</th>\n",
" <th>PDR_shelf</th>\n",
" <th>protocol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.GTTGAG</td>\n",
" <td>0.0</td>\n",
" <td>0.488591</td>\n",
" <td>0.320965</td>\n",
" <td>0.515461</td>\n",
" <td>0.090361</td>\n",
" <td>0.105259</td>\n",
" <td>0.121025</td>\n",
" <td>0.0</td>\n",
" <td>0.121025</td>\n",
" <td>0.263410</td>\n",
" <td>0.516635</td>\n",
" <td>0.618186</td>\n",
" <td>0.232372</td>\n",
" <td>0.087691</td>\n",
" <td>0.179349</td>\n",
" <td>0.855401</td>\n",
" <td>0.786055</td>\n",
" <td>0.541282</td>\n",
" <td>0.773497</td>\n",
" <td>0.0</td>\n",
" <td>0.281265</td>\n",
" <td>0.250894</td>\n",
" <td>0.285832</td>\n",
" <td>0.152622</td>\n",
" <td>0.193308</td>\n",
" <td>0.192678</td>\n",
" <td>0.0</td>\n",
" <td>0.192678</td>\n",
" <td>0.276641</td>\n",
" <td>0.645913</td>\n",
" <td>0.369431</td>\n",
" <td>0.183830</td>\n",
" <td>0.159538</td>\n",
" <td>0.183626</td>\n",
" <td>0.488850</td>\n",
" <td>0.429191</td>\n",
" <td>0.356704</td>\n",
" <td>0.371891</td>\n",
" <td>RRBS_NormalBCD19pCD27pcell67_88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.TAGCGG</td>\n",
" <td>0.0</td>\n",
" <td>0.482284</td>\n",
" <td>0.313186</td>\n",
" <td>0.509034</td>\n",
" <td>0.085526</td>\n",
" <td>0.094721</td>\n",
" <td>0.126048</td>\n",
" <td>0.0</td>\n",
" <td>0.126048</td>\n",
" <td>0.258379</td>\n",
" <td>0.331160</td>\n",
" <td>0.603295</td>\n",
" <td>0.229917</td>\n",
" <td>0.074569</td>\n",
" <td>0.173552</td>\n",
" <td>0.891344</td>\n",
" <td>0.781317</td>\n",
" <td>0.519032</td>\n",
" <td>0.765147</td>\n",
" <td>0.0</td>\n",
" <td>0.275113</td>\n",
" <td>0.244301</td>\n",
" <td>0.281934</td>\n",
" <td>0.159508</td>\n",
" <td>0.186889</td>\n",
" <td>0.213556</td>\n",
" <td>0.0</td>\n",
" <td>0.213556</td>\n",
" <td>0.284529</td>\n",
" <td>0.364407</td>\n",
" <td>0.374666</td>\n",
" <td>0.194492</td>\n",
" <td>0.150189</td>\n",
" <td>0.202991</td>\n",
" <td>0.220994</td>\n",
" <td>0.416715</td>\n",
" <td>0.372722</td>\n",
" <td>0.386118</td>\n",
" <td>RRBS_NormalBCD19pCD27pcell67_88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.TATCTC</td>\n",
" <td>0.0</td>\n",
" <td>0.486434</td>\n",
" <td>0.337771</td>\n",
" <td>0.509877</td>\n",
" <td>0.104169</td>\n",
" <td>0.131287</td>\n",
" <td>0.145222</td>\n",
" <td>0.0</td>\n",
" <td>0.145222</td>\n",
" <td>0.275499</td>\n",
" <td>0.528938</td>\n",
" <td>0.594743</td>\n",
" <td>0.239506</td>\n",
" <td>0.108890</td>\n",
" <td>0.177779</td>\n",
" <td>0.812648</td>\n",
" <td>0.734134</td>\n",
" <td>0.536591</td>\n",
" <td>0.741314</td>\n",
" <td>0.0</td>\n",
" <td>0.317291</td>\n",
" <td>0.273234</td>\n",
" <td>0.322569</td>\n",
" <td>0.168070</td>\n",
" <td>0.214958</td>\n",
" <td>0.197274</td>\n",
" <td>0.0</td>\n",
" <td>0.197274</td>\n",
" <td>0.298599</td>\n",
" <td>0.433302</td>\n",
" <td>0.385105</td>\n",
" <td>0.203708</td>\n",
" <td>0.181556</td>\n",
" <td>0.193979</td>\n",
" <td>0.399823</td>\n",
" <td>0.492227</td>\n",
" <td>0.388837</td>\n",
" <td>0.420926</td>\n",
" <td>RRBS_NormalBCD19pCD27pcell67_88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.TCTCTG</td>\n",
" <td>0.0</td>\n",
" <td>0.504985</td>\n",
" <td>0.354574</td>\n",
" <td>0.527785</td>\n",
" <td>0.126590</td>\n",
" <td>0.162859</td>\n",
" <td>0.148021</td>\n",
" <td>0.0</td>\n",
" <td>0.148021</td>\n",
" <td>0.298728</td>\n",
" <td>0.675342</td>\n",
" <td>0.654135</td>\n",
" <td>0.247424</td>\n",
" <td>0.123843</td>\n",
" <td>0.198148</td>\n",
" <td>0.793358</td>\n",
" <td>0.753129</td>\n",
" <td>0.541053</td>\n",
" <td>0.740954</td>\n",
" <td>0.0</td>\n",
" <td>0.308032</td>\n",
" <td>0.268731</td>\n",
" <td>0.311831</td>\n",
" <td>0.174724</td>\n",
" <td>0.213951</td>\n",
" <td>0.206692</td>\n",
" <td>0.0</td>\n",
" <td>0.206692</td>\n",
" <td>0.310745</td>\n",
" <td>0.335160</td>\n",
" <td>0.390648</td>\n",
" <td>0.196147</td>\n",
" <td>0.168175</td>\n",
" <td>0.209597</td>\n",
" <td>0.402829</td>\n",
" <td>0.469717</td>\n",
" <td>0.383694</td>\n",
" <td>0.375906</td>\n",
" <td>RRBS_NormalBCD19pCD27pcell67_88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.TGCTGC</td>\n",
" <td>0.0</td>\n",
" <td>0.461103</td>\n",
" <td>0.285016</td>\n",
" <td>0.490647</td>\n",
" <td>0.061652</td>\n",
" <td>0.073687</td>\n",
" <td>0.091623</td>\n",
" <td>0.0</td>\n",
" <td>0.091623</td>\n",
" <td>0.224748</td>\n",
" <td>0.583333</td>\n",
" <td>0.622024</td>\n",
" <td>0.196912</td>\n",
" <td>0.058647</td>\n",
" <td>0.151447</td>\n",
" <td>1.000000</td>\n",
" <td>0.882502</td>\n",
" <td>0.575498</td>\n",
" <td>0.874932</td>\n",
" <td>0.0</td>\n",
" <td>0.166791</td>\n",
" <td>0.159804</td>\n",
" <td>0.165718</td>\n",
" <td>0.099319</td>\n",
" <td>0.130943</td>\n",
" <td>0.126309</td>\n",
" <td>0.0</td>\n",
" <td>0.126309</td>\n",
" <td>0.179091</td>\n",
" <td>0.083333</td>\n",
" <td>0.245536</td>\n",
" <td>0.094161</td>\n",
" <td>0.100645</td>\n",
" <td>0.120579</td>\n",
" <td>0.000000</td>\n",
" <td>0.205599</td>\n",
" <td>0.257229</td>\n",
" <td>0.231186</td>\n",
" <td>RRBS_NormalBCD19pCD27pcell67_88</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" filename methylation_tssDistance \\\n",
"85 RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.GTTGAG 0.0 \n",
"86 RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.TAGCGG 0.0 \n",
"87 RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.TATCTC 0.0 \n",
"88 RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.TCTCTG 0.0 \n",
"89 RRBS_NormalBCD19pCD27pcell67_88_GCTACGCT.TGCTGC 0.0 \n",
"\n",
" methylation_genesDistance methylation_exonsDistance \\\n",
"85 0.488591 0.320965 \n",
"86 0.482284 0.313186 \n",
"87 0.486434 0.337771 \n",
"88 0.504985 0.354574 \n",
"89 0.461103 0.285016 \n",
"\n",
" methylation_intronsDistance methylation_promoterDistance \\\n",
"85 0.515461 0.090361 \n",
"86 0.509034 0.085526 \n",
"87 0.509877 0.104169 \n",
"88 0.527785 0.126590 \n",
"89 0.490647 0.061652 \n",
"\n",
" methylation_cgiDistance methylation_ctcfDistance \\\n",
"85 0.105259 0.121025 \n",
"86 0.094721 0.126048 \n",
"87 0.131287 0.145222 \n",
"88 0.162859 0.148021 \n",
"89 0.073687 0.091623 \n",
"\n",
" methylation_ctcfUpDistance methylation_ctcfDownDistance \\\n",
"85 0.0 0.121025 \n",
"86 0.0 0.126048 \n",
"87 0.0 0.145222 \n",
"88 0.0 0.148021 \n",
"89 0.0 0.091623 \n",
"\n",
" methylation_geneDistalRegulatoryModulesDistance \\\n",
"85 0.263410 \n",
"86 0.258379 \n",
"87 0.275499 \n",
"88 0.298728 \n",
"89 0.224748 \n",
"\n",
" methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance \\\n",
"85 0.516635 0.618186 \n",
"86 0.331160 0.603295 \n",
"87 0.528938 0.594743 \n",
"88 0.675342 0.654135 \n",
"89 0.583333 0.622024 \n",
"\n",
" methylation_5PrimeUTRDistance methylation_firstExonDistance \\\n",
"85 0.232372 0.087691 \n",
"86 0.229917 0.074569 \n",
"87 0.239506 0.108890 \n",
"88 0.247424 0.123843 \n",
"89 0.196912 0.058647 \n",
"\n",
" methylation_geneDistalRegulatoryModulesK562Distance \\\n",
"85 0.179349 \n",
"86 0.173552 \n",
"87 0.177779 \n",
"88 0.198148 \n",
"89 0.151447 \n",
"\n",
" methylation_hypoInHues64Distance methylation_intergenic \\\n",
"85 0.855401 0.786055 \n",
"86 0.891344 0.781317 \n",
"87 0.812648 0.734134 \n",
"88 0.793358 0.753129 \n",
"89 1.000000 0.882502 \n",
"\n",
" methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance \\\n",
"85 0.541282 0.773497 0.0 0.281265 \n",
"86 0.519032 0.765147 0.0 0.275113 \n",
"87 0.536591 0.741314 0.0 0.317291 \n",
"88 0.541053 0.740954 0.0 0.308032 \n",
"89 0.575498 0.874932 0.0 0.166791 \n",
"\n",
" PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance \\\n",
"85 0.250894 0.285832 0.152622 \n",
"86 0.244301 0.281934 0.159508 \n",
"87 0.273234 0.322569 0.168070 \n",
"88 0.268731 0.311831 0.174724 \n",
"89 0.159804 0.165718 0.099319 \n",
"\n",
" PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance \\\n",
"85 0.193308 0.192678 0.0 \n",
"86 0.186889 0.213556 0.0 \n",
"87 0.214958 0.197274 0.0 \n",
"88 0.213951 0.206692 0.0 \n",
"89 0.130943 0.126309 0.0 \n",
"\n",
" PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance \\\n",
"85 0.192678 0.276641 \n",
"86 0.213556 0.284529 \n",
"87 0.197274 0.298599 \n",
"88 0.206692 0.310745 \n",
"89 0.126309 0.179091 \n",
"\n",
" PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance \\\n",
"85 0.645913 0.369431 0.183830 \n",
"86 0.364407 0.374666 0.194492 \n",
"87 0.433302 0.385105 0.203708 \n",
"88 0.335160 0.390648 0.196147 \n",
"89 0.083333 0.245536 0.094161 \n",
"\n",
" PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance \\\n",
"85 0.159538 0.183626 \n",
"86 0.150189 0.202991 \n",
"87 0.181556 0.193979 \n",
"88 0.168175 0.209597 \n",
"89 0.100645 0.120579 \n",
"\n",
" PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf \\\n",
"85 0.488850 0.429191 0.356704 0.371891 \n",
"86 0.220994 0.416715 0.372722 0.386118 \n",
"87 0.399823 0.492227 0.388837 0.420926 \n",
"88 0.402829 0.469717 0.383694 0.375906 \n",
"89 0.000000 0.205599 0.257229 0.231186 \n",
"\n",
" protocol \n",
"85 RRBS_NormalBCD19pCD27pcell67_88 \n",
"86 RRBS_NormalBCD19pCD27pcell67_88 \n",
"87 RRBS_NormalBCD19pCD27pcell67_88 \n",
"88 RRBS_NormalBCD19pCD27pcell67_88 \n",
"89 RRBS_NormalBCD19pCD27pcell67_88 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pcell.tail()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mcell[\"protocol\"] = mcell[\"filename\"].str[:31]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" if __name__ == '__main__':\n",
"/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" from ipykernel import kernelapp as app\n",
"/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" app.launch_new_instance()\n",
"/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
}
],
"source": [
"mcell[\"filename\"][mcell[\"protocol\"]=='RRBS_NormalBCD19pCD27mcell1_22_'] = mcell[\"filename\"].str[:46]\n",
"mcell[\"filename\"][mcell[\"protocol\"]=='RRBS_NormalBCD19pCD27mcell23_44'] = mcell[\"filename\"].str[:47]\n",
"mcell[\"filename\"][mcell[\"protocol\"]=='RRBS_NormalBCD19pCD27mcell45_66'] = mcell[\"filename\"].str[:47]\n",
"mcell[\"filename\"][mcell[\"protocol\"]=='RRBS_NormalBCD19pCD27mcell67_88'] = mcell[\"filename\"].str[:47]"
]
},
{
"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>filename</th>\n",
" <th>methylation_tssDistance</th>\n",
" <th>methylation_genesDistance</th>\n",
" <th>methylation_exonsDistance</th>\n",
" <th>methylation_intronsDistance</th>\n",
" <th>methylation_promoterDistance</th>\n",
" <th>methylation_cgiDistance</th>\n",
" <th>methylation_ctcfDistance</th>\n",
" <th>methylation_ctcfUpDistance</th>\n",
" <th>methylation_ctcfDownDistance</th>\n",
" <th>methylation_geneDistalRegulatoryModulesDistance</th>\n",
" <th>methylation_vistaEnhancersDistance</th>\n",
" <th>methylation_3PrimeUTRDistance</th>\n",
" <th>methylation_5PrimeUTRDistance</th>\n",
" <th>methylation_firstExonDistance</th>\n",
" <th>methylation_geneDistalRegulatoryModulesK562Distance</th>\n",
" <th>methylation_hypoInHues64Distance</th>\n",
" <th>methylation_intergenic</th>\n",
" <th>methylation_shore</th>\n",
" <th>methylation_shelf</th>\n",
" <th>PDR_tssDistance</th>\n",
" <th>PDR_genesDistance</th>\n",
" <th>PDR_exonsDistance</th>\n",
" <th>PDR_intronsDistance</th>\n",
" <th>PDR_promoterDistance</th>\n",
" <th>PDR_cgiDistance</th>\n",
" <th>PDR_ctcfDistance</th>\n",
" <th>PDR_ctcfUpDistance</th>\n",
" <th>PDR_ctcfDownDistance</th>\n",
" <th>PDR_geneDistalRegulatoryModulesDistance</th>\n",
" <th>PDR_vistaEnhancersDistance</th>\n",
" <th>PDR_3PrimeUTRDistance</th>\n",
" <th>PDR_5PrimeUTRDistance</th>\n",
" <th>PDR_firstExonDistance</th>\n",
" <th>PDR_geneDistalRegulatoryModulesK562Distance</th>\n",
" <th>PDR_hypoInHues64Distance</th>\n",
" <th>PDR_intergenic</th>\n",
" <th>PDR_shore</th>\n",
" <th>PDR_shelf</th>\n",
" <th>protocol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.GTGAGG</td>\n",
" <td>0.0</td>\n",
" <td>0.472029</td>\n",
" <td>0.279894</td>\n",
" <td>0.507985</td>\n",
" <td>0.062184</td>\n",
" <td>0.068979</td>\n",
" <td>0.099081</td>\n",
" <td>0.0</td>\n",
" <td>0.099081</td>\n",
" <td>0.254673</td>\n",
" <td>0.414678</td>\n",
" <td>0.646722</td>\n",
" <td>0.202133</td>\n",
" <td>0.052964</td>\n",
" <td>0.180330</td>\n",
" <td>0.825413</td>\n",
" <td>0.880407</td>\n",
" <td>0.567798</td>\n",
" <td>0.868663</td>\n",
" <td>0.0</td>\n",
" <td>0.162036</td>\n",
" <td>0.157993</td>\n",
" <td>0.162275</td>\n",
" <td>0.105846</td>\n",
" <td>0.134560</td>\n",
" <td>0.144832</td>\n",
" <td>0.0</td>\n",
" <td>0.144832</td>\n",
" <td>0.210529</td>\n",
" <td>0.343079</td>\n",
" <td>0.229286</td>\n",
" <td>0.102900</td>\n",
" <td>0.115445</td>\n",
" <td>0.160677</td>\n",
" <td>0.270661</td>\n",
" <td>0.208397</td>\n",
" <td>0.252791</td>\n",
" <td>0.195733</td>\n",
" <td>RRBS_NormalBCD19pCD27mcell67_88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.GTTGAG</td>\n",
" <td>0.0</td>\n",
" <td>0.475509</td>\n",
" <td>0.284108</td>\n",
" <td>0.507053</td>\n",
" <td>0.067481</td>\n",
" <td>0.074098</td>\n",
" <td>0.107121</td>\n",
" <td>0.0</td>\n",
" <td>0.107121</td>\n",
" <td>0.252745</td>\n",
" <td>0.376858</td>\n",
" <td>0.661143</td>\n",
" <td>0.200471</td>\n",
" <td>0.058478</td>\n",
" <td>0.178968</td>\n",
" <td>0.852344</td>\n",
" <td>0.884411</td>\n",
" <td>0.593522</td>\n",
" <td>0.879363</td>\n",
" <td>0.0</td>\n",
" <td>0.157373</td>\n",
" <td>0.152483</td>\n",
" <td>0.156989</td>\n",
" <td>0.105841</td>\n",
" <td>0.133327</td>\n",
" <td>0.140077</td>\n",
" <td>0.0</td>\n",
" <td>0.140077</td>\n",
" <td>0.195673</td>\n",
" <td>0.159236</td>\n",
" <td>0.214858</td>\n",
" <td>0.102166</td>\n",
" <td>0.103687</td>\n",
" <td>0.136311</td>\n",
" <td>0.139063</td>\n",
" <td>0.208999</td>\n",
" <td>0.275148</td>\n",
" <td>0.201310</td>\n",
" <td>RRBS_NormalBCD19pCD27mcell67_88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.TAGCGG</td>\n",
" <td>0.0</td>\n",
" <td>0.465384</td>\n",
" <td>0.270854</td>\n",
" <td>0.497950</td>\n",
" <td>0.070138</td>\n",
" <td>0.075311</td>\n",
" <td>0.113918</td>\n",
" <td>0.0</td>\n",
" <td>0.113918</td>\n",
" <td>0.265460</td>\n",
" <td>0.515006</td>\n",
" <td>0.626513</td>\n",
" <td>0.197863</td>\n",
" <td>0.054605</td>\n",
" <td>0.191544</td>\n",
" <td>0.873469</td>\n",
" <td>0.874056</td>\n",
" <td>0.568923</td>\n",
" <td>0.844443</td>\n",
" <td>0.0</td>\n",
" <td>0.165017</td>\n",
" <td>0.151922</td>\n",
" <td>0.166105</td>\n",
" <td>0.117741</td>\n",
" <td>0.146015</td>\n",
" <td>0.161420</td>\n",
" <td>0.0</td>\n",
" <td>0.161420</td>\n",
" <td>0.211532</td>\n",
" <td>0.650660</td>\n",
" <td>0.257800</td>\n",
" <td>0.104910</td>\n",
" <td>0.104068</td>\n",
" <td>0.157382</td>\n",
" <td>0.273469</td>\n",
" <td>0.210667</td>\n",
" <td>0.270156</td>\n",
" <td>0.202981</td>\n",
" <td>RRBS_NormalBCD19pCD27mcell67_88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.TATCTC</td>\n",
" <td>0.0</td>\n",
" <td>0.475148</td>\n",
" <td>0.283056</td>\n",
" <td>0.509076</td>\n",
" <td>0.067527</td>\n",
" <td>0.076951</td>\n",
" <td>0.117951</td>\n",
" <td>0.0</td>\n",
" <td>0.117951</td>\n",
" <td>0.258988</td>\n",
" <td>0.298264</td>\n",
" <td>0.644925</td>\n",
" <td>0.206166</td>\n",
" <td>0.057636</td>\n",
" <td>0.178186</td>\n",
" <td>0.948181</td>\n",
" <td>0.881155</td>\n",
" <td>0.586620</td>\n",
" <td>0.867445</td>\n",
" <td>0.0</td>\n",
" <td>0.160243</td>\n",
" <td>0.152014</td>\n",
" <td>0.161938</td>\n",
" <td>0.115076</td>\n",
" <td>0.146481</td>\n",
" <td>0.159352</td>\n",
" <td>0.0</td>\n",
" <td>0.159352</td>\n",
" <td>0.204830</td>\n",
" <td>0.377937</td>\n",
" <td>0.193244</td>\n",
" <td>0.110812</td>\n",
" <td>0.107856</td>\n",
" <td>0.135752</td>\n",
" <td>0.152150</td>\n",
" <td>0.215250</td>\n",
" <td>0.266874</td>\n",
" <td>0.199644</td>\n",
" <td>RRBS_NormalBCD19pCD27mcell67_88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.TCTCTG</td>\n",
" <td>0.0</td>\n",
" <td>0.428225</td>\n",
" <td>0.251018</td>\n",
" <td>0.461970</td>\n",
" <td>0.061715</td>\n",
" <td>0.068326</td>\n",
" <td>0.092568</td>\n",
" <td>0.0</td>\n",
" <td>0.092568</td>\n",
" <td>0.211492</td>\n",
" <td>0.450980</td>\n",
" <td>0.630726</td>\n",
" <td>0.171360</td>\n",
" <td>0.056249</td>\n",
" <td>0.140668</td>\n",
" <td>0.892283</td>\n",
" <td>0.877888</td>\n",
" <td>0.557980</td>\n",
" <td>0.854488</td>\n",
" <td>0.0</td>\n",
" <td>0.159956</td>\n",
" <td>0.156530</td>\n",
" <td>0.159670</td>\n",
" <td>0.108323</td>\n",
" <td>0.135232</td>\n",
" <td>0.156855</td>\n",
" <td>0.0</td>\n",
" <td>0.156855</td>\n",
" <td>0.199040</td>\n",
" <td>0.287582</td>\n",
" <td>0.263901</td>\n",
" <td>0.103231</td>\n",
" <td>0.117227</td>\n",
" <td>0.144726</td>\n",
" <td>0.075563</td>\n",
" <td>0.210384</td>\n",
" <td>0.254628</td>\n",
" <td>0.219492</td>\n",
" <td>RRBS_NormalBCD19pCD27mcell67_88</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" filename methylation_tssDistance \\\n",
"83 RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.GTGAGG 0.0 \n",
"84 RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.GTTGAG 0.0 \n",
"85 RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.TAGCGG 0.0 \n",
"86 RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.TATCTC 0.0 \n",
"87 RRBS_NormalBCD19pCD27mcell67_88_CGTACTAG.TCTCTG 0.0 \n",
"\n",
" methylation_genesDistance methylation_exonsDistance \\\n",
"83 0.472029 0.279894 \n",
"84 0.475509 0.284108 \n",
"85 0.465384 0.270854 \n",
"86 0.475148 0.283056 \n",
"87 0.428225 0.251018 \n",
"\n",
" methylation_intronsDistance methylation_promoterDistance \\\n",
"83 0.507985 0.062184 \n",
"84 0.507053 0.067481 \n",
"85 0.497950 0.070138 \n",
"86 0.509076 0.067527 \n",
"87 0.461970 0.061715 \n",
"\n",
" methylation_cgiDistance methylation_ctcfDistance \\\n",
"83 0.068979 0.099081 \n",
"84 0.074098 0.107121 \n",
"85 0.075311 0.113918 \n",
"86 0.076951 0.117951 \n",
"87 0.068326 0.092568 \n",
"\n",
" methylation_ctcfUpDistance methylation_ctcfDownDistance \\\n",
"83 0.0 0.099081 \n",
"84 0.0 0.107121 \n",
"85 0.0 0.113918 \n",
"86 0.0 0.117951 \n",
"87 0.0 0.092568 \n",
"\n",
" methylation_geneDistalRegulatoryModulesDistance \\\n",
"83 0.254673 \n",
"84 0.252745 \n",
"85 0.265460 \n",
"86 0.258988 \n",
"87 0.211492 \n",
"\n",
" methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance \\\n",
"83 0.414678 0.646722 \n",
"84 0.376858 0.661143 \n",
"85 0.515006 0.626513 \n",
"86 0.298264 0.644925 \n",
"87 0.450980 0.630726 \n",
"\n",
" methylation_5PrimeUTRDistance methylation_firstExonDistance \\\n",
"83 0.202133 0.052964 \n",
"84 0.200471 0.058478 \n",
"85 0.197863 0.054605 \n",
"86 0.206166 0.057636 \n",
"87 0.171360 0.056249 \n",
"\n",
" methylation_geneDistalRegulatoryModulesK562Distance \\\n",
"83 0.180330 \n",
"84 0.178968 \n",
"85 0.191544 \n",
"86 0.178186 \n",
"87 0.140668 \n",
"\n",
" methylation_hypoInHues64Distance methylation_intergenic \\\n",
"83 0.825413 0.880407 \n",
"84 0.852344 0.884411 \n",
"85 0.873469 0.874056 \n",
"86 0.948181 0.881155 \n",
"87 0.892283 0.877888 \n",
"\n",
" methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance \\\n",
"83 0.567798 0.868663 0.0 0.162036 \n",
"84 0.593522 0.879363 0.0 0.157373 \n",
"85 0.568923 0.844443 0.0 0.165017 \n",
"86 0.586620 0.867445 0.0 0.160243 \n",
"87 0.557980 0.854488 0.0 0.159956 \n",
"\n",
" PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance \\\n",
"83 0.157993 0.162275 0.105846 \n",
"84 0.152483 0.156989 0.105841 \n",
"85 0.151922 0.166105 0.117741 \n",
"86 0.152014 0.161938 0.115076 \n",
"87 0.156530 0.159670 0.108323 \n",
"\n",
" PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance \\\n",
"83 0.134560 0.144832 0.0 \n",
"84 0.133327 0.140077 0.0 \n",
"85 0.146015 0.161420 0.0 \n",
"86 0.146481 0.159352 0.0 \n",
"87 0.135232 0.156855 0.0 \n",
"\n",
" PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance \\\n",
"83 0.144832 0.210529 \n",
"84 0.140077 0.195673 \n",
"85 0.161420 0.211532 \n",
"86 0.159352 0.204830 \n",
"87 0.156855 0.199040 \n",
"\n",
" PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance \\\n",
"83 0.343079 0.229286 0.102900 \n",
"84 0.159236 0.214858 0.102166 \n",
"85 0.650660 0.257800 0.104910 \n",
"86 0.377937 0.193244 0.110812 \n",
"87 0.287582 0.263901 0.103231 \n",
"\n",
" PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance \\\n",
"83 0.115445 0.160677 \n",
"84 0.103687 0.136311 \n",
"85 0.104068 0.157382 \n",
"86 0.107856 0.135752 \n",
"87 0.117227 0.144726 \n",
"\n",
" PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf \\\n",
"83 0.270661 0.208397 0.252791 0.195733 \n",
"84 0.139063 0.208999 0.275148 0.201310 \n",
"85 0.273469 0.210667 0.270156 0.202981 \n",
"86 0.152150 0.215250 0.266874 0.199644 \n",
"87 0.075563 0.210384 0.254628 0.219492 \n",
"\n",
" protocol \n",
"83 RRBS_NormalBCD19pCD27mcell67_88 \n",
"84 RRBS_NormalBCD19pCD27mcell67_88 \n",
"85 RRBS_NormalBCD19pCD27mcell67_88 \n",
"86 RRBS_NormalBCD19pCD27mcell67_88 \n",
"87 RRBS_NormalBCD19pCD27mcell67_88 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mcell.tail()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"26"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(\"RRBS_NormalBCD19pcell1_22_\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cd19cell[\"protocol\"] = cd19cell[\"filename\"].str[:26]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"41"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len('RRBS_NormalBCD19pcell1_22_TAAGGCGA.ACAACC')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" if __name__ == '__main__':\n",
"/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" from ipykernel import kernelapp as app\n",
"/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" app.launch_new_instance()\n",
"/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
}
],
"source": [
"cd19cell[\"filename\"][cd19cell[\"protocol\"]=='RRBS_NormalBCD19pcell1_22_'] = cd19cell[\"filename\"].str[:41]\n",
"cd19cell[\"filename\"][cd19cell[\"protocol\"]=='RRBS_NormalBCD19pcell23_44'] = cd19cell[\"filename\"].str[:42]\n",
"cd19cell[\"filename\"][cd19cell[\"protocol\"]=='RRBS_NormalBCD19pcell45_66'] = cd19cell[\"filename\"].str[:42]\n",
"cd19cell[\"filename\"][cd19cell[\"protocol\"]=='RRBS_NormalBCD19pcell67_88'] = cd19cell[\"filename\"].str[:42]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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>filename</th>\n",
" <th>methylation_tssDistance</th>\n",
" <th>methylation_genesDistance</th>\n",
" <th>methylation_exonsDistance</th>\n",
" <th>methylation_intronsDistance</th>\n",
" <th>methylation_promoterDistance</th>\n",
" <th>methylation_cgiDistance</th>\n",
" <th>methylation_ctcfDistance</th>\n",
" <th>methylation_ctcfUpDistance</th>\n",
" <th>methylation_ctcfDownDistance</th>\n",
" <th>methylation_geneDistalRegulatoryModulesDistance</th>\n",
" <th>methylation_vistaEnhancersDistance</th>\n",
" <th>methylation_3PrimeUTRDistance</th>\n",
" <th>methylation_5PrimeUTRDistance</th>\n",
" <th>methylation_firstExonDistance</th>\n",
" <th>methylation_geneDistalRegulatoryModulesK562Distance</th>\n",
" <th>methylation_hypoInHues64Distance</th>\n",
" <th>methylation_intergenic</th>\n",
" <th>methylation_shore</th>\n",
" <th>methylation_shelf</th>\n",
" <th>PDR_tssDistance</th>\n",
" <th>PDR_genesDistance</th>\n",
" <th>PDR_exonsDistance</th>\n",
" <th>PDR_intronsDistance</th>\n",
" <th>PDR_promoterDistance</th>\n",
" <th>PDR_cgiDistance</th>\n",
" <th>PDR_ctcfDistance</th>\n",
" <th>PDR_ctcfUpDistance</th>\n",
" <th>PDR_ctcfDownDistance</th>\n",
" <th>PDR_geneDistalRegulatoryModulesDistance</th>\n",
" <th>PDR_vistaEnhancersDistance</th>\n",
" <th>PDR_3PrimeUTRDistance</th>\n",
" <th>PDR_5PrimeUTRDistance</th>\n",
" <th>PDR_firstExonDistance</th>\n",
" <th>PDR_geneDistalRegulatoryModulesK562Distance</th>\n",
" <th>PDR_hypoInHues64Distance</th>\n",
" <th>PDR_intergenic</th>\n",
" <th>PDR_shore</th>\n",
" <th>PDR_shelf</th>\n",
" <th>protocol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>RRBS_NormalBCD19pcell67_88_TCCTGAGC.GTTGAG</td>\n",
" <td>0.0</td>\n",
" <td>0.365003</td>\n",
" <td>0.198154</td>\n",
" <td>0.403464</td>\n",
" <td>0.045692</td>\n",
" <td>0.049036</td>\n",
" <td>0.067145</td>\n",
" <td>0.0</td>\n",
" <td>0.067145</td>\n",
" <td>0.175783</td>\n",
" <td>0.170213</td>\n",
" <td>0.603492</td>\n",
" <td>0.133213</td>\n",
" <td>0.039203</td>\n",
" <td>0.124610</td>\n",
" <td>0.990164</td>\n",
" <td>0.857586</td>\n",
" <td>0.529461</td>\n",
" <td>0.830424</td>\n",
" <td>0.0</td>\n",
" <td>0.151630</td>\n",
" <td>0.135355</td>\n",
" <td>0.155304</td>\n",
" <td>0.086338</td>\n",
" <td>0.115382</td>\n",
" <td>0.124663</td>\n",
" <td>0.0</td>\n",
" <td>0.124663</td>\n",
" <td>0.173466</td>\n",
" <td>0.045593</td>\n",
" <td>0.233981</td>\n",
" <td>0.088479</td>\n",
" <td>0.087266</td>\n",
" <td>0.128772</td>\n",
" <td>0.000000</td>\n",
" <td>0.238726</td>\n",
" <td>0.295577</td>\n",
" <td>0.208543</td>\n",
" <td>RRBS_NormalBCD19pcell67_88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>RRBS_NormalBCD19pcell67_88_TCCTGAGC.TAGCGG</td>\n",
" <td>0.0</td>\n",
" <td>0.427826</td>\n",
" <td>0.249504</td>\n",
" <td>0.457398</td>\n",
" <td>0.062149</td>\n",
" <td>0.072737</td>\n",
" <td>0.087771</td>\n",
" <td>0.0</td>\n",
" <td>0.087771</td>\n",
" <td>0.207407</td>\n",
" <td>0.428877</td>\n",
" <td>0.564334</td>\n",
" <td>0.176146</td>\n",
" <td>0.050720</td>\n",
" <td>0.137381</td>\n",
" <td>0.662519</td>\n",
" <td>0.804080</td>\n",
" <td>0.516266</td>\n",
" <td>0.779332</td>\n",
" <td>0.0</td>\n",
" <td>0.224285</td>\n",
" <td>0.181686</td>\n",
" <td>0.230307</td>\n",
" <td>0.109675</td>\n",
" <td>0.134092</td>\n",
" <td>0.141826</td>\n",
" <td>0.0</td>\n",
" <td>0.141826</td>\n",
" <td>0.200233</td>\n",
" <td>0.200000</td>\n",
" <td>0.300089</td>\n",
" <td>0.122736</td>\n",
" <td>0.101102</td>\n",
" <td>0.129729</td>\n",
" <td>0.395023</td>\n",
" <td>0.371977</td>\n",
" <td>0.332454</td>\n",
" <td>0.371777</td>\n",
" <td>RRBS_NormalBCD19pcell67_88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>RRBS_NormalBCD19pcell67_88_TCCTGAGC.TATCTC</td>\n",
" <td>0.0</td>\n",
" <td>0.424818</td>\n",
" <td>0.246150</td>\n",
" <td>0.456708</td>\n",
" <td>0.058898</td>\n",
" <td>0.070105</td>\n",
" <td>0.105839</td>\n",
" <td>0.0</td>\n",
" <td>0.105839</td>\n",
" <td>0.221580</td>\n",
" <td>0.363380</td>\n",
" <td>0.632963</td>\n",
" <td>0.176894</td>\n",
" <td>0.050543</td>\n",
" <td>0.149377</td>\n",
" <td>0.885320</td>\n",
" <td>0.825866</td>\n",
" <td>0.539768</td>\n",
" <td>0.819517</td>\n",
" <td>0.0</td>\n",
" <td>0.195755</td>\n",
" <td>0.165491</td>\n",
" <td>0.200067</td>\n",
" <td>0.101588</td>\n",
" <td>0.130197</td>\n",
" <td>0.151039</td>\n",
" <td>0.0</td>\n",
" <td>0.151039</td>\n",
" <td>0.215342</td>\n",
" <td>0.302535</td>\n",
" <td>0.281348</td>\n",
" <td>0.112925</td>\n",
" <td>0.096018</td>\n",
" <td>0.148490</td>\n",
" <td>0.410017</td>\n",
" <td>0.335954</td>\n",
" <td>0.310619</td>\n",
" <td>0.307484</td>\n",
" <td>RRBS_NormalBCD19pcell67_88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>RRBS_NormalBCD19pcell67_88_TCCTGAGC.TCTCTG</td>\n",
" <td>0.0</td>\n",
" <td>0.472135</td>\n",
" <td>0.286358</td>\n",
" <td>0.503827</td>\n",
" <td>0.066952</td>\n",
" <td>0.077275</td>\n",
" <td>0.107918</td>\n",
" <td>0.0</td>\n",
" <td>0.107918</td>\n",
" <td>0.244037</td>\n",
" <td>0.413347</td>\n",
" <td>0.619841</td>\n",
" <td>0.199015</td>\n",
" <td>0.058952</td>\n",
" <td>0.168218</td>\n",
" <td>0.894972</td>\n",
" <td>0.879557</td>\n",
" <td>0.591964</td>\n",
" <td>0.864421</td>\n",
" <td>0.0</td>\n",
" <td>0.157952</td>\n",
" <td>0.150639</td>\n",
" <td>0.158441</td>\n",
" <td>0.108958</td>\n",
" <td>0.132628</td>\n",
" <td>0.156623</td>\n",
" <td>0.0</td>\n",
" <td>0.156623</td>\n",
" <td>0.199528</td>\n",
" <td>0.254980</td>\n",
" <td>0.243718</td>\n",
" <td>0.096941</td>\n",
" <td>0.102134</td>\n",
" <td>0.130035</td>\n",
" <td>0.138547</td>\n",
" <td>0.206872</td>\n",
" <td>0.276050</td>\n",
" <td>0.196075</td>\n",
" <td>RRBS_NormalBCD19pcell67_88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>RRBS_NormalBCD19pcell67_88_TCCTGAGC.TGCTGC</td>\n",
" <td>0.0</td>\n",
" <td>0.386120</td>\n",
" <td>0.188577</td>\n",
" <td>0.424230</td>\n",
" <td>0.057304</td>\n",
" <td>0.069063</td>\n",
" <td>0.110272</td>\n",
" <td>0.0</td>\n",
" <td>0.110272</td>\n",
" <td>0.226848</td>\n",
" <td>0.000000</td>\n",
" <td>0.601695</td>\n",
" <td>0.166264</td>\n",
" <td>0.051560</td>\n",
" <td>0.174515</td>\n",
" <td>0.933333</td>\n",
" <td>0.866931</td>\n",
" <td>0.560803</td>\n",
" <td>0.818182</td>\n",
" <td>0.0</td>\n",
" <td>0.167532</td>\n",
" <td>0.149592</td>\n",
" <td>0.180484</td>\n",
" <td>0.103296</td>\n",
" <td>0.120248</td>\n",
" <td>0.193353</td>\n",
" <td>0.0</td>\n",
" <td>0.193353</td>\n",
" <td>0.230246</td>\n",
" <td>0.000000</td>\n",
" <td>0.152542</td>\n",
" <td>0.106332</td>\n",
" <td>0.105665</td>\n",
" <td>0.193906</td>\n",
" <td>0.466667</td>\n",
" <td>0.299404</td>\n",
" <td>0.284534</td>\n",
" <td>0.258182</td>\n",
" <td>RRBS_NormalBCD19pcell67_88</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" filename methylation_tssDistance \\\n",
"84 RRBS_NormalBCD19pcell67_88_TCCTGAGC.GTTGAG 0.0 \n",
"85 RRBS_NormalBCD19pcell67_88_TCCTGAGC.TAGCGG 0.0 \n",
"86 RRBS_NormalBCD19pcell67_88_TCCTGAGC.TATCTC 0.0 \n",
"87 RRBS_NormalBCD19pcell67_88_TCCTGAGC.TCTCTG 0.0 \n",
"88 RRBS_NormalBCD19pcell67_88_TCCTGAGC.TGCTGC 0.0 \n",
"\n",
" methylation_genesDistance methylation_exonsDistance \\\n",
"84 0.365003 0.198154 \n",
"85 0.427826 0.249504 \n",
"86 0.424818 0.246150 \n",
"87 0.472135 0.286358 \n",
"88 0.386120 0.188577 \n",
"\n",
" methylation_intronsDistance methylation_promoterDistance \\\n",
"84 0.403464 0.045692 \n",
"85 0.457398 0.062149 \n",
"86 0.456708 0.058898 \n",
"87 0.503827 0.066952 \n",
"88 0.424230 0.057304 \n",
"\n",
" methylation_cgiDistance methylation_ctcfDistance \\\n",
"84 0.049036 0.067145 \n",
"85 0.072737 0.087771 \n",
"86 0.070105 0.105839 \n",
"87 0.077275 0.107918 \n",
"88 0.069063 0.110272 \n",
"\n",
" methylation_ctcfUpDistance methylation_ctcfDownDistance \\\n",
"84 0.0 0.067145 \n",
"85 0.0 0.087771 \n",
"86 0.0 0.105839 \n",
"87 0.0 0.107918 \n",
"88 0.0 0.110272 \n",
"\n",
" methylation_geneDistalRegulatoryModulesDistance \\\n",
"84 0.175783 \n",
"85 0.207407 \n",
"86 0.221580 \n",
"87 0.244037 \n",
"88 0.226848 \n",
"\n",
" methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance \\\n",
"84 0.170213 0.603492 \n",
"85 0.428877 0.564334 \n",
"86 0.363380 0.632963 \n",
"87 0.413347 0.619841 \n",
"88 0.000000 0.601695 \n",
"\n",
" methylation_5PrimeUTRDistance methylation_firstExonDistance \\\n",
"84 0.133213 0.039203 \n",
"85 0.176146 0.050720 \n",
"86 0.176894 0.050543 \n",
"87 0.199015 0.058952 \n",
"88 0.166264 0.051560 \n",
"\n",
" methylation_geneDistalRegulatoryModulesK562Distance \\\n",
"84 0.124610 \n",
"85 0.137381 \n",
"86 0.149377 \n",
"87 0.168218 \n",
"88 0.174515 \n",
"\n",
" methylation_hypoInHues64Distance methylation_intergenic \\\n",
"84 0.990164 0.857586 \n",
"85 0.662519 0.804080 \n",
"86 0.885320 0.825866 \n",
"87 0.894972 0.879557 \n",
"88 0.933333 0.866931 \n",
"\n",
" methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance \\\n",
"84 0.529461 0.830424 0.0 0.151630 \n",
"85 0.516266 0.779332 0.0 0.224285 \n",
"86 0.539768 0.819517 0.0 0.195755 \n",
"87 0.591964 0.864421 0.0 0.157952 \n",
"88 0.560803 0.818182 0.0 0.167532 \n",
"\n",
" PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance \\\n",
"84 0.135355 0.155304 0.086338 \n",
"85 0.181686 0.230307 0.109675 \n",
"86 0.165491 0.200067 0.101588 \n",
"87 0.150639 0.158441 0.108958 \n",
"88 0.149592 0.180484 0.103296 \n",
"\n",
" PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance \\\n",
"84 0.115382 0.124663 0.0 \n",
"85 0.134092 0.141826 0.0 \n",
"86 0.130197 0.151039 0.0 \n",
"87 0.132628 0.156623 0.0 \n",
"88 0.120248 0.193353 0.0 \n",
"\n",
" PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance \\\n",
"84 0.124663 0.173466 \n",
"85 0.141826 0.200233 \n",
"86 0.151039 0.215342 \n",
"87 0.156623 0.199528 \n",
"88 0.193353 0.230246 \n",
"\n",
" PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance \\\n",
"84 0.045593 0.233981 0.088479 \n",
"85 0.200000 0.300089 0.122736 \n",
"86 0.302535 0.281348 0.112925 \n",
"87 0.254980 0.243718 0.096941 \n",
"88 0.000000 0.152542 0.106332 \n",
"\n",
" PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance \\\n",
"84 0.087266 0.128772 \n",
"85 0.101102 0.129729 \n",
"86 0.096018 0.148490 \n",
"87 0.102134 0.130035 \n",
"88 0.105665 0.193906 \n",
"\n",
" PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf \\\n",
"84 0.000000 0.238726 0.295577 0.208543 \n",
"85 0.395023 0.371977 0.332454 0.371777 \n",
"86 0.410017 0.335954 0.310619 0.307484 \n",
"87 0.138547 0.206872 0.276050 0.196075 \n",
"88 0.466667 0.299404 0.284534 0.258182 \n",
"\n",
" protocol \n",
"84 RRBS_NormalBCD19pcell67_88 \n",
"85 RRBS_NormalBCD19pcell67_88 \n",
"86 RRBS_NormalBCD19pcell67_88 \n",
"87 RRBS_NormalBCD19pcell67_88 \n",
"88 RRBS_NormalBCD19pcell67_88 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cd19cell.tail()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"27"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(\"RRBS_cw154_Tris_protease_GR\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cw154[\"protocol\"] = cw154[\"filename\"].str[:27]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"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>filename</th>\n",
" <th>methylation_tssDistance</th>\n",
" <th>methylation_genesDistance</th>\n",
" <th>methylation_exonsDistance</th>\n",
" <th>methylation_intronsDistance</th>\n",
" <th>methylation_promoterDistance</th>\n",
" <th>methylation_cgiDistance</th>\n",
" <th>methylation_ctcfDistance</th>\n",
" <th>methylation_ctcfUpDistance</th>\n",
" <th>methylation_ctcfDownDistance</th>\n",
" <th>methylation_geneDistalRegulatoryModulesDistance</th>\n",
" <th>methylation_vistaEnhancersDistance</th>\n",
" <th>methylation_3PrimeUTRDistance</th>\n",
" <th>methylation_5PrimeUTRDistance</th>\n",
" <th>methylation_firstExonDistance</th>\n",
" <th>methylation_geneDistalRegulatoryModulesK562Distance</th>\n",
" <th>methylation_hypoInHues64Distance</th>\n",
" <th>methylation_intergenic</th>\n",
" <th>methylation_shore</th>\n",
" <th>methylation_shelf</th>\n",
" <th>PDR_tssDistance</th>\n",
" <th>PDR_genesDistance</th>\n",
" <th>PDR_exonsDistance</th>\n",
" <th>PDR_intronsDistance</th>\n",
" <th>PDR_promoterDistance</th>\n",
" <th>PDR_cgiDistance</th>\n",
" <th>PDR_ctcfDistance</th>\n",
" <th>PDR_ctcfUpDistance</th>\n",
" <th>PDR_ctcfDownDistance</th>\n",
" <th>PDR_geneDistalRegulatoryModulesDistance</th>\n",
" <th>PDR_vistaEnhancersDistance</th>\n",
" <th>PDR_3PrimeUTRDistance</th>\n",
" <th>PDR_5PrimeUTRDistance</th>\n",
" <th>PDR_firstExonDistance</th>\n",
" <th>PDR_geneDistalRegulatoryModulesK562Distance</th>\n",
" <th>PDR_hypoInHues64Distance</th>\n",
" <th>PDR_intergenic</th>\n",
" <th>PDR_shore</th>\n",
" <th>PDR_shelf</th>\n",
" <th>protocol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACAA...</td>\n",
" <td>0.0</td>\n",
" <td>0.557445</td>\n",
" <td>0.387638</td>\n",
" <td>0.583685</td>\n",
" <td>0.148984</td>\n",
" <td>0.173551</td>\n",
" <td>0.197546</td>\n",
" <td>0.0</td>\n",
" <td>0.197546</td>\n",
" <td>0.330378</td>\n",
" <td>0.534917</td>\n",
" <td>0.659609</td>\n",
" <td>0.300969</td>\n",
" <td>0.142909</td>\n",
" <td>0.246735</td>\n",
" <td>0.805699</td>\n",
" <td>0.820512</td>\n",
" <td>0.590658</td>\n",
" <td>0.806973</td>\n",
" <td>0.0</td>\n",
" <td>0.370149</td>\n",
" <td>0.420504</td>\n",
" <td>0.359511</td>\n",
" <td>0.408136</td>\n",
" <td>0.450911</td>\n",
" <td>0.436709</td>\n",
" <td>0.0</td>\n",
" <td>0.436709</td>\n",
" <td>0.446102</td>\n",
" <td>0.393463</td>\n",
" <td>0.409348</td>\n",
" <td>0.376109</td>\n",
" <td>0.438725</td>\n",
" <td>0.388377</td>\n",
" <td>0.397393</td>\n",
" <td>0.379858</td>\n",
" <td>0.378600</td>\n",
" <td>0.345222</td>\n",
" <td>RRBS_cw154_CutSmart_protein</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACCG...</td>\n",
" <td>0.0</td>\n",
" <td>0.495537</td>\n",
" <td>0.324467</td>\n",
" <td>0.521993</td>\n",
" <td>0.140566</td>\n",
" <td>0.157658</td>\n",
" <td>0.175575</td>\n",
" <td>0.0</td>\n",
" <td>0.175575</td>\n",
" <td>0.296219</td>\n",
" <td>0.366917</td>\n",
" <td>0.587535</td>\n",
" <td>0.261936</td>\n",
" <td>0.137202</td>\n",
" <td>0.237040</td>\n",
" <td>0.690698</td>\n",
" <td>0.796567</td>\n",
" <td>0.530634</td>\n",
" <td>0.780923</td>\n",
" <td>0.0</td>\n",
" <td>0.388093</td>\n",
" <td>0.435418</td>\n",
" <td>0.383256</td>\n",
" <td>0.418755</td>\n",
" <td>0.458484</td>\n",
" <td>0.442186</td>\n",
" <td>0.0</td>\n",
" <td>0.442186</td>\n",
" <td>0.440632</td>\n",
" <td>0.840602</td>\n",
" <td>0.376294</td>\n",
" <td>0.386659</td>\n",
" <td>0.445932</td>\n",
" <td>0.394716</td>\n",
" <td>0.346512</td>\n",
" <td>0.418598</td>\n",
" <td>0.404386</td>\n",
" <td>0.362456</td>\n",
" <td>RRBS_cw154_CutSmart_protein</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACGT...</td>\n",
" <td>0.0</td>\n",
" <td>0.520409</td>\n",
" <td>0.357209</td>\n",
" <td>0.544602</td>\n",
" <td>0.138190</td>\n",
" <td>0.161856</td>\n",
" <td>0.202110</td>\n",
" <td>0.0</td>\n",
" <td>0.202110</td>\n",
" <td>0.314849</td>\n",
" <td>0.463004</td>\n",
" <td>0.656101</td>\n",
" <td>0.275719</td>\n",
" <td>0.138640</td>\n",
" <td>0.225783</td>\n",
" <td>0.761359</td>\n",
" <td>0.792970</td>\n",
" <td>0.551861</td>\n",
" <td>0.785731</td>\n",
" <td>0.0</td>\n",
" <td>0.383971</td>\n",
" <td>0.430167</td>\n",
" <td>0.374988</td>\n",
" <td>0.410647</td>\n",
" <td>0.455464</td>\n",
" <td>0.453277</td>\n",
" <td>0.0</td>\n",
" <td>0.453277</td>\n",
" <td>0.450694</td>\n",
" <td>0.324865</td>\n",
" <td>0.443011</td>\n",
" <td>0.387172</td>\n",
" <td>0.439933</td>\n",
" <td>0.414011</td>\n",
" <td>0.280260</td>\n",
" <td>0.411414</td>\n",
" <td>0.389000</td>\n",
" <td>0.363575</td>\n",
" <td>RRBS_cw154_CutSmart_protein</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACTC...</td>\n",
" <td>0.0</td>\n",
" <td>0.569906</td>\n",
" <td>0.398614</td>\n",
" <td>0.595372</td>\n",
" <td>0.158028</td>\n",
" <td>0.178889</td>\n",
" <td>0.196911</td>\n",
" <td>0.0</td>\n",
" <td>0.196911</td>\n",
" <td>0.340942</td>\n",
" <td>0.608100</td>\n",
" <td>0.665446</td>\n",
" <td>0.310607</td>\n",
" <td>0.154927</td>\n",
" <td>0.247978</td>\n",
" <td>0.867355</td>\n",
" <td>0.831256</td>\n",
" <td>0.609382</td>\n",
" <td>0.812445</td>\n",
" <td>0.0</td>\n",
" <td>0.363173</td>\n",
" <td>0.416487</td>\n",
" <td>0.354378</td>\n",
" <td>0.421272</td>\n",
" <td>0.454977</td>\n",
" <td>0.438265</td>\n",
" <td>0.0</td>\n",
" <td>0.438265</td>\n",
" <td>0.452257</td>\n",
" <td>0.477332</td>\n",
" <td>0.376957</td>\n",
" <td>0.382624</td>\n",
" <td>0.449764</td>\n",
" <td>0.400321</td>\n",
" <td>0.171406</td>\n",
" <td>0.369736</td>\n",
" <td>0.385435</td>\n",
" <td>0.345905</td>\n",
" <td>RRBS_cw154_CutSmart_protein</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.AGGA...</td>\n",
" <td>0.0</td>\n",
" <td>0.554293</td>\n",
" <td>0.390193</td>\n",
" <td>0.576205</td>\n",
" <td>0.148946</td>\n",
" <td>0.173784</td>\n",
" <td>0.194668</td>\n",
" <td>0.0</td>\n",
" <td>0.194668</td>\n",
" <td>0.339245</td>\n",
" <td>0.494894</td>\n",
" <td>0.652036</td>\n",
" <td>0.291318</td>\n",
" <td>0.145185</td>\n",
" <td>0.250083</td>\n",
" <td>0.894932</td>\n",
" <td>0.825017</td>\n",
" <td>0.592558</td>\n",
" <td>0.807747</td>\n",
" <td>0.0</td>\n",
" <td>0.365658</td>\n",
" <td>0.414198</td>\n",
" <td>0.357211</td>\n",
" <td>0.407138</td>\n",
" <td>0.447926</td>\n",
" <td>0.433596</td>\n",
" <td>0.0</td>\n",
" <td>0.433596</td>\n",
" <td>0.428716</td>\n",
" <td>0.406863</td>\n",
" <td>0.397655</td>\n",
" <td>0.376272</td>\n",
" <td>0.425466</td>\n",
" <td>0.383864</td>\n",
" <td>0.274772</td>\n",
" <td>0.382212</td>\n",
" <td>0.378352</td>\n",
" <td>0.346732</td>\n",
" <td>RRBS_cw154_CutSmart_protein</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" filename methylation_tssDistance \\\n",
"0 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACAA... 0.0 \n",
"1 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACCG... 0.0 \n",
"2 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACGT... 0.0 \n",
"3 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.ACTC... 0.0 \n",
"4 RRBS_cw154_CutSmart_proteinase_K_TAGGCATG.AGGA... 0.0 \n",
"\n",
" methylation_genesDistance methylation_exonsDistance \\\n",
"0 0.557445 0.387638 \n",
"1 0.495537 0.324467 \n",
"2 0.520409 0.357209 \n",
"3 0.569906 0.398614 \n",
"4 0.554293 0.390193 \n",
"\n",
" methylation_intronsDistance methylation_promoterDistance \\\n",
"0 0.583685 0.148984 \n",
"1 0.521993 0.140566 \n",
"2 0.544602 0.138190 \n",
"3 0.595372 0.158028 \n",
"4 0.576205 0.148946 \n",
"\n",
" methylation_cgiDistance methylation_ctcfDistance \\\n",
"0 0.173551 0.197546 \n",
"1 0.157658 0.175575 \n",
"2 0.161856 0.202110 \n",
"3 0.178889 0.196911 \n",
"4 0.173784 0.194668 \n",
"\n",
" methylation_ctcfUpDistance methylation_ctcfDownDistance \\\n",
"0 0.0 0.197546 \n",
"1 0.0 0.175575 \n",
"2 0.0 0.202110 \n",
"3 0.0 0.196911 \n",
"4 0.0 0.194668 \n",
"\n",
" methylation_geneDistalRegulatoryModulesDistance \\\n",
"0 0.330378 \n",
"1 0.296219 \n",
"2 0.314849 \n",
"3 0.340942 \n",
"4 0.339245 \n",
"\n",
" methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance \\\n",
"0 0.534917 0.659609 \n",
"1 0.366917 0.587535 \n",
"2 0.463004 0.656101 \n",
"3 0.608100 0.665446 \n",
"4 0.494894 0.652036 \n",
"\n",
" methylation_5PrimeUTRDistance methylation_firstExonDistance \\\n",
"0 0.300969 0.142909 \n",
"1 0.261936 0.137202 \n",
"2 0.275719 0.138640 \n",
"3 0.310607 0.154927 \n",
"4 0.291318 0.145185 \n",
"\n",
" methylation_geneDistalRegulatoryModulesK562Distance \\\n",
"0 0.246735 \n",
"1 0.237040 \n",
"2 0.225783 \n",
"3 0.247978 \n",
"4 0.250083 \n",
"\n",
" methylation_hypoInHues64Distance methylation_intergenic \\\n",
"0 0.805699 0.820512 \n",
"1 0.690698 0.796567 \n",
"2 0.761359 0.792970 \n",
"3 0.867355 0.831256 \n",
"4 0.894932 0.825017 \n",
"\n",
" methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance \\\n",
"0 0.590658 0.806973 0.0 0.370149 \n",
"1 0.530634 0.780923 0.0 0.388093 \n",
"2 0.551861 0.785731 0.0 0.383971 \n",
"3 0.609382 0.812445 0.0 0.363173 \n",
"4 0.592558 0.807747 0.0 0.365658 \n",
"\n",
" PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance \\\n",
"0 0.420504 0.359511 0.408136 \n",
"1 0.435418 0.383256 0.418755 \n",
"2 0.430167 0.374988 0.410647 \n",
"3 0.416487 0.354378 0.421272 \n",
"4 0.414198 0.357211 0.407138 \n",
"\n",
" PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance \\\n",
"0 0.450911 0.436709 0.0 \n",
"1 0.458484 0.442186 0.0 \n",
"2 0.455464 0.453277 0.0 \n",
"3 0.454977 0.438265 0.0 \n",
"4 0.447926 0.433596 0.0 \n",
"\n",
" PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance \\\n",
"0 0.436709 0.446102 \n",
"1 0.442186 0.440632 \n",
"2 0.453277 0.450694 \n",
"3 0.438265 0.452257 \n",
"4 0.433596 0.428716 \n",
"\n",
" PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance \\\n",
"0 0.393463 0.409348 0.376109 \n",
"1 0.840602 0.376294 0.386659 \n",
"2 0.324865 0.443011 0.387172 \n",
"3 0.477332 0.376957 0.382624 \n",
"4 0.406863 0.397655 0.376272 \n",
"\n",
" PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance \\\n",
"0 0.438725 0.388377 \n",
"1 0.445932 0.394716 \n",
"2 0.439933 0.414011 \n",
"3 0.449764 0.400321 \n",
"4 0.425466 0.383864 \n",
"\n",
" PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf \\\n",
"0 0.397393 0.379858 0.378600 0.345222 \n",
"1 0.346512 0.418598 0.404386 0.362456 \n",
"2 0.280260 0.411414 0.389000 0.363575 \n",
"3 0.171406 0.369736 0.385435 0.345905 \n",
"4 0.274772 0.382212 0.378352 0.346732 \n",
"\n",
" protocol \n",
"0 RRBS_cw154_CutSmart_protein \n",
"1 RRBS_cw154_CutSmart_protein \n",
"2 RRBS_cw154_CutSmart_protein \n",
"3 RRBS_cw154_CutSmart_protein \n",
"4 RRBS_cw154_CutSmart_protein "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cw154.head() # RRBS_cw154_CutSmart_protein # RRBS_cw154_Tris_protease_CT # RRBS_cw154_Tris_protease_GR"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" if __name__ == '__main__':\n",
"/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" from ipykernel import kernelapp as app\n",
"/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" app.launch_new_instance()\n"
]
}
],
"source": [
"cw154[\"filename\"][cw154[\"protocol\"] == \"RRBS_cw154_CutSmart_protein\"] = cw154[\"filename\"].str[:48]\n",
"cw154[\"filename\"][cw154[\"protocol\"] == \"RRBS_cw154_Tris_protease_CT\"] = cw154[\"filename\"].str[:40]\n",
"cw154[\"filename\"][cw154[\"protocol\"] == \"RRBS_cw154_Tris_protease_GR\"] = cw154[\"filename\"].str[:43]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"files = [trito, normal, pcell, mcell, cw154, cd19cell]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"total_region_files = pd.concat([trito, normal, pcell, mcell, cw154, cd19cell])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(513, 40)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_region_files.shape"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"total_region_files = total_region_files[[\"filename\", \"methylation_tssDistance\",\"methylation_genesDistance\",\"methylation_exonsDistance\",\n",
" \"methylation_intronsDistance\", \"methylation_promoterDistance\",\"methylation_cgiDistance\",\n",
" \"methylation_ctcfDistance\",\"methylation_ctcfUpDistance\",\"methylation_ctcfDownDistance\",\n",
" \"methylation_geneDistalRegulatoryModulesDistance\",\"methylation_vistaEnhancersDistance\",\n",
" \"methylation_3PrimeUTRDistance\",\"methylation_5PrimeUTRDistance\",\n",
" \"methylation_firstExonDistance\",\"methylation_geneDistalRegulatoryModulesK562Distance\",\n",
" \"methylation_hypoInHues64Distance\",\"methylation_intergenic\",\n",
" \"methylation_shore\",\"methylation_shelf\",\"PDR_tssDistance\",\n",
" \"PDR_genesDistance\",\"PDR_exonsDistance\",\"PDR_intronsDistance\", \"PDR_promoterDistance\",\n",
" \"PDR_cgiDistance\",\"PDR_ctcfDistance\",\"PDR_ctcfUpDistance\",\"PDR_ctcfDownDistance\",\n",
" \"PDR_geneDistalRegulatoryModulesDistance\",\"PDR_vistaEnhancersDistance\",\"PDR_3PrimeUTRDistance\",\n",
" \"PDR_5PrimeUTRDistance\",\"PDR_firstExonDistance\",\"PDR_geneDistalRegulatoryModulesK562Distance\",\n",
" \"PDR_hypoInHues64Distance\",\"PDR_intergenic\",\"PDR_shore\",\"PDR_shelf\"]]\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"total_region_files = total_region_files.reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"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>filename</th>\n",
" <th>methylation_tssDistance</th>\n",
" <th>methylation_genesDistance</th>\n",
" <th>methylation_exonsDistance</th>\n",
" <th>methylation_intronsDistance</th>\n",
" <th>methylation_promoterDistance</th>\n",
" <th>methylation_cgiDistance</th>\n",
" <th>methylation_ctcfDistance</th>\n",
" <th>methylation_ctcfUpDistance</th>\n",
" <th>methylation_ctcfDownDistance</th>\n",
" <th>methylation_geneDistalRegulatoryModulesDistance</th>\n",
" <th>methylation_vistaEnhancersDistance</th>\n",
" <th>methylation_3PrimeUTRDistance</th>\n",
" <th>methylation_5PrimeUTRDistance</th>\n",
" <th>methylation_firstExonDistance</th>\n",
" <th>methylation_geneDistalRegulatoryModulesK562Distance</th>\n",
" <th>methylation_hypoInHues64Distance</th>\n",
" <th>methylation_intergenic</th>\n",
" <th>methylation_shore</th>\n",
" <th>methylation_shelf</th>\n",
" <th>PDR_tssDistance</th>\n",
" <th>PDR_genesDistance</th>\n",
" <th>PDR_exonsDistance</th>\n",
" <th>PDR_intronsDistance</th>\n",
" <th>PDR_promoterDistance</th>\n",
" <th>PDR_cgiDistance</th>\n",
" <th>PDR_ctcfDistance</th>\n",
" <th>PDR_ctcfUpDistance</th>\n",
" <th>PDR_ctcfDownDistance</th>\n",
" <th>PDR_geneDistalRegulatoryModulesDistance</th>\n",
" <th>PDR_vistaEnhancersDistance</th>\n",
" <th>PDR_3PrimeUTRDistance</th>\n",
" <th>PDR_5PrimeUTRDistance</th>\n",
" <th>PDR_firstExonDistance</th>\n",
" <th>PDR_geneDistalRegulatoryModulesK562Distance</th>\n",
" <th>PDR_hypoInHues64Distance</th>\n",
" <th>PDR_intergenic</th>\n",
" <th>PDR_shore</th>\n",
" <th>PDR_shelf</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.ACAACC</td>\n",
" <td>0.0</td>\n",
" <td>0.570721</td>\n",
" <td>0.404589</td>\n",
" <td>0.594704</td>\n",
" <td>0.146371</td>\n",
" <td>0.169386</td>\n",
" <td>0.199815</td>\n",
" <td>0.0</td>\n",
" <td>0.199815</td>\n",
" <td>0.342986</td>\n",
" <td>0.444522</td>\n",
" <td>0.680480</td>\n",
" <td>0.305206</td>\n",
" <td>0.145730</td>\n",
" <td>0.254209</td>\n",
" <td>0.877142</td>\n",
" <td>0.820339</td>\n",
" <td>0.607671</td>\n",
" <td>0.803716</td>\n",
" <td>0.0</td>\n",
" <td>0.343014</td>\n",
" <td>0.371890</td>\n",
" <td>0.339596</td>\n",
" <td>0.347748</td>\n",
" <td>0.388709</td>\n",
" <td>0.385297</td>\n",
" <td>0.0</td>\n",
" <td>0.385297</td>\n",
" <td>0.401254</td>\n",
" <td>0.509555</td>\n",
" <td>0.386285</td>\n",
" <td>0.324236</td>\n",
" <td>0.367271</td>\n",
" <td>0.354046</td>\n",
" <td>0.240359</td>\n",
" <td>0.386809</td>\n",
" <td>0.377907</td>\n",
" <td>0.359143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.ACGTGG</td>\n",
" <td>0.0</td>\n",
" <td>0.545781</td>\n",
" <td>0.383371</td>\n",
" <td>0.568638</td>\n",
" <td>0.141545</td>\n",
" <td>0.161519</td>\n",
" <td>0.191404</td>\n",
" <td>0.0</td>\n",
" <td>0.191404</td>\n",
" <td>0.326140</td>\n",
" <td>0.589834</td>\n",
" <td>0.670559</td>\n",
" <td>0.290196</td>\n",
" <td>0.140779</td>\n",
" <td>0.240221</td>\n",
" <td>0.809942</td>\n",
" <td>0.816166</td>\n",
" <td>0.573089</td>\n",
" <td>0.795932</td>\n",
" <td>0.0</td>\n",
" <td>0.348110</td>\n",
" <td>0.381251</td>\n",
" <td>0.341950</td>\n",
" <td>0.349891</td>\n",
" <td>0.398898</td>\n",
" <td>0.415058</td>\n",
" <td>0.0</td>\n",
" <td>0.415058</td>\n",
" <td>0.408417</td>\n",
" <td>0.548192</td>\n",
" <td>0.382172</td>\n",
" <td>0.332749</td>\n",
" <td>0.373615</td>\n",
" <td>0.359217</td>\n",
" <td>0.364148</td>\n",
" <td>0.391925</td>\n",
" <td>0.386808</td>\n",
" <td>0.354120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.ACTCAC</td>\n",
" <td>0.0</td>\n",
" <td>0.564547</td>\n",
" <td>0.401760</td>\n",
" <td>0.588136</td>\n",
" <td>0.148529</td>\n",
" <td>0.174413</td>\n",
" <td>0.209041</td>\n",
" <td>0.0</td>\n",
" <td>0.209041</td>\n",
" <td>0.346473</td>\n",
" <td>0.553062</td>\n",
" <td>0.696068</td>\n",
" <td>0.296809</td>\n",
" <td>0.148360</td>\n",
" <td>0.255392</td>\n",
" <td>0.795883</td>\n",
" <td>0.832812</td>\n",
" <td>0.609544</td>\n",
" <td>0.812564</td>\n",
" <td>0.0</td>\n",
" <td>0.338412</td>\n",
" <td>0.371890</td>\n",
" <td>0.332321</td>\n",
" <td>0.351391</td>\n",
" <td>0.393829</td>\n",
" <td>0.392313</td>\n",
" <td>0.0</td>\n",
" <td>0.392313</td>\n",
" <td>0.412311</td>\n",
" <td>0.471703</td>\n",
" <td>0.378630</td>\n",
" <td>0.327488</td>\n",
" <td>0.370494</td>\n",
" <td>0.338321</td>\n",
" <td>0.334783</td>\n",
" <td>0.378580</td>\n",
" <td>0.378799</td>\n",
" <td>0.353949</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.AGGATG</td>\n",
" <td>0.0</td>\n",
" <td>0.567309</td>\n",
" <td>0.399934</td>\n",
" <td>0.592890</td>\n",
" <td>0.143897</td>\n",
" <td>0.168936</td>\n",
" <td>0.200661</td>\n",
" <td>0.0</td>\n",
" <td>0.200661</td>\n",
" <td>0.342257</td>\n",
" <td>0.665920</td>\n",
" <td>0.661426</td>\n",
" <td>0.308680</td>\n",
" <td>0.141673</td>\n",
" <td>0.242236</td>\n",
" <td>0.787966</td>\n",
" <td>0.824659</td>\n",
" <td>0.602995</td>\n",
" <td>0.799836</td>\n",
" <td>0.0</td>\n",
" <td>0.342724</td>\n",
" <td>0.374419</td>\n",
" <td>0.337654</td>\n",
" <td>0.346109</td>\n",
" <td>0.389718</td>\n",
" <td>0.399153</td>\n",
" <td>0.0</td>\n",
" <td>0.399153</td>\n",
" <td>0.405627</td>\n",
" <td>0.359189</td>\n",
" <td>0.391002</td>\n",
" <td>0.324431</td>\n",
" <td>0.360431</td>\n",
" <td>0.343730</td>\n",
" <td>0.304035</td>\n",
" <td>0.380413</td>\n",
" <td>0.373345</td>\n",
" <td>0.347372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.ATAGCG</td>\n",
" <td>0.0</td>\n",
" <td>0.529224</td>\n",
" <td>0.367743</td>\n",
" <td>0.555131</td>\n",
" <td>0.136090</td>\n",
" <td>0.156827</td>\n",
" <td>0.175426</td>\n",
" <td>0.0</td>\n",
" <td>0.175426</td>\n",
" <td>0.307402</td>\n",
" <td>0.479145</td>\n",
" <td>0.644411</td>\n",
" <td>0.273473</td>\n",
" <td>0.134137</td>\n",
" <td>0.220729</td>\n",
" <td>0.815944</td>\n",
" <td>0.808981</td>\n",
" <td>0.575050</td>\n",
" <td>0.788587</td>\n",
" <td>0.0</td>\n",
" <td>0.349254</td>\n",
" <td>0.376307</td>\n",
" <td>0.342617</td>\n",
" <td>0.343348</td>\n",
" <td>0.388623</td>\n",
" <td>0.403861</td>\n",
" <td>0.0</td>\n",
" <td>0.403861</td>\n",
" <td>0.390288</td>\n",
" <td>0.471324</td>\n",
" <td>0.392438</td>\n",
" <td>0.332882</td>\n",
" <td>0.358450</td>\n",
" <td>0.319824</td>\n",
" <td>0.401641</td>\n",
" <td>0.398275</td>\n",
" <td>0.373236</td>\n",
" <td>0.363320</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.ATCGAC</td>\n",
" <td>0.0</td>\n",
" <td>0.566031</td>\n",
" <td>0.393281</td>\n",
" <td>0.591518</td>\n",
" <td>0.145246</td>\n",
" <td>0.162973</td>\n",
" <td>0.199969</td>\n",
" <td>0.0</td>\n",
" <td>0.199969</td>\n",
" <td>0.333682</td>\n",
" <td>0.490492</td>\n",
" <td>0.667285</td>\n",
" <td>0.304792</td>\n",
" <td>0.138075</td>\n",
" <td>0.248255</td>\n",
" <td>0.853356</td>\n",
" <td>0.817065</td>\n",
" <td>0.601387</td>\n",
" <td>0.800064</td>\n",
" <td>0.0</td>\n",
" <td>0.343104</td>\n",
" <td>0.371148</td>\n",
" <td>0.338015</td>\n",
" <td>0.350522</td>\n",
" <td>0.390895</td>\n",
" <td>0.405243</td>\n",
" <td>0.0</td>\n",
" <td>0.405243</td>\n",
" <td>0.397617</td>\n",
" <td>0.586487</td>\n",
" <td>0.367571</td>\n",
" <td>0.325332</td>\n",
" <td>0.362838</td>\n",
" <td>0.333114</td>\n",
" <td>0.328968</td>\n",
" <td>0.389536</td>\n",
" <td>0.375595</td>\n",
" <td>0.350909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.CAAGAG</td>\n",
" <td>0.0</td>\n",
" <td>0.566742</td>\n",
" <td>0.402345</td>\n",
" <td>0.590378</td>\n",
" <td>0.152305</td>\n",
" <td>0.173906</td>\n",
" <td>0.195518</td>\n",
" <td>0.0</td>\n",
" <td>0.195518</td>\n",
" <td>0.346568</td>\n",
" <td>0.533040</td>\n",
" <td>0.656980</td>\n",
" <td>0.304390</td>\n",
" <td>0.149536</td>\n",
" <td>0.253048</td>\n",
" <td>0.805577</td>\n",
" <td>0.816897</td>\n",
" <td>0.603907</td>\n",
" <td>0.803092</td>\n",
" <td>0.0</td>\n",
" <td>0.350942</td>\n",
" <td>0.386671</td>\n",
" <td>0.344248</td>\n",
" <td>0.364405</td>\n",
" <td>0.406190</td>\n",
" <td>0.400039</td>\n",
" <td>0.0</td>\n",
" <td>0.400039</td>\n",
" <td>0.421197</td>\n",
" <td>0.545560</td>\n",
" <td>0.392756</td>\n",
" <td>0.338890</td>\n",
" <td>0.381441</td>\n",
" <td>0.356823</td>\n",
" <td>0.307240</td>\n",
" <td>0.394851</td>\n",
" <td>0.384458</td>\n",
" <td>0.357089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.CATGAC</td>\n",
" <td>0.0</td>\n",
" <td>0.566995</td>\n",
" <td>0.407400</td>\n",
" <td>0.589923</td>\n",
" <td>0.148278</td>\n",
" <td>0.174522</td>\n",
" <td>0.209616</td>\n",
" <td>0.0</td>\n",
" <td>0.209616</td>\n",
" <td>0.337079</td>\n",
" <td>0.476748</td>\n",
" <td>0.678752</td>\n",
" <td>0.300825</td>\n",
" <td>0.146025</td>\n",
" <td>0.243253</td>\n",
" <td>0.863080</td>\n",
" <td>0.824824</td>\n",
" <td>0.602455</td>\n",
" <td>0.801551</td>\n",
" <td>0.0</td>\n",
" <td>0.345160</td>\n",
" <td>0.379410</td>\n",
" <td>0.338777</td>\n",
" <td>0.353197</td>\n",
" <td>0.399885</td>\n",
" <td>0.399392</td>\n",
" <td>0.0</td>\n",
" <td>0.399392</td>\n",
" <td>0.398004</td>\n",
" <td>0.482805</td>\n",
" <td>0.394881</td>\n",
" <td>0.334431</td>\n",
" <td>0.375372</td>\n",
" <td>0.332435</td>\n",
" <td>0.345932</td>\n",
" <td>0.381070</td>\n",
" <td>0.373821</td>\n",
" <td>0.350244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.CCTTCG</td>\n",
" <td>0.0</td>\n",
" <td>0.544137</td>\n",
" <td>0.377628</td>\n",
" <td>0.568732</td>\n",
" <td>0.136603</td>\n",
" <td>0.163191</td>\n",
" <td>0.191971</td>\n",
" <td>0.0</td>\n",
" <td>0.191971</td>\n",
" <td>0.312724</td>\n",
" <td>0.428419</td>\n",
" <td>0.644483</td>\n",
" <td>0.284865</td>\n",
" <td>0.139631</td>\n",
" <td>0.229110</td>\n",
" <td>0.812046</td>\n",
" <td>0.810739</td>\n",
" <td>0.567608</td>\n",
" <td>0.784795</td>\n",
" <td>0.0</td>\n",
" <td>0.345857</td>\n",
" <td>0.376641</td>\n",
" <td>0.339524</td>\n",
" <td>0.349919</td>\n",
" <td>0.393570</td>\n",
" <td>0.413810</td>\n",
" <td>0.0</td>\n",
" <td>0.413810</td>\n",
" <td>0.407238</td>\n",
" <td>0.334977</td>\n",
" <td>0.388436</td>\n",
" <td>0.324229</td>\n",
" <td>0.367053</td>\n",
" <td>0.354882</td>\n",
" <td>0.307155</td>\n",
" <td>0.397849</td>\n",
" <td>0.390108</td>\n",
" <td>0.346731</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.CGGTAG</td>\n",
" <td>0.0</td>\n",
" <td>0.540051</td>\n",
" <td>0.369671</td>\n",
" <td>0.565375</td>\n",
" <td>0.139633</td>\n",
" <td>0.163692</td>\n",
" <td>0.198751</td>\n",
" <td>0.0</td>\n",
" <td>0.198751</td>\n",
" <td>0.312778</td>\n",
" <td>0.531099</td>\n",
" <td>0.642033</td>\n",
" <td>0.284431</td>\n",
" <td>0.138257</td>\n",
" <td>0.223869</td>\n",
" <td>0.851775</td>\n",
" <td>0.809572</td>\n",
" <td>0.567265</td>\n",
" <td>0.792353</td>\n",
" <td>0.0</td>\n",
" <td>0.354063</td>\n",
" <td>0.391736</td>\n",
" <td>0.346245</td>\n",
" <td>0.357297</td>\n",
" <td>0.403435</td>\n",
" <td>0.414073</td>\n",
" <td>0.0</td>\n",
" <td>0.414073</td>\n",
" <td>0.409215</td>\n",
" <td>0.462336</td>\n",
" <td>0.404820</td>\n",
" <td>0.330088</td>\n",
" <td>0.374945</td>\n",
" <td>0.334288</td>\n",
" <td>0.296450</td>\n",
" <td>0.398979</td>\n",
" <td>0.390262</td>\n",
" <td>0.360787</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.CTATTG</td>\n",
" <td>0.0</td>\n",
" <td>0.579098</td>\n",
" <td>0.413628</td>\n",
" <td>0.602551</td>\n",
" <td>0.149996</td>\n",
" <td>0.174348</td>\n",
" <td>0.206507</td>\n",
" <td>0.0</td>\n",
" <td>0.206507</td>\n",
" <td>0.340917</td>\n",
" <td>0.479200</td>\n",
" <td>0.681367</td>\n",
" <td>0.314310</td>\n",
" <td>0.147705</td>\n",
" <td>0.250151</td>\n",
" <td>0.842639</td>\n",
" <td>0.825690</td>\n",
" <td>0.610104</td>\n",
" <td>0.808571</td>\n",
" <td>0.0</td>\n",
" <td>0.334826</td>\n",
" <td>0.363741</td>\n",
" <td>0.331545</td>\n",
" <td>0.340101</td>\n",
" <td>0.389592</td>\n",
" <td>0.384261</td>\n",
" <td>0.0</td>\n",
" <td>0.384261</td>\n",
" <td>0.399187</td>\n",
" <td>0.295595</td>\n",
" <td>0.361252</td>\n",
" <td>0.316965</td>\n",
" <td>0.356730</td>\n",
" <td>0.331182</td>\n",
" <td>0.275036</td>\n",
" <td>0.384923</td>\n",
" <td>0.370680</td>\n",
" <td>0.338867</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.GACACG</td>\n",
" <td>0.0</td>\n",
" <td>0.549829</td>\n",
" <td>0.379579</td>\n",
" <td>0.572968</td>\n",
" <td>0.142349</td>\n",
" <td>0.163858</td>\n",
" <td>0.194206</td>\n",
" <td>0.0</td>\n",
" <td>0.194206</td>\n",
" <td>0.320273</td>\n",
" <td>0.511181</td>\n",
" <td>0.628813</td>\n",
" <td>0.297707</td>\n",
" <td>0.137945</td>\n",
" <td>0.231191</td>\n",
" <td>0.913175</td>\n",
" <td>0.808944</td>\n",
" <td>0.575022</td>\n",
" <td>0.795743</td>\n",
" <td>0.0</td>\n",
" <td>0.347308</td>\n",
" <td>0.375613</td>\n",
" <td>0.343617</td>\n",
" <td>0.356474</td>\n",
" <td>0.398687</td>\n",
" <td>0.405318</td>\n",
" <td>0.0</td>\n",
" <td>0.405318</td>\n",
" <td>0.403924</td>\n",
" <td>0.414471</td>\n",
" <td>0.390100</td>\n",
" <td>0.335772</td>\n",
" <td>0.368552</td>\n",
" <td>0.331528</td>\n",
" <td>0.181846</td>\n",
" <td>0.396579</td>\n",
" <td>0.382919</td>\n",
" <td>0.352013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.GCATTC</td>\n",
" <td>0.0</td>\n",
" <td>0.577097</td>\n",
" <td>0.411205</td>\n",
" <td>0.599875</td>\n",
" <td>0.149844</td>\n",
" <td>0.176529</td>\n",
" <td>0.209171</td>\n",
" <td>0.0</td>\n",
" <td>0.209171</td>\n",
" <td>0.340387</td>\n",
" <td>0.512575</td>\n",
" <td>0.667962</td>\n",
" <td>0.312442</td>\n",
" <td>0.150562</td>\n",
" <td>0.251066</td>\n",
" <td>0.868216</td>\n",
" <td>0.824084</td>\n",
" <td>0.608142</td>\n",
" <td>0.803492</td>\n",
" <td>0.0</td>\n",
" <td>0.340215</td>\n",
" <td>0.377824</td>\n",
" <td>0.332149</td>\n",
" <td>0.354279</td>\n",
" <td>0.402852</td>\n",
" <td>0.387265</td>\n",
" <td>0.0</td>\n",
" <td>0.387265</td>\n",
" <td>0.416357</td>\n",
" <td>0.373845</td>\n",
" <td>0.376155</td>\n",
" <td>0.331282</td>\n",
" <td>0.370933</td>\n",
" <td>0.363017</td>\n",
" <td>0.378312</td>\n",
" <td>0.390375</td>\n",
" <td>0.366399</td>\n",
" <td>0.364220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.GCTGCC</td>\n",
" <td>0.0</td>\n",
" <td>0.544259</td>\n",
" <td>0.384439</td>\n",
" <td>0.564795</td>\n",
" <td>0.139736</td>\n",
" <td>0.162409</td>\n",
" <td>0.176248</td>\n",
" <td>0.0</td>\n",
" <td>0.176248</td>\n",
" <td>0.318136</td>\n",
" <td>0.574486</td>\n",
" <td>0.634002</td>\n",
" <td>0.289043</td>\n",
" <td>0.139641</td>\n",
" <td>0.223191</td>\n",
" <td>0.856793</td>\n",
" <td>0.800237</td>\n",
" <td>0.554718</td>\n",
" <td>0.776976</td>\n",
" <td>0.0</td>\n",
" <td>0.354565</td>\n",
" <td>0.388414</td>\n",
" <td>0.347581</td>\n",
" <td>0.360744</td>\n",
" <td>0.402607</td>\n",
" <td>0.390989</td>\n",
" <td>0.0</td>\n",
" <td>0.390989</td>\n",
" <td>0.431375</td>\n",
" <td>0.409462</td>\n",
" <td>0.385777</td>\n",
" <td>0.340581</td>\n",
" <td>0.375888</td>\n",
" <td>0.372911</td>\n",
" <td>0.350551</td>\n",
" <td>0.400626</td>\n",
" <td>0.404546</td>\n",
" <td>0.364278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.GGCATC</td>\n",
" <td>0.0</td>\n",
" <td>0.555539</td>\n",
" <td>0.392705</td>\n",
" <td>0.579332</td>\n",
" <td>0.145652</td>\n",
" <td>0.164935</td>\n",
" <td>0.184172</td>\n",
" <td>0.0</td>\n",
" <td>0.184172</td>\n",
" <td>0.333482</td>\n",
" <td>0.421168</td>\n",
" <td>0.661779</td>\n",
" <td>0.296269</td>\n",
" <td>0.147555</td>\n",
" <td>0.241141</td>\n",
" <td>0.825601</td>\n",
" <td>0.813275</td>\n",
" <td>0.589382</td>\n",
" <td>0.790102</td>\n",
" <td>0.0</td>\n",
" <td>0.349269</td>\n",
" <td>0.378454</td>\n",
" <td>0.343167</td>\n",
" <td>0.352282</td>\n",
" <td>0.398483</td>\n",
" <td>0.383984</td>\n",
" <td>0.0</td>\n",
" <td>0.383984</td>\n",
" <td>0.394837</td>\n",
" <td>0.570345</td>\n",
" <td>0.396641</td>\n",
" <td>0.337457</td>\n",
" <td>0.375483</td>\n",
" <td>0.341387</td>\n",
" <td>0.228666</td>\n",
" <td>0.393968</td>\n",
" <td>0.384816</td>\n",
" <td>0.358044</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.GTGAGG</td>\n",
" <td>0.0</td>\n",
" <td>0.530569</td>\n",
" <td>0.367508</td>\n",
" <td>0.554714</td>\n",
" <td>0.134121</td>\n",
" <td>0.155524</td>\n",
" <td>0.173857</td>\n",
" <td>0.0</td>\n",
" <td>0.173857</td>\n",
" <td>0.315881</td>\n",
" <td>0.463581</td>\n",
" <td>0.615143</td>\n",
" <td>0.280369</td>\n",
" <td>0.133060</td>\n",
" <td>0.232378</td>\n",
" <td>0.794342</td>\n",
" <td>0.804363</td>\n",
" <td>0.553666</td>\n",
" <td>0.777878</td>\n",
" <td>0.0</td>\n",
" <td>0.358688</td>\n",
" <td>0.390493</td>\n",
" <td>0.351879</td>\n",
" <td>0.361701</td>\n",
" <td>0.404384</td>\n",
" <td>0.398420</td>\n",
" <td>0.0</td>\n",
" <td>0.398420</td>\n",
" <td>0.422339</td>\n",
" <td>0.427769</td>\n",
" <td>0.417731</td>\n",
" <td>0.341625</td>\n",
" <td>0.380159</td>\n",
" <td>0.367890</td>\n",
" <td>0.326313</td>\n",
" <td>0.400965</td>\n",
" <td>0.391422</td>\n",
" <td>0.373351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.GTTGAG</td>\n",
" <td>0.0</td>\n",
" <td>0.557958</td>\n",
" <td>0.396493</td>\n",
" <td>0.580842</td>\n",
" <td>0.143581</td>\n",
" <td>0.165943</td>\n",
" <td>0.198223</td>\n",
" <td>0.0</td>\n",
" <td>0.198223</td>\n",
" <td>0.335718</td>\n",
" <td>0.511515</td>\n",
" <td>0.653486</td>\n",
" <td>0.294972</td>\n",
" <td>0.142042</td>\n",
" <td>0.251258</td>\n",
" <td>0.808442</td>\n",
" <td>0.821536</td>\n",
" <td>0.592957</td>\n",
" <td>0.795827</td>\n",
" <td>0.0</td>\n",
" <td>0.344146</td>\n",
" <td>0.371379</td>\n",
" <td>0.337804</td>\n",
" <td>0.354805</td>\n",
" <td>0.397635</td>\n",
" <td>0.404505</td>\n",
" <td>0.0</td>\n",
" <td>0.404505</td>\n",
" <td>0.420580</td>\n",
" <td>0.415804</td>\n",
" <td>0.359175</td>\n",
" <td>0.330021</td>\n",
" <td>0.365134</td>\n",
" <td>0.366449</td>\n",
" <td>0.413398</td>\n",
" <td>0.375629</td>\n",
" <td>0.381035</td>\n",
" <td>0.346115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.TAGCGG</td>\n",
" <td>0.0</td>\n",
" <td>0.540878</td>\n",
" <td>0.368191</td>\n",
" <td>0.564637</td>\n",
" <td>0.133724</td>\n",
" <td>0.153977</td>\n",
" <td>0.166738</td>\n",
" <td>0.0</td>\n",
" <td>0.166738</td>\n",
" <td>0.307876</td>\n",
" <td>0.486900</td>\n",
" <td>0.632573</td>\n",
" <td>0.286827</td>\n",
" <td>0.128397</td>\n",
" <td>0.230826</td>\n",
" <td>0.856077</td>\n",
" <td>0.814219</td>\n",
" <td>0.577274</td>\n",
" <td>0.793247</td>\n",
" <td>0.0</td>\n",
" <td>0.346260</td>\n",
" <td>0.378683</td>\n",
" <td>0.339097</td>\n",
" <td>0.356597</td>\n",
" <td>0.396187</td>\n",
" <td>0.381571</td>\n",
" <td>0.0</td>\n",
" <td>0.381571</td>\n",
" <td>0.400928</td>\n",
" <td>0.570090</td>\n",
" <td>0.362619</td>\n",
" <td>0.339981</td>\n",
" <td>0.379723</td>\n",
" <td>0.344153</td>\n",
" <td>0.377799</td>\n",
" <td>0.385887</td>\n",
" <td>0.384042</td>\n",
" <td>0.349956</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.TATCTC</td>\n",
" <td>0.0</td>\n",
" <td>0.575676</td>\n",
" <td>0.409355</td>\n",
" <td>0.600599</td>\n",
" <td>0.148591</td>\n",
" <td>0.174386</td>\n",
" <td>0.194566</td>\n",
" <td>0.0</td>\n",
" <td>0.194566</td>\n",
" <td>0.336279</td>\n",
" <td>0.554679</td>\n",
" <td>0.662141</td>\n",
" <td>0.309579</td>\n",
" <td>0.142574</td>\n",
" <td>0.240360</td>\n",
" <td>0.844196</td>\n",
" <td>0.822712</td>\n",
" <td>0.607978</td>\n",
" <td>0.806099</td>\n",
" <td>0.0</td>\n",
" <td>0.341342</td>\n",
" <td>0.367808</td>\n",
" <td>0.337694</td>\n",
" <td>0.339362</td>\n",
" <td>0.386849</td>\n",
" <td>0.380734</td>\n",
" <td>0.0</td>\n",
" <td>0.380734</td>\n",
" <td>0.394130</td>\n",
" <td>0.514109</td>\n",
" <td>0.395076</td>\n",
" <td>0.319727</td>\n",
" <td>0.356904</td>\n",
" <td>0.318327</td>\n",
" <td>0.309309</td>\n",
" <td>0.390633</td>\n",
" <td>0.376339</td>\n",
" <td>0.360353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.TCTCTG</td>\n",
" <td>0.0</td>\n",
" <td>0.573932</td>\n",
" <td>0.402771</td>\n",
" <td>0.597337</td>\n",
" <td>0.153348</td>\n",
" <td>0.175088</td>\n",
" <td>0.200765</td>\n",
" <td>0.0</td>\n",
" <td>0.200765</td>\n",
" <td>0.347808</td>\n",
" <td>0.610443</td>\n",
" <td>0.676611</td>\n",
" <td>0.311039</td>\n",
" <td>0.144939</td>\n",
" <td>0.256872</td>\n",
" <td>0.854733</td>\n",
" <td>0.826905</td>\n",
" <td>0.607012</td>\n",
" <td>0.802475</td>\n",
" <td>0.0</td>\n",
" <td>0.342377</td>\n",
" <td>0.370775</td>\n",
" <td>0.337035</td>\n",
" <td>0.356039</td>\n",
" <td>0.402495</td>\n",
" <td>0.412892</td>\n",
" <td>0.0</td>\n",
" <td>0.412892</td>\n",
" <td>0.408307</td>\n",
" <td>0.464521</td>\n",
" <td>0.389068</td>\n",
" <td>0.330017</td>\n",
" <td>0.366829</td>\n",
" <td>0.337809</td>\n",
" <td>0.241373</td>\n",
" <td>0.383198</td>\n",
" <td>0.378137</td>\n",
" <td>0.345602</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.TGACAG</td>\n",
" <td>0.0</td>\n",
" <td>0.567869</td>\n",
" <td>0.399077</td>\n",
" <td>0.591561</td>\n",
" <td>0.148038</td>\n",
" <td>0.176237</td>\n",
" <td>0.202154</td>\n",
" <td>0.0</td>\n",
" <td>0.202154</td>\n",
" <td>0.343355</td>\n",
" <td>0.522213</td>\n",
" <td>0.665558</td>\n",
" <td>0.303592</td>\n",
" <td>0.146534</td>\n",
" <td>0.252288</td>\n",
" <td>0.796484</td>\n",
" <td>0.830742</td>\n",
" <td>0.598186</td>\n",
" <td>0.806577</td>\n",
" <td>0.0</td>\n",
" <td>0.345410</td>\n",
" <td>0.381678</td>\n",
" <td>0.340699</td>\n",
" <td>0.363333</td>\n",
" <td>0.411799</td>\n",
" <td>0.398106</td>\n",
" <td>0.0</td>\n",
" <td>0.398106</td>\n",
" <td>0.406224</td>\n",
" <td>0.468663</td>\n",
" <td>0.352417</td>\n",
" <td>0.345042</td>\n",
" <td>0.391606</td>\n",
" <td>0.339766</td>\n",
" <td>0.370686</td>\n",
" <td>0.369564</td>\n",
" <td>0.369690</td>\n",
" <td>0.345702</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.TGCTGC</td>\n",
" <td>0.0</td>\n",
" <td>0.549810</td>\n",
" <td>0.381938</td>\n",
" <td>0.576183</td>\n",
" <td>0.143244</td>\n",
" <td>0.162055</td>\n",
" <td>0.182065</td>\n",
" <td>0.0</td>\n",
" <td>0.182065</td>\n",
" <td>0.322882</td>\n",
" <td>0.522536</td>\n",
" <td>0.636949</td>\n",
" <td>0.297776</td>\n",
" <td>0.142530</td>\n",
" <td>0.244198</td>\n",
" <td>0.808005</td>\n",
" <td>0.807068</td>\n",
" <td>0.566512</td>\n",
" <td>0.789342</td>\n",
" <td>0.0</td>\n",
" <td>0.361772</td>\n",
" <td>0.392851</td>\n",
" <td>0.356422</td>\n",
" <td>0.365182</td>\n",
" <td>0.413081</td>\n",
" <td>0.408349</td>\n",
" <td>0.0</td>\n",
" <td>0.408349</td>\n",
" <td>0.431775</td>\n",
" <td>0.422301</td>\n",
" <td>0.386494</td>\n",
" <td>0.344547</td>\n",
" <td>0.390997</td>\n",
" <td>0.368241</td>\n",
" <td>0.364905</td>\n",
" <td>0.392520</td>\n",
" <td>0.386162</td>\n",
" <td>0.373449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.ACAACC</td>\n",
" <td>0.0</td>\n",
" <td>0.571434</td>\n",
" <td>0.403317</td>\n",
" <td>0.595518</td>\n",
" <td>0.150534</td>\n",
" <td>0.172412</td>\n",
" <td>0.197820</td>\n",
" <td>0.0</td>\n",
" <td>0.197820</td>\n",
" <td>0.335046</td>\n",
" <td>0.542060</td>\n",
" <td>0.674691</td>\n",
" <td>0.311226</td>\n",
" <td>0.145333</td>\n",
" <td>0.251228</td>\n",
" <td>0.782911</td>\n",
" <td>0.825876</td>\n",
" <td>0.601882</td>\n",
" <td>0.804140</td>\n",
" <td>0.0</td>\n",
" <td>0.356830</td>\n",
" <td>0.396188</td>\n",
" <td>0.349903</td>\n",
" <td>0.388446</td>\n",
" <td>0.425970</td>\n",
" <td>0.423147</td>\n",
" <td>0.0</td>\n",
" <td>0.423147</td>\n",
" <td>0.414028</td>\n",
" <td>0.406933</td>\n",
" <td>0.353024</td>\n",
" <td>0.363997</td>\n",
" <td>0.410352</td>\n",
" <td>0.353075</td>\n",
" <td>0.404312</td>\n",
" <td>0.376888</td>\n",
" <td>0.385532</td>\n",
" <td>0.352623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.ACGTGG</td>\n",
" <td>0.0</td>\n",
" <td>0.547223</td>\n",
" <td>0.381861</td>\n",
" <td>0.572699</td>\n",
" <td>0.142991</td>\n",
" <td>0.167937</td>\n",
" <td>0.187875</td>\n",
" <td>0.0</td>\n",
" <td>0.187875</td>\n",
" <td>0.322376</td>\n",
" <td>0.494384</td>\n",
" <td>0.655945</td>\n",
" <td>0.290386</td>\n",
" <td>0.138317</td>\n",
" <td>0.228471</td>\n",
" <td>0.824135</td>\n",
" <td>0.809657</td>\n",
" <td>0.575763</td>\n",
" <td>0.782319</td>\n",
" <td>0.0</td>\n",
" <td>0.367896</td>\n",
" <td>0.410155</td>\n",
" <td>0.360025</td>\n",
" <td>0.392362</td>\n",
" <td>0.439690</td>\n",
" <td>0.423503</td>\n",
" <td>0.0</td>\n",
" <td>0.423503</td>\n",
" <td>0.424893</td>\n",
" <td>0.371724</td>\n",
" <td>0.396436</td>\n",
" <td>0.364995</td>\n",
" <td>0.415006</td>\n",
" <td>0.358457</td>\n",
" <td>0.266702</td>\n",
" <td>0.393868</td>\n",
" <td>0.396684</td>\n",
" <td>0.361810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.ACTCAC</td>\n",
" <td>0.0</td>\n",
" <td>0.574078</td>\n",
" <td>0.410369</td>\n",
" <td>0.599662</td>\n",
" <td>0.154807</td>\n",
" <td>0.180722</td>\n",
" <td>0.204649</td>\n",
" <td>0.0</td>\n",
" <td>0.204649</td>\n",
" <td>0.346719</td>\n",
" <td>0.484916</td>\n",
" <td>0.687740</td>\n",
" <td>0.314513</td>\n",
" <td>0.155080</td>\n",
" <td>0.260803</td>\n",
" <td>0.812211</td>\n",
" <td>0.828639</td>\n",
" <td>0.608617</td>\n",
" <td>0.812883</td>\n",
" <td>0.0</td>\n",
" <td>0.360783</td>\n",
" <td>0.413536</td>\n",
" <td>0.352271</td>\n",
" <td>0.396930</td>\n",
" <td>0.438502</td>\n",
" <td>0.437218</td>\n",
" <td>0.0</td>\n",
" <td>0.437218</td>\n",
" <td>0.434233</td>\n",
" <td>0.243128</td>\n",
" <td>0.391187</td>\n",
" <td>0.371005</td>\n",
" <td>0.424675</td>\n",
" <td>0.381663</td>\n",
" <td>0.401646</td>\n",
" <td>0.380932</td>\n",
" <td>0.388726</td>\n",
" <td>0.364671</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.AGGATG</td>\n",
" <td>0.0</td>\n",
" <td>0.574464</td>\n",
" <td>0.411157</td>\n",
" <td>0.598317</td>\n",
" <td>0.153244</td>\n",
" <td>0.179052</td>\n",
" <td>0.205426</td>\n",
" <td>0.0</td>\n",
" <td>0.205426</td>\n",
" <td>0.347422</td>\n",
" <td>0.549476</td>\n",
" <td>0.674730</td>\n",
" <td>0.318179</td>\n",
" <td>0.150916</td>\n",
" <td>0.255366</td>\n",
" <td>0.825079</td>\n",
" <td>0.819016</td>\n",
" <td>0.600086</td>\n",
" <td>0.803518</td>\n",
" <td>0.0</td>\n",
" <td>0.361346</td>\n",
" <td>0.394711</td>\n",
" <td>0.356405</td>\n",
" <td>0.385541</td>\n",
" <td>0.430138</td>\n",
" <td>0.435397</td>\n",
" <td>0.0</td>\n",
" <td>0.435397</td>\n",
" <td>0.430261</td>\n",
" <td>0.509647</td>\n",
" <td>0.397243</td>\n",
" <td>0.354870</td>\n",
" <td>0.405106</td>\n",
" <td>0.376955</td>\n",
" <td>0.349346</td>\n",
" <td>0.390618</td>\n",
" <td>0.387689</td>\n",
" <td>0.355591</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.ATAGCG</td>\n",
" <td>0.0</td>\n",
" <td>0.530317</td>\n",
" <td>0.372598</td>\n",
" <td>0.555500</td>\n",
" <td>0.139056</td>\n",
" <td>0.162002</td>\n",
" <td>0.188584</td>\n",
" <td>0.0</td>\n",
" <td>0.188584</td>\n",
" <td>0.314593</td>\n",
" <td>0.662600</td>\n",
" <td>0.651363</td>\n",
" <td>0.276445</td>\n",
" <td>0.139170</td>\n",
" <td>0.226507</td>\n",
" <td>0.782103</td>\n",
" <td>0.805115</td>\n",
" <td>0.577048</td>\n",
" <td>0.796431</td>\n",
" <td>0.0</td>\n",
" <td>0.368334</td>\n",
" <td>0.399350</td>\n",
" <td>0.363279</td>\n",
" <td>0.375962</td>\n",
" <td>0.421184</td>\n",
" <td>0.422536</td>\n",
" <td>0.0</td>\n",
" <td>0.422536</td>\n",
" <td>0.423652</td>\n",
" <td>0.422062</td>\n",
" <td>0.390546</td>\n",
" <td>0.352241</td>\n",
" <td>0.394821</td>\n",
" <td>0.362621</td>\n",
" <td>0.349869</td>\n",
" <td>0.400868</td>\n",
" <td>0.397587</td>\n",
" <td>0.375378</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.ATCGAC</td>\n",
" <td>0.0</td>\n",
" <td>0.579706</td>\n",
" <td>0.417754</td>\n",
" <td>0.601652</td>\n",
" <td>0.154766</td>\n",
" <td>0.179131</td>\n",
" <td>0.214474</td>\n",
" <td>0.0</td>\n",
" <td>0.214474</td>\n",
" <td>0.347695</td>\n",
" <td>0.592746</td>\n",
" <td>0.693724</td>\n",
" <td>0.318722</td>\n",
" <td>0.152873</td>\n",
" <td>0.251772</td>\n",
" <td>0.829067</td>\n",
" <td>0.818502</td>\n",
" <td>0.603463</td>\n",
" <td>0.804070</td>\n",
" <td>0.0</td>\n",
" <td>0.365534</td>\n",
" <td>0.410172</td>\n",
" <td>0.359211</td>\n",
" <td>0.392262</td>\n",
" <td>0.438666</td>\n",
" <td>0.432635</td>\n",
" <td>0.0</td>\n",
" <td>0.432635</td>\n",
" <td>0.437134</td>\n",
" <td>0.550723</td>\n",
" <td>0.406531</td>\n",
" <td>0.367066</td>\n",
" <td>0.412751</td>\n",
" <td>0.381165</td>\n",
" <td>0.291126</td>\n",
" <td>0.388891</td>\n",
" <td>0.385826</td>\n",
" <td>0.368804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.CAAGAG</td>\n",
" <td>0.0</td>\n",
" <td>0.562102</td>\n",
" <td>0.395170</td>\n",
" <td>0.587195</td>\n",
" <td>0.146861</td>\n",
" <td>0.171793</td>\n",
" <td>0.193740</td>\n",
" <td>0.0</td>\n",
" <td>0.193740</td>\n",
" <td>0.336475</td>\n",
" <td>0.643228</td>\n",
" <td>0.644603</td>\n",
" <td>0.303090</td>\n",
" <td>0.145796</td>\n",
" <td>0.249175</td>\n",
" <td>0.867900</td>\n",
" <td>0.811712</td>\n",
" <td>0.594372</td>\n",
" <td>0.798705</td>\n",
" <td>0.0</td>\n",
" <td>0.371383</td>\n",
" <td>0.417443</td>\n",
" <td>0.364244</td>\n",
" <td>0.392824</td>\n",
" <td>0.439427</td>\n",
" <td>0.424017</td>\n",
" <td>0.0</td>\n",
" <td>0.424017</td>\n",
" <td>0.430895</td>\n",
" <td>0.419896</td>\n",
" <td>0.406159</td>\n",
" <td>0.358254</td>\n",
" <td>0.421774</td>\n",
" <td>0.375869</td>\n",
" <td>0.285465</td>\n",
" <td>0.405000</td>\n",
" <td>0.385932</td>\n",
" <td>0.366543</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.CATGAC</td>\n",
" <td>0.0</td>\n",
" <td>0.572795</td>\n",
" <td>0.406324</td>\n",
" <td>0.595511</td>\n",
" <td>0.149580</td>\n",
" <td>0.173139</td>\n",
" <td>0.207084</td>\n",
" <td>0.0</td>\n",
" <td>0.207084</td>\n",
" <td>0.341760</td>\n",
" <td>0.582188</td>\n",
" <td>0.686507</td>\n",
" <td>0.311526</td>\n",
" <td>0.145188</td>\n",
" <td>0.255092</td>\n",
" <td>0.898922</td>\n",
" <td>0.822096</td>\n",
" <td>0.602356</td>\n",
" <td>0.802416</td>\n",
" <td>0.0</td>\n",
" <td>0.363315</td>\n",
" <td>0.403748</td>\n",
" <td>0.356727</td>\n",
" <td>0.385169</td>\n",
" <td>0.426971</td>\n",
" <td>0.428521</td>\n",
" <td>0.0</td>\n",
" <td>0.428521</td>\n",
" <td>0.437559</td>\n",
" <td>0.666602</td>\n",
" <td>0.399967</td>\n",
" <td>0.360502</td>\n",
" <td>0.402785</td>\n",
" <td>0.382317</td>\n",
" <td>0.213772</td>\n",
" <td>0.390465</td>\n",
" <td>0.395524</td>\n",
" <td>0.364513</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.CCTTCG</td>\n",
" <td>0.0</td>\n",
" <td>0.544424</td>\n",
" <td>0.372864</td>\n",
" <td>0.566449</td>\n",
" <td>0.141793</td>\n",
" <td>0.161766</td>\n",
" <td>0.188699</td>\n",
" <td>0.0</td>\n",
" <td>0.188699</td>\n",
" <td>0.320217</td>\n",
" <td>0.520051</td>\n",
" <td>0.648292</td>\n",
" <td>0.287153</td>\n",
" <td>0.134590</td>\n",
" <td>0.231480</td>\n",
" <td>0.918757</td>\n",
" <td>0.808219</td>\n",
" <td>0.556881</td>\n",
" <td>0.786660</td>\n",
" <td>0.0</td>\n",
" <td>0.372677</td>\n",
" <td>0.409840</td>\n",
" <td>0.367423</td>\n",
" <td>0.386818</td>\n",
" <td>0.428182</td>\n",
" <td>0.410043</td>\n",
" <td>0.0</td>\n",
" <td>0.410043</td>\n",
" <td>0.437956</td>\n",
" <td>0.431314</td>\n",
" <td>0.417184</td>\n",
" <td>0.370701</td>\n",
" <td>0.403697</td>\n",
" <td>0.379120</td>\n",
" <td>0.132574</td>\n",
" <td>0.402506</td>\n",
" <td>0.408092</td>\n",
" <td>0.392411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.CGGTAG</td>\n",
" <td>0.0</td>\n",
" <td>0.541543</td>\n",
" <td>0.377491</td>\n",
" <td>0.566615</td>\n",
" <td>0.144500</td>\n",
" <td>0.166361</td>\n",
" <td>0.187541</td>\n",
" <td>0.0</td>\n",
" <td>0.187541</td>\n",
" <td>0.323718</td>\n",
" <td>0.570565</td>\n",
" <td>0.649134</td>\n",
" <td>0.290349</td>\n",
" <td>0.139695</td>\n",
" <td>0.231105</td>\n",
" <td>0.875553</td>\n",
" <td>0.805793</td>\n",
" <td>0.574939</td>\n",
" <td>0.781107</td>\n",
" <td>0.0</td>\n",
" <td>0.366749</td>\n",
" <td>0.406201</td>\n",
" <td>0.359577</td>\n",
" <td>0.382439</td>\n",
" <td>0.427216</td>\n",
" <td>0.412182</td>\n",
" <td>0.0</td>\n",
" <td>0.412182</td>\n",
" <td>0.424271</td>\n",
" <td>0.326364</td>\n",
" <td>0.387866</td>\n",
" <td>0.358136</td>\n",
" <td>0.400989</td>\n",
" <td>0.367082</td>\n",
" <td>0.233944</td>\n",
" <td>0.405535</td>\n",
" <td>0.387747</td>\n",
" <td>0.372620</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.CTATTG</td>\n",
" <td>0.0</td>\n",
" <td>0.571516</td>\n",
" <td>0.403810</td>\n",
" <td>0.595260</td>\n",
" <td>0.147111</td>\n",
" <td>0.169791</td>\n",
" <td>0.199903</td>\n",
" <td>0.0</td>\n",
" <td>0.199903</td>\n",
" <td>0.345774</td>\n",
" <td>0.461720</td>\n",
" <td>0.665058</td>\n",
" <td>0.308691</td>\n",
" <td>0.144474</td>\n",
" <td>0.257436</td>\n",
" <td>0.833091</td>\n",
" <td>0.825356</td>\n",
" <td>0.606750</td>\n",
" <td>0.808127</td>\n",
" <td>0.0</td>\n",
" <td>0.352923</td>\n",
" <td>0.389431</td>\n",
" <td>0.346998</td>\n",
" <td>0.367228</td>\n",
" <td>0.410854</td>\n",
" <td>0.408465</td>\n",
" <td>0.0</td>\n",
" <td>0.408465</td>\n",
" <td>0.420192</td>\n",
" <td>0.435325</td>\n",
" <td>0.384775</td>\n",
" <td>0.344882</td>\n",
" <td>0.388064</td>\n",
" <td>0.354390</td>\n",
" <td>0.322671</td>\n",
" <td>0.384292</td>\n",
" <td>0.385328</td>\n",
" <td>0.353731</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.GACACG</td>\n",
" <td>0.0</td>\n",
" <td>0.547611</td>\n",
" <td>0.384121</td>\n",
" <td>0.571736</td>\n",
" <td>0.141024</td>\n",
" <td>0.160152</td>\n",
" <td>0.183884</td>\n",
" <td>0.0</td>\n",
" <td>0.183884</td>\n",
" <td>0.324428</td>\n",
" <td>0.516251</td>\n",
" <td>0.658554</td>\n",
" <td>0.296090</td>\n",
" <td>0.142236</td>\n",
" <td>0.243037</td>\n",
" <td>0.840847</td>\n",
" <td>0.810208</td>\n",
" <td>0.580500</td>\n",
" <td>0.780899</td>\n",
" <td>0.0</td>\n",
" <td>0.362036</td>\n",
" <td>0.396972</td>\n",
" <td>0.355362</td>\n",
" <td>0.379360</td>\n",
" <td>0.423028</td>\n",
" <td>0.407819</td>\n",
" <td>0.0</td>\n",
" <td>0.407819</td>\n",
" <td>0.430720</td>\n",
" <td>0.413402</td>\n",
" <td>0.375936</td>\n",
" <td>0.353105</td>\n",
" <td>0.395058</td>\n",
" <td>0.377523</td>\n",
" <td>0.239142</td>\n",
" <td>0.397595</td>\n",
" <td>0.385070</td>\n",
" <td>0.369290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.GCATTC</td>\n",
" <td>0.0</td>\n",
" <td>0.574308</td>\n",
" <td>0.399926</td>\n",
" <td>0.599169</td>\n",
" <td>0.146626</td>\n",
" <td>0.171425</td>\n",
" <td>0.202222</td>\n",
" <td>0.0</td>\n",
" <td>0.202222</td>\n",
" <td>0.339589</td>\n",
" <td>0.601239</td>\n",
" <td>0.673941</td>\n",
" <td>0.311423</td>\n",
" <td>0.142003</td>\n",
" <td>0.251027</td>\n",
" <td>0.900174</td>\n",
" <td>0.822302</td>\n",
" <td>0.607857</td>\n",
" <td>0.804399</td>\n",
" <td>0.0</td>\n",
" <td>0.350723</td>\n",
" <td>0.388557</td>\n",
" <td>0.343231</td>\n",
" <td>0.379437</td>\n",
" <td>0.423172</td>\n",
" <td>0.413884</td>\n",
" <td>0.0</td>\n",
" <td>0.413884</td>\n",
" <td>0.430536</td>\n",
" <td>0.484843</td>\n",
" <td>0.385927</td>\n",
" <td>0.351172</td>\n",
" <td>0.395395</td>\n",
" <td>0.377211</td>\n",
" <td>0.260417</td>\n",
" <td>0.387313</td>\n",
" <td>0.379687</td>\n",
" <td>0.352217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.GCTGCC</td>\n",
" <td>0.0</td>\n",
" <td>0.557827</td>\n",
" <td>0.389318</td>\n",
" <td>0.583295</td>\n",
" <td>0.146131</td>\n",
" <td>0.170323</td>\n",
" <td>0.216651</td>\n",
" <td>0.0</td>\n",
" <td>0.216651</td>\n",
" <td>0.332222</td>\n",
" <td>0.465542</td>\n",
" <td>0.685189</td>\n",
" <td>0.303248</td>\n",
" <td>0.142679</td>\n",
" <td>0.253152</td>\n",
" <td>0.925723</td>\n",
" <td>0.812384</td>\n",
" <td>0.566898</td>\n",
" <td>0.789918</td>\n",
" <td>0.0</td>\n",
" <td>0.364344</td>\n",
" <td>0.411073</td>\n",
" <td>0.357567</td>\n",
" <td>0.386643</td>\n",
" <td>0.436352</td>\n",
" <td>0.455234</td>\n",
" <td>0.0</td>\n",
" <td>0.455234</td>\n",
" <td>0.427594</td>\n",
" <td>0.441989</td>\n",
" <td>0.410031</td>\n",
" <td>0.357034</td>\n",
" <td>0.414984</td>\n",
" <td>0.377580</td>\n",
" <td>0.122134</td>\n",
" <td>0.390103</td>\n",
" <td>0.391888</td>\n",
" <td>0.356613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.GGCATC</td>\n",
" <td>0.0</td>\n",
" <td>0.558443</td>\n",
" <td>0.393582</td>\n",
" <td>0.581411</td>\n",
" <td>0.149267</td>\n",
" <td>0.173254</td>\n",
" <td>0.202211</td>\n",
" <td>0.0</td>\n",
" <td>0.202211</td>\n",
" <td>0.336075</td>\n",
" <td>0.497985</td>\n",
" <td>0.658613</td>\n",
" <td>0.307422</td>\n",
" <td>0.145063</td>\n",
" <td>0.253500</td>\n",
" <td>0.822348</td>\n",
" <td>0.810850</td>\n",
" <td>0.579463</td>\n",
" <td>0.788819</td>\n",
" <td>0.0</td>\n",
" <td>0.369847</td>\n",
" <td>0.416267</td>\n",
" <td>0.362248</td>\n",
" <td>0.393373</td>\n",
" <td>0.438305</td>\n",
" <td>0.432106</td>\n",
" <td>0.0</td>\n",
" <td>0.432106</td>\n",
" <td>0.447392</td>\n",
" <td>0.283823</td>\n",
" <td>0.414114</td>\n",
" <td>0.369362</td>\n",
" <td>0.423329</td>\n",
" <td>0.390531</td>\n",
" <td>0.294508</td>\n",
" <td>0.403624</td>\n",
" <td>0.394708</td>\n",
" <td>0.370958</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.GTGAGG</td>\n",
" <td>0.0</td>\n",
" <td>0.548763</td>\n",
" <td>0.378005</td>\n",
" <td>0.573823</td>\n",
" <td>0.146951</td>\n",
" <td>0.166034</td>\n",
" <td>0.189554</td>\n",
" <td>0.0</td>\n",
" <td>0.189554</td>\n",
" <td>0.329531</td>\n",
" <td>0.417063</td>\n",
" <td>0.656924</td>\n",
" <td>0.298588</td>\n",
" <td>0.143700</td>\n",
" <td>0.237655</td>\n",
" <td>0.837979</td>\n",
" <td>0.818187</td>\n",
" <td>0.578995</td>\n",
" <td>0.795407</td>\n",
" <td>0.0</td>\n",
" <td>0.369274</td>\n",
" <td>0.411458</td>\n",
" <td>0.362212</td>\n",
" <td>0.399234</td>\n",
" <td>0.439421</td>\n",
" <td>0.426244</td>\n",
" <td>0.0</td>\n",
" <td>0.426244</td>\n",
" <td>0.437661</td>\n",
" <td>0.387644</td>\n",
" <td>0.379763</td>\n",
" <td>0.378844</td>\n",
" <td>0.416184</td>\n",
" <td>0.393381</td>\n",
" <td>0.327526</td>\n",
" <td>0.390663</td>\n",
" <td>0.404334</td>\n",
" <td>0.359301</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.GTTGAG</td>\n",
" <td>0.0</td>\n",
" <td>0.560388</td>\n",
" <td>0.392703</td>\n",
" <td>0.585744</td>\n",
" <td>0.148877</td>\n",
" <td>0.173148</td>\n",
" <td>0.191432</td>\n",
" <td>0.0</td>\n",
" <td>0.191432</td>\n",
" <td>0.337372</td>\n",
" <td>0.531839</td>\n",
" <td>0.657189</td>\n",
" <td>0.308189</td>\n",
" <td>0.144341</td>\n",
" <td>0.245608</td>\n",
" <td>0.856433</td>\n",
" <td>0.820191</td>\n",
" <td>0.594452</td>\n",
" <td>0.790093</td>\n",
" <td>0.0</td>\n",
" <td>0.357508</td>\n",
" <td>0.399570</td>\n",
" <td>0.348983</td>\n",
" <td>0.382424</td>\n",
" <td>0.428961</td>\n",
" <td>0.426739</td>\n",
" <td>0.0</td>\n",
" <td>0.426739</td>\n",
" <td>0.436677</td>\n",
" <td>0.623022</td>\n",
" <td>0.378906</td>\n",
" <td>0.357031</td>\n",
" <td>0.402640</td>\n",
" <td>0.380493</td>\n",
" <td>0.320648</td>\n",
" <td>0.385596</td>\n",
" <td>0.378942</td>\n",
" <td>0.359409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.TAGCGG</td>\n",
" <td>0.0</td>\n",
" <td>0.542448</td>\n",
" <td>0.379673</td>\n",
" <td>0.565934</td>\n",
" <td>0.141666</td>\n",
" <td>0.161357</td>\n",
" <td>0.190254</td>\n",
" <td>0.0</td>\n",
" <td>0.190254</td>\n",
" <td>0.322077</td>\n",
" <td>0.564060</td>\n",
" <td>0.661459</td>\n",
" <td>0.291782</td>\n",
" <td>0.141023</td>\n",
" <td>0.236705</td>\n",
" <td>0.775427</td>\n",
" <td>0.812801</td>\n",
" <td>0.584395</td>\n",
" <td>0.788413</td>\n",
" <td>0.0</td>\n",
" <td>0.373111</td>\n",
" <td>0.419487</td>\n",
" <td>0.363822</td>\n",
" <td>0.398524</td>\n",
" <td>0.443218</td>\n",
" <td>0.429929</td>\n",
" <td>0.0</td>\n",
" <td>0.429929</td>\n",
" <td>0.428916</td>\n",
" <td>0.508213</td>\n",
" <td>0.389595</td>\n",
" <td>0.371629</td>\n",
" <td>0.425177</td>\n",
" <td>0.379944</td>\n",
" <td>0.390322</td>\n",
" <td>0.401379</td>\n",
" <td>0.390136</td>\n",
" <td>0.365038</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" filename methylation_tssDistance \\\n",
"0 RRBS_trito_pool_1_TAAGGCGA.ACAACC 0.0 \n",
"1 RRBS_trito_pool_1_TAAGGCGA.ACGTGG 0.0 \n",
"2 RRBS_trito_pool_1_TAAGGCGA.ACTCAC 0.0 \n",
"3 RRBS_trito_pool_1_TAAGGCGA.AGGATG 0.0 \n",
"4 RRBS_trito_pool_1_TAAGGCGA.ATAGCG 0.0 \n",
"5 RRBS_trito_pool_1_TAAGGCGA.ATCGAC 0.0 \n",
"6 RRBS_trito_pool_1_TAAGGCGA.CAAGAG 0.0 \n",
"7 RRBS_trito_pool_1_TAAGGCGA.CATGAC 0.0 \n",
"8 RRBS_trito_pool_1_TAAGGCGA.CCTTCG 0.0 \n",
"9 RRBS_trito_pool_1_TAAGGCGA.CGGTAG 0.0 \n",
"10 RRBS_trito_pool_1_TAAGGCGA.CTATTG 0.0 \n",
"11 RRBS_trito_pool_1_TAAGGCGA.GACACG 0.0 \n",
"12 RRBS_trito_pool_1_TAAGGCGA.GCATTC 0.0 \n",
"13 RRBS_trito_pool_1_TAAGGCGA.GCTGCC 0.0 \n",
"14 RRBS_trito_pool_1_TAAGGCGA.GGCATC 0.0 \n",
"15 RRBS_trito_pool_1_TAAGGCGA.GTGAGG 0.0 \n",
"16 RRBS_trito_pool_1_TAAGGCGA.GTTGAG 0.0 \n",
"17 RRBS_trito_pool_1_TAAGGCGA.TAGCGG 0.0 \n",
"18 RRBS_trito_pool_1_TAAGGCGA.TATCTC 0.0 \n",
"19 RRBS_trito_pool_1_TAAGGCGA.TCTCTG 0.0 \n",
"20 RRBS_trito_pool_1_TAAGGCGA.TGACAG 0.0 \n",
"21 RRBS_trito_pool_1_TAAGGCGA.TGCTGC 0.0 \n",
"22 RRBS_trito_pool_2_CGTACTAG.ACAACC 0.0 \n",
"23 RRBS_trito_pool_2_CGTACTAG.ACGTGG 0.0 \n",
"24 RRBS_trito_pool_2_CGTACTAG.ACTCAC 0.0 \n",
"25 RRBS_trito_pool_2_CGTACTAG.AGGATG 0.0 \n",
"26 RRBS_trito_pool_2_CGTACTAG.ATAGCG 0.0 \n",
"27 RRBS_trito_pool_2_CGTACTAG.ATCGAC 0.0 \n",
"28 RRBS_trito_pool_2_CGTACTAG.CAAGAG 0.0 \n",
"29 RRBS_trito_pool_2_CGTACTAG.CATGAC 0.0 \n",
"30 RRBS_trito_pool_2_CGTACTAG.CCTTCG 0.0 \n",
"31 RRBS_trito_pool_2_CGTACTAG.CGGTAG 0.0 \n",
"32 RRBS_trito_pool_2_CGTACTAG.CTATTG 0.0 \n",
"33 RRBS_trito_pool_2_CGTACTAG.GACACG 0.0 \n",
"34 RRBS_trito_pool_2_CGTACTAG.GCATTC 0.0 \n",
"35 RRBS_trito_pool_2_CGTACTAG.GCTGCC 0.0 \n",
"36 RRBS_trito_pool_2_CGTACTAG.GGCATC 0.0 \n",
"37 RRBS_trito_pool_2_CGTACTAG.GTGAGG 0.0 \n",
"38 RRBS_trito_pool_2_CGTACTAG.GTTGAG 0.0 \n",
"39 RRBS_trito_pool_2_CGTACTAG.TAGCGG 0.0 \n",
"\n",
" methylation_genesDistance methylation_exonsDistance \\\n",
"0 0.570721 0.404589 \n",
"1 0.545781 0.383371 \n",
"2 0.564547 0.401760 \n",
"3 0.567309 0.399934 \n",
"4 0.529224 0.367743 \n",
"5 0.566031 0.393281 \n",
"6 0.566742 0.402345 \n",
"7 0.566995 0.407400 \n",
"8 0.544137 0.377628 \n",
"9 0.540051 0.369671 \n",
"10 0.579098 0.413628 \n",
"11 0.549829 0.379579 \n",
"12 0.577097 0.411205 \n",
"13 0.544259 0.384439 \n",
"14 0.555539 0.392705 \n",
"15 0.530569 0.367508 \n",
"16 0.557958 0.396493 \n",
"17 0.540878 0.368191 \n",
"18 0.575676 0.409355 \n",
"19 0.573932 0.402771 \n",
"20 0.567869 0.399077 \n",
"21 0.549810 0.381938 \n",
"22 0.571434 0.403317 \n",
"23 0.547223 0.381861 \n",
"24 0.574078 0.410369 \n",
"25 0.574464 0.411157 \n",
"26 0.530317 0.372598 \n",
"27 0.579706 0.417754 \n",
"28 0.562102 0.395170 \n",
"29 0.572795 0.406324 \n",
"30 0.544424 0.372864 \n",
"31 0.541543 0.377491 \n",
"32 0.571516 0.403810 \n",
"33 0.547611 0.384121 \n",
"34 0.574308 0.399926 \n",
"35 0.557827 0.389318 \n",
"36 0.558443 0.393582 \n",
"37 0.548763 0.378005 \n",
"38 0.560388 0.392703 \n",
"39 0.542448 0.379673 \n",
"\n",
" methylation_intronsDistance methylation_promoterDistance \\\n",
"0 0.594704 0.146371 \n",
"1 0.568638 0.141545 \n",
"2 0.588136 0.148529 \n",
"3 0.592890 0.143897 \n",
"4 0.555131 0.136090 \n",
"5 0.591518 0.145246 \n",
"6 0.590378 0.152305 \n",
"7 0.589923 0.148278 \n",
"8 0.568732 0.136603 \n",
"9 0.565375 0.139633 \n",
"10 0.602551 0.149996 \n",
"11 0.572968 0.142349 \n",
"12 0.599875 0.149844 \n",
"13 0.564795 0.139736 \n",
"14 0.579332 0.145652 \n",
"15 0.554714 0.134121 \n",
"16 0.580842 0.143581 \n",
"17 0.564637 0.133724 \n",
"18 0.600599 0.148591 \n",
"19 0.597337 0.153348 \n",
"20 0.591561 0.148038 \n",
"21 0.576183 0.143244 \n",
"22 0.595518 0.150534 \n",
"23 0.572699 0.142991 \n",
"24 0.599662 0.154807 \n",
"25 0.598317 0.153244 \n",
"26 0.555500 0.139056 \n",
"27 0.601652 0.154766 \n",
"28 0.587195 0.146861 \n",
"29 0.595511 0.149580 \n",
"30 0.566449 0.141793 \n",
"31 0.566615 0.144500 \n",
"32 0.595260 0.147111 \n",
"33 0.571736 0.141024 \n",
"34 0.599169 0.146626 \n",
"35 0.583295 0.146131 \n",
"36 0.581411 0.149267 \n",
"37 0.573823 0.146951 \n",
"38 0.585744 0.148877 \n",
"39 0.565934 0.141666 \n",
"\n",
" methylation_cgiDistance methylation_ctcfDistance \\\n",
"0 0.169386 0.199815 \n",
"1 0.161519 0.191404 \n",
"2 0.174413 0.209041 \n",
"3 0.168936 0.200661 \n",
"4 0.156827 0.175426 \n",
"5 0.162973 0.199969 \n",
"6 0.173906 0.195518 \n",
"7 0.174522 0.209616 \n",
"8 0.163191 0.191971 \n",
"9 0.163692 0.198751 \n",
"10 0.174348 0.206507 \n",
"11 0.163858 0.194206 \n",
"12 0.176529 0.209171 \n",
"13 0.162409 0.176248 \n",
"14 0.164935 0.184172 \n",
"15 0.155524 0.173857 \n",
"16 0.165943 0.198223 \n",
"17 0.153977 0.166738 \n",
"18 0.174386 0.194566 \n",
"19 0.175088 0.200765 \n",
"20 0.176237 0.202154 \n",
"21 0.162055 0.182065 \n",
"22 0.172412 0.197820 \n",
"23 0.167937 0.187875 \n",
"24 0.180722 0.204649 \n",
"25 0.179052 0.205426 \n",
"26 0.162002 0.188584 \n",
"27 0.179131 0.214474 \n",
"28 0.171793 0.193740 \n",
"29 0.173139 0.207084 \n",
"30 0.161766 0.188699 \n",
"31 0.166361 0.187541 \n",
"32 0.169791 0.199903 \n",
"33 0.160152 0.183884 \n",
"34 0.171425 0.202222 \n",
"35 0.170323 0.216651 \n",
"36 0.173254 0.202211 \n",
"37 0.166034 0.189554 \n",
"38 0.173148 0.191432 \n",
"39 0.161357 0.190254 \n",
"\n",
" methylation_ctcfUpDistance methylation_ctcfDownDistance \\\n",
"0 0.0 0.199815 \n",
"1 0.0 0.191404 \n",
"2 0.0 0.209041 \n",
"3 0.0 0.200661 \n",
"4 0.0 0.175426 \n",
"5 0.0 0.199969 \n",
"6 0.0 0.195518 \n",
"7 0.0 0.209616 \n",
"8 0.0 0.191971 \n",
"9 0.0 0.198751 \n",
"10 0.0 0.206507 \n",
"11 0.0 0.194206 \n",
"12 0.0 0.209171 \n",
"13 0.0 0.176248 \n",
"14 0.0 0.184172 \n",
"15 0.0 0.173857 \n",
"16 0.0 0.198223 \n",
"17 0.0 0.166738 \n",
"18 0.0 0.194566 \n",
"19 0.0 0.200765 \n",
"20 0.0 0.202154 \n",
"21 0.0 0.182065 \n",
"22 0.0 0.197820 \n",
"23 0.0 0.187875 \n",
"24 0.0 0.204649 \n",
"25 0.0 0.205426 \n",
"26 0.0 0.188584 \n",
"27 0.0 0.214474 \n",
"28 0.0 0.193740 \n",
"29 0.0 0.207084 \n",
"30 0.0 0.188699 \n",
"31 0.0 0.187541 \n",
"32 0.0 0.199903 \n",
"33 0.0 0.183884 \n",
"34 0.0 0.202222 \n",
"35 0.0 0.216651 \n",
"36 0.0 0.202211 \n",
"37 0.0 0.189554 \n",
"38 0.0 0.191432 \n",
"39 0.0 0.190254 \n",
"\n",
" methylation_geneDistalRegulatoryModulesDistance \\\n",
"0 0.342986 \n",
"1 0.326140 \n",
"2 0.346473 \n",
"3 0.342257 \n",
"4 0.307402 \n",
"5 0.333682 \n",
"6 0.346568 \n",
"7 0.337079 \n",
"8 0.312724 \n",
"9 0.312778 \n",
"10 0.340917 \n",
"11 0.320273 \n",
"12 0.340387 \n",
"13 0.318136 \n",
"14 0.333482 \n",
"15 0.315881 \n",
"16 0.335718 \n",
"17 0.307876 \n",
"18 0.336279 \n",
"19 0.347808 \n",
"20 0.343355 \n",
"21 0.322882 \n",
"22 0.335046 \n",
"23 0.322376 \n",
"24 0.346719 \n",
"25 0.347422 \n",
"26 0.314593 \n",
"27 0.347695 \n",
"28 0.336475 \n",
"29 0.341760 \n",
"30 0.320217 \n",
"31 0.323718 \n",
"32 0.345774 \n",
"33 0.324428 \n",
"34 0.339589 \n",
"35 0.332222 \n",
"36 0.336075 \n",
"37 0.329531 \n",
"38 0.337372 \n",
"39 0.322077 \n",
"\n",
" methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance \\\n",
"0 0.444522 0.680480 \n",
"1 0.589834 0.670559 \n",
"2 0.553062 0.696068 \n",
"3 0.665920 0.661426 \n",
"4 0.479145 0.644411 \n",
"5 0.490492 0.667285 \n",
"6 0.533040 0.656980 \n",
"7 0.476748 0.678752 \n",
"8 0.428419 0.644483 \n",
"9 0.531099 0.642033 \n",
"10 0.479200 0.681367 \n",
"11 0.511181 0.628813 \n",
"12 0.512575 0.667962 \n",
"13 0.574486 0.634002 \n",
"14 0.421168 0.661779 \n",
"15 0.463581 0.615143 \n",
"16 0.511515 0.653486 \n",
"17 0.486900 0.632573 \n",
"18 0.554679 0.662141 \n",
"19 0.610443 0.676611 \n",
"20 0.522213 0.665558 \n",
"21 0.522536 0.636949 \n",
"22 0.542060 0.674691 \n",
"23 0.494384 0.655945 \n",
"24 0.484916 0.687740 \n",
"25 0.549476 0.674730 \n",
"26 0.662600 0.651363 \n",
"27 0.592746 0.693724 \n",
"28 0.643228 0.644603 \n",
"29 0.582188 0.686507 \n",
"30 0.520051 0.648292 \n",
"31 0.570565 0.649134 \n",
"32 0.461720 0.665058 \n",
"33 0.516251 0.658554 \n",
"34 0.601239 0.673941 \n",
"35 0.465542 0.685189 \n",
"36 0.497985 0.658613 \n",
"37 0.417063 0.656924 \n",
"38 0.531839 0.657189 \n",
"39 0.564060 0.661459 \n",
"\n",
" methylation_5PrimeUTRDistance methylation_firstExonDistance \\\n",
"0 0.305206 0.145730 \n",
"1 0.290196 0.140779 \n",
"2 0.296809 0.148360 \n",
"3 0.308680 0.141673 \n",
"4 0.273473 0.134137 \n",
"5 0.304792 0.138075 \n",
"6 0.304390 0.149536 \n",
"7 0.300825 0.146025 \n",
"8 0.284865 0.139631 \n",
"9 0.284431 0.138257 \n",
"10 0.314310 0.147705 \n",
"11 0.297707 0.137945 \n",
"12 0.312442 0.150562 \n",
"13 0.289043 0.139641 \n",
"14 0.296269 0.147555 \n",
"15 0.280369 0.133060 \n",
"16 0.294972 0.142042 \n",
"17 0.286827 0.128397 \n",
"18 0.309579 0.142574 \n",
"19 0.311039 0.144939 \n",
"20 0.303592 0.146534 \n",
"21 0.297776 0.142530 \n",
"22 0.311226 0.145333 \n",
"23 0.290386 0.138317 \n",
"24 0.314513 0.155080 \n",
"25 0.318179 0.150916 \n",
"26 0.276445 0.139170 \n",
"27 0.318722 0.152873 \n",
"28 0.303090 0.145796 \n",
"29 0.311526 0.145188 \n",
"30 0.287153 0.134590 \n",
"31 0.290349 0.139695 \n",
"32 0.308691 0.144474 \n",
"33 0.296090 0.142236 \n",
"34 0.311423 0.142003 \n",
"35 0.303248 0.142679 \n",
"36 0.307422 0.145063 \n",
"37 0.298588 0.143700 \n",
"38 0.308189 0.144341 \n",
"39 0.291782 0.141023 \n",
"\n",
" methylation_geneDistalRegulatoryModulesK562Distance \\\n",
"0 0.254209 \n",
"1 0.240221 \n",
"2 0.255392 \n",
"3 0.242236 \n",
"4 0.220729 \n",
"5 0.248255 \n",
"6 0.253048 \n",
"7 0.243253 \n",
"8 0.229110 \n",
"9 0.223869 \n",
"10 0.250151 \n",
"11 0.231191 \n",
"12 0.251066 \n",
"13 0.223191 \n",
"14 0.241141 \n",
"15 0.232378 \n",
"16 0.251258 \n",
"17 0.230826 \n",
"18 0.240360 \n",
"19 0.256872 \n",
"20 0.252288 \n",
"21 0.244198 \n",
"22 0.251228 \n",
"23 0.228471 \n",
"24 0.260803 \n",
"25 0.255366 \n",
"26 0.226507 \n",
"27 0.251772 \n",
"28 0.249175 \n",
"29 0.255092 \n",
"30 0.231480 \n",
"31 0.231105 \n",
"32 0.257436 \n",
"33 0.243037 \n",
"34 0.251027 \n",
"35 0.253152 \n",
"36 0.253500 \n",
"37 0.237655 \n",
"38 0.245608 \n",
"39 0.236705 \n",
"\n",
" methylation_hypoInHues64Distance methylation_intergenic \\\n",
"0 0.877142 0.820339 \n",
"1 0.809942 0.816166 \n",
"2 0.795883 0.832812 \n",
"3 0.787966 0.824659 \n",
"4 0.815944 0.808981 \n",
"5 0.853356 0.817065 \n",
"6 0.805577 0.816897 \n",
"7 0.863080 0.824824 \n",
"8 0.812046 0.810739 \n",
"9 0.851775 0.809572 \n",
"10 0.842639 0.825690 \n",
"11 0.913175 0.808944 \n",
"12 0.868216 0.824084 \n",
"13 0.856793 0.800237 \n",
"14 0.825601 0.813275 \n",
"15 0.794342 0.804363 \n",
"16 0.808442 0.821536 \n",
"17 0.856077 0.814219 \n",
"18 0.844196 0.822712 \n",
"19 0.854733 0.826905 \n",
"20 0.796484 0.830742 \n",
"21 0.808005 0.807068 \n",
"22 0.782911 0.825876 \n",
"23 0.824135 0.809657 \n",
"24 0.812211 0.828639 \n",
"25 0.825079 0.819016 \n",
"26 0.782103 0.805115 \n",
"27 0.829067 0.818502 \n",
"28 0.867900 0.811712 \n",
"29 0.898922 0.822096 \n",
"30 0.918757 0.808219 \n",
"31 0.875553 0.805793 \n",
"32 0.833091 0.825356 \n",
"33 0.840847 0.810208 \n",
"34 0.900174 0.822302 \n",
"35 0.925723 0.812384 \n",
"36 0.822348 0.810850 \n",
"37 0.837979 0.818187 \n",
"38 0.856433 0.820191 \n",
"39 0.775427 0.812801 \n",
"\n",
" methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance \\\n",
"0 0.607671 0.803716 0.0 0.343014 \n",
"1 0.573089 0.795932 0.0 0.348110 \n",
"2 0.609544 0.812564 0.0 0.338412 \n",
"3 0.602995 0.799836 0.0 0.342724 \n",
"4 0.575050 0.788587 0.0 0.349254 \n",
"5 0.601387 0.800064 0.0 0.343104 \n",
"6 0.603907 0.803092 0.0 0.350942 \n",
"7 0.602455 0.801551 0.0 0.345160 \n",
"8 0.567608 0.784795 0.0 0.345857 \n",
"9 0.567265 0.792353 0.0 0.354063 \n",
"10 0.610104 0.808571 0.0 0.334826 \n",
"11 0.575022 0.795743 0.0 0.347308 \n",
"12 0.608142 0.803492 0.0 0.340215 \n",
"13 0.554718 0.776976 0.0 0.354565 \n",
"14 0.589382 0.790102 0.0 0.349269 \n",
"15 0.553666 0.777878 0.0 0.358688 \n",
"16 0.592957 0.795827 0.0 0.344146 \n",
"17 0.577274 0.793247 0.0 0.346260 \n",
"18 0.607978 0.806099 0.0 0.341342 \n",
"19 0.607012 0.802475 0.0 0.342377 \n",
"20 0.598186 0.806577 0.0 0.345410 \n",
"21 0.566512 0.789342 0.0 0.361772 \n",
"22 0.601882 0.804140 0.0 0.356830 \n",
"23 0.575763 0.782319 0.0 0.367896 \n",
"24 0.608617 0.812883 0.0 0.360783 \n",
"25 0.600086 0.803518 0.0 0.361346 \n",
"26 0.577048 0.796431 0.0 0.368334 \n",
"27 0.603463 0.804070 0.0 0.365534 \n",
"28 0.594372 0.798705 0.0 0.371383 \n",
"29 0.602356 0.802416 0.0 0.363315 \n",
"30 0.556881 0.786660 0.0 0.372677 \n",
"31 0.574939 0.781107 0.0 0.366749 \n",
"32 0.606750 0.808127 0.0 0.352923 \n",
"33 0.580500 0.780899 0.0 0.362036 \n",
"34 0.607857 0.804399 0.0 0.350723 \n",
"35 0.566898 0.789918 0.0 0.364344 \n",
"36 0.579463 0.788819 0.0 0.369847 \n",
"37 0.578995 0.795407 0.0 0.369274 \n",
"38 0.594452 0.790093 0.0 0.357508 \n",
"39 0.584395 0.788413 0.0 0.373111 \n",
"\n",
" PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance \\\n",
"0 0.371890 0.339596 0.347748 \n",
"1 0.381251 0.341950 0.349891 \n",
"2 0.371890 0.332321 0.351391 \n",
"3 0.374419 0.337654 0.346109 \n",
"4 0.376307 0.342617 0.343348 \n",
"5 0.371148 0.338015 0.350522 \n",
"6 0.386671 0.344248 0.364405 \n",
"7 0.379410 0.338777 0.353197 \n",
"8 0.376641 0.339524 0.349919 \n",
"9 0.391736 0.346245 0.357297 \n",
"10 0.363741 0.331545 0.340101 \n",
"11 0.375613 0.343617 0.356474 \n",
"12 0.377824 0.332149 0.354279 \n",
"13 0.388414 0.347581 0.360744 \n",
"14 0.378454 0.343167 0.352282 \n",
"15 0.390493 0.351879 0.361701 \n",
"16 0.371379 0.337804 0.354805 \n",
"17 0.378683 0.339097 0.356597 \n",
"18 0.367808 0.337694 0.339362 \n",
"19 0.370775 0.337035 0.356039 \n",
"20 0.381678 0.340699 0.363333 \n",
"21 0.392851 0.356422 0.365182 \n",
"22 0.396188 0.349903 0.388446 \n",
"23 0.410155 0.360025 0.392362 \n",
"24 0.413536 0.352271 0.396930 \n",
"25 0.394711 0.356405 0.385541 \n",
"26 0.399350 0.363279 0.375962 \n",
"27 0.410172 0.359211 0.392262 \n",
"28 0.417443 0.364244 0.392824 \n",
"29 0.403748 0.356727 0.385169 \n",
"30 0.409840 0.367423 0.386818 \n",
"31 0.406201 0.359577 0.382439 \n",
"32 0.389431 0.346998 0.367228 \n",
"33 0.396972 0.355362 0.379360 \n",
"34 0.388557 0.343231 0.379437 \n",
"35 0.411073 0.357567 0.386643 \n",
"36 0.416267 0.362248 0.393373 \n",
"37 0.411458 0.362212 0.399234 \n",
"38 0.399570 0.348983 0.382424 \n",
"39 0.419487 0.363822 0.398524 \n",
"\n",
" PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance \\\n",
"0 0.388709 0.385297 0.0 \n",
"1 0.398898 0.415058 0.0 \n",
"2 0.393829 0.392313 0.0 \n",
"3 0.389718 0.399153 0.0 \n",
"4 0.388623 0.403861 0.0 \n",
"5 0.390895 0.405243 0.0 \n",
"6 0.406190 0.400039 0.0 \n",
"7 0.399885 0.399392 0.0 \n",
"8 0.393570 0.413810 0.0 \n",
"9 0.403435 0.414073 0.0 \n",
"10 0.389592 0.384261 0.0 \n",
"11 0.398687 0.405318 0.0 \n",
"12 0.402852 0.387265 0.0 \n",
"13 0.402607 0.390989 0.0 \n",
"14 0.398483 0.383984 0.0 \n",
"15 0.404384 0.398420 0.0 \n",
"16 0.397635 0.404505 0.0 \n",
"17 0.396187 0.381571 0.0 \n",
"18 0.386849 0.380734 0.0 \n",
"19 0.402495 0.412892 0.0 \n",
"20 0.411799 0.398106 0.0 \n",
"21 0.413081 0.408349 0.0 \n",
"22 0.425970 0.423147 0.0 \n",
"23 0.439690 0.423503 0.0 \n",
"24 0.438502 0.437218 0.0 \n",
"25 0.430138 0.435397 0.0 \n",
"26 0.421184 0.422536 0.0 \n",
"27 0.438666 0.432635 0.0 \n",
"28 0.439427 0.424017 0.0 \n",
"29 0.426971 0.428521 0.0 \n",
"30 0.428182 0.410043 0.0 \n",
"31 0.427216 0.412182 0.0 \n",
"32 0.410854 0.408465 0.0 \n",
"33 0.423028 0.407819 0.0 \n",
"34 0.423172 0.413884 0.0 \n",
"35 0.436352 0.455234 0.0 \n",
"36 0.438305 0.432106 0.0 \n",
"37 0.439421 0.426244 0.0 \n",
"38 0.428961 0.426739 0.0 \n",
"39 0.443218 0.429929 0.0 \n",
"\n",
" PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance \\\n",
"0 0.385297 0.401254 \n",
"1 0.415058 0.408417 \n",
"2 0.392313 0.412311 \n",
"3 0.399153 0.405627 \n",
"4 0.403861 0.390288 \n",
"5 0.405243 0.397617 \n",
"6 0.400039 0.421197 \n",
"7 0.399392 0.398004 \n",
"8 0.413810 0.407238 \n",
"9 0.414073 0.409215 \n",
"10 0.384261 0.399187 \n",
"11 0.405318 0.403924 \n",
"12 0.387265 0.416357 \n",
"13 0.390989 0.431375 \n",
"14 0.383984 0.394837 \n",
"15 0.398420 0.422339 \n",
"16 0.404505 0.420580 \n",
"17 0.381571 0.400928 \n",
"18 0.380734 0.394130 \n",
"19 0.412892 0.408307 \n",
"20 0.398106 0.406224 \n",
"21 0.408349 0.431775 \n",
"22 0.423147 0.414028 \n",
"23 0.423503 0.424893 \n",
"24 0.437218 0.434233 \n",
"25 0.435397 0.430261 \n",
"26 0.422536 0.423652 \n",
"27 0.432635 0.437134 \n",
"28 0.424017 0.430895 \n",
"29 0.428521 0.437559 \n",
"30 0.410043 0.437956 \n",
"31 0.412182 0.424271 \n",
"32 0.408465 0.420192 \n",
"33 0.407819 0.430720 \n",
"34 0.413884 0.430536 \n",
"35 0.455234 0.427594 \n",
"36 0.432106 0.447392 \n",
"37 0.426244 0.437661 \n",
"38 0.426739 0.436677 \n",
"39 0.429929 0.428916 \n",
"\n",
" PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance \\\n",
"0 0.509555 0.386285 0.324236 \n",
"1 0.548192 0.382172 0.332749 \n",
"2 0.471703 0.378630 0.327488 \n",
"3 0.359189 0.391002 0.324431 \n",
"4 0.471324 0.392438 0.332882 \n",
"5 0.586487 0.367571 0.325332 \n",
"6 0.545560 0.392756 0.338890 \n",
"7 0.482805 0.394881 0.334431 \n",
"8 0.334977 0.388436 0.324229 \n",
"9 0.462336 0.404820 0.330088 \n",
"10 0.295595 0.361252 0.316965 \n",
"11 0.414471 0.390100 0.335772 \n",
"12 0.373845 0.376155 0.331282 \n",
"13 0.409462 0.385777 0.340581 \n",
"14 0.570345 0.396641 0.337457 \n",
"15 0.427769 0.417731 0.341625 \n",
"16 0.415804 0.359175 0.330021 \n",
"17 0.570090 0.362619 0.339981 \n",
"18 0.514109 0.395076 0.319727 \n",
"19 0.464521 0.389068 0.330017 \n",
"20 0.468663 0.352417 0.345042 \n",
"21 0.422301 0.386494 0.344547 \n",
"22 0.406933 0.353024 0.363997 \n",
"23 0.371724 0.396436 0.364995 \n",
"24 0.243128 0.391187 0.371005 \n",
"25 0.509647 0.397243 0.354870 \n",
"26 0.422062 0.390546 0.352241 \n",
"27 0.550723 0.406531 0.367066 \n",
"28 0.419896 0.406159 0.358254 \n",
"29 0.666602 0.399967 0.360502 \n",
"30 0.431314 0.417184 0.370701 \n",
"31 0.326364 0.387866 0.358136 \n",
"32 0.435325 0.384775 0.344882 \n",
"33 0.413402 0.375936 0.353105 \n",
"34 0.484843 0.385927 0.351172 \n",
"35 0.441989 0.410031 0.357034 \n",
"36 0.283823 0.414114 0.369362 \n",
"37 0.387644 0.379763 0.378844 \n",
"38 0.623022 0.378906 0.357031 \n",
"39 0.508213 0.389595 0.371629 \n",
"\n",
" PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance \\\n",
"0 0.367271 0.354046 \n",
"1 0.373615 0.359217 \n",
"2 0.370494 0.338321 \n",
"3 0.360431 0.343730 \n",
"4 0.358450 0.319824 \n",
"5 0.362838 0.333114 \n",
"6 0.381441 0.356823 \n",
"7 0.375372 0.332435 \n",
"8 0.367053 0.354882 \n",
"9 0.374945 0.334288 \n",
"10 0.356730 0.331182 \n",
"11 0.368552 0.331528 \n",
"12 0.370933 0.363017 \n",
"13 0.375888 0.372911 \n",
"14 0.375483 0.341387 \n",
"15 0.380159 0.367890 \n",
"16 0.365134 0.366449 \n",
"17 0.379723 0.344153 \n",
"18 0.356904 0.318327 \n",
"19 0.366829 0.337809 \n",
"20 0.391606 0.339766 \n",
"21 0.390997 0.368241 \n",
"22 0.410352 0.353075 \n",
"23 0.415006 0.358457 \n",
"24 0.424675 0.381663 \n",
"25 0.405106 0.376955 \n",
"26 0.394821 0.362621 \n",
"27 0.412751 0.381165 \n",
"28 0.421774 0.375869 \n",
"29 0.402785 0.382317 \n",
"30 0.403697 0.379120 \n",
"31 0.400989 0.367082 \n",
"32 0.388064 0.354390 \n",
"33 0.395058 0.377523 \n",
"34 0.395395 0.377211 \n",
"35 0.414984 0.377580 \n",
"36 0.423329 0.390531 \n",
"37 0.416184 0.393381 \n",
"38 0.402640 0.380493 \n",
"39 0.425177 0.379944 \n",
"\n",
" PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf \n",
"0 0.240359 0.386809 0.377907 0.359143 \n",
"1 0.364148 0.391925 0.386808 0.354120 \n",
"2 0.334783 0.378580 0.378799 0.353949 \n",
"3 0.304035 0.380413 0.373345 0.347372 \n",
"4 0.401641 0.398275 0.373236 0.363320 \n",
"5 0.328968 0.389536 0.375595 0.350909 \n",
"6 0.307240 0.394851 0.384458 0.357089 \n",
"7 0.345932 0.381070 0.373821 0.350244 \n",
"8 0.307155 0.397849 0.390108 0.346731 \n",
"9 0.296450 0.398979 0.390262 0.360787 \n",
"10 0.275036 0.384923 0.370680 0.338867 \n",
"11 0.181846 0.396579 0.382919 0.352013 \n",
"12 0.378312 0.390375 0.366399 0.364220 \n",
"13 0.350551 0.400626 0.404546 0.364278 \n",
"14 0.228666 0.393968 0.384816 0.358044 \n",
"15 0.326313 0.400965 0.391422 0.373351 \n",
"16 0.413398 0.375629 0.381035 0.346115 \n",
"17 0.377799 0.385887 0.384042 0.349956 \n",
"18 0.309309 0.390633 0.376339 0.360353 \n",
"19 0.241373 0.383198 0.378137 0.345602 \n",
"20 0.370686 0.369564 0.369690 0.345702 \n",
"21 0.364905 0.392520 0.386162 0.373449 \n",
"22 0.404312 0.376888 0.385532 0.352623 \n",
"23 0.266702 0.393868 0.396684 0.361810 \n",
"24 0.401646 0.380932 0.388726 0.364671 \n",
"25 0.349346 0.390618 0.387689 0.355591 \n",
"26 0.349869 0.400868 0.397587 0.375378 \n",
"27 0.291126 0.388891 0.385826 0.368804 \n",
"28 0.285465 0.405000 0.385932 0.366543 \n",
"29 0.213772 0.390465 0.395524 0.364513 \n",
"30 0.132574 0.402506 0.408092 0.392411 \n",
"31 0.233944 0.405535 0.387747 0.372620 \n",
"32 0.322671 0.384292 0.385328 0.353731 \n",
"33 0.239142 0.397595 0.385070 0.369290 \n",
"34 0.260417 0.387313 0.379687 0.352217 \n",
"35 0.122134 0.390103 0.391888 0.356613 \n",
"36 0.294508 0.403624 0.394708 0.370958 \n",
"37 0.327526 0.390663 0.404334 0.359301 \n",
"38 0.320648 0.385596 0.378942 0.359409 \n",
"39 0.390322 0.401379 0.390136 0.365038 "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_region_files[:40]"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"stats = pd.read_csv(\"RRBS_anno_statistics_full_446files_filter50K.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(446, 18)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.shape"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"stats_files = stats.filename"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"merged = stats.merge(total_region_files, on='filename')"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"merged = merged.drop(['thisMeth', 'mixedReadCount', 'total_reads', 'total_cpg_no_filter', 'total_cpg_gtrthan1',\n",
" 'total_cpg_gtrthan38', 'avgReadCpgs_nofilter','avgReadCpgs_lessthan1CpG', 'avgReadCpgs_gtreql3.8CpG', 'bsRate',], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"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>filename</th>\n",
" <th>methylation</th>\n",
" <th>PDR_total</th>\n",
" <th>methylation_unweighted</th>\n",
" <th>PDR_unweighted</th>\n",
" <th>type</th>\n",
" <th>bio</th>\n",
" <th>protocol</th>\n",
" <th>methylation_tssDistance</th>\n",
" <th>methylation_genesDistance</th>\n",
" <th>methylation_exonsDistance</th>\n",
" <th>methylation_intronsDistance</th>\n",
" <th>methylation_promoterDistance</th>\n",
" <th>methylation_cgiDistance</th>\n",
" <th>methylation_ctcfDistance</th>\n",
" <th>methylation_ctcfUpDistance</th>\n",
" <th>methylation_ctcfDownDistance</th>\n",
" <th>methylation_geneDistalRegulatoryModulesDistance</th>\n",
" <th>methylation_vistaEnhancersDistance</th>\n",
" <th>methylation_3PrimeUTRDistance</th>\n",
" <th>methylation_5PrimeUTRDistance</th>\n",
" <th>methylation_firstExonDistance</th>\n",
" <th>methylation_geneDistalRegulatoryModulesK562Distance</th>\n",
" <th>methylation_hypoInHues64Distance</th>\n",
" <th>methylation_intergenic</th>\n",
" <th>methylation_shore</th>\n",
" <th>methylation_shelf</th>\n",
" <th>PDR_tssDistance</th>\n",
" <th>PDR_genesDistance</th>\n",
" <th>PDR_exonsDistance</th>\n",
" <th>PDR_intronsDistance</th>\n",
" <th>PDR_promoterDistance</th>\n",
" <th>PDR_cgiDistance</th>\n",
" <th>PDR_ctcfDistance</th>\n",
" <th>PDR_ctcfUpDistance</th>\n",
" <th>PDR_ctcfDownDistance</th>\n",
" <th>PDR_geneDistalRegulatoryModulesDistance</th>\n",
" <th>PDR_vistaEnhancersDistance</th>\n",
" <th>PDR_3PrimeUTRDistance</th>\n",
" <th>PDR_5PrimeUTRDistance</th>\n",
" <th>PDR_firstExonDistance</th>\n",
" <th>PDR_geneDistalRegulatoryModulesK562Distance</th>\n",
" <th>PDR_hypoInHues64Distance</th>\n",
" <th>PDR_intergenic</th>\n",
" <th>PDR_shore</th>\n",
" <th>PDR_shelf</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.ACAACC</td>\n",
" <td>0.591346</td>\n",
" <td>0.259001</td>\n",
" <td>0.691996</td>\n",
" <td>0.254835</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.572922</td>\n",
" <td>0.388607</td>\n",
" <td>0.597003</td>\n",
" <td>0.127235</td>\n",
" <td>0.135145</td>\n",
" <td>0.178152</td>\n",
" <td>0.0</td>\n",
" <td>0.178152</td>\n",
" <td>0.313712</td>\n",
" <td>0.354954</td>\n",
" <td>0.728744</td>\n",
" <td>0.282305</td>\n",
" <td>0.125011</td>\n",
" <td>0.230720</td>\n",
" <td>0.926937</td>\n",
" <td>0.902930</td>\n",
" <td>0.637052</td>\n",
" <td>0.896802</td>\n",
" <td>0.0</td>\n",
" <td>0.249665</td>\n",
" <td>0.320241</td>\n",
" <td>0.237896</td>\n",
" <td>0.363833</td>\n",
" <td>0.388255</td>\n",
" <td>0.419154</td>\n",
" <td>0.0</td>\n",
" <td>0.419154</td>\n",
" <td>0.364421</td>\n",
" <td>0.489309</td>\n",
" <td>0.214752</td>\n",
" <td>0.295134</td>\n",
" <td>0.379389</td>\n",
" <td>0.352525</td>\n",
" <td>0.020851</td>\n",
" <td>0.183311</td>\n",
" <td>0.285294</td>\n",
" <td>0.169240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.ACCGCG</td>\n",
" <td>0.531169</td>\n",
" <td>0.411448</td>\n",
" <td>0.620106</td>\n",
" <td>0.390562</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.505145</td>\n",
" <td>0.359230</td>\n",
" <td>0.526446</td>\n",
" <td>0.134970</td>\n",
" <td>0.171891</td>\n",
" <td>0.198475</td>\n",
" <td>0.0</td>\n",
" <td>0.198475</td>\n",
" <td>0.352037</td>\n",
" <td>0.516644</td>\n",
" <td>0.625871</td>\n",
" <td>0.266546</td>\n",
" <td>0.123009</td>\n",
" <td>0.233159</td>\n",
" <td>0.865658</td>\n",
" <td>0.777870</td>\n",
" <td>0.528428</td>\n",
" <td>0.750640</td>\n",
" <td>0.0</td>\n",
" <td>0.389613</td>\n",
" <td>0.436141</td>\n",
" <td>0.383879</td>\n",
" <td>0.426575</td>\n",
" <td>0.439299</td>\n",
" <td>0.424045</td>\n",
" <td>0.0</td>\n",
" <td>0.424045</td>\n",
" <td>0.468612</td>\n",
" <td>0.632490</td>\n",
" <td>0.413364</td>\n",
" <td>0.384389</td>\n",
" <td>0.446504</td>\n",
" <td>0.426731</td>\n",
" <td>0.016904</td>\n",
" <td>0.426067</td>\n",
" <td>0.413426</td>\n",
" <td>0.430125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.ACGTGG</td>\n",
" <td>0.586403</td>\n",
" <td>0.278568</td>\n",
" <td>0.699736</td>\n",
" <td>0.266418</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.553568</td>\n",
" <td>0.359975</td>\n",
" <td>0.583731</td>\n",
" <td>0.117959</td>\n",
" <td>0.125268</td>\n",
" <td>0.176109</td>\n",
" <td>0.0</td>\n",
" <td>0.176109</td>\n",
" <td>0.309668</td>\n",
" <td>0.712070</td>\n",
" <td>0.718911</td>\n",
" <td>0.277957</td>\n",
" <td>0.101747</td>\n",
" <td>0.227526</td>\n",
" <td>0.942223</td>\n",
" <td>0.895722</td>\n",
" <td>0.618027</td>\n",
" <td>0.868715</td>\n",
" <td>0.0</td>\n",
" <td>0.276292</td>\n",
" <td>0.353180</td>\n",
" <td>0.264764</td>\n",
" <td>0.381896</td>\n",
" <td>0.403526</td>\n",
" <td>0.431742</td>\n",
" <td>0.0</td>\n",
" <td>0.431742</td>\n",
" <td>0.362307</td>\n",
" <td>0.148328</td>\n",
" <td>0.312959</td>\n",
" <td>0.323942</td>\n",
" <td>0.389617</td>\n",
" <td>0.349141</td>\n",
" <td>0.150021</td>\n",
" <td>0.199184</td>\n",
" <td>0.311731</td>\n",
" <td>0.206646</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.AGGATG</td>\n",
" <td>0.628623</td>\n",
" <td>0.248006</td>\n",
" <td>0.732036</td>\n",
" <td>0.240201</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.600840</td>\n",
" <td>0.392730</td>\n",
" <td>0.633467</td>\n",
" <td>0.130846</td>\n",
" <td>0.134532</td>\n",
" <td>0.228113</td>\n",
" <td>0.0</td>\n",
" <td>0.228113</td>\n",
" <td>0.344969</td>\n",
" <td>0.738883</td>\n",
" <td>0.721101</td>\n",
" <td>0.315410</td>\n",
" <td>0.110561</td>\n",
" <td>0.254307</td>\n",
" <td>0.955880</td>\n",
" <td>0.916222</td>\n",
" <td>0.672012</td>\n",
" <td>0.891286</td>\n",
" <td>0.0</td>\n",
" <td>0.242686</td>\n",
" <td>0.329575</td>\n",
" <td>0.226780</td>\n",
" <td>0.380465</td>\n",
" <td>0.395003</td>\n",
" <td>0.428452</td>\n",
" <td>0.0</td>\n",
" <td>0.428452</td>\n",
" <td>0.378543</td>\n",
" <td>0.293278</td>\n",
" <td>0.226409</td>\n",
" <td>0.306003</td>\n",
" <td>0.391967</td>\n",
" <td>0.380095</td>\n",
" <td>0.156554</td>\n",
" <td>0.167441</td>\n",
" <td>0.275408</td>\n",
" <td>0.184916</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.ATAGCG</td>\n",
" <td>0.568354</td>\n",
" <td>0.434929</td>\n",
" <td>0.648127</td>\n",
" <td>0.425702</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.553723</td>\n",
" <td>0.424441</td>\n",
" <td>0.572004</td>\n",
" <td>0.201759</td>\n",
" <td>0.254062</td>\n",
" <td>0.243405</td>\n",
" <td>0.0</td>\n",
" <td>0.243405</td>\n",
" <td>0.365090</td>\n",
" <td>0.703915</td>\n",
" <td>0.690610</td>\n",
" <td>0.324217</td>\n",
" <td>0.225215</td>\n",
" <td>0.250693</td>\n",
" <td>0.628902</td>\n",
" <td>0.744224</td>\n",
" <td>0.591154</td>\n",
" <td>0.764444</td>\n",
" <td>0.0</td>\n",
" <td>0.396151</td>\n",
" <td>0.413207</td>\n",
" <td>0.393634</td>\n",
" <td>0.414282</td>\n",
" <td>0.434310</td>\n",
" <td>0.404922</td>\n",
" <td>0.0</td>\n",
" <td>0.404922</td>\n",
" <td>0.454559</td>\n",
" <td>0.520165</td>\n",
" <td>0.398875</td>\n",
" <td>0.398097</td>\n",
" <td>0.431908</td>\n",
" <td>0.403162</td>\n",
" <td>0.649024</td>\n",
" <td>0.500065</td>\n",
" <td>0.432751</td>\n",
" <td>0.402732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.ATCGAC</td>\n",
" <td>0.622386</td>\n",
" <td>0.272543</td>\n",
" <td>0.716552</td>\n",
" <td>0.270967</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.598241</td>\n",
" <td>0.400137</td>\n",
" <td>0.625556</td>\n",
" <td>0.135943</td>\n",
" <td>0.142543</td>\n",
" <td>0.197461</td>\n",
" <td>0.0</td>\n",
" <td>0.197461</td>\n",
" <td>0.328983</td>\n",
" <td>0.561384</td>\n",
" <td>0.724674</td>\n",
" <td>0.319542</td>\n",
" <td>0.123392</td>\n",
" <td>0.245854</td>\n",
" <td>0.957901</td>\n",
" <td>0.896857</td>\n",
" <td>0.631902</td>\n",
" <td>0.876983</td>\n",
" <td>0.0</td>\n",
" <td>0.263207</td>\n",
" <td>0.329618</td>\n",
" <td>0.253472</td>\n",
" <td>0.378263</td>\n",
" <td>0.392845</td>\n",
" <td>0.387199</td>\n",
" <td>0.0</td>\n",
" <td>0.387199</td>\n",
" <td>0.376833</td>\n",
" <td>0.406406</td>\n",
" <td>0.247637</td>\n",
" <td>0.313278</td>\n",
" <td>0.389120</td>\n",
" <td>0.356423</td>\n",
" <td>0.008145</td>\n",
" <td>0.210394</td>\n",
" <td>0.305067</td>\n",
" <td>0.196524</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.CAAGAG</td>\n",
" <td>0.580746</td>\n",
" <td>0.358441</td>\n",
" <td>0.670718</td>\n",
" <td>0.348679</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.557673</td>\n",
" <td>0.393037</td>\n",
" <td>0.581964</td>\n",
" <td>0.136496</td>\n",
" <td>0.154840</td>\n",
" <td>0.185085</td>\n",
" <td>0.0</td>\n",
" <td>0.185085</td>\n",
" <td>0.336342</td>\n",
" <td>0.548913</td>\n",
" <td>0.724639</td>\n",
" <td>0.289102</td>\n",
" <td>0.134083</td>\n",
" <td>0.242662</td>\n",
" <td>0.914303</td>\n",
" <td>0.839622</td>\n",
" <td>0.609142</td>\n",
" <td>0.828499</td>\n",
" <td>0.0</td>\n",
" <td>0.346284</td>\n",
" <td>0.400937</td>\n",
" <td>0.334616</td>\n",
" <td>0.403498</td>\n",
" <td>0.421695</td>\n",
" <td>0.446341</td>\n",
" <td>0.0</td>\n",
" <td>0.446341</td>\n",
" <td>0.415259</td>\n",
" <td>0.457161</td>\n",
" <td>0.375382</td>\n",
" <td>0.381060</td>\n",
" <td>0.431724</td>\n",
" <td>0.366805</td>\n",
" <td>0.323921</td>\n",
" <td>0.349326</td>\n",
" <td>0.361848</td>\n",
" <td>0.326459</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.CATGAC</td>\n",
" <td>0.579873</td>\n",
" <td>0.374401</td>\n",
" <td>0.668592</td>\n",
" <td>0.364613</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.555496</td>\n",
" <td>0.372118</td>\n",
" <td>0.585104</td>\n",
" <td>0.130767</td>\n",
" <td>0.149758</td>\n",
" <td>0.174434</td>\n",
" <td>0.0</td>\n",
" <td>0.174434</td>\n",
" <td>0.325693</td>\n",
" <td>0.489904</td>\n",
" <td>0.691227</td>\n",
" <td>0.303725</td>\n",
" <td>0.117398</td>\n",
" <td>0.252648</td>\n",
" <td>0.877326</td>\n",
" <td>0.830718</td>\n",
" <td>0.612466</td>\n",
" <td>0.819436</td>\n",
" <td>0.0</td>\n",
" <td>0.352702</td>\n",
" <td>0.397442</td>\n",
" <td>0.342892</td>\n",
" <td>0.398493</td>\n",
" <td>0.420841</td>\n",
" <td>0.398592</td>\n",
" <td>0.0</td>\n",
" <td>0.398592</td>\n",
" <td>0.433256</td>\n",
" <td>0.586659</td>\n",
" <td>0.322179</td>\n",
" <td>0.351247</td>\n",
" <td>0.405937</td>\n",
" <td>0.401367</td>\n",
" <td>0.420977</td>\n",
" <td>0.381797</td>\n",
" <td>0.388051</td>\n",
" <td>0.361306</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.CGGTAG</td>\n",
" <td>0.580833</td>\n",
" <td>0.285978</td>\n",
" <td>0.701418</td>\n",
" <td>0.271634</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.547152</td>\n",
" <td>0.343528</td>\n",
" <td>0.574055</td>\n",
" <td>0.117823</td>\n",
" <td>0.121103</td>\n",
" <td>0.155409</td>\n",
" <td>0.0</td>\n",
" <td>0.155409</td>\n",
" <td>0.304843</td>\n",
" <td>0.593398</td>\n",
" <td>0.648511</td>\n",
" <td>0.280638</td>\n",
" <td>0.096895</td>\n",
" <td>0.228697</td>\n",
" <td>0.986942</td>\n",
" <td>0.887179</td>\n",
" <td>0.600183</td>\n",
" <td>0.863762</td>\n",
" <td>0.0</td>\n",
" <td>0.280334</td>\n",
" <td>0.358519</td>\n",
" <td>0.267715</td>\n",
" <td>0.392784</td>\n",
" <td>0.396120</td>\n",
" <td>0.372254</td>\n",
" <td>0.0</td>\n",
" <td>0.372254</td>\n",
" <td>0.399106</td>\n",
" <td>0.174794</td>\n",
" <td>0.363332</td>\n",
" <td>0.337774</td>\n",
" <td>0.402650</td>\n",
" <td>0.366518</td>\n",
" <td>0.060128</td>\n",
" <td>0.231404</td>\n",
" <td>0.321895</td>\n",
" <td>0.210547</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.CTATTG</td>\n",
" <td>0.582590</td>\n",
" <td>0.427069</td>\n",
" <td>0.650146</td>\n",
" <td>0.424804</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.569780</td>\n",
" <td>0.422603</td>\n",
" <td>0.591459</td>\n",
" <td>0.171423</td>\n",
" <td>0.213337</td>\n",
" <td>0.222178</td>\n",
" <td>0.0</td>\n",
" <td>0.222178</td>\n",
" <td>0.366234</td>\n",
" <td>0.698103</td>\n",
" <td>0.685389</td>\n",
" <td>0.327695</td>\n",
" <td>0.170005</td>\n",
" <td>0.263775</td>\n",
" <td>0.864895</td>\n",
" <td>0.775154</td>\n",
" <td>0.610494</td>\n",
" <td>0.773103</td>\n",
" <td>0.0</td>\n",
" <td>0.393549</td>\n",
" <td>0.428448</td>\n",
" <td>0.389104</td>\n",
" <td>0.421122</td>\n",
" <td>0.442946</td>\n",
" <td>0.424529</td>\n",
" <td>0.0</td>\n",
" <td>0.424529</td>\n",
" <td>0.465571</td>\n",
" <td>0.501746</td>\n",
" <td>0.424570</td>\n",
" <td>0.388460</td>\n",
" <td>0.445857</td>\n",
" <td>0.430022</td>\n",
" <td>0.365360</td>\n",
" <td>0.478388</td>\n",
" <td>0.410114</td>\n",
" <td>0.419770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.CTCAGC</td>\n",
" <td>0.577931</td>\n",
" <td>0.441120</td>\n",
" <td>0.640678</td>\n",
" <td>0.433420</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.565528</td>\n",
" <td>0.437396</td>\n",
" <td>0.585564</td>\n",
" <td>0.207370</td>\n",
" <td>0.274443</td>\n",
" <td>0.235145</td>\n",
" <td>0.0</td>\n",
" <td>0.235145</td>\n",
" <td>0.377706</td>\n",
" <td>0.636860</td>\n",
" <td>0.669217</td>\n",
" <td>0.337121</td>\n",
" <td>0.216485</td>\n",
" <td>0.247102</td>\n",
" <td>0.909352</td>\n",
" <td>0.737474</td>\n",
" <td>0.578117</td>\n",
" <td>0.735609</td>\n",
" <td>0.0</td>\n",
" <td>0.409112</td>\n",
" <td>0.426369</td>\n",
" <td>0.406486</td>\n",
" <td>0.421935</td>\n",
" <td>0.442577</td>\n",
" <td>0.428453</td>\n",
" <td>0.0</td>\n",
" <td>0.428453</td>\n",
" <td>0.502348</td>\n",
" <td>0.678498</td>\n",
" <td>0.373862</td>\n",
" <td>0.418496</td>\n",
" <td>0.439376</td>\n",
" <td>0.451782</td>\n",
" <td>0.296798</td>\n",
" <td>0.502111</td>\n",
" <td>0.430704</td>\n",
" <td>0.404902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.GACACG</td>\n",
" <td>0.603615</td>\n",
" <td>0.259780</td>\n",
" <td>0.705313</td>\n",
" <td>0.246132</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.568850</td>\n",
" <td>0.361969</td>\n",
" <td>0.599111</td>\n",
" <td>0.122396</td>\n",
" <td>0.127940</td>\n",
" <td>0.159862</td>\n",
" <td>0.0</td>\n",
" <td>0.159862</td>\n",
" <td>0.320582</td>\n",
" <td>0.725028</td>\n",
" <td>0.694968</td>\n",
" <td>0.304324</td>\n",
" <td>0.117228</td>\n",
" <td>0.238581</td>\n",
" <td>0.971203</td>\n",
" <td>0.898648</td>\n",
" <td>0.616594</td>\n",
" <td>0.874494</td>\n",
" <td>0.0</td>\n",
" <td>0.259987</td>\n",
" <td>0.346939</td>\n",
" <td>0.247972</td>\n",
" <td>0.376738</td>\n",
" <td>0.384038</td>\n",
" <td>0.371028</td>\n",
" <td>0.0</td>\n",
" <td>0.371028</td>\n",
" <td>0.370863</td>\n",
" <td>0.041421</td>\n",
" <td>0.276435</td>\n",
" <td>0.305850</td>\n",
" <td>0.396597</td>\n",
" <td>0.374203</td>\n",
" <td>0.122058</td>\n",
" <td>0.193916</td>\n",
" <td>0.294751</td>\n",
" <td>0.178923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.GCTGCC</td>\n",
" <td>0.602191</td>\n",
" <td>0.274941</td>\n",
" <td>0.696495</td>\n",
" <td>0.261564</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.569876</td>\n",
" <td>0.362369</td>\n",
" <td>0.602931</td>\n",
" <td>0.119385</td>\n",
" <td>0.123399</td>\n",
" <td>0.166912</td>\n",
" <td>0.0</td>\n",
" <td>0.166912</td>\n",
" <td>0.312508</td>\n",
" <td>0.483566</td>\n",
" <td>0.664175</td>\n",
" <td>0.295046</td>\n",
" <td>0.104887</td>\n",
" <td>0.227071</td>\n",
" <td>0.888816</td>\n",
" <td>0.894996</td>\n",
" <td>0.621557</td>\n",
" <td>0.873106</td>\n",
" <td>0.0</td>\n",
" <td>0.270732</td>\n",
" <td>0.350990</td>\n",
" <td>0.255018</td>\n",
" <td>0.379740</td>\n",
" <td>0.403785</td>\n",
" <td>0.393961</td>\n",
" <td>0.0</td>\n",
" <td>0.393961</td>\n",
" <td>0.374419</td>\n",
" <td>0.151892</td>\n",
" <td>0.287812</td>\n",
" <td>0.327504</td>\n",
" <td>0.390677</td>\n",
" <td>0.347520</td>\n",
" <td>0.373774</td>\n",
" <td>0.199908</td>\n",
" <td>0.307455</td>\n",
" <td>0.208747</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.GGCATC</td>\n",
" <td>0.592896</td>\n",
" <td>0.309917</td>\n",
" <td>0.688324</td>\n",
" <td>0.293166</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.568737</td>\n",
" <td>0.375639</td>\n",
" <td>0.597084</td>\n",
" <td>0.126210</td>\n",
" <td>0.134600</td>\n",
" <td>0.173529</td>\n",
" <td>0.0</td>\n",
" <td>0.173529</td>\n",
" <td>0.311749</td>\n",
" <td>0.609371</td>\n",
" <td>0.670949</td>\n",
" <td>0.303669</td>\n",
" <td>0.104746</td>\n",
" <td>0.230130</td>\n",
" <td>0.933383</td>\n",
" <td>0.874730</td>\n",
" <td>0.619164</td>\n",
" <td>0.858757</td>\n",
" <td>0.0</td>\n",
" <td>0.302826</td>\n",
" <td>0.370106</td>\n",
" <td>0.290653</td>\n",
" <td>0.398204</td>\n",
" <td>0.419242</td>\n",
" <td>0.411253</td>\n",
" <td>0.0</td>\n",
" <td>0.411253</td>\n",
" <td>0.405264</td>\n",
" <td>0.556019</td>\n",
" <td>0.334586</td>\n",
" <td>0.327526</td>\n",
" <td>0.392053</td>\n",
" <td>0.379147</td>\n",
" <td>0.134353</td>\n",
" <td>0.259203</td>\n",
" <td>0.328873</td>\n",
" <td>0.271502</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.GTGAGG</td>\n",
" <td>0.576342</td>\n",
" <td>0.269746</td>\n",
" <td>0.692209</td>\n",
" <td>0.255067</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.543913</td>\n",
" <td>0.345264</td>\n",
" <td>0.578228</td>\n",
" <td>0.119943</td>\n",
" <td>0.117022</td>\n",
" <td>0.186957</td>\n",
" <td>0.0</td>\n",
" <td>0.186957</td>\n",
" <td>0.304374</td>\n",
" <td>0.275972</td>\n",
" <td>0.686426</td>\n",
" <td>0.258585</td>\n",
" <td>0.103817</td>\n",
" <td>0.243962</td>\n",
" <td>0.973117</td>\n",
" <td>0.893765</td>\n",
" <td>0.601321</td>\n",
" <td>0.862469</td>\n",
" <td>0.0</td>\n",
" <td>0.269974</td>\n",
" <td>0.351114</td>\n",
" <td>0.255608</td>\n",
" <td>0.384957</td>\n",
" <td>0.398879</td>\n",
" <td>0.421579</td>\n",
" <td>0.0</td>\n",
" <td>0.421579</td>\n",
" <td>0.369955</td>\n",
" <td>0.324316</td>\n",
" <td>0.242306</td>\n",
" <td>0.326748</td>\n",
" <td>0.397716</td>\n",
" <td>0.360025</td>\n",
" <td>0.077235</td>\n",
" <td>0.189490</td>\n",
" <td>0.295756</td>\n",
" <td>0.182461</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.GTTGAG</td>\n",
" <td>0.573082</td>\n",
" <td>0.434043</td>\n",
" <td>0.645717</td>\n",
" <td>0.421790</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.567695</td>\n",
" <td>0.412468</td>\n",
" <td>0.587454</td>\n",
" <td>0.152377</td>\n",
" <td>0.169104</td>\n",
" <td>0.213248</td>\n",
" <td>0.0</td>\n",
" <td>0.213248</td>\n",
" <td>0.325752</td>\n",
" <td>0.478594</td>\n",
" <td>0.672947</td>\n",
" <td>0.300417</td>\n",
" <td>0.138325</td>\n",
" <td>0.267002</td>\n",
" <td>0.718321</td>\n",
" <td>0.755050</td>\n",
" <td>0.585532</td>\n",
" <td>0.768680</td>\n",
" <td>0.0</td>\n",
" <td>0.394381</td>\n",
" <td>0.413518</td>\n",
" <td>0.390813</td>\n",
" <td>0.422574</td>\n",
" <td>0.436592</td>\n",
" <td>0.393126</td>\n",
" <td>0.0</td>\n",
" <td>0.393126</td>\n",
" <td>0.423319</td>\n",
" <td>0.545367</td>\n",
" <td>0.442718</td>\n",
" <td>0.413598</td>\n",
" <td>0.426612</td>\n",
" <td>0.399205</td>\n",
" <td>0.439942</td>\n",
" <td>0.496714</td>\n",
" <td>0.422975</td>\n",
" <td>0.392376</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.TAGCGG</td>\n",
" <td>0.563537</td>\n",
" <td>0.344400</td>\n",
" <td>0.671286</td>\n",
" <td>0.324550</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.537509</td>\n",
" <td>0.353348</td>\n",
" <td>0.562371</td>\n",
" <td>0.118371</td>\n",
" <td>0.130877</td>\n",
" <td>0.170794</td>\n",
" <td>0.0</td>\n",
" <td>0.170794</td>\n",
" <td>0.303084</td>\n",
" <td>0.460163</td>\n",
" <td>0.660977</td>\n",
" <td>0.281453</td>\n",
" <td>0.108285</td>\n",
" <td>0.226112</td>\n",
" <td>0.777019</td>\n",
" <td>0.856849</td>\n",
" <td>0.597661</td>\n",
" <td>0.836136</td>\n",
" <td>0.0</td>\n",
" <td>0.334426</td>\n",
" <td>0.403578</td>\n",
" <td>0.325768</td>\n",
" <td>0.395605</td>\n",
" <td>0.424208</td>\n",
" <td>0.433552</td>\n",
" <td>0.0</td>\n",
" <td>0.433552</td>\n",
" <td>0.385425</td>\n",
" <td>0.504680</td>\n",
" <td>0.365191</td>\n",
" <td>0.353273</td>\n",
" <td>0.424782</td>\n",
" <td>0.364329</td>\n",
" <td>0.507417</td>\n",
" <td>0.312744</td>\n",
" <td>0.359828</td>\n",
" <td>0.303938</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.TATCTC</td>\n",
" <td>0.592870</td>\n",
" <td>0.383162</td>\n",
" <td>0.663549</td>\n",
" <td>0.384798</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.574181</td>\n",
" <td>0.422529</td>\n",
" <td>0.596448</td>\n",
" <td>0.157292</td>\n",
" <td>0.185985</td>\n",
" <td>0.197790</td>\n",
" <td>0.0</td>\n",
" <td>0.197790</td>\n",
" <td>0.333505</td>\n",
" <td>0.549745</td>\n",
" <td>0.695153</td>\n",
" <td>0.306368</td>\n",
" <td>0.162632</td>\n",
" <td>0.244439</td>\n",
" <td>0.958932</td>\n",
" <td>0.817648</td>\n",
" <td>0.615515</td>\n",
" <td>0.796791</td>\n",
" <td>0.0</td>\n",
" <td>0.359375</td>\n",
" <td>0.390368</td>\n",
" <td>0.355666</td>\n",
" <td>0.395329</td>\n",
" <td>0.415846</td>\n",
" <td>0.400304</td>\n",
" <td>0.0</td>\n",
" <td>0.400304</td>\n",
" <td>0.420615</td>\n",
" <td>0.631283</td>\n",
" <td>0.386431</td>\n",
" <td>0.353516</td>\n",
" <td>0.402680</td>\n",
" <td>0.372923</td>\n",
" <td>0.120302</td>\n",
" <td>0.406370</td>\n",
" <td>0.387130</td>\n",
" <td>0.361715</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.TCTCTG</td>\n",
" <td>0.566829</td>\n",
" <td>0.459303</td>\n",
" <td>0.621230</td>\n",
" <td>0.456004</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.561735</td>\n",
" <td>0.445770</td>\n",
" <td>0.575198</td>\n",
" <td>0.219102</td>\n",
" <td>0.270627</td>\n",
" <td>0.262653</td>\n",
" <td>0.0</td>\n",
" <td>0.262653</td>\n",
" <td>0.388225</td>\n",
" <td>0.425444</td>\n",
" <td>0.658489</td>\n",
" <td>0.339626</td>\n",
" <td>0.221938</td>\n",
" <td>0.268746</td>\n",
" <td>0.693445</td>\n",
" <td>0.707242</td>\n",
" <td>0.582715</td>\n",
" <td>0.734725</td>\n",
" <td>0.0</td>\n",
" <td>0.424692</td>\n",
" <td>0.428625</td>\n",
" <td>0.423983</td>\n",
" <td>0.416932</td>\n",
" <td>0.438851</td>\n",
" <td>0.435457</td>\n",
" <td>0.0</td>\n",
" <td>0.435457</td>\n",
" <td>0.458630</td>\n",
" <td>0.506799</td>\n",
" <td>0.461580</td>\n",
" <td>0.424516</td>\n",
" <td>0.410753</td>\n",
" <td>0.402906</td>\n",
" <td>0.446482</td>\n",
" <td>0.535373</td>\n",
" <td>0.447916</td>\n",
" <td>0.449113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>RRBS_normal_B_cell_A1_24_TAAGGCGA.TGACAG</td>\n",
" <td>0.572760</td>\n",
" <td>0.339617</td>\n",
" <td>0.670456</td>\n",
" <td>0.330762</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_A1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.545209</td>\n",
" <td>0.366002</td>\n",
" <td>0.571545</td>\n",
" <td>0.129616</td>\n",
" <td>0.136915</td>\n",
" <td>0.184688</td>\n",
" <td>0.0</td>\n",
" <td>0.184688</td>\n",
" <td>0.302508</td>\n",
" <td>0.516610</td>\n",
" <td>0.667369</td>\n",
" <td>0.283649</td>\n",
" <td>0.109472</td>\n",
" <td>0.226291</td>\n",
" <td>0.815052</td>\n",
" <td>0.858191</td>\n",
" <td>0.595584</td>\n",
" <td>0.824065</td>\n",
" <td>0.0</td>\n",
" <td>0.328561</td>\n",
" <td>0.362106</td>\n",
" <td>0.322815</td>\n",
" <td>0.385218</td>\n",
" <td>0.409109</td>\n",
" <td>0.420144</td>\n",
" <td>0.0</td>\n",
" <td>0.420144</td>\n",
" <td>0.416980</td>\n",
" <td>0.540203</td>\n",
" <td>0.342566</td>\n",
" <td>0.359376</td>\n",
" <td>0.387962</td>\n",
" <td>0.391970</td>\n",
" <td>0.426135</td>\n",
" <td>0.310284</td>\n",
" <td>0.363570</td>\n",
" <td>0.329527</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>RRBS_normal_B_cell_B1_24_CGTACTAG.ACAACC</td>\n",
" <td>0.626281</td>\n",
" <td>0.266847</td>\n",
" <td>0.723172</td>\n",
" <td>0.246680</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_B1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.600708</td>\n",
" <td>0.393908</td>\n",
" <td>0.630897</td>\n",
" <td>0.130491</td>\n",
" <td>0.132780</td>\n",
" <td>0.202374</td>\n",
" <td>0.0</td>\n",
" <td>0.202374</td>\n",
" <td>0.352837</td>\n",
" <td>0.713675</td>\n",
" <td>0.725713</td>\n",
" <td>0.320354</td>\n",
" <td>0.106622</td>\n",
" <td>0.268270</td>\n",
" <td>0.927542</td>\n",
" <td>0.898352</td>\n",
" <td>0.640993</td>\n",
" <td>0.877308</td>\n",
" <td>0.0</td>\n",
" <td>0.259897</td>\n",
" <td>0.334198</td>\n",
" <td>0.248877</td>\n",
" <td>0.382296</td>\n",
" <td>0.401650</td>\n",
" <td>0.425809</td>\n",
" <td>0.0</td>\n",
" <td>0.425809</td>\n",
" <td>0.382371</td>\n",
" <td>0.255144</td>\n",
" <td>0.252366</td>\n",
" <td>0.309392</td>\n",
" <td>0.380196</td>\n",
" <td>0.367431</td>\n",
" <td>0.180845</td>\n",
" <td>0.201513</td>\n",
" <td>0.301175</td>\n",
" <td>0.213756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>RRBS_normal_B_cell_B1_24_CGTACTAG.ACCGCG</td>\n",
" <td>0.537494</td>\n",
" <td>0.432684</td>\n",
" <td>0.620718</td>\n",
" <td>0.409340</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_B1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.519674</td>\n",
" <td>0.378490</td>\n",
" <td>0.541383</td>\n",
" <td>0.159885</td>\n",
" <td>0.190980</td>\n",
" <td>0.207475</td>\n",
" <td>0.0</td>\n",
" <td>0.207475</td>\n",
" <td>0.318937</td>\n",
" <td>0.704963</td>\n",
" <td>0.615859</td>\n",
" <td>0.290296</td>\n",
" <td>0.155550</td>\n",
" <td>0.229599</td>\n",
" <td>0.820416</td>\n",
" <td>0.733765</td>\n",
" <td>0.532819</td>\n",
" <td>0.736470</td>\n",
" <td>0.0</td>\n",
" <td>0.398962</td>\n",
" <td>0.428732</td>\n",
" <td>0.396038</td>\n",
" <td>0.410177</td>\n",
" <td>0.437335</td>\n",
" <td>0.420623</td>\n",
" <td>0.0</td>\n",
" <td>0.420623</td>\n",
" <td>0.464244</td>\n",
" <td>0.680829</td>\n",
" <td>0.429482</td>\n",
" <td>0.377868</td>\n",
" <td>0.416697</td>\n",
" <td>0.435796</td>\n",
" <td>0.262760</td>\n",
" <td>0.487506</td>\n",
" <td>0.420668</td>\n",
" <td>0.395321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>RRBS_normal_B_cell_B1_24_CGTACTAG.ACTCAC</td>\n",
" <td>0.641663</td>\n",
" <td>0.246022</td>\n",
" <td>0.731753</td>\n",
" <td>0.227309</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_B1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.612460</td>\n",
" <td>0.404062</td>\n",
" <td>0.641619</td>\n",
" <td>0.138321</td>\n",
" <td>0.134888</td>\n",
" <td>0.200057</td>\n",
" <td>0.0</td>\n",
" <td>0.200057</td>\n",
" <td>0.365257</td>\n",
" <td>0.464153</td>\n",
" <td>0.743855</td>\n",
" <td>0.331324</td>\n",
" <td>0.115728</td>\n",
" <td>0.271711</td>\n",
" <td>0.888724</td>\n",
" <td>0.914558</td>\n",
" <td>0.655884</td>\n",
" <td>0.902320</td>\n",
" <td>0.0</td>\n",
" <td>0.242492</td>\n",
" <td>0.329096</td>\n",
" <td>0.230329</td>\n",
" <td>0.387293</td>\n",
" <td>0.406756</td>\n",
" <td>0.409925</td>\n",
" <td>0.0</td>\n",
" <td>0.409925</td>\n",
" <td>0.381945</td>\n",
" <td>0.411628</td>\n",
" <td>0.222693</td>\n",
" <td>0.307246</td>\n",
" <td>0.402911</td>\n",
" <td>0.373759</td>\n",
" <td>0.088080</td>\n",
" <td>0.165203</td>\n",
" <td>0.278304</td>\n",
" <td>0.167580</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>RRBS_normal_B_cell_B1_24_CGTACTAG.ATAGCG</td>\n",
" <td>0.589376</td>\n",
" <td>0.261165</td>\n",
" <td>0.710628</td>\n",
" <td>0.230766</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_B1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.564847</td>\n",
" <td>0.359529</td>\n",
" <td>0.595146</td>\n",
" <td>0.118704</td>\n",
" <td>0.114490</td>\n",
" <td>0.173252</td>\n",
" <td>0.0</td>\n",
" <td>0.173252</td>\n",
" <td>0.310655</td>\n",
" <td>0.503806</td>\n",
" <td>0.691586</td>\n",
" <td>0.285765</td>\n",
" <td>0.103678</td>\n",
" <td>0.228965</td>\n",
" <td>0.970596</td>\n",
" <td>0.901415</td>\n",
" <td>0.619064</td>\n",
" <td>0.889049</td>\n",
" <td>0.0</td>\n",
" <td>0.258756</td>\n",
" <td>0.349989</td>\n",
" <td>0.242980</td>\n",
" <td>0.378504</td>\n",
" <td>0.394998</td>\n",
" <td>0.419274</td>\n",
" <td>0.0</td>\n",
" <td>0.419274</td>\n",
" <td>0.350099</td>\n",
" <td>0.564988</td>\n",
" <td>0.274140</td>\n",
" <td>0.311913</td>\n",
" <td>0.397878</td>\n",
" <td>0.335510</td>\n",
" <td>0.049096</td>\n",
" <td>0.181269</td>\n",
" <td>0.296877</td>\n",
" <td>0.184334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>RRBS_normal_B_cell_B1_24_CGTACTAG.CAAGAG</td>\n",
" <td>0.573636</td>\n",
" <td>0.410016</td>\n",
" <td>0.649119</td>\n",
" <td>0.394524</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_B1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.558144</td>\n",
" <td>0.410151</td>\n",
" <td>0.580173</td>\n",
" <td>0.160099</td>\n",
" <td>0.195220</td>\n",
" <td>0.200590</td>\n",
" <td>0.0</td>\n",
" <td>0.200590</td>\n",
" <td>0.349321</td>\n",
" <td>0.549857</td>\n",
" <td>0.668894</td>\n",
" <td>0.309413</td>\n",
" <td>0.163017</td>\n",
" <td>0.243278</td>\n",
" <td>0.919356</td>\n",
" <td>0.784926</td>\n",
" <td>0.568173</td>\n",
" <td>0.761311</td>\n",
" <td>0.0</td>\n",
" <td>0.386441</td>\n",
" <td>0.419907</td>\n",
" <td>0.381549</td>\n",
" <td>0.411113</td>\n",
" <td>0.441415</td>\n",
" <td>0.406298</td>\n",
" <td>0.0</td>\n",
" <td>0.406298</td>\n",
" <td>0.448031</td>\n",
" <td>0.547251</td>\n",
" <td>0.410927</td>\n",
" <td>0.394507</td>\n",
" <td>0.435718</td>\n",
" <td>0.393209</td>\n",
" <td>0.308112</td>\n",
" <td>0.437090</td>\n",
" <td>0.407888</td>\n",
" <td>0.396319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>RRBS_normal_B_cell_B1_24_CGTACTAG.CATGAC</td>\n",
" <td>0.624309</td>\n",
" <td>0.250108</td>\n",
" <td>0.726315</td>\n",
" <td>0.228687</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_B1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.596158</td>\n",
" <td>0.388236</td>\n",
" <td>0.627931</td>\n",
" <td>0.130745</td>\n",
" <td>0.131519</td>\n",
" <td>0.172315</td>\n",
" <td>0.0</td>\n",
" <td>0.172315</td>\n",
" <td>0.338750</td>\n",
" <td>0.456898</td>\n",
" <td>0.722962</td>\n",
" <td>0.315504</td>\n",
" <td>0.112215</td>\n",
" <td>0.257224</td>\n",
" <td>0.969047</td>\n",
" <td>0.914006</td>\n",
" <td>0.639293</td>\n",
" <td>0.894982</td>\n",
" <td>0.0</td>\n",
" <td>0.248352</td>\n",
" <td>0.341477</td>\n",
" <td>0.232416</td>\n",
" <td>0.376812</td>\n",
" <td>0.406716</td>\n",
" <td>0.399623</td>\n",
" <td>0.0</td>\n",
" <td>0.399623</td>\n",
" <td>0.368001</td>\n",
" <td>0.332834</td>\n",
" <td>0.242602</td>\n",
" <td>0.312927</td>\n",
" <td>0.398130</td>\n",
" <td>0.356175</td>\n",
" <td>0.101891</td>\n",
" <td>0.168523</td>\n",
" <td>0.281624</td>\n",
" <td>0.169076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>RRBS_normal_B_cell_B1_24_CGTACTAG.CCTTCG</td>\n",
" <td>0.596452</td>\n",
" <td>0.329267</td>\n",
" <td>0.698844</td>\n",
" <td>0.306790</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_B1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.571273</td>\n",
" <td>0.377748</td>\n",
" <td>0.599836</td>\n",
" <td>0.130880</td>\n",
" <td>0.142904</td>\n",
" <td>0.178021</td>\n",
" <td>0.0</td>\n",
" <td>0.178021</td>\n",
" <td>0.331112</td>\n",
" <td>0.590600</td>\n",
" <td>0.684578</td>\n",
" <td>0.300336</td>\n",
" <td>0.120516</td>\n",
" <td>0.255226</td>\n",
" <td>0.910927</td>\n",
" <td>0.858774</td>\n",
" <td>0.607147</td>\n",
" <td>0.851038</td>\n",
" <td>0.0</td>\n",
" <td>0.313604</td>\n",
" <td>0.385012</td>\n",
" <td>0.301194</td>\n",
" <td>0.409911</td>\n",
" <td>0.429555</td>\n",
" <td>0.440444</td>\n",
" <td>0.0</td>\n",
" <td>0.440444</td>\n",
" <td>0.411064</td>\n",
" <td>0.403216</td>\n",
" <td>0.356594</td>\n",
" <td>0.351884</td>\n",
" <td>0.427722</td>\n",
" <td>0.393187</td>\n",
" <td>0.197940</td>\n",
" <td>0.299706</td>\n",
" <td>0.348462</td>\n",
" <td>0.263281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>RRBS_normal_B_cell_B1_24_CGTACTAG.CGGTAG</td>\n",
" <td>0.525054</td>\n",
" <td>0.433851</td>\n",
" <td>0.609985</td>\n",
" <td>0.410107</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_B1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.507179</td>\n",
" <td>0.348300</td>\n",
" <td>0.529348</td>\n",
" <td>0.131065</td>\n",
" <td>0.146562</td>\n",
" <td>0.159320</td>\n",
" <td>0.0</td>\n",
" <td>0.159320</td>\n",
" <td>0.291927</td>\n",
" <td>0.585224</td>\n",
" <td>0.598317</td>\n",
" <td>0.271001</td>\n",
" <td>0.122086</td>\n",
" <td>0.204311</td>\n",
" <td>0.831863</td>\n",
" <td>0.748557</td>\n",
" <td>0.519638</td>\n",
" <td>0.731533</td>\n",
" <td>0.0</td>\n",
" <td>0.407883</td>\n",
" <td>0.431943</td>\n",
" <td>0.402297</td>\n",
" <td>0.405059</td>\n",
" <td>0.427618</td>\n",
" <td>0.414400</td>\n",
" <td>0.0</td>\n",
" <td>0.414400</td>\n",
" <td>0.426879</td>\n",
" <td>0.483459</td>\n",
" <td>0.421985</td>\n",
" <td>0.395431</td>\n",
" <td>0.423924</td>\n",
" <td>0.373547</td>\n",
" <td>0.410587</td>\n",
" <td>0.484246</td>\n",
" <td>0.438821</td>\n",
" <td>0.430328</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>RRBS_normal_B_cell_B1_24_CGTACTAG.CTATTG</td>\n",
" <td>0.593262</td>\n",
" <td>0.424328</td>\n",
" <td>0.660035</td>\n",
" <td>0.416186</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_B1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.579434</td>\n",
" <td>0.434890</td>\n",
" <td>0.600699</td>\n",
" <td>0.188624</td>\n",
" <td>0.233725</td>\n",
" <td>0.230526</td>\n",
" <td>0.0</td>\n",
" <td>0.230526</td>\n",
" <td>0.366878</td>\n",
" <td>0.612085</td>\n",
" <td>0.672638</td>\n",
" <td>0.334540</td>\n",
" <td>0.186837</td>\n",
" <td>0.249289</td>\n",
" <td>0.849944</td>\n",
" <td>0.774006</td>\n",
" <td>0.594751</td>\n",
" <td>0.778275</td>\n",
" <td>0.0</td>\n",
" <td>0.393161</td>\n",
" <td>0.420478</td>\n",
" <td>0.388782</td>\n",
" <td>0.413085</td>\n",
" <td>0.445061</td>\n",
" <td>0.400803</td>\n",
" <td>0.0</td>\n",
" <td>0.400803</td>\n",
" <td>0.437013</td>\n",
" <td>0.527192</td>\n",
" <td>0.421103</td>\n",
" <td>0.396852</td>\n",
" <td>0.430744</td>\n",
" <td>0.381902</td>\n",
" <td>0.351360</td>\n",
" <td>0.469681</td>\n",
" <td>0.420649</td>\n",
" <td>0.397929</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>RRBS_normal_B_cell_B1_24_CGTACTAG.CTCAGC</td>\n",
" <td>0.615582</td>\n",
" <td>0.270868</td>\n",
" <td>0.718351</td>\n",
" <td>0.245807</td>\n",
" <td>normal</td>\n",
" <td>normal_B</td>\n",
" <td>normal_B_cell_B1_24</td>\n",
" <td>0.0</td>\n",
" <td>0.584390</td>\n",
" <td>0.381342</td>\n",
" <td>0.614639</td>\n",
" <td>0.128080</td>\n",
" <td>0.134331</td>\n",
" <td>0.181411</td>\n",
" <td>0.0</td>\n",
" <td>0.181411</td>\n",
" <td>0.326547</td>\n",
" <td>0.521315</td>\n",
" <td>0.699421</td>\n",
" <td>0.307323</td>\n",
" <td>0.114798</td>\n",
" <td>0.250506</td>\n",
" <td>0.958901</td>\n",
" <td>0.900439</td>\n",
" <td>0.617142</td>\n",
" <td>0.875827</td>\n",
" <td>0.0</td>\n",
" <td>0.267871</td>\n",
" <td>0.351355</td>\n",
" <td>0.252870</td>\n",
" <td>0.389412</td>\n",
" <td>0.415080</td>\n",
" <td>0.433702</td>\n",
" <td>0.0</td>\n",
" <td>0.433702</td>\n",
" <td>0.390515</td>\n",
" <td>0.388477</td>\n",
" <td>0.260598</td>\n",
" <td>0.320489</td>\n",
" <td>0.397438</td>\n",
" <td>0.368093</td>\n",
" <td>0.046859</td>\n",
" <td>0.194196</td>\n",
" <td>0.307630</td>\n",
" <td>0.192950</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",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\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>416</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.GCTGCC</td>\n",
" <td>0.565787</td>\n",
" <td>0.379120</td>\n",
" <td>0.636582</td>\n",
" <td>0.361267</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_1</td>\n",
" <td>0.0</td>\n",
" <td>0.544259</td>\n",
" <td>0.384439</td>\n",
" <td>0.564795</td>\n",
" <td>0.139736</td>\n",
" <td>0.162409</td>\n",
" <td>0.176248</td>\n",
" <td>0.0</td>\n",
" <td>0.176248</td>\n",
" <td>0.318136</td>\n",
" <td>0.574486</td>\n",
" <td>0.634002</td>\n",
" <td>0.289043</td>\n",
" <td>0.139641</td>\n",
" <td>0.223191</td>\n",
" <td>0.856793</td>\n",
" <td>0.800237</td>\n",
" <td>0.554718</td>\n",
" <td>0.776976</td>\n",
" <td>0.0</td>\n",
" <td>0.354565</td>\n",
" <td>0.388414</td>\n",
" <td>0.347581</td>\n",
" <td>0.360744</td>\n",
" <td>0.402607</td>\n",
" <td>0.390989</td>\n",
" <td>0.0</td>\n",
" <td>0.390989</td>\n",
" <td>0.431375</td>\n",
" <td>0.409462</td>\n",
" <td>0.385777</td>\n",
" <td>0.340581</td>\n",
" <td>0.375888</td>\n",
" <td>0.372911</td>\n",
" <td>0.350551</td>\n",
" <td>0.400626</td>\n",
" <td>0.404546</td>\n",
" <td>0.364278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>417</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.GGCATC</td>\n",
" <td>0.578365</td>\n",
" <td>0.372803</td>\n",
" <td>0.639499</td>\n",
" <td>0.362328</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_1</td>\n",
" <td>0.0</td>\n",
" <td>0.555539</td>\n",
" <td>0.392705</td>\n",
" <td>0.579332</td>\n",
" <td>0.145652</td>\n",
" <td>0.164935</td>\n",
" <td>0.184172</td>\n",
" <td>0.0</td>\n",
" <td>0.184172</td>\n",
" <td>0.333482</td>\n",
" <td>0.421168</td>\n",
" <td>0.661779</td>\n",
" <td>0.296269</td>\n",
" <td>0.147555</td>\n",
" <td>0.241141</td>\n",
" <td>0.825601</td>\n",
" <td>0.813275</td>\n",
" <td>0.589382</td>\n",
" <td>0.790102</td>\n",
" <td>0.0</td>\n",
" <td>0.349269</td>\n",
" <td>0.378454</td>\n",
" <td>0.343167</td>\n",
" <td>0.352282</td>\n",
" <td>0.398483</td>\n",
" <td>0.383984</td>\n",
" <td>0.0</td>\n",
" <td>0.383984</td>\n",
" <td>0.394837</td>\n",
" <td>0.570345</td>\n",
" <td>0.396641</td>\n",
" <td>0.337457</td>\n",
" <td>0.375483</td>\n",
" <td>0.341387</td>\n",
" <td>0.228666</td>\n",
" <td>0.393968</td>\n",
" <td>0.384816</td>\n",
" <td>0.358044</td>\n",
" </tr>\n",
" <tr>\n",
" <th>418</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.GTGAGG</td>\n",
" <td>0.554707</td>\n",
" <td>0.379959</td>\n",
" <td>0.630143</td>\n",
" <td>0.365870</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_1</td>\n",
" <td>0.0</td>\n",
" <td>0.530569</td>\n",
" <td>0.367508</td>\n",
" <td>0.554714</td>\n",
" <td>0.134121</td>\n",
" <td>0.155524</td>\n",
" <td>0.173857</td>\n",
" <td>0.0</td>\n",
" <td>0.173857</td>\n",
" <td>0.315881</td>\n",
" <td>0.463581</td>\n",
" <td>0.615143</td>\n",
" <td>0.280369</td>\n",
" <td>0.133060</td>\n",
" <td>0.232378</td>\n",
" <td>0.794342</td>\n",
" <td>0.804363</td>\n",
" <td>0.553666</td>\n",
" <td>0.777878</td>\n",
" <td>0.0</td>\n",
" <td>0.358688</td>\n",
" <td>0.390493</td>\n",
" <td>0.351879</td>\n",
" <td>0.361701</td>\n",
" <td>0.404384</td>\n",
" <td>0.398420</td>\n",
" <td>0.0</td>\n",
" <td>0.398420</td>\n",
" <td>0.422339</td>\n",
" <td>0.427769</td>\n",
" <td>0.417731</td>\n",
" <td>0.341625</td>\n",
" <td>0.380159</td>\n",
" <td>0.367890</td>\n",
" <td>0.326313</td>\n",
" <td>0.400965</td>\n",
" <td>0.391422</td>\n",
" <td>0.373351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>419</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.GTTGAG</td>\n",
" <td>0.581650</td>\n",
" <td>0.365969</td>\n",
" <td>0.651666</td>\n",
" <td>0.354063</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_1</td>\n",
" <td>0.0</td>\n",
" <td>0.557958</td>\n",
" <td>0.396493</td>\n",
" <td>0.580842</td>\n",
" <td>0.143581</td>\n",
" <td>0.165943</td>\n",
" <td>0.198223</td>\n",
" <td>0.0</td>\n",
" <td>0.198223</td>\n",
" <td>0.335718</td>\n",
" <td>0.511515</td>\n",
" <td>0.653486</td>\n",
" <td>0.294972</td>\n",
" <td>0.142042</td>\n",
" <td>0.251258</td>\n",
" <td>0.808442</td>\n",
" <td>0.821536</td>\n",
" <td>0.592957</td>\n",
" <td>0.795827</td>\n",
" <td>0.0</td>\n",
" <td>0.344146</td>\n",
" <td>0.371379</td>\n",
" <td>0.337804</td>\n",
" <td>0.354805</td>\n",
" <td>0.397635</td>\n",
" <td>0.404505</td>\n",
" <td>0.0</td>\n",
" <td>0.404505</td>\n",
" <td>0.420580</td>\n",
" <td>0.415804</td>\n",
" <td>0.359175</td>\n",
" <td>0.330021</td>\n",
" <td>0.365134</td>\n",
" <td>0.366449</td>\n",
" <td>0.413398</td>\n",
" <td>0.375629</td>\n",
" <td>0.381035</td>\n",
" <td>0.346115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>420</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.TAGCGG</td>\n",
" <td>0.564165</td>\n",
" <td>0.368605</td>\n",
" <td>0.635522</td>\n",
" <td>0.354078</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_1</td>\n",
" <td>0.0</td>\n",
" <td>0.540878</td>\n",
" <td>0.368191</td>\n",
" <td>0.564637</td>\n",
" <td>0.133724</td>\n",
" <td>0.153977</td>\n",
" <td>0.166738</td>\n",
" <td>0.0</td>\n",
" <td>0.166738</td>\n",
" <td>0.307876</td>\n",
" <td>0.486900</td>\n",
" <td>0.632573</td>\n",
" <td>0.286827</td>\n",
" <td>0.128397</td>\n",
" <td>0.230826</td>\n",
" <td>0.856077</td>\n",
" <td>0.814219</td>\n",
" <td>0.577274</td>\n",
" <td>0.793247</td>\n",
" <td>0.0</td>\n",
" <td>0.346260</td>\n",
" <td>0.378683</td>\n",
" <td>0.339097</td>\n",
" <td>0.356597</td>\n",
" <td>0.396187</td>\n",
" <td>0.381571</td>\n",
" <td>0.0</td>\n",
" <td>0.381571</td>\n",
" <td>0.400928</td>\n",
" <td>0.570090</td>\n",
" <td>0.362619</td>\n",
" <td>0.339981</td>\n",
" <td>0.379723</td>\n",
" <td>0.344153</td>\n",
" <td>0.377799</td>\n",
" <td>0.385887</td>\n",
" <td>0.384042</td>\n",
" <td>0.349956</td>\n",
" </tr>\n",
" <tr>\n",
" <th>421</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.TATCTC</td>\n",
" <td>0.598086</td>\n",
" <td>0.365747</td>\n",
" <td>0.640908</td>\n",
" <td>0.360269</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_1</td>\n",
" <td>0.0</td>\n",
" <td>0.575676</td>\n",
" <td>0.409355</td>\n",
" <td>0.600599</td>\n",
" <td>0.148591</td>\n",
" <td>0.174386</td>\n",
" <td>0.194566</td>\n",
" <td>0.0</td>\n",
" <td>0.194566</td>\n",
" <td>0.336279</td>\n",
" <td>0.554679</td>\n",
" <td>0.662141</td>\n",
" <td>0.309579</td>\n",
" <td>0.142574</td>\n",
" <td>0.240360</td>\n",
" <td>0.844196</td>\n",
" <td>0.822712</td>\n",
" <td>0.607978</td>\n",
" <td>0.806099</td>\n",
" <td>0.0</td>\n",
" <td>0.341342</td>\n",
" <td>0.367808</td>\n",
" <td>0.337694</td>\n",
" <td>0.339362</td>\n",
" <td>0.386849</td>\n",
" <td>0.380734</td>\n",
" <td>0.0</td>\n",
" <td>0.380734</td>\n",
" <td>0.394130</td>\n",
" <td>0.514109</td>\n",
" <td>0.395076</td>\n",
" <td>0.319727</td>\n",
" <td>0.356904</td>\n",
" <td>0.318327</td>\n",
" <td>0.309309</td>\n",
" <td>0.390633</td>\n",
" <td>0.376339</td>\n",
" <td>0.360353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>422</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.TCTCTG</td>\n",
" <td>0.598637</td>\n",
" <td>0.367210</td>\n",
" <td>0.649959</td>\n",
" <td>0.358410</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_1</td>\n",
" <td>0.0</td>\n",
" <td>0.573932</td>\n",
" <td>0.402771</td>\n",
" <td>0.597337</td>\n",
" <td>0.153348</td>\n",
" <td>0.175088</td>\n",
" <td>0.200765</td>\n",
" <td>0.0</td>\n",
" <td>0.200765</td>\n",
" <td>0.347808</td>\n",
" <td>0.610443</td>\n",
" <td>0.676611</td>\n",
" <td>0.311039</td>\n",
" <td>0.144939</td>\n",
" <td>0.256872</td>\n",
" <td>0.854733</td>\n",
" <td>0.826905</td>\n",
" <td>0.607012</td>\n",
" <td>0.802475</td>\n",
" <td>0.0</td>\n",
" <td>0.342377</td>\n",
" <td>0.370775</td>\n",
" <td>0.337035</td>\n",
" <td>0.356039</td>\n",
" <td>0.402495</td>\n",
" <td>0.412892</td>\n",
" <td>0.0</td>\n",
" <td>0.412892</td>\n",
" <td>0.408307</td>\n",
" <td>0.464521</td>\n",
" <td>0.389068</td>\n",
" <td>0.330017</td>\n",
" <td>0.366829</td>\n",
" <td>0.337809</td>\n",
" <td>0.241373</td>\n",
" <td>0.383198</td>\n",
" <td>0.378137</td>\n",
" <td>0.345602</td>\n",
" </tr>\n",
" <tr>\n",
" <th>423</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.TGACAG</td>\n",
" <td>0.592655</td>\n",
" <td>0.364070</td>\n",
" <td>0.651817</td>\n",
" <td>0.350381</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_1</td>\n",
" <td>0.0</td>\n",
" <td>0.567869</td>\n",
" <td>0.399077</td>\n",
" <td>0.591561</td>\n",
" <td>0.148038</td>\n",
" <td>0.176237</td>\n",
" <td>0.202154</td>\n",
" <td>0.0</td>\n",
" <td>0.202154</td>\n",
" <td>0.343355</td>\n",
" <td>0.522213</td>\n",
" <td>0.665558</td>\n",
" <td>0.303592</td>\n",
" <td>0.146534</td>\n",
" <td>0.252288</td>\n",
" <td>0.796484</td>\n",
" <td>0.830742</td>\n",
" <td>0.598186</td>\n",
" <td>0.806577</td>\n",
" <td>0.0</td>\n",
" <td>0.345410</td>\n",
" <td>0.381678</td>\n",
" <td>0.340699</td>\n",
" <td>0.363333</td>\n",
" <td>0.411799</td>\n",
" <td>0.398106</td>\n",
" <td>0.0</td>\n",
" <td>0.398106</td>\n",
" <td>0.406224</td>\n",
" <td>0.468663</td>\n",
" <td>0.352417</td>\n",
" <td>0.345042</td>\n",
" <td>0.391606</td>\n",
" <td>0.339766</td>\n",
" <td>0.370686</td>\n",
" <td>0.369564</td>\n",
" <td>0.369690</td>\n",
" <td>0.345702</td>\n",
" </tr>\n",
" <tr>\n",
" <th>424</th>\n",
" <td>RRBS_trito_pool_1_TAAGGCGA.TGCTGC</td>\n",
" <td>0.572118</td>\n",
" <td>0.380804</td>\n",
" <td>0.638604</td>\n",
" <td>0.366845</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_1</td>\n",
" <td>0.0</td>\n",
" <td>0.549810</td>\n",
" <td>0.381938</td>\n",
" <td>0.576183</td>\n",
" <td>0.143244</td>\n",
" <td>0.162055</td>\n",
" <td>0.182065</td>\n",
" <td>0.0</td>\n",
" <td>0.182065</td>\n",
" <td>0.322882</td>\n",
" <td>0.522536</td>\n",
" <td>0.636949</td>\n",
" <td>0.297776</td>\n",
" <td>0.142530</td>\n",
" <td>0.244198</td>\n",
" <td>0.808005</td>\n",
" <td>0.807068</td>\n",
" <td>0.566512</td>\n",
" <td>0.789342</td>\n",
" <td>0.0</td>\n",
" <td>0.361772</td>\n",
" <td>0.392851</td>\n",
" <td>0.356422</td>\n",
" <td>0.365182</td>\n",
" <td>0.413081</td>\n",
" <td>0.408349</td>\n",
" <td>0.0</td>\n",
" <td>0.408349</td>\n",
" <td>0.431775</td>\n",
" <td>0.422301</td>\n",
" <td>0.386494</td>\n",
" <td>0.344547</td>\n",
" <td>0.390997</td>\n",
" <td>0.368241</td>\n",
" <td>0.364905</td>\n",
" <td>0.392520</td>\n",
" <td>0.386162</td>\n",
" <td>0.373449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>425</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.ACAACC</td>\n",
" <td>0.593766</td>\n",
" <td>0.374765</td>\n",
" <td>0.655123</td>\n",
" <td>0.357956</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.571434</td>\n",
" <td>0.403317</td>\n",
" <td>0.595518</td>\n",
" <td>0.150534</td>\n",
" <td>0.172412</td>\n",
" <td>0.197820</td>\n",
" <td>0.0</td>\n",
" <td>0.197820</td>\n",
" <td>0.335046</td>\n",
" <td>0.542060</td>\n",
" <td>0.674691</td>\n",
" <td>0.311226</td>\n",
" <td>0.145333</td>\n",
" <td>0.251228</td>\n",
" <td>0.782911</td>\n",
" <td>0.825876</td>\n",
" <td>0.601882</td>\n",
" <td>0.804140</td>\n",
" <td>0.0</td>\n",
" <td>0.356830</td>\n",
" <td>0.396188</td>\n",
" <td>0.349903</td>\n",
" <td>0.388446</td>\n",
" <td>0.425970</td>\n",
" <td>0.423147</td>\n",
" <td>0.0</td>\n",
" <td>0.423147</td>\n",
" <td>0.414028</td>\n",
" <td>0.406933</td>\n",
" <td>0.353024</td>\n",
" <td>0.363997</td>\n",
" <td>0.410352</td>\n",
" <td>0.353075</td>\n",
" <td>0.404312</td>\n",
" <td>0.376888</td>\n",
" <td>0.385532</td>\n",
" <td>0.352623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>426</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.ACGTGG</td>\n",
" <td>0.569259</td>\n",
" <td>0.388716</td>\n",
" <td>0.648626</td>\n",
" <td>0.369196</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.547223</td>\n",
" <td>0.381861</td>\n",
" <td>0.572699</td>\n",
" <td>0.142991</td>\n",
" <td>0.167937</td>\n",
" <td>0.187875</td>\n",
" <td>0.0</td>\n",
" <td>0.187875</td>\n",
" <td>0.322376</td>\n",
" <td>0.494384</td>\n",
" <td>0.655945</td>\n",
" <td>0.290386</td>\n",
" <td>0.138317</td>\n",
" <td>0.228471</td>\n",
" <td>0.824135</td>\n",
" <td>0.809657</td>\n",
" <td>0.575763</td>\n",
" <td>0.782319</td>\n",
" <td>0.0</td>\n",
" <td>0.367896</td>\n",
" <td>0.410155</td>\n",
" <td>0.360025</td>\n",
" <td>0.392362</td>\n",
" <td>0.439690</td>\n",
" <td>0.423503</td>\n",
" <td>0.0</td>\n",
" <td>0.423503</td>\n",
" <td>0.424893</td>\n",
" <td>0.371724</td>\n",
" <td>0.396436</td>\n",
" <td>0.364995</td>\n",
" <td>0.415006</td>\n",
" <td>0.358457</td>\n",
" <td>0.266702</td>\n",
" <td>0.393868</td>\n",
" <td>0.396684</td>\n",
" <td>0.361810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>427</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.ACTCAC</td>\n",
" <td>0.595647</td>\n",
" <td>0.380070</td>\n",
" <td>0.652962</td>\n",
" <td>0.364404</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.574078</td>\n",
" <td>0.410369</td>\n",
" <td>0.599662</td>\n",
" <td>0.154807</td>\n",
" <td>0.180722</td>\n",
" <td>0.204649</td>\n",
" <td>0.0</td>\n",
" <td>0.204649</td>\n",
" <td>0.346719</td>\n",
" <td>0.484916</td>\n",
" <td>0.687740</td>\n",
" <td>0.314513</td>\n",
" <td>0.155080</td>\n",
" <td>0.260803</td>\n",
" <td>0.812211</td>\n",
" <td>0.828639</td>\n",
" <td>0.608617</td>\n",
" <td>0.812883</td>\n",
" <td>0.0</td>\n",
" <td>0.360783</td>\n",
" <td>0.413536</td>\n",
" <td>0.352271</td>\n",
" <td>0.396930</td>\n",
" <td>0.438502</td>\n",
" <td>0.437218</td>\n",
" <td>0.0</td>\n",
" <td>0.437218</td>\n",
" <td>0.434233</td>\n",
" <td>0.243128</td>\n",
" <td>0.391187</td>\n",
" <td>0.371005</td>\n",
" <td>0.424675</td>\n",
" <td>0.381663</td>\n",
" <td>0.401646</td>\n",
" <td>0.380932</td>\n",
" <td>0.388726</td>\n",
" <td>0.364671</td>\n",
" </tr>\n",
" <tr>\n",
" <th>428</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.AGGATG</td>\n",
" <td>0.595616</td>\n",
" <td>0.384004</td>\n",
" <td>0.658358</td>\n",
" <td>0.369689</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.574464</td>\n",
" <td>0.411157</td>\n",
" <td>0.598317</td>\n",
" <td>0.153244</td>\n",
" <td>0.179052</td>\n",
" <td>0.205426</td>\n",
" <td>0.0</td>\n",
" <td>0.205426</td>\n",
" <td>0.347422</td>\n",
" <td>0.549476</td>\n",
" <td>0.674730</td>\n",
" <td>0.318179</td>\n",
" <td>0.150916</td>\n",
" <td>0.255366</td>\n",
" <td>0.825079</td>\n",
" <td>0.819016</td>\n",
" <td>0.600086</td>\n",
" <td>0.803518</td>\n",
" <td>0.0</td>\n",
" <td>0.361346</td>\n",
" <td>0.394711</td>\n",
" <td>0.356405</td>\n",
" <td>0.385541</td>\n",
" <td>0.430138</td>\n",
" <td>0.435397</td>\n",
" <td>0.0</td>\n",
" <td>0.435397</td>\n",
" <td>0.430261</td>\n",
" <td>0.509647</td>\n",
" <td>0.397243</td>\n",
" <td>0.354870</td>\n",
" <td>0.405106</td>\n",
" <td>0.376955</td>\n",
" <td>0.349346</td>\n",
" <td>0.390618</td>\n",
" <td>0.387689</td>\n",
" <td>0.355591</td>\n",
" </tr>\n",
" <tr>\n",
" <th>429</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.ATAGCG</td>\n",
" <td>0.554055</td>\n",
" <td>0.387759</td>\n",
" <td>0.635741</td>\n",
" <td>0.364157</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.530317</td>\n",
" <td>0.372598</td>\n",
" <td>0.555500</td>\n",
" <td>0.139056</td>\n",
" <td>0.162002</td>\n",
" <td>0.188584</td>\n",
" <td>0.0</td>\n",
" <td>0.188584</td>\n",
" <td>0.314593</td>\n",
" <td>0.662600</td>\n",
" <td>0.651363</td>\n",
" <td>0.276445</td>\n",
" <td>0.139170</td>\n",
" <td>0.226507</td>\n",
" <td>0.782103</td>\n",
" <td>0.805115</td>\n",
" <td>0.577048</td>\n",
" <td>0.796431</td>\n",
" <td>0.0</td>\n",
" <td>0.368334</td>\n",
" <td>0.399350</td>\n",
" <td>0.363279</td>\n",
" <td>0.375962</td>\n",
" <td>0.421184</td>\n",
" <td>0.422536</td>\n",
" <td>0.0</td>\n",
" <td>0.422536</td>\n",
" <td>0.423652</td>\n",
" <td>0.422062</td>\n",
" <td>0.390546</td>\n",
" <td>0.352241</td>\n",
" <td>0.394821</td>\n",
" <td>0.362621</td>\n",
" <td>0.349869</td>\n",
" <td>0.400868</td>\n",
" <td>0.397587</td>\n",
" <td>0.375378</td>\n",
" </tr>\n",
" <tr>\n",
" <th>430</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.ATCGAC</td>\n",
" <td>0.598233</td>\n",
" <td>0.384663</td>\n",
" <td>0.661048</td>\n",
" <td>0.366513</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.579706</td>\n",
" <td>0.417754</td>\n",
" <td>0.601652</td>\n",
" <td>0.154766</td>\n",
" <td>0.179131</td>\n",
" <td>0.214474</td>\n",
" <td>0.0</td>\n",
" <td>0.214474</td>\n",
" <td>0.347695</td>\n",
" <td>0.592746</td>\n",
" <td>0.693724</td>\n",
" <td>0.318722</td>\n",
" <td>0.152873</td>\n",
" <td>0.251772</td>\n",
" <td>0.829067</td>\n",
" <td>0.818502</td>\n",
" <td>0.603463</td>\n",
" <td>0.804070</td>\n",
" <td>0.0</td>\n",
" <td>0.365534</td>\n",
" <td>0.410172</td>\n",
" <td>0.359211</td>\n",
" <td>0.392262</td>\n",
" <td>0.438666</td>\n",
" <td>0.432635</td>\n",
" <td>0.0</td>\n",
" <td>0.432635</td>\n",
" <td>0.437134</td>\n",
" <td>0.550723</td>\n",
" <td>0.406531</td>\n",
" <td>0.367066</td>\n",
" <td>0.412751</td>\n",
" <td>0.381165</td>\n",
" <td>0.291126</td>\n",
" <td>0.388891</td>\n",
" <td>0.385826</td>\n",
" <td>0.368804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>431</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.CAAGAG</td>\n",
" <td>0.583609</td>\n",
" <td>0.393008</td>\n",
" <td>0.649029</td>\n",
" <td>0.373233</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.562102</td>\n",
" <td>0.395170</td>\n",
" <td>0.587195</td>\n",
" <td>0.146861</td>\n",
" <td>0.171793</td>\n",
" <td>0.193740</td>\n",
" <td>0.0</td>\n",
" <td>0.193740</td>\n",
" <td>0.336475</td>\n",
" <td>0.643228</td>\n",
" <td>0.644603</td>\n",
" <td>0.303090</td>\n",
" <td>0.145796</td>\n",
" <td>0.249175</td>\n",
" <td>0.867900</td>\n",
" <td>0.811712</td>\n",
" <td>0.594372</td>\n",
" <td>0.798705</td>\n",
" <td>0.0</td>\n",
" <td>0.371383</td>\n",
" <td>0.417443</td>\n",
" <td>0.364244</td>\n",
" <td>0.392824</td>\n",
" <td>0.439427</td>\n",
" <td>0.424017</td>\n",
" <td>0.0</td>\n",
" <td>0.424017</td>\n",
" <td>0.430895</td>\n",
" <td>0.419896</td>\n",
" <td>0.406159</td>\n",
" <td>0.358254</td>\n",
" <td>0.421774</td>\n",
" <td>0.375869</td>\n",
" <td>0.285465</td>\n",
" <td>0.405000</td>\n",
" <td>0.385932</td>\n",
" <td>0.366543</td>\n",
" </tr>\n",
" <tr>\n",
" <th>432</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.CATGAC</td>\n",
" <td>0.593843</td>\n",
" <td>0.383170</td>\n",
" <td>0.659853</td>\n",
" <td>0.366034</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.572795</td>\n",
" <td>0.406324</td>\n",
" <td>0.595511</td>\n",
" <td>0.149580</td>\n",
" <td>0.173139</td>\n",
" <td>0.207084</td>\n",
" <td>0.0</td>\n",
" <td>0.207084</td>\n",
" <td>0.341760</td>\n",
" <td>0.582188</td>\n",
" <td>0.686507</td>\n",
" <td>0.311526</td>\n",
" <td>0.145188</td>\n",
" <td>0.255092</td>\n",
" <td>0.898922</td>\n",
" <td>0.822096</td>\n",
" <td>0.602356</td>\n",
" <td>0.802416</td>\n",
" <td>0.0</td>\n",
" <td>0.363315</td>\n",
" <td>0.403748</td>\n",
" <td>0.356727</td>\n",
" <td>0.385169</td>\n",
" <td>0.426971</td>\n",
" <td>0.428521</td>\n",
" <td>0.0</td>\n",
" <td>0.428521</td>\n",
" <td>0.437559</td>\n",
" <td>0.666602</td>\n",
" <td>0.399967</td>\n",
" <td>0.360502</td>\n",
" <td>0.402785</td>\n",
" <td>0.382317</td>\n",
" <td>0.213772</td>\n",
" <td>0.390465</td>\n",
" <td>0.395524</td>\n",
" <td>0.364513</td>\n",
" </tr>\n",
" <tr>\n",
" <th>433</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.CCTTCG</td>\n",
" <td>0.568050</td>\n",
" <td>0.392622</td>\n",
" <td>0.647402</td>\n",
" <td>0.371530</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.544424</td>\n",
" <td>0.372864</td>\n",
" <td>0.566449</td>\n",
" <td>0.141793</td>\n",
" <td>0.161766</td>\n",
" <td>0.188699</td>\n",
" <td>0.0</td>\n",
" <td>0.188699</td>\n",
" <td>0.320217</td>\n",
" <td>0.520051</td>\n",
" <td>0.648292</td>\n",
" <td>0.287153</td>\n",
" <td>0.134590</td>\n",
" <td>0.231480</td>\n",
" <td>0.918757</td>\n",
" <td>0.808219</td>\n",
" <td>0.556881</td>\n",
" <td>0.786660</td>\n",
" <td>0.0</td>\n",
" <td>0.372677</td>\n",
" <td>0.409840</td>\n",
" <td>0.367423</td>\n",
" <td>0.386818</td>\n",
" <td>0.428182</td>\n",
" <td>0.410043</td>\n",
" <td>0.0</td>\n",
" <td>0.410043</td>\n",
" <td>0.437956</td>\n",
" <td>0.431314</td>\n",
" <td>0.417184</td>\n",
" <td>0.370701</td>\n",
" <td>0.403697</td>\n",
" <td>0.379120</td>\n",
" <td>0.132574</td>\n",
" <td>0.402506</td>\n",
" <td>0.408092</td>\n",
" <td>0.392411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>434</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.CGGTAG</td>\n",
" <td>0.569010</td>\n",
" <td>0.390108</td>\n",
" <td>0.645339</td>\n",
" <td>0.372348</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.541543</td>\n",
" <td>0.377491</td>\n",
" <td>0.566615</td>\n",
" <td>0.144500</td>\n",
" <td>0.166361</td>\n",
" <td>0.187541</td>\n",
" <td>0.0</td>\n",
" <td>0.187541</td>\n",
" <td>0.323718</td>\n",
" <td>0.570565</td>\n",
" <td>0.649134</td>\n",
" <td>0.290349</td>\n",
" <td>0.139695</td>\n",
" <td>0.231105</td>\n",
" <td>0.875553</td>\n",
" <td>0.805793</td>\n",
" <td>0.574939</td>\n",
" <td>0.781107</td>\n",
" <td>0.0</td>\n",
" <td>0.366749</td>\n",
" <td>0.406201</td>\n",
" <td>0.359577</td>\n",
" <td>0.382439</td>\n",
" <td>0.427216</td>\n",
" <td>0.412182</td>\n",
" <td>0.0</td>\n",
" <td>0.412182</td>\n",
" <td>0.424271</td>\n",
" <td>0.326364</td>\n",
" <td>0.387866</td>\n",
" <td>0.358136</td>\n",
" <td>0.400989</td>\n",
" <td>0.367082</td>\n",
" <td>0.233944</td>\n",
" <td>0.405535</td>\n",
" <td>0.387747</td>\n",
" <td>0.372620</td>\n",
" </tr>\n",
" <tr>\n",
" <th>435</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.CTATTG</td>\n",
" <td>0.595037</td>\n",
" <td>0.373701</td>\n",
" <td>0.659609</td>\n",
" <td>0.357034</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.571516</td>\n",
" <td>0.403810</td>\n",
" <td>0.595260</td>\n",
" <td>0.147111</td>\n",
" <td>0.169791</td>\n",
" <td>0.199903</td>\n",
" <td>0.0</td>\n",
" <td>0.199903</td>\n",
" <td>0.345774</td>\n",
" <td>0.461720</td>\n",
" <td>0.665058</td>\n",
" <td>0.308691</td>\n",
" <td>0.144474</td>\n",
" <td>0.257436</td>\n",
" <td>0.833091</td>\n",
" <td>0.825356</td>\n",
" <td>0.606750</td>\n",
" <td>0.808127</td>\n",
" <td>0.0</td>\n",
" <td>0.352923</td>\n",
" <td>0.389431</td>\n",
" <td>0.346998</td>\n",
" <td>0.367228</td>\n",
" <td>0.410854</td>\n",
" <td>0.408465</td>\n",
" <td>0.0</td>\n",
" <td>0.408465</td>\n",
" <td>0.420192</td>\n",
" <td>0.435325</td>\n",
" <td>0.384775</td>\n",
" <td>0.344882</td>\n",
" <td>0.388064</td>\n",
" <td>0.354390</td>\n",
" <td>0.322671</td>\n",
" <td>0.384292</td>\n",
" <td>0.385328</td>\n",
" <td>0.353731</td>\n",
" </tr>\n",
" <tr>\n",
" <th>436</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.GACACG</td>\n",
" <td>0.570886</td>\n",
" <td>0.384283</td>\n",
" <td>0.645343</td>\n",
" <td>0.367303</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.547611</td>\n",
" <td>0.384121</td>\n",
" <td>0.571736</td>\n",
" <td>0.141024</td>\n",
" <td>0.160152</td>\n",
" <td>0.183884</td>\n",
" <td>0.0</td>\n",
" <td>0.183884</td>\n",
" <td>0.324428</td>\n",
" <td>0.516251</td>\n",
" <td>0.658554</td>\n",
" <td>0.296090</td>\n",
" <td>0.142236</td>\n",
" <td>0.243037</td>\n",
" <td>0.840847</td>\n",
" <td>0.810208</td>\n",
" <td>0.580500</td>\n",
" <td>0.780899</td>\n",
" <td>0.0</td>\n",
" <td>0.362036</td>\n",
" <td>0.396972</td>\n",
" <td>0.355362</td>\n",
" <td>0.379360</td>\n",
" <td>0.423028</td>\n",
" <td>0.407819</td>\n",
" <td>0.0</td>\n",
" <td>0.407819</td>\n",
" <td>0.430720</td>\n",
" <td>0.413402</td>\n",
" <td>0.375936</td>\n",
" <td>0.353105</td>\n",
" <td>0.395058</td>\n",
" <td>0.377523</td>\n",
" <td>0.239142</td>\n",
" <td>0.397595</td>\n",
" <td>0.385070</td>\n",
" <td>0.369290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>437</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.GCATTC</td>\n",
" <td>0.596410</td>\n",
" <td>0.375766</td>\n",
" <td>0.656557</td>\n",
" <td>0.357551</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.574308</td>\n",
" <td>0.399926</td>\n",
" <td>0.599169</td>\n",
" <td>0.146626</td>\n",
" <td>0.171425</td>\n",
" <td>0.202222</td>\n",
" <td>0.0</td>\n",
" <td>0.202222</td>\n",
" <td>0.339589</td>\n",
" <td>0.601239</td>\n",
" <td>0.673941</td>\n",
" <td>0.311423</td>\n",
" <td>0.142003</td>\n",
" <td>0.251027</td>\n",
" <td>0.900174</td>\n",
" <td>0.822302</td>\n",
" <td>0.607857</td>\n",
" <td>0.804399</td>\n",
" <td>0.0</td>\n",
" <td>0.350723</td>\n",
" <td>0.388557</td>\n",
" <td>0.343231</td>\n",
" <td>0.379437</td>\n",
" <td>0.423172</td>\n",
" <td>0.413884</td>\n",
" <td>0.0</td>\n",
" <td>0.413884</td>\n",
" <td>0.430536</td>\n",
" <td>0.484843</td>\n",
" <td>0.385927</td>\n",
" <td>0.351172</td>\n",
" <td>0.395395</td>\n",
" <td>0.377211</td>\n",
" <td>0.260417</td>\n",
" <td>0.387313</td>\n",
" <td>0.379687</td>\n",
" <td>0.352217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>438</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.GCTGCC</td>\n",
" <td>0.583331</td>\n",
" <td>0.384057</td>\n",
" <td>0.650519</td>\n",
" <td>0.361400</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.557827</td>\n",
" <td>0.389318</td>\n",
" <td>0.583295</td>\n",
" <td>0.146131</td>\n",
" <td>0.170323</td>\n",
" <td>0.216651</td>\n",
" <td>0.0</td>\n",
" <td>0.216651</td>\n",
" <td>0.332222</td>\n",
" <td>0.465542</td>\n",
" <td>0.685189</td>\n",
" <td>0.303248</td>\n",
" <td>0.142679</td>\n",
" <td>0.253152</td>\n",
" <td>0.925723</td>\n",
" <td>0.812384</td>\n",
" <td>0.566898</td>\n",
" <td>0.789918</td>\n",
" <td>0.0</td>\n",
" <td>0.364344</td>\n",
" <td>0.411073</td>\n",
" <td>0.357567</td>\n",
" <td>0.386643</td>\n",
" <td>0.436352</td>\n",
" <td>0.455234</td>\n",
" <td>0.0</td>\n",
" <td>0.455234</td>\n",
" <td>0.427594</td>\n",
" <td>0.441989</td>\n",
" <td>0.410031</td>\n",
" <td>0.357034</td>\n",
" <td>0.414984</td>\n",
" <td>0.377580</td>\n",
" <td>0.122134</td>\n",
" <td>0.390103</td>\n",
" <td>0.391888</td>\n",
" <td>0.356613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>439</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.GGCATC</td>\n",
" <td>0.583463</td>\n",
" <td>0.392946</td>\n",
" <td>0.648295</td>\n",
" <td>0.372514</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.558443</td>\n",
" <td>0.393582</td>\n",
" <td>0.581411</td>\n",
" <td>0.149267</td>\n",
" <td>0.173254</td>\n",
" <td>0.202211</td>\n",
" <td>0.0</td>\n",
" <td>0.202211</td>\n",
" <td>0.336075</td>\n",
" <td>0.497985</td>\n",
" <td>0.658613</td>\n",
" <td>0.307422</td>\n",
" <td>0.145063</td>\n",
" <td>0.253500</td>\n",
" <td>0.822348</td>\n",
" <td>0.810850</td>\n",
" <td>0.579463</td>\n",
" <td>0.788819</td>\n",
" <td>0.0</td>\n",
" <td>0.369847</td>\n",
" <td>0.416267</td>\n",
" <td>0.362248</td>\n",
" <td>0.393373</td>\n",
" <td>0.438305</td>\n",
" <td>0.432106</td>\n",
" <td>0.0</td>\n",
" <td>0.432106</td>\n",
" <td>0.447392</td>\n",
" <td>0.283823</td>\n",
" <td>0.414114</td>\n",
" <td>0.369362</td>\n",
" <td>0.423329</td>\n",
" <td>0.390531</td>\n",
" <td>0.294508</td>\n",
" <td>0.403624</td>\n",
" <td>0.394708</td>\n",
" <td>0.370958</td>\n",
" </tr>\n",
" <tr>\n",
" <th>440</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.GTGAGG</td>\n",
" <td>0.572670</td>\n",
" <td>0.387705</td>\n",
" <td>0.655986</td>\n",
" <td>0.366706</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.548763</td>\n",
" <td>0.378005</td>\n",
" <td>0.573823</td>\n",
" <td>0.146951</td>\n",
" <td>0.166034</td>\n",
" <td>0.189554</td>\n",
" <td>0.0</td>\n",
" <td>0.189554</td>\n",
" <td>0.329531</td>\n",
" <td>0.417063</td>\n",
" <td>0.656924</td>\n",
" <td>0.298588</td>\n",
" <td>0.143700</td>\n",
" <td>0.237655</td>\n",
" <td>0.837979</td>\n",
" <td>0.818187</td>\n",
" <td>0.578995</td>\n",
" <td>0.795407</td>\n",
" <td>0.0</td>\n",
" <td>0.369274</td>\n",
" <td>0.411458</td>\n",
" <td>0.362212</td>\n",
" <td>0.399234</td>\n",
" <td>0.439421</td>\n",
" <td>0.426244</td>\n",
" <td>0.0</td>\n",
" <td>0.426244</td>\n",
" <td>0.437661</td>\n",
" <td>0.387644</td>\n",
" <td>0.379763</td>\n",
" <td>0.378844</td>\n",
" <td>0.416184</td>\n",
" <td>0.393381</td>\n",
" <td>0.327526</td>\n",
" <td>0.390663</td>\n",
" <td>0.404334</td>\n",
" <td>0.359301</td>\n",
" </tr>\n",
" <tr>\n",
" <th>441</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.GTTGAG</td>\n",
" <td>0.584506</td>\n",
" <td>0.379222</td>\n",
" <td>0.659633</td>\n",
" <td>0.362664</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.560388</td>\n",
" <td>0.392703</td>\n",
" <td>0.585744</td>\n",
" <td>0.148877</td>\n",
" <td>0.173148</td>\n",
" <td>0.191432</td>\n",
" <td>0.0</td>\n",
" <td>0.191432</td>\n",
" <td>0.337372</td>\n",
" <td>0.531839</td>\n",
" <td>0.657189</td>\n",
" <td>0.308189</td>\n",
" <td>0.144341</td>\n",
" <td>0.245608</td>\n",
" <td>0.856433</td>\n",
" <td>0.820191</td>\n",
" <td>0.594452</td>\n",
" <td>0.790093</td>\n",
" <td>0.0</td>\n",
" <td>0.357508</td>\n",
" <td>0.399570</td>\n",
" <td>0.348983</td>\n",
" <td>0.382424</td>\n",
" <td>0.428961</td>\n",
" <td>0.426739</td>\n",
" <td>0.0</td>\n",
" <td>0.426739</td>\n",
" <td>0.436677</td>\n",
" <td>0.623022</td>\n",
" <td>0.378906</td>\n",
" <td>0.357031</td>\n",
" <td>0.402640</td>\n",
" <td>0.380493</td>\n",
" <td>0.320648</td>\n",
" <td>0.385596</td>\n",
" <td>0.378942</td>\n",
" <td>0.359409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>442</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.TAGCGG</td>\n",
" <td>0.567804</td>\n",
" <td>0.392930</td>\n",
" <td>0.650606</td>\n",
" <td>0.371967</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.542448</td>\n",
" <td>0.379673</td>\n",
" <td>0.565934</td>\n",
" <td>0.141666</td>\n",
" <td>0.161357</td>\n",
" <td>0.190254</td>\n",
" <td>0.0</td>\n",
" <td>0.190254</td>\n",
" <td>0.322077</td>\n",
" <td>0.564060</td>\n",
" <td>0.661459</td>\n",
" <td>0.291782</td>\n",
" <td>0.141023</td>\n",
" <td>0.236705</td>\n",
" <td>0.775427</td>\n",
" <td>0.812801</td>\n",
" <td>0.584395</td>\n",
" <td>0.788413</td>\n",
" <td>0.0</td>\n",
" <td>0.373111</td>\n",
" <td>0.419487</td>\n",
" <td>0.363822</td>\n",
" <td>0.398524</td>\n",
" <td>0.443218</td>\n",
" <td>0.429929</td>\n",
" <td>0.0</td>\n",
" <td>0.429929</td>\n",
" <td>0.428916</td>\n",
" <td>0.508213</td>\n",
" <td>0.389595</td>\n",
" <td>0.371629</td>\n",
" <td>0.425177</td>\n",
" <td>0.379944</td>\n",
" <td>0.390322</td>\n",
" <td>0.401379</td>\n",
" <td>0.390136</td>\n",
" <td>0.365038</td>\n",
" </tr>\n",
" <tr>\n",
" <th>443</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.TATCTC</td>\n",
" <td>0.599881</td>\n",
" <td>0.371286</td>\n",
" <td>0.647639</td>\n",
" <td>0.360518</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.575018</td>\n",
" <td>0.407772</td>\n",
" <td>0.599556</td>\n",
" <td>0.150032</td>\n",
" <td>0.172842</td>\n",
" <td>0.207183</td>\n",
" <td>0.0</td>\n",
" <td>0.207183</td>\n",
" <td>0.348191</td>\n",
" <td>0.524038</td>\n",
" <td>0.668196</td>\n",
" <td>0.314785</td>\n",
" <td>0.147052</td>\n",
" <td>0.263346</td>\n",
" <td>0.860790</td>\n",
" <td>0.826304</td>\n",
" <td>0.608059</td>\n",
" <td>0.807089</td>\n",
" <td>0.0</td>\n",
" <td>0.350740</td>\n",
" <td>0.387199</td>\n",
" <td>0.345054</td>\n",
" <td>0.369375</td>\n",
" <td>0.416003</td>\n",
" <td>0.412018</td>\n",
" <td>0.0</td>\n",
" <td>0.412018</td>\n",
" <td>0.418236</td>\n",
" <td>0.488627</td>\n",
" <td>0.380793</td>\n",
" <td>0.343215</td>\n",
" <td>0.395603</td>\n",
" <td>0.371997</td>\n",
" <td>0.304058</td>\n",
" <td>0.381193</td>\n",
" <td>0.377228</td>\n",
" <td>0.343486</td>\n",
" </tr>\n",
" <tr>\n",
" <th>444</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.TCTCTG</td>\n",
" <td>0.597122</td>\n",
" <td>0.387602</td>\n",
" <td>0.651297</td>\n",
" <td>0.369044</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.574766</td>\n",
" <td>0.414443</td>\n",
" <td>0.597902</td>\n",
" <td>0.153941</td>\n",
" <td>0.177219</td>\n",
" <td>0.200535</td>\n",
" <td>0.0</td>\n",
" <td>0.200535</td>\n",
" <td>0.344312</td>\n",
" <td>0.593102</td>\n",
" <td>0.687200</td>\n",
" <td>0.318141</td>\n",
" <td>0.152659</td>\n",
" <td>0.260521</td>\n",
" <td>0.857143</td>\n",
" <td>0.820577</td>\n",
" <td>0.596070</td>\n",
" <td>0.810128</td>\n",
" <td>0.0</td>\n",
" <td>0.364954</td>\n",
" <td>0.410597</td>\n",
" <td>0.357541</td>\n",
" <td>0.390213</td>\n",
" <td>0.434403</td>\n",
" <td>0.418302</td>\n",
" <td>0.0</td>\n",
" <td>0.418302</td>\n",
" <td>0.436308</td>\n",
" <td>0.430239</td>\n",
" <td>0.403836</td>\n",
" <td>0.358261</td>\n",
" <td>0.411722</td>\n",
" <td>0.383838</td>\n",
" <td>0.307817</td>\n",
" <td>0.397070</td>\n",
" <td>0.395306</td>\n",
" <td>0.363937</td>\n",
" </tr>\n",
" <tr>\n",
" <th>445</th>\n",
" <td>RRBS_trito_pool_2_CGTACTAG.TGACAG</td>\n",
" <td>0.588475</td>\n",
" <td>0.377043</td>\n",
" <td>0.654169</td>\n",
" <td>0.362253</td>\n",
" <td>CLL</td>\n",
" <td>CLL</td>\n",
" <td>trito_pool_2</td>\n",
" <td>0.0</td>\n",
" <td>0.566762</td>\n",
" <td>0.398915</td>\n",
" <td>0.590485</td>\n",
" <td>0.146940</td>\n",
" <td>0.169929</td>\n",
" <td>0.200901</td>\n",
" <td>0.0</td>\n",
" <td>0.200901</td>\n",
" <td>0.330337</td>\n",
" <td>0.614493</td>\n",
" <td>0.668077</td>\n",
" <td>0.309691</td>\n",
" <td>0.140807</td>\n",
" <td>0.241382</td>\n",
" <td>0.877043</td>\n",
" <td>0.819644</td>\n",
" <td>0.588788</td>\n",
" <td>0.797851</td>\n",
" <td>0.0</td>\n",
" <td>0.352289</td>\n",
" <td>0.386013</td>\n",
" <td>0.345792</td>\n",
" <td>0.381209</td>\n",
" <td>0.425696</td>\n",
" <td>0.436417</td>\n",
" <td>0.0</td>\n",
" <td>0.436417</td>\n",
" <td>0.417572</td>\n",
" <td>0.441868</td>\n",
" <td>0.379378</td>\n",
" <td>0.356776</td>\n",
" <td>0.394481</td>\n",
" <td>0.368598</td>\n",
" <td>0.322557</td>\n",
" <td>0.387040</td>\n",
" <td>0.387626</td>\n",
" <td>0.360044</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>446 rows × 46 columns</p>\n",
"</div>"
],
"text/plain": [
" filename methylation PDR_total \\\n",
"0 RRBS_normal_B_cell_A1_24_TAAGGCGA.ACAACC 0.591346 0.259001 \n",
"1 RRBS_normal_B_cell_A1_24_TAAGGCGA.ACCGCG 0.531169 0.411448 \n",
"2 RRBS_normal_B_cell_A1_24_TAAGGCGA.ACGTGG 0.586403 0.278568 \n",
"3 RRBS_normal_B_cell_A1_24_TAAGGCGA.AGGATG 0.628623 0.248006 \n",
"4 RRBS_normal_B_cell_A1_24_TAAGGCGA.ATAGCG 0.568354 0.434929 \n",
"5 RRBS_normal_B_cell_A1_24_TAAGGCGA.ATCGAC 0.622386 0.272543 \n",
"6 RRBS_normal_B_cell_A1_24_TAAGGCGA.CAAGAG 0.580746 0.358441 \n",
"7 RRBS_normal_B_cell_A1_24_TAAGGCGA.CATGAC 0.579873 0.374401 \n",
"8 RRBS_normal_B_cell_A1_24_TAAGGCGA.CGGTAG 0.580833 0.285978 \n",
"9 RRBS_normal_B_cell_A1_24_TAAGGCGA.CTATTG 0.582590 0.427069 \n",
"10 RRBS_normal_B_cell_A1_24_TAAGGCGA.CTCAGC 0.577931 0.441120 \n",
"11 RRBS_normal_B_cell_A1_24_TAAGGCGA.GACACG 0.603615 0.259780 \n",
"12 RRBS_normal_B_cell_A1_24_TAAGGCGA.GCTGCC 0.602191 0.274941 \n",
"13 RRBS_normal_B_cell_A1_24_TAAGGCGA.GGCATC 0.592896 0.309917 \n",
"14 RRBS_normal_B_cell_A1_24_TAAGGCGA.GTGAGG 0.576342 0.269746 \n",
"15 RRBS_normal_B_cell_A1_24_TAAGGCGA.GTTGAG 0.573082 0.434043 \n",
"16 RRBS_normal_B_cell_A1_24_TAAGGCGA.TAGCGG 0.563537 0.344400 \n",
"17 RRBS_normal_B_cell_A1_24_TAAGGCGA.TATCTC 0.592870 0.383162 \n",
"18 RRBS_normal_B_cell_A1_24_TAAGGCGA.TCTCTG 0.566829 0.459303 \n",
"19 RRBS_normal_B_cell_A1_24_TAAGGCGA.TGACAG 0.572760 0.339617 \n",
"20 RRBS_normal_B_cell_B1_24_CGTACTAG.ACAACC 0.626281 0.266847 \n",
"21 RRBS_normal_B_cell_B1_24_CGTACTAG.ACCGCG 0.537494 0.432684 \n",
"22 RRBS_normal_B_cell_B1_24_CGTACTAG.ACTCAC 0.641663 0.246022 \n",
"23 RRBS_normal_B_cell_B1_24_CGTACTAG.ATAGCG 0.589376 0.261165 \n",
"24 RRBS_normal_B_cell_B1_24_CGTACTAG.CAAGAG 0.573636 0.410016 \n",
"25 RRBS_normal_B_cell_B1_24_CGTACTAG.CATGAC 0.624309 0.250108 \n",
"26 RRBS_normal_B_cell_B1_24_CGTACTAG.CCTTCG 0.596452 0.329267 \n",
"27 RRBS_normal_B_cell_B1_24_CGTACTAG.CGGTAG 0.525054 0.433851 \n",
"28 RRBS_normal_B_cell_B1_24_CGTACTAG.CTATTG 0.593262 0.424328 \n",
"29 RRBS_normal_B_cell_B1_24_CGTACTAG.CTCAGC 0.615582 0.270868 \n",
".. ... ... ... \n",
"416 RRBS_trito_pool_1_TAAGGCGA.GCTGCC 0.565787 0.379120 \n",
"417 RRBS_trito_pool_1_TAAGGCGA.GGCATC 0.578365 0.372803 \n",
"418 RRBS_trito_pool_1_TAAGGCGA.GTGAGG 0.554707 0.379959 \n",
"419 RRBS_trito_pool_1_TAAGGCGA.GTTGAG 0.581650 0.365969 \n",
"420 RRBS_trito_pool_1_TAAGGCGA.TAGCGG 0.564165 0.368605 \n",
"421 RRBS_trito_pool_1_TAAGGCGA.TATCTC 0.598086 0.365747 \n",
"422 RRBS_trito_pool_1_TAAGGCGA.TCTCTG 0.598637 0.367210 \n",
"423 RRBS_trito_pool_1_TAAGGCGA.TGACAG 0.592655 0.364070 \n",
"424 RRBS_trito_pool_1_TAAGGCGA.TGCTGC 0.572118 0.380804 \n",
"425 RRBS_trito_pool_2_CGTACTAG.ACAACC 0.593766 0.374765 \n",
"426 RRBS_trito_pool_2_CGTACTAG.ACGTGG 0.569259 0.388716 \n",
"427 RRBS_trito_pool_2_CGTACTAG.ACTCAC 0.595647 0.380070 \n",
"428 RRBS_trito_pool_2_CGTACTAG.AGGATG 0.595616 0.384004 \n",
"429 RRBS_trito_pool_2_CGTACTAG.ATAGCG 0.554055 0.387759 \n",
"430 RRBS_trito_pool_2_CGTACTAG.ATCGAC 0.598233 0.384663 \n",
"431 RRBS_trito_pool_2_CGTACTAG.CAAGAG 0.583609 0.393008 \n",
"432 RRBS_trito_pool_2_CGTACTAG.CATGAC 0.593843 0.383170 \n",
"433 RRBS_trito_pool_2_CGTACTAG.CCTTCG 0.568050 0.392622 \n",
"434 RRBS_trito_pool_2_CGTACTAG.CGGTAG 0.569010 0.390108 \n",
"435 RRBS_trito_pool_2_CGTACTAG.CTATTG 0.595037 0.373701 \n",
"436 RRBS_trito_pool_2_CGTACTAG.GACACG 0.570886 0.384283 \n",
"437 RRBS_trito_pool_2_CGTACTAG.GCATTC 0.596410 0.375766 \n",
"438 RRBS_trito_pool_2_CGTACTAG.GCTGCC 0.583331 0.384057 \n",
"439 RRBS_trito_pool_2_CGTACTAG.GGCATC 0.583463 0.392946 \n",
"440 RRBS_trito_pool_2_CGTACTAG.GTGAGG 0.572670 0.387705 \n",
"441 RRBS_trito_pool_2_CGTACTAG.GTTGAG 0.584506 0.379222 \n",
"442 RRBS_trito_pool_2_CGTACTAG.TAGCGG 0.567804 0.392930 \n",
"443 RRBS_trito_pool_2_CGTACTAG.TATCTC 0.599881 0.371286 \n",
"444 RRBS_trito_pool_2_CGTACTAG.TCTCTG 0.597122 0.387602 \n",
"445 RRBS_trito_pool_2_CGTACTAG.TGACAG 0.588475 0.377043 \n",
"\n",
" methylation_unweighted PDR_unweighted type bio \\\n",
"0 0.691996 0.254835 normal normal_B \n",
"1 0.620106 0.390562 normal normal_B \n",
"2 0.699736 0.266418 normal normal_B \n",
"3 0.732036 0.240201 normal normal_B \n",
"4 0.648127 0.425702 normal normal_B \n",
"5 0.716552 0.270967 normal normal_B \n",
"6 0.670718 0.348679 normal normal_B \n",
"7 0.668592 0.364613 normal normal_B \n",
"8 0.701418 0.271634 normal normal_B \n",
"9 0.650146 0.424804 normal normal_B \n",
"10 0.640678 0.433420 normal normal_B \n",
"11 0.705313 0.246132 normal normal_B \n",
"12 0.696495 0.261564 normal normal_B \n",
"13 0.688324 0.293166 normal normal_B \n",
"14 0.692209 0.255067 normal normal_B \n",
"15 0.645717 0.421790 normal normal_B \n",
"16 0.671286 0.324550 normal normal_B \n",
"17 0.663549 0.384798 normal normal_B \n",
"18 0.621230 0.456004 normal normal_B \n",
"19 0.670456 0.330762 normal normal_B \n",
"20 0.723172 0.246680 normal normal_B \n",
"21 0.620718 0.409340 normal normal_B \n",
"22 0.731753 0.227309 normal normal_B \n",
"23 0.710628 0.230766 normal normal_B \n",
"24 0.649119 0.394524 normal normal_B \n",
"25 0.726315 0.228687 normal normal_B \n",
"26 0.698844 0.306790 normal normal_B \n",
"27 0.609985 0.410107 normal normal_B \n",
"28 0.660035 0.416186 normal normal_B \n",
"29 0.718351 0.245807 normal normal_B \n",
".. ... ... ... ... \n",
"416 0.636582 0.361267 CLL CLL \n",
"417 0.639499 0.362328 CLL CLL \n",
"418 0.630143 0.365870 CLL CLL \n",
"419 0.651666 0.354063 CLL CLL \n",
"420 0.635522 0.354078 CLL CLL \n",
"421 0.640908 0.360269 CLL CLL \n",
"422 0.649959 0.358410 CLL CLL \n",
"423 0.651817 0.350381 CLL CLL \n",
"424 0.638604 0.366845 CLL CLL \n",
"425 0.655123 0.357956 CLL CLL \n",
"426 0.648626 0.369196 CLL CLL \n",
"427 0.652962 0.364404 CLL CLL \n",
"428 0.658358 0.369689 CLL CLL \n",
"429 0.635741 0.364157 CLL CLL \n",
"430 0.661048 0.366513 CLL CLL \n",
"431 0.649029 0.373233 CLL CLL \n",
"432 0.659853 0.366034 CLL CLL \n",
"433 0.647402 0.371530 CLL CLL \n",
"434 0.645339 0.372348 CLL CLL \n",
"435 0.659609 0.357034 CLL CLL \n",
"436 0.645343 0.367303 CLL CLL \n",
"437 0.656557 0.357551 CLL CLL \n",
"438 0.650519 0.361400 CLL CLL \n",
"439 0.648295 0.372514 CLL CLL \n",
"440 0.655986 0.366706 CLL CLL \n",
"441 0.659633 0.362664 CLL CLL \n",
"442 0.650606 0.371967 CLL CLL \n",
"443 0.647639 0.360518 CLL CLL \n",
"444 0.651297 0.369044 CLL CLL \n",
"445 0.654169 0.362253 CLL CLL \n",
"\n",
" protocol methylation_tssDistance methylation_genesDistance \\\n",
"0 normal_B_cell_A1_24 0.0 0.572922 \n",
"1 normal_B_cell_A1_24 0.0 0.505145 \n",
"2 normal_B_cell_A1_24 0.0 0.553568 \n",
"3 normal_B_cell_A1_24 0.0 0.600840 \n",
"4 normal_B_cell_A1_24 0.0 0.553723 \n",
"5 normal_B_cell_A1_24 0.0 0.598241 \n",
"6 normal_B_cell_A1_24 0.0 0.557673 \n",
"7 normal_B_cell_A1_24 0.0 0.555496 \n",
"8 normal_B_cell_A1_24 0.0 0.547152 \n",
"9 normal_B_cell_A1_24 0.0 0.569780 \n",
"10 normal_B_cell_A1_24 0.0 0.565528 \n",
"11 normal_B_cell_A1_24 0.0 0.568850 \n",
"12 normal_B_cell_A1_24 0.0 0.569876 \n",
"13 normal_B_cell_A1_24 0.0 0.568737 \n",
"14 normal_B_cell_A1_24 0.0 0.543913 \n",
"15 normal_B_cell_A1_24 0.0 0.567695 \n",
"16 normal_B_cell_A1_24 0.0 0.537509 \n",
"17 normal_B_cell_A1_24 0.0 0.574181 \n",
"18 normal_B_cell_A1_24 0.0 0.561735 \n",
"19 normal_B_cell_A1_24 0.0 0.545209 \n",
"20 normal_B_cell_B1_24 0.0 0.600708 \n",
"21 normal_B_cell_B1_24 0.0 0.519674 \n",
"22 normal_B_cell_B1_24 0.0 0.612460 \n",
"23 normal_B_cell_B1_24 0.0 0.564847 \n",
"24 normal_B_cell_B1_24 0.0 0.558144 \n",
"25 normal_B_cell_B1_24 0.0 0.596158 \n",
"26 normal_B_cell_B1_24 0.0 0.571273 \n",
"27 normal_B_cell_B1_24 0.0 0.507179 \n",
"28 normal_B_cell_B1_24 0.0 0.579434 \n",
"29 normal_B_cell_B1_24 0.0 0.584390 \n",
".. ... ... ... \n",
"416 trito_pool_1 0.0 0.544259 \n",
"417 trito_pool_1 0.0 0.555539 \n",
"418 trito_pool_1 0.0 0.530569 \n",
"419 trito_pool_1 0.0 0.557958 \n",
"420 trito_pool_1 0.0 0.540878 \n",
"421 trito_pool_1 0.0 0.575676 \n",
"422 trito_pool_1 0.0 0.573932 \n",
"423 trito_pool_1 0.0 0.567869 \n",
"424 trito_pool_1 0.0 0.549810 \n",
"425 trito_pool_2 0.0 0.571434 \n",
"426 trito_pool_2 0.0 0.547223 \n",
"427 trito_pool_2 0.0 0.574078 \n",
"428 trito_pool_2 0.0 0.574464 \n",
"429 trito_pool_2 0.0 0.530317 \n",
"430 trito_pool_2 0.0 0.579706 \n",
"431 trito_pool_2 0.0 0.562102 \n",
"432 trito_pool_2 0.0 0.572795 \n",
"433 trito_pool_2 0.0 0.544424 \n",
"434 trito_pool_2 0.0 0.541543 \n",
"435 trito_pool_2 0.0 0.571516 \n",
"436 trito_pool_2 0.0 0.547611 \n",
"437 trito_pool_2 0.0 0.574308 \n",
"438 trito_pool_2 0.0 0.557827 \n",
"439 trito_pool_2 0.0 0.558443 \n",
"440 trito_pool_2 0.0 0.548763 \n",
"441 trito_pool_2 0.0 0.560388 \n",
"442 trito_pool_2 0.0 0.542448 \n",
"443 trito_pool_2 0.0 0.575018 \n",
"444 trito_pool_2 0.0 0.574766 \n",
"445 trito_pool_2 0.0 0.566762 \n",
"\n",
" methylation_exonsDistance methylation_intronsDistance \\\n",
"0 0.388607 0.597003 \n",
"1 0.359230 0.526446 \n",
"2 0.359975 0.583731 \n",
"3 0.392730 0.633467 \n",
"4 0.424441 0.572004 \n",
"5 0.400137 0.625556 \n",
"6 0.393037 0.581964 \n",
"7 0.372118 0.585104 \n",
"8 0.343528 0.574055 \n",
"9 0.422603 0.591459 \n",
"10 0.437396 0.585564 \n",
"11 0.361969 0.599111 \n",
"12 0.362369 0.602931 \n",
"13 0.375639 0.597084 \n",
"14 0.345264 0.578228 \n",
"15 0.412468 0.587454 \n",
"16 0.353348 0.562371 \n",
"17 0.422529 0.596448 \n",
"18 0.445770 0.575198 \n",
"19 0.366002 0.571545 \n",
"20 0.393908 0.630897 \n",
"21 0.378490 0.541383 \n",
"22 0.404062 0.641619 \n",
"23 0.359529 0.595146 \n",
"24 0.410151 0.580173 \n",
"25 0.388236 0.627931 \n",
"26 0.377748 0.599836 \n",
"27 0.348300 0.529348 \n",
"28 0.434890 0.600699 \n",
"29 0.381342 0.614639 \n",
".. ... ... \n",
"416 0.384439 0.564795 \n",
"417 0.392705 0.579332 \n",
"418 0.367508 0.554714 \n",
"419 0.396493 0.580842 \n",
"420 0.368191 0.564637 \n",
"421 0.409355 0.600599 \n",
"422 0.402771 0.597337 \n",
"423 0.399077 0.591561 \n",
"424 0.381938 0.576183 \n",
"425 0.403317 0.595518 \n",
"426 0.381861 0.572699 \n",
"427 0.410369 0.599662 \n",
"428 0.411157 0.598317 \n",
"429 0.372598 0.555500 \n",
"430 0.417754 0.601652 \n",
"431 0.395170 0.587195 \n",
"432 0.406324 0.595511 \n",
"433 0.372864 0.566449 \n",
"434 0.377491 0.566615 \n",
"435 0.403810 0.595260 \n",
"436 0.384121 0.571736 \n",
"437 0.399926 0.599169 \n",
"438 0.389318 0.583295 \n",
"439 0.393582 0.581411 \n",
"440 0.378005 0.573823 \n",
"441 0.392703 0.585744 \n",
"442 0.379673 0.565934 \n",
"443 0.407772 0.599556 \n",
"444 0.414443 0.597902 \n",
"445 0.398915 0.590485 \n",
"\n",
" methylation_promoterDistance methylation_cgiDistance \\\n",
"0 0.127235 0.135145 \n",
"1 0.134970 0.171891 \n",
"2 0.117959 0.125268 \n",
"3 0.130846 0.134532 \n",
"4 0.201759 0.254062 \n",
"5 0.135943 0.142543 \n",
"6 0.136496 0.154840 \n",
"7 0.130767 0.149758 \n",
"8 0.117823 0.121103 \n",
"9 0.171423 0.213337 \n",
"10 0.207370 0.274443 \n",
"11 0.122396 0.127940 \n",
"12 0.119385 0.123399 \n",
"13 0.126210 0.134600 \n",
"14 0.119943 0.117022 \n",
"15 0.152377 0.169104 \n",
"16 0.118371 0.130877 \n",
"17 0.157292 0.185985 \n",
"18 0.219102 0.270627 \n",
"19 0.129616 0.136915 \n",
"20 0.130491 0.132780 \n",
"21 0.159885 0.190980 \n",
"22 0.138321 0.134888 \n",
"23 0.118704 0.114490 \n",
"24 0.160099 0.195220 \n",
"25 0.130745 0.131519 \n",
"26 0.130880 0.142904 \n",
"27 0.131065 0.146562 \n",
"28 0.188624 0.233725 \n",
"29 0.128080 0.134331 \n",
".. ... ... \n",
"416 0.139736 0.162409 \n",
"417 0.145652 0.164935 \n",
"418 0.134121 0.155524 \n",
"419 0.143581 0.165943 \n",
"420 0.133724 0.153977 \n",
"421 0.148591 0.174386 \n",
"422 0.153348 0.175088 \n",
"423 0.148038 0.176237 \n",
"424 0.143244 0.162055 \n",
"425 0.150534 0.172412 \n",
"426 0.142991 0.167937 \n",
"427 0.154807 0.180722 \n",
"428 0.153244 0.179052 \n",
"429 0.139056 0.162002 \n",
"430 0.154766 0.179131 \n",
"431 0.146861 0.171793 \n",
"432 0.149580 0.173139 \n",
"433 0.141793 0.161766 \n",
"434 0.144500 0.166361 \n",
"435 0.147111 0.169791 \n",
"436 0.141024 0.160152 \n",
"437 0.146626 0.171425 \n",
"438 0.146131 0.170323 \n",
"439 0.149267 0.173254 \n",
"440 0.146951 0.166034 \n",
"441 0.148877 0.173148 \n",
"442 0.141666 0.161357 \n",
"443 0.150032 0.172842 \n",
"444 0.153941 0.177219 \n",
"445 0.146940 0.169929 \n",
"\n",
" methylation_ctcfDistance methylation_ctcfUpDistance \\\n",
"0 0.178152 0.0 \n",
"1 0.198475 0.0 \n",
"2 0.176109 0.0 \n",
"3 0.228113 0.0 \n",
"4 0.243405 0.0 \n",
"5 0.197461 0.0 \n",
"6 0.185085 0.0 \n",
"7 0.174434 0.0 \n",
"8 0.155409 0.0 \n",
"9 0.222178 0.0 \n",
"10 0.235145 0.0 \n",
"11 0.159862 0.0 \n",
"12 0.166912 0.0 \n",
"13 0.173529 0.0 \n",
"14 0.186957 0.0 \n",
"15 0.213248 0.0 \n",
"16 0.170794 0.0 \n",
"17 0.197790 0.0 \n",
"18 0.262653 0.0 \n",
"19 0.184688 0.0 \n",
"20 0.202374 0.0 \n",
"21 0.207475 0.0 \n",
"22 0.200057 0.0 \n",
"23 0.173252 0.0 \n",
"24 0.200590 0.0 \n",
"25 0.172315 0.0 \n",
"26 0.178021 0.0 \n",
"27 0.159320 0.0 \n",
"28 0.230526 0.0 \n",
"29 0.181411 0.0 \n",
".. ... ... \n",
"416 0.176248 0.0 \n",
"417 0.184172 0.0 \n",
"418 0.173857 0.0 \n",
"419 0.198223 0.0 \n",
"420 0.166738 0.0 \n",
"421 0.194566 0.0 \n",
"422 0.200765 0.0 \n",
"423 0.202154 0.0 \n",
"424 0.182065 0.0 \n",
"425 0.197820 0.0 \n",
"426 0.187875 0.0 \n",
"427 0.204649 0.0 \n",
"428 0.205426 0.0 \n",
"429 0.188584 0.0 \n",
"430 0.214474 0.0 \n",
"431 0.193740 0.0 \n",
"432 0.207084 0.0 \n",
"433 0.188699 0.0 \n",
"434 0.187541 0.0 \n",
"435 0.199903 0.0 \n",
"436 0.183884 0.0 \n",
"437 0.202222 0.0 \n",
"438 0.216651 0.0 \n",
"439 0.202211 0.0 \n",
"440 0.189554 0.0 \n",
"441 0.191432 0.0 \n",
"442 0.190254 0.0 \n",
"443 0.207183 0.0 \n",
"444 0.200535 0.0 \n",
"445 0.200901 0.0 \n",
"\n",
" methylation_ctcfDownDistance \\\n",
"0 0.178152 \n",
"1 0.198475 \n",
"2 0.176109 \n",
"3 0.228113 \n",
"4 0.243405 \n",
"5 0.197461 \n",
"6 0.185085 \n",
"7 0.174434 \n",
"8 0.155409 \n",
"9 0.222178 \n",
"10 0.235145 \n",
"11 0.159862 \n",
"12 0.166912 \n",
"13 0.173529 \n",
"14 0.186957 \n",
"15 0.213248 \n",
"16 0.170794 \n",
"17 0.197790 \n",
"18 0.262653 \n",
"19 0.184688 \n",
"20 0.202374 \n",
"21 0.207475 \n",
"22 0.200057 \n",
"23 0.173252 \n",
"24 0.200590 \n",
"25 0.172315 \n",
"26 0.178021 \n",
"27 0.159320 \n",
"28 0.230526 \n",
"29 0.181411 \n",
".. ... \n",
"416 0.176248 \n",
"417 0.184172 \n",
"418 0.173857 \n",
"419 0.198223 \n",
"420 0.166738 \n",
"421 0.194566 \n",
"422 0.200765 \n",
"423 0.202154 \n",
"424 0.182065 \n",
"425 0.197820 \n",
"426 0.187875 \n",
"427 0.204649 \n",
"428 0.205426 \n",
"429 0.188584 \n",
"430 0.214474 \n",
"431 0.193740 \n",
"432 0.207084 \n",
"433 0.188699 \n",
"434 0.187541 \n",
"435 0.199903 \n",
"436 0.183884 \n",
"437 0.202222 \n",
"438 0.216651 \n",
"439 0.202211 \n",
"440 0.189554 \n",
"441 0.191432 \n",
"442 0.190254 \n",
"443 0.207183 \n",
"444 0.200535 \n",
"445 0.200901 \n",
"\n",
" methylation_geneDistalRegulatoryModulesDistance \\\n",
"0 0.313712 \n",
"1 0.352037 \n",
"2 0.309668 \n",
"3 0.344969 \n",
"4 0.365090 \n",
"5 0.328983 \n",
"6 0.336342 \n",
"7 0.325693 \n",
"8 0.304843 \n",
"9 0.366234 \n",
"10 0.377706 \n",
"11 0.320582 \n",
"12 0.312508 \n",
"13 0.311749 \n",
"14 0.304374 \n",
"15 0.325752 \n",
"16 0.303084 \n",
"17 0.333505 \n",
"18 0.388225 \n",
"19 0.302508 \n",
"20 0.352837 \n",
"21 0.318937 \n",
"22 0.365257 \n",
"23 0.310655 \n",
"24 0.349321 \n",
"25 0.338750 \n",
"26 0.331112 \n",
"27 0.291927 \n",
"28 0.366878 \n",
"29 0.326547 \n",
".. ... \n",
"416 0.318136 \n",
"417 0.333482 \n",
"418 0.315881 \n",
"419 0.335718 \n",
"420 0.307876 \n",
"421 0.336279 \n",
"422 0.347808 \n",
"423 0.343355 \n",
"424 0.322882 \n",
"425 0.335046 \n",
"426 0.322376 \n",
"427 0.346719 \n",
"428 0.347422 \n",
"429 0.314593 \n",
"430 0.347695 \n",
"431 0.336475 \n",
"432 0.341760 \n",
"433 0.320217 \n",
"434 0.323718 \n",
"435 0.345774 \n",
"436 0.324428 \n",
"437 0.339589 \n",
"438 0.332222 \n",
"439 0.336075 \n",
"440 0.329531 \n",
"441 0.337372 \n",
"442 0.322077 \n",
"443 0.348191 \n",
"444 0.344312 \n",
"445 0.330337 \n",
"\n",
" methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance \\\n",
"0 0.354954 0.728744 \n",
"1 0.516644 0.625871 \n",
"2 0.712070 0.718911 \n",
"3 0.738883 0.721101 \n",
"4 0.703915 0.690610 \n",
"5 0.561384 0.724674 \n",
"6 0.548913 0.724639 \n",
"7 0.489904 0.691227 \n",
"8 0.593398 0.648511 \n",
"9 0.698103 0.685389 \n",
"10 0.636860 0.669217 \n",
"11 0.725028 0.694968 \n",
"12 0.483566 0.664175 \n",
"13 0.609371 0.670949 \n",
"14 0.275972 0.686426 \n",
"15 0.478594 0.672947 \n",
"16 0.460163 0.660977 \n",
"17 0.549745 0.695153 \n",
"18 0.425444 0.658489 \n",
"19 0.516610 0.667369 \n",
"20 0.713675 0.725713 \n",
"21 0.704963 0.615859 \n",
"22 0.464153 0.743855 \n",
"23 0.503806 0.691586 \n",
"24 0.549857 0.668894 \n",
"25 0.456898 0.722962 \n",
"26 0.590600 0.684578 \n",
"27 0.585224 0.598317 \n",
"28 0.612085 0.672638 \n",
"29 0.521315 0.699421 \n",
".. ... ... \n",
"416 0.574486 0.634002 \n",
"417 0.421168 0.661779 \n",
"418 0.463581 0.615143 \n",
"419 0.511515 0.653486 \n",
"420 0.486900 0.632573 \n",
"421 0.554679 0.662141 \n",
"422 0.610443 0.676611 \n",
"423 0.522213 0.665558 \n",
"424 0.522536 0.636949 \n",
"425 0.542060 0.674691 \n",
"426 0.494384 0.655945 \n",
"427 0.484916 0.687740 \n",
"428 0.549476 0.674730 \n",
"429 0.662600 0.651363 \n",
"430 0.592746 0.693724 \n",
"431 0.643228 0.644603 \n",
"432 0.582188 0.686507 \n",
"433 0.520051 0.648292 \n",
"434 0.570565 0.649134 \n",
"435 0.461720 0.665058 \n",
"436 0.516251 0.658554 \n",
"437 0.601239 0.673941 \n",
"438 0.465542 0.685189 \n",
"439 0.497985 0.658613 \n",
"440 0.417063 0.656924 \n",
"441 0.531839 0.657189 \n",
"442 0.564060 0.661459 \n",
"443 0.524038 0.668196 \n",
"444 0.593102 0.687200 \n",
"445 0.614493 0.668077 \n",
"\n",
" methylation_5PrimeUTRDistance methylation_firstExonDistance \\\n",
"0 0.282305 0.125011 \n",
"1 0.266546 0.123009 \n",
"2 0.277957 0.101747 \n",
"3 0.315410 0.110561 \n",
"4 0.324217 0.225215 \n",
"5 0.319542 0.123392 \n",
"6 0.289102 0.134083 \n",
"7 0.303725 0.117398 \n",
"8 0.280638 0.096895 \n",
"9 0.327695 0.170005 \n",
"10 0.337121 0.216485 \n",
"11 0.304324 0.117228 \n",
"12 0.295046 0.104887 \n",
"13 0.303669 0.104746 \n",
"14 0.258585 0.103817 \n",
"15 0.300417 0.138325 \n",
"16 0.281453 0.108285 \n",
"17 0.306368 0.162632 \n",
"18 0.339626 0.221938 \n",
"19 0.283649 0.109472 \n",
"20 0.320354 0.106622 \n",
"21 0.290296 0.155550 \n",
"22 0.331324 0.115728 \n",
"23 0.285765 0.103678 \n",
"24 0.309413 0.163017 \n",
"25 0.315504 0.112215 \n",
"26 0.300336 0.120516 \n",
"27 0.271001 0.122086 \n",
"28 0.334540 0.186837 \n",
"29 0.307323 0.114798 \n",
".. ... ... \n",
"416 0.289043 0.139641 \n",
"417 0.296269 0.147555 \n",
"418 0.280369 0.133060 \n",
"419 0.294972 0.142042 \n",
"420 0.286827 0.128397 \n",
"421 0.309579 0.142574 \n",
"422 0.311039 0.144939 \n",
"423 0.303592 0.146534 \n",
"424 0.297776 0.142530 \n",
"425 0.311226 0.145333 \n",
"426 0.290386 0.138317 \n",
"427 0.314513 0.155080 \n",
"428 0.318179 0.150916 \n",
"429 0.276445 0.139170 \n",
"430 0.318722 0.152873 \n",
"431 0.303090 0.145796 \n",
"432 0.311526 0.145188 \n",
"433 0.287153 0.134590 \n",
"434 0.290349 0.139695 \n",
"435 0.308691 0.144474 \n",
"436 0.296090 0.142236 \n",
"437 0.311423 0.142003 \n",
"438 0.303248 0.142679 \n",
"439 0.307422 0.145063 \n",
"440 0.298588 0.143700 \n",
"441 0.308189 0.144341 \n",
"442 0.291782 0.141023 \n",
"443 0.314785 0.147052 \n",
"444 0.318141 0.152659 \n",
"445 0.309691 0.140807 \n",
"\n",
" methylation_geneDistalRegulatoryModulesK562Distance \\\n",
"0 0.230720 \n",
"1 0.233159 \n",
"2 0.227526 \n",
"3 0.254307 \n",
"4 0.250693 \n",
"5 0.245854 \n",
"6 0.242662 \n",
"7 0.252648 \n",
"8 0.228697 \n",
"9 0.263775 \n",
"10 0.247102 \n",
"11 0.238581 \n",
"12 0.227071 \n",
"13 0.230130 \n",
"14 0.243962 \n",
"15 0.267002 \n",
"16 0.226112 \n",
"17 0.244439 \n",
"18 0.268746 \n",
"19 0.226291 \n",
"20 0.268270 \n",
"21 0.229599 \n",
"22 0.271711 \n",
"23 0.228965 \n",
"24 0.243278 \n",
"25 0.257224 \n",
"26 0.255226 \n",
"27 0.204311 \n",
"28 0.249289 \n",
"29 0.250506 \n",
".. ... \n",
"416 0.223191 \n",
"417 0.241141 \n",
"418 0.232378 \n",
"419 0.251258 \n",
"420 0.230826 \n",
"421 0.240360 \n",
"422 0.256872 \n",
"423 0.252288 \n",
"424 0.244198 \n",
"425 0.251228 \n",
"426 0.228471 \n",
"427 0.260803 \n",
"428 0.255366 \n",
"429 0.226507 \n",
"430 0.251772 \n",
"431 0.249175 \n",
"432 0.255092 \n",
"433 0.231480 \n",
"434 0.231105 \n",
"435 0.257436 \n",
"436 0.243037 \n",
"437 0.251027 \n",
"438 0.253152 \n",
"439 0.253500 \n",
"440 0.237655 \n",
"441 0.245608 \n",
"442 0.236705 \n",
"443 0.263346 \n",
"444 0.260521 \n",
"445 0.241382 \n",
"\n",
" methylation_hypoInHues64Distance methylation_intergenic \\\n",
"0 0.926937 0.902930 \n",
"1 0.865658 0.777870 \n",
"2 0.942223 0.895722 \n",
"3 0.955880 0.916222 \n",
"4 0.628902 0.744224 \n",
"5 0.957901 0.896857 \n",
"6 0.914303 0.839622 \n",
"7 0.877326 0.830718 \n",
"8 0.986942 0.887179 \n",
"9 0.864895 0.775154 \n",
"10 0.909352 0.737474 \n",
"11 0.971203 0.898648 \n",
"12 0.888816 0.894996 \n",
"13 0.933383 0.874730 \n",
"14 0.973117 0.893765 \n",
"15 0.718321 0.755050 \n",
"16 0.777019 0.856849 \n",
"17 0.958932 0.817648 \n",
"18 0.693445 0.707242 \n",
"19 0.815052 0.858191 \n",
"20 0.927542 0.898352 \n",
"21 0.820416 0.733765 \n",
"22 0.888724 0.914558 \n",
"23 0.970596 0.901415 \n",
"24 0.919356 0.784926 \n",
"25 0.969047 0.914006 \n",
"26 0.910927 0.858774 \n",
"27 0.831863 0.748557 \n",
"28 0.849944 0.774006 \n",
"29 0.958901 0.900439 \n",
".. ... ... \n",
"416 0.856793 0.800237 \n",
"417 0.825601 0.813275 \n",
"418 0.794342 0.804363 \n",
"419 0.808442 0.821536 \n",
"420 0.856077 0.814219 \n",
"421 0.844196 0.822712 \n",
"422 0.854733 0.826905 \n",
"423 0.796484 0.830742 \n",
"424 0.808005 0.807068 \n",
"425 0.782911 0.825876 \n",
"426 0.824135 0.809657 \n",
"427 0.812211 0.828639 \n",
"428 0.825079 0.819016 \n",
"429 0.782103 0.805115 \n",
"430 0.829067 0.818502 \n",
"431 0.867900 0.811712 \n",
"432 0.898922 0.822096 \n",
"433 0.918757 0.808219 \n",
"434 0.875553 0.805793 \n",
"435 0.833091 0.825356 \n",
"436 0.840847 0.810208 \n",
"437 0.900174 0.822302 \n",
"438 0.925723 0.812384 \n",
"439 0.822348 0.810850 \n",
"440 0.837979 0.818187 \n",
"441 0.856433 0.820191 \n",
"442 0.775427 0.812801 \n",
"443 0.860790 0.826304 \n",
"444 0.857143 0.820577 \n",
"445 0.877043 0.819644 \n",
"\n",
" methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance \\\n",
"0 0.637052 0.896802 0.0 0.249665 \n",
"1 0.528428 0.750640 0.0 0.389613 \n",
"2 0.618027 0.868715 0.0 0.276292 \n",
"3 0.672012 0.891286 0.0 0.242686 \n",
"4 0.591154 0.764444 0.0 0.396151 \n",
"5 0.631902 0.876983 0.0 0.263207 \n",
"6 0.609142 0.828499 0.0 0.346284 \n",
"7 0.612466 0.819436 0.0 0.352702 \n",
"8 0.600183 0.863762 0.0 0.280334 \n",
"9 0.610494 0.773103 0.0 0.393549 \n",
"10 0.578117 0.735609 0.0 0.409112 \n",
"11 0.616594 0.874494 0.0 0.259987 \n",
"12 0.621557 0.873106 0.0 0.270732 \n",
"13 0.619164 0.858757 0.0 0.302826 \n",
"14 0.601321 0.862469 0.0 0.269974 \n",
"15 0.585532 0.768680 0.0 0.394381 \n",
"16 0.597661 0.836136 0.0 0.334426 \n",
"17 0.615515 0.796791 0.0 0.359375 \n",
"18 0.582715 0.734725 0.0 0.424692 \n",
"19 0.595584 0.824065 0.0 0.328561 \n",
"20 0.640993 0.877308 0.0 0.259897 \n",
"21 0.532819 0.736470 0.0 0.398962 \n",
"22 0.655884 0.902320 0.0 0.242492 \n",
"23 0.619064 0.889049 0.0 0.258756 \n",
"24 0.568173 0.761311 0.0 0.386441 \n",
"25 0.639293 0.894982 0.0 0.248352 \n",
"26 0.607147 0.851038 0.0 0.313604 \n",
"27 0.519638 0.731533 0.0 0.407883 \n",
"28 0.594751 0.778275 0.0 0.393161 \n",
"29 0.617142 0.875827 0.0 0.267871 \n",
".. ... ... ... ... \n",
"416 0.554718 0.776976 0.0 0.354565 \n",
"417 0.589382 0.790102 0.0 0.349269 \n",
"418 0.553666 0.777878 0.0 0.358688 \n",
"419 0.592957 0.795827 0.0 0.344146 \n",
"420 0.577274 0.793247 0.0 0.346260 \n",
"421 0.607978 0.806099 0.0 0.341342 \n",
"422 0.607012 0.802475 0.0 0.342377 \n",
"423 0.598186 0.806577 0.0 0.345410 \n",
"424 0.566512 0.789342 0.0 0.361772 \n",
"425 0.601882 0.804140 0.0 0.356830 \n",
"426 0.575763 0.782319 0.0 0.367896 \n",
"427 0.608617 0.812883 0.0 0.360783 \n",
"428 0.600086 0.803518 0.0 0.361346 \n",
"429 0.577048 0.796431 0.0 0.368334 \n",
"430 0.603463 0.804070 0.0 0.365534 \n",
"431 0.594372 0.798705 0.0 0.371383 \n",
"432 0.602356 0.802416 0.0 0.363315 \n",
"433 0.556881 0.786660 0.0 0.372677 \n",
"434 0.574939 0.781107 0.0 0.366749 \n",
"435 0.606750 0.808127 0.0 0.352923 \n",
"436 0.580500 0.780899 0.0 0.362036 \n",
"437 0.607857 0.804399 0.0 0.350723 \n",
"438 0.566898 0.789918 0.0 0.364344 \n",
"439 0.579463 0.788819 0.0 0.369847 \n",
"440 0.578995 0.795407 0.0 0.369274 \n",
"441 0.594452 0.790093 0.0 0.357508 \n",
"442 0.584395 0.788413 0.0 0.373111 \n",
"443 0.608059 0.807089 0.0 0.350740 \n",
"444 0.596070 0.810128 0.0 0.364954 \n",
"445 0.588788 0.797851 0.0 0.352289 \n",
"\n",
" PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance \\\n",
"0 0.320241 0.237896 0.363833 \n",
"1 0.436141 0.383879 0.426575 \n",
"2 0.353180 0.264764 0.381896 \n",
"3 0.329575 0.226780 0.380465 \n",
"4 0.413207 0.393634 0.414282 \n",
"5 0.329618 0.253472 0.378263 \n",
"6 0.400937 0.334616 0.403498 \n",
"7 0.397442 0.342892 0.398493 \n",
"8 0.358519 0.267715 0.392784 \n",
"9 0.428448 0.389104 0.421122 \n",
"10 0.426369 0.406486 0.421935 \n",
"11 0.346939 0.247972 0.376738 \n",
"12 0.350990 0.255018 0.379740 \n",
"13 0.370106 0.290653 0.398204 \n",
"14 0.351114 0.255608 0.384957 \n",
"15 0.413518 0.390813 0.422574 \n",
"16 0.403578 0.325768 0.395605 \n",
"17 0.390368 0.355666 0.395329 \n",
"18 0.428625 0.423983 0.416932 \n",
"19 0.362106 0.322815 0.385218 \n",
"20 0.334198 0.248877 0.382296 \n",
"21 0.428732 0.396038 0.410177 \n",
"22 0.329096 0.230329 0.387293 \n",
"23 0.349989 0.242980 0.378504 \n",
"24 0.419907 0.381549 0.411113 \n",
"25 0.341477 0.232416 0.376812 \n",
"26 0.385012 0.301194 0.409911 \n",
"27 0.431943 0.402297 0.405059 \n",
"28 0.420478 0.388782 0.413085 \n",
"29 0.351355 0.252870 0.389412 \n",
".. ... ... ... \n",
"416 0.388414 0.347581 0.360744 \n",
"417 0.378454 0.343167 0.352282 \n",
"418 0.390493 0.351879 0.361701 \n",
"419 0.371379 0.337804 0.354805 \n",
"420 0.378683 0.339097 0.356597 \n",
"421 0.367808 0.337694 0.339362 \n",
"422 0.370775 0.337035 0.356039 \n",
"423 0.381678 0.340699 0.363333 \n",
"424 0.392851 0.356422 0.365182 \n",
"425 0.396188 0.349903 0.388446 \n",
"426 0.410155 0.360025 0.392362 \n",
"427 0.413536 0.352271 0.396930 \n",
"428 0.394711 0.356405 0.385541 \n",
"429 0.399350 0.363279 0.375962 \n",
"430 0.410172 0.359211 0.392262 \n",
"431 0.417443 0.364244 0.392824 \n",
"432 0.403748 0.356727 0.385169 \n",
"433 0.409840 0.367423 0.386818 \n",
"434 0.406201 0.359577 0.382439 \n",
"435 0.389431 0.346998 0.367228 \n",
"436 0.396972 0.355362 0.379360 \n",
"437 0.388557 0.343231 0.379437 \n",
"438 0.411073 0.357567 0.386643 \n",
"439 0.416267 0.362248 0.393373 \n",
"440 0.411458 0.362212 0.399234 \n",
"441 0.399570 0.348983 0.382424 \n",
"442 0.419487 0.363822 0.398524 \n",
"443 0.387199 0.345054 0.369375 \n",
"444 0.410597 0.357541 0.390213 \n",
"445 0.386013 0.345792 0.381209 \n",
"\n",
" PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance \\\n",
"0 0.388255 0.419154 0.0 \n",
"1 0.439299 0.424045 0.0 \n",
"2 0.403526 0.431742 0.0 \n",
"3 0.395003 0.428452 0.0 \n",
"4 0.434310 0.404922 0.0 \n",
"5 0.392845 0.387199 0.0 \n",
"6 0.421695 0.446341 0.0 \n",
"7 0.420841 0.398592 0.0 \n",
"8 0.396120 0.372254 0.0 \n",
"9 0.442946 0.424529 0.0 \n",
"10 0.442577 0.428453 0.0 \n",
"11 0.384038 0.371028 0.0 \n",
"12 0.403785 0.393961 0.0 \n",
"13 0.419242 0.411253 0.0 \n",
"14 0.398879 0.421579 0.0 \n",
"15 0.436592 0.393126 0.0 \n",
"16 0.424208 0.433552 0.0 \n",
"17 0.415846 0.400304 0.0 \n",
"18 0.438851 0.435457 0.0 \n",
"19 0.409109 0.420144 0.0 \n",
"20 0.401650 0.425809 0.0 \n",
"21 0.437335 0.420623 0.0 \n",
"22 0.406756 0.409925 0.0 \n",
"23 0.394998 0.419274 0.0 \n",
"24 0.441415 0.406298 0.0 \n",
"25 0.406716 0.399623 0.0 \n",
"26 0.429555 0.440444 0.0 \n",
"27 0.427618 0.414400 0.0 \n",
"28 0.445061 0.400803 0.0 \n",
"29 0.415080 0.433702 0.0 \n",
".. ... ... ... \n",
"416 0.402607 0.390989 0.0 \n",
"417 0.398483 0.383984 0.0 \n",
"418 0.404384 0.398420 0.0 \n",
"419 0.397635 0.404505 0.0 \n",
"420 0.396187 0.381571 0.0 \n",
"421 0.386849 0.380734 0.0 \n",
"422 0.402495 0.412892 0.0 \n",
"423 0.411799 0.398106 0.0 \n",
"424 0.413081 0.408349 0.0 \n",
"425 0.425970 0.423147 0.0 \n",
"426 0.439690 0.423503 0.0 \n",
"427 0.438502 0.437218 0.0 \n",
"428 0.430138 0.435397 0.0 \n",
"429 0.421184 0.422536 0.0 \n",
"430 0.438666 0.432635 0.0 \n",
"431 0.439427 0.424017 0.0 \n",
"432 0.426971 0.428521 0.0 \n",
"433 0.428182 0.410043 0.0 \n",
"434 0.427216 0.412182 0.0 \n",
"435 0.410854 0.408465 0.0 \n",
"436 0.423028 0.407819 0.0 \n",
"437 0.423172 0.413884 0.0 \n",
"438 0.436352 0.455234 0.0 \n",
"439 0.438305 0.432106 0.0 \n",
"440 0.439421 0.426244 0.0 \n",
"441 0.428961 0.426739 0.0 \n",
"442 0.443218 0.429929 0.0 \n",
"443 0.416003 0.412018 0.0 \n",
"444 0.434403 0.418302 0.0 \n",
"445 0.425696 0.436417 0.0 \n",
"\n",
" PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance \\\n",
"0 0.419154 0.364421 \n",
"1 0.424045 0.468612 \n",
"2 0.431742 0.362307 \n",
"3 0.428452 0.378543 \n",
"4 0.404922 0.454559 \n",
"5 0.387199 0.376833 \n",
"6 0.446341 0.415259 \n",
"7 0.398592 0.433256 \n",
"8 0.372254 0.399106 \n",
"9 0.424529 0.465571 \n",
"10 0.428453 0.502348 \n",
"11 0.371028 0.370863 \n",
"12 0.393961 0.374419 \n",
"13 0.411253 0.405264 \n",
"14 0.421579 0.369955 \n",
"15 0.393126 0.423319 \n",
"16 0.433552 0.385425 \n",
"17 0.400304 0.420615 \n",
"18 0.435457 0.458630 \n",
"19 0.420144 0.416980 \n",
"20 0.425809 0.382371 \n",
"21 0.420623 0.464244 \n",
"22 0.409925 0.381945 \n",
"23 0.419274 0.350099 \n",
"24 0.406298 0.448031 \n",
"25 0.399623 0.368001 \n",
"26 0.440444 0.411064 \n",
"27 0.414400 0.426879 \n",
"28 0.400803 0.437013 \n",
"29 0.433702 0.390515 \n",
".. ... ... \n",
"416 0.390989 0.431375 \n",
"417 0.383984 0.394837 \n",
"418 0.398420 0.422339 \n",
"419 0.404505 0.420580 \n",
"420 0.381571 0.400928 \n",
"421 0.380734 0.394130 \n",
"422 0.412892 0.408307 \n",
"423 0.398106 0.406224 \n",
"424 0.408349 0.431775 \n",
"425 0.423147 0.414028 \n",
"426 0.423503 0.424893 \n",
"427 0.437218 0.434233 \n",
"428 0.435397 0.430261 \n",
"429 0.422536 0.423652 \n",
"430 0.432635 0.437134 \n",
"431 0.424017 0.430895 \n",
"432 0.428521 0.437559 \n",
"433 0.410043 0.437956 \n",
"434 0.412182 0.424271 \n",
"435 0.408465 0.420192 \n",
"436 0.407819 0.430720 \n",
"437 0.413884 0.430536 \n",
"438 0.455234 0.427594 \n",
"439 0.432106 0.447392 \n",
"440 0.426244 0.437661 \n",
"441 0.426739 0.436677 \n",
"442 0.429929 0.428916 \n",
"443 0.412018 0.418236 \n",
"444 0.418302 0.436308 \n",
"445 0.436417 0.417572 \n",
"\n",
" PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance \\\n",
"0 0.489309 0.214752 0.295134 \n",
"1 0.632490 0.413364 0.384389 \n",
"2 0.148328 0.312959 0.323942 \n",
"3 0.293278 0.226409 0.306003 \n",
"4 0.520165 0.398875 0.398097 \n",
"5 0.406406 0.247637 0.313278 \n",
"6 0.457161 0.375382 0.381060 \n",
"7 0.586659 0.322179 0.351247 \n",
"8 0.174794 0.363332 0.337774 \n",
"9 0.501746 0.424570 0.388460 \n",
"10 0.678498 0.373862 0.418496 \n",
"11 0.041421 0.276435 0.305850 \n",
"12 0.151892 0.287812 0.327504 \n",
"13 0.556019 0.334586 0.327526 \n",
"14 0.324316 0.242306 0.326748 \n",
"15 0.545367 0.442718 0.413598 \n",
"16 0.504680 0.365191 0.353273 \n",
"17 0.631283 0.386431 0.353516 \n",
"18 0.506799 0.461580 0.424516 \n",
"19 0.540203 0.342566 0.359376 \n",
"20 0.255144 0.252366 0.309392 \n",
"21 0.680829 0.429482 0.377868 \n",
"22 0.411628 0.222693 0.307246 \n",
"23 0.564988 0.274140 0.311913 \n",
"24 0.547251 0.410927 0.394507 \n",
"25 0.332834 0.242602 0.312927 \n",
"26 0.403216 0.356594 0.351884 \n",
"27 0.483459 0.421985 0.395431 \n",
"28 0.527192 0.421103 0.396852 \n",
"29 0.388477 0.260598 0.320489 \n",
".. ... ... ... \n",
"416 0.409462 0.385777 0.340581 \n",
"417 0.570345 0.396641 0.337457 \n",
"418 0.427769 0.417731 0.341625 \n",
"419 0.415804 0.359175 0.330021 \n",
"420 0.570090 0.362619 0.339981 \n",
"421 0.514109 0.395076 0.319727 \n",
"422 0.464521 0.389068 0.330017 \n",
"423 0.468663 0.352417 0.345042 \n",
"424 0.422301 0.386494 0.344547 \n",
"425 0.406933 0.353024 0.363997 \n",
"426 0.371724 0.396436 0.364995 \n",
"427 0.243128 0.391187 0.371005 \n",
"428 0.509647 0.397243 0.354870 \n",
"429 0.422062 0.390546 0.352241 \n",
"430 0.550723 0.406531 0.367066 \n",
"431 0.419896 0.406159 0.358254 \n",
"432 0.666602 0.399967 0.360502 \n",
"433 0.431314 0.417184 0.370701 \n",
"434 0.326364 0.387866 0.358136 \n",
"435 0.435325 0.384775 0.344882 \n",
"436 0.413402 0.375936 0.353105 \n",
"437 0.484843 0.385927 0.351172 \n",
"438 0.441989 0.410031 0.357034 \n",
"439 0.283823 0.414114 0.369362 \n",
"440 0.387644 0.379763 0.378844 \n",
"441 0.623022 0.378906 0.357031 \n",
"442 0.508213 0.389595 0.371629 \n",
"443 0.488627 0.380793 0.343215 \n",
"444 0.430239 0.403836 0.358261 \n",
"445 0.441868 0.379378 0.356776 \n",
"\n",
" PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance \\\n",
"0 0.379389 0.352525 \n",
"1 0.446504 0.426731 \n",
"2 0.389617 0.349141 \n",
"3 0.391967 0.380095 \n",
"4 0.431908 0.403162 \n",
"5 0.389120 0.356423 \n",
"6 0.431724 0.366805 \n",
"7 0.405937 0.401367 \n",
"8 0.402650 0.366518 \n",
"9 0.445857 0.430022 \n",
"10 0.439376 0.451782 \n",
"11 0.396597 0.374203 \n",
"12 0.390677 0.347520 \n",
"13 0.392053 0.379147 \n",
"14 0.397716 0.360025 \n",
"15 0.426612 0.399205 \n",
"16 0.424782 0.364329 \n",
"17 0.402680 0.372923 \n",
"18 0.410753 0.402906 \n",
"19 0.387962 0.391970 \n",
"20 0.380196 0.367431 \n",
"21 0.416697 0.435796 \n",
"22 0.402911 0.373759 \n",
"23 0.397878 0.335510 \n",
"24 0.435718 0.393209 \n",
"25 0.398130 0.356175 \n",
"26 0.427722 0.393187 \n",
"27 0.423924 0.373547 \n",
"28 0.430744 0.381902 \n",
"29 0.397438 0.368093 \n",
".. ... ... \n",
"416 0.375888 0.372911 \n",
"417 0.375483 0.341387 \n",
"418 0.380159 0.367890 \n",
"419 0.365134 0.366449 \n",
"420 0.379723 0.344153 \n",
"421 0.356904 0.318327 \n",
"422 0.366829 0.337809 \n",
"423 0.391606 0.339766 \n",
"424 0.390997 0.368241 \n",
"425 0.410352 0.353075 \n",
"426 0.415006 0.358457 \n",
"427 0.424675 0.381663 \n",
"428 0.405106 0.376955 \n",
"429 0.394821 0.362621 \n",
"430 0.412751 0.381165 \n",
"431 0.421774 0.375869 \n",
"432 0.402785 0.382317 \n",
"433 0.403697 0.379120 \n",
"434 0.400989 0.367082 \n",
"435 0.388064 0.354390 \n",
"436 0.395058 0.377523 \n",
"437 0.395395 0.377211 \n",
"438 0.414984 0.377580 \n",
"439 0.423329 0.390531 \n",
"440 0.416184 0.393381 \n",
"441 0.402640 0.380493 \n",
"442 0.425177 0.379944 \n",
"443 0.395603 0.371997 \n",
"444 0.411722 0.383838 \n",
"445 0.394481 0.368598 \n",
"\n",
" PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf \n",
"0 0.020851 0.183311 0.285294 0.169240 \n",
"1 0.016904 0.426067 0.413426 0.430125 \n",
"2 0.150021 0.199184 0.311731 0.206646 \n",
"3 0.156554 0.167441 0.275408 0.184916 \n",
"4 0.649024 0.500065 0.432751 0.402732 \n",
"5 0.008145 0.210394 0.305067 0.196524 \n",
"6 0.323921 0.349326 0.361848 0.326459 \n",
"7 0.420977 0.381797 0.388051 0.361306 \n",
"8 0.060128 0.231404 0.321895 0.210547 \n",
"9 0.365360 0.478388 0.410114 0.419770 \n",
"10 0.296798 0.502111 0.430704 0.404902 \n",
"11 0.122058 0.193916 0.294751 0.178923 \n",
"12 0.373774 0.199908 0.307455 0.208747 \n",
"13 0.134353 0.259203 0.328873 0.271502 \n",
"14 0.077235 0.189490 0.295756 0.182461 \n",
"15 0.439942 0.496714 0.422975 0.392376 \n",
"16 0.507417 0.312744 0.359828 0.303938 \n",
"17 0.120302 0.406370 0.387130 0.361715 \n",
"18 0.446482 0.535373 0.447916 0.449113 \n",
"19 0.426135 0.310284 0.363570 0.329527 \n",
"20 0.180845 0.201513 0.301175 0.213756 \n",
"21 0.262760 0.487506 0.420668 0.395321 \n",
"22 0.088080 0.165203 0.278304 0.167580 \n",
"23 0.049096 0.181269 0.296877 0.184334 \n",
"24 0.308112 0.437090 0.407888 0.396319 \n",
"25 0.101891 0.168523 0.281624 0.169076 \n",
"26 0.197940 0.299706 0.348462 0.263281 \n",
"27 0.410587 0.484246 0.438821 0.430328 \n",
"28 0.351360 0.469681 0.420649 0.397929 \n",
"29 0.046859 0.194196 0.307630 0.192950 \n",
".. ... ... ... ... \n",
"416 0.350551 0.400626 0.404546 0.364278 \n",
"417 0.228666 0.393968 0.384816 0.358044 \n",
"418 0.326313 0.400965 0.391422 0.373351 \n",
"419 0.413398 0.375629 0.381035 0.346115 \n",
"420 0.377799 0.385887 0.384042 0.349956 \n",
"421 0.309309 0.390633 0.376339 0.360353 \n",
"422 0.241373 0.383198 0.378137 0.345602 \n",
"423 0.370686 0.369564 0.369690 0.345702 \n",
"424 0.364905 0.392520 0.386162 0.373449 \n",
"425 0.404312 0.376888 0.385532 0.352623 \n",
"426 0.266702 0.393868 0.396684 0.361810 \n",
"427 0.401646 0.380932 0.388726 0.364671 \n",
"428 0.349346 0.390618 0.387689 0.355591 \n",
"429 0.349869 0.400868 0.397587 0.375378 \n",
"430 0.291126 0.388891 0.385826 0.368804 \n",
"431 0.285465 0.405000 0.385932 0.366543 \n",
"432 0.213772 0.390465 0.395524 0.364513 \n",
"433 0.132574 0.402506 0.408092 0.392411 \n",
"434 0.233944 0.405535 0.387747 0.372620 \n",
"435 0.322671 0.384292 0.385328 0.353731 \n",
"436 0.239142 0.397595 0.385070 0.369290 \n",
"437 0.260417 0.387313 0.379687 0.352217 \n",
"438 0.122134 0.390103 0.391888 0.356613 \n",
"439 0.294508 0.403624 0.394708 0.370958 \n",
"440 0.327526 0.390663 0.404334 0.359301 \n",
"441 0.320648 0.385596 0.378942 0.359409 \n",
"442 0.390322 0.401379 0.390136 0.365038 \n",
"443 0.304058 0.381193 0.377228 0.343486 \n",
"444 0.307817 0.397070 0.395306 0.363937 \n",
"445 0.322557 0.387040 0.387626 0.360044 \n",
"\n",
"[446 rows x 46 columns]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['NormalBCD19pCD27mcell1_22_' 'NormalBCD19pCD27mcell23_44'\n",
" 'NormalBCD19pCD27mcell45_66' 'NormalBCD19pCD27mcell67_88'\n",
" 'NormalBCD19pCD27pcell1_22_' 'NormalBCD19pCD27pcell23_44'\n",
" 'NormalBCD19pCD27pcell45_66' 'NormalBCD19pCD27pcell67_88'\n",
" 'RRBS_NormalBCD19pcell1_22_' 'RRBS_NormalBCD19pcell23_44'\n",
" 'RRBS_NormalBCD19pcell45_66' 'RRBS_NormalBCD19pcell67_88'\n",
" 'cw154_CutSmart_proteinase_K' 'cw154_Tris_protease'\n",
" 'cw154_Tris_protease_GR' 'normal_B_cell_A1_24' 'normal_B_cell_B1_24'\n",
" 'normal_B_cell_C1_24' 'normal_B_cell_D1_24' 'normal_B_cell_G1_22'\n",
" 'normal_B_cell_H1_22' 'trito_pool_1' 'trito_pool_2']\n"
]
}
],
"source": [
"print(np.unique(merged.protocol)) # there are 23 'protocol' fields"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# merged.to_csv(\"total_genomic_region.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(446, 46)"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged.shape"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['filename', 'methylation', 'PDR_total', 'methylation_unweighted',\n",
" 'PDR_unweighted', 'type', 'bio', 'protocol', 'methylation_tssDistance',\n",
" 'methylation_genesDistance', 'methylation_exonsDistance',\n",
" 'methylation_intronsDistance', 'methylation_promoterDistance',\n",
" 'methylation_cgiDistance', 'methylation_ctcfDistance',\n",
" 'methylation_ctcfUpDistance', 'methylation_ctcfDownDistance',\n",
" 'methylation_geneDistalRegulatoryModulesDistance',\n",
" 'methylation_vistaEnhancersDistance', 'methylation_3PrimeUTRDistance',\n",
" 'methylation_5PrimeUTRDistance', 'methylation_firstExonDistance',\n",
" 'methylation_geneDistalRegulatoryModulesK562Distance',\n",
" 'methylation_hypoInHues64Distance', 'methylation_intergenic',\n",
" 'methylation_shore', 'methylation_shelf', 'PDR_tssDistance',\n",
" 'PDR_genesDistance', 'PDR_exonsDistance', 'PDR_intronsDistance',\n",
" 'PDR_promoterDistance', 'PDR_cgiDistance', 'PDR_ctcfDistance',\n",
" 'PDR_ctcfUpDistance', 'PDR_ctcfDownDistance',\n",
" 'PDR_geneDistalRegulatoryModulesDistance', 'PDR_vistaEnhancersDistance',\n",
" 'PDR_3PrimeUTRDistance', 'PDR_5PrimeUTRDistance',\n",
" 'PDR_firstExonDistance', 'PDR_geneDistalRegulatoryModulesK562Distance',\n",
" 'PDR_hypoInHues64Distance', 'PDR_intergenic', 'PDR_shore', 'PDR_shelf'],\n",
" dtype='object')"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged.columns"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#\n",
"# First do pairs by CLL vs Normal B; We could discuss protocols at a later point\n",
"#\n",
"normal = merged[merged[\"type\"]==\"normal\"]\n",
"CLL = merged[merged[\"type\"]==\"CLL\"]"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"342\n",
"104\n"
]
}
],
"source": [
"print(len(normal))\n",
"print(len(CLL))"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#CLL_pairs = CLL\n",
"normal_pairs = normal"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['filename', 'methylation', 'PDR_total', 'methylation_unweighted',\n",
" 'PDR_unweighted', 'type', 'bio', 'protocol', 'methylation_tssDistance',\n",
" 'methylation_genesDistance', 'methylation_exonsDistance',\n",
" 'methylation_intronsDistance', 'methylation_promoterDistance',\n",
" 'methylation_cgiDistance', 'methylation_ctcfDistance',\n",
" 'methylation_ctcfUpDistance', 'methylation_ctcfDownDistance',\n",
" 'methylation_geneDistalRegulatoryModulesDistance',\n",
" 'methylation_vistaEnhancersDistance', 'methylation_3PrimeUTRDistance',\n",
" 'methylation_5PrimeUTRDistance', 'methylation_firstExonDistance',\n",
" 'methylation_geneDistalRegulatoryModulesK562Distance',\n",
" 'methylation_hypoInHues64Distance', 'methylation_intergenic',\n",
" 'methylation_shore', 'methylation_shelf', 'PDR_tssDistance',\n",
" 'PDR_genesDistance', 'PDR_exonsDistance', 'PDR_intronsDistance',\n",
" 'PDR_promoterDistance', 'PDR_cgiDistance', 'PDR_ctcfDistance',\n",
" 'PDR_ctcfUpDistance', 'PDR_ctcfDownDistance',\n",
" 'PDR_geneDistalRegulatoryModulesDistance', 'PDR_vistaEnhancersDistance',\n",
" 'PDR_3PrimeUTRDistance', 'PDR_5PrimeUTRDistance',\n",
" 'PDR_firstExonDistance', 'PDR_geneDistalRegulatoryModulesK562Distance',\n",
" 'PDR_hypoInHues64Distance', 'PDR_intergenic', 'PDR_shore', 'PDR_shelf'],\n",
" dtype='object')"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"normal_pairs.columns"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['NormalBCD19pCD27mcell1_22_' 'NormalBCD19pCD27mcell23_44'\n",
" 'NormalBCD19pCD27mcell45_66' 'NormalBCD19pCD27mcell67_88'\n",
" 'NormalBCD19pCD27pcell1_22_' 'NormalBCD19pCD27pcell23_44'\n",
" 'NormalBCD19pCD27pcell45_66' 'NormalBCD19pCD27pcell67_88'\n",
" 'RRBS_NormalBCD19pcell1_22_' 'RRBS_NormalBCD19pcell23_44'\n",
" 'RRBS_NormalBCD19pcell45_66' 'RRBS_NormalBCD19pcell67_88'\n",
" 'normal_B_cell_A1_24' 'normal_B_cell_B1_24' 'normal_B_cell_C1_24'\n",
" 'normal_B_cell_D1_24' 'normal_B_cell_G1_22' 'normal_B_cell_H1_22']\n"
]
}
],
"source": [
"print(np.unique(normal_pairs.protocol))"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"protocol = normal_pairs[normal_pairs[\"protocol\"] == \"NormalBCD19pCD27mcell23_44\"]"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(19, 46)"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"protocol.shape"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(342, 46)"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"normal_pairs.shape"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"protocol = protocol.reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DANGER!!!!\n",
"\"\"\"\n",
"\n",
"# Falsely named variable!!! I simply do this to not modify the code below\n",
"\n",
"normal_pairs = protocol"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation, normal_pairs.methylation)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_difference': stacked})[['filename', 'methylation_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs1 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs1.shape)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_total, normal_pairs.PDR_total)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"PDR_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_difference': stacked})[['filename', 'PDR_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs2 = pd.merge(out, PDR_differences, how='inner')\n",
"print(pairs2.shape)\n"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_unweighted, normal_pairs.methylation_unweighted)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_unweighted_difference': stacked})[['filename', 'methylation_unweighted_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs3 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs3.shape)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_unweighted, normal_pairs.PDR_unweighted)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"PDR_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_unweighted_difference': stacked})[['filename', 'PDR_unweighted_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs4 = pd.merge(out, PDR_differences, how='inner')\n",
"print(pairs4.shape)\n"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"\\n 'methylation_tssDistance',\\n 'methylation_genesDistance', 'methylation_exonsDistance',\\n 'methylation_intronsDistance', 'methylation_promoterDistance',\\n 'methylation_cgiDistance', 'methylation_ctcfDistance',\\n 'methylation_ctcfUpDistance', 'methylation_ctcfDownDistance',\\n 'methylation_geneDistalRegulatoryModulesDistance',\\n 'methylation_vistaEnhancersDistance', 'methylation_3PrimeUTRDistance',\\n 'methylation_5PrimeUTRDistance', 'methylation_firstExonDistance',\\n 'methylation_geneDistalRegulatoryModulesK562Distance',\\n 'methylation_hypoInHues64Distance', 'methylation_intergenic',\\n 'methylation_shore', 'methylation_shelf'\\n\\n\""
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
" 'methylation_tssDistance',\n",
" 'methylation_genesDistance', 'methylation_exonsDistance',\n",
" 'methylation_intronsDistance', 'methylation_promoterDistance',\n",
" 'methylation_cgiDistance', 'methylation_ctcfDistance',\n",
" 'methylation_ctcfUpDistance', 'methylation_ctcfDownDistance',\n",
" 'methylation_geneDistalRegulatoryModulesDistance',\n",
" 'methylation_vistaEnhancersDistance', 'methylation_3PrimeUTRDistance',\n",
" 'methylation_5PrimeUTRDistance', 'methylation_firstExonDistance',\n",
" 'methylation_geneDistalRegulatoryModulesK562Distance',\n",
" 'methylation_hypoInHues64Distance', 'methylation_intergenic',\n",
" 'methylation_shore', 'methylation_shelf'\n",
"\n",
"\"\"\" "
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_tssDistance, normal_pairs.methylation_tssDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_tssDistance_difference': stacked})[['filename', 'methylation_tssDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs5 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs5.shape)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_genesDistance, normal_pairs.methylation_genesDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_genesDistance_difference': stacked})[['filename', 'methylation_genesDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs6 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs6.shape)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_exonsDistance, normal_pairs.methylation_exonsDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_exonsDistance_difference': stacked})[['filename', 'methylation_exonsDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs7 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs7.shape)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_intronsDistance, normal_pairs.methylation_intronsDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_intronsDistance_difference': stacked})[['filename', 'methylation_intronsDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs8 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs8.shape)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_promoterDistance, normal_pairs.methylation_promoterDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_promoterDistance_difference': stacked})[['filename', 'methylation_promoterDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs9 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs9.shape)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_cgiDistance, normal_pairs.methylation_cgiDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_cgiDistance_difference': stacked})[['filename', 'methylation_cgiDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs10 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs10.shape)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_ctcfDistance, normal_pairs.methylation_ctcfDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_ctcfDistance_difference': stacked})[['filename', 'methylation_ctcfDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs11 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs11.shape)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_ctcfUpDistance, normal_pairs.methylation_ctcfUpDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_ctcfUpDistance_difference': stacked})[['filename', 'methylation_ctcfUpDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs12 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs12.shape)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_ctcfDownDistance, normal_pairs.methylation_ctcfDownDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_ctcfDownDistance_difference': stacked})[['filename', 'methylation_ctcfDownDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs13 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs13.shape)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_geneDistalRegulatoryModulesDistance, normal_pairs.methylation_geneDistalRegulatoryModulesDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_geneDistalRegulatoryModulesDistance_difference': stacked})[['filename', 'methylation_geneDistalRegulatoryModulesDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs14 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs14.shape)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_vistaEnhancersDistance, normal_pairs.methylation_vistaEnhancersDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_vistaEnhancersDistance_difference': stacked})[['filename', 'methylation_vistaEnhancersDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs15 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs15.shape)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_3PrimeUTRDistance, normal_pairs.methylation_3PrimeUTRDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_3PrimeUTRDistance_difference': stacked})[['filename', 'methylation_3PrimeUTRDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs16 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs16.shape)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_5PrimeUTRDistance, normal_pairs.methylation_5PrimeUTRDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_5PrimeUTRDistance_difference': stacked})[['filename', 'methylation_5PrimeUTRDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs17 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs17.shape)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_firstExonDistance, normal_pairs.methylation_firstExonDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_firstExonDistance_difference': stacked})[['filename', 'methylation_firstExonDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs18 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs18.shape)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_geneDistalRegulatoryModulesK562Distance, normal_pairs.methylation_geneDistalRegulatoryModulesK562Distance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_geneDistalRegulatoryModulesK562Distance_difference': stacked})[['filename', 'methylation_geneDistalRegulatoryModulesK562Distance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs19 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs19.shape)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_hypoInHues64Distance, normal_pairs.methylation_hypoInHues64Distance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_hypoInHues64Distance_difference': stacked})[['filename', 'methylation_hypoInHues64Distance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs20 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs20.shape)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_intergenic, normal_pairs.methylation_intergenic)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_intergenic_difference': stacked})[['filename', 'methylation_intergenic_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs21 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs21.shape)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_shore, normal_pairs.methylation_shore)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_shore_difference': stacked})[['filename', 'methylation_shore_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs22 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs22.shape)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.methylation_shelf, normal_pairs.methylation_shelf)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'methylation_shelf_difference': stacked})[['filename', 'methylation_shelf_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs23 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs23.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'\\n###\\nPDR by genomic regions\\n###\\n'"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
"###\n",
"PDR by genomic regions\n",
"###\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_tssDistance, normal_pairs.PDR_tssDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_tssDistance_difference': stacked})[['filename', 'PDR_tssDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs24 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs24.shape)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_genesDistance, normal_pairs.PDR_genesDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_genesDistance_difference': stacked})[['filename', 'PDR_genesDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs25 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs25.shape)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_exonsDistance, normal_pairs.PDR_exonsDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_exonsDistance_difference': stacked})[['filename', 'PDR_exonsDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs26 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs26.shape)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_intronsDistance, normal_pairs.PDR_intronsDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_intronsDistance_difference': stacked})[['filename', 'PDR_intronsDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs27 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs27.shape)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_promoterDistance, normal_pairs.PDR_promoterDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_promoterDistance_difference': stacked})[['filename', 'PDR_promoterDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs28 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs28.shape)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_cgiDistance, normal_pairs.PDR_cgiDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_cgiDistance_difference': stacked})[['filename', 'PDR_cgiDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs29 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs29.shape)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_ctcfDistance, normal_pairs.PDR_ctcfDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_ctcfDistance_difference': stacked})[['filename', 'PDR_ctcfDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs30 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs30.shape)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_ctcfUpDistance, normal_pairs.PDR_ctcfUpDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_ctcfUpDistance_difference': stacked})[['filename', 'PDR_ctcfUpDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs31 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs31.shape)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_ctcfDownDistance, normal_pairs.PDR_ctcfDownDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_ctcfDownDistance_difference': stacked})[['filename', 'PDR_ctcfDownDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs32 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs32.shape)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_geneDistalRegulatoryModulesDistance, normal_pairs.PDR_geneDistalRegulatoryModulesDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_geneDistalRegulatoryModulesDistance_difference': stacked})[['filename', 'PDR_geneDistalRegulatoryModulesDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs33 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs33.shape)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_vistaEnhancersDistance, normal_pairs.PDR_vistaEnhancersDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_vistaEnhancersDistance_difference': stacked})[['filename', 'PDR_vistaEnhancersDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs34 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs34.shape)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_3PrimeUTRDistance, normal_pairs.PDR_3PrimeUTRDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_3PrimeUTRDistance_difference': stacked})[['filename', 'PDR_3PrimeUTRDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs35 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs35.shape)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_5PrimeUTRDistance, normal_pairs.PDR_5PrimeUTRDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_5PrimeUTRDistance_difference': stacked})[['filename', 'PDR_5PrimeUTRDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs36 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs36.shape)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_firstExonDistance, normal_pairs.PDR_firstExonDistance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_firstExonDistance_difference': stacked})[['filename', 'PDR_firstExonDistance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs37 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs37.shape)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_geneDistalRegulatoryModulesK562Distance, normal_pairs.PDR_geneDistalRegulatoryModulesK562Distance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_geneDistalRegulatoryModulesK562Distance_difference': stacked})[['filename', 'PDR_geneDistalRegulatoryModulesK562Distance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs38 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs38.shape)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_hypoInHues64Distance, normal_pairs.PDR_hypoInHues64Distance)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_hypoInHues64Distance_difference': stacked})[['filename', 'PDR_hypoInHues64Distance_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs39 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs39.shape)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_intergenic, normal_pairs.PDR_intergenic)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_intergenic_difference': stacked})[['filename', 'PDR_intergenic_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs40 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs40.shape)"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_shore, normal_pairs.PDR_shore)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_shore_difference': stacked})[['filename', 'PDR_shore_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs41 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs41.shape)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(171, 44)\n"
]
}
],
"source": [
"normal_pairsA = normal_pairs.set_index(\"filename\")\n",
"from itertools import combinations\n",
"cc = list(combinations(normal_pairs.filename, 2)) # combines into all pairs\n",
"out = pd.DataFrame([normal_pairsA.loc[c,:].mean() for c in cc], index=cc) # covariates between pairs == mean\n",
"df_ex = pd.DataFrame(np.abs(np.subtract.outer(normal_pairs.PDR_shelf, normal_pairs.PDR_shelf)), normal_pairs.filename, normal_pairs.filename)\n",
"stacked = df_ex.stack()\n",
"methylation_differences = pd.DataFrame({'filename': stacked.index.to_series(), 'PDR_shelf_difference': stacked})[['filename', 'PDR_shelf_difference']].reset_index(drop=True)\n",
"out['filename'] = out.index\n",
"out = out.reset_index(drop=True)\n",
"pairs42 = pd.merge(out, methylation_differences, how='inner')\n",
"print(pairs42.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 95,
"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>methylation</th>\n",
" <th>PDR_total</th>\n",
" <th>methylation_unweighted</th>\n",
" <th>PDR_unweighted</th>\n",
" <th>methylation_tssDistance</th>\n",
" <th>methylation_genesDistance</th>\n",
" <th>methylation_exonsDistance</th>\n",
" <th>methylation_intronsDistance</th>\n",
" <th>methylation_promoterDistance</th>\n",
" <th>methylation_cgiDistance</th>\n",
" <th>methylation_ctcfDistance</th>\n",
" <th>methylation_ctcfUpDistance</th>\n",
" <th>methylation_ctcfDownDistance</th>\n",
" <th>methylation_geneDistalRegulatoryModulesDistance</th>\n",
" <th>methylation_vistaEnhancersDistance</th>\n",
" <th>methylation_3PrimeUTRDistance</th>\n",
" <th>methylation_5PrimeUTRDistance</th>\n",
" <th>methylation_firstExonDistance</th>\n",
" <th>methylation_geneDistalRegulatoryModulesK562Distance</th>\n",
" <th>methylation_hypoInHues64Distance</th>\n",
" <th>methylation_intergenic</th>\n",
" <th>methylation_shore</th>\n",
" <th>methylation_shelf</th>\n",
" <th>PDR_tssDistance</th>\n",
" <th>PDR_genesDistance</th>\n",
" <th>PDR_exonsDistance</th>\n",
" <th>PDR_intronsDistance</th>\n",
" <th>PDR_promoterDistance</th>\n",
" <th>PDR_cgiDistance</th>\n",
" <th>PDR_ctcfDistance</th>\n",
" <th>PDR_ctcfUpDistance</th>\n",
" <th>PDR_ctcfDownDistance</th>\n",
" <th>PDR_geneDistalRegulatoryModulesDistance</th>\n",
" <th>PDR_vistaEnhancersDistance</th>\n",
" <th>PDR_3PrimeUTRDistance</th>\n",
" <th>PDR_5PrimeUTRDistance</th>\n",
" <th>PDR_firstExonDistance</th>\n",
" <th>PDR_geneDistalRegulatoryModulesK562Distance</th>\n",
" <th>PDR_hypoInHues64Distance</th>\n",
" <th>PDR_intergenic</th>\n",
" <th>PDR_shore</th>\n",
" <th>PDR_shelf</th>\n",
" <th>filename</th>\n",
" <th>PDR_shelf_difference</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.479927</td>\n",
" <td>0.211825</td>\n",
" <td>0.598410</td>\n",
" <td>0.213551</td>\n",
" <td>0.0</td>\n",
" <td>0.449520</td>\n",
" <td>0.267668</td>\n",
" <td>0.483687</td>\n",
" <td>0.067515</td>\n",
" <td>0.078676</td>\n",
" <td>0.100928</td>\n",
" <td>0.0</td>\n",
" <td>0.100928</td>\n",
" <td>0.243361</td>\n",
" <td>0.330658</td>\n",
" <td>0.591722</td>\n",
" <td>0.193698</td>\n",
" <td>0.057629</td>\n",
" <td>0.166310</td>\n",
" <td>0.916325</td>\n",
" <td>0.848614</td>\n",
" <td>0.554686</td>\n",
" <td>0.831449</td>\n",
" <td>0.0</td>\n",
" <td>0.193781</td>\n",
" <td>0.180520</td>\n",
" <td>0.197211</td>\n",
" <td>0.118727</td>\n",
" <td>0.149712</td>\n",
" <td>0.163970</td>\n",
" <td>0.0</td>\n",
" <td>0.163970</td>\n",
" <td>0.215506</td>\n",
" <td>0.256394</td>\n",
" <td>0.261533</td>\n",
" <td>0.125087</td>\n",
" <td>0.121044</td>\n",
" <td>0.154530</td>\n",
" <td>0.270776</td>\n",
" <td>0.276170</td>\n",
" <td>0.290322</td>\n",
" <td>0.271030</td>\n",
" <td>(RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.ACAA...</td>\n",
" <td>0.059358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.510815</td>\n",
" <td>0.212417</td>\n",
" <td>0.621270</td>\n",
" <td>0.213140</td>\n",
" <td>0.0</td>\n",
" <td>0.479725</td>\n",
" <td>0.297167</td>\n",
" <td>0.511913</td>\n",
" <td>0.076270</td>\n",
" <td>0.087780</td>\n",
" <td>0.117836</td>\n",
" <td>0.0</td>\n",
" <td>0.117836</td>\n",
" <td>0.260816</td>\n",
" <td>0.376710</td>\n",
" <td>0.601329</td>\n",
" <td>0.207560</td>\n",
" <td>0.066333</td>\n",
" <td>0.183775</td>\n",
" <td>0.917272</td>\n",
" <td>0.863566</td>\n",
" <td>0.577783</td>\n",
" <td>0.844587</td>\n",
" <td>0.0</td>\n",
" <td>0.194231</td>\n",
" <td>0.184724</td>\n",
" <td>0.196954</td>\n",
" <td>0.123469</td>\n",
" <td>0.154484</td>\n",
" <td>0.157504</td>\n",
" <td>0.0</td>\n",
" <td>0.157504</td>\n",
" <td>0.222399</td>\n",
" <td>0.277329</td>\n",
" <td>0.277501</td>\n",
" <td>0.128742</td>\n",
" <td>0.127362</td>\n",
" <td>0.160260</td>\n",
" <td>0.220213</td>\n",
" <td>0.268432</td>\n",
" <td>0.291102</td>\n",
" <td>0.254058</td>\n",
" <td>(RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.ACAA...</td>\n",
" <td>0.093303</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.532978</td>\n",
" <td>0.214784</td>\n",
" <td>0.633539</td>\n",
" <td>0.215382</td>\n",
" <td>0.0</td>\n",
" <td>0.505779</td>\n",
" <td>0.318911</td>\n",
" <td>0.536958</td>\n",
" <td>0.079573</td>\n",
" <td>0.091920</td>\n",
" <td>0.122659</td>\n",
" <td>0.0</td>\n",
" <td>0.122659</td>\n",
" <td>0.279853</td>\n",
" <td>0.390240</td>\n",
" <td>0.634088</td>\n",
" <td>0.221209</td>\n",
" <td>0.069256</td>\n",
" <td>0.198566</td>\n",
" <td>0.910118</td>\n",
" <td>0.867438</td>\n",
" <td>0.598956</td>\n",
" <td>0.851913</td>\n",
" <td>0.0</td>\n",
" <td>0.195622</td>\n",
" <td>0.180383</td>\n",
" <td>0.198551</td>\n",
" <td>0.126705</td>\n",
" <td>0.156709</td>\n",
" <td>0.167375</td>\n",
" <td>0.0</td>\n",
" <td>0.167375</td>\n",
" <td>0.228736</td>\n",
" <td>0.257438</td>\n",
" <td>0.256796</td>\n",
" <td>0.128609</td>\n",
" <td>0.122432</td>\n",
" <td>0.164352</td>\n",
" <td>0.252867</td>\n",
" <td>0.271939</td>\n",
" <td>0.287226</td>\n",
" <td>0.255894</td>\n",
" <td>(RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.ACAA...</td>\n",
" <td>0.089631</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.524757</td>\n",
" <td>0.209161</td>\n",
" <td>0.631273</td>\n",
" <td>0.210507</td>\n",
" <td>0.0</td>\n",
" <td>0.497676</td>\n",
" <td>0.318725</td>\n",
" <td>0.528371</td>\n",
" <td>0.077818</td>\n",
" <td>0.088105</td>\n",
" <td>0.121085</td>\n",
" <td>0.0</td>\n",
" <td>0.121085</td>\n",
" <td>0.271990</td>\n",
" <td>0.367118</td>\n",
" <td>0.652783</td>\n",
" <td>0.220800</td>\n",
" <td>0.065502</td>\n",
" <td>0.191460</td>\n",
" <td>0.927917</td>\n",
" <td>0.866008</td>\n",
" <td>0.592442</td>\n",
" <td>0.854033</td>\n",
" <td>0.0</td>\n",
" <td>0.191223</td>\n",
" <td>0.176037</td>\n",
" <td>0.193845</td>\n",
" <td>0.118732</td>\n",
" <td>0.149313</td>\n",
" <td>0.157583</td>\n",
" <td>0.0</td>\n",
" <td>0.157583</td>\n",
" <td>0.219240</td>\n",
" <td>0.311056</td>\n",
" <td>0.278299</td>\n",
" <td>0.123833</td>\n",
" <td>0.114435</td>\n",
" <td>0.155329</td>\n",
" <td>0.198957</td>\n",
" <td>0.264981</td>\n",
" <td>0.287984</td>\n",
" <td>0.257222</td>\n",
" <td>(RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.ACAA...</td>\n",
" <td>0.086973</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.505953</td>\n",
" <td>0.209542</td>\n",
" <td>0.619024</td>\n",
" <td>0.210612</td>\n",
" <td>0.0</td>\n",
" <td>0.475963</td>\n",
" <td>0.297809</td>\n",
" <td>0.508730</td>\n",
" <td>0.072301</td>\n",
" <td>0.086216</td>\n",
" <td>0.110602</td>\n",
" <td>0.0</td>\n",
" <td>0.110602</td>\n",
" <td>0.263933</td>\n",
" <td>0.407868</td>\n",
" <td>0.639506</td>\n",
" <td>0.199685</td>\n",
" <td>0.061699</td>\n",
" <td>0.180957</td>\n",
" <td>0.922535</td>\n",
" <td>0.861449</td>\n",
" <td>0.575959</td>\n",
" <td>0.842457</td>\n",
" <td>0.0</td>\n",
" <td>0.191796</td>\n",
" <td>0.178494</td>\n",
" <td>0.193245</td>\n",
" <td>0.117895</td>\n",
" <td>0.151175</td>\n",
" <td>0.156774</td>\n",
" <td>0.0</td>\n",
" <td>0.156774</td>\n",
" <td>0.221501</td>\n",
" <td>0.329722</td>\n",
" <td>0.263398</td>\n",
" <td>0.122234</td>\n",
" <td>0.119651</td>\n",
" <td>0.158287</td>\n",
" <td>0.183439</td>\n",
" <td>0.266621</td>\n",
" <td>0.282830</td>\n",
" <td>0.243251</td>\n",
" <td>(RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.ACAA...</td>\n",
" <td>0.114917</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" methylation PDR_total methylation_unweighted PDR_unweighted \\\n",
"0 0.479927 0.211825 0.598410 0.213551 \n",
"1 0.510815 0.212417 0.621270 0.213140 \n",
"2 0.532978 0.214784 0.633539 0.215382 \n",
"3 0.524757 0.209161 0.631273 0.210507 \n",
"4 0.505953 0.209542 0.619024 0.210612 \n",
"\n",
" methylation_tssDistance methylation_genesDistance \\\n",
"0 0.0 0.449520 \n",
"1 0.0 0.479725 \n",
"2 0.0 0.505779 \n",
"3 0.0 0.497676 \n",
"4 0.0 0.475963 \n",
"\n",
" methylation_exonsDistance methylation_intronsDistance \\\n",
"0 0.267668 0.483687 \n",
"1 0.297167 0.511913 \n",
"2 0.318911 0.536958 \n",
"3 0.318725 0.528371 \n",
"4 0.297809 0.508730 \n",
"\n",
" methylation_promoterDistance methylation_cgiDistance \\\n",
"0 0.067515 0.078676 \n",
"1 0.076270 0.087780 \n",
"2 0.079573 0.091920 \n",
"3 0.077818 0.088105 \n",
"4 0.072301 0.086216 \n",
"\n",
" methylation_ctcfDistance methylation_ctcfUpDistance \\\n",
"0 0.100928 0.0 \n",
"1 0.117836 0.0 \n",
"2 0.122659 0.0 \n",
"3 0.121085 0.0 \n",
"4 0.110602 0.0 \n",
"\n",
" methylation_ctcfDownDistance \\\n",
"0 0.100928 \n",
"1 0.117836 \n",
"2 0.122659 \n",
"3 0.121085 \n",
"4 0.110602 \n",
"\n",
" methylation_geneDistalRegulatoryModulesDistance \\\n",
"0 0.243361 \n",
"1 0.260816 \n",
"2 0.279853 \n",
"3 0.271990 \n",
"4 0.263933 \n",
"\n",
" methylation_vistaEnhancersDistance methylation_3PrimeUTRDistance \\\n",
"0 0.330658 0.591722 \n",
"1 0.376710 0.601329 \n",
"2 0.390240 0.634088 \n",
"3 0.367118 0.652783 \n",
"4 0.407868 0.639506 \n",
"\n",
" methylation_5PrimeUTRDistance methylation_firstExonDistance \\\n",
"0 0.193698 0.057629 \n",
"1 0.207560 0.066333 \n",
"2 0.221209 0.069256 \n",
"3 0.220800 0.065502 \n",
"4 0.199685 0.061699 \n",
"\n",
" methylation_geneDistalRegulatoryModulesK562Distance \\\n",
"0 0.166310 \n",
"1 0.183775 \n",
"2 0.198566 \n",
"3 0.191460 \n",
"4 0.180957 \n",
"\n",
" methylation_hypoInHues64Distance methylation_intergenic \\\n",
"0 0.916325 0.848614 \n",
"1 0.917272 0.863566 \n",
"2 0.910118 0.867438 \n",
"3 0.927917 0.866008 \n",
"4 0.922535 0.861449 \n",
"\n",
" methylation_shore methylation_shelf PDR_tssDistance PDR_genesDistance \\\n",
"0 0.554686 0.831449 0.0 0.193781 \n",
"1 0.577783 0.844587 0.0 0.194231 \n",
"2 0.598956 0.851913 0.0 0.195622 \n",
"3 0.592442 0.854033 0.0 0.191223 \n",
"4 0.575959 0.842457 0.0 0.191796 \n",
"\n",
" PDR_exonsDistance PDR_intronsDistance PDR_promoterDistance \\\n",
"0 0.180520 0.197211 0.118727 \n",
"1 0.184724 0.196954 0.123469 \n",
"2 0.180383 0.198551 0.126705 \n",
"3 0.176037 0.193845 0.118732 \n",
"4 0.178494 0.193245 0.117895 \n",
"\n",
" PDR_cgiDistance PDR_ctcfDistance PDR_ctcfUpDistance \\\n",
"0 0.149712 0.163970 0.0 \n",
"1 0.154484 0.157504 0.0 \n",
"2 0.156709 0.167375 0.0 \n",
"3 0.149313 0.157583 0.0 \n",
"4 0.151175 0.156774 0.0 \n",
"\n",
" PDR_ctcfDownDistance PDR_geneDistalRegulatoryModulesDistance \\\n",
"0 0.163970 0.215506 \n",
"1 0.157504 0.222399 \n",
"2 0.167375 0.228736 \n",
"3 0.157583 0.219240 \n",
"4 0.156774 0.221501 \n",
"\n",
" PDR_vistaEnhancersDistance PDR_3PrimeUTRDistance PDR_5PrimeUTRDistance \\\n",
"0 0.256394 0.261533 0.125087 \n",
"1 0.277329 0.277501 0.128742 \n",
"2 0.257438 0.256796 0.128609 \n",
"3 0.311056 0.278299 0.123833 \n",
"4 0.329722 0.263398 0.122234 \n",
"\n",
" PDR_firstExonDistance PDR_geneDistalRegulatoryModulesK562Distance \\\n",
"0 0.121044 0.154530 \n",
"1 0.127362 0.160260 \n",
"2 0.122432 0.164352 \n",
"3 0.114435 0.155329 \n",
"4 0.119651 0.158287 \n",
"\n",
" PDR_hypoInHues64Distance PDR_intergenic PDR_shore PDR_shelf \\\n",
"0 0.270776 0.276170 0.290322 0.271030 \n",
"1 0.220213 0.268432 0.291102 0.254058 \n",
"2 0.252867 0.271939 0.287226 0.255894 \n",
"3 0.198957 0.264981 0.287984 0.257222 \n",
"4 0.183439 0.266621 0.282830 0.243251 \n",
"\n",
" filename PDR_shelf_difference \n",
"0 (RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.ACAA... 0.059358 \n",
"1 (RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.ACAA... 0.093303 \n",
"2 (RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.ACAA... 0.089631 \n",
"3 (RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.ACAA... 0.086973 \n",
"4 (RRBS_NormalBCD19pCD27mcell23_44_GTAGAGGA.ACAA... 0.114917 "
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pairs42.head()"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"\\n 'methylation_tssDistance',\\n 'methylation_genesDistance', 'methylation_exonsDistance',\\n 'methylation_intronsDistance', 'methylation_promoterDistance',\\n 'methylation_cgiDistance', 'methylation_ctcfDistance',\\n 'methylation_ctcfUpDistance', 'methylation_ctcfDownDistance',\\n 'methylation_geneDistalRegulatoryModulesDistance',\\n 'methylation_vistaEnhancersDistance', 'methylation_3PrimeUTRDistance',\\n 'methylation_5PrimeUTRDistance', 'methylation_firstExonDistance',\\n 'methylation_geneDistalRegulatoryModulesK562Distance',\\n 'methylation_hypoInHues64Distance', 'methylation_intergenic',\\n 'methylation_shore', 'methylation_shelf'\\n\\n\""
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
" 'methylation_tssDistance',\n",
" 'methylation_genesDistance', 'methylation_exonsDistance',\n",
" 'methylation_intronsDistance', 'methylation_promoterDistance',\n",
" 'methylation_cgiDistance', 'methylation_ctcfDistance',\n",
" 'methylation_ctcfUpDistance', 'methylation_ctcfDownDistance',\n",
" 'methylation_geneDistalRegulatoryModulesDistance',\n",
" 'methylation_vistaEnhancersDistance', 'methylation_3PrimeUTRDistance',\n",
" 'methylation_5PrimeUTRDistance', 'methylation_firstExonDistance',\n",
" 'methylation_geneDistalRegulatoryModulesK562Distance',\n",
" 'methylation_hypoInHues64Distance', 'methylation_intergenic',\n",
" 'methylation_shore', 'methylation_shelf'\n",
"\n",
"\"\"\" "
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pairs1 = pairs1[[\"filename\", \"methylation_difference\"]]\n",
"pairs2 = pairs2[[\"filename\", \"PDR_difference\"]]\n",
"pairs3 = pairs3[[\"filename\", \"methylation_unweighted_difference\"]]\n",
"pairs4 = pairs4[[\"filename\", \"PDR_unweighted_difference\"]]\n",
"pairs5 = pairs5[[\"filename\", \"methylation_tssDistance_difference\"]]\n",
"pairs6 = pairs6[[\"filename\", \"methylation_genesDistance_difference\"]]\n",
"pairs7 = pairs7[[\"filename\", \"methylation_exonsDistance_difference\"]]\n",
"pairs8 = pairs8[[\"filename\", \"methylation_intronsDistance_difference\"]]\n",
"pairs9 = pairs9[[\"filename\", \"methylation_promoterDistance_difference\"]]\n",
"pairs10 = pairs10[[\"filename\", \"methylation_cgiDistance_difference\"]]\n",
"pairs11 = pairs11[[\"filename\", \"methylation_ctcfDistance_difference\"]]\n",
"pairs12 = pairs12[[\"filename\", \"methylation_ctcfUpDistance_difference\"]]\n",
"pairs13 = pairs13[[\"filename\", \"methylation_ctcfDownDistance_difference\"]]\n",
"pairs14 = pairs14[[\"filename\", \"methylation_geneDistalRegulatoryModulesDistance\"]]\n",
"pairs15 = pairs15[[\"filename\", \"methylation_vistaEnhancersDistance_difference\"]]\n",
"pairs16 = pairs16[[\"filename\", \"methylation_3PrimeUTRDistance_difference\"]]\n",
"pairs17 = pairs17[[\"filename\", \"methylation_5PrimeUTRDistance_difference\"]]\n",
"pairs18 = pairs18[[\"filename\", \"methylation_firstExonDistance_difference\"]]\n",
"pairs19 = pairs19[[\"filename\", \"methylation_geneDistalRegulatoryModulesK562Distance_difference\"]]\n",
"pairs20 = pairs20[[\"filename\", \"methylation_hypoInHues64Distance_difference\"]]\n",
"pairs21 = pairs21[[\"filename\", \"methylation_intergenic_difference\"]]\n",
"pairs22 = pairs22[[\"filename\", \"methylation_shore_difference\"]]\n",
"pairs23 = pairs23[[\"filename\", \"methylation_shelf_difference\"]]\n",
"pairs24 = pairs24[[\"filename\", \"PDR_tssDistance_difference\"]]\n",
"pairs25 = pairs25[[\"filename\", \"PDR_genesDistance_difference\"]]\n",
"pairs26 = pairs26[[\"filename\", \"PDR_exonsDistance_difference\"]]\n",
"pairs27 = pairs27[[\"filename\", \"PDR_intronsDistance_difference\"]]\n",
"pairs28 = pairs28[[\"filename\", \"PDR_promoterDistance_difference\"]]\n",
"pairs29 = pairs29[[\"filename\", \"PDR_cgiDistance_difference\"]]\n",
"pairs30 = pairs30[[\"filename\", \"PDR_ctcfDistance_difference\"]]\n",
"pairs31 = pairs31[[\"filename\", \"PDR_ctcfUpDistance_difference\"]]\n",
"pairs32 = pairs32[[\"filename\", \"PDR_ctcfDownDistance_difference\"]]\n",
"pairs33 = pairs33[[\"filename\", \"PDR_geneDistalRegulatoryModulesDistance\"]]\n",
"pairs34 = pairs34[[\"filename\", \"PDR_vistaEnhancersDistance_difference\"]]\n",
"pairs35 = pairs35[[\"filename\", \"PDR_3PrimeUTRDistance_difference\"]]\n",
"pairs36 = pairs36[[\"filename\", \"PDR_5PrimeUTRDistance_difference\"]]\n",
"pairs37 = pairs37[[\"filename\", \"PDR_firstExonDistance_difference\"]]\n",
"pairs38 = pairs38[[\"filename\", \"PDR_geneDistalRegulatoryModulesK562Distance_difference\"]]\n",
"pairs39 = pairs39[[\"filename\", \"PDR_hypoInHues64Distance_difference\"]]\n",
"pairs40 = pairs40[[\"filename\", \"PDR_intergenic_difference\"]]\n",
"pairs41 = pairs41[[\"filename\", \"PDR_shore_difference\"]]\n",
"pairs42 = pairs42[[\"filename\", \"PDR_shelf_difference\"]]"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pairs_total = [pairs1, pairs2, pairs3, pairs4, pairs5, pairs6, pairs7, pairs8, pairs9, pairs10,\n",
" pairs11, pairs12, pairs13, pairs14, pairs15, pairs16, pairs17, pairs18, pairs19, pairs20,\n",
" pairs21, pairs22, pairs23, pairs24, pairs25, pairs26, pairs27, pairs28, pairs29, pairs30,\n",
" pairs31, pairs32, pairs33, pairs34, pairs35, pairs36, pairs37, pairs38, pairs39, pairs40,\n",
" pairs41, pairs42]"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"\n",
"total_normal_pairs = pd.concat([df.set_index(\"filename\") for df in pairs_total], axis=1).reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(171, 43)"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_normal_pairs.shape"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"total_normal_pairs.to_csv(\"total_normal_pairs_NormalBCD19pCD27mcell23_44.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.4.5"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
hall1467/wikidata_usage_tracking | jupyter_notebooks/misalignment/dissonance-201509.ipynb | 2 | 685275 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"library(data.table)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Using a table of article assessments and views, build tables\n",
"## (matrices) that shows the number of dissonant articles per\n",
"## assessment category based on sorting by popularity.\n",
"##\n",
"## The underlying assumption is that in an ideal system with a limited\n",
"## and fixed amount of resources (in other words, popularity and high quality\n",
"## artefacts does not increase the amount of resources in the system),\n",
"## popularity ranking and assessment class follow a 1-to-1 relationship.\n",
"## We can therefore sort by popularity and group articles that way\n",
"## because work will be prioritised by popularity.\n",
"\n",
"## DATA ASSUMPTION: views_with_redirects from resolve-redirects.R\n",
"## is loaded into memory.\n",
"\n",
"## 3: build a 2x2 matrix of assessment classes and popularity classes\n",
"## \n",
"\n",
"## Assessment classes in ascending order of quality."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"assessment_classes = c('E', 'D', 'C', 'B', 'A');"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"quality_prediction_and_page_views <- read.table(\"../../results/sql_queries/entity_views_and_aggregated_revisions/entity_views_and_aggregated_revisions_and_quality_scoring_20150901.tsv\", header=FALSE, sep=\"\\t\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"quality_prediction_and_page_views <- data.table(quality_prediction_and_page_views)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"colnames(quality_prediction_and_page_views) <- c('entity_id','number_of_revisions', 'page_views', 'prediction')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" entity_id number_of_revisions page_views prediction \n",
" Q1 : 1 Min. : 1.00 Min. :0.000e+00 A: 285 \n",
" Q100 : 1 1st Qu.: 7.00 1st Qu.:3.300e+01 B: 196496 \n",
" Q1000 : 1 Median : 15.00 Median :2.440e+02 C: 3466684 \n",
" Q10000 : 1 Mean : 20.63 Mean :3.023e+04 D: 2650426 \n",
" Q100000 : 1 3rd Qu.: 27.00 3rd Qu.:1.464e+03 E:11018416 \n",
" Q1000000: 1 Max. :21863.00 Max. :1.253e+10 \n",
" (Other) :17332301 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"summary(quality_prediction_and_page_views)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## 0: calculate number of articles in each assessment class\n",
"n_per_class = quality_prediction_and_page_views[, list(narticles=sum(.N)), by='prediction']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"setkey(n_per_class, prediction);\n",
"## NOTE: setkey allows us to do n_per_class['GA']$narticles to get counts"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## 1: order articles by popularity\n",
"articles_by_pop = quality_prediction_and_page_views[order(quality_prediction_and_page_views$page_views)][,list(entity_id, prediction, page_views)];\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10750354</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10766855</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q12443525</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td> </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td> </td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818</td><td> </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td> </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td> </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927</td><td> </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td> </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224</td><td> </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td> </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td> </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td> </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td> </td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157</td><td> </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td> </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td> </td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498</td><td> </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td> </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td> </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td> </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td> </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td> </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td> </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td> </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td> </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td> </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td> </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td> </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td> </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td> </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721</td><td> </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td> </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & \\\\\n",
"\t Q10069140 & C & 0 & \\\\\n",
"\t Q10081695 & C & 0 & \\\\\n",
"\t Q10092002 & E & 0 & \\\\\n",
"\t Q10111267 & E & 0 & \\\\\n",
"\t Q10149726 & E & 0 & \\\\\n",
"\t Q10180230 & E & 0 & \\\\\n",
"\t Q10185035 & E & 0 & \\\\\n",
"\t Q10205202 & E & 0 & \\\\\n",
"\t Q10252966 & E & 0 & \\\\\n",
"\t Q10444494 & C & 0 & \\\\\n",
"\t Q10624171 & C & 0 & \\\\\n",
"\t Q10704108 & C & 0 & \\\\\n",
"\t Q10750354 & E & 0 & \\\\\n",
"\t Q10766855 & E & 0 & \\\\\n",
"\t Q10827611 & E & 0 & \\\\\n",
"\t Q11093044 & E & 0 & \\\\\n",
"\t Q11934537 & E & 0 & \\\\\n",
"\t Q12133466 & E & 0 & \\\\\n",
"\t Q12264503 & E & 0 & \\\\\n",
"\t Q12267516 & E & 0 & \\\\\n",
"\t Q12304084 & E & 0 & \\\\\n",
"\t Q12443525 & E & 0 & \\\\\n",
"\t Q12543904 & E & 0 & \\\\\n",
"\t Q12890205 & E & 0 & \\\\\n",
"\t Q12891524 & E & 0 & \\\\\n",
"\t Q12918202 & E & 0 & \\\\\n",
"\t Q13005653 & E & 0 & \\\\\n",
"\t Q13073896 & E & 0 & \\\\\n",
"\t Q13163823 & E & 0 & \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & \\\\\n",
"\t Q31165 & C & 2048330818 & \\\\\n",
"\t Q40629 & C & 2049755644 & \\\\\n",
"\t Q105584 & C & 2049926923 & \\\\\n",
"\t Q4584301 & D & 2052339927 & \\\\\n",
"\t Q565 & C & 2052996261 & \\\\\n",
"\t Q1868372 & E & 2056080224 & \\\\\n",
"\t Q209330 & C & 2060928966 & \\\\\n",
"\t Q14005 & D & 2063120071 & \\\\\n",
"\t Q918 & C & 2063217449 & \\\\\n",
"\t Q150248 & C & 2068796814 & \\\\\n",
"\t Q866 & C & 2079749157 & \\\\\n",
"\t Q477675 & C & 2080785713 & \\\\\n",
"\t Q1967876 & C & 2084215818 & \\\\\n",
"\t Q750403 & C & 2084693498 & \\\\\n",
"\t Q355 & C & 2093900731 & \\\\\n",
"\t Q623578 & C & 2097991400 & \\\\\n",
"\t Q17299517 & D & 2105487660 & \\\\\n",
"\t Q33999 & C & 2108672678 & \\\\\n",
"\t Q2494649 & C & 2114531894 & \\\\\n",
"\t Q2597810 & C & 2128920607 & \\\\\n",
"\t Q193563 & C & 2130725560 & \\\\\n",
"\t Q423048 & C & 2136131564 & \\\\\n",
"\t Q37312 & C & 2142913121 & \\\\\n",
"\t Q54919 & C & 2148531382 & \\\\\n",
"\t Q36578 & C & 2229315598 & \\\\\n",
"\t Q30 & A & 2277746226 & \\\\\n",
"\t Q6581097 & D & 3273952711 & \\\\\n",
"\t Q5 & A & 5668008721 & \\\\\n",
"\t Q5296 & C & 12530369761 & \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | | \n",
"| Q10069140 | C | 0 | | \n",
"| Q10081695 | C | 0 | | \n",
"| Q10092002 | E | 0 | | \n",
"| Q10111267 | E | 0 | | \n",
"| Q10149726 | E | 0 | | \n",
"| Q10180230 | E | 0 | | \n",
"| Q10185035 | E | 0 | | \n",
"| Q10205202 | E | 0 | | \n",
"| Q10252966 | E | 0 | | \n",
"| Q10444494 | C | 0 | | \n",
"| Q10624171 | C | 0 | | \n",
"| Q10704108 | C | 0 | | \n",
"| Q10750354 | E | 0 | | \n",
"| Q10766855 | E | 0 | | \n",
"| Q10827611 | E | 0 | | \n",
"| Q11093044 | E | 0 | | \n",
"| Q11934537 | E | 0 | | \n",
"| Q12133466 | E | 0 | | \n",
"| Q12264503 | E | 0 | | \n",
"| Q12267516 | E | 0 | | \n",
"| Q12304084 | E | 0 | | \n",
"| Q12443525 | E | 0 | | \n",
"| Q12543904 | E | 0 | | \n",
"| Q12890205 | E | 0 | | \n",
"| Q12891524 | E | 0 | | \n",
"| Q12918202 | E | 0 | | \n",
"| Q13005653 | E | 0 | | \n",
"| Q13073896 | E | 0 | | \n",
"| Q13163823 | E | 0 | | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | | \n",
"| Q31165 | C | 2048330818 | | \n",
"| Q40629 | C | 2049755644 | | \n",
"| Q105584 | C | 2049926923 | | \n",
"| Q4584301 | D | 2052339927 | | \n",
"| Q565 | C | 2052996261 | | \n",
"| Q1868372 | E | 2056080224 | | \n",
"| Q209330 | C | 2060928966 | | \n",
"| Q14005 | D | 2063120071 | | \n",
"| Q918 | C | 2063217449 | | \n",
"| Q150248 | C | 2068796814 | | \n",
"| Q866 | C | 2079749157 | | \n",
"| Q477675 | C | 2080785713 | | \n",
"| Q1967876 | C | 2084215818 | | \n",
"| Q750403 | C | 2084693498 | | \n",
"| Q355 | C | 2093900731 | | \n",
"| Q623578 | C | 2097991400 | | \n",
"| Q17299517 | D | 2105487660 | | \n",
"| Q33999 | C | 2108672678 | | \n",
"| Q2494649 | C | 2114531894 | | \n",
"| Q2597810 | C | 2128920607 | | \n",
"| Q193563 | C | 2130725560 | | \n",
"| Q423048 | C | 2136131564 | | \n",
"| Q37312 | C | 2142913121 | | \n",
"| Q54919 | C | 2148531382 | | \n",
"| Q36578 | C | 2229315598 | | \n",
"| Q30 | A | 2277746226 | | \n",
"| Q6581097 | D | 3273952711 | | \n",
"| Q5 | A | 5668008721 | | \n",
"| Q5296 | C | 12530369761 | | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class\n",
"1 Q10040378 E 0 \n",
"2 Q10069140 C 0 \n",
"3 Q10081695 C 0 \n",
"4 Q10092002 E 0 \n",
"5 Q10111267 E 0 \n",
"6 Q10149726 E 0 \n",
"7 Q10180230 E 0 \n",
"8 Q10185035 E 0 \n",
"9 Q10205202 E 0 \n",
"10 Q10252966 E 0 \n",
"11 Q10444494 C 0 \n",
"12 Q10624171 C 0 \n",
"13 Q10704108 C 0 \n",
"14 Q10750354 E 0 \n",
"15 Q10766855 E 0 \n",
"16 Q10827611 E 0 \n",
"17 Q11093044 E 0 \n",
"18 Q11934537 E 0 \n",
"19 Q12133466 E 0 \n",
"20 Q12264503 E 0 \n",
"21 Q12267516 E 0 \n",
"22 Q12304084 E 0 \n",
"23 Q12443525 E 0 \n",
"24 Q12543904 E 0 \n",
"25 Q12890205 E 0 \n",
"26 Q12891524 E 0 \n",
"27 Q12918202 E 0 \n",
"28 Q13005653 E 0 \n",
"29 Q13073896 E 0 \n",
"30 Q13163823 E 0 \n",
"⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 \n",
"17332279 Q31165 C 2048330818 \n",
"17332280 Q40629 C 2049755644 \n",
"17332281 Q105584 C 2049926923 \n",
"17332282 Q4584301 D 2052339927 \n",
"17332283 Q565 C 2052996261 \n",
"17332284 Q1868372 E 2056080224 \n",
"17332285 Q209330 C 2060928966 \n",
"17332286 Q14005 D 2063120071 \n",
"17332287 Q918 C 2063217449 \n",
"17332288 Q150248 C 2068796814 \n",
"17332289 Q866 C 2079749157 \n",
"17332290 Q477675 C 2080785713 \n",
"17332291 Q1967876 C 2084215818 \n",
"17332292 Q750403 C 2084693498 \n",
"17332293 Q355 C 2093900731 \n",
"17332294 Q623578 C 2097991400 \n",
"17332295 Q17299517 D 2105487660 \n",
"17332296 Q33999 C 2108672678 \n",
"17332297 Q2494649 C 2114531894 \n",
"17332298 Q2597810 C 2128920607 \n",
"17332299 Q193563 C 2130725560 \n",
"17332300 Q423048 C 2136131564 \n",
"17332301 Q37312 C 2142913121 \n",
"17332302 Q54919 C 2148531382 \n",
"17332303 Q36578 C 2229315598 \n",
"17332304 Q30 A 2277746226 \n",
"17332305 Q6581097 D 3273952711 \n",
"17332306 Q5 A 5668008721 \n",
"17332307 Q5296 C 12530369761 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td> </td><td> 1 </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td> </td><td> 2 </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td> </td><td> 3 </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td> </td><td> 4 </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td> </td><td> 5 </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td> </td><td> 6 </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td> </td><td> 7 </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td> </td><td> 8 </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td> </td><td> 9 </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td> </td><td>10 </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td> </td><td>11 </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td> </td><td>12 </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td> </td><td>13 </td></tr>\n",
"\t<tr><td>Q10750354</td><td>E </td><td>0 </td><td> </td><td>14 </td></tr>\n",
"\t<tr><td>Q10766855</td><td>E </td><td>0 </td><td> </td><td>15 </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td> </td><td>16 </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td> </td><td>17 </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td> </td><td>18 </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td> </td><td>19 </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td> </td><td>20 </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td> </td><td>21 </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td> </td><td>22 </td></tr>\n",
"\t<tr><td>Q12443525</td><td>E </td><td>0 </td><td> </td><td>23 </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td> </td><td>24 </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td> </td><td>25 </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td> </td><td>26 </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td> </td><td>27 </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td> </td><td>28 </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td> </td><td>29 </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td> </td><td>30 </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td> </td><td>17332278 </td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818</td><td> </td><td>17332279 </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td> </td><td>17332280 </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td> </td><td>17332281 </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927</td><td> </td><td>17332282 </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td> </td><td>17332283 </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224</td><td> </td><td>17332284 </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td> </td><td>17332285 </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td> </td><td>17332286 </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td> </td><td>17332287 </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td> </td><td>17332288 </td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157</td><td> </td><td>17332289 </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td> </td><td>17332290 </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td> </td><td>17332291 </td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498</td><td> </td><td>17332292 </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td> </td><td>17332293 </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td> </td><td>17332294 </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td> </td><td>17332295 </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td> </td><td>17332296 </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td> </td><td>17332297 </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td> </td><td>17332298 </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td> </td><td>17332299 </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td> </td><td>17332300 </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td> </td><td>17332301 </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td> </td><td>17332302 </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td> </td><td>17332303 </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td> </td><td>17332304 </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td> </td><td>17332305 </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721</td><td> </td><td>17332306 </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td> </td><td>17332307 </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & & 1 \\\\\n",
"\t Q10069140 & C & 0 & & 2 \\\\\n",
"\t Q10081695 & C & 0 & & 3 \\\\\n",
"\t Q10092002 & E & 0 & & 4 \\\\\n",
"\t Q10111267 & E & 0 & & 5 \\\\\n",
"\t Q10149726 & E & 0 & & 6 \\\\\n",
"\t Q10180230 & E & 0 & & 7 \\\\\n",
"\t Q10185035 & E & 0 & & 8 \\\\\n",
"\t Q10205202 & E & 0 & & 9 \\\\\n",
"\t Q10252966 & E & 0 & & 10 \\\\\n",
"\t Q10444494 & C & 0 & & 11 \\\\\n",
"\t Q10624171 & C & 0 & & 12 \\\\\n",
"\t Q10704108 & C & 0 & & 13 \\\\\n",
"\t Q10750354 & E & 0 & & 14 \\\\\n",
"\t Q10766855 & E & 0 & & 15 \\\\\n",
"\t Q10827611 & E & 0 & & 16 \\\\\n",
"\t Q11093044 & E & 0 & & 17 \\\\\n",
"\t Q11934537 & E & 0 & & 18 \\\\\n",
"\t Q12133466 & E & 0 & & 19 \\\\\n",
"\t Q12264503 & E & 0 & & 20 \\\\\n",
"\t Q12267516 & E & 0 & & 21 \\\\\n",
"\t Q12304084 & E & 0 & & 22 \\\\\n",
"\t Q12443525 & E & 0 & & 23 \\\\\n",
"\t Q12543904 & E & 0 & & 24 \\\\\n",
"\t Q12890205 & E & 0 & & 25 \\\\\n",
"\t Q12891524 & E & 0 & & 26 \\\\\n",
"\t Q12918202 & E & 0 & & 27 \\\\\n",
"\t Q13005653 & E & 0 & & 28 \\\\\n",
"\t Q13073896 & E & 0 & & 29 \\\\\n",
"\t Q13163823 & E & 0 & & 30 \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & & 17332278 \\\\\n",
"\t Q31165 & C & 2048330818 & & 17332279 \\\\\n",
"\t Q40629 & C & 2049755644 & & 17332280 \\\\\n",
"\t Q105584 & C & 2049926923 & & 17332281 \\\\\n",
"\t Q4584301 & D & 2052339927 & & 17332282 \\\\\n",
"\t Q565 & C & 2052996261 & & 17332283 \\\\\n",
"\t Q1868372 & E & 2056080224 & & 17332284 \\\\\n",
"\t Q209330 & C & 2060928966 & & 17332285 \\\\\n",
"\t Q14005 & D & 2063120071 & & 17332286 \\\\\n",
"\t Q918 & C & 2063217449 & & 17332287 \\\\\n",
"\t Q150248 & C & 2068796814 & & 17332288 \\\\\n",
"\t Q866 & C & 2079749157 & & 17332289 \\\\\n",
"\t Q477675 & C & 2080785713 & & 17332290 \\\\\n",
"\t Q1967876 & C & 2084215818 & & 17332291 \\\\\n",
"\t Q750403 & C & 2084693498 & & 17332292 \\\\\n",
"\t Q355 & C & 2093900731 & & 17332293 \\\\\n",
"\t Q623578 & C & 2097991400 & & 17332294 \\\\\n",
"\t Q17299517 & D & 2105487660 & & 17332295 \\\\\n",
"\t Q33999 & C & 2108672678 & & 17332296 \\\\\n",
"\t Q2494649 & C & 2114531894 & & 17332297 \\\\\n",
"\t Q2597810 & C & 2128920607 & & 17332298 \\\\\n",
"\t Q193563 & C & 2130725560 & & 17332299 \\\\\n",
"\t Q423048 & C & 2136131564 & & 17332300 \\\\\n",
"\t Q37312 & C & 2142913121 & & 17332301 \\\\\n",
"\t Q54919 & C & 2148531382 & & 17332302 \\\\\n",
"\t Q36578 & C & 2229315598 & & 17332303 \\\\\n",
"\t Q30 & A & 2277746226 & & 17332304 \\\\\n",
"\t Q6581097 & D & 3273952711 & & 17332305 \\\\\n",
"\t Q5 & A & 5668008721 & & 17332306 \\\\\n",
"\t Q5296 & C & 12530369761 & & 17332307 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | | 1 | \n",
"| Q10069140 | C | 0 | | 2 | \n",
"| Q10081695 | C | 0 | | 3 | \n",
"| Q10092002 | E | 0 | | 4 | \n",
"| Q10111267 | E | 0 | | 5 | \n",
"| Q10149726 | E | 0 | | 6 | \n",
"| Q10180230 | E | 0 | | 7 | \n",
"| Q10185035 | E | 0 | | 8 | \n",
"| Q10205202 | E | 0 | | 9 | \n",
"| Q10252966 | E | 0 | | 10 | \n",
"| Q10444494 | C | 0 | | 11 | \n",
"| Q10624171 | C | 0 | | 12 | \n",
"| Q10704108 | C | 0 | | 13 | \n",
"| Q10750354 | E | 0 | | 14 | \n",
"| Q10766855 | E | 0 | | 15 | \n",
"| Q10827611 | E | 0 | | 16 | \n",
"| Q11093044 | E | 0 | | 17 | \n",
"| Q11934537 | E | 0 | | 18 | \n",
"| Q12133466 | E | 0 | | 19 | \n",
"| Q12264503 | E | 0 | | 20 | \n",
"| Q12267516 | E | 0 | | 21 | \n",
"| Q12304084 | E | 0 | | 22 | \n",
"| Q12443525 | E | 0 | | 23 | \n",
"| Q12543904 | E | 0 | | 24 | \n",
"| Q12890205 | E | 0 | | 25 | \n",
"| Q12891524 | E | 0 | | 26 | \n",
"| Q12918202 | E | 0 | | 27 | \n",
"| Q13005653 | E | 0 | | 28 | \n",
"| Q13073896 | E | 0 | | 29 | \n",
"| Q13163823 | E | 0 | | 30 | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | | 17332278 | \n",
"| Q31165 | C | 2048330818 | | 17332279 | \n",
"| Q40629 | C | 2049755644 | | 17332280 | \n",
"| Q105584 | C | 2049926923 | | 17332281 | \n",
"| Q4584301 | D | 2052339927 | | 17332282 | \n",
"| Q565 | C | 2052996261 | | 17332283 | \n",
"| Q1868372 | E | 2056080224 | | 17332284 | \n",
"| Q209330 | C | 2060928966 | | 17332285 | \n",
"| Q14005 | D | 2063120071 | | 17332286 | \n",
"| Q918 | C | 2063217449 | | 17332287 | \n",
"| Q150248 | C | 2068796814 | | 17332288 | \n",
"| Q866 | C | 2079749157 | | 17332289 | \n",
"| Q477675 | C | 2080785713 | | 17332290 | \n",
"| Q1967876 | C | 2084215818 | | 17332291 | \n",
"| Q750403 | C | 2084693498 | | 17332292 | \n",
"| Q355 | C | 2093900731 | | 17332293 | \n",
"| Q623578 | C | 2097991400 | | 17332294 | \n",
"| Q17299517 | D | 2105487660 | | 17332295 | \n",
"| Q33999 | C | 2108672678 | | 17332296 | \n",
"| Q2494649 | C | 2114531894 | | 17332297 | \n",
"| Q2597810 | C | 2128920607 | | 17332298 | \n",
"| Q193563 | C | 2130725560 | | 17332299 | \n",
"| Q423048 | C | 2136131564 | | 17332300 | \n",
"| Q37312 | C | 2142913121 | | 17332301 | \n",
"| Q54919 | C | 2148531382 | | 17332302 | \n",
"| Q36578 | C | 2229315598 | | 17332303 | \n",
"| Q30 | A | 2277746226 | | 17332304 | \n",
"| Q6581097 | D | 3273952711 | | 17332305 | \n",
"| Q5 | A | 5668008721 | | 17332306 | \n",
"| Q5296 | C | 12530369761 | | 17332307 | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum \n",
"1 Q10040378 E 0 1 \n",
"2 Q10069140 C 0 2 \n",
"3 Q10081695 C 0 3 \n",
"4 Q10092002 E 0 4 \n",
"5 Q10111267 E 0 5 \n",
"6 Q10149726 E 0 6 \n",
"7 Q10180230 E 0 7 \n",
"8 Q10185035 E 0 8 \n",
"9 Q10205202 E 0 9 \n",
"10 Q10252966 E 0 10 \n",
"11 Q10444494 C 0 11 \n",
"12 Q10624171 C 0 12 \n",
"13 Q10704108 C 0 13 \n",
"14 Q10750354 E 0 14 \n",
"15 Q10766855 E 0 15 \n",
"16 Q10827611 E 0 16 \n",
"17 Q11093044 E 0 17 \n",
"18 Q11934537 E 0 18 \n",
"19 Q12133466 E 0 19 \n",
"20 Q12264503 E 0 20 \n",
"21 Q12267516 E 0 21 \n",
"22 Q12304084 E 0 22 \n",
"23 Q12443525 E 0 23 \n",
"24 Q12543904 E 0 24 \n",
"25 Q12890205 E 0 25 \n",
"26 Q12891524 E 0 26 \n",
"27 Q12918202 E 0 27 \n",
"28 Q13005653 E 0 28 \n",
"29 Q13073896 E 0 29 \n",
"30 Q13163823 E 0 30 \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 17332278\n",
"17332279 Q31165 C 2048330818 17332279\n",
"17332280 Q40629 C 2049755644 17332280\n",
"17332281 Q105584 C 2049926923 17332281\n",
"17332282 Q4584301 D 2052339927 17332282\n",
"17332283 Q565 C 2052996261 17332283\n",
"17332284 Q1868372 E 2056080224 17332284\n",
"17332285 Q209330 C 2060928966 17332285\n",
"17332286 Q14005 D 2063120071 17332286\n",
"17332287 Q918 C 2063217449 17332287\n",
"17332288 Q150248 C 2068796814 17332288\n",
"17332289 Q866 C 2079749157 17332289\n",
"17332290 Q477675 C 2080785713 17332290\n",
"17332291 Q1967876 C 2084215818 17332291\n",
"17332292 Q750403 C 2084693498 17332292\n",
"17332293 Q355 C 2093900731 17332293\n",
"17332294 Q623578 C 2097991400 17332294\n",
"17332295 Q17299517 D 2105487660 17332295\n",
"17332296 Q33999 C 2108672678 17332296\n",
"17332297 Q2494649 C 2114531894 17332297\n",
"17332298 Q2597810 C 2128920607 17332298\n",
"17332299 Q193563 C 2130725560 17332299\n",
"17332300 Q423048 C 2136131564 17332300\n",
"17332301 Q37312 C 2142913121 17332301\n",
"17332302 Q54919 C 2148531382 17332302\n",
"17332303 Q36578 C 2229315598 17332303\n",
"17332304 Q30 A 2277746226 17332304\n",
"17332305 Q6581097 D 3273952711 17332305\n",
"17332306 Q5 A 5668008721 17332306\n",
"17332307 Q5296 C 12530369761 17332307"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## 2: assign popularity assessment class based on rank\n",
"## (buckets based on number of articles in each class)\n",
"articles_by_pop[, pop_class := ''];\n",
"articles_by_pop[, seqNum := seq_len(nrow(articles_by_pop))];"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"assign_pop_class = function(dataset, classes, class_n) {\n",
" ## Based on the per-class number of articles in class_n\n",
" ## assign popularity based on classes to dataset.\n",
" prev_idx = 0;\n",
" for(rating in classes) {\n",
" start_idx = prev_idx + 1;\n",
" end_idx = start_idx + class_n[prediction == rating]$narticles;\n",
" print(paste('start_idx =', start_idx, ', end_idx = ', end_idx));\n",
" dataset[seqNum >= start_idx & seqNum <= end_idx, pop_class := rating];\n",
" prev_idx = end_idx -1;\n",
" }\n",
" dataset;\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1] \"start_idx = 1 , end_idx = 11018417\"\n",
"[1] \"start_idx = 11018417 , end_idx = 13668843\"\n",
"[1] \"start_idx = 13668843 , end_idx = 17135527\"\n",
"[1] \"start_idx = 17135527 , end_idx = 17332023\"\n",
"[1] \"start_idx = 17332023 , end_idx = 17332308\"\n"
]
}
],
"source": [
"articles_by_pop = assign_pop_class(articles_by_pop,\n",
" assessment_classes, n_per_class);"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"create_dissonance_matrix = function(articledata, classes) {\n",
" d_mtrx = matrix(0, nrow=length(classes), ncol=length(classes));\n",
" rownames(d_mtrx) = classes;\n",
" colnames(d_mtrx) = classes;\n",
"\n",
" for(real_rating in classes) {\n",
" for(pop_rating in classes) {\n",
" d_mtrx[real_rating, pop_rating] = length(articledata[prediction == real_rating & pop_class == pop_rating]$entity_id);\n",
" }\n",
" }\n",
" d_mtrx;\n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>E</th><th scope=col>D</th><th scope=col>C</th><th scope=col>B</th><th scope=col>A</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>E</th><td>7390748</td><td>1675081</td><td>1904939</td><td> 47625 </td><td> 23 </td></tr>\n",
"\t<tr><th scope=row>D</th><td>1398509</td><td> 557220</td><td> 661379</td><td> 33262 </td><td> 56 </td></tr>\n",
"\t<tr><th scope=row>C</th><td>2111108</td><td> 402118</td><td> 850756</td><td>102534 </td><td>168 </td></tr>\n",
"\t<tr><th scope=row>B</th><td> 118051</td><td> 16007</td><td> 49602</td><td> 12829 </td><td> 7 </td></tr>\n",
"\t<tr><th scope=row>A</th><td> 0</td><td> 0</td><td> 8</td><td> 246 </td><td> 31 </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" & E & D & C & B & A\\\\\n",
"\\hline\n",
"\tE & 7390748 & 1675081 & 1904939 & 47625 & 23 \\\\\n",
"\tD & 1398509 & 557220 & 661379 & 33262 & 56 \\\\\n",
"\tC & 2111108 & 402118 & 850756 & 102534 & 168 \\\\\n",
"\tB & 118051 & 16007 & 49602 & 12829 & 7 \\\\\n",
"\tA & 0 & 0 & 8 & 246 & 31 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| <!--/--> | E | D | C | B | A | \n",
"|---|---|---|---|---|\n",
"| E | 7390748 | 1675081 | 1904939 | 47625 | 23 | \n",
"| D | 1398509 | 557220 | 661379 | 33262 | 56 | \n",
"| C | 2111108 | 402118 | 850756 | 102534 | 168 | \n",
"| B | 118051 | 16007 | 49602 | 12829 | 7 | \n",
"| A | 0 | 0 | 8 | 246 | 31 | \n",
"\n",
"\n"
],
"text/plain": [
" E D C B A \n",
"E 7390748 1675081 1904939 47625 23\n",
"D 1398509 557220 661379 33262 56\n",
"C 2111108 402118 850756 102534 168\n",
"B 118051 16007 49602 12829 7\n",
"A 0 0 8 246 31"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Based on direct hits to articles:\n",
"create_dissonance_matrix(articles_by_pop, assessment_classes)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dissonance_matrix = create_dissonance_matrix(articles_by_pop,\n",
" assessment_classes);"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"0.508390717981167"
],
"text/latex": [
"0.508390717981167"
],
"text/markdown": [
"0.508390717981167"
],
"text/plain": [
"[1] 0.5083907"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Total misaligned entities\n",
"(dissonance_matrix[1,1]+dissonance_matrix[2,2]+dissonance_matrix[3,3]+dissonance_matrix[4,4]+dissonance_matrix[5,5])/sum(dissonance_matrix[,])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"0.108771929824561"
],
"text/latex": [
"0.108771929824561"
],
"text/markdown": [
"0.108771929824561"
],
"text/plain": [
"[1] 0.1087719"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# A class quality and A class views over A class quality\n",
"dissonance_matrix[5,5]/sum(dissonance_matrix[5,])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"0"
],
"text/latex": [
"0"
],
"text/markdown": [
"0"
],
"text/plain": [
"[1] 0"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# A class quality and E and D class views over A class quality\n",
"(dissonance_matrix[5,1]+dissonance_matrix[5,2])/sum(dissonance_matrix[5,])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"0.891228070175439"
],
"text/latex": [
"0.891228070175439"
],
"text/markdown": [
"0.891228070175439"
],
"text/plain": [
"[1] 0.8912281"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# A class quality and < A class views\n",
"(dissonance_matrix[5,1]+dissonance_matrix[5,2]+dissonance_matrix[5,3]+dissonance_matrix[5,4])/sum(dissonance_matrix[5,])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"0.891228070175439"
],
"text/latex": [
"0.891228070175439"
],
"text/markdown": [
"0.891228070175439"
],
"text/plain": [
"[1] 0.8912281"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# < A class quality and A class views\n",
"(dissonance_matrix[1,5]+dissonance_matrix[2,5]+dissonance_matrix[3,5]+dissonance_matrix[4,5])/sum(dissonance_matrix[,5])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"prediction_e_pop_class_a <- merge(articles_by_pop[prediction == 'E' & pop_class == 'A'],quality_prediction_and_page_views, by='entity_id')[, c(\"entity_id\",\"page_views.x\", \"number_of_revisions\")]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>page_views.x</th><th scope=col>number_of_revisions</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q1002972 </td><td>2045659200</td><td>30 </td></tr>\n",
"\t<tr><td>Q1097348 </td><td> 76241976</td><td>30 </td></tr>\n",
"\t<tr><td>Q15401930 </td><td> 215204156</td><td>21 </td></tr>\n",
"\t<tr><td>Q15980804 </td><td> 84469470</td><td>34 </td></tr>\n",
"\t<tr><td>Q18241050 </td><td>2045553487</td><td> 6 </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>2056080224</td><td>45 </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lll}\n",
" entity\\_id & page\\_views.x & number\\_of\\_revisions\\\\\n",
"\\hline\n",
"\t Q1002972 & 2045659200 & 30 \\\\\n",
"\t Q1097348 & 76241976 & 30 \\\\\n",
"\t Q15401930 & 215204156 & 21 \\\\\n",
"\t Q15980804 & 84469470 & 34 \\\\\n",
"\t Q18241050 & 2045553487 & 6 \\\\\n",
"\t Q1868372 & 2056080224 & 45 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | page_views.x | number_of_revisions | \n",
"|---|---|---|---|---|---|\n",
"| Q1002972 | 2045659200 | 30 | \n",
"| Q1097348 | 76241976 | 30 | \n",
"| Q15401930 | 215204156 | 21 | \n",
"| Q15980804 | 84469470 | 34 | \n",
"| Q18241050 | 2045553487 | 6 | \n",
"| Q1868372 | 2056080224 | 45 | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id page_views.x number_of_revisions\n",
"1 Q1002972 2045659200 30 \n",
"2 Q1097348 76241976 30 \n",
"3 Q15401930 215204156 21 \n",
"4 Q15980804 84469470 34 \n",
"5 Q18241050 2045553487 6 \n",
"6 Q1868372 2056080224 45 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"head(prediction_e_pop_class_a)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Q: why do I get _two_ pageid columns? Solution is to do the selection\n",
"## on the joined table, not as a select _in_ the join.\n",
"\n",
"## Dissonance matrix proportions by row (..., 1) and column (..., 2)\n",
"## rounded to 1 decimal places."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>E</th><th scope=col>D</th><th scope=col>C</th><th scope=col>B</th><th scope=col>A</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>E</th><td>67.1</td><td>15.2</td><td>17.3</td><td> 0.4</td><td> 0.0</td></tr>\n",
"\t<tr><th scope=row>D</th><td>52.8</td><td>21.0</td><td>25.0</td><td> 1.3</td><td> 0.0</td></tr>\n",
"\t<tr><th scope=row>C</th><td>60.9</td><td>11.6</td><td>24.5</td><td> 3.0</td><td> 0.0</td></tr>\n",
"\t<tr><th scope=row>B</th><td>60.1</td><td> 8.1</td><td>25.2</td><td> 6.5</td><td> 0.0</td></tr>\n",
"\t<tr><th scope=row>A</th><td> 0.0</td><td> 0.0</td><td> 2.8</td><td>86.3</td><td>10.9</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" & E & D & C & B & A\\\\\n",
"\\hline\n",
"\tE & 67.1 & 15.2 & 17.3 & 0.4 & 0.0\\\\\n",
"\tD & 52.8 & 21.0 & 25.0 & 1.3 & 0.0\\\\\n",
"\tC & 60.9 & 11.6 & 24.5 & 3.0 & 0.0\\\\\n",
"\tB & 60.1 & 8.1 & 25.2 & 6.5 & 0.0\\\\\n",
"\tA & 0.0 & 0.0 & 2.8 & 86.3 & 10.9\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| <!--/--> | E | D | C | B | A | \n",
"|---|---|---|---|---|\n",
"| E | 67.1 | 15.2 | 17.3 | 0.4 | 0.0 | \n",
"| D | 52.8 | 21.0 | 25.0 | 1.3 | 0.0 | \n",
"| C | 60.9 | 11.6 | 24.5 | 3.0 | 0.0 | \n",
"| B | 60.1 | 8.1 | 25.2 | 6.5 | 0.0 | \n",
"| A | 0.0 | 0.0 | 2.8 | 86.3 | 10.9 | \n",
"\n",
"\n"
],
"text/plain": [
" E D C B A \n",
"E 67.1 15.2 17.3 0.4 0.0\n",
"D 52.8 21.0 25.0 1.3 0.0\n",
"C 60.9 11.6 24.5 3.0 0.0\n",
"B 60.1 8.1 25.2 6.5 0.0\n",
"A 0.0 0.0 2.8 86.3 10.9"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"round(100*prop.table(dissonance_matrix, 1), 1);"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>E</th><th scope=col>D</th><th scope=col>C</th><th scope=col>B</th><th scope=col>A</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>E</th><td>67.1</td><td>63.2</td><td>54.9</td><td>24.2</td><td> 8.1</td></tr>\n",
"\t<tr><th scope=row>D</th><td>12.7</td><td>21.0</td><td>19.1</td><td>16.9</td><td>19.6</td></tr>\n",
"\t<tr><th scope=row>C</th><td>19.2</td><td>15.2</td><td>24.5</td><td>52.2</td><td>58.9</td></tr>\n",
"\t<tr><th scope=row>B</th><td> 1.1</td><td> 0.6</td><td> 1.4</td><td> 6.5</td><td> 2.5</td></tr>\n",
"\t<tr><th scope=row>A</th><td> 0.0</td><td> 0.0</td><td> 0.0</td><td> 0.1</td><td>10.9</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" & E & D & C & B & A\\\\\n",
"\\hline\n",
"\tE & 67.1 & 63.2 & 54.9 & 24.2 & 8.1\\\\\n",
"\tD & 12.7 & 21.0 & 19.1 & 16.9 & 19.6\\\\\n",
"\tC & 19.2 & 15.2 & 24.5 & 52.2 & 58.9\\\\\n",
"\tB & 1.1 & 0.6 & 1.4 & 6.5 & 2.5\\\\\n",
"\tA & 0.0 & 0.0 & 0.0 & 0.1 & 10.9\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| <!--/--> | E | D | C | B | A | \n",
"|---|---|---|---|---|\n",
"| E | 67.1 | 63.2 | 54.9 | 24.2 | 8.1 | \n",
"| D | 12.7 | 21.0 | 19.1 | 16.9 | 19.6 | \n",
"| C | 19.2 | 15.2 | 24.5 | 52.2 | 58.9 | \n",
"| B | 1.1 | 0.6 | 1.4 | 6.5 | 2.5 | \n",
"| A | 0.0 | 0.0 | 0.0 | 0.1 | 10.9 | \n",
"\n",
"\n"
],
"text/plain": [
" E D C B A \n",
"E 67.1 63.2 54.9 24.2 8.1\n",
"D 12.7 21.0 19.1 16.9 19.6\n",
"C 19.2 15.2 24.5 52.2 58.9\n",
"B 1.1 0.6 1.4 6.5 2.5\n",
"A 0.0 0.0 0.0 0.1 10.9"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"round(100*prop.table(dissonance_matrix, 2), 1);"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>pop_class</th><th scope=col>prediction.x</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q1002972 </td><td>A </td><td>E </td></tr>\n",
"\t<tr><td>Q1028181 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q103204 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1048694 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q105584 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1061861 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1063819 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q10726338</td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q10798782</td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q10800557</td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q1097348 </td><td>A </td><td>E </td></tr>\n",
"\t<tr><td>Q11028 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1123836 </td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q1133733 </td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q11399 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q11424 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q11573 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q116933 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q11789729</td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q11920 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1200925 </td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q12136 </td><td>A </td><td>B </td></tr>\n",
"\t<tr><td>Q1250916 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1257444 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1278335 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q130232 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q131454 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q131524 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q1321 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q13219454</td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q732353 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q739 </td><td>A </td><td>B </td></tr>\n",
"\t<tr><td>Q750403 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q753110 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q7725310</td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q7727 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q7737 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q7867 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q788926 </td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q809830 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q8261 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q82799 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q82955 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q829984 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q830077 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q83042 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q83552 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q8447 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q853614 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q855091 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q864380 </td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q866 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q918 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q937857 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q9384291</td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q938726 </td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q947873 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q948329 </td><td>A </td><td>D </td></tr>\n",
"\t<tr><td>Q953058 </td><td>A </td><td>C </td></tr>\n",
"\t<tr><td>Q974144 </td><td>A </td><td>D </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lll}\n",
" entity\\_id & pop\\_class & prediction.x\\\\\n",
"\\hline\n",
"\t Q1002972 & A & E \\\\\n",
"\t Q1028181 & A & C \\\\\n",
"\t Q103204 & A & C \\\\\n",
"\t Q1048694 & A & C \\\\\n",
"\t Q105584 & A & C \\\\\n",
"\t Q1061861 & A & C \\\\\n",
"\t Q1063819 & A & C \\\\\n",
"\t Q10726338 & A & D \\\\\n",
"\t Q10798782 & A & D \\\\\n",
"\t Q10800557 & A & D \\\\\n",
"\t Q1097348 & A & E \\\\\n",
"\t Q11028 & A & C \\\\\n",
"\t Q1123836 & A & D \\\\\n",
"\t Q1133733 & A & D \\\\\n",
"\t Q11399 & A & C \\\\\n",
"\t Q11424 & A & C \\\\\n",
"\t Q11573 & A & C \\\\\n",
"\t Q116933 & A & C \\\\\n",
"\t Q11789729 & A & D \\\\\n",
"\t Q11920 & A & C \\\\\n",
"\t Q1200925 & A & D \\\\\n",
"\t Q12136 & A & B \\\\\n",
"\t Q1250916 & A & C \\\\\n",
"\t Q1257444 & A & C \\\\\n",
"\t Q1278335 & A & C \\\\\n",
"\t Q130232 & A & C \\\\\n",
"\t Q131454 & A & C \\\\\n",
"\t Q131524 & A & C \\\\\n",
"\t Q1321 & A & C \\\\\n",
"\t Q13219454 & A & D \\\\\n",
"\t ⋮ & ⋮ & ⋮\\\\\n",
"\t Q732353 & A & C \\\\\n",
"\t Q739 & A & B \\\\\n",
"\t Q750403 & A & C \\\\\n",
"\t Q753110 & A & C \\\\\n",
"\t Q7725310 & A & D \\\\\n",
"\t Q7727 & A & C \\\\\n",
"\t Q7737 & A & C \\\\\n",
"\t Q7867 & A & C \\\\\n",
"\t Q788926 & A & D \\\\\n",
"\t Q809830 & A & C \\\\\n",
"\t Q8261 & A & C \\\\\n",
"\t Q82799 & A & C \\\\\n",
"\t Q82955 & A & C \\\\\n",
"\t Q829984 & A & C \\\\\n",
"\t Q830077 & A & C \\\\\n",
"\t Q83042 & A & C \\\\\n",
"\t Q83552 & A & C \\\\\n",
"\t Q8447 & A & C \\\\\n",
"\t Q853614 & A & C \\\\\n",
"\t Q855091 & A & C \\\\\n",
"\t Q864380 & A & D \\\\\n",
"\t Q866 & A & C \\\\\n",
"\t Q918 & A & C \\\\\n",
"\t Q937857 & A & C \\\\\n",
"\t Q9384291 & A & D \\\\\n",
"\t Q938726 & A & D \\\\\n",
"\t Q947873 & A & C \\\\\n",
"\t Q948329 & A & D \\\\\n",
"\t Q953058 & A & C \\\\\n",
"\t Q974144 & A & D \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | pop_class | prediction.x | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q1002972 | A | E | \n",
"| Q1028181 | A | C | \n",
"| Q103204 | A | C | \n",
"| Q1048694 | A | C | \n",
"| Q105584 | A | C | \n",
"| Q1061861 | A | C | \n",
"| Q1063819 | A | C | \n",
"| Q10726338 | A | D | \n",
"| Q10798782 | A | D | \n",
"| Q10800557 | A | D | \n",
"| Q1097348 | A | E | \n",
"| Q11028 | A | C | \n",
"| Q1123836 | A | D | \n",
"| Q1133733 | A | D | \n",
"| Q11399 | A | C | \n",
"| Q11424 | A | C | \n",
"| Q11573 | A | C | \n",
"| Q116933 | A | C | \n",
"| Q11789729 | A | D | \n",
"| Q11920 | A | C | \n",
"| Q1200925 | A | D | \n",
"| Q12136 | A | B | \n",
"| Q1250916 | A | C | \n",
"| Q1257444 | A | C | \n",
"| Q1278335 | A | C | \n",
"| Q130232 | A | C | \n",
"| Q131454 | A | C | \n",
"| Q131524 | A | C | \n",
"| Q1321 | A | C | \n",
"| Q13219454 | A | D | \n",
"| ⋮ | ⋮ | ⋮ | \n",
"| Q732353 | A | C | \n",
"| Q739 | A | B | \n",
"| Q750403 | A | C | \n",
"| Q753110 | A | C | \n",
"| Q7725310 | A | D | \n",
"| Q7727 | A | C | \n",
"| Q7737 | A | C | \n",
"| Q7867 | A | C | \n",
"| Q788926 | A | D | \n",
"| Q809830 | A | C | \n",
"| Q8261 | A | C | \n",
"| Q82799 | A | C | \n",
"| Q82955 | A | C | \n",
"| Q829984 | A | C | \n",
"| Q830077 | A | C | \n",
"| Q83042 | A | C | \n",
"| Q83552 | A | C | \n",
"| Q8447 | A | C | \n",
"| Q853614 | A | C | \n",
"| Q855091 | A | C | \n",
"| Q864380 | A | D | \n",
"| Q866 | A | C | \n",
"| Q918 | A | C | \n",
"| Q937857 | A | C | \n",
"| Q9384291 | A | D | \n",
"| Q938726 | A | D | \n",
"| Q947873 | A | C | \n",
"| Q948329 | A | D | \n",
"| Q953058 | A | C | \n",
"| Q974144 | A | D | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id pop_class prediction.x\n",
"1 Q1002972 A E \n",
"2 Q1028181 A C \n",
"3 Q103204 A C \n",
"4 Q1048694 A C \n",
"5 Q105584 A C \n",
"6 Q1061861 A C \n",
"7 Q1063819 A C \n",
"8 Q10726338 A D \n",
"9 Q10798782 A D \n",
"10 Q10800557 A D \n",
"11 Q1097348 A E \n",
"12 Q11028 A C \n",
"13 Q1123836 A D \n",
"14 Q1133733 A D \n",
"15 Q11399 A C \n",
"16 Q11424 A C \n",
"17 Q11573 A C \n",
"18 Q116933 A C \n",
"19 Q11789729 A D \n",
"20 Q11920 A C \n",
"21 Q1200925 A D \n",
"22 Q12136 A B \n",
"23 Q1250916 A C \n",
"24 Q1257444 A C \n",
"25 Q1278335 A C \n",
"26 Q130232 A C \n",
"27 Q131454 A C \n",
"28 Q131524 A C \n",
"29 Q1321 A C \n",
"30 Q13219454 A D \n",
"⋮ ⋮ ⋮ ⋮ \n",
"225 Q732353 A C \n",
"226 Q739 A B \n",
"227 Q750403 A C \n",
"228 Q753110 A C \n",
"229 Q7725310 A D \n",
"230 Q7727 A C \n",
"231 Q7737 A C \n",
"232 Q7867 A C \n",
"233 Q788926 A D \n",
"234 Q809830 A C \n",
"235 Q8261 A C \n",
"236 Q82799 A C \n",
"237 Q82955 A C \n",
"238 Q829984 A C \n",
"239 Q830077 A C \n",
"240 Q83042 A C \n",
"241 Q83552 A C \n",
"242 Q8447 A C \n",
"243 Q853614 A C \n",
"244 Q855091 A C \n",
"245 Q864380 A D \n",
"246 Q866 A C \n",
"247 Q918 A C \n",
"248 Q937857 A C \n",
"249 Q9384291 A D \n",
"250 Q938726 A D \n",
"251 Q947873 A C \n",
"252 Q948329 A D \n",
"253 Q953058 A C \n",
"254 Q974144 A D "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Let's write the stubs out to a file\n",
"write.table(merge(articles_by_pop[(prediction == 'E' | prediction == 'D' | prediction == 'C' | prediction == 'B') & pop_class == 'A'], quality_prediction_and_page_views, by='entity_id')[, c(\"entity_id\",\"pop_class\", \"prediction.x\")],\n",
" '../../results/entity_categorization/201509_a_class_views_less_than_a_quality.tsv', row.names=FALSE, col.names=FALSE, quote=FALSE, sep='\\t');\n",
"merge(articles_by_pop[(prediction == 'E' | prediction == 'D' | prediction == 'C' | prediction == 'B') & pop_class == 'A'], quality_prediction_and_page_views, by='entity_id')[, c(\"entity_id\",\"pop_class\", \"prediction.x\")]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"write.table(merge(articles_by_pop[prediction == 'A' & (pop_class == 'B' | pop_class == 'C' | pop_class == 'D' | pop_class == 'E')], quality_prediction_and_page_views, by='entity_id')[, c(\"entity_id\",\"pop_class\", \"prediction.x\")],\n",
" '../../results/entity_categorization/201509_a_class_quality_less_than_a_views.tsv', row.names=FALSE, col.names=FALSE, quote=FALSE, sep='\\t');"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"write.table(merge(articles_by_pop[(prediction == 'A' & pop_class == 'A') | (prediction == 'B' & pop_class == 'B') | (prediction == 'C' & pop_class == 'C') | (prediction == 'D' & pop_class == 'D') | (prediction == 'E' & pop_class == 'E')], quality_prediction_and_page_views, by='entity_id')[, c(\"entity_id\",\"pop_class\", \"prediction.x\")],\n",
" '../../results/entity_categorization/201509_aligned.tsv', row.names=FALSE, col.names=FALSE, quote=FALSE, sep='\\t');"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"write.table(merge(articles_by_pop[(prediction == 'A' & pop_class != 'A') | (prediction == 'B' & pop_class != 'B') | (prediction == 'C' & pop_class != 'C') | (prediction == 'D' & pop_class != 'D') | (prediction == 'E' & pop_class != 'E')], quality_prediction_and_page_views, by='entity_id')[, c(\"entity_id\",\"pop_class\", \"prediction.x\")],\n",
" '../../results/entity_categorization/201509_misaligned.tsv', row.names=FALSE, col.names=FALSE, quote=FALSE, sep='\\t');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dissonance Measures (was seperate file)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Various ways of measuring dissonance.\n",
"\n",
"## DATA ASSUMPTION: articles_by_pop from build-dissonance-table.R\n",
"## is loaded into memory.\n",
"\n",
"## None/Moderate/High measure of dissonance"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td>E </td><td> 1 </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td>E </td><td> 2 </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td>E </td><td> 3 </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td>E </td><td> 4 </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td>E </td><td> 5 </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td>E </td><td> 6 </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td>E </td><td> 7 </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td>E </td><td> 8 </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td>E </td><td> 9 </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td>E </td><td>10 </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td>E </td><td>11 </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td>E </td><td>12 </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td>E </td><td>13 </td></tr>\n",
"\t<tr><td>Q10750354</td><td>E </td><td>0 </td><td>E </td><td>14 </td></tr>\n",
"\t<tr><td>Q10766855</td><td>E </td><td>0 </td><td>E </td><td>15 </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td>E </td><td>16 </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td>E </td><td>17 </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td>E </td><td>18 </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td>E </td><td>19 </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td>E </td><td>20 </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td>E </td><td>21 </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td>E </td><td>22 </td></tr>\n",
"\t<tr><td>Q12443525</td><td>E </td><td>0 </td><td>E </td><td>23 </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td>E </td><td>24 </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td>E </td><td>25 </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td>E </td><td>26 </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td>E </td><td>27 </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td>E </td><td>28 </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td>E </td><td>29 </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td>E </td><td>30 </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>17332278 </td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818</td><td>A </td><td>17332279 </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>17332280 </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>17332281 </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927</td><td>A </td><td>17332282 </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>17332283 </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224</td><td>A </td><td>17332284 </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>17332285 </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>17332286 </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>17332287 </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>17332288 </td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157</td><td>A </td><td>17332289 </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>17332290 </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>17332291 </td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498</td><td>A </td><td>17332292 </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>17332293 </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>17332294 </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>17332295 </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>17332296 </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>17332297 </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>17332298 </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>17332299 </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>17332300 </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>17332301 </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>17332302 </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>17332303 </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>17332304 </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>17332305 </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721</td><td>A </td><td>17332306 </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>17332307 </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 \\\\\n",
"\t Q10069140 & C & 0 & E & 2 \\\\\n",
"\t Q10081695 & C & 0 & E & 3 \\\\\n",
"\t Q10092002 & E & 0 & E & 4 \\\\\n",
"\t Q10111267 & E & 0 & E & 5 \\\\\n",
"\t Q10149726 & E & 0 & E & 6 \\\\\n",
"\t Q10180230 & E & 0 & E & 7 \\\\\n",
"\t Q10185035 & E & 0 & E & 8 \\\\\n",
"\t Q10205202 & E & 0 & E & 9 \\\\\n",
"\t Q10252966 & E & 0 & E & 10 \\\\\n",
"\t Q10444494 & C & 0 & E & 11 \\\\\n",
"\t Q10624171 & C & 0 & E & 12 \\\\\n",
"\t Q10704108 & C & 0 & E & 13 \\\\\n",
"\t Q10750354 & E & 0 & E & 14 \\\\\n",
"\t Q10766855 & E & 0 & E & 15 \\\\\n",
"\t Q10827611 & E & 0 & E & 16 \\\\\n",
"\t Q11093044 & E & 0 & E & 17 \\\\\n",
"\t Q11934537 & E & 0 & E & 18 \\\\\n",
"\t Q12133466 & E & 0 & E & 19 \\\\\n",
"\t Q12264503 & E & 0 & E & 20 \\\\\n",
"\t Q12267516 & E & 0 & E & 21 \\\\\n",
"\t Q12304084 & E & 0 & E & 22 \\\\\n",
"\t Q12443525 & E & 0 & E & 23 \\\\\n",
"\t Q12543904 & E & 0 & E & 24 \\\\\n",
"\t Q12890205 & E & 0 & E & 25 \\\\\n",
"\t Q12891524 & E & 0 & E & 26 \\\\\n",
"\t Q12918202 & E & 0 & E & 27 \\\\\n",
"\t Q13005653 & E & 0 & E & 28 \\\\\n",
"\t Q13073896 & E & 0 & E & 29 \\\\\n",
"\t Q13163823 & E & 0 & E & 30 \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 \\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 \\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 \\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 \\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 \\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 \\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 \\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 \\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 \\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 \\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 \\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 \\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 \\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 \\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 \\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 \\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 \\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 \\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 \\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 \\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 \\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 \\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | \n",
"| Q10069140 | C | 0 | E | 2 | \n",
"| Q10081695 | C | 0 | E | 3 | \n",
"| Q10092002 | E | 0 | E | 4 | \n",
"| Q10111267 | E | 0 | E | 5 | \n",
"| Q10149726 | E | 0 | E | 6 | \n",
"| Q10180230 | E | 0 | E | 7 | \n",
"| Q10185035 | E | 0 | E | 8 | \n",
"| Q10205202 | E | 0 | E | 9 | \n",
"| Q10252966 | E | 0 | E | 10 | \n",
"| Q10444494 | C | 0 | E | 11 | \n",
"| Q10624171 | C | 0 | E | 12 | \n",
"| Q10704108 | C | 0 | E | 13 | \n",
"| Q10750354 | E | 0 | E | 14 | \n",
"| Q10766855 | E | 0 | E | 15 | \n",
"| Q10827611 | E | 0 | E | 16 | \n",
"| Q11093044 | E | 0 | E | 17 | \n",
"| Q11934537 | E | 0 | E | 18 | \n",
"| Q12133466 | E | 0 | E | 19 | \n",
"| Q12264503 | E | 0 | E | 20 | \n",
"| Q12267516 | E | 0 | E | 21 | \n",
"| Q12304084 | E | 0 | E | 22 | \n",
"| Q12443525 | E | 0 | E | 23 | \n",
"| Q12543904 | E | 0 | E | 24 | \n",
"| Q12890205 | E | 0 | E | 25 | \n",
"| Q12891524 | E | 0 | E | 26 | \n",
"| Q12918202 | E | 0 | E | 27 | \n",
"| Q13005653 | E | 0 | E | 28 | \n",
"| Q13073896 | E | 0 | E | 29 | \n",
"| Q13163823 | E | 0 | E | 30 | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | \n",
"| Q565 | C | 2052996261 | A | 17332283 | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | \n",
"| Q918 | C | 2063217449 | A | 17332287 | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | \n",
"| Q866 | C | 2079749157 | A | 17332289 | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | \n",
"| Q355 | C | 2093900731 | A | 17332293 | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | \n",
"| Q30 | A | 2277746226 | A | 17332304 | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | \n",
"| Q5 | A | 5668008721 | A | 17332306 | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum \n",
"1 Q10040378 E 0 E 1 \n",
"2 Q10069140 C 0 E 2 \n",
"3 Q10081695 C 0 E 3 \n",
"4 Q10092002 E 0 E 4 \n",
"5 Q10111267 E 0 E 5 \n",
"6 Q10149726 E 0 E 6 \n",
"7 Q10180230 E 0 E 7 \n",
"8 Q10185035 E 0 E 8 \n",
"9 Q10205202 E 0 E 9 \n",
"10 Q10252966 E 0 E 10 \n",
"11 Q10444494 C 0 E 11 \n",
"12 Q10624171 C 0 E 12 \n",
"13 Q10704108 C 0 E 13 \n",
"14 Q10750354 E 0 E 14 \n",
"15 Q10766855 E 0 E 15 \n",
"16 Q10827611 E 0 E 16 \n",
"17 Q11093044 E 0 E 17 \n",
"18 Q11934537 E 0 E 18 \n",
"19 Q12133466 E 0 E 19 \n",
"20 Q12264503 E 0 E 20 \n",
"21 Q12267516 E 0 E 21 \n",
"22 Q12304084 E 0 E 22 \n",
"23 Q12443525 E 0 E 23 \n",
"24 Q12543904 E 0 E 24 \n",
"25 Q12890205 E 0 E 25 \n",
"26 Q12891524 E 0 E 26 \n",
"27 Q12918202 E 0 E 27 \n",
"28 Q13005653 E 0 E 28 \n",
"29 Q13073896 E 0 E 29 \n",
"30 Q13163823 E 0 E 30 \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278\n",
"17332279 Q31165 C 2048330818 A 17332279\n",
"17332280 Q40629 C 2049755644 A 17332280\n",
"17332281 Q105584 C 2049926923 A 17332281\n",
"17332282 Q4584301 D 2052339927 A 17332282\n",
"17332283 Q565 C 2052996261 A 17332283\n",
"17332284 Q1868372 E 2056080224 A 17332284\n",
"17332285 Q209330 C 2060928966 A 17332285\n",
"17332286 Q14005 D 2063120071 A 17332286\n",
"17332287 Q918 C 2063217449 A 17332287\n",
"17332288 Q150248 C 2068796814 A 17332288\n",
"17332289 Q866 C 2079749157 A 17332289\n",
"17332290 Q477675 C 2080785713 A 17332290\n",
"17332291 Q1967876 C 2084215818 A 17332291\n",
"17332292 Q750403 C 2084693498 A 17332292\n",
"17332293 Q355 C 2093900731 A 17332293\n",
"17332294 Q623578 C 2097991400 A 17332294\n",
"17332295 Q17299517 D 2105487660 A 17332295\n",
"17332296 Q33999 C 2108672678 A 17332296\n",
"17332297 Q2494649 C 2114531894 A 17332297\n",
"17332298 Q2597810 C 2128920607 A 17332298\n",
"17332299 Q193563 C 2130725560 A 17332299\n",
"17332300 Q423048 C 2136131564 A 17332300\n",
"17332301 Q37312 C 2142913121 A 17332301\n",
"17332302 Q54919 C 2148531382 A 17332302\n",
"17332303 Q36578 C 2229315598 A 17332303\n",
"17332304 Q30 A 2277746226 A 17332304\n",
"17332305 Q6581097 D 3273952711 A 17332305\n",
"17332306 Q5 A 5668008721 A 17332306\n",
"17332307 Q5296 C 12530369761 A 17332307"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"articles_by_pop[, pop_class := ordered(pop_class, assessment_classes)];"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dissonance_metric = c('High negative', 'Moderate negative',\n",
" 'None', 'Moderate positive', 'High positive');"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10750354</td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855</td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525</td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>17332278 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818</td><td>A </td><td>17332279 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>17332280 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>17332281 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927</td><td>A </td><td>17332282 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>17332283 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224</td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>17332285 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>17332286 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>17332287 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>17332288 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157</td><td>A </td><td>17332289 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>17332290 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>17332291 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498</td><td>A </td><td>17332292 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>17332293 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>17332294 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>17332295 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>17332296 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>17332297 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>17332298 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>17332299 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>17332300 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>17332301 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>17332302 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>17332303 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>17332304 </td><td>NA </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>17332305 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721</td><td>A </td><td>17332306 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>17332307 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & NA \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & NA \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & NA \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & NA \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & NA \\\\\n",
"\t Q10750354 & E & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & NA \\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & NA \\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & NA \\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & NA \\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & NA \\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & NA \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & NA \\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & NA \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | NA | \n",
"| Q10081695 | C | 0 | E | 3 | NA | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | NA | \n",
"| Q10624171 | C | 0 | E | 12 | NA | \n",
"| Q10704108 | C | 0 | E | 13 | NA | \n",
"| Q10750354 | E | 0 | E | 14 | NA | \n",
"| Q10766855 | E | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | E | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | NA | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | NA | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | NA | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | NA | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | NA | \n",
"| Q565 | C | 2052996261 | A | 17332283 | NA | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | NA | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | NA | \n",
"| Q918 | C | 2063217449 | A | 17332287 | NA | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | NA | \n",
"| Q866 | C | 2079749157 | A | 17332289 | NA | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | NA | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | NA | \n",
"| Q355 | C | 2093900731 | A | 17332293 | NA | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | NA | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | NA | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | NA | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | NA | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | NA | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | NA | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | NA | \n",
"| Q30 | A | 2277746226 | A | 17332304 | NA | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | NA | \n",
"| Q5 | A | 5668008721 | A | 17332306 | NA | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance\n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 NA \n",
"3 Q10081695 C 0 E 3 NA \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 NA \n",
"12 Q10624171 C 0 E 12 NA \n",
"13 Q10704108 C 0 E 13 NA \n",
"14 Q10750354 E 0 E 14 NA \n",
"15 Q10766855 E 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 E 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 NA \n",
"17332279 Q31165 C 2048330818 A 17332279 NA \n",
"17332280 Q40629 C 2049755644 A 17332280 NA \n",
"17332281 Q105584 C 2049926923 A 17332281 NA \n",
"17332282 Q4584301 D 2052339927 A 17332282 NA \n",
"17332283 Q565 C 2052996261 A 17332283 NA \n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 NA \n",
"17332286 Q14005 D 2063120071 A 17332286 NA \n",
"17332287 Q918 C 2063217449 A 17332287 NA \n",
"17332288 Q150248 C 2068796814 A 17332288 NA \n",
"17332289 Q866 C 2079749157 A 17332289 NA \n",
"17332290 Q477675 C 2080785713 A 17332290 NA \n",
"17332291 Q1967876 C 2084215818 A 17332291 NA \n",
"17332292 Q750403 C 2084693498 A 17332292 NA \n",
"17332293 Q355 C 2093900731 A 17332293 NA \n",
"17332294 Q623578 C 2097991400 A 17332294 NA \n",
"17332295 Q17299517 D 2105487660 A 17332295 NA \n",
"17332296 Q33999 C 2108672678 A 17332296 NA \n",
"17332297 Q2494649 C 2114531894 A 17332297 NA \n",
"17332298 Q2597810 C 2128920607 A 17332298 NA \n",
"17332299 Q193563 C 2130725560 A 17332299 NA \n",
"17332300 Q423048 C 2136131564 A 17332300 NA \n",
"17332301 Q37312 C 2142913121 A 17332301 NA \n",
"17332302 Q54919 C 2148531382 A 17332302 NA \n",
"17332303 Q36578 C 2229315598 A 17332303 NA \n",
"17332304 Q30 A 2277746226 A 17332304 NA \n",
"17332305 Q6581097 D 3273952711 A 17332305 NA \n",
"17332306 Q5 A 5668008721 A 17332306 NA \n",
"17332307 Q5296 C 12530369761 A 17332307 NA "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"articles_by_pop[, dissonance := factor(NA, dissonance_metric)];"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## NOTE: because pop_class is of class ordered, we can use\n",
"## expressions like \"pop_class < 'C'\" as expected"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10750354</td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855</td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525</td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>17332278 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818</td><td>A </td><td>17332279 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>17332280 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>17332281 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927</td><td>A </td><td>17332282 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>17332283 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224</td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>17332285 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>17332286 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>17332287 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>17332288 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157</td><td>A </td><td>17332289 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>17332290 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>17332291 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498</td><td>A </td><td>17332292 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>17332293 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>17332294 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>17332295 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>17332296 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>17332297 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>17332298 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>17332299 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>17332300 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>17332301 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>17332302 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>17332303 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>17332304 </td><td>NA </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>17332305 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721</td><td>A </td><td>17332306 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>17332307 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & NA \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & NA \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & NA \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & NA \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & NA \\\\\n",
"\t Q10750354 & E & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & NA \\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & NA \\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & NA \\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & NA \\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & NA \\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & NA \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & NA \\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & NA \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | NA | \n",
"| Q10081695 | C | 0 | E | 3 | NA | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | NA | \n",
"| Q10624171 | C | 0 | E | 12 | NA | \n",
"| Q10704108 | C | 0 | E | 13 | NA | \n",
"| Q10750354 | E | 0 | E | 14 | NA | \n",
"| Q10766855 | E | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | E | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | NA | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | NA | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | NA | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | NA | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | NA | \n",
"| Q565 | C | 2052996261 | A | 17332283 | NA | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | NA | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | NA | \n",
"| Q918 | C | 2063217449 | A | 17332287 | NA | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | NA | \n",
"| Q866 | C | 2079749157 | A | 17332289 | NA | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | NA | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | NA | \n",
"| Q355 | C | 2093900731 | A | 17332293 | NA | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | NA | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | NA | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | NA | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | NA | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | NA | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | NA | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | NA | \n",
"| Q30 | A | 2277746226 | A | 17332304 | NA | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | NA | \n",
"| Q5 | A | 5668008721 | A | 17332306 | NA | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance\n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 NA \n",
"3 Q10081695 C 0 E 3 NA \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 NA \n",
"12 Q10624171 C 0 E 12 NA \n",
"13 Q10704108 C 0 E 13 NA \n",
"14 Q10750354 E 0 E 14 NA \n",
"15 Q10766855 E 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 E 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 NA \n",
"17332279 Q31165 C 2048330818 A 17332279 NA \n",
"17332280 Q40629 C 2049755644 A 17332280 NA \n",
"17332281 Q105584 C 2049926923 A 17332281 NA \n",
"17332282 Q4584301 D 2052339927 A 17332282 NA \n",
"17332283 Q565 C 2052996261 A 17332283 NA \n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 NA \n",
"17332286 Q14005 D 2063120071 A 17332286 NA \n",
"17332287 Q918 C 2063217449 A 17332287 NA \n",
"17332288 Q150248 C 2068796814 A 17332288 NA \n",
"17332289 Q866 C 2079749157 A 17332289 NA \n",
"17332290 Q477675 C 2080785713 A 17332290 NA \n",
"17332291 Q1967876 C 2084215818 A 17332291 NA \n",
"17332292 Q750403 C 2084693498 A 17332292 NA \n",
"17332293 Q355 C 2093900731 A 17332293 NA \n",
"17332294 Q623578 C 2097991400 A 17332294 NA \n",
"17332295 Q17299517 D 2105487660 A 17332295 NA \n",
"17332296 Q33999 C 2108672678 A 17332296 NA \n",
"17332297 Q2494649 C 2114531894 A 17332297 NA \n",
"17332298 Q2597810 C 2128920607 A 17332298 NA \n",
"17332299 Q193563 C 2130725560 A 17332299 NA \n",
"17332300 Q423048 C 2136131564 A 17332300 NA \n",
"17332301 Q37312 C 2142913121 A 17332301 NA \n",
"17332302 Q54919 C 2148531382 A 17332302 NA \n",
"17332303 Q36578 C 2229315598 A 17332303 NA \n",
"17332304 Q30 A 2277746226 A 17332304 NA \n",
"17332305 Q6581097 D 3273952711 A 17332305 NA \n",
"17332306 Q5 A 5668008721 A 17332306 NA \n",
"17332307 Q5296 C 12530369761 A 17332307 NA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10750354</td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855</td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525</td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>17332278 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818</td><td>A </td><td>17332279 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>17332280 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>17332281 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927</td><td>A </td><td>17332282 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>17332283 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224</td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>17332285 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>17332286 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>17332287 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>17332288 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157</td><td>A </td><td>17332289 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>17332290 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>17332291 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498</td><td>A </td><td>17332292 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>17332293 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>17332294 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>17332295 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>17332296 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>17332297 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>17332298 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>17332299 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>17332300 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>17332301 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>17332302 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>17332303 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>17332304 </td><td>NA </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>17332305 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721</td><td>A </td><td>17332306 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>17332307 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & NA \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & NA \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & NA \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & NA \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & NA \\\\\n",
"\t Q10750354 & E & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & NA \\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & NA \\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & NA \\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & NA \\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & NA \\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & NA \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & NA \\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & NA \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | NA | \n",
"| Q10081695 | C | 0 | E | 3 | NA | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | NA | \n",
"| Q10624171 | C | 0 | E | 12 | NA | \n",
"| Q10704108 | C | 0 | E | 13 | NA | \n",
"| Q10750354 | E | 0 | E | 14 | NA | \n",
"| Q10766855 | E | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | E | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | NA | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | NA | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | NA | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | NA | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | NA | \n",
"| Q565 | C | 2052996261 | A | 17332283 | NA | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | NA | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | NA | \n",
"| Q918 | C | 2063217449 | A | 17332287 | NA | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | NA | \n",
"| Q866 | C | 2079749157 | A | 17332289 | NA | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | NA | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | NA | \n",
"| Q355 | C | 2093900731 | A | 17332293 | NA | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | NA | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | NA | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | NA | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | NA | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | NA | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | NA | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | NA | \n",
"| Q30 | A | 2277746226 | A | 17332304 | NA | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | NA | \n",
"| Q5 | A | 5668008721 | A | 17332306 | NA | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance\n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 NA \n",
"3 Q10081695 C 0 E 3 NA \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 NA \n",
"12 Q10624171 C 0 E 12 NA \n",
"13 Q10704108 C 0 E 13 NA \n",
"14 Q10750354 E 0 E 14 NA \n",
"15 Q10766855 E 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 E 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 NA \n",
"17332279 Q31165 C 2048330818 A 17332279 NA \n",
"17332280 Q40629 C 2049755644 A 17332280 NA \n",
"17332281 Q105584 C 2049926923 A 17332281 NA \n",
"17332282 Q4584301 D 2052339927 A 17332282 NA \n",
"17332283 Q565 C 2052996261 A 17332283 NA \n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 NA \n",
"17332286 Q14005 D 2063120071 A 17332286 NA \n",
"17332287 Q918 C 2063217449 A 17332287 NA \n",
"17332288 Q150248 C 2068796814 A 17332288 NA \n",
"17332289 Q866 C 2079749157 A 17332289 NA \n",
"17332290 Q477675 C 2080785713 A 17332290 NA \n",
"17332291 Q1967876 C 2084215818 A 17332291 NA \n",
"17332292 Q750403 C 2084693498 A 17332292 NA \n",
"17332293 Q355 C 2093900731 A 17332293 NA \n",
"17332294 Q623578 C 2097991400 A 17332294 NA \n",
"17332295 Q17299517 D 2105487660 A 17332295 NA \n",
"17332296 Q33999 C 2108672678 A 17332296 NA \n",
"17332297 Q2494649 C 2114531894 A 17332297 NA \n",
"17332298 Q2597810 C 2128920607 A 17332298 NA \n",
"17332299 Q193563 C 2130725560 A 17332299 NA \n",
"17332300 Q423048 C 2136131564 A 17332300 NA \n",
"17332301 Q37312 C 2142913121 A 17332301 NA \n",
"17332302 Q54919 C 2148531382 A 17332302 NA \n",
"17332303 Q36578 C 2229315598 A 17332303 NA \n",
"17332304 Q30 A 2277746226 A 17332304 NA \n",
"17332305 Q6581097 D 3273952711 A 17332305 NA \n",
"17332306 Q5 A 5668008721 A 17332306 NA \n",
"17332307 Q5296 C 12530369761 A 17332307 NA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10750354</td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855</td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525</td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>17332278 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818</td><td>A </td><td>17332279 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>17332280 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>17332281 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927</td><td>A </td><td>17332282 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>17332283 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224</td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>17332285 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>17332286 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>17332287 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>17332288 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157</td><td>A </td><td>17332289 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>17332290 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>17332291 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498</td><td>A </td><td>17332292 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>17332293 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>17332294 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>17332295 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>17332296 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>17332297 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>17332298 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>17332299 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>17332300 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>17332301 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>17332302 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>17332303 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>17332304 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>17332305 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721</td><td>A </td><td>17332306 </td><td>None </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>17332307 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & NA \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & NA \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & NA \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & NA \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & NA \\\\\n",
"\t Q10750354 & E & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & NA \\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & NA \\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & NA \\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & NA \\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & NA \\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & NA \\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & None \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | NA | \n",
"| Q10081695 | C | 0 | E | 3 | NA | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | NA | \n",
"| Q10624171 | C | 0 | E | 12 | NA | \n",
"| Q10704108 | C | 0 | E | 13 | NA | \n",
"| Q10750354 | E | 0 | E | 14 | NA | \n",
"| Q10766855 | E | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | E | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | NA | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | NA | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | NA | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | NA | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | NA | \n",
"| Q565 | C | 2052996261 | A | 17332283 | NA | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | NA | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | NA | \n",
"| Q918 | C | 2063217449 | A | 17332287 | NA | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | NA | \n",
"| Q866 | C | 2079749157 | A | 17332289 | NA | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | NA | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | NA | \n",
"| Q355 | C | 2093900731 | A | 17332293 | NA | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | NA | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | NA | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | NA | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | NA | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | NA | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | NA | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | NA | \n",
"| Q30 | A | 2277746226 | A | 17332304 | None | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | NA | \n",
"| Q5 | A | 5668008721 | A | 17332306 | None | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance\n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 NA \n",
"3 Q10081695 C 0 E 3 NA \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 NA \n",
"12 Q10624171 C 0 E 12 NA \n",
"13 Q10704108 C 0 E 13 NA \n",
"14 Q10750354 E 0 E 14 NA \n",
"15 Q10766855 E 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 E 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 NA \n",
"17332279 Q31165 C 2048330818 A 17332279 NA \n",
"17332280 Q40629 C 2049755644 A 17332280 NA \n",
"17332281 Q105584 C 2049926923 A 17332281 NA \n",
"17332282 Q4584301 D 2052339927 A 17332282 NA \n",
"17332283 Q565 C 2052996261 A 17332283 NA \n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 NA \n",
"17332286 Q14005 D 2063120071 A 17332286 NA \n",
"17332287 Q918 C 2063217449 A 17332287 NA \n",
"17332288 Q150248 C 2068796814 A 17332288 NA \n",
"17332289 Q866 C 2079749157 A 17332289 NA \n",
"17332290 Q477675 C 2080785713 A 17332290 NA \n",
"17332291 Q1967876 C 2084215818 A 17332291 NA \n",
"17332292 Q750403 C 2084693498 A 17332292 NA \n",
"17332293 Q355 C 2093900731 A 17332293 NA \n",
"17332294 Q623578 C 2097991400 A 17332294 NA \n",
"17332295 Q17299517 D 2105487660 A 17332295 NA \n",
"17332296 Q33999 C 2108672678 A 17332296 NA \n",
"17332297 Q2494649 C 2114531894 A 17332297 NA \n",
"17332298 Q2597810 C 2128920607 A 17332298 NA \n",
"17332299 Q193563 C 2130725560 A 17332299 NA \n",
"17332300 Q423048 C 2136131564 A 17332300 NA \n",
"17332301 Q37312 C 2142913121 A 17332301 NA \n",
"17332302 Q54919 C 2148531382 A 17332302 NA \n",
"17332303 Q36578 C 2229315598 A 17332303 NA \n",
"17332304 Q30 A 2277746226 A 17332304 None \n",
"17332305 Q6581097 D 3273952711 A 17332305 NA \n",
"17332306 Q5 A 5668008721 A 17332306 None \n",
"17332307 Q5296 C 12530369761 A 17332307 NA "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## A: None if A, Moderate if A, High elsewhere\n",
"articles_by_pop[prediction == 'A' & pop_class <= 'C',\n",
" dissonance := 'High negative'];\n",
"articles_by_pop[prediction == 'A' & pop_class == 'B',\n",
" dissonance := 'Moderate negative'];\n",
"articles_by_pop[prediction == 'A' & pop_class == 'A',\n",
" dissonance := 'None'];\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10750354</td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855</td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525</td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>17332278 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818</td><td>A </td><td>17332279 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>17332280 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>17332281 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927</td><td>A </td><td>17332282 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>17332283 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224</td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>17332285 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>17332286 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>17332287 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>17332288 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157</td><td>A </td><td>17332289 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>17332290 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>17332291 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498</td><td>A </td><td>17332292 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>17332293 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>17332294 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>17332295 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>17332296 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>17332297 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>17332298 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>17332299 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>17332300 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>17332301 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>17332302 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>17332303 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>17332304 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>17332305 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721</td><td>A </td><td>17332306 </td><td>None </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>17332307 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & NA \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & NA \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & NA \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & NA \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & NA \\\\\n",
"\t Q10750354 & E & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & NA \\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & NA \\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & NA \\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & NA \\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & NA \\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & NA \\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & None \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | NA | \n",
"| Q10081695 | C | 0 | E | 3 | NA | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | NA | \n",
"| Q10624171 | C | 0 | E | 12 | NA | \n",
"| Q10704108 | C | 0 | E | 13 | NA | \n",
"| Q10750354 | E | 0 | E | 14 | NA | \n",
"| Q10766855 | E | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | E | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | NA | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | NA | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | NA | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | NA | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | NA | \n",
"| Q565 | C | 2052996261 | A | 17332283 | NA | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | NA | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | NA | \n",
"| Q918 | C | 2063217449 | A | 17332287 | NA | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | NA | \n",
"| Q866 | C | 2079749157 | A | 17332289 | NA | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | NA | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | NA | \n",
"| Q355 | C | 2093900731 | A | 17332293 | NA | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | NA | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | NA | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | NA | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | NA | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | NA | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | NA | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | NA | \n",
"| Q30 | A | 2277746226 | A | 17332304 | None | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | NA | \n",
"| Q5 | A | 5668008721 | A | 17332306 | None | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance\n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 NA \n",
"3 Q10081695 C 0 E 3 NA \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 NA \n",
"12 Q10624171 C 0 E 12 NA \n",
"13 Q10704108 C 0 E 13 NA \n",
"14 Q10750354 E 0 E 14 NA \n",
"15 Q10766855 E 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 E 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 NA \n",
"17332279 Q31165 C 2048330818 A 17332279 NA \n",
"17332280 Q40629 C 2049755644 A 17332280 NA \n",
"17332281 Q105584 C 2049926923 A 17332281 NA \n",
"17332282 Q4584301 D 2052339927 A 17332282 NA \n",
"17332283 Q565 C 2052996261 A 17332283 NA \n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 NA \n",
"17332286 Q14005 D 2063120071 A 17332286 NA \n",
"17332287 Q918 C 2063217449 A 17332287 NA \n",
"17332288 Q150248 C 2068796814 A 17332288 NA \n",
"17332289 Q866 C 2079749157 A 17332289 NA \n",
"17332290 Q477675 C 2080785713 A 17332290 NA \n",
"17332291 Q1967876 C 2084215818 A 17332291 NA \n",
"17332292 Q750403 C 2084693498 A 17332292 NA \n",
"17332293 Q355 C 2093900731 A 17332293 NA \n",
"17332294 Q623578 C 2097991400 A 17332294 NA \n",
"17332295 Q17299517 D 2105487660 A 17332295 NA \n",
"17332296 Q33999 C 2108672678 A 17332296 NA \n",
"17332297 Q2494649 C 2114531894 A 17332297 NA \n",
"17332298 Q2597810 C 2128920607 A 17332298 NA \n",
"17332299 Q193563 C 2130725560 A 17332299 NA \n",
"17332300 Q423048 C 2136131564 A 17332300 NA \n",
"17332301 Q37312 C 2142913121 A 17332301 NA \n",
"17332302 Q54919 C 2148531382 A 17332302 NA \n",
"17332303 Q36578 C 2229315598 A 17332303 NA \n",
"17332304 Q30 A 2277746226 A 17332304 None \n",
"17332305 Q6581097 D 3273952711 A 17332305 NA \n",
"17332306 Q5 A 5668008721 A 17332306 None \n",
"17332307 Q5296 C 12530369761 A 17332307 NA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10750354</td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855</td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525</td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>17332278 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818</td><td>A </td><td>17332279 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>17332280 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>17332281 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927</td><td>A </td><td>17332282 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>17332283 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224</td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>17332285 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>17332286 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>17332287 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>17332288 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157</td><td>A </td><td>17332289 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>17332290 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>17332291 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498</td><td>A </td><td>17332292 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>17332293 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>17332294 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>17332295 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>17332296 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>17332297 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>17332298 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>17332299 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>17332300 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>17332301 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>17332302 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>17332303 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>17332304 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>17332305 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721</td><td>A </td><td>17332306 </td><td>None </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>17332307 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & NA \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & NA \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & NA \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & NA \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & NA \\\\\n",
"\t Q10750354 & E & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & NA \\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & NA \\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & NA \\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & NA \\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & NA \\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & NA \\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & None \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | NA | \n",
"| Q10081695 | C | 0 | E | 3 | NA | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | NA | \n",
"| Q10624171 | C | 0 | E | 12 | NA | \n",
"| Q10704108 | C | 0 | E | 13 | NA | \n",
"| Q10750354 | E | 0 | E | 14 | NA | \n",
"| Q10766855 | E | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | E | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | NA | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | NA | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | NA | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | NA | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | NA | \n",
"| Q565 | C | 2052996261 | A | 17332283 | NA | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | NA | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | NA | \n",
"| Q918 | C | 2063217449 | A | 17332287 | NA | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | NA | \n",
"| Q866 | C | 2079749157 | A | 17332289 | NA | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | NA | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | NA | \n",
"| Q355 | C | 2093900731 | A | 17332293 | NA | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | NA | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | NA | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | NA | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | NA | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | NA | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | NA | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | NA | \n",
"| Q30 | A | 2277746226 | A | 17332304 | None | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | NA | \n",
"| Q5 | A | 5668008721 | A | 17332306 | None | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance\n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 NA \n",
"3 Q10081695 C 0 E 3 NA \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 NA \n",
"12 Q10624171 C 0 E 12 NA \n",
"13 Q10704108 C 0 E 13 NA \n",
"14 Q10750354 E 0 E 14 NA \n",
"15 Q10766855 E 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 E 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 NA \n",
"17332279 Q31165 C 2048330818 A 17332279 NA \n",
"17332280 Q40629 C 2049755644 A 17332280 NA \n",
"17332281 Q105584 C 2049926923 A 17332281 NA \n",
"17332282 Q4584301 D 2052339927 A 17332282 NA \n",
"17332283 Q565 C 2052996261 A 17332283 NA \n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 NA \n",
"17332286 Q14005 D 2063120071 A 17332286 NA \n",
"17332287 Q918 C 2063217449 A 17332287 NA \n",
"17332288 Q150248 C 2068796814 A 17332288 NA \n",
"17332289 Q866 C 2079749157 A 17332289 NA \n",
"17332290 Q477675 C 2080785713 A 17332290 NA \n",
"17332291 Q1967876 C 2084215818 A 17332291 NA \n",
"17332292 Q750403 C 2084693498 A 17332292 NA \n",
"17332293 Q355 C 2093900731 A 17332293 NA \n",
"17332294 Q623578 C 2097991400 A 17332294 NA \n",
"17332295 Q17299517 D 2105487660 A 17332295 NA \n",
"17332296 Q33999 C 2108672678 A 17332296 NA \n",
"17332297 Q2494649 C 2114531894 A 17332297 NA \n",
"17332298 Q2597810 C 2128920607 A 17332298 NA \n",
"17332299 Q193563 C 2130725560 A 17332299 NA \n",
"17332300 Q423048 C 2136131564 A 17332300 NA \n",
"17332301 Q37312 C 2142913121 A 17332301 NA \n",
"17332302 Q54919 C 2148531382 A 17332302 NA \n",
"17332303 Q36578 C 2229315598 A 17332303 NA \n",
"17332304 Q30 A 2277746226 A 17332304 None \n",
"17332305 Q6581097 D 3273952711 A 17332305 NA \n",
"17332306 Q5 A 5668008721 A 17332306 None \n",
"17332307 Q5296 C 12530369761 A 17332307 NA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10750354</td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855</td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525</td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>17332278 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818</td><td>A </td><td>17332279 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>17332280 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>17332281 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927</td><td>A </td><td>17332282 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>17332283 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224</td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>17332285 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>17332286 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>17332287 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>17332288 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157</td><td>A </td><td>17332289 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>17332290 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>17332291 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498</td><td>A </td><td>17332292 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>17332293 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>17332294 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>17332295 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>17332296 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>17332297 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>17332298 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>17332299 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>17332300 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>17332301 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>17332302 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>17332303 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>17332304 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>17332305 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721</td><td>A </td><td>17332306 </td><td>None </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>17332307 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & NA \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & NA \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & NA \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & NA \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & NA \\\\\n",
"\t Q10750354 & E & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & NA \\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & NA \\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & NA \\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & NA \\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & NA \\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & NA \\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & None \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | NA | \n",
"| Q10081695 | C | 0 | E | 3 | NA | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | NA | \n",
"| Q10624171 | C | 0 | E | 12 | NA | \n",
"| Q10704108 | C | 0 | E | 13 | NA | \n",
"| Q10750354 | E | 0 | E | 14 | NA | \n",
"| Q10766855 | E | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | E | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | NA | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | NA | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | NA | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | NA | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | NA | \n",
"| Q565 | C | 2052996261 | A | 17332283 | NA | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | NA | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | NA | \n",
"| Q918 | C | 2063217449 | A | 17332287 | NA | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | NA | \n",
"| Q866 | C | 2079749157 | A | 17332289 | NA | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | NA | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | NA | \n",
"| Q355 | C | 2093900731 | A | 17332293 | NA | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | NA | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | NA | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | NA | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | NA | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | NA | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | NA | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | NA | \n",
"| Q30 | A | 2277746226 | A | 17332304 | None | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | NA | \n",
"| Q5 | A | 5668008721 | A | 17332306 | None | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance\n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 NA \n",
"3 Q10081695 C 0 E 3 NA \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 NA \n",
"12 Q10624171 C 0 E 12 NA \n",
"13 Q10704108 C 0 E 13 NA \n",
"14 Q10750354 E 0 E 14 NA \n",
"15 Q10766855 E 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 E 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 NA \n",
"17332279 Q31165 C 2048330818 A 17332279 NA \n",
"17332280 Q40629 C 2049755644 A 17332280 NA \n",
"17332281 Q105584 C 2049926923 A 17332281 NA \n",
"17332282 Q4584301 D 2052339927 A 17332282 NA \n",
"17332283 Q565 C 2052996261 A 17332283 NA \n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 NA \n",
"17332286 Q14005 D 2063120071 A 17332286 NA \n",
"17332287 Q918 C 2063217449 A 17332287 NA \n",
"17332288 Q150248 C 2068796814 A 17332288 NA \n",
"17332289 Q866 C 2079749157 A 17332289 NA \n",
"17332290 Q477675 C 2080785713 A 17332290 NA \n",
"17332291 Q1967876 C 2084215818 A 17332291 NA \n",
"17332292 Q750403 C 2084693498 A 17332292 NA \n",
"17332293 Q355 C 2093900731 A 17332293 NA \n",
"17332294 Q623578 C 2097991400 A 17332294 NA \n",
"17332295 Q17299517 D 2105487660 A 17332295 NA \n",
"17332296 Q33999 C 2108672678 A 17332296 NA \n",
"17332297 Q2494649 C 2114531894 A 17332297 NA \n",
"17332298 Q2597810 C 2128920607 A 17332298 NA \n",
"17332299 Q193563 C 2130725560 A 17332299 NA \n",
"17332300 Q423048 C 2136131564 A 17332300 NA \n",
"17332301 Q37312 C 2142913121 A 17332301 NA \n",
"17332302 Q54919 C 2148531382 A 17332302 NA \n",
"17332303 Q36578 C 2229315598 A 17332303 NA \n",
"17332304 Q30 A 2277746226 A 17332304 None \n",
"17332305 Q6581097 D 3273952711 A 17332305 NA \n",
"17332306 Q5 A 5668008721 A 17332306 None \n",
"17332307 Q5296 C 12530369761 A 17332307 NA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378</td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140</td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10081695</td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10092002</td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267</td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726</td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230</td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035</td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202</td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966</td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494</td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10624171</td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10704108</td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10750354</td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855</td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611</td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044</td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537</td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466</td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503</td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516</td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084</td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525</td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904</td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205</td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524</td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202</td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653</td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896</td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823</td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>17332278 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818</td><td>A </td><td>17332279 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>17332280 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>17332281 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927</td><td>A </td><td>17332282 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>17332283 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224</td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>17332285 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>17332286 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>17332287 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>17332288 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157</td><td>A </td><td>17332289 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>17332290 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>17332291 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498</td><td>A </td><td>17332292 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>17332293 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>17332294 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>17332295 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>17332296 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>17332297 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>17332298 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>17332299 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>17332300 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>17332301 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>17332302 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>17332303 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>17332304 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>17332305 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721</td><td>A </td><td>17332306 </td><td>None </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>17332307 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & NA \\\\\n",
"\t Q10081695 & C & 0 & E & 3 & NA \\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & NA \\\\\n",
"\t Q10624171 & C & 0 & E & 12 & NA \\\\\n",
"\t Q10704108 & C & 0 & E & 13 & NA \\\\\n",
"\t Q10750354 & E & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & NA \\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & NA \\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & NA \\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & NA \\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & NA \\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & NA \\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & None \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | NA | \n",
"| Q10081695 | C | 0 | E | 3 | NA | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | NA | \n",
"| Q10624171 | C | 0 | E | 12 | NA | \n",
"| Q10704108 | C | 0 | E | 13 | NA | \n",
"| Q10750354 | E | 0 | E | 14 | NA | \n",
"| Q10766855 | E | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | E | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | NA | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | NA | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | NA | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | NA | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | NA | \n",
"| Q565 | C | 2052996261 | A | 17332283 | NA | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | NA | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | NA | \n",
"| Q918 | C | 2063217449 | A | 17332287 | NA | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | NA | \n",
"| Q866 | C | 2079749157 | A | 17332289 | NA | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | NA | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | NA | \n",
"| Q355 | C | 2093900731 | A | 17332293 | NA | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | NA | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | NA | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | NA | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | NA | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | NA | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | NA | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | NA | \n",
"| Q30 | A | 2277746226 | A | 17332304 | None | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | NA | \n",
"| Q5 | A | 5668008721 | A | 17332306 | None | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance\n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 NA \n",
"3 Q10081695 C 0 E 3 NA \n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 NA \n",
"12 Q10624171 C 0 E 12 NA \n",
"13 Q10704108 C 0 E 13 NA \n",
"14 Q10750354 E 0 E 14 NA \n",
"15 Q10766855 E 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 E 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 NA \n",
"17332279 Q31165 C 2048330818 A 17332279 NA \n",
"17332280 Q40629 C 2049755644 A 17332280 NA \n",
"17332281 Q105584 C 2049926923 A 17332281 NA \n",
"17332282 Q4584301 D 2052339927 A 17332282 NA \n",
"17332283 Q565 C 2052996261 A 17332283 NA \n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 NA \n",
"17332286 Q14005 D 2063120071 A 17332286 NA \n",
"17332287 Q918 C 2063217449 A 17332287 NA \n",
"17332288 Q150248 C 2068796814 A 17332288 NA \n",
"17332289 Q866 C 2079749157 A 17332289 NA \n",
"17332290 Q477675 C 2080785713 A 17332290 NA \n",
"17332291 Q1967876 C 2084215818 A 17332291 NA \n",
"17332292 Q750403 C 2084693498 A 17332292 NA \n",
"17332293 Q355 C 2093900731 A 17332293 NA \n",
"17332294 Q623578 C 2097991400 A 17332294 NA \n",
"17332295 Q17299517 D 2105487660 A 17332295 NA \n",
"17332296 Q33999 C 2108672678 A 17332296 NA \n",
"17332297 Q2494649 C 2114531894 A 17332297 NA \n",
"17332298 Q2597810 C 2128920607 A 17332298 NA \n",
"17332299 Q193563 C 2130725560 A 17332299 NA \n",
"17332300 Q423048 C 2136131564 A 17332300 NA \n",
"17332301 Q37312 C 2142913121 A 17332301 NA \n",
"17332302 Q54919 C 2148531382 A 17332302 NA \n",
"17332303 Q36578 C 2229315598 A 17332303 NA \n",
"17332304 Q30 A 2277746226 A 17332304 None \n",
"17332305 Q6581097 D 3273952711 A 17332305 NA \n",
"17332306 Q5 A 5668008721 A 17332306 None \n",
"17332307 Q5296 C 12530369761 A 17332307 NA "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## B: \n",
"articles_by_pop[prediction == 'B' & pop_class <= 'D',\n",
" dissonance := 'High negative'];\n",
"articles_by_pop[prediction == 'B' & pop_class == 'C',\n",
" dissonance := 'Moderate negative'];\n",
"articles_by_pop[prediction == 'B' & pop_class == 'B',\n",
" dissonance := 'None'];\n",
"articles_by_pop[prediction == 'B' & pop_class == 'A',\n",
" dissonance := 'Moderate positive'];"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10750354 </td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>17332278 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818</td><td>A </td><td>17332279 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>17332280 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>17332281 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927</td><td>A </td><td>17332282 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>17332283 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224</td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>17332285 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>17332286 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>17332287 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>17332288 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157</td><td>A </td><td>17332289 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>17332290 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>17332291 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498</td><td>A </td><td>17332292 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>17332293 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>17332294 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>17332295 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>17332296 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>17332297 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>17332298 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>17332299 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>17332300 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>17332301 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>17332302 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>17332303 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>17332304 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>17332305 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721</td><td>A </td><td>17332306 </td><td>None </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>17332307 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative\\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative\\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative\\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative\\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative\\\\\n",
"\t Q10750354 & E & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & NA \\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & NA \\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & NA \\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & NA \\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & NA \\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & NA \\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & None \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | E | 0 | E | 14 | NA | \n",
"| Q10766855 | E | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | E | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | NA | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | NA | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | NA | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | NA | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | NA | \n",
"| Q565 | C | 2052996261 | A | 17332283 | NA | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | NA | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | NA | \n",
"| Q918 | C | 2063217449 | A | 17332287 | NA | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | NA | \n",
"| Q866 | C | 2079749157 | A | 17332289 | NA | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | NA | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | NA | \n",
"| Q355 | C | 2093900731 | A | 17332293 | NA | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | NA | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | NA | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | NA | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | NA | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | NA | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | NA | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | NA | \n",
"| Q30 | A | 2277746226 | A | 17332304 | None | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | NA | \n",
"| Q5 | A | 5668008721 | A | 17332306 | None | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 High negative\n",
"3 Q10081695 C 0 E 3 High negative\n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 High negative\n",
"12 Q10624171 C 0 E 12 High negative\n",
"13 Q10704108 C 0 E 13 High negative\n",
"14 Q10750354 E 0 E 14 NA \n",
"15 Q10766855 E 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 E 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 NA \n",
"17332279 Q31165 C 2048330818 A 17332279 NA \n",
"17332280 Q40629 C 2049755644 A 17332280 NA \n",
"17332281 Q105584 C 2049926923 A 17332281 NA \n",
"17332282 Q4584301 D 2052339927 A 17332282 NA \n",
"17332283 Q565 C 2052996261 A 17332283 NA \n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 NA \n",
"17332286 Q14005 D 2063120071 A 17332286 NA \n",
"17332287 Q918 C 2063217449 A 17332287 NA \n",
"17332288 Q150248 C 2068796814 A 17332288 NA \n",
"17332289 Q866 C 2079749157 A 17332289 NA \n",
"17332290 Q477675 C 2080785713 A 17332290 NA \n",
"17332291 Q1967876 C 2084215818 A 17332291 NA \n",
"17332292 Q750403 C 2084693498 A 17332292 NA \n",
"17332293 Q355 C 2093900731 A 17332293 NA \n",
"17332294 Q623578 C 2097991400 A 17332294 NA \n",
"17332295 Q17299517 D 2105487660 A 17332295 NA \n",
"17332296 Q33999 C 2108672678 A 17332296 NA \n",
"17332297 Q2494649 C 2114531894 A 17332297 NA \n",
"17332298 Q2597810 C 2128920607 A 17332298 NA \n",
"17332299 Q193563 C 2130725560 A 17332299 NA \n",
"17332300 Q423048 C 2136131564 A 17332300 NA \n",
"17332301 Q37312 C 2142913121 A 17332301 NA \n",
"17332302 Q54919 C 2148531382 A 17332302 NA \n",
"17332303 Q36578 C 2229315598 A 17332303 NA \n",
"17332304 Q30 A 2277746226 A 17332304 None \n",
"17332305 Q6581097 D 3273952711 A 17332305 NA \n",
"17332306 Q5 A 5668008721 A 17332306 None \n",
"17332307 Q5296 C 12530369761 A 17332307 NA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10750354 </td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>17332278 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818</td><td>A </td><td>17332279 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>17332280 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>17332281 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927</td><td>A </td><td>17332282 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>17332283 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224</td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>17332285 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>17332286 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>17332287 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>17332288 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157</td><td>A </td><td>17332289 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>17332290 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>17332291 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498</td><td>A </td><td>17332292 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>17332293 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>17332294 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>17332295 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>17332296 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>17332297 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>17332298 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>17332299 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>17332300 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>17332301 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>17332302 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>17332303 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>17332304 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>17332305 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721</td><td>A </td><td>17332306 </td><td>None </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>17332307 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative\\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative\\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative\\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative\\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative\\\\\n",
"\t Q10750354 & E & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & NA \\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & NA \\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & NA \\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & NA \\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & NA \\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & NA \\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & None \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | E | 0 | E | 14 | NA | \n",
"| Q10766855 | E | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | E | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | NA | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | NA | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | NA | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | NA | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | NA | \n",
"| Q565 | C | 2052996261 | A | 17332283 | NA | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | NA | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | NA | \n",
"| Q918 | C | 2063217449 | A | 17332287 | NA | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | NA | \n",
"| Q866 | C | 2079749157 | A | 17332289 | NA | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | NA | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | NA | \n",
"| Q355 | C | 2093900731 | A | 17332293 | NA | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | NA | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | NA | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | NA | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | NA | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | NA | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | NA | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | NA | \n",
"| Q30 | A | 2277746226 | A | 17332304 | None | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | NA | \n",
"| Q5 | A | 5668008721 | A | 17332306 | None | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 High negative\n",
"3 Q10081695 C 0 E 3 High negative\n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 High negative\n",
"12 Q10624171 C 0 E 12 High negative\n",
"13 Q10704108 C 0 E 13 High negative\n",
"14 Q10750354 E 0 E 14 NA \n",
"15 Q10766855 E 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 E 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 NA \n",
"17332279 Q31165 C 2048330818 A 17332279 NA \n",
"17332280 Q40629 C 2049755644 A 17332280 NA \n",
"17332281 Q105584 C 2049926923 A 17332281 NA \n",
"17332282 Q4584301 D 2052339927 A 17332282 NA \n",
"17332283 Q565 C 2052996261 A 17332283 NA \n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 NA \n",
"17332286 Q14005 D 2063120071 A 17332286 NA \n",
"17332287 Q918 C 2063217449 A 17332287 NA \n",
"17332288 Q150248 C 2068796814 A 17332288 NA \n",
"17332289 Q866 C 2079749157 A 17332289 NA \n",
"17332290 Q477675 C 2080785713 A 17332290 NA \n",
"17332291 Q1967876 C 2084215818 A 17332291 NA \n",
"17332292 Q750403 C 2084693498 A 17332292 NA \n",
"17332293 Q355 C 2093900731 A 17332293 NA \n",
"17332294 Q623578 C 2097991400 A 17332294 NA \n",
"17332295 Q17299517 D 2105487660 A 17332295 NA \n",
"17332296 Q33999 C 2108672678 A 17332296 NA \n",
"17332297 Q2494649 C 2114531894 A 17332297 NA \n",
"17332298 Q2597810 C 2128920607 A 17332298 NA \n",
"17332299 Q193563 C 2130725560 A 17332299 NA \n",
"17332300 Q423048 C 2136131564 A 17332300 NA \n",
"17332301 Q37312 C 2142913121 A 17332301 NA \n",
"17332302 Q54919 C 2148531382 A 17332302 NA \n",
"17332303 Q36578 C 2229315598 A 17332303 NA \n",
"17332304 Q30 A 2277746226 A 17332304 None \n",
"17332305 Q6581097 D 3273952711 A 17332305 NA \n",
"17332306 Q5 A 5668008721 A 17332306 None \n",
"17332307 Q5296 C 12530369761 A 17332307 NA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10750354 </td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>17332278 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818</td><td>A </td><td>17332279 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>17332280 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>17332281 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927</td><td>A </td><td>17332282 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>17332283 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224</td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>17332285 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>17332286 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>17332287 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>17332288 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157</td><td>A </td><td>17332289 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>17332290 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>17332291 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498</td><td>A </td><td>17332292 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>17332293 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>17332294 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>17332295 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>17332296 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>17332297 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>17332298 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>17332299 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>17332300 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>17332301 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>17332302 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>17332303 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>17332304 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>17332305 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721</td><td>A </td><td>17332306 </td><td>None </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>17332307 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative\\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative\\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative\\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative\\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative\\\\\n",
"\t Q10750354 & E & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & NA \\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & NA \\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & NA \\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & NA \\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & NA \\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & NA \\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & None \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | E | 0 | E | 14 | NA | \n",
"| Q10766855 | E | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | E | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | NA | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | NA | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | NA | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | NA | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | NA | \n",
"| Q565 | C | 2052996261 | A | 17332283 | NA | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | NA | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | NA | \n",
"| Q918 | C | 2063217449 | A | 17332287 | NA | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | NA | \n",
"| Q866 | C | 2079749157 | A | 17332289 | NA | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | NA | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | NA | \n",
"| Q355 | C | 2093900731 | A | 17332293 | NA | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | NA | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | NA | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | NA | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | NA | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | NA | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | NA | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | NA | \n",
"| Q30 | A | 2277746226 | A | 17332304 | None | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | NA | \n",
"| Q5 | A | 5668008721 | A | 17332306 | None | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 High negative\n",
"3 Q10081695 C 0 E 3 High negative\n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 High negative\n",
"12 Q10624171 C 0 E 12 High negative\n",
"13 Q10704108 C 0 E 13 High negative\n",
"14 Q10750354 E 0 E 14 NA \n",
"15 Q10766855 E 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 E 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 NA \n",
"17332279 Q31165 C 2048330818 A 17332279 NA \n",
"17332280 Q40629 C 2049755644 A 17332280 NA \n",
"17332281 Q105584 C 2049926923 A 17332281 NA \n",
"17332282 Q4584301 D 2052339927 A 17332282 NA \n",
"17332283 Q565 C 2052996261 A 17332283 NA \n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 NA \n",
"17332286 Q14005 D 2063120071 A 17332286 NA \n",
"17332287 Q918 C 2063217449 A 17332287 NA \n",
"17332288 Q150248 C 2068796814 A 17332288 NA \n",
"17332289 Q866 C 2079749157 A 17332289 NA \n",
"17332290 Q477675 C 2080785713 A 17332290 NA \n",
"17332291 Q1967876 C 2084215818 A 17332291 NA \n",
"17332292 Q750403 C 2084693498 A 17332292 NA \n",
"17332293 Q355 C 2093900731 A 17332293 NA \n",
"17332294 Q623578 C 2097991400 A 17332294 NA \n",
"17332295 Q17299517 D 2105487660 A 17332295 NA \n",
"17332296 Q33999 C 2108672678 A 17332296 NA \n",
"17332297 Q2494649 C 2114531894 A 17332297 NA \n",
"17332298 Q2597810 C 2128920607 A 17332298 NA \n",
"17332299 Q193563 C 2130725560 A 17332299 NA \n",
"17332300 Q423048 C 2136131564 A 17332300 NA \n",
"17332301 Q37312 C 2142913121 A 17332301 NA \n",
"17332302 Q54919 C 2148531382 A 17332302 NA \n",
"17332303 Q36578 C 2229315598 A 17332303 NA \n",
"17332304 Q30 A 2277746226 A 17332304 None \n",
"17332305 Q6581097 D 3273952711 A 17332305 NA \n",
"17332306 Q5 A 5668008721 A 17332306 None \n",
"17332307 Q5296 C 12530369761 A 17332307 NA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10750354 </td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025</td><td>A </td><td>17332278 </td><td>NA </td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818</td><td>A </td><td>17332279 </td><td>NA </td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644</td><td>A </td><td>17332280 </td><td>NA </td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923</td><td>A </td><td>17332281 </td><td>NA </td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927</td><td>A </td><td>17332282 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261</td><td>A </td><td>17332283 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224</td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966</td><td>A </td><td>17332285 </td><td>NA </td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071</td><td>A </td><td>17332286 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449</td><td>A </td><td>17332287 </td><td>NA </td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814</td><td>A </td><td>17332288 </td><td>NA </td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157</td><td>A </td><td>17332289 </td><td>NA </td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713</td><td>A </td><td>17332290 </td><td>NA </td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818</td><td>A </td><td>17332291 </td><td>NA </td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498</td><td>A </td><td>17332292 </td><td>NA </td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731</td><td>A </td><td>17332293 </td><td>NA </td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400</td><td>A </td><td>17332294 </td><td>NA </td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660</td><td>A </td><td>17332295 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678</td><td>A </td><td>17332296 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894</td><td>A </td><td>17332297 </td><td>NA </td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607</td><td>A </td><td>17332298 </td><td>NA </td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560</td><td>A </td><td>17332299 </td><td>NA </td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564</td><td>A </td><td>17332300 </td><td>NA </td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121</td><td>A </td><td>17332301 </td><td>NA </td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382</td><td>A </td><td>17332302 </td><td>NA </td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598</td><td>A </td><td>17332303 </td><td>NA </td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226</td><td>A </td><td>17332304 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711</td><td>A </td><td>17332305 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721</td><td>A </td><td>17332306 </td><td>None </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761</td><td>A </td><td>17332307 </td><td>NA </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative\\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative\\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative\\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative\\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative\\\\\n",
"\t Q10750354 & E & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & NA \\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & NA \\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & NA \\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & NA \\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & NA \\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & NA \\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & NA \\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & NA \\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & NA \\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & NA \\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & NA \\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & NA \\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & NA \\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & NA \\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & NA \\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & NA \\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & NA \\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & NA \\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & NA \\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & NA \\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & NA \\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & NA \\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & NA \\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & None \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & NA \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | E | 0 | E | 14 | NA | \n",
"| Q10766855 | E | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | E | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | NA | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | NA | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | NA | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | NA | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | NA | \n",
"| Q565 | C | 2052996261 | A | 17332283 | NA | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | NA | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | NA | \n",
"| Q918 | C | 2063217449 | A | 17332287 | NA | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | NA | \n",
"| Q866 | C | 2079749157 | A | 17332289 | NA | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | NA | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | NA | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | NA | \n",
"| Q355 | C | 2093900731 | A | 17332293 | NA | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | NA | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | NA | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | NA | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | NA | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | NA | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | NA | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | NA | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | NA | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | NA | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | NA | \n",
"| Q30 | A | 2277746226 | A | 17332304 | None | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | NA | \n",
"| Q5 | A | 5668008721 | A | 17332306 | None | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | NA | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 High negative\n",
"3 Q10081695 C 0 E 3 High negative\n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 High negative\n",
"12 Q10624171 C 0 E 12 High negative\n",
"13 Q10704108 C 0 E 13 High negative\n",
"14 Q10750354 E 0 E 14 NA \n",
"15 Q10766855 E 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 E 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 NA \n",
"17332279 Q31165 C 2048330818 A 17332279 NA \n",
"17332280 Q40629 C 2049755644 A 17332280 NA \n",
"17332281 Q105584 C 2049926923 A 17332281 NA \n",
"17332282 Q4584301 D 2052339927 A 17332282 NA \n",
"17332283 Q565 C 2052996261 A 17332283 NA \n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 NA \n",
"17332286 Q14005 D 2063120071 A 17332286 NA \n",
"17332287 Q918 C 2063217449 A 17332287 NA \n",
"17332288 Q150248 C 2068796814 A 17332288 NA \n",
"17332289 Q866 C 2079749157 A 17332289 NA \n",
"17332290 Q477675 C 2080785713 A 17332290 NA \n",
"17332291 Q1967876 C 2084215818 A 17332291 NA \n",
"17332292 Q750403 C 2084693498 A 17332292 NA \n",
"17332293 Q355 C 2093900731 A 17332293 NA \n",
"17332294 Q623578 C 2097991400 A 17332294 NA \n",
"17332295 Q17299517 D 2105487660 A 17332295 NA \n",
"17332296 Q33999 C 2108672678 A 17332296 NA \n",
"17332297 Q2494649 C 2114531894 A 17332297 NA \n",
"17332298 Q2597810 C 2128920607 A 17332298 NA \n",
"17332299 Q193563 C 2130725560 A 17332299 NA \n",
"17332300 Q423048 C 2136131564 A 17332300 NA \n",
"17332301 Q37312 C 2142913121 A 17332301 NA \n",
"17332302 Q54919 C 2148531382 A 17332302 NA \n",
"17332303 Q36578 C 2229315598 A 17332303 NA \n",
"17332304 Q30 A 2277746226 A 17332304 None \n",
"17332305 Q6581097 D 3273952711 A 17332305 NA \n",
"17332306 Q5 A 5668008721 A 17332306 None \n",
"17332307 Q5296 C 12530369761 A 17332307 NA "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10750354 </td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>17332278 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818 </td><td>A </td><td>17332279 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>17332280 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>17332281 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927 </td><td>A </td><td>17332282 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>17332283 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224 </td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>17332285 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>17332286 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>17332287 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>17332288 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157 </td><td>A </td><td>17332289 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>17332290 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>17332291 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498 </td><td>A </td><td>17332292 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>17332293 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>17332294 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>17332295 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>17332296 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>17332297 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>17332298 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>17332299 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>17332300 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>17332301 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>17332302 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>17332303 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>17332304 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>17332305 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721 </td><td>A </td><td>17332306 </td><td>None </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>17332307 </td><td>High positive</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative\\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative\\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative\\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative\\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative\\\\\n",
"\t Q10750354 & E & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & High positive\\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & High positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & High positive\\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & High positive\\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & High positive\\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & High positive\\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & High positive\\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & High positive\\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & High positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & High positive\\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & High positive\\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & High positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & High positive\\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & High positive\\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & High positive\\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & High positive\\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & High positive\\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & High positive\\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & High positive\\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & High positive\\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & High positive\\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & High positive\\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & NA \\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & None \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & High positive\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | E | 0 | E | 14 | NA | \n",
"| Q10766855 | E | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | E | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | High positive | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | High positive | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | High positive | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | High positive | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | NA | \n",
"| Q565 | C | 2052996261 | A | 17332283 | High positive | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | High positive | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | NA | \n",
"| Q918 | C | 2063217449 | A | 17332287 | High positive | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | High positive | \n",
"| Q866 | C | 2079749157 | A | 17332289 | High positive | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | High positive | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | High positive | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | High positive | \n",
"| Q355 | C | 2093900731 | A | 17332293 | High positive | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | High positive | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | NA | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | High positive | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | High positive | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | High positive | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | High positive | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | High positive | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | High positive | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | High positive | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | High positive | \n",
"| Q30 | A | 2277746226 | A | 17332304 | None | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | NA | \n",
"| Q5 | A | 5668008721 | A | 17332306 | None | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | High positive | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 High negative\n",
"3 Q10081695 C 0 E 3 High negative\n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 High negative\n",
"12 Q10624171 C 0 E 12 High negative\n",
"13 Q10704108 C 0 E 13 High negative\n",
"14 Q10750354 E 0 E 14 NA \n",
"15 Q10766855 E 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 E 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 High positive\n",
"17332279 Q31165 C 2048330818 A 17332279 High positive\n",
"17332280 Q40629 C 2049755644 A 17332280 High positive\n",
"17332281 Q105584 C 2049926923 A 17332281 High positive\n",
"17332282 Q4584301 D 2052339927 A 17332282 NA \n",
"17332283 Q565 C 2052996261 A 17332283 High positive\n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 High positive\n",
"17332286 Q14005 D 2063120071 A 17332286 NA \n",
"17332287 Q918 C 2063217449 A 17332287 High positive\n",
"17332288 Q150248 C 2068796814 A 17332288 High positive\n",
"17332289 Q866 C 2079749157 A 17332289 High positive\n",
"17332290 Q477675 C 2080785713 A 17332290 High positive\n",
"17332291 Q1967876 C 2084215818 A 17332291 High positive\n",
"17332292 Q750403 C 2084693498 A 17332292 High positive\n",
"17332293 Q355 C 2093900731 A 17332293 High positive\n",
"17332294 Q623578 C 2097991400 A 17332294 High positive\n",
"17332295 Q17299517 D 2105487660 A 17332295 NA \n",
"17332296 Q33999 C 2108672678 A 17332296 High positive\n",
"17332297 Q2494649 C 2114531894 A 17332297 High positive\n",
"17332298 Q2597810 C 2128920607 A 17332298 High positive\n",
"17332299 Q193563 C 2130725560 A 17332299 High positive\n",
"17332300 Q423048 C 2136131564 A 17332300 High positive\n",
"17332301 Q37312 C 2142913121 A 17332301 High positive\n",
"17332302 Q54919 C 2148531382 A 17332302 High positive\n",
"17332303 Q36578 C 2229315598 A 17332303 High positive\n",
"17332304 Q30 A 2277746226 A 17332304 None \n",
"17332305 Q6581097 D 3273952711 A 17332305 NA \n",
"17332306 Q5 A 5668008721 A 17332306 None \n",
"17332307 Q5296 C 12530369761 A 17332307 High positive"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## C: \n",
"articles_by_pop[prediction == 'C' & pop_class == 'E',\n",
" dissonance := 'High negative'];\n",
"articles_by_pop[prediction == 'C' & pop_class == 'D',\n",
" dissonance := 'Moderate negative'];\n",
"articles_by_pop[prediction == 'C' & pop_class == 'C',\n",
" dissonance := 'None'];\n",
"articles_by_pop[prediction == 'C' & pop_class == 'B',\n",
" dissonance := 'Moderate positive'];\n",
"articles_by_pop[prediction == 'C' & pop_class == 'A',\n",
" dissonance := 'High positive'];"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10750354 </td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>17332278 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818 </td><td>A </td><td>17332279 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>17332280 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>17332281 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927 </td><td>A </td><td>17332282 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>17332283 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224 </td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>17332285 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>17332286 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>17332287 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>17332288 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157 </td><td>A </td><td>17332289 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>17332290 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>17332291 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498 </td><td>A </td><td>17332292 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>17332293 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>17332294 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>17332295 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>17332296 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>17332297 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>17332298 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>17332299 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>17332300 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>17332301 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>17332302 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>17332303 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>17332304 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>17332305 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721 </td><td>A </td><td>17332306 </td><td>None </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>17332307 </td><td>High positive</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative\\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative\\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative\\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative\\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative\\\\\n",
"\t Q10750354 & E & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & High positive\\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & High positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & High positive\\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & High positive\\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & High positive\\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & High positive\\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & High positive\\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & High positive\\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & High positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & High positive\\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & High positive\\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & High positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & High positive\\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & High positive\\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & High positive\\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & High positive\\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & High positive\\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & High positive\\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & High positive\\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & High positive\\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & High positive\\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & High positive\\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & NA \\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & None \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & High positive\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | E | 0 | E | 14 | NA | \n",
"| Q10766855 | E | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | E | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | High positive | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | High positive | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | High positive | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | High positive | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | NA | \n",
"| Q565 | C | 2052996261 | A | 17332283 | High positive | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | High positive | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | NA | \n",
"| Q918 | C | 2063217449 | A | 17332287 | High positive | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | High positive | \n",
"| Q866 | C | 2079749157 | A | 17332289 | High positive | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | High positive | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | High positive | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | High positive | \n",
"| Q355 | C | 2093900731 | A | 17332293 | High positive | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | High positive | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | NA | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | High positive | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | High positive | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | High positive | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | High positive | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | High positive | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | High positive | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | High positive | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | High positive | \n",
"| Q30 | A | 2277746226 | A | 17332304 | None | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | NA | \n",
"| Q5 | A | 5668008721 | A | 17332306 | None | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | High positive | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 High negative\n",
"3 Q10081695 C 0 E 3 High negative\n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 High negative\n",
"12 Q10624171 C 0 E 12 High negative\n",
"13 Q10704108 C 0 E 13 High negative\n",
"14 Q10750354 E 0 E 14 NA \n",
"15 Q10766855 E 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 E 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 High positive\n",
"17332279 Q31165 C 2048330818 A 17332279 High positive\n",
"17332280 Q40629 C 2049755644 A 17332280 High positive\n",
"17332281 Q105584 C 2049926923 A 17332281 High positive\n",
"17332282 Q4584301 D 2052339927 A 17332282 NA \n",
"17332283 Q565 C 2052996261 A 17332283 High positive\n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 High positive\n",
"17332286 Q14005 D 2063120071 A 17332286 NA \n",
"17332287 Q918 C 2063217449 A 17332287 High positive\n",
"17332288 Q150248 C 2068796814 A 17332288 High positive\n",
"17332289 Q866 C 2079749157 A 17332289 High positive\n",
"17332290 Q477675 C 2080785713 A 17332290 High positive\n",
"17332291 Q1967876 C 2084215818 A 17332291 High positive\n",
"17332292 Q750403 C 2084693498 A 17332292 High positive\n",
"17332293 Q355 C 2093900731 A 17332293 High positive\n",
"17332294 Q623578 C 2097991400 A 17332294 High positive\n",
"17332295 Q17299517 D 2105487660 A 17332295 NA \n",
"17332296 Q33999 C 2108672678 A 17332296 High positive\n",
"17332297 Q2494649 C 2114531894 A 17332297 High positive\n",
"17332298 Q2597810 C 2128920607 A 17332298 High positive\n",
"17332299 Q193563 C 2130725560 A 17332299 High positive\n",
"17332300 Q423048 C 2136131564 A 17332300 High positive\n",
"17332301 Q37312 C 2142913121 A 17332301 High positive\n",
"17332302 Q54919 C 2148531382 A 17332302 High positive\n",
"17332303 Q36578 C 2229315598 A 17332303 High positive\n",
"17332304 Q30 A 2277746226 A 17332304 None \n",
"17332305 Q6581097 D 3273952711 A 17332305 NA \n",
"17332306 Q5 A 5668008721 A 17332306 None \n",
"17332307 Q5296 C 12530369761 A 17332307 High positive"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10750354 </td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>17332278 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818 </td><td>A </td><td>17332279 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>17332280 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>17332281 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927 </td><td>A </td><td>17332282 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>17332283 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224 </td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>17332285 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>17332286 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>17332287 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>17332288 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157 </td><td>A </td><td>17332289 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>17332290 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>17332291 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498 </td><td>A </td><td>17332292 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>17332293 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>17332294 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>17332295 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>17332296 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>17332297 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>17332298 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>17332299 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>17332300 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>17332301 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>17332302 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>17332303 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>17332304 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>17332305 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721 </td><td>A </td><td>17332306 </td><td>None </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>17332307 </td><td>High positive</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative\\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative\\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative\\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative\\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative\\\\\n",
"\t Q10750354 & E & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & High positive\\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & High positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & High positive\\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & High positive\\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & High positive\\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & High positive\\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & High positive\\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & High positive\\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & High positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & High positive\\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & High positive\\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & High positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & High positive\\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & High positive\\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & High positive\\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & High positive\\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & High positive\\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & High positive\\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & High positive\\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & High positive\\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & High positive\\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & High positive\\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & NA \\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & None \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & High positive\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | E | 0 | E | 14 | NA | \n",
"| Q10766855 | E | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | E | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | High positive | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | High positive | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | High positive | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | High positive | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | NA | \n",
"| Q565 | C | 2052996261 | A | 17332283 | High positive | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | High positive | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | NA | \n",
"| Q918 | C | 2063217449 | A | 17332287 | High positive | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | High positive | \n",
"| Q866 | C | 2079749157 | A | 17332289 | High positive | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | High positive | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | High positive | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | High positive | \n",
"| Q355 | C | 2093900731 | A | 17332293 | High positive | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | High positive | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | NA | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | High positive | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | High positive | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | High positive | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | High positive | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | High positive | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | High positive | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | High positive | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | High positive | \n",
"| Q30 | A | 2277746226 | A | 17332304 | None | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | NA | \n",
"| Q5 | A | 5668008721 | A | 17332306 | None | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | High positive | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 High negative\n",
"3 Q10081695 C 0 E 3 High negative\n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 High negative\n",
"12 Q10624171 C 0 E 12 High negative\n",
"13 Q10704108 C 0 E 13 High negative\n",
"14 Q10750354 E 0 E 14 NA \n",
"15 Q10766855 E 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 E 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 High positive\n",
"17332279 Q31165 C 2048330818 A 17332279 High positive\n",
"17332280 Q40629 C 2049755644 A 17332280 High positive\n",
"17332281 Q105584 C 2049926923 A 17332281 High positive\n",
"17332282 Q4584301 D 2052339927 A 17332282 NA \n",
"17332283 Q565 C 2052996261 A 17332283 High positive\n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 High positive\n",
"17332286 Q14005 D 2063120071 A 17332286 NA \n",
"17332287 Q918 C 2063217449 A 17332287 High positive\n",
"17332288 Q150248 C 2068796814 A 17332288 High positive\n",
"17332289 Q866 C 2079749157 A 17332289 High positive\n",
"17332290 Q477675 C 2080785713 A 17332290 High positive\n",
"17332291 Q1967876 C 2084215818 A 17332291 High positive\n",
"17332292 Q750403 C 2084693498 A 17332292 High positive\n",
"17332293 Q355 C 2093900731 A 17332293 High positive\n",
"17332294 Q623578 C 2097991400 A 17332294 High positive\n",
"17332295 Q17299517 D 2105487660 A 17332295 NA \n",
"17332296 Q33999 C 2108672678 A 17332296 High positive\n",
"17332297 Q2494649 C 2114531894 A 17332297 High positive\n",
"17332298 Q2597810 C 2128920607 A 17332298 High positive\n",
"17332299 Q193563 C 2130725560 A 17332299 High positive\n",
"17332300 Q423048 C 2136131564 A 17332300 High positive\n",
"17332301 Q37312 C 2142913121 A 17332301 High positive\n",
"17332302 Q54919 C 2148531382 A 17332302 High positive\n",
"17332303 Q36578 C 2229315598 A 17332303 High positive\n",
"17332304 Q30 A 2277746226 A 17332304 None \n",
"17332305 Q6581097 D 3273952711 A 17332305 NA \n",
"17332306 Q5 A 5668008721 A 17332306 None \n",
"17332307 Q5296 C 12530369761 A 17332307 High positive"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10750354 </td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>17332278 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818 </td><td>A </td><td>17332279 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>17332280 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>17332281 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927 </td><td>A </td><td>17332282 </td><td>NA </td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>17332283 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224 </td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>17332285 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>17332286 </td><td>NA </td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>17332287 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>17332288 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157 </td><td>A </td><td>17332289 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>17332290 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>17332291 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498 </td><td>A </td><td>17332292 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>17332293 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>17332294 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>17332295 </td><td>NA </td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>17332296 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>17332297 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>17332298 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>17332299 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>17332300 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>17332301 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>17332302 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>17332303 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>17332304 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>17332305 </td><td>NA </td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721 </td><td>A </td><td>17332306 </td><td>None </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>17332307 </td><td>High positive</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative\\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative\\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative\\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative\\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative\\\\\n",
"\t Q10750354 & E & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & High positive\\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & High positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & High positive\\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & High positive\\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & NA \\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & High positive\\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & High positive\\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & NA \\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & High positive\\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & High positive\\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & High positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & High positive\\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & High positive\\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & High positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & High positive\\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & High positive\\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & NA \\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & High positive\\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & High positive\\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & High positive\\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & High positive\\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & High positive\\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & High positive\\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & High positive\\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & High positive\\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & NA \\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & None \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & High positive\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | E | 0 | E | 14 | NA | \n",
"| Q10766855 | E | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | E | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | High positive | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | High positive | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | High positive | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | High positive | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | NA | \n",
"| Q565 | C | 2052996261 | A | 17332283 | High positive | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | High positive | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | NA | \n",
"| Q918 | C | 2063217449 | A | 17332287 | High positive | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | High positive | \n",
"| Q866 | C | 2079749157 | A | 17332289 | High positive | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | High positive | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | High positive | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | High positive | \n",
"| Q355 | C | 2093900731 | A | 17332293 | High positive | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | High positive | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | NA | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | High positive | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | High positive | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | High positive | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | High positive | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | High positive | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | High positive | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | High positive | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | High positive | \n",
"| Q30 | A | 2277746226 | A | 17332304 | None | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | NA | \n",
"| Q5 | A | 5668008721 | A | 17332306 | None | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | High positive | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 High negative\n",
"3 Q10081695 C 0 E 3 High negative\n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 High negative\n",
"12 Q10624171 C 0 E 12 High negative\n",
"13 Q10704108 C 0 E 13 High negative\n",
"14 Q10750354 E 0 E 14 NA \n",
"15 Q10766855 E 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 E 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 High positive\n",
"17332279 Q31165 C 2048330818 A 17332279 High positive\n",
"17332280 Q40629 C 2049755644 A 17332280 High positive\n",
"17332281 Q105584 C 2049926923 A 17332281 High positive\n",
"17332282 Q4584301 D 2052339927 A 17332282 NA \n",
"17332283 Q565 C 2052996261 A 17332283 High positive\n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 High positive\n",
"17332286 Q14005 D 2063120071 A 17332286 NA \n",
"17332287 Q918 C 2063217449 A 17332287 High positive\n",
"17332288 Q150248 C 2068796814 A 17332288 High positive\n",
"17332289 Q866 C 2079749157 A 17332289 High positive\n",
"17332290 Q477675 C 2080785713 A 17332290 High positive\n",
"17332291 Q1967876 C 2084215818 A 17332291 High positive\n",
"17332292 Q750403 C 2084693498 A 17332292 High positive\n",
"17332293 Q355 C 2093900731 A 17332293 High positive\n",
"17332294 Q623578 C 2097991400 A 17332294 High positive\n",
"17332295 Q17299517 D 2105487660 A 17332295 NA \n",
"17332296 Q33999 C 2108672678 A 17332296 High positive\n",
"17332297 Q2494649 C 2114531894 A 17332297 High positive\n",
"17332298 Q2597810 C 2128920607 A 17332298 High positive\n",
"17332299 Q193563 C 2130725560 A 17332299 High positive\n",
"17332300 Q423048 C 2136131564 A 17332300 High positive\n",
"17332301 Q37312 C 2142913121 A 17332301 High positive\n",
"17332302 Q54919 C 2148531382 A 17332302 High positive\n",
"17332303 Q36578 C 2229315598 A 17332303 High positive\n",
"17332304 Q30 A 2277746226 A 17332304 None \n",
"17332305 Q6581097 D 3273952711 A 17332305 NA \n",
"17332306 Q5 A 5668008721 A 17332306 None \n",
"17332307 Q5296 C 12530369761 A 17332307 High positive"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10750354 </td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>NA </td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>NA </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>NA </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>NA </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>NA </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>17332278 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818 </td><td>A </td><td>17332279 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>17332280 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>17332281 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927 </td><td>A </td><td>17332282 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>17332283 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224 </td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>17332285 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>17332286 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>17332287 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>17332288 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157 </td><td>A </td><td>17332289 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>17332290 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>17332291 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498 </td><td>A </td><td>17332292 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>17332293 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>17332294 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>17332295 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>17332296 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>17332297 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>17332298 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>17332299 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>17332300 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>17332301 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>17332302 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>17332303 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>17332304 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>17332305 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721 </td><td>A </td><td>17332306 </td><td>None </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>17332307 </td><td>High positive</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & NA \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative\\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative\\\\\n",
"\t Q10092002 & E & 0 & E & 4 & NA \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & NA \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & NA \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & NA \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & NA \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & NA \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & NA \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative\\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative\\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative\\\\\n",
"\t Q10750354 & E & 0 & E & 14 & NA \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & NA \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & NA \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & NA \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & NA \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & NA \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & NA \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & NA \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & NA \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & NA \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & NA \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & NA \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & NA \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & NA \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & NA \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & NA \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & NA \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & High positive\\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & High positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & High positive\\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & High positive\\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & High positive\\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & High positive\\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & High positive\\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & High positive\\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & High positive\\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & High positive\\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & High positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & High positive\\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & High positive\\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & High positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & High positive\\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & High positive\\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & High positive\\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & High positive\\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & High positive\\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & High positive\\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & High positive\\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & High positive\\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & High positive\\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & High positive\\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & High positive\\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & High positive\\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & None \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & High positive\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | NA | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | NA | \n",
"| Q10111267 | E | 0 | E | 5 | NA | \n",
"| Q10149726 | E | 0 | E | 6 | NA | \n",
"| Q10180230 | E | 0 | E | 7 | NA | \n",
"| Q10185035 | E | 0 | E | 8 | NA | \n",
"| Q10205202 | E | 0 | E | 9 | NA | \n",
"| Q10252966 | E | 0 | E | 10 | NA | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | E | 0 | E | 14 | NA | \n",
"| Q10766855 | E | 0 | E | 15 | NA | \n",
"| Q10827611 | E | 0 | E | 16 | NA | \n",
"| Q11093044 | E | 0 | E | 17 | NA | \n",
"| Q11934537 | E | 0 | E | 18 | NA | \n",
"| Q12133466 | E | 0 | E | 19 | NA | \n",
"| Q12264503 | E | 0 | E | 20 | NA | \n",
"| Q12267516 | E | 0 | E | 21 | NA | \n",
"| Q12304084 | E | 0 | E | 22 | NA | \n",
"| Q12443525 | E | 0 | E | 23 | NA | \n",
"| Q12543904 | E | 0 | E | 24 | NA | \n",
"| Q12890205 | E | 0 | E | 25 | NA | \n",
"| Q12891524 | E | 0 | E | 26 | NA | \n",
"| Q12918202 | E | 0 | E | 27 | NA | \n",
"| Q13005653 | E | 0 | E | 28 | NA | \n",
"| Q13073896 | E | 0 | E | 29 | NA | \n",
"| Q13163823 | E | 0 | E | 30 | NA | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | High positive | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | High positive | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | High positive | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | High positive | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | High positive | \n",
"| Q565 | C | 2052996261 | A | 17332283 | High positive | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | High positive | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | High positive | \n",
"| Q918 | C | 2063217449 | A | 17332287 | High positive | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | High positive | \n",
"| Q866 | C | 2079749157 | A | 17332289 | High positive | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | High positive | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | High positive | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | High positive | \n",
"| Q355 | C | 2093900731 | A | 17332293 | High positive | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | High positive | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | High positive | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | High positive | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | High positive | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | High positive | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | High positive | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | High positive | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | High positive | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | High positive | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | High positive | \n",
"| Q30 | A | 2277746226 | A | 17332304 | None | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | High positive | \n",
"| Q5 | A | 5668008721 | A | 17332306 | None | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | High positive | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 NA \n",
"2 Q10069140 C 0 E 2 High negative\n",
"3 Q10081695 C 0 E 3 High negative\n",
"4 Q10092002 E 0 E 4 NA \n",
"5 Q10111267 E 0 E 5 NA \n",
"6 Q10149726 E 0 E 6 NA \n",
"7 Q10180230 E 0 E 7 NA \n",
"8 Q10185035 E 0 E 8 NA \n",
"9 Q10205202 E 0 E 9 NA \n",
"10 Q10252966 E 0 E 10 NA \n",
"11 Q10444494 C 0 E 11 High negative\n",
"12 Q10624171 C 0 E 12 High negative\n",
"13 Q10704108 C 0 E 13 High negative\n",
"14 Q10750354 E 0 E 14 NA \n",
"15 Q10766855 E 0 E 15 NA \n",
"16 Q10827611 E 0 E 16 NA \n",
"17 Q11093044 E 0 E 17 NA \n",
"18 Q11934537 E 0 E 18 NA \n",
"19 Q12133466 E 0 E 19 NA \n",
"20 Q12264503 E 0 E 20 NA \n",
"21 Q12267516 E 0 E 21 NA \n",
"22 Q12304084 E 0 E 22 NA \n",
"23 Q12443525 E 0 E 23 NA \n",
"24 Q12543904 E 0 E 24 NA \n",
"25 Q12890205 E 0 E 25 NA \n",
"26 Q12891524 E 0 E 26 NA \n",
"27 Q12918202 E 0 E 27 NA \n",
"28 Q13005653 E 0 E 28 NA \n",
"29 Q13073896 E 0 E 29 NA \n",
"30 Q13163823 E 0 E 30 NA \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 High positive\n",
"17332279 Q31165 C 2048330818 A 17332279 High positive\n",
"17332280 Q40629 C 2049755644 A 17332280 High positive\n",
"17332281 Q105584 C 2049926923 A 17332281 High positive\n",
"17332282 Q4584301 D 2052339927 A 17332282 High positive\n",
"17332283 Q565 C 2052996261 A 17332283 High positive\n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 High positive\n",
"17332286 Q14005 D 2063120071 A 17332286 High positive\n",
"17332287 Q918 C 2063217449 A 17332287 High positive\n",
"17332288 Q150248 C 2068796814 A 17332288 High positive\n",
"17332289 Q866 C 2079749157 A 17332289 High positive\n",
"17332290 Q477675 C 2080785713 A 17332290 High positive\n",
"17332291 Q1967876 C 2084215818 A 17332291 High positive\n",
"17332292 Q750403 C 2084693498 A 17332292 High positive\n",
"17332293 Q355 C 2093900731 A 17332293 High positive\n",
"17332294 Q623578 C 2097991400 A 17332294 High positive\n",
"17332295 Q17299517 D 2105487660 A 17332295 High positive\n",
"17332296 Q33999 C 2108672678 A 17332296 High positive\n",
"17332297 Q2494649 C 2114531894 A 17332297 High positive\n",
"17332298 Q2597810 C 2128920607 A 17332298 High positive\n",
"17332299 Q193563 C 2130725560 A 17332299 High positive\n",
"17332300 Q423048 C 2136131564 A 17332300 High positive\n",
"17332301 Q37312 C 2142913121 A 17332301 High positive\n",
"17332302 Q54919 C 2148531382 A 17332302 High positive\n",
"17332303 Q36578 C 2229315598 A 17332303 High positive\n",
"17332304 Q30 A 2277746226 A 17332304 None \n",
"17332305 Q6581097 D 3273952711 A 17332305 High positive\n",
"17332306 Q5 A 5668008721 A 17332306 None \n",
"17332307 Q5296 C 12530369761 A 17332307 High positive"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## D\n",
"articles_by_pop[prediction == 'D' & pop_class == 'E',\n",
" dissonance := 'Moderate negative'];\n",
"articles_by_pop[prediction == 'D' & pop_class == 'D',\n",
" dissonance := 'None'];\n",
"articles_by_pop[prediction == 'D' & pop_class == 'C',\n",
" dissonance := 'Moderate positive'];\n",
"articles_by_pop[prediction == 'D' & pop_class >= 'B',\n",
" dissonance := 'High positive'];"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>None </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>None </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>None </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>None </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>None </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>None </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>None </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>None </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10750354 </td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>None </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>None </td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>None </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>None </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>None </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>None </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>None </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>None </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>None </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>None </td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>None </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>None </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>None </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>None </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>None </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>None </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>None </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>17332278 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818 </td><td>A </td><td>17332279 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>17332280 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>17332281 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927 </td><td>A </td><td>17332282 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>17332283 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224 </td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>17332285 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>17332286 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>17332287 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>17332288 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157 </td><td>A </td><td>17332289 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>17332290 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>17332291 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498 </td><td>A </td><td>17332292 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>17332293 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>17332294 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>17332295 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>17332296 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>17332297 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>17332298 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>17332299 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>17332300 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>17332301 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>17332302 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>17332303 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>17332304 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>17332305 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721 </td><td>A </td><td>17332306 </td><td>None </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>17332307 </td><td>High positive</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & None \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative\\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative\\\\\n",
"\t Q10092002 & E & 0 & E & 4 & None \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & None \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & None \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & None \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & None \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & None \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & None \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative\\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative\\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative\\\\\n",
"\t Q10750354 & E & 0 & E & 14 & None \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & None \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & None \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & None \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & None \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & None \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & None \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & None \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & None \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & None \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & None \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & None \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & None \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & None \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & None \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & None \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & None \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & High positive\\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & High positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & High positive\\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & High positive\\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & High positive\\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & High positive\\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & High positive\\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & High positive\\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & High positive\\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & High positive\\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & High positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & High positive\\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & High positive\\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & High positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & High positive\\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & High positive\\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & High positive\\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & High positive\\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & High positive\\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & High positive\\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & High positive\\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & High positive\\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & High positive\\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & High positive\\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & High positive\\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & High positive\\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & None \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & High positive\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | None | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | None | \n",
"| Q10111267 | E | 0 | E | 5 | None | \n",
"| Q10149726 | E | 0 | E | 6 | None | \n",
"| Q10180230 | E | 0 | E | 7 | None | \n",
"| Q10185035 | E | 0 | E | 8 | None | \n",
"| Q10205202 | E | 0 | E | 9 | None | \n",
"| Q10252966 | E | 0 | E | 10 | None | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | E | 0 | E | 14 | None | \n",
"| Q10766855 | E | 0 | E | 15 | None | \n",
"| Q10827611 | E | 0 | E | 16 | None | \n",
"| Q11093044 | E | 0 | E | 17 | None | \n",
"| Q11934537 | E | 0 | E | 18 | None | \n",
"| Q12133466 | E | 0 | E | 19 | None | \n",
"| Q12264503 | E | 0 | E | 20 | None | \n",
"| Q12267516 | E | 0 | E | 21 | None | \n",
"| Q12304084 | E | 0 | E | 22 | None | \n",
"| Q12443525 | E | 0 | E | 23 | None | \n",
"| Q12543904 | E | 0 | E | 24 | None | \n",
"| Q12890205 | E | 0 | E | 25 | None | \n",
"| Q12891524 | E | 0 | E | 26 | None | \n",
"| Q12918202 | E | 0 | E | 27 | None | \n",
"| Q13005653 | E | 0 | E | 28 | None | \n",
"| Q13073896 | E | 0 | E | 29 | None | \n",
"| Q13163823 | E | 0 | E | 30 | None | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | High positive | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | High positive | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | High positive | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | High positive | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | High positive | \n",
"| Q565 | C | 2052996261 | A | 17332283 | High positive | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | High positive | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | High positive | \n",
"| Q918 | C | 2063217449 | A | 17332287 | High positive | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | High positive | \n",
"| Q866 | C | 2079749157 | A | 17332289 | High positive | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | High positive | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | High positive | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | High positive | \n",
"| Q355 | C | 2093900731 | A | 17332293 | High positive | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | High positive | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | High positive | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | High positive | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | High positive | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | High positive | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | High positive | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | High positive | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | High positive | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | High positive | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | High positive | \n",
"| Q30 | A | 2277746226 | A | 17332304 | None | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | High positive | \n",
"| Q5 | A | 5668008721 | A | 17332306 | None | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | High positive | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 None \n",
"2 Q10069140 C 0 E 2 High negative\n",
"3 Q10081695 C 0 E 3 High negative\n",
"4 Q10092002 E 0 E 4 None \n",
"5 Q10111267 E 0 E 5 None \n",
"6 Q10149726 E 0 E 6 None \n",
"7 Q10180230 E 0 E 7 None \n",
"8 Q10185035 E 0 E 8 None \n",
"9 Q10205202 E 0 E 9 None \n",
"10 Q10252966 E 0 E 10 None \n",
"11 Q10444494 C 0 E 11 High negative\n",
"12 Q10624171 C 0 E 12 High negative\n",
"13 Q10704108 C 0 E 13 High negative\n",
"14 Q10750354 E 0 E 14 None \n",
"15 Q10766855 E 0 E 15 None \n",
"16 Q10827611 E 0 E 16 None \n",
"17 Q11093044 E 0 E 17 None \n",
"18 Q11934537 E 0 E 18 None \n",
"19 Q12133466 E 0 E 19 None \n",
"20 Q12264503 E 0 E 20 None \n",
"21 Q12267516 E 0 E 21 None \n",
"22 Q12304084 E 0 E 22 None \n",
"23 Q12443525 E 0 E 23 None \n",
"24 Q12543904 E 0 E 24 None \n",
"25 Q12890205 E 0 E 25 None \n",
"26 Q12891524 E 0 E 26 None \n",
"27 Q12918202 E 0 E 27 None \n",
"28 Q13005653 E 0 E 28 None \n",
"29 Q13073896 E 0 E 29 None \n",
"30 Q13163823 E 0 E 30 None \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 High positive\n",
"17332279 Q31165 C 2048330818 A 17332279 High positive\n",
"17332280 Q40629 C 2049755644 A 17332280 High positive\n",
"17332281 Q105584 C 2049926923 A 17332281 High positive\n",
"17332282 Q4584301 D 2052339927 A 17332282 High positive\n",
"17332283 Q565 C 2052996261 A 17332283 High positive\n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 High positive\n",
"17332286 Q14005 D 2063120071 A 17332286 High positive\n",
"17332287 Q918 C 2063217449 A 17332287 High positive\n",
"17332288 Q150248 C 2068796814 A 17332288 High positive\n",
"17332289 Q866 C 2079749157 A 17332289 High positive\n",
"17332290 Q477675 C 2080785713 A 17332290 High positive\n",
"17332291 Q1967876 C 2084215818 A 17332291 High positive\n",
"17332292 Q750403 C 2084693498 A 17332292 High positive\n",
"17332293 Q355 C 2093900731 A 17332293 High positive\n",
"17332294 Q623578 C 2097991400 A 17332294 High positive\n",
"17332295 Q17299517 D 2105487660 A 17332295 High positive\n",
"17332296 Q33999 C 2108672678 A 17332296 High positive\n",
"17332297 Q2494649 C 2114531894 A 17332297 High positive\n",
"17332298 Q2597810 C 2128920607 A 17332298 High positive\n",
"17332299 Q193563 C 2130725560 A 17332299 High positive\n",
"17332300 Q423048 C 2136131564 A 17332300 High positive\n",
"17332301 Q37312 C 2142913121 A 17332301 High positive\n",
"17332302 Q54919 C 2148531382 A 17332302 High positive\n",
"17332303 Q36578 C 2229315598 A 17332303 High positive\n",
"17332304 Q30 A 2277746226 A 17332304 None \n",
"17332305 Q6581097 D 3273952711 A 17332305 High positive\n",
"17332306 Q5 A 5668008721 A 17332306 None \n",
"17332307 Q5296 C 12530369761 A 17332307 High positive"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>None </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>None </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>None </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>None </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>None </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>None </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>None </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>None </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10750354 </td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>None </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>None </td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>None </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>None </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>None </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>None </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>None </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>None </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>None </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>None </td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>None </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>None </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>None </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>None </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>None </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>None </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>None </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>17332278 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818 </td><td>A </td><td>17332279 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>17332280 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>17332281 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927 </td><td>A </td><td>17332282 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>17332283 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224 </td><td>A </td><td>17332284 </td><td>NA </td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>17332285 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>17332286 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>17332287 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>17332288 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157 </td><td>A </td><td>17332289 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>17332290 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>17332291 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498 </td><td>A </td><td>17332292 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>17332293 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>17332294 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>17332295 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>17332296 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>17332297 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>17332298 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>17332299 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>17332300 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>17332301 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>17332302 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>17332303 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>17332304 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>17332305 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721 </td><td>A </td><td>17332306 </td><td>None </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>17332307 </td><td>High positive</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & None \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative\\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative\\\\\n",
"\t Q10092002 & E & 0 & E & 4 & None \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & None \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & None \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & None \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & None \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & None \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & None \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative\\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative\\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative\\\\\n",
"\t Q10750354 & E & 0 & E & 14 & None \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & None \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & None \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & None \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & None \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & None \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & None \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & None \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & None \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & None \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & None \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & None \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & None \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & None \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & None \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & None \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & None \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & High positive\\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & High positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & High positive\\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & High positive\\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & High positive\\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & High positive\\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & NA \\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & High positive\\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & High positive\\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & High positive\\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & High positive\\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & High positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & High positive\\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & High positive\\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & High positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & High positive\\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & High positive\\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & High positive\\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & High positive\\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & High positive\\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & High positive\\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & High positive\\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & High positive\\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & High positive\\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & High positive\\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & High positive\\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & High positive\\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & None \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & High positive\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | None | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | None | \n",
"| Q10111267 | E | 0 | E | 5 | None | \n",
"| Q10149726 | E | 0 | E | 6 | None | \n",
"| Q10180230 | E | 0 | E | 7 | None | \n",
"| Q10185035 | E | 0 | E | 8 | None | \n",
"| Q10205202 | E | 0 | E | 9 | None | \n",
"| Q10252966 | E | 0 | E | 10 | None | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | E | 0 | E | 14 | None | \n",
"| Q10766855 | E | 0 | E | 15 | None | \n",
"| Q10827611 | E | 0 | E | 16 | None | \n",
"| Q11093044 | E | 0 | E | 17 | None | \n",
"| Q11934537 | E | 0 | E | 18 | None | \n",
"| Q12133466 | E | 0 | E | 19 | None | \n",
"| Q12264503 | E | 0 | E | 20 | None | \n",
"| Q12267516 | E | 0 | E | 21 | None | \n",
"| Q12304084 | E | 0 | E | 22 | None | \n",
"| Q12443525 | E | 0 | E | 23 | None | \n",
"| Q12543904 | E | 0 | E | 24 | None | \n",
"| Q12890205 | E | 0 | E | 25 | None | \n",
"| Q12891524 | E | 0 | E | 26 | None | \n",
"| Q12918202 | E | 0 | E | 27 | None | \n",
"| Q13005653 | E | 0 | E | 28 | None | \n",
"| Q13073896 | E | 0 | E | 29 | None | \n",
"| Q13163823 | E | 0 | E | 30 | None | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | High positive | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | High positive | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | High positive | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | High positive | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | High positive | \n",
"| Q565 | C | 2052996261 | A | 17332283 | High positive | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | NA | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | High positive | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | High positive | \n",
"| Q918 | C | 2063217449 | A | 17332287 | High positive | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | High positive | \n",
"| Q866 | C | 2079749157 | A | 17332289 | High positive | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | High positive | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | High positive | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | High positive | \n",
"| Q355 | C | 2093900731 | A | 17332293 | High positive | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | High positive | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | High positive | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | High positive | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | High positive | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | High positive | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | High positive | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | High positive | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | High positive | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | High positive | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | High positive | \n",
"| Q30 | A | 2277746226 | A | 17332304 | None | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | High positive | \n",
"| Q5 | A | 5668008721 | A | 17332306 | None | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | High positive | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 None \n",
"2 Q10069140 C 0 E 2 High negative\n",
"3 Q10081695 C 0 E 3 High negative\n",
"4 Q10092002 E 0 E 4 None \n",
"5 Q10111267 E 0 E 5 None \n",
"6 Q10149726 E 0 E 6 None \n",
"7 Q10180230 E 0 E 7 None \n",
"8 Q10185035 E 0 E 8 None \n",
"9 Q10205202 E 0 E 9 None \n",
"10 Q10252966 E 0 E 10 None \n",
"11 Q10444494 C 0 E 11 High negative\n",
"12 Q10624171 C 0 E 12 High negative\n",
"13 Q10704108 C 0 E 13 High negative\n",
"14 Q10750354 E 0 E 14 None \n",
"15 Q10766855 E 0 E 15 None \n",
"16 Q10827611 E 0 E 16 None \n",
"17 Q11093044 E 0 E 17 None \n",
"18 Q11934537 E 0 E 18 None \n",
"19 Q12133466 E 0 E 19 None \n",
"20 Q12264503 E 0 E 20 None \n",
"21 Q12267516 E 0 E 21 None \n",
"22 Q12304084 E 0 E 22 None \n",
"23 Q12443525 E 0 E 23 None \n",
"24 Q12543904 E 0 E 24 None \n",
"25 Q12890205 E 0 E 25 None \n",
"26 Q12891524 E 0 E 26 None \n",
"27 Q12918202 E 0 E 27 None \n",
"28 Q13005653 E 0 E 28 None \n",
"29 Q13073896 E 0 E 29 None \n",
"30 Q13163823 E 0 E 30 None \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 High positive\n",
"17332279 Q31165 C 2048330818 A 17332279 High positive\n",
"17332280 Q40629 C 2049755644 A 17332280 High positive\n",
"17332281 Q105584 C 2049926923 A 17332281 High positive\n",
"17332282 Q4584301 D 2052339927 A 17332282 High positive\n",
"17332283 Q565 C 2052996261 A 17332283 High positive\n",
"17332284 Q1868372 E 2056080224 A 17332284 NA \n",
"17332285 Q209330 C 2060928966 A 17332285 High positive\n",
"17332286 Q14005 D 2063120071 A 17332286 High positive\n",
"17332287 Q918 C 2063217449 A 17332287 High positive\n",
"17332288 Q150248 C 2068796814 A 17332288 High positive\n",
"17332289 Q866 C 2079749157 A 17332289 High positive\n",
"17332290 Q477675 C 2080785713 A 17332290 High positive\n",
"17332291 Q1967876 C 2084215818 A 17332291 High positive\n",
"17332292 Q750403 C 2084693498 A 17332292 High positive\n",
"17332293 Q355 C 2093900731 A 17332293 High positive\n",
"17332294 Q623578 C 2097991400 A 17332294 High positive\n",
"17332295 Q17299517 D 2105487660 A 17332295 High positive\n",
"17332296 Q33999 C 2108672678 A 17332296 High positive\n",
"17332297 Q2494649 C 2114531894 A 17332297 High positive\n",
"17332298 Q2597810 C 2128920607 A 17332298 High positive\n",
"17332299 Q193563 C 2130725560 A 17332299 High positive\n",
"17332300 Q423048 C 2136131564 A 17332300 High positive\n",
"17332301 Q37312 C 2142913121 A 17332301 High positive\n",
"17332302 Q54919 C 2148531382 A 17332302 High positive\n",
"17332303 Q36578 C 2229315598 A 17332303 High positive\n",
"17332304 Q30 A 2277746226 A 17332304 None \n",
"17332305 Q6581097 D 3273952711 A 17332305 High positive\n",
"17332306 Q5 A 5668008721 A 17332306 None \n",
"17332307 Q5296 C 12530369761 A 17332307 High positive"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>entity_id</th><th scope=col>prediction</th><th scope=col>page_views</th><th scope=col>pop_class</th><th scope=col>seqNum</th><th scope=col>dissonance</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>Q10040378 </td><td>E </td><td>0 </td><td>E </td><td> 1 </td><td>None </td></tr>\n",
"\t<tr><td>Q10069140 </td><td>C </td><td>0 </td><td>E </td><td> 2 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10081695 </td><td>C </td><td>0 </td><td>E </td><td> 3 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10092002 </td><td>E </td><td>0 </td><td>E </td><td> 4 </td><td>None </td></tr>\n",
"\t<tr><td>Q10111267 </td><td>E </td><td>0 </td><td>E </td><td> 5 </td><td>None </td></tr>\n",
"\t<tr><td>Q10149726 </td><td>E </td><td>0 </td><td>E </td><td> 6 </td><td>None </td></tr>\n",
"\t<tr><td>Q10180230 </td><td>E </td><td>0 </td><td>E </td><td> 7 </td><td>None </td></tr>\n",
"\t<tr><td>Q10185035 </td><td>E </td><td>0 </td><td>E </td><td> 8 </td><td>None </td></tr>\n",
"\t<tr><td>Q10205202 </td><td>E </td><td>0 </td><td>E </td><td> 9 </td><td>None </td></tr>\n",
"\t<tr><td>Q10252966 </td><td>E </td><td>0 </td><td>E </td><td>10 </td><td>None </td></tr>\n",
"\t<tr><td>Q10444494 </td><td>C </td><td>0 </td><td>E </td><td>11 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10624171 </td><td>C </td><td>0 </td><td>E </td><td>12 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10704108 </td><td>C </td><td>0 </td><td>E </td><td>13 </td><td>High negative</td></tr>\n",
"\t<tr><td>Q10750354 </td><td>E </td><td>0 </td><td>E </td><td>14 </td><td>None </td></tr>\n",
"\t<tr><td>Q10766855 </td><td>E </td><td>0 </td><td>E </td><td>15 </td><td>None </td></tr>\n",
"\t<tr><td>Q10827611 </td><td>E </td><td>0 </td><td>E </td><td>16 </td><td>None </td></tr>\n",
"\t<tr><td>Q11093044 </td><td>E </td><td>0 </td><td>E </td><td>17 </td><td>None </td></tr>\n",
"\t<tr><td>Q11934537 </td><td>E </td><td>0 </td><td>E </td><td>18 </td><td>None </td></tr>\n",
"\t<tr><td>Q12133466 </td><td>E </td><td>0 </td><td>E </td><td>19 </td><td>None </td></tr>\n",
"\t<tr><td>Q12264503 </td><td>E </td><td>0 </td><td>E </td><td>20 </td><td>None </td></tr>\n",
"\t<tr><td>Q12267516 </td><td>E </td><td>0 </td><td>E </td><td>21 </td><td>None </td></tr>\n",
"\t<tr><td>Q12304084 </td><td>E </td><td>0 </td><td>E </td><td>22 </td><td>None </td></tr>\n",
"\t<tr><td>Q12443525 </td><td>E </td><td>0 </td><td>E </td><td>23 </td><td>None </td></tr>\n",
"\t<tr><td>Q12543904 </td><td>E </td><td>0 </td><td>E </td><td>24 </td><td>None </td></tr>\n",
"\t<tr><td>Q12890205 </td><td>E </td><td>0 </td><td>E </td><td>25 </td><td>None </td></tr>\n",
"\t<tr><td>Q12891524 </td><td>E </td><td>0 </td><td>E </td><td>26 </td><td>None </td></tr>\n",
"\t<tr><td>Q12918202 </td><td>E </td><td>0 </td><td>E </td><td>27 </td><td>None </td></tr>\n",
"\t<tr><td>Q13005653 </td><td>E </td><td>0 </td><td>E </td><td>28 </td><td>None </td></tr>\n",
"\t<tr><td>Q13073896 </td><td>E </td><td>0 </td><td>E </td><td>29 </td><td>None </td></tr>\n",
"\t<tr><td>Q13163823 </td><td>E </td><td>0 </td><td>E </td><td>30 </td><td>None </td></tr>\n",
"\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
"\t<tr><td>Q1048694 </td><td>C </td><td> 2048095025 </td><td>A </td><td>17332278 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q31165 </td><td>C </td><td> 2048330818 </td><td>A </td><td>17332279 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q40629 </td><td>C </td><td> 2049755644 </td><td>A </td><td>17332280 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q105584 </td><td>C </td><td> 2049926923 </td><td>A </td><td>17332281 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q4584301 </td><td>D </td><td> 2052339927 </td><td>A </td><td>17332282 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q565 </td><td>C </td><td> 2052996261 </td><td>A </td><td>17332283 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q1868372 </td><td>E </td><td> 2056080224 </td><td>A </td><td>17332284 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q209330 </td><td>C </td><td> 2060928966 </td><td>A </td><td>17332285 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q14005 </td><td>D </td><td> 2063120071 </td><td>A </td><td>17332286 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q918 </td><td>C </td><td> 2063217449 </td><td>A </td><td>17332287 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q150248 </td><td>C </td><td> 2068796814 </td><td>A </td><td>17332288 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q866 </td><td>C </td><td> 2079749157 </td><td>A </td><td>17332289 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q477675 </td><td>C </td><td> 2080785713 </td><td>A </td><td>17332290 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q1967876 </td><td>C </td><td> 2084215818 </td><td>A </td><td>17332291 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q750403 </td><td>C </td><td> 2084693498 </td><td>A </td><td>17332292 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q355 </td><td>C </td><td> 2093900731 </td><td>A </td><td>17332293 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q623578 </td><td>C </td><td> 2097991400 </td><td>A </td><td>17332294 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q17299517 </td><td>D </td><td> 2105487660 </td><td>A </td><td>17332295 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q33999 </td><td>C </td><td> 2108672678 </td><td>A </td><td>17332296 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q2494649 </td><td>C </td><td> 2114531894 </td><td>A </td><td>17332297 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q2597810 </td><td>C </td><td> 2128920607 </td><td>A </td><td>17332298 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q193563 </td><td>C </td><td> 2130725560 </td><td>A </td><td>17332299 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q423048 </td><td>C </td><td> 2136131564 </td><td>A </td><td>17332300 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q37312 </td><td>C </td><td> 2142913121 </td><td>A </td><td>17332301 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q54919 </td><td>C </td><td> 2148531382 </td><td>A </td><td>17332302 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q36578 </td><td>C </td><td> 2229315598 </td><td>A </td><td>17332303 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q30 </td><td>A </td><td> 2277746226 </td><td>A </td><td>17332304 </td><td>None </td></tr>\n",
"\t<tr><td>Q6581097 </td><td>D </td><td> 3273952711 </td><td>A </td><td>17332305 </td><td>High positive</td></tr>\n",
"\t<tr><td>Q5 </td><td>A </td><td> 5668008721 </td><td>A </td><td>17332306 </td><td>None </td></tr>\n",
"\t<tr><td>Q5296 </td><td>C </td><td>12530369761 </td><td>A </td><td>17332307 </td><td>High positive</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllll}\n",
" entity\\_id & prediction & page\\_views & pop\\_class & seqNum & dissonance\\\\\n",
"\\hline\n",
"\t Q10040378 & E & 0 & E & 1 & None \\\\\n",
"\t Q10069140 & C & 0 & E & 2 & High negative\\\\\n",
"\t Q10081695 & C & 0 & E & 3 & High negative\\\\\n",
"\t Q10092002 & E & 0 & E & 4 & None \\\\\n",
"\t Q10111267 & E & 0 & E & 5 & None \\\\\n",
"\t Q10149726 & E & 0 & E & 6 & None \\\\\n",
"\t Q10180230 & E & 0 & E & 7 & None \\\\\n",
"\t Q10185035 & E & 0 & E & 8 & None \\\\\n",
"\t Q10205202 & E & 0 & E & 9 & None \\\\\n",
"\t Q10252966 & E & 0 & E & 10 & None \\\\\n",
"\t Q10444494 & C & 0 & E & 11 & High negative\\\\\n",
"\t Q10624171 & C & 0 & E & 12 & High negative\\\\\n",
"\t Q10704108 & C & 0 & E & 13 & High negative\\\\\n",
"\t Q10750354 & E & 0 & E & 14 & None \\\\\n",
"\t Q10766855 & E & 0 & E & 15 & None \\\\\n",
"\t Q10827611 & E & 0 & E & 16 & None \\\\\n",
"\t Q11093044 & E & 0 & E & 17 & None \\\\\n",
"\t Q11934537 & E & 0 & E & 18 & None \\\\\n",
"\t Q12133466 & E & 0 & E & 19 & None \\\\\n",
"\t Q12264503 & E & 0 & E & 20 & None \\\\\n",
"\t Q12267516 & E & 0 & E & 21 & None \\\\\n",
"\t Q12304084 & E & 0 & E & 22 & None \\\\\n",
"\t Q12443525 & E & 0 & E & 23 & None \\\\\n",
"\t Q12543904 & E & 0 & E & 24 & None \\\\\n",
"\t Q12890205 & E & 0 & E & 25 & None \\\\\n",
"\t Q12891524 & E & 0 & E & 26 & None \\\\\n",
"\t Q12918202 & E & 0 & E & 27 & None \\\\\n",
"\t Q13005653 & E & 0 & E & 28 & None \\\\\n",
"\t Q13073896 & E & 0 & E & 29 & None \\\\\n",
"\t Q13163823 & E & 0 & E & 30 & None \\\\\n",
"\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
"\t Q1048694 & C & 2048095025 & A & 17332278 & High positive\\\\\n",
"\t Q31165 & C & 2048330818 & A & 17332279 & High positive\\\\\n",
"\t Q40629 & C & 2049755644 & A & 17332280 & High positive\\\\\n",
"\t Q105584 & C & 2049926923 & A & 17332281 & High positive\\\\\n",
"\t Q4584301 & D & 2052339927 & A & 17332282 & High positive\\\\\n",
"\t Q565 & C & 2052996261 & A & 17332283 & High positive\\\\\n",
"\t Q1868372 & E & 2056080224 & A & 17332284 & High positive\\\\\n",
"\t Q209330 & C & 2060928966 & A & 17332285 & High positive\\\\\n",
"\t Q14005 & D & 2063120071 & A & 17332286 & High positive\\\\\n",
"\t Q918 & C & 2063217449 & A & 17332287 & High positive\\\\\n",
"\t Q150248 & C & 2068796814 & A & 17332288 & High positive\\\\\n",
"\t Q866 & C & 2079749157 & A & 17332289 & High positive\\\\\n",
"\t Q477675 & C & 2080785713 & A & 17332290 & High positive\\\\\n",
"\t Q1967876 & C & 2084215818 & A & 17332291 & High positive\\\\\n",
"\t Q750403 & C & 2084693498 & A & 17332292 & High positive\\\\\n",
"\t Q355 & C & 2093900731 & A & 17332293 & High positive\\\\\n",
"\t Q623578 & C & 2097991400 & A & 17332294 & High positive\\\\\n",
"\t Q17299517 & D & 2105487660 & A & 17332295 & High positive\\\\\n",
"\t Q33999 & C & 2108672678 & A & 17332296 & High positive\\\\\n",
"\t Q2494649 & C & 2114531894 & A & 17332297 & High positive\\\\\n",
"\t Q2597810 & C & 2128920607 & A & 17332298 & High positive\\\\\n",
"\t Q193563 & C & 2130725560 & A & 17332299 & High positive\\\\\n",
"\t Q423048 & C & 2136131564 & A & 17332300 & High positive\\\\\n",
"\t Q37312 & C & 2142913121 & A & 17332301 & High positive\\\\\n",
"\t Q54919 & C & 2148531382 & A & 17332302 & High positive\\\\\n",
"\t Q36578 & C & 2229315598 & A & 17332303 & High positive\\\\\n",
"\t Q30 & A & 2277746226 & A & 17332304 & None \\\\\n",
"\t Q6581097 & D & 3273952711 & A & 17332305 & High positive\\\\\n",
"\t Q5 & A & 5668008721 & A & 17332306 & None \\\\\n",
"\t Q5296 & C & 12530369761 & A & 17332307 & High positive\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"entity_id | prediction | page_views | pop_class | seqNum | dissonance | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| Q10040378 | E | 0 | E | 1 | None | \n",
"| Q10069140 | C | 0 | E | 2 | High negative | \n",
"| Q10081695 | C | 0 | E | 3 | High negative | \n",
"| Q10092002 | E | 0 | E | 4 | None | \n",
"| Q10111267 | E | 0 | E | 5 | None | \n",
"| Q10149726 | E | 0 | E | 6 | None | \n",
"| Q10180230 | E | 0 | E | 7 | None | \n",
"| Q10185035 | E | 0 | E | 8 | None | \n",
"| Q10205202 | E | 0 | E | 9 | None | \n",
"| Q10252966 | E | 0 | E | 10 | None | \n",
"| Q10444494 | C | 0 | E | 11 | High negative | \n",
"| Q10624171 | C | 0 | E | 12 | High negative | \n",
"| Q10704108 | C | 0 | E | 13 | High negative | \n",
"| Q10750354 | E | 0 | E | 14 | None | \n",
"| Q10766855 | E | 0 | E | 15 | None | \n",
"| Q10827611 | E | 0 | E | 16 | None | \n",
"| Q11093044 | E | 0 | E | 17 | None | \n",
"| Q11934537 | E | 0 | E | 18 | None | \n",
"| Q12133466 | E | 0 | E | 19 | None | \n",
"| Q12264503 | E | 0 | E | 20 | None | \n",
"| Q12267516 | E | 0 | E | 21 | None | \n",
"| Q12304084 | E | 0 | E | 22 | None | \n",
"| Q12443525 | E | 0 | E | 23 | None | \n",
"| Q12543904 | E | 0 | E | 24 | None | \n",
"| Q12890205 | E | 0 | E | 25 | None | \n",
"| Q12891524 | E | 0 | E | 26 | None | \n",
"| Q12918202 | E | 0 | E | 27 | None | \n",
"| Q13005653 | E | 0 | E | 28 | None | \n",
"| Q13073896 | E | 0 | E | 29 | None | \n",
"| Q13163823 | E | 0 | E | 30 | None | \n",
"| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n",
"| Q1048694 | C | 2048095025 | A | 17332278 | High positive | \n",
"| Q31165 | C | 2048330818 | A | 17332279 | High positive | \n",
"| Q40629 | C | 2049755644 | A | 17332280 | High positive | \n",
"| Q105584 | C | 2049926923 | A | 17332281 | High positive | \n",
"| Q4584301 | D | 2052339927 | A | 17332282 | High positive | \n",
"| Q565 | C | 2052996261 | A | 17332283 | High positive | \n",
"| Q1868372 | E | 2056080224 | A | 17332284 | High positive | \n",
"| Q209330 | C | 2060928966 | A | 17332285 | High positive | \n",
"| Q14005 | D | 2063120071 | A | 17332286 | High positive | \n",
"| Q918 | C | 2063217449 | A | 17332287 | High positive | \n",
"| Q150248 | C | 2068796814 | A | 17332288 | High positive | \n",
"| Q866 | C | 2079749157 | A | 17332289 | High positive | \n",
"| Q477675 | C | 2080785713 | A | 17332290 | High positive | \n",
"| Q1967876 | C | 2084215818 | A | 17332291 | High positive | \n",
"| Q750403 | C | 2084693498 | A | 17332292 | High positive | \n",
"| Q355 | C | 2093900731 | A | 17332293 | High positive | \n",
"| Q623578 | C | 2097991400 | A | 17332294 | High positive | \n",
"| Q17299517 | D | 2105487660 | A | 17332295 | High positive | \n",
"| Q33999 | C | 2108672678 | A | 17332296 | High positive | \n",
"| Q2494649 | C | 2114531894 | A | 17332297 | High positive | \n",
"| Q2597810 | C | 2128920607 | A | 17332298 | High positive | \n",
"| Q193563 | C | 2130725560 | A | 17332299 | High positive | \n",
"| Q423048 | C | 2136131564 | A | 17332300 | High positive | \n",
"| Q37312 | C | 2142913121 | A | 17332301 | High positive | \n",
"| Q54919 | C | 2148531382 | A | 17332302 | High positive | \n",
"| Q36578 | C | 2229315598 | A | 17332303 | High positive | \n",
"| Q30 | A | 2277746226 | A | 17332304 | None | \n",
"| Q6581097 | D | 3273952711 | A | 17332305 | High positive | \n",
"| Q5 | A | 5668008721 | A | 17332306 | None | \n",
"| Q5296 | C | 12530369761 | A | 17332307 | High positive | \n",
"\n",
"\n"
],
"text/plain": [
" entity_id prediction page_views pop_class seqNum dissonance \n",
"1 Q10040378 E 0 E 1 None \n",
"2 Q10069140 C 0 E 2 High negative\n",
"3 Q10081695 C 0 E 3 High negative\n",
"4 Q10092002 E 0 E 4 None \n",
"5 Q10111267 E 0 E 5 None \n",
"6 Q10149726 E 0 E 6 None \n",
"7 Q10180230 E 0 E 7 None \n",
"8 Q10185035 E 0 E 8 None \n",
"9 Q10205202 E 0 E 9 None \n",
"10 Q10252966 E 0 E 10 None \n",
"11 Q10444494 C 0 E 11 High negative\n",
"12 Q10624171 C 0 E 12 High negative\n",
"13 Q10704108 C 0 E 13 High negative\n",
"14 Q10750354 E 0 E 14 None \n",
"15 Q10766855 E 0 E 15 None \n",
"16 Q10827611 E 0 E 16 None \n",
"17 Q11093044 E 0 E 17 None \n",
"18 Q11934537 E 0 E 18 None \n",
"19 Q12133466 E 0 E 19 None \n",
"20 Q12264503 E 0 E 20 None \n",
"21 Q12267516 E 0 E 21 None \n",
"22 Q12304084 E 0 E 22 None \n",
"23 Q12443525 E 0 E 23 None \n",
"24 Q12543904 E 0 E 24 None \n",
"25 Q12890205 E 0 E 25 None \n",
"26 Q12891524 E 0 E 26 None \n",
"27 Q12918202 E 0 E 27 None \n",
"28 Q13005653 E 0 E 28 None \n",
"29 Q13073896 E 0 E 29 None \n",
"30 Q13163823 E 0 E 30 None \n",
"⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n",
"17332278 Q1048694 C 2048095025 A 17332278 High positive\n",
"17332279 Q31165 C 2048330818 A 17332279 High positive\n",
"17332280 Q40629 C 2049755644 A 17332280 High positive\n",
"17332281 Q105584 C 2049926923 A 17332281 High positive\n",
"17332282 Q4584301 D 2052339927 A 17332282 High positive\n",
"17332283 Q565 C 2052996261 A 17332283 High positive\n",
"17332284 Q1868372 E 2056080224 A 17332284 High positive\n",
"17332285 Q209330 C 2060928966 A 17332285 High positive\n",
"17332286 Q14005 D 2063120071 A 17332286 High positive\n",
"17332287 Q918 C 2063217449 A 17332287 High positive\n",
"17332288 Q150248 C 2068796814 A 17332288 High positive\n",
"17332289 Q866 C 2079749157 A 17332289 High positive\n",
"17332290 Q477675 C 2080785713 A 17332290 High positive\n",
"17332291 Q1967876 C 2084215818 A 17332291 High positive\n",
"17332292 Q750403 C 2084693498 A 17332292 High positive\n",
"17332293 Q355 C 2093900731 A 17332293 High positive\n",
"17332294 Q623578 C 2097991400 A 17332294 High positive\n",
"17332295 Q17299517 D 2105487660 A 17332295 High positive\n",
"17332296 Q33999 C 2108672678 A 17332296 High positive\n",
"17332297 Q2494649 C 2114531894 A 17332297 High positive\n",
"17332298 Q2597810 C 2128920607 A 17332298 High positive\n",
"17332299 Q193563 C 2130725560 A 17332299 High positive\n",
"17332300 Q423048 C 2136131564 A 17332300 High positive\n",
"17332301 Q37312 C 2142913121 A 17332301 High positive\n",
"17332302 Q54919 C 2148531382 A 17332302 High positive\n",
"17332303 Q36578 C 2229315598 A 17332303 High positive\n",
"17332304 Q30 A 2277746226 A 17332304 None \n",
"17332305 Q6581097 D 3273952711 A 17332305 High positive\n",
"17332306 Q5 A 5668008721 A 17332306 None \n",
"17332307 Q5296 C 12530369761 A 17332307 High positive"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## E\n",
"articles_by_pop[prediction == 'E' & pop_class == 'E',\n",
" dissonance := 'None'];\n",
"articles_by_pop[prediction == 'E' & pop_class == 'D',\n",
" dissonance := 'Moderate positive'];\n",
"articles_by_pop[prediction == 'E' & pop_class >= 'C',\n",
" dissonance := 'High positive'];"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Build a matrix where columns are the metric and rows are classes\n",
"create_alt_diss_matrix = function(articledata, metric, classes) {\n",
" d_mtrx = matrix(0, nrow=length(classes), ncol=length(metric));\n",
" rownames(d_mtrx) = classes;\n",
" colnames(d_mtrx) = metric;\n",
"\n",
" ## NOTE: R matrix values are [row,col] dimensions\n",
" for(real_rating in classes) {\n",
" for(diss_rating in metric) {\n",
" d_mtrx[real_rating, diss_rating] = length(articledata[prediction == real_rating & dissonance == diss_rating]$entity_id);\n",
" }\n",
" }\n",
" d_mtrx;\n",
"}\n",
"\n",
"alternative_dissonance_matrix.1 = create_alt_diss_matrix(articles_by_pop,\n",
" dissonance_metric, assessment_classes);\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>High negative</th><th scope=col>Moderate negative</th><th scope=col>None</th><th scope=col>Moderate positive</th><th scope=col>High positive</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>E</th><td> 0.0</td><td> 0.0</td><td>67.1</td><td>15.2</td><td>17.7</td></tr>\n",
"\t<tr><th scope=row>D</th><td> 0.0</td><td>52.8</td><td>21.0</td><td>25.0</td><td> 1.3</td></tr>\n",
"\t<tr><th scope=row>C</th><td>60.9</td><td>11.6</td><td>24.5</td><td> 3.0</td><td> 0.0</td></tr>\n",
"\t<tr><th scope=row>B</th><td>68.2</td><td>25.2</td><td> 6.5</td><td> 0.0</td><td> 0.0</td></tr>\n",
"\t<tr><th scope=row>A</th><td> 2.8</td><td>86.3</td><td>10.9</td><td> 0.0</td><td> 0.0</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" & High negative & Moderate negative & None & Moderate positive & High positive\\\\\n",
"\\hline\n",
"\tE & 0.0 & 0.0 & 67.1 & 15.2 & 17.7\\\\\n",
"\tD & 0.0 & 52.8 & 21.0 & 25.0 & 1.3\\\\\n",
"\tC & 60.9 & 11.6 & 24.5 & 3.0 & 0.0\\\\\n",
"\tB & 68.2 & 25.2 & 6.5 & 0.0 & 0.0\\\\\n",
"\tA & 2.8 & 86.3 & 10.9 & 0.0 & 0.0\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"| <!--/--> | High negative | Moderate negative | None | Moderate positive | High positive | \n",
"|---|---|---|---|---|\n",
"| E | 0.0 | 0.0 | 67.1 | 15.2 | 17.7 | \n",
"| D | 0.0 | 52.8 | 21.0 | 25.0 | 1.3 | \n",
"| C | 60.9 | 11.6 | 24.5 | 3.0 | 0.0 | \n",
"| B | 68.2 | 25.2 | 6.5 | 0.0 | 0.0 | \n",
"| A | 2.8 | 86.3 | 10.9 | 0.0 | 0.0 | \n",
"\n",
"\n"
],
"text/plain": [
" High negative Moderate negative None Moderate positive High positive\n",
"E 0.0 0.0 67.1 15.2 17.7 \n",
"D 0.0 52.8 21.0 25.0 1.3 \n",
"C 60.9 11.6 24.5 3.0 0.0 \n",
"B 68.2 25.2 6.5 0.0 0.0 \n",
"A 2.8 86.3 10.9 0.0 0.0 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Normalise by row\n",
"round(100*prop.table(alternative_dissonance_matrix.1, 1), 1);\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>prediction</th><th scope=col>dissonance</th><th scope=col>dissonant_views</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>E </td><td>None </td><td> 974270873 </td></tr>\n",
"\t<tr><td>C </td><td>High negative </td><td> 191333791 </td></tr>\n",
"\t<tr><td>D </td><td>Moderate negative</td><td> 271085622 </td></tr>\n",
"\t<tr><td>B </td><td>High negative </td><td> 29993947 </td></tr>\n",
"\t<tr><td>D </td><td>None </td><td> 643757672 </td></tr>\n",
"\t<tr><td>E </td><td>Moderate positive</td><td> 1936794472 </td></tr>\n",
"\t<tr><td>C </td><td>Moderate negative</td><td> 479573341 </td></tr>\n",
"\t<tr><td>C </td><td>None </td><td> 19310577750 </td></tr>\n",
"\t<tr><td>E </td><td>High positive </td><td> 84541030940 </td></tr>\n",
"\t<tr><td>D </td><td>Moderate positive</td><td> 11178081936 </td></tr>\n",
"\t<tr><td>B </td><td>Moderate negative</td><td> 1373753598 </td></tr>\n",
"\t<tr><td>A </td><td>High negative </td><td> 449871 </td></tr>\n",
"\t<tr><td>D </td><td>High positive </td><td> 88694398564 </td></tr>\n",
"\t<tr><td>C </td><td>Moderate positive</td><td>101248862230 </td></tr>\n",
"\t<tr><td>B </td><td>None </td><td> 17323238794 </td></tr>\n",
"\t<tr><td>A </td><td>Moderate negative</td><td> 3409307010 </td></tr>\n",
"\t<tr><td>A </td><td>None </td><td> 13960100385 </td></tr>\n",
"\t<tr><td>C </td><td>High positive </td><td>175561739664 </td></tr>\n",
"\t<tr><td>B </td><td>Moderate positive</td><td> 2806922193 </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lll}\n",
" prediction & dissonance & dissonant\\_views\\\\\n",
"\\hline\n",
"\t E & None & 974270873 \\\\\n",
"\t C & High negative & 191333791 \\\\\n",
"\t D & Moderate negative & 271085622 \\\\\n",
"\t B & High negative & 29993947 \\\\\n",
"\t D & None & 643757672 \\\\\n",
"\t E & Moderate positive & 1936794472 \\\\\n",
"\t C & Moderate negative & 479573341 \\\\\n",
"\t C & None & 19310577750 \\\\\n",
"\t E & High positive & 84541030940 \\\\\n",
"\t D & Moderate positive & 11178081936 \\\\\n",
"\t B & Moderate negative & 1373753598 \\\\\n",
"\t A & High negative & 449871 \\\\\n",
"\t D & High positive & 88694398564 \\\\\n",
"\t C & Moderate positive & 101248862230 \\\\\n",
"\t B & None & 17323238794 \\\\\n",
"\t A & Moderate negative & 3409307010 \\\\\n",
"\t A & None & 13960100385 \\\\\n",
"\t C & High positive & 175561739664 \\\\\n",
"\t B & Moderate positive & 2806922193 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"prediction | dissonance | dissonant_views | \n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| E | None | 974270873 | \n",
"| C | High negative | 191333791 | \n",
"| D | Moderate negative | 271085622 | \n",
"| B | High negative | 29993947 | \n",
"| D | None | 643757672 | \n",
"| E | Moderate positive | 1936794472 | \n",
"| C | Moderate negative | 479573341 | \n",
"| C | None | 19310577750 | \n",
"| E | High positive | 84541030940 | \n",
"| D | Moderate positive | 11178081936 | \n",
"| B | Moderate negative | 1373753598 | \n",
"| A | High negative | 449871 | \n",
"| D | High positive | 88694398564 | \n",
"| C | Moderate positive | 101248862230 | \n",
"| B | None | 17323238794 | \n",
"| A | Moderate negative | 3409307010 | \n",
"| A | None | 13960100385 | \n",
"| C | High positive | 175561739664 | \n",
"| B | Moderate positive | 2806922193 | \n",
"\n",
"\n"
],
"text/plain": [
" prediction dissonance dissonant_views\n",
"1 E None 974270873 \n",
"2 C High negative 191333791 \n",
"3 D Moderate negative 271085622 \n",
"4 B High negative 29993947 \n",
"5 D None 643757672 \n",
"6 E Moderate positive 1936794472 \n",
"7 C Moderate negative 479573341 \n",
"8 C None 19310577750 \n",
"9 E High positive 84541030940 \n",
"10 D Moderate positive 11178081936 \n",
"11 B Moderate negative 1373753598 \n",
"12 A High negative 449871 \n",
"13 D High positive 88694398564 \n",
"14 C Moderate positive 101248862230 \n",
"15 B None 17323238794 \n",
"16 A Moderate negative 3409307010 \n",
"17 A None 13960100385 \n",
"18 C High positive 175561739664 \n",
"19 B Moderate positive 2806922193 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Number of dissonant views per assessment class and amount of dissonance\n",
"articles_by_pop[, list(dissonant_views=sum(page_views)), by=list(prediction, dissonance)];\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>dissonance</th><th scope=col>dissonant_views</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>None </td><td> 52211945474 </td></tr>\n",
"\t<tr><td>High negative </td><td> 221777609 </td></tr>\n",
"\t<tr><td>Moderate negative</td><td> 5533719571 </td></tr>\n",
"\t<tr><td>Moderate positive</td><td>117170660831 </td></tr>\n",
"\t<tr><td>High positive </td><td>348797169168 </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|ll}\n",
" dissonance & dissonant\\_views\\\\\n",
"\\hline\n",
"\t None & 52211945474 \\\\\n",
"\t High negative & 221777609 \\\\\n",
"\t Moderate negative & 5533719571 \\\\\n",
"\t Moderate positive & 117170660831 \\\\\n",
"\t High positive & 348797169168 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"dissonance | dissonant_views | \n",
"|---|---|---|---|---|\n",
"| None | 52211945474 | \n",
"| High negative | 221777609 | \n",
"| Moderate negative | 5533719571 | \n",
"| Moderate positive | 117170660831 | \n",
"| High positive | 348797169168 | \n",
"\n",
"\n"
],
"text/plain": [
" dissonance dissonant_views\n",
"1 None 52211945474 \n",
"2 High negative 221777609 \n",
"3 Moderate negative 5533719571 \n",
"4 Moderate positive 117170660831 \n",
"5 High positive 348797169168 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Calculations of total number of dissonant views per dissonance\n",
"articles_by_pop[, list(dissonant_views=sum(page_views)), by=list(dissonance)];"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"523935272653"
],
"text/latex": [
"523935272653"
],
"text/markdown": [
"523935272653"
],
"text/plain": [
"[1] 523935272653"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"articles_by_pop[,sum(as.numeric(page_views))];"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"12.0947727255594"
],
"text/latex": [
"12.0947727255594"
],
"text/markdown": [
"12.0947727255594"
],
"text/plain": [
"[1] 12.09477"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"0.0229368304409811"
],
"text/latex": [
"0.0229368304409811"
],
"text/markdown": [
"0.0229368304409811"
],
"text/plain": [
"[1] 0.02293683"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"1.23145971375505"
],
"text/latex": [
"1.23145971375505"
],
"text/markdown": [
"1.23145971375505"
],
"text/plain": [
"[1] 1.23146"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"22.107092420945"
],
"text/latex": [
"22.107092420945"
],
"text/markdown": [
"22.107092420945"
],
"text/plain": [
"[1] 22.10709"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"64.5437383092995"
],
"text/latex": [
"64.5437383092995"
],
"text/markdown": [
"64.5437383092995"
],
"text/plain": [
"[1] 64.54374"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Proportions\n",
"100*65938379920/545180810059;\n",
"100*125047198/545180810059;\n",
"100*6713682043/545180810059;\n",
"100*120523625541/545180810059;\n",
"100*351880075357/545180810059;"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 87% of views are high positive"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>dissonant_views</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>9.965343</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|l}\n",
" dissonant\\_views\\\\\n",
"\\hline\n",
"\t 9.965343\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"dissonant_views | \n",
"|---|\n",
"| 9.965343 | \n",
"\n",
"\n"
],
"text/plain": [
" dissonant_views\n",
"1 9.965343 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"100*(articles_by_pop[, list(dissonant_views=sum(page_views)), by=list(dissonance)][1][,c('dissonant_views')]/articles_by_pop[,sum(as.numeric(page_views))])"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>dissonant_views</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>0.0423292</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|l}\n",
" dissonant\\_views\\\\\n",
"\\hline\n",
"\t 0.0423292\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"dissonant_views | \n",
"|---|\n",
"| 0.0423292 | \n",
"\n",
"\n"
],
"text/plain": [
" dissonant_views\n",
"1 0.0423292 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"100*(articles_by_pop[, list(dissonant_views=sum(page_views)), by=list(dissonance)][2][,c('dissonant_views')]/articles_by_pop[,sum(as.numeric(page_views))])"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>dissonant_views</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>1.056184</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|l}\n",
" dissonant\\_views\\\\\n",
"\\hline\n",
"\t 1.056184\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"dissonant_views | \n",
"|---|\n",
"| 1.056184 | \n",
"\n",
"\n"
],
"text/plain": [
" dissonant_views\n",
"1 1.056184 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"100*(articles_by_pop[, list(dissonant_views=sum(page_views)), by=list(dissonance)][3][,c('dissonant_views')]/articles_by_pop[,sum(as.numeric(page_views))])"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>dissonant_views</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>22.36358</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|l}\n",
" dissonant\\_views\\\\\n",
"\\hline\n",
"\t 22.36358\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"dissonant_views | \n",
"|---|\n",
"| 22.36358 | \n",
"\n",
"\n"
],
"text/plain": [
" dissonant_views\n",
"1 22.36358 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"100*(articles_by_pop[, list(dissonant_views=sum(page_views)), by=list(dissonance)][4][,c('dissonant_views')]/articles_by_pop[,sum(as.numeric(page_views))])"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>dissonant_views</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>66.57257</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|l}\n",
" dissonant\\_views\\\\\n",
"\\hline\n",
"\t 66.57257\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"dissonant_views | \n",
"|---|\n",
"| 66.57257 | \n",
"\n",
"\n"
],
"text/plain": [
" dissonant_views\n",
"1 66.57257 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"100*(articles_by_pop[, list(dissonant_views=sum(page_views)), by=list(dissonance)][5][,c('dissonant_views')]/articles_by_pop[,sum(as.numeric(page_views))])"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "R [r]",
"language": "R",
"name": "R [r]"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.3.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
jmwerner/TXT2PYNB | Examples/Example_1.ipynb | 1 | 1776 | {
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"language": "python",
"input": "a = 5\nd = 5000\ne = 50928734",
"outputs": [],
"collapsed": false,
"metadata": {}
},
{
"cell_type": "code",
"language": "python",
"input": "b = 10\nprint(b * 20)",
"outputs": [],
"collapsed": false,
"metadata": {}
},
{
"cell_type": "code",
"language": "python",
"input": "# This code uses 4 spaces instead of a tab (pep8 compliance)\nfor i in range(0,5):\n b = a + i\n print(b)",
"outputs": [],
"collapsed": false,
"metadata": {}
},
{
"cell_type": "code",
"language": "python",
"input": "# This code uses tabs\na = b * 25\nfor i in range(0,5):\n\tb = a + i\n\tprint(b)",
"outputs": [],
"collapsed": false,
"metadata": {}
},
{
"source": "This here is markdown\n\nThis is line two of markdown. Isn't it beautiful?\n\nThis is line three of markdown\n\n#Markdown header! ",
"cell_type": "markdown",
"metadata": {}
}
]
}
],
"metadata": {
"name": ""
}
} | mit |
pm4py/pm4py-core | notebooks/2_event_data_filtering.ipynb | 1 | 37210 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Event Data Filtering\n",
"*by: Sebastiaan J. van Zelst*"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Like any data-driven field, the successful application of process mining needs *data munging and crunching*.\n",
"In pm4py, you can munge and crunch your data in two ways, i.e., you can write ```lambda``` functions and apply them on\n",
"your event log, or, you can apply pre-built filtering and transformation functions.\n",
"Hence, in this turtorial, we briefly explain how to filter event data in various different ways in pm4py."
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Generic Lambda Functions"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"In a nutshell, a lambda function allows you to specify a function that needs to be applied on a given element.\n",
"As a simple example, consider the following snippet:"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": "10"
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f = lambda x: 2 * x\n",
"f(5)"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"In the code, we assign a ```lambda``` function to variable ```f```.\n",
"The function specifies that on each possible input it receives, the resulting function that is applied is a multiplication by 2.\n",
"Hence ```f(1)=2```, ```f(2)=4```, etc."
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Note that, invoking ```f``` only works if we provide an argument that can be combined with the ```* 2``` operation.\n",
"For example, for ```strings```, the ```* 2``` operation concatenates the input argument with itself:"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": "'PetePete'"
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f('Pete')"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Filter and Map"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Lambda functions allow us to write short, type-independent functions.\n",
"Given a list of objects, Python provides two core functions that can apply a given lambda function on each element of\n",
"the given list (in fact, any iterable):"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- ```filter(f,l)```\n",
" - apply the given lambda function ```f``` as a filter on the iterable ```l```.\n",
"- ```map(f,l)```\n",
" - apply the given lambda function ```f``` as a transformation on the iterable ```l```."
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"For more information, study the concept of ‘higher order functions’ in Python, e.g., as introduced [here](https://www.codespeedy.com/higher-order-functions-in-python-map-filter-sorted-reduce/).\n",
"Let's consider a few simple examples."
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": "<filter at 0x1fc59e9a4f0>"
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
"filter(lambda n: n >= 5, l)"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"The previous example needs little to no explanation, i.e., the filter retains all numbers in the list greater or equal to five.\n",
"However, what is interesting, is the fact that the resulting objects are not a list (or an iterables), rather a ```filter``` object.\n",
"Such an objects can be easily transformed to a list by wrapping it with a ```list()``` cast:"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": "[5, 6, 7, 8, 9, 10]"
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(filter(lambda n: n >= 5, l))"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"The same holds for the ```map()``` function:"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": "<map at 0x1fc59e7adf0>"
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"map(lambda n: n * 3, l)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": "[3, 6, 9, 12, 15, 18, 21, 24, 27, 30]"
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(map(lambda n: n * 3, l))"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Observe that, the previous map function simply muliplies each element of list ```l``` by three."
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Lambda-Based Filtering in pm4py"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"In pm4py, event log objects mimic lists of traces, which in turn, mimic lists of events.\n",
"Clearly, ```lambda``` functions can therefore be applied to event logs and traces.\n",
"However, as we have shown in the previous example, after applying such a lamda-based filter, the resulting object is no longer an event log.\n",
"Furthermore, casting a filter object or map object to an event log in ```pm4py``` is a bit more involved, i.e., it is\n",
"not so trivial as ```list(filter(...))``` in the previous example.\n",
"This is due to the fact that various meta-data is stored in the event log object as well.\n",
"To this end, pm4py offers wrapper functions that make sure that after applying your higher-order function with a lambda function,\n",
"the resulting object is again an Event Log object.\n",
"In the upcoming scripts, we'll take a look at some lambda-based fitlering.\n",
"First, let's inspect the length of each trace in our running example log by applying a generic map function"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": "parsing log, completed traces :: 0%| | 0/6 [00:00<?, ?it/s]",
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "f7c29377364e465f953bc55686df17c3"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": "[9, 5, 5, 5, 13, 5]"
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pm4py\n",
"\n",
"log = pm4py.read_xes('data/running_example.xes')\n",
"# inspect the length of each trace using a generic map function\n",
"list(map(lambda t: len(t), log))"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"As we can see, there are four traces describing a trace of length 5, one trace of length 9 and one trace of length 13.\n",
"Let's retain all traces that have a lenght greater than 5."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": "[9, 13]"
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lf = pm4py.filter_log(lambda t: len(t) > 5, log)\n",
"list(map(lambda t: len(t), lf))"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"The traces of length 9 and 13 have repeated behavior in them, i.e., the *reinitiate request* activity has been performed at least once:"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": "[(9, 1), (5, 0), (5, 0), (5, 0), (13, 2), (5, 0)]"
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(map(lambda t: (len(t), len(list(filter(lambda e: e['concept:name'] == 'reinitiate request', t)))), log))"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Observe that the map function maps each trace onto a tuple.\n",
"The first element describes the length of the trace.\n",
"The second element describes the number of occurrences of the activity *register request*.\n",
"Observe that we obtain said counter by filtering the trace, i.e., by retaining only those events that describe the\n",
"*reinitiate request* activity and counting the length of the resulting list.\n",
"Note that the traces describe a list of events, and, events are implementing a dictionary.\n",
"In this case, the activity name is captured by the ```concept:name``` attribute."
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"In general, PM4PY supports the following *generic filtering functions*:"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- ```pm4py.filter_log(f, log)```\n",
" - filter the log according to a function ```f```.\n",
"- ```pm4py.filter_trace(f,trace)```\n",
" - filter the trace according to function ```f```.\n",
"- ```pm4py.sort_log(log, key, reverse)```\n",
" - sort the event log according to a given ```key```, reversed order if ```reverse==True```.\n",
"- ```pm4py.sort_trace(trace, key, reverse)```\n",
" - sort the trace according to a given ```key```, reversed order if ```reverse==True```."
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Let's see these functions in action:"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6\n",
"2\n"
]
}
],
"source": [
"print(len(log))\n",
"lf = pm4py.filter_log(lambda t: len(t) > 5, log)\n",
"print(len(lf))"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9\n",
"2\n"
]
}
],
"source": [
"print(len(log[0])) #log[0] fetches the 1st trace\n",
"tf = pm4py.filter_trace(lambda e: e['concept:name'] in {'register request', 'pay compensation'}, log[0])\n",
"print(len(tf))"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9\n",
"5\n",
"13\n"
]
}
],
"source": [
"print(len(log[0]))\n",
"ls = pm4py.sort_log(log, lambda t: len(t))\n",
"print(len(ls[0]))\n",
"ls = pm4py.sort_log(log, lambda t: len(t), reverse=True)\n",
"print(len(ls[0]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Specific Filters"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"There are various pre-built filters in PM4Py, which make commonly needed process mining filtering functionality a lot easier.\n",
"In the upcoming overview, we briefly give present these functions.\n",
"We describe how to call them, their main input parameters and their return objects.\n",
"Note that, all of the filters work on both DataFrames and pm4py event log objects."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Start Activities\n",
"- ```filter_start_activities(log, activities, retain=True)```\n",
" - retains (or drops) the traces that contain the given activity as the final event."
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": "[{'attributes': {'concept:name': '3'}, 'events': [{'concept:name': 'register request', 'org:resource': 'Pete', 'time:timestamp': datetime.datetime(2010, 12, 30, 14, 32, tzinfo=datetime.timezone(datetime.timedelta(seconds=3600))), 'Activity': 'register request', 'Resource': 'Pete', 'Costs': '50'}, '..', {'concept:name': 'pay compensation', 'org:resource': 'Ellen', 'time:timestamp': datetime.datetime(2011, 1, 15, 10, 45, tzinfo=datetime.timezone(datetime.timedelta(seconds=3600))), 'Activity': 'pay compensation', 'Resource': 'Ellen', 'Costs': '200'}]}, '....', {'attributes': {'concept:name': '4'}, 'events': [{'concept:name': 'register request', 'org:resource': 'Pete', 'time:timestamp': datetime.datetime(2011, 1, 6, 15, 2, tzinfo=datetime.timezone(datetime.timedelta(seconds=3600))), 'Activity': 'register request', 'Resource': 'Pete', 'Costs': '50'}, '..', {'concept:name': 'reject request', 'org:resource': 'Ellen', 'time:timestamp': datetime.datetime(2011, 1, 12, 15, 44, tzinfo=datetime.timezone(datetime.timedelta(seconds=3600))), 'Activity': 'reject request', 'Resource': 'Ellen', 'Costs': '200'}]}]"
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pm4py.filter_start_activities(log, {'register request'})"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": "[]"
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pm4py.filter_start_activities(log, {'register request TYPO!'})"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": " case:concept:name concept:name time:timestamp costs \\\n14 1 register request 2010-12-30 10:02:00+00:00 50 \n15 1 examine thoroughly 2010-12-31 09:06:00+00:00 400 \n16 1 check ticket 2011-01-05 14:12:00+00:00 100 \n17 1 decide 2011-01-06 10:18:00+00:00 200 \n18 1 reject request 2011-01-07 13:24:00+00:00 200 \n9 2 register request 2010-12-30 10:32:00+00:00 50 \n10 2 check ticket 2010-12-30 11:12:00+00:00 100 \n11 2 examine casually 2010-12-30 13:16:00+00:00 400 \n12 2 decide 2011-01-05 10:22:00+00:00 200 \n13 2 pay compensation 2011-01-08 11:05:00+00:00 200 \n0 3 register request 2010-12-30 13:32:00+00:00 50 \n1 3 examine casually 2010-12-30 14:06:00+00:00 400 \n2 3 check ticket 2010-12-30 15:34:00+00:00 100 \n3 3 decide 2011-01-06 08:18:00+00:00 200 \n4 3 reinitiate request 2011-01-06 11:18:00+00:00 200 \n5 3 examine thoroughly 2011-01-06 12:06:00+00:00 400 \n6 3 check ticket 2011-01-08 10:43:00+00:00 100 \n7 3 decide 2011-01-09 08:55:00+00:00 200 \n8 3 pay compensation 2011-01-15 09:45:00+00:00 200 \n37 4 register request 2011-01-06 14:02:00+00:00 50 \n38 4 check ticket 2011-01-07 11:06:00+00:00 100 \n39 4 examine thoroughly 2011-01-08 13:43:00+00:00 400 \n40 4 decide 2011-01-09 11:02:00+00:00 200 \n41 4 reject request 2011-01-12 14:44:00+00:00 200 \n24 5 register request 2011-01-06 08:02:00+00:00 50 \n25 5 examine casually 2011-01-07 09:16:00+00:00 400 \n26 5 check ticket 2011-01-08 10:22:00+00:00 100 \n27 5 decide 2011-01-10 12:28:00+00:00 200 \n28 5 reinitiate request 2011-01-11 15:18:00+00:00 200 \n29 5 check ticket 2011-01-14 13:33:00+00:00 100 \n30 5 examine casually 2011-01-16 14:50:00+00:00 400 \n31 5 decide 2011-01-19 10:18:00+00:00 200 \n32 5 reinitiate request 2011-01-20 11:48:00+00:00 200 \n33 5 examine casually 2011-01-21 08:06:00+00:00 400 \n34 5 check ticket 2011-01-21 10:34:00+00:00 100 \n35 5 decide 2011-01-23 12:12:00+00:00 200 \n36 5 reject request 2011-01-24 13:56:00+00:00 200 \n19 6 register request 2011-01-06 14:02:00+00:00 50 \n20 6 examine casually 2011-01-06 15:06:00+00:00 400 \n21 6 check ticket 2011-01-07 15:22:00+00:00 100 \n22 6 decide 2011-01-07 15:52:00+00:00 200 \n23 6 pay compensation 2011-01-16 10:47:00+00:00 200 \n\n org:resource @@index \n14 Pete 14 \n15 Sue 15 \n16 Mike 16 \n17 Sara 17 \n18 Pete 18 \n9 Mike 9 \n10 Mike 10 \n11 Sean 11 \n12 Sara 12 \n13 Ellen 13 \n0 Pete 0 \n1 Mike 1 \n2 Ellen 2 \n3 Sara 3 \n4 Sara 4 \n5 Sean 5 \n6 Pete 6 \n7 Sara 7 \n8 Ellen 8 \n37 Pete 37 \n38 Mike 38 \n39 Sean 39 \n40 Sara 40 \n41 Ellen 41 \n24 Ellen 24 \n25 Mike 25 \n26 Pete 26 \n27 Sara 27 \n28 Sara 28 \n29 Ellen 29 \n30 Mike 30 \n31 Sara 31 \n32 Sara 32 \n33 Sue 33 \n34 Pete 34 \n35 Sara 35 \n36 Mike 36 \n19 Mike 19 \n20 Ellen 20 \n21 Mike 21 \n22 Sara 22 \n23 Mike 23 ",
"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>case:concept:name</th>\n <th>concept:name</th>\n <th>time:timestamp</th>\n <th>costs</th>\n <th>org:resource</th>\n <th>@@index</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>14</th>\n <td>1</td>\n <td>register request</td>\n <td>2010-12-30 10:02:00+00:00</td>\n <td>50</td>\n <td>Pete</td>\n <td>14</td>\n </tr>\n <tr>\n <th>15</th>\n <td>1</td>\n <td>examine thoroughly</td>\n <td>2010-12-31 09:06:00+00:00</td>\n <td>400</td>\n <td>Sue</td>\n <td>15</td>\n </tr>\n <tr>\n <th>16</th>\n <td>1</td>\n <td>check ticket</td>\n <td>2011-01-05 14:12:00+00:00</td>\n <td>100</td>\n <td>Mike</td>\n <td>16</td>\n </tr>\n <tr>\n <th>17</th>\n <td>1</td>\n <td>decide</td>\n <td>2011-01-06 10:18:00+00:00</td>\n <td>200</td>\n <td>Sara</td>\n <td>17</td>\n </tr>\n <tr>\n <th>18</th>\n <td>1</td>\n <td>reject request</td>\n <td>2011-01-07 13:24:00+00:00</td>\n <td>200</td>\n <td>Pete</td>\n <td>18</td>\n </tr>\n <tr>\n <th>9</th>\n <td>2</td>\n <td>register request</td>\n <td>2010-12-30 10:32:00+00:00</td>\n <td>50</td>\n <td>Mike</td>\n <td>9</td>\n </tr>\n <tr>\n <th>10</th>\n <td>2</td>\n <td>check ticket</td>\n <td>2010-12-30 11:12:00+00:00</td>\n <td>100</td>\n <td>Mike</td>\n <td>10</td>\n </tr>\n <tr>\n <th>11</th>\n <td>2</td>\n <td>examine casually</td>\n <td>2010-12-30 13:16:00+00:00</td>\n <td>400</td>\n <td>Sean</td>\n <td>11</td>\n </tr>\n <tr>\n <th>12</th>\n <td>2</td>\n <td>decide</td>\n <td>2011-01-05 10:22:00+00:00</td>\n <td>200</td>\n <td>Sara</td>\n <td>12</td>\n </tr>\n <tr>\n <th>13</th>\n <td>2</td>\n <td>pay compensation</td>\n <td>2011-01-08 11:05:00+00:00</td>\n <td>200</td>\n <td>Ellen</td>\n <td>13</td>\n </tr>\n <tr>\n <th>0</th>\n <td>3</td>\n <td>register request</td>\n <td>2010-12-30 13:32:00+00:00</td>\n <td>50</td>\n <td>Pete</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>3</td>\n <td>examine casually</td>\n <td>2010-12-30 14:06:00+00:00</td>\n <td>400</td>\n <td>Mike</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>check ticket</td>\n <td>2010-12-30 15:34:00+00:00</td>\n <td>100</td>\n <td>Ellen</td>\n <td>2</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3</td>\n <td>decide</td>\n <td>2011-01-06 08:18:00+00:00</td>\n <td>200</td>\n <td>Sara</td>\n <td>3</td>\n </tr>\n <tr>\n <th>4</th>\n <td>3</td>\n <td>reinitiate request</td>\n <td>2011-01-06 11:18:00+00:00</td>\n <td>200</td>\n <td>Sara</td>\n <td>4</td>\n </tr>\n <tr>\n <th>5</th>\n <td>3</td>\n <td>examine thoroughly</td>\n <td>2011-01-06 12:06:00+00:00</td>\n <td>400</td>\n <td>Sean</td>\n <td>5</td>\n </tr>\n <tr>\n <th>6</th>\n <td>3</td>\n <td>check ticket</td>\n <td>2011-01-08 10:43:00+00:00</td>\n <td>100</td>\n <td>Pete</td>\n <td>6</td>\n </tr>\n <tr>\n <th>7</th>\n <td>3</td>\n <td>decide</td>\n <td>2011-01-09 08:55:00+00:00</td>\n <td>200</td>\n <td>Sara</td>\n <td>7</td>\n </tr>\n <tr>\n <th>8</th>\n <td>3</td>\n <td>pay compensation</td>\n <td>2011-01-15 09:45:00+00:00</td>\n <td>200</td>\n <td>Ellen</td>\n <td>8</td>\n </tr>\n <tr>\n <th>37</th>\n <td>4</td>\n <td>register request</td>\n <td>2011-01-06 14:02:00+00:00</td>\n <td>50</td>\n <td>Pete</td>\n <td>37</td>\n </tr>\n <tr>\n <th>38</th>\n <td>4</td>\n <td>check ticket</td>\n <td>2011-01-07 11:06:00+00:00</td>\n <td>100</td>\n <td>Mike</td>\n <td>38</td>\n </tr>\n <tr>\n <th>39</th>\n <td>4</td>\n <td>examine thoroughly</td>\n <td>2011-01-08 13:43:00+00:00</td>\n <td>400</td>\n <td>Sean</td>\n <td>39</td>\n </tr>\n <tr>\n <th>40</th>\n <td>4</td>\n <td>decide</td>\n <td>2011-01-09 11:02:00+00:00</td>\n <td>200</td>\n <td>Sara</td>\n <td>40</td>\n </tr>\n <tr>\n <th>41</th>\n <td>4</td>\n <td>reject request</td>\n <td>2011-01-12 14:44:00+00:00</td>\n <td>200</td>\n <td>Ellen</td>\n <td>41</td>\n </tr>\n <tr>\n <th>24</th>\n <td>5</td>\n <td>register request</td>\n <td>2011-01-06 08:02:00+00:00</td>\n <td>50</td>\n <td>Ellen</td>\n <td>24</td>\n </tr>\n <tr>\n <th>25</th>\n <td>5</td>\n <td>examine casually</td>\n <td>2011-01-07 09:16:00+00:00</td>\n <td>400</td>\n <td>Mike</td>\n <td>25</td>\n </tr>\n <tr>\n <th>26</th>\n <td>5</td>\n <td>check ticket</td>\n <td>2011-01-08 10:22:00+00:00</td>\n <td>100</td>\n <td>Pete</td>\n <td>26</td>\n </tr>\n <tr>\n <th>27</th>\n <td>5</td>\n <td>decide</td>\n <td>2011-01-10 12:28:00+00:00</td>\n <td>200</td>\n <td>Sara</td>\n <td>27</td>\n </tr>\n <tr>\n <th>28</th>\n <td>5</td>\n <td>reinitiate request</td>\n <td>2011-01-11 15:18:00+00:00</td>\n <td>200</td>\n <td>Sara</td>\n <td>28</td>\n </tr>\n <tr>\n <th>29</th>\n <td>5</td>\n <td>check ticket</td>\n <td>2011-01-14 13:33:00+00:00</td>\n <td>100</td>\n <td>Ellen</td>\n <td>29</td>\n </tr>\n <tr>\n <th>30</th>\n <td>5</td>\n <td>examine casually</td>\n <td>2011-01-16 14:50:00+00:00</td>\n <td>400</td>\n <td>Mike</td>\n <td>30</td>\n </tr>\n <tr>\n <th>31</th>\n <td>5</td>\n <td>decide</td>\n <td>2011-01-19 10:18:00+00:00</td>\n <td>200</td>\n <td>Sara</td>\n <td>31</td>\n </tr>\n <tr>\n <th>32</th>\n <td>5</td>\n <td>reinitiate request</td>\n <td>2011-01-20 11:48:00+00:00</td>\n <td>200</td>\n <td>Sara</td>\n <td>32</td>\n </tr>\n <tr>\n <th>33</th>\n <td>5</td>\n <td>examine casually</td>\n <td>2011-01-21 08:06:00+00:00</td>\n <td>400</td>\n <td>Sue</td>\n <td>33</td>\n </tr>\n <tr>\n <th>34</th>\n <td>5</td>\n <td>check ticket</td>\n <td>2011-01-21 10:34:00+00:00</td>\n <td>100</td>\n <td>Pete</td>\n <td>34</td>\n </tr>\n <tr>\n <th>35</th>\n <td>5</td>\n <td>decide</td>\n <td>2011-01-23 12:12:00+00:00</td>\n <td>200</td>\n <td>Sara</td>\n <td>35</td>\n </tr>\n <tr>\n <th>36</th>\n <td>5</td>\n <td>reject request</td>\n <td>2011-01-24 13:56:00+00:00</td>\n <td>200</td>\n <td>Mike</td>\n <td>36</td>\n </tr>\n <tr>\n <th>19</th>\n <td>6</td>\n <td>register request</td>\n <td>2011-01-06 14:02:00+00:00</td>\n <td>50</td>\n <td>Mike</td>\n <td>19</td>\n </tr>\n <tr>\n <th>20</th>\n <td>6</td>\n <td>examine casually</td>\n <td>2011-01-06 15:06:00+00:00</td>\n <td>400</td>\n <td>Ellen</td>\n <td>20</td>\n </tr>\n <tr>\n <th>21</th>\n <td>6</td>\n <td>check ticket</td>\n <td>2011-01-07 15:22:00+00:00</td>\n <td>100</td>\n <td>Mike</td>\n <td>21</td>\n </tr>\n <tr>\n <th>22</th>\n <td>6</td>\n <td>decide</td>\n <td>2011-01-07 15:52:00+00:00</td>\n <td>200</td>\n <td>Sara</td>\n <td>22</td>\n </tr>\n <tr>\n <th>23</th>\n <td>6</td>\n <td>pay compensation</td>\n <td>2011-01-16 10:47:00+00:00</td>\n <td>200</td>\n <td>Mike</td>\n <td>23</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas\n",
"\n",
"ldf = pm4py.format_dataframe(pandas.read_csv('data/running_example.csv', sep=';'), case_id='case_id',\n",
" activity_key='activity', timestamp_key='timestamp')\n",
"pm4py.filter_start_activities(ldf, {'register request'})"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": "Empty DataFrame\nColumns: [case:concept:name, concept:name, time:timestamp, costs, org:resource, @@index]\nIndex: []",
"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>case:concept:name</th>\n <th>concept:name</th>\n <th>time:timestamp</th>\n <th>costs</th>\n <th>org:resource</th>\n <th>@@index</th>\n </tr>\n </thead>\n <tbody>\n </tbody>\n</table>\n</div>"
},
"execution_count": 74,
"metadata": {},
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}
],
"source": [
"pm4py.filter_start_activities(ldf, {'register request TYPO!'})"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
},
"slideshow": {
"slide_type": "slide"
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},
"source": [
"### End Activities\n",
"- ```filter_end_activities(log, activities, retain=True)```\n",
" - retains (or drops) the traces that contain the given activity as the final event."
]
},
{
"cell_type": "markdown",
"metadata": {
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"name": "#%% md\n"
},
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"source": [
"For example, we can retain the number of cases that end with a \"payment of the compensation\":"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"pycharm": {
"name": "#%%\n"
},
"slideshow": {
"slide_type": "subslide"
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"outputs": [
{
"data": {
"text/plain": "3"
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(pm4py.filter_end_activities(log, 'pay compensation'))"
]
},
{
"cell_type": "markdown",
"source": [
"### Event Attribute Values"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"- ```filter_event_attribute_values(log, attribute_key, values, level=\"case\", retain=True)```\n",
" - retains (or drops) traces (or events) based on a given collection of ```values``` that need to be matched for the\n",
" given ```attribute_key```. If ```level=='case'```, complete traces are matched (or dropped if ```retain==False```) that\n",
" have at least one event that describes a specifeid value for the given attribute. If ```level=='event'```, only events\n",
" that match are retained (or dropped)."
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "code",
"execution_count": 87,
"outputs": [
{
"data": {
"text/plain": "[['Pete', 'Mike', 'Ellen', 'Sara', 'Sara', 'Sean', 'Pete', 'Sara', 'Ellen'],\n ['Mike', 'Mike', 'Sean', 'Sara', 'Ellen'],\n ['Pete', 'Sue', 'Mike', 'Sara', 'Pete'],\n ['Mike', 'Ellen', 'Mike', 'Sara', 'Mike'],\n ['Ellen',\n 'Mike',\n 'Pete',\n 'Sara',\n 'Sara',\n 'Ellen',\n 'Mike',\n 'Sara',\n 'Sara',\n 'Sue',\n 'Pete',\n 'Sara',\n 'Mike'],\n ['Pete', 'Mike', 'Sean', 'Sara', 'Ellen']]"
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# retain any case that has either Peter or Mike working on it\n",
"lf = pm4py.filter_event_attribute_values(log, 'org:resource', {'Pete', 'Mike'})\n",
"list(map(lambda t: list(map(lambda e: e['org:resource'], t)), lf))"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 88,
"outputs": [
{
"data": {
"text/plain": "[['Pete', 'Mike', 'Pete'],\n ['Mike', 'Mike'],\n ['Pete', 'Mike', 'Pete'],\n ['Mike', 'Mike', 'Mike'],\n ['Mike', 'Pete', 'Mike', 'Pete', 'Mike'],\n ['Pete', 'Mike']]"
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# retain only those events that have Pete or Mik working on it\n",
"lf = pm4py.filter_event_attribute_values(log, 'org:resource', {'Pete', 'Mike'}, level='event')\n",
"list(map(lambda t: list(map(lambda e: e['org:resource'], t)), lf))\n"
],
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"name": "#%%\n"
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}
},
"nbformat": 4,
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} | gpl-3.0 |
yigong/AY250 | hw7/.ipynb_checkpoints/test-checkpoint.ipynb | 1 | 154381 | {
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{
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"input": [
"import pandas as pd\n",
"import json\n",
"import matplotlib.pyplot as plt\n",
"import pdb\n",
"import numpy as np\n",
"import datetime\n",
"file_json = open('closed.json')\n",
"data_json = json.load(file_json)\n",
"data_df = pd.DataFrame(data_json, copy=True)\n",
"git_df_temp = data_df[['title', 'created_at', 'labels', 'closed_at', 'id']]\n",
"git_user_temp = data_df['user']\n",
"value_list = []\n",
"for i, row_entry in git_user_temp.iteritems():\n",
" val = row_entry['login']\n",
" value_list.append(val)\n",
"user_df = pd.Series(value_list,index=git_user_temp.index)\n",
"user_df.name = 'user'\n",
"git_df = git_df_temp.join(user_df)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 75
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data_df.user.head()"
],
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"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 110,
"text": [
"0 {u'following_url': u'https://api.github.com/us...\n",
"1 {u'following_url': u'https://api.github.com/us...\n",
"2 {u'following_url': u'https://api.github.com/us...\n",
"3 {u'following_url': u'https://api.github.com/us...\n",
"4 {u'following_url': u'https://api.github.com/us...\n",
"Name: user, dtype: object"
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Question\n",
"---------"
]
},
{
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"input": [
"git_df"
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"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<pre>\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 2968 entries, 0 to 2967\n",
"Data columns (total 6 columns):\n",
"title 2968 non-null values\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 2968 entries, 0 to 2967\n",
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"input": [
"git_df_2 = pd.concat([git_df_temp,user_df],axis=1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
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"evalue": "'Series' object has no attribute '_data'",
"output_type": "pyerr",
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"\u001b[0;32m<ipython-input-40-c1fcf8b76128>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgit_df_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mgit_df_temp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0muser_df\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/Yigong/anaconda/python.app/Contents/lib/python2.7/site-packages/pandas/tools/merge.pyc\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity)\u001b[0m\n\u001b[1;32m 876\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mignore_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjoin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 877\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 878\u001b[0;31m verify_integrity=verify_integrity)\n\u001b[0m\u001b[1;32m 879\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 880\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/Yigong/anaconda/python.app/Contents/lib/python2.7/site-packages/pandas/tools/merge.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity)\u001b[0m\n\u001b[1;32m 952\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverify_integrity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mverify_integrity\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 953\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 954\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew_axes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_new_axes\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 955\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 956\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_result\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[0;32m/Users/Yigong/anaconda/python.app/Contents/lib/python2.7/site-packages/pandas/tools/merge.pyc\u001b[0m in \u001b[0;36m_get_new_axes\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1129\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1130\u001b[0m \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1131\u001b[0;31m \u001b[0mnew_axes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_comb_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1132\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1133\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin_axes\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mndim\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\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/Yigong/anaconda/python.app/Contents/lib/python2.7/site-packages/pandas/tools/merge.pyc\u001b[0m in \u001b[0;36m_get_comb_axis\u001b[0;34m(self, i)\u001b[0m\n\u001b[1;32m 1154\u001b[0m \u001b[0mall_indexes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobjs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1155\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1156\u001b[0;31m \u001b[0mall_indexes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobjs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1158\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_get_combined_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_indexes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mintersect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersect\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute '_data'"
]
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"git_df.drop_duplicates(cols='id',inplace=True)\n",
"git_df['created_at'] = pd.to_datetime(git_df['created_at'])\n",
"git_df['closed_at'] = pd.to_datetime(git_df['closed_at'])\n",
"git_df.set_index('created_at', inplace=True, drop=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 76
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(5)\n",
"----"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"git_df.head()"
],
"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>title</th>\n",
" <th>created_at</th>\n",
" <th>labels</th>\n",
" <th>closed_at</th>\n",
" <th>id</th>\n",
" <th>user</th>\n",
" </tr>\n",
" <tr>\n",
" <th>created_at</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>2010-09-29 00:45:31</th>\n",
" <td> Enable element-wise comparison operations in D...</td>\n",
" <td>2010-09-29 00:45:31</td>\n",
" <td> []</td>\n",
" <td>2011-02-19 23:13:48</td>\n",
" <td> 337721</td>\n",
" <td> wesm</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:50:13</th>\n",
" <td> reindex_like function</td>\n",
" <td>2010-09-29 00:50:13</td>\n",
" <td> []</td>\n",
" <td>2010-12-17 02:57:33</td>\n",
" <td> 337726</td>\n",
" <td> wesm</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:50:52</th>\n",
" <td> Binary operations on int DataMatrix</td>\n",
" <td>2010-09-29 00:50:52</td>\n",
" <td> []</td>\n",
" <td>2011-01-01 23:50:12</td>\n",
" <td> 337728</td>\n",
" <td> wesm</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:51:27</th>\n",
" <td> Plot keyword arguments are unused in DataFrame...</td>\n",
" <td>2010-09-29 00:51:27</td>\n",
" <td> []</td>\n",
" <td>2010-12-11 06:14:32</td>\n",
" <td> 337730</td>\n",
" <td> wesm</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:57:00</th>\n",
" <td> Python 2.7 testing</td>\n",
" <td>2010-09-29 00:57:00</td>\n",
" <td> []</td>\n",
" <td>2010-12-17 02:46:34</td>\n",
" <td> 337736</td>\n",
" <td> wesm</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 82,
"text": [
" title \\\n",
"created_at \n",
"2010-09-29 00:45:31 Enable element-wise comparison operations in D... \n",
"2010-09-29 00:50:13 reindex_like function \n",
"2010-09-29 00:50:52 Binary operations on int DataMatrix \n",
"2010-09-29 00:51:27 Plot keyword arguments are unused in DataFrame... \n",
"2010-09-29 00:57:00 Python 2.7 testing \n",
"\n",
" created_at labels closed_at id \\\n",
"created_at \n",
"2010-09-29 00:45:31 2010-09-29 00:45:31 [] 2011-02-19 23:13:48 337721 \n",
"2010-09-29 00:50:13 2010-09-29 00:50:13 [] 2010-12-17 02:57:33 337726 \n",
"2010-09-29 00:50:52 2010-09-29 00:50:52 [] 2011-01-01 23:50:12 337728 \n",
"2010-09-29 00:51:27 2010-09-29 00:51:27 [] 2010-12-11 06:14:32 337730 \n",
"2010-09-29 00:57:00 2010-09-29 00:57:00 [] 2010-12-17 02:46:34 337736 \n",
"\n",
" user \n",
"created_at \n",
"2010-09-29 00:45:31 wesm \n",
"2010-09-29 00:50:13 wesm \n",
"2010-09-29 00:50:52 wesm \n",
"2010-09-29 00:51:27 wesm \n",
"2010-09-29 00:57:00 wesm "
]
}
],
"prompt_number": 82
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def count(created_in):\n",
" return len(created_in)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 73
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"git_df.set_index('created_at', inplace = True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"git_df."
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 72,
"text": [
"created_at\n",
"2010-09-29T00:45:31Z Enable element-wise comparison operations in D...\n",
"2010-09-29T00:45:31Z Enable element-wise comparison operations in D...\n",
"2010-09-29T00:50:13Z reindex_like function\n",
"2010-09-29T00:50:13Z reindex_like function\n",
"2010-09-29T00:50:52Z Binary operations on int DataMatrix\n",
"Name: title, dtype: object"
]
}
],
"prompt_number": 72
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"issue_df = git_df.title.resample('M', how= count)\n",
"issue_df.name = 'Number of issues'\n",
"plt.figure()\n",
"issue_ax = issue_df.plot()\n",
"issue_ax.set_ylabel(issue_df.name)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 79,
"text": [
"<matplotlib.text.Text at 0x10eea2790>"
]
}
],
"prompt_number": 79
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Using matplotlib backend: MacOSX\n"
]
}
],
"prompt_number": 78
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def distinct_user(resample_input):\n",
" #print resample_input.user\n",
" distinct_input = resample_input.drop_duplicates()\n",
" #print distinct_input.user\n",
" return len(distinct_input.index)\n",
"user_monthly = git_df.user.resample('M', how = distinct_user)\n",
"user_monthly.name = 'Number of distinct user'\n",
"plt.figure()\n",
"user_ax = user_monthly.plot()\n",
"user_ax.set_ylabel(user_monthly.name)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 85,
"text": [
"<matplotlib.text.Text at 0x10eea2950>"
]
}
],
"prompt_number": 85
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import datetime"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 86
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def mean_day(close_at_input):\n",
" try:\n",
" create_at_series = pd.Series(close_at_input.index, index = close_at_input.index, name = close_at_input.index.name)\n",
" diff = (close_at_input - create_at_series)\n",
" sec_mean = diff.sum()/(len(diff))\n",
" #pdb.set_trace()\n",
" return (datetime.timedelta(microseconds = int(sec_mean/1000))).days\n",
" except:\n",
" return np.nan\n",
" \n",
"open_day = git_df.closed_at.resample('M', mean_day)\n",
"open_day.name = 'mean_days'\n",
"open_day_nissue = pd.concat([issue_df, open_day], axis=1)\n",
"plt.figure()\n",
"line1 = open_day_nissue['Number of issues'].plot()\n",
"line1.set_ylabel('Number of issues')\n",
"plt.figure()\n",
"line2 = open_day_nissue['mean_days'].plot()\n",
"line2.set_ylabel('mean_days')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 87,
"text": [
"<matplotlib.text.Text at 0x113711c50>"
]
}
],
"prompt_number": 87
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Table for (6)\n",
"----"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"open_day_nissue.head()"
],
"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>Number of issues</th>\n",
" <th>mean_days</th>\n",
" </tr>\n",
" <tr>\n",
" <th>created_at</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010-09-30</th>\n",
" <td> 11</td>\n",
" <td> 138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-10-31</th>\n",
" <td> 8</td>\n",
" <td> 250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-11-30</th>\n",
" <td> 2</td>\n",
" <td> 13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-12-31</th>\n",
" <td> 4</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-31</th>\n",
" <td> 9</td>\n",
" <td> 52</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 88,
"text": [
" Number of issues mean_days\n",
"created_at \n",
"2010-09-30 11 138\n",
"2010-10-31 8 250\n",
"2010-11-30 2 13\n",
"2010-12-31 4 2\n",
"2011-01-31 9 52"
]
}
],
"prompt_number": 88
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"P(7)\n",
"----"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"comments_df = data_df[['created_at','comments', 'id' ]]\n",
"#print comments_df.head()\n",
"comments_df.drop_duplicates(cols='id', inplace=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 79
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"comments_df.comments[100][0]['text']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 50,
"text": [
"u'duplicate issue'"
]
}
],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def comment_func(comment_list):\n",
" try:\n",
" return comment_list[0]['text']\n",
" except:\n",
" return np.nan\n",
"comments_df.comments = comments_df.comments.map(comment_func)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 80
},
{
"cell_type": "code",
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"input": [
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],
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"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>created_at</th>\n",
" <th>comments</th>\n",
" <th>id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 2010-09-29T00:45:31Z</td>\n",
" <td> implemented in git HEAD</td>\n",
" <td> 337721</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 2010-09-29T00:50:13Z</td>\n",
" <td> done</td>\n",
" <td> 337726</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 2010-09-29T00:50:52Z</td>\n",
" <td> I guess I &quot;accidentally&quot; fixed this ...</td>\n",
" <td> 337728</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td> 2010-09-29T00:51:27Z</td>\n",
" <td> fixed</td>\n",
" <td> 337730</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td> 2010-09-29T00:57:00Z</td>\n",
" <td> Everything seems to be working in Python 2.7 w...</td>\n",
" <td> 337736</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 81,
"text": [
" created_at comments id\n",
"0 2010-09-29T00:45:31Z implemented in git HEAD 337721\n",
"2 2010-09-29T00:50:13Z done 337726\n",
"4 2010-09-29T00:50:52Z I guess I "accidentally" fixed this ... 337728\n",
"6 2010-09-29T00:51:27Z fixed 337730\n",
"8 2010-09-29T00:57:00Z Everything seems to be working in Python 2.7 w... 337736"
]
}
],
"prompt_number": 81
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"p(8)\n",
"----"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"chattiest_df = git_df[['created_at', 'user', 'id']]\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"chattiest_df.head()"
],
"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>created_at</th>\n",
" <th>user</th>\n",
" <th>id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 2010-09-29T00:45:31Z</td>\n",
" <td> wesm</td>\n",
" <td> 337721</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2010-09-29T00:45:31Z</td>\n",
" <td> wesm</td>\n",
" <td> 337721</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td> 2010-09-29T00:50:13Z</td>\n",
" <td> wesm</td>\n",
" <td> 337726</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 2010-09-29T00:50:13Z</td>\n",
" <td> wesm</td>\n",
" <td> 337726</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 2010-09-29T00:50:52Z</td>\n",
" <td> wesm</td>\n",
" <td> 337728</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
" created_at user id\n",
"0 2010-09-29T00:45:31Z wesm 337721\n",
"1 2010-09-29T00:45:31Z wesm 337721\n",
"2 2010-09-29T00:50:13Z wesm 337726\n",
"3 2010-09-29T00:50:13Z wesm 337726\n",
"4 2010-09-29T00:50:52Z wesm 337728"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"chattiest_df['created_at'] = pd.to_datetime(chattiest_df['created_at'])\n",
"chattiest_df.set_index('created_at', inplace=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"chattiest_df.head()"
],
"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>user</th>\n",
" <th>id</th>\n",
" </tr>\n",
" <tr>\n",
" <th>created_at</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010-09-29 00:45:31</th>\n",
" <td> wesm</td>\n",
" <td> 337721</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:45:31</th>\n",
" <td> wesm</td>\n",
" <td> 337721</td>\n",
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" <tr>\n",
" <th>2010-09-29 00:50:13</th>\n",
" <td> wesm</td>\n",
" <td> 337726</td>\n",
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" <tr>\n",
" <th>2010-09-29 00:50:13</th>\n",
" <td> wesm</td>\n",
" <td> 337726</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:50:52</th>\n",
" <td> wesm</td>\n",
" <td> 337728</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
" user id\n",
"created_at \n",
"2010-09-29 00:45:31 wesm 337721\n",
"2010-09-29 00:45:31 wesm 337721\n",
"2010-09-29 00:50:13 wesm 337726\n",
"2010-09-29 00:50:13 wesm 337726\n",
"2010-09-29 00:50:52 wesm 337728"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"del chattiest_df['id']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 100
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"chattiest_df.head()"
],
"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",
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" <td> wesm</td>\n",
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" <td> wesm</td>\n",
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" <th>2010-09-29 00:50:13</th>\n",
" <td> wesm</td>\n",
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" <th>2010-09-29 00:50:13</th>\n",
" <td> wesm</td>\n",
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" <tr>\n",
" <th>2010-09-29 00:50:52</th>\n",
" <td> wesm</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 102,
"text": [
" user\n",
"created_at \n",
"2010-09-29 00:45:31 wesm\n",
"2010-09-29 00:45:31 wesm\n",
"2010-09-29 00:50:13 wesm\n",
"2010-09-29 00:50:13 wesm\n",
"2010-09-29 00:50:52 wesm"
]
}
],
"prompt_number": 102
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"chattiest_se = chattiest_df['user']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def distinct(in_df):\n",
" return len(in_df.unique())\n",
"nDistinct_se = chattiest_se.resample('M', how = distinct)\n",
"nDistinct_se.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"created_at\n",
"2010-09-30 2\n",
"2010-10-31 3\n",
"2010-11-30 2\n",
"2010-12-31 3\n",
"2011-01-31 5\n",
"Freq: M, dtype: int64"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"chattiest_se.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 112,
"text": [
"created_at\n",
"2010-09-29 00:45:31 wesm\n",
"2010-09-29 00:45:31 wesm\n",
"2010-09-29 00:50:13 wesm\n",
"2010-09-29 00:50:13 wesm\n",
"2010-09-29 00:50:52 wesm\n",
"Name: user, dtype: object"
]
}
],
"prompt_number": 112
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"chattiest_se.resample('M', how = [distinct, percent, chattiest_user, count])\n"
],
"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>distinct</th>\n",
" <th>percent</th>\n",
" <th>chattiest_user</th>\n",
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" <tr>\n",
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" <th>2010-09-30</th>\n",
" <td> 2</td>\n",
" <td> 0.818182</td>\n",
" <td> wesm</td>\n",
" <td> 22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-10-31</th>\n",
" <td> 3</td>\n",
" <td> 0.750000</td>\n",
" <td> wesm</td>\n",
" <td> 16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-11-30</th>\n",
" <td> 2</td>\n",
" <td> 0.500000</td>\n",
" <td> mpenning</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-12-31</th>\n",
" <td> 3</td>\n",
" <td> 0.500000</td>\n",
" <td> knm</td>\n",
" <td> 8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-31</th>\n",
" <td> 5</td>\n",
" <td> 0.333333</td>\n",
" <td> triplechess</td>\n",
" <td> 18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-02-28</th>\n",
" <td> 2</td>\n",
" <td> 0.500000</td>\n",
" <td> ghost</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-03-31</th>\n",
" <td> 1</td>\n",
" <td> 1.000000</td>\n",
" <td> ghost</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-04-30</th>\n",
" <td> 0</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-05-31</th>\n",
" <td> 3</td>\n",
" <td> 0.714286</td>\n",
" <td> wesm</td>\n",
" <td> 7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-06-30</th>\n",
" <td> 3</td>\n",
" <td> 0.777778</td>\n",
" <td> wesm</td>\n",
" <td> 9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-31</th>\n",
" <td> 9</td>\n",
" <td> 0.733333</td>\n",
" <td> wesm</td>\n",
" <td> 30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-08-31</th>\n",
" <td> 10</td>\n",
" <td> 0.677419</td>\n",
" <td> wesm</td>\n",
" <td> 31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-09-30</th>\n",
" <td> 14</td>\n",
" <td> 0.712121</td>\n",
" <td> wesm</td>\n",
" <td> 66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-10-31</th>\n",
" <td> 17</td>\n",
" <td> 0.661017</td>\n",
" <td> wesm</td>\n",
" <td> 118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-11-30</th>\n",
" <td> 25</td>\n",
" <td> 0.536364</td>\n",
" <td> wesm</td>\n",
" <td> 110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-31</th>\n",
" <td> 22</td>\n",
" <td> 0.496000</td>\n",
" <td> wesm</td>\n",
" <td> 125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-31</th>\n",
" <td> 41</td>\n",
" <td> 0.363636</td>\n",
" <td> wesm</td>\n",
" <td> 154</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-02-29</th>\n",
" <td> 26</td>\n",
" <td> 0.485149</td>\n",
" <td> wesm</td>\n",
" <td> 101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-03-31</th>\n",
" <td> 47</td>\n",
" <td> 0.261905</td>\n",
" <td> wesm</td>\n",
" <td> 126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-04-30</th>\n",
" <td> 39</td>\n",
" <td> 0.549708</td>\n",
" <td> wesm</td>\n",
" <td> 171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-05-31</th>\n",
" <td> 26</td>\n",
" <td> 0.497207</td>\n",
" <td> wesm</td>\n",
" <td> 179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-06-30</th>\n",
" <td> 55</td>\n",
" <td> 0.284024</td>\n",
" <td> wesm</td>\n",
" <td> 169</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-07-31</th>\n",
" <td> 52</td>\n",
" <td> 0.290780</td>\n",
" <td> wesm</td>\n",
" <td> 141</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-08-31</th>\n",
" <td> 46</td>\n",
" <td> 0.137255</td>\n",
" <td> wesm</td>\n",
" <td> 102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-09-30</th>\n",
" <td> 53</td>\n",
" <td> 0.129496</td>\n",
" <td> changhiskhan</td>\n",
" <td> 139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-10-31</th>\n",
" <td> 47</td>\n",
" <td> 0.141593</td>\n",
" <td> y-p</td>\n",
" <td> 113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-11-30</th>\n",
" <td> 63</td>\n",
" <td> 0.245370</td>\n",
" <td> y-p</td>\n",
" <td> 216</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-12-31</th>\n",
" <td> 66</td>\n",
" <td> 0.154286</td>\n",
" <td> y-p</td>\n",
" <td> 175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-31</th>\n",
" <td> 55</td>\n",
" <td> 0.130769</td>\n",
" <td> jreback</td>\n",
" <td> 130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-02-28</th>\n",
" <td> 56</td>\n",
" <td> 0.192308</td>\n",
" <td> jreback</td>\n",
" <td> 130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-03-31</th>\n",
" <td> 56</td>\n",
" <td> 0.363208</td>\n",
" <td> jreback</td>\n",
" <td> 212</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-04-30</th>\n",
" <td> 49</td>\n",
" <td> 0.274648</td>\n",
" <td> y-p</td>\n",
" <td> 142</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 113,
"text": [
" distinct percent chattiest_user count\n",
"created_at \n",
"2010-09-30 2 0.818182 wesm 22\n",
"2010-10-31 3 0.750000 wesm 16\n",
"2010-11-30 2 0.500000 mpenning 4\n",
"2010-12-31 3 0.500000 knm 8\n",
"2011-01-31 5 0.333333 triplechess 18\n",
"2011-02-28 2 0.500000 ghost 2\n",
"2011-03-31 1 1.000000 ghost 2\n",
"2011-04-30 0 NaN NaN 0\n",
"2011-05-31 3 0.714286 wesm 7\n",
"2011-06-30 3 0.777778 wesm 9\n",
"2011-07-31 9 0.733333 wesm 30\n",
"2011-08-31 10 0.677419 wesm 31\n",
"2011-09-30 14 0.712121 wesm 66\n",
"2011-10-31 17 0.661017 wesm 118\n",
"2011-11-30 25 0.536364 wesm 110\n",
"2011-12-31 22 0.496000 wesm 125\n",
"2012-01-31 41 0.363636 wesm 154\n",
"2012-02-29 26 0.485149 wesm 101\n",
"2012-03-31 47 0.261905 wesm 126\n",
"2012-04-30 39 0.549708 wesm 171\n",
"2012-05-31 26 0.497207 wesm 179\n",
"2012-06-30 55 0.284024 wesm 169\n",
"2012-07-31 52 0.290780 wesm 141\n",
"2012-08-31 46 0.137255 wesm 102\n",
"2012-09-30 53 0.129496 changhiskhan 139\n",
"2012-10-31 47 0.141593 y-p 113\n",
"2012-11-30 63 0.245370 y-p 216\n",
"2012-12-31 66 0.154286 y-p 175\n",
"2013-01-31 55 0.130769 jreback 130\n",
"2013-02-28 56 0.192308 jreback 130\n",
"2013-03-31 56 0.363208 jreback 212\n",
"2013-04-30 49 0.274648 y-p 142"
]
}
],
"prompt_number": 113
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def percent(in_se):\n",
" try:\n",
" counts = in_se.value_counts().order(ascending = False)\n",
" return counts.ix[0]/float(counts.sum())\n",
" except:\n",
" return np.nan\n",
"percent_se = 100*chattiest_se.resample('M', how = percent) "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def chattiest_user(in_se):\n",
" try:\n",
" counts = in_se.value_counts().order(ascending = False)\n",
" return counts.index[0]\n",
" except:\n",
" return np.nan\n",
"chatUser_se = chattiest_se.resample('M', how = chattiest_user) "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"chatUser_se.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"created_at\n",
"2010-09-30 wesm\n",
"2010-10-31 wesm\n",
"2010-11-30 mpenning\n",
"2010-12-31 knm\n",
"2011-01-31 triplechess\n",
"Freq: M, dtype: object"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nComments_se = chattiest_se.resample('M', how = count)\n",
"print nComments_se.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"created_at\n",
"2010-09-30 22\n",
"2010-10-31 16\n",
"2010-11-30 4\n",
"2010-12-31 8\n",
"2011-01-31 18\n",
"Freq: M, dtype: int64\n"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"user = pd.DataFrame({'nIssue':nComments_se, 'chattiest':chatUser_se,\\\n",
" 'percentage of the chattiest':percent_se, 'nParticipants':nDistinct_se},\\\n",
" columns = ['nParticipants', 'nIssue', 'chattiest', 'percentage of the chattiest'])\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"user.columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 117,
"text": [
"Index([u'nParticipants', u'nIssue', u'chattiest', u'percentage of the chattiest'], dtype=object)"
]
}
],
"prompt_number": 117
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"P(9)\n",
"----"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"id_labels_df = data_df[['id', 'labels', 'created_at']].drop_duplicates(cols = 'id', inplace = False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 98
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"id_labels_df.columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 99,
"text": [
"Index([u'id', u'labels', u'created_at'], dtype=object)"
]
}
],
"prompt_number": 99
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"id_labels_list = []\n",
"for (idx,Id) in id_labels_df.id.iteritems():\n",
" if len(id_labels_df.labels.ix[idx]):\n",
" for label_dict in id_labels_df.labels.ix[idx]:\n",
" id_labels_list.append((Id, label_dict['name'], id_labels_df.created_at[idx]))\n",
" else:\n",
" id_labels_list.append((Id, np.nan,id_labels_df.created_at[idx]))\n",
" \n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 101
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"id_labels_df = pd.DataFrame(id_labels_list, columns=['id', 'label', 'created_at'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 102
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"id_labels_df['created_at'] = pd.to_datetime(id_labels_df['created_at'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 104
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"id_labels_df.set_index('created_at', inplace=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 106
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"id_labels_df.head()"
],
"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>id</th>\n",
" <th>label</th>\n",
" </tr>\n",
" <tr>\n",
" <th>created_at</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010-09-29 00:45:31</th>\n",
" <td> 337721</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:50:13</th>\n",
" <td> 337726</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:50:52</th>\n",
" <td> 337728</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:51:27</th>\n",
" <td> 337730</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:57:00</th>\n",
" <td> 337736</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 107,
"text": [
" id label\n",
"created_at \n",
"2010-09-29 00:45:31 337721 NaN\n",
"2010-09-29 00:50:13 337726 NaN\n",
"2010-09-29 00:50:52 337728 NaN\n",
"2010-09-29 00:51:27 337730 NaN\n",
"2010-09-29 00:57:00 337736 NaN"
]
}
],
"prompt_number": 107
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"issue_df"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 108,
"text": [
"created_at\n",
"2010-09-30 11\n",
"2010-10-31 8\n",
"2010-11-30 2\n",
"2010-12-31 4\n",
"2011-01-31 9\n",
"2011-02-28 2\n",
"2011-03-31 2\n",
"2011-04-30 0\n",
"2011-05-31 7\n",
"2011-06-30 9\n",
"2011-07-31 30\n",
"2011-08-31 31\n",
"2011-09-30 66\n",
"2011-10-31 118\n",
"2011-11-30 110\n",
"2011-12-31 125\n",
"2012-01-31 154\n",
"2012-02-29 101\n",
"2012-03-31 126\n",
"2012-04-30 171\n",
"2012-05-31 179\n",
"2012-06-30 169\n",
"2012-07-31 141\n",
"2012-08-31 102\n",
"2012-09-30 139\n",
"2012-10-31 113\n",
"2012-11-30 216\n",
"2012-12-31 175\n",
"2013-01-31 130\n",
"2013-02-28 130\n",
"2013-03-31 212\n",
"2013-04-30 142\n",
"Freq: M, Name: Number of issues, dtype: int64"
]
}
],
"prompt_number": 108
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"git_df.labels.ix[2956]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 56,
"text": [
"[{u'color': u'e10c02',\n",
" u'name': u'Bug',\n",
" u'url': u'https://api.github.com/repos/pydata/pandas/labels/Bug'},\n",
" {u'color': u'0b02e1',\n",
" u'name': u'Indexing',\n",
" u'url': u'https://api.github.com/repos/pydata/pandas/labels/Indexing'},\n",
" {u'color': u'e102d8',\n",
" u'name': u'Dtypes',\n",
" u'url': u'https://api.github.com/repos/pydata/pandas/labels/Dtypes'}]"
]
}
],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data_df.columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 54,
"text": [
"Index([u'assignee', u'body', u'closed_at', u'comments', u'comments_url', u'created_at', u'events_url', u'html_url', u'id', u'labels', u'labels_url', u'milestone', u'number', u'pull_request', u'state', u'title', u'updated_at', u'url', u'user'], dtype=object)"
]
}
],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"user"
],
"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>nParticipants</th>\n",
" <th>nIssue</th>\n",
" <th>chattiest</th>\n",
" <th>percentage of the chattiest</th>\n",
" </tr>\n",
" <tr>\n",
" <th>created_at</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010-09-30</th>\n",
" <td> 2</td>\n",
" <td> 22</td>\n",
" <td> wesm</td>\n",
" <td> 81.818182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-10-31</th>\n",
" <td> 3</td>\n",
" <td> 16</td>\n",
" <td> wesm</td>\n",
" <td> 75.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-11-30</th>\n",
" <td> 2</td>\n",
" <td> 4</td>\n",
" <td> mpenning</td>\n",
" <td> 50.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-12-31</th>\n",
" <td> 3</td>\n",
" <td> 8</td>\n",
" <td> knm</td>\n",
" <td> 50.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-31</th>\n",
" <td> 5</td>\n",
" <td> 18</td>\n",
" <td> triplechess</td>\n",
" <td> 33.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-02-28</th>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" <td> ghost</td>\n",
" <td> 50.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-03-31</th>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> ghost</td>\n",
" <td> 100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-04-30</th>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-05-31</th>\n",
" <td> 3</td>\n",
" <td> 7</td>\n",
" <td> wesm</td>\n",
" <td> 71.428571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-06-30</th>\n",
" <td> 3</td>\n",
" <td> 9</td>\n",
" <td> wesm</td>\n",
" <td> 77.777778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-31</th>\n",
" <td> 9</td>\n",
" <td> 30</td>\n",
" <td> wesm</td>\n",
" <td> 73.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-08-31</th>\n",
" <td> 10</td>\n",
" <td> 31</td>\n",
" <td> wesm</td>\n",
" <td> 67.741935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-09-30</th>\n",
" <td> 14</td>\n",
" <td> 66</td>\n",
" <td> wesm</td>\n",
" <td> 71.212121</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-10-31</th>\n",
" <td> 17</td>\n",
" <td> 118</td>\n",
" <td> wesm</td>\n",
" <td> 66.101695</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-11-30</th>\n",
" <td> 25</td>\n",
" <td> 110</td>\n",
" <td> wesm</td>\n",
" <td> 53.636364</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-31</th>\n",
" <td> 22</td>\n",
" <td> 125</td>\n",
" <td> wesm</td>\n",
" <td> 49.600000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-31</th>\n",
" <td> 41</td>\n",
" <td> 154</td>\n",
" <td> wesm</td>\n",
" <td> 36.363636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-02-29</th>\n",
" <td> 26</td>\n",
" <td> 101</td>\n",
" <td> wesm</td>\n",
" <td> 48.514851</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-03-31</th>\n",
" <td> 47</td>\n",
" <td> 126</td>\n",
" <td> wesm</td>\n",
" <td> 26.190476</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-04-30</th>\n",
" <td> 39</td>\n",
" <td> 171</td>\n",
" <td> wesm</td>\n",
" <td> 54.970760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-05-31</th>\n",
" <td> 26</td>\n",
" <td> 179</td>\n",
" <td> wesm</td>\n",
" <td> 49.720670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-06-30</th>\n",
" <td> 55</td>\n",
" <td> 169</td>\n",
" <td> wesm</td>\n",
" <td> 28.402367</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-07-31</th>\n",
" <td> 52</td>\n",
" <td> 141</td>\n",
" <td> wesm</td>\n",
" <td> 29.078014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-08-31</th>\n",
" <td> 46</td>\n",
" <td> 102</td>\n",
" <td> wesm</td>\n",
" <td> 13.725490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-09-30</th>\n",
" <td> 53</td>\n",
" <td> 139</td>\n",
" <td> changhiskhan</td>\n",
" <td> 12.949640</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-10-31</th>\n",
" <td> 47</td>\n",
" <td> 113</td>\n",
" <td> y-p</td>\n",
" <td> 14.159292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-11-30</th>\n",
" <td> 63</td>\n",
" <td> 216</td>\n",
" <td> y-p</td>\n",
" <td> 24.537037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-12-31</th>\n",
" <td> 66</td>\n",
" <td> 175</td>\n",
" <td> y-p</td>\n",
" <td> 15.428571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-31</th>\n",
" <td> 55</td>\n",
" <td> 130</td>\n",
" <td> jreback</td>\n",
" <td> 13.076923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-02-28</th>\n",
" <td> 56</td>\n",
" <td> 130</td>\n",
" <td> jreback</td>\n",
" <td> 19.230769</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-03-31</th>\n",
" <td> 56</td>\n",
" <td> 212</td>\n",
" <td> jreback</td>\n",
" <td> 36.320755</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-04-30</th>\n",
" <td> 49</td>\n",
" <td> 142</td>\n",
" <td> y-p</td>\n",
" <td> 27.464789</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 81,
"text": [
" nParticipants nIssue chattiest percentage of the chattiest\n",
"created_at \n",
"2010-09-30 2 22 wesm 81.818182\n",
"2010-10-31 3 16 wesm 75.000000\n",
"2010-11-30 2 4 mpenning 50.000000\n",
"2010-12-31 3 8 knm 50.000000\n",
"2011-01-31 5 18 triplechess 33.333333\n",
"2011-02-28 2 2 ghost 50.000000\n",
"2011-03-31 1 2 ghost 100.000000\n",
"2011-04-30 0 0 NaN NaN\n",
"2011-05-31 3 7 wesm 71.428571\n",
"2011-06-30 3 9 wesm 77.777778\n",
"2011-07-31 9 30 wesm 73.333333\n",
"2011-08-31 10 31 wesm 67.741935\n",
"2011-09-30 14 66 wesm 71.212121\n",
"2011-10-31 17 118 wesm 66.101695\n",
"2011-11-30 25 110 wesm 53.636364\n",
"2011-12-31 22 125 wesm 49.600000\n",
"2012-01-31 41 154 wesm 36.363636\n",
"2012-02-29 26 101 wesm 48.514851\n",
"2012-03-31 47 126 wesm 26.190476\n",
"2012-04-30 39 171 wesm 54.970760\n",
"2012-05-31 26 179 wesm 49.720670\n",
"2012-06-30 55 169 wesm 28.402367\n",
"2012-07-31 52 141 wesm 29.078014\n",
"2012-08-31 46 102 wesm 13.725490\n",
"2012-09-30 53 139 changhiskhan 12.949640\n",
"2012-10-31 47 113 y-p 14.159292\n",
"2012-11-30 63 216 y-p 24.537037\n",
"2012-12-31 66 175 y-p 15.428571\n",
"2013-01-31 55 130 jreback 13.076923\n",
"2013-02-28 56 130 jreback 19.230769\n",
"2013-03-31 56 212 jreback 36.320755\n",
"2013-04-30 49 142 y-p 27.464789"
]
}
],
"prompt_number": 81
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data_df.columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 111,
"text": [
"Index([u'assignee', u'body', u'closed_at', u'comments', u'comments_url', u'created_at', u'events_url', u'html_url', u'id', u'labels', u'labels_url', u'milestone', u'number', u'pull_request', u'state', u'title', u'updated_at', u'url', u'user'], dtype=object)"
]
}
],
"prompt_number": 111
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"from numpy import nan as NaN"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 115
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"NaN"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 116,
"text": [
"nan"
]
}
],
"prompt_number": 116
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pdb\n",
"import datetime\n",
"import json\n",
"\n",
"import pandas as pd \n",
"import matplotlib.pyplot as plt \n",
"import numpy as np \n",
"from numpy import nan as NaN\n",
"\n",
"# Functions defined\n",
"def user_extract(dict_in):\n",
"\t''' Extract 'login' key's value '''''\n",
"\treturn dict_in['login']\n",
"\n",
"def count(resample_in):\n",
"\t''' Count the number of samples in resampled data'''\n",
"\treturn len(resample_in)\n",
"\n",
"def distinct_user(resample_in):\n",
"\t''' Count the number of distinct user in resampled data'''\n",
"\tunique_in = resample_in.drop_duplicates()\n",
"\treturn len(unique_in)\n",
"\n",
"def mean_day(resample_in):\n",
"\t''' Calculate the mean open day of an issue in resampled data'''\n",
"\ttry:\n",
"\t\t#Create a Series has value equals to created_at\n",
"\t\tcreated_at_series = pd.Series(\n",
"\t\t\tresample_in.index, index=resample_in.index)\n",
"\t\tdiff = resample_in - created_at_series\n",
"\t\tsec_mean = diff.sum()/float(len(diff))\n",
"\t\t#Convert to timedelta object\n",
"\t\ttd = datetime.timedelta(microseconds=int(sec_mean/1000.))\t\t\n",
"\t\t#return day attribute of the time difference \n",
"\t\treturn td.days \n",
"\texcept Exception:\n",
"\t\treturn NaN\n",
"\n",
"def comment_func(comment_list):\n",
"\t''' Extract the comment string from the comments column'''\n",
"\ttry:\n",
"\t\treturn comment_list[0]['text']\n",
"\texcept Exception:\n",
"\t\treturn NaN\n",
"\n",
"def chattiest_user(resample_se):\n",
"\t''' Find the chattiest user in a month'''\n",
"\ttry:\n",
"\t\tcounts = resample_se.value_counts().order(ascending=True)\n",
"\t\treturn counts.index[0]\n",
"\texcept Exception:\n",
"\t\treturn NaN\n",
"\n",
"def distinct(resample_se):\n",
"\t''' Find the number of distinct user who comment on 'pandas' '''\n",
"\treturn len(resample_se.unique())\n",
"\n",
"def percent(resample_se):\n",
"\t''' Find the percentage of comments provided by the chattiest user'''\n",
"\ttry:\n",
"\t\tcounts = resample_se.value_counts().order(ascending=False)\n",
"\t\treturn int(counts.ix[0]/float(counts.sum())*100)\n",
"\texcept Exception:\n",
"\t\treturn NaN\n",
"\n",
"\n",
"# Read and load data\n",
"json_file = open('closed.json')\n",
"json_data = json.load(json_file)\n",
"all_data_df = pd.DataFrame(json_data)\n",
"\n",
"###########\n",
"# part(1) #\n",
"###########\n",
"\n",
"# p1 is the dataframe have title, created_at, labels, closed_at, user, \\\n",
"# id as columns\n",
"p1 = all_data_df[['title', 'created_at', 'labels', 'closed_at',\\\n",
"\t\t\t\t\t'user', 'id']]\n",
"# transfer the user values to username string\n",
"p1.user = p1.user.map(user_extract)\n",
"\n",
"###########\n",
"# part(2) #\n",
"###########\n",
"\n",
"# Drop the duplicate rows using id inplace\n",
"p1.drop_duplicates(cols='id', inplace=True)\n",
"\n",
"###########\n",
"# part(4) #\n",
"###########\n",
"\n",
"# Convert created_at and closed_at columns from string to datetime\n",
"p1['created_at'] = pd.to_datetime(p1['created_at'])\n",
"p1['closed_at'] = pd.to_datetime(p1['closed_at'])\n",
"\n",
"###########\n",
"# part(5) #\n",
"###########\n",
"\n",
"# Set 'created_at' as index\n",
"p1.set_index('created_at', inplace=True)\n",
"# Make the monthly number of issue plot\n",
"issue_month = p1.title.resample('M', how=count)\n",
"issue_month.name = 'Number of Issues'\n",
"plt.figure()\n",
"issue_ax = issue_month.plot()\n",
"issue_ax.set_ylabel(issue_month.name)\n",
"# Make the monthly distinct user number plot\n",
"distinct_month = p1.user.resample('M', how=distinct_user)\n",
"distinct_month.name = 'Number of Distinct User'\n",
"plt.figure()\n",
"distinct_ax = distinct_month.plot()\n",
"distinct_ax.set_ylabel(distinct_month.name)\n",
"\n",
"###########\n",
"# part(6) #\n",
"###########\n",
"\n",
"# Resample the closed_at and return a series of mean open day\n",
"open_day = p1.closed_at.resample('M', how=mean_day)\n",
"open_day.name = 'Mean Open Day'\n",
"# Concatenate monthly issue number with mean open day\n",
"open_day = pd.concat([issue_month, open_day], axis=1)\n",
"open_day.columns = ['nIssues', 'mean_days']\n",
"plt.figure()\n",
"line1 = open_day['nIssues'].plot()\n",
"line1.set_ylabel('Number of issues')\n",
"plt.figure()\n",
"line2 = open_day['mean_days'].plot()\n",
"line2.set_ylabel('Mean Open Day')\n",
"open_day.to_pickle('mean_day.pkl')\n",
"print '_'*80\n",
"print open_day.head(20)\n",
"\n",
"###########\n",
"# part(7) #\n",
"###########\n",
"\n",
"# Create the comment dateframe\n",
"comm_df = all_data_df[['created_at', 'comments', 'id']]\n",
"comm_df.drop_duplicates(cols='id', inplace=True)\n",
"comm_df.comments = comm_df.comments.map(comment_func)\n",
"comm_df.created_at = pd.to_datetime(comm_df.created_at)\n",
"comm_df.set_index('created_at', inplace=True)\n",
"print '_'*80\n",
"print comm_df.head(20)\n",
"comm_df.to_pickle('comments.pkl')\n",
"\n",
"###########\n",
"# part(8) #\n",
"###########\n",
"\n",
"# Create user dataframe\n",
"user_df = all_data_df[['created_at', 'user', 'id']]\n",
"user_df.user = user_df.user.map(user_extract)\n",
"user_df.created_at = pd.to_datetime(user_df.created_at)\n",
"user_df.set_index('created_at', inplace=True)\n",
"user_se = user_df['user']\n",
"chattiest_df = user_se.resample('M', how=[count,chattiest_user,percent,distinct])\n",
"chattiest_df.columns = ['Number of comments', 'The chattiest', 'Percentage of the chattiest(%)',\\\n",
"\t\t\t\t\t\t'Number of participants']\n",
"print '_'*80\t\t\t\t\t\t\n",
"print chattiest_df.head(20)\n",
"chattiest_df.to_pickle('chattiest.pkl')\n",
"\n",
"###########\n",
"# part(9) #\n",
"###########\n",
"\n",
"# Create id_label dataframe with create time as index\n",
"id_labels_temp = data_df[['id', 'labels', 'created_at']]\n",
"id_labels_temp.drop_duplicates(cols='id', inplace=True)\n",
"id_labels_list = []\n",
"for (idx,Id) in id_labels_temp.id.iteritems():\n",
" if len(id_labels_temp.labels.ix[idx]):\n",
" for label_dict in id_labels_temp.labels.ix[idx]:\n",
" id_labels_list.append((Id, label_dict['name'], id_labels_temp.created_at[idx]))\n",
" else:\n",
" id_labels_list.append((Id, np.nan,id_labels_temp.created_at[idx]))\n",
"id_labels_df = pd.DataFrame(id_labels_list, columns=['id', 'label', 'created_at'])\n",
"id_labels_df['created_at'] = pd.to_datetime(id_labels_df['created_at'])\n",
"id_labels_df.set_index('created_at', inplace=True)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"________________________________________________________________________________\n",
" nIssues mean_days\n",
"created_at \n",
"2010-09-30 11 138\n",
"2010-10-31 8 250\n",
"2010-11-30 2 13\n",
"2010-12-31 4 2\n",
"2011-01-31 9 52\n",
"2011-02-28 2 3\n",
"2011-03-31 2 6\n",
"2011-04-30 0 NaN\n",
"2011-05-31 7 64\n",
"2011-06-30 9 49\n",
"2011-07-31 30 67\n",
"2011-08-31 31 53\n",
"2011-09-30 66 39\n",
"2011-10-31 118 23\n",
"2011-11-30 110 21\n",
"2011-12-31 125 26\n",
"2012-01-31 154 20\n",
"2012-02-29 101 23\n",
"2012-03-31 126 30\n",
"2012-04-30 171 13\n",
"________________________________________________________________________________\n",
" comments id\n",
"created_at \n",
"2010-09-29 00:45:31 implemented in git HEAD 337721\n",
"2010-09-29 00:50:13 done 337726\n",
"2010-09-29 00:50:52 I guess I "accidentally" fixed this ... 337728\n",
"2010-09-29 00:51:27 fixed 337730\n",
"2010-09-29 00:57:00 Everything seems to be working in Python 2.7 w... 337736\n",
"2010-09-29 05:30:56 All fixed up and wrote unit tests--hopefully d... 337994\n",
"2010-09-29 15:41:55 This is a bug. DataMatrix as input to the Data... 338909\n",
"2010-09-29 19:45:47 In principle I agree with you that fill should... 339355\n",
"2010-09-30 22:29:36 A user suggested this version to start with:\\r... 341577\n",
"2010-09-30 22:33:14 Done in latest git HEAD 341581\n",
"2010-09-30 22:34:26 This will not go in forthcoming 0.3 release bu... 341583\n",
"2010-10-03 17:20:41 You make a good point, and I think it might be... 344725\n",
"2010-10-07 23:42:34 Hi Surbas,\\r\\n\\r\\nI'm sorry this has taken me ... 352369\n",
"2010-10-11 03:19:39 implemented in recent commits 356064\n",
"2010-10-12 16:10:48 added apply and applymap functions to Series f... 358943\n",
"2010-10-12 16:13:04 Haven't been able to reproduce this so closing 358947\n",
"2010-10-12 16:13:55 Done for DataFrame / WidePanel. Needs to be ad... 358950\n",
"2010-10-12 16:15:10 duplicate issue 358952\n",
"2010-10-22 17:59:31 fixed in git HEAD 376890\n",
"2010-11-19 14:50:11 Should be safe to use the git HEAD, I will try... 428564\n",
"________________________________________________________________________________"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
" Number of comments The chattiest Percentage of the chattiest(%) \\\n",
"created_at \n",
"2010-09-30 22 andylei 81 \n",
"2010-10-31 16 hector13 75 \n",
"2010-11-30 4 wesm 50 \n",
"2010-12-31 8 mpenning 50 \n",
"2011-01-31 18 wesm 33 \n",
"2011-02-28 2 tgefell 50 \n",
"2011-03-31 2 ghost 100 \n",
"2011-04-30 0 NaN NaN \n",
"2011-05-31 7 surbas 71 \n",
"2011-06-30 9 dieterv77 77 \n",
"2011-07-31 30 talltom 73 \n",
"2011-08-31 31 xdong 67 \n",
"2011-09-30 66 scottza 71 \n",
"2011-10-31 118 Komnomnomnom 66 \n",
"2011-11-30 110 algotr8der 53 \n",
"2011-12-31 125 MaxBo 49 \n",
"2012-01-31 154 fonnesbeck 36 \n",
"2012-02-29 101 yarikoptic 48 \n",
"2012-03-31 126 brentp 26 \n",
"2012-04-30 171 nspies 54 \n",
"\n",
" Number of participants \n",
"created_at \n",
"2010-09-30 2 \n",
"2010-10-31 3 \n",
"2010-11-30 2 \n",
"2010-12-31 3 \n",
"2011-01-31 5 \n",
"2011-02-28 2 \n",
"2011-03-31 1 \n",
"2011-04-30 0 \n",
"2011-05-31 3 \n",
"2011-06-30 3 \n",
"2011-07-31 9 \n",
"2011-08-31 10 \n",
"2011-09-30 14 \n",
"2011-10-31 17 \n",
"2011-11-30 25 \n",
"2011-12-31 22 \n",
"2012-01-31 41 \n",
"2012-02-29 26 \n",
"2012-03-31 47 \n",
"2012-04-30 39 \n"
]
}
],
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},
{
"cell_type": "code",
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"id_labels_df.head()"
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"language": "python",
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{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>id</th>\n",
" <th>label</th>\n",
" <th>created_at</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 337721</td>\n",
" <td> NaN</td>\n",
" <td> 2010-09-29T00:45:31Z</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 337726</td>\n",
" <td> NaN</td>\n",
" <td> 2010-09-29T00:50:13Z</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 337728</td>\n",
" <td> NaN</td>\n",
" <td> 2010-09-29T00:50:52Z</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td> 337730</td>\n",
" <td> NaN</td>\n",
" <td> 2010-09-29T00:51:27Z</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 337736</td>\n",
" <td> NaN</td>\n",
" <td> 2010-09-29T00:57:00Z</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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" id label created_at\n",
"0 337721 NaN 2010-09-29T00:45:31Z\n",
"1 337726 NaN 2010-09-29T00:50:13Z\n",
"2 337728 NaN 2010-09-29T00:50:52Z\n",
"3 337730 NaN 2010-09-29T00:51:27Z\n",
"4 337736 NaN 2010-09-29T00:57:00Z"
]
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"prompt_number": 124
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"open_day.head()"
],
"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>nIssues</th>\n",
" <th>mean_days</th>\n",
" </tr>\n",
" <tr>\n",
" <th>created_at</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010-09-30</th>\n",
" <td> 11</td>\n",
" <td> 138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-10-31</th>\n",
" <td> 8</td>\n",
" <td> 250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-11-30</th>\n",
" <td> 2</td>\n",
" <td> 13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-12-31</th>\n",
" <td> 4</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-31</th>\n",
" <td> 9</td>\n",
" <td> 52</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
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" nIssues mean_days\n",
"created_at \n",
"2010-09-30 11 138\n",
"2010-10-31 8 250\n",
"2010-11-30 2 13\n",
"2010-12-31 4 2\n",
"2011-01-31 9 52"
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}
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},
{
"cell_type": "code",
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"input": [
"id_labels_df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>id</th>\n",
" <th>label</th>\n",
" </tr>\n",
" <tr>\n",
" <th>created_at</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010-09-29 00:45:31</th>\n",
" <td> 337721</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:50:13</th>\n",
" <td> 337726</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:50:52</th>\n",
" <td> 337728</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:51:27</th>\n",
" <td> 337730</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:57:00</th>\n",
" <td> 337736</td>\n",
" <td> NaN</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
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" id label\n",
"created_at \n",
"2010-09-29 00:45:31 337721 NaN\n",
"2010-09-29 00:50:13 337726 NaN\n",
"2010-09-29 00:50:52 337728 NaN\n",
"2010-09-29 00:51:27 337730 NaN\n",
"2010-09-29 00:57:00 337736 NaN"
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"prompt_number": 132
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p1.closed_at.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 133,
"text": [
"created_at\n",
"2010-09-29 00:45:31 2011-02-19 23:13:48\n",
"2010-09-29 00:50:13 2010-12-17 02:57:33\n",
"2010-09-29 00:50:52 2011-01-01 23:50:12\n",
"2010-09-29 00:51:27 2010-12-11 06:14:32\n",
"2010-09-29 00:57:00 2010-12-17 02:46:34\n",
"Name: closed_at, dtype: datetime64[ns]"
]
}
],
"prompt_number": 133
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"id_labels_df.ix[60:100]"
],
"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>id</th>\n",
" <th>label</th>\n",
" </tr>\n",
" <tr>\n",
" <th>created_at</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2011-07-18 15:37:46</th>\n",
" <td> 1242420</td>\n",
" <td> Enhancement</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-18 15:37:46</th>\n",
" <td> 1242420</td>\n",
" <td> Testing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-18 15:39:18</th>\n",
" <td> 1242434</td>\n",
" <td> Enhancement</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-18 15:39:18</th>\n",
" <td> 1242434</td>\n",
" <td> timeseries</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-18 15:43:35</th>\n",
" <td> 1242459</td>\n",
" <td> Enhancement</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-18 15:45:19</th>\n",
" <td> 1242473</td>\n",
" <td> Bug</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-18 15:46:39</th>\n",
" <td> 1242483</td>\n",
" <td> Refactor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-18 15:47:17</th>\n",
" <td> 1242492</td>\n",
" <td> Enhancement</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-18 15:48:30</th>\n",
" <td> 1242501</td>\n",
" <td> Enhancement</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-18 15:49:35</th>\n",
" <td> 1242511</td>\n",
" <td> Enhancement</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-18 15:50:10</th>\n",
" <td> 1242517</td>\n",
" <td> Enhancement</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-18 15:52:21</th>\n",
" <td> 1242529</td>\n",
" <td> Enhancement</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-18 15:54:10</th>\n",
" <td> 1242542</td>\n",
" <td> Enhancement</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-18 15:54:45</th>\n",
" <td> 1242545</td>\n",
" <td> Testing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-18 16:02:37</th>\n",
" <td> 1242597</td>\n",
" <td> Enhancement</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-18 21:00:39</th>\n",
" <td> 1245597</td>\n",
" <td> Bug</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-21 19:32:25</th>\n",
" <td> 1265368</td>\n",
" <td> Build problem</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-24 20:08:48</th>\n",
" <td> 1278525</td>\n",
" <td> Enhancement</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-26 01:42:29</th>\n",
" <td> 1286029</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-26 15:41:00</th>\n",
" <td> 1289636</td>\n",
" <td> Build problem</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-27 23:02:15</th>\n",
" <td> 1299665</td>\n",
" <td> Bug</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-28 15:04:34</th>\n",
" <td> 1303422</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-29 15:53:40</th>\n",
" <td> 1310939</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-07-29 16:26:50</th>\n",
" <td> 1311144</td>\n",
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" <th>2011-07-29 16:26:50</th>\n",
" <td> 1311144</td>\n",
" <td> timeseries</td>\n",
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" <th>2011-07-29 22:19:39</th>\n",
" <td> 1313137</td>\n",
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" <tr>\n",
" <th>2011-07-30 01:01:42</th>\n",
" <td> 1313688</td>\n",
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" <th>2011-08-01 20:13:57</th>\n",
" <td> 1325504</td>\n",
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" <td> 1351978</td>\n",
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" <th>2011-08-06 08:02:19</th>\n",
" <td> 1355531</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-08-07 02:54:27</th>\n",
" <td> 1359677</td>\n",
" <td> NaN</td>\n",
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" <tr>\n",
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" <td> 1361833</td>\n",
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" <td> 1361845</td>\n",
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" <td> 1361862</td>\n",
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" <td> 1369747</td>\n",
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" <td> 1373938</td>\n",
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" <td> 1374309</td>\n",
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" <th>2011-08-09 20:53:42</th>\n",
" <td> 1375374</td>\n",
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" <th>2011-08-09 22:51:30</th>\n",
" <td> 1376176</td>\n",
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"</table>\n",
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"created_at \n",
"2011-07-18 15:37:46 1242420 Enhancement\n",
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"2011-07-18 15:39:18 1242434 Enhancement\n",
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"2011-07-29 22:19:39 1313137 Enhancement\n",
"2011-07-30 01:01:42 1313688 Enhancement\n",
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"2011-08-09 02:25:36 1369747 NaN\n",
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"p1.head()"
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{
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"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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" <thead>\n",
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" <th></th>\n",
" <th>title</th>\n",
" <th>labels</th>\n",
" <th>closed_at</th>\n",
" <th>user</th>\n",
" <th>id</th>\n",
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" <th>created_at</th>\n",
" <th></th>\n",
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" <tr>\n",
" <th>2010-09-29 00:45:31</th>\n",
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" <td> []</td>\n",
" <td>2011-02-19 23:13:48</td>\n",
" <td> wesm</td>\n",
" <td> 337721</td>\n",
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" <th>2010-09-29 00:50:13</th>\n",
" <td> reindex_like function</td>\n",
" <td> []</td>\n",
" <td>2010-12-17 02:57:33</td>\n",
" <td> wesm</td>\n",
" <td> 337726</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:50:52</th>\n",
" <td> Binary operations on int DataMatrix</td>\n",
" <td> []</td>\n",
" <td>2011-01-01 23:50:12</td>\n",
" <td> wesm</td>\n",
" <td> 337728</td>\n",
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" <th>2010-09-29 00:51:27</th>\n",
" <td> Plot keyword arguments are unused in DataFrame...</td>\n",
" <td> []</td>\n",
" <td>2010-12-11 06:14:32</td>\n",
" <td> wesm</td>\n",
" <td> 337730</td>\n",
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" <th>2010-09-29 00:57:00</th>\n",
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" <td> []</td>\n",
" <td>2010-12-17 02:46:34</td>\n",
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" title labels \\\n",
"created_at \n",
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"2010-09-29 00:50:52 Binary operations on int DataMatrix [] \n",
"2010-09-29 00:51:27 Plot keyword arguments are unused in DataFrame... [] \n",
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"input": [
"def open_day_per_issue(series_in):\n",
"\t''' Calculate the days an issue takes to be solved '''\n",
"\tcreated_at_series = pd.Series(series_in.index, index=series_in.index)\n",
"\tdiff = series_in - created_at_series\n",
"\treturn float(diff.days)"
],
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{
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"input": [
"open_day_per_issue_se = p1.closed_at.apply(open_day_per_issue)"
],
"language": "python",
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"outputs": [
{
"ename": "AttributeError",
"evalue": "'Timestamp' object has no attribute 'index'",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-147-014c63418d2e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mopen_day_per_issue_se\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclosed_at\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen_day_per_issue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/Yigong/anaconda/python.app/Contents/lib/python2.7/site-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[1;32m 2534\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimestamp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2535\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2536\u001b[0;31m \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2537\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2538\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/Yigong/anaconda/python.app/Contents/lib/python2.7/site-packages/pandas/lib.so\u001b[0m in \u001b[0;36mpandas.lib.map_infer (pandas/lib.c:42840)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m<ipython-input-141-838498043c83>\u001b[0m in \u001b[0;36mopen_day_per_issue\u001b[0;34m(series_in)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mopen_day_per_issue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_in\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m''' Calculate the days an issue takes to be solved '''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mcreated_at_series\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_in\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseries_in\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mdiff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseries_in\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mcreated_at_series\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'Timestamp' object has no attribute 'index'"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"> \u001b[0;32m<ipython-input-141-838498043c83>\u001b[0m(3)\u001b[0;36mopen_day_per_issue\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m 2 \u001b[0;31m \u001b[0;34m''' Calculate the days an issue takes to be solved '''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m----> 3 \u001b[0;31m \u001b[0mcreated_at_series\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_in\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseries_in\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 4 \u001b[0;31m \u001b[0mdiff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseries_in\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mcreated_at_series\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"ipdb> series_in\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Timestamp('2011-02-19 23:13:48', tz=None)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"ipdb> d\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"*** Newest frame\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"ipdb> u\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"> \u001b[0;32m/Users/Yigong/anaconda/python.app/Contents/lib/python2.7/site-packages/pandas/core/series.py\u001b[0m(2536)\u001b[0;36mapply\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m 2535 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m-> 2536 \u001b[0;31m \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 2537 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"ipdb> u\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"> \u001b[0;32m<ipython-input-147-014c63418d2e>\u001b[0m(1)\u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m----> 1 \u001b[0;31m\u001b[0mopen_day_per_issue_se\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclosed_at\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen_day_per_issue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"ipdb> u\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"*** Oldest frame\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"ipdb> d\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"> \u001b[0;32m/Users/Yigong/anaconda/python.app/Contents/lib/python2.7/site-packages/pandas/core/series.py\u001b[0m(2536)\u001b[0;36mapply\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m 2535 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m-> 2536 \u001b[0;31m \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 2537 \u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"ipdb> d\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"> \u001b[0;32m<ipython-input-141-838498043c83>\u001b[0m(3)\u001b[0;36mopen_day_per_issue\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m 2 \u001b[0;31m \u001b[0;34m''' Calculate the days an issue takes to be solved '''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m----> 3 \u001b[0;31m \u001b[0mcreated_at_series\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_in\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseries_in\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 4 \u001b[0;31m \u001b[0mdiff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseries_in\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mcreated_at_series\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"ipdb> exit\n"
]
}
],
"prompt_number": 147
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(p1.closed_at)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 143,
"text": [
"pandas.core.series.TimeSeries"
]
}
],
"prompt_number": 143
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p1.closed_at.index"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 144,
"text": [
"<class 'pandas.tseries.index.DatetimeIndex'>\n",
"[2010-09-29 00:45:31, ..., 2013-04-28 15:27:23]\n",
"Length: 2934, Freq: None, Timezone: None"
]
}
],
"prompt_number": 144
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pdb"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Automatic pdb calling has been turned ON\n"
]
}
],
"prompt_number": 145
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"user.ix[0][1].index"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'numpy.int64' object has no attribute 'index'",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-149-53ba29158ff5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0muser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'numpy.int64' object has no attribute 'index'"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"> \u001b[0;32m<ipython-input-149-53ba29158ff5>\u001b[0m(1)\u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m----> 1 \u001b[0;31m\u001b[0muser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"ipdb> exit\n"
]
}
],
"prompt_number": 149
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p1_reset = p1.reset_index()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 151
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p1_reset.head()"
],
"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>created_at</th>\n",
" <th>title</th>\n",
" <th>labels</th>\n",
" <th>closed_at</th>\n",
" <th>user</th>\n",
" <th>id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2010-09-29 00:45:31</td>\n",
" <td> Enable element-wise comparison operations in D...</td>\n",
" <td> []</td>\n",
" <td>2011-02-19 23:13:48</td>\n",
" <td> wesm</td>\n",
" <td> 337721</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2010-09-29 00:50:13</td>\n",
" <td> reindex_like function</td>\n",
" <td> []</td>\n",
" <td>2010-12-17 02:57:33</td>\n",
" <td> wesm</td>\n",
" <td> 337726</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2010-09-29 00:50:52</td>\n",
" <td> Binary operations on int DataMatrix</td>\n",
" <td> []</td>\n",
" <td>2011-01-01 23:50:12</td>\n",
" <td> wesm</td>\n",
" <td> 337728</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2010-09-29 00:51:27</td>\n",
" <td> Plot keyword arguments are unused in DataFrame...</td>\n",
" <td> []</td>\n",
" <td>2010-12-11 06:14:32</td>\n",
" <td> wesm</td>\n",
" <td> 337730</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2010-09-29 00:57:00</td>\n",
" <td> Python 2.7 testing</td>\n",
" <td> []</td>\n",
" <td>2010-12-17 02:46:34</td>\n",
" <td> wesm</td>\n",
" <td> 337736</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 152,
"text": [
" created_at title \\\n",
"0 2010-09-29 00:45:31 Enable element-wise comparison operations in D... \n",
"1 2010-09-29 00:50:13 reindex_like function \n",
"2 2010-09-29 00:50:52 Binary operations on int DataMatrix \n",
"3 2010-09-29 00:51:27 Plot keyword arguments are unused in DataFrame... \n",
"4 2010-09-29 00:57:00 Python 2.7 testing \n",
"\n",
" labels closed_at user id \n",
"0 [] 2011-02-19 23:13:48 wesm 337721 \n",
"1 [] 2010-12-17 02:57:33 wesm 337726 \n",
"2 [] 2011-01-01 23:50:12 wesm 337728 \n",
"3 [] 2010-12-11 06:14:32 wesm 337730 \n",
"4 [] 2010-12-17 02:46:34 wesm 337736 "
]
}
],
"prompt_number": 152
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(p1_reset['created_at']-p1_reset['closed_at']).apply(datetime.timedelta.total_seconds())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "descriptor 'total_seconds' of 'datetime.timedelta' object needs an argument",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-161-b15a1a9d3ddc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mp1_reset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'created_at'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mp1_reset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'closed_at'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimedelta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal_seconds\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[0;31mTypeError\u001b[0m: descriptor 'total_seconds' of 'datetime.timedelta' object needs an argument"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"> \u001b[0;32m<ipython-input-161-b15a1a9d3ddc>\u001b[0m(1)\u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m----> 1 \u001b[0;31m\u001b[0;34m(\u001b[0m\u001b[0mp1_reset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'created_at'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mp1_reset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'closed_at'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimedelta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal_seconds\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\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"ipdb> exit\n"
]
}
],
"prompt_number": 161
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = -(p1_reset['created_at']-p1_reset['closed_at'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 169
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a.map(datetime.timedelta.total_seconds)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "descriptor 'total_seconds' requires a 'datetime.timedelta' object but received a 'numpy.timedelta64'",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-164-65772c304ca2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimedelta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal_seconds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/Yigong/anaconda/python.app/Contents/lib/python2.7/site-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, arg, na_action)\u001b[0m\n\u001b[1;32m 2495\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2496\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2497\u001b[0;31m \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2498\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/Yigong/anaconda/python.app/Contents/lib/python2.7/site-packages/pandas/lib.so\u001b[0m in \u001b[0;36mpandas.lib.map_infer (pandas/lib.c:42840)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: descriptor 'total_seconds' requires a 'datetime.timedelta' object but received a 'numpy.timedelta64'"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"> \u001b[0;32m/Users/Yigong/Documents/Python/AY250/hw7/inference.pyx\u001b[0m(864)\u001b[0;36mpandas.lib.map_infer (pandas/lib.c:42840)\u001b[0;34m()\u001b[0m\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"ipdb> exit\n"
]
}
],
"prompt_number": 164
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a.ix[0][1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "IndexError",
"evalue": "invalid index to scalar variable.",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-167-f5444cee41aa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m: invalid index to scalar variable."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"> \u001b[0;32m<ipython-input-167-f5444cee41aa>\u001b[0m(1)\u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m----> 1 \u001b[0;31m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"ipdb> exit\n"
]
}
],
"prompt_number": 167
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a.ix[0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 171,
"text": [
"numpy.timedelta64(12436097000000000,'ns')"
]
}
],
"prompt_number": 171
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p1_reset"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<pre>\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 2934 entries, 0 to 2933\n",
"Data columns (total 6 columns):\n",
"created_at 2934 non-null values\n",
"title 2934 non-null values\n",
"labels 2934 non-null values\n",
"closed_at 2934 non-null values\n",
"user 2934 non-null values\n",
"id 2934 non-null values\n",
"dtypes: datetime64[ns](2), int64(1), object(3)\n",
"</pre>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 173,
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 2934 entries, 0 to 2933\n",
"Data columns (total 6 columns):\n",
"created_at 2934 non-null values\n",
"title 2934 non-null values\n",
"labels 2934 non-null values\n",
"closed_at 2934 non-null values\n",
"user 2934 non-null values\n",
"id 2934 non-null values\n",
"dtypes: datetime64[ns](2), int64(1), object(3)"
]
}
],
"prompt_number": 173
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"created_at = p1_reset['created_at']\n",
"closed_at = p1_reset['closed_at']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 174
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"time_diff = closed_at - created_at"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 175
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"time_diff.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 176,
"text": [
"0 143 days, 22:28:17\n",
"1 79 days, 02:07:20\n",
"2 94 days, 22:59:20\n",
"3 73 days, 05:23:05\n",
"4 79 days, 01:49:34\n",
"dtype: timedelta64[ns]"
]
}
],
"prompt_number": 176
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"time_diff.map(datetime.timedelta.total_seconds)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "descriptor 'total_seconds' requires a 'datetime.timedelta' object but received a 'numpy.timedelta64'",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-177-074ab4b0a9de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtime_diff\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimedelta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal_seconds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/Yigong/anaconda/python.app/Contents/lib/python2.7/site-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, arg, na_action)\u001b[0m\n\u001b[1;32m 2495\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2496\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2497\u001b[0;31m \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2498\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/Yigong/anaconda/python.app/Contents/lib/python2.7/site-packages/pandas/lib.so\u001b[0m in \u001b[0;36mpandas.lib.map_infer (pandas/lib.c:42840)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: descriptor 'total_seconds' requires a 'datetime.timedelta' object but received a 'numpy.timedelta64'"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"> \u001b[0;32m/Users/Yigong/Documents/Python/AY250/hw7/inference.pyx\u001b[0m(864)\u001b[0;36mpandas.lib.map_infer (pandas/lib.c:42840)\u001b[0;34m()\u001b[0m\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"ipdb> exit\n"
]
}
],
"prompt_number": 177
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = np.timedelta64(2069211000000000, 'ns')\n",
">>> days = x.astype('timedelta64[D]')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 178
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"days"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 179,
"text": [
"numpy.timedelta64(23,'D')"
]
}
],
"prompt_number": 179
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a.map(lambda td : td.days)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'numpy.timedelta64' object has no attribute 'days'",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-181-dfe5f4c2ec11>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mtd\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mtd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/Yigong/anaconda/python.app/Contents/lib/python2.7/site-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, arg, na_action)\u001b[0m\n\u001b[1;32m 2495\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2496\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2497\u001b[0;31m \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2498\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/Yigong/anaconda/python.app/Contents/lib/python2.7/site-packages/pandas/lib.so\u001b[0m in \u001b[0;36mpandas.lib.map_infer (pandas/lib.c:42840)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m<ipython-input-181-dfe5f4c2ec11>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(td)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mtd\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mtd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'numpy.timedelta64' object has no attribute 'days'"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"> \u001b[0;32m<ipython-input-181-dfe5f4c2ec11>\u001b[0m(1)\u001b[0;36m<lambda>\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m----> 1 \u001b[0;31m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mtd\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mtd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"ipdb> eit\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"*** NameError: name 'eit' is not defined\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"ipdb> exit\n"
]
}
],
"prompt_number": 181
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t = a[0]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 189
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 190,
"text": [
"numpy.timedelta64(12436097000000000,'ns')"
]
}
],
"prompt_number": 190
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"24*60*60*1e9"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 191,
"text": [
"86400000000000.0"
]
}
],
"prompt_number": 191
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"created = pd.Series(p1.index, index=p1.index)\n",
"closed = p1.closed_at"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 192
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"time_diff = closed - created"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 195
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"time_diff.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 196,
"text": [
"created_at\n",
"2010-09-29 00:45:31 143 days, 22:28:17\n",
"2010-09-29 00:50:13 79 days, 02:07:20\n",
"2010-09-29 00:50:52 94 days, 22:59:20\n",
"2010-09-29 00:51:27 73 days, 05:23:05\n",
"2010-09-29 00:57:00 79 days, 01:49:34\n",
"dtype: timedelta64[ns]"
]
}
],
"prompt_number": 196
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"created = pd.Series(p1.index, index=p1.index)\n",
"closed = p1.closed_at\n",
"time_diff = closed - created\n",
"SEC_DAY = 24*60*60*1e9\n",
"open_day_per_issue = time_diff.map(lambda td: td/SEC_DAY)\n",
"open_day_per_issue.name = 'open_day'\n",
"print open_day_per_issue.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"created_at\n",
"2010-09-29 00:45:31 143\n",
"2010-09-29 00:50:13 79\n",
"2010-09-29 00:50:52 94\n",
"2010-09-29 00:51:27 73\n",
"2010-09-29 00:57:00 79\n",
"Name: open_day, dtype: int64\n"
]
}
],
"prompt_number": 204
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"open_day_per_issue"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"id_labels_df.head()"
],
"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>id</th>\n",
" <th>label</th>\n",
" </tr>\n",
" <tr>\n",
" <th>created_at</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2010-09-29 00:45:31</th>\n",
" <td> 337721</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:50:13</th>\n",
" <td> 337726</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:50:52</th>\n",
" <td> 337728</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:51:27</th>\n",
" <td> 337730</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010-09-29 00:57:00</th>\n",
" <td> 337736</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 199,
"text": [
" id label\n",
"created_at \n",
"2010-09-29 00:45:31 337721 NaN\n",
"2010-09-29 00:50:13 337726 NaN\n",
"2010-09-29 00:50:52 337728 NaN\n",
"2010-09-29 00:51:27 337730 NaN\n",
"2010-09-29 00:57:00 337736 NaN"
]
}
],
"prompt_number": 199
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"label_open_df = pd.merge(id_labels_df.reset_index(), open_day_per_issue.reset_index(), on = 'created_at')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 219
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"label_open_df.ix[label_open_df['label']=='Bug']['open_day']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 236,
"text": [
"7 266\n",
"17 347\n",
"43 33\n",
"46 11\n",
"58 2\n",
"65 12\n",
"75 1\n",
"80 3\n",
"98 0\n",
"100 9\n",
"107 3\n",
"108 14\n",
"113 11\n",
"121 3\n",
"128 1\n",
"...\n",
"3716 0\n",
"3719 0\n",
"3737 9\n",
"3765 4\n",
"3771 3\n",
"3778 2\n",
"3785 0\n",
"3791 0\n",
"3795 0\n",
"3798 0\n",
"3803 0\n",
"3806 0\n",
"3807 0\n",
"3811 0\n",
"3816 0\n",
"Name: open_day, Length: 956, dtype: int64"
]
}
],
"prompt_number": 236
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = label_open_df['label'].drop_duplicates()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 231
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a.values"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 235,
"text": [
"array([nan, u'Bug', u'Enhancement', u'Testing', u'timeseries', u'Refactor',\n",
" u'Build problem', u'Docs', u'groupby', u'Ideas', u'unicode',\n",
" u'Data IO', u'prio-high', u'prio-medium', u'prio-low',\n",
" u'Visualization', u'Community', u'missing-data', u'Stats',\n",
" u'Indexing', u'Output-Formatting', u\"Can't Repro\", u'Performance',\n",
" u'Reshaping', u'Multithreading', u'Dtypes', u'Good as first PR',\n",
" u'API', u'Note To Selves', u'Regression', u'Usage'], dtype=object)"
]
}
],
"prompt_number": 235
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"open_day_per_issue.reset_index()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<pre>\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 2934 entries, 0 to 2933\n",
"Data columns (total 2 columns):\n",
"created_at 2934 non-null values\n",
"open_day 2934 non-null values\n",
"dtypes: datetime64[ns](1), int64(1)\n",
"</pre>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 212,
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 2934 entries, 0 to 2933\n",
"Data columns (total 2 columns):\n",
"created_at 2934 non-null values\n",
"open_day 2934 non-null values\n",
"dtypes: datetime64[ns](1), int64(1)"
]
}
],
"prompt_number": 212
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"label_open_df = pd.merge(id_labels_df.reset_index(), open_day_per_issue.reset_index(),\\\n",
"\t\t on='created_at')\n",
"label_open_df.set_index('created_at', inplace=True)\n",
"bug_df = label_open_df.ix[label_open_df['label']=='Bug']\n",
"LABELS = label_open_df['label'].drop_duplicates().values[1:]\n",
"time_per_label = pd.DataFrame([])\n",
"for label_str in LABELS:\n",
"\ttemp = label_open_df.ix[label_open_df['label']==label_str]['open_day']\n",
"\tlabel_month = temp.resample('M', how='mean')\n",
"\ttime_per_label[label_str] = label_month\n",
"time_per_label.index = label_month.index"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "string indices must be integers",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-238-d30eff139fd9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mtime_per_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\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 6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mlabel_str\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mLABELS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mtemp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel_open_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlabel_open_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'label'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mlabel_str\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'open_day'\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 8\u001b[0m \u001b[0mlabel_month\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtemp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'M'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'mean'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mtime_per_label\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlabel_str\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel_month\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: string indices must be integers"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"> \u001b[0;32m<ipython-input-238-d30eff139fd9>\u001b[0m(7)\u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m 6 \u001b[0;31m\u001b[0;32mfor\u001b[0m \u001b[0mlabel_str\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mLABELS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m----> 7 \u001b[0;31m \u001b[0mtemp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel_open_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlabel_open_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'label'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mlabel_str\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'open_day'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 8 \u001b[0;31m \u001b[0mlabel_month\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtemp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'M'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'mean'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"ipdb> temp\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"*** NameError: name 'temp' is not defined\n"
]
}
],
"prompt_number": "*"
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
pioneers/topgear | ipython-in-depth/examples/IPython Kernel/Terminal Usage.ipynb | 1 | 6380 | {
"metadata": {
"name": "",
"signature": "sha256:993106eecfd7abe1920e1dbe670c4518189c26e7b29dcc541835f7dcf6fffbb2"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"A few things that work best/only at the IPython terminal or Qt console clients"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running code with `%run`"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%writefile script.py\n",
"x = 10\n",
"y = 20\n",
"z = x+y\n",
"print 'z is:', z"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Writing script.py\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%run script"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"z is: 30\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"10"
]
}
],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Event loop and GUI integration"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `%gui` magic enables the integration of GUI event loops with the interactive execution loop, allowing you to run GUI code without blocking IPython.\n",
"\n",
"Consider for example the execution of Qt-based code. Once we enable the Qt gui support:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%gui qt"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can define a simple Qt application class (simplified version from [this Qt tutorial](http://zetcode.com/tutorials/pyqt4/firstprograms)):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sys\n",
"from PyQt4 import QtGui, QtCore\n",
"\n",
"class SimpleWindow(QtGui.QWidget):\n",
" def __init__(self, parent=None):\n",
" QtGui.QWidget.__init__(self, parent)\n",
"\n",
" self.setGeometry(300, 300, 200, 80)\n",
" self.setWindowTitle('Hello World')\n",
"\n",
" quit = QtGui.QPushButton('Close', self)\n",
" quit.setGeometry(10, 10, 60, 35)\n",
"\n",
" self.connect(quit, QtCore.SIGNAL('clicked()'),\n",
" self, QtCore.SLOT('close()'))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And now we can instantiate it:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"app = QtCore.QCoreApplication.instance()\n",
"if app is None:\n",
" app = QtGui.QApplication([])\n",
"\n",
"sw = SimpleWindow()\n",
"sw.show()\n",
"\n",
"from IPython.lib.guisupport import start_event_loop_qt4\n",
"start_event_loop_qt4(app)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But IPython still remains responsive:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"10+2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"12"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `%gui` magic can be similarly used to control Wx, Tk, glut and pyglet applications, [as can be seen in our examples](https://github.com/ipython/ipython/tree/master/examples/lib)."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Embedding IPython in a terminal application"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%writefile simple-embed.py\n",
"# This shows how to use the new top-level embed function. It is a simpler\n",
"# API that manages the creation of the embedded shell.\n",
"\n",
"from IPython import embed\n",
"\n",
"a = 10\n",
"b = 20\n",
"\n",
"embed(header='First time', banner1='')\n",
"\n",
"c = 30\n",
"d = 40\n",
"\n",
"embed(header='The second time')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Writing simple-embed.py\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The example in kernel-embedding shows how to embed a full kernel into an application and how to connect to this kernel from an external process."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Logging terminal sessions and transitioning to a notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `%logstart` magic lets you log a terminal session with various degrees of control, and the `%notebook` one will convert an interactive console session into a notebook with all input cells already created for you (but no output)."
]
}
],
"metadata": {}
}
]
} | apache-2.0 |
Knewton/lentil | nb/data_explorations.ipynb | 2 | 29140 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from __future__ import division\n",
"\n",
"from collections import defaultdict\n",
"import pickle\n",
"import os\n",
"import sys\n",
"\n",
"from matplotlib import pyplot as plt\n",
"import pygraphviz as pgv\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import seaborn as sns\n",
"sns.set_style('whitegrid')\n",
"\n",
"from lentil import datatools\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import logging\n",
"logging.getLogger().setLevel(logging.DEBUG)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Grockit Kaggle Comp](https://www.kaggle.com/c/WhatDoYouKnow)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def interaction_history_from_grockit_data_set(data):\n",
" \"\"\"\n",
" Parse Grockit data set into an interaction history\n",
" \n",
" :param pd.DataFrame data: A dataframe of raw interactions\n",
" :rtype: datatools.InteractionHistory\n",
" :return: An interaction history object\n",
" \"\"\"\n",
" \n",
" data['round_started_at'] = pd.to_datetime(data['round_started_at'], format='%Y-%m-%d %H:%M:%S', coerce=True)\n",
" data['answered_at'] = pd.to_datetime(data['answered_at'], format='%Y-%m-%d %H:%M:%S', coerce=True)\n",
" \n",
" # sort by timestamp\n",
" data.sort('answered_at', inplace=True, axis=0)\n",
" \n",
" # compute response times\n",
" data['duration'] = (data['answered_at'] - data['round_started_at']) / np.timedelta64(1, 's')\n",
" \n",
" # get relevant columns and rename them\n",
" data = data[['user_id', 'correct', 'question_id', 'answered_at', 'duration']]\n",
" data.columns = ['student_id', 'outcome', 'module_id', 'timestamp', 'duration']\n",
" \n",
" # only keep interactions with binary outcomes and positive response times\n",
" data = data[((data['outcome']==1) | (data['outcome']==0)) & (data['duration'] > 0)]\n",
" \n",
" # cast outcomes from 0/1 to False/True\n",
" data['outcome'] = data['outcome'].apply(lambda x: x == 1)\n",
" \n",
" student_timesteps = defaultdict(int)\n",
" timesteps = [None] * len(data)\n",
" for i, (_, ixn) in enumerate(data.iterrows()):\n",
" student_timesteps[ixn['student_id']] += 1\n",
" timesteps[i] = student_timesteps[ixn['student_id']]\n",
" data['timestep'] = timesteps\n",
" \n",
" data['module_type'] = [datatools.AssessmentInteraction.MODULETYPE] * len(data)\n",
" \n",
" lesson_data = data.copy(deep=True)\n",
" lesson_data['module_type'] = [datatools.LessonInteraction.MODULETYPE] * len(lesson_data)\n",
" \n",
" return datatools.InteractionHistory(\n",
" pd.concat([data, lesson_data], axis=0),\n",
" sort_by_timestep=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_path = os.path.join('data', 'grockit', 'valid_training.csv')\n",
"df = pd.read_csv(data_path, delimiter=',')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print '\\n'.join(df.columns)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print \"Number of interactions = %d\" % len(df)\n",
"print \"Number of unique students = %d\" % len(df['user_id'].unique())\n",
"print \"Number of unique modules = %d\" % len(df['question_id'].unique())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.xlabel('Number of interactions per student')\n",
"plt.ylabel('Frequency (number of students)')\n",
"plt.hist(df['user_id'].value_counts().values)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.xlabel('Number of interactions per problem')\n",
"plt.ylabel('Frequency (number of problems)')\n",
"plt.hist(df['question_id'].value_counts().values)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unfiltered_history = interaction_history_from_grockit_data_set(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[KDD Cup 2010](https://pslcdatashop.web.cmu.edu/KDDCup/downloads.jsp)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def interaction_history_from_kdd_cup_data_set(data):\n",
" \"\"\"\n",
" Parse a KDD Cup data set into an interaction history\n",
" \n",
" :param pd.DataFrame data: A dataframe of raw interactions\n",
" :rtype: datatools.InteractionHistory\n",
" :return: An interaction history object\n",
" \"\"\"\n",
" # sort by timestamp\n",
" data.sort('Step Start Time', inplace=True, axis=0)\n",
" \n",
" # get relevant columns and rename them\n",
" data = data[['Anon Student Id', 'Correct First Attempt', 'Problem Name', 'Step Duration (sec)']]\n",
" data.columns = ['student_id', 'outcome', 'module_id', 'duration']\n",
" \n",
" # only keep interactions with binary outcomes and positive response times\n",
" data = data[((data['outcome']==1) | (data['outcome']==0)) & (data['duration'] > 0)]\n",
" \n",
" # cast outcomes from 0/1 to False/True\n",
" data['outcome'] = data['outcome'].apply(lambda x: x == 1)\n",
" \n",
" student_timesteps = defaultdict(int)\n",
" timesteps = [None] * len(data)\n",
" for i, (_, ixn) in enumerate(data.iterrows()):\n",
" student_timesteps[ixn['student_id']] += 1\n",
" timesteps[i] = student_timesteps[ixn['student_id']]\n",
" data['timestep'] = timesteps\n",
" \n",
" data['module_type'] = [datatools.AssessmentInteraction.MODULETYPE] * len(data)\n",
" \n",
" lesson_data = data.copy(deep=True)\n",
" lesson_data['module_type'] = [datatools.LessonInteraction.MODULETYPE] * len(lesson_data)\n",
" \n",
" return datatools.InteractionHistory(\n",
" pd.concat([data, lesson_data], axis=0),\n",
" sort_by_timestep=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_path = os.path.join('data',\n",
" 'bridge_to_algebra_2006_2007',\n",
" 'bridge_to_algebra_2006_2007_train.txt')\n",
"df = pd.read_csv(data_path, delimiter='\\t')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print '\\n'.join(df.columns)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print \"Number of interactions = %d\" % len(df)\n",
"print \"Number of unique students = %d\" % len(df['Anon Student Id'].unique())\n",
"print \"Number of unique modules = %d\" % len(df['Problem Name'].unique())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.xlabel('Number of interactions per student')\n",
"plt.ylabel('Frequency (number of students)')\n",
"plt.hist(df['Anon Student Id'].value_counts().values)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.xlabel('Number of interactions per problem')\n",
"plt.ylabel('Frequency (number of problems)')\n",
"plt.hist(df['Problem Name'].value_counts().values)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unfiltered_history = interaction_history_from_kdd_cup_data_set(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Assistments](https://sites.google.com/site/assistmentsdata/home/assistment-2009-2010-data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def interaction_history_from_assistments_data_set(\n",
" data,\n",
" duration_column='ms_first_response_time',\n",
" module_id_column='problem_id'):\n",
" \"\"\"\n",
" Parse dataframe of assistments interactions into an interaction history\n",
"\n",
" :param pd.DataFrame assistments_data: A raw history from assistments\n",
" :param str duration_column: Column to use as interaction duration\n",
" :param str module_id_column: Column to use as module_id\n",
" :rtype: datatools.InteractionHistory\n",
" :return: An interaction history\n",
" \"\"\"\n",
" # sort by order_id\n",
" data.sort('order_id', inplace=True, axis=0)\n",
" \n",
" # get relevant columns and rename them\n",
" data = data[['user_id', 'correct', duration_column, module_id_column]]\n",
" data.columns = ['student_id', 'outcome', 'duration', 'module_id']\n",
"\n",
" # only keep interactions with binary outcomes and positive response times\n",
" data = data[((data['outcome']==1) | (data['outcome']==0)) & (data['duration'] > 0)]\n",
" \n",
" # cast outcomes from int to bool\n",
" data['outcome'] = data['outcome'].apply(lambda x: x == 1)\n",
"\n",
" # map response times from milliseconds to seconds\n",
" data['duration'] = data['duration'].apply(lambda x: x / 1000)\n",
"\n",
" # existing interactions are all assessment interactions\n",
" data['module_type'] = [datatools.AssessmentInteraction.MODULETYPE] * len(data)\n",
"\n",
" # add timesteps\n",
" timesteps = [None] * len(data)\n",
" student_timesteps = defaultdict(int)\n",
" for i, (_, ixn) in enumerate(data.iterrows()):\n",
" student_timesteps[ixn['student_id']] += 1\n",
" timesteps[i] = student_timesteps[ixn['student_id']]\n",
" data['timestep'] = timesteps\n",
"\n",
" # add artificial lesson interactions\n",
" lesson_data = data.copy(deep=True)\n",
" lesson_data['module_type'] = [datatools.LessonInteraction.MODULETYPE] * len(data)\n",
"\n",
" return datatools.InteractionHistory(\n",
" pd.concat([data, lesson_data]),\n",
" sort_by_timestep=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_path = os.path.join('data', 'assistments_2009_2010.csv')\n",
"df = pd.read_csv(data_path, delimiter=',')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print '\\n'.join(df.columns)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print \"Number of interactions = %d\" % (len(df))\n",
"print \"Number of unique students = %d\" % (len(df['user_id'].unique()))\n",
"print \"Number of unique modules = %d\" % (len(df['problem_id'].unique()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.xlabel('Number of interactions per student')\n",
"plt.ylabel('Frequency (number of students)')\n",
"plt.hist(df['user_id'].value_counts().values)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.xlabel('Number of interactions per problem')\n",
"plt.ylabel('Frequency (number of problems)')\n",
"plt.hist(df['problem_id'].value_counts().values)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unfiltered_history = interaction_history_from_assistments_data_set(\n",
" df,\n",
" module_id_column='problem_id',\n",
" duration_column='ms_first_response_time')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Filter the interaction history"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def filter_history(history, min_num_ixns=5, max_num_ixns=sys.maxint):\n",
" \"\"\"\n",
" Filter history for students with histories of bounded length,\n",
" and modules with enough interactions\n",
" \n",
" :param datatools.InteractionHistory history: An interaction history\n",
" :param int min_num_ixns: Minimum number of timesteps in student history,\n",
" and minimum number of interactions for module\n",
" \n",
" :param int max_num_ixns: Maximum number of timesteps in student history\n",
" :rtype: datatools.InteractionHistory\n",
" :return: A filtered interaction history\n",
" \"\"\"\n",
" students = set(history.data['student_id'][(\n",
" history.data['timestep'] > min_num_ixns) & (\n",
" history.data['module_type']==datatools.AssessmentInteraction.MODULETYPE)])\n",
" students -= set(history.data['student_id'][history.data['timestep'] >= max_num_ixns])\n",
" \n",
" modules = {module_id for module_id, group in history.data.groupby('module_id') if len(group) > min_num_ixns}\n",
"\n",
" return datatools.InteractionHistory(\n",
" history.data[(history.data['student_id'].isin(students)) & (\n",
" history.data['module_id'].isin(modules))],\n",
" reindex_timesteps=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# apply the filter a couple of times, since removing student histories\n",
"# may cause certain modules to drop below the min_num_ixns threshold,\n",
"# and removing modules may cause student histories to drop below\n",
"# the min_num_ixns threshold\n",
"REPEATED_FILTER = 3 # number of times to repeat filtering\n",
"history = reduce(\n",
" lambda acc, _: filter_history(acc, min_num_ixns=75, max_num_ixns=1000), \n",
" range(REPEATED_FILTER), unfiltered_history)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# path to pickled interaction history file\n",
"history_path = os.path.join('data', 'assistments_2009_2010_history.pkl')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# load history from file\n",
"with open(history_path, 'rb') as f:\n",
" history = pickle.load(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# serialize history\n",
"with open(history_path, 'wb') as f:\n",
" pickle.dump(history, f, pickle.HIGHEST_PROTOCOL)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Explore basic stats about interaction history"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = history.data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"num_interactions = len(df)\n",
"value_counts = df['module_type'].value_counts()\n",
"num_assessment_ixns = value_counts.get(datatools.AssessmentInteraction.MODULETYPE, 0)\n",
"num_lesson_ixns = value_counts.get(datatools.LessonInteraction.MODULETYPE, 0)\n",
"\n",
"print \"Number of interactions = %d\" % (num_interactions)\n",
"print \"Number of assessment interactions = %d\" % (num_assessment_ixns)\n",
"print \"Number of lesson interactions = %d\" % (num_lesson_ixns)\n",
"\n",
"num_students = history.num_students()\n",
"\n",
"print \"Number of unique students: %d\" % (num_students)\n",
"\n",
"num_assessments = history.num_assessments()\n",
"\n",
"print \"Number of unique assessments: %d\" % (num_assessments)\n",
"\n",
"num_lessons = history.num_lessons()\n",
"\n",
"print \"Number of unique lessons: %d\" % (num_lessons)\n",
"\n",
"value_counts = df['outcome'].value_counts()\n",
"num_passes = value_counts.get(True, 0)\n",
"num_fails = value_counts.get(False, 0)\n",
"pass_rate = num_passes / (num_passes + num_fails)\n",
"\n",
"print \"Overall pass rate: %f\" % (pass_rate)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d = []\n",
"for _, group in df.groupby('student_id'):\n",
" d.extend(group['timestep'].value_counts().values)\n",
"d = np.array(d) - 1 # remove the lesson interaction at each timestep\n",
"\n",
"plt.xlabel('Number of assessment interactions per timestep')\n",
"plt.ylabel('Frequency (number of timesteps)')\n",
"plt.hist(d)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"timestamps = pd.DatetimeIndex(df['timestamp'])\n",
"\n",
"print \"Beginning of data set = %s\" % (min(timestamps))\n",
"print \"End of data set = %s\" % (max(timestamps))\n",
"\n",
"hours = timestamps.hour\n",
"plt.xlabel('Hour of interaction')\n",
"plt.ylabel('Frequency (number of interactions)')\n",
"plt.hist(hours, bins=24)\n",
"plt.show()\n",
"\n",
"# Monday=0, Sunday=6\n",
"days = timestamps.weekday\n",
"plt.xlabel('Day of interaction')\n",
"plt.ylabel('Frequency (number of interactions)')\n",
"plt.hist(days, bins=7)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.xlabel('Timestep')\n",
"plt.ylabel('Frequency (number of interactions)')\n",
"plt.hist(df['timestep'].values)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"durations = np.array([x for x in df['duration'].values])\n",
"\n",
"plt.xlabel('ln(response time, in seconds)')\n",
"plt.ylabel('Frequency (number of interactions)')\n",
"plt.hist(np.log(durations+1))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"counts = df['student_id'].value_counts().values\n",
"plt.xlabel('Number of interactions per student')\n",
"plt.ylabel('Frequency (number of students)')\n",
"plt.hist(counts)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"counts = df['module_id'][df['module_type'] == datatools.LessonInteraction.MODULETYPE].value_counts().values\n",
"\n",
"plt.xlabel('Number of interactions per lesson module')\n",
"plt.ylabel('Frequency (number of lesson modules)')\n",
"plt.hist(counts)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"counts = df['module_id'][df['module_type'] == datatools.AssessmentInteraction.MODULETYPE].value_counts().values\n",
"\n",
"plt.xlabel('Number of interactions per assessment module')\n",
"plt.ylabel('Frequency (number of assessment modules)')\n",
"plt.hist(counts)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"counts = df.groupby(['student_id', 'module_id']).size().values\n",
"\n",
"plt.xlabel('Number of interactions per student per module')\n",
"plt.ylabel('Frequency (number of student-module pairs)')\n",
"plt.hist(counts)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"num_students_per_module = [len(group['student_id'].unique()) for _, group in df.groupby('module_id')]\n",
"\n",
"plt.xlabel('Number of students per module')\n",
"plt.ylabel('Frequency (number of modules)')\n",
"plt.hist(num_students_per_module)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"grouped = df[df['module_type']==datatools.AssessmentInteraction.MODULETYPE].groupby('student_id')\n",
"num_assessments_per_student = [len(group['module_id']) for _, group in grouped]\n",
"\n",
"plt.xlabel('Number of assessment modules per student')\n",
"plt.ylabel('Frequency (number of students)')\n",
"plt.hist(num_assessments_per_student)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"grouped = df[df['module_type']==datatools.LessonInteraction.MODULETYPE].groupby('student_id')\n",
"num_lessons_per_student = [len(group['module_id']) for _, group in grouped]\n",
"\n",
"plt.xlabel('Number of lesson modules per student')\n",
"plt.ylabel('Frequency (number of students)')\n",
"plt.hist(num_lessons_per_student)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_pass_rates(grouped):\n",
" \"\"\"\n",
" Get pass rate for each group\n",
" \n",
" :param pd.GroupBy grouped: A grouped dataframe\n",
" :rtype: dict[str, float]\n",
" :return: A dictionary mapping group name to pass rate\n",
" \"\"\"\n",
" pass_rates = {}\n",
" for name, group in grouped:\n",
" vc = group['outcome'].value_counts()\n",
" if True not in vc:\n",
" pass_rates[name] = 0\n",
" else:\n",
" pass_rates[name] = vc[True] / len(group)\n",
" return pass_rates"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"grouped = df[df['module_type']==datatools.AssessmentInteraction.MODULETYPE].groupby('student_id')\n",
"\n",
"plt.xlabel('Student pass rate')\n",
"plt.ylabel('Frequency (number of students)')\n",
"plt.hist(get_pass_rates(grouped).values())\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"grouped = df[df['module_type']==datatools.AssessmentInteraction.MODULETYPE].groupby('module_id')\n",
"\n",
"plt.xlabel('Assessment pass rate')\n",
"plt.ylabel('Frequency (number of assessments)')\n",
"plt.hist(get_pass_rates(grouped).values())\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def make_flow_graph(interaction_logs):\n",
" \"\"\"\n",
" Create a graphviz object for the graph of \n",
" module transitions across all student paths\n",
" \n",
" :param pd.DataFrame interaction_logs: An interaction history\n",
" :rtype pgv.AGraph\n",
" :return Graph of module transitions in student paths\n",
" \"\"\"\n",
" G = pgv.AGraph(directed=True)\n",
"\n",
" for module_id in interaction_logs['module_id'].unique():\n",
" G.add_node(module_id)\n",
"\n",
" E = defaultdict(set)\n",
" grouped = interaction_logs.groupby('student_id')\n",
" for student_id, group in grouped:\n",
" module_ids_in_student_path = group['module_id']\n",
" for source_node, target_node in zip(module_ids_in_student_path[:-1], module_ids_in_student_path[1:]):\n",
" if source_node != target_node: # stationary\n",
" E[(source_node, target_node)] |= {student_id}\n",
"\n",
" for (source_node, target_node), students_that_made_transition in E.iteritems():\n",
" G.add_edge(\n",
" source_node,\n",
" target_node,\n",
" weight=len(students_that_made_transition))\n",
"\n",
" return G"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"G = make_flow_graph(df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"flow_graph_path = os.path.join('data', 'assistments_flow_graph.dot')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"G.write(flow_graph_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def make_conn_graph(interaction_logs):\n",
" \"\"\"\n",
" Create a graphviz object for the bi-partite graph connecting students\n",
" with the modules they've interacted with\n",
" \n",
" :param pd.DataFrame interaction_logs: An interaction history\n",
" :rtype pgv.AGraph\n",
" :return Bi-partite graph of student-module interactions\n",
" \"\"\"\n",
" G = pgv.AGraph(directed=True)\n",
"\n",
" for module_id in interaction_logs['module_id'].unique():\n",
" G.add_node(module_id, label='module')\n",
"\n",
" grouped = interaction_logs.groupby('student_id')\n",
" for student_id, group in grouped:\n",
" G.add_node(student_id, label='student')\n",
" for module_id in set(group['module_id'].values):\n",
" G.add_edge(student_id, module_id)\n",
"\n",
" return G"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"G = make_conn_graph(df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"conn_graph_path = os.path.join('data', 'assistments_conn_graph.dot')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"G.write(conn_graph_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
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"display_name": "Python 2",
"language": "python",
"name": "python2"
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"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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| apache-2.0 |
aerosara/thesis | notebooks_archive_10112014/Haloize with odelay.ipynb | 1 | 75153 | {
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"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"CRTBP Derivatives, Stopping Conditions, and Initial Conditions"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"%reset\n",
"%pylab inline\n",
"\n",
"import scipy.integrate as integrate\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"import numpy as np\n",
"import math\n",
"from pycse import odelay\n",
"from scipy.optimize import fsolve\n",
"\n",
"from thesis_functions.astro import FindOrbitCenter, ComputeLibrationPoints, stop_yEquals0, stop_zEquals0, PropagateSatellite, BuildRICFrame, BuildVNBFrame #nonlinearDerivativesFunction\n",
"from thesis_functions.visualization import PlotGrid\n",
"\n",
"def stop_maxTime(state, t):\n",
" isterminal = True\n",
" direction = 0\n",
" value = abs(t)-3 # stop if time is greater than 3 units\n",
" return value, isterminal, direction\n",
"\n",
"def nonlinearDerivativesFunction(inputstate, timespan):\n",
" \n",
" x, y, z, xdot, ydot, zdot = inputstate\n",
" \n",
" # distances\n",
" r1 = np.sqrt((mu+x)**2.0 + y**2.0 + z**2.0);\n",
" r2 = np.sqrt((1-mu-x)**2.0 + y**2.0 + z**2.0);\n",
" \n",
" derivs = [xdot, \n",
" ydot,\n",
" zdot, \n",
" x + 2.0*ydot + (1 - mu)*(-mu - x)/(r1**3.0) + mu*(1 - mu - x)/(r2**3.0),\n",
" y - 2.0*xdot - (1 - mu)*y/(r1**3.0) - mu*y/(r2**3.0),\n",
" -(1 - mu)*z/(r1**3.0) - mu*z/(r2**3.0)]\n",
" \n",
" return derivs\n",
"\n",
"#muArray_H = [0.04]\n",
"#testcases_H = np.array([[0.723268, 0.040000, 0.198019, 1.300177*4.0]]) # x, z, ydot, T\n",
"\n",
"testcase = 0\n",
"#mu = muArray_H[testcase]\n",
"#timespan = np.linspace(0, testcases_H[testcase, 3], 2000) # Howell\n",
"#initialstate_H = [testcases_H[testcase, 0], 0, testcases_H[testcase,1], 0, testcases_H[testcase, 2], 0] # Howell\n",
"\n",
"# create a perturbed initial state\n",
"#initialstate2 = np.zeros(len(initialstate_H))\n",
"#initialstate2[0:3] = initialstate_H[0:3]\n",
"#initialstate2[3:6] = [x * 1.001 for x in initialstate_H[3:6]]\n",
"\n",
"#print initialstate2\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"Once deleted, variables cannot be recovered. Proceed (y/[n])? y\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 113
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Define functions: ForceForwardToNextEvent, ForceForwardNEvents, FuncToSolve, Haloize"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"''' from the pysce source code at https://github.com/jkitchin/pycse/blob/2c9ff7fe81b0dfb34b3edf87356817312910b799/pycse/PYCSE.py#L78\n",
"odelay(func, y0, xspan, events, fsolve_args=None, **kwargs):\n",
" ode wrapper with events func is callable, with signature func(Y, x)\n",
" y0 are the initial conditions xspan is what you want to integrate\n",
" over\n",
"\n",
" events is a list of callable functions with signature event(Y, x).\n",
" These functions return zero when an event has happened.\n",
" \n",
" [value, isterminal, direction] = event(Y, x)\n",
" value is the value of the event function. When value = 0, an event is triggered\n",
"\n",
" isterminal = True if the integration is to terminate at a zero of\n",
" this event function, otherwise, False.\n",
"\n",
" direction = 0 if all zeros are to be located (the default), +1\n",
" if only zeros where the event function is increasing, and -1 if\n",
" only zeros where the event function is decreasing.\n",
"\n",
" fsolve_args is a dictionary of options for fsolve\n",
" kwargs are any additional options you want to send to odeint.\n",
" '''\n",
"\n",
"def ForceForwardToNextEvent(initialstate, eventType):\n",
" \n",
" # take a small step forward (0.1 time units) so that the integration is sure to move forward from the current state\n",
" shorttimespan = np.linspace(0.0, 0.1, 10)\n",
" computeOneStep = integrate.odeint(nonlinearDerivativesFunction, initialstate, shorttimespan)\n",
" \n",
" # this is the new initial state (last vector from short propagation above)\n",
" initialstate = computeOneStep[9]\n",
" \n",
" # step to the next event (given maximum time constraint)\n",
" t, statesOverTime, EventTime, EventState, EventIndex = odelay(nonlinearDerivativesFunction, initialstate, timespan, events=[eventType, stop_maxTime])\n",
" \n",
" EventTime = EventTime + 0.1;\n",
" \n",
" return EventTime, EventState\n",
"\n",
"def ForceForwardNEvents(inputstate, eventType, nEvents):\n",
" \n",
" # propagate forward the specified nEvents\n",
" for x in xrange(0, nEvents):\n",
" eTime, eState = ForceForwardToNextEvent(inputstate, eventType)\n",
" inputstate = eState[0]\n",
" # seems like you can't look ahead more than 2-3 plane crossings without doing deltaV's\n",
" \n",
" return eTime, eState\n",
" \n",
"\n",
"# step to abs(y) < 1e-11 (per Howell)\n",
"\n",
"# adjust so xdot, zdot are zero (within 1e-8) when y is zero\n",
"# can adjust: initial x, z, or ydot\n",
"\n",
"def FuncToSolve(initialguesses, inputstate, holdXorZ, numEventsToLookAhead):\n",
" ''' initialguesses will contain [x and ydot] or [z and ydot] or just [ydot], depending on value of holdXorZ. \n",
" inputstate will contain the full position and velocity state.\n",
" the values inside initialguesses will be adjusted to get perpendicular XZ plane crossings after numEventsToLookAhead.'''\n",
" \n",
" # set the initialguesses into the appropriate components of the inputstate\n",
" if (holdXorZ == 'X'):\n",
" inputstate[2] = initialguesses[0] # Z\n",
" inputstate[4] = initialguesses[1] # Ydot\n",
" elif (holdXorZ == 'Z'):\n",
" inputstate[0] = initialguesses[0] # X\n",
" inputstate[4] = initialguesses[1] # Ydot\n",
" elif (holdXorZ == 'both'):\n",
" # limit to 1% updates\n",
" #if ((abs(initialguesses[0]) - abs(inputstate[4]))/abs(inputstate[4]) < 0.01):\n",
" inputstate[4] = initialguesses[0] # Ydot\n",
" #inputstate[5] = initialguesses[1]\n",
" \n",
" # propagate forward the specified numEventsToLookAhead\n",
" eTime, eState = ForceForwardNEvents(inputstate, stop_yEquals0, numEventsToLookAhead)\n",
" \n",
" result = [eState[0,3], eState[0,5]] # Vx, Vz = 0 gives a perpendicular XZ plane crossing\n",
" \n",
" return result # fsolve will look for a solution that makes the results = 0\n",
"\n",
"\n",
"def Haloize(inputstate, holdXorZ, numEventsToLookAhead):\n",
" \n",
" # the 'initialguesses' vector that gets passed to the FuncToSolve has to be the parameters \n",
" # that fsolve is allowed to vary in order to get the results to be zero\n",
" if (holdXorZ == 'X'):\n",
" initialguesses = [inputstate[2], inputstate[4]] # Z and Ydot\n",
" elif (holdXorZ == 'Z'):\n",
" initialguesses = [inputstate[0], inputstate[4]] # X and Ydot\n",
" elif (holdXorZ == 'both'):\n",
" #initialguesses = [inputstate[4], inputstate[5]] # Ydot and Zdot\n",
" initialguesses = inputstate[4] # Ydot only\n",
" \n",
" # refine initial guesses\n",
" ''' from fsolve documentation at http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fsolve.html:\n",
" scipy.optimize.fsolve(func, x0, args=(), ...)\n",
" func: A function that takes at least one (possibly vector) argument.\n",
" x0: The starting estimate for the roots of func(x) = 0.\n",
" args: Any extra arguments to func. '''\n",
" solution = fsolve(FuncToSolve, initialguesses, (inputstate, holdXorZ, numEventsToLookAhead))\n",
"\n",
" # print the update applied to X/Z and Vy\n",
" print 'solution = ', solution, 'solution-initialguesses = ', solution-initialguesses \n",
"\n",
" # TODO: make this work even if the input state isn't at a plane crossing... I guess you would need to let the solver adjust the input Vx and Vz (in addition to Vy) if you're not at the XZ plane crossing.......\n",
"\n",
" # copy solution from fsolve into haloState\n",
" haloState = inputstate\n",
" \n",
" if (holdXorZ == 'X'):\n",
" haloState[2] = solution[0]\n",
" haloState[4] = solution[1]\n",
" elif (holdXorZ == 'Z'):\n",
" haloState[0] = solution[0]\n",
" haloState[4] = solution[1]\n",
" elif (holdXorZ == 'both'):\n",
" haloState[4] = solution[0]\n",
" #haloState[5] = solution[1]\n",
" \n",
" return haloState\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 114
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Set input state and call Haloize"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"# test case\n",
"#initialguesses_x_ydot = [0.723268, 0.198019] # x, ydot\n",
"#initz = 0.04000\n",
"\n",
"#initialguesses_x_ydot = [0.723268, 0.198019] # x, ydot\n",
"#initz = 0.050\n",
"\n",
"#inputstate = [initialguesses_x_ydot[0], 0.0, initz, 0.0, initialguesses_x_ydot[1], 0.0]\n",
"\n",
"#inputstate = [0.72311425511821148, 0.0, 0.05, 0.0, 0.20832957653736492, 0.0] # solution from Z = 0.05\n",
"mu = 0.04\n",
"timespan = np.linspace(0.0, 3.0, 200)\n",
"inputstate = [0.723, 0.0, 0.05, 0.0, 0.208, 0.0]\n",
"\n",
"print 'inputstate = ', inputstate\n",
"\n",
"\n",
"# inputs to halo-ization\n",
"numEventsToLookAhead = 1\n",
"holdXorZ = 'both' # 'X' 'Z' 'both'\n",
"\n",
"\n",
"# result from function using initial guesses (what are the Vx and Vz components at the \n",
"# specified XZ plane crossing when just using the input state? fsolve will adjust inputs so that this result gets close to zero)\n",
"eTime, initialGuessResult = ForceForwardNEvents(inputstate, stop_yEquals0, numEventsToLookAhead) \n",
"print 'eTime = ', eTime, ', initialGuessResult = ', initialGuessResult\n",
"\n",
"# we have the option to hold the X or Z component constant; the other component (X or Z)\n",
"# and the Ydot will be adjusted to get a halo orbit (perpendicular XZ plane crossings)\n",
"haloState = Haloize(inputstate, holdXorZ, numEventsToLookAhead) \n",
"print 'haloState = ', haloState\n",
"\n",
"# result from function using solution (Vx and Vz should be close to zero)\n",
"eTime, SolutionResult = ForceForwardNEvents(haloState, stop_yEquals0, numEventsToLookAhead) \n",
"print 'eTime = ', eTime, ', SolutionResult = ', SolutionResult\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"inputstate = [0.721, 0.0, 0.05, 0.0, 0.208, 0.0]\n",
"eTime = "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" [ 2.31833947] , initialGuessResult = [[ -2.01468801e-01 3.53016227e-16 3.72434581e-03 -1.07554566e+00\n",
" -2.73483587e+00 -1.98084986e-01]]\n",
"solution = "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" [-0.00090222] solution-initialguesses = [-0.20890222]\n",
"haloState = [0.721, 0.0, 0.05, 0.0, -0.00090221604697969201, 0.0]\n",
"eTime = [ 0.09955968] , SolutionResult = [[ 0.71965036 0. 0.04878872 -0.02723166 0.00180579 -0.02421457]]\n"
]
}
],
"prompt_number": 115
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"# determine orbit period\n",
"eTime, eState = ForceForwardToNextEvent(haloState, stop_yEquals0)\n",
"\n",
"# propagate for X orbits (eTime[0]*4.0 = 2 orbits)\n",
"timespan = np.linspace(0.0, eTime[0]*2.0, 200)\n",
"\n",
"statesOverTime = integrate.odeint(nonlinearDerivativesFunction, haloState, timespan)\n",
"\n",
"print 'eTime[0] = ', eTime[0]\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"eTime[0] = 0.0995596773342\n"
]
}
],
"prompt_number": 116
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"x1, y1, z1, xdot1, ydot1, zdot1 = statesOverTime.T\n",
"\n",
"center = FindOrbitCenter(x1, y1, z1);\n",
"\n",
"data = {'sat1': {'x':x1, 'y':y1, 'z':z1}}\n",
"points = {'center': center}\n",
"PlotGrid('Satellite 1 in RLP Frame', 'X', 'Y', 'Z', data, points, 'equal')\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"center = [0.7155290589683363, 0.0, 0.04522479993307775]\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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Wyzo4OKBly5a4evVqzTtAouFtXyIiIg303nvvoWnTpvjmm29q1b424Q0A8vPzYWBggKZN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\nEYY/IiIiIhVh+CMiIiJSkf8Hb3q1jFll3q8AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10808e290>"
]
}
],
"prompt_number": 117
}
],
"metadata": {}
}
]
} | mit |
adityaka/misc_scripts | python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/05_04/Begin/Tick Marks.ipynb | 2 | 1692 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1>Tick Marks, Labels, and Grids</h1>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('ggplot')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"number_of_data_points = 10\n",
"\n",
"my_figure = plt.figure()\n",
"subplot_1 = my_figure.add_subplot(1, 1, 1)\n",
"my_data_set = np.random.rand(number_of_data_points).cumsum()\n",
"subplot_1.plot(np.random.rand(number_of_data_points).cumsum())\n",
"\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Line styles for grid lines\n",
"<list>\n",
" <li>- solid line</li>\n",
" <li>-- dashed line</li>\n",
" <li>-. dash dot line</li>\n",
" <li>: dotted</li>\n",
"</list>\n",
"\n",
"More information about lines available at: http://matplotlib.org/api/lines_api.html"
]
}
],
"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
}
| bsd-3-clause |
jpallas/beakerx | doc/python/ChartingAPI.ipynb | 1 | 20656 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python API to BeakerX Interactive Plotting\n",
"\n",
"You can access Beaker's native interactive plotting library from Python.\n",
"\n",
"## Plot with simple properties\n",
"\n",
"Python plots has syntax very similar to Groovy plots. Property names are the same."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from beakerx import *\n",
"import pandas as pd\n",
"\n",
"tableRows = pd.read_csv('../resources/data/interest-rates.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Plot(title=\"Title\",\n",
" xLabel=\"Horizontal\",\n",
" yLabel=\"Vertical\",\n",
" initWidth=500,\n",
" initHeight=200)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot items\n",
"\n",
"### Lines, Bars, Points and Right yAxis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x = [1, 4, 6, 8, 10]\n",
"y = [3, 6, 4, 5, 9]\n",
"\n",
"pp = Plot(title='Bars, Lines, Points and 2nd yAxis', \n",
" xLabel=\"xLabel\", \n",
" yLabel=\"yLabel\", \n",
" legendLayout=LegendLayout.HORIZONTAL,\n",
" legendPosition=LegendPosition(position=LegendPosition.Position.RIGHT),\n",
" omitCheckboxes=True)\n",
"\n",
"pp.add(YAxis(label=\"Right yAxis\"))\n",
"pp.add(Bars(displayName=\"Bar\", \n",
" x=[1,3,5,7,10], \n",
" y=[100, 120,90,100,80], \n",
" width=1))\n",
"pp.add(Line(displayName=\"Line\", \n",
" x=x, \n",
" y=y, \n",
" width=6, \n",
" yAxis=\"Right yAxis\"))\n",
"pp.add(Points(x=x, \n",
" y=y, \n",
" size=10, \n",
" shape=ShapeType.DIAMOND,\n",
" yAxis=\"Right yAxis\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot = Plot(title= \"Setting line properties\")\n",
"ys = [0, 1, 6, 5, 2, 8]\n",
"ys2 = [0, 2, 7, 6, 3, 8]\n",
"plot.add(Line(y= ys, width= 10, color= Color.red))\n",
"plot.add(Line(y= ys, width= 3, color= Color.yellow))\n",
"plot.add(Line(y= ys, width= 4, color= Color(33, 87, 141), style= StrokeType.DASH, interpolation= 0))\n",
"plot.add(Line(y= ys2, width= 2, color= Color(212, 57, 59), style= StrokeType.DOT))\n",
"plot.add(Line(y= [5, 0], x= [0, 5], style= StrokeType.LONGDASH))\n",
"plot.add(Line(y= [4, 0], x= [0, 5], style= StrokeType.DASHDOT))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot = Plot(title= \"Changing Point Size, Color, Shape\")\n",
"y1 = [6, 7, 12, 11, 8, 14]\n",
"y2 = [4, 5, 10, 9, 6, 12]\n",
"y3 = [2, 3, 8, 7, 4, 10]\n",
"y4 = [0, 1, 6, 5, 2, 8]\n",
"plot.add(Points(y= y1))\n",
"plot.add(Points(y= y2, shape= ShapeType.CIRCLE))\n",
"plot.add(Points(y= y3, size= 8.0, shape= ShapeType.DIAMOND))\n",
"plot.add(Points(y= y4, size= 12.0, color= Color.orange, outlineColor= Color.red))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot = Plot(title= \"Changing point properties with list\")\n",
"cs = [Color.black, Color.red, Color.orange, Color.green, Color.blue, Color.pink]\n",
"ss = [6.0, 9.0, 12.0, 15.0, 18.0, 21.0]\n",
"fs = [False, False, False, True, False, False]\n",
"plot.add(Points(y= [5] * 6, size= 12.0, color= cs))\n",
"plot.add(Points(y= [4] * 6, size= 12.0, color= Color.gray, outlineColor= cs))\n",
"plot.add(Points(y= [3] * 6, size= ss, color= Color.red))\n",
"plot.add(Points(y= [2] * 6, size= 12.0, color= Color.black, fill= fs, outlineColor= Color.black))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot = Plot()\n",
"y1 = [1.5, 1, 6, 5, 2, 8]\n",
"cs = [Color.black, Color.red, Color.gray, Color.green, Color.blue, Color.pink]\n",
"ss = [StrokeType.SOLID, StrokeType.SOLID, StrokeType.DASH, StrokeType.DOT, StrokeType.DASHDOT, StrokeType.LONGDASH]\n",
"plot.add(Stems(y= y1, color= cs, style= ss, width= 5))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot = Plot(title= \"Setting the base of Stems\")\n",
"ys = [3, 5, 2, 3, 7]\n",
"y2s = [2.5, -1.0, 3.5, 2.0, 3.0]\n",
"plot.add(Stems(y= ys, width= 2, base= y2s))\n",
"plot.add(Points(y= ys))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot = Plot(title= \"Bars\")\n",
"cs = [Color(255, 0, 0, 128)] * 5 # transparent bars\n",
"cs[3] = Color.red # set color of a single bar, solid colored bar\n",
"plot.add(Bars(x= [1, 2, 3, 4, 5], y= [3, 5, 2, 3, 7], color= cs, outlineColor= Color.black, width= 0.3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Lines, Points with Pandas"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot = Plot(title= \"Pandas line\")\n",
"plot.add(Line(y= tableRows.y1, width= 2, color= Color(216, 154, 54)))\n",
"plot.add(Line(y= tableRows.y10, width= 2, color= Color.lightGray))\n",
"\n",
"plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot = Plot(title= \"Pandas Series\")\n",
"plot.add(Line(y= pd.Series([0, 6, 1, 5, 2, 4, 3]), width=2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot = Plot(title= \"Bars\")\n",
"cs = [Color(255, 0, 0, 128)] * 7 # transparent bars\n",
"cs[3] = Color.red # set color of a single bar, solid colored bar\n",
"plot.add(Bars(pd.Series([0, 6, 1, 5, 2, 4, 3]), color= cs, outlineColor= Color.black, width= 0.3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Areas, Stems and Crosshair"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ch = Crosshair(color=Color.black, width=2, style=StrokeType.DOT)\n",
"plot = Plot(crosshair=ch)\n",
"y1 = [4, 8, 16, 20, 32]\n",
"base = [2, 4, 8, 10, 16]\n",
"cs = [Color.black, Color.orange, Color.gray, Color.yellow, Color.pink]\n",
"ss = [StrokeType.SOLID, \n",
" StrokeType.SOLID, \n",
" StrokeType.DASH, \n",
" StrokeType.DOT, \n",
" StrokeType.DASHDOT, \n",
" StrokeType.LONGDASH]\n",
"plot.add(Area(y=y1, base=base, color=Color(255, 0, 0, 50)))\n",
"plot.add(Stems(y=y1, base=base, color=cs, style=ss, width=5))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot = Plot()\n",
"y = [3, 5, 2, 3]\n",
"x0 = [0, 1, 2, 3]\n",
"x1 = [3, 4, 5, 8]\n",
"plot.add(Area(x= x0, y= y))\n",
"plot.add(Area(x= x1, y= y, color= Color(128, 128, 128, 50), interpolation= 0))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"p = Plot()\n",
"p.add(Line(y= [3, 6, 12, 24], displayName= \"Median\"))\n",
"p.add(Area(y= [4, 8, 16, 32], base= [2, 4, 8, 16],\n",
" color= Color(255, 0, 0, 50), displayName= \"Q1 to Q3\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ch = Crosshair(color= Color(255, 128, 5), width= 2, style= StrokeType.DOT)\n",
"pp = Plot(crosshair= ch, omitCheckboxes= True,\n",
" legendLayout= LegendLayout.HORIZONTAL, legendPosition= LegendPosition(position=LegendPosition.Position.TOP))\n",
"x = [1, 4, 6, 8, 10]\n",
"y = [3, 6, 4, 5, 9]\n",
"pp.add(Line(displayName= \"Line\", x= x, y= y, width= 3))\n",
"pp.add(Bars(displayName= \"Bar\", x= [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], y= [2, 2, 4, 4, 2, 2, 0, 2, 2, 4], width= 0.5))\n",
"pp.add(Points(x= x, y= y, size= 10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Constant Lines, Constant Bands"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"p = Plot ()\n",
"p.add(Line(y=[-1, 1]))\n",
"p.add(ConstantLine(x=0.65, style=StrokeType.DOT, color=Color.blue))\n",
"p.add(ConstantLine(y=0.1, style=StrokeType.DASHDOT, color=Color.blue))\n",
"p.add(ConstantLine(x=0.3, y=0.4, color=Color.gray, width=5, showLabel=True))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Plot().add(Line(y=[-3, 1, 3, 4, 5])).add(ConstantBand(x=[1, 2], y=[1, 3]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"p = Plot() \n",
"p.add(Line(x= [-3, 1, 2, 4, 5], y= [4, 2, 6, 1, 5]))\n",
"p.add(ConstantBand(x= ['-Infinity', 1], color= Color(128, 128, 128, 50)))\n",
"p.add(ConstantBand(x= [1, 2]))\n",
"p.add(ConstantBand(x= [4, 'Infinity']))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from decimal import Decimal\n",
"pos_inf = Decimal('Infinity')\n",
"neg_inf = Decimal('-Infinity')\n",
"print (pos_inf)\n",
"print (neg_inf)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from beakerx.plot import Text as BeakerxText\n",
"plot = Plot()\n",
"xs = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
"ys = [8.6, 6.1, 7.4, 2.5, 0.4, 0.0, 0.5, 1.7, 8.4, 1]\n",
"def label(i):\n",
" if ys[i] > ys[i+1] and ys[i] > ys[i-1]:\n",
" return \"max\"\n",
" if ys[i] < ys[i+1] and ys[i] < ys[i-1]:\n",
" return \"min\"\n",
" if ys[i] > ys[i-1]:\n",
" return \"rising\"\n",
" if ys[i] < ys[i-1]:\n",
" return \"falling\"\n",
" return \"\"\n",
"\n",
"for i in xs:\n",
" i = i - 1\n",
" if i > 0 and i < len(xs)-1:\n",
" plot.add(BeakerxText(x= xs[i], y= ys[i], text= label(i), pointerAngle= -i/3.0))\n",
"\n",
"plot.add(Line(x= xs, y= ys))\n",
"plot.add(Points(x= xs, y= ys))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot = Plot(title= \"Setting 2nd Axis bounds\")\n",
"ys = [0, 2, 4, 6, 15, 10]\n",
"ys2 = [-40, 50, 6, 4, 2, 0]\n",
"ys3 = [3, 6, 3, 6, 70, 6]\n",
"plot.add(YAxis(label=\"Spread\"))\n",
"plot.add(Line(y= ys))\n",
"plot.add(Line(y= ys2, yAxis=\"Spread\"))\n",
"plot.setXBound([-2, 10])\n",
"#plot.setYBound(1, 5)\n",
"plot.getYAxes()[0].setBound(1,5)\n",
"plot.getYAxes()[1].setBound(3,6)\n",
"\n",
"\n",
"plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot = Plot(title= \"Setting 2nd Axis bounds\")\n",
"ys = [0, 2, 4, 6, 15, 10]\n",
"ys2 = [-40, 50, 6, 4, 2, 0]\n",
"ys3 = [3, 6, 3, 6, 70, 6]\n",
"plot.add(YAxis(label=\"Spread\"))\n",
"plot.add(Line(y= ys))\n",
"plot.add(Line(y= ys2, yAxis=\"Spread\"))\n",
"plot.setXBound([-2, 10])\n",
"plot.setYBound(1, 5)\n",
"\n",
"plot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TimePlot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"\n",
"millis = current_milli_time()\n",
"\n",
"hour = round(1000 * 60 * 60)\n",
"xs = []\n",
"ys = []\n",
"for i in range(11):\n",
" xs.append(millis + hour * i)\n",
" ys.append(i)\n",
"\n",
"plot = TimePlot(timeZone=\"America/New_York\")\n",
"# list of milliseconds\n",
"plot.add(Points(x=xs, y=ys, size=10, displayName=\"milliseconds\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot = TimePlot()\n",
"plot.add(Line(x=tableRows['time'], y=tableRows['m3']))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### numpy datatime64"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y = pd.Series([7.5, 7.9, 7, 8.7, 8, 8.5])\n",
"dates = [np.datetime64('2015-02-01'), \n",
" np.datetime64('2015-02-02'), \n",
" np.datetime64('2015-02-03'),\n",
" np.datetime64('2015-02-04'),\n",
" np.datetime64('2015-02-05'),\n",
" np.datetime64('2015-02-06')]\n",
"plot = TimePlot()\n",
"\n",
"plot.add(Line(x=dates, y=y))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Timestamp"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y = pd.Series([7.5, 7.9, 7, 8.7, 8, 8.5])\n",
"dates = pd.Series(['2015-02-01',\n",
" '2015-02-02',\n",
" '2015-02-03',\n",
" '2015-02-04',\n",
" '2015-02-05',\n",
" '2015-02-06']\n",
" , dtype='datetime64[ns]')\n",
"plot = TimePlot()\n",
"plot.add(Line(x=dates, y=y))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Datetime and date"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import datetime\n",
"\n",
"y = pd.Series([7.5, 7.9, 7, 8.7, 8, 8.5])\n",
"dates = [datetime.date(2015, 2, 1),\n",
" datetime.date(2015, 2, 2),\n",
" datetime.date(2015, 2, 3),\n",
" datetime.date(2015, 2, 4),\n",
" datetime.date(2015, 2, 5),\n",
" datetime.date(2015, 2, 6)]\n",
"plot = TimePlot()\n",
"plot.add(Line(x=dates, y=y))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import datetime\n",
"\n",
"y = pd.Series([7.5, 7.9, 7, 8.7, 8, 8.5])\n",
"dates = [datetime.datetime(2015, 2, 1),\n",
" datetime.datetime(2015, 2, 2),\n",
" datetime.datetime(2015, 2, 3),\n",
" datetime.datetime(2015, 2, 4),\n",
" datetime.datetime(2015, 2, 5),\n",
" datetime.datetime(2015, 2, 6)]\n",
"plot = TimePlot()\n",
"plot.add(Line(x=dates, y=y))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NanoPlot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"millis = current_milli_time()\n",
"nanos = millis * 1000 * 1000\n",
"xs = []\n",
"ys = []\n",
"for i in range(11):\n",
" xs.append(nanos + 7 * i)\n",
" ys.append(i)\n",
"\n",
"nanoplot = NanoPlot()\n",
"nanoplot.add(Points(x=xs, y=ys))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stacking"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y1 = [1,5,3,2,3]\n",
"y2 = [7,2,4,1,3]\n",
"p = Plot(title='Plot with XYStacker', initHeight=200)\n",
"a1 = Area(y=y1, displayName='y1')\n",
"a2 = Area(y=y2, displayName='y2')\n",
"stacker = XYStacker()\n",
"p.add(stacker.stack([a1, a2]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## SimpleTime Plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"SimpleTimePlot(tableRows, [\"y1\", \"y10\"], # column names\n",
" timeColumn=\"time\", # time is default value for a timeColumn\n",
" yLabel=\"Price\", \n",
" displayNames=[\"1 Year\", \"10 Year\"],\n",
" colors = [[216, 154, 54], Color.lightGray],\n",
" displayLines=True, # no lines (true by default)\n",
" displayPoints=False) # show points (false by default))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#time column base on DataFrame index \n",
"tableRows.index = tableRows['time']\n",
"\n",
"SimpleTimePlot(tableRows, ['m3'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"rng = pd.date_range('1/1/2011', periods=72, freq='H')\n",
"ts = pd.Series(np.random.randn(len(rng)), index=rng)\n",
"df = pd.DataFrame(ts, columns=['y'])\n",
"SimpleTimePlot(df, ['y'])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Second Y Axis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The plot can have two y-axes. Just add a `YAxis` to the plot object, and specify its label.\n",
"Then for data that should be scaled according to this second axis,\n",
"specify the property `yAxis` with a value that coincides with the label given.\n",
"You can use `upperMargin` and `lowerMargin` to restrict the range of the data leaving more white, perhaps for the data on the other axis."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"p = TimePlot(xLabel= \"Time\", yLabel= \"Interest Rates\")\n",
"p.add(YAxis(label= \"Spread\", upperMargin= 4))\n",
"p.add(Area(x= tableRows.time, y= tableRows.spread, displayName= \"Spread\",\n",
" yAxis= \"Spread\", color= Color(180, 50, 50, 128)))\n",
"p.add(Line(x= tableRows.time, y= tableRows.m3, displayName= \"3 Month\"))\n",
"p.add(Line(x= tableRows.time, y= tableRows.y10, displayName= \"10 Year\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Combined Plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"points = 100\n",
"logBase = 10\n",
"expys = []\n",
"xs = []\n",
"for i in range(0, points):\n",
" xs.append(i / 15.0)\n",
" expys.append(math.exp(xs[i]))\n",
"\n",
"\n",
"cplot = CombinedPlot(xLabel= \"Linear\")\n",
"logYPlot = Plot(title= \"Linear x, Log y\", yLabel= \"Log\", logY= True, yLogBase= logBase)\n",
"logYPlot.add(Line(x= xs, y= expys, displayName= \"f(x) = exp(x)\"))\n",
"logYPlot.add(Line(x= xs, y= xs, displayName= \"g(x) = x\"))\n",
"cplot.add(logYPlot, 4)\n",
"\n",
"linearYPlot = Plot(title= \"Linear x, Linear y\", yLabel= \"Linear\")\n",
"linearYPlot.add(Line(x= xs, y= expys, displayName= \"f(x) = exp(x)\"))\n",
"linearYPlot.add(Line(x= xs, y= xs, displayName= \"g(x) = x\"))\n",
"cplot.add(linearYPlot,4)\n",
"\n",
"cplot\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot = Plot(title= \"Log x, Log y\", xLabel= \"Log\", yLabel= \"Log\",\n",
" logX= True, xLogBase= logBase, logY= True, yLogBase= logBase)\n",
"\n",
"plot.add(Line(x= xs, y= expys, displayName= \"f(x) = exp(x)\"))\n",
"plot.add(Line(x= xs, y= xs, displayName= \"f(x) = x\"))\n",
"\n",
"plot"
]
}
],
"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
}
| apache-2.0 |
LimeeZ/phys292-2015-work | assignments/assignment11/OptimizationEx01.ipynb | 1 | 33649 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Optimization Exercise 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"nbgrader": {}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import scipy.optimize as opt"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Hat potential"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"The following potential is often used in Physics and other fields to describe symmetry breaking and is often known as the \"hat potential\":\n",
"\n",
"$$ V(x) = -a x^2 + b x^4 $$\n",
"\n",
"Write a function `hat(x,a,b)` that returns the value of this function:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [],
"source": [
"def hat(x,a,b):\n",
" v = -a*x**2 + b*x**4\n",
" return v\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "7204bd97cd003430289f171b6ba70d63",
"grade": true,
"grade_id": "optimizationex01a",
"points": 2
}
},
"outputs": [],
"source": [
"assert hat(0.0, 1.0, 1.0)==0.0\n",
"assert hat(0.0, 1.0, 1.0)==0.0\n",
"assert hat(1.0, 10.0, 1.0)==-9.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Plot this function over the range $x\\in\\left[-3,3\\right]$ with $b=1.0$ and $a=5.0$:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true,
"nbgrader": {}
},
"outputs": [],
"source": [
"a = 5.0\n",
"b = 1.0"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f20ec0a04e0>]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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J48oq2IcNg6VLfd9kEZGWuu8+7wHo2DF2JY0rq2Bv1QrOO0+DqCKSn1LuhoGM7+7YmFdf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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f20ec13c240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x1 = np.arange(-3,3,0.1)\n",
"plt.plot(x1, hat(x1, 5,1))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "bd49ce2f030e3366ee640213f26fdaa6",
"grade": true,
"grade_id": "optimizationex01b",
"points": 2
}
},
"outputs": [],
"source": [
"assert True # leave this to grade the plot"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Write code that finds the two local minima of this function for $b=1.0$ and $a=5.0$.\n",
"\n",
"* Use `scipy.optimize.minimize` to find the minima. You will have to think carefully about how to get this function to find both minima.\n",
"* Print the x values of the minima.\n",
"* Plot the function as a blue line.\n",
"* On the same axes, show the minima as red circles.\n",
"* Customize your visualization to make it beatiful and effective."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-1.5811388245\n",
"1.58113880951\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f20e58c3ef0>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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jjeDUU8OZw+OPw6pVYYuTr3wFHn20Yznak/V/95dfhr/+Fb7//fLmaUlWjmWh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\nE/8fPej+Xv3q0DYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f20e5871438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def hat(x):\n",
" b = 1\n",
" a = 5\n",
" v = -a*x**2 + b*x**4\n",
" return v\n",
"\n",
"xmin1 = opt.minimize(hat,-1.5)['x'][0]\n",
"xmin2 = opt.minimize(hat,1.5)['x'][0]\n",
"xmins = np.array([xmin1,xmin2])\n",
"\n",
"print(xmin1)\n",
"print(xmin2)\n",
"\n",
"x1 = np.arange(-3,3,0.1)\n",
"plt.plot(x1, hat(x1))\n",
"plt.scatter(xmins,hat(xmins), c = 'r',marker = 'o')\n",
"plt.grid(True)\n",
"plt.title('Hat Potential')\n",
"plt.xlabel('Range')\n",
"plt.ylabel('Potential')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "235361d4c954cf9fd6a8ecef309b3a44",
"grade": true,
"grade_id": "optimizationex01c",
"points": 4
}
},
"outputs": [],
"source": [
"assert True # leave this for grading the plot"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"To check your numerical results, find the locations of the minima analytically. Show and describe the steps in your derivation using LaTeX equations. Evaluate the location of the minima using the above parameters."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false,
"nbgrader": {
"checksum": "d7d37614ffa0d469a42ff3fd121335f2",
"grade": true,
"grade_id": "optimizationex01d",
"points": 2,
"solution": true
}
},
"source": [
"$$\n",
"V(x) = -a x^2 + b x^4 \\\\\n",
"V'(x) = -2ax + 4bx^3 \\\\\n",
"V'(x) = x (-2a + 4bx^2)\\\\\n",
"4x^2 - 10 = 2(2x^2 - 5) \\\\ \n",
"2(2x^2 - 5) = - \\sqrt{10}-2x , \\sqrt{10}+2 x \\\\\n",
"$$\n",
"The minimums or maximums are at $$ x = \\frac{\\sqrt{10}}{2}, x = \\frac{-\\sqrt{10}}{2}, x = 0\\\\$$\n",
"Checking to see if they are a minimum or a maximum:\\\\\n",
"$$\n",
"V''(x) = -10 + 12x^2\\\\\n",
"V''(0) = -10 + 12(0)^2 = -10\\\\\n",
"$$\n",
"x = 0 is a maxima.\n",
"$$\n",
"V''(\\frac{\\sqrt{10}}{2}) = -10 + 12(\\frac{\\sqrt{10}}{2})^2 = 350 \\\\\n",
"$$\n",
"The x above is a minima.\n",
"$$\n",
"V''(\\frac{-\\sqrt{10}}{2}) = -10 + 12(\\frac{-\\sqrt{10}}{2})^2 = 350\\\\\n",
"$$\n",
"The x above is a minima."
]
},
{
"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": "3.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
code-shoily/data-playground | পাইথন ও ডাটাঃ নামপাই - ১.ipynb | 1 | 29024 | {
"metadata": {
"name": "\u09aa\u09be\u0987\u09a5\u09a8 \u0993 \u09a1\u09be\u099f\u09be\u0983 \u09a8\u09be\u09ae\u09aa\u09be\u0987 - \u09e7"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"\u09a8\u09be\u09ae\u09aa\u09be\u0987 \u09aa\u09b0\u09bf\u099a\u09bf\u09a4\u09bf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u09b8\u09ac\u09be\u0987\u0995\u09c7 \u0988\u09a6 \u09ae\u09cb\u09ac\u09be\u09b0\u0995 \u099c\u09be\u09a8\u09bf\u09df\u09c7 \u09b6\u09c1\u09b0\u09c1 \u0995\u09b0\u09a4\u09c7 \u09af\u09be\u099a\u09cd\u099b\u09bf \u09a8\u09be\u09ae\u09aa\u09be\u0987 \u09a8\u09bf\u09df\u09c7 \u0986\u09ae\u09be\u09b0 \u09aa\u09cd\u09b0\u09a5\u09ae \u09aa\u09cb\u09b8\u09cd\u099f\u0964 \u0985\u09a8\u09cd\u09af\u09be\u09a8\u09cd\u09af \u09aa\u09cb\u09b8\u09cd\u099f\u09c7\u09b0 \u09ae\u09a4 \u098f\u099f\u09bf \u099f\u09be\u09ae\u09cd\u09ac\u09b2\u09be\u09b0 \u098f \u09b9\u09cb\u09b8\u09cd\u099f\u09c7\u09a1 \u09a8\u09be \u09b9\u09df\u09c7 \u09ac\u09b0\u0982 ipython \u09a8\u09cb\u099f\u09ac\u09c1\u0995 \u0986\u0995\u09be\u09b0\u09c7 \u09a5\u09be\u0995\u09ac\u09c7 \u09af\u09c7\u09a8 \u0986\u09aa\u09a8\u09be\u09b0\u09be \u098f\u099f\u09bf \u09a1\u09be\u0989\u09a8\u09b2\u09cb\u09a1 \u0995\u09b0\u09c7 \u098f\u09b0 \u0995\u09cb\u09a1 \u09a8\u09bf\u09df\u09c7 \u0996\u09c7\u09b2\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \u0986\u09ae\u09bf \u0986\u0997\u09be\u09ae\u09c0 \u0995\u09bf\u099b\u09c1 \u09a6\u09bf\u09a8 \u09aa\u09be\u0987\u09a5\u09a8\u09c7\u09b0 \u09a1\u09be\u099f\u09be \u09a8\u09bf\u09df\u09c7 \u09ae\u09be\u09b8\u09cd\u09a4\u09be\u09a8\u09bf \u0995\u09b0\u09be\u09b0 \u0995\u09cd\u09b7\u09ae\u09a4\u09be \u09ac\u09bf\u09b7\u09df\u0995 \u0986\u09b2\u09cb\u099a\u09a8\u09be \u0995\u09b0\u09ac \u098f\u09ac\u0982 \u09a8\u09bf\u099c\u09c7\u0993 \u0995\u09bf\u099b\u09c1 \u0995\u09b8\u09b0\u09a4 \u09b6\u09bf\u0996\u09c7 \u09a8\u09bf\u09ac\u0964 \u0986\u09b0 \u09b8\u09be\u09a5\u09c7 \u09b8\u09be\u09a5\u09c7 \u09af\u09a5\u09be \u09b8\u09ae\u09cd\u09ad\u09ac \u09b6\u09c7\u09df\u09be\u09b0 \u0995\u09b0\u09ac \u0986\u09aa\u09a8\u09be\u09a6\u09c7\u09b0 \u09b8\u09be\u09a5\u09c7 \u0986\u09ae\u09be\u09a6\u09c7\u09b0 \u0985\u09ad\u09bf\u099c\u09cd\u099e\u09a4\u09be\u0964\n",
"\n",
"\u0986\u09aa\u09a8\u09bf \u0995\u09bf\u09ad\u09be\u09ac\u09c7 \u098f\u0987 \u09a8\u09cb\u099f\u09ac\u09c1\u0995 \u09b0\u09be\u09a8 \u0995\u09b0\u09ac\u09c7\u09a8 \u09b8\u09c7\u099f\u09bf [\u098f\u0996\u09be\u09a8\u09c7](http://nbviewer.ipython.org/gist/code-shoily/8084957) \u09aa\u09be\u09ac\u09c7\u09a8\u0964 \u0986\u09b0 \u09a8\u09be\u09ae\u09aa\u09be\u0987 \u0987\u09a8\u09cd\u09b8\u099f\u09b2\u09c7\u09b6\u09a8 \u09ae\u09c7\u09a5\u09a1 [\u098f\u0996\u09be\u09a8 \u09a5\u09c7\u0995\u09c7](http://docs.scipy.org/doc/numpy/user/install.html) \u09aa\u09be\u09ac\u09c7\u09a8\u0964 \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf (\u0986\u09b2\u09cd\u09b2\u09be\u09b9 \u09a8\u09be \u0995\u09b0\u09c1\u09a8) \u0989\u0987\u09a8\u09cd\u09a1\u09cb\u099c \u0987\u0989\u099c\u09be\u09b0 \u09b9\u09df\u09c7 \u09a5\u09be\u0995\u09c7\u09a8 \u09a4\u09be\u09b9\u09b2\u09c7 Python (x,y) \u09a1\u09be\u0989\u09a8\u09b2\u09cb\u09a1 \u0995\u09b0\u09c7 \u09a8\u09bf\u09a8, \u0986\u09ae\u09bf \u09b6\u09c1\u09a8\u09c7\u099b\u09bf \u099c\u09bf\u09a8\u09bf\u09b8\u099f\u09be \u09ad\u09be\u09b2\u0964 \u0986\u09aa\u09a8\u09bf \u0989\u09aa\u09b0\u09c7\u09b0 \u09b2\u09bf\u0999\u09cd\u0995 \u09a5\u09c7\u0995\u09c7 \u098f\u0987 \u09b0\u09bf\u09aa\u09cb\u099c\u09bf\u099f\u09b0\u09bf \u09a8\u09be\u09ae\u09bf\u09df\u09c7 \u09af\u09a5\u09be\u09b0\u09c0\u09a4\u09bf \u09ab\u09cb\u09b2\u09cd\u09a1\u09be\u09b0\u09c7 \u0997\u09bf\u09df\u09c7 ipython notebook \u099f\u09be\u09b0\u09cd\u09ae\u09bf\u09a8\u09be\u09b2\u09c7 \u09b2\u09bf\u0996\u09b2\u09c7\u0987 \u099a\u09b2\u09ac\u09c7\u0964 \n",
"\n",
"\u098f\u0996\u09a8 \u09a4\u09be\u09b9\u09b2\u09c7 \u09b6\u09c1\u09b0\u09c1 \u0995\u09b0\u09be \u09af\u09be\u0995\u0964"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u09a1\u09be\u099f\u09be \u09a8\u09bf\u09df\u09c7 \u0996\u09c7\u09b2\u09be \u0995\u09b0\u09a4\u09c7 \u0997\u09c7\u09b2\u09c7 \u0995\u09cd\u09b7\u09c1\u09a6\u09cd\u09b0\u09a4\u09ae \u09af\u09c7 \u09b2\u09be\u0987\u09ac\u09cd\u09b0\u09c7\u09b0\u09bf (\u09aa\u09be\u0987\u09a5\u09a8\u09c7\u09b0 \u09a8\u09bf\u099c\u09b8\u09cd\u09ac \u09a1\u09be\u099f\u09be \u09b8\u09cd\u099f\u09cd\u09b0\u09be\u0995\u099a\u09be\u09b0 \u09ac\u09cd\u09af\u09a4\u09bf\u09b0\u09c7\u0995\u09c7) \u09ae\u09be\u09a5\u09be\u09df \u0986\u09b8\u09c7 \u09a4\u09be \u09b9\u09b2 \u09a8\u09be\u09ae\u09aa\u09be\u0987 (Numpy)\u0964 \u098f\u099f\u09bf \u0986\u09aa\u09a8\u09be\u0995\u09c7 \u0995\u09cd\u09b7\u09ae\u09a4\u09be\u09ac\u09be\u09a8 \u0995\u09b0\u09c7 \u09ac\u09b9\u09c1\u09ae\u09be\u09a4\u09cd\u09b0\u09bf\u0995 \u0985\u09cd\u09af\u09be\u09b0\u09c7 \u0993 \u09a4\u09be\u09a6\u09c7\u09b0 \u0989\u09aa\u09b0 \u09ac\u09bf\u09ad\u09bf\u09a8\u09cd\u09a8 \u0985\u09aa\u09be\u09b0\u09c7\u09b6\u09be\u09a8 (\u09aa\u09cd\u09b0\u09a4\u09bf \u09b8\u09a6\u09b8\u09cd\u09af\u09c7\u09b0 \u0989\u09aa\u09b0 \u09ab\u09be\u0982\u09b6\u09a8, \u09aa\u09c1\u09b0\u09cb \u0985\u09cd\u09af\u09be\u09b0\u09c7 \u098f\u09b0 \u0989\u09aa\u09b0 \u09ab\u09be\u0982\u09b6\u09a8, \u0985\u09a5\u09ac\u09be \u09ac\u09bf\u09ad\u09bf\u09a8\u09cd\u09a8 \u09b8\u09be\u09b9\u09be\u09af\u09cd\u09af\u0995\u09be\u09b0\u09c0 \u09ab\u09be\u0982\u09b6\u09a8 \u0987\u09a4\u09cd\u09af\u09be\u09a6\u09bf), \u0997\u09c1\u09b0\u09c1\u09a4\u09cd\u09ac\u09aa\u09c2\u09b0\u09cd\u09a3 \u0997\u09be\u09a3\u09bf\u09a4\u09bf\u0995 \u09ab\u09be\u0982\u09b6\u09a8, \u09b2\u09bf\u09a8\u09bf\u09df\u09be\u09b0 \u0985\u09cd\u09af\u09be\u09b2\u099c\u09c7\u09ac\u09cd\u09b0\u09be, \u098f\u09ac\u0982 C/Fortran \u098f\u09b0 \u0995\u09cb\u09a1 \u09b6\u09c7\u09df\u09be\u09b0\u09bf\u0982\u0964 \u098f\u0995\u099f\u09bf \u0997\u09c1\u09b0\u09c1\u09a4\u09cd\u09ac\u09aa\u09c2\u09b0\u09cd\u09a3 \u09ac\u09cd\u09af\u09be\u09aa\u09be\u09b0 \u09b9\u09b2\u0993 \u09af\u09c7 \u09a8\u09be\u09ae\u09aa\u09be\u0987 \u0985\u09a4\u09cd\u09af\u09a8\u09cd\u09a4 \u09a6\u09cd\u09b0\u09c1\u09a4 \u0995\u09be\u099c \u0995\u09b0\u09c7, \u09a6\u09c7\u0996\u09be \u09af\u09be\u0995 \u0995\u09a4 \u09a6\u09cd\u09b0\u09c1\u09a4 \u09a8\u09bf\u099a\u09c7\u09b0 \u0995\u09cb\u09a1\u09c7\u0983 "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from numpy import arange\n",
"\n",
"big_ndarray = arange(1e7)\n",
"big_list = big_ndarray.tolist()\n",
"\n",
"%timeit [i**2 for i in big_list]\n",
"%timeit big_ndarray ** 2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1 loops, best of 3: 1.7 s per loop\n",
"10 loops, best of 3: 59.7 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u09a6\u09c7\u0996\u09b2\u09c7\u09a8? \u09a6\u09cd\u09b0\u09c1\u09a4\u09a4\u09b0 \u09b9\u0993\u09df\u09be\u09b0 \u09aa\u09be\u09b6\u09be\u09aa\u09b6\u09bf \u09b8\u09c1\u09a8\u09cd\u09a6\u09b0 API\u0993 \u09a6\u09bf\u09df\u09c7\u099b\u09c7 \u0986\u09aa\u09a8\u09be\u0995\u09c7 \u09a8\u09be\u09ae\u09aa\u09be\u0987\u0964 \u098f\u09ac\u09be\u09b0 \u0995\u09bf\u099b\u09c1 \u09ac\u09b0\u09cd\u09a3\u09a8\u09be \u0995\u09b0\u09be \u09af\u09be\u0995, \u0986\u09ae\u09be\u09a6\u09c7\u09b0 \u09aa\u09cd\u09b0\u09a5\u09ae \u099f\u09aa\u09bf\u0995, ndarray\u0964"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"ndarray"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ndarray \u09b9\u09b2\u0993 \u09a8\u09be\u09ae\u09aa\u09be\u0987 \u098f\u09b0 \u09b8\u09cd\u09aa\u09c7\u09b6\u09be\u09b2 \u099f\u09be\u0987\u09aa\u0964 \u098f\u099f\u09bf \u09aa\u09be\u0987\u09a5\u09a8\u09c7\u09b0 \u09b2\u09bf\u09b8\u09cd\u099f\u09c7\u09b0 \u09ae\u09a4\u0987 \u0995\u09be\u099c \u0995\u09b0\u09c7, \u0995\u09bf\u09a8\u09cd\u09a4\u09c1 \u0986\u09b0\u0993 \u0985\u09a8\u09c7\u0995 \u09ac\u09c7\u09b6\u09bf \u09b6\u0995\u09cd\u09a4\u09bf\u09b6\u09be\u09b2\u09c0\u0964 \u098f\u09a4\u09c7 \u0986\u09aa\u09a8\u09bf \u0986\u09b0\u0993 \u09aa\u09be\u099a\u09cd\u099b\u09c7\u09a8 \u099f\u09c1\u09b2\u09b8\u09c7\u099f \u0993 \u09ab\u09be\u0982\u09b6\u09a8 \u09af\u09c7\u09ae\u09a8 \u09ae\u09cd\u09af\u09be\u099f\u09cd\u09b0\u09bf\u0995\u09cd\u09b8 \u0985\u09aa\u09be\u09b0\u09c7\u09b6\u09be\u09a8, \u09b0\u200d\u09cd\u09af\u09be\u09a8\u09cd\u09a1\u09cb\u09ae \u09a8\u09be\u09ae\u09cd\u09ac\u09be\u09b0 \u099c\u09c7\u09a8\u09be\u09b0\u09c7\u09b6\u09a8, \u098f\u09ac\u0982 \u09b2\u09c1\u09aa/\u09ac\u09cd\u09b0\u09cd\u09af\u09be\u099e\u09cd\u099b\u09c7\u09b0 \u09aa\u09b0\u09bf\u09ac\u09b0\u09cd\u09a4\u09c7 \u0995\u09bf\u099b\u09c1 \u098f\u0995\u09cd\u09b8\u09aa\u09cd\u09b0\u09c7\u09b6\u09a8\u0964\u098f\u0995\u09cd\u09b8\u09aa\u09cd\u09b0\u09c7\u09b6\u09a8\u0964 \u09ac\u09b9\u09c1 \u0985\u09aa\u09c7\u09b0\u09be\u099f\u09b0 \u09ad\u09bf\u09a8\u09cd\u09a8\u09ad\u09be\u09ac\u09c7 \u0995\u09be\u099c \u0995\u09b0\u09c7 ndarray \u098f\u09b0 \u0989\u09aa\u09b0\u0964 \u0995\u09bf\u099b\u09c1 \u0989\u09a6\u09be\u09b9\u09b0\u09a3 \u09a8\u09bf\u099a\u09c7 \u09a6\u09c7\u09df\u09be \u09b9\u09b2-"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"\n",
"# \u09b6\u09c2\u09a8\u09cd\u09af\u09c7 \u09ad\u09b0\u09be \u0995\u09bf\u099b\u09c1 \u0985\u09cd\u09af\u09be\u09b0\u09c7\u0964 \u09ae\u09a8\u09c7 \u09b0\u09be\u0996\u09c1\u09a8 RC Cola \u09b8\u09c2\u09a4\u09cd\u09b0, Row \u0986\u0997\u09c7, Column \u09aa\u09b0\u09c7\n",
"z5 = np.zeros(5) # \u09ae\u09be\u09a4\u09cd\u09b0\u09be \u09e7, \u09a6\u09c8\u09b0\u09cd\u0998\u09cd\u09af \u09eb\n",
"z22 = np.zeros([2, 2]) # \u09ae\u09be\u09a4\u09cd\u09b0\u09be \u09e8, \u09b0\u09cb \u09e8, \u0995\u09b2\u09be\u09ae \u09e8\n",
"z56 = np.zeros([5,6]) # \u09ae\u09be\u09a4\u09cd\u09b0\u09be \u09e8, \u09b0\u09cb \u09eb, \u0995\u09b2\u09be\u09ae \u09ec\n",
"z567 = np.zeros([5,6,7]) # \u09ae\u09be\u09a4\u09cd\u09b0\u09be \u09e9, (\u09eb, \u09ec, \u09ed)\n",
"\n",
"# \u0995\u09cd\u09b0\u09ae\u09be\u09a8\u09c1\u09af\u09be\u09df\u09c0 \u09a8\u09be\u09ae\u09cd\u09ac\u09be\u09b0 \u09b8\u09c3\u09b7\u09cd\u099f\u09bf \n",
"r1_10 = np.arange(1, 10) # \u098f\u0995 \u09a5\u09c7\u0995\u09c7 \u09a6\u09b6\n",
"r1_10_01 = np.arange(1, 10, 0.1) # \u098f\u0995 \u09a5\u09c7\u0995\u09c7 \u09a6\u09b6, \u09e6.\u09e7 \u0995\u09b0\u09c7 \u09ac\u09cd\u09b0\u09bf\u09a6\u09cd\u09a7\u09bf\n",
"ls_1_10_2 = np.linspace(10, 50, 25) # \u09a6\u09b6 \u09a5\u09c7\u0995\u09c7 \u09aa\u099e\u09cd\u099a\u09be\u09b6 \u09aa\u09b0\u09cd\u09af\u09a8\u09cd\u09a4 \u09e8\u09eb \u09b8\u09a6\u09b8\u09cd\u09af \u09b8\u09ae\u09be\u09a8 \u09ad\u09be\u0997\u09c7 \u09ac\u09bf\u09ad\u09be\u099c\u09cd\u09af\n",
"l2a = np.array(range(10), dtype=np.int32) # \u0985\u09cd\u09af\u09be\u09b0\u09c7 \u09b8\u09c3\u09b7\u09cd\u099f\u09bf \u09b2\u09bf\u09b8\u09cd\u099f \u09a5\u09c7\u0995\u09c7 dtype \u09a8\u09be \u09a6\u09bf\u09b2\u09c7\u0993 \u099a\u09b2\u09a4\n",
"identity_matrix = np.identity(5) # \u0995\u09b0\u09cd\u09a3 \u09ac\u09b0\u09be\u09ac\u09b0 \u09e7, \u0985\u09a8\u09cd\u09af \u09b8\u09ac \u09b6\u09c2\u09a8\u09cd\u09af, \u09b6\u09c1\u09a7\u09c1\u09ae\u09be\u09a4\u09cd\u09b0 \u09b8\u09cd\u0995\u09cb\u09df\u09be\u09b0 \u09ae\u09cd\u09af\u09be\u099f\u09cd\u09b0\u09bf\u0995\u09cd\u09b8\u09c7\u09b0 \u099c\u09a8\u09cd\u09af \u09aa\u09cd\u09b0\u09af\u09cb\u099c\u09cd\u09af\n",
"random_array = np.random.randn(5, 6) # \u09ac\u09b2\u09be\u09b0 \u0995\u09bf \u09aa\u09cd\u09b0\u09df\u09cb\u099c\u09a8 \u0986\u099b\u09c7? normal distribution \u09a5\u09c7\u0995\u09c7\u0964 beta, normal, gamma, chisquare \u0987\u09a4\u09cd\u09af\u09be\u09a6\u09bf\u0993 \u09b0\u09df\u09c7\u099b\u09c7\u0964 "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u09a8\u09be\u09ae\u09aa\u09be\u0987\u09df\u09c7\u09b0 \u09ae\u09be\u09a7\u09cd\u09af\u09ae\u09c7 \u09ae\u09cd\u09af\u09be\u099f\u09cd\u09b0\u09bf\u0995\u09cd\u09b8\u09c7\u09b0 \u09ac\u09b9\u09c1 \u0985\u09aa\u09be\u09b0\u09c7\u09b6\u09be\u09a8 \u0995\u09b0\u09be \u09af\u09be\u09df, \u09a4\u09be\u09b0 \u09b8\u09be\u09a5\u09c7 \u09b8\u09be\u09a5\u09c7 \u09ac\u09c7\u09b6 \u0995\u09bf\u099b\u09c1 \u0985\u09aa\u09be\u09b0\u09c7\u09b6\u09be\u09a8 \u09b0\u09df\u09c7\u099b\u09c7 \u09af\u09be \u0985\u09cd\u09af\u09be\u09b0\u09c7\u09b0 \u09aa\u09cd\u09b0\u09a4\u09bf\u099f\u09bf \u09b8\u09a6\u09b8\u09cd\u09af\u09c7\u09b0 \u0989\u09aa\u09b0 \u0995\u09be\u099c \u0995\u09b0\u09c7 (\u0985\u09a8\u09c7\u0995\u099f\u09be \u09b9\u09be\u0987\u09df\u09be\u09b0 \u0985\u09b0\u09cd\u09a1\u09be\u09b0 \u09ab\u09be\u0982\u09b6\u09a8\u09c7\u09b0 \u09ae\u09a4)\u0964 \u0985\u09b0\u09cd\u09a5\u09be\u09ce \u0986\u09aa\u09a8\u09bf for loop/list comprehension \u09a8\u09be \u099a\u09be\u09b2\u09bf\u09df\u09c7\u0987 \u09b2\u09bf\u09b8\u09cd\u099f\u09c7\u09b0 \u09aa\u09cd\u09b0\u09a4\u09bf\u099f\u09bf \u09ae\u09c7\u09ae\u09cd\u09ac\u09be\u09b0\u09c7\u09b0 \u0989\u09aa\u09b0 \u0995\u09be\u099c \u0995\u09b0\u09be\u09a4\u09c7 \u09aa\u09be\u09b0\u09ac\u09c7\u09a8\u0964 \u098f\u0995\u0987\u09ad\u09be\u09ac\u09c7, if \u099b\u09be\u09b0\u09be\u0987 \u09b2\u09bf\u09b8\u09cd\u099f\u09c7\u09b0 \u0989\u09aa\u09b0 \u09ab\u09bf\u09b2\u09cd\u099f\u09be\u09b0 \u0995\u09b0\u09be\u09a8\u09cb \u09af\u09be\u09df, \u09ac\u09c7\u09b6 \u09b8\u09c1\u09a8\u09cd\u09a6\u09b0\u09ad\u09be\u09ac\u09c7, [] \u0985\u09ad\u09be\u09b0\u09b2\u09cb\u09a1\u09bf\u0999\u09cd\u0997\u09c7\u09b0 \u09ae\u09be\u09a7\u09cd\u09af\u09ae\u09c7\u0964 \u09ac\u09b2\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8, numpy \u0986\u09aa\u09a8\u09be\u0995\u09c7 \u09b8\u09c1\u09a8\u09cd\u09a6\u09b0 \u098f\u0995\u099f\u09be DSL \u09a6\u09c7\u09df, \u09a8\u09be\u09ae\u09cd\u09ac\u09be\u09b0 \u09a8\u09bf\u09df\u09c7 \u0996\u09c7\u09b2\u09be \u0995\u09b0\u09be\u09b0 \u099c\u09a8\u09cd\u09af\u0964"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# \u09ae\u09cd\u09af\u09be\u099f\u09cd\u09b0\u09bf\u0995\u09cd\u09b8 \n",
"matrix_ = np.matrix([[1, 2, 4], [5, 6, 7]])\n",
"matrix_multiplication = np.matrix([[1, 2, 3], [4, 5, 6]]) * np.matrix([[1, 2], [3, 4], [5, 6]])\n",
"# \u0989\u09a6\u09be\u09b0\u09b9\u09a8\u09c7\u09b0\u09b8\u09cd\u09ac\u09b0\u09c2\u09aa \u098f\u0995\u099f\u09bf \u09ad\u09cd\u09af\u09be\u09b0\u09bf\u09df\u09be\u09ac\u09b2 \n",
"lst = [1, 4, 6, -1, 5, 3]\n",
"nd_array = np.array(lst)\n",
"\n",
"# \u09ac\u09bf\u09ad\u09bf\u09a8\u09cd\u09a8 \u0993\u09aa\u09c7\u09b0\u09be\u099f\u09b0\u09c7\u09b0 \u09ab\u09be\u0982\u09b6\u09a8 ndarray \u098f\u09b0 \u0989\u09aa\u09b0-\n",
"square_ = nd_array ** 2 # \u09aa\u09cd\u09b0\u09a4\u09bf\u099f\u09bf \u09b8\u09a6\u09b8\u09cd\u09af\u09c7\u09b0 \u0989\u09aa\u09b0 ** \u098f\u09b0 \u09aa\u09cd\u09b0\u09df\u09cb\u0997 \n",
"double_ = nd_array * 2 # \u09aa\u09cd\u09b0\u09a4\u09bf\u099f\u09bf \u09b8\u09a6\u09b8\u09cd\u09af\u09c7\u09b0 \u0989\u09aa\u09b0 * \u098f\u09b0 \u09aa\u09cd\u09b0\u09df\u09cb\u0997\n",
"triple_ = nd_array + nd_array + nd_array # \u09ad\u09cd\u09af\u09be\u09b0\u09bf\u09df\u09c7\u09ac\u09b2\u09c7\u09b0 \u09aa\u09cd\u09b0\u09a4\u09bf\u099f\u09bf \u09b8\u09a6\u09b8\u09cd\u09af \u098f\u0995\u09c7 \u0985\u09aa\u09b0\u09c7\u09b0 \u09b8\u09be\u09a5\u09c7 \u09af\u09cb\u0997 \u0995\u09b0\u09be \u09b9\u09df\u09c7\u099b\u09c7\u0964 \n",
"inverse_ = 1. / nd_array # \u09e7 \u0995\u09c7 \u09aa\u09cd\u09b0\u09a4\u09bf\u099f\u09bf \u09b8\u09a6\u09b8\u09cd\u09af \u09a6\u09bf\u09df\u09c7 \u09ad\u09be\u0997 \u09a6\u09c7\u09df\u09be \u09b9\u09df\u09c7\u099b\u09c7\u0964 \n",
"\n",
"# \u0989\u09aa\u09b0\u09c7\u09b0 \u09ae\u09a4\u0987, \u0995\u09bf\u09a8\u09cd\u09a4\u09c1 \u0985\u09cd\u09af\u09be\u09b0\u09c7\u0995\u09c7 \u09aa\u09cd\u09af\u09be\u09b0\u09be\u09ae\u09c7\u099f\u09be\u09b0 \u09b9\u09bf\u09b8\u09c7\u09ac\u09c7 \u09ac\u09cd\u09af\u09ac\u09b9\u09be\u09b0 \u0995\u09b0\u09be \u09b9\u09df\u09c7\u099b\u09c7\u0964 \n",
"square_root = np.sqrt(nd_array) \n",
"sin_ = np.sin(nd_array)\n",
"floor_ = np.floor(nd_array)\n",
"\n",
"# \u09ac\u09c1\u09b2\u09bf\u09df\u09be\u09a8 \u0985\u09aa\u09c7\u09b0\u09be\u099f\u09b0 \u09ae\u09cd\u09af\u09be\u09aa\u09c7\u09b0 \u0995\u09be\u099c \u0995\u09b0\u09c7, \u098f\u0995\u09c7 \u09af\u0996\u09a8 \u0986\u09ac\u09be\u09b0 \u0985\u09a8\u09cd\u09af \u0985\u09cd\u09af\u09be\u09b0\u09c7\u09b0 \u09ad\u09bf\u09a4\u09b0 \u09a2\u09c1\u0995\u09be\u09a8 \u09b9\u09df, \u09a4\u0996\u09a8 \u0993\u099f\u09bf \n",
"# \u09ab\u09bf\u09b2\u09cd\u099f\u09be\u09b0\u09c7\u09b0 \u0995\u09be\u099c \u0995\u09b0\u09c7, if \u098f\u09b0 \u09aa\u09cd\u09b0\u09df\u09cb\u099c\u09a8 \u09b9\u09df \u09a8\u09be, \u09ac\u09b0\u0982 \u0986\u09b0\u0993 \u09a6\u09cd\u09b0\u09c1\u09a4 \u0995\u09be\u099c \u0995\u09b0\u09c7 \u098f\u0987 \u09aa\u09cd\u09b0\u0995\u09cd\u09b0\u09bf\u09df\u09be\u0964\n",
"positive_elements = nd_array[nd_array > 0]\n",
"odd_elements = nd_array[nd_array%2 == 1]\n",
"\n",
"# \u0995\u09bf\u099b\u09c1 \u09ae\u09c7\u099f\u09be \u09ab\u09be\u0982\u09b6\u09a8, \u09af\u09be \u0985\u09cd\u09af\u09be\u09b0\u09c7\u09b0 \u09ad\u09bf\u09a4\u09b0\u0995\u09be\u09b0 \u09a4\u09a5\u09cd\u09af \u09a6\u09c7\u09df- \n",
"dim_ = nd_array.ndim\n",
"shape_ = nd_array.shape\n",
"type_ = nd_array.dtype\n",
"\n",
"# \u0986\u09b0\u0993 \u0995\u09bf\u099b \u09ab\u09be\u0982\u09b6\u09a8\n",
"convert_to_a23 = nd_array.reshape(2, 3) # \u09ec -> (\u09e8, \u09e9)\n",
"flattened_back = convert_to_a23.ravel() # \u09ac\u09b9\u09c1\u09ae\u09be\u09a4\u09cd\u09b0\u09bf\u0995 -> \u098f\u0995 \u09ae\u09be\u09a4\u09cd\u09b0\u09bf\u0995 \n",
"repeatedly_ = np.tile(nd_array, 5) # \u09eb \u09ac\u09be\u09b0 \u0985\u09cd\u09af\u09be\u09b0\u09c7\u099f\u09bf\u0995\u09c7 \u09b0\u09bf\u09aa\u09bf\u099f \u0995\u09b0\u09ac\u09c7\u0964\n",
"\n",
"# \u0986\u09b0\u0993 \u0995\u09bf\u099b\u09c1 \u09ae\u09cd\u09af\u09be\u099f\u09cd\u09b0\u09bf\u0995\u09cd\u09b8 \n",
"m35 = np.arange(15).reshape(3, 5)\n",
"m35T = m35.T\n",
"dot_product = m35.dot(m35T)\n",
"\n",
"# \u098f\u09ac\u09be\u09b0 \u098f\u09b2\u09cb \u09aa\u09b0\u09bf\u09b8\u0982\u0996\u09cd\u09af\u09be\u09a8 \n",
"mean_ = nd_array.mean()\n",
"standard_deviation = nd_array.std()\n",
"cumulative_sum = nd_array.cumsum()\n",
"cumulative_product = nd_array.cumprod()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 105
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"\u09b8\u09cd\u09b2\u09be\u0987\u099a\u09bf\u0982"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u09a8\u09be\u09ae\u09aa\u09be\u0987\u098f\u09b0 \u09b8\u09cd\u09b2\u09be\u0987\u099a\u09bf\u0982 \u098f\u0995\u099f\u09c1 \u09ad\u09bf\u09a8\u09cd\u09a8\u09a7\u09b0\u09cd\u09ae\u09c0, \u0995\u09bf\u09a8\u09cd\u09a4\u09c1 \u09af\u09a5\u09c7\u09b7\u09cd\u099f \u09b2\u099c\u09bf\u0995\u09cd\u09af\u09be\u09b2\u0964 \u09aa\u09be\u0987\u09a5\u09a8\u09c7\u09b0 \u09aa\u09b0\u09bf\u099a\u09bf\u09a4 [][] \u098f\u09b0 \u09aa\u09b0\u09bf\u09ac\u09b0\u09cd\u09a4\u09c7 [,] \u09ac\u09cd\u09af\u09ac\u09b9\u09c3\u09a4 \u09b9\u09df\u0964 \u0985\u09b0\u09cd\u09a5\u09be\u09ce \u0986\u09aa\u09a8\u09bf x[0][0] \u09a8\u09be \u09b2\u09bf\u0996\u09c7 \u09b2\u09bf\u0996\u09ac\u09c7\u09a8 x[0,0]\u0964 \u098f\u0996\u09a8, \u09a8\u09bf\u099a\u09c7\u09b0 \u0995\u09cb\u09a1 \u09a6\u09c7\u0996\u09c1\u09a8 "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a34 = np.arange(12).reshape(3, 4)\n",
"\n",
"print a34"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 0 1 2 3]\n",
" [ 4 5 6 7]\n",
" [ 8 9 10 11]]\n"
]
}
],
"prompt_number": 106
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a34[2, 3]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 107,
"text": [
"11"
]
}
],
"prompt_number": 107
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a34[:, 1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 108,
"text": [
"array([1, 5, 9])"
]
}
],
"prompt_number": 108
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a34[0, :]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 109,
"text": [
"array([0, 1, 2, 3])"
]
}
],
"prompt_number": 109
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a34[1:, 1:]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 110,
"text": [
"array([[ 5, 6, 7],\n",
" [ 9, 10, 11]])"
]
}
],
"prompt_number": 110
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a34[1:3, 2:3]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 111,
"text": [
"array([[ 6],\n",
" [10]])"
]
}
],
"prompt_number": 111
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"\u0989\u09a6\u09be\u09b9\u09b0\u09a3-\u09e7 \u09b8\u09ae\u09c0\u0995\u09b0\u09a3\u09c7\u09b0 \u09b8\u09ae\u09be\u09a7\u09be\u09a8"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u09a6\u09c7\u0996\u09be \u09af\u09be\u099a\u09cd\u099b\u09c7 \u0986\u09ae\u09bf \u09ae\u09c7\u099f\u09cd\u09b0\u09bf\u0995\u09cd\u09b8 \u09aa\u09c1\u09b0\u09be \u09ad\u09c1\u09b2\u09c7 \u0997\u09c7\u099b\u09bf, \u09a4\u09be\u0993 \u099a\u09c7\u09b7\u09cd\u099f\u09be \u0995\u09b0\u09c7 \u09a6\u09c7\u0996\u09bf\u0964 \u09a8\u09bf\u099a\u09c7\u09b0 \u09b8\u09ae\u09c0\u0995\u09b0\u09a3\u099f\u09bf \u09a6\u09c7\u0996\u09be \u09af\u09be\u0995- \n",
"\n",
"2x + 3y + z = 24 \n",
"4x - 6y + 2z = 36 \n",
"x + 5y - 2z = 18 \n",
"\n",
"\u0986\u09ae\u09b0\u09be \u09af\u09a6\u09bf \u098f\u0995\u09c7 \u09ae\u09cd\u09af\u09be\u099f\u09cd\u09b0\u09bf\u0995\u09cd\u09b8\u09c7 \u09aa\u09b0\u09bf\u09a3\u09a4 \u0995\u09b0\u09bf \u09a4\u09be\u09b9\u09b2\u09c7 \u09a4\u09be \u09a6\u09be\u0981\u09dc\u09be\u09df\n",
"\n",
"$$\n",
"\\begin{bmatrix}\n",
" 2 & 3 & 1 \\\\\\\n",
" 4 & -6 & 2 \\\\\\\n",
" 1 & 5 & -2\n",
" \\end{bmatrix}\n",
"\\begin{bmatrix}\n",
" x \\\\\\\n",
" y \\\\\\\n",
" z\n",
" \\end{bmatrix} = \n",
"\\begin{bmatrix}\n",
" 24 \\\\\\\n",
" 36 \\\\\\\n",
" 18\n",
" \\end{bmatrix}\n",
"$$\n",
"\n",
"\u098f\u099f\u09bf \u0986\u099b\u09c7 $AX = B$ \u09ab\u09b0\u09ae\u09cd\u09af\u09be\u099f\u09c7 \u0986\u09b0 \u098f\u09b0 \u09b8\u09ae\u09be\u09a7\u09be\u09a8 \u09b9\u09b2 $X = A^{-1}B$ \n",
"\n",
"\u098f\u09ac\u09be\u09b0 \u09aa\u09be\u0987\u09a5\u09a8\u09c7\u09b0 \u09aa\u09be\u09b2\u09be "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"A = np.matrix([[2, 3, 1],\n",
" [4, -6, 2],\n",
" [1, 5, -2]])\n",
"B = np.matrix([[24],\n",
" [36],\n",
" [18]])\n",
"result = A**(-1) * B\n",
"\n",
"print result"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[ 11.]\n",
" [ 1.]\n",
" [ -1.]]\n"
]
}
],
"prompt_number": 93
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"\u0989\u09a6\u09be\u09b9\u09b0\u09a3-\u09e8 \u0987\u0989\u0995\u09cd\u09b2\u09bf\u09a1\u09c0\u09df \u09a6\u09c2\u09b0\u09a4\u09cd\u09ac \u09a8\u09bf\u09b0\u09cd\u09a3\u09df "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0986\u09ae\u09b0\u09be \u099c\u09be\u09a8\u09bf \u0987\u0989\u0995\u09cd\u09b2\u09bf\u09a1\u09c0\u09df \u09a6\u09c2\u09b0\u09a4\u09cd\u09ac \u09b9\u09b2 $d = \\sqrt{(x_{1} - x_{2})^{2} + (y_{1} - y_{2})^{2}}$ \u09af\u09a6\u09bf\u0993 \u09aa\u09be\u0987\u09a5\u09a8 \u09b2\u09bf\u09b8\u09cd\u099f\u09c7\u09b0 \u09ae\u09be\u09a7\u09cd\u09af\u09ae\u09c7 \u098f\u09b0 \u09b8\u09ae\u09be\u09a7\u09be\u09a8 \u09b8\u09b9\u099c \u09a4\u09ac\u09c1\u0993 \u098f\u09b0 \u098f\u0995\u099f\u09bf \u09a8\u09be\u09ae\u09aa\u09be\u0987 \u09b8\u09ae\u09be\u09a7\u09be\u09a8 \u09a6\u09bf\u09b2\u09be\u09ae \u0995\u09be\u09b0\u09a3 \u098f\u099f\u09bf \u0986\u09b0\u0993 \u09b6\u0995\u09cd\u09a4\u09bf\u09b6\u09be\u09b2\u09c0, \u09a6\u09cd\u09b0\u09c1\u09a4\u09a4\u09b0 \u098f\u09ac\u0982 \u09ae\u09c7\u09b6\u09bf\u09a8 \u09b2\u09be\u09b0\u09cd\u09a8\u09bf\u0999\u09c7\u09b0 \u0985\u09a8\u09c7\u0995 \u09ac\u0987\u09a4\u09c7\u0987 \u0986\u09aa\u09a8\u09bf \u098f\u0987 \u0995\u09cc\u09b6\u09b2\u099f\u09bf\u0987 \u09a6\u09c7\u0996\u09ac\u09c7\u09a8\u0964"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def dist(x,y): \n",
" return np.sqrt(np.sum((x-y)**2))\n",
"\n",
"p1 = np.array([1, 2])\n",
"p2 = np.array([4, -1])\n",
"print dist(p1, p2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"4.24264068712\n"
]
}
],
"prompt_number": 97
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u0986\u099c\u0995\u09c7\u09b0 \u09ae\u09a4 \u098f\u0996\u09be\u09a8\u09c7\u0987 \u09b6\u09c7\u09b7 \u0995\u09b0\u099b\u09bf \u0986\u09ae\u09bf, \u09af\u09a6\u09bf\u0993 \u0986\u09ae\u09bf \u098f\u0995\u09c7 \u0986\u09b0\u0993 \u0986\u09aa\u09a1\u09c7\u099f \u0995\u09b0\u09a4\u09c7 \u09a5\u09be\u0995\u09ac\u0964 \u09a8\u09be\u09ae\u09aa\u09be\u0987 \u09a4\u09cb \u09ae\u09be\u09a4\u09cd\u09b0 \u09b6\u09c1\u09b0\u09c1 \u09b9\u09b2\u0964 \u0986\u09ae\u09be\u09b0 \u09aa\u09b0\u09ac\u09b0\u09cd\u09a4\u09c0 \u09aa\u09cd\u09b0\u09a4\u09bf\u099f\u09bf \u09a8\u09cb\u099f\u09ac\u09c1\u0995\u09c7\u0987 \u0995\u09bf\u099b\u09c1 \u09a8\u09be \u0995\u09bf\u099b\u09c1 \u09a5\u09be\u0995\u09ac\u09c7 \u09a8\u09be\u09ae\u09aa\u09be\u0987 \u09a8\u09bf\u09df\u09c7\u0964 \u0986\u09b0 \u09af\u09a6\u09bf \u0986\u09aa\u09a8\u09bf \u0986\u09b0\u0993 \u09aa\u09cd\u09b0\u09cd\u09af\u09be\u0995\u099f\u09bf\u09b8 \u0995\u09b0\u09a4\u09c7 \u099a\u09be\u09a8, \u09a4\u09be\u09b9\u09b2\u09c7 [\u098f\u0987 \u09b2\u09bf\u0999\u09cd\u0995\u099f\u09bf](http://www.loria.fr/~rougier/teaching/numpy.100/) \u09ad\u09bf\u099c\u09bf\u099f \u0995\u09b0\u09c1\u09a8\u0964 \u0986\u0997\u09be\u09ae\u09c0 \u09aa\u09b0\u09cd\u09ac\u09c7 \u09a5\u09be\u0995\u09ac\u09c7 \u0986\u09b0\u0993 \u0995\u09bf\u099b\u09c1 \u09a8\u09be\u09ae\u09aa\u09be\u0987 \u098f\u09ac\u0982 \u09ae\u09cd\u09af\u09be\u099f\u09aa\u09cd\u09b2\u099f\u09b2\u09bf\u09ac, \u0985\u09b0\u09cd\u09a5\u09be\u09ce \u0985\u09a8\u09c7\u0995 \u0985\u09a8\u09c7\u0995 \u099a\u09bf\u09a4\u09cd\u09b0\u0964 \u0987\u09a8\u09b6\u09be\u0986\u09b2\u09cd\u09b2\u09be\u09b9 \u09a4\u0996\u09a8 \u0995\u09a5\u09be \u09b9\u09ac\u09c7, \u0986\u09aa\u09a8\u09be\u09a6\u09c7\u09b0 \u0986\u09ac\u09be\u09b0\u0993 \u099c\u09be\u09a8\u09be\u099a\u09cd\u099b\u09bf \u0988\u09a6 \u09ae\u09cb\u09ac\u09be\u09b0\u0995\u0964"
]
}
],
"metadata": {}
}
]
} | mit |
ecervera/mindstorms-nb | task/quadrat.ipynb | 1 | 4098 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercici: fer un quadrat\n",
"\n",
"<img src=\"img/bart-simpson-chalkboard.jpg\" align=\"right\" width=250>\n",
"A partir de les instruccions dels moviments bàsics, heu de fer un programa per a que el robot avance i gire 90 graus, de manera de faça una trajectòria quadrada.\n",
"\n",
"L'estratègia és simple: repetiu quatre vegades el codi necessari per a fer avançar el robot un temps, i girar (a l'esquerra o a la dreta).\n",
"\n",
"Abans que res, no oblideu connectar-vos al robot!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from functions import connect, forward, stop, left, right, disconnect, next_notebook\n",
"from time import sleep\n",
"\n",
"connect() # Executeu, polsant Majúscules + Enter"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Programa principal\n",
"Substituïu els comentaris per les ordres necessàries:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# avançar\n",
"# girar\n",
"# avançar\n",
"# girar\n",
"# avançar\n",
"# girar\n",
"# avançar\n",
"# girar\n",
"# parar"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ha funcionat a la primera? Fer un quadrat perfecte no és fàcil, i el més normal és que calga ajustar un parell de coses:\n",
"\n",
"* el gir de 90 graus: si el robot gira massa, heu de disminuir el temps del `sleep`; si gira massa poc, augmentar-lo (podeu posar decimals)\n",
"\n",
"* si no va recte: és normal que un dels motors gire una mica més ràpid que l'altre; podeu ajustar les velocitats de cada motor individualment entre 0 (mínim) i 100 (màxim), per exemple:\n",
"\n",
" `forward(speed_B=90,speed_C=75)`\n",
" \n",
"Canvieu els valors i torneu a provar fins aconseguir un quadrat decent (la perfecció és impossible)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"### Versió pro\n",
"\n",
"Els llenguatges de programació tenen estructures per a repetir blocs d'instruccions sense haver d'escriure-les tantes vegades. És el que s'anomena **bucle** o, en anglès, [*for loop*](https://en.wikipedia.org/wiki/For_loop).\n",
"\n",
"En Python, un bucle per a repetir un bloc d'instruccions quatre vegades s'escriu així:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for i in range(4):\n",
" # avançar\n",
" # girar\n",
"# parar"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"És important que les instruccions de *dins* del bucle estiguen desplaçades cap a la dreta, és a dir [**indentades**](https://es.wikipedia.org/wiki/Indentaci%C3%B3n).\n",
"\n",
"Substituïu els comentaris per les instruccions i proveu."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"### Recapitulem\n",
"Per a acabar l'exercici, i abans de passar a la següent pàgina, desconnecteu el robot:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"disconnect()\n",
"next_notebook('sensors')"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:py34]",
"language": "python",
"name": "conda-env-py34-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": "3.4.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
JamesSample/icpw | correct_toc_elev.ipynb | 1 | 11562 | {
"cells": [
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import imp\n",
"from sqlalchemy import create_engine"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TOC and elevation corrections\n",
"\n",
"Some further changes to the ICPW trends analysis are required:\n",
"\n",
" 1. Heleen has discovered some strange results for TOC for some of the Canadian sites (see e-mail received 14/03/2017 at 17.45) <br><br>\n",
" \n",
" 2. We now have elevation data for the remaining sites (see e-mail received 15/03/2017 at 08.37) <br><br>\n",
" \n",
" 3. Heleen would like a \"grid cell ID\" adding to the climate processing output (see e-mail received 15/03/2017 13.33)\n",
" \n",
"Having made the above changes, the whole climate data and trends analysis needs re-running. This notebook deals with points 1 and 2 above; point 3 requires a small modification to the existing climate code.\n",
"\n",
"## 1. Correct TOC\n",
"\n",
"This is a bit more complicated than it first appears. It looks as though a lot of dupicate data was uploaded to the database at some point, and some of the duplicates have incorrect method names. For the Ontairo lakes, the same values have been uploaded both as DOC (in mg-C/l) and as \"DOCx\", which is in umol-C/l. The conversion factor from DOCx to DOC is therefore 0.012, which is very close to Heleen's estimated correction factor of dividing by 100. The problem is that the database appears to be selecting which values to display more-or-less at random. This is illustrated below."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create db connection\n",
"r2_func_path = r'C:\\Data\\James_Work\\Staff\\Heleen_d_W\\ICP_Waters\\Upload_Template\\useful_resa2_code.py'\n",
"resa2 = imp.load_source('useful_resa2_code', r2_func_path)\n",
"\n",
"engine, conn = resa2.connect_to_resa2()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"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>value_id</th>\n",
" <th>sample_id</th>\n",
" <th>method_id</th>\n",
" <th>value</th>\n",
" <th>flag1</th>\n",
" <th>flag2</th>\n",
" <th>approved</th>\n",
" <th>remark</th>\n",
" <th>entered_by</th>\n",
" <th>entered_date</th>\n",
" <th>detection_limit</th>\n",
" <th>labware_status</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4011483</td>\n",
" <td>330853</td>\n",
" <td>10294</td>\n",
" <td>290</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" <td>None</td>\n",
" <td>RESA2</td>\n",
" <td>2006-03-21 14:07:47</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3688111</td>\n",
" <td>330853</td>\n",
" <td>10313</td>\n",
" <td>290</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" <td>None</td>\n",
" <td>RESA2</td>\n",
" <td>2006-02-17 13:38:39</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" value_id sample_id method_id value flag1 flag2 approved remark \\\n",
"0 4011483 330853 10294 290 None None YES None \n",
"1 3688111 330853 10313 290 None None YES None \n",
"\n",
" entered_by entered_date detection_limit labware_status \n",
"0 RESA2 2006-03-21 14:07:47 None None \n",
"1 RESA2 2006-02-17 13:38:39 None None "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get example data\n",
"sql = (\"SELECT * FROM resa2.water_chemistry_values2 \"\n",
" \"WHERE sample_id = (SELECT water_sample_id \"\n",
" \"FROM resa2.water_samples \"\n",
" \"WHERE station_id = 23466 \"\n",
" \"AND sample_date = DATE '2000-05-23') \"\n",
" \"AND method_id IN (10313, 10294)\")\n",
"\n",
"df = pd.read_sql_query(sql, engine)\n",
"\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`method_id=10294` is DOC in mg-C/l, whereas `method_id=10313` is DOCx in umol-C/l. Both were uploaded within the space of a few weeks back in 2006. I assume that the values with `method_id=10313` are correct, and those with `method_id=10294` are wrong. \n",
"\n",
"It seems as though, when both methods are present, RESA2 preferentially chooses `method_id=10313`, which is why most of the data look OK. However, if `method_id=10313` is not available, the database uses the values for `method_id=10294` instead, and these values are wrong. The problem is that this selection isn't deliberate: the database only prefers `method_id=10313` because it appears lower in the table than `method_id=10294`. Essentially, it's just a fluke that most of the data turn out OK - it could easily have been the other way around.\n",
"\n",
"To fix this, I need to:\n",
"\n",
" 1. Go through all the samples from the Ontario sites and see whether there are values for both `method_id=10313` and `method_id=10294` <br><br>\n",
" \n",
" 2. If yes, see whether the raw values are the same. If so, delete the value for `method_id=10294` <br><br>\n",
" \n",
" 3. If values are only entered with `method_id=10294`, check to see whether they are too large and, if so, switch the `method_id` to `10313`\n",
" \n",
"This is done below."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Finished.\n"
]
}
],
"source": [
"# Get a list of all water samples associated with\n",
"# stations in the 'ICPW_TOCTRENDS_2015_CA_ICPW' project\n",
"sql = (\"SELECT water_sample_id FROM resa2.water_samples \"\n",
" \"WHERE station_id IN ( \"\n",
" \"SELECT station_id FROM resa2.stations \"\n",
" \"WHERE station_id IN ( \"\n",
" \"SELECT station_id FROM resa2.projects_stations \"\n",
" \"WHERE project_id IN ( \"\n",
" \"SELECT project_id FROM resa2.projects \"\n",
" \"WHERE project_name = 'ICPW_TOCTRENDS_2015_CA_ICPW')))\")\n",
" \n",
"samp_df = pd.read_sql_query(sql, engine)\n",
"\n",
"# Loop over samples and check whether both method_ids are present\n",
"for samp_id in samp_df['water_sample_id'].values:\n",
" # Get data for this sample\n",
" sql = (\"SELECT method_id, value \"\n",
" \"FROM resa2.water_chemistry_values2 \"\n",
" \"WHERE sample_id = %s \"\n",
" \"AND method_id IN (10294, 10313)\" % samp_id)\n",
" df = pd.read_sql_query(sql, engine)\n",
" df.index = df['method_id']\n",
" del df['method_id']\n",
" \n",
" # How many entries for DOC?\n",
" if len(df) == 1:\n",
" # We have just one of the two methods\n",
" if df.index[0] == 10294:\n",
" # Should be DOC in mg-C/l and values should be <50\n",
" if df['value'].values[0] > 50:\n",
" # Method_ID must be wrong\n",
" sql = ('UPDATE resa2.water_chemistry_values2 '\n",
" 'SET method_id = 10313 '\n",
" 'WHERE sample_id = %s '\n",
" 'AND method_id = 10294' % samp_id)\n",
" result = conn.execute(sql)\n",
" \n",
" # Otherwise we have both methods\n",
" elif len(df) == 2:\n",
" # Are they the same and large?\n",
" if (df.loc[10313].value == df.loc[10294].value) and (df.loc[10313].value > 50):\n",
" # Delete record for method_id=10294\n",
" sql = ('DELETE FROM resa2.water_chemistry_values2 '\n",
" 'WHERE sample_id = %s '\n",
" 'AND method_id = 10294' % samp_id)\n",
" result = conn.execute(sql)\n",
"\n",
"print 'Finished.'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Update station elevations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Heleen has provided the missing elevation data, which I copied here:\n",
"\n",
"C:\\Data\\James_Work\\Staff\\Heleen_d_W\\ICP_Waters\\TOC_Trends_Analysis_2015\\CRU_Climate_Data\\missing_elev_data.xlsx"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"120\n",
"170\n",
"87\n",
"86\n",
"100\n",
"120\n",
"113\n",
"105\n",
"100\n",
"90\n",
"129\n",
"160\n",
"100\n",
"90\n",
"135\n",
"105\n",
"98\n",
"665\n",
"571\n",
"457\n",
"673\n",
"30\n",
"61\n",
"197\n",
"130\n",
"73\n",
"43\n",
"152\n",
"150\n",
"300\n",
"236\n"
]
}
],
"source": [
"# Read elev data\n",
"in_xlsx = (r'C:\\Data\\James_Work\\Staff\\Heleen_d_W\\ICP_Waters\\TOC_Trends_Analysis_2015'\n",
" r'\\CRU_Climate_Data\\missing_elev_data.xlsx')\n",
"elev_df = pd.read_excel(in_xlsx)\n",
"elev_df.index = elev_df['station_id']\n",
"\n",
"# Loop over stations and update info\n",
"for stn_id in elev_df['station_id'].values:\n",
" # Get elev\n",
" elev = elev_df.loc[stn_id]['altitude']\n",
"\n",
" # Update rows\n",
" sql = ('UPDATE resa2.stations '\n",
" 'SET altitude = %s '\n",
" 'WHERE station_id = %s' % (elev, stn_id))\n",
" result = conn.execute(sql)"
]
}
],
"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 |
TomDecroos/matplotsoccer | experimental-notebooks/pieter.ipynb | 1 | 261974 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import os\n",
"import sys\n",
"sys.path.append(\"../src/\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import matplotsoccer as mps\n",
"import pandas as pd\n",
"from tqdm import tqdm\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/tomd/software/miniconda3/envs/soccer/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
" return f(*args, **kwds)\n",
"100%|██████████| 380/380 [00:04<00:00, 90.25it/s]\n"
]
},
{
"data": {
"text/plain": [
"Index(['game_id', 'period_id', 'time_seconds', 'timestamp', 'team_id',\n",
" 'player_id', 'start_x', 'start_y', 'end_x', 'end_y', 'bodypart',\n",
" 'type_id', 'result', 'left_to_right', 'type_name', 'id_x', 'first_name',\n",
" 'last_name', 'soccer_name', 'birthday', 'nation_id', 'id_y',\n",
" 'full_name', 'short_name', 'abbreviation'],\n",
" dtype='object')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = \"../data/spadl-v2.hdf\"\n",
"games = pd.read_hdf(data,key=\"games\")\n",
"epl16 = games[(games.competition_id == 8) & (games.season_id == 2016)]\n",
"epl16[:5]\n",
"\n",
"def get_actions(games, hdf_url):\n",
" actions = []\n",
" for game in tqdm(list(games.itertuples())):\n",
" a = pd.read_hdf(hdf_url, key=\"actions/\" + str(game.id))\n",
" a[\"left_to_right\"] = a[\"team_id\"] == game.home_team_id\n",
" actions.append(a)\n",
" actions = pd.concat(actions)\n",
"\n",
" #actions = always_ltr(actions)\n",
" return actions\n",
"\n",
"\n",
"def always_ltr(actions):\n",
" away_idx = ~actions.left_to_right\n",
" actions.loc[away_idx, \"start_x\"] = 105 - actions[away_idx].start_x.values\n",
" actions.loc[away_idx, \"start_y\"] = 68 - actions[away_idx].start_y.values\n",
" actions.loc[away_idx, \"end_x\"] = 105 - actions[away_idx].end_x.values\n",
" actions.loc[away_idx, \"end_y\"] = 68 - actions[away_idx].end_y.values\n",
" return actions\n",
"\n",
"actions = get_actions(epl16,data)\n",
"\n",
"actiontypes = pd.read_hdf(data, key=\"actiontypes\")\n",
"actiontypes.columns = [\"type_id\",\"type_name\"]\n",
"actions = actions.merge(actiontypes, on=\"type_id\")\n",
"\n",
"players = pd.read_hdf(data,key=\"players\")\n",
"actions = actions.merge(players,left_on=\"player_id\",right_on=\"id\")\n",
"\n",
"teams = pd.read_hdf(data,key=\"teams\")\n",
"actions = actions.merge(teams,left_on=\"team_id\",right_on=\"id\")\n",
"\n",
"actions = actions.sort_values([\"game_id\",\"period_id\",\"time_seconds\",\"timestamp\"])\n",
"\n",
"actions.columns"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"player_actions = actions[actions.last_name.str.contains(\"Kompany\")].copy()\n",
"set(player_actions.soccer_name)\n",
"player_actions = always_ltr(player_actions)\n",
"x,y = player_actions.start_x, player_actions.start_y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Field"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x384 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f = mps.field()\n",
"f = mps.field(color=\"green\",figsize=8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Heatmap"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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"
},
{
"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": [
"ax = mps.field(show=False)\n",
"ax.scatter(x,y,s=2); plt.show()\n",
"\n",
"matrix = mps.count(x,y,n=20,m=20)\n",
"hm = mps.heatmap(matrix)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Actions"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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yO+OxxWJxRFlZWfcrv/6Gek4IIYQQ0uv27t3rnZmZWd1duWnTpjXJ5XJ7LpfbxWKxmgsKClx++yb2LgqFgu3v76/evHmzr5eXl8bPz09dU1PDBqDcvHlzoFwuL+NwOLra2tpe/XxTWVlpv3v3bt/y8vJymUzmMG7cuNuGmp05c8a1sLDwnFQqVTU0NLAAIDc3tzojI8O7pKTE5dayKpWKmZiY2Lhnz54r48aNE+Tn57tOmTKlzdx2U/HU1tbabdq0KfDEiROVbm5uuqlTp4YeOnTI7emnn27tzXbfD0pKSlyFQqFILpc7LFu27CqXy72Z0Jq6LpmZmVfCwsLUnZ2dDIFAIE5OTm7kcrkapVLJTE5OvrFz584aHo8XWV1dzebxeGpz19HW+83Ucf/4xz82bdy4MVCn04HJZOLo0aMemzdvVth6DizVY0t7Ozs7GTt37vQ7e/ZsRVdXF2PkyJGiWbNmNVuq39z5Wb58OXfy5MnNq1evrr9x4wZTqVRa3SFCyQkhhBDygNLr9aetKbdq1aqGVatWNfTWcUtLSx2USiUzOjq628X0vLy8NC0tLSzjz6ampm7HaL366qv1AHDw4EGOcVtUVFT73LlzeVOnTm1OSkpqurcW3K6oqMhZKpW2ubq66seOHasKCAi4bdiQSCTqkEqlKgDw8fHRWqqLxWIhPj6+hcViITY2tuXkyZPOU6ZMaTO33VQdx44dc6mpqbGXSqVCAOjo6GBWVVU5ABhyyYlxWFdjYyPz8ccfF5w8ebJx/PjxSsD0ddm2bZvv4cOHPfV6Pa5du8b+LTHWsNls/aRJk9oBYPjw4V01NTVsHo+nNndcW+83U8d97LHHOkaMGKH68ccfne3t7fXDhw/v7O7+McXLy0tnrh5b2nvp0iX7cePGtXl4eOgAQCKRtBcXFzvFx8e32hpnUVGRW25u7mVjfAB01raHkhNCCCGE9Krs7Gzv+Pj4G9aUZTAYt720Wq3ZhTCDgoLUV69eZRvfX7t2jT18+HA1ABQWFl44cuSIa05OjteOHTv8ysvLz917S/4vRkvc3d1t/kDZXd23bmcwGHctOj1hwoSWQ4cOXe7pcR80HA5HN2HChJaCggJXY3Jy53XJy8tzKygocD916pTMzc1NFxkZGaHTGT4z29nZ3TzHDAYDxu3mWLrf7rxelo47Y8aMxn379nHs7e318fHxjeghU/X0Znt7K05r0DMnhBBCCOlVBw4c8EpJSbEqObHFE0880X7+/Hmn2tpau6qqKnZdXR17/PjxSp1Oh6qqKvvp06e3paen1yoUim5n0rLFI4880lFSUuLa0dHBOH36tOOvv/7a4/q1Wi0OHTrkrtVqcezYMfdHHnmk3dJ2AOBwOJorV67cPGZsbGx7cXGx68WLF9kAcP78eXu5XD6kv3DWarU4e/asc0hIiNmH4Zubm5kcDkfj5uamKykpcZTJZE49OVZ399ud18vScWfPnt30/fffe+Tn53s+++yzPe7xM1WPre0dP358R3FxsWtLSwuzoaGBVVZW5iKVSpU9iTM6Orp169at3gDQ0tLCND5LZo0hfSMTQgghpHcdPXrUxdnZWSeRSDpv3T5v3jxufn6+Z2Njo52/v//I9PT0K5bqMVU+KSmp+a233qqJiYkRAsC77777C4vFglarRVJS0kOtra0snU7HWL9+fU1vtik8PLwrOTm5PjIyUsTn81VBQUGdlspXVlbaP/PMMyOam5tZKpWKKRQKXdevX187e/bsZkdHR11OTo7Xq6++GvzYY4+1/P73v28HAHPbAWDlypVXly5d+tDGjRsD8/PzL/B4PPWWLVvkcXFxfK1WCycnJ11OTs5lABqzQT2gjM+caLVaxqOPPtoye/Zss7NGJSQktGRlZfmGhYWJw8LCVCKRqMNS3eau46xZs5ot3W93Xi9Lx/X399f6+vqq1Wo1MygoyOL1U6lUjIcffjiioaGBzWQy9QcOHPD6+eefz5mrx9b2CgSCrqVLl14bM2ZMBAC8+uqrCmNdpuq3dJ9v375dvmDBAl5WVpYfm83W5+TkXAoMDLTq/mTo9Xf1FN5P7uvgCSGEDDqWx+8McqWlpdUSiaTXnh150GVkZHhXV1c7pKen2/wQMuk7dF2GhtLSUh+JRMK7c/uQ6zkZNWoU6uvrMWLEiIEOhZB+UVVVBQB0z5Mho6qqCr6+vjhz5sxAh0IIIcRGQy45qa+vR1ubyckvCHkg0f1Ohhq654m1xo4d2xEaGtqrC/aRe0fXZWgbcsmJ8dvjY8eODWwghPST2NhYAHTPk6HDeM8T85RKJeOzzz7jZH38ybDKSpljR3s7w9nFRS8QCFVLlyyuS0lJaXRycnrgh04bV68ngwtdl6GNZusihBBChpAtW7Z4B3O5D3+258uQ5GWvOOWfKGeUXLyO/BPljORlrzh9ujsnJJjLfXjLli02r1RNCCH3asj1nBBCCCFD1apVfwncu2+//46cPGZ4xG2LnMPdk4Mnpz6NJ6c+zTx/rhypS5/l1tYqHN5//z16KJkQ0m+o54QQQggZArZs2eK9d99+/88O/nBXYnKn8AgxPjv4A3Pvvv3+1INCCOlPlJwQQgghDzilUsnYmJbG3ZL1JdPbx9eqfbx9fJGe+QVzY1oaV6VSWTXF8v79+92FQqHI+GKz2aNPnDjhBADLli0b7u3tLeHz+ZYzo9+YKn/58mX2mDFjBCNGjBCLRKKI3Nxcd6saY0ZGRoZ3ampq4L3UQXqftdclLy/Pzc3N7WGhUCji8/ni1atXD+uLePbs2eOxdu3abusuLS11EAqFImdn51GFhYXOvR2HSqViCIVCkY+Pj8TPz29kZGRkRG8foyesPT/WouSEEEIIecDt2rWLwxeKwReKbNovPEKMEQIRdu3a5WlN+YSEhBaZTFYhk8kqjhw5ciEwMLDL+HBzYmJiY25u7gVrj22qvJ2dnf7DDz+UV1VVlR88ePDi8uXLeTY1iDxwxo4d2yaTySoqKirKv/rqK5/z58/bd7+XbZKSkprT0tLquisnkUg6ZTJZRWRkpMXFDnvK0dFRL5PJKlJSUuqfe+65q8YFGAeatefHWpScEELIAPj000/B4z2ETz/9dKBDIUNAZtbHw2anLOvR//Nnz1/GzMz62OZvRbOzszlxcXGNxveTJk1q9/f3v22F6NTU1ECpVCrgcrmR8+bN4/J4vEiFQmFnrnxwcLAmOjpaCRhWbVer1QylUskAgA0bNviFhoaKw8PDRc8//3xQT9pqTmVlpT2Xy42cNm1aaFhYmHjdunU3z8fEiRNHCAQCUWRkZERaWtrNbilz8fRlnENZa2srU61WMxwdHXWAoVclJiaGP2XKlFA+ny9etGhRMGD+ejk7O4+aP39+MI/Hi5wzZ06IcfuMGTN4AQEBUSkpKdxbj2frdTR13O3bt3vdWu+iRYuCP/zwQ5uHUVqqx9b2vvPOO358Pl/M5/PFH3zwgXd39Zs7P3K53M54bLFYHFFWVuZgbXvogfj+0tkJ7N8P/QdbgIpzQIcScHYCRBFgvJIKzJwJOFh93Qgh96n29na88MKfcPLUKbz+zma8+84bOHbsR3z00Ta4uLgMdHjkAVVZKXOURk/o0b7SRybgzVUvONm63969e70zMzOruys3bdq0Jrlcbs/lcrtYLFZzQUGBS1JSUnN3++3fv99dLBZ3GKc83rx5c6BcLi/jcDi62traXv98U1NT43Dw4MGq8PDwTrFYLJ4/f/6N8PDwrszMzCthYWHqzs5OhkAgECcnJzdyuVyNuXj6Os6hpqSkxFUoFIrkcrnDsmXLrnK53JsJ7ZkzZ1wLCwvPSaVSVUNDAwsAzF0vpVLJTE5OvrFz584aHo8XWV1dzebxeOrc3NzqjIwM75KSktv+QNt6HU0d949//GPTxo0bA3U6HZhMJo4ePeqxefNmmyegsFSPLe3t7Oxk7Ny50+/s2bMVXV1djJEjR4pmzZrVbKl+c+dn+fLl3MmTJzevXr26/saNG0ylUmn1lyPUc9IP9FlZ0AcGQPf2OuiCnaBdOhXaV/8I7dKp0AU7QffWWugDA6DPyhroUAl5oJSVlaGzs3Ogw7ipvLwcY8dKoVTrcOj745gy/Rl8/a8T6OjSYuxYKcrLywc6RPKA6mhvZzi7uvVoX2dXN7S3tVn1zIlRaWmpg1KpZBp7OSzx8vLSeHp6ar28vDQcDkfb1NTE6m4fuVxut2bNmuAdO3bIjduioqLa586dy8vIyPB2dHTs9TVaAgICukaPHq1ydXXVS6XStqKiImcA2LZtm29ERIRIIpFEXLt2jS2Xy+0txdPXcQ41xmFdtbW1pT/88IPHyZMnbybSIpGoQyqVqgDAx8dHC5i/Xmw2Wz9p0qR2BwcH/fDhw7tqamrYlo5r63U0dVwvLy/diBEjVD/++KPz8ePHnYYPH95pjNMWluqxpb0nT550HjduXJuHh4fO19dXK5FI2ouLi516EmdRUZHbyy+/3GCMLygoSGOp/K0oOelj+rfeBNatgXZmNHQJMdALggEne4DJBJzsoRcEQzcrBtqZ0cDrawzl70ednUBODjSPTYDOwxN6Fgs6D09oHpsAfPGF4feE3KKzsxM5OTl47LEJ8PD0BIvFgoenJx57bAK++OKLHicVer0eP/74I6KjoyGRSLBly5ZejrxnPvnkEzwRG4ulL6YifVsWnH/rJXF2ccGWjz7G0hdT8URsLA3zIn3C2cVF39HW2qN9O9pa4eLqatOH6OzsbO/4+Pgb1pRlMBi3vbRarcVEqKOjgzFjxoywTZs2/SIWi2/+oSgsLLzw4osvXjt58qTL448/Hm5LvD2Vl5fnVlBQ4H7q1CmZTCarCA0NVel0OovxDEScQwGHw9FNmDChpaCgwNW4zd3d/bYP0Jaul52d3c17nMFgwLjdHEvXkcFg3PbvxdJxZ8yY0bhv3z7Ovn37OPHx8Y3oIVP19GZ7eytOa1By0of0WVnA37dB++wTgF83zxL6eUI75wng79vuux4U3ccfQ8sNQfNHmTg3ZymOf38aP5YpcPz70zg3Zymat+2AlhsC3ccfD3SoZJD4+OOPweWGYNuOLMya/zy+/fdZlFy8jm//fRaz5j+PrX/fCS43BB/bcM/o9Xrk5+dj1KhRmD59OoqKisBkMrv9g9vX2tvbMX/+Avy/ze/hq39+jz/OTTFZ7o9zU/DVP7/Hu/9vM+bPX4D29vZ+jpQ8yAQCoar4Pz/1aN/iop8gEAhtWrH7wIEDXikpKVYlJ7bQ6XSYPXs2LzEx8casWbNabt1eVVVlP3369Lb09PRahULR6w9F//rrr/alpaUOHR0djJKSEtfx48d3NDc3MzkcjsbNzU1XUlLiKJPJnCzF0x9xDlVarRZnz551DgkJ6TJXxtz1slV315HD4WiuXLlyc5ul486ePbvp+++/98jPz/d89tlnm3oSj7l6bG3v+PHjO4qLi11bWlqYDQ0NrLKyMhepVKrsSZzR0dGtW7du9QaAlpYWpvFZMmvQWMe+0tkJrH7V0CPi4mjdPi6O0D49HqzVrwIpKYD94P+bpXv7bag//QylmXvRLrh9FhiNhycaJk1Hw6TpcKmsgOTlBWDX1oL5xhsDFC0ZDN5++218kr0LH33+T1hYBA7nz5Vj5fIk1NbW4g0L94xOp8M///lPvPbaa6ipqRlUH+rLy8sxa1YiokaNxdf/OnGzt8QcQYQIX//rBNb95c8YO1aKffv2Qiy2atZVQixaumRxXfaunSFPTn3a5i8lv/psp27pksVWz8Rz9OhRF2dnZ51EIrmt+3PevHnc/Px8z8bGRjt/f/+R6enpVyzVY6q8j4+P9vDhw5wLFy44ZWdn+wJAfn7+heDgYHVSUtJDra2tLJ1Ox1i/fn2Nre3sTlBQUOeqVauGX7p0yTE5ObleIBB0hYSEqLOysnzDwsLEYWFhKpFI1AEYviwxFY+57aTnjM+caLVaxqOPPtoye/Zss88rJSQktJi6XuZUVlbaP/PMMyOam5tZKpWKKRQKXdevX187a9asZkvXceXKlVeXLl360MaNGwPz8/MvWDquv7+/1tfXV61Wq5ndDX1SqVSMhx9+OKKhoYHNZDL1Bw4c8DLO2GWqHlt3nob9AAAgAElEQVTbKxAIupYuXXptzJgxEQDw6quvKox1marf3PmZPXt28/bt2+ULFizgZWVl+bHZbH1OTs6lwMBAq4Z2MfT6+3q4o83Bx8bGAgCOHTvWy6Hc4YsvoFu/FrqEGJt3Ze47Aeb6TcCcOX0QWO/Rffwx1O+koXjPN1B7dz9vPvt6PaRJ08F+fS2Yixf3Q4QE6Md73goff/wxNmzchOwD38OatRauN9RjwcxJ+Ou617D4jntGo9Hgq6++wrp163D9+nW0tbXdtT+TycSGDRuwdu3aXmuDLbghIVj6witYuOxPNu/76c5tyPzob5Bfsfj5jZhwj/e8Tc9WDDalpaXVEomk4c7tSqWSEczlPmxqZXhLzp8rx/K5cbqaX3753wfx+YiMjAzv6upqh/T0dIsPIVdWVtrHxcXxL1y4QA+G9QNrrwu5v5WWlvpIJBLendup56SP6P+WDn1kcM/2jQyG/m/pYAzm5KSzE/q161Catc+qxAQA1N6+KP0gG2OWJgLz5vV6z5BKpcLly5cRETEo1iQid+js7MTatevw9z2HrEpMAMMicP+zYw9eSH4Go0ePxr///W8cO3YMXV1dOH36NFpbW00mJUZ6vR7r1q3Dxo0be6sZNuns7MSGv67Bprdfv+t3Dg6OyNz9DzwSY3oGJSaLhehHovs6RDJEODk56detXStPXfos97ODP1i1EOP1hnqkLn1Wt27tWvmDmJgQQgYnSk56ES8kBFfkholD2llM2D8602Q5Rsl5gOMKfZjpxU/1IX7A90f6LM5eceAA2vgRaA+3LRFoF4jQxhfC48CBXusZ6uzsRGZmJt544w00NTWho6MDjo5WDqUj/ebAgQMYIRT1aBE4/6BgjB49+uY2V1dX2Nvbw9qe346OPlkPyyparRZq9d1DoJUdHehUmR/Gv++L3di0cUNfhkaGmBUrVlyvrVU4zI9/0j898wuLPSjnz5UjdemzusRZCVdXrFhxvR/D7Fdjx47tCA0NNfuMgpFAIOiiXpP+Y+11IQ8mSk560RW5HLpameFNcAS0DqZPL0PRAOZ3JQAAbfyj0ItCbi9gzwbaB+7DlDU02z6CfM7SHu0rn70QEds+gt09JiddXV3IysrCG2+8AZVKhfb2drDZbGg0Vs9WR/rRtm0fIXH+8z3ad+Hzr+Dt1S9h+rRp+N3vfoff/e53CAoKwmeffYY333wTbW1tJntQGAwG3nnnnQEb1qXRaBDM5WLP/sMIF1qfyFeeq8DVOgUmTZrUh9GRoej9999TBAUFdi6fG8cdIRBh9vxlTOkjE+Ds6oaOtlYUF/2EL7N36C6eP4d1a9fKH+TEBACMq9eTwYWuy9BGyUlfcXICOjWGaYPvoHs6BrrYh8HK+hasg8eBg8ehnSyFfgzfUKBLDbg493PAtmGePYum9Efv2s7QaOD/9T5oHR1RPzXe5L5N4x4F468vQ6/Xg8GwfXi3Wq3GJ598gtdffx1KpfK2B6CZTCbUarXNdZLuabVaqFQqKJXKbl8dHR13bTtVfAoc/+H4/tt/olOlhEqluvlTKI7C2nfSYWdn+k+S9JEJ0Ov1+Pzzz2/bvmzZMixatAhffvkl1q5di8bGRovDvPqbnZ0d5iUnY9+Xu7H2rTSr99v35W7MS04Gi9Xtcg+E2GzFihXXn3vuuRu7du3iZGVm+L+56gWn9rY2hourq14gECqXLllcl5KS0kRDuQghA4GSk74SHgaG/KphXRNT3J2hTZ0FdHSC9dkRsI4UA0eKoX18JODjAYgG93MTjLZWaF1cbyYjvA83w6n25lpYqItLMJucaF1cwWhpgZeXF27cuHEzQfnb3/4GBoOBoKAgDB8+HEFBQRg2bBjYbMM6SGq1Gp999hnWrl2Ljo4Ok7MyMRiMAUlO9Hq91R/c7/Vl/OBv6xS5PUkEe1vegS9Nbm9rbUanSgk7M4vEObu6obWlxeTv7OzskJycjLlz5yI3NxevvfYaFArFoJm1a8GCBZj45FN49fW3zSZft9JoNMj9Rw4KCo72Q3RkqHJyctIvX778xvLly3t9ul9CCLkXlJz0lSXzwfggw3xyYuTsAO3zTwNdarByjoJVWGbYPm0aoNcDg+AD5W00GmDPHkCvR2zksNt+VT9xCqr/9Be0iSUWq2C1t0HJtoeHh8dtH5g3bdqEa9eu3VaWwWDA398fkydPRn5+Ptrb2y1+M65SqeDv79+Dht1/WCwWnJ2d4eTkZPFVWFgIJpOJ2bNnd1v21tedddvb299TguPh6Ylv/30W7p4cm/ftaGuFm7u7xTJMJhMJCQmYOXMmDh8+jDVr1uDnn38e8KRMJBIhODgYPxX8C7+bNKXb8oUF34MbEkITOxBCCBmSKDnpK1N/D8brG4BrTd0vwAgA9mxoF0wGfr0O1uc/gPHtt4ZV5OfNAz79FBio4R3GZOStt4Dq6pubGQBaokah8q33u01G7uR56jgcxo1DxZH8m9v0ej1WrFiBX375BbW1tTdfdXV1qKurw3//+1/cuHHDpl4Rcx+y++Ll6Og4aIfgGKdVHeiV0qMio1D8n5/w5NSnbd63uOgnREVGWVWWwWBg2rRpmDp1KoqKijBy5Eibj9fbFi6Yj71f7rYqOdn3xW4sXDC/H6IihBBCBh9KTvqKgz30a1eBtXkLtHNjrVuIsV0F1rclwEd/BxYuBBYvBj77DNi929CTcuAA4ODQt3FrNEBOjiEZuXz59t89/TTwxhvAmDHAF19Av22nzYkJAHC/+hR2L74AO+f/e66GwWBgzZo1d5VVq9Woq6uDs7MzKioq8MYbb+DkyZPo7Ow0OazJ1dUV//3vf8Hn822Oi/StP/3pBXy4PbNHycne3Vn4859esGkfBoOB6OjBMRXvs88+i7feegvBXt3/+/X19cVnn2T2Q1SE9L79+/e7r1u3brjx/cWLFx1//PHHczExMcply5YN379/v7eXl5fGmpmvTJW/fPkye9asWaHNzc129vb2uo0bN9bOmDHD9JhPK/R0PQ0+ny/Oy8u7IBAILM4otWfPHo/y8nKntLS0m4tYOjs7j+ro6DhzZ1lT20tLSx1mz54dJpfLHfLz8ysff/zxwT1bTi+x5bp8/vnnnhs2bAjU6/X485//fPXFF198oCdxGApsXimWWI8xNxH6ec+C8flRQw+KJdeaoPr4MPDcn8BYssTQU5KdDeh0wIoVwLffAo6OwCOPAL35wK9GA+zaBYSGGoaQsdnA/PmGxOTpp4GSEsPwMr0e+Oc/DYkJAMycCdcL5+BSWWHT4VwqK+B6QQbMND3N8p3YbDaCg4Ph7e2NCRMmoKCgAKdOnUJCQgIcHR1hf8daKQwGg2brGqRmzpyJKlkFzp+zbTbO8+fKcbHyHGZaec8MRhwOB9euXYNer+/2de3aNXA4tg99I2QwSEhIaJHJZBUymaziyJEjFwIDA7uMMy8lJiY25ubmXrC2LlPl7ezs9B9++KG8qqqq/ODBgxeXL1/O6+Um9KqkpKTmWxMTW0kkkk6ZTFYRGRk5JJISWzU3NzNXrVrFPXr06PmioiLZpk2bAuVyOX3xfp+j5KSPMVa+BMaalWDu+zfwVSEYsl8AZSeg1QHKTjBkv4C59yew9h/HK21KMN5af0cFDCA93ZAcvPMOcPIk4OYGjBgBXO/BlwMajaEnJizMtmTkTg4OYKRthOTlBWBfr7fq0Ozr9ZC8vACMtI33tABjZGQk/vGPf+DChQtYtmzZzaFVwMA9EE+65+DggLS0jVi5PAnXG6y7Z6431GPl8iSkpW28KxElhHSPwWCMMfd6//33fYzl3n//fR9LZXty7OzsbE5cXFyj8f2kSZPa/f39b/v2KDU1NVAqlQq4XG7kvHnzuDweL1KhUNiZKx8cHKyJjo5WAkB4eHiXWq1mKJVKBgBs2LDBLzQ0VBweHi56/vnng3oSsyWvv/66f2hoqHjq1KmhnZ2dNx9my8vLc4uJieFPmTIllM/nixctWhQMADNmzOAFBAREpaSkcG+tR6/XY86cOSF8Pl+8cOHC4O62m5OTk+MRFRUVIRAIREuWLBneXfkH0bFjx1xEIlFHUFCQxsvLSxcTE9P63XffuQHm7we5XG43ceLEEQKBQCQWiyPKysocAMC4LTIyMiItLe3mKqXmri/pO5Sc9APG3EQw/vtvsFJfAbO6GazMw2C99xVYmYfBrG4GY8UrwH9/wsfGHVQq4Pjxuytat86QNPz978DFi4CPD8DhADU15g9uKhlJSQEuXQL+8AeguNi6ZMQE5uLFYC+cD2nS9G57UFwqKyBNmg72wvlgLl5s9TEsGT58OLZu3QqFQoF169bB09MTra2t1HMyiC1evBiLFqRgwcxJ3fagnD9XjgUzJ2HRghQs7qV7hhDSf/bu3eudnJzc7Wxg06ZNa3ryySebQ0NDO2NjY5sLCgpcrKl///797mKxuMPJyUkPAJs3bw48ffr0ufPnz1e8/vrrV+81/ltVVlba796927esrKxiw4YNipqamtvGaJ45c8Z1w4YNigsXLpRv3rxZAQC5ubnVr7322l3DklQqFTMxMbFRJpOVl5WVOefn57ta2m5KbW2t3aZNmwJPnDhRWVlZWVFbW2t/6NAh09MdPsAUCgXbz8/v5jeSvr6+6rq6OjZg/n5Yvnw5d/Lkyc2VlZUVP/30U6W3t7cWADIzM69UVlZWnD59WrZz507/W3tgTF1f0neo66u/ONgD8XGGlyVXrwKTJgFnzwJyORBsIkF/7jnD66uvDKusG8ucPw889BDwxReGZ0YuXbp9v7g44M03gbFje6VJAMB84w2wg4IwZmki2vhCyGcvRNO4R6F1cQWrvQ2ep44j5MtP4FJVCUbaxl5LTG7l6emJdevWYeXKlSgqKsKoUaN6/Rik97zxxhsICgrCC8nPIEwQgcR5S3DnInD/2JWJS+dlSEvbSIkJIfdAr9eftqbcqlWrGlatWtXQW8ctLS11UCqVTGMvhyVeXl6alpYWlvFnU1NTt7OLyOVyuzVr1gQfPHiwyrgtKiqqfe7cubypU6c2JyUldTOW2jZFRUXOUqm0zdXVVT927FhVQEDAbc+aiESiDqlUqgIAHx8fraW6WCwW4uPjW1gsFmJjY1tOnjzpPGXKlDZz203VcezYMZeamhp7qVQqBICOjg5mVVWVA4DWXmryfenW2RnN3Q9FRUVuubm5lwHAy8tLB0AHANu2bfM9fPiw52/Da9lyudyey+VqANuuL7l3lJwMIpEAEBUFNDYCLi7A998DixaZ32H2bCAhAXjtNeD994Hw8Nt/3wfJiCnMxYuB5GR4HDiAiI/+DuZfXwGjtQV6N3fooqJg99ILhmdM+nhYjqOj482ZqcjgtnjxYiQnJ+PAgQP46KO/Y/2rL6K1pQVu7u6IiozCy396ATNnzqShXITcp7Kzs73j4+OtWkOFwWDc9tJqtRbn/+7o6GDMmDEjbNOmTb+IxeJO4/bCwsILR44ccc3JyfHasWOHX3l5+bl7bcetMVri7u7e4w+s5uq+dTuDwbhrQcwJEya0HDp06PKd24eSwMBAdX19/c3PsvX19XZjxozpAGy7H/Ly8twKCgrcT506JXNzc9NFRkZG3Drpzr1cX2I7GtY1WPyrAP8BgPp6w1Cs9nbg4MG7y5kapvX++4bfxcTcXvYvf+nzxOQmBwfg2Wdh91MhmE2NYGi1YDY1wu6nQkPvDn3IJHdwcHDAs88+i59+KkRTYyO0Wi2aGhvx00+FmDNnDiUmhNzHDhw44JWSktLrCzzqdDrMnj2bl5iYeGPWrFktt26vqqqynz59elt6enqtQqHo1T8gjzzySEdJSYlrR0cH4/Tp046//vprj+vXarU4dOiQu1arxbFjx9wfeeSRdkvbAYDD4WiuXLly85ixsbHtxcXFrhcvXmQDwPnz5+2H4oPgsbGx7RUVFc4KhcKusbGReeLECfennnqq1dL9EB0d3bp161ZvAGhpaWEqFAq75uZmJofD0bi5uelKSkocZTKZ08C1ilByMtD0euCjj4Hlr+CuwaXHjgFqNfD554YH4O98ZiQuDjh16v+eGTl+3PCztNSw/xNPGPbJy+vnRhFCCBmqjh496uLs7KyTSCSdt26fN28e97HHHhNevnzZwd/ff+SePXs8LNVjqvz333/vevjwYU52dravUCgUCYVCUXV1NVuv1yMpKemh8PBw0aOPPipcv369hYcxbRceHt6VnJxcHxkZKVq7dm1QUFBQp6XylZWV9kKhULRp06bAr7/+miMUCkVfffWVBwA4OjrqcnJyvIRCoXjkyJEdv//979stbQeAlStXXv3rX/863NjeoKAgzZYtW+RxcXH88PBwUWJiYmhra+vgXGyrD3l4eOjee++9X2JjY8PHjx8vXLNmjYLL5Wos3Q/bt2+XHz582FMgEIhiYmIEDQ0NrISEhBa9Xo+wsDDx2rVrg0QiEc2ONoAYev1dPYX3E5uDNw77OXbsWC+HYuiC1dXKrN9BrQZWvQ588x0Yym6H5QLTpxuGaUml1tV/8SIgEADa33ojd+8GkpOtj488EPrynidkMLrHe97y+J1BrrS0tFoikfTasyMPup6uc0L6Fl2XoaG0tNRHIpHw7txOPScDpakZmJEE5B0xn5iw2cCSJf/XM5KXZ31iAhiGfmk0gEIB+PkZVptnMICtW3unDYQQQgghhPSiITc+sS+FcLlgBgm7LccDUAjAD4DF9aLVasOsXfcqIMAwC1hjI/DYY8Cf/2x4vfmm4dXNg36EEELIg2js2LEdoaGhFld5J/2PrsvQRj0nvaj6yhWrVoC+/J//INjPDw729oC7O2BnIUc8c8aw7klv4HCA8nLDCvMTJgDr1wNMJvDii4aV6AkhhAwNSiUD27d7acZKRTp399F6FmuMzt19tGasVIQdO7zw28KGD7qYmBhlXFzckJ5+dzCi6zK0UXIyEB55xNCTUVtrWKvk9deB2FjAwwNwdDQkLMbeDDs74MSJ3j2+iwtQWAh0dgIzZgDbtgEslmFWLVrAkBBCHmi6LVu8tcHch5t3fRFybsFLTsf/dYbxY5kCx/91hnFuwUtOzZ/tCdEGcx/WbdniPdCxEkKGHhrWNZB8fIApUwyvN980bKutNazaXlRkmK3r55+BlhaL1fSYvT1w4IDhgfnnngOysgzJ0qRJwKFDhkSJEELIA0O3alWgeu9+/9JPDjDbBaLbfqfx8ETDpOlomDSd6VJZAclLKVx2ba0D8/336aFkQki/oZ6TwSYoCIiPB95915CgtLYa3vclFgvIzDQM7VqzxrD4o5MTMHp03yVGhBBC+pVuyxZv9d79/sVfHL4rMblTu0CE4i8OM9V79/tTDwohpD9RcjLY9efD6gwGsGmTYWawzZsNz7t4eABcLnDtWv/FQQghpHcplQz9xjRu6Ye7mWpvX6t2UXv7onTrLqZ+YxoXKpVV/zPav3+/u3H9EaFQKGKz2aNPnDjhBADLli0b7u3tLeHz+WJr6jJVvq6ujhUZGRkhEAhEQqFQtHv3bk+rGmNGRkaGd2pqauC91NFfpk+fHnr27Nnb5tHJz893DQsLEwuFQtHp06e7He6wZ88ej7Vr1w4zvi8tLXUQCoUiZ2fnUYWFhc63llUoFHaPPfYYv/daYD1brsvnn3/uKRAIROHh4aIPP/yQEukHACUnxLS//MWQpGRlAb/8Avj7A66ugFw+0JERQgix1a5dnLZwEdrDI2zarV0gQlt4BLBrl1VJQEJCQotMJquQyWQVR44cuRAYGNgVExOjBIDExMTG3NzcC9Ye21R5DoejO378eGVlZWVFQUHB+dTUVK7WuJbXA6ykpMRRpVIxoqKiblv8cdeuXV4rV678VSaTVYwZM6bb2XOSkpKa09LS6ozvJRJJp0wmq4iMjLxr0cHAwEDNsGHD1IcPH75rjejBorm5mblq1Sru0aNHzxcVFck2bdoUKJfL6ZGF+xwlJ8SyxYsNScq+fUB7OxASYuhhOXduoCMjhBBiJU1m1jD53MU9+n++/NnFTE1m1rDuS94uOzubExcX12h8P2nSpHZ/f//bZl1JTU0NlEqlAi6XGzlv3jwuj8eLVCgUdubKOzg46D08PHQAcOPGDVZXVxdTrVYzAGDDhg1+oaGh4vDwcNHzzz8f1JO2mlNZWWnP5XIjp02bFhoWFiZet27dzfMxceLEEQKBQBQZGRmRlpZ2s1vKXDw9iTM7O9v7mWeeaTK+b2hoYAmFQtE333zjtXHjxqBbe07MxTNjxgxeQEBAVEpKCtfadsfHxzft2rVr0PZGHDt2zEUkEnUEBQVpvLy8dDExMa3fffedG2D+PMvlcjvjORKLxRFlZWUOgPnzlpeX5xYTE8OfMmVKKJ/PFy9atCi4/1s6tFB2SayTkGBIUn74AXjqKUD023jl4mJg7NiBjY0QQohFzPOVjk3jHu3Rvk3jHgVz3Z+dbN1v79693pmZmdXdlZs2bVqTXC6353K5XSwWq7mgoMAlKSmp2Vz5xsZG5vjx44W//PKLwwcffFDt6OioB4DNmzcHyuXyMg6Ho6utre31zzc1NTUOBw8erAoPD+8Ui8Xi+fPn3wgPD+/KzMy8EhYWpu7s7GQIBAJxcnJyI5fL1ZiLpydxnjx50jUlJeWG8b2Pj49WJpNVJCQk8OLi4poXLlx4Mwk0F09ubm51RkaGd0lJiYu1bX700Ufb169f36uJXm9SKBRsPz8/tfG9r6+vuq6ujg2YP8/Lly/nTp48uXn16tX1N27cYCqVSiZg/rwBwJkzZ1wLCwvPSaVSVUNDA6u/2znUUM8Jsc2TTxqSlFOnDO+lUkNPytGjAxsXIYQQsxjt7QytS89G52hdXMFob7PpAcjS0lIHpVLJjI6OVnZX1svLS+Pp6an18vLScDgcbVNTk8UPfxwOR3f+/PmK48ePn9uxY4dfZ2cnAwCioqLa586dy8vIyPA2Jiy9KSAgoGv06NEqV1dXvVQqbSsqKnIGgG3btvlGRESIJBJJxLVr19hyudzeUjw9ibO+vp49bNgwdfclzcfTE4GBgZr6+np2T/fvb4xbntM1d56LiorcXn755QYA8PLy0gUFBWkAy+dNJBJ1SKVSFWBIDPutQUMUJSekZ6RSQ5JSXm54/+SThiQlN3dg4yKEEHIXvYuLntXe1qN9We1t0Lu42vRhPzs72zs+Pv5G9yUNHyhvfWm1WqsSodGjR6vYbLb+5MmTTgBQWFh44cUXX7x28uRJl8cffzzclnh7Ki8vz62goMD91KlTMplMVhEaGqrS/baosbl4ehKng4ODzvgNf0/jsYTBYJi8vh0dHQwHB4dBu0pzYGCgur6+/mavSH19vV1gYKAasO08d3fe3N3dKSHpR5SckHsjEhmSlOpqw/TDM2cakpRPPx3oyAghhPxGFy5QeZ463qN9PU8dh04g6LYH5FYHDhzwunUYUm+5fPkyu66ujgUYnh2oqqpyCgkJUet0OlRVVdlPnz69LT09vVahUPS4t8CcX3/91b60tNSho6ODUVJS4jp+/PiO5uZmJofD0bi5uelKSkocZTKZEwCYi6encYaHhyvPnTvn0F05c/F0h8PhaK5cuXJXLD///LPjiBEjbLr2/Sk2Nra9oqLCWaFQ2DU2NjJPnDjh/tRTT7VaOs/R0dGtW7du9QaAlpYWpkKhsOvpeSN9g545Ib0jJATo6DBMOTxmDLBokeH1P/8DpKYOdHSEEDKk2S1dUsf97OOQhknTbf5SkvvFxzq7pUvqui9pcPToURdnZ2edRCK5bWapefPmcfPz8z0bGxvt/P39R6anp1+xVI+p8v7+/poXXnghBAC0Wi3jzTffrAkJCVFrtVokJSU91NraytLpdIz169fX2NrO7gQFBXWuWrVq+KVLlxyTk5PrBQJBV0hIiDorK8s3LCxMHBYWphKJRB0AoNfrTcZjbnt3pk2b1vzDDz+4x8fHt1oql5CQ0GIqnsrKSvtnnnlmRHNzM0ulUjGFQqHr+vXra2fPnt0MACtXrry6dOnShzZu3BiYn59/gcfjqQHg+++/d5syZYrZ538GmoeHh+699977JTY2NhwA1qxZo+ByuRpL98P27dvlCxYs4GVlZfmx2Wx9Tk7OJXPnjQwMhl7f68My+5PNwcfGxgIAjh071suhkNs0NwNPPAGUlhrev/YasHFj/67bQgDQPU+Gnnu85+/rP1KlpaXVEomk4a5fKJUMbTD34dMmVoa3xKWyAmMWzdSxan75X/TBcxwDLSMjw7u6utohPT1dYalcZWWlfVxcHP/ChQvl/RXbrdra2hiPPvqooKSkRMZm998jIGPGjBF8++23Vf7+/v06rMna60Lub6WlpT4SiYR353Ya1kX6hocH8L//a+hNefJJw+KOTCawfLlhJXpCCCH9x8lJz1i3Vi55KUXHvl5v1S7s6/WQvJSiY6xbK38QE5P7iaurq/6tt95SXLp0qdeHq5mjUCjsXnnllav9nZgQQskJ6VtOTsC//gV0dQGzZwM7dwIsFjBrFqC2auIRQgghvYC5YsV1dmLCVemzU3UulRUWy7pUVkD67FQdOzHhKnPFiuv9FGK/Gzt2bMfEiRMtDpUCAIFA0DVQvSZGM2bMaBEIBF39dbzAwEDNvHnzmrov2fusvS7kwTTkhnW5urpCrVYjOjq6L+IZdKqqquDr64szZ84MdCgGOh3w0kvARx8Z3j/xBPDtt4Cz88DG9QCjYV1k1KhRqK+vx4gRIwY6lH7xn//8B2w2G21tPZqd6oEc1jU8KEhS+9vihj0RFBioqamtLb236Agh5P+YG9Y15B6IV6vV0GqHTg9lD//n3HeYTGDbNuDDD4E33wQ2bABcXACxGPj3vwFPz4GOkJAHTn19/eD7W9CHhtLfeGvVKhR2ulpZj/dnBgmH3OcFQsjAGHJ/bJycDLPDDZVvkR93fTEAACAASURBVI3fmg86DAbw9tuG1wcfAK+8AnA4wLBhwJkzhp+EkF5h7DEZKn/3POlLDkIIuW/RMydk4L38smGtlM8+A+rqgIAAwN4euHx5oCMjhBBCCCH9iJITMnikpBiSlEOHDA/Lh4Yaelh+/nmgIyOEEEIIIf2AkhMy+PzhD4YkxTgEJSrKkKQUFQ1oWIQQQizbv3+/u1AoFBlfbDZ79IkTJ5wAYNmyZcO9vb0lfD5fbE1d5sqzWKwxxvoXLFgQfC/xZmRkeKempgZaW37Pnj0ea9eutXrc8dtvv+3X2to6aD5rmYvH1nb1NVuuy4kTJ5yEQqEoICAgKiUlhdvXsQGG+G491u9+97sReXl5bubKl5aWOgiFQpGzs/OowsJCq2YAcnZ2HtUbsd6PBs0/GELu8sQThiTl9GnD++hoQ5Ly3XcDGxchhBCTEhISWmQyWYVMJqs4cuTIhcDAwK6YmBglACQmJjbm5uZesLYuc+UdHBx0xmNkZ2f/0pvxdycpKak5LS2tztryO3bs8G9raxs0n7XMxWNruwaTmJgYpUwmq3jttdcG7YKNEomkUyaTVURGRtLK81YYNP9gCDFr9GhDklJZaXg/ebIhSdm7d2DjIoQQYlZ2djYnLi6u0fh+0qRJ7f7+/ppby6SmpgZKpVIBl8uNnDdvHpfH40Uqfpvy2FR5SzZs2OAXGhoqDg8PFz3//PNBvdcSgxkzZvBMfTvv7Ow8av78+cE8Hi9yzpw5IQBw8OBBN6FQKLp27Rr7iSeeCBcKhaLq6mo2AOTk5HhERUVFCAQC0ZIlS4Yb68nLy3OLiYnhT5kyJZTP54sXLVoUDAByudxu4sSJIwQCgUgsFkeUlZU5mKunsrLSnsvlRk6bNi00LCxMvG7dumHdxWOuXe+8844fn88X8/l88QcffOBtqb2Dkbn4zd0n5q5Lbx3XHHPH1ev1mDNnTgifzxcvXLjwZg+h8V6IjIyMSEtL8+2uXfejITdbF7mPhYcbkpSaGiAyEvjjHw3bd+wAli0b2NgIIYTcZu/evd6ZmZnV3ZWbNm1ak1wut+dyuV0sFqu5oKDAJSkpqdlc+a6uLqZIJIpwdHTUb9y4sWbq1KltALB58+ZAuVxexuFwdLW1tb3++SY3N7c6IyPDu6SkxOXW7UqlkpmcnHxj586dNTweL7K6upodHx/fGh8fXxEUFBT1448/ng8ICNAAQG1trd2mTZsCT5w4Uenm5qabOnVq6KFDh9yefvrpVgA4c+aMa2Fh4TmpVKpqaGhgAcDy5cu5kydPbl69enX9jRs3mEqlkmmuHoFA0FlTU+Nw8ODBqvDw8E6xWCyeP3/+DXPxmGtXZWWl/c6dO/3Onj1b0dXVxRg5cqRo1qxZzUFBQRpT7eXxeINqVWVL8Zu6T7q7LqZ8/fXXHKFQ6Prb/vYArlo6rqk6LB1XpVIxExMTG/fs2XNl3Lhxgvz8fNcpU6a0ZWZmXgkLC1N3dnYyBAKBODk5uZHL5Zps1/2Kek7I/Wf4cKCpCaivNzw0v3y5oSfl3XcHOjJCCCEwjLFXKpXM6OhoZXdlvby8NJ6enlovLy8Nh8PRNjU1sSyVv3TpUmlFRcW5LVu2yBcsWBDa1tbGAICoqKj2uXPn8jIyMrwdHR37bYVpNputnzRpUruDg4N++PDhXTU1NWxzZY8dO+ZSU1NjL5VKhUKhUFReXu5cVVXlYPy9SCTqkEqlKgDw8fHRAkBRUZHbyy+/3AAAXl5euqCgII2legICArpGjx6tcnV11Uul0raioiKbVzk+efKk87hx49o8PDx0vr6+WolE0l5cXOxka3sHiqX4Td0n3V0XU/7whz80GocXjh07tq2745pi6bgsFgvx8fEtLBYLsbGxLSdPnnQGgG3btvlGRESIJBJJxLVr19hyudzeXLvuV/d1ZkWGOB8f4OJFoLUVeOop4LXXDK+VK4H33jMkLIQQQvpddna2d3x8/A1ryjIYjNteWq3W4h9vLperAYAnnniiw8/PT33+/HmH0aNHqwoLCy8cOXLENScnx2vHjh1+5eXl53qjLd2xs7O7+UGQwWBAp9NZLD9hwoSWQ4cOmZwr393d3eoVRE3VU1lZaW/t/j1la3v7i0ajYTg4OHQbjLn7xNJ16S0MBuOupMHa4zIYDOTl5bkVFBS4nzp1Subm5qaLjIyMMJ7/gbr/+wL1nJD7n5sbcPIkoFIBU6cC//M/hpXoFy4EaKVoQgjpdwcOHPBKSUmxKjmxxdWrV1nGnpLKykr7q1evskeMGNGl0+lQVVVlP3369Lb09PRahULR5x/SreHi4qKtr6+/2RMUGxvbXlxc7Hrx4kU2AJw/f95eLpdb/KI4Ojq6devWrd4A0NLSwlQoFHaW6vn111/tS0tLHTo6OhglJSWu48ePv/kQ9p3xmDN+/PiO4uJi15aWFmZDQwOrrKzMRSqVdtsL1t++/vprN+Pwt7KyMqewsLBOwHz85u6TnlwXU7o7bxwOR3PlypWb9+b/Z+/Mw5o81jZ+JyEsIYABZIvEsGQhgFEBEUWxLnUpp4Uip8pm3au12qK1Vdt+7rbWomJta7V1q1iPVay1VdtaFM9xKagHXJrIIkQ2AWVPQiDJ9weF40LYDIRlfteVS5l33pl75l2fd555pqV61Wo1Tp48aalWq3H+/HnL4cOH11RUVFBZLFa9hYWFJjU11VQikZgBQHc9/zsKGTkh9B5MTIBffgHq64FZs4B9+xp+//hHw+R5kxZHaAkEAoGgB/744w9zBoOhEYvFtY+nR0dHc86cOdOvrKzMyN7eflBcXFxuS+U0l9/Ozq5+7ty5LsbGxhoajYbPP/88x9LSUqNWqxEZGelSVVVF02g0lDVr1uTps01SqdT4lVdeca+oqKAplUqqUChkrlmzJv+1117TOTcGAObPn18cGhrq3q9fv/rExMQsDodTv3XrVllwcDBPrVbDzMxMk5CQcA+Azon/X331lez111/n7tmzx45Op2sTEhKyBw0aVNtcOUZGRlo2m127bNmyAdnZ2aZRUVElAoFApUuPQqGg6mrX3Llzi318fDwAYPny5QW65k0YktLSUiNfX18PY2NjTf/+/es+++yzAgAQCASq5vTrOk/YbHa7j0tz6Kq3cfvSpUsfzJ0712XDhg1OZ86cyeByuXW66jU1NdUkJCRYL1++3DkwMLDyxRdfrFEqlZQ9e/b0d3Nz83Rzc1OKRCI50DB5vjPP/66GotX2aLe0dovv168fAKC8vFzvYrojY8aMAQCcb1wzpC+h1QKxscC2bQ1/BwQ0hCFmMg2rq4vp0+cAAUDfOwee8z7fo/1B09LScsRicenT6RQKxUeTL+lwuVS2EFqt9tpzieuGxMfH2+Tk5JjExcV12zC07UEqlRoHBwfzMjIybhtay/PQ244LoXnS0tJsxWIx9+l04tZF6L1QKMDWrQ1Gyvr1wOXLDS5gAgHwqBVvA4kE4PGA9PSu0UogEAgEAoFAIMYJoY+walWDkbJzJ3D3LmBj0zChvkDHR5l3322YbD95ckNkMAKBQCD0Onx9feVjx47VGS62pyEQCFQ9fdQE6H3HhdA+yJwTQt9i4cKG3+HDQEQEwP57naKMDMDdveH/6enAuXMNxkxpKRAeDpw92zDJnkAgEHogbCeneipb2OFnPtvJqdvNN9AHjavXE7oX5Lj0bYhxQuibTJ/e8Dt9GpgypcGFCwD++9+GUMRKZcPfKhVw6VKDW9hHHxlOL4FAIDwHefn5aYbWQCAQCG2BfAom9G0mT24YIfn3vxv+Hjz4f6MmjcjlDQs8/vabYTQSCAQCgUAg9BGIcUIgAMDIkQ0GiUDQ/HaFApg6Fbh/v2t1EQgEAoFAIPQhiHFCIDRy8WLLxkdNTcNIS22t7jwEAoHQhzl27JilUCgUNf7odPrQS5cumQHAvHnzBtjY2Ih5PJ5nW8rSlf+PP/4w5/P5IldXV88pU6a4Po/e+Ph4m9jYWKf27sfj8Tzbshr7oUOHrFauXOnweBqDwRjSXN7m0tPS0kyEQqGIwWAMSU5OZrRXZ0+lrcclLCyM6+Tk5A0A9fX1YLFY4piYGE7j9ub6Xx+097icOnXKwsLCYrBQKBTxeDzP9957r02adOlfu3atXVVVVa99h++1DSMQ2oVWC7z9doMLly7UaiA7G1iw4Ilk54EDQaFQOvxzHjiwkxtHIBAIXUNYWFilRCK5I5FI7pw9ezbDyclJ1Ti5OTw8vCwxMTGjrWU1l1+tVmPWrFkuO3bskGVnZ9/es2ePTN9t0CeRkZEVGzduLOro/mKxuFYikdzx8vJq4eHUt2EwGJrLly+b/fbbb0xbW9snAjc8b//roiPHxdfXt1oikdy5c+fO7SNHjtjevXu3VeNWl/5du3bZV1dX99p3+F7bMAKhXfz+e9vWNFEogP37gQMHmpLyZDIcTL3f4V+erFs/WwkEAqFD7Nu3jxUcHFzW+PeECRNq7O3tn3h5jI2NdfLz8xNwOByv6OhoDpfL9SooKDDSlf/f//43w9raun7ixInVAOD0WBSxdevW2bm6unry+XzRggUL2PpuzwcffGDv6urqOXnyZNfa2tqmxTpPnTplMWLECN6kSZNceTye56xZs5wBIDQ0lOvo6Oj9+Jd8oGE172nTpg3k8XieM2fOdG4tXRcJCQlW3t7eHgKBQDRnzpwB+mxrT2LixInlx48f75eYmNhv4sSJTbH/dfX/hx9+aO/i4uI5duxYd2dnZ6/GEbDHR66GDRsmaBwR0Xc/V1VVUevq6iimpqYaoH3nz4kTJyyEQqGouLiYHhQUxBcKhaKcnBw6AKxfv96Ox+N58ng8z+3bt9s07tPZ10VnQKJ1EQhAw6rxvr7/+5vy2CLRj4cQbkw3IpcOgUDo/lAoFB9d2z799NPcZcuWlQLAli1bbN99912dw7gdWR3+6NGjNrt3785pLd+UKVPKZTKZMYfDUdFotIqkpCTzyMjIiuby3rt3z9jCwqJ+1KhRvNLSUvqMGTNK3n///RIA2Lx5s5NMJktnsVia/Px8vd6kpVKp8cGDB/vfvn37tkQiMRk2bNgTrmY3btxgJicn/+Xn56csLS2lAUBiYmJOfHy8TWpqqvnjeZVKJTU8PLzs0KFDucOGDROcOXOGOWnSpGpd6c3pyc/PN9q0aZPTpUuXpBYWFprJkye7njx50uLll1/uc2uD+Pr6yg8fPmxdX19PCQkJKWvs7+b6PyMjw3j//v39b926dScrK8vYx8enRRdDffZzamoqUygUimQymcm8efMecDicJsO6redPSEhIVUhIyB02m+194cKFu46OjvVAw/n59ddf2928efOOSqWiDBo0SDR16tQKNptd35nXRWfRI0QSCJ1OQEDDCvIEAoFAeG7S0tJMFAoFNSAgoNX1KqytresrKytpjf+Wl5fTdOVVKBTU69evM69fv37HxsamfujQoaKXX365QiQSqby9vWsiIiK4kydProiMjNTr6rlXrlxh+Pn5VTOZTK2vr6/S0dFR9fh2kUgk9/PzUwKAra2tuqWyaDQaQkJCKmk0GsaMGVN59epVxqRJk6p1pTdXxvnz583z8vKM/fz8hAAgl8upmZmZJgD6nHECAHZ2dnVMJlPTWr7Lly8zhg0bVm1paakZMmTIM8fxafTZz76+vtVJSUmZZWVl1NGjRwuuXr1a5u/vrwDad/40x9WrVxnDhg2rtrKy0gCAWCyuSUlJMWOz2VWdeV10FsQ4IRAIBAKhl9LWEY9ly5aVNo6i6IN9+/bZhISEPGpL3qfn4anVaoquvI6OjnVubm5KHo+nAgBvb++amzdvmolEIlVycnLG2bNnmQkJCda7du2yu3379l/6ag+FolMSAMDS0rLdL5Stlf14OoVC0T69fdSoUZUnT56819F6exPbtm3Lp1Kp2u+++47V0TIe72+1+n+Hs6V+bu64tAaLxdKMGjWqMikpidlonDzP+dManXlddBZkzgmBQCAQCAS9cvz4ceuYmJg2GSftYdSoUfKCggLjBw8e0JRKJUUikTD4fH6tRqNBZmam8UsvvVQdFxeXX1BQ0Opk4/YwfPhweWpqKlMul1OuXbtmWlhY2OHy1Wo1Tp48aalWq3H+/HnL4cOH17SUDgAsFqs+Nze3qc4xY8bUpKSkMLOysugAcPfuXWOZTNZnPzjb2tqqra2tWx05CQgIkKekpDCrqqqoN27ceOI4MplM9YMHD2jV1dWU7OxsU6D1fn76uLQFtVqNmzdvMgYOHNjiqE1LmJubq0tKSppGGP39/eUpKSnMyspKamlpKS09Pd3cz89P0dnXRWfRZ09kAoFAIBAI+uePP/4wZzAYGrFY/ETc9ejoaM6ZM2f6lZWVGdnb2w+Ki4vLbamc5vJHRkZWfPzxx/eDgoIE9fX1lLCwsIdDhgxRqtVqREZGulRVVdE0Gg1lzZo1efpsE5/PV0VFRZV4eXmJeDyeks1mtxhTXiqVGr/yyivuFRUVNKVSSRUKhcw1a9bkv/baaxWmpqaahIQE6+XLlzsHBgZWvvjiizUAoCsdAJYuXfpg7ty5Lhs2bHA6c+ZMBpfLrdu6dassODiYp1arYWZmpklISLgHoF6nqD5ES/0fExNTMmjQII/G42hqaqoFgMWLFxeNHz+eLxaLaxwcHOoAgM1m17fUz80dF12aGuecqNVqysiRIytfe+21ZudVtaYfAObPn18cGhrq3q9fv/rExMQsgUCgmjt3brGPj48HACxfvryAzWbXd/Z10VlQtNp2j0h1J9otvl+/fgCA8vIe4XanE61W2+owMwCMGTMGAHD+/PnOFdSHoVAoOJj67PooWq0Wd9NSIBg8rMX9o32d0ZnXITkHCH3tHHjO+3zrN9ZuTFpaWo5YLNabe1ZvJz4+3iYnJ8ckLi6uwNBaCP+jM49LWVkZlcViaQoLC418fHw88vLyblKpxJHIEKSlpdmKxWLu0+nkaPRANBoNYmJiEBQUZGgpBB0oaqrx1UdLsH5OGC6f/dHQcggEAoFAIAB44403nPl8vigoKIi/efPm+8Qw6X4Qt64ehkajwaxZs3Ds2DHU19dDo9GAXFjdi1zpbXy+YiGKZNkwNjWDVtuqGyyBQCAQDICvr6/c1dW1w77/hM6hM4/LkSNHWnQnJBgeYpz0ILRaLebMmYOjR49CoVDA3Nwc9+7dg5ubm6GlEdBwfM4dO4iEuLWoU9XC2V2INzd9AbYLz9DSCAQC4UkUCgr272dpv9zpgMwsUyiUFJiZauHupqQsXFSEmJgymJn1aL/vttC4ej2he0GOS9+GGCc9BK1Wi3nz5uHIkSOQy+UAGmKl37x5kxgn3QB5dSX2rFuOlHM/AwBeCI1E1NL/g7GpmYGVEQgEwpNo4+JssG4tR9vfCtpBA6nacS8BJkZAbT2FIntgRtmyYSDl/fcG4sOPZJTY2IeG1ksgEPoWxDjpAWi1WixYsAAJCQlNhgkA1NTUID09HSEhIQZURwAACiiQ3b0NU3MmZq38GAETXzG0JAKBQHgGbew7Tvhuv7166kgq7Po9udHMGFqBM7QCZyqKy0H7eD1Hm3ffhBK3lUwWJxAIXQYxTro5Wq0Wb731Fg4ePPiEYQI0xMq+evWqgZQRtFot6uoaogaaMS2wePPXMDE1hb2zi4GVEQgEwrNo4+Js8N1+e3XEC1SYm7ac2a4f1BEvUGnf7bfXDnCuJSMoBAKhqyDGSTdGq9Xi7bffxt69e58xTBq5efNmF6siAMCjR4/w+uuvw8nJqSmNw/MwoCICgUBoAYWCgnVrOeqpI1s3TBoxN4X6lQAqbd1aDhYufIS/14MgEAiEzoSEeerGvPvuu9izZ49OwwQACgsLoVKRQCNdyX/+8x8MHjwYP/30E44cOWJoOQQCgdA6Bw6wtP2t8IwrV2vY9YPW1hI4cKBNOx47dsxSKBSKGn90On3opUuXzABg3rx5A2xsbMQ8Hs+zLWU1l7+l8jtCfHy8TWxsrFPrOQ3PSy+95Hrz5k2Tx9POnDnDdHNz8xQKhaJr1661anUeOnTIauXKlQ6Nf6elpZkIhUIRg8EYkpyczHg8b0FBgVFgYKBBIrq09biEhYVxnZycvAGgvr4eLBZLHBMTw2nc/nR79UVL/dYcp06dsrCwsBgsFApFPB7P87333muTJl36165da1dVVdVr3+HJyEk35tq1a9BoNGAwGDoNFDMzM0ilUnh7e3exur6HRqPB5s2b8cEHH0CtVmP48OH4/vvvETh6NKJ9nTtc7gAOp/VMBAKB8Bxov/jcQTtoYIdeZrSDuFTtF587UObNK2stb1hYWGVYWNgdAMjNzaWPHj1a0Bh5KTw8vCwqKurR7Nmz2+T72lz+lsrvzaSmppoqlUqKt7f3EyvTHzhwwHrp0qWFCxcufNSWciIjIysANK1MLhaLayUSyZ1hw4YJns7r5ORU7+DgUHf69Gnm5MmTq5+7EZ0Eg8HQXL582ay8vJxma2tb//i2p9urL1rqN134+vpWJyUlZdbV1YHH43nNnj37EZ/Pb/Hrsi79u3btsp87d+4jCwuLXrlWQZ8zTqqqqqDVaptWTH5eMjMz0b9/f9y4cUMv5T1OUlISZDIZTp06hcOHD+Pf//53s/lu3bpFjJNOpri4GDExMTh79iyAhlGtDRs2gE6n435u9wqZXlFRgfT0dDx8+BBKpRJFRUUAgMOHD8PMzAwsFguDBg0Ci8UysFICoYEhQ4agpKQE7u7ueimvsrISFEqPXuhd/2RmmWrHvdShXbUD7YCz19o9OrFv3z5WcHBwk0EzYcKEGqlUavx4ntjYWKeLFy9aPHjwgB4UFFR58eJFy0uXLkmcnJzqm8vfUvnr1q2z27t3b38jIyPtuHHjKr788sv89mrWhVQqNZ4wYQLfy8tLLpVKzaZNm/Zww4YNRQAwduxY9/z8fGM6na6NiIgoXblyZUlLejqic9++fTavvPJKeePfpaWltMDAQEFhYaHxhQsXrOLj4x0OHTqU7ePjo9SlJzQ0lHvlyhWLCRMmVBw4cEDWlnaHhISUHzhwwKY7GycTJ04sP378eL+KigraxIkTyysrK2mA7vZ++OGH9t99952ti4tLbUZGhunvv/9+VyAQqBgMxhC5XH4DAIYNGybYsmXL/dGjR8sTEhKsNm3a5KRSqSijRo2q3LNnT97z6K2qqqLW1dVRTE1NNUDDqMrGjRsdLC0t1VlZWWajRo2q/Pbbb+83p//EiRMW77//vnNxcTE9KCiIT6VScebMmQwul1u3fv16u/379/cHgEWLFhUtWbLkIdC510Vn0eeME61WC61Wf26z1dWde71yOBwsXLgQCxcubHrYRkdH46effkJ9fT1qampw48YNTJ8+vVN19HXWrFmDs2fPwsbGBgcOHMCUKVMMLQlAg9F0/fp1XL9+HTdu3MD169fx4MEDeHt7w97eHqampigvb3ie/fjjj1AqlSgpKUF6ejpsbW0xdOhQDBkyBEOGDIGPjw8cHPQ++k0gtEpJSYle76X6vMf3GhRKCkw6+Mg3pgNyRbutvaNHj9rs3r07p7V8U6ZMKZfJZMYcDkdFo9EqkpKSzP/+Ytyu8jdv3uwkk8nSWSyWJj8/X+/vN3l5eSYnTpzI5PP5tZ6enp4zZsx4xOfzVbt37851c3Orq62tpQgEAs+oqKgyDodTr0tPR3RevXqVGRMT0zQ6Ymtrq5ZIJHfCwsK4wcHBFTNnzmwy0nTpSUxMzImPj7dJTU01b2ubR44cWbNmzRp2W/MbAl9fX/nhw4et6+vrKSEhIWWN7WuuvRkZGcb79+/vf+vWrTtZWVnGPj4+LboY5ufnG23atMnp0qVLUgsLC83kyZNdT548afHyyy9XtVdnamoqUygUimQymcm8efMecDicplGeGzduMJOTk//y8/NTlpaW0nTpDwkJqQoJCbnDZrO9L1y4cNfR0bEeaDCev/76a7ubN2/eUalUlEGDBommTp1awWazdZ6H3ZkeIVKfNK6mfv78eb2Up68RmLby6aefItbaBorfkyCZOxsnocWLL77YpRr6Ips2bUJVVRU2btyIAQMGGFSLSqXCyZMnsWvXLqSmpmLo0KEYOnQoQkNDsXbtWvD5fNBotKb8jefo999/35Sm0WiQmZnZZNRs374dqampEIvFmDdvHl599VWYmJg8XTWB0Ck0jpjo675sZNTnHm2tY2aqRW09BWY6ByJ0o6oDGO1bkDEtLc1EoVBQAwICWnW5sra2rq+srKQ1/lteXk5rbZ/myvf29q6JiIjgTp48uSIyMrK8pf07gqOjo2ro0KFKAPDz86u+cuUKg8/nq3bu3Nn/9OnT/bRaLYqLi+l/G1r1uvR0RGdJSQndwcGhri15denpSJudnJzqS0pK6B3Ztyuxs7OrYzKZrbo4Xb58mTFs2LBqS0tLzZAhQ5SOjo4tulWdP3/ePC8vz9jPz08IAHK5nJqZmWkCoN3GSaNbV1lZGXX06NGCq1evlvn7+ysAQCQSyf38/JRAg+HZ3rKvXr3KGDZsWLWVlZUGAMRicU1KSooZm82u6uzrojPotZNpehunT58GAMxPvYH8lWvxpud02H21D+9nZGPk4MEGVtf7KCgowIIFC6BQNDz3LC0tceDAAYMaJllZWVixYgU4HA4+//xzzJo1C4WFhTh37hw+/fRTREREwMPD4wnDRBdUKhV8Ph/Tpk3DJ598gl9//RVFRUVYuHAhvv32Wzg7O2PZsmWQSqVd0DICgdDpuLspKbIHHdqVklsMuLu1a17Hvn37bEJCQto0D4JCoTzxU6vVrY7SNFd+cnJyxqJFi4qvXr1qPnr0aH579HaUU6dOWSQlJVn++eefEolEcsfV1VWp0Wha1NMRnSYmJhqFQtHqO1tLelqCQqE0a3zK5XKKiYlJt5/XsG3btvyNGzc+13o8j7uCqtX/+nYF1AAAIABJREFUsw9GjRpVKZFI7kgkkjsymexWbGxs6WP7tHuYlsViaUaNGlWZlJTEbEyztLRst0HSVgxxXTwvxDjpIURHRAAA/rhZhKBJ63CGOwxBk9fj17R8yAcNBW7fNrDC3sPZs2cxePBgfPXVV1i9erWh5eCvv/7CpEmTEBAQgLq6Oly4cAHnz5/H9OnTYWraxpCgbcDY2Bjh4eH47bffcPnyZdDpdAQFBWHs2LFIS0vTWz0EAqHroSxcVERJz+3QSyblZq6GsnBRUXv2OX78uPXjbkj65uny/x4NNn7ppZeq4+Li8gsKCjowRNQyhYWFxmlpaSZyuZySmprK9Pf3l1dUVFBZLFa9hYWFJjU11VQikZi1pKejOvl8vuKvv/5qdThbl57WYLFY9bm5uc9ouXXrlqm7u3u3Dzhga2urtra2bvX8DggIkKekpDCrqqqoN27cMC0sLGxqM5PJVD948IBWXV1Nyc7ONgWAMWPG1KSkpDCzsrLoAHD37l1jmUzWNDSrq99aQq1W4+bNm4yBAwd2ONSqubm5uqSkpOlLpL+/vzwlJYVZWVlJLS0tpaWnp5v7+fkpuuK66AyIcdID0HzzLR6Wl2PogMGYP2IB5PSGF1I53RTzRyzESufxkI8YBc23ew2stGdTV1eHFStWYNKkSSgpKcH48eMRGxtrMD0qlQpr167F6NGj8Y9//AMymQxbtmyBQNDm4CAdxs3NDZs2bYJMJkNERAQmTJiAVatWQalUdnrdBAKhE4iJKaOUVADF7fTqKC4HpaQCiIlp845//PGHOYPB0IjF4iciS0VHR3MCAwOF9+7dM7G3tx906NAhq5bK0ZW/ufK1Wi0iIyNd+Hy+aOTIkcI1a9Y816Tl5mCz2bXLli0b4O3tLYqKiioRCASqsLCwSq1WCzc3N8+VK1eyRSKRvCU9HdU5ZcqUinPnzlm2lk+XHqlUaiwUCkWbNm1y+umnn1hCoVB05MiRpv5funTpgw8//HCAUCgU5eTkNLlx/fbbbxaTJk3Se7SrzkZXe3k8niomJqZk0KBBHu+99x6bzWbXmv69fs/ixYuLxo8fz3/jjTecG13o2Gx2/datW2XBwcE8Pp8vCg8Pd62qqmoyCnT1W3M0zjkRCoWebm5uytdee01nv7Z2vObPn18cGhrq7uPjI5DJZEYCgUA1d+7cYh8fH4+AgADh8uXLC9hsdn1XXBedAaWHTxxst/hGX+T6+g65Xz5Doz+/vnyln6CmBoq5b+Dcmd/xj7Ii2L3+HShGzX84EZTdx3cXt8N23CiY7f4KMDfvfH29CJlMhunTp+PSpUugUqlYu3YtVqxY0TRHqau5evUq5syZAy6Xiy+++ALOzh0PVayPc6CoqAhvvfUW0tPTsXv3bowePbrDZRG6nu5+H9C3vue8z/foMF9paWk5YrG4tLlt2rg4G3y8ntOmFeIBoEYJWkKSBu9/IOutK8THx8fb5OTkmMTFxbXoEiSVSo2Dg4N5GRkZBnFTqK6upowcOVKQmpoqodO7bgqIj4+P4Jdffsm0t7fvNLej5mjrcekIZWVlVBaLpSksLDTy8fHxyMvLu2moZ31fJy0tzVYsFnOfTidHo7ty+zbkg4bg17QCvKppMNJ1GSYAIGU5N+vmVVFRgczMTNTW1urct6+Tm5uLwYMH49KlS2Cz2Th//jxWrVplEMNELpfjnXfewSuvvIJVq1bh5MmTz2WY6AsHBwccPXoUH3/8MaZPn44FCxagqqrd8wEJBqCurg7Z2dl49KjTPGwIPQRKbOxDRM14QEtI0rQ6glJc3mCYRM140FsNk54Ek8nUrl69uiA7O7vL3HIKCgqM3n777QddbZh0Nm+88YYzn88XBQUF8Tdv3nyfGCbdDxLSpJuimDARG7kTscdzMur2hMPUvfUv1Y1uXnNun8aqCRNhVpAHc3Nz3L9/H2PGjMGpU6dgY2PTBep7FhwOB+PHj0dNTQ32798PW1tbg+goLS1FcHAwBg4ciFu3bhlMR0uEhobihRdewNtvv43Ro0fj9OnTJPxwN6aiogIhISGorq7uFkYuwfBQ4rYWaAc419LWreVobS0bFlgcaNcQLlhVB0puMSjpORpKaSXw4Ue9dsSkEV9fX7mrq2urvv8CgUBlqFGTRkJDQyu7sj4nJ6f66Ohog0R3autx6QhHjhzpXouTEZ6BGCfdFM0wf9SVUlFfUQgAsBg+o2mbzwMplEbGuG3T/CK79VQqNP7DATS4N3h5ecHf3x+jRo3Cr7/+avBQuN2B7OxsaDQauLu7g0KhYP/+/TAxMTGYG1dubi4mTpyI0NBQbNy4sVsvINevXz/s3bsXGzZswMiRI3HmzBnweDxDyyI8xYMHDzBp0iSMHDmSrPtBgKMTW1xUWPDkM7+8Asi831z2hhvh0qVcLF3KBQAHR6f6woL8XhcZoy+sLt8TIcelb0OMk26K+fw5mD0/FluzrwAAqKYN8+ACCm8j8efVWDFitk7jZLbs3zD/YOsTaZ988glsbW0RGBiIX3/9FXx+j4gm1ykcPXoUc+bMgYuLC65cuQJTU1OYmbV78WO9kZOTg6CgIMTGxmLJkiUG09EeKBQKPvjgA9jb22PMmDFISkrq0+dUdyM3Nxfjx49HVFQUPvroI7zwwguGlkQwMEWFBUYHU5s1RNpEtK8zeV8gEAhdArnZdFcmTICz/BHqHhbDmC0GALhUFCLx59XItnTAXtGkZncTlN0HW1EGTJjwzLZ3330XVlZWmDhxIq5cuQJ7e/tObUJ3Q6lUIjY2Fl9++SUAwNXVFSqVSq/heNtLXl4exo0bh3fffReLFi0ymI6OMnfuXNBoNIwbNw4XLlyAq6uroSX1eR49eoSJEydi4cKFeOeddwwth0AgEAiEdkGMk+6KkRFKp4YBu76EZeBcWNVW4/LRxQCAEf/coXO3iOxkGM2IAXQsxDdv3jzk5+fj5ZdfRlJSEhgMRqfI725IpVK89tprSEtLg7GxMT777DO8+eabBnWfqq6uxvjx47Fw4cIeaZg0MmvWLNTW1mLcuHG4fv06WCyWoSX1WWpra/Hqq68iODiYGCYEAoFA6JGQEAXdmPfuN8zZMjftB+nBmQAAp1nf68xP06gxLfsijGfPbLHc1atXg8fjISYmpk/4oh8+fBg+Pj5IS0uDu7s7Ll++jEWLFhl8XkdsbCxGjBiBpUuXGlSHPliwYAGCg4Px5ptvGlpKn0Wr1WL+/PmwtrbG5s2bDS2H0Ec5duyY5d9rOYiEQqGITqcPvXTpkhkAzJs3b4CNjY2Yx+N5tqUsXfnfeustNo/H8xSJRB4HDx7s9zx64+PjbWJjY53amv/QoUNWK1eubHMUkLVr19pVVVV1m3ctXXra267Opj3H5dy5c+YikcjDzc3N09/fn//w4cPmv87qmfT0dBMfHx+Bm5ubp4eHhygjI6PFSGoMBmOIrm1P939aWpqJUCgUMRiMIcnJyYzW0nsb3eaCITzL4V9+gbeJCXL2RwEAXGccgIaq+5obk58GKncg4OHRYrkUCgXffPMNsrOz8c033+hVc3ekoqICNTU1mDZtGq5du4ahQ4caWhJOnTqF3377Ddu2bTO0FL3xySef4Pr16zhy5IihpfRJjhw5gqtXr+LgwYMGC+xAIISFhVVKJJI7EonkztmzZzOcnJxUjZObw8PDyxITEzPaWlZz+ZOTkxlJSUmWd+7cuX3u3LmMd999l1NWVtZlJ3xkZGTFxo0bi9qaf9euXfbV1dXd5oLUpae97eouKJVKysyZM1327t2bk5WVdfuzzz7Lq6ur65K6o6KiXDZu3JiflZV1+/Tp0xkWFhYdDrn8dP+LxeJaiURyx8vLS/54Pl3pvY1uc8EQnqS6uhoAcP3vr/tDp30JOb3lSdszcy+COX9Om8o3MTHB/v37sWLFCuTm9r6oenL5/67b+fPn4+zZs0hISIClZasL7HY6paWlmDdvHvbt29ct9OgLBoOBgwcPYvHixSgo0Pu6WYQWKCoqwpIlS7B//36Y/70AK4FgaPbt28cKDg4ua/x7woQJNfb29k+sjBkbG+vk5+cn4HA4XtHR0Rwul+tVUNAQVay5/FKp1MTLy0tOp9Ph6OhYb29vr7p48aI5AKxbt87O1dXVk8/nixYsWMDWd3tCQ0O5jo6O3jExMZzH0xkMxpAZM2Y4c7lcr2nTpg0EgBMnTlgIhUJRcXExPSgoiP/4CuIJCQlW3t7eHgKBQDRnzpym8JmnTp2yGDFiBG/SpEmuPB7Pc9asWc4AIJPJjMaOHesuEAhEnp6eHunp6Sa6ypFKpcYcDsdrypQprm5ubp6rVq1yaE2PrnatX7/ejsfjefJ4PM/t27c3rUPQXHsNxQ8//GAlFAoV/v7+CgAIDAyUOzg4qAGgsc+8vLw8Nm7c2L9xn+b6OSEhwerll19uijL0zjvvOK1Zs8ZOV72XL182MzY21k6cOLEaALhcbl1r9QJAc/2mq//7MmTOSTflww8/xC8AjJRK1FlY4fr3C1rdR8WyAWXatDbX4e3tjaVLl2LWrFn4/fffDe7mpA+0Wi2+/fZbrFy5Ev/+97/B4/FAoVDw4osvGloagAZ9b7zxBiIiIhAUFGRoOXrHz88PCxYswKxZs3D69OlecU51dxrduebOnYthw4YZWg6B0MTRo0dtdu/endNavilTppTLZDJjDoejotFoFUlJSeaRkZEVzeUVi8XKTz/91LGqqopaVFRklJ2dbVpQUEAHgM2bNzvJZLJ0Foulyc/P1/v7TWJiYk58fLxNamrqE18AFAoFNSoq6tHXX3+dx+VyvXJycughISFVISEhd9hstveFCxfuOjo61gNAfn6+0aZNm5wuXboktbCw0EyePNn15MmTFi+//HIVANy4cYOZnJz8l5+fn7K0tJQGAPPnz+dMnDix4r333it59OgRVaFQUHWVIxAIavPy8kxOnDiRyefzaz09PT1nzJjxSJceXe2SSqXGX3/9td3NmzfvqFQqyqBBg0RTp06tYLPZ9c21l8vlds1wxVNkZWUZu7i4NLvK9O7du3Pd3NzqamtrKQKBwDMqKqqMw+HUA8/2s5WVlXr58uWcyspKqqWlpebHH39kXbhwQaqr3oyMDBMul9uuenX1m67zqi9DRk66KfbbtmEyAPzrX6BXlgNabas/40elQDsnIy9btgwPHz5EYmJip7SjK6mqqkJUVBTmzJmD4uJi/Otf/zK0pGc4cuQIpFIp1q9fb2gpncaqVavw8OFD7Nmzx9BS+gS//vor/vrrL3z00UeGlkIgNJGWlmaiUCioAQEBra5XYW1tXd+vXz+1tbV1PYvFUpeXl+v0Xx42bJgiIiLioZ+fn3Du3Lkcf3//KjMzMw0AeHt710RERHDj4+NtTE1Nu2xCJZ1O106YMKHGxMREO2DAAFVeXh5dV97z58+b5+XlGfv5+QmFQqHo9u3bjMzMTJPG7SKRSO7n56cEAFtbWzUAXLlyxWLJkiWlAGBtba1hs9n1LZXj6OioGjp0qJLJZGr9/Pyqr1y50u65CVevXmUMGzas2srKStO/f3+1WCyuSUlJMWtve7uCxo9gb731FtvZ2dlr9+7dLADYuXNnfw8PD5FYLPYoLi6my2SypjkhT/cznU7HxIkTyw8dOtQvKSmJweFwap2dneubrRBocb6urnq7W791Z8jISTek/ptv8D6AzNdfh3t4eKfWZWRkhE8++QSLFy/GK6+8ApqOKF/dnRs3buCf//wnMjMzYW5uji+//BLR0dGGlvUEWq0WGzZsQFxcnEHDF3c2dDodO3bsQGRkJGbNmtVjz6megFarxfvvv49NmzbB2LjFuZgEQpeyb98+m5CQkEdtyUuhUJ74qdXqFodcV69e/WD16tUPAEAsFgtdXFxUAJCcnJxx9uxZZkJCgvWuXbvsbt++/dfzt6R1jIyMmt5UKRQKNBpNi/lHjRpVefLkyXvNbbO0tGzzvIXmypFKpZ1+I2hvezsTV1dX1ZUrV5gAsGPHjvyKigqaQqGgnjp1yiIpKcnyzz//lFhYWGi8vLw8HtfZXD/Pnj27dMWKFWxXV9fayMjIhy3Vy+PxanNyckyeTm+p3o70G4VCadYK0pXeW+iVIydDhgzBgAEDMGbMmGd+arUaanWH5yx1PsnJMJozB0cAuO/d2yVVvvjii+jfv3+3mchcW1uLhIQE+I8IhIWlFWg0GiwsreA/IhCHDx9Gbe3/RlK1Wi127tyJ4cOHIzMzE4MGDcK1a9e6nWECAOfPn4darcb48eMNLaXT8ff3h42NDU6fPm1oKb2an376CQDw6quvGlhJ96LxPt/cM2DAgAEYMkRn0ByCnjh+/Lh1TExMm4yT9lJUVEQDgJ9//plZUVFhFBgYKNdoNMjMzDR+6aWXquPi4vILCgq6hbVubm6uLikpafpCM2bMmJqUlBRmVlYWHQDu3r1rLJPJWvxQHBAQULVjxw4bAKisrKQWFBQYtVROYWGhcVpamolcLqekpqYy/f39myZhPq1HF/7+/vKUlBRmZWUltbS0lJaenm7u5+fX7VZtnzp1asWtW7cY169fNwXQ9H5XUVFBZbFY9RYWFprU1FRTiUTS6krLI0aMUJSWltJ/++23ftOnTy9vLa9cLqeePXuWCQC5ubn0oqIiWkfqbQkWi1Wfm5v7zLmsK7230CuNk5KSkqYJ5T2KzEwgKAhZAKZ3oa8+hULBqlWrsGXLli6rUxd79nwDtjMH67Z9Ce6E6Vi49xxWnbqFhXvPgTthOtbE7QTbmYM9exqijGVnZ2Pp0qVQqVRYsGABrly5AoFAYOBWNE98fDwWL17cJ+ZhUCgULF68GPHx8YaW0qv57LPPsHLlyj5xTumL6upqlJSUGFpGr+aPP/4wZzAYGrFY/IRPfnR0NCcwMFB47949E3t7+0GHDh2yaqkcXfkjIiJc3NzcPGNjYzl79+7NplKp0Gq1iIyMdOHz+aKRI0cK16xZk6fPNkmlUmOhUCjatGmT008//cQSCoWiI0eOtKgfAObPn18cGhrq7uPjI5DJZEZsNrt+69atsuDgYB6fzxeFh4e7VlVVtWgsfPXVV7LTp0/3EwgEohEjRghKS0tpLZXDZrNrly1bNsDb21sUFRVVIhAIVLr06GqXQCBQzZ07t9jHx8cjICBAuHz58gI2m63TzclQmJmZaXfv3p0TERHhKhAIRNnZ2abjxo2rDgsLq9RqtXBzc/NcuXIlWyQStSm6VXBwcJmfn18Vk8lsdWTi4MGD91asWDHA1dXVc8KECbyqqipae+tt7bxaunTpgw8//HDA4wEMWkrvLVB6+DoXzYofM2YMgIYv1U/T+BDXV7tbqqtdlJUB1tYAAAqAgwcPIioq6vnKRNv1qdVqcLlc/Pzzzxg0aNBz19sRVq9Zi6++2YvQVTtg76LbwHhwT4rEDW/hjdkzsfr/PsI333wDCwsL/POf/+xCte3j3r178PX1hUwm6/JoSno7R9tJbW0tuFwuzp07B5FI1KV19wWysrIQEBCAvLy8Vl26DHUOtBV962vpPt+Gunq0pZeWlpYjFotLn06nUCg+B1Pvd7jcaF9naLXaa88lrhsSHx9vk5OTYxIXF9crQgxKpVLj4OBgXkZGxm1Da3keDHVcgoKC3N95550HISEhVV1Zb18lLS3NViwWc59O75UjJz2Ouromw2Tf35OIIyMju1QCjUZDTEwM9u/f36X1NrJnzzf46pu9iN5yuEXDBADsXQSI3nIYX32zF3v2fIPZs2d3a8MEAL744gvMnDmzT4V5NTExwfz58/H5558bWkqv5MCBA5g+fTqZa0IgEAjPSVFREY3L5XrZ2NjUN0ZOIxgOMiHe0Gi1QOPLRWUlZv697oUh3DRmzJiB0aNHY/PmzV06ibm2thbvr1yJf67/Fub9bFrfAYB5PxuErtqB91fORkxMdLd+QVOr1fj222+RkpJiaCldzvz58+Hh4dHrgwB0NVqtFgcPHsQPP/xgaCkEQo/G19dX7urqqmo9Z89AIBCoevqoCdD1x8XBwUGdk5Nzq6vqI7QMGTkxNHZ/r/GTmwtYWACAweZ+8Pl89OvXDzdv3uzSeo8fP47+XD7suPx27WfvIoDtQB6OHz/eScr0g1QqBYvFgqurq6GldDmOjo7gcrlIT083tJReRVZWFlQqFZnYTSA8JyNGjFAEBweTL+XdDHJc+jZk5MSQvPQSUFoKXLkCcDj4+eefAQBLliwxmKSgoCAkJydj8ODBXVbnth07MWjy9A7tK548Ddt27MS0diw+2dWkpKTAz8/P0DIMhp+fH1JSUsgCgXokOTkZQUFBZCI8oc04ODrVR/s6d/iZ7+Do1O0mQxMIhN4JMU4MxYoVwC+/AEeOAP7+AICYmBgADWuPGIrRo0fj+PHjWLx4cZfVeefWTYxZuq3ZbYqqCtDodBibNr+GFHeQP05v/6Az5T03f/75Z583Ti5dumRoGb2KCxcuYPTo0YaWQehBFBbkpxlaA4FAILQF4tZlCA4cAD7+GFi7FnhsIvejR4/w7rvvGlAYMGLECPz5559dWqe8phomjOYniu9a+Ap+3PK+zn2NGeaoqe7eI79k5MSvT8636Uz+/PNPBAQEGFoGgUAgEAh6hxgnXc3Fi8CMGcDUqcCHHzYlNxoEq1evNpCwBpydnfHgwQOoVF03P5BhzkStvKbZbRXFBah6VKxzX5W8BuZMi86S9tyoVCrcunULQ4cONbQUg+Hl5YWcnBxUVXVvI7KnoNVqkZub2yfnMBEIBAKh90OMk64kKwsYPRoYOBA4evSJTa+//joAgMFo3n2pqzAyMoKjoyPy8vS6flWLiLy8kZN+Ved2C2t7ndty0q9C5OXdGbL0wu3bt+Hq6tqnQgg/DZ1Oh7e3N9LSiFeJPigpKYGZmRmYTKahpRAIz3Ds2DFLoVAoavzR6fShly5dMgOAefPmDbCxsRHzeDzP1sq5d+8e3cfHR+Du7u4pEok8EhMTLRu3taec1oiPj7eJjY11au9+PB7PUyqVthom8tChQ1YrV650eDyNwWA0G8miufS0tDQToVAoYjAYQ5KTkw37gtCFtPW4xMfH29Dp9KGFhYVGAPDCCy+4nzp1yuLx7U+Xc+7cOXORSOTh5ubm6e/vz3/48KHO8KT66v/4+HibmJgYDgAolUqKv78/f/v27W0LT9oMa9eutauqquq17/C9tmEGo6gIqG9m3mB5OeDu3vD/nJxnNv/111+IiYmBWq2GWq3uXI2tMHDgQOTm5nZZfW+/9SbST3+vc7uFjZ3Obemnv8fbb73ZGbL0QllZGezsdOvvCrRard4WHe0o/fv3x6NHjwyqobcgk8kwcOBAg2rQaDSob+4+R+jzhIWFVUokkjsSieTO2bNnM5ycnFQjRoxQAEB4eHhZYmJiRlvKMTIy0n7++eeyzMzM2ydOnMiaP38+t3Fbe8oxNJGRkRUbN24s6uj+YrG4ViKR3PHy8mrTCud9EVNTU823335r3Za8SqWSMnPmTJe9e/fmZGVl3f7ss8/y6urqdObvjP6fNm0ad/jw4dVLlix52NEydu3aZV9dXd1r3+F7bcMMgkIBcDgNoyNlZf9Lr6sDWKz//R8AMjOBH38EAGRkNNxjAwICYG5uDiaTadD1C2xsbFD2uP5O5tVXX0VJzl08uCdtdrsu4+TBPSlKcjPw6quvdqa850KhUBh0fY/jx4/j4sWLuHjxIg4ePGgwHWZmZlAoFAarvzfx6NEjWFu36TncKZw+fRoWFhYwNzfH119/bTAdhLZBoVB8dP22bNli25hvy5Ytti3l7Ujd+/btYwUHBzc9TCZMmFBjb2//hFUbGxvr5OfnJ+BwOF7R0dEcLpfrVVBQYOTs7FwfEBCgAAA+n6+qq6ujKBQKiq5yAGDdunV2rq6unnw+X7RgwQJ2RzS3xAcffGDv6urqOXnyZNfa2tqmUHmnTp2yGDFiBG/SpEmuPB7Pc9asWc4AEBoaynV0dPRu/GLeiFarxbRp0wbyeDzPmTNnOreWrouEhAQrb29vD4FAIJozZ84Afba1JzFu3LiKxMREVnPbzMzMNEwms+mL7w8//GAlFAoV/v7+CgAIDAyUOzg4qAFg/fr1djwez5PH43m2ZVSjvfkB4J133nHSarXYvn17QWPa2LFj3QUCgcjLy8tj48aN/RvTGQzGkBkzZjhzuVyvadOmDQSAEydOWAiFQlFxcTE9KCiILxQKRTk5OXSgd50PxDjRJ//5D2BqCly7Bnh7A1IpOA4OTYssWgKg0ul4h0KBksdDWUgIKBQK+PyG9T0WLFiA2tpaKJVKhIeHg0KhgOPo2OXNoNPpaOlLgr4xMTHBxxs3InHDW6gpf/ZDQnPGSU35QyRueAsfb9zYrRdgNLRxsnDhwqaRk0WLFhlMBzFO9EddXZ1Bz/mFCxdCLpdDpVJ1aVQ/Qs/j6NGjNlFRUa0OmU6ZMqV83LhxFa6urrVjxoypSEpKesIP9tixY5aenp5yMzOzFoeAN2/e7HTt2rW/7t69e+eDDz548Lz6H0cqlRofPHiwf3p6+p1169YV5OXlmTy+/caNG8x169YVZGRk3N68eXMBACQmJuasWLGi4OmylEolNTw8vEwikdxOT09nnDlzhtlSenPk5+cbbdq0yenSpUtSqVR6Jz8/3/jkyZPddwJmJ2Jpaal2cHCou379+jMP27lz55atXbu26VzIysoydnFxqX06n1QqNf7666/tUlNT/7p06ZLk448/dsrPz9cZOrW9+QEgKSnJctu2bY4rV658YiRt9+7duVKp9M61a9ckX3/9tb1MJjMCAIVCQY2KinoklUpvX7hwwTInJ4ceEhJSJZFI7tjZ2dVduHDhrkQiucPlcut62/lAQgnrk7NngZoaQKMBCgoAX1/IqqsBAP9h2eEMAK/KMjDUdTACQKFQ8bs12zmSAAAgAElEQVSNA8aXFsCXboKPrZ41vMcXPXNf63S0Wi2o1K61W+fMmY28/Hx8tWw6QlftgL2LoGnb08bJg3tSJG54C2/Mnok5c2Z3qc72QqPRoNFoDFa/g4MDHjxouC/b2tq2krvzUKvVoNF0uvUS2gGVSjWom56DgwPu378PjUYDG5sOu0wTugitVnutLfmWLVtWumzZslJ91ZuWlmaiUCiojaMfLWFtbV1fWVlJa/y3vLy86WYhk8mM3n//fecTJ05ktlaOt7d3TUREBHfy5MkVkZGR5c/bhse5cuUKw8/Pr5rJZGp9fX2Vjo6OT0SNEYlEcj8/PyUA2NratuibTaPREBISUkmj0TBmzJjKq1evMiZNmlStK725Ms6fP2+el5dn7OfnJwQAuVxOzczMNAHQJyOPREdHP9yzZ0+bbkiN60O99dZb7BMnTrA++uijfBMTE+2wYcOqraysNAAgFotrUlJSzNhsdrP9efXqVUZ78gMAnU7Xrl+//v6SJUucr1y5crcxfefOnf1Pnz7dT6vVori4mC6TyYw5HE49nU7XTpgwoQYABgwYoMrLy6Nzudxmvxz3tvOBGCf65NSpBsMEALRa4G/DpNDYDP2VCrgpqkGBtmm4SkOhoEzTcA9bwrQygODmqa2tNciX2dX/9xEGsNl4f+Vs2A7kQTy5YWFFhpU1FJXlyEm/irRfDqNUlomPN27s9oYJ0DBioFQqDVb/jz/+CF9fX2i1Wpw9e9ZgOhQKBczMzAxWf2/C2NjYoOfUsWPH8Pbbb6OmpgZbtmwxmA5C92bfvn02ISEhbZpoRqFQnvip1WoKAMjlckpoaKjbpk2b7nt6ej7ztftpkpOTM86ePctMSEiw3rVrl93t27f/et52PK6xJSwtLTs8WVRX2Y+nUyiUZ75IjBo1qvLkyZP3OlpvbyIsLKxi3bp1TjY2Ni26fbi6uqquXLnCBIAdO3bkV1RU0BQKBdXExKTF49dc/7eXwMDAqhUrVhQfO3bMeufOndZvvvnmo1OnTlkkJSVZ/vnnnxILCwuNl5eXR+MHTSMjo6Y6KRRKqx86e9P5QNy69EVFBZCd3ewmB5UC7ooq0B4zTABATaFgZ3UFAMCR1n3sxIKCAjg4OLSesROYM2c28u/L8H+xbyL33BEAwK4FL+PL2ROQe+4IVi97CwV593uEYQIY3p1p4MCB8PT0hJeXF9wbAzIYAGKc6A8HBwcUFhYarH4nJyf861//ws8//wwPDw+D6SB0b44fP24dExPT4SgYGo0Gr732Gjc8PPzR1KlTK9uSPzMz0/ill16qjouLyy8oKNDrF7bhw4fLU1NTmXK5nHLt2jXTwsLCDpevVqtx8uRJS7VajfPnz1sOHz68pqV0AGCxWPW5ublNdY4ZM6YmJSWFmZWVRQeAu3fvGje6A/VFjIyMMHz48Krr16+3GMZw6tSpFbdu3WI0uoA1BiDy9/eXp6SkMCsrK6mlpaW09PR0cz8/v6aH99P931p+XVCpVMTHx8vWrFkzoKSkhFZRUUFlsVj1FhYWmtTUVFOJRNKmB6W5ubm6pKSkaYSxt50PxDjRF8nJDfNNmoGC5jtaQ6HgvEoJt25kmABAbm6uQaMBmZiYYPr06fjtzC8AGm4elRXluPKfi5g2bVq3nmPyNAMGDEC2DqO1L5GdnQ1n51bndxLawMCBAyGTyQwegY1A0MUff/xhzmAwNGKx+InRjujoaE5gYKDw3r17Jvb29oMOHTqk02Xgt99+Y54+fZq1b9++/o1hiRsn/jZXjlarRWRkpAufzxeNHDlSuGbNGr3Gw+fz+aqoqKgSLy8v0cqVK9lsNrvFkRypVGosFApFmzZtcvrpp59YQqFQdOTIESugIbpUQkKCtVAo9Bw0aJD8xRdfrGkpHQCWLl364MMPPxzQ2A9sNrt+69atsuDgYB6fzxeFh4e7VlVV9Wnf2dmzZz+srq5usQ/MzMy0u3fvzomIiHAVCASi7Oxs03HjxlULBALV3Llzi318fDwCAgKEy5cvL2Cz2U1BF57u/9byt0RgYKB8/Pjx5UuWLBkQFhZWqdVq4ebm5rly5Uq2SCRqU1Sw+fPnF4eGhrr7+PgIZDKZUW87Hyg9/AHXrPgxY8YAAM6fP//MtsZhUn21u6kuDw9g164Gd642IqPRMVBdhy/79QfPiN5snvGlBc+ltaW+aA65XA4bGxvU1NR0+byTp7l79y4EAkGPfgnTarWwtbXFrVu34GiA4AZA+88BfVNRUQE2m43y8nIYGXUvQ7ynYmNjA4lEgv79+7eeGYY/B1pD3/paus+3oa6W/Xe6OWlpaTlisVhvc0d6O/Hx8TY5OTkmcXFxXT/Bk6ATclz6BmlpabZisZj7dDoZOdEXp0+3yzABgA3aBv9BXYaJIZBKpXB1dTW4YQI0uJf1dCgUCnx9fZGSkmJoKQbj2rVrGDx4MDFM9Ii7uzv++ktv7vQEAoFAIHQbDP8G2gvop1IBT/uAm5kBRkaoA6BrltVPGjUGULvXqFtycjICAwMNLQNA7zBOAMDPz69PGycpKSnw8/MztIxeRWBgIC5evGhoGQRCj8fX11c+duzYHhnRqDdDjkvfhhgnemBIeTlA/3v0g8kEHB2BNWuAoiLYAnhEbwiH/nSche/ppviS1Ta3jK4iOTkZQUFBhpYBgBgnvQVinOifoKAgJCcnG1oGoQejUCgoX331lbV4qK/I3MJyKJVG8zG3sBwqHuor2rVrl3Xjgoe9nREjRiiCg4PJS3A3gxyXvg0xTvSAoKqqYXX4SZOAEyeAvDzg3XcBGxtUAuhf1zBvrtCEATX+Z6RwaDSYUbrPIVCr1UhOTsaoUaMMLQVA7zFOhg0bhpSUlB49d+Z5IMaJ/gkMDMTly5ehUqlaz0wgPEXc1q02TgOcB2/dc3Cg8B8zzRbt+4PywalbWLTvD4rwHzPNPvt6/0CnAc6D47ZuJQvZEAiELoc4geuBb7lcTPvzz4YRk6dY+Pe/F63toaLSkMNggldTif4qJeq7kWECAElJSXB2du42UZV6i3Hi6OgIR0dHJCUlYezYsYaW06VcvXoVVCoVbm5uhpbSq7C2tsbgwYPxyy+/ICQkxNByCD2I2KXLnL478oP9tI37qI8vdgsAZhZW8Bj5IjxGvkh9cE+Kj9ct4uTl5ZvEfbald9yMCQRCj6B7vR33UFQ0WrOGCTQa7ARQbkSH6u+5JUqaEW5aWuMyyw4yRovhuLucffv24fXXXze0jCZ6i3ECAIsWLUJ8fLyhZXQ5O3bswKJFi7pFgIXexuuvv479+/cbWgahBxG3davNd0d+sJ8R9/0zhsnT2LsIMCPue+p3R36wJyMoBAKhK+mVbwyZmZn473//izFjxjzz61LCwgAA161sn9mkoBlB28qKs11JZWUlTp06hYiICENLaaI3GSfR0dG4ePEi7t3rFYu3tomioiL8/PPPmDVrlqGl9ErCw8ORlJSEkpISQ0vptjT3DPjvf/+LzMxMQ0vrchQKBWXd+g2cqR/tpJr3a5utYd7PBmEffk5dt34DR6lUtumBdezYMcvGdUmEQqGITqcPvXTpkhkAzJs3b4CNjY2Yx+N5tlbOvXv36D4+PgJ3d3dPkUjkkZiYaAkARUVFNC8vLw+BQCASCoWigwcP9mtTY3QQHx9vExsb6/Q8ZRD0T1uPS3x8vA2dTh9aWFhoBAAvvPCC+6lTpyxaKufcuXPmIpHIw83NzdPf35//8OFDnZGJ0tLSTIRCoYjBYAxJTk5mPE97YmJiOACgVCop/v7+/O3bt3fY6F+7dq1dVVVVr3yHB3qpcdK/f38wmQYelXj0CDhxAh8A3coI0cXevXsxfvx42No+a0gZit5knJibm2PmzJn44osvDC2ly9i1axemTZsGFotlaCm9EgsLC4SGhmL37t2GltKjYDKZbV4fpjdx4MABlp2LAHZcfrv2s3cRoD+XjwMHDrTJCAgLC6uUSCR3JBLJnbNnz2Y4OTmpRowYoQCA8PDwssTExIy2lGNkZKT9/PPPZZmZmbdPnDiRNX/+fC4AsFgszX/+8x+pVCq9k5SUdDc2NpbTuMo3oW9iamqq+fbbb63bklepVFJmzpzpsnfv3pysrKzbn332WV5dXZ3O/GKxuFYikdzx8vJq0+KIbWHatGnc4cOHVy9ZsuRhR8vYtWuXfXV1da98hwd6qXFy48YN5OXl4fz588/8aDQaaLQuCN/797yNDZ1f03OjVCrx6aefYsWKFYaW8gQKhQIWFhatZ+whvPnmm9i7dy9qampaz9zDUalU+Oqrr7Bo0SJDS+nVLF++HNu2bUNVFQlq8ziN9/nmngF5eXm4ceOGoSV2OV/s2u0w+KWIDj3zh7wUQf1i126H9u63b98+VnBwcFnj3xMmTKixt7d/YhXt2NhYJz8/PwGHw/GKjo7mcLlcr4KCAiNnZ+f6gIAABdCwOntdXR1FoVBQTExMtFZWVhoAePToEU2lUlHr6uooALBu3To7V1dXTz6fL1qwYAG7I23VhVQqNeZwOF5TpkxxdXNz81y1alVTf4wdO9ZdIBCIvLy8PDZu3Nhk+erS05k6+yLjxo2rSExMbPYrmJmZmYbJZDZZrz/88IOVUChU+Pv7K4CG1dodHBzUALB+/Xo7Ho/nyePxPNsyqtHe/ADwzjvvOGm1Wmzfvr3p66uu84fBYAyZMWOGM5fL9Zo2bdpAADhx4oSFUCgUFRcX04OCgviNK9YDQEJCgpW3t7eHQCAQzZkzZ0Bb9HRXyIT4zuDaNUAuB37/HRg/3tBqWmXnzp3w8fGBj4+PoaU8g5NT7xltd3FxQWBgIL755hssXrzY0HI6lUOHDsHT0xOenq16bxCeAw8PD0yYMAFxcXH4v//7P0PLIXRjMjPumk4a5N+hfbmD/PHz1lVm7d3v6NGjNrt3785pLd+UKVPKZTKZMYfDUdFotIqkpCTzyMjIisbtx44ds/T09JSbmZlpAaCsrIzq7+8vvH//vsn27dtzTE1NtQCwefNmJ5lMls5isTT5+fl6f7/Jy8szOXHiRCafz6/19PT0nDFjxiM+n6/avXt3rpubW11tbS1FIBB4RkVFlXE4nHpdejpbZ1/D0tJS7eDgUHf9+nXTp7fNnTu37PG/s7KyjF1cXP6fvTsPa+pa9wf+TUIICUEIIMhgoEAGEjAVidbeolZrceBnsYhaAWelWntb0eNcrUOlVYuK2jpgixOeXo/A9dhTrec40NZCGRStmAAqRkBUZCaBQJLfHzRcB8JkmNfneXhsdvZe+032Kux3r6n2xf3kcrnpwYMH7W7evJmlVqspgwYNEk2ZMqXcycmp/sV927M/AFy6dKlffn4+Izk5OevZ7Ybqj0qlooaGhpYcPHgw39XV1SsvL48eGBhYGRgYmOXk5OR95cqVbAcHh3oAKCgoMImMjHS8evWq3MLCQjt+/Hi3M2fOWEyaNKlHPrki/1N0BF/fhn/HjMHAAQPwTlH7uycNHNDmh1Vt8ujRI3z11Ve4fPlyh56nvXpTcgIAW7Zswdtvv42AgAC4ubl1dTgdoqCgAKtWrcLZs2e7OpQ+YcuWLZBKpZgzZw64XG5Xh0N0UyplNYXBMm/XsaYscyiVVW3qn5yZmclQqVRUfetHc6ytresrKipo+n/LysoauzcoFAqTVatWDUxMTGwcKMThcLTZ2dlZGRkZZgsWLHCZNWtWGYPB0Hl7e1fPmDHDdfz48eUhISFlbfuULXNwcFD7+PjUAIBUKq1KTk5m8fl89b59+/r/9NNPVjqdDo8fP6b/lWjVG4qno+Psi8LCwp7GxMS0qvWC8ldX+48//tgpMTGRs379+gIGg6EbOnRolb5VTiKRVKempjKdnJyavLlPSUlhtWV/AKDT6botW7Y8+OSTTwYmJydn67cbqj90Ol03duzYagBwdnZW5+fn011dXZvsg3b58mXz/Px8U6lUKgQApVJJzc3NZQDokclJr+zW1aUOHmz498EDAIDi4UPodLp2/yheXHneiHQ6HT788EPMmzcPIpGow87zKnpbcuLl5YWVK1di9uzZ6I39pHU6HebNm4dFixaRtU06yWuvvYalS5di3rx5fXYtHaJlTJa5rlbZvi6lamU1WCx2mypXbGysTWBgYElr9qVQKM/9aDQaCgAolUrK5MmT3SMjIx+IxeKXnnb7+PjU0Ol0XUpKChMAkpKScpYsWfI4JSXFfMSIEW0bXNNOZ8+etbh06VK/P/74QyaTybLc3NxqtNqG1cwMxdMVcfZ2QUFB5UlJSf30370hbm5u6vv375sCwJ49ewrefvvtCpVK1eK9MIVCeeVfrm+99Vbl6tWrH6vVauq+ffusgebrj4mJSeM5KRQKWvpsfn5+jeO9FArFnxEREcWvGnNXIcmJMel0QHg4MGwY4Nz9u/sdP34cubm5+Pzzz7s6FIN6W3ICAEuXLgWFQkFUVFRXh2J03377LZ4+fYq1a9d2dSh9ysqVK1FeXo79+/d3dShEN+XB49fk3Uhp17F5N1Lgwee32ALyrPj4eOuZM2e2KjlpilarxbRp01yDg4NLpkyZUqHffu/ePXpRURENaGhVyc3NZbq4uNRptVrk5uaaTpw4sSoqKqqgsLDQtL3nNuThw4emmZmZDKVSSUlLS2MPGzZMWV5eTuVwOPUWFhbatLQ0M5lMxtTH31Q8nRFnX2RiYoI33nijMiMjo9nZkKZMmVL+559/svRdwPQPCYcNG6ZMTU1lV1RUUIuLi2k3btwwl0qljXWew+HU65Oa1uxvCJVKRXR0tGLjxo3OT548oRmqPy0xNzfXPHnypLGFcdSoUdWpqansO3fu0AEgOzvbVKFQ9NjeUT028G5p+vSGf3/5pWvjaIXc3FwsW7YM586dA4PB6OpwDOqNyQmNRsORI0cglUrh7++PQYMGdXVIRpGdnY3169fj119/BZ1O7+pw+hQTExMcOXIEfn5+GDFiBBnrQ7xkcfiCoqhDR108/+vdNj+UvP5jnHZZ+IKi1u5/8eJFcxaLpZVIJM+1doSFhXHPnTtnVVpaamJvbz8oKirqvqEyLly4wP7pp584OTk5zNjY2P4AcO7cuZw7d+6YLl682AUANBoNZcOGDfkuLi51Go0GISEhr1VWVtK0Wi1l48aN+W39nC1xcnKqXb58ufPdu3fNQkNDnwgEArWLi0tdTExMf3d3d7G7u3uNSCRSAg2tyE3FY2g78ermzZv39PDhw/bN7cNkMnWHDh3KmzFjhptOp4O1tXX9mjVrHgkEAvWCBQseDxkyxBMAVqxYUfjs+JFly5Y9WrBgwWtffPGF47lz53Ja2r85b731lvKdd94p++STT5y/++47RVP1pyXh4eGPJ0+e7GFlZVWfkJBwh8vl1u/cuVMREBDA02g0YDKZ2ri4uHsAWhVTd0Pp4d0A2hy8iUlDPlZfb5zrpV875XJiIsDhAOvXAxs3GqVsY2iM75kxJU+fPsWbb76JpUuX4sMPP+yawFqBQqHghx9+wNSpU7s6lA7x/fffY9euXbh69SrMzdvXF7w1mqoDxlZTU4NRo0YhJCQEH3/8cYedh2je8ePHsW7dOiQnJ2PAM+PVOqMOvApjx/eKv+e7/9zvzcjMzMyTSCQvdedQqVQUR+eBrze1MnxzHt2T4+9rZmsfFuRf1w88702io6Nt8vLyGFFRUc0ODpXL5aYBAQG8nJycW50VW1/W2utC9GyZmZm2EonE9cXtpFuXsbi6NvzbjRKTptTW1uL999/HpEmTunViotcbW070Zs+eDR8fH7z33ntQqdrUY6JbUavVCA4OBpfLxUcffdTV4fRpoaGhmDNnDiZNmgSl0mjT8hO9AJPJ1H22bq3i9OYl2uqy1i2vUF32FKc3L9F+tm6tojcmJgRBdE8kOTEC96oqoLwcOHeuq0NpVk1NDaZNmwZbW1t89dVXXR1Oq/Tm5IRCoSAmJgb9+/dHUFAQamtfGu/Z7dXX12PGjBmgUqk4ceIEqFTyK6WrrV+/Hp6enpg8eXKfWFOHaL2IpUufhk6b8uhIxHTto3vyZvd9dE+OIxHTtaHTpjyKWLq03YvFdXe+vr7K0aNHtzijkUAgUJNWk87T2utC9E5kzIkRHE5Pb/gPf/+uDaQZ5eXleO+99zBgwAAcPXq0299E6rsbOjg4dHEkHYtGo+Ho0aOYMWMGAgICEB8f32MWnlQqlZg2bRq0Wi3i4+PJOJNugkKh4PDhw1i4cCHeeecd/Pjjj10dEtGNRH29o9DZ2al285rZ3P6ufAyeOIPqOmgYTFnmUCurkXcjBdfOntA+uZ+Dz9atVfTmxAQA9KvXE90LuS59W/e+Q+0BxhX9NUbwvsFxfW12+vRpbN++Hf/5z3+MMjWoWq3G22+/DbFYjBMnTsDUtPtPDvL0acPfQyazzet+9Th0Oh1///vf4e7ujlGjRuHRo0ddHVKLSkpK8M4774DD4SAxMbFbT6rQF5mYmODw4cPw8/ODn58fampqujokohuJWLr0aWH+g+vLFs66L//xiHLfnHd0X/w/b+yb845O/uMR5fIP5+Q9LMi/3tsTE4IguieSnLwKnQ6r5HJks9mAkRY/u3XrFsLCwrBixQqMGzcOXC4Xe/bsQWVl+1o3KysrkZ6ejqCgIOzduxc0Gq3lg7qBwsK+NQaORqPh22+/xaRJkzBkyBAkJiZ2dUgGnTt3Dj4+PvDz80NsbCxpMemmKBQKtm3bhgULFiAjIwPl5eUtH0T0GUwmUxceHl5yPT31dlVleYZWo0mvqizPuJ6eenvhwoWlZIwJQRBdhSQnr2LmTADAR4MHG6W46upqBAcHQ6VSQSgUQqPRID8/H6tWrYK9vT0WLFgAmUzWpjIZDAY8PT2xdu3axlVRe4K+lpwADTeTGzZsQFxcHFauXIng4GAUFbV69s4OV1xcjLCwMCxevBiHDh3CV1991e27BxLAp59+CrFYTFq3CIIgiB6B3Fm0V0UFcPw4jg8ciDoj3aB99NFHuH37Njw9PbF58+bGsQdKpRIqlQrff/89fHx8MHz4cJw5c6ZVK4ybmprCysrKKPF1pr6YnOiNGDECmZmZ4PF4kEgk+P7777t05W+dToeTJ0/C29sbdnZ2uHnzJsaOHdtl8RBtZ2lpCTMzs64OgyAIgiBaRJKT9nJ3BwDEuLkZpbjvv/8eR44cAYvFwqlTpzBmzJiXppfVaDRQqVRITk5GaGgoBgwYgMjIyF45I09fTk4AwMzMDFu3bsX58+exd+9evPPOO/j555+h1Wo7LQadTodLly5h4sSJiIyMxP/+7//i66+/7tA1WQiC6NlOnz7dTygUivQ/dDrd5+rVq0wAWLhwobONjY2Ex+O1uErovXv36EOGDBF4eHiIRSKRZ0JCQj/9excvXjTn8/kiNzc38YQJE17pj3B0dLRNREREm6eF5PF4Yrlc3uIAzhMnTliuWbNmwLPbWCxWk90tmtqemZnJEAqFIhaLNTgpKYnV1jh7qtZel9DQUG5kZGR//evt27fbTp061UX/uqnvvzmbNm2yq6ysNPq9cVBQkKujo6M30DDLJYfDkcycOdM44wF6IZKctMfNm0BxMXD2rFGK+/PPPxvXh/jmm28gFovB4XDg7Oxs8JjKykoUFxdjzZo1uHWr981u2NeTE73XX38dKSkpmD59OlauXAkPDw9ERkZ2aHevJ0+eYMeOHRAIBPjv//5vTJw4EWlpaRg6dGiHnZMgiN4hKCioQiaTZclksqzz58/nODo6qvUzLwUHB5cmJCTktKYcExMT3d69exW5ubm3EhMT74SHh7sCDQ/p5s6d+9qePXsUd+/evRUTE6PowI/zykJCQsq3bt3a7l/YEomkViaTZXl5eZGFi5owduzYiqSkpMYpLpOSkizGjBnTOEi3rd//gQMH7Kuqqjrk3pjFYml///135oULF9i2trY9cuX2zkKSk/YYNKjh34kTjVKcQqGAiYkJZs+ejVmzZjVub6nrDIvFwg8//NArbxpJcvJ/TExMGgc1//DDD7h79y48PT0xZcoU/PjjjygtLX3lc5SXl+P8+fP44IMPwOPx8Oeff+LIkSO4ceMGPvroox4xwxtBEC+jUChDDP3s2LHDVr/fjh07bJvbtz3njo2N5QQEBDT+gho7dmy1vb39czdlERERjlKpVMDlcr3CwsK4rq6uXoWFhSYDBw6sHz58uAoA+Hy+uq6ujqJSqSi//vory9raut7f378KABwdHRvL27x5s52bm5uYz+eLFi1a5NSemJuzbt06ezc3N/H48ePdamtrGwdxnj171uLNN9/kjRs3zo3H44nnzp07EAAmT57s6uDg4P3iE3KdTofp06e78Hg88Zw5cwa2tN2QuLg4S29vb0+BQCCaP3++4aeZvdjEiRMr09PT2frXqampFgEBARWA4e9foVCYjB492kMgEIjEYrHnjRs3GImJiRZCoVD0+PFj+siRI/lCoVCUl5dHB4AtW7bY8Xg8MY/HE+/evdtGX46h626Iv79/WXx8vFVCQoKVv79/mX67ofIN1WdD172j639nIuuctNXx4w3/3rtntCInTJiAa9euYcCA51se3333XZw8efKlmbpMTU1hbW2Nn3/+Gd7e3kaLozshycnLKBQKpFIppFIpvv76a5w8eRJfffUVrl27hv79+2Pw4MEYPHgwfHx8MHjwYNjZ2b00O5tGo0FxcTGuXbuGa9euISMjA9euXUNRUREkEgmmT5+Ob775BhwOp4s+JUEQvcWpU6dsDh06lNfSfhMmTChTKBSmXC5XTaPRyi9dumQeEhLSOL3c6dOn+4nFYiWTydTdu3fP1MLCot7Pz49XXFxMnzVr1pNVq1Y9AYBt27Y5KhSKGxwOR1tQUGDU+xu5XG567Nix/rdu3bolk8kYQ4cOfa5r2rVr19hJSUm3pVJpTXFxMQ0AEhIS8uotCD0AACAASURBVKKjo23S0tKe6wtbU1NDDQ4OLj1x4sT9oUOHCs6dO8ceN25claHtTcVTUFBgEhkZ6Xj16lW5hYWFdvz48W5nzpyxmDRpUp9auNDW1lZjb29fl56ebsZgMHRWVlb1Tk5O9YDh7z88PJzr7+9fvnLlyiclJSVUlUpFHTRoUGVgYGCWk5OT95UrV7IdHBzqgYbrfvDgQbubN29mqdVqyqBBg0RTpkwp15+jqetuiK+vr/LkyZPW9fX1lMDAwNK0tDTz5spvqj43d907sv53th4dfHtotVrodDqMGjWq7QfrdLiclIS7LBbmzp4NALh+/TrYbHbzxxlQXl4OS0tLAID7X2NYnuXn5/fSquEUCgVqtRq//vprk8f0FiQ5aV6/fv0QHh6O8PBwaDQa5ObmNiYbO3fuxPXr1/H06VPQaDRotVpQKBTQ6XRoNBpwOBy8/vrrGDx4MCZPnoxNmzaBz+f3mGmmid4nNzcXVVVV7fu93ASNRtOjZifsSDqdLr01+y1fvrx4+fLlxcY6b2ZmJkOlUlH1rR/Nsba2rq+oqKDp/y0rK2v8ZaRQKExWrVo1MDExMRcAVCoVNSMjg52RkZFlY2NT7+PjI5o0aVK5SCRSe3t7V8+YMcN1/Pjx5SEhIWWGz9h2ycnJLKlUWsVms3W+vr41Dg4O6mffF4lESqlUWgM03DA3VxaNRkNgYGAFjUbDqFGjKlJSUljjxo2rMrS9qTIuX75snp+fbyqVSoUAoFQqqbm5uQwAfSo5AYCRI0dW/Pvf/7YwNTXVjRw5sqKl/ZOTky0SEhLuAYC1tbUWgMHBnCkpKayhQ4dWWVpaagFAIpFUp6amMp2cnCqBtl13ALCzs6tjs9mN52uu/Kbqc3PXvSPrf2frc8nJq/zB+ptcDgD4cMj/tXCz2Wz079/f0CEG3bx5E35+ftiyZQs++uijJuOys7ODnZ0d8vPzATR045o7dy727t0LDw8P1NXVwcSkd15Ckpy0Ho1Gg0AggEAgwPTp0xu363Q61NfXY/To0dDpdLh48SLodDq5aSO6nfb8Dm0OhUIh9byLxcbG2gQGBpa0Zl/99dL/aDQaCgAolUrK5MmT3SMjIx+IxeJaAHBwcKhzd3ev4fF4agDw9vauvnnzJlMkEqmTkpJyzp8/z46Li7M+cOCA3a1bt24b6/O0VJ/69evX8vSZbSz72e0UCuWlKRv9/Pwqzpw5Y7xuHD3Uu+++W3HgwIH+pqam2tDQ0FbVOWNp63XftWtXAZVK1R0/frzF7gmG6rOh696R9b+z9c4722bop+e9fPly2w6sqgIsLIBly/Dzjh2vFENVVRWCg4NRXl6Oa9euNftLb/To0Th69ChYLBYOHjyIkJAQbNmyBVZWVqDT6V06xWxH0ul0Rr9h6Wv0rSX6FhEyboTorq5du2bU8nri9Om9TXx8vPWZM2daNfi9KVqtFtOmTXMNDg4umTJlSuPTcD8/P2VhYaHpo0ePaJaWllqZTMbi8/kPtVotcnNzTSdOnFjl6+urEgqFXsb5JA3eeOMN5fr1652VSiXl9u3bjIcPH7b7F6pGo8GZM2f6TZo0qeLy5cv9IiMj85vbDgAcDqf+/v37pgCUADBq1Kjq5cuXc+/cuUN3d3evy87ONjUzM9Nyudw+N9B67NixVYsXL3Y1MTHRHTt27H5L+w8fPrxyz549Nn/729+KKyoqqFVVVVT92CVzc3PNkydPaPpuXcOGDVN+9tlnzhUVFVS1Wk25ceOGuVQqbfckDC+2rhgq31B9NnTdnZ2d6zuy/ne2PpectJtA0PDvKyYmOp0OixYtglwuh1gsxp49e5rd/4MPPsBvv/2GU6dOYfBfiz1aWlpCJpNBKBRCKpUiNTX1lWLqrhwd2zy7I0EQBNHFLl68aM5isbQSieS5fslhYWHcc+fOWZWWlprY29sPioqKMngjeeHCBfZPP/3EycnJYcbGxvYHgHPnzuW4urrWffnllw9GjhwpqK+vpwQFBT0dPHhwjUajQUhIyGuVlZU0rVZL2bhxY76hstuDz+erQ0NDn3h5eYl4PF6Nk5NTbXP7y+Vy0/fee8+jvLycVlNTQxUKheyNGzcWTJs2rdzMzEwbFxdnvWLFioFvvfVWxbvvvlsNAIa2A8CyZcseLViw4LUvvvjCUf897Ny5UxEQEMDTaDRgMpnauLi4ewD6XHLCZDJ1Li4uNRqNhsJms3VA89///v37FbNnz3aNiYmxo9Ppuri4uLv65CQ8PPzx5MmTPaysrOoTEhLuCAQC9YIFCx4PGTLEEwBWrFhRqB9vYgyGyjdUn52cnOqbuu46na6+I+t/Z6P08CfvbQ5e/0StrOyF7ni1tcDp06javRemWbdgoqxCPYsNtUgM9pTJwIoVQGIi8N57zZZfW1uL06dPIyp6H27f+hMqZRWYLDY8xV5Y9skSlJWVYfHixWCxWEhLS4Onp2dbP0Kjf/3rX5g4cSKWLl2KqKioJvfR9+Fuc0tRF6NQKBg/fjz+9a9/dXUoPV5PrQOE8fS1OmDw93zr9Oj+YJmZmXkSicRoY0d6u+joaJu8vDxGVFQU6UvcjZDr0jdkZmbaSiQS1xe3k5YTAJqYGNStXIPs/q447DUeqW8tRzWDBfNaJaT3M7F7xYqG/Z48QXNDhmNiDuNvq1bD3MEdnKHvwSdwPWgMFjS1SpTlXEPE5ig8zr0JUCjYv3//KyUmQMMsX5GRkVi9ejWGDBmCkJCQVyqvuyEtJwRBEARBEH1Ln09O1J9vRNn+wwgP2oIcu9eee6+CaQFTTR0AYNG0Ldi4bhOs8gtg+vmGl8pZ//lG7D34HXjzo8B2fH4WLSqrH/pLRqK/ZCQGFt7BrUN/Q86du0aJf9WqVbh69SpCQ0MhFAoxZEi7pqPvlkhyQhAEQXQkX19fpZubm7rlPYnORK5L39ankxNNTAzK9h/GlA92oMS8iYkTdDpsT4zEPWtn/OIxFFMcePjH/uWwcXYCbf78xt1iYg5j78HvIProG5haWDd7TrajOwZHxGDvvsXgOjtj/vx5r/w5zpw5A3Nzc/j6+uLhw4cvrZfSU5HkhCAIomOoVCrKkSNHON8ciBmQmyM3q1EpKWZMls6DJ6j56MMFRTNnzixlMpk9ut93a+hXrye6F3Jd+ra+u0J8bS3qVq7Bwknrmk5MAHz2UzQAYMr8bwEAJeYchE9ah7qVawC1+q9iavG3VavhMfOLFhMTPVMLa3jM/AJ/W7UaarVxHgxUVTVMhe7g4GC0MrsaSU4IgiCML2rnThsHp4Gvf/ntERebt0OY7+38iRJ6JBXv7fyJYvN2CHPrN9+7ODgNfD1q506blksjCIIwrr7bchIfj+z+rsh9oSuXHlOtwrRrP+K4NBC1dEbj9hy715Dd3wWD4uOB6dMRHx8Pcwf3l7pytYTt6A7WADfEx8c/tzZFe1EoFFRXV8Pc3BwMBqNx4b2ejCQnBEEQxrV02XLHoydP2Y9ceYDK4fKee4/B7gcX6Ri4SMdQSxU5+GJ7BPdBfgFj59c7yKBkgiA6TZ9tOanatQeHvcYbfD/h4EIAwJfvLn7pve+8JqBqV8MUwF/v3gvO0OZn8DLEeth7+Hr33nYd2xQWi4W8vDwAgJubm9HK7SokOSEIgjCeqJ07bY6ePGX/zvrYlxKTF3G4PLyzPpZ69OQpe9KCQhBEZ+qzyYlp1i2kukiafM+59CGcyx/h06DPmnz/D5dBoGf9CQC4fetPWPEGtysGKw8f3L71Z7uONcTFxQVXrlxBXl4e5s6da9SyO0t9fcMU4nZ2dl0cCUEQRO+gUqkomzZ/wX3r0ygq07J1uQbT0gZvfRJF3bT5C25NTU2rmuJPnz7dTygUivQ/dDrd5+rVq0wAWLhwobONjY2Ex+OJWyqnqKiI5uXl5SkQCERCoVB07NixxpU1Y2JiOC4uLl6urq5ecXFxlq36MAZER0fbREREkCdh3Uxrr0toaCg3MjKyccXm7du3206dOtVF//rEiROWa9asafVA3E2bNtlVVlYa/d44KCjI1dHR0RtouMfhcDiSmTNnco19nt6izyYnJsoqVDNYTb6noVLxzVuh+LfQr8n3laYs0KsbxniolFWgGSinJTQzFpTVle06tjkjRozAvn378P3336OwsOe1xj969AgAYGLSd3sdEgRBGNPRo0c5VgN54AxsvsXkRRwuD5YDPXD06FGrlvcGgoKCKmQyWZZMJss6f/58jqOjo1o/uDk4OLg0ISGhVavGczgc7W+//SaXy+VZly5dyo6IiOBqNBrU1NRQNmzY4Pzrr7/KLly4kL1y5cqBGo2m5QKJXmns2LEVSUlJFvrXSUlJFmPGjGm8sQoJCSnfunVrUWvLO3DggH1VVVWH3BuzWCzt77//zrxw4QLb1ta2zy2W2RZ9NjmpZ7FhXqts8r2Hlvb4ZuRMg8ey1ErUmbMBAEwWGxoD5bREU6MEy9yi5R3bYfHixZg9ezZycnLauxBZl+mJCRVBEER3tm//oQGvjZnarr/5bqOnUvfuP9TmaSBjY2M5AQEBpfrXY8eOrba3t3/upiwiIsJRKpUKuFyuV1hYGNfV1dWrsLDQhMFg6CwtLbUAUFJSQlOr1dS6ujrK5cuXzfl8vmrgwIH1PB5P7eDgUJeSksIEgM2bN9u5ubmJ+Xy+aNGiRU7t+ayGyOVyUy6X6zVhwgQ3d3d38dq1axu/j9GjR3sIBAKRl5eX59atWxuf4huKpyPj7GsmTpxYmZ6ezta/Tk1NtQgICKgAgMmTJ7s6ODh4v9hCoVAoTPTXTCwWe964cYORmJhoIRQKRY8fP6aPHDmSLxQKRXl5eXQA2LJlix2PxxPzeDzx7t27G5sdz549a/Hmm2/yxo0b58bj8cRz584d2Fys/v7+ZfHx8VYJCQlW/v7+jTdmhso3VE/i4uIsvb29PQUCgWj+/PnOLe3fE/XZR9NqkRjS+5n4j/CtNh879P4N1Im8wADgKfZCWc419JeMbHM5ZbkZ8BR7tfm41vr+++9x8uRJZGZmQqFQgMvtGS2IJDkhCIIwrtwcuZl4sbRdxw4QSfFHzOfMth536tQpm0OHDuW1tN+ECRPKFAqFKZfLVdNotPJLly6Zh4SElJeWllKHDRsmfPDgAWP37t15ZmZmusLCQrq9vX3dtm3b+ltbW9fb2dnV5efn0wGotm3b5qhQKG5wOBxtQUGB0e9v8vPzGYmJibl8Pr9WLBaLZ82aVcLn89WHDh267+7uXldbW0sRCATi0NDQUi6XW28ono6Osy+xtbXV2Nvb16Wnp5sxGAydlZVVvZOTUz0AJCQk5EVHR9ukpaWZP3tMeHg419/fv3zlypVPSkpKqCqVijpo0KDKwMDALCcnJ+8rV65kOzg41AMNSenBgwftbt68maVWqymDBg0STZkypVx/jmvXrrGTkpJuS6XSmuLi4ubW6Yavr6/y5MmT1vX19ZTAwMDStLQ08+bKb6qeFBQUmERGRjpevXpVbmFhoR0/frzbmTNnLCZNmlTZm+pVn205YX/6Meb9+VO7jp3757/A/vRjAMCyT5ag9I//bVc5pSn/i2WfLGnXsa01bNgwAA1jUZTK9rXwdDaSnBAEQRhXjUpJoTPb1wWZbsaCSlndpukfMzMzGSqVijp8+PAW16uwtraut7Ky0lhbW9dzOBxNWVkZDWjo2pWdnZ3122+/3T5w4IBdbW1tYwwrVqx4Mn/+/NJny/H29q6eMWOGa3R0tI2ZmZnR12hxcHBQ+/j41LDZbJ1UKq1KTk5mAcC+ffv6e3p6iiQSiefjx4/pCoXCtLl4OjrOvmbkyJEV//73vy0uXLhgMXLkyIqW9k9OTrb45JNPigHA2tpaq080mpKSksIaOnRolaWlpbZ///4aiURSnZqa2pioi0QipVQqrQEaEqWWzm1nZ1fH5/NrWlN+U/Xk8uXL5vn5+aZSqVQoFApFt27dYuXm5jIM7d9T9dnkBO+/D/6TPPAe32vTYbzH98B/ch94//2/inkf1Q/voKrwTpvKqSq8g+qiu3j/r3I6CoVCgZ9fw9gZc3Nz6HTdv76S5IQgCMK4zJgsXZ2qfQ+o6mqUYLLM2/THIzY21iYwMLCkNftSKJTnfjQazXOJkI+PTw2dTtelpKQwnZyc6h49ekTXv/f48WO6s7NzHQAkJSXlLFmy5HFKSor5iBEj+G2Jt73Onj1rcenSpX5//PGHTCaTZbm5udVotVo0F09XxNmbvfvuuxVJSUkWSUlJ7HfffbfF5MSY+vXr16YBT7t27SrYunVrq25yDNUTPz+/xnFdCoXiz4iIiOLm9u+J+m5ywmCA/tVWHDizBdbVpS3vD8C6uhQHzmwB/autyFUoUF5eDgaDge1fRiL36FqoK1v1exjqyhLkHl2L7V9GwtTU9FU+RatQqVQ8fPgQANCvX78OP9+rIskJQRCEcXnwBDVFt1PbdWxRVirceYI2rdgdHx9vPXPmzNb9UWzCvXv36EVFRTSgYYxAbm4u08XFpW7kyJHV2dnZzIKCApPc3Fx6UVERfdiwYSqtVovc3FzTiRMnVkVFRRUUFhYa/Y/rw4cPTTMzMxlKpZKSlpbGHjZsmLK8vJzK4XDqLSwstGlpaWYymYwJAIbi6Yw4+5qxY8dWZWZmml+7do3t7+/f4ixDw4cPr9yzZ48NAFRUVFALCwsbu0CZm5trnjx50tg9a9iwYcrU1FR2RUUFtbi4mHbjxg1zqVTa7tXrbW1tNdbW1tqWyjdUT0aNGlWdmprKvnPnDh0AsrOzTRUKhUlvq1c9uk/aq6LNnw+r/AL8Y/9yhE9ahxwDCzICDS0mB85sgdWH86CcNg0BUinq6+tx6tQpzJ8/D4r8fOzdtxgeM79odkHGqsI7yD26FksWzsX8+fM64mM1acCAAUhNTYVUKkVgYCASExNRW1uL06dPY+eefbh960+oqqvANGfDU+yFiP9egvfffx8MBqPlwo2MJCcEQRDG9dGHC4oiv4l1cZGOafNDyXsX/0e75qMFrZ7x6OLFi+YsFksrkUhqn90eFhbGPXfunFVpaamJvb39oKioqPuGyrhz547p4sWLXQBAo9FQNmzYkO/i4lIHAJ9//nn+m2++KQSAL7/88gGNRoNGo0FISMhrlZWVNK1WS9m4cWN+Wz9nS5ycnGqXL1/ufPfuXbPQ0NAnAoFA7eLiUhcTE9Pf3d1d7O7uXiMSiZQAoNPpmozH0Hai/ZhMps7FxaVGo9FQ2Gy2DmgYK/Lee+95lJeX02pqaqhCoZC9cePGgmnTppXv379fMXv2bNeYmBg7Op2ui4uLu+vo6FgPAOHh4Y8nT57sYWVlVZ+QkHBHIBCoFyxY8HjIkCGeALBixYrC5rqBtZWh8g3VZycnp/qdO3cqAgICeBqNBkwmUxsXF3dPp9PV96Z6RekJ3Xya0ebgrawaZkN8dgYrTUwM6lauQXZ/F3znNQF/uAyC0pQFllqJofdvYN7NH8ErVoD+1VbQ5s/HgwcPMGnSJFy/fh0MBgPR0dFYsGABDh/+Dn9btRqsAW6wHvYerDx8QDNjQVOjRFluBkpSEqEsuoftX0Z2WmIyatQoAMDly5cBAMePH0dYWBiCgoJw8covsBroAZdRUzDAUwo6k4U6lRJFt1Nx/9IplOXfwbbIrZ2aRAHA66+/jszMzB7RBa0neLEOEH1PX6sDTf2eb4M2ja3objIzM/MkEknxi9tVKhXFwWng602tDN+cUkUOrnwVri0qzL/e0/uxNyU6OtomLy+PERUV1exTMblcbhoQEMDLycm51Vmx9WWtvS5Ez5aZmWkrkUhcX9zep1tO9Gjz54MWFoZB8fHYsnsv6D/vAr26CnXmbNSJvMDeuLxhjMlfXbAGDhyIq1ev4tNPP8XBgwcRHh6OX375Bfv370dYWCji4+MRFb0P1059BWV1JVjmFvAUe+HrdUvx/vvvd0pXLkNCQ0Oxd+8+nD59GsPmrIFw7LTn3mew+8FFOgYu0jEoVeRg9efL8KCgABs3rO+0GEnLCUEQhHExmUzd+s/WKr7YHsF9Z31sqxZiVJU/xa+7I7TrP1ur6I2JCUEQ3RNJTvQYDOCDD8D+4IP/2/TXT1OYTCYOHDgAPz8/hIeH4/jx48jIyMCpU6fwwQcf4INnyulOYmIO407+Q3C4fKR8vxUOXsNg6eDa5L4cLg+j132Pb7bMwUAnp05rQXny5Amo1L47HIogCKIjRCxd+vRBfgHj6KbZ9m99EtVsC0qpIge/7o7Qzvwg+FHE0qVPOzHMTuXr66t0c3NTt7SfQCBQk1aTztPa60L0TiQ5eUWhoaEYPHgwgoODkZWVhfT0dIhEoq4Oq0m1tbVYsXoN/P72DTgDeTgyQ4LEZe/hg5hfYcpqejFIpqUN3vz4a6xYvRgzZ4Z1WquPo6Njp5yHIAiiL9n59Y7Cgc5OtZs2h3MtB3rAbfRU6gCRFHQzFupqlCjKSsXd//ygLc+/g/WfrVX05sQEAPSr1xPdC7kufRt5PG0EYrEYf/zxBw4fPoywsLCuDseg+Ph4WA30AGdgw9OysGPpAICT89+CVmt4NjwOlwcrZ3fEx8d3SpwASU4IgiA6SsTSpU8fFjy4vmbxnPtPr5xUnomYqDs+eyjOREzUPb1yUrl2yby8osL86709MSEIonsiyYmRsNlszJ07t6vDaFZU9F64jJrS+JpKM8H0g78AAI6F+jR7rMuoKYiK3tuh8T2LJCcEQRAdh8lk6sLDw0tuZKTerq6syNBqNOnVlRUZNzJSby9cuLCUjDEhCKKrkOSkD7l9608M8JQ+t43B7odJ204DAJ7k3jR47ACRFLdv/dmh8T2LJCcEQRAEQRB9Dxlz0oeoqqtAZ7Je2s5x9kDY8Yxmj6WbsaCsanFtI6MhyQlBEARBEETfQ1pO+hCmORt1KmWT71GpNFCptCbfA4C6GiVY7KYHzXcEkpwQBEH0PKdPn+4nFApF+h86ne5z9epVJgAsXLjQ2cbGRsLj8cQtlVNUVETz8vLyFAgEIqFQKDp27JiV/r22lNOS6Ohom4iIiDb/weHxeGK5XN7iDDEnTpywXLNmzYBnt7FYrMFN7dvU9szMTIZQKBSxWKzBSUlJLz9d7KVae12io6Nt6HS6z8OHD00A4O233/Y4e/Zsp92s5OXl0ceNG+fW3uODgoJcHR0dvQGgvr4eHA5HMnPmTG5Lx23atMmusrLypXv4pupbT0SSkz7EU+yFotup7Tq2KCsVnmIvI0dkGElOCIIgep6goKAKmUyWJZPJss6fP5/j6Oio1s+8FBwcXJqQkJDTmnI4HI72t99+k8vl8qxLly5lR0REcDWaholb2lJOVwsJCSnfunVrUXuPl0gktTKZLMvLy6vpJ4sEzMzMtN999511V5zb1dW17ty5c3dfpQwWi6X9/fffmRcuXGDb2tq2avX5AwcO2FdVVb10D/+q9a27IMlJHxLx30tw//I/2nXs/cv/QMR/LzFyRIaR5IQgCOLVUSiUIYZ+duzYYavfb8eOHbbN7duec8fGxnICAgJK9a/Hjh1bbW9v/9zNV0REhKNUKhVwuVyvsLAwrqurq1dhYaEJg8HQWVpaagGgpKSEplarqXV1dRRD5QDA5s2b7dzc3MR8Pl+0aNEip/bE3Jx169bZu7m5icePH+9WW1tL0W8/e/asxZtvvskbN26cG4/HE8+dO3cgAEyePNnVwcHB+8Un4TqdDtOnT3fh8XjiOXPmDGxpuyFxcXGW3t7engKBQDR//nxnY37WnmTMmDHlCQkJnBe3N/X9GKpvADB69GgPgUAg8vLy8ty6dWt/fTksFmvwrFmzBrq6unpNnz7dRb89PDzc2d3dXfxiC56hcgzx9/cvi4+Pt0pISLDy9/cva66cxMREC6FQKHr8+DF95MiRfKFQKMrLy6MDhuubQqEw0ZclFos9b9y4YWgJv26DJCd9yPvvv4+yB7koVbTtgVOpIgdl+Xfw/vvvd1BkLyPJCUEQRM926tQpm9DQ0JKW9pswYULZmDFjyt3c3GpHjRpVfunSJXMAKC0tpfL5fJGvr69o+/bt91uaQWzbtm2O6enpt7Ozs7PWrVv3yFifAwDkcrnpsWPH+t+4cSNr8+bNhfn5+c/d4F27do29efPmwpycnFvbtm0rBICEhIS81atXF75YVk1NDTU4OLhUJpPdunHjBuvcuXPs5rY3paCgwCQyMtLx6tWrcrlcnlVQUGB65syZzut73Y3069dPM2DAgLqMjAwz/bbmvh9D9e3QoUP35XJ5Vnp6uuzgwYP2CoXCBABUKhU1NDS0RC6X37py5Uo/fTJw4MCB/H/9618v3VAZKscQX19fZWZmJis3N9eMz+fXNFdOYGBgpUwmy7Kzs6u7cuVKtkwmy3J1da0DDNe38PBwrr+/f7lcLs/65Zdf5DY2NobXjugmyID4PoTBYGBb5Fas/nwZRq/7HkxLmxaPUZU/xcUdS7BpzepOWYBRpWpYd8nauktaaAmCIHoVnU6X3pr9li9fXrx8+fJiY503MzOToVKpqMOHD29xMT1ra+v6iooKmv7fsrIyGtDQtSs7OzsrIyPDbMGCBS6zZs0qYzAYBhMUb2/v6hkzZriOHz++PCQkpMzQfu2RnJzMkkqlVWw2W+fr61vj4ODw3OrlIpFIKZVKawDA1ta22Zs/Go2GwMDAChqNhlGjRlWkpKSwxo0bV2Voe1NlXL582Tw/P99UKpUKAUCpVFJzc3MZADpv5ppuJCws7GlMTEzjTU0z34/B+rZv377+P/30k5VOp8Pjx4/pCoXClMvl1tPpdN3YsWOrAcDZ2Vmdn59P1ycETTFUTnPx29nZ1bHZbO2rltOU5ORki4SEhHt/fXYtAG0Lh3Q50nLSx8yfPw+LF8zFxS1zWmxBKVXk4F/rZkBZWowjR2JRUtLiA7BX9vDhQwAAJzQXYgAAIABJREFUhUJpYU+CIAiiu4qNjbUJDAxs1R8NCoXy3I9Go3nuD4CPj08NnU7XpaSkMJsrJykpKWfJkiWPU1JSzEeMGMF/lfibirE5/fr1a/fTaENlP7udQqG8lJT5+fk1ju9RKBR/RkREGC257GmCgoLKk5KS+mm1/3ffbej7aaq+nT171uLSpUv9/vjjD5lMJstyc3Or0ZdlYmLS+N1TKBQ8e44XNVdOc3bt2lWwdevWxlaP9pbTW5DkpA/auGE9Ij//DL/sWIykbR/i/h//Rm1VObT1daitKsf9P/6NK1+F45cdi/H5mr/Bw90NmZmZePfdd1FWZtSHUS8pLHypRZIgCILoYeLj461nzpzZ7ida9+7doxcVFdGAhj7zubm5TBcXF4NPq7VaLXJzc00nTpxYFRUVVVBYWGjUpv433nhDmZaWxlYqlZT09HSzhw8ftrt8jUaDM2fO9NNoNLh8+XK/N954o7q57QDA4XDq79+/33jOUaNGVaemprLv3LlDB4Ds7GzTlroP9WYmJiZ44403KjMyMthA27+f8vJyKofDqbewsNCmpaWZyWSyZhNhY5dja2ur+atVo1XlmJuba548eWJ4itVnDB8+vHLPnj02AFBRUUHVj7Hpzkhy0kfNnz8PD/MV+GLFx6hOicfZ5f8PJ2YPxdnl/w/VKfGIXPUJigoeYFlEBC5evAg3Nzekp6dj/PjxqKzsuFZjkpwQBEH0bBcvXjRnsVhaiURS++z2sLAw7ltvvSW8d+8ew97eftCJEycsDZVx584d0xEjRgj4fL7o7bffFmzYsCFfn5w0VY5Op0NISMhrfD5f9F//9V/CjRs35hvzM/H5fHVoaOgTLy8v0Zo1a5ycnJxqm9tfLpebCoVCUWRkpOM///lPjlAoFP3www+WQMPsUnFxcdZCoVA8aNAg5bvvvlvd3HYAWLZs2aPPPvvMWT8A2snJqX7nzp2KgIAAHp/PFwUHB7tVVla26ma1t5o3b97TqqoqGgC09fsJCgqq0Ol0cHd3F69Zs8ZJJBK1ena0Z1u1XqWctsQTHh7+ePLkyR5DhgwRKBQKk+bq2/79+xU//fSTlUAgEL355puC4uLibl9PKDpds+PLurs2B29l1TBVeke3AHQXo0aNAgBcvnz5lcq5f/8+RowYAYVCAT8/P/z0008wNzd/9QBfsGvXLixduhQ9vF52K8aqA0TP1dfqwCv+nu/RfUozMzPzJBJJn+3e01bR0dE2eXl5jKioKPJkrBvpKdfl559/Nl+/fr1TcnJydlfH0hNlZmbaSiQS1xe3k5YTolVcXFxw8eJFODk54ZdffsE///nPDjkPaTkhCIIgCKI7y83NpQuFQtGCBQteW7FiRY9fV6S76fb9zojuw93dHf/5z39w+fJlTJ8+vUPOQZITgiAIorP4+voq3dzc1C3vSXSm7n5dPDw86mQyWVZXx9Fb9bnkRKVSQaPRNHZz6O2uX78ONtvgVOltJhAIIBAIGl/n5+fD3t4edDrdKOWT5IQgjC83NxdVVVV95vdeVVUVaLRu3626S6lUKsqRI0c4e/cfGnAnN9usVqWkMJgsnbsHv+bjRQuLZs6cWcpkMnt9/1r96vVE90KuS9/W57p10en0PvVHi81mo3//FhcobZfs7Gy88cYbCAkJQX19m6febhJJTgjC+Pr372/UhxTdHY1GM9oDk94oKmqnzQCnga9v3PO9C2VIMNN33T8oI3Zchu+6f1AoQ4KZn+8+7DLAaeDrUVE7W14MiyAIwsj6XMuJr68vgL4zMLQjVVRUoLKyEqdOnYKpqSmOHDnyyokfSU4IwviuXbvW1SF0qr7SQtQen0Ysc4yN+x97wcJdVLaj+3PvUVn90F8yEv0lI6lVhXewadtqriI/n7Er6mvyi5kgiE7T51pOCOPx9fXFuXPnwGazceLECSxcuLDZxYlao7KyEkxmu6YXJwiCIJoRFbXTJjbuf+y9Pt7/UmLyIrajO7w+3k+Njfsfe9KCQhBEZyLJCfFKhg8fjh9//BFMJhPfffcdlixZ8srTADs6OhopOoIgCAJoGGOyccsXXP7sSKqphXWrjjG1sAZ/diR145YvuDU1Na2aYvn06dP9hEKhSP9Dp9N9rl69ygSAhQsXOtvY2Eh4PJ64pXKKiopoXl5engKBQCQUCkXHjh2zam57e0VHR9tERESQPzrdTGuvS3R0tA2dTvd5+PChCQC8/fbbHmfPnrXo+Agb5OXl0ceNG+fW3uODgoJcHR0dvQGgvr4eHA5HMnPmTG5Lx23atMmusrLypXv4EydOWK5Zs2ZAe+PpLkhyQryyESNG4MyZM2AwGPj2229feZ0SkpwQBEEY19GjRzksB3e01GLyIrajO1gD3HD06NFWJQFBQUEVMpksSyaTZZ0/fz7H0dFRrR/cHBwcXJqQkJDTmnI4HI72t99+k8vl8qxLly5lR0REcDUajcHtRN9lZmam/e6771qXcRuZq6tr3blz5+6+ShksFkv7+++/My9cuMC2tbVt1QDeAwcO2FdVVb10Dx8SElK+devWHj+1MUlOCKN45513kJCQAFNTU3C5XFAo7V/HjCQnBEEQxrXn24MDbN+Y3K6/+bbDJ1P3fHuwzU9jY2NjOQEBAaX612PHjq22t7d/7uYrIiLCUSqVCrhcrldYWBjX1dXVq7Cw0ITBYOgsLS21AFBSUkJTq9XUuro6iqHtALB582Y7Nzc3MZ/PFy1atMipPZ/VELlcbsrlcr0mTJjg5u7uLl67dm3j9zF69GgPgUAg8vLy8ty6dWvjDDSG4unIOPuiMWPGlCckJHBe3B4XF2fp7e3tKRAIRPPnz3cGDNc3wPB1ZLFYg2fNmjXQ1dXVa/r06S767eHh4c7u7u7iF1sCDZVjiL+/f1l8fLxVQkKClb+/f+PKsU2Vk5iYaCEUCkWPHz+mjxw5ki8UCkV5eXl0AJg8ebKrg4OD94stLwqFwkRfllgs9rxx4wajtd9tV+lzA+KJjjN+/HjI5XK4urq+UjkkOSEIgjCuO7nZZr7TB7frWCsPH6T/ENnmwYCnTp2yOXToUF5L+02YMKFMoVCYcrlcNY1GK7906ZJ5SEhIeWlpKXXYsGHCBw8eMHbv3p1nZmamAwBD27dt2+aoUChucDgcbUFBgdHvb/Lz8xmJiYm5fD6/ViwWi2fNmlXC5/PVhw4duu/u7l5XW1tLEQgE4tDQ0FIul1tvKJ6OjrOv6devn2bAgAF1GRkZZvptBQUFJpGRkY5Xr16VW1hYaMePH+925swZC8BwfTN0HVUqFTU0NLTk4MGD+a6url55eXl0V1fXugMHDuTL5fLHAQEBvGfjMVSOofh9fX2VJ0+etK6vr6cEBgaWpqWlmRsqJzAwsDIwMDDLycnJ+8qVK9kODg6N5SYkJORFR0fb6I/XCw8P5/r7+5evXLnySUlJCVWlUnX7holuHyDRszybmOTk5GDfvn1tLoMkJwRBEMZVq1JSaAxWu46lmbFQo6puU3N4ZmYmQ6VSUYcPH97iehXW1tb1VlZWGmtr63oOh6MpKyujAQ1du7Kzs7N+++232wcOHLCrra2lNLfd29u7esaMGa7R0dE2+oTFmBwcHNQ+Pj41bDZbJ5VKq5KTk1kAsG/fvv6enp4iiUTi+fjxY7pCoTBtLp6OjrMvCgsLexoTE9M4ccPly5fN8/PzTaVSqVAoFIpu3brFys3NZQCG65uh60in03Vjx46tZjAYOmdnZ3V+fn6z85QbKqc5dnZ2dXw+v+ZVy2lKcnKyxSeffFL812fXOjk5GWfthw5EkhOiQ1RXV+Ptt9/GkiVLsHPnzjYdS5ITgiAI42IwWTpNrbJdx2pqlDBjmrfpJjo2NtYmMDCwpDX7UiiU5340Gs1ziZCPj08NnU7XpaSkMJvbnpSUlLNkyZLHKSkp5iNGjOC3Jd72Onv2rMWlS5f6/fHHHzKZTJbl5uZWo5+10lA8XRFnbxcUFFSelJTU79kZQ/38/BrHPykUij8jIiKKgabrW3PX0cTEpLHuUyiUZmclba6c5uzatatg69atjVN2t7ec3oIkJ0SHMDc3x6ZNmwAAERER+Oabb1p9LElOCIIgjMvdg19TltO+9W7KcjPgzuO3acXu+Ph465kzZ7YqOWnKvXv36EVFRTSgoc98bm4u08XFpc7Qdq1Wi9zcXNOJEydWRUVFFRQWFrbrKXNzHj58aJqZmclQKpWUtLQ09rBhw5Tl5eVUDodTb2FhoU1LSzOTyWRMADAUT2fE2ReZmJjgjTfeqMzIyGADwKhRo6pTU1PZd+7coQNAdna2qUKhMNiFztB1bKv2lmNra6uxtrZuzD5aKsfc3Fzz5MmTVi0sN3z48Mo9e/bYAEBFRQVVP8amO+v2ARI919y5c1FbW4vFixfjo48+AoPBwLx58xrfX716NXJzc2FpaQkOhwNLS0sAwH/+8x88evQIbDYbbDYbjo6O4PF4hk5DEARBtODjRQuLPo/+zqW/ZGSbH0oW/56g3fjpwlbPAHTx4kVzFoullUgktc9uDwsL4547d86qtLTUxN7eflBUVNR9Q2XcuXPHdPHixS4AoNFoKBs2bMh3cXGp+/e//23e1HaNRoOQkJDXKisraVqtlrJx48b8tn7Oljg5OdUuX77c+e7du2ahoaFPBAKB2sXFpS4mJqa/u7u72N3dvUYkEikBQKfTNRmPoe3Eq5s3b97Tw4cP2wOAk5NT/c6dOxUBAQE8jUYDJpOpjYuLu2fo2KCgoIqmrmNrUCiUxpaVVymnLfGEh4c/njx5soeVlVV9QkLCHZVKRX3vvfc8ysvLaTU1NVShUMjeuHFjwbRp08r379+vmD17tmtMTIwdnU7XxcXF3XV0dOzWXbsor7omRRdrc/D6lYPJCvGdZ9euXVi6dCkoFAqOHDmCsLAwAMCgQYNw8+bNxv30M3wxmUzQaDRQqVTU19eDTqfj6dOnoFJJQ197kDpP9DWvWOfbP9VgN5CZmZknkUiKX9yuUqkoA5wGvt7UyvDNqSq8A/nBT7WPCvOv98bxEdHR0TZ5eXmMqKiowub2k8vlpgEBAbycnJxbnRVbX9ba69LVfv75Z/P169c7JScnZ3d1LD1RZmamrUQicX1xO7nbIzrcp59+ii+//BI6nQ5z5sxBdnbD/8MbN24Em81u3E+n00Gn00GpVKKyshLl5eXQarVYtGgRSUwIgiBeAZPJ1G1Yt1aRHbtaq65sXW8rdWUJsmNXazesW6vojYkJQbRXbm4uXSgUihYsWPDaihUrevy6It0N6dZFdIqVK1eitrYW1tbW4PMbxv9NmjQJFhYWqKqqMngci8XCunXrOitMgiCIXisiYulTRX4+I3bPh/b82ZHNtqBUFd5Bduxq7ewZUx9FRCx92olhdipfX1+lm5ubuqX9BAKBmrSadJ7WXpeu4uHhUSeTybK6Oo7eiiQnRKdZv379c681Gg3WrVuHFStWoLq6+qX9zc3NsW/fPrBY7Zv+kiAIgnjerqivC7nOzrUbt3zKZQ1wg+3wyVQrDx/QzFjQ1ChRlpuB4t/jtcqie9iwbq2iNycmAKBfvZ7oXsh16dtIXxmiS8hkMnh6eoLL5TbZZYtCoUAoFGLq1KldEB1BEETvFRGx9GlRwYPrn38y7z4yTivTvwjWJS0fhfQvgnXIOK3c+OmCvEeF+dd7e2JCEET3RFpOiC5x7Ngx3L17F1OnTsWUKVNw6tQp1NT83/pDZmZmOHz4cOMgeYIgCMJ4mEymLjw8vCQ8PLzd0/0SBEF0BNJyQnSJzZs3Y968eVCpVDh9+jSenTXO1NQU06ZNg0Qi6cIICYIgCIIgiM5GkhOiS1CpVBw4cABhYWFQKpXQarWg0RrWEzI1NcX27du7OEKCIAiCIAiis5HkhOgyNBoN3333HaZOnYq6ujpoNBqYmZlhy5YtsLW17erwCIIgiDY6ffp0P6FQKNL/0Ol0n6tXrzIBYOHChc42NjYSHo8nbqmcoqIimpeXl6dAIBAJhULRsWPHrJ59v7S0lGpnZzdo/fr19q8Sb3R0tE1ERIRjW4/j8XhiuVze4uruJ06csFyzZs2AZ7exWKzBTe3b1PbMzEyGUCgUsViswUlJSX1mdpjWXpfQ0FBuZGRkf/3r7du3206dOtVF/7qp7785mzZtsqusrDT6vXFQUJCro6OjNwDU19eDw+FIZs6cyTX2eXoLkpwQXcrExATHjx9HYGAggIYZuj766KMujoogCIJoj6CgoAqZTJYlk8myzp8/n+Po6KjWz7wUHBxcmpCQkNOacjgcjva3336Ty+XyrEuXLmVHRERwNRpN4/tr16518PLyatfq250pJCSkfOvWre1eB0MikdTKZLKsnvBZu8LYsWMrkpKSLPSvk5KSLMaMGVOpf93W7//AgQP2VVVVHXJvzGKxtL///jvzwoULbFtb2269QntXI8kJ0eXodDr+/ve/4x//+Afu3r0LExMyTwNBEIQxUCiUIYZ+duzY0dhEvWPHDtvm9m3PuWNjYzkBAQGl+tdjx46ttre3f+6mLCIiwlEqlQq4XK5XWFgY19XV1auwsNCEwWDoLC0ttQBQUlJCU6vV1Lq6OgrQ0Jrw5MkT+uuvv/7cDfvmzZvt3NzcxHw+X7Ro0SKn9sTcnHXr1tm7ubmJx48f71ZbW9s4W8vZs2ct3nzzTd64cePceDyeeO7cuQMBYPLkya4ODg7eLz4h1+l0mD59uguPxxPPmTNnYEvbDYmLi7P09vb2FAgEovnz5zsb87P2FBMnTqxMT09vXM05NTXVIiAgoAIw/P0rFAqT0aNHewgEApFYLPa8ceMGIzEx0UIoFIoeP35MHzlyJF8oFIry8vLoALBlyxY7Ho8n5vF44t27d9voyzF03Q3x9/cvi4+Pt0pISLDy9/cv0283VL6h+mzound0/e9M5C6Q6BYYDAaCgoK6OgyCIAjCSE6dOmVz6NChvJb2mzBhQplCoTDlcrlqGo1WfunSJfOQkJDy0tJS6rBhw4QPHjxg7N69O0+/Sv3y5cud9+3b92D//v3P9f/dtm2bo0KhuMHhcLQFBQVGvb+Ry+Wmx44d63/r1q1bMpmMMXTo0Oe6pl27do2dlJR0WyqV1hQXF9MAICEhIS86OtomLS3N/Nl9a2pqqMHBwaUnTpy4P3ToUMG5c+fY48aNqzK0val4CgoKTCIjIx2vXr0qt7Cw0I4fP97tzJkzFpMmTapsav/eytbWVmNvb1+Xnp5uxmAwdFZWVvVOTk71gOHvPzw8nOvv71++cuXKJyUlJVSVSkUdNGhQZWBgYJaTk5P3lStXsh0cHOqBhut+8OBBu5s3b2ap1WrKoEGDRFOmTCnXn6Op626Ir6+v8uTJk9b19fWUwMDA0rS0NPPmym+qPjd33Tuy/ne2Hh08QRAEQRCG6XS69Nbst3z58uLly5cXG+u8mZmZDJVKRR0+fHiLi+lZW1vXV1RU0PT/lpWV0YCGrl3Z2dlZGRkZZgsWLHCZNWtW2enTp/t5eHjU8Pn8l1YP9/b2rp4xY4br+PHjy0NCQspePlP7JScns6RSaRWbzdb5+vrWODg4PHd+kUiklEqlNUDDDXNzZdFoNAQGBlbQaDSMGjWq4v+3d/dRTZ154sC/SUhCQgKEV0k0RCAvhEBGMVDrC9aposjWcLBrVxCtLVL9dXdncdbOz3mx+DqncwYrPd0RdWeptXTn9FQ8Dp3ir61odrVSsDS+YIJBIEJEoIQESALkJr8/bBi1JAHKO9/PORzPvXnuc7/3Phe839znuU9VVRVz3bp1ve7WD1fH5cuX/VpaWmgKhUICAGCxWMg6nY4OAHMqOQEASElJMX/55ZdsGo3mTElJMXsrf/36dXZZWVkjAEBQUJADABzuylZVVTGTkpJ6XU/x5HJ5X3V1NYPH4/UAjK7dAQDCwsIGWSzW0P481T/c9eyp3Sfy+p9smJwghBBCaFyVlJQEK5XKEc2hQiKRnvohCOKpCa4WL15so1KpzqqqKsb169f9PvvsM87ChQsDu7u7fUgkEkRERAzu2rWrS6VS3bt48SKrtLQ0qLi4OOzOnTt3x+t4vM255e/v7/XGdLR1P7meRCI5n/18xYoV5gsXLjSOdb+zxdq1a83FxcWhNBrNkZ2dPanz9oy23d99991WMpnsPHv2LMdbWXfXs7t2n8jrf7LhmBOEEEIIjatz584F5eTkjPlGsbGxkdrW1kYBeDxGQKfTMSIjIweLiooMer3+dmNj451XX321480332zbtWtXl8PhAJ1OR9uwYUNvYWFhq8Fg8PomrdF47rnnLDU1NSyLxUK6ceOG78OHD8dcP0EQcOHCBX+CIODy5cv+zz33XJ+n9QAAHA7H3tzcPLTPVatW9VVXV7MaGhqoAAD19fU0vV4/J79wXrNmTa9arfarra1lpaamen1ytHTp0p733nsvGADAbDaTDQbD0Hnz8/MjOjo6hrpnJScnW6qrq1lms5nc2dlJuXnzpp9CofD6NNCdkJAQ4oenNR7rd3c9u2v3ib7+J9ucvJARQgghNDEuXbrkx2QyHXK5vP/J9Vu3buVXVFQEGo1Gn/Dw8ITCwsJmd3U0NDTQdu/eHQkAQBAEaf/+/S2RkZGD7so7nU7Iyspa2NPTQ3E4HKSCgoKW8TsiAJFINJCdnd0hk8mkQqHQxuPx+j2V12q1tI0bN8aYTCaKzWYjSyQSVkFBQevmzZtNvr6+jtLS0qC9e/cuWL58uXnt2rV9AADu1gMA7Nmz51Fubu7Cw4cPcysqKu4JBILBY8eO6dPT04UEQQCDwXCUlpY2AsCcewsUg8FwRkZG2giCILFYLCeA5/N/4sQJ/fbt2wWnT58Oo1KpztLS0vtcLtcOAJCXl9eekZERExgYaC8rK2sQi8UDubm57YmJibEAAHv37jW4xpuMB3f1EwQx7PXM4/Hsw7W70+m0T+T1P9lIT87MPQONOvhVq1YBAMDly5fHORSEpie85tFc8xOvec/9d6Y5tVrdJJfLx23syGxXVFQU3NTURC8sLDRMdSzo77Bd5ga1Wh0il8sFz67Hbl0IIYQQQgihaQG7dSGEEEJoTlqyZIklKirqR2/+QlML22Vuw+QEIYQQmmusVhJ88AGn90+n5tF09b4+VgvJzmA6B2JENtbunW2Qk2MEBmNG9/seCdfs9Wh6wXaZ2zA5QQghhOYQovBY8ODBQ3xt6EL4c0I6ufpFOfTRmeDXbyEpmtWM1/54MlL0q32R1N/+Rk/J/7fvpzpehNDcgskJQgghNEcM5O/hGs/+JfyNl4+S74UtfOozM4MNX0mWw1eS5WRheyOc+P0BPqelhU4r/CMOSkYITRocEI8QQgjNAUThsWDj2b+Ev5xV+KPE5Fn3whbCy1mFZOPZv4QThceCJylEhBDC5AQhhBCa9axW0uDBQ/w85e/IXX5eJ6cGAIAuPw68ofwdefDgIT7YbCN6xfKnn37qL5FIpK4fKpW6+Nq1awwAgJ07d84PDg6WC4XCOG/1tLW1UWQyWaxYLJZKJBLphx9+GOj67NKlS34ikUgaFRUVl5aWFjWig3GjqKgoOD8/n/tT6pgsGzZsiLp16xb9yXUVFRWs6OjoOIlEIr1x44avtzo++uijgH379s1zLavVarpEIpEymcxFKpWK+WRZg8Hgs3z5cuH4HcHIjbRdsrOz+UePHg11Lf/hD38I+cd//MdI1/Kzx+vNgQMHwnp6esb93jgzM1PA5XLjAQDsdjtwOBx5Tk4Of7z3M1tgcoIQQgjNdmfOcLShC0Hn5YnJs+6FLYT6UAHAmTOBXgsDQGZmplmj0dRpNJq6ixcv3uNyuQOuwc0vv/yysays7N5I6uFwOI6rV69qtVptXWVlZX1+fj6fIAggCAJ27Nix8L333tPfv3//zunTp/WjOqAZqqamxtdms5Hi4+OfmvzxzJkzQXv27Hmo0WjqEhMTbd7qycrKMh05cqTNtSyXy/s1Gk2dTCazPFuWy+Xa582bN/j555+zxucoxt+aNWvMKpWK7VpWqVTsn//850OzxD97vN4UFxeH9/b2Tsi9MZPJdHz99deML774ghUSEjLnJsscDUxOEEIIoVmu9z9OzvtzwoYx/Z//54R0cs9/FI/422eXkpISTnp6utG1vGbNmr7w8PCnbsry8/O5CoVCzOfzZVu3buULBAKZwWDwodPpzoCAAAcAQFdXF2VgYIA8ODhI+t///V9mUFCQPTU1tRfg8Q20q66DBw+GRUVFxYlEIumuXbt4YzlWd7RaLY3P58vS0tKioqOj4379618PnY/Vq1fHiMViqUwmiz1y5MjQt/ju4hlLnCUlJcEbN27sdi13dnZSJBKJ9LPPPgs6fPgw78knJ+7iycjIEERERMSP5ht7pVLZfebMmWnbrW/Dhg09N27cGEqeqqur2enp6WYA98er1+t9XOcoLi4u9ubNm/Tz58+zJRKJtL29nZqSkiKSSCTSpqYmKgDAoUOHwoRCYZxQKIw7fvz40LkoLy9nP//888J169ZFCYXCuB07dizwFGtqamr3uXPnAsvKygJTU1OH2tJd/e6uk9LS0oD4+PhYsVgsff311+d7Kz8T4YB4hBBCaJaj6ep9q1+Uj2nbbyITgFZxjDHa7T755JPgU6dONXkrl5aW1q3X62l8Pn+AQqGYKisr/bKyskxGo5GcnJwsefDgAf348eNNvr6+zsbGRhqbzbavWLFC2NnZSd22bVvHr371qw4AgHfeeYer1+tvcjgcR2tr67jf37S0tNDPnz+vE4lE/XFxcXHbtm3rEolEA6dOnWqOjo4e7O/vJ4nF4rjs7Gwjn8+3u4tnLHFWVVWxcnJyulzLISEhhEajqcvMzBSkp6ebXn311aEk0F08ZWVlTUVFRcE1NTV+Iz3mZcuW9RUUFEzbG92QkBAiPDx88MaNG750Ot1/SD7bAAAc2ElEQVQZGBho5/F4dgAAd8ebl5fHT01NNb311lsdXV1dZKvVSk5ISOhRKpV1PB4v/sqVK/URERF2gMdJ6cmTJ8Nu3bpVNzAwQEpISJBu2rTJ5NpHbW0tS6VS3VUoFLbOzk6Kp1iXLFli+fjjj4PsdjtJqVQaa2pq/DzVP9x10tra6nP06FHutWvXtGw227F+/fqoCxcusF966aWeib7+JxM+OUEIIYRmOR+rhdRHZ3ovOAwLjQlUS9+Ixpy4qNVqutVqJS9dutTrfBVBQUH2wMBAIigoyM7hcIju7m4KwOOuXfX19XVXr169W1xcHNbf30+yWq3kb7/9lvXnP/+5+erVq5oTJ06E19XV0QAA4uPj+7Zs2SIoKioK9vX1Hfc5WiIiIgYWL15sY7FYToVC0Xv9+nUmAMD7778fGhsbK5XL5bHt7e1UvV7vMZ6xxNnR0UGdN2/e4EjKuotnLLhcrr2jo4M61u0nQ0pKivnLL79kf/HFF+yUlBSzt/LXr19n/+u//msnAEBQUJDDlWgMp6qqipmUlNQbEBDgCA0NJeRyeV91dfVQoi6VSi0KhcIG8DhR8rbvsLCwQZFINNT9zlP9w10nly9f9mtpaaEpFAqJRCKR3rlzh6nT6ejuys9UmJwghBBCs5ydwXT69f9oWMGIMAcsMMj0G9XNTklJSbBSqezyXhKARCI99UMQxFOJ0OLFi21UKtVZVVXFiIiIGIyOjrYJhcKBoKAgR3x8fN+tW7cYAAAqlerem2++2V5VVeW3cuVK0WjiHavy8nJ2ZWWl/zfffKPRaDR1UVFRNofDAZ7iGUucdDrdYbVavd6zeYrHExKJNGz7WiwWEp1O917BFFq7dq1ZpVKxVSoVa+3atV6Tk/Hk7+/vNSF50rvvvtt65MiREb2a2911smLFiqFxXXq9/nZ+fn6np/IzESYnCCGE0Cw3ECOyKZrVY9o2qfkmDMQIRzVj97lz54Ke7IY0Wo2NjdS2tjYKwOMxAjqdjhEZGTm4YsUKi8FgoD169Ihis9lIGo2GKRKJ+h0OB+h0OtqGDRt6CwsLWw0Gw5ifFrjz8OFDmlqtplssFlJNTQ0rOTnZYjKZyBwOx85msx01NTW+Go2GAQDgLp6xxikSiax3796leyvnLh5vOByOvbm5+Uex3L592zcmJmZaz9a+Zs2aXrVa7VdbW8tKTU3t8VZ+6dKlPe+9914wAIDZbCYbDIahLlB+fn5ER0fHUPes5ORkS3V1NctsNpM7OzspN2/e9FMoFGM+HyEhIURQUNBQsueufnfXyapVq/qqq6tZDQ0NVACA+vp6ml6v95mM638yzeg+aQghhBDyjrV7Z9uOP56K/EqyfNRfSu64We5g/zJvxG88unTpkh+TyXTI5fKn3iy1detWfkVFRaDRaPQJDw9PKCwsbHZXR0NDA2337t2RAAAEQZD279/fEhkZOQgA8Pvf//5BSkqK2G63kzIzM79ftGiRjSAIyMrKWtjT00NxOBykgoKCltEepzc8Hq//l7/85fz79+/7Zmdnd4jF4oHIyMjB06dPh0ZHR8dFR0fbpFKpBQDA6XQOG4+79d6kpaWZvvrqK3+lUunx5jszM9M8XDxarZa2cePGGJPJRLHZbGSJRMIqKCho3bx5swkAYM+ePY9yc3MXHj58mFtRUXFPIBAMAgB88cUX7HXr1pl+ynmbaAwGwxkZGWkjCILEYrGcAJ6P98SJE/rt27cLTp8+HUalUp2lpaX3XS9WyMvLa8/IyIgJDAy0l5WVNYjF4oHc3Nz2xMTEWACAvXv3Gjx1Axstd/W7u555PJ792LFj+vT0dCFBEMBgMBylpaWNTqfTPtHX/2QiOZ0zulvaqINftWoVAABcvnx5nENBaHrCax7NNT/xmh/V2IrpRq1WN8nl8s4ffWC1kmzc+T/7p2FmhvdE2N4IH3/yfx2+D1u/gxnej304RUVFwU1NTfTCwkKPXW20Wi0tPT1deO/evTuTFduTent7ScuWLRPX1NRoqNTJGwKSmJgo/tvf/qYLDw8fVfeln2qk7YJmNrVaHSKXywXPrsduXQghhNBsx2A4qb/9jf7E+QOOoD6j9/IAENRnhBPnDziov/2NfjYmJjMJi8Vyvv3224b79+9PWncdg8Hg84tf/OLRZCcmCGG3LoQQQmgOoOT/2/eclhb6J2fzw99Q/s7jExRheyOcOH/Awcne/IiS/2/fT2KYk2rJkiWWqKioAW/lxGLxwFQ9NXHJyMiY1MHeXC7XvnXr1m7vJcffSNsFzU6YnCCEEEKzHDckVP7w+86//59/Ks9j+TsAcB6ADMcKI+BYYUREcIjd0NkxthH105hr9no0vWC7zG2YnCCEEEKz3MPvO32+DOGOefsXOw14v4AQmhRz7o+NTqeD3t7eoQGTCM123333HQAAXvNozvjuu++AxWJNdRgIIYTGYM4lJ6GhoVMdAkKTCm/S0FzDYrHwbz1CCM1Qcy45qa2tneoQEEIIIYQQQsPAVwkjhBBCaFx8+umn/hKJROr6oVKpi69du8YAANi5c+f84OBguVAojPNWT1tbG0Umk8WKxWKpRCKRfvjhh4He6h+LoqKi4Pz8/DEPxlGr1XSJRCJlMpmLVCoVc6z1oKeNtF0yMzMFXC43HgDAbrcDh8OR5+Tk8F2ff/TRRwH79u2bN97xjbbdy8vL2Ww2+2cSiUQqFArj3nrrrRHF5C7+AwcOhPX09Mzae/g59+QEIYQQQhMjMzPTnJmZWQcA0NzcTF25cqXY9eall19+2Zidnd312muveZ0FksPhOK5evaoNCAhwPHz40CchIUG6ZcuWbk/1TwW5XN6v0WjqkpKSxFMVw1zHZDIdX3/9NaO7u5sSEhLy1OztWVlZJgAY9xnux9LuS5Ys6a2srNQNDg6CUCiUvfbaa10ikcjj65LdxV9cXByem5vbxWazHWMIf9qbtVkXQgghhKZOSUkJJz09fWjGxzVr1vSFh4c/dfOYn5/PVSgUYj6fL9u6dStfIBDIDAaDD51OdwYEBDgAALq6uigDAwPkwcFBkqf6Dx48GBYVFRUnEomku3bt4o338ej1ep/Vq1fHiMViaVxcXOzNmzfpnsqXlpYGxMfHx4rFYunrr78+37XeVYdMJos9cuTI0OCo8vJy9vPPPy9ct25dlFAojNuxY8cCT+WZTOaibdu2LRAIBLJXXnkl0luc7uKZ6VJTU7vPnTsXWFZWFpiamjo0L0tGRoYgIiIi/sknKQAAv/3tb8MXLlwYt3r16pgFCxbItFotDeDx+XSVSUpKErueiIz3eevp6SEPDg6SfH19HQDu2324+M+fP8+WSCTS9vZ2akpKikgikUibmpqoAACHDh0KEwqFcUKhMO748ePBrm0m+vdiIuCTE4QQQgiNu08++ST41KlTTd7KpaWldev1ehqfzx+gUCimyspKv6ysLJPRaCQnJydLHjx4QD9+/HiT7zOz1D9b/zvvvMPV6/U3ORyOo7W1ddzvb/Ly8vipqammt956q6Orq4tstVrdfsHb2trqc/ToUe61a9e0bDbbsX79+qgLFy6wX3rppZ5Tp041R0dHD/b395PEYnFcdna2kc/n2wEAamtrWSqV6q5CobB1dnZSAADclbdareTs7OyukydPtggEAllTUxNVIBAMDhenp3jG+zxNtiVLllg+/vjjILvdTlIqlcaamho/AICysrKmoqKiYNcyAMC9e/doH3zwQejt27frGhoaaImJiR67GI7neaupqWFJJBKpXq+n79y585GrzQGGb/fh4lcqlT1KpbKOx+PFX7lypT4iIsIOAKDVamknT54Mu3XrVt3AwAApISFBumnTJhOPx7NP9O/FRJgRQSKEEEJo5lCr1XSr1UpeunSp1y5XQUFBdrPZTHH9293dTQF43LWrvr6+7ttvv/XNzc2N3LZtWzedTne6qz8+Pr5vy5YtgvXr15uysrLGfWbz69evs8vKyhp/iNkBAG671Fy+fNmvpaWFplAoJAAAFouFrNPp6ADQ8/7774d+/vnngU6nE9rb26k/JGZ2AACpVGpRKBQ2AICQkBACAMBdeSqV6lyzZk0fAMD8+fMHWlpaqAKBYHC4OD/66KMAd/GM93maCmFhYYMsFstrF6evv/6amZSU1Ovv7+9YtGiRLSIiwmO3Kk/tONoYXd26jEYjeeXKleKqqipjcnKyFWD4dh+NqqoqZlJSUq/raaNcLu+rrq5m8Hi8non+vZgImJwghBBCaFyVlJQEK5XKrpGUJZFIT/0QBPFU963FixfbqFSqs6qqirFy5UqLu/pVKtW9ixcvskpLS4OKi4vD7ty5c3f8jsjrMTifXbdixQrzhQsXGp9cV15ezq6srPT/5ptvNGw22yGTyWIdjr/fU/v7+xMjLe/j4zO0TxKJBE/WM5zh4pkt3n333VYymew8e/YsZ6x1kEh/v+wI4u/N4Om8Ddfu3nA4HMeKFSvMlZWVLFdy8my7j6ep/L0YKxxzghBCCKFxde7cuaCcnJwRJSfDaWxspLa1tVEAHo+h0Ol0jMjIyEF39TscDtDpdLQNGzb0FhYWthoMBtpPO4IfW7p0ac97770XDABgNpvJBoNh6AteDodjb25uHtrnqlWr+qqrq1kNDQ1UAID6+nqaXq/3MZlMZA6HY2ez2Y6amhpfjUbj8U1joy3vLk538YztTEw/ISEhxA9PiTxaunSppbq6mtXT00Oura31ffjw4VCbsVgs4tGjR5Te3l7S/fv3fQHct6Nrm2fbfSQIgoBbt24xIyMjPT618cTPz4/o6OiguJaTk5Mt1dXVLLPZTO7s7KTcvHnTT6FQWCfj92IizJoLEyGEEEJT79KlS35MJtMhl8v7n1y/detWfkVFRaDRaPQJDw9PKCwsbHZXR0NDA2337t2RAAAEQZD279/f4kpOhqvf6XRCVlbWwp6eHorD4SAVFBS0jPdxnThxQr99+3bB6dOnw6hUqrO0tPQ+l8u1AwDs2bPnUW5u7sLDhw9zKyoq7gkEgsFjx47p09PThQRBAIPBcJSWljZmZmaaT58+HRodHR0XHR1tk0qlFk/7HG15d3EmJCT0DxcPANi91TcTabVa2saNG2NMJhPFZrORJRIJq6CgoHXz5s2mnJycjoSEhFihUGjj8Xj9rrFM//Iv/9L24osviuRyed+8efMGAQB4PJ7d03kbrt3dxeQac0IQBGnZsmXmzZs3u32LmKf4AQDy8vLaMzIyYgIDA+1lZWUNYrF4IDc3tz0xMTEWAGDv3r0GHo9nJwhiwn8vJgLJ6Rz1E6npZEYHjxBCaNoheS8yfanV6ia5XN757HoSiZT4ZciYp/OAFzsN4HQ6b/yk4KahoqKi4KamJnphYaFhqmNBfzeR7WI0GskcDsfx8OFDn8TExNiWlpZbZDJ2JJoKarU6RC6XC55dj09OEEIIIYTQnPDGG28sqK2t9QMAeOeddx5gYjL9YHKCEEIIoTlpyZIllqioqDH3/UcTYyLb5S9/+Yvb7oRoesDkBCGEEJrlIoJD7C92Gob9P38bALxNocHbxAB84GH7iYtu6kzl7PLIPWyXuW2mJyczum8wQgghNBkMnR3qH600m8nW7G2CjuvfBvxOvpl88Lv/dvxpaaKJcfaDJvD39/rmI4QQmgjY0Q4hhBCaa2pqfPtk8riLOlPgqrTD5ApBEqRsOEL+f/e6A/tk8jioqfGd6hARQnMTJicIIYTQHOJ4991gywsvxv46Ko32xrLdJAv1cR5iofpC3vL/Q/p1VBrN8sKLsY7jx4OnOFSE0ByEyQlCCCE0F5jNZOtLGVGthwv561P3k/9b+MKwxf5b+AKsT91Pbj30R771pYwoMJtHfK/w6aef+kskEqnrh0qlLr527RoDAGDnzp3zg4OD5UKhMM5bPW1tbRSZTBYrFoulEolE+uGHHwa6Pvvnf/5nnlAojJNKpbFPrh+LoqKi4Pz8/LG/YxkAmEzmotFuc+DAgbCenp5R3YNt2LAh6tatW/Qn11VUVLCio6PjJBKJ9MaNG16fdn300UcB+/btm+daVqvVdIlEImUymYtUKhXzybIGg8Fn+fLlwtHEOF5G0y4UCiXxyWuuoqKC9WwZoVAYp9Vqx20CwqKiouCcnBy+a/mFF16IKS8vZ49X/SM13sc1XWByghBCCM12T3TjStlwhKzlLPBYXMtZMKZuXpmZmWaNRlOn0WjqLl68eI/L5Q64Bje//PLLxrKysnsjqYfD4TiuXr2q1Wq1dZWVlfX5+fl8giBApVIxKysr/evq6u589dVX9/793/+dbzQaZ9y9THFxcXhvb++I466pqfG12Wyk+Pj4pya2PHPmTNCePXseajSausTERJu3erKyskxHjhxpcy3L5fJ+jUZTJ5PJfjS5I5fLtc+bN2/w888//9HN/nRCp9MdrmtOo9HUrVu3rneqY0I/zYz7hUYIIYTQ6FjT0sVH569+qhuXN65uXkfnr6ZZ09LFo91nSUkJJz093ehaXrNmTV94ePhTb/3Kz8/nKhQKMZ/Pl23dupUvEAhkBoPBh06nOwMCAhwAAF1dXZSBgQHy4OAgSavV0mUymYVKpUJERIQ9PDx84H/+53/8AAAOHjwYFhUVFScSiaS7du3ijTZebzzVv23btgUCgUD2yiuvRLrWHTp0KEwoFMYJhcK44z90kTt//jxbIpFI29vbqSkpKSKJRCJtamqiett3SUlJ8MaNG7tdy52dnRSJRCL97LPPgg4fPsx78snJ6tWrY8RisVQmk8UeOXIk1LVNRkaGICIiIv7Jb/y9USqV3WfOnJmR3ft+85vfhEdFRcWtX78+qr+/f+gFSqWlpQHx8fGxYrFY+vrrr893rWcymYuGa8fRcnf+U1JSYlxPdygUSuL3339P8VS+vLyc/fzzzwvXrVsXJRQK43bs2LHA03HNJjP9bV0IIYQQ8sIh/1nfoIUcMJZt7SQyED9b1Dfa7T755JPgU6dONXkrl5aW1q3X62l8Pn+AQqGYKisr/bKyskxGo5GcnJwsefDgAf348eNNvr6+TrlcbvvDH/4Q0dPTQ25ra/O5f/++r8FgoAIAvPPOO1y9Xn+Tw+E4Wltbx/3+xl39VquVnJ2d3XXy5MkWgUAga2pqovb395NOnjwZduvWrbqBgQFSQkKCdNOmTSalUtmjVCrreDxe/JUrV+ojIiJG9IrmqqoqVk5OTpdrOSQkhNBoNHWZmZmC9PR006uvvjqUBJ46dao5Ojp6sL+/nyQWi+Oys7ONfD7fXlZW1lRUVBRcU1PjN9JjXrZsWV9BQcG4J3rjqb+/nyyRSKSu5fLy8nsEQZA+/PDD0Dt37tzRaDT0pKSkOACA1tZWn6NHj3KvXbumZbPZjvXr10dduHCB/dJLL/UM144CgWDQ3X7/+te/ciQSCeuHemkA8AjA/fm/cuWKDgDg+PHjwZWVlf7BwcGEp/IAALW1tSyVSnVXoVDYOjs7KVqtljbccc02+OQEIYQQmuX8cne0v9asGtPrgXc0qRysna+1j2YbtVpNt1qt5KVLl3qdryIoKMgeGBhIBAUF2TkcDtHd3U0BeNy1q76+vu7q1at3i4uLw/r7+0lJSUnWLVu2fK9QKCS5ubn85OTkHgaD4QAAiI+P79uyZYugqKgo2NfX1zmWY/XEXf1UKtW5Zs2aPjqd7pw/f/5AS0sLtaqqipmUlNQbEBDgCA0NJeRyeV91dTVjrPvu6Oigzps3z+2N8pPef//90NjYWKlcLo9tb2+n6vX6MY9J4HK59o6ODq9PdqbSs926YmJiBq9fv85UKBS9LBbLuWTJEltERMQAAMDly5f9WlpaaAqFQiKRSKR37txh6nQ6OsDw7ehpv//wD/9gdO1zyZIlQ13JPJ3/2tpa3xMnToR/8MEHzSMpL5VKLQqFwgbwOCF1d1yzDSYnCCGE0GyXkWFe0Pe9U2R8MKrNxMYHMN/a5QSl0jya7UpKSoKVSmWX95IAJBLpqR+CIJ7qqrJ48WIblUp1VlVVMQAA3n777Ucajabu8uXLuo6ODurChQsHAABUKtW9N998s72qqspv5cqVotHEOxLu6vfx8RlKVEgkEjgc4z9FDJ1Od1itVq/3bOXl5ezKykr/b775RqPRaOqioqJsI4mHRCINm8xZLBYSnU6fcXPekEjuezutWLFiaFyUXq+/nZ+f3wkwPu3o6fxbrVbStm3bBKdOnWpydVn01l7+/v7ESI9rNsHkBCGEEJrtqFSgbMrs3NKgGtUThS0NV5w+mzI7wWd0vaTOnTsX9GQ3pNFqbGyktrW1UQAA9Hq9j06nY0RGRg4CPH6TFwDAZ599xjKZTD7Lly+3OBwO0Ol0tA0bNvQWFha2GgyGcX2D0WjrT05OtlRXV7PMZjO5s7OTcvPmTT+FQjH0FMnPz4/o6OigjHT/IpHIevfuXbq3ciaTiczhcOxsNttRU1Pjq9FoRvS0hsPh2Jubm390TLdv3/aNiYmZcbO1P/fcc5aamhqWxWIh3bhxw/fhw4c0AIBVq1b1VVdXsxoaGqgAAPX19TS9Xj9uXQA9nf+8vLwFmzZt6lq+fLllJOVHc1yzDY45QQghhOYA2hs7O/9p9drQg4tfIRFk7/fFFAcBrzSogPafX3aOZj+XLl3yYzKZDrlc/tSbpbZu3cqvqKgINBqNPuHh4QmFhYXN7upoaGig7d69OxIAgCAI0v79+1tcycmWLVsWNjc30318fJz/9V//dZ9MJgNBEJCVlbWwp6eH4nA4SAUFBS2jidkbp9M5qvrFYvFAbm5ue2JiYiwAwN69ew08Hm9ofEleXl57RkZGTGBgoL2srKzBNcbAnbS0NNNXX33lr1QqezyVy8zMNJ8+fTo0Ojo6Ljo62iaVSi0AAFqtlrZx48YYk8lEsdlsZIlEwiooKGjdvHmzCQBgz549j3JzcxcePnyYW1FRcc811uKLL75gr1u3zjSyszQ1nh1zsm/fPkNOTk53dnZ2h0wmkwqFQhuPx+sHAODxePZjx47p09PThQRBAIPBcJSWljYCwIjG/njj7vwDAJw9ezZUKBRaS0tLQwAAvv76a42n8sMRiUQDwx3XbENyOse9WyZCCCGEpoBarW6Sy+Vuk4keiSwuT7DR99IC71Nz/PzBt/Cn5r/a/O/eujOuQU4jRUVFwU1NTfTCwkLDVMfiSW9vL2nZsmXimpoaDZU6eUNAEhMTxX/729904eHhhPfS42emtAv6adRqdYhcLhc8ux67dSGEEEJzhN/27PbtjSMbGL+9UeVgbc8e1UB4NDFYLJbz7bffNty/f3/SuvEYDAafX/ziF48mOzFBCJ+cIIQQQrOEtycn0NFB6Y8RxdPN3V77dfX7BxJ0Xf0tCA2dtTen165dY3R1dfmkp6d77C6FJhe2y9zg7skJjjlBCCGE5orQUIJuMn43kqJeR1/PAq7Z69H0gu0yt2G3LoQQQmj2cDgcjrnxvlGE0Iz1w9+pYbuYYnKCEEIIzR63Ozo6AjBBQQhNVw6Hg9TR0REAALeH+xy7dSGEEEKzhN1uf72tre10W1ubDPALSITQ9OQAgNt2u/314T7EAfEIIYQQQgihaQG/VUEIIYQQQghNC5icIIQQQgghhKYFTE4QQgghhBBC0wImJwghhBBCCKFpAZMThBBCCCGE0LSAyQlCCCGEEEJoWsDkBCGEEEIIITQtYHKCEEIIIYQQmhYwOUEIIYQQQghNC5icIIQQQgghhKYFTE4QQgghhBBC0wImJwghhBBCCKFpAZMThBBCCCGE0LSAyQlCCCGEEEJoWsDkBCGEEEIIITQtYHKCEEIIIYQQmhYwOUEIIYQQQghNC5icIIQQQgghhKaF/w86WBSoZndqaAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 576x384 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x428.689 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"start = 29411\n",
"delta = 19\n",
"for i in range(1):\n",
" phase = actions[start+i*delta:start+delta+i*delta].copy()\n",
" phase[\"team\"] = phase.full_name\n",
" phase[\"player\"] = phase.soccer_name\n",
" phase = phase[[\"team\",\"player\",\"time_seconds\",\"type_name\",\"result\",\"start_x\",\"start_y\",\"end_x\",\"end_y\"]]\n",
"\n",
" # Full field\n",
" mps.actions(phase,figsize = 8)\n",
"\n",
" ## Zoomed in\n",
" mps.actions(phase,color=\"green\",zoom=True,figsize=8)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff65dc11a20>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shot_chart(x,y,kind=\"kde\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy.stats import binned_statistic_2d\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.patches import Circle, Rectangle, Arc\n",
"import seaborn as sns\n",
"# from bokeh.plotting import figure, ColumnDataSource\n",
"# from bokeh.models import HoverTool\n",
"from math import pi\n",
"\n",
"\n",
"sns.set_style('white')\n",
"sns.set_color_codes()\n",
"\n",
"# In meters\n",
"PITCH_WIDTH = 68.0\n",
"PITCH_LENGTH = 105.0\n",
"\n",
"def draw_pitch(ax=None, color='gray', lw=1, despine=False):\n",
" \"\"\"Returns an axes with a basketball court drawn onto to it.\n",
" This function draws a court based on the x and y-axis values that the NBA\n",
" stats API provides for the shot chart data. For example the center of the\n",
" hoop is located at the (0,0) coordinate. Twenty-two feet from the left of\n",
" the center of the hoop in is represented by the (-220,0) coordinates.\n",
" So one foot equals ±10 units on the x and y-axis.\n",
" Parameters\n",
" ----------\n",
" ax : Axes, optional\n",
" The Axes object to plot the court onto.\n",
" color : matplotlib color, optional\n",
" The color of the court lines.\n",
" lw : float, optional\n",
" The linewidth the of the court lines.\n",
" Returns\n",
" -------\n",
" ax : Axes\n",
" The Axes object with the court on it.\n",
" \"\"\"\n",
"\n",
" #Create figure\n",
" if ax is None:\n",
" ax = plt.gca()\n",
"\n",
" ax.tick_params(labelbottom=False, labelleft=False)\n",
" ax.set_xlim(-5, PITCH_LENGTH + 5)\n",
" ax.set_ylim(-5, PITCH_WIDTH + 5)\n",
"\n",
" # Create an empty array of strings with the same shape as the meshgrid, and\n",
" # populate it with two colors in a checkerboard pattern.\n",
" # ylen = int(PITCH_LENGTH/10)\n",
" # xlen = int(PITCH_WIDTH/10)\n",
" # colortuple = ((86,176,0, 100), (99,201,0, 100))\n",
" # colors = np.empty((xlen,ylen,4), dtype=int)\n",
" # for y in range(ylen):\n",
" # for x in range(xlen):\n",
" # colors[x, y] = colortuple[(x + y) % len(colortuple)]\n",
" # x0, x1 = ax.get_xlim()\n",
" # y0, y1 = ax.get_ylim()\n",
" # ax.imshow(colors, extent=[x0, x1, y0, y1], aspect='auto')\n",
"\n",
" # size of the pitch is 120, 80\n",
"\n",
" #Pitch Outline & Centre Line\n",
" outerLinesLeftHalf = Rectangle((0, 0), PITCH_LENGTH/2, PITCH_WIDTH, linewidth=lw, color=color, fill=False)\n",
" ax.add_patch(outerLinesLeftHalf)\n",
" outerLinesRightHalf = Rectangle((PITCH_LENGTH/2, 0), PITCH_LENGTH/2, PITCH_WIDTH, linewidth=lw, color=color, fill=False)\n",
" ax.add_patch(outerLinesRightHalf)\n",
"\n",
" #Left Penalty Area\n",
" leftPenaltyArea = Rectangle((0, PITCH_WIDTH/2 - 20.16), 16.5, 40.32, linewidth=lw, color=color, fill=False)\n",
" ax.add_patch(leftPenaltyArea)\n",
"\n",
" #Right Penalty Area\n",
" rightPenaltyArea = Rectangle((PITCH_LENGTH-16.5, PITCH_WIDTH/2 - 20.16), 16.5, 40.32, linewidth=lw, color=color, fill=False)\n",
" ax.add_patch(rightPenaltyArea)\n",
"\n",
" #Left 6-yard Box\n",
" leftSixYardBox = Rectangle((0, PITCH_WIDTH/2 - 9.16), 5.5, 18.32, linewidth=lw, color=color, fill=False)\n",
" ax.add_patch(leftSixYardBox)\n",
"\n",
" #Right 6-yard Box\n",
" rightSixYardBox = Rectangle((PITCH_LENGTH-5.5, PITCH_WIDTH/2 - 9.16), 5.5, 18.32, linewidth=lw, color=color, fill=False)\n",
" ax.add_patch(rightSixYardBox)\n",
"\n",
" #Left goal\n",
" leftGoal = Rectangle((-1, PITCH_WIDTH/2 - 3.66), 2, 7.32, linewidth=lw, color=color, fill=True)\n",
" ax.add_patch(leftGoal)\n",
"\n",
" #Right goal\n",
" rightGoal = Rectangle((PITCH_LENGTH-1, PITCH_WIDTH/2 - 3.66), 2, 7.32, linewidth=lw, color=color, fill=True)\n",
" ax.add_patch(rightGoal)\n",
"\n",
" #Prepare Circles\n",
" centreCircle = Circle((PITCH_LENGTH/2, PITCH_WIDTH/2), 8.1, color=color, fill=False)\n",
" ax.add_patch(centreCircle)\n",
" centreSpot = Circle((PITCH_LENGTH/2, PITCH_WIDTH/2), 0.3, color=color)\n",
" ax.add_patch(centreSpot)\n",
" leftPenSpot = Circle((11, PITCH_WIDTH/2), 0.1, color=color)\n",
" ax.add_patch(leftPenSpot)\n",
" rightPenSpot = Circle((PITCH_LENGTH-11, PITCH_WIDTH/2), 0.1, color=color)\n",
" ax.add_patch(rightPenSpot)\n",
"\n",
" #Prepare Arcs\n",
" # arguments for arc\n",
" # x, y coordinate of centerpoint of arc\n",
" # width, height as arc might not be circle, but oval\n",
" # angle: degree of rotation of the shape, anti-clockwise\n",
" # theta1, theta2, start and end location of arc in degree\n",
" leftArc = Arc((11,PITCH_WIDTH/2), height=18.3, width=18.3, angle=0, theta1=310, theta2=50, color=color)\n",
" ax.add_patch(leftArc)\n",
" rightArc = Arc((PITCH_LENGTH-11, PITCH_WIDTH/2), height=18.3, width=18.3, angle=0, theta1=130, theta2=230, color=color)\n",
" ax.add_patch(rightArc)\n",
"\n",
"\n",
" # Set the spines to match the rest of court lines, makes outer_lines\n",
" # somewhate unnecessary\n",
" for spine in ax.spines:\n",
" ax.spines[spine].set_lw(lw)\n",
" ax.spines[spine].set_color(color)\n",
"\n",
" if despine:\n",
" ax.spines[\"top\"].set_visible(False)\n",
" ax.spines[\"bottom\"].set_visible(False)\n",
" ax.spines[\"right\"].set_visible(False)\n",
" ax.spines[\"left\"].set_visible(False)\n",
" \n",
" return ax\n",
"\n",
"\n",
"def shot_chart(x, y, kind=\"scatter\", title=\"\", color=\"b\", cmap=None,\n",
" xlim=(0,120), ylim=(0,80),\n",
" court_color=\"gray\", court_lw=1, outer_lines=False,\n",
" flip_court=False, kde_shade=True, gridsize=None, ax=None,\n",
" despine=False, **kwargs):\n",
" \"\"\"\n",
" Returns an Axes object with player shots plotted.\n",
" Parameters\n",
" ----------\n",
" x, y : strings or vector\n",
" The x and y coordinates of the shots taken. They can be passed in as\n",
" vectors (such as a pandas Series) or as columns from the pandas\n",
" DataFrame passed into ``data``.\n",
" data : DataFrame, optional\n",
" DataFrame containing shots where ``x`` and ``y`` represent the\n",
" shot location coordinates.\n",
" kind : { \"scatter\", \"kde\", \"hex\" }, optional\n",
" The kind of shot chart to create.\n",
" title : str, optional\n",
" The title for the plot.\n",
" color : matplotlib color, optional\n",
" Color used to plot the shots\n",
" cmap : matplotlib Colormap object or name, optional\n",
" Colormap for the range of data values. If one isn't provided, the\n",
" colormap is derived from the valuue passed to ``color``. Used for KDE\n",
" and Hexbin plots.\n",
" {x, y}lim : two-tuples, optional\n",
" The axis limits of the plot.\n",
" court_color : matplotlib color, optional\n",
" The color of the court lines.\n",
" court_lw : float, optional\n",
" The linewidth the of the court lines.\n",
" outer_lines : boolean, optional\n",
" If ``True`` the out of bound lines are drawn in as a matplotlib\n",
" Rectangle.\n",
" flip_court : boolean, optional\n",
" If ``True`` orients the hoop towards the bottom of the plot. Default\n",
" is ``False``, which orients the court where the hoop is towards the top\n",
" of the plot.\n",
" kde_shade : boolean, optional\n",
" Default is ``True``, which shades in the KDE contours.\n",
" gridsize : int, optional\n",
" Number of hexagons in the x-direction. The default is calculated using\n",
" the Freedman-Diaconis method.\n",
" ax : Axes, optional\n",
" The Axes object to plot the court onto.\n",
" despine : boolean, optional\n",
" If ``True``, removes the spines.\n",
" kwargs : key, value pairs\n",
" Keyword arguments for matplotlib Collection properties or seaborn plots.\n",
" Returns\n",
" -------\n",
" ax : Axes\n",
" The Axes object with the shot chart plotted on it.\n",
" \"\"\"\n",
"\n",
" if ax is None:\n",
" ax = plt.gca()\n",
"\n",
" if cmap is None:\n",
" cmap = sns.light_palette(color, as_cmap=True)\n",
"\n",
" if not flip_court:\n",
" ax.set_xlim(xlim)\n",
" ax.set_ylim(ylim)\n",
" else:\n",
" ax.set_xlim(xlim[::-1])\n",
" ax.set_ylim(ylim[::-1])\n",
"\n",
" ax.tick_params(labelbottom=False, labelleft=False)\n",
" ax.set_title(title, fontsize=18)\n",
"\n",
" draw_pitch(ax, color=court_color, lw=court_lw)\n",
"\n",
" if kind == \"scatter\":\n",
" ax.scatter(x, y, c=color, **kwargs)\n",
"\n",
" elif kind == \"kde\":\n",
" sns.kdeplot(x, y, shade=kde_shade, cmap=cmap, ax=ax, **kwargs)\n",
" ax.set_xlabel('')\n",
" ax.set_ylabel('')\n",
"\n",
" elif kind == \"hex\":\n",
" if gridsize is None:\n",
" # Get the number of bins for hexbin using Freedman-Diaconis rule\n",
" # This is idea was taken from seaborn, which got the calculation\n",
" # from http://stats.stackexchange.com/questions/798/\n",
" from seaborn.distributions import _freedman_diaconis_bins\n",
" x_bin = _freedman_diaconis_bins(x)\n",
" y_bin = _freedman_diaconis_bins(y)\n",
" gridsize = int(np.mean([x_bin, y_bin]))\n",
"\n",
" ax.hexbin(x, y, gridsize=gridsize, cmap=cmap, **kwargs)\n",
"\n",
" else:\n",
" raise ValueError(\"kind must be 'scatter', 'kde', or 'hex'.\")\n",
"\n",
" # Set the spines to match the rest of court lines, makes outer_lines\n",
" # somewhate unnecessary\n",
" for spine in ax.spines:\n",
" ax.spines[spine].set_lw(court_lw)\n",
" ax.spines[spine].set_color(court_color)\n",
"\n",
" if despine:\n",
" ax.spines[\"top\"].set_visible(False)\n",
" ax.spines[\"bottom\"].set_visible(False)\n",
" ax.spines[\"right\"].set_visible(False)\n",
" ax.spines[\"left\"].set_visible(False)\n",
"\n",
" return ax\n",
"\n",
"def pass_chart(x, y, kind=\"scatter\", title=\"\", color=\"b\", cmap=None,\n",
" xlim=(0,120), ylim=(0,80),\n",
" court_color=\"gray\", court_lw=1, outer_lines=False,\n",
" flip_court=False, kde_shade=True, gridsize=None, ax=None,\n",
" despine=False, **kwargs):\n",
" \"\"\"\n",
" Returns an Axes object with player shots plotted.\n",
" Parameters\n",
" ----------\n",
" x, y : strings or vector\n",
" The x and y coordinates of the shots taken. They can be passed in as\n",
" vectors (such as a pandas Series) or as columns from the pandas\n",
" DataFrame passed into ``data``.\n",
" data : DataFrame, optional\n",
" DataFrame containing shots where ``x`` and ``y`` represent the\n",
" shot location coordinates.\n",
" kind : { \"scatter\", \"kde\", \"hex\" }, optional\n",
" The kind of shot chart to create.\n",
" title : str, optional\n",
" The title for the plot.\n",
" color : matplotlib color, optional\n",
" Color used to plot the shots\n",
" cmap : matplotlib Colormap object or name, optional\n",
" Colormap for the range of data values. If one isn't provided, the\n",
" colormap is derived from the valuue passed to ``color``. Used for KDE\n",
" and Hexbin plots.\n",
" {x, y}lim : two-tuples, optional\n",
" The axis limits of the plot.\n",
" court_color : matplotlib color, optional\n",
" The color of the court lines.\n",
" court_lw : float, optional\n",
" The linewidth the of the court lines.\n",
" outer_lines : boolean, optional\n",
" If ``True`` the out of bound lines are drawn in as a matplotlib\n",
" Rectangle.\n",
" flip_court : boolean, optional\n",
" If ``True`` orients the hoop towards the bottom of the plot. Default\n",
" is ``False``, which orients the court where the hoop is towards the top\n",
" of the plot.\n",
" kde_shade : boolean, optional\n",
" Default is ``True``, which shades in the KDE contours.\n",
" gridsize : int, optional\n",
" Number of hexagons in the x-direction. The default is calculated using\n",
" the Freedman-Diaconis method.\n",
" ax : Axes, optional\n",
" The Axes object to plot the court onto.\n",
" despine : boolean, optional\n",
" If ``True``, removes the spines.\n",
" kwargs : key, value pairs\n",
" Keyword arguments for matplotlib Collection properties or seaborn plots.\n",
" Returns\n",
" -------\n",
" ax : Axes\n",
" The Axes object with the shot chart plotted on it.\n",
" \"\"\"\n",
"\n",
" if ax is None:\n",
" ax = plt.gca()\n",
"\n",
" if cmap is None:\n",
" cmap = sns.light_palette(color, as_cmap=True)\n",
"\n",
" if not flip_court:\n",
" ax.set_xlim(xlim)\n",
" ax.set_ylim(ylim)\n",
" else:\n",
" ax.set_xlim(xlim[::-1])\n",
" ax.set_ylim(ylim[::-1])\n",
"\n",
" ax.tick_params(labelbottom=False, labelleft=False)\n",
" ax.set_title(title, fontsize=18)\n",
"\n",
" draw_pitch(ax, color=court_color, lw=court_lw)\n",
"\n",
" if kind == \"scatter\":\n",
" for i in range(len(x)):\n",
" ax.plot((x.values[i,0],y.values[i,0]), (x.values[i,1],y.values[i,1]), c=color, **kwargs)\n",
" ax.plot(x.values[i, 0], x.values[i, 1],\"o\", color=color)\n",
"\n",
" elif kind == \"kde\":\n",
" sns.kdeplot(x, y, shade=kde_shade, cmap=cmap, ax=ax, **kwargs)\n",
" ax.set_xlabel('')\n",
" ax.set_ylabel('')\n",
"\n",
" elif kind == \"hex\":\n",
" if gridsize is None:\n",
" # Get the number of bins for hexbin using Freedman-Diaconis rule\n",
" # This is idea was taken from seaborn, which got the calculation\n",
" # from http://stats.stackexchange.com/questions/798/\n",
" from seaborn.distributions import _freedman_diaconis_bins\n",
" x_bin = _freedman_diaconis_bins(x)\n",
" y_bin = _freedman_diaconis_bins(y)\n",
" gridsize = int(np.mean([x_bin, y_bin]))\n",
"\n",
" ax.hexbin(x, y, gridsize=gridsize, cmap=cmap, **kwargs)\n",
"\n",
" else:\n",
" raise ValueError(\"kind must be 'scatter', 'kde', or 'hex'.\")\n",
"\n",
" # Set the spines to match the rest of court lines, makes outer_lines\n",
" # somewhate unnecessary\n",
" for spine in ax.spines:\n",
" ax.spines[spine].set_lw(court_lw)\n",
" ax.spines[spine].set_color(court_color)\n",
"\n",
" if despine:\n",
" ax.spines[\"top\"].set_visible(False)\n",
" ax.spines[\"bottom\"].set_visible(False)\n",
" ax.spines[\"right\"].set_visible(False)\n",
" ax.spines[\"left\"].set_visible(False)\n",
"\n",
" return ax"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"version": "3.7.1"
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},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
WeatherSuperMan/Udacity-Self-driving-Car-Nanodegree | Term_1/Project_5_Vehicle_Detection/Project_5_Final_Submission_codes.ipynb | 1 | 1859987 | null | mit |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/05_artandscience/labs/c_neuralnetwork.ipynb | 1 | 10656 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "4f3CKqFUqL2-",
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Neural Network"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Learning Objectives:**\n",
" * Use the `DNNRegressor` class in TensorFlow to predict median housing price"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data is based on 1990 census data from California. This data is at the city block level, so these features reflect the total number of rooms in that block, or the total number of people who live on that block, respectively.\n",
"<p>\n",
"Let's use a set of features to predict house value."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "6TjLjL9IU80G"
},
"source": [
"## Set Up\n",
"In this first cell, we'll load the necessary libraries."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"import shutil\n",
"import numpy as np\n",
"import pandas as pd\n",
"import tensorflow as tf\n",
"\n",
"tf.logging.set_verbosity(tf.logging.INFO)\n",
"pd.options.display.max_rows = 10\n",
"pd.options.display.float_format = '{:.1f}'.format"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ipRyUHjhU80Q"
},
"source": [
"Next, we'll load our data set."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"https://storage.googleapis.com/ml_universities/california_housing_train.csv\", sep=\",\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "HzzlSs3PtTmt",
"slideshow": {
"slide_type": "-"
}
},
"source": [
"## Examine the data\n",
"\n",
"It's a good idea to get to know your data a little bit before you work with it.\n",
"\n",
"We'll print out a quick summary of a few useful statistics on each column.\n",
"\n",
"This will include things like mean, standard deviation, max, min, and various quantiles."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"test": {
"output": "ignore",
"timeout": 600
}
},
"colab_type": "code",
"id": "gzb10yoVrydW",
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This data is at the city block level, so these features reflect the total number of rooms in that block, or the total number of people who live on that block, respectively. Let's create a different, more appropriate feature. Because we are predicing the price of a single house, we should try to make all our features correspond to a single house as well"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df['num_rooms'] = df['total_rooms'] / df['households']\n",
"df['num_bedrooms'] = df['total_bedrooms'] / df['households']\n",
"df['persons_per_house'] = df['population'] / df['households']\n",
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.drop(['total_rooms', 'total_bedrooms', 'population', 'households'], axis = 1, inplace = True)\n",
"df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Lr6wYl2bt2Ep",
"slideshow": {
"slide_type": "-"
}
},
"source": [
"## Build a neural network model\n",
"\n",
"In this exercise, we'll be trying to predict `median_house_value`. It will be our label (sometimes also called a target). We'll use the remaining columns as our input features.\n",
"\n",
"To train our model, we'll first use the [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/contrib/learn/LinearRegressor) interface. Then, we'll change to DNNRegressor\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"featcols = {\n",
" colname : tf.feature_column.numeric_column(colname) \\\n",
" for colname in 'housing_median_age,median_income,num_rooms,num_bedrooms,persons_per_house'.split(',')\n",
"}\n",
"# Bucketize lat, lon so it's not so high-res; California is mostly N-S, so more lats than lons\n",
"featcols['longitude'] = tf.feature_column.bucketized_column(tf.feature_column.numeric_column('longitude'),\n",
" np.linspace(-124.3, -114.3, 5).tolist())\n",
"featcols['latitude'] = tf.feature_column.bucketized_column(tf.feature_column.numeric_column('latitude'),\n",
" np.linspace(32.5, 42, 10).tolist())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"featcols.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Split into train and eval\n",
"msk = np.random.rand(len(df)) < 0.8\n",
"traindf = df[msk]\n",
"evaldf = df[~msk]\n",
"\n",
"SCALE = 100000\n",
"BATCH_SIZE= 100\n",
"OUTDIR = './housing_trained'\n",
"train_input_fn = tf.estimator.inputs.pandas_input_fn(x = traindf[list(featcols.keys())],\n",
" y = traindf[\"median_house_value\"] / SCALE,\n",
" num_epochs = None,\n",
" batch_size = BATCH_SIZE,\n",
" shuffle = True)\n",
"eval_input_fn = tf.estimator.inputs.pandas_input_fn(x = evaldf[list(featcols.keys())],\n",
" y = evaldf[\"median_house_value\"] / SCALE, # note the scaling\n",
" num_epochs = 1, \n",
" batch_size = len(evaldf), \n",
" shuffle=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Linear Regressor\n",
"def train_and_evaluate(output_dir, num_train_steps):\n",
" myopt = tf.train.FtrlOptimizer(learning_rate = 0.01) # note the learning rate\n",
" estimator = tf.estimator.LinearRegressor(\n",
" model_dir = output_dir, \n",
" feature_columns = featcols.values(),\n",
" optimizer = myopt)\n",
" \n",
" #Add rmse evaluation metric\n",
" def rmse(labels, predictions):\n",
" pred_values = tf.cast(predictions['predictions'],tf.float64)\n",
" return {'rmse': tf.metrics.root_mean_squared_error(labels*SCALE, pred_values*SCALE)}\n",
" estimator = tf.contrib.estimator.add_metrics(estimator,rmse)\n",
" \n",
" train_spec=tf.estimator.TrainSpec(\n",
" input_fn = train_input_fn,\n",
" max_steps = num_train_steps)\n",
" eval_spec=tf.estimator.EvalSpec(\n",
" input_fn = eval_input_fn,\n",
" steps = None,\n",
" start_delay_secs = 1, # start evaluating after N seconds\n",
" throttle_secs = 10, # evaluate every N seconds\n",
" )\n",
" tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)\n",
"\n",
"# Run training \n",
"shutil.rmtree(OUTDIR, ignore_errors = True) # start fresh each time\n",
"train_and_evaluate(OUTDIR, num_train_steps = (100 * len(traindf)) / BATCH_SIZE) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# DNN Regressor\n",
"def train_and_evaluate(output_dir, num_train_steps):\n",
" myopt = tf.train.FtrlOptimizer(learning_rate = 0.01) # note the learning rate\n",
" estimator = # TODO: Implement DNN Regressor model\n",
" \n",
" #Add rmse evaluation metric\n",
" def rmse(labels, predictions):\n",
" pred_values = tf.cast(predictions['predictions'],tf.float64)\n",
" return {'rmse': tf.metrics.root_mean_squared_error(labels*SCALE, pred_values*SCALE)}\n",
" estimator = tf.contrib.estimator.add_metrics(estimator,rmse)\n",
" \n",
" train_spec=tf.estimator.TrainSpec(\n",
" input_fn = train_input_fn,\n",
" max_steps = num_train_steps)\n",
" eval_spec=tf.estimator.EvalSpec(\n",
" input_fn = eval_input_fn,\n",
" steps = None,\n",
" start_delay_secs = 1, # start evaluating after N seconds\n",
" throttle_secs = 10, # evaluate every N seconds\n",
" )\n",
" tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)\n",
"\n",
"# Run training \n",
"shutil.rmtree(OUTDIR, ignore_errors = True) # start fresh each time\n",
"tf.summary.FileWriterCache.clear() # ensure filewriter cache is clear for TensorBoard events file\n",
"train_and_evaluate(OUTDIR, num_train_steps = (100 * len(traindf)) / BATCH_SIZE) "
]
}
],
"metadata": {
"colab": {
"default_view": {},
"name": "first_steps_with_tensor_flow.ipynb",
"provenance": [],
"version": "0.3.2",
"views": {}
},
"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
}
| apache-2.0 |
nproctor/phys202-2015-work | assignments/assignment06/DisplayEx01.ipynb | 1 | 102545 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Display Exercise 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Imports"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Put any needed imports needed to display rich output the following cell:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [],
"source": [
"from IPython.display import Image\n",
"from IPython.display import HTML"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "fed914a9fcfb3c6780f71b2a57ca435f",
"grade": true,
"grade_id": "displayex01a",
"points": 2
}
},
"outputs": [],
"source": [
"assert True # leave this to grade the import statements"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Basic rich display"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Find a Physics related image on the internet and display it in this notebook using the `Image` object.\n",
"\n",
"* Load it using the `url` argument to `Image` (don't upload the image to this server).\n",
"* Make sure the set the `embed` flag so the image is embedded in the notebook data.\n",
"* Set the width and height to `600px`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [
{
"data": {
"image/png": 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TyHFRWOBklzIjyLGrjblMDBWvvGIMaetu7NN+OBNMgaEmJPZCoCm5bCNvvHEKTydrWZta\nbspFHKQwzqW0asa6LGMBjmFeUacsQtmonlqjjPZKC44u74mtZyjKCAcrjGeNh6v2f4unNkecy88y\n2zQwRWpYYqBMgU4JSYMCR9oufikFCFLucAFfUsc5YjnSlqgXYawrM1CKjCnszFubY+6bw5AhnPnZ\nQzmib1+yd/aPG0MlDNHXXqeQZn+doy66mHVLlzH9XYarJsCq/+2+4TwchOwcBNwshleDgB1NQA5P\nJyENJqQRKEaw3gW0RyFrrXJJGHIkwmuE/Nk4vu2U60QZCWyZi7jeBVyRxXwjX8dnreMKo3wnMAQE\ndISOS7WJu8ZeRG9qaTchXXjWkOdNdYSijHGWPsTUpWv50/zvcLdaDs1SVDMucY6tUYYB3aoswFP0\nwiQs7QolZ2nA0995alEWK8zwKkdjAifO/lkl2FFxTxu4bt+lrPl3xLn8ONdrDYeZOh41IbMlzwhC\nLkA50QQ0SY5Zpsx1qfBgLmK3DC6WhDDL6BukBL7CMBfTkiyhvdfn6P53zHlzGzmMU++5lU+OGkHy\nXn9n/PZsem0h+7/Lj95XAqw+DeZD6o8DGJjPcxfCAIF5wEhVWrzn6dSxRSTEznJhGDBFlQk+5Pdi\nmOwTpps8X3OeQSZkSzJe1hzrjLKLEW6yyg6uxCYX8miU42w8d4tnf3X8WiNOcCkL+n2aXZr3ZKIX\nysbzJnA2MAlLGxlbkeNYE/AVcmwZtbH76LPYbcGJ3GZTfm88YwRGqLBBMmYR0Vc909QzyCv13tNl\nPd14VgF/RBkO4TcgmoclVWp2dy77tjGFsxzFfvcMiXoZk10/ZwkPnw7+XxHr7rs4wCmHCTwlIUVV\n6tWxUSzfzwV8K7P8Csc4ZzjbKUemMReg1BhLh3E86pUKAduL8PF8Oy+pMkzk/V3T+q/iQSs4rfwD\nZU3vHhV9x9d/KAlWE+C/kSZshaeHIhul7V9zvUpB7h/GccB+KIkYnrCemyNoIWAHHCsIma3K4WHA\nrh7uDgzjUHZKDDdHGcNdmUUYLjaGE8KAL3pHnyCHzQxXGNjkQnZX5d6sTIOpYTsxtEyZycVzPoZv\nncJeTR9jHzGsEcNLXnnGJ3xfMt5UGKwVrgxbOUGVV7AsyEpsU9PGtAmXsf8LX2GezRiHsjqAuS6i\nHc8hzlFnHc9lKXMEtgUwwt0Os7OS38Nr/ho02E18tFiNf1oCzkOzZ6DtTSM+8TY7asrQ7iNm+PAt\nCexV+y754E4z9QYau3PcJhlvhrWktpM4zDHUOB62lu8kNfzYRDwcxbwQK5cUMt5IQyJgroWK87yl\nZc7XmLw6jAqt3XfwB+DQD2qO/6kcgKVWk/eehwS63r37/aneBf4X0yJ9fQ8PaEwZwywiXvCNrPNl\nSn4jD6pS+KC2dd9gxs0Yyl0II4C8hy711Ap818JOeMQpnwo9NaI8hTISQ28Pz6gwJOdpVpgnwphA\nKKnljSxjQ1rhy2mRWh9zg7csVGH7kuFZFfLSyUkuod+zH2P3wii6+x/Bx7XEmx5+b7up0x56e+VI\nTSlIxMogone2kS5iNnlLY65ALoNVEpLb6gLOFKVbHX/2jr1txlHlCp1phUtwPBEIUxGcEW5Qgs/i\n6vfKpOEYdfW7urT5IU/tNOeG/tK53k59r61UzWfDSjjTU7/e2aF/8Nq61mvDxTOGcvXdw3OHz538\nz5egbDQ8i9KFstTGzHXruTGrsNYl9DaW/YkZ7kpMTlK+Io6jEsO16nnWJwxJNjKbCmfqGh60HTzu\nu+j0GS975ZPrr+YrH8Ah8Z/Ng62QugrJe236AZfAVxPgv5Bbxak2YTHCDggP+JRb1fKUERbjeFYj\ntnQbWZeu4Kv/zHZu2ZLcvUPMTzQIz1Q1D6iLtvFE1wisU+XnznGdZGTOcVeaEWcZx9uM3TLLaMn4\nfurZiOK8Z6oYZgF4z04CT6LUC/R3jtuzIotswjbOMqIuY7TC/DTHnpJxhhFOG/4FvgaUyLNOU0YY\nwx815eNhhVNMnrlBJ8c7w0Hl1ZygGX3VMsCV2WgcFyFcEQSYrX7Goeo5KUkZlFWYYz2/E0PBKbsD\nHVa4x2n+NGvbhjjX7xsmaz4slLo/S1A4xGn9F4TKberNbxLf/1rn+2XlqP5KvNwu8Ik41uusbV9h\ns9H3SlY7YtWG2ltmDOH8O4Yy8f3Eff1VfC/IM1QddyKsF2UaNZwbZFRcN3uqkrqMl2wnj/qY0fkx\nLHI95F0PFVEuNMo1WZErTAuPRK2EmoBR5vmMVUa4TK+j7p85Lv4rZKDJe28f9IWBagL8F0nfZJ73\nnCAwXwK+5bppUaEXGfXZJo4mJK/CbFHeMIZL49e49/1s5/6h4a51RXO1So33Gj2ObzgkseYLamt3\nU801K8GpzjLDKUWvDA48Z7uMN53llizmgjglNhnTbcJQb/kUMdOBVQJT6uFFEVIv7CDwlELkY36V\nVVjiHZeGludEmGZSHh51PNspoHCF85yPskQ9dSgH+yI1WQc7ZRFnRI7PF/rw23Q1E7VMLMIlmrCf\nL3KoCKuDAr0GfIY+WcZiDE/klFqFnYwQW809F7qGH7lkVOp8r1O95ndTqM9swz5Oey/wrnWkdf2W\nee13qHj32ZaVzYc7N6ygOuAoD73DQsNEQZ4C2ckGzZc6O/wtZ8esjrRwxL1D626eMYQjb9mS+vcS\n9zXnUacZ5+JYphnb25hNtoNfq+VpdSySgE22zHWBcg3CRB9zVc+j3CHK1Ti2zop8h5THCXk43ciP\nXEJR6khsmek4jIbYtT3MfT/HxH+TfyT5acLfu4q7F1AGPv43/dcAyf/7+q6qCfBfoPISbxEwXGG5\nesppFz/XmA3i2CAhPwjyXJat4ZfiSQl4Uj3zJc9u5Rd59L1u467RNMwYGl7oJPicMy0P4EPU104X\nLdwWhc1nqEQLNGs7x/mWN01Qdz3UJXgmE2DFMB/Yo6mBZ3CsdhkdFccxSYWFZcvQJGaizZjS6fm0\nel4Eto4NSxGKqkx3nquSMm8lGd/A0zL8JK5Rgyg8J5ZpUuHbPqGfLXJYaR3DvOFZk3GyrzCiUqZE\nRlkKPCaeC9NNXKuOQtbJz5NNrDSWV/pMZvyIT9JlFeNhNxEqmW0WbPMRaTxuhaVws3P5FlWZ4nRg\nf+uHdPXk+JGh5uwo5YfO1z/p6dV7fXvTWK81T6bZ8Du926JOXP0pPvMzQQomq5skylOemhGpHXJO\nmm2x2OrgibWl4IJ7htb+bsYgpv2ff4A8C8RQchVWkDAHj2qeBaK84lPGlYUvaJke28OPxdK/fmdu\nUE97VmSxLXGXJAxW+CGb+CmgdLNGK8zH8bA6rtESc4xh4KqL+Pw/dzT+B/OgKfjkvbe/swI8EdgN\nOP5v+j8DTAE+/femUC2D+YD1PMmsXDOTNMf1ppYaAp4Vz6tYfii1dAq8lXayPmpiEI4CGb28ZZ3G\nNGnCvjbh4frt3vU2//+4ezifEp//opf6BzXLrTQm20d9U+yRQTmtO9Fiz0NtD0E5alP90jotXGZM\n910i2TEExUoQcJJo+j2UO9QQiLIvnosI2EdgMDE/Ic8ZQY4mIzQEEaEGXBYo9erZtdSf79et4nxn\nuK1lND8behjbSY6lJs+j8vZ7iA8O6rlcDa3A18OI61zEOGM4V+C3IlztlTHZasaGBVq94zjXya8Q\nWqnjz/Qw3hbZbt7PuDopmxpv23rFvulltQN6G43n2ZA5kcu+arW9r9PWmQEVpyaKxOZ7eUIU2x4E\n8Q3qOQvTdbD61tMbGjcdV+xpvU3MujRg3VIxvhxqckkmhbMgmKF4DXywnw3chaF3n5Jg/QRj1mUi\n+fmhrVy59wo2/SX+K8/iwKCGP5h6FoR1XCGGNlPL9j6kIwj4DQGfV8/vVJlolAZv2Ttq4BRNmaZv\n33W3mrJMhBuyMjdKhWukwDYYXrTKhtAxLqswWEoMycrs6J6ledCtVP7lB++/2aA+nLnTeL7eUPfX\n67qzjmZ1oQZ/5tX07Sr99QNbZs7h1aVr+NjfDFUB6oAiUPuO/l8DXwcuB77xbnOoJsAPUPejXBnW\n8nlTz2tEdEqOTWRM9parw17EvsI9Js8VLuOL4rnQCK8orDfCW7aLH2Ow2sNYW+L8ht047W/Hv2cg\nAyQwp6m2tHtfN8NgFjmyI502bxSigTbNvhzlghmq5qfe1EXGx98Xk9wN8UahMEDwf/RwTiBrk8Ck\njRIWV5saf7JWOFOFeQE87+BoEW5WpV3gY5HnNBdxbhQx2MOjYcAg8VytASNwjBn/fb4peYyp53FN\nGeS6eDRowYbNbNFQy+fLnrO9YwOGPet3YlppNvdLyDNRxF3WchHCEVi+mW5k3yAidQ5IqPOW/vEq\nMc9dMHC2s0N+Y7Xm48a4Hmv0itBl37S+90RoPcv73Oddao4L8un9Zl33zr53y9moFEyh9iRfiW6C\nZIUJ14NrutTDvpaCj4KVW4bB6hF4e5IEpWmo6RNhzrfoGQhzELNAnT8SkRsCWT/NmK7t1PRsDHx4\nz9DG9P7WL7CBkPUmIkcNqyTi1TDkVQ87SS3rjGe4z3BRA5/1GWcFNfzeWc5zIXsEjkMUpmkNx7KB\nRzXjkWwTO+b6c596HgZ+5FMaXCd9Ram1ZXq5CqsGn8mIf/8R/a81rJ1Tbz2dA0f0f+91gNt/i66F\nK/7XAuFPwE+AU4F93tFfBrYHnuGvE+P/qJ4Cf0C67+FgcXwReNVVuFtTFvmEO73nSLUcaDuYIMIJ\nYcynBS41wmNqqXcV5roKJzvlSco85pWXRTm+43Z2+cvYCnL3MHOMBjXnWTusxmnLnzDRAi/+UOt7\ndxgthPi6hyVqO8y7PvPV9/mcuPzBEf33V9+6f6aDGxQZpb428Vozz9rRcxMd9PM0HTIs7e51pfpC\n0QiTSjFLRehUZWeE2QqaBkzzluuSmNfJeMlaRng4XoUnhhzMoYTUSo4/iJI3Ibn8YCaHTfQ1Ifmi\n40ve8EfxVHDM7LiX6ypLuBWDtSmna8zNySqmeMdQE/Gcr/AYGXPU8aAryvNRA4UtDul6xmr9joik\n1nNv6MweVvtOxvc919ree6rqbSYX/c7b9t/a1i2/7PzgVseg5rRSd3QmuUuU2rVpNrTdCZ9yAXcG\nmC1sPOzEJN3hDU//MzM3sh/Q5pFBKPNQ2UbVLDZCt6ibZrTtxswPXStu7CMqNRNkO3nTp2CUok95\nXivM15hSFnO/WmbRQ2fYw2Eo+azI50UolTYwN11Da+csjtMKO/giWa6DgiqpC/iNh8htZDYpv/Ad\ndNqNXOWVC5MulqKsE2HwouP4zOY7uv91NCX4R9rfOQW+CHgIuPCdQwN3Ac8Dd/y97VcT4Adg4w00\nEnITwlIV5ihEeFZphS9ohdck5AFvuc6XGMoGylqi4soM9nAZjnM05jTXwzIvtCG8aD0LDNypV1Mz\nczhbzBgS3uJdS53PRhWFcKmEwcOofNr7Xi1Omtc4268+yHrdiK/9iqPldOsaHvOupa2Y1jU71+cZ\ncfk6Z9v7OzGfQrlX0TGRbXvN2SFPZ+nWc7Os37okbZ+Qj+rP8gQvGGGAempFWCieHV0dzwOptbTY\nCifbFAka+U2hjeF4lvgi09OYPxPzXV9ivi8xS8r8Uit8WnsY6VImNUzntKCWoTV9eDqeT3fWQ6G2\nN1eaPCe7Mut8zCoCxHvu7V6ez1Gjr2vGW81Dek4JIteqwr2BRn2tb/mizYbflrqB9aCN6JDY276t\nCzflrlJhXxUWu9AejxY+O3BDOMv69oGB1F+grm3f2kL7Jq9kEuWmCjV3JdmkNx2tM2y25UjrWn+k\n0rhAlMh4OwXlKUWGZKp1grxm1WzbZxt3d11f7e9SSj7jeUL+KIZHCAhMyF7quANorHgGxst5UjKG\n2QpBLuCgqIGjC+18OUsobniKexPP9Xh+bDK+naziXh9wSdbFS95xaRjSYmBSWOA1rzyuGV0RXL25\nj/N/BU1wGr/39neGeQAoADPf0SfA4bz96LQj/t72qwnwAyCGeVqhyyudGjNEi2zvysRklKgw01Z4\nQCx7u5Tj4gJ3ezgfZb4W+YWrcKcmVMIaxolDXMJ44ylIQGVNWRbHvu50K6NvVzuwv8dvMsj9JBzg\nXNNI59qzTSiKAAAgAElEQVSeIx3YB5VfJEH4B1zz+d43nOxp3tmauhtMEF7pXHCtc4Oa1bef5W3b\nIZkbkxOIU8+u4s2jEA6xjL4lTSbem6RbhzZt3yOzzWPxNV8Wx2wVak2ZcSLM8cI2gfC6tbw5Yj92\nUKFDY14HHg4NQ6VAhDITmFJazXO+xKzMMlgt0vMnxkvGDBVOkmb2VM8VHUv5NJbUl7hdHe0rH6p7\nEcMBDQOS8VpmVxNoyTu123z5kYO8q/WWtu+m6aQXRfveqeieoam5x6s/1WrLJcOa+n4ys0NtZoe0\najJsgPUDnn2rZeDpqqZYlmHLUz/8+e7uhtswNS95zzTnzROAsfEoYjvh1+V0ejlLRx2R+UFbeAqf\nS9P8s0bUGuOmG9XHBK3pNbB0syAlEZ5QwYjySU04lJjByQa+Fm/kezZhsSgH51r4RVYhpymvq/Kl\n5td5PBAeKy9i19qhPKDdNKWbeEE8rm4oW2mRJUa5VpQxDrYS6BRLp1HEhNzmPeHCb3z4HpZgM5xP\nse+1qX6whTDVBPhPWn85ZxDR3ytlt4GFWZlf+ZjXfIlhLuBo75ljSnzXlRlYG/KKL1Hy3UyzXWSu\nSFC3mF975VhXpI8tsVhLLPExs9OU16NG7TX+6OQVsvz2iIQ2Mnc5oq0dzZ+wfvCczI0oq5A56d/H\n6cDGcjzkESHvjS9s6Ltm+J1q++UcQ7YF2otp7wWZG/OUiH7DukGNKmZqkfAZRJ23OhWRJ71vybJ0\n8iVJsu09qRuxZZr1+47ThsEQHuAcjwOhh2lDdyWvGXnjWSAhT4nHkTHGVzheErZ2CeRb+BSeP5oS\ndcYyM97EtRte5vcu5lkjrGrai1vF83XvOTYrmq/7bm5r2650jHQyzHZys1a4ONnIq+pZHuRcS9uw\njkuSZLsFGhUutyqHCayrZP0PSd2oji90fv1Ojx4tKo+jpuQwxyl6mve5fk77rJXM7o9G1zg7/NUs\nHboHhCPEhy14eUuEnY34WVAoJm7sfbHd8ZeVbIcmNaMuTv2w4c4XDshcQ6515IYahBE20adQan2F\nLdJNuLTIy7aL77kOZifrmWQ72C3r4SBCigKVjYu4O91Ir/UDmUCeCo6NRrnEwc9MwLeSbjakPWQY\n7gFwjo9rTJ0rsbWtMDRLGGgztpWQteLZcd6XP0QPUvWgGeISzHtt+A/2vkU1Af4TFp9DkxeOdzEr\nRXnIww5hjk8AT4lQKyUmq3KvpjymllLPBn6qyiVAo4F91PKTYgvftZsYVn6VNzVmqDhmSsA8o7yi\njiV1bfb0fH3aaHCPhmnUpL71iMyOX+jtyBkCk31Yd5VX88MobjwyyJnjs6x/u/V9+61p5QtOC+ej\nsqNzTT01QW5/Fb0rS0evV2m90bqB2+Zd8wEKr4iwbV0qz4N6Ub+DJ/eYy4ausOmka1w8/j6bDprk\nXfvPva1vVmMOaBjKkcawEMG4Hj6TlfGug9OyLuZnRTJinMs4dMUfsSrUNtRzdWCoNA6mnRIT0/W0\nvvUrdtRuNrkuhoW1/hUCjnSdeBtzlQqrybFFUE8Rx3NqWT1i+uzRKuY2TWtGoDpa3aCip169Bndf\n13z7rk4Hm8wN3ahh3VXONg37UueXuqzvj9XaQeJze36x67PPqwa5JBt4XObGNDtp/qnBzBIY9PZf\nU5eBbJ8lhdneDXgrtVs9VnITTo2zXTL1g77fPnzd4Xg6FPqm3eSylJ87NbOMoZcEbBnVcVNdL+ap\ncLRanovX8KTAzJo8Rygcqwnn4ZllMj5vYKf2r3KHwsBAGGOgFBluVcfnKNG/vIoXsi5e8yUsMc+7\nCk/YTrrE0F3j3nup1H88j2AJ/6FWLYT+z1EIeUwMRgJme8vYIOJ518EQLJM0oUEzTqCbRxC2Wn09\nx2mJA00nC7SD1HWSq5/G4wTs6T1Hhq1sgXAHSrs6DnEJe4mA92QTjlxwsGht6un1w9hOWiS2zy8c\nerigs0haz7J2xNIe02u9JzdAxDynYk911Bz12U1nPujc0NTRhtfcYYvWN8wSpClJxyxM063v97Qf\nlLotdkCDPsW8GSUqL6gwMfTZcoFub3SqS/vdnmWjFiXZln+u2GG3D9rJfForWJcR2CIv+IwnRanz\nNcTAHOcYGHl+0PM6Sb+9OMfHLOhYwV4+4cKsg1/FnURZynfrauWKfJFvGDgoW4+xMWkQcpUKy1H2\nNFDrE0KUKIh4WHF22hHHnulV9nRa32m1ZpBzA0y/7rqbnTenqjeXe/H7JFnvI7y0XHdlwxMno7LE\na8OZlj79r2h8dDtvWCYmv4t1bT9J3aig7EZ+wxE1QbCDen0apFcUuEGIvAyMDZN80bvG2UO2eXwr\nxGPTMCHhpSDHW0HAiGxj4WXxbCqt41gRnkcoi2NPTfmNL7KyewknE7Fdv2OZHa9BuxZzcNTGJBxz\nFh/PHq6HclZmmcADcYVdXZmxLuOMXIG8BOQl4pkgYq4xdAa1PGRTXvRC4dXDOG9zH/sfBG9QMqwm\n7739g4XQR/J2aczfvQlS/VCk92n5j/lUkOObYQ0LfUZBhIcUbkIxeBq6inw3suwgAUd44bWmUSz3\nSkzA4RqQQ/lD8gZHaEKXJNRiWJRs4GARtvZFHncJD7kevHh6qde+SaV5yqZle75ibP1FVnR3I7Zf\n5gZ1Ot80yhvOCDGf965tgNOWOu97veG1buiLdQf2Md4Oqon6n5pk+SN71eVDiHtEgkHqzSzv+42R\nIDjXud6HG/XTkML9QnlrL9KNmpWITjCkr6mRHOq3HrnzW3Ma+3d/Fdhkcsx0qZmnoS5Wy1YofV2B\nWwLPTs6SF+XPWFoRRnrHlN51nJM4RpsQDeBRteyXOe5PK4zTmKkCb5mQ3/qM75TWtO4sPo2w2qkx\nfWzKcHUE6hhZ7Bi8sbtrco31g1dj4md6gpp+GS17ZFn7FRr2rA29fOZrxenf2zf6yg/V1FytvnFb\nNbUPGp+dLoG7Xny0Z6VYuVqi5l1F6q7wvmU6Yj8WaK8ZanrGIxS86BMG3YHQEtZ2vDpk7IPfFSEV\n428g5HW17IVj23xdNsZa+uRqpL60PtrJq38izLHdgJ25qriCHcMmzjfKfpWnuU0ME/L1LEuKfC4N\n+WJtHT9WTwlLW/8GLty0iRnO8ktSGjFspcpDopS9ME0sW1rLYIFOTUhcxoHHbMH1l71G5+Z+Hfwz\nWgvssttIpuSEqBJT85dWExDhyXUUKZTf0V+Jqfn9c/R0lLnpb4a6Avgi8APg+nf03w4cwNv1gBnv\noloH+D4tP5tykKPLGG4xtZSkQJupYYEEPCyWY9Vwh0Cgnp1NyIikh36F3hyI4ySBtP5Afth1D7Ob\n8uzZVeKXdPJa6jksiLjBhMz1Kfu5lN4+JbJF2UnLMuTJy48/ymrTMqgc4XxhPlozxfm+S8PMX+qj\n0tWe/AVoNEZEdinloi/VpvG16mWGBDVd6qUUmq6veBc8G4Ybhr3RUTxmeFOfXzgTzTBKpOgnQ7Nk\nUxh0TwjM+jU1gZ6c+tJJiltgVB/zuGMmHvDwXmG99JJI7wjribyhFU/ORHgby9Y+03KuydxtAt+n\nbwOfW5/xq65lnFHXn5l1np2SAj8n4A1bMpO1wf8sdHxeMlp8QiZ5rvbKJInlcz2r657PRek6DdLR\nRui2Cd7FTAkMI9Nirv7xm2cd6YVdeor57zXVd8yExpMzzX8S8s85lSMayubAYp29UtS70KSVNcXx\n3+pd8+Z1BCXJmY5YTXg2Pvyk+sg50ftFzEk5WdkjPtsiDNaoEv4gMF17CWnfrXf9wXQT6biwlock\noNFnjHWWx2rqWWIiIs1zme/JfUnybqJE+Fyzs6KcjrCjJnSZHP01YLLkuCqtsCgvXNv/NHZZegoP\nEXF5GBL4jIFJB1/f8CJf7T+VI4zwdHk9g4I69iejyyW8oRZRxzZAnaswMI2x29xL8+Z+HfwzBjRz\n5uRBHFWf++u7u6fvxdJCDn/2Qwzuiv/6STFPLGbx8s73XAjtgId5e3V40LvNoboCfB+WncClCFNM\nxFNBLctUGCowUC3jJGWay6gJa+hfVH6d8xxUWsz3Cn34skm4IU05JOuhvesZ+gQBK+IKIzVhbVLi\nEJPnASPMUM+nVWnwFZptRg7DKlId0Wf0U5OWv7gbqFS8to23fnjJa+VSAvNN5/vUWXpVclnvOzOR\nL7Z2ZFeWcn0/jtZsgWQDG7r9r4vRgF3EJIu91kxrzocLjIBoNDEkvclTM91Jnwd9OuBZp723z1y4\nq2pNX4wdY13Twn6jX5nY0N6xs75dajDIWxo0YzXKq0HMTwmIc3let04XmUB2X7eaMFdDZ1hLITA8\nmwX81MM9nd1clzOc0P+bnBE/w2FWmY8j9Ja7Bc7SlBuCMH1Dcm5iELDMW+YZAyKs0hSD80NqaheP\nXrdqz+trwmBy7HtNzKTtMuPL2xkxkwXz0yTUk9TzeCBmntNgephV7jQRU9WHNzvNH6hChMtmYoJP\nJKG5I3TBRK+tq7BtF2fSa7oXdvS+dnBjr3nb9Or/3JaG8DmvMo6KJkGBe3O13O2VxSJsnZUYGNS5\nu0V0hBE9TzO21JDDI+HXznPw2rWcWchzFBl/low9nbLkxOn0VqUdx/AVLfyiscy58QaONXl+XGjl\nFZ9RMIadtcx8nzGz0kUc5IjVsdYVyeNpEqX56EEMvmLJ2zdP/hu11rHjuXsz8NNbsWmPkXT9pYUg\n6jBTh9Lzzv49RtJ14wt0byrz+78ZajqwhLev5974jv5vA98CzuLtQun/pXoN8B+06qv0JuJrgeMN\ngdasi318D8socZLv5mlbZL46Lsg6mFxIOFaUV6MmdvEplycxt5EyX5o5ptDCYYuv50qX8DEbs5vt\npMlXuCbuDo8sra7b1ZUYSkolSMhpkaneUQojP6x9izmTMj9yZJyNmq/KGmfbSt7X71CbBSeImq1i\n449x2nJJZ0PfE43KKkf4U+sax/X07p8X1YrNhs/L7JDnldYLEpqf90ZbMmpbgcV43Tkmmm211/LU\njnuqGE85sZJO7fHa79NtQ5Ye6j0d6szwrEjmHBdlcTRXlKZSkU96y0OZowFl7tqXZKEry0+zlDLC\nx1LPrVkPXb6LPtEmPheo3rf6fA6KN9LHd9GAcE3SwUO2h4Va5hlgFxFeEWGOhGwnyi5ZXP8JIxix\n+lLvfn8eP3jsaa9lhEfj8l/DuaMcjSsT11j+VnnEHNXCuJRej1V8a33qW7qCgn5LRB500jg+pfUU\nnzUe4HVAnSKuJg2nisosr36UkzhVrZnj3KDVhWzgd4eMvHKMOl3nnB2kFbnE5LkWMKpMrxVWIMwP\naoKh+YA1BtY4y8dJODvbxNq4mx+oUtOUx2iFRT7jaAzjB4f83HqOCwxXAav7reQEW+b1XB1Lalqx\nWZlyVmGCLbJU4QE8xVwt7QIDgJzJswDlPudYZAxfeG4Pxm7WF8Q/w4O3WJ+99/YPFkKf9//63+0z\nRIBqAvyHlUPm+piSN7ism4dx3C/Q6pUtRZghSqtPSEvLeYCYsbbEaJ9xSFLhRm9Z5h2qGxjhulg/\nbFf6k7Dcpryelpjd+Sr7BKqTjZYWuCI32hTrLWtU+b2PeU48HcMm3TMV33qFwQwUdddL5M9w2vfV\nTUH/6cbU3qISbPG14k63qs9PF2m+3/t+I71vf8RWol+KkZle2CN1Db9O7fgl+N7Hqq9rQZhu1DwB\npjkSO0KR51R0XCCBxdc9NWryzTurOvWODeL8M2GBV4OQcRpnr9iMjeo5qOfVUL1lI8redW3+h7l6\nvV2U/4+994zWqzjS/X/VvfcbTk46ko5yFspCQoCEkMgCjDFgG9vgBMbjjG1wAEccAacxtknGA8YG\nbHLGZBFlQEQBAiUUUA4nvWnv3d31/6CZexkPzAJf/oPv3HnWeta7V+319uovVat219NVTZoxauhQ\nUqtsFcuQXCPvWnrRfpdUd8Q/TkrxCqAj2UWIctSiKl8PwvEC2/rWMSgtx2f2bug6OOtno6alh5J+\nVC2N6jUdNuj+JWkYvLKaNmwQdFTQ+llB85Vz8z1jMm18WdR8EWX/vlrHJ7zm35Now7NomPjl6sDH\nEu1cpiInet/cHCQsqEbmUcGEYOP9Q5AHESmMnHnslSYKGlJKBu6OGh1JH8M0ozVk7NVXjS7Mymac\nz6JRznOwD9wjwujeAtsENoWEK1yZ0XGOI1W4xziudxXq5buErJ+01stIX+Uqhfkm4yyxfDwX82xI\nGe0rlNSwBOgVy742pl6VDQJehbpgaLERj2igJ0tZevX7/i/+ksuI9C3wDVZ5IyH0T4ACu+8Dvy7+\nJwC+Baw4hePEMtQH+oLnMfJs9CVeru5ghA8cHEr0AKuMclRhML92FbKkh1sFJgzeTEpGRT0DEE6h\njs9oE5/xjlbjGOj7ObO+i69p6p9EuVaVBaq8IobHrWGtyZsVPuMRIYSpC776k4A85Wga4FzrtD1L\n9gugh1aytv0yN6ByDtsb09C5K/GNJwKL2ivRj502NCducJeIjohrYZNCKQ2jr8jcmE2Zb/1E1XTV\noZQRmR8CDwkimGx+oXHd0nzTq6OCkqnnVud4QT1DXYX32BxfF8cMa3O5qC36fXVbvAWly+bYGUVs\nDX0s9mXYuJwvqvCAq3GJZtS9a8XDa6I4WxlF2dMKf1AXXeOrXJEGJqC01krU6lsZG/qyFyWNburb\nwnoJbDCGJ9J+1hF0m0TVaM5ec12cNx/IpL7bEb9ssb+2yEWgP0dNj6rNAS6haZXX3Dkq1v8iV54i\n8HzVd6xwNF+bhdZ9TdrwOa/ysqjOLlLckC+8XIlzW2drphvFco0Yhvoa7zJWZlVLHYv7t+QfcxW3\nLKvJ7ZImK7MKJ2mNd6FoQ5mFIfBASJmtGd9yGV9pSrkvKbMnGWtXfYEPoFwvcKhmHBP62GFjtiCM\n8xnDqdAHrAiO59VzpAbafJWRvsZkX6I9KzE41BiY1ThUIRUhG7aBx95Zz/j7ERxB3wLf7qEGb2cA\n/Ltnc/7fgpBysQb6DNxhoIWU96rlFMnoSnuY4i0/zyqMC8pY64hU2WShnPTwzOqSPB0C9256sPU6\nX2H0gA+zOdnJOEm4FWVJw0iOVE9PLeEcoxyBskng/hCYHzzHZNXigWoZqqrPNXQ8P3HK3mc+o1r4\niTLwmiWFjvkSoqWiboJa+6NiXd95gt7k0Oed5tpfZJJzoXWLEg3w1Le5Yn6vIPKYqs5PpPVHSTb5\nRXXFYzKGTFQtHKKhYSjCS2BmjJv+w4twGoxhsSj7uyqnpb3c78vcmVVZUd1aOKd7bcejVtInC/V+\naKgy3uY4TgP3BeVw18cp6jjSl3k+JCzCc8+6b/A+AstDZhfterlhmKS+u67AZdWdfNeV6LYpo12Z\nniAsierWL2kYgKphvBraotiuUcN9wbOmruGlExqKLx0afH6gC/nRPUnjK1koNKZJ4/ZU67anoW5Q\nHLkF4u19wUd/cb5huAoHGTX3KTK+t9p+V+o7l2Ta1JrRMddr/YyK8I0J40/6GIJXrXs2eA6u9bBq\n+4Z9Eg36spUdWVrLPWzBi/gRocKvsxIvupRlvsSoAJ/wCfdjGDrmfJaEEju6Ez4PDCqkfAfHqaRc\nERx/CcrcYLig2s8HXJnGNOEWDy3GcE1kONJlzMxKeFfiMlfhFZfRHzJe9Z77g+OyNGFJ8PQD0x+e\nzYXvtH+8ZQRQR11IqX+zfLu38HYGwFPfxrX+4fDSCfxADE04HnRV5royDVkfL2vK01GeT5qU83wf\nq2PPr10P7eXtXJ9WuCsoR264hbtVdHBaZnPntO6ZtZ74qQ3ftUdEEa+4hAPJuJLApwh8wnrep1Dy\nGS+lpfinfWuH7Zvu4rlQLV/uS6wnUFSvSWP74/cmrqtcKjderMKxSRgwJPWDwufLY5YFLYytmIYH\nXejsz7S1pbVuyyIlLKn6zmczN6DiqftSFGQx0OlDHCn21dQN2VbLxv9T4qf2ejo/47Jh+zZ3PLPQ\nIuOCLzzlatGcpA8Vw01RgbUi3GcUwdYOaRiy6ZdiQ7erhEdCicu0ysdCHxmBIaN+yTbXz/rg+LqB\nicMifizKF4znDzvXDYlsLn+uc/qHvh6mx0VqrsbItIIqrI2VJ41hLsoIFI+jOc3qRxAYZIQn8PTP\nmH7AUUHia0V4JpcL3/DYi0IunOphdhD9tghn5bL4Vie5qQR7dqbxB+rShnWC+kI+mSfIo55iHhq+\nXPFDFo8afsohopVGI6xO09xYTXkyzvNYa8tfz0HpRZjd2t6/KhiWizI5tvRIYGOoMc45Pu77qCPw\nDWDHqo8zMyi3o7QET3eSErIy/T5jvqvS6frR8RfxoDG8Xz33S8pcYLEkjHUJh/oyj2nCparMQWgR\nWC/CoxKxjQixu6ftPeIzNqnykQencs476yVvDSGAz6j4jPKbZfhvKITeHzgPOPtv+A9T4l96FHXe\n8RWUrd4x2SdsDYHfWuEuwITAAckmngV8UmaKS/iOUTYax0kG2gqf5JpiI9dEyqdE2Hvt4vHfsHX+\nK87bq1Xpq+xo+LrrN2tIeFUCM12FVSR8MPRnL0W5bb+q9tNrIxKxbHQ1XlYnawSXm73Pu2tRXuer\naixGc8Hoip/nexdlofhXcYXTCLoAkU+EEH0n5PRWAwcWav4zLjTV1bTjEypUIw37gX9IkSHeu0Yf\nio+kftA259q+MXzk+ePxutbYmkkTfhrluUjAq2OeKLuSwIu9ypAcoMoKEfbTjJt9H8tVeY96zLov\nMjPAY6Rc71Ma5buEtJ9yUmFm56D1VxTqdy5LHFeKcmJaidp8wt3WUDYJtzg4JDhm+CojK9u7Gmo9\nxZ0h7dviatg0ZX+ExNpq2GvKtF/XkvjSoGHRmZm91CijRHn1m1n8hNfIlmLGoLRUXPRETXOv7Ir8\n1XjWorqfpMlDApJ6O3/kwG9ubWlaPALVra7CA7H0XKyCz1x8WKnSdVSl1GGS/vb9M+VwozyIILXA\nfqI8hGGQ9WQ+YWlIKfsyY4PnqNhzW1am7CuMqnreF1t+QeADwDSEW5efyNysRKMv755lXA7c7wLf\nSco8TsINQTkUQx2BJ0PMChPTIYE5JjBTYJRChyoVVZZ4OPWeSZz1TvrKW4EB8Fiyt8C3JoSG3c1Q\n3zBs/iNMhZsM/BZY9k5v5I0QG5aqJyXQQ+BOW0eTGuamJR6VOlbl6hmv9bQDSwPM0Y1cy2A2e88d\neL48sMrBCPeroyIJHz9g8QuldXvbiJxvsJbfR8XyJeueXHDm8KkPnpSlYaekHBRStmF4ITLJs3Ez\ni8JusXFirN2h3m8LHlvf+Oxhbc3P9+zsmRNnxC8Xor4Lgw+3Si46TFO+iiGcVm3fcHY+6TZJ44ct\ntWIfg0VD9eVgKtutb5tjKdXV1xpPKxXLx9lI9gvwsAlMnDZr/xtEyJyjEsU8Uyi41qyXoVETA0Mm\nE22sE9f00nPDWmacOZejjeVeAp8EmhXWZWU2iWe+jfhSUM7QjA+JpXv15/mgKNfo7upwpp7VeaWu\n1tO+l5XSN8W4Y4NwjUo8zdXssVqrPR8ybrJm08zgGOlT2o3wOMoztXLH1Mju2LO+sHzo1NEfO/OZ\ndZet/1aesZGjN4OB342Y54Xfo3wHzGKJmEfg+prYKVbys/PBNUk+1xZgVWy3z+/q+MkngqNfoVJs\npOjV7COZdonk22K7vRQbvSNJO3bGVQ5FmEZEp4kZQpGPk+BSz3wxPOgTDsvnOE89P3rS8YMpEV2x\n5es+cPmGNSwYNJjpKO0Nnj+W8tyuyk9V+SBwSyHh4jRhC8rvvLAIaDOBJzTQFxwnqaPBZVQJbAgZ\nW1G6sGSaMgjoEeGMO8cx87CVvPsddZo3gUzJXf0y7c35fy9S/sh4tuYsetVKOsvu3ydpaXjdxrD/\n1hH6e8Bdr7Hngc7/bA9vZwaof/P73wIvHsueYhkbdg8NulZgi3qGaZkDTMxnbcYMV2EPMZyTVTCi\n5KLBTAeepMYEV+EnZHyrPnCXL7FnlrJ64zc4nshfZwLvcilHlzZEraNnLX4qKYd5vofZrp9Nqqxd\n98wpSX/vqJMRBqOmGBJGZGluhveMw7BRAn1Tpx3x/iD6sIgOqlQ6qIUGl/Q32tTX76y4hs4fx+lM\ngtwEjFCN+slXForR+1Nt7CzVGj6faMu43rycazGrUZ2eC2Fne8efImv6xwQXVhvhVlX2SBNZiJXx\nte6hd1Z3NC1JSqxtVi4+ehj3aJlDXJkvBk+swkJRFivMHrudT7oqI7RGBzCuvshp6vmCplwbBe5Q\nR50Iv0H4imiyHpeMEtgiNdbUqvlv1Hryq7XCb9g9oGmYtazLRdxBzDojpLncjpXqecx73TSw/fKv\nTBtz4IU4Tg0QGTGrvfBPZFydBbc80TBbYZF67g0aBvVl5qSK5mpVnzvfa9w0dcT0j+ruuSkPRIZc\nUrHTSv3DWrZuX3RlyEo/c2nulXKpXWOz5bSswqqkxn2un++kfWjWw++yjFhgP1EeESg6x4tpldV7\nKBcDK0u97HQl0o4O5viMMSFlYLmRyFcoaomnUKK0wlBXQ3zGVQSGijDBwvOpZ8/gODnrpzsrs9gn\nXOwTlmrAe49TzxYCDwfPDcHzjAgH3zGGzXeM4Zh31nv+c1jwezRTm9FG9bU0gQhPNPl13kX6ujc6\n5gKPA/v9jf1d/CfzQODtDYDyGv63QVrh7lDDWcN9PuOYtMSXsjLXuyrX+jKrKz1ck/Xxq5CwVDKK\naYUJ6jnFGu5Rw/hRP+EvaT+7evv4rMDwkOPbznMqnt+L5xY8C3auP/CSck/dxyVQRfidWOolcPWI\nqb9tqm94ZUh1B7O2r9uzbteWyes0qT4dEvpchQkombGp7DNz+qcEbpU4/WexnE4ufEmFCWK4PIh8\n08fxjYnPb0+0MMgH8/5yUnc/IgOK5LszrX+4ps07ar5lcqb1UxORH43s+u4HDWwNFLY7x4JKNw/v\n2A7oVaoAACAASURBVHTgNg26Pld81eSL6WUCviPP9LEFzs+qrMlKXO8qBJ9xck8PLyjY1QXasxqP\noHwL2LZzHZmr0OMTDq6Vmekr5Md0ssan7GOz0icRZmG4yin/4kthZS6tnOdjDgiGdmCFKk+o0JlV\n248PgdkCMzLfNFkCmapWO4v339Zct2yoxzyw03G+Vz8GKHmRJivmIo/uXYJEhHLBssDBv9RUVswa\n0XZ8HPXkndZtCCE3tr86/pcE/+t8vHVTR+P9wyN4IbLl5cX81i4xVMWwAmXPFNZ4x/NJmUc1YYlL\n2CdUmCbKRlfjAJQrSElCP/kIjnPwLVFO08C2YPgX38slqvyg+RC+P+rbDBp2Eqe37kclFHmEwJEa\nKNUqHBLKmKTMOg08EZRnsQwQw3SgU2CdwlIb8YyJSIxllTH81Rh2IPz2tpG8dOsIPvNmhz39lyLg\nJzZSmdlM+bUUj/oMJje9zjuDe52VHmR3EFz8N/ZLgPK/Pl/7elv4RzgD/IfFEwdwnheaBe7JUvZy\nfWwm4jYDRHC/CmVTYO9CzCMCFVfDaR+nJf0Mq/byTxJIV3+OqRq4C6XZeXrdTtSX6HNlDshKtGYl\ndP7dd/4y7cmfIWoWk7G3KIt7tgydoxlH+DKrvecL9XXLVtfXrWh2CU0aeFmES9Ja3WPqw65CYc2E\nvaZOF6/x4DNr8UOqdEWYZ7+ZRtdlQbqyCllQRmSZPcVpNNZHzES1O8v5gzRwn6rpqJnG76Wu7S/T\nxu5zhIZgMq0rJ5V6o4EXc0W2tLbe+1MMO1SZbgrVKrBMlYkmIlNlpQh7JxVO971sLMZcqYGtmXAw\nyu2+wpOuxKQox5Em8BsMxym0An9cuYov+wovpYH9FbN81/qZP037KFtT+ZnPZ2NF2cMEng2e1Kf2\nq1nCQVrrqbkyL9QqPIqW7/chroUgkQhu1vCZRzQW7jmizqYH+ZDdXDHpd1V1Q9X1rq9p9kps/WVe\nzQNIWOg8D+41YuT7oqh3lKi/1lC53tr00briikFY1lhTe0psbcDW7iM/XypPeUIFE5T9VbjfQGSF\nfQw8JMIo28vNWS9/9cpXshSPsF8pxyOZw3hDBBw9/QYe8TW2pClZYX+eGvJZFk66nCu7Psn7i5M4\novUwhg4/lSNn/p4Vw7/MIT5jsktY7B1GhTUqPGUNYwnsiVARZTkwAM+7M8dHfco0zYhc4OkQeFQD\nj4pylwrvqitz860juPi20cx+R53qb+EB9xb4+ngjIXQr/zshe93BSP8IAsq9gPXAtnd6I6/F0qMY\nrgl/tJaVCJkRLgp5HjKGAWIYZgqsVtghMBllaxC6UeY0Of6UeKZqxKaQMdMI9UG4QWuMV88eGkCE\nhzC8NwSGiHLPF/ehXlw219fCpcYyfuuakffWN+28vLYrXqM2+7UNHILxHYawViMeNUKkSt5IFvuE\nvpDRYaXnw5u6P3TdPD2v3wWdlag2LojO2h4wVRE+aoQtmmO1C6YQ448VtesEndDuossrVg82GgrT\nxkyYXyy+Ok0l3pClXTfk6zY9IBo1i5qpteqwWSGr9841HxpFfYGYWwTmqJIzyhMIc8XwlA+0asoq\nCcwRy/hKHedFVY5HuFwtZ5SKfDuqMCV40j3GceG2Hfykw3FiRficutxi1B1ptfQXhaXA+8WYHSHR\nPVzGhKyqmzTjOYI+Uk1ao7yp5UOQeHvPQWPzsqEk6l/yQdsGNf5phvPFrnWVhZ/NmfTTGtJ7MWa2\nD+GqIGFOwE/Jme7GeaMaz87ZvjYwy2yk1RDoNDAAGOmz4lHgGtQzJI66pxSj9YMU7QL2CI4VxlA0\nMLZS4M+x4wBncBhWhoxOFbYJzDNlrreGfZ3jV8Fxcu80bkgzDm+ZwNARn+KiwjByErMaYZXWKJPj\nRhy9oYwvDmd457sZUVsHtQ28qJa/CsxQZRyeKGSM9I7hLiHvMnZ5x1MSuN4LnsDQoOww0Cdi5wSR\nO6xGDZ6oSYPmP9hmvnRimw4+sZ01V+yi8k75WHPM/MMHMakpwmuAN8M/r6Pak/2Hq3CrgB8Ca15j\nO+sNnv8d/icDfANkO1hqDH1phbwIq41hsnHsEzIG4JiaVjk9K3OYBgZ6eK9NuF/A9MXsi+FhLdEp\nyi814/CxW1gblGFq+WokfK47405JeDZk9OTz/DE4vm9y/sdZ1nyywHXtXa/+SLPasiyJvm8CC4Iw\nWGCpBPuSKDNqtbZjg2eB90xMQ+vkgJRDINln/IRPI9UvIGxBzVYJfNo5LvEStiYEgucwY7glQdZl\nYsqZ2mnbIvYDXdrVfOGxjfnVxwWnSyObLCkW10xSz3uzNDe/VmsYl7dbuozZsiWr6ctZlTm+xLd9\nlTHqOC5J2YrQrY4DRblPlfGT1nB8VmVI/TbmAdVsB0tcmXxuJ2NcxjDNGPDck8xxNbZuD4wTwyay\n5DOR2fKiWK5FOU4EU+1pOry/f+BOX2E7yoYgPIKhqZjvHhQM7UZCqb647mrEPNhTm5EhTXeKye4d\n337GPseMMU+2Fm+LnHHHKbrnL2hYZqUn2XfwtB2zh4w+DqIW0Bes+qezjB717Moc55QqnX8OWn3a\np9ywdevHHgpux+IkCfcnJb6e9bPdZSzKEvbyGbPqauwtwrPA/knCwwqRlrklLbNZhEtR7lbHET7j\nh5UKl3W+h77hp3G4BrYrPISyTjzX1NbxcKgwLaQ02SJr/S5+HwLdI7/AYWPPwBPYQwP7hpRxoUZa\nS7khS9nslB4RHgiGF4IwF2WcQtUEmRF8bqIT+SohHu8pFEWLQShOlqz+jBCa5qnjGzePtJfeMpKF\n+g4dXXkPwb15vt34R6gC/8Phwb24GGhJa6yRiGfUMTYoQqCfmMuSCi1ima1wB4GRGN5lLBdbZZPk\nOMAJP7PCwbKTJ9MC61c0cKEJvOLLbMs8rt6wV6qMRanf0A1tRYqa8rT3xeOzcu+gEFwO5fy6xr52\nMYwS5QWf0ug1nKgptVCrbnSOFaqUre8eG5R5qI9UkXljhx39yOo1x2Wu6Ylg/Q1nE3V/TdMmI3ar\nVQ77bsaFZ1oKNVe7kSjqymNPa6/7S8/QzlMXeXhKNJLgdVSWRMGFIb3OD/ttXeMDm7OscGxI2k0h\n3nimq/EZFV6I4EGJOMvk+GVWpWxi2tNOfh9vJTw9mElS5UopchqOGyhyuMIPrHKOGJao4w6b4zdi\neG/DdP65/TDG22baNbC+92kWbbuKKZW+5tG9O+c80dp8V73COpRnBEYHNdNFQ+ICr8ae0YXo5YGS\nUaiL1kwiVDakvslEVJaCHz6z8/jDQ4hc5jv6Fpr6bcjYBiFJhIZXTDC3YvvbkjCgvLN0wP2Dm6+e\nIcoRGbk/FAKDsOzX3HHZReo5QoTpznCnBF6kRkfBcnINzo5yfCoIfVEeky8ygIxXgnCoZPzCec5Q\nwyHkaNzrXj71yvc5a8BCDgspm0lYQkwqwp8Vvm4auU+U3mQ79SbluaiNk2J4LFUmNE7gW0OOYfMr\nV/BKqPEzEYZKYJYKVZTHFZIIDgzCwOClIWi0mlDYbtT/KmA+gtZ3qdY9IuiLgezj1voPqCZPeO3c\n4kV3WE0/d+uI/pNuUbPRWHfhka+w7r/M2Twa/FsonL7NJdb/+QT+GzwyjSnGcL6JeVyENiu4tMbt\nojyshuag4HLcZ5WpWAbmA5e5jPHqWIqwk8CRJuUxIkZkgVg9zwiM8YGKBAYGz61G+DywxSjX5/Oc\niefSoLw7ktKtYvg0CY8F+Mvu+bLRLpeE2WQMcFU2ecdLJnP3Jmn9EDFZQaCogc1ZQpsoeTE1O6jx\nd4te6T/xFU+uNk++OQqRYr+Lfmwk+9Rc1Q1WpKiGwcFw78C6P8+dMOj9R4nEKcQ71NXFIunFarN7\nYlvqzOXXt+ajcB/qWk1cGW4jlirUC0yOAnd6oUszduK5VAMfkX6axLAGx7QIbsmqTESZagyjs05+\nQQ8HZo7tLmFJ2zy+MPp7fKl5PpPjwTTF7WRxC031kzikdYGd1r6/u7X3nu2NxtR2xsLtKkxBmeFd\n3KnOD9Ys35Sm1vvU79xe3jM2Xl9wvvWZSGr9IpWSKs8o+owQ8qitUy2UhcpqI7krhGqkUhtBsEEl\nt29jYemIAGotozXrnL6h9InlbXWPDLeKGOEJYE8reIHlCNNTZasoO5ynNQjnWseHVNnPWq7SwKI5\nj3DphqEcIob7BRa2Xc6rjZM4jQIvmDxbUWIsvQjjTcZnbYGTA6xyFX4aN/FDAscjjAbWpj00NI5i\nXK6NP3Q/RaLKPghVhbuNoT4EjlIvY72P1/nQeLf63ETwl3spHBhC095B235E8AMhfu+7X1377hca\nhp5ufH0ekgm5srvQx40zVDseEKItqPnKB1vd4Se2mOKJHbryil1vOIjo/xjNlvmHDmB80+5zZH0z\nvHYTyet8Ah/GbhndE8Dq19jPZ3dPwJnA1a+3h//5BH4NlkLsDUt1t9TFhYwbgrIiLlIfGZajrAbG\n5jMGqbBEAu01ZbQalqoyKRdzWdLPU075pitTQ1jgcjzoasSaUVTPsZPGcb+mbM6qVDauZ3FI2GPi\nWG4QpVkz3p2V6c6Uq43ybqM0Zv3Fhb7G8rRGP8pLNvCMFBiQL5ZbjTAQ2CaGVQJLvNqHNehT1nTn\nFo4cdcnCUa13EHhfQO/J2/5DArUfGwmnZaJrwcw8YEjdkRM6P3KkRTejyQ98aL5StVBG/P6ithPN\nloq4lu07jz29nI15QkCCsh/KQwjGG+aK8CjQrgk9WZUbfcb0rMp+IoyfsjevINiicLpPGQegGd4G\nRk84m8WDT2awKj3BsAzPCyhfQXhGa+yIm/36Qlf4xPSL+o5uHMKDvdXR04PPHeFSJvos/WuacFWW\nJZtV0mqAla0NT11UKG5Yrpo/FtUmURN5ZXI1HbCfqn7D2Oozil3pwnj1wb6/ko29C3iukixw1cpe\nv3KOTRK4Kqk1LlPZPHJI4feLyuXx+7vACZkyBmGLwOyi5XmBigj7OGWJUazJGJPW+GVSwadlTjVK\n81/3YQbwtE+JbY7lIeKeoFRDlVWhylV4ervvYYMrs4dznOwdT0jgmbjIRWp5N/BhX2a4lpkVC4UQ\n2NYxi281jeMwDBUCD8fQFLyc7LK4q5y0/cK75qc1mP1FuVapm41vXSRp1wdQe7KjY48stD11Q9de\nX1Otv8P55ptcGNjl6trOMCI3EHT/ai3cloVBa1064UHv6wf5tPHq20aYn906jCn/f/mcBmxwb55v\nkAH+mw7w9L+x3wpM5D/KY/4X/icDfA2On8Q2Y1GE543hLmt5TC0RlhkGBhnYgWUPYJiJuE6EWQba\nguUOgf1qVfqMsMVXaTIxPaLs3e/4Ux7mp8pPjONTW1ZxpUQcLUpTsUijevq3b6NZlQqGQQRWCDwb\n1J7oE+3SJL1IA5PFsMHDgyLMVm+nErSs4IJjdHBMRGlWzQ8WaDBBtyomZzT95MCGSzq21qZ1ejei\nIw2NF2D653U1XjxneufR7zNRdW/QTSK2RySMFMrjI1tZr6oDNRT3Vcky9Qy3duukXLRpCJ4uFabi\nWWsssRpGFzxXe9g/CBGWZ7xjUhRzkU/56LYNbBYoVVPqVOmTPj5uiiyecC4fy7Uw0FiuIc82K/xJ\nhNU+5UQJ9FPlL1h24nWsyZFvmW+P6VvSN2LHpmHtcdb9w6CUgH1RjLE8QmAHgT1VdZqS1Wso9wb1\n/erZXHbTf2Ok8r0QunqtJBqZnh0e/7lYklMFomBkXr7w+NkC9apML4b0F+Vs0Fwj5ceTrPojoTaF\nQIcGRhsYV8sYJJblAlM08IAII4FRCNfh2cfDpd5zsDXM9ZZf4Thxwtf4iLH0G8OjtoHMCC8R81Jx\nGKNyKZ9zgS+oZ4sv0eC7GWtj7lfPITiWhYBKynViWBUSmluns3DzYn7uMvb23h7jKs3rNTT8ixFy\nKnqgiD4ZtKVRfdt71Q/8lLfRh7zUbzNa1/XezQ987n0Ne/wo0wFXG2OOrCbhn2zceiq+uUWkP2fi\nYtkgmxEz1/bbX7jcoImq7aUo6p/1gZb4kye0uvx7Glh5Td9/Uo99CyhGHPpyHwc+sIvme3fQ+m/c\np5mdkeLPWcOI27fR/tp3m1NKJf8ftH0Xsbvv34XsLob8GzYAv2J3p+hXXm8P/wiavU8DD/MO3wS5\neyKP24gpkeVCiRhmDQNVcSZiqwhRgAYT8ajAABEmSkSvtdSZiCizfCsvHOCVYmS4IHOc5Rw/zMWc\nKzEhirjapRQRctZymBgeRHhIAxdPnsrCVdu5YsDxzKwbS4upZ126BbZdHzdsfyD7tHEcB/QauMcF\npiDs5ZOoq1YdOCRi8wNeg1NHoVIb1oDPtUZ262PYSsUHGRV8cbqE8mivtkVDXU1IXSCXi6Tilfod\nITTuiqJXV9iIazWQM5ZFCA+awGoMJ4jw7Pbu0+3Ajp9OFnhOlSXEfDFn6RXLCGNpFrhYchSCMiNk\nfN1YfiqB63zEyFj4qLV8K8CxaZUz8vVcPfEHzIg6aFPLWptnuynygAqHEPFrgYNwBIQ+TZmBcgGe\n91a3Nh8QaW/7q7/lo1sewWSefQVSYKkqfUrh+DRpmNGf5nZUk8472/LLh4kk2yMT35r6pl+Xkhml\nunjdKvWFV9ToYSLZtcb0NiKF52zoP0ptOikK8scQ9+8lgWvEUK/KoaL8swqHizBKhR8Y5WtiabKG\n2EaMkIg7UJb+613ecxHeH0AQ/iSB39iYVWM+ylEN4xli8jwdlIGmnpeiPPUSswFLQ7KSS/PjmKCB\nkq/x6bpeZlRbeaC2i5uKHUzDcqGvcrIJjAuO2PUyIdlC7ckfREt82rElqL9JlCaMHCji12ahU/F1\nBykD/6DINiOl9wfXmKrkXgYaFLvGkB0Y8B5b+KEGPSkSt0lD6bgo2rw1zVc/HyWFc4zYm0MgNmoW\nWYl+7NWfqLmdYyI2ejE5Y7R2yRFr+ev/ic8Ny3HmueP54JA86Zv9z4dfZNO6Kkf9jflOdjc8PRM4\n4jX2nwC/A156o/X+n88AFWTfsSyzhpEYlgkMV8NSn/EiQFAeAm4mMEyV/gCX4BgTMlZH/fw4UebG\nynz1DJaIZhLuFMtklDYTuFmVfdOMojXssauPcwuGEzXwsK9RJmbRgLP4bOsi5hWG0WCb6BZLZIs0\nNe8bBgw8Wj4SXOxLL4eLevtmzJeQvT9Lac6y9Bqh/z51OkwDMYFlNdexsT63fi02He2yQYcpbpNm\n4o1NS6Bb0PgJ1WKnMbW1me98VgijrK2UVVJvNJrfl03dVYy3rjGwt4l4QsGJYUZzw6PXqlJUYYoK\ni0XpdJ66NOZ0X2MawkzvmSiGZrEsRzABpmvgj94xHZgtQnNjN9eP/CHn2WbqjeUWU8dVYulWR3OU\n8RUNfF0d6wjcScYxNvCJpJezXT+dcV3Sq9DUNJljNt9Ln0/xUcS9GmgLnk/4zDWUKnOeqIuX/bYY\nbdlXjN8V2/jGWjrkipBNWJ63W67ocSM762zfnu/Z+MKhyxv2uLS7NqsUSzi0eWPXx5I6O0eFvix0\nHR5J/5RimrvIxW6uGBol8ADCviL0KmxWZbQGzvGBBpR5OCZhaRFDTQ1PoSx0EbeIY+LAvdCWqSyS\nmI0IL0mOeyXwK1WaNGVFbTNrCsM5Bc8PNWUfDNt6NlMX1zE6l2cHea4JNb5GwqDQx/nZTurUkdiI\niVK0tZ4Xi38KRvqNmMNBq5kfniO0TnE6/OVcCH8IEk5zvqsZGdA7YOv675caBpxTLo/8gdjiHkh+\nEBpmqPJbDc1H5UP2qUwHniKufl9jK6tVdawz+ier5iCPimCWiW+Y5Fzn75Bmo8gJJ7RUj/pQW270\nR9v96j92/y/B8ZtGY8z8g5sZ1WyooKRvhjdsJ+31/+EMcAdwG/AF/rcURoF92d0V+rvAb+A/Sn7+\nnw6A945nyMutvGCgQyx3+cDjRvCidOcMd6qhXQyTosCyYKgamGxgQ1B6xDDNRTwnIM6RTy3f0xof\nI2I/gdtVWbiqmX9pKXGYNTyrgTkbWvlzW42FBKa17M+7R53JTImJTIHVBFZJxLkSGIvBGcuVqtQa\nJoSFdVM65pUeWjey0teBhB1fF2WgBPZEyQw8LbA5F3ePD4TJ6uNBEmp49QWjNUG5W+FmgXchdpnL\nJgy0Utkz+Aknm2hFS+T4jdN8Sy56dQa+MMTRMEWx80Syu1HGAq0h4i9GmYtBDDyNMNt6dgDbg2Og\nVb6ujvegHO6VWyPDXsZzBzAnOK4LgSNHnsHJuRaGY9jkarQbZSJC0IT9qq+SjwqsE8uLvsoZseeo\nVDhfEupEWB9qdtfGZXu9VN+ysWvAvsxZf0f7H4JLDvFO93UZ61zgqtiuXanK4U7bXH+ycI2o/7Fq\nw21e/AUG97Gczdr6w4Dla5oGzEf8rcY6sWRjak07+wyyPGjzhMzFn1Faj0hNdrgQF41kA8XrzWLZ\nA2VEDa6LlQUIURAeU8eYRPmtKKMEjjTKahWGiCPYiDUj3ss5No+r9HBFBJnJM8Xm6dCYK1GOTiLO\njlJmIrxLoKEY+JzmuTYq8nENzAtVpoQaAzTh2yZiD8kxFMtOUsp1LWGfyg5urG7PzwXi4McODqG9\nz/uh5CW7OCE+VrVtfAjN1yheSsWhh6svXmcj/awVimb79n/yhQHfCAy831Deqyady1SbIh86ykHr\nJllJxxojS4TQgsgePbnmqwua7CdIk9HcXeqbpoXQcaMaGoLaL32oOT3khPacG9/tVy9+k7Xapoj9\nD2pmUqNFVTFvhjfspPY6AfCNdICv5evqHf+fDYC3jORTQbngX1sMrcWSt0qZ3ZXAKR7GBGWN7O5D\n16lwgzHMROmycKMK80QoiucRseyrjjXqWaeeEarMshH51oRngI6sxmpgSnuNoSK8MOJ0GlsXcKIx\nvCwFnlbD+QgFHIepYbURLsj6+LbErNOMbXE+ndF5kB+484HeL4UaHSLsDaRquKdSmTnS5rYf4F1+\nL82cS9NBjzpXJyI9/Qh/QONW1cYvZW7G3Yak3djerCaDvhiZV74dQlc1k+KiXZVvXVKM7xwUaHgu\nS6dfg64cK76lq+KG7W/NrpHqcMbQbwwTKhE3xoE9RRgc4CaB/UOg5uERzRhmldEqDDXQg7A6KAuH\nf5C76kbxebFsVsfNaYXzc03kxLG5ZwXnFYZzqqTcFmrMTXeQq22kLSowBeWG2ibMztVTXyjWr31P\nXK/bUd/YOjGZv3mxXVVKptZZ2XqrCNtVWaSCJtnUlXnbfUrwbY/WxcUfu+DO0DCgEFxLZdDW5Ev9\ndYN+5X3hN5FE88D/TEPzz/Btv1JTOrRUW7ChYLem3g/sxpqrgqs/Tk04VH38rDHp6DiwXIUCwnSb\n5zocc/61G/ilmjE+CKKeWSLsMfEUZoplfFTPY1GOCeoZ4naSI0+HZOyrgch0M1rz3Bhq5CRlfCjx\neLAMCTVGlNezQR2HZa9wldQzRpR56hHNGCGeQvA0tQzOjtj81+ZtaTapK9Ghd6kfOErI7nYaFSD6\nkJfmq9W3LsC2/lyUr31g17lfebr42Dy0rhbqBu3S0LxDbO2jQQoviIZxaLhNtOlQK4M+n/iBB1kN\nx3kaX0X7BtT7ZJtCv2BmIea+YMIoRcaaOP+7kLZM9bQtN9o/tKsl+tKH2sKUDzfq+iv66P7PfLDB\nMP+gZsY1WTKU8GZ4467XzQD/bvy9AfCNys5rgH8GjmL3qLo3g//SAHj7WMZ8sJnLjDABoRsQLA+I\nI9PAMAwP+Yze4Bmr0OsdEYHpqiyQQKcKY7wn2JiNwPQ6w20ZTBUYbD13hMDePnARjqOBCSL8Cc/7\nmzK+nFjOGnMqO4qjOF0iNklMtwgVDIfZhPN0tzbw1VDlA6GH5VqiLHlTSvrra3E+Gdi5P0duvZ0+\nDVRVeRTPyMhsOTpLKaa1wpXq0ofF9I9H+lNjuSH41vc4N/DD2yonXN8YPzZOddCuEEJDnvLmamjK\nLLU7Rc0BTYVbhgeyS43WDjPxuuucGzJJpT9Btn2WwGSgTgNjRJgQe7qC5SmB6dawNAQ6RJjkS1xp\nchzoPDfhiTB8WDyX2wKLhhzBdwQeR9llGhibq2daUF41MeN9N/fmm1CXMCc4RjfM5nDr+KGHMwUO\nMHXcVd+29QsR1W3lVU1bCFFcaKkNkyxUKuu2/n6rGxXqpGeuGPKJmyiijfsF3/KyGHNBNRt2soi0\neTqfykLr1p7i6O+Vs0P+UrSvfsRRiGNt/lMW2kc6iU7yvqnH2J2j41ztClV7ONr0qHdtfcENjY0p\n9wetWyAS9jDCjSJhtnp6RdmGMBvhfmC0eowLnFVo56TOqRwoOTYEx2pVVknEhnwD3wkeE8qkYrgF\nOM0KPxc4Plh+lWX80RT4WLqJUwudjMhKvJhr5882zyddQp2WeVyrBGr0un7TKjEDo2Jz/ZaX9vmq\naNNCcN31ccONmYZvuTCqqm7I40jVacid6H3L7cvqnpwAhWlKrldUDs60eq6GUXsCjUbCnJbW7MJq\nLXeA93GmJrc8uBEtIv4F0ebDlGx6MIUrDOk+Kl4JvADsZdDNCP2iOsuEwgVO2waq5jqMrU45oSU6\n+YOtXj7cwerXk9M0WuYf3Mi4JiF7c+EPvan77Q2Af68M5o3Kzg1AM7yl+4YCNLH73t5r+bZKdO5f\nSHTbaM5QJz81mOdRaQoqVVFWaGBcZrhAhWVBGReEyxVeChnFasSPXMrTWcpzmeNTaT+rvWNWWuXg\n4JhYCRyvysMoI62nX4UdBPbKEj7vE0b6MgsQGko1ao0TqeY6OQvHTvq5SDNeDAkPapWzMsf3g2OV\nVljn+uhS4Y8Kc7Qa2nO2v9bzyujl1mKm/YgTfGh+AS8nOsch1SrPe6d/8La0TiMO8Jhi2XfemiST\nfpb6wfulftox7YU7W5Osc1XQ8uD3bFx5dOYHfU607QVC/YI0aTgkS0Z2BDf0NKW5cVfpI2eLRT4j\npQAAIABJREFUumsMtRYDI9Sz1HnyLuP0apmHkpRhocqR3jHBp5yohoeBBltkpAjPCiw0ynlJlZU+\ncOboEzkOQ1U9g1xKn+vhMtdNb+hlsy9RqBvO2c5wKYGNVlhffoiPuhpLqHGCS7nJpJxmEi4wlvOb\nxuzaVGjuX0bgqcELmDdgLD0Dolcmi5pOn40bZGiolGsLBxjkdg25QZbNk4JvejK44tR6wx+tyuiY\nv/5CaFkm2ljIAidnWrhUdcDjXjs2JtrxkUjbakLYhbIgIA8oEifJtMeydM+Lk2RaMfGdn3aubSwh\nPj4EHjWCCiyUwAMBuqwlHn04M32KU8eztc28FGo85rrZM3O8T2P+LDHNLuVZn3Gjz/hddTu9fhcv\nSkZP2MkI71npe3H5Aj9Uy1m+REQ/v8exhyprFZ6q9RYWZyW7sn3iqyPrGqqHK6rGckN/pp92blyn\nrbaeqqLvUjP8Qe+bh3+k99QLg3CCEP/FdLef7WgdW9VxgoRCRUd/J3Xj7K6dQ//ZqD6qhgXO8WAQ\noiwb31PLZv2k6hZolo4+MwvDRyt177Nxx0qRUPLeLCQUHkLEexMdGqm5DS3mXOi6z/mBN6tv/oB3\n5oJbRthf3zaSGf/OKQP4gHEe+2bJ21y4/XszwDcqO58FfAbYkzfovvA62I/dh5UTgFmv4XNA8v9x\n955RdhVX2vCzq845N3ffzknd6m61cs4ISQhZImdsbMAYD5icjY3BxgwCjDEGGwzGARsbE8YmmCyS\nSRISMoicBMoB5VbHG845VbX394Pxt3j9DmPNfPOtd+Z91nrWurdqVa26P/Zzq3bt2vs/ub7/BYtb\nMc0MqBvFBRsh/g4W/2DreScS03zNaqUQTdGMIxlYrRU6lELEwNsKmOEx9hBhOwkmkWCdCEIBGgFc\nYUM0iWAfxRgFhbxVGASwRoD9qoq4t+RhGhMMBG1BB6TjG/g2A3tEUCQPo4kQQ2H07uX4K5UwhwKs\n4jLOTXZhMke4SWs8JxbpwR3t91FgrYCYJG7LjYhn7Vgi78UmU9QwT4MQeoQviEBK4ehNmuTbivUG\nYlyqdM9McKpeK6kiJP6yqqL1JAHuJ8lNZuQnBn78VyafyNWtLdmJLRWJF4cp+CuVGqgToJEETxJh\nrhCKWrCJBR1WcBsbGAZmE2MigCqlESjGXwDsb4G3ANQPOQjD0q0YC8Jq8rBCpdCjE3iZNHYojUnp\nEs6JDY6FwQzy4KUcLrYav06XcKb1MFRifNUO4v3Ni/FGRQdO5whNzJQ3pXSWxOjMUBy7dVm1KZtJ\nQwum6x7DDRPS3tYVLOopIlxquZ0tN7+jdOmd2KWvU6TPYQqutUwqhLvEp/Q/F4LanyZd6Vg/rr9W\nK6/JSfgN6NRKSHmsF/LD7PkzSFEVW/c8ITOZbfNjwtVvsiTnCnkHC+sUKdNKKbkHBrNa5+C4dAPG\nko/ntA+lA8xThA7yYOEwWQyGQ2GNAsZown1cRodSaPc95IXxO3b4nlKQgS0qdiIfa4evk8WpYJzB\nBiuLa9I1kUsdnqkrJLTHvVxGZd3wD/f/5M0ZP2KbGcqoPdZR3UVGZQ7WFA84Dk42qFxyTPL4Ekv1\nPrHLpSTp5a1OvhKIu0rDPask2caUepY5PZ1RNUxRf8rjxOsgamFQZ8YmH4jJn26lbolD45OO62eL\nk4NYsk1K2Q7HCVYU7yTYaVbbFQqSFeF9xDMPKOd3QoIdHtQTDLXoa1V89Al5SR+dxeqnS5g1P42x\nOdp7H+ATA/+mD/DzTqT/EP9ZAfy8Opw5ABfj0+wMP9jLuSYDeBSfRmwv/wz/P4vf/UOQOqXKWyTk\nHSzIPgCiqeDEZFb6W8r6vyZ4jwt4ipcx57kwOxWgjQx0gTFfAKMUfCI0Fz38OWDsK4IKJjynCLNY\nYED4UByGgnCrs5hAwGHk8AIpjC562KOAIhyqIXhz+Lm4U/voJo0dYvCA0ogFWKwZO1JDcI7rxR1+\nGuOVj9dsHyZjEG2ugBpP45d+ou+bHg1M4IJfFKiBVM50ikPct9Y8tDMcLRm/ew6AFEtlv0fhjNiM\nGSQyd5Td+HZP71wgkoNwjV8uZ37iVOpqktpfE6vxBPMaxLtYu8ErnUqfBFc+n9E0UrQdC67sIlVo\ndZ57QjE6AAyLLR7QCvMUEAhjhQiGOcYf4eCR4FDHqCAFAqHKr8bS5v3wY+VhByXwMJXRAIt9oTCN\nC9gkgvbYQ0kEb8Eg6wyGuBgfi0U+KmEuBMloD1r71+KKhrk4nQwabAEbol2IPW164gFdrz00wm+s\n2LBmv0vTZCcFZJ2Dd8+A01f63NYM03weVPxP1tavIMos9Hr8H7tk5pj+ONgaSlV5wLaOq8HgFzWi\nV40XNEOpV9i21RO7sqNgH+XLHii3mVjNFFHPkqIRAIY66PuFqyY6rl+hrL9UJH0QW+8IUl65dZ/o\nJGi8qgg5ATZIjM2isIliXCUMBYeEEFpsCXOrVuGGYiW+MvAx7vfqcNryi/Dz1nmYwQ6bUnVytefj\nbo5xLcc4lDQ2gCGUtl+jsgMbXicxQIK8xFJbUb9lx44184+MefgzzuVe0wi/FLmGHcIVdZzTl6uY\nb4OoJwikQDjoG31HX/FG0L2AkaklmKG2f89vJGg+gIh+yZw9SbStVuSeBWjfsufeU0xVUGqMp+NH\n2FZ2WanZnuDhV0WudrpQqk04VSOUGiWcXiCSy2tynQQ926n0ToVoHybqUKzeBskEJaLuHpDzt8TY\nPDWNURpwZQb+Rp9gAXA/Q5U+015m4PkBRP2Mf/k7U78dwNfxabzf3f8RjfjPCuDnXTtvxqfp7X+J\nT0vV7Q3+f/EBPj4Uh3oq+AE4Pcgu9weCO5ElW82S6YFKOeFEN7FXy9ofbUPloPh1keQs4uDbzqXG\nOPYKsK6Ooab5LJ2ksI6A0V6AF5jRQcBwQ3hAEWZbixQJHrQR2qHRBMEIj1DjBP9CwGGjvoMWL4lp\nSuFVSmGVyuB1pfA2FA7d+Q5uCRIYFzSgRQhVyRG48Pvn4McqxmXIAM7ga1zGIEdY5CVip2Adl2Gz\nLZhpt+N+19s/TRHnja1tdCa3a9CMrQiUeyO27a8H6pOzhBsTu+IRPcqOfDSZ2nWjBJkzxGZvIqaw\nx5/8Q1j9daWT00XiT0gnMyD5QGxDp9W5i2DrjofzjxLKrNc00KQU1pGCB8LEZAoPG4upJGgSwW+c\nwxgLbFQOU0CYOup4nAGFvE7jYzHoZIdVpY14XvvIOkEaQD0R5lY04qrSVpxDAe4CYVFZcAbvwNXw\n0U+ExzL1qJYQxyDC9yAY5yUQCqt33/vrpOn11Ts3Zxv6Oj95u6GgomSKyP45sg0HJKlylkXFqUzp\nE4FwhVL6myLVV9h08ktAKpVQQS6jMTarceaAqT9KUaZBqbC50L3zdj9Tc5hzTfcx12qQ/QpzfUFR\nT15pHSmW9aJoima1mpUEJDJe29QDBlX1wjV7xhy95jBKSDUERaXwVwmxhBJYSx5GkQ8fwBIwpiqL\nq8hh7oDCOV4Cj3qV+Mj2Y17DdPR5SYwEoyoaxHuIcKQX4RfwcDBH2CgxjkaEFb7m3zMjLu6or9d+\ncRss1XpeYeGGD77xIjn/NoGcBKLdxN5kR/ltiL1XnWRPcK7qQSO5j5hTxy4zW/7oB8EBBH6WxTs4\nmah5gkkmsuRL1lW9RMo7ijnfrNRgoEAKjJUgmg7x+khoFymewFLeIuLvEfiNhOwDhus+FqmsBicW\nK9S9aSQ1TotYKPW2iBpOunwQEMei5MUrdoqfVirutXrhR6Gqeq+sqv/G8UHKKATBfX3U+kaJaj/b\n94mV/gLjD39n7p93Iv2H+M8K4OddOz+ETwMS91b8gP9iAXyoBTUn1+JG4arZxFWvCSUXC9wFhGpt\nURkkUu5UF6dvYuT/IAimeKAfClL/TC6/GqrUxKRiiGwEJSaJoStYKlvgfJ8RVwvURBIZToSlBExQ\nhE0AHASTBkp4OOFjOjvscA4PQvB1FpjKDjTnx+F0AtaaMtrZoJNizHQxurRCJtOMNi+B210RHW4A\nzZd/Da0qwPsO+CLKKEfbMd324mLt46tRL6b1ftLVlUiVS8LWq+zAMVuW5EwUjmyHG3KH4YbOSDzj\nIb47obv/iV3tUEN1P8qocLzW8SvOVRxAXPdndv4w1sn6wWLXwl0m+UZaJwWuYqITPb4iF99gY3UE\nu+w25orNzrUFSpk14ioPgsJkEv0EIZ5iHfoh2KUIkxThbQaqCBharsDFVQ04pmo4ppCP3RLjJVJY\nrwKkkvV4kTQ2KUKnMTiLLI61BcyNC9jsEx6UIk6hGEspidZoOzpeuwY3DpmH263D5baAQ5WPQQFe\n7F1TcU7j8J17QBKS4YqWkeunb1059m6R3CAkcTZz511ART8QzxVuzkTc2PBPfaf96NDgy7eWJH/P\nlshfkVX66xv7dv26OllzMGn6kXDi1GSu5nWwyTOCLmbvQZbW0QD9laXiYCCeGofJf/H8aIaQpEWw\njAgzWTtDgtXZmr4FNR3b5mif1ivCMmvRIsCE3nV+ItvA/VE/DvarcA/HGGP60akSuFv5GB8XkNYJ\njGEPl6Qy+AErPEoOz9hB9WTf63JLshkLyGK9K+FQjrARwENxiFHawwQvVbSulAjFqG6xbnhj65NN\nm1d9daUoM5NdU2uMhj5fFW9lJBaBk8+JCo4i0p1C3u+DgL4Lck9a19DNSA1zwvMV4idA/sJUtnRn\nOWqaaeE9p7hyPyI7lVTN44TB0ULchEg9Au1mi+Iao+UvHtwUiGmFFJ8jrdpA4QTCwCqB2qwong2K\npmi9a6ZGaV1S2eMO3igvATisQumes6tq6/ZPV+ycmsx2/40KqUgkFY9PZHo+2z41me1+plAqDDD/\n/RH4806k/xD/V4XBLG73zyKtz2bT5jlKb4pY3e+RO9dJTSWQf08gD1mTulkouEKcOlWUsjFXvgKp\nJFbURpwaRVQeFoX6Vq2xUMhnIqxmeBPIVN7iXJrZ6WEWpgufpqWvUw6PQGOWr1GgT9+LzowN7gXQ\nqRg1w76C01UahoBBG+HxdBb/zAJWjD06wHUc4kIXYwcpjMtU4FQH/CyOcInpRzsJJrsQd/jVqCTB\ngWSAcnfYHaTKA2FvVYuXCCt0siqz/YO2UxjJBQzkfSndqjx9iHDDFzRVnONs/kSB/b3j/B9yCe/g\n0OEWBd0rwr9IqeI55/ZfdcKq9H5fSnrReYYz58aRHxLi3Qp6vGb7ICt/gbj6JUZa1sK2dgqrkQK/\nS5FpYzF3kcZcYfis8ToJZuoytnUdjkWkEWsfj+sE3tjzPmqVj6kqQHu5hDs9D/tqAgljuQsxWWk4\nUugE8HO2+IkibFYe3mr/AkZwjCSX8bpOYIqJ8bRmXKZgtefijVIiXynxYKSeM6X+vnVTD3N27Mqy\nojuVyDeIg3eYvEmDrv7t49JH5YFss8/JigovHRCnXk8kcxcHSv5iTaNjzuwU8Lcg+o9EPIcSVQ+J\ndbOd5AfE1v3JSetEz5c54EyzUuEwJ6knNIWdADrJqcdGHrjiNi8Qwxr3skWt8jGSPLhUJafl0z+K\nLrE4kARLbBmHNl6En/W+iCO8DN4DMKvxNNxxwRycJDEiG+J1Elng1+Ni38dRzmC4hCgQ4T4SNLsY\nx5KBb8rJThf6HTooJ1wMq2FaTZip7u+e62IK/kLi1QRe4ilrExcMlNKX+r6rAWj6uaWJPzzY/+Y5\nEVc+oiFHqcBeCU591UpVj6JyWxhmX1dAByFT42zuBuuaphFHsxjZdgAdpHxD2u4h2BEK0WbF0kvK\njvMINZEEJU+ZfYW92UzJGYG3LfC8DTPBdN/hm8Nj7+r7fwOmD8spb2BeqnFcBWWFJKX3houLveVB\ndnsTCL1X+L9CAJ8ZiY6v5PzbWNJFa4eygioS9L0K7nhBzWhw9f1OUpONq35VXMXBoKpnCLnx4hJM\noMMUx9eBaw8JSupcq2pO9309m1TpDYJMHUi4ewKn5gOSYKJXWfwxzmXvYUlvI+bZLHa2EOUUoSoq\n4wHtY64isBDWdx2HbwXVqBDCS9rDi14W1exB2z68KMCxjrDC7kKMJMajBGsjfGIjaA3M14RWlOBV\nalwfK9xsYzwNg62pCuMzq9Xr3p1YX53b2p2pLYzc+sHUSipFKMZT1/WZWfsReo4DD705djXVRK7O\ncdM4IGfjKPWodZWHC3JrtpuKTzydHPWqd8xxHoUbHeoL5Crfg/ARDlVaqcEaK94SRWo4lG5jTz0G\n9meKa3rCces7jir3VcgdAAnyoLAzod2/OMGk9tk4Jd2IoYrwmApQD8aUZB61XhobSNCqGMdIjCjq\nwQH1TfhBuQ9HweB9ncRBa3+Jx6vGY7wtY1NtBW4rGtxYTfhSmfEtWKxUFqe7fmwWJ484428q7Mw3\nbuvOUS5RCnLZ3n3XvHPKy+S8OzT0HELcXpSqfYq2KtNXzJ6TDMJHSNFFSvQQhiw4vXDUxUd7p32d\nUbFck12YDPxbwrhhNsiboVXJRyx9IGwH9GytC/dZVzHVutoPo7jh9w6NCxSwEFTZDHBb0+h3DstU\n9Y5UCbxIwDgi9CqL65jRyw5ZYjxS2IaQPHhwyIlg4vY/Y0emDgNcwEccY+63DsQm7SOtgMBGOE4L\nbu7bUHuhTpU8xFgPYJc4rISHM7mMJo7wvJTVZlPSAhMXwagGQeVy745b+9GZ9zHXdCjE98Qu+T3H\n2ZV+kBwhrpIZ2QnH2UsfDbU/TRHmabKJ3YXRDwTa7Udw/Qw93lNSFE3vCrz9NILnHVGnRdVAzBVX\niqufwSrRJi5Vy0iPEZefzZxtFyQanVSOIqQgkn1ZBH3ZxNKZonaMBVcvOnLLrkv+zmwPyyh/YG6q\nfUSOKowgLXvDp4rbo0ExexMIvVf4Hy2AiwB1cXviQrbp85y0/oq5ZoyCZIX4XgL2ZVQfIq7qZsvV\nX/Cs/EyIfh5X2iN0FNxCImsSXuNVxiXPjlXVLo1yQ6yq9zhVsdna+mpQ0KnItGsnaz2SIkNNjaT0\nkKf86SBV52l6zNjkBLa5J4jVLgEfqBXP0ArrhTC27SAszTThOyqJt5WHDRxiOht0SoxJHOIrwhj0\n0qjWPn4Vd+N4ncUHLsJ5sYfzVBlfcntgi3vw2wi4WRF2cDfeER8Tipv9FUHO5Sore5r9hCm5EOmm\nER9Pe3f5gud7TE26Klh3QiAdd8dS+bynzYks+SeFcJRz+ptK+5cCQQ/Ir8soTF43OOs3eX/b4ZDM\nGq3iMYEN7oyobpbS7kG2FSeQ5gqSeAmgZ/uklzNTG4iG+eXE/VblJjE3vSaoecy4ugUOqSOUzlS1\nTus/kDReJg8NwniTI2yHRq84/AYxemEAYTzgBTg2NFgND2H/h1jv5zC7ZhxeEMJkBVQP9GBIeaM3\nucjqXTCPImAmD6IHhKe4DHgJnhFkSjrjmwJZ2epiGdUw9Ln2de8d8TtPybFGhlQS1fzVJ/UR+aYp\nJY2jDde+BqnY6Tg46CBz2b1ewttXgBGkuK6nr/LhRILmGq7+k0h2DlF5JnHpFlCwUJDqBaiXCNM9\nTz8nnKqxUhNqGna5mKpRw6b/6Xil1HoViLgStpKPPiSgHGNp4KGLCW3pKvxCLOaww9MQFJM5nBET\nbibBcUEK3yvupr+m87ggHsQbWnBC82X43iULSz/Tknk3HEhNWL/kqIfSlet/agZyo32XuNBxNEzg\nhngq3mMNdsBhM5cRk8LQ5ra79Ka15/QOllMP+Z7+5vlh+ykr/OK5gBoiwK8ij79Jov5s2VsDSc5O\nBoX1BHGWK/qcBN2K7CmO07d7MLOdAND0IURmeqI2stIDgmQjKH2XdVXrBekcELzMqHqCRKeBOJsM\nXp2YTL50tEDXODfkpmO2fnjlv2G+h2VVamBO0DU2i0oWSam94dPhxvIgR//H4wD/j+PJoZg8tT31\nJ7G1o60bdiO79EwI8kT8F8d+g+XqE9i2PWS4rQbADqPqzmBu+8DbM7zNudaCobbmUux9VZD/rm/9\nIxw1FqBwsC/mKaFMMg4nXBmaGZ8o236ViYc3kJCfQGYGiXpFibTH1nkEWSdEs5TN3ReHte+auHpj\nbFSTczQ9W48VxOgGQFLCCBCW6hAnweLPyscj5Y1YZbpxvB3AmKASq7mE30iMdFBEPQwQFbCv2Y33\nfB9NroALKIMFAB5LN5kvuQKO0ba0xw6iDMKA0kxzjnugvSbYcISWUc8kkbrdA44n0GaIfw67lvVC\ndXtCWz0j1NX3xZK8nqVi3LV29lJFtZt8SkXMibFIJGIIFW3UkbE86ifWtO3DMvpoEZUwRuYw3DII\nqqNk3CYibwtkXCwVm4Wbl8XhhI9HH7R5vHiIxaHalPHW4JagCIsX8WlOvYP40+dxXnELKos78AcX\n4bsS4f3ccMyNGKfEIe4mwT3icBMJTlM5Oy9I2pPJosb24hNmvG1jrFdJfCEuYELcl5iByJtM5LoI\nsimVKFZ2TXj8cif5jGPvBbHpETbh/TZDwXcI7hwSd0q/9J0v0Nf72cJPnXPPOlt/b+wam5IVfIwQ\nvwDy54IbzoxlOAwPvw3QRWbMcb63TEDKOp7NhGVElDWuf2rXrEWzRcCAhKVdWG8G0QOLfomwwA8w\nLYrwpilh1PqXcxcRYyUJ5psSbuEyPtYlXEpAVd352JYI5KXyHhxAjCNLO1Hceg1GegFWkwlXpjID\nDw+b8+dskCr7FPQeGVP/FHEYBoW3ncVG38cu8qDCsF2clXc8rzy/rfVXHyZT+nbj8k/emCzMYw7W\nCPnd3wwbnnPwx3hR6k1BdmJMiceZ1ZVK2SeFMCsTNf28zEPWgVO/cJIpkpJ9EgV6h0jKonietmYZ\nGE4ECzyK3wbQLcAMj/rbiLZtz6QeOMzXa+ayq8tZN/71L25bednnW7GCcCWJVO01If+1e7b/cQJ4\n/xCkFrerRU5qFlkzcq3muj9AaCoBbSLyKlRykFDzXRuPWcGuZTkBB0JaljDSYzKB+Sa0+iFDv8kW\nTznxj//Krp8vj3loKCa3gyU/E8gCgt3QvECQ/11kZnxsXcPm2I4dwai+IHS1HzHIKVJziPVygmSN\n4jEEvMbsN1qp/EnzZC8khYQIBrmAx9jgSTjUuwRmK+BRsahJteIeLuK2cjduC3ejFxG+TAV835Xw\n+3AXeuNuPJ0egsejHv0zEZwmgtUS4gLbj5Aj3McO9zmL98NBvdxZ2pKp7f1KKjHiTWH1SFFSk4VQ\nV7JDOyPX8Qk0bgvZXEtSsVyZypOJG48ciBMvXZW+4wmCe9O69ieM61SFKHcpkbwkRHMZ/KKxw7eE\n3P5sbCfWMapOi4OmnUISETDHE1kGQCs2c8B6Sb5ue7uwdMJhHVsskxDD0rXx+MHe5GFiUWaHSSpA\nMymsyDRius7hjngndrgivuEGMK3lVGxxBewwvZhhBzCSI2ylAGk2SLDFCiL4oeBPUDjLhRgjFo9x\nyL91pXCFKWKjWBQdaLBpxMNfDLmyz0lbBYM+plLNPrFtsSG39BOCdwJKF84tzXjcusRYkcZ3WPkH\nCqmzxeYuYGl+hcANfUUuWM6vNar9+bLtarJUuZDj1CQh+ZhAM/2YVoOxJ5HdfFgq0zfRWWzp2z3M\n+UkM15VoE0a7KyEV7cTJXMCRUvZGNI8p7F/e478HoFZSSIhgk7N4whXQuOX7GC6MpRCMcIJpugon\nhIXkL4TwfGGg8mTejIcBdzlHuJ0sGiEYRR5ekTJKtoQL4jJOKfemvpTIbMxrT4rM2Ngx/IafOs7U\nUJy/kYS/wpwaHrpMw3XJsNNI6o2CH1wuxEO+E+Z/65BNhLZhugBeTzJsEdbvFaVmRYzK3dblp5WT\nqbMBeZtJtbOSGgK9SUCzKB7vO3lF4NUH/vLLctm7zyOgyJRPODe2/0vbH/3cPHwAAFYQ5OCQ32v+\nVx9a/0cJ4OIO74CUDn5nTNcY44a+r1gttdBjRDAKkC2ksuuNqV8URVPWkFTeZIETRfSrIvpS55pf\nKxZbm2Ju1paH9FjX2etkaP+9NdcfK8C2MrpeMLZzY2Rr7ybyXlaQaRU7vbeExFrpULGdvKgcLhgk\nbvqetZ0dgsQRJplcJ0QDIjIvdlgGEktW5ueHmf1dGbshWM4BVN9aTHJlVMLhQGsxhHy8YgdxRjKH\nO4nxERl0WsFhG+7CGoTY7iewXaVxk+2nEW9cV/mIlDDLDWKi68M2YbxgQ6xWwCxSaGEb5ITV62Ap\nTTtm0ZGx+J8Q5OCybcpYk9kdc6oq3ZNeD66eZV3dT0Tc1qLjr11YOun7ecoP32PSrzvgi1anz2Kp\nnBzL0DqAq+K4IiWEHWJzo43t+HYYz9qmSnVXGDe0S5BcaAOPCFgHUjMCP97UOnX5oaRQIsaTpowW\nKNRyhFQmFeZciNdNDzI8iGsH1+Noa9E2uA4557ArLuF2M4COVackDoKo10ShnTSO04QLZdC/wvbr\nKtOrOku7sDLBOLa8LX9g2KevoRL6lGfGqgRb5bCRHJbAyEu+svH8I77QSoLhz8W/WtLDW/+kdPIM\nFjo/srUNwm3q57lPahyqPjDK+z4Y9ecWxq+y3LjWOu9nLDpKZoI5wvKyYTUmU+w8IzJj1zpXd6K1\n7fME2THWD04mrd8ZNvLmk8AYIMKTFTVrrYt1WUpYJxF+62L8TATvEuNhMfZ8LssmnTDfdBGSfoRD\nBHhKShjlLB4Rwk8c40GOsQRFBB2L0GeL0kQFPJvK9jwfpvRVZgA7ksDjSnCEYnwoIcaaAi6L+0Gl\n7ShwofyY68NztoQeJUpYFE2btE97MRk2MpQnTB9qkns046ek6KdKS0RKD1yflmYj/jIRPpLJ6/PB\nC1noeU0YGpYTvytz3cshqocZ17CftZWjjeSviTg/y0p6bOwqzrLejkurKr8z1tPvJ0XS77OkW008\nURk9fPQ/tmgFy5Vwrnqv+Q8eghyET5MeHPh37b/Ap3HFf/77Af8jfIBPjEfViWl9syDFVIiFAAAg\nAElEQVTZGcejmCjRKyLPQlGzCGaQwh62TVsd58+OzeRNioLfC/QkpWy74SFD2Q2J2IaL4Hm3MoJH\niYN9AZ6IwJ4B5/3Uaf9Oz+mDrOhfMFpmQVJTQIV0lNIbQcoJvMlZ0B+NJGaya1wZo+ERMXXzyflH\nwGWzpAZbEpCVTnHQuf/A94M8Zz0PD5OHTnEY7qdhVIAnbRHDTD+OUUAtgLadK/GXXAfatjyHZ9KV\nOCvViIe8SgwXoEY5NYe0PNAyr9wMICcGO4VhA8G9onFyPIhZdiAxWpHUapgKG5PnwVYlVdS4c/vE\nnHM1O4SakgHCO7t9fY2H+nehUi3OVWwRlZ1+dHzm3TpZMzFJ6Vke2czp/QvvPiT49nwSUwKCcVrH\nA1D4kFjNUxqPCgfjLddvKnPFLYobDhRKLQRXNEKos3H40q9kKruHah9LBBinPq2KdiM7bAGhmgWr\nyzuwVntIkIc/cQFnJWuQ8NJ4BBYHewGuVRXu9oQnl9oIO+MSDmn5Dm66eH+5wpboV36KR1RX4fZy\nGdd6Ei6CFQ2N/Umwg8vYjAS6CaiISjUJciXLjP16+0Y+01A6qyGQIdmeUvOtaa/vOMBXRPQ4O+8c\nUvwbgScifttCddnHUJRT4r/HkplPKsqTxt1gfWDZl80AnJPKfFE1fBuuYgKQHNnS9ut9Kmo+HEE+\nXhTGeHa4w7r0x1qbOqVRHxfyy7xE2EwKw20JD2gfkznGSgUUQThBR7ieNb7ccT2uPn8GzuYIz5HD\nMSBs/NYMJMl3sRVMcBEeYlbnbXz7xGeyte81R8W2KWLL9VEIXdyVHSlx/Ib7NCC/xc8gdAaBjbxt\nzupKP9jdxBE6uvsPSjmly3Hs/x5E52Xj4LYBePs69kZqloQDv2QkUQXx2giYnDepW0LPHETaN0JY\nL/CGO/Zvd5Ld5SiVBtRDQPBGXe7qBfnsLT5c1QcKXsFJ3ResHetA+UtO2HnNS//A7g9LUSa00LM3\n2p3p1XZr9m8c6g0vEdJ4PlxZ+6HdmPts33q3pVCU0l2fM+fnBUQLPk2JdTWAGz874L/9DvDRNv90\nHgx+Z2XIe9YMCxSoAJKXPaEKYppJBBObjsqYaxaUzYyNkGB5LF4JQnOsbRXmzHaGXkuqrsJyfR3M\n0GWisnc4qWw7eeclxcgO3SZx/ZksakSmr2ULsz9QcM0/jnlsXiTzbQ1aCiBVgBohgneIaKIOK1ex\nDHk3tlPeAnVeGcX7pcoy8jov3XRIKm/awFjOBpNMD1bZAp4mh62ujN0w+LWv8UZcxNVmAFw3GdeB\n8EjLfhgX7cL5CPCsi3E/yrhFBTyq/kz8UhGOEwuPy2iB4K44DL7mYozXjJc8ia5WUbTUFvEmsWyy\nrAbrh714pLOtKYWOVxQh7IX3URpVnTuL8WWxlOeUqXiS55I/cRn/Wgg/QZy430hF7S8yb0wA5B1r\nWzdFrukVx7kL4LV8DADMehZAKwDpDCjhrNQsieORG6Q45tsmnrWkpnX1TAAfOkHKlvGGOBRIYQox\nVgqwmxhz8nksBqEAQSMpnEiCfVwZWQhqmy7Fa9pha1TCeDY4wGO8se7qYLxyslOTax7sr3q8p0ff\nbwt4uxzjr+ThAAK2xzHWWsYJdgDnF3dXnOlTzwwB6pXD9vETLvgWiRxmJHF9RdD7pZjy5UhlBi8o\n1z/CHIyhUtP7xlY0GslEWql/YkeLI0lNNOAznMtPd5KfQECPEp6jrVoGQKW4OIORXOyQ7q6ufXx/\nYRm0IariQaRIoz7sLVaVeyCF/oZp/b0t5zrnvQwg6WUxlRkrAEzZU8ZPogFsNRa/F0b3hovQLiGe\nB/A9R/ASjBucwiJyuFkJfBKco517f3zHvbc6ow/w1OZYXPy+il3sjH0xjoMPNYJRysM4AVKewjbn\njC8uWivM67qGXHd0IrXmKRFkSOOrhvR9fT7OhqDZ03SZBZ10ufFXiqCty+jTIwkyPb69QTG9Dcic\nKEq8JqwMFM0TUssJ4rLB0mOH1k24KOW93sN22EeOm4Yb1zguiqdYa5s+PH73tT/fG9tWUG6IHlHq\n1BMKn6XiGghXoU2P+d/6CL75d6bcF8Br+N9T4L8A4IcATvz7Af9td4CLOzD0+Mrg5wrZgOO2xSzJ\nfYmUVYS/iKI0hOYxvErnxrRZ1/COcyOSgOzyA3oKVr4oSNcxVe5w3NTk+cUbBN5vLCpuFegTnMt2\niqQ+OjJ5wgRFbotR2cXaJY4Os6VtxKqbkBljbfY2QdUBItm5hIKDcFITvwSofRRxgYm3AzLRcvo9\nkWyfcF163EGPHkA+HAEhCK9FIdj38SQl0KYVOk0KD8NgsorQyhY3myK+HPdjpJ9GvuU7uP67h+JA\n57BVHDpchPyqn7b3pPPFA8Ld6SUSxtmdH0wwXqJ4KWK5Dtb1smA2CAYOvWKxnqwUmDG8fcSftq1f\ndXqFqOju2LrfGal+M+s19IcoWQvu+mZ5xj8f4l14JZeqf2R08mBIsBrAaZrdTayC410xdY1VtfvB\nRkeBqEjkah3kAUXYH6IUQd4mYKrzjR0989SzgyCuEni9XMC7imQVPCRdiANFoxKsNkdRejbSZisI\nmwHMHboHd/QqTCWFMSQofnMONoDhkcJ+pJGOAlzudgWvk8YlRHxI/8e1kefHs7VnLyXBkUohYUqo\nRoT58S40xgUMhqXqV4jKy8RwJAq1bKg6mX6TSpvP+naso0sEfiiiK5cRehypwGieCHKdpShzoe/Z\nK2yU/7Xy4oNcVLNYlN8uYudbTiqNuM7z3ZMOahwJtfuIHxs3fv4tvl/0leBFY/G+9pDTAYYEAXWQ\np7NgaU2m+jpIRfWKUE1AAwN3KWBh0mG9CEg59HCMySTIasEjziINgxrSeNoU8FWJseJf04fNBeO3\nRZeeD3JdRPKKK2M4FNZ70M+wqFOVRoszubRzVBlH2VG+CkkpKBLsiq3fMLT6loPW7lx0NgFXw+JM\nEC5RCq8MxFguREcs8ZBTQHa3xk4H6hdGk8CbrIjzSvFHGrAgGZ/Ua1YPyZ95eC7z25Fim9c6rtfC\nlUOsdAyJ7TjH3JDv61PDn8JTe1NI6bA0VfQflDi2o1WNLFZTc/Q3ChLMCDhPDdFn26upOXrJPF0e\nlIG/vwX+2w7vBfzbAdHX4dPnuSv/fhH/7XaAiwC1uBNnCSd/Bal73KLmPSg+CJASxL0sJAFZ9QXj\nmsdGdnoilDF3WG5rJpC1RE8ao/YlFUxw3FqOTUc/gDU2bhpiXFNFLA1LwLRbgJZvDB7+I4g+sUSp\nJ8XmZ7Oq+KGYqqvgki8ANJILbWutq95SlmEPRHacz1L7RWuGZgXUw1r28RW/qqAsgeaCeUVD16vD\n4FBNwBoJ8YItoSNRgaHW4usSosI6DKcijot343VKojXMY7Xv4S3Xi7fiXnS9dTomWWBZ1BMcZyMc\nQwlc0jpn4ynkmdcS6dIsyuMHjRPePS/Q/b8DxWtFMNvFiLiM15WPbtHwrctqgbwg4ubWNy3OrQhf\n6QKqc1SuuYQIxydk6Li0jOr5SWpPq+Xq92xCX0pAx3nlrlusy6dD1zRZhJJIpeoEelXMrU/H0rLJ\nSPX+QvWHArSGRKbA8SaQ7K6sXH1wKlUY5Qw2m7Bqg0ryJCgcJg5DxGGr9GNm1KNGcr+M4jJd6gYA\nCGhzNSYQ4W3Xj49dGa0CnO2AB00JL3EBic7LMJBIhBtQsjuE8UK2pfesgZ2db0oEDguNC6IB3WwK\nSb+weyjFMdZwjBU+tov2bVYIAy7CW8zepvrqpa0VHf98oaFEOkTiGXHyO5C9huLkD0kwgZy3OgAy\nkaTfQRDfAdAGCeIvWMILkctvF0reZ1ExPrK5q0XoPRJUNbd897TA664kwWsUoOgHmAxKbi335jeY\nor9R2P1WceFCxcUlXMCTtoRHbBHjEeEGAXawwnzl8KIxaAPhx87hyPZfYJUYDBdGKSziLBCuZcIZ\ntoDD4zLMsF4stXHFORz5pXgQaQgicXg2jjNf9ALbKpZVoa/2g0K3v7V/V21zXMYncYhtYSlbo5VZ\nr5SYOaOq/hSLWADpCNwfiR3paxzJhB8DmCWEjU5woNL4Swz4vqVzQxckLNS1jvSYnP/oyGF18xb5\n3jpNrnmVcV1DY9cxo2yne7Hp3MR2SEtku669ABfs9Rt+BQXmnGap2Hv+25JF/8qbAPwFwE8/0ycA\nvgVg1b9+rv1f1/D5WAjgxb39Mfh8B+Re4/EOTJzW7v9RXOUCQcOt4lQNOcwHYUDAz4v4ncz588p2\n9ChjZjwubtgdYoIZAPlCamkCXk7EPzyKR2aM6fylQEbq2N3lgFsMZ7+nnHduzK2apTV1E1YkY9e8\nzosbvgORYaf2H/yccW2fxGj6MaBY53pmQOE1GD2TVNNF5XjqhlClz41d+yTm5NSYksOE5H2BjFQU\npRu6Xt2PCX0EvGCBZvKQtgMgipCNBrC4vBlsuulM53BR1E9dqfKn1dX8ShhFeLphOL4vwP1IuN+5\nMgWNZ2OXK6LNhbjfkdpgdgfn2AGUWOM35PBlAnYojbdtjPPKPTi7tLvxVHKFLjiMFcHWEROvOHaG\nPvocz7XeS5nESAh5orCNIXdqtj9j0E9Ek2Xyy9endzdbyiwDcKSVoFfELNCEF0AysbtY+bPIdr7l\nODcnNkPmOFSMZ1VxBiOxurXzmhMA9CqFZ/3k7hYx6I1jvZEN7nYxfssK72vfvuUHpRPiAfmQBFOl\nhE5hHOMsnmVghJ/CP3EZs4Jd2CkWU63F+g0X42sscicUzgl3VA2RULbmmz64lAUnJbwdRXLuJULI\nmdympUS4SxQ6ycMMABkfKLsYWTiz2gk2dLRddx3Y20BMh4Q2tdqSn0wBcchBT5m8SeK7o4T1XYa9\ntaEkR2uRuYkosURAacPZXWWX/1OZ8ynL1XMdeaNqqx/9nhP1gShkwiI6rMFHpULza4Hu+yOpuIcE\ns02AjWBsYcLMgRBPxCHejgv40BQwSRgHdw3FRxBQ3IOPbQn9H5+M0wCEXoRrhHHcsF/jGXHYyQAr\nxv0fJXGUCb264o6Gn0HQJYTHFWE6iA5xJXk56lc3+/SJ83U0gsiBXWaLZrwVxo2vGqOfi23y/cDr\nH7vfqHqKFF8skEHHapuQOy6wWBK6qD9iN4lIpvWH2E6E4oDGvpZwq9Zbto6o7xpfX3lDk5XmPnLp\nHubccOtaaqyd8DZzfdG5IaPK3P7xGYWFV/9H7J0BsKQdS3avCf53D61PA0gBeO4zbYRPde5vItn9\n2QH/ngBOxqdpjfYWn5cj8B+iNY3k40NxDbnEtYSKjYb1b8TFc6FopoLexpzPi22+yLqRBxSjuZ8Y\nM/52oWCdFcwTSCURvcFKdjtOnBHZUXlB6zmW+DgivBNSw9HGtUc7C9WvQKiTtGp2gsdTmfQiUXQn\nlPoIkvF+nXhvGEi/YZF9IXLNIxTUEWzpeSGqiljVCfx32YzYoezQU0Mz05i487uxGTaPXf3ojokP\nPyixQGkstwZzyULFA5lficPtzOglhXaVwOVi6K+BxpVKZBfF+J5EeFEB45svw48Qo871wNOh249D\nev+tLye+LQYfSqRO3v2a+lhxfKIy+CFKmM+MinjQ067kfSncicpoF7ZT3H2nifDrwp4h64nQKwIa\nP+WkLkkmfwtLX4u5NmdsVao/bFjqJNHkRXVbjK0MY65sU04fy6SeClG12nG+BhR8sb489DWAqDJJ\nMxhYblytEdV0btGNeMuiZWRn1xUXeH7sC/C+jTGNS7jdGNyt4SJxmOcLNgDYCMF0irBeAd1RGb1R\nAdfYEva3IYaSgh3oRt6FeCNM48cEmFjheiKc41nc6RiloKJ34mD30BIPVAfCGO9ifOBCtJNgj1F4\nJlODFuVhtLCXdUWMCUN8wVGyjTTy2pMPCK68/5TE4UIokofLHPSNfT7OFsBXpB5yUCdfbvCqE5VX\nrP8YK28yJ1GvRD4R4fmO7AssXsmR9+jE4aPHiHKxglXscI8pJp8EI06n1w/v658yyrjsK1FY3xkY\n7EOElwhIJTUmEfBXOLS/b/DleACpVavxc1F4Bx4WksNNLDjYhngrcqgyRez+6CTMsyVU2RJy2zbj\ncaVwUSL9yYU6WTzJxVU7ESG2MU5R0vuQ57sbkvmohtHUIZzdxqZ62Z49k2ZCoyKXXuv7ytYBtMfa\n7LJAdU+Z1dlxCVFxcZnLd8bi6vYAWQF06IrficUOSfl8loh6mYnnzGiqmTCmbt85vogiiT9ytmJY\nzJ0jo/iAzZaHvc6SrjVuyOiYuyq0rTv8P2r3gAfhdNpxOrO3/K+u4/ZZAXwVwBuf+f5ZAZwDwAK4\n6N+Z6/MckP8uGjxUXliBWx4cUEdes9uaX3QPLry7P/7lgKTmGtfQYqR1hI1HJI0d/4m1w14H+c8q\nZWInNBeQGlL0UaTdana5S0M3ukpsy2URq32JUGeRf4ZV4iuM4Ee1Ke9Yh9o9hit37SpOuNtJYq5X\nmLgsdg1p6/JkSJ1sgcUs+RGWMhdGrnpurBs6IehXQvuL0BIQVYacHeqQXWzssC1wXddSouqFTNX2\nISCslBhpV8ALIuhTXnk6a2yH4D0CxpVtRrFRmy0wj4o43/Sjz8S4CUC45XK02CKWisZ3SSGRCfj7\nVcPkTB3jhsGdjWMrO+xhpe7mPbtf2f+tcqHmy3ZA++FAVUNhe5MSh3USY6mLbZfWaEtmtm90IVaB\nZW1F1Zu140YcMQegdiK8AkEqE4THx5R8sJSIzhdCG5G6GOKdVCxXrARR58VR/sxIKnKbk32LwLIK\nUPN80ktFRMeMOcTqafJ2hZn0ykmA9JsyoriEvHjo8ALsFsFyY5KNe3Z2XVoqZVcQQTkfs0FYAsGo\nzADe5EGsVA6ncYwB7XCoA+6GhUiIrdSHhC2gWC5iApfRZ0ooVNat+lb/QM1txV31o7d9sNCJoJ4Y\nj5S2V53qIhzJETgesIsHdmJduQfWDFSv5zLWmBJqlZJ14kvzPmNbWwBMuDKmPwtjIYDlxRjPx4zk\nVcBoEMpli/Wx1atKVn5vhd4CZKwXB7uE0D2y5ZgfaK9QQ/8Pde8dbFdxbA+vntnhxJuTrnLOWUII\nJZIkQIicDBiMHxnDc8DGEYMfxsDDOBFsg40BG7DJiBwsEBmJIBRAAXFRlq5uPGnvPTPd3x+yf8b+\nwMZ+fr/6vlXVVVOzq+ecfap61ZmeNd0JVoGxIi5nD0/noy5ixGz16DDYdBHZ5AgS0yCCY9mhlwS7\niLE/M54DQY+OMYET3GkNHAqYQQrzdBdetiV0i8FCNjjDJfiSMM4lQizA1tp6jE8K8JMylofhnmql\nuh5MbPqHphcrXcz3OWA6CfrChbuiePALqVSnyuW2TEwS7whnMcY5DCKpNCtdSJPQqrS3Vc0bVH9b\nbfrtM9glPw5U6WZF9Bw0zbNivm/Fzg9SL86a2jLyFCj5rLi6HdYNaHZ2+GATz1kfmZkrDecqlmv3\nj9yAfOwG1sW2edlZ0aiPbTv5j8BIlVmypU9r8m/O2n10tRuxtwjpn/FRApyEvQVOf/x31lqGvST4\n3D/zBboZpR0Wj++Xlu4zar2Jh+TS8X5hw+ueNL0EbtjItqEslOpWRKsJvNpzSR4iswBUQbBakLMq\nav1eYsfH1jXdDfKNIt6XSL3CLvUjY/tudiW9FJDDHQdZdmFjlb9jipHaFZX07otEMMyP0hcIMvNL\npX6bwagPi7VtiWtZTtb/gqGGahZM8ctYB5KyEOYS/GUAkQHvP2bSeReJIAHglbvQrny8w4JNrsIL\nqIwvF9qrU8XdDVNzYWlOmLH30t4cRFocVpoYLyRFDAXjWMW411bwii1B93TAwCQ74xgtuert1yqS\n+u7tYx5CS+cXU0FHp4Jr81V7nK7e8iIIt1lgMhRGQ1BFLtVurK4VxlrFvL6udtkfjORyFU5HRPom\nhlw4NA5/DIcZYHqHo9SeGJk9KZ/PA1PxyiAZY1zmXpF0TUK1s9v4rSmWOQXCJmLZL6vMu+NHHnoc\nCIYdnhCD5QTsgMO+SUVfAoPFik2fVHbPvmldPNsk6EeCI2sZrxLAxRTmOsILpohNXEEdOSwa3ojl\nSRGeizFBEf4jZHwdwDliMUIMbOJhl1KlGTu2zDgnV7d+XwCvGot6L8ShcRFrK524yrJXpz1MB+Cl\ng+3vssIqAZ5jgz+KwXtpb8dJrQ3/nb8EUh3BVRKyo0i7oyzRpVbLDRA8D4UFVuPnsXCbhT6eCf05\ncAeP6nNoqir9xGTHeAgKSwH087y4KSmlTuopjih2tU9610Tp1RK752G7fhL3wLcVfDuOMEUcpoQO\ntQA2isKBYDyLMnwGbnUVjPUqICJEsPgpAbMm3IdNkqCLE3ygPNzoa9zIwJc041QQXhKL/+Ki3aQY\nD2hCHyWYam1mh9blDan0+ycqFCdr2fy4je2vkgi/Nxb3ssMKE+e3xaZWA3iPwKl9+ux/6YFDqj5X\nl1nSHKOQUVJZMKtlXN2MlhEzJtcvOtdDuphEU9jYqZVKsu8aYye/l3CfgDk3KOG+iywP2urcgHTi\nGvnCaMBB/0zM/xkMRY5D5ST1qe1f1AECwHH4mGZNHyXACfgLAWax9yTlz/00JwJ4+x+8z8clIP8h\nYob94R48cM52mdfHc+cMDqKmEZn2Y9LoPB5K+ihldiqJNkFVIFSZxBpTISpLqAqc9DvMmtbZsRv3\nvuPcixB5xyp7BAv1RrZlbOyaOiHeXUhnFzGqAnKZp5j0r7VSV4lvLoOTSQL6sAspP0H9O2HK/lKU\n3l0J9RzW+jkjrR+y1NxnXeOEOFNzMUS9BpLhLLEnwNrmAXceQ+RqxGGNOKwK0hjPMc6hBBMlxspy\np5oQeoW0K7l6V8FJcRmHK0anBRaA8STFGCMOV7HFcf2uxkokGAWHghfiHAKuIcGZzuAoU9LloP8z\nP6ltfWeCiWCTEiwRrIqxJFWLAV4KowVI2SLG27i0EKL6kUJfeNgNVmb29JohAtqvKEEQcdp8AGQS\nCbsjDkeYAIc71j9iYIaD2kLiHUyknk3Y1yrCxS2yf5WR9I8cUluEkBk6fJ9riUqhApaSQp1SOEAr\nrC12Nr/pSmqDKeMRW3CXwnYvTYpYigJuMiX0bY9xhxhsB2H/1N7Tuhaf8SUToc/7GzFEEWICvscO\niwbcjOViYUSwQglu9Uq4LOVvf7T/wCU6W715Y1xIr2Gjv6NM1wPO4Q/ZRoTiWsY5ruqyRi8rlVO1\n5fLQZqVQTYS84abtjv2VI5ovmTqi5eK7iGSNAATQwqssnk4k2hLDHCLEE65MsFqA2CpzQ5lpY23m\nnm9WpZ89j0ltAmioNZhBDre7yF7jmN/NeOuTTO6NX3rejveE3PiSxavi8IaJsTnVjdNNAUnZ4nqO\n4YtgcFUBO0ShYgsocYS3C1W4hQjP2wqQlBCtOhqjnUVgEsyQXmSTEpTXidUABrsyhiQldILMm06w\nRQgHsKASVfL7sZhBSZHaSuXqrU78pUK63bn68WzT+0Lgeaqw0vM6f6GIH4BHj7FUWyJvn4kNp04+\nqF+/G+b1G3Gq9nbdIpTTSTz7oSiZdK9zw5cWk0lbYu7DVqryEfebnbjBfaJk3NsJNweGc30Y+e8R\n6FN1gftbKIY4+JYRfGr7BwT4SWm4EEDTxzl4HxmPB/DYn8YTAKwF/k8H+EnYW+vv7+HPCch/GUdu\ntk8DdtiSgdVHQxfuCOjVuc41TbEyfbd1dRG7ahHOJha1HcbUb4A0bGb4LQLaw8zLSKk5CtycuJrd\nRF4dS6ooce0fdWrXLVb8HUzhAbZiL9OBznq9uYrxK3uEeZyXckc44HeGw3la9MSAnBf4lWui2D80\ncfl1xFWPaK84ykl2uEZPi5LkfAI91dx0+1wWtBFjLQkmuwTv2bhe+Wnzsla9r/shnyYCG+a7DjQF\nfE0RdCKYpAOMGbwdt29qwhlSwErjodB2IT7PBCcO34PC4wOqMOuDXkwXRr3n29to17AvRMnW0dvW\nTbu039gXF7Lg9z0d+VNy9cUZUkFc6pGHSWEYEbLKD971xRQdo79QuNXzKgMnDju66a0ND35bEX05\n8fBFFkSK8CGzHD/a0vFrtTpSQY1PqKPGM/U/9X1w2UsPFcbNCYcHBzoZks+/NCXwt09lq7ZwgD4u\nxiYitOkA76pw13JSOAaEfcjHcrZ4H4JJtowlsFgOQclLowYBphQFoeehKyphhijcssngtn45XIsy\nqqDQu/azGMcRUsKYOGIUbnpvA35ERSzgNK7eufmA96qq3v0tkp0vQXCPJgwWYJJw0B5zy4pAFyeK\nyOSUt2mdSYiIpaz17gCiyJKsHlj/4wVNuQfXvrRxzY3OM78+HmmfQUUieojBX704sOPYeqvg9EHT\n+45tz6c3DCNArKndrLjjYe1hQpRULUiHvTdbSVawBNPZDJ7seeuWicJRKcI0JryoGEdXNIazwUNs\nMQ0h2ohxWCGLxcR42RIWsMZ1vsW1BqiFxlSbwnl+jF8pwi1EKBvGz0jhO0mI0znBZgLqwOgRL//w\nnq5Z32+oebZSKTaO87ztv2ZgnB+gJ+DO50BoLJQmfcWjrhLIW1epjHRBuGkGsfR3jjUzdcaSezRI\nMrO13nK/4eaZiWQqJp5ZIampCNBCyA90rsopyeREapPINXUn3LTBJ3mfyZ/LLl8fubro6ya88l+N\nd6M4/aS9dlCIvP3o/BHeD7b4lOHH7KX9KtLzV6ceiSQb/86Sn5SGOxzAb7C3N/Bf4aMEOBZ76+oD\nf7399f70bOXff51/HxZ/uPuBO1unDkoB60n1ZtPBs86RIklGv99VOetd5ixIUv1ZJGTBBwJZrUmN\nFcFIi7zvVIqcy3sK9hEJu+cbl2qiIHONuPgEPxV8zbH+YRIkFxKpDAT3suD8cpw9OAjNZ3yu/DBR\n3jWmJ6VVaHtAaq7TtIxd1UAWdW2iaxcqFGZPHjfnGgU2irCbLfoS4Wcs6A5SHdaFLu0AACAASURB\nVHO0xij4WAHBWxDsQ4wqMFZZYGTDHhzfncUj79fjOiK86wgHkcVPHOEsF+FRSpAjjY6NZcwlRjU7\nZLa9v//jtS1thaTQ8s2qpm2LIXhdR+Ag1CcmvfTY5i1HLW+ofmKEVuXRIjCkSmuYAVF4X1xlEDlM\nbahesrCh9uEV57cf9fplWi4FcKNn8Vrk0TGvA6kM4JUdvuh8+1Q+wAksapUAc9nhFmjM0/7WTcP7\nHj6RBT1K8w5n8PskrqFMtru1Umg9s7c0q39L09OvJE6f4quOfZXgBSicLiH2AfCyEhyeWHxdx7hO\nh/gdE34BwgJP4Ud9gP05wlxFSEwW53a045aWLB4XILtqLb5KgteUxkQB4vq6pWOiqGq9UnheNFg7\n7BdH9T6paGcuKByalIJUkoTbJdRPKmvbWKOaBbXOYAg5bwCJ3xV6739hv8GDj3t56/Kf9POqriLx\nno1QhgZe9Ezw4zrvvS8PaTryqUz4frUT/TpcttX3i2JM30OdVOIw6K0vVAZcp1S0zEnTVKVoDKjv\naaG3rUuAmdkIPyyncKgDZolgCQH7JmV8QAq/90KcB+B65WHo1CXY8uZ8dCrGICiMnHYPNq9YgEgp\nKM1YzUDN5Kfw9FuH4hQx8NmiA4QnYAojtFRmx4XwPY+3X8kO+4tCBzRecQq1irEPyELp7RsIZqjv\n9/jORD1K8IQTPAuFsSlO9hVV/6TlfrOtHXRVHM0/p+BagrTueRdSm3dS86GzmQ5RaU+QahROWcAN\nrEjV/uLynEg2TQhm/k/iXLOqHOhf2taA4X8tnRHACrBA/aDtb32uk0nFj+k6/OeZp/DxabhbANT8\naXwv9m6HAfz1FrgZQOFP448S4GIA7QC2/4P3+bfi5O1v7LFqSF/mgS4xE8bBte5U/uqqutwVF9RX\nXbUwDFbUKsgqUma9JgxhqFmWq/obV7XR2NqEQF1KB8sBucCp1KNxrI+04t/mRE2/JAnvBctsYvVS\nJUk/lXAqHYQYRgxXMFWbLOfedaF/N4l+iwTTypX8SgWKmWQOOe/Jlpo7g9BrGybCJY6xDg5tSYRj\nPQUF0c+Vig3ju3eM+yo5vACBEcZcJrwAQdjuYaAzuM+UAFPARMXYP5XFC6aIbo4xH8BpzsfFJDhb\nCJ4IdvYf+tyoMGhb6YVtj+RrPoiF8Kh1+vsw3Y8x880Dhty/XSg7ykn1dsf+i1ExbC3Ho4YQoVkB\noSOsspAN4/ods89v+pwzN4atxGRnFrQ73BF+0a3fvGu3rF2pPDvL54ZrHctpCaBF0IoUAk3cNqnf\n+J9qEohgCQSrTVJ9WjbTXc+s8trv1LXVzx8fV7JfgZRriHFsaGHB2EnAzDTwqigwDGaxw/2mjI2u\njKMFGLLSoguCgiNsYGDkxFuwlWIkpoJdFcIvSbA4jvBNIZyoLPaIIPFV7ztxgBfJYTErgI0aJeJ6\nS73V2+Ik225t/pmo1L/e+cEAsBpLDn09cDuUfVGo8jNh/6XA35WZ13/AdyfVnXz8+PqjM43h0lNm\nN0/fNKll3ujRrVNeTgdtWUawiYAsJJWwJSG1OwRKz1tjl8SVdKJ4z7aAVt/pyYYS3LarbYxxLsH0\nosYpnuANYgxzFj0k2AWN2drhcVfGBgYmsKDujQMxGYy3ohiPugj1Kw7FUMVYLQ6HGof/dA6/efMA\nLJAYJBbPCuAyAV4EcFomfH69RvFC3hvwUooHvbalY9GcpDL4xDiuGhGoNV3szNa4gtXC0UYSPJE4\nLANSs4rxsMUi2jNc43UWL72/UPncWQ5jzw+5bkvshs+ouJahzmWnGaSPTFz64Njl5sdSdXQijTMT\nbm6LJdcnkeClSwyt+J9FuUcCLyvQuU9rkI/dAv8jHWAt/rJ3Pu6jjh/9B7gawJkAbsVeArwLwNHY\ne7/u4+p5/a/jhK33VJa0fmVM2fVuEISztfjbrR3YBtGdVZmb+6jc1UdGycKOjuIXG6xrVY5TN1kK\na0GYCqabjJLTLeWqEfl3wHc/cBwuckDnZZCaaG8T9DEUwDmnr1CQm0Byi+djoRN1h7Gpz7DSiz3E\nmWy6MB4ib4hgXz/74Qv9B3xjgQAlBdxeLiGbSqHRz6C21Nt4LcjrCjN7ikG+q6+zuAKMlA7RPwD+\nYAidijCXCMuswWg/xG9sgsu6S7A5wJLgJgguH3k9rlx3OjqE8b7n4T4H3EWErwhwGpvUq6aYv1O5\n9g9BuF8xhkFhtDi9i23+dS+lDxb2hxPv2moj6gEQgZFhl3ZM8eaB9Tc/s73n+Ct6knlZBVroG5za\n6E04HJAygIWh098p63hhwKEwYaRO+Jh9hmYv1DoJWdQfxalBWpuEUCpXKtlZJhnwRMX025xOrZ/u\nBbseVHHUHic41wT4sopQpX34icMICrAGglmK8N/W4fBEcElQwT2jCCeRwjL0YLfzccLKY9CPHNqE\ncZhXgV+/CNTyGSwVwUjSSItDwcXoWvu9WWN43XKuFBuH+mrbT7Vgsk7h7Z6eOWVx2RqD5BiUUns0\nda5goQ6SymCiqA9YMloJRyZPHpW3N+SeyYGTa2pSy3KK7AkaqkeRfCDIrglVz08d4ySld3XvKRz7\nWj547zw//HDIjtLJd/etvrXOCVp79px6R23db3MuRhgDP/Q1vqN9TIwZniY0ex6OYMJLJDjaz2Fn\nVEbRFfGm8pBWPq5WGqcriy86hQt1jFtZ4QItqHeCrxQZF+cIdzNjoxhM93z8tljBWWzRF8B51mEh\nkUor0B89Zakq3LSYuD22hpeQk5VC2I+ANAuWlypHqFzq4QOBqE8IVoX4xB+WCidfIdpuUM6/nol/\nTpL/TuzoMKCmmVmpishAkaCakOkgqtppWDMD+yfsaTj6l/W+f4GIhSpZ6E/fAI3wMUcZ/wcfl4aj\nTxgD+OurcEsBfAnAzwAMAHAs9raq/C8Av/rUX/Cfx98thnBX4ZXyMekjHnKu+StK1HaBrRPyqjTi\nTdYOU55e355L39+/KnNLLyiJy2bmCIJanYj3IYn+Suz8r0FjoWJ5U0FOYKW+BcIFRGgnwIF4v9GW\nrtmuaX9m3UcT9x1h6eZ2hcOt1d9x5B+iCIvAerUm7jd56ODvE0mgPTwCjZEeYRg8vFosDNsY6N09\nWhee2rN78cNp/90sJ1irCDebEg5zwNEC7ILGwFFZ3L4nxsIkwYvCGB4aHAUPS10R2xxj4YXjsEwE\n84XRaiM8KQ6f5QRXk8ZxUW/O4wT1pCuPOIUdinBEVGoONCpZQTQtLof1LqJ20p23wMpKcljrDAri\nXKQcZxyorl/17bPTeudPdpTnH3gl+7fPQPkggmokqAGeqAeN5hmO7Art7dg5pd+kK33drQVpIeUK\nGraNBQ+T44cYqiUId0o+telOpXsatbLDQsJDjtHIDtYm+DIz5jiFBZwg1D6qY8HzHmM0Jcg4i8eU\nxlcShRsU42TfwxXs8EsGrm8+AsHAi3Bdbhqq4CEkQhEabcrDFvKRb5y1Y5+GeWZg77uFM80ezAVg\nReGpMNgWW1tzCiDVobdlm0ixiaTS6EwEtryFjfdabPOv+ChuNtw4UaniA8KZwUT+GudovVLJW1D6\nEWI7yoEWJ6YpBwTTPJFZUOFvwrBtSHXq7V4B3oHLz0zlVnQKY61SmKIIHQLsZIsBJsH3mdHIggXC\naNAKOROjG3ubqc9JF3BZTDhWHPqTQv2M53DLGa04GRbtjjHWWWT8BC8xcIok+AURWk2ElRB81UX4\nHhgDBJi6ret02d1z+KCa1LPHabezw7nkp2CzCQoHkkKKBc+ToCv01s120H27e88PdvT87J5CNOk6\n3wvPsrbmRIvsSCu117IEP4BUvQJ47zvJNohU7waq2wxSewxQz+LPjNhrNpL88Arxn/ofxv2iDOp6\nx9PJIzLSYCAkn8Ze4+uTMvb82wqifpQAO7C3gsLPAXwee/823oK/lsb8b+AfVoO5r/LcniNTx3ew\npP+DpWUnSNVptTvQ1P0gKawj8boUvCAfPN83n71jeEP2R6VCfPy5Za59DRrPKofjLSkvgWq63NDP\n5qrvft1ZukYxXmHC577A9Js5xAsV6CkACzuZlkJjiGg0WNDDzN5AaNUxrO8h52bTbXUC2U0aESd4\nHYQuEAo+Opdo31VDybBs5r1XFcEIYYIjvEwObA2iQONNZ3HkboMOInQohyli8EsnOMwabNUKM5Xg\nSlH4iQBPkGAtNL4MixtYMAvAbqWSceKiXTW+3GIsTgdQm0T5MSLytol8ZhtYGO9lSbKpICxUiWCQ\nACmGRMRYHyV9toNK6Xzw5rnVweOlgcXjQiK9mdk+ZWCPNMp5GrRycO2lF41vPOFc7RUYoI3ai69n\n4bugVCNc1UwVxG8p5baJBJOjaHhWB3teBmGiA8CEVYowVQS7xWGXWNQECr+0DqdpYBApPMnAgnQN\nfhkXMFs5HEwKesIj+NVZQ3D6kAvQt3YuvowQThG2isUu0tjmOvAyZdBAQBGOu5SPbMMcnB53onvH\neyOe2b7n8EkZ3XWYp3bXaGxZA5SehEk2M5xAZJNAntKeTSvIPk6qBkB6ysb2meWpwteBpNGgjjTZ\nRIndN9bqIu2kD+neZco2/1HpdSO0t62aGANj2/eIUnlKuWinzc2lVrnqEA8Zh8kC9EsC3O85zFaE\ngIGXxGKQONwDwQJSmBkq3OAEB8chXmJGP9krWh/4+f7YQkAPEaYyoZ8TfF1p3KQEDzuH+c7DjYpw\nMxs8b4BnlMLJpLAtq9cuT3nrz2RTWAdxd8ChAsKBAAIR/Tq07BaXu7iSjMx92P5EphwdKk78WCj1\nQrfRIz2qa2bJRczpcZ0m+pGiqvNIssMNgi0Ez3eiG63QPMs0ImJtHZx/Faf+JdnL32BRBnW9Y9Qp\nI1OoZwbo09gKufF/jQD/jMnY29j81/+uD/kH+FQl8R+MHl++2PviBKJ4P8vNOyKM6oLLzwHJcE2l\nNAEbGfnnrBu1QyQ1rzF7o2rNXxaH+oNDCsncdRbhYGG8NEtflmGxYxxJgyMMcsqtmafoGAX1Tkx2\nCwv1Y0UnkMJtwjis7PBbX2POmD6H71OXf3Y8S6bL12Y5Aa9VSoNckOr+wCR9j+otHTDecdXDBJrr\nB4XQEB5VwEwSpJXCywD2MxU8jASAwnEUYxM0Ro9/ALefOxQLPIWQCGPG/AE3nj0CJwnjFWfxAlmc\nNe5+XHLBaFzgYgziRCKQPLx1+xGLs5n3x8WVbD8ynT8hqQz3dXlruTh1pSBojrn+yMTWDyZnP7Qc\n9NokHNFj82NywZ4sHMCss2m1vXlgzfULBuavHlGTWbFgYP7Hb7bk7v3S8Lr/XFyTenEsSLaS+Bu0\nZ2PH1XOVBNOJWSmv0gRgVhz3HW25dqjSboJm87z2TJqA0V6Ih8RiAgitAB4iYJ4z2CyMt93ey/2D\nQEhHRSxXhAEseFEYs87rhxWDv4bPZYZjITQ2JzvxtFLYlnTide3D86rQnxN0AigCuEcE4yFIqkdj\nopfKrecNaw9hU6kmLt0MFz3hbGYGVFIHYHU5OjgOvU0tLJjnxNXDZfaAvCS2Q+91kv8PQqWXJN3r\nXJXH2t3hibqKECwD4hlKuu9mhZwwKi7Bd60LJ2ZS7xbT/ioHwsjYYpgQ3lYKoxRjvQKUKEzyBfcy\nYQYEPhxuMQb7MXAogBIEIYA3xGCSUlhJgouCBN8xhL6cYGzK4tdGcB6XcRP56A+DhTaBpxV+QMBJ\nRIBNQAI3n5PC2kIybKOvOlaTwgIQ8iT6xV7bPNglw769rfu7Hdu6r99lpGmnUsFXHfvHgqtWEqkz\nu+IPz/FV61xwmBWVnlYgdaNwTTpimpWIV5sIZSOhTge/xHAjLVdmvYKrdvwb4n5RBnW9o9VJI1Ko\nMwKRT2Nv/F8gwIOxVwf48L/rQ/4BPnVPkIeSu/+wyL/gAhKdUlRsiN0gqyS9ShSlmRtGaCBQtLVJ\nU2UXk79UmeqXMuGbg+tzv5qbSz0eDKy7/M4dvRdeqRRfIUT7ABhzlfW/Nou+81VxfK8oXmSdd7Wo\neJEVGutB+aFG9/g+8+ZVZ589AoS1GrgFnoudqz41TO8CSzCYkW4Nw839tJSm+8EeBXIDSbDHU+gW\nwsQgjwfYYBqAGlFY6hIMUz56hTHmrFF4RxFyroKnncOxF03EY2wxjhymCmM4M9aeNxKBGIwF8BoL\nAnZ4MFe17nRb4Rq45DJhLIRCiQjPhf4WGK49ETC1qWDDNiVRozMmR04ySkAu8jeKq9ti2F/NkvYJ\n4sAeZfwNAwLVOTVU2wskUoTyHxbus8S56oNJuq4Bxw8TJS3M9Fwi6g+l8ojZgf7wcY+6lhC6R0HM\nGMforwhD2WGQUlgOwUQAyxXQLAqj/Bh3O8I+nsYf2OIYUkhrwVJncRADjw06B/dmh6K/TmO9K+BD\nvwFNXMJgrxqjPB/nuQSHA7DcjhfF4byeZ3F7d+fQWkWs6kbsmu96yu3RhugqY12HKMwjlaRF8I4w\n1gTephkWNMc5f5d12XtI3ECW5E5Pd01XIqkkmXm/rzoikDfdSnAXST7NygtEclNKbuzn0+HWS8Rh\nMRQ2OakxQKFVA9+NY9SLQzMIeXGYTMBYAR4iwlQr6IVgNynMTFVwr9WYwAYvCdBKhAW9DbgmLOOY\n2hKurBBOdUCZBTMVcKdT+IYIbhSFz8HhLceYIYxfsaAPEaYbg5yz0NagAsEqnzqWg4L5sWsa7lN5\nRWzzh5bjmWM3td91eSGa+7YQDRdl/4sluNSxVyNEzZ4OzvOozy1Q3q0Jp0SRN9LjzBgn3lNOaJdQ\nUGBRsZNkpIMdU5He13+Mhiv+TXG/KIW63lHqpNEh6oQh6tPYW3JT/HcIcCH2KlmWA3j/I/OfB/AC\n9nLN7z/q8HEEeBKAbQBe+h+/4qfDP9UU6TD69sNC/ldFqrs09dSy8v8YJdM6IKndjmsmK3ArIWuZ\naQ950TBBOgPWr/tqx3Jf7T6+NvXQtAHVX2vrLB22OpKaqQvk+3+oUOUAJtlPQWfaRP++ipK5HuMh\no3j+qIbPnlqfeWwStLeNiATAaCXYBZe8lbiq6igZtKLSO+P3kF3Nvt76HIz7MVvMIGCkYwxSgoHW\nQGjvIdPsxmrcVY6wP8dYIQ4ZUvis1viJsziRCDeyxY8B3KQZ7wE4xkvjP9ngcmaUXYImItwmCidz\nBdMSwhkwOIEImZ29By3v6Jl7YOi1H2e5UpvWG5eINXda6xzEmYpt2OHpjkeUl6wnFFsI0sqcqWHo\nBhDrOOlzo1KVeoJ5VSm7BGQmwPFMI7XblIonbC+euiXjb+9Wykxr23NFuT7/8HaNZER39xEvhME6\n5QS+Ynwjsmglhz6OMUAEIxWhlhWeUoLZTHgZhCE2QQ4at5PgfLF4RBTmjvwm4nR/HA0fL0iAlfFm\nPOVlsFXnsdVnXOcUfm624wc6jfVO4/ykgjNzwzAvU909kGJTL9Bx9UgepCp4ufcDTICoGsvhK0XX\nwqEq/Id1/kAx2d+X4tlrtWo/BOCXBVUjrR15jCfBInid30hs6yhNvV8ApX6ldfAdtplFNXF4hvXi\nxeDwIJDZbrlhSpW34+csONAKHAFrAIwUh7ucwxZxGEOM0QIMhUJ/38evwTgo8VERwRYA89oZX0o5\nLA7LOIGAtSWNKgFeE8KhAmwqG9ypBV9SFtc5wiAmLIDFepvBvcriPGvRzA7PO4f+SrAzCPEMGAfs\nLM0/M+dtfLWYDDl21e4HmjcXzl1XkOK+IaqmiKjdzN7JQlRtjf46a0yKnZ5l4W+y8IYIKF9x+kUo\n1cqQw4hUSyKuNpLCLAtbW0FvdIO0jgEu/5dEzx+DRWlUF4bQgmkKvmdQDP9sHgJPwEEFe9LJR+YN\niuEqua1QQcedn7DmJxVEvR/AEQBuAvBX9QQ/jgCfwf898gP+SQJ8xN3QeZj/9b5OgnlO6jrBmWmh\n3uyLuJiRW2dk4INx0lK2MugMwGuFIM9cX9LimgWh9qn0vFIm35i565Tm7O/7xxQ1FszUN4RlGSu3\nsEq4TSkuNOYfnD2lz8yF2dTqJpEgSUz/Nqjkek0VgUg3peVuYlMdeLtb06nVz/m6p8IKo8TDaggi\ntqglxvesRYMIDlQOraRRVY6xgwg9cJhRifB9DzjeGXiKMHjy47jx7IE4jQ1WOMYgsRjiFfGQIZyk\nFX4nDn3KghcCxjdcgu/DYhiRHsfIv2RMv3xtdumxJo4jx8XvkrFbhTNzQCbNwPuRmb4r7bXVWZee\nD6FWw6lmkq411vRvEpc6O/Q7vkCMB0lpLezP1XFyCnt6RNEOXOnBDssFHx5DtHWzp22+Lvd0b6U4\n61mm8Kh87uVuUnhdBFNBKCtBmwgGi8PPnUUMwSyxmAKCLwQLhbewNwf2ayljBjQObxiDzqpp+BYR\nVpNGAoesrsZ+AtSoGEOtxl22B0NtBSd7eYyAwS89D5NdBQcX3sHqoFZ6k0rufaI4zAzBce3P44Ni\nefzWcmXi/hn9/kEmSnWIy95qKdzlqW0HK7LvOqlrMrbhAFK0WKC/WU5mzFVU2Fp0B+8KVFfJ2dTn\nlTK7Y8p3saQ+dHYg9SZTBwdqzxQm+UChUgZhQmJxj9aYyYJq5+FpMCZYhycBvCoWB7LDoUKoKKAP\n+fgtGAeHQKcAu4lhhTGBCJMaDK4uCQ4RRq1SmCqEXzngHDi0OAdNgv8mxufYYKIAt7kE4xWhKIxn\nmTEehImJRX5999kL326/amORq+/SKgQLTYTy3owdv+wU90+svkhp/koicZ8CNh+QuI5ztK45Rxht\nQjIwYbPeOLu0gs6BkfQ0GZg9CYpDE0nmvIWGnf/GuF+UQlUcoXfGLrzpb8Mr4Z+tD6Z3EVTyBq6v\n24zn0h99tgdrijF6Pqki9C8AXIC95xjf/8j8dQAGYS8J3v1Rh4/KYP5/g/PKI879SWrbMb7qaKqg\nuottphLotolO1NJQ7akXlR5SNP3EuL73h9T+nvL2nCCqoZmwq6KIm5REOaIuTqtC94Cqn507ouZb\nScI1rmLGvKMQPVyVersCRE1EvA2gJUQRh37bQOY+06xUvQN4x4f2wy0qST3CoTsnToadmM1suI04\nmaAYs5TG085iKgv2hcMz1qFJHG6lNL6rA3wrVjgrRZgHAM5gjQqQsxb69QXoLxYvgXAqEzQUvh1p\nXCEG79kIh5LCjSFwh7V4MSZs8gUXdvXMLzHb0bX5Z+ckFVlJjCUh4KCwH1DWYLyqNAp5f/n+ljFe\nXKXigNuIe6YDgOfkLPYrv2K4H0KlZgprVpL81Pj+vZrdldX+mmO1XzzWxVVXksptsS4KOOlzdiL+\n5yvRhHI6XDfFMB7zgHYR7Os8/MSzOJQJs0H4ozUY4QT3+Q7HeT7ONgrnBQAHZUwXwcswGNlwGL4K\nwU5hbC+tR5oEP86MxhQAlBQwJGjBr8MMPpNofI4LmN39EFZWH4LLdjyPm/vMwwwX4ZGwpneodKox\nlv09o7+cLF552TvLoXeWbByuIJK3QN42zeZEEHpjM6o/JBxZMLUP+F40MURqkEDuVhLflVUrbuwN\n3MF+adJvUrJ9iOjiJUHS8xmr49nO+V9hO+RsR8FnnDRY7bX5vp+MB/A6Eeb6hIgFW0HYx/dwbSXG\nAnZYqS1S7ONEcfimp7BeCw41Hn5CBrNUiP8wFg/uEJyrgJ1W4+e+xb3FAFfkDU4k4H0IdjFhCCwW\nwuL7TrAfKaQs4X6lMQYOi51gJ6vkuHU9h73pkRmgRM61zqz0KLzd2PhVpbzPKvh/NNp8KyY7zll9\nRUUV982opucSo+dAl29VQr0MCoxKxhpEzJCcleLoMtof+B0O/LdfhBBQPJHO7qnGkOSv55E2SDAZ\nX+j9W5+N8nj5Y5fai08SQndgr5rl2b91/P9sReh/hCO8S+6rcPWFCi7jKLUjtn3eIM7vJxTuQxTX\napTuDP3iJiG3P7E2Jen7lGer32auaRHOvWDckIdFqusU17wVmcGJ1jaX8TcMDf2tiUjSDRUuJYof\nUSITmfNtzrb0h5QnkKoE2uv02YSzImk40HF2kKdKg1h6NigfBRKM84DnHaEvEYZnEtxjFWazAhzj\nLkkwVTscAo2SBzjR+IATNJIgAGO+EtxkCWVxGLfPs7jhzAH4RqLwLSUYzxY1zmAcM76mGJ/TCiRq\ny/spb8fCYk9Dh6cK9wFICNgfQMjQbxhXVSKOz7ac9LUGD8ZJ5hmCmU1AzCr9oKjK1Y6rbhHU1oDD\nciI1Y0tmxN2hipuF/HEM1QoODANvMufne5G71FDYEtLmNzPByjXMarZi7CeQlVpjsAdsYgaUYHzZ\nYomvMZGAZqVwfZJgggfMIkFOBI1W464RZ+HmoAaB8nArgJ0UYKSXwwKzAbdKgPNVGmfDYpq1mE8K\nNUT4YTgMVzJwQ74fpinGY5zgHMQY3Pbm9DHVNTvWkZIhfp30tK+s+pCI3mDS7xO7Q5xkcpVkv8ns\ndBmo25Z2uMUh/HkA/TwRhpbsPpEiQ4Gp6t/rMM3AO8EX/wyrUqFScRyoqIVALzI3j4y4+nsemhaB\naD6bpnVKd/RlQaIFG0CYYhN8QABEMDEI8bM4Qn9heAyMVcAwT/CCEFrEoBUW9ymF44TRoRhjGLgv\nMPi6AyrOoV/o4YfG4hZn8CvHSAMYQ4QnlcNgFhzlGL9Y9AH++/6enq7RWPyGkLnASrGTEHYYlMYL\n8cEMqTNIaqwU+6/ly66sp9lf8KiulqDfLGPjxcRVX4+oa6aVSt6g0siwrRXsGB6js/A7zN/33x30\nABalUNM7DItHhsjFAus+ja3GbZUInX+bA7z8T7YHwKMALsJfmqTLn57d+id75KOO/woBflKicRP2\nVotZjL178U+Lf4kAH7XXdh2kLp8L+ENZwtrA60k7pLYa27BZqernCblpFVv7OSV+i0U+b7l2rFbJ\ndEWedtTSbDg8WUFXHOoyXcn4ZmdGrhA3oGBkSokl74i68yI6LzpeopCMXLFpjAAAIABJREFUJK/7\n20RlQ+JsEdnL48rgaWm94VVr4u8p6Z6sFIazxSAAQ52gv6fwEoDxlrAFBAfClHKEuz2NGezwtDhM\n0x72DUNcaxKcMONFXLStPy5h4D6xOEkYbef2Q70VkLIY41k8yAoXOYs7wCClMUsMeiBuhI2TdoZ6\nWyF+VxEOAZCpxPtsLpSmX+yprVOcKW82FkuYsUao4TRCXCGdf1xscEXJDn9Zc3oZYPaz3DrQo8qX\nQtV7M+nUF7uiUccRZW4j8b8TmWlvxG7oUUFq825rq99WKtnHudTNjjMAhxmmmIX1TBJMZC2PEmGi\nr1AEYQcY02HwvBAGsUU7C5aSwmdb5qI61x+HqDTWQdDHlpGGwZM6hQZVj4OUxi4AgxXw0/J2zIAg\nYyr4nTI4y0X4lfLQbHpwDvdCa8GVvXv6dgfp7l6OJZ2pc5N6twVvm15viTg+hBT1Fx7UpMhfKqip\n01y6DhrnOwwe3F4+PkkHa3/uU9fF69oHnFifiS/OwnvMp1R/42qfKdj6/2RUa1/31FVQ91sfbq5G\nFs7kn3JuUF/iym5B7WyCnSyp5nvIdk8lQpoFL5LCTDZ7fwcRjNEW37CC8Sw4mQXtBIw1jJuV4AAA\nqyE44qC1uHRDHU5jh3eVw+1GcCsznhLgPSIsEsLjECxmQoMvOP/QD/4iVVuNuzeNwsF1TPFBTLES\nsptY4jcF5ZssxeNZ1K112PfMMu3I98hKrSS7yqA0kkBjRfi+WBUaDO1xZWwbZKWnidA7cBXuj/6Z\n2PyUWJRCTe9QOmRMgJxjWHwaW4vfVSJ0fdIhyEbs3fpu+sjc5QBeBnAF/ob8gH+NAD8p0fgtAK3Y\nK6T+Z/AvESAAPM1X/HaZoksgvo0lp33YvNLmeTgdW8lXF2xtX5a6qxPbtN5TYZ2m6tUmab5PUD9M\nwfvlzmgUsgrVAYUFEpUC+qxh11iOkrG7o+igVDE5cCyY5oBUv8g2Hxn47ZcygkNKlcl1eX/LUqVK\nU0BuAwgd7NCHHL5nLQI2GOGA4QoYLntPQO8nxqxAoZ0IJQGm2ywukDI+axKMJoXiln7YJhYKgjNI\nY1fk4b8U45dpH+dYi8NFMC9JULCCGxThAmEIM2AiaALWC8VvsNQeaqV6MFDaQdi2r8cbVrMtbRZg\nqRZsBzBfU7ECV79JbPoMZwetsoQfeSq+VLhaCwfWo+wH1mUVw/+MVpVOD/l7xAV5UuU5WnWVIlez\nX2i2X0s+5pCiKo/lBUd6OnPVQ+wyH1gXDgNHQyAYDcHAXITfJD7mEoEYWKOAOVbhN0jQf/BR+AZp\nFITwFBjv2AL6cwUbdQ7PwKFGAde7En7Q8xKeyo3CdNJ4XhG+7DEuFoWLpYKUOBgd4MdCqK5q3DaZ\nKsk+8N0GitHaMKQyZeuyqnUQb1piRjVbHnyD5fwMEveCllCstJwjFJ0cqq7Dhet6WGpa6tL+akJ2\npiO/qWwbP7Oi66xfDM2sGuZJ8ReC3Km+lLsVXBvImw3Y3wr5Bzq0tBluvc+5ARNgKzOEc4NIRcOM\nzj7gIRohCoOSCu7VAeYywSeHZU7QH4ylBByuNcYpwiPOYSgEb39Qj+8w4U4laGTCkeKwRxNuZsYF\nILzHjMOE8fNFm3DjHV34f92iWIMHnhiKebMdekaTBGyo2CiQQxhRX0eliU5VjJXSlwCcIYjIoOcO\nB9vXoHBULB01RrpGJWgfnKDj0Htx1tp/Ni4/JRYFqOodSvMn+UgTw3ifxt7FHwoRuv9XZTD/CJ+U\naLwcwPnYqyG8959Yb38Ap2Cv/GbxR+xlAJW/53g5Lscc+q9VUOo0ImUSl+mwnN6flAzRykWRqGWR\n6CF5bQcokq3g9D1OyfFQcbu1jXUpZY63Lr/Uoa7JSeMTFc4eI1zXCwRbLA+sJHbIvcbN7ewpncTM\nDb5HW44iiJ8N3qvv6D3/viBYv4+vyk1gPAjCLCFoxXhDFEYJ484kRpsw5ghjllIIoJElxhIBDvAT\nrHYWPgSeEwwjQYs43O6A4SpBIzPegcV4EyNwQBcB/QX4jQamE2GyM/jAGAQkMB7hfhDmdVZmfzar\n1vY4I5IkWMsiGVJYBY3VwjiMCNWVaHY9kBlu3ISy05XLAtCF4KZ+VmpfDHThZ9albsrm9RlRue9M\nDa+vSNSPQL+20rTv+u6z/9gQrj5A+8lY8swfIbJvBeGDvpJpBGrSGo86p6c5m3sGnH3RWpljtD2E\nWaVISUtGcJslHEgMPeIkHKZz6O+FWKYCvOcrtHlVqNJ5tCrCrQJMtwm2QvCersJ1SuMeY/F7MjjP\nJvgFARPgYNliNRPWQXChVJBhxkpU0OoSaoQgk2qUWTvembPnw2jwL3IU7KfJdb9aLr/XGlR916Lx\nNnbNQ1JUeI3FPzXw5BQL/A6i7oxU0z0hzOEVbHm/1itXM/fZzKr6QwU+07nG16F6RhCFOyAUE2hq\naOQep1NT2TZvUHbwDYnrc7Cn7KHgmkag0qp9WQNiJsb/w955B91RHGv/6ZnZPfnNWa9yRAklQBJB\nIgcBBhEMxiZnTLYBkwwy0VyCMdmYbMDknDHRiCySAeWc3hxO2t2Z7u+Pc6mLufZFYBHM51/V1Kna\nU2d3T83ss90z3T0TjYcHRTCBASPA1cL4sXUYaRT6KIPfssMuYDQ4IBTBQKdxGhx+LQR2DlFVhOO2\nXvZ33tf/Yi6e+FM/jDnModjEyC2wKLwiaP2zgxsWSfufLILbCPwLh2AsI9ohQufcAO0vF7F4TAGr\nRgZoOfVxnL7ehOYfMCOGTE9/mjbcQyJgRG5d2ie4vxCg+zsRwC/beSkD4CSUEpK/SqzQhihZkn9E\nyUT9rP2f4vcZL8q587agc/ZmNhVEUhlBeoqSKCiYVoPkBE00HkS1gUvVB87bjyhe7VwmCpDeSEv5\noXlJb82ShhJvipa+J0XKnw5XORgwy0ViY5xUdTBnFhfcuLkd2cNbuoM9a0OXmmLMJyewS66MxZYn\nROFDrRADsGFc8JAlTBBGgyE84ATjEOIxZoAEO4LRqzQsC/pB4XnnMD3m40oX4ecJwvVWMJMJzyng\nNInhUHI4BoyBsOj1PNzuBOeGETrY4hMiDGCFR1gwgAQ7KteR7ggrb4lsLG0oX6cIH/a4puUx9E5x\nXLsBo7Ey4sZ5kApPq+z9ik19wP0OIq3OZ1u+fT6aMo4QfpwLGjYp2OE/SnjLfxa5ulnOlN3uSXZi\nVWL+bELqPULtECv+WChbG6Owg5UsFsHGAn4NrJpI0XDVnb+L/dR4cZnXRcmbsLRTyG5bAAtStdik\nbhJ+BMLLOo6MWAx1Fn0kh8UCjIRgVwjmk8KkAnCJcpgYrMU2sSQyULhVIhzjQtRFXWio7IMz8yvw\nvFgscFn8AYJRQcEPli8cmk3orkSyIqihsOLnvGbgWIAbrJMb6/zM2eyGFXRUcwFU8dCVwbCdYyrV\n1dFh7iGv70ERV3V6jGHM6oaCav9T4CpvSGneKi7RtUXpPw4m0MLJCYqKadZ4gEDTHal2AJ0ATVI+\nXmCOVzjXkNeUOdfZ5m2Z1HZky+uhXRM5UyGIXiaFbYjwMhgpBlYJIxDBCSK4gAT1jjEJwFGacYUQ\nIs04f6cl+PMfe2D/76eixEK8elUT+sxkuIkRuvoJ1GYBshKhe5sWvPA7AS5WwEtFrM0Jgg0DrJ0R\noGVMAYsv/AuuufgrPMNfhxkxpHv6Y9NxHnxiBGZd2jw81hvgf+0K97X5KgL4ZRONywBciVKszVNf\n4bxf2wX+jGF8zq1xJWcLefMNVBnDvq+Q+ZgRY5H0HLbp65gyHSIJDYmfwqjo5yRaTFRRp+BtJZK4\ng6VsDgufnXCVx+WkbGJA8eEsmZSIbnTQQ6zER4YwGxW4uqEjvw3W5vdID6w65jgIjyDBzpHgTa0w\n2AE5EawgjYmi8TYYtUIYiTQu4TxGOUKDWIwEoVE6cZOKY+sowpvCKAsJxyngeQ7wFxH8hIp4ioGx\nwihC4wYb4kRr0cgh/osIP1LA01pAjnGsi+DCYvH8uO6t8Sg/AIRVIng5Tr2bgGKTrB1QTlG/m0Uy\ng0HRPMXeOw7Ji0lit3UUx+8f93vO0eg8eJ+2Pxw4MzH9DE3qMeFEFuItU7C/0g5PMtFU8sNbra2Y\nHkqfyySqGkKQ3W3U0GJ0Zy0AEkXvk8hGzjfLQMoRZDwXzB1OJRoEqVaFgh64sxylYshqDY5CPKoE\nn4ggIRFeFsEbXESTLmVH7NB+P+YmB2JorAoXMuPgp0/CpQOn4GDJ4VWTwuuFLuwIh1pTwC/Z4BAI\nVhq4D9J+B7yErOI8mjNNK36y9PXJy0X4FYf6LZzru6FR8WMs9N4h+yvy1DElJuVnJpPx4wn0giZu\nIMjog3oOu+wnieMnpDUNV8glirb6L6QwxnLdUu0qnnfK7CO2qlaproCARv4s64UBRfQBiDdi1q3M\nqflOqqsUVVwVupoKpvhUSGo4oPsJ05aK488QhVMc4X4RRCLYg4F+8HEhMc5XCs++sQinH9qF1q/4\naMgSzLmxHjVbWHRN6sHiSBAUBbnVHtLbA+7KHiw8pYjWijyWjA6wvDJC+89exb3/q27eN8CMGFI9\nfbDRYAO/wAijdWkL8EwQoverBkLfglIA9BAAD33+B+t3h5Gvx1EAXsX/1CL8WvzKBKeJ0HkeqdcU\nVMInFwLuY1/jz85huCY7UVNwD4SqSLu92emspZ5R6SC5uY3xnULUqdg9XKDwv4zK/MSx3sMimYJI\nbyiFgZC4B4q1MsuIUOzUgHOHXI7KWwHgnkEoTzj8VBlsbghxRfiVEI4hwTsMfEwKB7LgZiKMgWCC\n9jELDndrjRVK41HHGG0NrjMRLtQaWoCVwriZCBcT4WNhqEjhbm1xrUTYQxROIY3VUsQb4uF8G2JJ\nAbgmxhjFgkmRZNpbc5ssbSr7iwZXzQyCsbnesPF8IX1kQinnm5ZZzLELrRtQ6bj8ZEZ0Qi+HdeWU\nmsvKf0CLPRLCGnDe/PxWZ1XF3r2l3LSsjJs1fsixo33w4Y7iHazwonH+MURL39Qmv7dWyzynK47z\nZdEhAqxQoh9lsicpwvMM7ibBHiNntrTGa3A6GczTSbxqkljMwCs6hi1JocbP4PxiN85QCk+KwTAO\ncQB343y/Fja7Er8Vh2NTzThIEfIo4mRO4Uk47E0WR4Z5LyF53WnKiltKARkOkGIm4awM617W+MG7\nDx17Q+gyByjU3kGk3iDqPoa5yrdIqld6qo6dllnxEHP5np0UbVFOOLUswp5Z3x6oiV4VKp5uVO8d\nxNFCh9Re6Ap/wWVll5JqW+Chd0ulV6eZaJaP9vFC6M+kL9QUnkjsLGL6Wonyv1ASdoiPu5QrHAsq\nrGYTPeJxz5kKWRD1aKXCPsSyVChIKyXvsMIGlnDKbgux/F99yMZjytYMdz8hRgqGNLxFBK85RFeR\noDIO0Vtrkf/RWnyQ+1evtY5ck0RNS19MPNBD8u8s2iHYqkXByCK8XGNR/DsjbTFeWZJD2zb/5JzP\no7QWMQt/XxY/B2AygNkA0p//wQ9GAAHgRNW9MEbxBgAv+WLeJiMDwTxMJNeY0Ml3GdoaihoA8yA7\nhVbMaapXGwQQGURQKwSKAPsRkToCYq5hogYraphlRQKYEMVpAqouSnHtFVwx5B/dw9PDMMKFOII0\nmkjDZ8bhRuFkEbQbwQNW45fkcBcITUqwAzz4pBGPRdg/MPiNI9xhCJe2MbatFdwJwidhEb83CTwE\niwsdoR8JBljGRwQc4CIsZMKDqhRasjWAXEc0bnYxrDu2OrYoYaMx7zGX36bIbQyxIwm4t41p1wo0\nbohibKbE/FO1eM8WyZ1Q11Gxa0d1z1PMcgkYnzClHy3Y6q3S/qrTOEpfp/zsVR6tXUaq52bmxE+r\ndPcv2l3tLBJvkWO8bLziaT7mW+hirdZL2Pk4ydj87hCpJ6cvYG1/NfbHLUfoFHpVDHeaJOp0HH3C\nLMpMGou0hyqVwHOO8LqOMHPZE7i57/Y4Ax5WkoZvNX5pCI8GnbhWKfSDxY4qhheVxbOI4SDXjTgB\nFS5AC1ssZEbYsyZ5SLKsWIaiNL5219mzC70bf8TOnqNN8RfEsYwjb25eCtmYlMcIfr2IMo6iKkNy\nK5E91zh7UqgqZxjkXxLKXqZD2Rex3Gmg1HUsMhnQg+LQFxYluCJulieYQ2P0qhRJdC95PWshPANU\nvI9IVZAUtweChxVEiSrsbFTuVSbXopCfCZXv8ChsYcnvqVShzUEe2HUJbvlXn4kvMhwDxzrQgQCP\nE7AHqNcMYufNxdzeL/3x+uWaJKpXjcKPZiZQ/ndxgGWoKwIKWbTFGPbvNGoObu/oRdtO/+ScBZTS\neLMAkp87fi2AI1Fatzjq8z/4QQngsWgvg7JrY0gtYYTsUXq2gTIQ/S4U3tWCvT2F+QS8BbgjCNRZ\n5PYftboPt+jrTbtIAV2R6JUMvKfgThPokAmdLK5vxFwTUKHCsU1lhRtuQu3/OWBeGIB4IJjJGruI\nIOd5SEYavzIOP4Eg6QRXKuB8Bq71NM4gDa00rnARNlIKS5eFOGSAjwcZaGCgnzhkyeEuMTiSCAus\nxUYcooWAB2BQxowthJFTpOdYrt+9EA4eBB74hoi6W5EaBUQTSNxrgZQbzakDrDReKKQzAbduZ1Bd\np1AxnxUu58jcqkk9qOBEwXb2oHhWUYqn1OjMEOX046x7LzAUrVVqWUwQ+xOUqyE209nqM8jQKQL0\nKle4U5neqzyzAoJC0ZhVSYXojuE7rjw8Vmk3VUk8phUqWSGvCR+zRiUBN4tgUxjsbdL4UCehXYhf\naIXjxMPfiLBnzc8ws+UGPAPBn3wPrxSy5mGqsJN1hMskgOcYyylAIwh/cRYEwYYIUdW6JrNBWax3\nYrGzIvrrA7cfokRyRMWDmGuWWynLrMn2Hl2f5jcL2cSmSHZc28Vt02YVDxx4dfK9Z5Si6xWyOxye\nnXr49Zn3blHSVm5M9xIwFijGC055P4dT1wmpyQIaZaHP83Xnbzy1utGoXAeoux8hz4zYEqPW9icU\nrFBshUbHQKUKvjCKyvSQ0t2+QssopYvPGeWO237h158S+jfhmiQqV0zEXjsnUb3OYTav4oZsDu27\nfuHw5wOhfwPgdACfF8k8gE1Q2vny88L47xsI/Y94E78NNpQjLCB7CGSJgu7H5Ap5rMjGqXy8Igoj\ni9dAci6L4gjU0UbvXVyOUfcZpG+2hAYhGh4xgl60vM6IeSHbTIGzNkIhb6U4AtJ7/PXo+9qX3cut\nXbB3dOPDP3Xi/n2r8J5j9NUO+0upsnZfpfAegDICxoaEC4ixg1iMU4S+HZ/ivEwV9qNSkdQ5wtiW\nLI6EwrnCICfQLoIlwvOsERBjexE44T5wtnk7Dif0MDfNV0o9S6AyAJsDbs3SKM0eVx5TtGM/Utbd\nUZTkhTFdvSBvK+Nk1CzNfFWOq58UcpMJssGBPQeevrs5eLskVea1zg9d1L3ytvJY/+nEyUciSu2i\nqNCfEd2soKeBTA+RtApovDPmDXDaj7iOi9n6s6D7bOT5hel1w5dPIx+LhSkOwcdK4xkmvK0M+pFC\nH9/DLewwgh0eQoCdo15sbNK4KWjFSBJUnbwLjAZagi7/RyA3Rojf0iFaOMAMzmIlLOqh8Sw75JXC\nVsLogyIaEvFwtVisUByM6t//oY+Wzt1jco+rGUauz0Odsri+Ol6fEldVTya5UktdJk39aGfz87eE\nTFok3IjE4Q2vYwURwdnquSzxYZqCyelE+XWBCzcXqBTEvQatNtHkchBvvnCm0XL63kia5jiprSKO\nv+q4+gVGTX8R1U068SfH5W1aRWmtVo/WevVIX+G0HZeEp93RiW/LDf0umeEh1lOLAcM1wIwirUtb\ngQ/DCMWvGgh9P4Df/ffn3+1ttH432fwe8AcMvbgore8H6ByXQ+ucwOXXairbPkAwIS/5oaEOjy+I\nndPrwrOdcLpCJu8HqvxLmywbHLCUF1zwpiO7i4fqzRl4L6DOVlbWC6hnWg6rOn+PIdd/1XvaZRGW\n7bII5/Yuwt7O4l1mWGdxAQsWAWj2CT47vLsixGuWYcuH44Y44VII5lvGoeJwvNU40wmqgtJG5BUk\neEIUAgh2YqSrmYcNj9ygbNFOXBghkxNSL7Iok7exvdrCsgFddki6FgN29WTMbE8HV3SrzCkBgbOR\n6efrTH5tx0Atrqo6ppOPx6TmLudqYzeln6gllf4YithxWbq5bOQkAb0ToHyDKBp4tOUh9c6OuMiJ\nlwdkik/F1wEizTKZwK8D4seSmOSQvnfQ9HemikjeBiYnBXlEAAvCBEXI9KyOzYtyNC4EGmHwBjls\n5AXYhwTjXDs8P4nRy9+rPE7ydA4XcGfnXLwX9WBcPMLl4nB4lEWyeykmCrCYI7wvFkfl1lbvFPYi\nb4tYJAUkwRgGlg6n87/uIbt5CmVHOqLp1dR0kWN3GgruUKXUQaT0IAbdy0S/0cx3Rtz0Voim/lrR\nbsqZxx2p8dW5jQ4puuHZ9mL8HnGpFqVkFCu1ikDtAKakXOwdISqA1LSIix+BpV1IbQyJcgT6i4Iu\ng0S7a+rezHh/+xGZ1v4WlVtvvyR/7fp/Ir6/MBxZdJkIHevc8H8vgH9WEfq5zx0jAPsC8FAKt/s7\nfnACCABzEE0N0JaPuHNnS8WxkdhnA9tzbii5NyMprmAOL1Hkzi9wd3OeW/s7VzhHiA/JueC+ALmN\ncq7rhohzVVlZcVrAHTNzvGJ8gVcWPOkc+q/c196A23kJnt5pEY5yBofbCMOdQ3lPgItBeLDGYGqs\nC4dbi0bH2IsdtgHjMcfYVQQbieA2bTHSaDxpNYRd/DC2/cZF0ej5RTvmXrZ9q4hgIZjNYvMkPD1p\ngh7mTDxymQ1CN3RVt6W5Ecf7lKuoMSX1Xb4MXiqMm1Lp1hsDStxAzpwUSmxHqPj1jmKXgfBAaJvb\nravPEuIniCdPQNTwQlYtCqO+c50b+qq1I5tYqrbOuuZxEHwKwkRf6cUK6ADR1OGb3WJ8L4rBIfSM\nfRQeIpvF1KAdu4dZnJVMBZtzJIOVw8+yy/GqLWKIK6BLE55iwvHFDqQmTuzMR13S3bkocXZZP2sU\nYWFgESHCIAh+qTw0aYc7teBil0NZ18IBV4Wtar6USka12xzedwGypMhNn3ZYVZ7rhhNkbYT6GRE3\ntQG1YKc7QWbecYXBd1mkBxaLDV0AJkC8QyOX2TpXKGsFgNXp5RtYxJ4Oov5zQqqLRVy7IaTsGBZ+\nE4KagsoOJeBNgKrixGMN1IsEmBZ07knw+gonhhrVMc73/zoZQrFQD9p0t6WrvtSr+OHBEqDHFtG5\nzk3A6/UOflAu8Gesxg08BNvOdsgdKQjXCnggw04R0KgQrUkhvZejcI2V8I9QXk+Enlmaa7YRyl4W\nSfhanpYcwORmO85/GqJHRegcncPKg+/AzH9xE5j/4a525O/qxpt3d6HvW3lU7ZTCBEUoszFsCuBC\nASaSoMoJ3gFhNzjcyMDOQv4jVhJ7kKvYkcNhSzkadIdIJoCYyUI6skSvGOEIojYDxZqdG9aYdenn\nkhjkPKDVmOg2EhxspWrQx27efTWqz2CTbb6D/eik1t6tjyp4s/ci0bU/z21zzk7+MSdSWH+P6Gjv\n1dlNj0qazqNhqzuhehs9L5YVUDfDG+/3xk4s6H6bKOXGs6sYQWQbLEOBwk8VudF9hr14NgvYi+MZ\n57AFOTQF3bjXxPAUBEyMJ213/C1b5P3j5TLEMYoUg2XGIptD0WiM7g7Qq2N4RUK7E5GqN3E5TwRH\nBD0xG3RUDLHZwkpm7MQWNRLi18nq1XHjyTgB8iRYI4RlELTnu+sa/XhbXTHbP9XZOfoKaDkdFP+l\nU2pXK+mhlpN12/unrhaYomjZi8A5Liafdzo5Xvt8EDG/QjDDldPPCqmtRCp/H3JZGcjbAJwaBqKh\nQnpcJJnAUDhKlNlQpGwYCfc1lO7jiW0uS1+dg1ozgLmuEdRntz2Wzn55fY2rfyNmGOieMiRHESJx\nKKh1aa1oKTrY7zQTZH2z3gUQAD7G/cuGYRttUdhLKGqNVPENJ10PFdHOpHR3Lwfn+oTfWioki9Sa\njaRjSwLfEqF3L6YgIXCDLGXHBmiZWMTqhx/AIWetz/tDKWPmYgEe7nK48s4uPL5bE57wi6gRwU8I\nSJPC48L4qQD3Q9T2JOkyduWb6ahPTyBNT7Dz3oWh8SJmOMO0eKLngJidxGeINIyyPCIfysDrPa4Z\nRiSeIHpwYbj4mIxqGJN18TsbaMOBBPtY3uu63OPae32vbeDS6NNDKmlsx87muNmMeKUQ76kQ5lSi\nezZxpq+SsJlVMqNUUOWE7yGY7SMTWwatrOPq8rxUnAxXtxEkNYaRHtQ87P4d0pVrqlm82SS8YVTA\n68rDSi+FvDJ4KQowVisM8Iy9HloaQ4c5RmFDGIyRBC6lCPtDcJFSOL8rwFkUqlrf54rms/D74yfT\nZdxhD47XFHZli5tFcIoSXC5AhxLsKkAbHN4EYRgsRkDQV5vcfBuipqriuQ2XLDu1GEYViWyx/MqY\nXzhDQb/EkI8IdKRXtKez8jdjmBbWutYpvGYlVskS20iTK7OBu4u0nibgmCaa4zg2XJC5JeKqRZGU\n99Xw5odStcRxZZ0oLBHxH6hI396YjD85jl1ZxKgacV7b2k+u6PwwRKn+Ztt6Hl/fd2YA6ElAD3UI\nogh5XpfWja7Qgf+ZAP4OwJMozQeuEz9YAQSAT/Doi4MwfhDDbioSlAPYQFOyb0DZbiPR0RFyj0eU\nFcvFVke5cQG66oDgvgCddUWsyuewaFKEjgUP4bgt1+NtJVHKqBmI0k72iz/74t5WhH/qxrt3duPB\nfcrwCYAdQcgS1C6MpHacagen37Rava/ZDQCZcYKYJ0ovJ5dqi1RqW45qt3YYnCoEw59nqn5AWG0O\nSA0pPEfwUa6q914T2Syh9iVfmXEfRq+9UKWG739sYexBb3ldp9XecQkgAAAgAElEQVSqoR8peE+S\nVocbdpc6k0k4JId6Do60+iBAeR9AzyHwXqwyTxkJhgDUIIqfIaHNPKKiKP8TlkQfP77iyQHDbzlI\nGVkFxxnycJWJ4yWtECONASD0FjqrF4KwmUrYFgKWwWFqWycOSWgcRTm0iUEjunGPY+znO3ziebIN\nGO1HTEmHQZuanu+sr4pV9L5jPJyYWwWYAJerBH5qi4hHOQyxxdQ2+c7qARJmsxyi6CyGkaiIrVQ2\n1Nw1eumK4y7wDfeP2N8kkvicTJR4qEhyVJozf+jVelcoqiLFQ8qL6WsCQ7trTadbNkeSUUorN09I\nTSkU/Hs87aYCUq8kfFJIjwaQjhn7tHNKG+/TkfUVR83UqstzrryNpWrzMBo7/6qelzZEqaDqj1Gq\nWlyFUkJ/iB8+MxTQo4FREQIJUVTr0vIIigz5ZwL4FErP1joL4A8qDOafsQ1+/qqP2gkOMqcCTfdb\nqFEG5W8CkU9I7CwIFhbRtSiOyjGMaHCEHrHonGDR1fMULmlcj7eyLYCfAbgIwDolmT8xBDFnsRux\nPk4Q6y9U3sthfY9DJhKpiFjKwJwBuK475IYwkqo11pl3PGVyLGokiekDuLms6CWIuTiwA6yH6oMK\n3H62Z4LnA4ejnFS+6KvEEyLBwcxmXnNQf9Wq+MpXu4p9tixPtF8GCf+qKTw4VmjboRhvuDJeVCcX\n4upKX7X3Adxso3oaNeM3VqlDIC4dj+srwzA4cdwmOx8YS+VScFim03hRJ+BxAbUmiW6KoVmsnwS5\nOVHk16eqC/NcD85XGVxEDlcKsL2KYSIB7ztgvnJg8nAwCB8qHze4EPd7QWZGpHKXsuOPJcKk9kV4\n1sTV1IoBzC6POgrwYaHoN4SFBlJuWbtOYj4s+ha667c1XstAjlDpsvH9X3infU8h84pjNV6DXrVQ\npIUribguE5nzs370SDH0do970aks3n0gN0JTMBMkc33KV5LIPaKQgsj2CvZuIarTUpyuqHtBc82+\ngyE9Wznus5SIFXPdxDAck1rdbitOwuWfT/ckAJujVJG9BsAlKGU0/FC5BqVsjTMBuC98txIAA2jE\n/65Z+hpKhU3/GYKvoGv/XwggAEzDATcpxH7mo3KJj7I1Fs76SMPCnQ9EpwskFqIlBmCpQ24Hh/y8\nl3HnRGC9zLpWo/Rm+gSlYMyvdc5HBpRNBrv7SHSFlb5vB9HY+XC1cFytnVRa4cwSZq8VRueY4xPA\nOsMw8xzRJ8SJE0Puw1aSs7S44UThOHbp15nwEz9Q+xQTcpE4kxIYQ6Rvg9iJADJK23Rzofqk5Yme\nFwB7tUF+gnDyOVGunDg2VVPbh1DhIYaC1wnFp4ncniTBA32ar9usod89p2qDxTqNe5XCVFEk2kmO\nNe4Ic14iXhZNJYUFJo4MediKFZ7wDLrEwSOFZzjCoeKhSnlQtojDNeESEHojg8tNgCdtiB1tWHsx\nIdrQT3a94CwuLnTXvpVMdayIQlxno7JdfL9zsRa86hyGi6COLao4gokipCTEZA5Q/8qcRW/nwoFH\nabgNRHDYmZHZ9jxjH1GCmyLS3QqYQMpt7ZG9zjpvjPHUraHlM33hPzPZM30VuEjhxrgE25Bya4j4\n/qT3/Ik16bPHWqknJVgMoZTlfuPCaFSlIPXTn3X88p5/0MXbofSCvAqlKZKNAcwBcAeAjq8zZr7H\nXINS8ZT1wWdxgJ/tGrzOuvZvWRH66/ASbj14ErZfQFhyhodUxiCzuhvFngz6nt6DFZ0GXp1D0Azw\nBha9d7+JZw5ZT5feD6WKN+eg9Gb72uy6pOd1AM0P9Rt4JaH3iGT8+aHMme4gmPyCs+MXAPm86ESz\ncLIGzIaVXuZcKgGb+kWR6ztA/sMG7DF4CsN7BooPZpS9n09LklzQ6OD/SUGE4E7uDct3S8d7b7Di\nv7zMFDcV599AwG6WMu8ostOcsVdQ6G/DXP82RFJscpuTK+5jdHagJ/kT6hrvnyZAr6Oksz35gwFc\nF3D/ZeXlSyYpjUndawa95cWXdRq/MED1YlbkQVMM/ZzCeIohN/AK/G7+UUhU9OC4riSeMVkQJ5Ek\nDTEhjhaHs43BMaXIkvwHSuMOAX6mKUsdyyofztR1bJbt3iBRVf3aCw7oD4URcCgXgThGmXIIGZjN\nwI6TRw4f89y7xQMC6Mch8AChonBWQW2ngIKzOFd8GudsbDtPsSkU0KE95AMxjQrm3KyNHxjT4YhQ\nxdIed27ZUHbGjLj3bplwxbtaKIikaryzA3xrB5Q7blh8YM8B/0j8jkBpSuoAlF6Qb6CUwTAepcDe\nBIAHUSpGsn6XQv/9+Uzw5HOf6ySC69MC/B1KAYhf9ZzfigX4GYMwsdxH4c8+EpMdyFNAjkFxBRUx\nCqs1Mnt8gL/OXQ+XGgDgbJQCM+9fD+f7O+6tn36CNr2/JfQsMLpjDcNIEG7ckC/uuspxVeBcJSBp\nE6nMstBWCkGvtKzu0xQeBGXByAx0kswJBxcpYKaDN8FBf+iH5hrrhQ8Hkb+557lrNXFaC68yUeE3\nBS8zSyN0WofD4576WRjxSSx6pQi9CpKjNfghJxTbaPToG+Op5Sk/KUuCYmV7PNG5QMfR4Rk8ysBA\npbEJDF4u9jTl4+WrdlDAU0woCGO38nJckA1wr/ZxESk0SgGfssIk7WE4FD5mhXvI4c4h12CLhUfj\nTjgssxa+7zAr8vBQd0vlvS7rzShrbFlCCrcLY5w42geBSFfHcJ2Mz3/dOa4lh2EsSLgITeIQz/YO\nzL/0yaJnATxCggZRqDaEtSKYca7FrmdovsEjdS+AUyG4VGskhbG5sZhlDU4z4N6muj2Xp2j16T61\nfWo5s1bDr424oU8hmNZEZDly/arS3SqzN/b+osu3LiQAzASwJYAlKFU3/pdeqN8x69MC/Nqsbxf4\nK5mf/82xKC2ALPjC8Q/xDU8GD0a6LkCiLIYYFmLFF6//ddEo/af+KE3Gdq2n8/4v7q4/8HiPll0i\nQvN8s/phhhsjxMMcqloK4eZvdWcPKzLH6wTxFRbqZRIZq4g3CDhVCZiPBCquPP/3sIW7FJtTAqiD\nIbQKSi2EcD3AfTTkdlF8xWlBbKvzPXd9KtKnFDx7r6FgrUfuTw7YOQzo7FhcnSxgmThieEsyueYy\nKFmrPNyuDd6DUU1K03QbJSuUdk9Dm53i6Z4lYFxAMeysBDUmgd8EOVwEwV0EDEIc+xHwe0XYXGuc\nxw7XM+FB5bCGgRMV42QQfh1Y/C0ex3MRY18UMBQWZ/b2NN+eqVsxSwRVXDSH5Xsya1tbt3+use7u\n0IZqDIGqBa6HHUJXRH9mTAMo3tax7eu7L3pmizO1fQVGH1wIio2e9n6jSJ+nGEMicsuMov5GaD+y\ndKLSOESAhyriL9Y1lv/y+LhZ0uG4uQWORFRmqI2G2DAc3ipk+od2yJjANp18dGGjK9ZD1w8GcBCA\nepResI/jC7ud/RvwHwH8b45HyRVf9oXjj6OUw/fvxIYAfolS1exvJbbrrupTLoLKnuCphZ9qtXoV\nlH2PgKWkV48TLtskH22xujs386YgmtzIpMc5SdQQq2eKiE/wyF4qUPtY1hs70BVxWLagk8+wZofz\nDD8pcJeHZFoN8EslPEcTshGpFsXoVcrtp4V8Q3lNJLcxRGK6e+b4kf32VwbtwuiOleEjA8QioRij\nHBqFuDbuhWJuYJ9k5fwm5cFpjUj7ICL82gk2JUaNsvgjE85CDKQNqobdjL0+3Q9/hEJMaWRZYZYS\nnMcObXBocCEO1XG8EOTwY21wRFfLmFGOaXV55bxNCj2Zj0jTo77fkoTDVHFQECxlh7wVMFlU2xD9\nOFRbiEijdXTH0x/nG5n4BQXVIBy73mh3p3J6T2vsCaE1Z3oqvNojU1CgAYOqj9U18duaI9QsIZvK\nMcr7iKvOFMNNcwJ/PkONidzg4dbVBEfmJlet5+7XKIWT7IySEXEHgE/X8zW+Kb4XArg+MkEE/+N7\nfx1ClJKY7/1C+3cSvwRK1t5uAA7FtyR+ALBv+29PY65+N7CTB1sZSM4N3ZQpNlBcPzBXvZM0by5v\nrtp3l741Wx+Ujj8zCaJvLZLfnwTLI+1nHWgbWDpcBPtkyfQvQqlzICYAshHMViTYVSL8ghVtHZYs\nw2nO4uXI6XwPqQtznDIh+6eELjli5IAN9xYggKV3jIZwXld3doyqtMX00zF0nc5Wf1rM1Y2Ima4L\nOcDbUQELCj04OuyB2ACXuQg9AJpUFm0OKIQ5XB/m0fS3fbEZAx9ahz9ahz4jbsIqG6DSFvE8CI/B\nw++jHGYbRhMBtemytQ+UlS+cyGHxLe3n79ZeSwoOUwQIiDAHhAUgbECCrSGY7NjfWkhIAW9DsNf0\nwdVjLHqHCaj+EtDCgAsriyp3IgmVGeTKCbw85b80d3xjv8FV/v1NhWhMhwsHDyzYSdOK4cTuQjR9\nTsTVeZHMpCgaGgtdY7wi17TRN9D9DsAjAA5HSVB+BOAmAAeiNCb/w5ewPi3Az4vgVznvtzoH+A2w\nFUruyG/xHf0HgajbKm/KKrUqa9D1Eunuwb5eyJD83SEG5GJqfgbKDRfJ99VmdXc23GZQwONOXdVx\n1oEhKysKV2vB9lCYwIzrGZhADCcKngEmnGNpjzO0u9FAadLsI1SnksEBjtChCH+Fw9FThzZO8GJt\nI33P/o0lHiiveB8ZzFNQm5LhWqXwMASe9rCDEF4kQjuAvUjwqBC0MGb4cSyBjy1BuEQDVc6hmYG/\nGh+XKeAnUYjpmrBjweD0hMWvRdBdBC6IadwdALv6Fr/uaNuyJh5bPMZXS94R4Fk26NAOu4tCyM5b\nGvTWHiTIi4eu1VGIHjgsjpxXrggJa/UWIkFcBBmx5C/pPHTex+0X7URKH0AwrZp4h6RZ/vTYmp32\nMjo3GVy5wLm6KLDDU+xqF2hkWh0bLUimQ67ZzLoBqy3SwyOufuOEQtO0b2k4fBZO82OUjIs7ALzz\nLV37q/C9sAD/vwmD+QaoRMnqmw/ganzHK3M3+Q+NRDx819cLVxNFjxAVe5Vq256kq8uPLVoN4dUC\nmaMgClTcQlHrkJwbX58wH1/37pp5Q0OXmKfIbXqB8/c7y8jLgcU+FlF9XNMV5MxPxfAhbN3jpNWl\nRuvntOAZZuwfOZw1sf+Wz1elXxylfLzsQvNBLGFBBmnR3juayhaA+cBCcdiQeO17P41zsD9ppMng\nUrY4BgBigv8KBBc4hbt9hSnKwwyjcLAFThrzII6cswseUhrvKYU+LDhDa9wpwHnksKUT1BKjIIKr\nSOM0V/TrraXVpIKFhvF8wOWHKVP0KQyqnEM2LPiRSMgEfAJBpyVosthAGNVhUD5BqfzsyCZ7FXpG\nM5uJFiZO1kQsCeuInKFcpXDF2igYMUe4YY2Tqk5HiRhJomg5Pci5+v5OqpKhDOgW1kMjrqrsKtam\nzwGtUxn79Uw5SmE1E1ESwTvx/Qmn+V4I4P83YTDrmUMBTEFJAL84d/mdcHC428c3+q9eHCJxCqh7\n97heOjty6fe1hJkoNDVCYW1Md1uh9gqSpGLp80Bc1rQR3PbjGkaNy4VjRq/IHr3ilJw0F13QJor3\n1KB6S2aW0uF1RP4sp6Jp5PgwkFwTsqyNkyebDBj4h1Rs6WgRehsi7cq3GWY0qAgJ5XgD1l2dAi9u\nzOIanQ+uiBRCZZAxEfZzMbxhGDsVHeqVxifaYctA4VqTwwjnY5bykXt3VzSRxdtKQ8NBb/gwWt6f\nAY8dclrhPtK4lAUHkeAoFyBpOXxRCUbECQ8VFX4M5v75rrqBygU3KtXSnyjUIHysCEvZYpJyaEKp\ngMRcKF7FnN4p4ngirrvXEkXvsK0ZqKk9UhQJXF1NMZj6kUOVAfsNoFSVkwzAGVWw9ZWCmqyTuqWW\nYymBHhhxZXUkqeO+I/EDgG6UYgqBkgh+Fk5zP4AX8K9NXf0g+I8F+NXoC+DXKL1Fa1BKY7od30Aa\n39fluuRHq6GyCV8vm+Pp1UZx9EBEWOMpHkho31qpVRlFuS6m/F98s7qOyJVD3CpQb4sQbyPiDQ5c\nv0c+aLuriV01fsuJA0/WvU8BuEGJt12KY0fnVPFqBbd0UtP04zKJd6q1wjytpE0UOiEmDkq9VAiG\nv5fJvLkzBN2rWk+Z26fhtxsQIUYxXKosToFCo/FhlUG11ngegmeZcKQwTgXwK1i8rjwcTwZPKoPn\nojwma4MdNeECB2wGhZFgtDGjXEKcbpL4g7N4mQOMII3nAHSHtv48V7R5cPc5StmZEMQE+AsrOGXN\nBIGtEwdlI5QLqDwsNjQbtH4SSOL1ZT3bxPqXPdhPiYbl8uG5wlaV1g5/lgitlmtHgys7LRLLi65q\nNCS20lAs4zgGkK50khhW5OqBllP+r0Iv/SVd9m2TALAHSlM38wHcilKNym+b/1iA/0YolDprKEr5\nu5+5EaMAnIySq/EQvtpmUN8INl8xWiVlbVEGlJNNzLFKDo+pJe872B5I4/vg5rdjpjPOtmem5Zo0\nqTYH0hWhJDdPmY+yXcHINWWJv5ZNqt9so5CrKu9Wg2a+tSr8gGG2BVwsQM9GI6sPraxJPbKLp6IE\nCR61nCJr60jH1l5tKDdVEIwoS775Ngte1oSt+zT89m0SvEyE3RBgnCM8rxg7how7jcIk52NXCFIm\nBgvBlgS8yIItLWF/43A3unELUpigBMc54C7nY2eE2E8BE0GYRQbHhAWUSYQ1yqA68vGWDrybe7v7\n1FbE3t0rBHaDIAZgNjt0EbAtMzYIgtp+7Gihs/l3srZhelqv/WtEiUc9KvYbUP7geGfLy4rBpH6F\ncMYn7OqvFOnelVV8PDj2jogKhBIjNWJJJlPmSCWF/P6hLc9YrqCQdLkOzbjvejz8AwoozQvegdJ4\nPgbAMAB3AXgU/37hNP8S/xHAL2cMSqJ3M/7HnfiMvwE4FUAMwGbf8n39Q36O5vbLsOomYj6IyIsJ\n83siFf08LIpB06NGdUjkUhuKxJc5W/EKlBRICjto02asHbYwoTCSo1GbOBUUE7pllabeK6f37ZuC\nwAu4xvm6bU+SMFTQAcPv0aqY1JLtdtqfoiV3hHOJtyLuP0n8QrnO1Z5tU/OmatWzqY7hCrGYzoKp\nnsLvWbAdBBsz8AjnMUxrLHYOUymGRqTwM/RiN9uLNhh87BK4XgMfdoRYnVEIpYjxBNQ7QmbSi1j4\n7pbYzGOcFpayKa5QBcyKipFkzLu75ovNM7XX0iwqfGV19zSqT80+zIVho7X2IxW0Xuo0NlOEfhlv\nySsC+1rBVW8eh5ochn379hQOa7duyIEk9qcCt//qoOHTBk9etSoxM7Kq0VMm7yTGLKYPS7oQSnnA\nEm+3oB0j9uacAfq+ezUrUBK8T1B6yV+FUlWaW1CyDn/w/McF/ufEURI3hVLxgnXap/j7wmXxzjZF\ngfVVWzuRu1eYWxW6pivd2+DrNRC451nyH8ZMcStCEMGEj/UWG7Yr9+Y2auSeFM/rr6R7e3HcQyqo\n6+bKytr4i2WWfQXd+0pStc5jhQcU4QgmvKE0upXD3oyK5c5WNyTjS5oiKlNaPDGxFiaNlzyFuaww\nTQh/IME4AiZqwgVOcIA4KGNwsyjcLBpzPYM2saXVS0eYrBQGKOAxpdAhwAHM6ILgRUWodoxNncUz\n2qDZOixXgoNdgN9DoQDRM6FkYW9h5NyU+fSkfLEiGYXJez297EMNjGNCfwKWqKBidmTcj3NBn63C\nYNrC3sIRuZQfHtwbuEcU4YHImYQoPlBT6i4l+s3IJXe3SMWtxDoInheIbdDkk3N6kwJ0Tc5S5eWg\n7/OYqQVwGUpFFz743PEmlNLxhqI0T3gfvpmx/71wgX/Q5bD+BbZAaYHjJgB340vqcH8f2dqee39E\n8ZME/qci8fGeCjMOyV5G9VJH1a+yxPtCqn9M4lcAmXgY1W2hlJukKJl3UjlOOD2IXfm1VuqGWOnz\nvuHUABdNvN7x6FZx5XWiegYTRZPA8QesG3S8x213g5AjFPsJdV5YCDYe7ukFS+Iqd1IhxEBl0S9y\naIBglBZsQA5PQGNDB1gC5gGYYi2eZUaTAnqcwyZKo8k5XA3GTEW4D4JjJ72EXy9sxFhhJCe/iv9a\n0RdXRoJ9FHC8jfCAAo6zEZ6DwZvE2JuUtHOQ7BWHk6Oge6GS/NOeys1WxJuB0MzAPGK8F5E+KReO\nGdQVHPPz7t7DhIjeLNjUOd2s3jFcNQoqWVZ0VVcqePtFkhgnEn9JtF5F4tUyUyoS2obZrymAmiOR\nWy4V/eB3PQa+hDyAhwGs+cLxXpQMkkdQKs91Akrzhe0AVq/H689AKdnhO+U/Avj3VAC4EKWafb/C\n+u3wb5VncV7n1ubcQSK0k5P4uw66lkSGEvHTJLFe4VQjJL3KSfqPedvQEXHZSF/Frghd/auE+BCH\nsnNEMidDyok5Ux5x040Ef1dnG1KRG/RgvrhHRRBsGUKv3Nw3KwmqMNFZXKc0JmqN6lhs5Z+FsVkE\n5LXCXBYMogi3Rw7LbISRSmGoEwzVhD49CjfGSnN/rAgLrMMkieMXEuI4ifCR1mgOQjyuLHZe1owV\nijAFgvaVzejrLDQsnNbohsORUYRlSuNadthHETI2gh+yN7pQiLX7KvsuGO+DeAII/UmwnNlfykLH\nrOi8pGdV94WP5sMxXYDdP8dqlEJFh1a1NxbY7dvtwrKEKh8Y2Jr/suL3E/K2CSU5sijR1AjxOgvV\nEQqVRwj9Szg++bvu/3Xky1aBlwB4AsDbKO2ydiRKC4HzAKzzTm7/hO+FAP7HBf4f9kCpU2ah1PE/\nCM71OO8RF7RE90Dli0ZoY6Oy1qhsh6boHsBBEG6nqPB6qGQJiT3XZ2on7TQp9zfFiWdEwj2EguZ2\nm3msRpk+FnZTQqLbiWljJDLMKZ2MPVmdSdw/QHsL/5byl/RCcCYp7EaEBg+4KBScAWCVJtzPhF8S\n4znWCI3Gz41BjhQ+JYWYLeLXOo5LJMQ1QtjS87EdDB4WiwoleBQGl0Fwt+/wcGDwVMxiiyLhRifo\nogi10HiAgZwC9rUW5ZHgdRVigAi6VmZ/8lFz2Z3VgU1vLNArwqhuLCNjF7Q81VqwPDJG1bOBwpYe\nmQsc1HhS8oRmvqgsDHZcY+xjIRVq01T7kZXE0wwUWTCWJapjaGJxo0KEo0KOpvwelXO+637/BpmI\nUoWjGIB7UMp6+jrhNP9xgf+b79oC7IPSXMgKAOfhGyxe8F2wGZ/zhhAdDDIZBe0LeQstp5aCvJWW\ny7Z24k2LYBogFRWB8w42qHiNpeFqhje4SNW/UyyXC/m3Rba6JabVvtZVPyRILihyTV/hSgglUxEn\nULATo7bcQR90FHapL9ra7Y1ePsVTHYugUO8ILZrQAcJEq/CKApqlVKb+Hvy/9s48TqrqyuPfe9+r\nqq7qla2BlgZZBAURCIsbUUQlKEo0aoJLTMxkVGJcYxwjIXFBjVkcIgKuSEwMSYwmGCFRsyjixAma\naMhETYAkiIIgNE1vVfXeu3f+uPQAPTS9VHWt9/v5+NGuT3W90132r86753fO0YxKKP4iNCVCc4Jy\n2SIFWghGKYdfqIBjhKIXkglTX2PRpf05QyuGJsFRHiQEgdD00wpfa+pKXVZ6AXODgFoR8ETgcSTQ\nsrnxnPcisn5wSO4ZL2VzU6Kl34StDbf+cdOu+6sSyhngEn06IBwWIvKKIvQGQpwRKGdioNwHWpzI\nHY4TvR8difpa1vrEj4/rPaOliG1UiHJfJMfEdfPRCbXr1cUM/Ea23/MeZivwHOZ88ETMBKhRmMnm\nXVmunhMZYDELoMRkn+diptL+d4avnxHWcNum48S86b4WI6Vwt2qtB4J4QelIsxKhUl+X/FW75d8M\nVHRAU9BSvU3VLagQ5VcoUfquDMRVG7ydPyhj0Ke0Lon59FqmtfiPgNJ6LSLPBCoy2FMx7RP240r1\n0UQqvaD36B2JE2Nrd183bHz5bb7SjAZmKJ8/CMEIKRBCsx7BJGCbFuyR8JFAcqefZLiAaVpRKSXV\nJWGe8jyOEx6PKMXZl9UQ0oIG6fArNNec9AYXv9ufO31FSPmUhQQLE3C5FzAxCPiaCpgqBDEhWV0R\nfjsoj2wc19AycMx7ez59xJs7H3+9IfGRuEL4jbz/lEuvhEQemfB5Vkru9ZCrfORAX8sdGlkTaFmu\nCG1JaC/ua/E/gUxOUbgDPBJVLaqu2qOhdCnDR2b7/c4gPvAm5hxxJ6Y5YA7m72oDHWeFOSGABbkW\nsxOMZl+p/2rMG1iw7ApCM3y8UFwHtXFCbzUH+is+co5SolRK/aLw5TShRX9B6feqZO33A102JKli\n7yd01dqBzlgVBFXxhO7TEqjSOQm/z42+XzbRC2Jf0kR0oIOdPqE+WodGJ9GHNZHoFwTezU8igjM3\n88qsf3CVjHOZL6hVAVVKMUfEeV9oWqTgBMTeDx6fjwr4dZBkuyNYmPQY2dzCVyWs9mGOm+AzymeO\n8ngn2cxQP8l7a0YzLggIaZ93pGBtPGBskGA6PneIgLFa0VvA7wKNQHOR5+Ou2X730W/UXeEm8Y8I\nRPJYn/jAMNXTPbzPJ/D7aSf5pRbl/djT/oZA+/UKb0ZL0PKVRrH7tIT2hggd2qrQQ3wV+mmT+mBD\nk9o6MC5217boXSdm+W3OJm8D8zCeQjBTzxdgxnblNMV2BhjGePqiwJ3k18SZlLiaHdeFZOSukCjZ\n5uD8Rmi2hyURiZgghQoh9Ha0+FAIykk618hI4h4d8JYQ/uxhXunpGyOJH6HkVgRlgQo9qOBs0OM9\nvD5opz6Jbgh0/HRfJJILg7592ovj2eEcLRRzpaC3I4kpuEUIThWCwU1JFkRdFgjJr9H40uFKLfin\nIymVcLVWfEMINILaHfDJvrBaCB4OFNO1ZCEBy5IJFkmJJwUzteAlND6KS5TPE2f8ix9ewoszXVHy\njEv4nyFdtVyCdEXJuF3idU8g1/bjxPGNavN3yp3hjwr0YqciEEwAABACSURBVLQcj9Zjt+lfzesl\npnzHo2FoiPK/eMRHIXQ4qesHBLrhye8x/ZJMvp95QA1m0dNQzK3x7RzYh5wTZ4DFJIBTMVWse4E/\n9vC1cpIviq3rHREeEiL6XojQ80LKhND0aRT/qqtk6CopuEyyZ3EzjdeW6YG9NfplIeQL6GCBRn8j\noZv/PUn9sTEx8M++cF9SeA1aO0cpHZT7OnmyR2PtbuXXPs6gDjPq1SOI+AlmOmEuFLDFcRmAYpGG\n0QLGV2rm74a7JLykXS6Xgr9pxWNCMjmAWY7JMK5Hs8lR/MiTLFEBvyTgOSG50oH1AQxQAQOkxw0z\nt+z74zufxx8NUfFpl6p3S0T17pAOr/LxNiZF3d0xUbs6obefWqpr5yrpnaxhsEZoRxP1tL5Xy8ZP\nBbRUe7qpl0fDOK1b3BWc27dH37j8xgGux3gLE8B3MF0nVgD30tMCWAnMx/Q73kceevrSxeW8FkPE\ndocp3+zI0u1hHYlIEXobLdZHhDNJoxtAlCOCasd35+gQ9yittqNlmRZm6EOgmkuEcCd4NI4RRLdq\n8AMd792iG47wdd3ihxl/Q1fjWjWMkcBlaI5SgpURh2M1PKU0VcDxcZ/bS12eUrDOkdTEy7ky0sRv\ncJgrPGZqzdRAsUmGeYgkX9SCnTpgjBI8MmsTzx7smp/gsbdADC6hckWY3lVJ0TSpRPddmaB+Q0RW\nD3GU84LEvVnjL/eIDwkTHRrQdEwLH7wBcqhCHeZR1z9Br5pfcHbR3EmkyHBMU8EETPEx6zMLC10A\nz8EMibwD2NQDr593fI5XzwuJqhWSyOtClPw0otwK5TiTPfVheVT2XaJ1+LSQE76vMdh4U5kYUQK6\nPhmou0pcvSyhxBMhKUclVJNsYkMQ4fAyX+2p0MKfltC7xHKOH5BKbKtHEPE8ZknBLFfSPxzwNd/l\n8gC+LwVHScFgpZmE4BmhUEhO0QEhLZAJuDYM8wWUKc27lSEWTH3nUFVJLc7izh0uVbEI1e8kqX+z\nhe1OXzFlUJK6nTF9WL/dJD9eSmJViLJXEuyWTbwTc6n4mMaP+jQ4DokTfs78jan8zEXIWZj9Qcsx\nf5dZpVAFsAazhW0g8BfMxIt8GRXe41zIsysiovLjDmVbIvTe4ovG9QnV+HylHDHdJbwrzvYRMVE9\nXAXyWimZjWbKn/W8Hx4pbroWxOak2N3o6FhpksYBCn98Uu904wQDnuT0+nTFuLKWGtdlroZRjiQS\nLuMLiT3cmZTMjwh+jOZbCmaLgN7KYR6KmwW4QnHPmf+iUz68C/iJU8ea90P09sP02egQDTtU/ELT\n0h+iv3QRt2hC9yTZ8eWAZLmD4yfZOVjR0suj4eRfs+jVdP28RUAY4xvsh0lKPsxuOIZCE0CBGQ8+\nFtPKtgOzOOazmIkXL2PG7Tel4Vp5zYU8vVkjq0OU/d4VZX2EDvlR0adOC/ftQO8ZUaarLm4WjSsc\nUfa+QCcVRAPVsjQp4jcr3TQYIRE6EAk+GKVp+uwKLlrRE3FqEM8M54SQx2cCQakj8R3Frb7DvVpT\n75Zwn4rzdV/xcvlQFp7yYteOOC7ggvA2Qi+H6TMyoLnepaolQr+tDqLZRzwhCa5R+D/wqf9Ykt1T\nAuJVDs3TXuRpK36dp/X8/X6go99bd7dLdotCEsBRmFL8Dzn4WKrWUeE1mP7eouYC7o0GRHc6VKiw\n6PdqMxtLpY5+u1KOOVmq0tfAm+7TMFBQskpKajThYVKrCl/rB6DhowmxY5Kv48f5ND79M664MBMx\n/2oQvQOXSwRMVMZn9hYwXgZ89cx3SelWdCInLXWJXezjfxAmWufSS7lEdBPb3oxQfoZHY3+flp0l\nhI9ey6q69PxEBU93z9+7s1ytWxSCAIaBG4EKzJlC0Wd3neUiFgypw3+7hOo6SaQJQqKEPtsQervQ\nYc+n+cYwVcsUem0dr00uY9jfHSLHBtRHEtQN9fjw96u4dVY2Yl85lGPmbeW69XH+TaRpsvEwhlWG\nif7EQY4A0UshSySOo4lvUySvf5u/P52O6xQJqZy/WwHsJMcBX8SkzevSGVSxMINrjobQSy5VdcCa\nJN6fqjj8FB/v1TAV50lCVwY036ZR2qfJjfNhkyZ+hkf9n37LkpOyHH5OWCksB9B6/r4WeLybr2EF\nsAPKMe1ruzC+oqK1tqSDaVzYN4D1LrFSSfnOMFVrJLoqIHjUIXaLwF/u0TArScN4UDUJ6tf+Fz/O\n1JazQ2EFMHc42Pl7d+judslukY8CeBZwAcYIWxRTazOEnMyMn2j82S7l9Q6Rv7vEwg28/1aMfpOS\n1NVqkhLkjet4fkm2g92LFcDcoKPz95wln0bi98ek1uswVd2i32iVZtQ6nj9/AhNq6tn8zTDR0xU6\nBoxpZku9Ivng39hwE2YZt8UC+87fyzGJjD1/7wZzMWlzewhMT+FSjAhaLK3kSiZajJyAWaw0KduB\npEKuZ4BHYM76nsQIpcViyS4VGGvLB5g7sbw+f89VAXQx6yZ7Y6q8XRm0aLFYuk4MKOPQczlnA5/A\nWFsKogUwFwVwMnAtnXONWyyW9FCKSToqMPuBn2Pfee8ATGX398BlFND5ey4JYA1mufgvMWd+yeyG\nY7EUFTvYt+N6Nmao6XtAPeYoaj65sbis4JiLWTq+ATg1y7FY8gtbBOk5RmF2Al+U7UB6klzJAD+L\nGZr4uyzHYbEUO2HMrXAl5u+yMavRFAm9gYcx+0ZXYwYmWiwdYTPA9HIcxtoyuYPnfQyzTmJGm8eX\nY6Y+L093YIXEdzn0IeoZmM6QlZhm6lAmgrLkJVYA00M5cA/mDLAzd4W/wYjl820eb8J4em3W2AGd\nqSKFMAL4IGaB0YgejciSj1gBTJ2zMAODj+jC97RgNkq2XQWwFPO3vTQ9oRUuXS2j1wC3AMuAS8iB\nXQKWnMAKYPfpjxGqz9H1mQDPYYacrm7zeDMmA7Q7Ujqguz4iAUwHFgPfBiamLSJLPmIFsOuko7V0\nJiYLPG2/xzRm25sHPJFKgIWO3u+fVGjtElm299+9Unw9S/5hBbBrjMTc7p6V7UAs6WUiZi7gYmAa\nuTHmy9LzWAHsHCHgZsxKyvIsx2LpQaLApZis8GbM2aGlcLEC2DFTMNaWY7MdiCWzjMRUjx+kfTvN\n8cCnMxmUJa1YAWyfMkzG9xWslayoaWunGYmZfHE3nfc9WXKTYhTAWCeecybmrG9kD8diyTNqMLfG\nazHiZ+00+U0xCuBUzBHP9ZhC4P5UY34nn8eeg3eKYv0lCUyx5Hwgjtll8Ho2A7J0i2LeCTIW+Awm\nI/wZMAjTnfF1YFsW47LkCJ3tV+zFgXaatp+qltylGDPAtowBXsIMKrVY/o/u9CtOxBisF2MM18Wa\nIecLxSyALnAT8E3MEFOL5QBS6VeMYlrulmGqaNZOk5sUqwBOwlhbju/gee3dBX0OkwD8LP2hWXKF\ndPUrHkHHdhpLdig2ASzFuBduwczt64j27oL+gfEFdqaibMlT0t2v2NZOMyoNMVpSo5gEcCbwOF37\n/669u6AAI4o/T09olmKjBmOjWYapxFk7TXYoBgHsh1kQdgXpm9qyHfgodu+OJUUEcDKmaHIvdjpN\npil0AbwUeIjun0G3dxf0ZYz964GUorNY9qOKju00rfuObcaYHgpVAIdhbFrnZDkOi6Vb7G+nORWT\nKY7DVO6Oy2JchUahCWDrB+S3MUuJLJa8JgpcDPwIU5E7PKvRFB6FJIATMR+QJ2Y7EIulJxiOqR4/\nhLXTpItCEMAYcBcwj85ZWyyWnKSzplOXA+00R2YqwAIk3wVwBsbaclS2A7FYUqU7ptMaTDvTMswS\nalsc6Rq5LIBXYz7gDrZtrS+wCJiL8elZLHlPKqZTAZyEKZosxNppOksuCyAcuM3wYva1WD4EHJbF\nuCyWtJMu02klcBV2Ok1nyHUBbEVgBHAddmqLpUDpCdPp/naa07DTadqSDwLoAjdglnZVZTkWiyUv\nabXTPAp8FTudppVcF8AJGGvL1A6e114BDczQ3lRXyFosBcNwYAHwMKZToJjtNLkqgFHMezQfiHTi\n+e0V0CKYidfpEsBTgAbgqf0em4qdIG3JQxxgNuZAfQHt22kGUbgtVbkogKcB38dMau4s7RXQzsO4\nB9KZAZ6GOY/uu/frBZglShZL3tI6neZRjJ0mhvmDmgvcR+EWUnJJAPtgftdfoOvWlvYKaHUY8dPA\nT1MNcD/WYarTD2Juvy2WgqDVTnM/sAZTRS5kckUAL8IcSQzq5ve3V0A72H+ng/MARccTpS2WvKUS\nk408hjHlFmIWmG0BPBzz+z0/y3F0hQrMUOBtwLlZjsWSx/wW+OtBHr+Ugy9cSjddqRx+hAPtNIXS\nfZAtAXSA64D/JL+sLcMxmerhmP3Yf8hqNJa8ZxP7DpNbiQEvZODa3akcttppHsHYafK9GyEbAtg6\n1uykLFw7FaZg3nNn79cDMKshTs1aRJa851H+v7P/HDLTypZq5XA4cAcmIziX/LTTZFIAo8BtmMXj\nJRm8brrYBoxu89jTmDWx+XQLb8khLsb07rYSw2RfmSBdlUMHOBtjp8m36TSZEsDpGGvL2Axdz2LJ\nCwYCr+/39Q2Y1YWZoCcqh23tNJn6WbpLqgLoYlrU2quG9ga+i6mmF8q5qcWSVtZjKmvTOHhBIh9p\ntdMswmS4k7MbTrukIwOsBC7HDJ+4Hqje+/gczFlpbRquYbEULPcDn8ScpxUilRhD9WPANeSWnSbd\nt8BjgaWYAtOn0vzaFktBci6wEbO/tdAZj7HTLOHQdpozycxw13QKoMQI/EKgVxpf12IpaG7FeP+K\nibZ2mtYOiH4Yr+FlZGZ0V7oE8BhMkWNaml7PYikKJgMrsx1ElhkO3A48gfEkDs7gtVMVwCjmA+xW\nOpextmc+XwIkOHDKisVSkCzC+MBOxzSV989uODmDA5yFsdPcRWbsNKkI4DRM1ndMF76nPfP5mcBQ\n4IMU4rFY8oJ/YsYJPQcMyW4oGaezGdAAjJ2mddlTWQ/F0x0B7IU557uGrltb2jOfl2D6a21XhcVS\nwHQ1A2q109yHEZ0paY6nqwL4SYzHsbsfXO2Zz79FfhnILRZLN0glA6oErsTYaa4lPXaazgpgLaZg\nc2GK12vPfK7Y133TtjfcYrEUCOnKgMbROTtNR3QkgBLTxfFdcsu/aLFY8pB0Z0AlHLjsqatdF4cS\nwLGYIsf0Lr6mxWKxZJzhmGkrD2Mm2nRmOs3BBLAEM7HlNjJjxrZYLJa04QCzMHaauzn0rXVbATwJ\nM6tvXM+EZrFYLJmjP8ZO8wpmAnfbdsNWARyCEb7r2Dfw02KxWAoCCXyNfeeMV+19fAmmmPI3Ol4+\nbrFYLHnPYOAN9onh4uyGY7FYLNkhnxYSWSwWi8VisVi6yqHWgHbneRaLxZI3tNeH3N3nWSwWS97Q\nXh9yd59nsdhNV5a8YQ1wAvBimp5nsVgsecPB+pBTeZ7Fwv8C2zZB/F85erMAAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 3,
"metadata": {
"image/png": {
"height": 600,
"width": 600
}
},
"output_type": "execute_result"
}
],
"source": [
"Image(url='http://upload.wikimedia.org/wikipedia/commons/thumb/6/6d/Particle2D.svg/320px-Particle2D.svg.png', embed=True, width = 600, height = 600)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "00df159bd7cb62dbf196367fd7395e7f",
"grade": true,
"grade_id": "displayex01b",
"points": 4
}
},
"outputs": [],
"source": [
"assert True # leave this to grade the image display"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Use the `HTML` object to display HTML in the notebook that reproduces the table of Quarks on [this page](http://en.wikipedia.org/wiki/List_of_particles). This will require you to learn about how to create HTML tables and then pass that to the HTML object for display. Don't worry about styling and formatting the table, but you should use LaTeX where appropriate."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<tr>\n",
"<th>Name</th>\n",
"<th>Symbol</th>\n",
"<th>Antiparticle</th>\n",
"<th>Charge</th>\n",
"<th>Mass</th>\n",
"</tr>\n",
"<tr>\n",
"<th>Up</th>\n",
"<td>u</td>\n",
"<td>$\\bar{u}$</td>\n",
"<td>+2/3</td>\n",
"<td>1.5-3.3</td>\n",
"</tr>\n",
"<tr>\n",
"<th>Down</th>\n",
"<td>d</td>\n",
"<td>$\\bar{d}$</td>\n",
"<td>-1/3</td>\n",
"<td>3.5-6.0</td>\n",
"</tr>\n",
"<tr>\n",
"<th>Charm</th>\n",
"<td>c</td>\n",
"<td>$\\bar{c}$</td>\n",
"<td>+2/3</td>\n",
"<td>1,160-1,340</td>\n",
"</tr>\n",
"<tr>\n",
"<th>Strange</th>\n",
"<td>s</td>\n",
"<td>$\\bar{s}$</td>\n",
"<td>-1/3</td>\n",
"<td>70-130</td>\n",
"</tr>\n",
"<tr>\n",
"<th>Top</th>\n",
"<td>t</td>\n",
"<td>$\\bar{t}$</td>\n",
"<td>+2/3</td>\n",
"<td>169,100-173,300</td>\n",
"</tr>\n",
"<tr>\n",
"<th>Bottom</th>\n",
"<td>b</td>\n",
"<td>$\\bar{b}$</td>\n",
"<td>-1/3</td>\n",
"<td>4,130-4,370</td>\n",
"</tr>\n",
"\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%html\n",
"<table>\n",
"<tr>\n",
"<th>Name</th>\n",
"<th>Symbol</th>\n",
"<th>Antiparticle</th>\n",
"<th>Charge</th>\n",
"<th>Mass</th>\n",
"</tr>\n",
"<tr>\n",
"<th>Up</th>\n",
"<td>u</td>\n",
"<td>$\\bar{u}$</td>\n",
"<td>+2/3</td>\n",
"<td>1.5-3.3</td>\n",
"</tr>\n",
"<tr>\n",
"<th>Down</th>\n",
"<td>d</td>\n",
"<td>$\\bar{d}$</td>\n",
"<td>-1/3</td>\n",
"<td>3.5-6.0</td>\n",
"</tr>\n",
"<tr>\n",
"<th>Charm</th>\n",
"<td>c</td>\n",
"<td>$\\bar{c}$</td>\n",
"<td>+2/3</td>\n",
"<td>1,160-1,340</td>\n",
"</tr>\n",
"<tr>\n",
"<th>Strange</th>\n",
"<td>s</td>\n",
"<td>$\\bar{s}$</td>\n",
"<td>-1/3</td>\n",
"<td>70-130</td>\n",
"</tr>\n",
"<tr>\n",
"<th>Top</th>\n",
"<td>t</td>\n",
"<td>$\\bar{t}$</td>\n",
"<td>+2/3</td>\n",
"<td>169,100-173,300</td>\n",
"</tr>\n",
"<tr>\n",
"<th>Bottom</th>\n",
"<td>b</td>\n",
"<td>$\\bar{b}$</td>\n",
"<td>-1/3</td>\n",
"<td>4,130-4,370</td>\n",
"</tr>\n",
"\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "cbfeafa3f168e0c4f55bc93ea685d014",
"grade": true,
"grade_id": "displayex01c",
"points": 4
}
},
"outputs": [],
"source": [
"assert True # leave this here to grade the quark table"
]
}
],
"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 |
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